Technology-Assisted Interventions for Substance Use Disorders 3031264444, 9783031264443

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Technology-Assisted Interventions for Substance Use Disorders Jonathan Avery Mashal Khan Editors

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Technology-Assisted Interventions for Substance Use Disorders

Jonathan Avery  •  Mashal Khan Editors

Technology-Assisted Interventions for Substance Use Disorders

Editors Jonathan Avery Weill Cornell Medicine NewYork–Presbyterian Hospital New York, NY, USA

Mashal Khan Weill Cornell Medicine NewYork Presbyterian Hospital New York, NY, USA

ISBN 978-3-031-26444-3    ISBN 978-3-031-26445-0 (eBook) https://doi.org/10.1007/978-3-031-26445-0 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Contents

1 Technology  Assisted Therapies for Substance Use Disorder��������   1 James Sherer, Elon Richman, and Keriann Shalvoy 2 T  elemedicine and Medication-­Assisted Treatment for Opioid Use Disorder������������������������������������������������������������������  13 Christine LaGrotta and Christine Collins 3 Technology-Assisted  Treatments for Co-Occurring Mental Illness and SUD��������������������������������������������������������������������������������  23 Anil Abraham Thomas, Matthew Antonello, and Rober Aziz 4 Online  Peer Support for Substance Use Disorders ����������������������  31 Kate Fruitman 5 Technology-Assisted  Prevention Interventions for Substance Use Disorders������������������������������������������������������������������������������������  41 Anil Abraham Thomas, Sonya Bakshi, and Mary Rockas 6 Brain  Stimulation Methods for Substance Use Disorders������������  49 Karanbir Padda 7 Technology-Assisted  Interventions for Behavioral Addictions Involving Problematic Use of the Internet ����������������  61 Sanya Virani and Marc N. Potenza 8 Technology-Assisted  Therapies for Substance Use Disorders in Children and Adolescents ����������������������������������������������������������  69 Miriam E. Goldblum 9 Technology  Assisted Treatment of Substance Use Disorders in Pregnancy ������������������������������������������������������������������������������������  75 Rosemary V. Busch Conn 10 Technology-Assisted  Therapies for Substance Use Disorders in LGBTQIA������������������������������������������������������������������������������������  81 Nia Harris 11 Technology-Assisted  Interventions for SUDs with Racial/Ethnic Minorities in the United States����������������������  87 Stephanie Chiao, Ariella Dagi, and Derek Iwamoto

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12 The  Media and Substance Use Disorders��������������������������������������  97 Charalambia Louka 13 Legal  Technologies in Substance Use Disorders���������������������������� 107 Sanya Virani and Patricia Ryan Recupero 14 Technology-Assisted  Therapies in Healthcare Professionals ������ 115 Adeolu Ilesanmi Index��������������������������������������������������������������������������������������������������������  127

Contents

Contributors

Matthew Antonello  NYU Grossman School of Medicine, New York, NY, USA Rober Aziz  NYU Grossman School of Medicine, New York, NY, USA Sonya Bakshi  NYU Grossman School of Medicine, New York, NY, USA Rosemary  V.  Busch  Conn, MD Department of Psychiatry, New York Presbyterian/Weill Cornell Medicine, New York, NY, USA Stephanie  Chiao Department of Psychiatry, NewYork-Presbyterian Weill Cornell Medicine, New York, NY, USA Christine  Collins Department of Psychiatry and Neuroscience, Lindner Center of HOPE/University of Cincinnati College of Medicine, Mason, OH, USA Ariella  Dagi Department of Psychiatry, NewYork-Presbyterian Weill Cornell Medicine, New York, NY, USA Weill Cornell Medical College of Cornell University, New York, New York, USA Kate Fruitman  Weill Cornell Medicine, New York, NY, USA Miriam E. Goldblum  New York Presbyterian/Weill Cornell Medicine, New York, NY, USA Nia Harris  Psychiatry Resident, New York, NY, USA Adeolu  Ilesanmi New York Presbyterian Hospital/Weill Cornell Medical College, New York, NY, USA Derek  Iwamoto Department of Psychology, University of Maryland, College Park, MD, USA Christine  LaGrotta  Department of Addiction Psychiatry, James J.  Peters Bronx VA Medical Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA Charalambia Louka  Weill Cornell Medicine/NewYork-Presbyterian, New York, NY, USA

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Karanbir  Padda  Department of Psychiatry, New York-Presbyterian/Weill Cornell Medical Center, New York, NY, USA Marc  N.  Potenza Yale University School of Medicine, New Haven, CT, USA Patricia Ryan Recupero  Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, RI, USA Elon  Richman  NYU Grossman School of Medicine/Bellevue HHC, New York, NY, USA Mary Rockas  NYU Grossman School of Medicine, New York, NY, USA Keriann Shalvoy  NYU Grossman School of Medicine/Bellevue HHC, New York, NY, USA James  Sherer NYU Grossman School of Medicine/Bellevue HHC, New York, NY, USA Anil Abraham Thomas  Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, USA Sanya  Virani Warren Alpert Medical School of Brown University, Providence, RI, USA Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, RI, USA

Contributors

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Technology Assisted Therapies for Substance Use Disorder James Sherer, Elon Richman, and Keriann Shalvoy

Introduction Technology has helped make the promise of widely available and affordable treatment for substance use disorders a reality. First line pharmacologic treatments for substance use disorders (SUDs) such as buprenorphine and naltrexone are highly effective but may come with considerable side effects. Suboxone, for example, can lead to sedation, anxiety, constipation, nausea, and headaches [1]. Having non-pharmacologic alternatives for treating SUDs may open the door to recovery for countless patients who found the side effects of common medications intolerable [2]. This chapter provides an overview of the breakthrough technologies in substance use treatment that have become essential for patient and provider alike. While subsequent chapters will provide a more in-depth look at each individual technology, this chapter serves as a general overview of the landscape of technologically assisted treatments of substance use disorders. The most important takeaway is a simple one—these interventions are readily available, well-researched, effective, and can augment care in almost any set-

J. Sherer (*) · E. Richman · K. Shalvoy NYU Grossman School of Medicine/Bellevue HHC, New York, NY, USA e-mail: [email protected]; [email protected]; [email protected]

ting. These treatments will become indispensable in our efforts to provide the best care to as many patients as possible.

 art I: Mobile Phone Technologies P for the Treatment of Substance Use Disorders As smartphones have become ubiquitous in the USA, there has been growing interest in the potential power of mobile devices to assist in healthcare. Particular attention has been paid to the treatment of substance use disorders owing to the potential for increased treatment availability and the potential to overcome barriers to treatment that prevents approximately 90% of people with substance use disorders from accessing treatment [3]. Substance use disorder treatment is increasingly available in primary care settings where medication assisted treatment may be prescribed, but capacity for psychotherapeutic interventions is limited [4, 5]. 97% of Americans own a mobile phone and 85% have an internet capable smartphone [6]. Several studies have demonstrated that vulnerable populations of low socio-­ economic status and racial/ethnic minority populations have access to mobile phones and apps at similar rates to the general population [7]. For this reason, both medical researchers and entrepreneurs have been developing apps that focus on substance use. These mobile apps hold

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Avery, M. Khan (eds.), Technology-Assisted Interventions for Substance Use Disorders, https://doi.org/10.1007/978-3-031-26445-0_1

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the promise of increasing patient contact with support for their substance use disorder from 30 min a week to an unlimited amount of time. Mobile health (mHealth) apps create a venue for patients to engage in treatment more continuously, at any moment that is most convenient or urgent. For many, engaging in treatment via mobile device also offers a sense of anonymity, as an individual can use their phone privately and access an app immediately in most cases. This perception of anonymity does not take into account the actual increased risk of compromised privacy that may result from sharing information online. Close to 80% of mHealth apps are estimated to be sharing user data with third parties [8]. With Americans spending more and more on mHealth apps (see Fig.  1.1), concerns about securing patient health information and financial information are growing. Still, engaging in this virtual way when discussing stigmatized behaviors may be more tolerable and less shame-­ inducing than in face-to-face encounters. Today, there are a handful of rigorously researched apps directed toward substance use disorders, and one FDA approved app. These few mHealth apps researched and developed by the medical community are far outnumbered by the thousands of unregulated, industry-developed apps available which are used much more widely by people with substance use disorders. Furthermore, there are hundreds of apps available to promote substance use through a variety of formats including creatFig. 1.1  Spending on mental health apps over time. (Source: Auxier, B., Bucaille, A., Westcott, K. (2021). Mental Health Goes Mobile: The Mental Health App Market Will Keep on Growing. Deloitte Dec. 1, 2021. https://www2.deloitte. com/us/en/insights/ industry/technology/ technology-­media-­and-­ telecom-­ predictions/2022/ mental-­health-­app-­ market.html)

ing virtual social networks around substance use, enabling home alcohol delivery, or gamifying substance use [9].

 ext Messaging and Integrative Voice T Recognition In addition to mHealth smartphone apps, less technologically complex options are also quite effective, including text messaging and interactive voice recognition [5, 10]. Text messaging is also cost-effective and while more limited in its capability can still involve multimedia and videos. Studies examining texting interventions for substance use suggest high rates of engagement by text messaging. While each intervention varies, multiple studies show high satisfaction and low annoyance with receiving texts, as well as high rates of opening and responding to text messages. Text messaging also circumvents the need for the patient to download and open an application on their phone, a common barrier experienced in mHealth smartphone app interventions. Interventions utilizing text messaging appear to have very promising results and it is plausible that text messaging may be more widely utilized and effective than mHealth apps in general based on the outcomes reported in individual studies of both formats [10, 11]. The superiority of text messaging as compared to mHealth apps is currently unknown and remains an area for future research.

Spending on Mental Health Apps (in US$ millions)

491 372 269 203

2019

2020

2021

2022 (projected)

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Integrative voice recognition (IVR) is an alternative to texting in which pre-recorded statements come to the patient by phone call and the individual using the platform can respond verbally. This approach may be helpful for ­ patients with impairments that limit their ability to use text messaging or those who are more comfortable speaking on the phone than texting. It has been shown to be effective for interventions related to some medical conditions, but thus far has not been utilized in substance use disorders. No studies have directly compared TM and/or IVR to mHealth applications for substance use disorders.

 bjective Data Collection by O mHealth Apps The majority of available mHealth apps are focused on active participation from the patient to engage with educational materials or to input subjective data, for example, related to their emotional state or severity of cravings similar to the information that may be collected in traditional in-person psychotherapeutic treatment. Smartphones also have the ability to collect unique data not previously used in the treatment setting. One area of objective data collection is via sensory hardware embedded in smartphones. Movement sensors can identify tremors or sleep patterns. External hardware can also collect data that is then sent to smartphone apps by Bluetooth, like a breathalyzer device that sends result to a contingency management app [12]. GPS technology can be used to intervene when a patient visits a defined high-risk area for relapse. It is important to note that such approaches may not be HIPAA compliant as an individual may reasonably be identified based on their routine patterns of movement on GPS [13]. In contrast to these passive forms of data collection (location monitoring, vital signs, sleep patterns), patients may also actively upload data electronically that can be useful to clinicians. For example, patients may be asked to record videos of themselves taking their medication as a method to improve adherence [14]. This approach may be useful in clini-

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cal situations at high risk for medication diversion or forensic settings [15]. As opposed to mHealth apps that aim to adapt evidence-based in-person techniques to a virtual format, many of the objective data measures described here are of unclear utility in clinical practice. Further research will be required to determine how to effectively apply this data to patient care.

 Health Smartphone Applications m for Substance Use Disorders Before further detailing some mHealth apps most commonly used today, it is important to highlight that research on mHealth apps for substance use disorders has broadly identified a gap between the hope for mHealth to uniquely meet the unmet needs of the population with substance use disorders and its real-world use. Many studies demonstrate feasibility and patient interest in using mHealth apps. Feasibility data supports the notion that the wide majority of patients enrolled in substance treatment, including low-income patients, have internet capable smartphones and that patient feedback studies indicate widespread interest in incorporating an app into traditional substance treatment. While access to smartphones and interest in using substance treatment apps are certainly necessary for implementation, there are many other variables that may prevent or limit an individual from using an mHealth app in the clinical setting. One issue has been that of access to smartphones, since access to the device, voice calling, Wi-Fi, or data may be inconsistent. Phone plans dramatically affect the way an individual uses their smartphone features including when and how they connect to the internet and take calls or texts. Pay-as-you-go plans are more common among low SES patients and vary in terms of limits of data, calls, and texts. mHealth apps are also likely to include video and audio, which utilizes more data [16]. Patients experiencing financial and housing psychosocial stressors are also more likely to lose their phones or replace their phones frequently, which may also include a change of phone number. One small 2015 study of patients

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enrolled in substance treatment suggested that about half have at least one change of phone number within 1  year [17]. People who have unlimited data plans may also have limited ­interaction with mHealth apps for reasons that do not have to do with access to the apps, but more to do with how interested they are in using the app or how user-friendly it is. In addition to having access to the device required, the mHealth application itself must demonstrate a positive outcome on treating substance use disorders and also retain users who sign up for the app. A few of the mHealth apps for substance use disorders found that only about half of studies showed positive results, which are typically mild to moderate in effect size [18]. 277 mHealth apps for alcohol use disorder were downloaded 2.7 million times in 2017, demonstrating widespread interest in this interface; however, retention is a challenge [19]. Incorporation of push notifications to remind patients to continue engaging with the app help mitigate app abandonment to some extent. One study in a VA population regarding an app for alcohol use had 66% of the study group abandon the app after 6 months when using push notifications which is a significant reduction compared to the average abandonment rate of all mental health apps which is 90% within 10  days of download [20, 21]. Elucidating the reasons why patients stop using mHealth apps, despite a lack of obvious barriers to access is difficult to ascertain in many cases. Patients report an interest in using mHealth apps and even among those where patients were highly involved in development, the rates of use and success of interventions may be modest or non-existent. In other words, the patients using an app may rate it very highly but still not use it. In addition to patient-related barriers to mHealth applications, it is also important to consider that apps must be cost-effective. Buy-in from clinicians is also essential for apps that are meant to augment traditional treatment [22]. The majority of mHealth apps available for SUD are directed toward alcohol or smoking cessation. There are relatively few apps for other

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substances of abuse. Over the 10 year period of 2009–2019 there were only 72 apps related to opioids, and the majority were simple calculators meant for clinicians to convert doses of different opioids [8]. Even fewer apps have been developed to address methamphetamine use. It is not clear how many apps exist on the app store for methamphetamine, but only a handful of studies have examined apps specific to this population, one of which was focused on education and harm reduction [23, 24]. Review articles assessing the content of substance use mHealth apps available also tend to categorize apps based on the substance of abuse. Relatively less is known about the quantity or quality of apps in different therapeutic categories—CBT, bias re-training, self-­ determination [25]. mHealth apps may be developed as standalone interventions or as adjunct to in-person treatment settings. They may focus on education, harm reduction, use reduction, or abstinence. mHealth app formats may involve an adaptation of a therapeutic modality like CBT or may take on novel formats like creating games to encourage engagement with the app, termed gamification. Studies of games created thus far are inconclusive as to whether it is beneficial in practice or if the games created thus far have not met user expectations [26]. In the in-person group settings, apps may assist in strengthening social networks by encouraging communication outside of the treatment center [3]. Patient survey data also shows that while patients are interested in online social support networks, they also report frequently encountering triggers for substance use while they are on social media platforms [27]. Studies of patient feedback on mHealth apps also consistently find that patients want a great ability to tailor app features to their specific situation, which to date is rarely incorporated into apps [28]. The incorporation of this kind of patient feedback in the planning stage of app development varies widely. Increasingly, medical researchers are calling for a participatory action research method wherein people with substance use disorders direct app development, along with input from clinicians and researchers [25, 29].

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Assessing the Quality of mHealth Apps The FDA has created an evaluation and approval process for mHealth applications. There is currently one FDA approved evidence-based app for substance use disorders, PEAR, which is designed to be used in conjunction with traditional individual treatment. As previously discussed, there are far more industry-developed apps than those developed by medical researchers. Of that minority, very few are seeking FDA approval. Of those that do register on clinicaltrials.gov, even fewer continue the process and report results of their evaluation studies. This may be related to bureaucratic hurdles in the FDA approval process or may alternatively represent a high rate of negative outcome results, which are less likely to be reported in the literature [30]. Given the limitations of the FDA approval process, alternative methods have been developed to assess mobile apps. The Mobile App Rating Scale (MARS) is an objective tool to assess overall user experience quality of mental health related applications based on multiple domains: engagement, functionality, esthetics, information quality, and subjective quality. A free non-profit online resource, “One Mind Psyberguide,” incorporates ratings on credibility, user experience (in part based on MARS rating), and transparency regarding privacy policies [31]. This method of assessment frequently yields different results than a simple evaluation of app store star ratings based on subjective experiences of users [32].

Part II: Web-Based Technologies for the Treatment of Substance Use Disorders There has been a trend toward increased use of web-based technologies and delivery platforms in mental health treatments including addictions. It is hoped that these developments will increase access and quality of care as well as decrease cost [33]. One meta-analysis involving 9764 clients who were treated with various internet-based psychological interventions for problems including substance use disorders found an overall

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mean effect size of 0.53, correlating with medium size, which is comparable to face-to-face therapies. Additionally, a comparison of internet-­ based therapies with face-to-face therapy revealed no difference in effectiveness [34]. Because of built-in fidelity measures, it is thought that this delivery of care is strongly adherent to evidence-­ based practices [35]. More robust research is needed regarding web-based modalities for substance use disorders. There is evidence that retention in these programs is at least as high as face-to-face modalities, if not higher, and that treatment satisfaction is comparable, with one study demonstrating high rates of therapeutic alliance strength and similar rates of urinary drug screen results across modalities [36]. Additionally, there is a sense of flexibility and titration of interactions for those who are more or less symptomatic over the course of treatment [37]. There are over 100 different computer assisted-therapy programs for behavioral health problems with specific examples for substance use and behavioral addictions (i.e. gambling). These technologies include multimedia that are tailored to individual needs and use information compiled from medical records and other bioinformatics. Most known psychotherapy modalities exist through these platforms as well as peer support groups like alcoholics anonymous. Web-­ based services may incorporate computers, tablets, and mobile devices as part of a treatment plan. While an exhaustive list of web-based substance use care delivery is beyond the scope of this review, some notable examples as endorsed by the National Institute on Drug Abuse (NIDA) and the Substance Abuse and Mental Health Services Administration (SAMHSA) include the following (further details can be found at such resource centers as: https://sudtech.org/ and https://www.c4tbh.org/): • Therapeutic Education System (TES): Web-­ based interactive multimedia modules based on community reinforcement. • CBT4CBT: Web-based cognitive behavioral skills training for substance use disorders.

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• MOTIV8: Web-based contingency manage- an idea of real-world efficacy on a broad scale ment program for smoking cessation includ- [38]. However, the heterogeneity of study results ing group support and biological verification may have more to do with study design than the of breath carbon monoxide samples. interventions themselves [39]. At the very least, • Project Quit: A research team at the Medical these interventions will likely become invaluable University of South Carolina providing help adjunctive therapies along with more common with smoking cessation. pharmacologic treatments. These interventions • ModerateDrinking.com: Web-based self-­are reviewed individually below and then comcontrol training program for reducing alcohol pared in a table (see Table 1.1). consumption and resulting alcohol related consequences. • Drinkers Checkup: Web-based brief motiva- Repetitive Transcranial Magnetic tional intervention for assessing alcohol use Stimulation (rTMS) and assisting with decision-making. Transcranial magnetic stimulation (TMS) is one of the most actively researched interventions in

 art III: Outpatient Procedures P and Devices for Substance Use Disorders

In 2008, the Food and Drug Administration (FDA) approved repetitive Transcranial Magnetic Stimulation (rTMS) for the treatment of major depressive disorder. For patients who found antidepressants ineffective or who suffered from serious side effects, rTMS became a much-needed alternative to the standard of care. Early research indicates that rTMS, as well as other novel interventions, may play a similar role as an alternative or adjunctive treatment for substance use. In this section, we will provide a brief overview of non-­ invasive brain stimulation (NIBS), deep brain stimulation (DBS), and electronic nicotine delivery systems (ENDS) as treatments for SUDs. As each of these subjects warrants a deeper investigation, these topics will be introduced here and explored in more detail in later chapters.

 on-Invasive Brain Stimulation (rTMS N and tDCS)

Table 1.1 Transcranial Direct Current Stimulation (tDCS) vs. Repetitive Transcranial Magnetic Stimulation (rTMS)

Type of stimulation

Transcranial direct current stimulation (tDCS) Weak, 1–2 mA current applied directly to scalp which changes membrane resting potentials in neurons Excites or inhibits

Effect on neuronal activity Brain target NAc SUD Alcohol, research cocaine, cannabis, nicotine Effect on Moderate cravings reduction Effect on Limited substance use Quality of Multiple clinical evidence/ trials but types of evidence studies inconsistent (variable effect sizes)

Repetitive transcranial magnetic stimulation (rTMS) Coil positioned above the scalp delivers magnetic pulses which become electrical currents in the brain tissue

Excites or inhibits

DLPFC, MPC, ACC Alcohol, cocaine, methamphetamine, opioid, cannabis, nicotine Significant reduction Limited

Multiple clinical trials with consistent evidence

Treating SUDs with safe, effective, and time-­ limited outpatient interventions is an alluring proposition for patient and provider alike. However, studies looking into the efficacy of NAc nucleus accumbens, DLPFC dorsolateral prefrontal rTMS and transcranial Direct Current Stimulation cortex, ACC anterior cingulate cortex (tDCS) have been mixed and do little to provide Sources: [40, 43]

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psychiatry today. TMS uses a coil placed against the skull to either depolarize or hyperpolarize neurons, depending on the frequency of the magnetic pulses administered. Depending on the shape and placement of the coil on the skull, different brain regions can be targeted. The shape of the coil also determines how deep the pulses penetrate, usually from 1 to 3 cm deep. When multiple pulses of TMS are administered in rapid succession (rTMS) it is possible to see differences in neuronal excitability and changes in addictive behavior in the short term [40]. When subjects are exposed to addiction related cues while receiving treatment, the effects of rTMS for SUDs may be enhanced significantly [41]. While most studies target the dorsolateral prefrontal cortex (DLPFC), this is not always the case, with some studies targeting more medial brain regions [42]. Studies also vary in the number of rTMS sessions provided. While most studies evaluate results after between eight and 15 sessions, this number fluctuates widely [43]. There is no standard for number of sessions or neuroanatomical target, so each of these details should be closely examined whenever reviewing the literature on rTMS. The results for rTMS as a treatment for alcohol use disorder (AUD) are mixed. Some studies that targeted the DLPFC showed a decrease in alcohol cravings and consumption, while others found no effect [39]. When rTMS was investigated for tobacco use disorder, many studies showed a reduction in cigarette cravings and use, but these results were heterogeneous and the results were somewhat transient, with many patients returning to their baseline use fairly quickly [2]. Cocaine use disorder and rTMS have not been as exhaustively researched, but preliminary results are promising with regard to a reduction in cravings and use [44]. There is a dearth of studies investigating rTMS for cannabis and methamphetamine use disorders, but preliminary findings are promising, with rTMS leading to reductions in cravings in both disorders [39]. Interestingly, there is some evidence to show that rTMS may be useful as therapy for behavioral addictions including gambling use disorders, causing reductions in cravings and problem

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behavior [43]. Even without targeting a specific use disorder, rTMS may be useful for decision-­ making, cognitive flexibility, and self-control [44]. Of all the NIBS techniques, rTMS shows the most promise for the widest array of use disorders. This, combined wide availability of rTMS, further broadens its appeal as an intervention for SUDs.

 ranscranial Direct Current T Stimulation (tDCS) Transcranial Direct Current Stimulation (tDCS) only modulates neuronal excitability unlike rTMS, which can modulate as well as stimulate brain cells. Electrodes placed on the scalp deliver a small, continuous current (1–2  mA) for anywhere from 10 to 30  min. There are two types, anodal and cathodal, which increase or decrease neuronal excitability, respectively. Depending on where the electrodes are placed, different brain regions can be targeted. The DLPFC is a commonly targeted brain region for SUD treatment. tDCS is more commonly used for chronic pain, Parkinson’s, and major depressive disorder [45]. Preliminary findings for tDCS as a treatment for various SUDs are quite promising, but the number of studies is limited. For alcohol use disorder, tDCS can reduce cravings and the amount of alcohol consumed [42], but in one study was associated with increased relapse rates [46]. The majority of studies evaluating tDCS for tobacco cravings and cigarette use reported improvements [39]. There are single studies looking at tDCS for cannabis [47], cocaine [48], and opioid use disorders [49], and they all show moderate effects with regard to cravings.

Deep Brain Stimulation (DBS) Deep brain stimulation (DBS) is more focal than rTMS or tDCS but it is also more invasive, requiring the implantation of microelectrodes in the brain parenchyma itself. DBS is a neurosurgical intervention—it subjects patients to typical surgical risks such as bleeding, stroke, and infection.

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That said, among the neurological and neurosurgical communities it is a safe and established treatment for those with movement disorders such as Parkinson’s, essential tremor, dystonia, and some seizure disorders [50]. Since the microelectrodes used in DBS can penetrate deeper than the magnetic pulses of rTMS or the electrical current of tDCS, deeper structures can be accessed. The nucleus accumbens, the pleasure-reward center of the brain, is often targeted for treatment when DBS is investigated for SUDs. Given the nature of the intervention, studies investigating DBS for SUDs are relatively small, but the results are still promising. With regard to tobacco use disorder, DBS can reduce cravings and use [51] and even potentially lead to complete smoking cessation [52]. DBS led to a reduction in alcohol use and alcohol cravings across a variety of studies [40]. For opioid use disorder, results for DBS are compelling, leading to a reduction of use across various studies [39]. DBS also seems to be effective for cocaine cravings, but the evidence is limited [53]. Early indications are that, despite being invasive, DBS may be a viable alternative for SUD treatment.

 lectronic Nicotine Delivery Systems E (ENDS) Tobacco remains the leading cause of preventable disability, disease, and death in the USA, responsible for nearly a half-million deaths annually [54]. E-cigarette use is on the rise globally, with many users citing reasons such as being able to smoke in more public places, and because they view e-cigarettes as a safe and cost-effective way to cut down on cigarette smoking [55]. E-cigarettes or electronic nicotine delivery systems (ENDS) are small, rechargeable, battery powered devices that heat a solution containing nicotine and flavorings to produce an aerosol that the user inhales. The vapor from e-cigarettes is typically odor free. There is a rapidly growing multibillion dollar industry of ENDS manufacturers and nicotine cartridge suppliers [56]. The literature is quite mixed when it comes to ENDS as a treatment for tobacco use disorder.

One major systematic review reports that ENDS are safer to the user than regular cigarettes, and that they can be effective for patients trying to cut down on cigarette smoking [57]. However, another meta-analysis showed the opposite, reporting that those who use ENDS are actually much less likely to quit and may be ingesting more nicotine than they realize [55]. Yet another review claimed that there was no association between ENDS use and smoking reduction/cessation, and that larger, more rigorous studies are needed [58]. Given these contrasting views, along with the fact that ENDS may not be healthier than regular cigarettes [59], recommending ENDS for smoking cessation remains an individual decision between patient and provider. Q&A 1. True or False: Most available smartphone apps related to substance use are either low quality or promote substance use. True: Resources like FDA approval status, the Mobile Application Rating Scale, and One Mind Psyberguide can assist clinicians in identifying trustworthy mHealth apps 2. True or False: Most Americans have a smartphone, so generally patients are more engaged with Health apps than texts or calls. False: Rates of engagement are higher with text or phone call interventions than applications 3. True or False: rTMS may be effective in reducing cigarette cravings and tobacco use over a five-year span. False: Early research indicates rTMS is highly effective in reducing cravings and smoking in the short term, but long-term effects are unclear.

References 1. Shulman M, Wai JM, Nunes EV.  Buprenorphine treatment for opioid use disorder: an overview. CNS Drugs. 2019;33(6):567–80. https://doi.org/10.1007/ s40263-­019-­00637-­z. 2. Bellamoli E, Manganotti P, Schwartz RP, Rimondo C, Gomma M, Serpelloni G. rTMS in the treatment of drug addiction: an update about human stud-

1  Technology Assisted Therapies for Substance Use Disorder ies. Behav Neurol. 2014;2014:1–11. https://doi. org/10.1155/2014/815215. 3. Lord S, Moore SK, Ramsey A, Dinauer S, Johnson K. Implementation of a substance use recovery support mobile phone app in community settings: qualitative study of clinician and staff perspectives of facilitators and barriers. JMIR Ment Health. 2016;3(2):e24. https://doi.org/10.2196/mental.4927. 4. Quanbeck A, Gustafson DH, Marsch LA, Chih MY, Kornfield R, McTavish F, Shah DV.  Implementing a mobile health system to integrate the treatment of addiction into primary care: a hybrid implementation-­ effectiveness study. J Med Internet Res. 2018;20(1):e37. https://doi.org/10.2196/jmir.8928. 5. Tofighi B, Abrantes A, Stein MD.  The role of technology-­ based interventions for substance use disorders in primary care: a review of the literature. Med Clin N Am. 2018;102(4):715–31. https://doi. org/10.1016/j.mcna.2018.02.011. 6. Center PR.  Mobile fact sheet. 2021. https://www. pewresearch.org/internet/fact-­sheet/mobile/. 7. Dahne J, Lejuez CW. Smartphone and mobile application utilization prior to and following treatment among individuals enrolled in residential substance use treatment. J Subst Abus Treat. 2015;58:95–9. https://doi.org/10.1016/j.jsat.2015.06.017. 8. Nuamah J, Mehta R, Sasangohar F.  Technologies for opioid use disorder management: mobile app search and scoping review. JMIR Mhealth Uhealth. 2020;8(6):e15752. https://doi.org/10.2196/15752. 9. Ghassemlou S, Marini C, Chemi C, Ranjit YS, Tofighi B. Harmful smartphone applications promoting alcohol and illicit substance use: a review and content analysis in the United States. Transl Behav Med. 2020;10(5):1233–42. https://doi.org/10.1093/tbm/ ibz135. 10. Tofighi B, Nicholson JM, McNeely J, Muench F, Lee JD. Mobile phone messaging for illicit drug and alcohol dependence: a systematic review of the literature. Drug Alcohol Rev. 2017;36(4):477–91. https://doi. org/10.1111/dar.12535. 11. Gonzales R, Ang A, Murphy DA, Glik DC, Anglin MD.  Substance use recovery outcomes among a cohort of youth participating in a mobile-based texting aftercare pilot program. J Subst Abus Treat. 2014;47(1):20–6. https://doi.org/10.1016/j. jsat.2014.01.010. 12. McPherson SM, Burduli E, Smith CL, Herron J, Oluwoye O, Hirchak K, Roll JM. A review of contingency management for the treatment of substance-use disorders: adaptation for underserved populations, use of experimental technologies, and personalized optimization strategies. Subst Abus Rehabil. 2018;9:43– 57. https://doi.org/10.2147/sar.s138439. 13. Carter A, Liddle J, Hall W, Chenery H. Mobile phones in research and treatment: ethical guidelines and future directions. JMIR Mhealth Uhealth. 2015;3(4):99– 106. https://doi.org/10.2196/mhealth.4538. 14. Godersky ME, Klein JW, Merrill JO, Blalock KL, Saxon AJ, Samet JH, Tsui JI.  Acceptability and

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feasibility of a mobile health application for video directly observed therapy of buprenorphine for opioid use disorders in an office-based setting. J Addict Med. 2020;14(4):319–25. https://doi.org/10.1097/ adm.0000000000000608. 15. Steinkamp JM, Goldblatt N, Borodovsky JT, LaVertu A, Kronish IM, Marsch LA, SchumanOlivier Z.  Technological interventions for medication adherence in adult mental health and substance use disorders: a systematic review. JMIR Ment Health. 2019;6(3):e12493. https://doi. org/10.2196/12493. 16. Scott CK, Dennis ML, Gustafson DH. Using smartphones to decrease substance use via self-monitoring and recovery support: study protocol for a randomized control trial. Trials. 2017;18:374. https://doi. org/10.1186/s13063-­017-­2096-­z. 17. Milward J, Day E, Wadsworth E, Strang J, Lynskey M.  Mobile phone ownership, usage and readiness to use by patients in drug treatment. Drug Alcohol Depend. 2015;146:111–5. https://doi.org/10.1016/j. drugalcdep.2014.11.001. 18. Nesvag S, McKay JR.  Feasibility and effects of digital interventions to support people in recovery from substance use disorders: systematic review. J Med Internet Res. 2018;20(8):e255. https://doi. org/10.2196/jmir.9873. 19. Hoeppner BB, Schick MR, Kelly LM, Hoeppner SS, Bergman B, Kelly JF. There is an app for that— or is there? A content analysis of publicly available smartphone apps for managing alcohol use. J Subst Abus Treat. 2017;82:67–73. https://doi.org/10.1016/j. jsat.2017.09.006. 20. Malte CA, Dulin PL, Baer JS, Fortney JC, Danner AN, Lott AMK, Hawkins EJ.  Usability and acceptability of a mobile app for the self-management of alcohol misuse among veterans (step away): pilot cohort study. JMIR Mhealth Uhealth. 2021;9(4):e25927. https://doi.org/10.2196/25927. 21. Torous J, Bucci S, Bell IH, Kessing LV, Faurholt-­ Jepsen M, Whelan P, Firth J.  The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality. World Psychiatry. 2021;20(3):318–35. https://doi. org/10.1002/wps.20883. 22. Ford JH, Alagoz E, Dinauer S, Johnson KA, Pe-Romashko K, Gustafson DH.  Successful organizational strategies to sustain use of A-CHESS: a mobile intervention for individuals with alcohol use ­disorders. J Med Internet Res. 2015;17(8):e201. https://doi.org/10.2196/jmir.3965. 23. Birrell L, Deen H, Champion KE, Newton NC, Stapinski LA, Kay-Lambkin F, Chapman C. A mobile app to provide evidence-based information about crystal methamphetamine (ice) to the community (cracks in the ice): co-design and beta testing. JMIR Mhealth Uhealth. 2018;6(12):e11107. https://doi. org/10.2196/11107. 24. Rubenis AJ, Baker AL, Arunogiri S. Methamphetamine use and technology-mediated

10 psychosocial interventions: a mini-review. Addict Behav. 2021;121:106881. https://doi.org/10.1016/j. addbeh.2021.106881. 25. Zhang M, Wing T, Fung DSS, Smith H.  Enhancing the quality and utility of content analyses for addictive disorders. Int J Environ Res Public Health. 2018;15(7):1389. https://doi.org/10.3390/ ijerph15071389. 26. Bindoff I, de Salas K, Peterson G, Ling T, Lewis I, Wells L, Ferguson SG. Quittr: the design of a video game to support smoking cessation. JMIR Serious Games. 2016;4(2):e19. https://doi.org/10.2196/ games.6258. 27. Ashford RD, Lynch K, Curtis B.  Technology and social media use among patients enrolled in outpatient addiction treatment programs: cross-sectional survey study. J Med Internet Res. 2018;20(3):e84. https://doi. org/10.2196/jmir.9172. 28. Hoeppner BB, Hoeppner SS, Seaboyer L, Schick MR, Wu GW, Bergman BG, Kelly JF.  How smart are smartphone apps for smoking cessation? A content analysis. Nicotine Tob Res. 2016;18(5):1025–31. https://doi.org/10.1093/ntr/ntv117. 29. Bagot K, Hodgdon E, Sidhu N, Patrick K, Kelly M, Lu Y, Bath E. End user-informed mobile health intervention development for adolescent cannabis use disorder: qualitative study. JMIR Mhealth Uhealth. 2019;7(10):e13691. https://doi.org/10.2196/13691. 30. Minen MT, Reichel JF, Pemmireddy P, Loder E, Torous J.  Characteristics of neuropsychiatric mobile health trials: cross-sectional analysis of studies registered on ClinicalTrials.gov. JMIR Mhealth Uhealth. 2020;8(8):e16180. https://doi.org/10.2196/16180. 31. Satre DD, Meacham MC, Asarnow LD, Fisher WS, Fortuna LR, Iturralde E.  Opportunities to integrate mobile app-based interventions into mental health and substance use disorder treatment services in the wake of COVID-19. Am J Health Promot. 2021;35(8):1178– 83. https://doi.org/10.1177/08901171211055314. 32. Neary M, Bunyi J, Palomares K, Mohr DC, Powell A, Ruzek J, Schueller SM.  A process for reviewing mental health apps: using the one mind PsyberGuide credibility rating system. Digit Health. 2021;7:20552076211053690. https://doi. org/10.1177/20552076211053690. 33. Ramsey A.  Integration of technology based behavioral health interventions in substance use disorders. Int J Ment Heal Addict. 2015;13(4):470–80. 34. Barak A, Hen L, Boniel-Nissim M, Shapira NA.  A comprehensive review and a meta-analysis of the effectiveness of internet-based psychotherapeutic interventions. J Technol Hum Serv. 2008;26(2–4):109–60. 35. Litvin EB, Abrantes AM, Brown RA. Computer and mobile technology-based interventions for substance use disorders: an organizing framework. Addict Behav. 2013;38(3):1747–56. 36. King VL, Brooner RK, Peirce JM, Kolodner K, Kidorf MS. A randomized trial of web-based video-

J. Sherer et al. conferencing for substance abuse counseling. J Subst Abus Treat. 2014;46(1):36–42. 37. King VL, Stoller KB, Kidorf M, Kindbom K, Hursh S, Brady T, Brooner RK. Assessing the effectiveness of an internet-based videoconferencing platform for delivering intensified substance abuse counseling. J Subst Abus Treat. 2009;36:331–8. 38. Luigjes J, Segrave R, de Joode N, Figee M, Denys D. Efficacy of invasive and non-invasive brain modulation interventions for addiction. Neuropsychol Rev. 2019;29(1):116–38. https://doi.org/10.1007/ s11065-­018-­9393-­5. 39. Coles AS, Kozak K, George TP.  A review of brain stimulation methods to treat substance use disorders: brain stimulation to treat SUDs. Am J Addict. 2018;27(2):71–91. https://doi.org/10.1111/ ajad.12674. 40. Mahoney JJ, Hanlon CA, Marshalek PJ, Rezai AR, Krinke L.  Transcranial magnetic stimulation, deep brain stimulation, and other forms of neuromodulation for substance use disorders: review of modalities and implications for treatment. J Neurol Sci. 2020;418:117149. https://doi.org/10.1016/j. jns.2020.117149. 41. Kearney-Ramos TE, Dowdle LT, Mithoefer OJ, Devries W, George MS, Hanlon CA. State-dependent effects of ventromedial prefrontal cortex continuous Thetaburst stimulation on cocaine cue reactivity in chronic cocaine users. Front Psychol. 2019;10:317. https://doi.org/10.3389/fpsyt.2019.00317. 42. Philip NS, Sorensen DO, McCalley DM, Hanlon CA.  Non-invasive brain stimulation for alcohol use disorders: state of the art and future directions. Neurotherapeutics. 2020;17(1):116–26. https://doi. org/10.1007/s13311-­019-­00780-­x. 43. Stein ER, Gibson BC, Votaw VR, Wilson AD, Clark VP, Witkiewitz K. Non-invasive brain stimulation in substance use disorders: implications for dissemination to clinical settings. Curr Opin Psychol. 2019;30:6– 10. https://doi.org/10.1016/j.copsyc.2018.12.009. 44. Gomis-Vicent E, Thoma V, Turner JJD, Hill KP, Pascual-Leone A. Review: non-invasive brain stimulation in behavioral addictions: insights from direct comparisons with substance use disorders. Am J Addict. 2019;28(6):431–54. https://doi.org/10.1111/ ajad.12945. 45. Woods AJ, Antal A, Bikson M, Boggio PS, Brunoni AR, Celnik P, Cohen LG, Fregni F, Herrmann CS, Kappenman ES, Knotkova H, Liebetanz D, Miniussi C, Miranda PC, Paulus W, Priori A, Reato D, Stagg C, Wenderoth N, Nitsche MA.  A technical guide to tDCS, and related non-invasive brain stimulation tools. Clin Neurophysiol. 2016;127(2):1031–48. https://doi.org/10.1016/j.clinph.2015.11.012. 46. da Silva MC, Conti CL, Klauss J, Alves LG, do Nascimento Cavalcante HM, Fregni F, Nitsche MA, Nakamura-Palacios EM.  Behavioral effects of transcranial direct current stimulation (tDCS) induced

1  Technology Assisted Therapies for Substance Use Disorder dorsolateral prefrontal cortex plasticity in alcohol dependence. J Physiol Paris. 2013;107(6):493–502. https://doi.org/10.1016/j.jphysparis.2013.07.003. 47. Boggio PS, Zaghi S, Villani AB, Fecteau S, Pascual-­ Leone A, Fregni F. Modulation of risk-taking in marijuana users by transcranial direct current stimulation (tDCS) of the dorsolateral prefrontal cortex (DLPFC). Drug Alcohol Depend. 2010;112(3):220–5. https:// doi.org/10.1016/j.drugalcdep.2010.06.019. 48. Shahbabaie A, Golesorkhi M, Zamanian B, Ebrahimpoor M, Keshvari F, Nejati V, Fregni F, Ekhtiari H. State dependent effect of transcranial direct current stimulation (tDCS) on methamphetamine craving. Int J Neuropsychopharmacol. 2014;17(10):1591–8. https:// doi.org/10.1017/S1461145714000686. 49. Wang Y, Shen Y, Cao X, Shan C, Pan J, He H, Ma Y, Yuan T-F. Transcranial direct current stimulation of the frontal-parietal-temporal area attenuates cue-induced craving for heroin. J Psychiatr Res. 2016;79:1–3. https://doi.org/10.1016/j.jpsychires.2016.04.001. 50. Hardesty DE, Sackeim HA. Deep brain stimulation in movement and psychiatric disorders. Biol Psychiatry. 2007;61(7):831–5. https://doi.org/10.1016/j. biopsych.2006.08.028. 51. Mantione M, Nieman D, Figee M, van den Munckhof P, Schuurman R, Denys D. Cognitive effects of deep brain stimulation in patients with obsessive-­compulsive disorder. J Psychiatry Neurosci. 2015;40(6):378–86. https:// doi.org/10.1503/jpn.140210. 52. Kuhn J, Bauer R, Pohl S, Lenartz D, Huff W, Kim EH, Klosterkoetter J, Sturm V.  Observations on unaided smoking cessation after deep brain stimulation of the nucleus accumbens. Eur Addict Res. 2009;15(4):196– 201. https://doi.org/10.1159/000228930.

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53. Gonçalves-Ferreira A, do Couto FS, Rainha Campos A, Lucas Neto LP, Gonçalves-Ferreira D, Teixeira J.  Deep brain stimulation for refractory cocaine dependence. Biol Psychiatry. 2016;79(11):e87–9. https://doi.org/10.1016/j. biopsych.2015.06.023. 54. Tobacco Use. Centers for disease control: National center for chronic disease prevention and health promotion. 2021. https://www.cdc.gov/chronicdisease/ resources/publications/factsheets/tobacco.htm. 55. Kalkhoran S, Glantz SA.  E-cigarettes and smoking cessation in real-world and clinical settings: a systematic review and meta-analysis. Lancet Respir Med. 2016;4(2):116–28. https://doi.org/10.1016/ S2213-­2600(15)00521-­4. 56. Bhatt JM, Ramphul M, Bush A.  An update on controversies in e-cigarettes. Paediatr Respir Rev. 2020;36:75–86. https://doi.org/10.1016/j. prrv.2020.09.003. 57. Glasser AM, Collins L, Pearson JL, Abudayyeh H, Niaura RS, Abrams DB, Villanti AC.  Overview of electronic nicotine delivery systems: a systematic review. Am J Prev Med. 2017;52(2):e33–66. https:// doi.org/10.1016/j.amepre.2016.10.036. 58. Patil S, Arakeri G, Patil S, Ali Baeshen H, Raj T, Sarode SC, Sarode GS, Awan KH, Gomez R, Brennan PA. Are electronic nicotine delivery systems (ENDs) helping cigarette smokers quit?—current evidence. J Oral Pathol Med. 2020;49(3):181–9. https://doi. org/10.1111/jop.12966. 59. Isik Andrikopoulos G, Farsalinos K, Poulas K. Electronic nicotine delivery systems (ENDS) and their relevance in oral health. Toxics. 2019;7(4):61. https://doi.org/10.3390/toxics7040061.

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Telemedicine and Medication-­Assisted Treatment for Opioid Use Disorder Christine LaGrotta and Christine Collins

Introduction In 2019, there were nearly 50,000 opioid-related overdose deaths in the United States (US) [1]. Despite the increase in prevalence and negative outcomes associated with opioid use disorders (OUDs) in the United States, utilization and adherence with evidence-based treatments remain challenging [2]. One major factor influencing the utilization of effective treatment is access to care, particularly in locations where there is difficulty in recruiting and retaining qualified clinicians, such as in rural areas. As such, there is an urgent need to develop new modes of delivering evidence-­ based treatments to individuals with opioid and other substance use disorders (SUDs). Telemedicine is an innovative option to deliver such care. It has the potential to produce comparable results to in-person treatment, reduce the burden of travel, and help reduce the perception of stigma. It may also be associated with high patient

C. LaGrotta (*) Department of Addiction Psychiatry, James J. Peters Bronx VA Medical Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA C. Collins Department of Psychiatry and Neuroscience, Lindner Center of HOPE/University of Cincinnati College of Medicine, Mason, OH, USA e-mail: [email protected]

and provider satisfaction with the delivery of care [3–5]. Several studies have shown that real-time video teleconferencing appointments are as effective in treating individuals with mental illnesses as in-person visits and are associated with high patient satisfaction [6, 7]. Prior to the COVID-19 pandemic, there were numerous barriers to conducting treatment via telehealth with this population; therefore, it was harder to study its effectiveness. SUD treatment is distinct from the treatment of other mental illnesses in that it involves both patient report and objective findings from urine drug screens; therefore, it is critical that SUD treatment delivery using telehealth is evaluated for its effectiveness. One recently published retrospective chart review conducted prior to the COVID-19 pandemic showed comparative treatment outcomes with telemedicine delivery to patients with OUDs on medication-assisted treatment [8]. COVID-19 has accelerated the use of telemedicine services in the treatment of substance use disorders and allowed for additional research on the effectiveness of this modality of treatment. This chapter will review the current role of telemedicine in the treatment of OUDs. Ongoing research, including gold standard randomized controlled trials, is needed to further evaluate the acceptance of telehealth services and OUD care outcomes.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Avery, M. Khan (eds.), Technology-Assisted Interventions for Substance Use Disorders, https://doi.org/10.1007/978-3-031-26445-0_2

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Fig. 2.1  Monthly volume of behavioral health outpatient encounters January–August, 2019 and 2020 [13]

Telemedicine “Telemedicine” and “telehealth” are broad terms that encompass a wide variety of ways in which healthcare is delivered using technology. The term “telehealth” is a broader term that includes both clinical assessment and non-clinical activities such as administrative meetings, patient ­education, and provider trainings. “Telemedicine” refers specifically to the use of telecommunication for the remote assessment and treatment of patients [9]. In the United States, each state differs in how they define the above terms. In some states, both of these terms are explicitly defined in law and/or policy and regulations, while in other states they are not [10]. Telemedicine modalities include both asynchronous and synchronous technologies. The synchronous model enables real-time consultation between patients and physicians. Asynchronous telemedicine uses a “store and forward” approach, where images, videos, or other medical information are captured, uploaded/ sent to a provider, who then reviews them [11]. Traditionally, in the United States, the key healthcare players Medicare and Medicaid did not reimburse synchronous telemedicine unless it involved both audio and visual components in real-time [12]. However, with the COVID-19 pandemic, this changed due to the rapid need for patients to continue treatment in a safe, socially distanced, environment. Figure  2.1 depicts the

shift from in-person to telehealth outpatient appointments at the start of the pandemic which occurred at a large behavioral health facility, McLean Hospital [13]. Similar shifts to telehealth appointments were happening at clinics across the country and impacted SUD treatment, including OUD care.

OUD Epidemiology In the United States in 2017, 2.1 million people had an opioid use disorder. Even with increased awareness of the opioid crisis, in 2017 there were still 58 opioid prescriptions written for every 100 Americans. Two million people misused prescription opioids for the first time in 2017. On average, 130 Americans die every day from an opioid overdose [14]. Research has shown that the proportion of adults seeking treatment for OUD increased 41% from 2004 to 2013 and then 53.5% from 2013 to 2015  in the midst of the US opioid crisis [15]. The 2019 Treatment Episode Data Set (TEDs) revealed that 24% of all substance use admissions were due to heroin (not other opioids) that year, a rise from 14% in 2009. The average age was 37 years old. The proportion of admissions aged 12 years or older for primary use of opiates other than heroin increased from 7% in 2009 to 10% in 2011 and 2012, before declining to 7% in 2019 (average age of 36 years old) [16].

2  Telemedicine and Medication-Assisted Treatment for Opioid Use Disorder

Medication Treatments The three FDA-approved medications for the treatment of opioid use disorder include methadone, naltrexone, and buprenorphine.

Methadone In order for a person to be prescribed methadone for the treatment of opioid use disorder, they need to be registered in a SAMHSA-approved methadone clinic. These methadone programs are highly concentrated in urban areas, so much so that 91–99% of rural communities lack a methadone clinic. Besides location, these programs are heavily federally regulated, usually require a person to have supervised administration of the medication, and have long waiting lists [17]. The above restrictions make methadone a poor candidate as the treatment of choice for opioid use disorder using telemedicine.

Naltrexone Naltrexone, a mu-opioid receptor antagonist, is another option for the treatment of opioid use disorder. It is less commonly used due to the need to be abstinent from opioids for 7–10 days prior to administration, making it a poor choice particularly in outpatient clinics. However, if a person was able to abstain for that period, then this would be an option to cautiously consider prescribing via telemedicine, ensuring that the patient is aware of precipitated withdrawal symptoms that may occur if taken too early after last opioid use.

Buprenorphine Buprenorphine is a synthetic opioid that acts as an opioid partial agonist and has been FDA approved for acute pain, chronic pain, and opioid use disorder. It is a long-acting, high affinity partial agonist

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at the mu-opioid receptor. Because of its high affinity, buprenorphine blocks other opioids from binding to the mu-opioid receptor. This makes it an ideal candidate medication for opioid use disorder, as it prevents abuse from other opioids [18]. As there are fewer federal regulations on buprenorphine than methadone, as well as the fact that it is often prescribed by primary care providers, psychiatrists, and other providers, it is the best candidate to treat opioid use disorder via telemedicine. Telemedicine with buprenorphine allows patients with OUD to stay in treatment and provides benefits such as enhanced convenience, reduced travel time, and cost savings. This benefits not just patients, but also physicians and the healthcare system as a whole [19]. The West Virginia University Department of Behavioral Medicine and Psychiatry CRC retrospectively reviewed clinic records to assess the difference between face-to-face and telepsychiatry buprenorphine programs for the treatment of opioid use disorder by looking at three outcomes: (1) additional substance use, (2) average time to achieve 30 and 90 consecutive days of abstinence, (3) treatment retention rates at 90 and 365  days. The researchers looked at medical records of 100 patients who were participating in telepsychiatry and face-to-face group based outpatient buprenorphine programs. They found that in comparison with the telepsychiatry group, the face-to-face buprenorphine group did not show any significant differences in terms of all three outcomes that were being studied [20]. University of Maryland’s Division of Alcohol and Drug Abuse Program began delivering treatment with buprenorphine to those with opioid use disorder at a drug treatment center in rural Maryland in August 2015. A chart view (as part of a quality assurance project) was performed on the first 150 patients in the program and looked at both continued opioid use as well as retention in treatment. They found that retention in treatment was 98% at 1 week, 91% at 1 month, and 59% at 3  months. Of those who were still engaged in treatment at 3  months, 94% had stopped using illicit opioids [8].

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At Home Buprenorphine Induction Since induction of buprenorphine can now be done via a telehealth visit (see Ryan Haight Act below), we must be aware of the steps to follow to complete a home induction, which are similar to the steps to take during an in-office induction. The telehealth visit should begin by using the DSM-5 to diagnose an opioid use disorder. Then, one should obtain a complete history of substance use, a full medical/social/psychiatric history, and evaluation for current depression and suicidal thoughts. After the evaluation is complete, just as with an in-person visit, the state’s prescription monitoring system (PMP) should be reviewed and the patient should be provided with medications for breakthrough withdrawal symptoms including insomnia, nausea, muscle aches, and abdominal cramping. The patient, as always, should be warned about precipitated withdrawal. The initial prescription should be sufficient for the patient to complete the induction phase, stabilization phase, and be able to return in 1 week or less (keeping in mind that most patients stabilize on 8–16 mg of buprenorphine). Furthermore, as is standard of care, a naloxone overdose kit should be provided [3].

Drug Enforcement Agency (DEA) registered hospital or clinic [23]. Although there have been positive impacts of the Haight Act, it has also stood in the way of clinicians’ ability to prescribe buprenorphine via telemedicine, as buprenorphine is a controlled substance. The Ryan Haight Act includes a requirement that the DEA (acting on behalf of the United States Attorney General) pass regulations creating a “special registration” process for certain providers. However, the DEA has not followed through on doing this for the prescribing of buprenorphine [19]. Of note, regulations resulting from the Ryan Haight Act were relaxed during the National State of Emergency associated with the COVID-­19 pandemic. This will be detailed more in the below section specific to COVID-19 and is further summarized in Table 2.1.

SUPPORT for Patients and Communities Act

In October 2018, President Trump signed the Substance Use Disorder Prevention that Promotes Opioid Recovery and Treatment for Patients and Communities Act (SUPPORT for Patients and Communities Act), which had several provisions in it. Most relevant to the substance use commuPertinent Legislation Prior nity and the opioid epidemic, this act intends to to COVID-19 increase access to evidence-based treatment and follow-up care, particularly for pregnant women, Ryan Haight Act children, people in rural areas and people in Ryan Haight was 18 years old when he died from recovery from substance use disorders. an overdose of Vicodin that was prescribed to Specifically related to telehealth services, this him by a telemedicine doctor, without an in-­ law further aims to reduce disparities in access to person medical evaluation. In 2008, Congress treatment. To do so, the law requires the passed the Ryan Haight Online Pharmacy Department of Health and Human Services Consumer Protection Act, which limited the con- (HHS) to issue guidance outlining opportunities ditions in which a controlled substance could be for starting to receive Medicaid reimbursement administered by telemedicine [21]. An in-person for assessment, medication-assisted treatment, assessment of the patient needs to be completed counseling, and related SUD services delivered prior to the prescribing of the controlled sub- using telehealth [24]. In addition, the act contains a Medicare stances (with certain specified exemptions) [22]. An example of an exemption would be if the Provision to Address the Opioid Crisis, which patient is treated by and is physically located at a exempts substance use disorder telehealth ser-

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Table 2.1  Opioid prescribing regulations before and during COVID-19 Legislation Ryan Haight online pharmacy consumer protection act Substance use disorder prevention that promotes opioid recovery and Treatment (SUPPORT) for patients and communities act National State of emergency due to COVID-19 pandemic

Year enacted 2008

2018

2020

Summary Required that an in-person appointment and exam be completed prior to prescribing controlled substances via telehealth with certain exceptions (e.g., patient is located at a DEA-­registered hospital or clinic) Included efforts to increase access to evidence-based care for OUDs by exempting certain requirements for telehealth services including geographic limitations and by requiring the Dept of HHS to provide guidelines for opportunities to receive Medicaid reimbursement for OUD treatment services via telehealth

Relaxed federal and state regulations governing the use of telehealth services for general medical and addiction services, including the following:  – Removal of requirement for in-person visit prior to prescribing controlled substances via telehealth  –  Waiver of requiring HIPAA compliant telehealth platforms  –  Expansion of Medicare reimbursement to include telehealth services  –  Flexibility of take-home doses at OTPs  –  Flexibility for prescribing controlled meds via telehealth  –  DEA exception to separate registration of clinician across state lines  – Medicaid and state-specific changes to reduce barriers to prescribing via telehealth

vices from specified requirements, such as geographic restrictions, under Medicare [25]. This may help alleviate barriers to opioid use disorder treatment for people in rural areas and those without easy access to providers who offer treatment.

Barriers Logistical barriers serve as another significant challenge to implementing telehealth services, including startup costs, especially for technologies to be HIPPA compliant. There is limited internet access, especially in rural areas [19]. In order to participate in telemedicine, a patient must have access to a telephone or a computer. The telephone must have video access (which federally funded phones often do not) and must remain active (i.e., if a person is unable to pay the phone bill, the phone may be switched off and thus the appointment cannot be held). For those who have a computer, they must have working wired or wireless internet service.

Many patients who are older, with limited English proficiency, or of low socioeconomic status do not have reliable smartphone access. Also, there are no current evidence-based protocols regarding appropriate frequency of telemedicine visits, how many refills to give, how to counsel patients on appropriate initiation to avoid precipitated withdrawal and when to use urine toxicology. There are also concerns among clinicians that telemedicine may provide limited treatment structure or lead to increased diversion. It is also not clear at this time which patients will benefit more from telemedicine versus in-person care, including which approach may lead to improved retention in care [26].

Effectiveness: Existing Literature Lin et  al. conducted a systematic review of telemedicine-­ delivered treatment interventions for substance use disorders. All of the studies examining medication delivered by telemedicine for opioid use disorder were non-randomized ret-

C. LaGrotta and C. Collins

18

rospective studies. The first study (Eibl and colleagues) found that the telemedicine group was more likely retained in treatment at 1 year compared to those who received a majority of visits in-person. The second, a study by Zheng and colleagues, found no significant difference in time to abstinence as well as 90 and 365  day retention comparing patients receiving medication virtually vs. in person. King and colleagues conducted two studies comparing therapy delivered to methadone patients in person as well as virtually and found no difference in the number of sessions attended and no differences in percent of drug positive urines [12]. Weintraub et  al. conducted a retrospective chart review of 472 patients with opioid use disorder between August 2015 and April 2019. Of these 472 individuals, 443 were prescribed buprenorphine (94%), 20 prescribed with once monthly 380  mg extended release naltrexone, 5 with oral daily naltrexone, and 3 were not given any medication. Those given buprenorphine vs. naltrexone were analyzed separately. The buprenorphine arm had 89% retention at 1 week, 79% retention at 1 month, and 50% retention at 3 months. However, at 3 months, of those patients who remained engaged in treatment, 93% had negative urine screens. In those taking naltrexone, 48% were engaged in treatment at 3 months and of those engaged, 100% had negative urines. They concluded that telemedicine prescribing of buprenorphine, especially in rural populations, was possible, and that prescribing via telemedicine led to similar treatment retention rates than in-person appointments, with the ability to prescribe to a much broader geographical area [8]. A 2021 scoping review included nine studies (3 controlled—2 of which were randomized and 6 observations studies) on the use of telemedicine interventions and applicability to medications for opioid use disorder. Eligible studies were those that examined telemedicine interventions and reported outcomes, such as treatment initiation and retention in care. All of the included studies showed that telemedicine delivery of care was associated with similar outcomes (treatment retention, positive urine toxicology) as compared to treatment as usual [17].

COVID-19 and Beyond As a result of the COVID-19 pandemic, a National State of Emergency was declared. This led to relaxation in federal and state regulations governing the use of telehealth for general medical services and addiction services including the use of phone only or audio/visual technologies for delivering care to patients [27]. During the pandemic, Federal policy changes included a waiver of regulatory requirements related to HIPPA compliant telehealth platforms (HHS/ Office of Civil Rights (HIPPA)), expansion of Medicare coverage to include telehealth services, and flexibility for take-home medication with Opioid Treatment Programs (OTPs through SAMHSA), flexibility for prescribing controlled substances via telehealth, DEA exception to separate registration requirements across state lines, and compliance with addiction treatment confidentiality regulations [28]. Additional state regulation changes included Medicaid changes and state-specific changes to licensing of clinicians.

Removal of Barriers As previously discussed, the Secretary of Health and Human Services, in coordination with the Attorney General, waived the Ryan Haight Act’s in-person examination requirement for the duration of the federally declared COVID-19 emergency. This allowed the initial consultation for buprenorphine treatment to be held via telemedicine. Initially, this was limited to communication conducted by an “audio-visual, real-time, two-­ way interactive communication system,” however, the DEA then authorized telephone consultations as well [29]. The use, and efficacy, of telephone-based encounters was highlighted by the development of the Addiction Treatment Program (ATP), which was a telephone-based program to reduce treatment access barriers for people with substance use disorders staying at 59 of San ­ Francisco’s COVID-19 Isolation and Quarantine (I&Q) sites. There were 12 identified patients who were diagnosed with opioid use disorder and

2  Telemedicine and Medication-Assisted Treatment for Opioid Use Disorder

newly prescribed buprenorphine via telephone. Although 4 left the I&Q site early, of the 8 remaining, 7 individuals continued to take buprenorphine at the time of I&Q discharge. None of the patients who were started on buprenorphine sustained significant adverse events, required emergency care, or experienced overdose [30]. While this was the experience of just one small group, it shows promise in the ability to increase delivery methods for medication treatment of opioid use disorder. Numerous initiatives such as the above were started in response to the COVID-19 pandemic, in which the prescribing of medications for opioid use disorder transitioned away from in-­person visits, many with promising results. Medical students at a student-run free clinic in Miami, Florida transitioned a closed down syringe service program into a telemedicine clinic (TeleMOUD) to use medications to treat those with opioid use disorder. They advertised the clinic on social media, coordinated appointments, and provided support while attending physicians prescribed the buprenorphine itself [31]. During the pandemic, the DEA also further improved access to treatment for opioid use disorder by, in some circumstances, waiving the requirement that a DEA-registered provider needs to obtain a separate DEA registration in each state in which they practice. This change may improve the ability of providers to prescribe buprenorphine via telemedicine, particularly in rural areas [29].

Patient and Provider Experience Since COVID-19 has catapulted telehealth delivery of substance use disorder treatment into widespread use, this has provided an opportunity for better understanding its effectiveness within this population. An online survey study of SUD treatment via telehealth suggested that telehealth services were generally perceived as easy to use by patients, who preferred video appointments over phone [32]. In April 2020, early in the COVID-19 pandemic, one researcher conducted semi-structured

19

interviews with 18 clinicians in 10 separate states who were licensed to prescribe buprenorphine. At that time, nearly all the interview participants were utilizing some telemedicine, and more than half were only doing telemedicine. Most participants noted that they changed their clinical care at that time to remain at home and minimize exposure to the virus. These changes largely included waiving of urine toxicology screening, sending patients home with a larger supply of medications, and requiring fewer visits. The positive impacts of this, as self-reported by the clinicians, included increased quality of patient interactions and increased access for some patients. Self-perceived negatives included less structure, less accountability, less information to inform clinical decision-making, difficulty establishing a connection, technological challenges, and shorter visits [33].

Beyond COVID-19 Prior to the COVID-19 pandemic in 2020, there were calls for the Federal Government to remove unnecessary restrictions to medications for opioid use disorder, with little effect, despite the opioid crisis being declared a United States public health emergency in 2017. Small steps were made, such as increasing the number of patients that those with a buprenorphine waiver could prescribe to and expanding the pool of prescribers [34]. However, even with these small, incremental steps, buprenorphine for the treatment of opioid use disorder remained more difficult to access than opioid analgesics, illicit opioids, and buprenorphine prescribed for pain. Once the COVID-19 pandemic has resolved, many of the previous barriers to treatment will likely come back into effect. The solution to breaking down these barriers includes actions by the Federal Government, including legislative action to remove the waiver requirement and amend the Ryan Haight Act to permit waivered providers to ­prescribe buprenorphine for opioid use disorder treatment without an initial in-person visit, where medically indicated. In addition, the HHS Secretary is authorized by existing law to waive

20

the Ryan Haight restrictions during any public health emergency and should do so for the duration of the opioid public health emergency [29].

Conclusions The opioid crisis and, more recently, the COVID-­ 19 pandemic have brought about a change in the way that medical services for the treatment of opioid use disorder are delivered. Federal regulations, though developed for a good cause, act as a barrier to treatment as they make obtaining the treatment for opioid use disorder harder than getting the opioids themselves. The COVID-19 pandemic catapulted the utilization of telemedicine services in the field of addiction psychiatry. Most notably, this change has been seen for the use of buprenorphine to treat opioid use disorder, in which many of the federal regulations have been temporarily halted. The future role of telemedicine in the treatment of opioid use disorders is uncertain. However, what is known is that in order to keep telemedicine regulations less stringent in the future, we must act now.

References 1. National Institute on Drug Abuse. Opioid overdose crisis. https://www.drugabuse.gov/drug-­topics/ opioids/opioid-­overdose-­crisis. 2. Park-Lee E, Lipari RN, Hedden SL, Kroutil LA, Porter JD.  Receipt of services for substance use and mental health issues among adults: results from the 2016 National survey on drug use and health. Rockville, MD: CBHSQ Data Review; 2012. 3. Oesterle TS, Kolla B, Risma CJ, Breitinger SA, Rakocevic DB, Loukianova LL, Hall-Flavin DK, Gentry MT, Rummans TA, Chauhan M, Gold MS.  Substance use disorders and telehealth in the COVID-19 pandemic era: a new outlook. Mayo Clin Proc. 2020;95(12):2709–18. 4. Shore JH, Yellowlees P, Caudill R, Johnston B, Turvey C, Mishkind M, Krupinski E, Myers K, Shore P, Kaftarian E, Hilty D.  Best practices in videoconferencing-­ based telemental health April 2018. Telemed J E Health. 2018;24(11):827–32. 5. Hilty DM, Crawford A, Teshima J, Chan S, Sunderji N, Yellowlees PM, Kramer G, O'neill P, Fore C, Luo

C. LaGrotta and C. Collins J, Li ST. A framework for telepsychiatric training and e-health: competency-based education, evaluation and implications. Int Rev Psychiatry. 2015;27(6):569–92. 6. Hilty DM, Ferrer DC, Parish MB, Johnston B, Callahan EJ, Yellowlees PM.  The effectiveness of telemental health: a 2013 review. Telemed J E Health. 2013;19(6):444–54. 7. Turgoose D, Ashwick R, Murphy D.  Systematic review of lessons learned from delivering tele-­therapy to veterans with post-traumatic stress disorder. J Telemed Telecare. 2018;24(9):575–85. 8. Weintraub E, Greenblatt AD, Chang J, Welsh CJ, Berthiaume AP, Goodwin SR, Arnold R, Himelhoch SS, Bennett ME, Belcher AM. Outcomes for patients receiving telemedicine-delivered medication-based treatment for opioid use disorder: a retrospective chart review. Heroin Addict Relat Clin Probl. 2021;23(2):5. 9. O’Brien M, McNicholas F.  The use of telepsychiatry during COVID-19 and beyond. Ir J Psychol Med. 2020;37(4):250–5. 10. Understanding telehealth policy. Center for Connected Health Policy. https://www.cchpca.org/. 11. Ting DS, Gunasekeran DV, Wickham L, Wong TY. Next generation telemedicine platforms to screen and triage. Br J Ophthalmol. 2020;104(3):299–300. 12. Lin LA, Casteel D, Shigekawa E, Weyrich MS, Roby DH, McMenamin SB. Telemedicine-delivered treatment interventions for substance use disorders: a systematic review. J Subst Abus Treat. 2019;101:38–49. 13. Busch AB, Sugarman DE, Horvitz LE, et  al. Telemedicine for treating mental health and substance use disorders: reflections since the pandemic. Neuropsychopharmacology. 2021;46:1068–70. Table 1, Monthly volume of behavioral health outpatient encounters January–August, 2019 and 2020. 14. ADOPT: Advancing Drug and Opioid Prevention and Treatment. UCSF. https://opioidpreventionandtreatment.ucsf.edu/sites/g/files/tkssra506/f/wysiwyg/Epi_ Pamphlet.pdf. 15. Huhn AS, Strain EC, Tompkins DA, Dunn KE. A hidden aspect of the US opioid crisis: rise in first-time treatment admissions for older adults with opioid use disorder. Drug Alcohol Depend. 2018;193:142–7. 16. SAMHSA, Treatment Episode Data Set (TEDS). Admissions to and discharges from publicly-funded substance abuse treatment, vol. 2019. Rockville, MD: Substance Abuse and Mental Health Services Administration; 2019. 17. Jones CM, Campopiano M, Baldwin G, McCance-­ Katz E. National and state treatment need and capacity for opioid agonist medication-assisted treatment. Am J Public Health. 2015;105(8):e55–63. 18. Velander JR.  Suboxone: rationale, science, misconceptions. Ochsner J. 2018;18(1):23–9. 19. Yang YT, Weintraub E, Haffajee RL. Telemedicine’s role in addressing the opioid epidemic. Mayo Clin Proc. 2018;93(9):1177.

2  Telemedicine and Medication-Assisted Treatment for Opioid Use Disorder 20. Zheng W, Nickasch M, Lander L, Wen S, Xiao M, Marshalek P, Dix E, Sullivan C. Treatment outcome comparison between telepsychiatry and face-to-face buprenorphine medication-assisted treatment (MAT) for opioid use disorder: a 2-year retrospective data analysis. J Addict Med. 2017;11(2):138. 21. Maheu M.  Telehealth opioids and the Ryan Haight act: update. Telehealthorg. 2021. https://telehealth. org/ryan-­haight-­act/. 22. Shore J.  Ryan Haight online pharmacy consumer protection act of 2008. Washington, DC: American Psychiatric Association; 2008. https://www.psychiatry.org/psychiatrists/practice/telepsychiatry/toolkit/ ryan-­haight-­act. 23. Congress US.  Ryan Haight online pharmacy consumer protection act of 2008. Washington, DC: Congress US; 2008. 24. H.R.6—SUPPORT for patients and communities act. https://www.congress.gov/bill/115th-­congress/house-­ bill/6?q=%7B%22search%22%3A%5B%22HR6%2 2%5D%7D&r=1. 25. Davis CS. The SUPPORT for patients and communities act—what will it mean for the opioid-overdose crisis? N Engl J Med. 2019;380(1):3–5. 26. Wang L, Weiss J, Ryan EB, Waldman J, Rubin S, Griffin JL. Telemedicine increases access to buprenorphine initiation during the COVID-19 pandemic. J Subst Abus Treat. 2021;124:108272. 27. Guidelines: COVID-19 resources. American Society of Addiction Medicine. https://www.asam.org/ quality-­care/clinical-­guidelines/covid.

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28. 42 CFR Part 2. https://www.samhsa.gov/ a b o u t -­u s / w h o -­w e -­a r e / l a w s -­r e g u l a t i o n s / confidentiality-­regulations-­faqs. 29. Davis CS, Samuels EA. Continuing increased access to buprenorphine in the United States via telemedicine after COVID-19. Int J Drug Policy. 2021;93:102905. 30. Mehtani NJ, Ristau JT, Snyder H, Surlyn C, Eveland J, Smith-Bernardin S, Knight KR.  COVID-19: a catalyst for change in telehealth service delivery for opioid use disorder management. Subst Abus. 2021;42(2):205–12. 31. Castillo M, Conte B, Hinkes S, Mathew M, Na CJ, Norindr A, Serota DP, Forrest DW, Deshpande AR, Bartholomew TS, Tookes HE.  Implementation of a medical student-run telemedicine program for medications for opioid use disorder during the COVID-19 pandemic. Harm Reduct J. 2020;17(1):1–6. 32. Molfenter T, Roget N, Chaple M, Behlman S, Cody O, Hartzler B, Johnson E, Nichols M, Stilen P, Becker S. Use of telehealth in substance use disorder services during and after COVID-19: online survey study. JMIR Ment Health. 2021;8(2):e25835. 33. Uscher-Pines L, Sousa J, Raja P, Mehrotra A, Barnett M, Huskamp HA.  Treatment of opioid use disorder during COVID-19: experiences of clinicians transitioning to telemedicine. J Subst Abus Treat. 2020;118:108124. 34. Chan B, Bougatsos C, Priest KC, McCarty D, Grusing S, Chou R.  Opioid treatment programs, telemedicine and COVID-19: a scoping review. Subst Abus. 2021;43:1–8.

3

Technology-Assisted Treatments for Co-Occurring Mental Illness and SUD Anil Abraham Thomas, Matthew Antonello, and Rober Aziz

Case Presentation Jane Smith is a 50-year-old female with a medical history of asthma and a psychiatric history of psychosis, obsessional thoughts and compulsions, early life trauma, alcohol use disorder, and cocaine use disorder who came to the intensive outpatient program for initial evaluation. She was referred to the intensive outpatient program after presenting to the psychiatric emergency room with suicidal ideations while intoxicated. She reported one brief period in fifth grade where she was seen by mental health professionals but not again until now. She endorsed auditory hallucinations since her teenage years as well as episodes that have a dissociative quality. She also has anxiety spectrum symptoms beginning in middle school which have persisted into adulthood without treatment. She experienced sexual trauma at a young age from a family member and physical and emotional trauma throughout her childhood. She has a real estate license A. A. Thomas (*) Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, USA e-mail: [email protected] M. Antonello · R. Aziz NYU Grossman School of Medicine, New York, NY, USA e-mail: [email protected]; [email protected]

and briefly went to nursing school before having seven children, the youngest of which is 10 years old at intake. She denies a lifetime history of rehab, detox, or inpatient psychiatric care. Her auditory hallucinations have been present since she was a teenager but have been threatening over the last 5 years and as a result she sent her children to live with their fathers. She has fears that people are out to get her and that her life is in danger; therefore, she spends a lot of time at home. She began to drink daily and uses cocaine 2–3 times a week over the last 5  years. She developed an alcohol dependence but has no history of complicated withdrawal. The patient has numerous symptoms of OCD which confounds the diagnostic picture further along with early life traumas. She has a history of suicidal ideations with a plan and even once wrote a note but has never made an attempt and reports protective factors. Her family history is positive for a father who had schizophrenia and drank alcohol heavily and a mother who has anxiety and depression. Both parents rarely showed the patient any love or attention. The patient was compliant with her prescribed medications including naltrexone, sertraline, and quetiapine but continued to use alcohol and have anxiety when leaving her home despite being engaged with therapists every week and psychiatrists monthly. Adjunctive technological assisted

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Avery, M. Khan (eds.), Technology-Assisted Interventions for Substance Use Disorders, https://doi.org/10.1007/978-3-031-26445-0_3

23

A. A. Thomas et al.

24

treatment in the form of app based interactive therapy was added to her regimen and the patient reported a positive response including maintaining full sobriety from alcohol for over 3 months. Her counselor and psychiatrist noticed a stark update in her mood and she was able to obtain part-time employment for the first time in 14 years.

Introduction Growing disparities in healthcare access and cost present particular challenges in the field of mental health. Patients with mental health and addictive disorders frequently report barriers to care, such as psychosocial instability, stigma, lack of healthcare literacy, and inability to afford care or have sufficient coverage [1, 2]. Simultaneously, technological advances have provided us with convenient communication, easy access to information, and streamlined many tasks. This has presented a variety of developments in healthcare delivery and access, a growing field that can be described as technology-assisted treatments. Technology-assisted treatments are treatments that can be administered virtually, by mobile device or computer, Table 3.1 Treatment utilization

both mental health and substance use treatment 9%

mental health care only 35%

with minimal to no live provider involvement, and are growing in prominence in various medical specialties and subspecialties.

Epidemiology According to the US Department of Health and Human Services, 7.7 million adults have co-­ occurring mental and substance use disorders. Out of 20.3 million adults with substance use disorders 37.9% also had mental illness. Among 42.1 million adults with mental illness, 18.2% also had substance use disorders. A comprehensive treatment approach should address both disorders at the same time but unfortunately, not everyone with co-occurring conditions will get the treatment they need. Over half of the patients with co-occurring mental illness and substance use disorder (52.5%) did not receive care for either illness. Around a third (34.5%) received mental health care only, less than 10% (9.1%) received care for both mental health and substance use and 3.9% received substance use treatment only [1], (Table 3.1). Among adults with co-occurring disorders who did not receive mental health care, 52.2% said they could not afford the cost, 23.8% said they did not

Who gets treatment?

substance use treatment only 4%

neither mental health care not substance use treatment 52%

3  Technology-Assisted Treatments for Co-Occurring Mental Illness and SUD

know where to go for treatment, 23.0% said they could handle the problem without treatment, 13.6% said they feared being committed, 12.4% said it might cause their neighbors to have a negative opinion of them, 11.1% said they did not think treatment would help, 10.6% said they did not have the time, and 10.1% said they were concerned about confidentiality [2] (Table 3.2). Among adults with co-occurring disorders who did not receive substance use care, 38.4%

25

said they were not ready to stop using, 35.1% said they had no health insurance and could not afford the cost, 13.1% said it might cause their neighbors to have a negative opinion of them, 13.0% said it might have a negative effect on their job, 11.5% said they did not know where to go for treatment, 9.9% said they had insurance, but it did not cover the treatment cost, and 9.0% said no program had the treatment type [2] (Table 3.3).

Among adults with co-occurring disorders who did not receive mental health care, their reasons for not receiving it were:

Table 3.2  Reasons for not utilizing treatment I 60.00% 50.00% 40.00% 30.00% 20.00% 10.00%

y lit

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go to re he

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0.00%

A. A. Thomas et al.

26 Table 3.3  Reasons for not utilizing treatment II Among adults with co-occurring disorders who did not receive substance abuse care, their reasons for not receiving it were: 45.00% 40.00% 35.00% 30.00% 25.00% 20.00% 15.00% 10.00% 5.00% 0.00% Not ready to stop using

Had no health Might cause insurance and neigh bors to could not have negative afford cost opinion

Existing Applications

Might have negative effect on job

No program Did not know Had had the where to go insurance, but for treatment did not cover treatmen type treatment cost

effective than treatment of individual disorders separately or in sequence [3]. Therefore, the Technology-assisted treatments involve the use importance of developing technology-assisted of technology to deliver psychotherapy or behav- treatments for comorbid SUD and other mental ioral treatment directly to patients. This can be disorders is high. Nevertheless, only a few of done with applications on cell phones, tablets, or such treatments for co-occurring SUDs and other websites on computers. Technological advances psychiatric disorders exist. Many patients attribute their sobriety to supnow provide around the clock support to patients. Whether they are in their home, at work, or in a port groups such as 12-step programs. Due to crisis, patients are a few taps and swipes away COVID-19, many groups have reverted to online from connecting to their doctor, therapist, peer and patients have responded positively. As a result, patients are more likely to utilize the 12 support group, or guided meditations. Although the existing literature on technology-­ Steps app which has sobriety reminders, meeting assisted treatments is positive overall with respect finders, daily meditations, and the Alcoholics to acceptability and efficacy, most of these treat- Anonymous big book. Other apps such as Sober Grid, a social media ments only target a single disorder. However, it is uncommon for an individual to present with a profile, allow people with substance use disorsingle diagnosis. Rather, many patients have ders to engage with each other with the goal of comorbid disorders, and this is especially true of recovery. Patients can even use location services individuals with SUDs. Integrated treatments if they wish to find people on the app nearby [4]. The only FDA approved app for treating subdelivered in person in individual and group forstance use disorders is reSET by Pear mats that address symptoms of SUDs and Therapeutics. It is an interactive therapy software comorbid psychiatric disorders simultaneously ­ have been shown to be effective and often more which can be prescribed to patients and provides

3  Technology-Assisted Treatments for Co-Occurring Mental Illness and SUD

a 12 week schedule with weekly check-ins with a clinician. Patients get rewards for completing quizzes after each class and results showed abstinence rates of 40.3% compared to 17.6% of those without the software [4]. Kathleen Carroll and colleagues at Yale designed CBT 4 CBT, a computer-based version of cognitive behavioral therapy used alongside standard care. Six modules focus on cravings, problem-solving, and decision-making. The most impressive result from CBT 4 CBT is sustained abstinence from cocaine, 36% for those in CBT 4 CBT and 17% for the usual treatment group [5]. In a review of six RCTs by Sugarman et  al. (2018) [6], the two technology-assisted treatments with the strongest support were VetChange and computer-delivered Self-Help for Alcohol and other drug use and Depression (SHADE). Both of these skills-focused treatments were associated with reductions in psychiatric symptoms and substance use. The evidence for computer-­delivered SHADE is particularly compelling in that it is the only technology-assisted treatment that has been compared to an in-person equivalent and tested in a replication study. Results showed that computer-delivered SHADE was equivalent to therapist-delivered SHADE in reducing depressive symptoms and substance use and even outperformed therapist-delivered SHADE in reducing alcohol use. Notably, computer-­delivered SHADE required an average of 16 min versus 60 min for therapist-delivered SHADE. This suggests that effective technology-­ assisted treatments like SHADE could be beneficial in reducing the time required for clinicians treating individuals with comorbid diagnoses [6–8]. Evidence is less robust for low-intensive and brief technology-assisted treatments. For example, a low-intensive supportive text messaging intervention was shown to reduce depressive symptoms, but not alcohol use [9]. SHADE was recently adapted for young adults with depressive symptoms and alcohol use disorder. The adapted technology-assisted treatment (DEAL: DEpression-ALcohol) is a 4-module, 1  h per week, internet-based intervention that includes CBT and MI techniques with homework exer-

27

cises. Although short-term reductions in alcohol use and depressive symptoms were observed, these reductions were not sustained at longer follow-up points. In addition, a single-session integrated intervention was not found to be effective in reducing alcohol use and depressive symptoms even in the shortterm [10]. Therefore, it can be inferred that the duration and intensity of technology-assisted treatments are important factors that impact the efficacy of technology-­assisted treatments for comorbid symptoms. There is a current RCT that is investigating DEAL in combination with social networking, which allows participants to access the intervention for 12 months. It would be salient to know if the results of this study demonstrated more long-­term sustained results for participants. Some efficacy has also been demonstrated for technology-assisted treatments focused on comorbid SUDs and borderline personality disorder as well as disaster-affected individuals with substance use, depression, and anxiety [6, 11]. However, the effects of these technology-assisted treatments on substance use and psychiatric outcomes have yet to be tested in controlled trials. Data from a study that longitudinally followed a cohort of New York City area residents after the terrorist attacks of September 11, 2001 revealed differences in gender, race, education level, and overall health between participants and non-­ participants [11]. This suggests an additional need for data collection focused on determinants of participation and acceptability of an intervention by demographic subgroups.

Future Directions Three RCTs of new technology-assisted treatments are currently underway that may add to the literature in important ways [12–14]. The Internet Treatment for Alcohol and Depression (iTreAD) study will examine a technology-assisted treatment with added access to a social networking support community. Nearly 65% of adults in the USA use social networking sites, up to 90% among young adults [15]. The potential to pro-

A. A. Thomas et al.

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vide supportive services through a platform that many people are already using could increase engagement. Similarly, the Climate Schools Combined intervention aims to use an internet-­ based treatment to engage school-aged children in universal prevention for substance use and mental health problems [14]. Similar to VetChange, Coming Home and Moving Forward are directed at recent combat veterans [13]. Technology-assisted treatments that address comorbidity for this population are needed given the high rates of both SUDs and PTSD observed [16]. Results from this study may provide insight into providing integrated treatment to patients who may be reluctant to seek help in outpatient settings due to stigma. Examination of the literature demonstrates that the majority of existing technology-assisted treatments address alcohol use, depression, and/ or some form of anxiety. Two significant areas for research in regard to technology-assisted treatments for SUDs and comorbid diagnoses are eating disorders and psychotic disorders. Many individuals with eating disorders often report problems with substances, with prevalence rates of comorbid SUDs and eating disorders estimated to be as much as 46% [17]. In addition, there is a high comorbidity of SUDs and non-­ substance induced psychotic symptoms [18]. Therefore, technology-assisted treatments for these individuals could be particularly useful. A pilot study using live text messaging between therapists and individuals with psychotic disorders found that participants reported that the intervention was helpful and useful [19]. This supports the potential of a mobile app for individuals with comorbid SUDs and psychotic disorders. The extent of integration, defined as the degree to which a treatment addresses both SUDs and other co-occurring psychiatric disorders, is variable between existing technology-assisted ­treatments. Technology-assisted treatments that associate substance use and a related psychiatric disorder and provide skills aimed to address these different diagnoses have demonstrated a high level of integration. Several technology-assisted treatments meet this level of integration (SHADE,

VetChange, DEAL, and Coming Home and Moving Forward) [6]. Lastly, patient adherence with technology-­ assisted treatments should be further researched to maximize potential engagement. A previous review of computer-assisted therapies for psychiatric disorders found that few studies reported rates of engagement or completion [20]. The initial SHADE trial did not find any differences between computer-based versus in-person delivery in regard to the number of sessions completed [7]. However, other studies reported much lower levels of engagement. Participants assigned to DEAL completed fewer sessions than those assigned to the control group, and only 68% of intervention participants completed at least one module [21]. The VetChange study reported high rates of completion for the first module, but only a third of the sample completed all 8 modules [22]. Another study found that completion rates varied by topic, with more participants completing modules related to depression and smoking and less participants completing modules that targeted drugs and anxiety [11]. Future research should examine methods to improve engagement with technology-assisted treatments and also investigate the optimal frequency and duration of technology-assisted treatments for substance use and comorbid disorders.

Conclusion Approximately 40% of persons with a substance use disorder have co-occurring mental illness [23]. These patients experience compounded barriers to care and incur greater economic costs [24]. Technology-based interventions present a unique opportunity to address the needs of these complex patients by providing accessible and cost-effective care. Given the early state of this field of research, limited data exists on technology-­assisted treatments, let alone those designed to treat comorbid psychiatric disorders. Yet, the literature demonstrates some notably effective treatments, such as reSET, VetChange, and SHADE [6]. Important future directions for technology-assisted treatments should include

3  Technology-Assisted Treatments for Co-Occurring Mental Illness and SUD

concomitant personality disorders, eating disorders, and psychotic disorders as well as deepen our understanding of and overcome barriers to access to care.

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12. Kay-Lambkin FJ, Baker AL, Geddes J, et  al. The iTreAD project: a study protocol for a randomised controlled clinical trial of online treatment and social networking for binge drinking and depression in young people. BMC Public Health. 2015;15:1025. 13. Possemato K, Acosta MC, Fuentes J, et  al. A web-­ based self-management program for recent combat References veterans with PTSD and substance misuse: program development and veteran feedback. Cogn Behav 1. Han B, Compton WM, Blanco C, Colpe Pract. 2015;22:345–58. LJ. Prevalence, treatment, and unmet treatment needs 14. Teesson M, Newton NC, Slade T, et al. The CLIMATE of US adults with mental health and substance use disschools combined study: a cluster randomised conorders. Health Aff (Millwood). 2017;36(10):1739–47. trolled trial of a universal internet-based prevention https://doi.org/10.1377/hlthaff.2017.0584. program for youth substance misuse, depression and 2. National Institute on Drug Abuse (https://nida.nih. anxiety. BMC Psychiatry. 2014;14:32. gov). National Institutes of Health (https://www.nih. 15. Perrin A.  Social media usage: 2005–2015. gov). U.S. Department of Health and Human Services Washington, DC: Pew Research Center; 2015. (https://www.hhs.gov). 2023. 16. Cohen BE, Gima K, Bertenthal D, Kim S, Marmar 3. Mangrum LF, Spence RT, Lopez M.  Integrated verCR, Seal KH.  Mental health diagnoses and utilizasus parallel treatment of co-occurring psychiatric tion of VA non-mental health medical services among and substance use disorders. J Subst Abus Treat. returning Iraq and Afghanistan veterans. J Gen Intern 2006;30:79–84. Med. 2010;25:18–24. 4. Ben Lesser Ben lesser is one of the most 17. Harrop EN, Marlatt GA.  The comorbidity of subsought-after experts in health. “Ben Lesser.” stance use disorders and eating disorders in women: Dualdiagnosis.org. 2021. https://dualdiagnosis.org/ prevalence, etiology, and treatment. Addict Behav. apps-­for-­addiction-­recovery-­and-­mental-­health/. 2010;35:392–8. 5. Carroll KM, et  al. Computer-assisted delivery of 18. Lechner WV, Dahne J, Chen KW, et  al. The prevacognitive-behavioral therapy: efficacy and durabillence of substance use disorders and psychiatric disity of CBT4CBT among cocaine-dependent indiorders as a function of psychotic symptoms. Drug viduals maintained on methadone. Am J Psychiatry. Alcohol Depend. 2013;131:78–84. 2014;171(4):436–44. 19. Ben-Zeev D, Kaiser SM, Krzos I. Remote “hovering” 6. Sugarman DE, Campbell ANC, Iles BR, Greenfield with individuals with psychotic disorders and subSF. Technology-based interventions for substance use stance use: feasibility, engagement, and therapeutic and comorbid disorders: an examination of the emergalliance with a text-messaging mobile interventionist. ing literature. Harv Rev Psychiatry. 2017;25(3):123– J Dual Diagn. 2014;10:197–203. 34. https://doi.org/10.1097/HRP.0000000000000148. 20. Kiluk BD, Sugarman DE, Nich C, et  al. A method7. Kay-Lambkin FJ, Baker AL, Lewin TJ, Carr ological analysis of randomized clinical trials of VJ.  Computer-based psychological treatment for computer-assisted therapies for psychiatric disorders: comorbid depression and problematic alcohol and/or toward improved standards for an emerging field. Am cannabis use: a randomized controlled trial of clinical J Psychiatry. 2011;168:790–9. efficacy. Addiction. 2009;104:378–88. 21. Deady M, Mills KL, Teesson M, Kay-Lambkin F. An 8. Kay-Lambkin FJ, Baker AL, Kelly B, Lewin online intervention for co-occurring depression and TJ. Clinician-assisted computerised versus therapist-­ problematic alcohol use in young people: primary delivered treatment for depressive and addictive disoutcomes from a randomized controlled trial. J Med orders: a randomised controlled trial. Med J Aust. Internet Res. 2016;18:e71. 2011;195:S44–50. 22. Brief DJ, Rubin A, Keane TM, et  al. Web interven9. Agyapong VIO, Ahern S, McLoughlin DM, Farren tion for OEF/OIF veterans with problem drinking CK.  Supportive text messaging for depression and and PTSD symptoms: a randomized clinical trial. J comorbid alcohol use disorder: single-blind ranConsult Clin Psychol. 2013;81:890–900. domised trial. J Affect Disord. 2012;141:168–76. 23. Substance Abuse and Mental Health Services 10. Geisner IM, Varvil-Weld L, Mittmann AJ, Mallett Administration. Results from the 2012 national surK, Turrisi R.  Brief web-based intervention for vey on drug use and health: summary of national find­college students with comorbid risky alcohol use and ings. Rockville, MD: Substance Abuse and Mental depressed mood: does it work and for whom? Addict Health Services Administration; 2013. Behav. 2015;42:36–43. 24. Kessler RC, Nelson CB, McGonagle KA, Edlund MJ, 11. Ruggiero KJ, Resnick HS, Acierno R, et al. Internet-­ Frank RG, Leaf PJ. The epidemiology of co-occurring based intervention for mental health and substance addictive and mental disorders. Am J Orthopsychiatry. use problems in disaster-affected populations: a pilot 1996;66:17–31. feasibility study. Behav Ther. 2006;37:190–205.

4

Online Peer Support for Substance Use Disorders Kate Fruitman

Introduction In a 2015 TED Talk that now has over 18 million views, Johann Hari, a journalist who has extensively written about societal perceptions of substance use, postulates that “the opposite of addiction is connection” [1]. Hari suggests that patients with SUDs, ostracized by friends, family, and colleagues, often lack social support [1]. In an argument substantiated by studies on the role of community in SUD treatment, he posits that rebuilding meaningful interpersonal connections is essential to recovery [1, 2]. Patients with SUDs have historically made these connections through engagement with peers, or individuals in various stages of recovery with experiential knowledge about life with an addiction [3, 4]. Peer support for patients with SUDs, a term which captures all services based on the provision of emotional and informational assistance through peers, can occur in a variety of formats. For instance, peer engagement is a central component of MAOs, or nonprofessional support groups composed of individuals with similar challenges and a shared motivation to recover from said challenge [5]. MAOs, the most utilized of which are 12-Step programs, provide patients with a theoretical framework to guide

K. Fruitman (*) Weill Cornell Medicine, New York, NY, USA e-mail: [email protected]

recovery and a strong community of like-minded peers [4, 5]. Patients can also seek mutual aid outside of formal MAOs by connecting with peers in support groups or on social media platforms [6]. For example, patients can access recovery-specific resources like InTheRooms. com, a website and mobile application designed to host virtual recovery group meetings, blog content, and chat functions. The premise of mutual aid is that nonprofessional, bidirectional support is provided by peers (Fig. 4.1). However, there is increasing recognition for the role of PRS, or individuals in recovery trained to provide professional assistance in the treatment of SUDs (Fig. 4.1) [4, 7]. PRS have the unique capacity to integrate the previously siloed clinical interventions (e.g., medication assisted treatment) and non-clinical treatments for SUDs (e.g., Alcoholics Anonymous meetings) [4, 8]. By engaging patients in settings like the emergency room or community clinics, PRS can provide the empathetic support often lacking in clinical environments while educating patients on the role of medication in SUD treatment and the existence of strong recovery communities. Therefore, PRS programs have been demonstrated to be effective in increasing both successful connection to SUD outpatient treatment following detoxification, as well as improved SUD treatment adherence following hospital discharge [9, 10]. Notably, regardless of the context of engagement, peers help patients

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Avery, M. Khan (eds.), Technology-Assisted Interventions for Substance Use Disorders, https://doi.org/10.1007/978-3-031-26445-0_4

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K. Fruitman

32 Peer

Peer

Mutual Aid

Patient

PRS

Peer Coaching

Fig. 4.1  Mutual aid services provide bidirectional nonprofessional peer support, while PRS programs offer unidirectional professional peer support Table 4.1  Benefits of online peer engagement • Diversity of available support groups/12-step meetings [14–17] • Convenience of logging in to meetings remotely [14–17] • 24/7 access to peers through social media platforms [6, 18, 19] • Increased anonymitya [6, 14, 16, 17] • Opportunity to globally expand social network of recovery-oriented peers [17] • Decreased perceptions of stigma [20, 21]  There is continued discord in the literature regarding the impact of virtual services on anonymity and whether increased anonymity positively or negatively affects recovery

Table 4.3  Future directions • Examine the impact of the transition to virtual mutual aid meetings on substance-use-specific outcomes • Explore the efficacy of online PRS services for SUD • Understand the trajectory for continued technology-­ assisted peer services in the years following the COVID-19 pandemic • Consider the feasibility and efficacy of “reverse telehealth” for PRS [23] • Provide training for peer specialists that specifically incorporates telehealth skills [25]

a

Table 4.2  Limitations of online peer engagement • Perception of social disconnectedness on virtual platforms [17, 22] • Inequity in access to technology [23, 24] • Fatigue associated with excessive videoconferencing [17]

develop a strong social support system and can increase perception of belonging within a community [11]. Unfortunately, during the COVID-19 pandemic, programming like MAO meetings or PRS encounters could no longer occur in-person. The transition to virtual platforms allowed for the continued provision of support at a time when patients with SUDs faced increasing challenges like housing and food insecurity, exacerbations of underlying medical and psychiatric conditions, and fewer opportunities to connect with timely SUD care [12, 13]. In the following chapter, I explore the use of online peer-based interventions for patients with SUDs, including a discussion of the benefits (Table  4.1), challenges (Table  4.2), and future

directions (Table  4.3) of virtual mutual aid and PRS engagement.

Online Mutual Aid Services In a recent review of nonprofessional digital recovery support services, Bergman and Kelly provide a framework to conceptualize the scope of presently available resources [6]. The authors offer four characteristics that can uniquely distinguish a given format of peer-to-peer digital engagement: type of service, type of platform, points of access, and organization/individuals responsible. Offered services may range from a synchronous virtual Alcoholics Anonymous meeting with videoconferencing capabilities to asynchronous interaction with recovery-related content like blog posts and images. The platform refers to the way in which participants access the service, including the use of conferencing technology (e.g., Zoom), recovery-specific social networking sites (e.g., InTheRooms), or general-interest social networking sites (e.g., Facebook). The point of access explains the way in which individuals can utilize the platform, ranging from a website to a smartphone applica-

4  Online Peer Support for Substance Use Disorders

tion. Lastly, digital recovery support services can be categorized based on the organizations/individuals responsible for developing and moderating the service. MAOs, such as Alcoholics Anonymous, are frequently involved in creating and maintaining these resources. However, other support services can be entirely regulated by volunteer peer monitors. Several studies have attempted to categorize the prevalence of technology-assisted recovery initiation and maintenance, including the use of digital mutual aid services. Data from the 2017 National Recovery Study, a nationally representative sample of adults in the USA in recovery from a SUD, reported that approximately 11% of patients utilized at least one type of online technology to decrease use or continue abstinence [18, 26]. When specifically considering mutual aid activity, 4.1% attended virtual MAO meetings (e.g., Alcoholics Anonymous, SMART Recovery), 4.9% utilized a general-interest social network site (e.g., Facebook), and 3% used recovery-specific social network sites (e.g., InTheRooms.com) as part of their recovery journey [18]. In a study specifically examining use patterns on InTheRooms. com, the authors found that participants engaged with the platform for an average of 30 min per day several times per week, with virtual video meeting and daily meditation participation being the most frequently utilized features [19]. Bergman and Kelly’s proposed categorization schema is useful to conceptualize the various ways in which patients can utilize technology to access mutual aid [6]. When specifically considering the integration of technology in MAOs, studies have demonstrated a long-standing practice of using devices to augment face-to-face meetings. For example, even prior to the COVID-­ 19 pandemic, members of Alcoholics Anonymous utilized technology like mobile devices, video-chat, and virtual messaging platforms to connect with peers outside of official meetings [27, 28]. Furthermore, through recovery-specific social media platforms like InTheRooms.com, patients can access a broad range of virtual meetings and engage with peers through live videoconferencing [14]. In a pre-pandemic study characterizing the

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use of virtual meeting attendance through InTheRooms.com, the authors found that while most participants preferred a combination of in-­ person and videoconference group attendance, some members exclusively accessed 12-Step meetings through the digital platform [14]. The authors demonstrated that participants found online meetings “almost as useful” as face-to-­face meetings, but virtual engagement offered the convenience of remote participation and an increased number of possible meetings to attend [14]. In addition to utilizing social media platforms to engage in MAOs, InTheRooms.com members can asynchronously interact with recovery-­ related content while enjoying 24/7 access to peers through chat features. By learning about the recovery stories and challenges of peers, participants build recovery-supportive ties, increase exposure to positive recovery outcomes, and bolster their recovery self-efficacy [6, 8]. As such, unsurprisingly, studies of InTheRoom.com participants found that members believed the platform to increase motivation for abstinence/ recovery and self-efficacy [19]. Similarly, the recovery-specific social media smartphone application, Sober Grid, provides users with opportunities for virtual engagement with peers through chats, location-sharing services, and interaction with posted images and blog content [29]. In a study investigating Sober Grid, the authors concluded that patients with a greater number of virtual social connections had longer lengths of sobriety, a finding that reinforces the importance of peer connection as a critical recovery resource [29]. Notably, although not described in this study, Sober Grid now also offers virtual access to certified PRS, a format of peer-based recovery that will be discussed in subsequent sections as it is not considered mutual aid.

Lessons Learned: The COVID-19 Pandemic and Mutual Aid The COVID-19 pandemic necessitated the transition to the virtual delivery of SUD treatment, including mutual aid services [15]. As such,

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unsurprisingly, investigators demonstrated a 143% increase in telehealth service availability in SUD treatment facilities [30]. Furthermore, InTheRoom.com reported that 12-Step meeting attendance through their site increased from an average of 170–465 weekly attendees [16]. Unfortunately, there is limited literature commenting on the impact of the transition to virtual meetings on patient engagement and recovery outcomes. However, based on a review of the relevant literature from both before and after the onset of the COVID-19 pandemic, the following section will summarize the potential benefits (Table 4.1) and limitations (Table 4.2) of virtual mutual aid services. One study examining meeting attendance among members of Narcotics Anonymous found that respondents attended more virtual meetings per week following the onset of the COVID-19 pandemic as compared to face-to-face meetings prior to the pandemic [16]. Notably, 64.9% of participants endorsed that “virtual meetings were as good or better than face-to-face meetings for maintaining their abstinence” [16]. Relative to White participants, Black and Hispanic participants were more likely to endorse this statement [16]. More than half of participants stated that they attended meetings outside of their time zone, including meetings hosted from overseas [16]. As such, the authors concluded that virtual meetings, in addition to abiding by state-mandated stay-at-­ home orders, increased scheduling flexibility and allowed patients to broaden the geographic range of their fellowship connections [16]. This data echoed the pre-pandemic work on the use of videoconference-­ aided meetings attendance through InTheRooms.com, which demonstrated that giving members the opportunity to engage in virtual meetings increased the number of available meetings to attend [14]. Similarly, a qualitative study of members of Gambler’s Anonymous following the onset of the COVID-19 pandemic demonstrated that the transition to teleconferencing allowed participants to attend meetings outside of their local branch [17]. This study suggested that virtual meetings not only increased the number of available sessions, but also allowed participants to become

K. Fruitman

“new members” at non-local meetings [17]. As a result, members could increase the scope of their recovery-oriented social network and benefit from the diverse perspective of new peers [17]. Notably, work prior to the pandemic demonstrated that the measurable benefit of 12-Step programs has been linked not only to patients enacting the required step-based process, but also to socialization with peers in meetings [31]. As such, by building a larger community of individuals in recovery, especially during a time of tremendous social isolation, members could continue to create and maintain the social connectedness that is critical to recovery [17, 32]. Notably, while some members in the aforementioned Gambler’s Anonymous [17] and Narcotics Anonymous [16] studies felt that virtual mutual aid meetings were sufficient to maintain connectedness, others believed that online meetings could not recreate the intimacy of face-­ to-­ face gatherings [17]. Specifically, members grappled with the “dehumanizing” nature of video-based interactions and the resulting disruption of the social bonds within the group [17]. Moreover, some members noted that they experienced “fatigue” from the excessive use of videoconferencing not only in the setting of mutual aid meetings but also for work-related responsibilities [17]. Interestingly, the extent to which anonymity, an integral element to many 12-Step recovery groups, was preserved in the transition to virtual meetings continues to be debated in the literature. In a pre-pandemic qualitative study on the use of videoconferencing on InTheRooms.com, participants disclosed that they faced the difficulty of balancing sufficient anonymity with sharing enough details to build strong connections [14]. The authors commented that surveyed members either advocated for the necessity of self-­ disclosure to build comradery or, conversely, believed that the highly public nature of InTheRoom.com meetings required greater precautions to protect member confidentiality [14]. The qualitative study on members of Gamblers Anonymous similarly raised the concern that Zoom meetings may violate the core tenant of confidentiality [17]. Specifically, as many mem-

4  Online Peer Support for Substance Use Disorders

bers participated in group meetings from home, participants had the concern that their own families may be privy to their self-disclosure and that of other group members [17]. Offering yet another perspective on anonymity, the authors of a study conducted on members of Narcotics Anonymous posited that virtual meeting attendance increased the perception of anonymity, thereby decreasing the potential “social anxiety” associated with meeting strangers in new groups [16]. Furthermore, for individuals interested in recovery from the perspective of harm reduction, attending the abstinence-­ focused 12-Step groups may be especially intimidating [6]. As such, the opportunity to observe or join meetings without self-identification may decrease fear of judgment, a significant barrier to participation [6]. Notably, given these disparate accounts of member experiences and perceptions of anonymity in virtual meetings, further research is warranted.

 irtual Peer Recovery Specialist V Services While participation in MAOs, support groups, or social networking modalities can provide nonprofessional SUD care, patients can also benefit from engagement with professionally trained peers [3, 4]. Peer providers, recovery coaches, or PRS are individuals in recovery who not only have experiential knowledge pertaining to addiction, but also have undergone formal training in SUD treatment [20]. Unlike the mutual aid treatments described in the above sub-sections, PRS programs largely provide unidirectional care, from provider to patient [33]. Although presently discussed in the context of SUDs, peer specialists have been utilized in treatment programs for other diseases, including other psychiatric illnesses [34] and chronic medical conditions like obesity [35, 36]. As was the case for mutual aid, peer specialists across various disciplines provided virtual services prior to the COVID-19 pandemic [34, 36]. When specifically considering psychiatric peer specialist services, studies have demon-

35

strated the efficacy of virtual peer programming [34]. For instance, peers specifically trained in the provision of psychotherapy were able to utilize online social media platforms like Facebook to provide supportive treatment for individuals with mental health conditions [37]. This intervention was demonstrated to decrease rates of anxiety among participants [37]. Furthermore, a pilot study in 2011 demonstrated that patients with bipolar disorder enrolled in an internet-­ based recovery program who received online PRS coaching had improved engagement and retention compared to patients without the virtual peer element [38]. After the onset of the COVID-19 pandemic, peer programs began to transition to virtual healthcare delivery. In a recent survey of mental health peer specialists in the USA, 73% of respondents indicated that they took on “new tasks” during the pandemic, the most common of which included the use of technology [39]. When asked to describe the specific technology-related novel tasks, 34% of respondents indicated that they participated in individual telehealth and 19% of participants facilitated online groups [39]. In terms of peer involvement in addiction treatment, a 2020 survey of SUD treatment centers demonstrated that 73% and 64.5% provided telephone and video-based PRS services, respectively [40]. Overall, these results clearly indicate that online interventions became integral to the delivery of peer support. Unfortunately, despite the growth in technology-assisted peer support, there is limited literature commenting on the outcomes of telehealth peer engagement in the treatment of SUDs specifically, including the impact on successful connection to treatment or maintenance of recovery.

 otential Benefits and Limitations P of Online Peer Recovery Specialist Services One qualitative study on PRS during the COVID-­19 pandemic found that online services increased peer specialist treatment access for patients with SUDs [22]. As discussed in the pre-

K. Fruitman

36

vious sections on mutual aid, the transition to virtual peer platforms allowed for individuals based out of geographically remote regions or patients with complex scheduling needs to interact remotely with peers [14, 22]. Mental health providers serving individuals in rural communities have identified the need to both integrate telehealth services and expand peer provider roles to better address disparities in access to care [41, 42]. As such, although there is no specific literature documenting the feasibility of efficacy of such efforts, virtual peer programming could expand available SUD services within remote communities. Furthermore, PRS have been demonstrated to decrease the stigma associated with seeking help for a SUD, a major barrier to treatment engagement [20, 43, 44]. Notably, studies on patient perceptions and attitudes toward telepsychiatry have similarly found that virtual treatment options decrease the stigma of SUD care [21]. Therefore, although the limited literature on virtual peer support for SUDs does not describe the role of stigma, it is possible that the combination of telehealth and peer engagement presents a welcoming option to individuals concerned about stigma or judgment in clinical spaces. In addition to the potential benefits for patients, the transition to virtual peer support has been a well-received change for some PRS.  Firstly, the attitudes of mental health providers, including licensed and unlicensed clinicians, toward telehealth have been largely supportive of the transition [45]. In a cross-­ sectional survey of mental health providers in the USA performed in the spring of 2020, most providers expressed positive experiences with telepsychiatry, with the majority reporting that they would want to continue to use telehealth platforms for at least 25% of their caseload [45]. In a large survey of peers in the USA, over half of the participants reported that changes in their job as a result of the pandemic were largely positive, with many specifically citing the benefits of integrating technology to allow for remote support services [39]. Despite the various benefits of online peer support, there are some potential limitations

raised by both patients and peers. In an article advocating for the expansion of the PRS role in the setting of the pandemic, Kleinman and colleagues comment on various challenges faced by peers providing care to patients during state lockdowns [23]. The authors argue that despite the convenience offered by virtual peer programming, there may be patients without access to technology who are further marginalized by the transition to online care [23]. The low-income and minority populations whose recovery is most impacted by the social stressors associated with the COVID-19 pandemic may also be the most likely to struggle with access to reliable technology [24, 46]. A study conducted on mental health and SUD-related visit volume at an integrated healthcare organization provides further evidence to the existence of a “digital divide” among lower-income and older community members [24]. The authors demonstrated that following the partial reopening of the health center, individuals enrolled in Medicaid or Medicare, as well as Hispanic and Black patients had a decrease in mental health and substance-related visits [24]. They postulate that this discrepancy may be due to differences in access to technology among minorities, individuals with disabilities, lower-­ income patients, and older adults [24]. In addition to difficulties with access, peer providers noted that online services, despite their necessity during the COVID-19 pandemic, may be inferior to in-person meetings in promoting interpersonal connectedness [22]. PRS specifically noted that the loss of physical touch deeply impacted their relationship with clients in early recovery, as they believed these patients often required the deeper connection forged by physical actions like hugging [22].

Future Directions The transition to online platforms during the COVID-19 pandemic changed the environment of peer programming, both in the setting of mutual aid and PRS services [15]. Despite the expansion of available online resources for peer engagement, the efficacy and longevity of these

4  Online Peer Support for Substance Use Disorders

virtual programs remain unclear. As such, future investigative efforts should be made to understand the impact of virtual PRS engagement and MAO meeting attendance on SUD-related outcomes [16, 17, 22, 23]. While there is available literature on the experience of members of Gambler’s Anonymous [17] and Narcotics Anonymous[16] during the COVID-19 pandemic, these studies did not comment on large organizations like Alcoholics Anonymous and largely focused on members’ perceptions of treatment efficacy. Therefore, future research may be warranted to quantify rates of relapse among members and explore the longitudinal impact of virtual meeting attendance across a broader range of MAOs. Additionally, while the study on members of Narcotics Anonymous [16] found that Black participants had more positive attitudes toward virtual meetings when compared to their White counterparts, further research should elucidate the way in which race and socioeconomic status influence user perceptions of virtual meetings. Given the demonstrated success of PRS in connecting patients to SUD treatment and improving adherence and outcomes, future research should explore the extent to which virtual PRS engagement is similarly effective for patients with SUDs [3, 4]. While qualitative studies on PRS support for SUD patients have indicated that virtual PRS services allowed for the continued provision of care during the COVID-­19 pandemic, the surveyed peers also identified several areas for improvement [22, 23]. For instance, peers noted that technology, despite providing an alternative to face-to-face meetings during the pandemic, posed a challenge to some clients who either lacked access to devices or the necessary skills to interface with virtual platforms [22, 23]. Peer providers have therefore suggested “reverse telemedicine” solutions, or interventions which involve clients utilizing technology within treatment centers to contact remote PRS [23]. However, additional research is needed to better understand the feasibility and efficacy of such initiatives. Moreover, given the expansion of virtual PRS services during the COVID-19 pandemic, there has been

37

effort to expand telehealth-specific training for PRS [25]. The training centers and employers of peer providers should consider the implementation of curricula that prepare PRS to engage clients in remote support [25].

Conclusion Patients with SUDs have been demonstrated to benefit from the support of peers, both in the context of membership to a MAO [5] or interaction with PRS [3, 4]. In the era of increased digital devices accessibility, some individuals in recovery built these peer communities through engagement in virtual settings [6]. Members could utilize social media platforms to communicate with peers through chat functions and blog posts or attend live MAO meetings through videoconferencing technology [14, 18, 19]. These online mutual aid resources not only allowed participants to engage with peers from the convenience of their own devices, but also were found to increase motivation for abstinence/recovery and bolster self-efficacy [8, 19]. Similarly, online PRS services for psychiatric patients were demonstrated to improve treatment engagement, retention, and outcomes for patients with mental illness [34, 37, 38]. After the onset of the COVID-19 pandemic, the utilization of technology for SUD peer support vastly expanded, including the transition of both MAO participation and PRS engagement to virtual platforms [15, 39, 40]. This transition allowed for the continued provision of safe peer services. Members of MAOs attended more virtual meetings during the pandemic than in-person meetings prior to the pandemic, citing the broad availability of online meetings outside of their local branch [16]. Peer providers similarly noted that virtual services may increase accessibility for some patients, but voiced concerns that reliance on technology could exclude low-income or elderly patients without access to devices [22, 23]. Despite the growth of telehealth services during the pandemic, there remains a paucity of literature describing the impact of virtual peer

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engagement on patients with SUDs, including possible inequities in accessibility of care. This chapter highlights not only the body of ­knowledge pertaining to online peer support, but also argues for the continued exploration of existing and novel virtual peer initiatives. Taken together, the opportunity to expand service diversity and availability through the integration of technology into peer programming is a promising venture that warrants further research.

References 1. Hari J.  Everything you know about addiction is wrong; 2015. 2. Stevens E, Jason LA, Ram D, Light J.  Investigating social support and network relationships in substance use disorder recovery. Subst Abus. 2015;36:396–9. 3. Eddie D, Hoffman L, Vilsaint C, Abry A, Bergman B, Hoeppner B, Weinstein C, Kelly JF. Lived experience in new models of care for substance use disorder: a systematic review of peer recovery support services and recovery coaching. Front Psychol. 2019;10:1052. 4. Knight RN. Peer support for substance use disorders. In: Avery JD, editor. Peer support in medicine: a quick guide. Cham: Springer; 2021. p. 1–30. 5. Humphreys K.  Circles of recovery: self-help organizations for addictions. Cambridge: Cambridge University Press; 2003. 6. Bergman BG, Kelly JF. Online digital recovery support services: an overview of the science and their potential to help individuals with substance use disorder during COVID-19 and beyond. J Subst Abus Treat. 2021;120:108152. 7. Liebling EJ, Perez JJS, Litterer MM, Greene C.  Implementing hospital-based peer recovery support services for substance use disorder. Am J Drug Alcohol Abuse. 2021;47:229–37. 8. White WL, Kelly JF, Roth JD. New addiction-­recovery support institutions: mobilizing support beyond professional addiction treatment and recovery mutual aid. J Groups Addict Recover. 2012;7:297–317. 9. Blondell RD, Behrens T, Smith SJ, Greene BJ, Servoss TJ.  Peer support during inpatient detoxification and aftercare outcomes. Addict Disord Treat. 2008;7:77–86. 10. Tracy K, Burton M, Nich C, Rounsaville B. Utilizing peer mentorship to engage high recidivism substance-­ abusing patients in treatment. Am J Drug Alcohol Abuse. 2011;37:525–31. 11. Boisvert RA, Martin LM, Grosek M, Clarie AJ.  Effectiveness of a peer-support community in addiction recovery: participation as intervention. Occup Ther Int. 2008;15:205–20.

K. Fruitman 12. Alexander GC, Stoller KB, Haffajee RL, Saloner B.  An epidemic in the midst of a pandemic: opioid use disorder and COVID-19. Ann Intern Med. 2020;173(1):57–8. 13. Wang QQ, Kaelber DC, Xu R, Volkow ND. COVID-­19 risk and outcomes in patients with substance use disorders: analyses from electronic health records in the United States. Mol Psychiatry. 2021;26:30–9. 14. Rubya S, Yarosh S.  Video-mediated peer support in an online community for recovery from substance use disorders. In: Proceedings of the 2017 ACM conference on computer supported cooperative work and social computing. New  York, NY: Association for Computing Machinery; 2017. p. 1454–69. 15. Krentzman AR.  Helping clients engage with remote mutual aid for addiction recovery during COVID-19 and beyond. Alcohol Treat Q. 2021;39:348–65. 16. Galanter M, White WL, Hunter B. Virtual twelve step meeting attendance during the COVID-19 period: a study of members of narcotics anonymous. J Addict Med. 2021;16(2):e81–6. 17. Penfold KL, Ogden J.  Exploring the experience of gamblers anonymous meetings during COVID-19: a qualitative study. Curr Psychol. 2021;41(11):1–14. 18. Bergman BG, Greene MC, Hoeppner BB, Kelly JF. Expanding the reach of alcohol and other drug services: prevalence and correlates of US adult engagement with online technology to address substance problems. Addict Behav. 2018;87:74–81. 19. Bergman BG, Kelly NW, Hoeppner BB, Vilsaint CL, Kelly JF.  Digital recovery management: characterizing recovery-specific social network site participation and perceived benefit. Psychol Addict Behav. 2017;31:506. 20. Jack HE, Oller D, Kelly J, Magidson JF, Wakeman SE.  Addressing substance use disorder in primary care: the role, integration, and impact of recovery coaches. Subst Abus. 2018;39:307–14. 21. Lin LA, Casteel D, Shigekawa E, Weyrich MS, Roby DH, McMenamin SB.  Telemedicine-delivered treatment interventions for substance use disorders: a systematic review. J Subst Abus Treat. 2019;101:38–49. 22. Anvari MS, Seitz-Brown C, Spencer J, Mulheron M, Abdelwahab S, Borba CP, Magidson JF, Felton JW. “How can I hug someone now [over the phone]?”: impacts of COVID-19 on peer recovery specialists and clients in substance use treatment. J Subst Abus Treat. 2021;131:108649. 23. Kleinman MB, Felton JW, Johnson A, Magidson JF. “I have to be around people that are doing what I’m doing”: the importance of expanding the peer recovery coach role in treatment of opioid use disorder in the face of COVID-19 health disparities. J Subst Abus Treat. 2021;122:108182. 24. Yang J, Landrum MB, Zhou L, Busch AB. Disparities in outpatient visits for mental health and/or substance use disorders during the COVID surge and partial reopening in Massachusetts. Gen Hosp Psychiatry. 2020;67:100–6.

4  Online Peer Support for Substance Use Disorders 25. Fortuna KL, Myers AL, Walsh D, Walker R, Mois G, Brooks JM.  Strategies to increase peer support specialists’ capacity to use digital technology in the era of COVID-19: pre-post study. JMIR Ment Health. 2020;7:e20429. 26. Kelly JF, Bergman BG, Hoeppner BB, Vilsaint C, White WL.  Prevalence and pathways of recovery from drug and alcohol problems in the United States population: implications for practice, research, and policy. Drug Alcohol Depend. 2017;181:162–9. 27. Campbell SW, Kelley MJ. Mobile phone use among alcoholics anonymous members: new sites for recovery. New Media Soc. 2008;10:915–33. 28. Yarosh S.  Shifting dynamics or breaking sacred traditions? The role of technology in twelve-step fellowships. In: Proceedings of the SIGCHI conference on human factors in computing systems. New York, NY: Association for Computing Machinery; 2013. p. 3413–22. 29. Ashford RD, Giorgi S, Mann B, Pesce C, Sherritt L, Ungar L, Curtis B. Digital recovery networks: characterizing user participation, engagement, and outcomes of a novel recovery social network smartphone application. J Subst Abus Treat. 2020;109:50–5. 30. Cantor J, McBain RK, Kofner A, Hanson R, Stein BD, Yu H.  Telehealth adoption by mental health and substance use disorder treatment facilities in the COVID-­19 pandemic. Psychiatric Serv. 2021;73(4):411–7. 31. McCrady B, Tonigan J.  Recent research into twelve step programs. In: Principles of addiction medicine. Philadelphia, PA: Lippincott Williams and Wilkins; 2009. p. 923–38. 32. Groh DR, Jason LA, Keys CB. Social network variables in alcoholics anonymous: a literature review. Clin Psychol Rev. 2008;28:430–50. 33. Salzer MS, Schwenk E, Brusilovskiy E. Certified peer specialist roles and activities: results from a national survey. Psychiatr Serv. 2010;61:520–3. 34. Fortuna KL, Venegas M, Umucu E, Mois G, Walker R, Brooks JM.  The future of peer support in digital psychiatry: promise, progress, and opportunities. Curr Treat Options Psychiatry. 2019;6:221–31. 35. Fisher EB, Ballesteros J, Bhushan N, et al. Key features of peer support in chronic disease prevention and management. Health Aff. 2015;34:1523–30. 36. Young AS, Cohen AN, Goldberg R, Hellemann G, Kreyenbuhl J, Niv N, Nowlin-Finch N, Oberman

39 R, Whelan F. Improving weight in people with serious mental illness: the effectiveness of computerized services with peer coaches. J Gen Intern Med. 2017;32:48–55. 37. O’Leary K, Schueller SM, Wobbrock JO, Pratt W. “Suddenly, we got to become therapists for each other” designing peer support chats for mental health. In: Proceedings of the 2018 CHI conference on human factors in computing systems; 2018. p. 1–14. 38. Simon GE, Ludman EJ, Goodale LC, Dykstra DM, Stone E, Cutsogeorge D, Operskalski B, Savarino J, Pabiniak C.  An online recovery plan program: can peer coaching increase participation? Psychiatr Serv. 2011;62:666–9. 39. Adams WE, Rogers ES, Edwards JP, Lord EM, McKnight L, Barbone M.  Impact of COVID-19 on peer support specialists in the United States: findings from a cross-sectional online survey. Psychiatr Serv. 2021;73(1):9–17. 40. Molfenter T, Roget N, Chaple M, Behlman S, Cody O, Hartzler B, Johnson E, Nichols M, Stilen P, Becker S. Use of telehealth in substance use disorder services during and after COVID-19: online survey study. JMIR Ment Health. 2021;8:e25835. 41. Myers CR. Using telehealth to remediate rural mental health and healthcare disparities. Issues Ment Health Nurs. 2019;40:233–9. 42. Morales DA, Barksdale CL, Beckel-Mitchener AC. A call to action to address rural mental health disparities. J Clin Trans Sci. 2020;4:463–7. 43. Avery JD.  The stigma of addiction in the medical community. In: The stigma of addiction. Cham: Springer; 2019. p. 81–92. 44. Kulesza M, Larimer ME, Rao D.  Substance use related stigma: what we know and the way forward. J Addict Behav Ther Rehabil. 2013;2:782. 45. Guinart D, Marcy P, Hauser M, Dwyer M, Kane JM.  Mental health care providers’ attitudes toward telepsychiatry: a systemwide, multisite survey during the COVID-19 pandemic. Psychiatr Serv. 2021;72:704–7. 46. Khatri UG, Pizzicato LN, Viner K, Bobyock E, Sun M, Meisel ZF, South EC.  Racial/ethnic disparities in unintentional fatal and nonfatal emergency medical services–attended opioid overdoses during the COVID-19 pandemic in Philadelphia. JAMA Netw Open. 2021;4:e2034878.

5

Technology-Assisted Prevention Interventions for Substance Use Disorders Anil Abraham Thomas, Sonya Bakshi, and Mary Rockas

Primary intervention

Secondary intervention

Tertiary intervention

Quaternary intervention

Early intervening before health effects occur Education and preventive measures such as vaccinations, altering risky behaviors:poor eating habits, tobacco use, and banning substances known to be associated with a disease or health condition

Early detection

Slow disease progress

Prevent relapse

Educations, screening and Screening to identify diseases in Managing disease after the earliest stages, before the diagnosis, to slow or stop treatment method onset of signs and symptoms; disease progress through measures include: mammography, treatment methods: chemotherapy, colonoscopy and regular blood rehabilitation and pressure testing. managing complications

Fig. 5.1  Levels of intervention

Technology-assisted prevention intervention for substance abuse can be divided into primary, secondary, tertiary, and quaternary interventions, Fig. 5.1.

A. A. Thomas (*) Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, USA e-mail: [email protected] S. Bakshi · M. Rockas NYU Grossman School of Medicine, New York, NY, USA e-mail: [email protected]; [email protected]

Primary Prevention Primary prevention is defined as an intervention aimed at stopping a disease process before it occurs through behavioral modification or treatment [1]. In the case of substance use disorders, primary preventative measures attempt to deter individuals from developing substance use disorders. For some disorders, particularly those which involve illegal substances such as cocaine, methamphetamine, and heroin among others, these preventative measures seek to deter all use. For other substances, such as alcohol, preventative measures may aim to educate individuals on keeping use within non-problematic limits. Traditionally, primary prevention consists mostly of public health interventions which can include population level programs such as televi-

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Avery, M. Khan (eds.), Technology-Assisted Interventions for Substance Use Disorders, https://doi.org/10.1007/978-3-031-26445-0_5

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A. A. Thomas et al.

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sion/radio campaigns or education programs. These can occur at the school, community, or national level [2–5]. Since primary prevention is most effective prior to the development of a disorder, youth are often targeted. Therefore, many well-known programs are both local and national school based prevention programs, such as D.A.R.E. (Drug Abuse Resistance Education) in the USA [3]. However, there is evidence of declining participation in these programs [4, 5], as well as some evidence that some popular programs, such as D.A.R.E. are not as effective as initially thought [6, 7]. Given some of these failings and as technology becomes more prevalent, attention has shifted to technology-based interventions for primary prevention which have proven effective in multiple settings including in medical settings, schools, and home. In the medical setting there have been some randomized trials showing that technologyassisted interventions are effective [8–10]. One exemplary study [8] randomized adolescent noncannabis smokers into computer-­delivered brief intervention, therapist-delivered brief intervention, or control. At 12 months follow-­up, those in the computer-delivered and therapist-­ delivered interventions had initiated less than the control. There was no difference between the computerand therapy-based interventions. There are multiple technology-assisted interventions that have been shown to be effective in school settings. Notable groundbreaking interventions include CLIMATE and Head-On; these interventions teach students about illicit substances and require them to participate in an interactive activity. Both have been shown to improve knowledge and understanding [11–14]. It is notable that these interventions did not track actual use. One home-based technology-assisted intervention did track outcomes. Real-Talk, a web-­ based program, had teens participate in realistic scenarios online to improve social skills and problem-solving. Six months after the intervention, rates of alcohol, cannabis, polysubstance use, and total substance use were lower for those exposed to the intervention compared to those who were not [15].

These studies demonstrate how technology has the potential to be effective. As technology and social media continue to evolve, there will be an even greater potential to target primary prevention toward high-yield (youth) or high-risk individuals.

Secondary Prevention Secondary prevention attempts to identify a disease at its earliest stages [1]. In the case of substance use disorders, this may include identifying sub-clinical problematic use before it fully meets DSM-5 (Diagnostic and Statistical Manual, fifth ed.) criteria [16]. Traditionally, secondary prevention has consisted of broad screenings in medical settings such as primary care offices, psychiatry settings, and hospitals. One traditional screening tool is the CAGE-5 for alcohol use disorder [17] or general use questions for illicit drugs. With the emergence of COVID, the majority of primary care and psychiatry visits switched to telemedicine, allowing patients to have continued access to care. Screenings were conducted via tele-visits, including verbally asking questions or messaging scales to patients to survey mental health and addiction concerns. Screening, Brief Intervention, and Referral to Treatment (SBIRT), for example, is an evidence-based screening and early intervention tool for individuals at risk of substance use disorders [18]. While it is mostly used in office settings, it can also be administered in-person and remotely using a secure online telehealth platform such as Doxy, Tele-doc, Doximity, or a HIPAAcompliant ZOOM account [19]. The items on questionnaires such as the SBIRT or CAGE, thus, can easily be asked during tele-visits which offers flexibility and use across several practice settings. Using telemedicine has in fact shown increased use of the SBIRT screening tool in some academic settings, such as in clinical social work school [20]. Incorporating training on how to monitor use of alcohol and other substances via telemedicine has been especially crucial during the COVID pandemic [19].

5  Technology-Assisted Prevention Interventions for Substance Use Disorders

In addition to conducting screenings during a telemedicine appointment, these and other screening questionnaires can be sent to patients in messages through secure portals in the electronic medical record (EMR). Gathering past psychiatric and substance use history, current substances use, and quantity and frequency of substance use can be easily accomplished by tele-visits as well. If patients are flagged for increased use of substances, they can be referred to proper treatment, such as therapy, addiction psychiatry/dual diagnosis clinics, or to virtual support groups.

Tertiary and Quaternary Prevention: Relapse Management Tertiary prevention refers to prevention aimed at slowing disease progression and is addressed elsewhere in this book. Quaternary prevention refers to relapse prevention. Technology can be particularly useful in prevention of relapse in patients struggling with substance use. Relapse rates especially during times of stress, isolation, and limited care access can be high; technology in the form of virtual AA and NA meetings, online recovery groups, apps, and social media can provide a platform for individuals with a substance use disorder to stay connected to each other, the community, and the provider for needed support. ReSet was the first FDA-authorized prescription digital therapeutic for substance use disorder to increase abstinence and treatment retention. ReSet is a 12-week interactive module that delivers CBT (cognitive behavioral therapy) in conjunction with the outpatient clinician. ReSet-O is similar to ReSet but more specific to opioid use disorder. Another computer-based program, CBT4CBT, provides computer-based access training for CBT to target substance dependence. One randomized control trial looked at 8 weeks of biweekly use of the CBT program versus standard treatment as usual. Significant differences between groups and improvement in drug use were seen using the CBT4CBT intervention. At six-month follow-up,

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enduring benefit was seen by both self-report and through urine specimens. Participants randomized to the intervention were more likely to maintain or increase gains made during the CBT training program, while those with the standard of care tended to increase substance use. Thus, a computerized intervention like CBT4CBT may provide both short- and long-term effects of preventing drug use [21]. Many individuals with substance use disorders turn to group meetings such as AA (Alcoholics Anonymous), NA (Narcotics Anonymous), and SMART (Specific, Measurable, Achievable, Relevant, Time-bound) Recovery to assist in maintenance of sobriety and relapse prevention. Virtual meetings were historically underutilized; however, with the emergence of COVID, virtual therapeutic has gained significance. An updated list of these meetings can be found on the Recovery Centers for America websites. These virtual platforms not only allowed recovery groups to continue, but also allowed individuals to attend multiple groups per day from the comfort of their homes. Size limits were not imposed as any number of individuals could join and there were no constraints of limiting the amount of people in a smaller space. Meetings were designed to be “fluid” in that they could be attended by phone, Web-chat, and other platforms to increase access to individuals. Many other recovery groups, such as hospital and clinic-based groups previously held in person, were also forced to become virtual seemingly overnight in reaction to COVID.  These groups could also be attended through platforms such as WebEx and Zoom for patients that had video camera access, and by phone for those who did not or preferred to not be seen on video. Smartphone-based apps have also been implemented to address the need for virtual access. One such app, A-CHESS (Addiction-­ Comprehensive Health Enhancement Support System), was designed to target alcohol use, particularly in individuals leaving alcohol treatment facilities to provide continuity of care. The app was created to provide monitoring, information, communication with counselors and support services for patients. A randomized control study

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showed that using A-CHESS decreased drinking days and enhanced long-term abstinence among alcohol users 8  months after leaving residential treatment [22]. Subsequent studies used A-CHESS to target both alcohol and opioid use. One study demonstrated that MAT (Medication-­ Assisted Treatment) + A-CHESS was superior to MAT alone in addressing both alcohol and opioid use [22]. Resources related to HIV and HCV were also incorporated into A-CHESS to see if these can improve screening and treatment for these comorbid conditions. Using smartphone apps and computer-based interventions such as ReSet, CBT4CBT, and A-CHESS has significant effects on relapse prevention. This is especially important in the acute, subacute phase after treatment from detoxification, rehabilitation, or residential based programs and while bridging to outpatient care. They can also provide an effective means for communication between patients and their providers; allow easy access to mental health and substance resources at the click of a mouse, or the touch of a button. Given that many substance users are lost to follow-up or often do not receive aftercare post-treatment, app-based and virtual interventions have the potential to facilitate relapse prevention and management [22].

 ocial Media, Networking Within S Addiction Community Communication about drug use is often prevalent on social media among substance users. Many messages on social media promote acceptance of using substances, and social networks often drive perceptions about marijuana, opioids, and other drugs (peer-to-peer). At times, individuals may glorify drug use via social media, posting pictures and videos of drug-related paraphernalia, or people using substances [23]. Social media users often tweet about illicit substance use, such as nonmedical prescription opioid use, due to perceived protection of their “real” identity. However, for the same reasons that social media can be problematic, it can also be helpful. The wide access that social media gives individuals to

A. A. Thomas et al.

communicate and connect can also be a way to outreach to current and former drug users, thus positively impacting relapse prevention [24]. Social media provides a means for substance users to share personal experiences and problems, ask questions, and obtain support from those who share similar struggles with addiction. Online forums in particular are well-utilized by drug users because of their anonymity and decreased stigma. Social media is considered a particularly viable source for collecting data and gaining insight into patient’s attitudes, behaviors, communication, use of prescription drugs and medications. Several studies have even used social media to identify individuals amenable to drug recovery interventions. Teenagers and young adults are particularly aware of communication regarding drugs in their environment, much of which happens in online communities and social media. Efforts have been made to engage teens and young adults in preventing substance use via digital technology [25, 26]. As previously mentioned, telemedicine during COVID allowed patients to still receive medication-­assisted treatment, despite the barriers created by a lack of in-person visitations and rules regarding controlled substances. For instance, the virtual buprenorphine clinic (VBC) orchestrated by NYC Health and Hospitals, as described, was created acutely to fill this need during a trying time for those in need of buprenorphine-­naloxone. This allowed patients to virtually connect with the addiction psychiatry team at Bellevue hospital. Through this tele-­ clinic, patients were instructed by clinicians to themselves learn how to induce Suboxone or continue with their maintenance. They were also provided resources and bridged to an office-­ based Suboxone clinic going forward. The ability to be prescribed Suboxone without physically seeing a clinician or being observed allowed for more access to care while maintaining similar outcomes. Initial studies of medication-assisted treatment via telehealth demonstrated high patient satisfaction, increased access to treatment, and no increase in adverse outcomes [27]. Further research is required to determine the

5  Technology-Assisted Prevention Interventions for Substance Use Disorders

long-term effects of telemedicine in prevention of substance use and relapse management.

 ros and Cons of Technology P Assistance for Prevention Telemedicine has opened doors for the mental health and substance use fields that were previously unimaginable. In a matter of weeks, clinics had switched from seeing patients in person to piloting secure video and telephone-based visits. The flexibility to see a patient from any place, at any time is a remarkable feat and one that has actually increased compliance rates for medical visits and improved access to care. However, the feeling of being in the room with a patient, making eye contact, reading body language, is something that remains a challenge even during video visits. The activation of going to a doctor’s office and having an in-person visit is something that still cannot be replaced. For substance users, the act of going to the methadone clinic, for instance, is a ritual that many individuals had done daily Fig. 5.2  Pros and cons of technology assistance for prevention

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for years. Despite medical visits taking place virtually, relapse rates still increased during COVID; mental health rates worsened and isolation remained prevalent. For example, in-person groups and AA/NA meetings, sessions with counselors and sponsors were all not possible due to rising fears of infection from COVID. Use of technology provides flexible, readily available, and personalized care for those with mental health and substance use disorders. Using apps such as A-CHESS is an easy-to-use, reliable method that can be used by patients in both inpatient and outpatient settings [22]. However, access to care is limited by smartphones, and not all patients are able to afford smartphones or have access to Wi-Fi on a daily basis. Often in patients who are homeless, living in shelters, or low income, the need of smartphones or computers poses a barrier for receiving adequate care through both telemedicine and app-based interventions. In older individuals, use of technology may also be a limiting factor and may not be a desirable means for management of substance use, Fig. 5.2.

Pros

Cons

Convenience-no need to travel to the clinic

Not in the same physical space, clinic/office setting

Flexibility-scheduled time can be extended beyond regular hours

Difficult to interpret no verbal communications-body language, eye contact, subtle gestures

Increased compliancecare is available from anywhere

No ritual of going to clinic routinely

Improved access to careless "no shows"

Limited by the need of smart devices, Wifi access and knowledge of how to operate the devices

Improved engagementfeel less stigmatized when contact is made from patients’ safe space

Expenses: patients-need smartp hones, wi-fi link; clinician:- needs secure and updated technologies

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Conclusion and Future Directions Technology-assisted prevention interventions for substance use disorders are no longer novel approaches, rather they are fully integrated into the treatment. Technology-assisted prevention intervention continues to evolve. Technology interventions are currently contributing and have further potential to contribute at all levels of prevention: primary, secondary, tertiary, and quaternary levels. They are proving to be efficacious and cost-effective in delivering quality treatment interventions to individuals with substance use disorders; it adds more tools in the toolbox to aid the patients [28]. As technology advances the interventions will advance; however, there is need to continuously gauge the effectiveness of the technology-based prevention interventions.

References 1. Wallace R.  Secondary prevention. In: Breslow LC, editor. Encyclopedia of public health. Cham: Springer; 2006. http://www.enotes.com/ public-­health-­encyclopedia/secondary-­prevention. 2. Afuseh E, Pike CA, Oruche UM.  Individualized approach to primary prevention of substance use disorder: age-related risks. Subst Abuse Treat Prev Policy. 2020;15(1):58. 3. Caputi TL, McLellan AT.  Truth and DARE: is DARE's new Keepin' it REAL curriculum suitable for American nationwide implementation? Drugs: Educ Prev Policy. 2017;24(1):49–57. 4. Johnson K, et  al. Long-term sustainability of evidence-­ based prevention interventions and community coalitions survival: a five and one-half year follow-up study. Prev Sci. 2017;18(5):610–21. 5. Salas-Wright CP, et al. Trends in substance use prevention program participation among adolescents in the U.S. J Adolesc Health. 2019;65(3):426–9. 6. West SL, O'Neal KK.  Project D.A.R.E. outcome effectiveness revisited. Am J Public Health. 2004;94(6):1027–9. 7. Lynam DR, et al. Project DARE: no effects at 10-year follow-up. J Consult Clin Psychol. 1999;67(4):590–3. 8. Walton MA, et al. A randomized controlled trial testing the efficacy of a brief cannabis universal prevention program among adolescents in primary care. Addiction. 2014;109(5):786–97. 9. Harris SK, et  al. Computer-facilitated substance use screening and brief advice for teens in primary care: an international trial. Pediatrics. 2012;129(6):1072–82.

A. A. Thomas et al. 10. Walton MA, et al. Components of brief alcohol interventions for youth in the emergency department. Subst Abus. 2015;36(3):339–49. 11. Vogl L, et  al. A computerized harm minimization prevention program for alcohol misuse and related harms: randomized controlled trial. Addiction. 2009;104(4):564–75. 12. Newton NC, et al. Delivering prevention for alcohol and cannabis using the internet: a cluster randomised controlled trial. Prev Med. 2009;48(6):579–84. 13. Botvin GJ, et al. Long-term follow-up results of a randomized drug abuse prevention trial in a white middle-­ class population. JAMA. 1995;273(14):1106–12. 14. Marsch LA, Bickel WK, Grabinski MJ.  Application of interactive, computer technology to adolescent substance abuse prevention and treatment. Adolesc Med State Art Rev. 2007;18(2):342–56. xii. 15. Schwinn TM, Schinke SP, Di Noia J.  Preventing drug abuse among adolescent girls: outcome data from an internet-based intervention. Prev Sci. 2010;11(1):24–32. 16. American-Psychiatric-Association. Diagnostic and Statistic Manual IV. 5th ed. Arlington, VA: American Psychiatric Association; 2013. 17. O'Brien CP.  The CAGE questionnaire for detection of alcoholism: a remarkably useful but simple tool. JAMA. 2008;300(17):2054–6. 18. SAMHSA. Screening, Brief Intervention and Referral to Treatment (SBIRT) In Behavioral Healthcare. Rockbille: SAMHSA; 2011. 19. Washburn M, et  al. A pilot study of peer-to-peer SBIRT simulation as a clinical telehealth training tool during COVID-19. Clin Soc Work J. 2021;49:1–15. 20. Sacco P, Ting I, Crouch T, Emery L, Moreland M, Bright C, et  al. SBIRT training in social work education: evaluating changes using standardized patient simulation. J Soc Work Pract Addict. 2017;17(1–2):150–68. 21. Carroll KM, et  al. Computer-assisted delivery of cognitive-behavioral therapy for addiction: a randomized trial of CBT4CBT.  Am J Psychiatry. 2008;165(7):881–8. 22. Gustafson DH, et al. A smartphone application to support recovery from alcoholism: a randomized clinical trial. JAMA Psychiat. 2014;71(5):566–72. 23. Evans W, et al. Peer-to-peer social media as an effective prevention strategy: quasi-experimental evaluation. JMIR Mhealth Uhealth. 2020;8(5):e16207. 24. Nasralah T, El-Gayar O, Wang Y.  Social media text mining framework for drug abuse: development and validation study with an opioid crisis case analysis. J Med Internet Res. 2020;22(8):e18350. 25. Marsch LA, Borodovsky JT. Technology-based interventions for preventing and treating substance use among youth. Child Adolesc Psychiatr Clin N Am. 2016;25(4):755–68. 26. Marsch LA, Carroll KM, Kiluk BD.  Technology-­ based interventions for the treatment and recovery

5  Technology-Assisted Prevention Interventions for Substance Use Disorders management of substance use disorders: a JSAT special issue. J Subst Abus Treat. 2014;46(1):1–4. 27. Tofighi B, Abrantes A, Stein MD.  The role of technology-­based interventions for substance use disorders in primary care: a review of the literature. Med Clin North Am. 2018;102(4):715–31. 28. Pradhan AM, et  al. Consumer health information Technology in the Prevention of substance abuse: scoping review. J Med Internet Res. 2019;21(1):e11297.

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29. Dan Noori SG.  Techonology assisted care (TAC), in innovation to impact. New Haven: Yale School of Medicine, Flare Capital Partners Scholar Class; 2019. 30. Marsch LA.  Leveraging technology to enhance addiction treatment and recovery. J Addict Dis. 2012;31(3):313–8. 31. Molfenter T, et al. Trends in telemedicine use in addiction treatment. Addict Sci Clin Pract. 2015;10:14.

6

Brain Stimulation Methods for Substance Use Disorders Karanbir Padda

Introduction Medication-assisted treatment (MAT) is often utilized for substance use disorders (SUD); however, currently FDA-approved MAT is limited to the management of tobacco, alcohol, and opioids. Furthermore, the effect sizes of successful MAT for tobacco, alcohol, and opioids are relatively small [1]. For instance, approximately 50% of patients with opioid use disorder relapse even with the use of MAT [2]. The current limitations of MAT necessitate research into novel therapeutics to treat or augment treatment of SUDs. Brain stimulation methods are showing increasing evidence of being effective for the treatment of a wide variety of SUDs. Current methods that have been researched include both non-invasive and invasive modalities, primarily transcranial direct current stimulation, repetitive transcranial magnetic stimulation, and deep brain stimulation. The use of brain stimulation methods may benefit patients who are unable to tolerate the MAT due to side effects or patients who fail to respond to MAT and other forms of addiction management.

K. Padda (*) Department of Psychiatry, New York-Presbyterian/ Weill Cornell Medical Center, New York, NY, USA

Here, I describe the brain stimulation methods used for SUDs and present the current evidence supporting their use.

 ranscranial Direct Current T Stimulation (tDCS) Description tDCS is a non-invasive method of neuromodulation involving two or more scalp electrodes that apply a low-intensity direct current (0.5–2.0 mA) at a constant rate to a targeted brain area. The anodal electrode increases cortical excitability, while the cathodal electrode decreases excitability of a targeted brain area [3]. tDCS is a low-­ cost, easily accessible, pain-free simulation method with only minor side effects of scalp irritation and pruritus. No recovery time is required following treatment. Current conditions treated by tDCS include major depression, chronic pain, and Parkinson’s disease [4]. The effects of tDCS have been evaluated in various SUDs, including alcohol, nicotine, opioid, methamphetamine, and cannabis (Table  6.1). The tDCS protocols used across studies are variable in terms of size of electrodes, intensity of applied current, targeted brain areas, stimulation duration, and number of stimulation sessions. The dorsolateral prefrontal cortex (DLPFC) is the most often targeted brain region of tDCS in treatment of SUDs, which is

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Avery, M. Khan (eds.), Technology-Assisted Interventions for Substance Use Disorders, https://doi.org/10.1007/978-3-031-26445-0_6

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50 Table 6.1 Transcranial summary

direct

current

stimulation

Method  – Non-invasive, uses scalp electrodes to deliver low-intensity direct current at constant rate to a targeted brain area Primary brain target  – Dorsolateral prefrontal cortex Overall evidence  – Strong positive treatment effects: alcohol, tobacco  – Promising treatment effects: opioid, methamphetamine  – Requires more research: cocaine, cannabis Treatment effects  – Reduces craving, consumption, anxiety, depression, impulsivity Abstinence  – Mixed evidence, 1 RCT suggests abstinence benefits in tobacco dependence Optimal settings  – Dorsolateral prefrontal cortex > other targets  – Multiple > single stimulation sessions

involved in decision-making and executive function. Dysfunction of this brain region has been implicated in addiction and craving response [5, 6]. Below is the current evidence available for individual substances.

Evidence Alcohol A meta-analysis reviewed 18 studies using varying parameters of tDCS for alcohol use disorder and showed positive treatment effects [7]. The primary targeted brain areas in these studies were the DLPFC and inferior frontal gyrus (IFG). The random-effects model meta-analysis revealed a small positive effect of tDCS in reducing alcohol craving and consumption. The placement of electrodes and number of stimulation sessions impacted the effect size. Bilateral tDCS, including anodal-tDCS of the right DLPFC and cathodal-­tDCS of the left DLPFC, produced the most significant reduction in alcohol craving with multiple sessions showing better overall effect [7]. Current evidence supports the use of tDCS for alcohol use disorder. Bilateral tDCS targeting the

DLPFC over multiple stimulation sessions is most effective. Many studies have included patients with comorbid psychiatric disorders, which is a strength of current evidence in determining real word applications with the high rates of psychiatric comorbidity in SUDs. There are also limitations in the current literature. The optimal tDCS protocol remains to be elucidated given the variation between studies. Another important question is the effect of tDCS on alcohol abstinence rates with current studies showing conflicting results regarding a positive effect [8, 9]. Duration of efficacy of tDCS is unclear with one current study showing no effect on abstinence rates beyond 2 weeks [10].

Tobacco A meta-analysis of 12 studies of varying treatment parameters revealed significant positive effects of tDCS in reducing cue-provoked nicotine craving and smoking intake. The studies targeted the DLPFC, occipital lobe or supraorbital area, and frontal-parietal-temporal association areas. Most studies evaluated anodal-tDCS of the DLPFC and cathodal-tDCS of the DLPFC, occipital lobe, and supraorbital area. Analyses revealed that anodal-tDCS of the right DLPFC had significant positive effects on cueprovoked craving, while cathodal-tDCS of DLPFC regions had positive effects on both cue-provoked craving and smoking intake than other brain regions evaluated [11]. These positive findings are further bolstered by a recent randomized control trial comparing 20 sessions of tDCS administered over 4 or 12  weeks to bupropion 300  mg daily for 8  weeks. tDCS administered over 12  weeks had comparable 6  months abstinence rates to bupropion treatment and resulted in significantly lower nicotine dependency [12]. Current evidence supports the use of tDCS for treatment of tobacco use disorder, although there are limitations in the current literature. A standardized tDCS protocol with the most potential benefit for tobacco use disorder requires further research although there is some evidence guiding placement of electrodes with anodal-tDCS of the right DLPFC and cathodal-tDCS of the

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DLPFC.  One study utilizing tDCS for tobacco dependency found a significantly increased time to first cigarette after overnight abstinence, which provides evidence of tDCS being beneficial for tobacco abstinence [13]. However, further research is needed to determine the effect of tDCS on overall tobacco abstinence rates and time to relapse. Another significant limitation is the lack of evidence evaluating the use of tDCS in patients with schizophrenia, a population with an estimated 62% prevalence of tobacco use [14]. One study found that tDCS improves cognitive performance but has no effect on tobacco craving or consumption in patients with schizophrenia. Further research is required to determine the efficacy of tDCS for tobacco use disorder in those with comorbid schizophrenia and other psychiatric disorders.

Cocaine Seven studies have evaluated the effects of tDCS in cocaine use disorder and show conflicting results. All studies targeted the DLPFC and primarily used 2 mA stimulation intensity of 20 min duration. Laterality of anodal and cathodal electrodes and number of sessions varied. tDCS was found to decrease activation of anterior cingulate cortex (ACC) [15] and increase P3 physiological responses [16] to cocaine related cues, brain areas implicated in the cognitive processing underlying addiction. Two studies found a significant decrease in craving [17, 18], while another study showed a decrease in risk-taking behaviors [19]. However, more recent randomized controlled trials found no effect on craving, cocaine use days over, cognition, or abstinence [20, 21]. Current evidence does not support the use of tDCS for cocaine use disorder although further research may elucidate benefits. Methamphetamine Six studies and one case report have evaluated tDCS in methamphetamine use disorder and have shown promising treatment effects. tDCS reduces overall methamphetamine craving [22–27], but there is conflicting evidence regarding cue-­ induced methamphetamine craving. One study showed an increase in craving in response to a

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cue [23], while another study showed a decrease in cue-induced craving [25]. tDCS also impacts cognitive processing involved in addiction with improved performance on executive function tests [22, 25, 26]. A study evaluating electrode placement for reducing cue-induced craving found that anodal left DLPFC/cathodal right shoulder and anodal left DLPFC/cathodal right PFC were optimal for reducing attentional bias to drug cues [28]. There are potentially positive additive effects in improving cognitive processing in combining tDCS with mindfulness-based substance abuse treatment [22] and computerized cognitive addiction therapy [25]. Overall, tDCS shows promising positive effects in reducing methamphetamine craving and improving cognitive processing involved in addiction. However, more research is required in developing an optimal tDCS protocol, evaluating effect in populations with comorbid psychiatric disorders, and determining impact on abstinence and relapse rates.

Opioid tDCS has shown positive treatment effects for pain management and opioid dependence. Two studies found that tDCS over the PFC and motor cortex significantly reduced opioid consumption in the post-operative period following total knee arthroplasty [29, 30] and lumbar spine surgery [31], signifying the potential opioid sparing effects of tDCS.  Furthermore, multiple studies have shown that variable parameters of tDCS significantly reduce craving [32–37], depression [33–35], anxiety [33–35], and impulsivity [37]. However, tDCS has not shown an effect on relapse rates [34]. Overall, tDCS shows an ability to reduce opioid craving and some associated psychiatric symptoms of opioid use disorder. However, more research is needed to standardize a tDCS protocol and evaluate potential benefits in abstinence. Cannabis Evidence is limited for tDCS in cannabis use disorder. One study found that a single session of right anodal/left cathodal-tDCS of the DLPFC diminished craving but also increased propensity

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for risk-taking behavior [38]. There is not enough literature to suggest benefits of tDCS for cannabis dependence.

Repetitive Transcranial Magnetic Stimulation (rTMS) Description rTMS is another non-invasive brain stimulation technique that involves the use of an electromagnetic coil producing repetitive trains of magnetic pulses against the scalp that produces a temporary electrical current to cortical tissue with downstream effects. rTMS modulates cortical excitability and can lead to changes in neuroplasticity [39, 40]. Parameters can vary in pulse frequency, number of pulses, and stimulus intensity. Low frequency (LF) rTMS (≤1 Hz) inhibits cortical excitability, whereas high frequency (HF) rTMS (10–20 Hz) increases cortical excitability [39, 40]. A modified protocol of rTMS is involved in theta burst stimulation (TBS), in which three pulses of stimulation at 50  Hz frequency are repeated every 200 ms and delivered in an intermittent (iTBS) or continuous (cTBS) pattern of 600–900 pulses during 40–190  s [41]. rTMS is currently used for major depression, obsessive-­ compulsive disorder, schizophrenia, Parkinson’s disease, and chronic pain. rTMS has been evaluated for multiple SUDs, including alcohol, nicotine, opioid, cocaine, methamphetamine, and cannabis (Table 6.2). The most commonly targeted brain area across studies is the DLPFC with fewer studies targeting the medial prefrontal cortex (mPFC), insula, and superior frontal gyrus (SFG); studies vary in coil type, stimulation intensity, pulse frequency, pulses per session, total number of pulses, and total number of sessions [42].

Evidence A meta-analysis of 26 studies consisting of 748 patients with varying SUDs (nicotine, alcohol, methamphetamine, cocaine, heroin, cannabis)

K. Padda Table 6.2  Repetitive transcranial magnetic stimulation summary Method  – Non-invasive, uses an electromagnetic coil delivering repetitive magnetic pulses against the scalp to produce a temporary electrical current in cortical tissue  – Primary brain target  – Dorsolateral prefrontal cortex Overall evidence  – Strong positive treatment effects: alcohol, tobacco, cocaine, methamphetamine  – Requires more evidence: opioid, cannabis Treatment effects  – Reduces craving, consumption, withdrawal symptoms, depression, anxiety  – Improves sleep quality Abstinence  – Mixed evidence, requires more research to establish benefit Optimal settings  – Left or bilateral dorsolateral prefrontal cortex > other targets  – High > low frequency delivery of magnetic pulses  – Multiple > single stimulation sessions

found that HF rTMS of the left DLPFC significantly reduced craving, while both HF rTMS of the left DLPFC and deep TMS (dTMS) of the bilateral DLPFC and insula significantly reduced substance consumption. Deep TMS had no anti-­ craving effect, while LF stimulation of the DLPFC and mPFC had neither anti-craving or substance-reducing effects. There was a positive association between total number of stimulation pulses and effect using HF TMS of the DLPFC, but no significant predictive effect of other rTMS parameters [42]. Below, I outline the current findings of rTMS in individual substances.

Alcohol Nine studies evaluating rTMS in alcohol use disorder have found overall positive treatment effects. Multiple stimulation sessions of HF rTMS targeting the DLPFC [43–45] and deep TMS of the mPFC [46, 47] have been shown to reduce alcohol craving with less efficacy shown for a single stimulation session of rTMS targeting the DLPFC [48–51]. The greater efficacy of multiple stimulation sessions may be mediated by the total number of pulses delivered [42]. Two stud-

6  Brain Stimulation Methods for Substance Use Disorders

ies also support the use of deep TMS in reducing alcohol consumption [47, 50]. Overall, the use of multiple stimulation sessions of rTMS is supported in the treatment of alcohol use disorder.

Tobacco Nine studies have evaluated rTMS in tobacco use disorder with varying parameters and show overall positive treatment effects. The primary brain area targeted is the DLPFC but the insula and suprafrontal gyrus have also been researched. Some studies have found that both single and multiple stimulation sessions of HF rTMS targeting the DLPFC result in a significant decrease in nicotine craving [52–55], but the evidence is mixed with negative findings on craving utilizing LF rTMS [56], iTBS [57], and a single stimulation session [58, 59]. Three studies support the use of rTMS in reducing nicotine consumption [52, 60–62], including in patients with schizophrenia [60]. The effects on nicotine abstinence are mixed; two studies showed a positive effect on abstinence of up to 3–6 months [57, 62] but two other studies showed no significant effect on abstinence [56, 61]. Overall, a systematic review of rTMS on nicotine consumption and craving suggests possible efficacy of HF rTMS of the left DLPFC although optimal rTMS parameters and impact on abstinence rates remain areas requiring more research. Cocaine Twelve studies have evaluated the use of rTMS in cocaine dependence, utilizing both HF rTMS and iTBS, and most commonly targeting the left DLPFC.  The right DLPFC, mPFC, and dorsal anterior cingulate cortex (dACC) have also been researched as targets. Several studies have demonstrated that rTMS significantly reduces craving [63–70] and one study showed a decrease in impulsivity [63]. There appears to be an advantage of HF rTMS over LF rTMS as one study found that HF rTMS reduced choice for smoked cocaine, while LF rTMS did not [71]. Multiple stimulation sessions also appear advantageous for longer lasting effects as one study evaluating a single stimulation session of HF TMS targeting the DLPFC only produced transient reduction in

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craving [65]. Importantly, five studies have also shown that rTMS significantly reduces overall cocaine intake [63, 68–70, 72] with only one study resulting in a negative finding [73]. One study showed positive effects for abstinence by elongating the latency to first relapse [74]. Overall, multiple sessions of rTMS most often targeting the left DLPFC is an effective treatment in cocaine dependence for reducing craving and overall cocaine intake, as well as increasing abstinence rates. Early evidence demonstrates advantages for HF rTMS over LF rTMS and multiple stimulation sessions over a single stimulation session although more research is needed to determine an optimal protocol.

Methamphetamine Eight studies evaluating rTMS in methamphetamine dependence have utilized HF rTMS, LF rTMS, iTBS, and cTBS and most commonly targeted the DLPFC with one study evaluating the vmPFC [75]. rTMS has been shown to reduce craving [75–79] and also improve factors modulating substance relapse, including impulsivity, withdrawal symptoms [75, 80], depression [75, 79–82], anxiety [79–82], and sleep quality [79, 80]. Reduction in craving was found to be positively correlated with improvement in anxiety and withdrawal [75] and can last up to 90 days [83]. In a study evaluating optimal rTMS parameters, advantages were seen for iTBS of the left DLPFC and cTBS of the right DLPFC over cTBS of the left DLPFC in reducing craving [79]. Additionally, combination treatment with iTBS of the left DLPFC and cTBS of the left vmPFC has advantages over either treatment alone with improvements in depression, withdrawal symptoms, and sleep quality [75]. Overall, rTMS shows strong promising effects for treatment of methamphetamine dependence but more research is required to determine the optimal rTMS protocol and the impact on abstinence. Opioid Three studies have evaluated rTMS in opioid use disorder utilizing both LF and HF rTMS applied over the left DLPFC. All studies demonstrated a reduction in craving [84–86] with effects lasting

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up to 60 days [84]. In combination with methadone maintenance therapy, rTMS also reduced heroin use [86]. Both LF and HF rTMS were found to be equally efficacious [84]. These results are promising although more research is needed to establish rTMS as an effective treatment for opioid dependence.

Cannabis Only two small studies have evaluated the effect of rTMS in cannabis use disorder. Both studies utilized HF rTMS targeting the left DLPFC.  A single stimulation session was not effective in reducing craving [87] but multiple stimulation sessions were effective in reducing craving and cannabis use. However, the latter study was a case series consisting of only three patients [88]. Neither study reported an adverse event. More research will be required to determine effectiveness of rTMS in cannabis use disorder.

Deep Brain Stimulation (DBS) Description DBS is an invasive neurosurgical stimulation method that involves the implantation of electrodes directly into the brain and allows for continuous modulation of brain activity. Electrodes are connected to an implantable pulse generator, most often in the chest wall, that can adjust stimulation parameters. DBS delivers high frequency stimulation (>130 Hz) to block neural transmission and can lead to changes in neuroplasticity and neurotransmitter release. DBS carries the risk of infection, stroke, intracranial hemorrhage, and seizure given it is an invasive neurosurgical technique. Current uses and areas of study for DBS include Parkinson’s disease, Alzheimer’s disease, chronic pain, obsessive-compulsive disorder, and major depression [89]. DBS has been studied for treatment of addiction in alcohol, nicotine, cocaine, heroin, and methamphetamines. These studies all targeted the nucleus accumbens, an area of the brain involved in reward circuitry and commonly implicated in addiction.

Evidence A systematic review identified 14 publications of case reports/case series evaluating the effects of DBS in SUDs with a total of 33 patients. The parameters and electrode placement varied among studies, along with inclusion/exclusion criteria of patients. Parameters ranged between 130 and 185 Hz pulse frequency, 90 and 240 ms duration of pulse, and 1.5 and 7.0 Volts of electrical potential. Nicotine (n  =  11) and heroin (n = 13) are the most studied with less evidence evaluating alcohol (n  =  6), methamphetamine (n  =  2), and cocaine (n  =  1) [90]. All studies showed a reduction in substance use in at least one patient with further reports of reduced craving and improved quality of life. Some studies showed decreased symptoms of depression, anxiety, and OCD, as well as maintenance of employment. Overall remission rates after the first attempt to quit at 6 months and 1 year following DBS were 61% and 53%, respectively. Evidence of remission was stronger for heroin and alcohol and weaker for nicotine and cocaine. The most common adverse effects included dizziness, insomnia, and weight change with one case of intracranial hemorrhage without neurologic deficit and one case of seizure in a patient with epilepsy [90]. Overall, DBS shows promising therapeutic potential for treatment of SUDs, especially in achieving remission, although studies are limited in size and do not have sham control groups. The impact of patient motivation, psychiatric comorbidity, and adjunctive addiction treatments could all potentially impact treatment outcomes of DBS. Adverse effects are mild but can be serious given the invasive nature of DBS. DBS may offer potential treatment for those with refractory SUDs and significant mortality.

Nerve Stimulation Stimulating nerves such as the vagus nerve, trigeminal nerve, and auricular nerve is another potential method of modulating brain networks in addiction [91]. Vagus nerve stimulation was

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6  Brain Stimulation Methods for Substance Use Disorders

found to inhibit heroin priming and cue-induced reinstatement of heroin-seeking behavior in rats, indicating that it may inhibit cue-induced relapse in opioid use disorder [92]. Transcutaneous auricular nerve stimulation via acupuncture has been shown to improve depression symptoms and sleep quality in protracted alcohol withdrawal [93]. Laser and transcutaneous auricular nerve stimulation has also been shown to improve depression symptoms in patients with alcohol use disorder undergoing therapy [94], indicating a potential adjunct treatment for alcohol use disorder. Transcutaneous auricular nerve stimulation has additionally been shown to potentially be an effective adjunctive treatment to increase treatment retention and decrease methadone detoxification and maintenance dosages in opioid use disorder [95]. This is further supported by evidence that percutaneous electrical auricular nerve stimulation reduces signs and symptoms of opioid withdrawal. Further research will be needed to determine the scope and efficacy of nerve stimulation in SUDs.

Conclusion Brain stimulation methods offer promising treatments for patients with refractory SUDs and intolerable side effects to MAT (Table  6.3). tDCS and rTMS are non-invasive and well-tolerated techniques with strong evidence supporting their use in the treatment of SUDs. DBS is an invasive technique that is generally well tolerated but more prone to adverse events. There is emerging evidence that DBS is efficacious in SUDs but studies are limited by the lack of sham control groups. Nerve stimulation is another potential modality for addiction treatment although evidence is very limited. Further research is required to determine the optimal protocols across brain stimulation methods and evaluate the impact of psychiatric comorbidity and adjunctive treatment on overall effectiveness. Treatment effects on abstinence rates and duration of efficacy of treatment also need to be further elucidated.

Table 6.3  Brain stimulation SUD treatment: key points  – Transcranial direct current stimulation, repetitive transcranial magnetic stimulation, and deep brain stimulation have all been shown positive effects in treating various SUDs  – tDCS and rTMS most commonly target the DLPFC, while DBS targets the nucleus accumbens,  – Brain stimulation methods commonly reduce craving, substance use, impulsivity, depression, and anxiety in SUDs  – Some studies suggest that brain stimulation treatment positively impacts abstinence although more research is needed to strengthen the evidence base  – More research is required to determine optimal treatment protocols in brain stimulation treatments for addiction

References 1. Wu L, He Y, Jiang B, et al. The effect of a very brief smoking-reduction intervention in smokers who have no intention to quit: study protocol for a randomized controlled trial. BMC Public Health. 2015;15:418. https://doi.org/10.1186/s12889-­015-­1749-­7. 2. Weiss RD, Potter JS, Fiellin DA, et  al. Adjunctive counseling during brief and extended buprenorphine-­ naloxone treatment for prescription opioid dependence: a 2-phase randomized controlled trial. Arch Gen Psychiatry. 2011;68(12):1238–46. https://doi. org/10.1001/archgenpsychiatry.2011.121. 3. Utz KS, Dimova V, Oppenländer K, Kerkhoff G.  Electrified minds: transcranial direct current stimulation (tDCS) and galvanic vestibular stimulation (GVS) as methods of non-invasive brain stimulation in neuropsychology—a review of current data and future implications. Neuropsychologia. 2010;48(10):2789–810. https://doi.org/10.1016/j. neuropsychologia.2010.06.002. 4. Woods AJ, Antal A, Bikson M, et al. A technical guide to tDCS, and related non-invasive brain stimulation tools. Clin Neurophysiol. 2016;127(2):1031–48. https://doi.org/10.1016/j.clinph.2015.11.012. 5. Goldstein RZ, Volkow ND.  Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications. Nat Rev Neurosci. 2011;12(11):652–69. https://doi.org/10.1038/ nrn3119. 6. Ditye T, Jacobson L, Walsh V, Lavidor M. Modulating behavioral inhibition by tDCS combined with cognitive training. Exp Brain Res. 2012;219(3):363–8. https://doi.org/10.1007/s00221-­012-­3098-­4. 7. Kim HJ, Kang N.  Bilateral transcranial direct current stimulation attenuated symptoms of alcohol use

56 disorder: a systematic review and meta-­ analysis. Prog Neuropsychopharmacol Biol Psychiatry. 2021;108:110160. https://doi.org/10.1016/j. pnpbp.2020.110160. 8. den Uyl TE, Gladwin TE, Rinck M, Lindenmeyer J, Wiers RW. A clinical trial with combined transcranial direct current stimulation and alcohol approach bias retraining. Addict Biol. 2017;22(6):1632–40. https:// doi.org/10.1111/adb.12463. 9. Klauss J, Penido Pinheiro LC, Silva Merlo BL, et al. A randomized controlled trial of targeted prefrontal cortex modulation with tDCS in patients with alcohol dependence. Int J Neuropsychopharmacol. 2014;17(11):1793–803. https://doi.org/10.1017/ S1461145714000984. 10. Dubuson M, Kornreich C, Vanderhasselt MA, et  al. Transcranial direct current stimulation combined with alcohol cue inhibitory control training reduces the risk of early alcohol relapse: a randomized placebo-­controlled clinical trial. Brain Stimul. 2021;14(6):1531–43. https://doi.org/10.1016/j. brs.2021.10.386. 11. Kang N, Kim RK, Kim HJ.  Effects of transcranial direct current stimulation on symptoms of nicotine dependence: a systematic review and meta-­ analysis. Addict Behav. 2019;96:133–9. https://doi. org/10.1016/j.addbeh.2019.05.006. 12. Ghorbani Behnam S, Mousavi SA, Emamian MH.  The effects of transcranial direct current stimulation compared to standard bupropion for the treatment of tobacco dependence: a randomized sham-controlled trial. Eur Psychiatry J Assoc Eur Psychiatr. 2019;60:41–8. https://doi.org/10.1016/j. eurpsy.2019.04.010. 13. Falcone M, Bernardo L, Ashare RL, et al. Transcranial direct current brain stimulation increases ability to resist smoking. Brain Stimul. 2016;9(2):191–6. https://doi.org/10.1016/j.brs.2015.10.004. 14. Chapman S, Ragg M, McGeechan K. Citation bias in reported smoking prevalence in people with schizophrenia. Aust N Z J Psychiatry. 2009;43(3):277–82. https://doi.org/10.1080/00048670802653372. 15. Conti CL, Nakamura-Palacios EM.  Bilateral transcranial direct current stimulation over dorsolateral prefrontal cortex changes the drug-cued reactivity in the anterior cingulate cortex of crack-cocaine addicts. Brain Stimul. 2014;7(1):130–2. https://doi. org/10.1016/j.brs.2013.09.007. 16. Conti CL, Moscon JA, Fregni F, Nitsche MA, Nakamura-Palacios EM.  Cognitive related electrophysiological changes induced by non-invasive cortical electrical stimulation in crack-cocaine addiction. Int J Neuropsychopharmacol. 2014;17(9):1465–75. https://doi.org/10.1017/S1461145714000522. 17. de Almeida RR, Taiar I, Trevizol AP, Shiozawa P, Cordeiro Q.  Effect of a ten-day prefrontal transcranial direct current stimulation protocol for crack craving: a proof-of-concept trial. J ECT. 2016;32(3):e8–9. https://doi.org/10.1097/YCT.0000000000000302.

K. Padda 18. Batista EK, Klauss J, Fregni F, Nitsche MA, Nakamura-­ Palacios EM.  A randomized placebo-controlled trial of targeted prefrontal cortex modulation with bilateral tDCS in patients with crack-cocaine dependence. Int J Neuropsychopharmacol. 2015;18(12):pyv066. https://doi.org/10.1093/ijnp/pyv066. 19. Gorini A, Lucchiari C, Russell-Edu W, Pravettoni G. Modulation of risky choices in recently abstinent dependent cocaine users: a transcranial direct-current stimulation study. Front Hum Neurosci. 2014;8:661. https://doi.org/10.3389/fnhum.2014.00661. 20. Verveer I, van der Veen FM, Shahbabaie A, Remmerswaal D, Franken IHA. Multi-session electrical neuromodulation effects on craving, relapse and cognitive functions in cocaine use disorder: a randomized, sham-controlled tDCS study. Drug Alcohol Depend. 2020;217:108429. https://doi.org/10.1016/j. drugalcdep.2020.108429. 21. Klauss J, Anders QS, Felippe LV, et al. Lack of effects of extended sessions of transcranial direct current stimulation (tDCS) over dorsolateral prefrontal cortex on craving and relapses in crack-cocaine users. Front Pharmacol. 2018;9:1198. https://doi.org/10.3389/ fphar.2018.01198. 22. Alizadehgoradel J, Imani S, Nejati V, et al. Improved executive functions and reduced craving in youths with methamphetamine addiction: evidence from combined transcranial direct current stimulation with mindfulness treatment. Clin Psychopharmacol Neurosci. 2021;19(4):653–68. https://doi. org/10.9758/cpn.2021.19.4.653. 23. Shahbabaie A, Golesorkhi M, Zamanian B, et  al. State dependent effect of transcranial direct current stimulation (tDCS) on methamphetamine craving. Int J Neuropsychopharmacol. 2014;17(10):1591–8. https://doi.org/10.1017/S1461145714000686. 24. Shahbabaie A, Ebrahimpoor M, Hariri A, et  al. Transcranial DC stimulation modifies functional connectivity of large-scale brain networks in abstinent methamphetamine users. Brain Behav. 2018;8(3):e00922. https://doi.org/10.1002/brb3.922. 25. Xu X, Ding X, Chen L, et al. The transcranial direct current stimulation over prefrontal cortex combined with the cognitive training reduced the cue-induced craving in female individuals with methamphetamine use disorder: a randomized controlled trial. J Psychiatr Res. 2021;134:102–10. https://doi.org/10.1016/j. jpsychires.2020.12.056. 26. Alizadehgoradel J, Nejati V, Sadeghi Movahed F, et al. Repeated stimulation of the dorsolateral-prefrontal cortex improves executive dysfunctions and craving in drug addiction: a randomized, double-blind, parallel-­group study. Brain Stimul. 2020;13(3):582– 93. https://doi.org/10.1016/j.brs.2019.12.028. 27. Shariatirad S, Vaziri A, Hassani-Abharian P, Sharifi Fardshad M, Molavi N, Fitzgerald PB.  Cumulative and booster effects of tdcs sessions on drug cravings, lapse, and cognitive impairment in methamphetamine use disorder: a case study report. Am J

6  Brain Stimulation Methods for Substance Use Disorders Addict. 2016;25(4):264–6. https://doi.org/10.1111/ ajad.12373. 28. Shahbabaie A, Hatami J, Farhoudian A, Ekhtiari H, Khatibi A, Nitsche MA.  Optimizing electrode montages of transcranial direct current stimulation for attentional bias modification in early abstinent methamphetamine users. Front Pharmacol. 2018;9:907. https://doi.org/10.3389/fphar.2018.00907. 29. Borckardt JJ, Reeves ST, Milliken C, et al. Prefrontal versus motor cortex transcranial direct current stimulation (tDCS) effects on post-surgical opioid use. Brain Stimul. 2017;10(6):1096–101. https://doi. org/10.1016/j.brs.2017.09.006. 30. Khedr EM, Sharkawy ESA, Attia AMA, Ibrahim Osman NM, Sayed ZM.  Role of transcranial direct current stimulation on reduction of postsurgical opioid consumption and pain in total knee arthroplasty: double randomized clinical trial. Eur J Pain Lond Engl. 2017;21(8):1355–65. https://doi.org/10.1002/ ejp.1034. 31. Glaser J, Reeves ST, Stoll WD, et al. Motor/prefrontal transcranial direct current stimulation (tDCS) following lumbar surgery reduces postoperative analgesia use. Spine. 2016;41(10):835–9. https://doi. org/10.1097/BRS.0000000000001525. 32. Wang Y, Shen Y, Cao X, et  al. Transcranial direct current stimulation of the frontal-parietal-temporal area attenuates cue-induced craving for heroin. J Psychiatr Res. 2016;79:1–3. https://doi.org/10.1016/j. jpsychires.2016.04.001. 33. Taremian F, Nazari S, Moradveisi L, Moloodi R.  Transcranial direct current stimulation on opium craving, depression, and anxiety: a preliminary study. J ECT. 2019;35(3):201–6. https://doi.org/10.1097/ YCT.0000000000000568. 34. Sadeghi Bimorgh M, Omidi A, Ghoreishi FS, Rezaei Ardani A, Ghaderi A, Banafshe HR.  The effect of transcranial direct current stimulation on relapse, anxiety, and depression in patients with opioid dependence under methadone maintenance treatment: a pilot study. Front Pharmacol. 2020;11:401. https:// doi.org/10.3389/fphar.2020.00401. 35. Eskandari Z, Dadashi M, Mostafavi H, Armani Kia A, Pirzeh R. Comparing the efficacy of anodal, cathodal, and sham transcranial direct current stimulation on brain-derived neurotrophic factor and psychological symptoms in opioid-addicted patients. Basic Clin Neurosci. 2019;10(6):641–50. https://doi. org/10.32598/BCN.10.6.1710.1. 36. Kooteh BR, Dolatshahi B, Nosratabadi M, Bakhshani NM, Mahdavi A, Hakami MC. Combination therapy and opioids: effectiveness of transcranial direct-­ current stimulation (tDCS) and emotion regulation training in reducing current drug craving. Maedica. 2020;15(1):53–60. https://doi.org/10.26574/ maedica.2020.15.1.53. 37. Eskandari Z, Mostafavi H, Hosseini M, Mousavi SE, Ramazani S, Dadashi M. A sham-controlled clinical trial to examine the effect of bilateral tDCS on crav-

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ing, TNF-α and IL-6 expression levels, and impulsivity of males with opioid use disorder. J Addict Dis. 2021;39(3):347–56. https://doi.org/10.1080/1055088 7.2021.1883208. 38. Boggio PS, Zaghi S, Villani AB, Fecteau S, Pascual-­ Leone A, Fregni F. Modulation of risk-taking in marijuana users by transcranial direct current stimulation (tDCS) of the dorsolateral prefrontal cortex (DLPFC). Drug Alcohol Depend. 2010;112(3):220–5. https:// doi.org/10.1016/j.drugalcdep.2010.06.019. 39. Barr MS, Farzan F, Rajji TK, et  al. Can repetitive magnetic stimulation improve cognition in schizophrenia? Pilot data from a randomized controlled trial. Biol Psychiatry. 2013;73(6):510–7. https://doi. org/10.1016/j.biopsych.2012.08.020. 40. Barr MS, Farzan F, Rusjan PM, Chen R, Fitzgerald PB, Daskalakis ZJ.  Potentiation of gamma oscillatory activity through repetitive transcranial magnetic stimulation of the dorsolateral prefrontal cortex. Neuropsychopharmacology. 2009;34(11):2359–67. https://doi.org/10.1038/npp.2009.79. 41. Huang YZ, Edwards MJ, Rounis E, Bhatia KP, Rothwell JC.  Theta burst stimulation of the human motor cortex. Neuron. 2005;45(2):201–6. https://doi. org/10.1016/j.neuron.2004.12.033. 42. Zhang JJQ, Fong KNK, Ouyang RG, Siu AMH, Kranz GS.  Effects of repetitive transcranial magnetic stimulation (rTMS) on craving and substance consumption in patients with substance dependence: a systematic review and meta-analysis. Addiction. 2019;114(12):2137–49. https://doi.org/10.1111/ add.14753. 43. Herremans SC, Van Schuerbeek P, De Raedt R, et al. The impact of accelerated right prefrontal high-­ frequency repetitive transcranial magnetic stimulation (rTMS) on cue-reactivity: an FMRI study on craving in recently detoxified alcohol-dependent patients. PLoS One. 2015;10(8):e0136182. https:// doi.org/10.1371/journal.pone.0136182. 44. Mishra BR, Nizamie SH, Das B, Praharaj SK.  Efficacy of repetitive transcranial magnetic stimulation in alcohol dependence: a sham-controlled study. Addiction. 2010;105(1):49–55. https://doi. org/10.1111/j.1360-­0443.2009.02777.x. 45. Mishra BR, Praharaj SK, Katshu MZUH, Sarkar S, Nizamie SH.  Comparison of anticraving efficacy of right and left repetitive transcranial magnetic stimulation in alcohol dependence: a randomized double-­blind study. J Neuropsychiatry Clin Neurosci. 2015;27(1):e54–9. https://doi.org/10.1176/appi. neuropsych.13010013. 46. Girardi P, Rapinesi C, Chiarotti F, et al. Add-on deep transcranial magnetic stimulation (dTMS) in patients with dysthymic disorder comorbid with alcohol use disorder: a comparison with standard treatment. World J Biol Psychiatry. 2015;16(1):66–73. https:// doi.org/10.3109/15622975.2014.925583. 47. Ceccanti M, Inghilleri M, Attilia ML, et  al. Deep TMS on alcoholics: effects on cortisolemia and

58 dopamine pathway modulation. A pilot study. Can J Physiol Pharmacol. 2015;93(4):283–90. https://doi. org/10.1139/cjpp-­2014-­0188. 48. Herremans SC, Baeken C, Vanderbruggen N, et  al. No influence of one right-sided prefrontal HF-rTMS session on alcohol craving in recently detoxified alcohol-dependent patients: results of a naturalistic study. Drug Alcohol Depend. 2012;120(1-3):209–13. https://doi.org/10.1016/j.drugalcdep.2011.07.021. 49. Herremans SC, Vanderhasselt MA, De Raedt R, Baeken C.  Reduced intra-individual reaction time variability during a Go-NoGo task in detoxified alcohol-­ dependent patients after one right-sided dorsolateral prefrontal HF-rTMS session. Alcohol Alcohol. 2013;48(5):552–7. https://doi.org/10.1093/ alcalc/agt054. 50. Addolorato G, Antonelli M, Cocciolillo F, et  al. Deep transcranial magnetic stimulation of the dorsolateral prefrontal cortex in alcohol use disorder patients: effects on dopamine transporter availability and alcohol intake. Eur Neuropsychopharmacol. 2017;27(5):450–61. https://doi.org/10.1016/j. euroneuro.2017.03.008. 51. Höppner J, Broese T, Wendler L, Berger C, Thome J.  Repetitive transcranial magnetic stimulation (rTMS) for treatment of alcohol dependence. World J Biol Psychiatry. 2011;12(Suppl 1):57–62. https://doi. org/10.3109/15622975.2011.598383. 52. Pripfl J, Tomova L, Riecansky I, Lamm C. Transcranial magnetic stimulation of the left dorsolateral prefrontal cortex decreases cue-induced nicotine craving and EEG delta power. Brain Stimul. 2014;7(2):226–33. https://doi.org/10.1016/j.brs.2013.11.003. 53. Li X, Hartwell KJ, Owens M, et al. Repetitive transcranial magnetic stimulation of the dorsolateral prefrontal cortex reduces nicotine cue craving. Biol Psychiatry. 2013;73(8):714–20. https://doi. org/10.1016/j.biopsych.2013.01.003. 54. Wing VC, Bacher I, Wu BS, Daskalakis ZJ, George TP.  High frequency repetitive transcranial magnetic stimulation reduces tobacco craving in schizophrenia. Schizophr Res. 2012;139(1-3):264–6. https://doi. org/10.1016/j.schres.2012.03.006. 55. Rose JE, McClernon FJ, Froeliger B, Behm FM, Preud’homme X, Krystal AD. Repetitive transcranial magnetic stimulation of the superior frontal gyrus modulates craving for cigarettes. Biol Psychiatry. 2011;70(8):794–9. https://doi.org/10.1016/j. biopsych.2011.05.031. 56. Trojak B, Meille V, Achab S, et al. Transcranial magnetic stimulation combined with nicotine replacement therapy for smoking cessation: a randomized controlled trial. Brain Stimul. 2015;8(6):1168–74. https:// doi.org/10.1016/j.brs.2015.06.004. 57. Dieler AC, Dresler T, Joachim K, Deckert J, Herrmann MJ, Fallgatter AJ.  Can intermittent theta burst stimulation as add-on to psychotherapy improve nicotine abstinence? Results from a pilot study.

K. Padda Eur Addict Res. 2014;20(5):248–53. https://doi. org/10.1159/000357941. 58. Eichhammer P, Johann M, Kharraz A, et  al. High-­ frequency repetitive transcranial magnetic stimulation decreases cigarette smoking. J Clin Psychiatry. 2003;64(8):951–3. https://doi.org/10.4088/jcp. v64n0815. 59. Li X, Du L, Sahlem GL, Badran BW, Henderson S, George MS.  Repetitive transcranial magnetic stimulation (rTMS) of the dorsolateral prefrontal cortex reduces resting-state insula activity and modulates functional connectivity of the orbitofrontal cortex in cigarette smokers. Drug Alcohol Depend. 2017;174:98–105. https://doi.org/10.1016/j. drugalcdep.2017.02.002. 60. Prikryl R, Ustohal L, Kucerova HP, et al. Repetitive transcranial magnetic stimulation reduces cigarette consumption in schizophrenia patients. Prog Neuropsychopharmacol Biol Psychiatry. 2014;49:30– 5. https://doi.org/10.1016/j.pnpbp.2013.10.019. 61. Dinur-Klein L, Dannon P, Hadar A, et  al. Smoking cessation induced by deep repetitive transcranial magnetic stimulation of the prefrontal and insular cortices: a prospective, randomized controlled trial. Biol Psychiatry. 2014;76(9):742–9. https://doi. org/10.1016/j.biopsych.2014.05.020. 62. Amiaz R, Levy D, Vainiger D, Grunhaus L, Zangen A.  Repeated high-frequency transcranial magnetic stimulation over the dorsolateral prefrontal cortex reduces cigarette craving and consumption. Addiction. 2009;104(4):653–60. https://doi. org/10.1111/j.1360-­0443.2008.02448.x. 63. Lolli F, Salimova M, Scarpino M, et al. A randomised, double-blind, sham-controlled study of left prefrontal cortex 15 Hz repetitive transcranial magnetic stimulation in cocaine consumption and craving. PLoS One. 2021;16(11):e0259860. https://doi.org/10.1371/journal.pone.0259860. 64. Gómez Pérez LJ, Cardullo S, Cellini N, et al. Sleep quality improves during treatment with repetitive transcranial magnetic stimulation (rTMS) in patients with cocaine use disorder: a retrospective observational study. BMC Psychiatry. 2020;20(1):153. https://doi. org/10.1186/s12888-­020-­02568-­2. 65. Camprodon JA, Martínez-Raga J, Alonso-Alonso M, Shih MC, Pascual-Leone A.  One session of high frequency repetitive transcranial magnetic stimulation (rTMS) to the right prefrontal cortex transiently reduces cocaine craving. Drug Alcohol Depend. 2007;86(1):91–4. https://doi.org/10.1016/j. drugalcdep.2006.06.002. 66. Politi E, Fauci E, Santoro A, Smeraldi E. Daily sessions of transcranial magnetic stimulation to the left prefrontal cortex gradually reduce cocaine craving. Am J Addict. 2008;17(4):345–6. https://doi. org/10.1080/10550490802139283. 67. Rapinesi C, Del Casale A, Di Pietro S, et al. Add-on high frequency deep transcranial magnetic stimula-

6  Brain Stimulation Methods for Substance Use Disorders tion (dTMS) to bilateral prefrontal cortex reduces cocaine craving in patients with cocaine use disorder. Neurosci Lett. 2016;629:43–7. https://doi. org/10.1016/j.neulet.2016.06.049. 68. Terraneo A, Leggio L, Saladini M, Ermani M, Bonci A, Gallimberti L.  Transcranial magnetic stimulation of dorsolateral prefrontal cortex reduces cocaine use: a pilot study. Eur Neuropsychopharmacol. 2016;26(1):37–44. https://doi.org/10.1016/j. euroneuro.2015.11.011. 69. Pettorruso M, Spagnolo PA, Leggio L, et al. Repetitive transcranial magnetic stimulation of the left dorsolateral prefrontal cortex may improve symptoms of anhedonia in individuals with cocaine use disorder: a pilot study. Brain Stimul. 2018;11(5):1195–7. https:// doi.org/10.1016/j.brs.2018.06.001. 70. Sanna A, Fattore L, Badas P, Corona G, Cocco V, Diana M.  Intermittent theta burst stimulation of the prefrontal cortex in cocaine use disorder: a pilot study. Front Neurosci. 2019;13:765. https://doi.org/10.3389/ fnins.2019.00765. 71. Martinez D, Urban N, Grassetti A, et al. Transcranial magnetic stimulation of medial prefrontal and cingulate cortices reduces cocaine self-administration: a pilot study. Front Psych. 2018;9:80. https://doi. org/10.3389/fpsyt.2018.00080. 72. Steele VR, Maxwell AM, Ross TJ, Stein EA, Salmeron BJ.  Accelerated intermittent theta-burst stimulation as a treatment for cocaine use disorder: a proof-of-­ concept study. Front Neurosci. 2019;13:1147. https:// doi.org/10.3389/fnins.2019.01147. 73. Bolloni C, Panella R, Pedetti M, et  al. Bilateral transcranial magnetic stimulation of the prefrontal cortex reduces cocaine intake: a pilot study. Front Psych. 2016;7:133. https://doi.org/10.3389/ fpsyt.2016.00133. 74. Madeo G, Terraneo A, Cardullo S, et al. Long-term outcome of repetitive transcranial magnetic stimulation in a large cohort of patients with cocaine-use disorder: an observational study. Front Psych. 2020;11:158. https://doi.org/10.3389/fpsyt.2020.00158. 75. Chen T, Su H, Li R, et  al. The exploration of optimized protocol for repetitive transcranial magnetic stimulation in the treatment of methamphetamine use disorder: a randomized sham-controlled study. EBioMedicine. 2020;60:103027. https://doi. org/10.1016/j.ebiom.2020.103027. 76. Li X, Malcolm RJ, Huebner K, et al. Low frequency repetitive transcranial magnetic stimulation of the left dorsolateral prefrontal cortex transiently increases cue-induced craving for methamphetamine: a preliminary study. Drug Alcohol Depend. 2013;133(2):641– 6. https://doi.org/10.1016/j.drugalcdep.2013.08.012. 77. Liu Q, Shen Y, Cao X, et  al. Either at left or right, both high and low frequency rTMS of dorsolateral prefrontal cortex decreases cue induced craving for methamphetamine. Am J Addict. 2017;26(8):776–9. https://doi.org/10.1111/ajad.12638. 78. Su H, Zhong N, Gan H, et al. High frequency repetitive transcranial magnetic stimulation of the left dor-

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solateral prefrontal cortex for methamphetamine use disorders: a randomised clinical trial. Drug Alcohol Depend. 2017;175:84–91. https://doi.org/10.1016/j. drugalcdep.2017.01.037. 79. Zhao D, Li Y, Liu T, Voon V, Yuan TF.  Twice-daily theta burst stimulation of the dorsolateral prefrontal cortex reduces methamphetamine craving: a pilot study. Front Neurosci. 2020;14:208. https://doi. org/10.3389/fnins.2020.00208. 80. Liang Y, Wang L, Yuan TF.  Targeting withdrawal symptoms in men addicted to methamphetamine with transcranial magnetic stimulation: a randomized clinical trial. JAMA Psychiat. 2018;75(11):1199–201. https://doi.org/10.1001/jamapsychiatry.2018.2383. 81. Mansouriyeh N, Mahmoud-Aliloo M, Rostami R.  The effect of high-frequency repetitive transcranial magnetic stimulation on reducing depression and anxiety in methamphetamine users. Addict Health. 2020;12(4):278–86. https://doi.org/10.22122/ahj. v12i4.288. 82. Chen T, Su H, Jiang H, et al. Cognitive and emotional predictors of real versus sham repetitive transcranial magnetic stimulation treatment response in methamphetamine use disorder. J Psychiatr Res. 2020;126:73– 80. https://doi.org/10.1016/j.jpsychires.2020.05.007. 83. Liu X, Zhao X, Shen Y, et al. The effects of DLPFC-­ targeted repetitive transcranial magnetic stimulation on craving in male methamphetamine patients. Clin Transl Med. 2020;10(2):e48. https://doi.org/10.1002/ ctm2.48. 84. Liu X, Zhao X, Liu T, et  al. The effects of repetitive transcranial magnetic stimulation on cue-­ induced craving in male patients with heroin use disorder. EBioMedicine. 2020;56:102809. https://doi. org/10.1016/j.ebiom.2020.102809. 85. Shen Y, Cao X, Tan T, et al. 10-Hz repetitive transcranial magnetic stimulation of the left dorsolateral prefrontal cortex reduces heroin cue craving in long-term addicts. Biol Psychiatry. 2016;80(3):e13–4. https:// doi.org/10.1016/j.biopsych.2016.02.006. 86. Tsai TY, Wang TY, Liu YC, et  al. Add-on repetitive transcranial magnetic stimulation in patients with opioid use disorder undergoing methadone maintenance therapy. Am J Drug Alcohol Abuse. 2021;47(3):330–43. https://doi.org/10.1080/009529 90.2020.1849247. 87. Sahlem GL, Baker NL, George MS, Malcolm RJ, McRae-Clark AL.  Repetitive transcranial magnetic stimulation (rTMS) administration to heavy cannabis users. Am J Drug Alcohol Abuse. 2018;44(1):47–55. https://doi.org/10.1080/00952990.2017.1355920. 88. Sahlem GL, Caruso MA, Short EB, et al. A case series exploring the effect of twenty sessions of repetitive transcranial magnetic stimulation (rTMS) on cannabis use and craving. Brain Stimul. 2020;13(1):265–6. https://doi.org/10.1016/j.brs.2019.09.014. 89. Fitzgerald PB, Segrave RA. Deep brain stimulation in mental health: review of evidence for clinical efficacy. Aust N Z J Psychiatry. 2015;49(11):979–93. https:// doi.org/10.1177/0004867415598011.

60 90. Hassan O, Phan S, Wiecks N, Joaquin C, Bondarenko V. Outcomes of deep brain stimulation surgery for substance use disorder: a systematic review. Neurosurg Rev. 2021;44(4):1967–76. https://doi.org/10.1007/ s10143-­020-­01415-­y. 91. Mahoney JJ, Hanlon CA, Marshalek PJ, Rezai AR, Krinke L.  Transcranial magnetic stimulation, deep brain stimulation, and other forms of neuromodulation for substance use disorders: review of modalities and implications for treatment. J Neurol Sci. 2020;418:117149. https://doi.org/10.1016/j. jns.2020.117149. 92. Liu H, Liu Y, Yu J, et  al. Vagus nerve stimulation inhibits heroin-seeking behavior induced by heroin priming or heroin-associated cues in rats. Neurosci

K. Padda Lett. 2011;494(1):70–4. https://doi.org/10.1016/j. neulet.2011.02.059. 93. Wang YM, Xu YY, Zhai Y, et al. Effect of transcutaneous auricular vagus nerve stimulation on protracted alcohol withdrawal symptoms in male alcohol-­ dependent patients. Front Psych. 2021;12:678594. https://doi.org/10.3389/fpsyt.2021.678594. 94. Zalewska-Kaszubska J, Obzejta D. Use of low-energy laser as adjunct treatment of alcohol addiction. Lasers Med Sci. 2004;19(2):100–4. https://doi.org/10.1007/ s10103-­004-­0307-­9. 95. Baker TE, Chang G.  The use of auricular acupuncture in opioid use disorder: a systematic literature review. Am J Addict. 2016;25(8):592–602. https://doi. org/10.1111/ajad.12453.

7

Technology-Assisted Interventions for Behavioral Addictions Involving Problematic Use of the Internet Sanya Virani

and Marc N. Potenza

Introduction Behavioral addictions may include maladaptive use of the internet. Such behaviors have been termed internet addiction (IA), internet dependence, and pathological internet use, among other names [1, 2]. The term problematic use of the internet (PUI) [3] describes a maladaptive pattern of involves impaired control over online behaviors, the occurrence of psychological, social, or professional negative consequences and distress related to such behaviors, and often obsessive thoughts when it is not possible to engage in such behaviors [4]. The term includes, but is not limited to, online gaming, gambling, buying, and pornography viewing, as well as social networking and cyberbullying [3]. Controversy about over-pathologizing the condition, including the relevance of specific criteria adopted from other disorders, has appeared in the literature [4, 5]. Some clinical aspects of PUI resemble those of addictions to substances, and these may include impaired control over behavior, craving, persis-

S. Virani (*) Warren Alpert Medical School of Brown University, Providence, RI, USA e-mail: [email protected] M. N. Potenza Yale University School of Medicine, New Haven, CT, USA e-mail: [email protected]

tence despite damaging effects, and preoccupation despite associated functional impairment. As reviewed elsewhere [6], multiple problems may arise from PUI (e.g., difficulty cutting down, poor sleep, fatigue, irritability, apathy, racing thoughts, declining grades, poor job performance, and/or neglecting other responsibilities). Thus, the presence of addiction symptoms (e.g., tolerance), impairment in daily functioning, frequent comorbidity (i.e., anxiety, depression, and obsessive-compulsive disorder), and potential risk factors (e.g., fearful attachment styles) have been described. It is unclear whether all PUI behaviors have tolerance and withdrawal [2] and some PUI behaviors may share similarities with symptoms of other disorders such as obsessive-­ compulsive disorder, anxiety disorders, and impulse control disorders [1, 2]. PUI may also be linked to “workaholism” [7]. In May 2019, the World Health Organization included gaming disorder as a mental disorder in the eleventh revision of the International Classification of Diseases (ICD-11). However, individuals affected by gaming disorder or PUI broadly may not receive adequate therapy due to ambivalence, absence of adequate local treatment options, or other factors. The current mainstays of prevention or treatment for PUI include cognitive behavioral therapy (CBT) approaches, and family therapy may also be helpful [8]. Pharmacological treatments have not been thoroughly investigated, with no medications for for-

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Avery, M. Khan (eds.), Technology-Assisted Interventions for Substance Use Disorders, https://doi.org/10.1007/978-3-031-26445-0_7

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mal indications for gaming disorder or PUI. With the COVID-19 pandemic having exacerbated PUI, additional interventions have been proposed [9–11]. Innovative approaches include offerings through digital platforms that provide assessment, prevention, and intervention. These approaches include web-based tools, mobile apps, wearables, and virtual reality platforms, with varying degrees of efficacy in the assessment and treatment of behavioral addictions. This chapter will focus on outlining the various technology-based interventions for three major categories of behavioral addictions. The chapter will focus on internet gaming disorder (IGD), gambling disorder (GD), and problematic pornography use (PPU).

Identifying Target Groups A review [12] suggested that interventions for PUI may be divided broadly into universal and selected/indicated. Table 7.1 identifies four main target groups for universal prevention and six specific biopsychosocial risk factors for selective or indicated prevention. Children and adolescents may be particularly responsive to positive influences, and as a group, they can be accessed easily while at schools [13]. On the other end of the life spectrum, less attention has been paid to PUI among older adults and Table 7.1  Universal and selective/indicated prevention Universal prevention: target groups  –  Children and adolescents  –  University students  – Parents and others close to members of the target groups  – Gambling employees and employees with regular access to the internet Selective/indicated prevention: biopsychosocial risk factors  –  Psychopathological factors  –  Personality characteristics  –  Physiological characteristics  –  Patterns of internet use  –  Sociodemographic factors  –  Current situation Adapted from [12]

S. Virani and M. N. Potenza

other less accessible adults like unemployed individuals and those on parental leave, who may be at high risk for developing PUI [14, 15]. While attempting to monitor children’s usage of the internet, it is important to have knowledge and awareness of their online activities [16] and to understand their needs (COST Action CA16207). Monitoring internet use should involve establishing rules, regulating the content of online activities, and limiting internet use [17]. Involving children in the discussion and joint internet use may be helpful [18]. Regarding employees, Young [19] encourages company management to teach employees how to detect early signs of PUI and factors that may contribute to its development. The organization of educational seminars and the monitoring of internet use by employers have been recommended [20].

Technology-Based Interventions for Internet Gaming Disorder Although not as well examined as psychotherapies and pharmacological treatments, technology-­ based interventions have been explored in treating IGD with some early promising results. Some work has examined the efficacy of internet-­ based platforms in decreasing IGD symptoms and reducing time spent online [21, 22]. Web-­ based tailored interventions for IGD may deliver educational material in personalized messages. Individuals who found the message relevant to their own situation tended to pay more attention to them, leading to more positive outcomes. “Smart gamers” in Malaysia is one web-based intervention [21]. The Gagne’s Learning Theory, Cognitive Dissonance, and Fogg’s Behavioral Model were applied to the approach during which gamers were presented with video clips tailored to their gaming behaviors, followed by persuasive communication emphasizing the negative effects of excessive gaming and suggesting different approaches to healthy living. Preliminary data suggested that both learning theory and cognitive dissonance may trigger thoughts and feelings regarding gaming behavior and lead to formation of intent to make positive behavioral

7  Technology-Assisted Interventions for Behavioral Addictions Involving Problematic Use of the Internet

changes. However, no observable behavioral changes were appreciated at the end of the experiment. In Korea, virtual reality therapy (VRT) has been explored for IGD as for alcohol dependence [23, 24]. During a high-risk situation phase, participants were exposed for 10 min to gaming cues from the game that he/she usually played (for example, scenes of betting or shooting). The approach allowed participants to select which over-gaming situations to view during the high-­ risk situation with the purpose of inducing desires for gameplay. VRT also involved sound-assisted cognitive reconstruction during which an aversion-­ inducing noise (an annoying beep or siren) was played to pair the exposure of high-­ risk situation stimuli and aversion-inducing stimuli when experiencing game cues. This aversion-inducing noise led participants to feel annoyed and anxious, so that they develop aversive responses to the game cues. Almost simultaneous with aversion-inducing noise, participants were exposed to stimuli illustrating negative consequences of long-term gaming. The main strategies ultimately rested on highlighting the long-term consequences of IGD, such as conflicts with family members, low school achievement, losing his/her job, and health problems. VRT was compared to CBT, with VRT potentially having advantages for helping individuals who may feel uncomfortable with group CBT.  Further, VRT may help engage individuals with attentional concerns. Another advantage of the VRT program is the shorter duration of sessions (30 min including preparation and wrap-up) in comparison with CBT sessions (about 2 h per session). A study of primary school students in Hong Kong [25] tested whether Wise IT-use (WIT), which consists of online training and an onsite workshop, was successful in preventing and treating IGD among students, increasing awareness and equipping them with tools and knowledge to game in a healthy manner. During the online training, students were asked to complete online modules on topics such as cybersecurity and digital citizenship. The study found a reduction in symptoms and proportion of students at risk of IGD.

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An online-based motivational intervention to reduce PUI (OMPRIS) is being tested for IGD [26]. It includes a 4-week web-based intervention that adopts motivational interviewing, behavioral therapy techniques, and social counseling. An uncontrolled study for PUI had participants assess an online service for internet addiction (OASIS) and despite having only a few sessions to do this, a significant reduction in mean time spent on the internet and PUI symptoms was observed [27]. One novel technique utilized an eCoach to provide feedback as part of the mobileor internet-based intervention, based on one’s personal needs and values, sleep, relaxation, alcohol use, mood, and levels of appreciation, gratitude, and procrastination [28].

Technology-Based Interventions for Gambling Disorder (GD) While one study [29] suggested that virtual reality (VR) increased craving reactivity in GD, others [30] have suggested the efficacy of VR cue-exposure therapy (VRCET) for substance use disorders and GD in reducing craving in response to addiction-related cues and extinction of cue-response association. An extensive scoping review [31] found that CBT was the most common form of internet-­ based intervention generally shown to be effective in reducing problem-gambling scores and gambling behaviors. The wide range of interventions that made use of internet resources included text-based interactions with counselors and peers, automated personalized and normative feedback on gambling behaviors, and interactive CBT. Most commonly, information technologies (especially made available through video or chat sessions) were used to modify or extend existing forms of treatment for GD, particularly CBT.  Other approaches included motivational interviewing, monitoring feedback and support, and exposure therapy. Significant reductions in problem-gambling severity and time and money spent gambling were seen. Internet-based resources have also been used to collect and analyze large amounts of data to improve the detec-

S. Virani and M. N. Potenza

64 Table 7.2  Advantages of internet-based interventions for GD

Table 7.3  Clinical questions to ask and tips for managing PUI

 1. Greater anonymity [34]  2. Reduction in barriers associated with stigma of seeking treatment [34]  3. Promotion of openness/honesty [35]  4. Elimination of/reduction in practical barriers [36]    (a) Distance to treatment facilities    (b) Conflicts between treatment availability and other constraints on time such as childcare or paid work    (c) Cost of transportation to treatment facilities    (d) Treatment relevant to cultural or language needs

 1. Potential barriers to healthy sexual expression or experiences as well as potential excessive and risky behaviors, including pornography use  2. Impairment related to pornography use, understanding that such impairment may be in relational, occupational, educational, or other domains  3. Symptom severity of problematic pornography use including impairment, poor control, preoccupation, and continued use despite negative consequences instead of only asking for time and frequency of pornography use  4. Potential conflicts between pornography use and moral values  5. Individual goals and treatment motivation

tion of potential problems or to allow potential participants to contextualize their own gambling behaviors, thereby allowing gambling providers and responsible gambling site operators to improve their harm prevention strategies efficiently. While several self-help intervention formats (online, telephone, CDRoM, bibliotherapy) delivered alone or in combination have been described to manage various substance use and gambling disorders [32], another review [33] found no efficacy study on a completely online intervention for GD. The authors noted that different groups had different motivations for enrolling in online intervention programs, thus potentially biasing conclusions drawn about the efficacy of these interventions (Table 7.2).

Technology-Based Interventions for Problematic Pornography Use Despite the scarcity of systematic studies, clinicians should consider treatment motivations and individual goals of those seeking treatment for problematic pornography use [37]. Clinically, it is important to consider situations in which there may be difficulties in controlling pornography use (e.g., when using the internet and experiencing stress or negative mood states). Some ideas for questions to ask and tips for clinical encounters for this problem are provided in Table 7.3. Decreased self-efficacy in these situations has been linked to pornography use frequency and

Adapted from [37]

hypersexuality [38]; as such, alternate decision-­ making strategies in these situations may represent a focus of therapy. Help options include online forums. For example, NoFap and Reboot Nation were founded to help young males quit pornography viewing. Some men experienced erectile dysfunction, which they attributed to altered sexual arousal altered resulting from use of pornography [23, 24]. Given the accessibility of pornography through online access, parental oversight of children and adolescents’ internet activity represents an early preventative intervention [23, 24]. The use of pornography filter blocking software such as Net Nanny (https://www.netnanny.com/features/porn-­blocking/) or others (e.g., https:// www.raymond.cc/blog/block-­p ornographic-­ pictures-­by-­pixelating-­images/) may reduce unwanted exposure.

Conclusion Research on the topic of managing behavioral addictions is ongoing, and some concepts are relatively new. Empirical studies on prevention of or treatment for addictive behaviors in subjects with various forms of PUI are at early stages. Specific types of integrative approaches based on a public health model of PUI may lead to better outcomes in the long run. A comprehensive

7  Technology-Assisted Interventions for Behavioral Addictions Involving Problematic Use of the Internet

approach to PUI may involve multiple aspects of diagnosis and treatment. As an “agent,” online media should be assessed to determine which characteristics may reinforce compulsive or maladaptive use of the internet. Biological, cognitive, affective, and psychological characteristics and their interactions with environmental factors should be considered in developing interventions for PUI [39]. Behavioral interventions involving craving modification [40] and VRT have been shown to be efficacious, not only in decreasing craving and severity of internet addiction, but also in altering neural activity and functional connectivity. Similarly, neuromodulatory approaches based on brain-behavior relations have shown initial promise in the treatment of PUI, particularly IGD [41–43]. Therefore, technology-­based interventions are showing initial promise, but more extensive and ongoing research is needed, especially to determine efficacy and tolerability in larger samples and over longer durations.

References 1. Spada MM.  An overview of problematic internet use. Addict Behav. 2014;39(1):3–6. https://doi. org/10.1016/j.addbeh.2013.09.007. 2. Volpe U, Dell’Osso B, Fiorillo A, Mucic D, Aboujaoude E.  Internet-related psychopathology: clinical phenotypes and perspectives in an evolving field. J Psychopathol. 2015;21(4):406–14. 3. Fineberg NA, Demetrovics Z, Stein DJ, Ioannidis K, Potenza MN, Grünblatt E, Brand M, Billieux J, Carmi L, King DL, Grant JE, Yücel M, Dell’Osso B, Rumpf HJ, Hall N, Hollander E, Goudriaan A, Menchon J, Zohar J, et  al. Manifesto for a European research network into problematic usage of the internet. Eur Neuropsychopharmacol. 2018;28(11):1232–46. https://doi.org/10.1016/j.euroneuro.2018.08.004. 4. D’Hondt F, Billieux J, Maurage P. Electrophysiological correlates of problematic internet use: critical review and perspectives for future research. Neurosci Biobehav Rev. 2015;59:64–82. https://doi. org/10.1016/j.neubiorev.2015.10.005. 5. Starcevic V.  Is internet addiction a useful concept? Aust N Z J Psychiatry. 2013;47(1):16–9. https://doi. org/10.1177/0004867412461693. 6. Kuss DJ, Lopez-Fernandez O. Internet addiction and problematic internet use: a systematic review of clinical research. World J Psychiatry. 2016;6(1):143–76. https://doi.org/10.5498/wjp.v6.i1.143.

65

7. Quinones C, Griffiths MD, Kakabadse NK.  Compulsive internet use and workaholism: an exploratory two-wave longitudinal study. Comput Hum Behav. 2016;60:492–9. https://doi. org/10.1016/j.chb.2016.02.060. 8. Xu L-X, Wu L-L, Geng X-M, Wang Z-L, Guo X-Y, Song K-R, Liu G-Q, Deng L-Y, Zhang J-T, Potenza MN.  A review of psychological interventions for internet addiction. Psychiatry Res. 2021;302:114016. https://doi.org/10.1016/j.psychres.2021.114016. 9. Awan HA, Aamir A, Diwan MN, Ullah I, Pereira-­ Sanchez V, Ramalho R, Orsolini L, de Filippis R, Ojeahere MI, Ransing R, Vadsaria AK, Virani S.  Internet and pornography use during the COVID-­19 pandemic: presumed impact and what can be done. Front Psych. 2021;12:623508. https://doi. org/10.3389/fpsyt.2021.623508. 10. Gjoneska B, Potenza MN, Jones J, Corazza O, Hall N, Sales CMD, Grünblatt E, Martinotti G, Burkauskas J, Werling AM, Walitza S, Zohar J, Menchón JM, Király O, Chamberlain SR, Fineberg NA, Demetrovics Z.  Problematic use of the internet during the COVID-­19 pandemic: good practices and mental health recommendations. Compr Psychiatry. 2022;112:152279. https://doi.org/10.1016/j. comppsych.2021.152279. 11. Király O, Potenza MN, Stein DJ, King DL, Hodgins DC, Saunders JB, Griffiths MD, Gjoneska B, Billieux J, Brand M, Abbott MW, Chamberlain SR, Corazza O, Burkauskas J, Sales CMD, Montag C, Lochner C, Grünblatt E, Wegmann E, et al. Preventing problematic internet use during the COVID-19 pandemic: consensus guidance. Compr Psychiatry. 2020;100:152180. https://doi.org/10.1016/j.comppsych.2020.152180. 12. Vondráčková P, Gabrhelík R.  Prevention of internet addiction: a systematic review. J Behav Addict. 2016;5(4):568–79. https://doi. org/10.1556/2006.5.2016.085. 13. Kalina K. Developing the system of drug services in the Czech republic. J Drug Issues. 2007;37(1):181– 204. https://doi.org/10.1177/002204260703700109. 14. Müller KW, Glaesmer H, Brähler E, Woelfling K, Beutel ME.  Prevalence of internet addiction in the general population: results from a German population-based survey. Behav Inform Technol. 2014;33(7):757–66. https://doi.org/10.1080/01449 29X.2013.810778. 15. Young KS. Internet addiction: the emergence of a new clinical disorder. Cyberpsychol Behav. 1998;1(3):237– 44. https://doi.org/10.1089/cpb.1998.1.237. 16. Li X, Li D, Newman J.  Parental behavioral and psychological control and problematic internet use among Chinese adolescents: the mediating role of self-­ control. Cyberpsychol Behav Soc Netw. 2013;16(6):442–7. https://doi.org/10.1089/ cyber.2012.0293. 17. van den Eijnden RJJM, Spijkerman R, Vermulst AA, van Rooij TJ, Engels RCME. Compulsive internet use among adolescents: bidirectional parent–child rela-

66 tionships. J Abnorm Child Psychol. 2010;38(1):77– 89. https://doi.org/10.1007/s10802-­009-­9347-­8. 18. Xiuqin H, Huimin Z, Mengchen L, Jinan W, Ying Z, Ran T. Mental health, personality, and parental rearing styles of adolescents with internet addiction disorder. Cyberpsychol Behav Soc Netw. 2010;13:401–6. https://doi.org/10.1089/cyber.2009.0222. 19. Young K.  Policies and procedures to manage employee internet abuse. Comput Hum Behav. 2010;26(6):1467–71. https://doi.org/10.1016/j. chb.2010.04.025. 20. Frangos C, Sotiropoulos I.  P02-246–factors predicting the use of internet at work for non-work purposes for a random sample of company workers in Greece. Eur Psychiatry. 2010;25:881. https://doi.org/10.1016/ S0924-­9338(10)70872-­5. 21. Chen YY, Goh KN, Razak MFA.  Development of a web-based tailored intervention for excessive gaming, vol. 6; 2012. 22. Park J-A, Park M-H, Shin J-H, Li B, Rolfe DT, Yoo J-Y, Dittmore SW. Effect of sports participation on internet addiction mediated by self-control: a case of Korean adolescents. Kasetsart J Soc Sci. 2016b;37(3):164–9. https://doi.org/10.1016/j.kjss.2016.08.003. 23. Park BY, Wilson G, Berger J, Christman M, Reina B, Bishop F, Klam WP, Doan AP. Is internet pornography causing sexual dysfunctions? A review with clinical reports. Behav Sci (Basel). 2016a;6(3):E17. https://doi.org/10.3390/bs6030017. 24. Park SY, Kim SM, Roh S, Soh M-A, Lee SH, Kim H, Lee YS, Han DH. The effects of a virtual reality treatment program for online gaming addiction. Comput Methods Programs Biomed. 2016c;129:99–108. https://doi.org/10.1016/j.cmpb.2016.01.015. 25. Chau C, Tsui YY, Cheng C.  Gamification for internet gaming disorder prevention: evaluation of a wise IT-use (WIT) program for Hong Kong primary students. Front Psychol. 2019;10:2468. https://doi. org/10.3389/fpsyg.2019.02468. 26. Dieris-Hirche J, Bottel L, Pape M, te Wildt BT, Wölfling K, Henningsen P, Timmesfeld N, Neumann A, Neusser S, Beckers R, Herpertz S.  Effects of an online-based motivational intervention to reduce problematic internet use and promote treatment motivation in internet gaming disorder and internet use disorder (OMPRIS): study protocol for a randomised controlled trial. BMJ Open. 2021;11(8):e045840. https://doi.org/10.1136/bmjopen-­2020-­045840. 27. Determinants for the effectiveness of an Online Ambulatory Service for Internet Addicts (OASIS)|Lisbon Addictions. 2019. https://www. lisbonaddictions.eu/lisbon-­a ddictions-­2 019/presentations/determinants-­e ffectiveness-­o nline-­ ambulatory-­service-­internet-­addicts-­oasis. Accessed 13 Feb 2022. 28. Saruhanjan K, Zarski A-C, Schaub MP, Ebert DD.  Design of a guided internet- and mobile-based intervention for internet use disorder—study protocol for a two-armed randomized controlled trial. Front

S. Virani and M. N. Potenza Psychiatry. 2020;11:190. https://doi.org/10.3389/ fpsyt.2020.00190. 29. Loranger C, Bouchard S, Boulanger J, Robillard G.  Validation of two virtual environments for the prevention and treatment of pathological gambling. J Cyber Ther Rehabil. 2011;4(2):233–6. 30. Park C-B, Park SM, Gwak AR, Sohn BK, Lee J-Y, Jung HY, Choi S-W, Kim DJ, Choi J-S.  The effect of repeated exposure to virtual gambling cues on the urge to gamble. Addict Behav. 2015;41:61–4. https:// doi.org/10.1016/j.addbeh.2014.09.027. 31. van der Maas M, Shi J, Elton-Marshall T, Hodgins DC, Sanchez S, Lobo DS, Hagopian S, Turner NE.  Internet-based interventions for problem gambling: scoping review. JMIR Ment Health. 2019;6(1):e65. https://doi.org/10.2196/mental.9419. 32. Danielsson A-K, Eriksson A-K, Allebeck P. Technology-based support via telephone or web: a systematic review of the effects on smoking, alcohol use and gambling. Addict Behav. 2014;39(12):1846– 68. https://doi.org/10.1016/j.addbeh.2014.06.007. 33. Giroux I, Goulet A, Mercier J, Jacques C, Bouchard S.  Online and mobile interventions for problem gambling, alcohol, and drugs: a systematic review. Front Psychol. 2017;8:954. https://doi.org/10.3389/ fpsyg.2017.00954. 34. Cooper G. Exploring and understanding online assistance for problem gamblers: the pathways disclosure model. Int J Ment Health Addict. 2004;1:32–8. 35. Rochlen AB, Zack JS, Speyer C.  Online therapy: review of relevant definitions, debates, and current empirical support. J Clin Psychol. 2004;60(3):269– 83. https://doi.org/10.1002/jclp.10263. 36. Suurvali H, Cordingley J, Hodgins DC, Cunningham J.  Barriers to seeking help for gambling problems: a review of the empirical literature. J Gambl Stud. 2009;25(3):407–24. https://doi.org/10.1007/ s10899-­009-­9129-­9. 37. Brand M, Blycker G, Potenza MN. When pornography becomes a problem: clinical insights. Psychiatr Times. 2019;36:12. https://www.psychiatrictimes. com/view/when-­p ornography-­b ecomes-­p roblem-­ clinical-­insights. Accessed 17 Feb 2022. 38. Kraus SW, Rosenberg H, Martino S, Nich C, Potenza MN.  The development and initial evaluation of the pornography-use avoidance self-efficacy scale. J Behav Addict. 2017;6(3):354–63. https://doi. org/10.1556/2006.6.2017.057. 39. Brand M, Young KS, Laier C, Wölfling K, Potenza MN.  Integrating psychological and neurobiological considerations regarding the development and maintenance of specific internet-use disorders: an interaction of person-affect-cognition-execution (I-PACE) model. Neurosci Biobehav Rev. 2016;71:252–66. https://doi.org/10.1016/j.neubiorev.2016.08.033. 40. Zhang J-T, Yao Y-W, Potenza MN, Xia C-C, Lan J, Liu L, Wang L-J, Liu B, Ma S-S, Fang X-Y. Altered resting-state neural activity and changes following a craving behavioral intervention for internet gam-

7  Technology-Assisted Interventions for Behavioral Addictions Involving Problematic Use of the Internet ing disorder. Sci Rep. 2016;6:28109. https://doi. org/10.1038/srep28109. 41. Wu L-L, Potenza MN, Zhou N, Kober H, Shi X-H, Yip SW, Xu J-H, Zhu L, Wang R, Liu G-Q, Zhang J-T. A role for the right dorsolateral prefrontal cortex in enhancing regulation of both craving and negative emotions in internet gaming disorder: a randomized trial. Eur Neuropsychopharmacol. 2020a;36:29–37. https://doi.org/10.1016/j.euroneuro.2020.04.003. 42. Wu L-L, Potenza MN, Zhou N, Kober H, Shi X-H, Yip SW, Xu J-H, Zhu L, Wang R, Liu G-Q, Zhang J-T. Efficacy of single-session transcranial direct cur-

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rent stimulation on addiction-related inhibitory control and craving: a randomized trial in males with internet gaming disorder. J Psychiatry Neurosci. 2021;46(1):E111–8. https://doi.org/10.1503/ jpn.190137. 43. Wu L-L, Zhu L, Shi X-H, Zhou N, Wang R, Liu G-Q, Song K-R, Xu L-X, Potenza MN, Zhang J-T. Impaired regulation of both addiction-related and primary rewards in individuals with internet gaming disorder. Psychiatry Res. 2020b;286:112892. https:// doi.org/10.1016/j.psychres.2020.112892.

8

Technology-Assisted Therapies for Substance Use Disorders in Children and Adolescents Miriam E. Goldblum

Introduction

increased sexual risk-taking; unintended pregnancies; new medical conditions or exacerbation Substance use disorders among the child and of existing conditions such as asthma; co-morbid adolescent populations continue to be a growing mental health issues, including anxiety and public health concern with rates of use consis- depression; and impairment in cognitive functently rising since 2015 [1]. Use of nicotine vap- tioning [3–5]. Further, substance use significantly ing products, marijuana, and alcohol are of contributes to the three leading causes of death significant concern in recent years. Childhood, among adolescents—accidents, homicides, and and adolescence more specifically, is a critical suicides [6]. Substance use has also been conperiod as neuronal development and brain matu- nected to poor educational performance, acaration overlap with the period of most frequent demic achievement, and delinquency [7–9]. initiation of alcohol and other drug use [2]. A variety of risk factors for using substances According to the Center on Addiction and and developing substance use disorders in this age Substance Abuse, 90% of US adults suffering group, including demographic, psychosocial, and from chronic substance use disorders began environmental, have been identified. These smoking, drinking, or using other drugs before include but are not limited to engagement with the the age of 18. Reaching this population and pro- child welfare system, gender, age, history of viding support for prevention, treatment, and abuse, and co-morbid mental health conditions continued engagement is crucial, as the long-­ [2]. Of the myriad factors associated with term effects of youth substance use have consid- increased risk of substance use, there are two in erable costs. From a financial perspective, particular that have consistently shown strong evisubstance misuse is estimated to cost the USA dence. Those include teens’ reported substance approximately $442 billion each year in health use among their peer group, and parental involvecare costs, lost productivity, and criminal justice ment and attitudes toward substances [10]. costs, with underage drinking alone accounting With the range and severity of consequences of for $68 billion annually [1]. Beyond the long-­ youth substance use, preventing and mitigating term financial costs, youth substance use poses this issue is of utmost concern from both a child immediate health risks, including physical injury; welfare and public health standpoint. There are multiple points of intervention: those based on risk level include universal prevention, selective M. E. Goldblum (*) New York Presbyterian/Weill Cornell Medicine, prevention, and indicated prevention and/or treatNew York, NY, USA ment. In addition, within those levels, there are © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Avery, M. Khan (eds.), Technology-Assisted Interventions for Substance Use Disorders, https://doi.org/10.1007/978-3-031-26445-0_8

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M. E. Goldblum

70

multiple domains for prevention including the individual, family, peer, school, community, and environment/society (IOM risk level; CSAP domains). While a number of interventions among and within each of these levels are a­ vailable, they are often not accessed by the child and adolescent populations given the hurdles such as cost, limited dissemination, and even logistical barriers such as lack of transportation [11]. At each intervention point, new and distinctive challenges can arise. Technology-based approaches provide a means for reaching this at-­ risk population. While there are some evidence-­ based interventions that can deliver treatment in a reproducible and cost-effective manner, many of these have not been validated for use in the child or adolescent populations.

 ccessibility of Technological Uses A for the Adolescent Population Using technology-assisted therapies for substance use in adolescents has many advantages for access to care. These therapies are able to reach a population who previously could either not afford to travel to the clinic, have difficulty accessing public transportation, or did not have access to a personal vehicle. This is particularly relevant to the adolescent population, who is not earning a substantial income, and who may not have the ability to travel to a clinic after-school hours while parents are working [12]. A taxi ride to the clinic in the suburbs may be very expensive, and while transport may be less expensive in the city, it may take up a lot of time and involve different modes of transportation and transfers on trains. Additionally, it may not be safe to travel in an inner-city neighborhood during after-school hours, particularly for a population that is attempting to cut down or refrain from substance use. Having the ability to utilize technology for substance use treatment from the comfort of home, without needing to worry about financial means to travel to the clinic, figuring out transportation, accessing public transport, and worrying about personal safety/exposure to substances en route to the clinic, has been shown to enhance access for this population. Additionally, during a pandemic period (as the world is now facing the

COVID-19 pandemic), adolescents have not had the ability to consistently meet with their providers in-person, due to risks of exposure either in the clinic (particularly if the clinic is located in a hospital) or on potentially crowded modes of transportation. In this way, utilizing therapies remotely could further enhance the safety and compliance of both clinicians and patients. Despite the logistical advantages in terms of safety and accessibility, the use of technology for substance abuse treatment is not without potential cons, especially when it comes to access for the most at-risk groups within the child and adolescent populations. Though increasingly rare to be in a technology-free household, there still remain many households that do not have access to personal technology, a private internet network, or may only have limited access in public settings. Further, and of particular concern for dependent youth, accessing care from home may impact a patient’s ability to access a private area in the home where the individual can utilize the therapies. In this way, a therapeutic alliance may be more difficult to establish as the patient may not feel safe or free to fully express their extent of abuse or personal feelings with a clinician.

Technologic Integration The COVID-19 pandemic has had an effect not only on access to technology but has the way in which individuals interact with technology on a daily basis. Given the dangers of in-person interaction, particularly before vaccines for COVID-­19 were extended to the adolescent population, teens utilized technology for nearly all forms of interaction outside of the household. Though interacting with social media on a daily basis was an important facet of life for many teens prior to the pandemic, interacting with technology became increasingly important when in-person gatherings and activities were severely restricted by the advice, and even mandates, of governments on regional, state, and national levels. Teens became even more comfortable with interacting with technology for their own personal needs. Given adolescents’ near constant interaction with technology, this group particularly may feel even more comfortable utilizing

8  Technology-Assisted Therapies for Substance Use Disorders in Children and Adolescents

t­echnology-­assisted therapies as opposed to inperson therapies. Answering an automated daily text to rate their mood and desire to use substances will likely take less effort than actually filling out a scale with pen and paper. Additionally, if the scale is filled out in the clinician’s office and involves thinking about how the patient felt over the last week, it will likely be less accurate than a scale completed on a daily basis due to the level of recall involved [13]. Answering a text may feel much more natural to an adolescent, and less “clinical,” than filling out a survey with pen and paper. This would not only improve the accuracy of reported data, it is likely to increase compliance and reduce stigma largely associated with mental health treatment, and around substance abuse in particular [14]. When a teen has been sending and receiving hundreds of texts a day, one more text is unlikely to be burdensome and instead can be incorporated naturally into the person’s daily life.

Parental Involvement There are also limitations to consider on the side of providers, with limits to confidentiality of particular consideration. Aside from potential breaches in data in the cyberspace, which may limit what patients reveal on a technological platform, there may also be concern that parents can gain access to their confidential treatment. This is particularly important to consider with substance use, as it is a protected area and is not typically disclosed by clinicians to parents unless there is a concerning safety issue. MyChart, a platform attached to the Epic Electronic Medical Record, widely used across the country in hospitals and clinics, does not give automatic access of the medical record to parents of teens; it requires that teens permit proxy access to their parents in order for their parents to have access. With parents frequently providing the financial means for the access to technology (paying for a laptop or Wi-Fi, paying a phone bill), parents may feel that they should be able to access the content that their teens interact with via technology. Parents of teens who struggle with substance abuse may be especially wary of their teens’ activities and may increase monitoring of their children, which may involve reading the teens’ texts or

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posts on social media [15]. Also, though many parents may not have felt comfortable utilizing technology a few years ago, the COVID-19 pandemic also increased many adults’ comfort level with utilizing technology on a daily basis—for both work and socialization. Parents who want to know the details of their teens’ treatments, without wanting the teens to be aware of this access, may be able to breach the confidentiality due to their comfort level with technology much more easily than prior to the pandemic. The potential for limits of confidentiality may limit the teens’ comfort level with utilizing technology-assisted therapies, particularly if they are disclosing details regarding substance use which they do not want their parents to know [16]. Though many parents are more comfortable with using technology than they had been in the past, they still might not be as adept at using technology as their teens. If teens are not motivated themselves to utilize treatment, then parents may be the driving force behind compliance with treatment. Parents may be providing the means of transportation to the clinic and may be setting up a rewards system for attending therapy sessions, etc. With teens’ ability to utilize technology for treatment, parents may be more removed from the logistical aspects of treatment as compared to in-person treatment. Parents may not know if teens are attending therapy sessions or utilizing the treatment in the ways intended by the clinicians. Parents may not be able to help enforce compliance with treatment in ways that they might be able to with traditional treatment. A parent may not know if a teen misses virtual sessions unless the clinician reaches out to the parent directly, whereas a parent who brings the teen into a clinic for treatment will clearly know if the teen attended the session.

Conclusion In conclusion, the child and adolescent populations are the fastest growing subgroup of those developing and dying from substance abuse and related disorders. Given the myriad difficulties inherent in reaching this dependent population, including

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cost, privacy concerns, and logistical barriers such as transportation, technology-based interventions can help improve access and potentially mitigate non-adherence to treatment. However, despite the advantages of technology-based interventions, there remain certain barriers and frank disadvantages to these modalities including lack of access to personal technology or private inter-

M. E. Goldblum

net, lack of privacy required to appropriately engage in treatment, and lack of necessary parental engagement. Further efforts should be made to increase awareness of the growing issue of youth substance abuse and substance-related death and provide easily accessible, and potentially, technology-based approaches to reaching this highly vulnerable population [12].

8  Technology-Assisted Therapies for Substance Use Disorders in Children and Adolescents

Key Points • Substance abuse is rising in the child and adolescent populations. • Youth substance use has significant societal costs. • Enhances access to care. • Ease of incorporation into daily life for teens. • Confidentiality concerns.

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5. Kelly AB, Evans-Whipp TJ, Smith R, et al. A longitudinal study of the association of adolescent polydrug use, alcohol use and high school non-completion. Addiction. 2015;110(4):627–35. 6. Lynskey M, Hall W. The effects of adolescent cannabis use on educational attainment: a review. Addiction. 2000;95(11):1621–30. 7. Pardini D, White HR, Xiong SY, et  al. Unfazed or dazed and confused: does early adolescent marijuana use cause sustained impairments in attention and academic functioning. J Abnorm Child Psychol. 2015;43(7):1203–17. Pearls and Pitfalls 8. Guilamo-Ramos V, Litardo HA, Jaccard J. Prevention Pearl: Enables access to treatment for those who programs for reducing adolescent problem behavare unable to be transported to clinic easily. iors: implications of the co-occurrence of problem behaviors in adolescence. J Adolesc Health. Pitfall: May be cost-prohibitive to obtain com2005;36(1):82–6. puter/smartphone access for families with 9. Miller PG, Butler E, Richardson B, et al. Relationships financial difficulties. between problematic alcohol consumption and delinPearl: Teens may be more comfortable answering quent behaviour from adolescence to young adulthood. Drug Alcohol Rev. 2016;35(3):317–25. texts as opposed to completing surveys, and 10. Youth Risk Behavior Surveillance System (YRBSS). daily texts may give more accurate informaCDC; 2022. https://www.cdc.gov/healthyyouth/data/ tion than a survey completed retrospectively yrbs/. in a clinician’s office. 11. Martins SS, Alexandre PK.  The association of ecstasy use and academic achievement among adoPitfall: Concern for confidentiality given parents lescents in two U.S. national surveys. Addict Behav. frequently paying for phones/computers. 2009;34:9–16. 12. Teens insights into drugs, alcohol, and nicotine: a national survey of adolescent attitudes toward addictive substances. Center for Addiction; 2019. 13. Eklund JM, af Klinteberg B. Alcohol use and patterns References of delinquent behaviour in male and female adolescents. Alcohol Alcohol. 2009;44:607–14. 1. Substance Abuse and Addiction Statistics [2022]. 14. Richardson GB, Montgomery L, Brubaker Gainesville, FL: NCDAS, National Center for Drug MD.  Interpersonal contact and attitudes toward Abuse Statistics; 2022. https://drugabusestatistics. adolescents who abuse substances. J Drug Educ. org/. 2016;46(3-4):113–30. 2. U.S.  Department of Health and Human Services 15. Marsch LA, Borodovsky JT.  Technology-based (HHS). Office of the surgeon general, facing addicinterventions for preventing and treating substance tion in America: the surgeon general’s report on alcouse among youth. Child Adolesc Psychiatr Clin N hol, drugs, and health. Washington, DC: HHS; 2016. Am. 2016;25(4):755–68. https://doi.org/10.1016/j. 3. Fettes DL, Aarons GA, Green AE. Higher rates of adochc.2016.06.005. lescent substance use in child welfare versus commu16. VanDeMark NR, Burrell NR, Lamendola WF, Hoich nity populations in the United States. J Stud Alcohol CA, Berg NP, Medina E.  An exploratory study of Drugs. 2013;74(6):825–34. https://doi.org/10.15288/ engagement in a technology-supported substance jsad.2013.74.825. abuse intervention. Subst Abuse Treat Prev Policy. 4. Spirito A, Rasile DA, Vinnick LA, Jelalian E, Arrigan 2010;5:10. https://doi.org/10.1186/1747-­597X-­5-­10. ME.  Relationship between substance use and self-­ PMID: 20529338; PMCID: PMC2898791. reported injuries among adolescents. J Adolesc Health. 1997;21:221–4.

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Technology Assisted Treatment of Substance Use Disorders in Pregnancy Rosemary V. Busch Conn

Introduction As independent problems, substance use disorders can wreak havoc in the life of an individual and their loved ones. In the context of pregnancy, this havoc is exponentially compounded not only for the mother but also for the developing fetus. However, despite the risks associated, substance use in pregnancy is common. Presented here is a discussion of how using technology may aid with widespread substance use in pregnancy through broadened and improved screening, treatment, and research. Efficacious means by which to measure, uncover, and treat the increase in alcohol and drug use in pregnancy is a necessity. Substance use disorders are more likely to co-occur rather than exist in isolation (referred to in DSM-IV as “polysubstance dependence”), and this applies in the setting of pregnancy as well. In addition, pregnant women who use alcohol and drugs in pregnancy are more likely to suffer from both concurrent psychiatric disease and emotional, physical, or sexual abuse. In addition to detrimental effects on the mother and baby, substance use in pregnancy can erupt into legal challenges and add to the complexity of social and familial difficulty.

Though there exists a system to provide expectant mothers with substance use treatment, about 5% of women use addictive substances while pregnant [1]. To improve outreach and engagement of the pregnant population, one must consider the utilization of technology in screening and treatment of substance use disorders and substance misuse. There is an abundance of solutions to lessen the rates of alcohol and drug use during pregnancy through targeted use of an electronic medical record, utilization of tablets in clinical settings, mobile applications, virtual care, and online networks. This chapter on utilizing those listed technologies to treat substance use disorders in pregnancy is not comprehensive but rather a collection of exciting examples from clinical, research, and public health settings. Access to technology is higher now than at any point in history. Two-thirds of the world’s population use a mobile phone and more than half of those are smartphones [2]. These fractions are even higher among women of reproductive age. The opportunities are innumerable for clinical applications, research, and public health initiatives in this population at a time during which not only pregnancy is likely, but also development of a substance use disorder is at its peak [3].

R. V. Busch Conn (*) Department of Psychiatry, New York Presbyterian/ Weill Cornell Medicine, New York, NY, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Avery, M. Khan (eds.), Technology-Assisted Interventions for Substance Use Disorders, https://doi.org/10.1007/978-3-031-26445-0_9

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Clinical Applications  linical Barriers: Why Do We Need C Technology to Assist in Substance Use in Pregnancy? Clinical applications are foundational when considering technology utilization for substance use in pregnancy. A primary reason this topic is of importance is that there exist numerous barriers which limit screening and treatment for expectant mothers. Employment of electronic medical record systems and mobile applications to address and treat alcohol, tobacco, and drug use in pregnancy minimizes these barriers from both the clinician and patient side. Barriers from the clinical side include reticence of clinicians to screen for alcohol and illicit substance use in pregnancy for various reasons. Indeed, underreporting of tobacco, alcohol, and drug use in pregnancy is a significant issue as demonstrated by many studies involving various substances. In one such study, 38% of mothers who had previously denied cocaine and/or opioid use went on to deliver babies with a positive meconium assay for one or both of these substances [2]. The urine drug screen is an imperative part of screening. One study demonstrated that less than half of pregnant women who provided a positive urine drug screen had accurately selfreported illicit drug use. Fig. 9.1 Increased screening and interventions could improve the health of women and their children. https://www. cdc.gov/pregnancy/ polysubstance-­use-­in-­ pregnancy.html. Open access [4]

Mothers may avoid reporting due to concerns about stigma from their clinical team or misunderstand the necessity of this information in optimizing health in pregnancy. Practically, they may be concerned about involvement of the criminal justice system or threatened custody of their child with endorsed substance use, which is of greater significance in some geographic regions. Pregnancy and postpartum is a particularly difficult time to enter substance use treatment, in part because of the new physical and social demands, but also due to the notable lack of services offered to and facilities able to accommodate this population, especially in rural regions. In early clinical encounters clinicians aim to develop rapport with patients, leading some to avoid questions which could upset the idealized dynamic of caretaker and compliant patient. Other barriers are the constraint of time to ask a robust substance history, the misperception that use of alcohol and illicit drugs is low in pregnant women, and the accompanying concern for the lack of a referral pathway should the answers mandate one. A clinician may be uncertain about what information to give and how best to deliver it to an expectant mother who screens positive for substance use. If a clinician perceives a patient to be of a certain socioeconomic class or if a family member is present, they may be more likely to forgo sensitive questions. For clinicians, technology can come into play to alleviate some of the burden with increased screening modalities and linked referral protocols (Fig. 9.1) (Table 9.1).

Data from a national survey showed that among pregnant women

about 10% had at least one alcoholic drink in the past 30 days

of those using alcohol, 40% also used other substances (most often tobacco & marijuana)

Increased screening and interventions could improve the health of women and their children Source: National Survey on Drug Use and Health (NSDUH, United States, 2015-2018

9  Technology Assisted Treatment of Substance Use Disorders in Pregnancy Table 9.1  Clinical barriers to substance use treatment in pregnancy https://www.cdc.gov/pregnancy/polysubstance-use-in-pregnancy.html Clinician barriers • Hesitance to screen for substance use with aim to develop rapport • Incomplete substance use screens, e.g. limiting questions to certain substances or curtailed quantification of use • Underutilization of urine drug screens • Assumptions of patient socioeconomic status and implications Patient barriers • Underreporting or omissions in reporting • Concern for stigma • Misunderstanding or lack of knowledge regarding the necessity of complete answers • Involvement in the criminal justice system or concern for threatened custody of their child Additional barriers • Misperception of alcohol and drug use is low in pregnant women • Lack of referral pathways, facilities, and services which accommodate pregnant women • Limitations of clinical encounters, e.g. presence of family members or time constraints • New physical and social demands of pregnancy • Lack of referral pathways, facilities, and services which accommodate pregnant women

 linical Applications: What Are C the Tools We Can Use? Technology can be utilized in a number of ways in the treatment of substance use disorders in pregnancy. At a foundational level, electronic medical records are now ubiquitous in clinical settings. Though different in structure, most electronic medical records, or EMRs, contain required substance use screening sections. In screening patients for substance use as part of their intake and documenting this within the EMR lies an opportunity for any treating physician or clinical team member to address substance use, assess motivation for change, and guide to appropriate treatment. Within a health system an EMR can typically be viewed by all clinical team members involved in patient care. Collection of screening data at this fundamental level is the first step in using technology to address substance use. As women are often highly motivated to change behavior in

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pregnancy, previously documented substance use can be asked about in prenatal counseling or early in the course of pregnancy. Additionally, for reproductive aged women who have otherwise been healthy and well, the preparation for and start of pregnancy are prime opportunities to obtain a detailed substance use history, especially with the guided thoroughness offered by an EMR. A limitation of utilizing an EMR as a modality for identifying problematic substance use is that they capture a diagnosis of a substance use disorder, frequently omitting substance use that may not meet full criterion of a disorder but may be hazardous nonetheless [5]. Considering barriers and practical applications in the clinical setting are fundamental in not only treating substance use disorders with technology in pregnancy, but understanding why this approach is imperative and how it may best be applied.

Research Applications The field of research extends to the understanding, implementation practices, and continued development of technology assisted treatment of substance use disorders. Areas of ongoing research described here include using mobile applications focused on peer coaching, lifestyle coaching, and substance specific adaptations of technology assisted treatments. Further studies are warranted to determine disparities in efficacy of delivery format of SBIRT [6].

Peer Coaching Peer coaching is one modality demonstrating promising research outcomes. In a small scale randomized control trial, an application which centers around empowerment through personal storytelling known as “S4E” (Storytelling “4” Empowerment) was studied to evaluate the feasibility of use of this type of technology. The outcomes were congruent with the hypotheses: in the youth clinic where the application was stud-

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ied rates of substance use decreased, sexual risk behaviors decreased, and there was an improvement in the uptake of testing for HIV and sexually transmitted infections. Additionally, the study participants who used S4E instead of enhanced usual practice demonstrated better communication with clinicians which increased prevention knowledge and self-efficacy of ­participants [7]. Although the study was small, the outcome demonstrated significant power in the reduction of risky behavior, a primary aim in minimizing substance use.

Lifestyle Coaching An area of research that can work together with peer coaching is of lifestyle coaching. Such applications have focused directly on instruction for optimizing pregnancy conditions, such as the lifestyle coaching app “Smarter Pregnancy.” This application which focuses on nutrition, alcohol, and smoking has been studied in couples undergoing in  vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI). Participants were assigned a lifestyle risk score and coached on relevant behavioral changes. Smarter pregnancy was shown to be an effective modality for improving lifestyle behaviors among participating couples and additional studies, such as a multicenter randomized control trial, were recommended to further gather data of the application’s effects on pregnancy, live births, and neonatal outcomes [8].

Substance Specific Adaptations As the opioid epidemic has flourished in the USA, including among women of childbearing age, novel strategies for treatment have emerged as an important area of study. A chapter on recent clinical trials targeting opioid use disorder in particular comments on such novel strategies, such as web based interventions and applications delivered via smartphone. Pregnant women com-

prise a special population that has been a subject of particular focus [9]. Interpersonal modalities to decrease substance use and increase healthy behavior, such as peer and lifestyle coaching, are promising areas for research on substance use treatment in pregnancy as they are easily adaptable to a mobile platform and enhance the support system around a woman during a unique life event. Consideration of how technologic adaptations of interventions might apply best to particular substances is worth considering. As technology assisted treatments of substance use disorders broaden across general and subpopulations, areas of further research abound.

Public Health Application With regard to a larger scale across populations is the utilization of technology assisted treatment of substance use disorders in pregnancy in the public health arena. Increasingly health systems are utilizing technology to obtain information, perhaps at a rate which exceeds that of the application of such data. Despite a number of initiatives, alcohol and drug use during pregnancy is escalating. Technology has enormous potential in helping the reduction and treatment of this increasingly prevalent public health issue. In clinical settings which lack advanced EMR technology or are understaffed, or as an adjunctive tool to be used by a patient and/or their physicians, a growing number of mobile screening applications are employable to capture data related to substance use in pregnancy. These mobile applications belong to the growing toolset entitled as “mHealth,” which is defined by the World Health Organization (WHO) as, “the use of mobile devices (mobile phones, personal digital assistants, or patient monitoring devices) for medical and public health practice” [2]. In other words, while not everyone has access to an EMR or a smartphone, the majority of the population has a basic mobile phone. “mHealth” occurs when phones are used for healthcare. These tools are vital for screening and have been shown to be

9  Technology Assisted Treatment of Substance Use Disorders in Pregnancy

feasible and generalizable in use to gather data about an individual’s substance use, in addition to general and medication health history. The bulk of the cost with these technologies is in the front end of development; once made they are efficient for gathering data as they are easy to use and allow for self-reporting. In one study the participants answered questions about substance use before and since pregnancy, differentiating substances into categories of alcohol, cigarettes, e-cigarettes, “recreational drugs” (e.g. cannabis), and “street drugs” (non-cannabis drugs). Participants who answered questions about substance use were mostly older pregnant women and more likely to be white. At least 95% of those who participated in any screening surveys answered at least one questionnaire about substance use; more than 60% answered all questions about substance use [5].

Tools Adaptable to mHealth Among developed tools for tobacco, alcohol, and drug use screening is the evidence-based “SBIRT” model. SBIRT, an intervention and treatment tool comprised of Screening, Brief Intervention, and Referral to treatment, is recommended by the WHO, Centers for Disease Control, U.S.  Preventive Services Task Force, American College of Obstetricians and Gynecologists, and Substance Abuse and Mental Health Association. Each step of SIBRT is adaptable to a technology assisted platform. Not only there is evidence that apps and social media are useful platforms by which these steps can be facilitated, a growing body of evidence demonstrates that SBIRT delivered via mHealth is effective and functional. This approach of adaptation of electronic delivery, sometimes referred to as eSBIRT, allows for the circumvention of many common barriers which inhibit women’s access to substance use treatment in pregnancy. Screening for amount and frequency of consumption of alcohol and illicit substances can be done via electronic questionnaires. Motivational and evidence-driven

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messaging regarding benefits of reducing or abstaining from use of substances serve as brief interventions and can be delivered via pre-­ recorded video or audio, or live from a remote clinician or peer counselor. Brief interventions are tailored to each participant’s responses to screening questionnaires. When women meet standardized criteria for problematic substance use they can be referred to specialist services for substance use treatment. Utilization of the SBIRT tool, especially in conjunction with tailored patient education and decision support, offers enormous potential in the improvement of maternal and child health outcomes, already demonstrating prevention of maternal substance use, decreased effects on the developing child, and the provision of options for treatment. To understand the full extent of benefits offered by eSBIRT further studies are warranted [2]. One such mHealth application developed is SmartStart which is specifically designed to provide evidence-based prenatal screening, brief intervention, and referral to treatment within pregnancy. In early use of this application tablet based screening during clinic visits was beneficial for adherence to recommended guidelines. In addition, use of SmartStart and other mHealth tools increases comprehensive care as they provide a medium by which to screen and, when needed, counsel pregnant patients on risk and protective factors [10]. mHealth applications have shown to be functional and effective in a variety of areas relevant to pregnancy such as the reduction of gestational weight gain through measures such as improved nutrition, management of gestational diabetes, treatment of peri-partum depression and anxiety, and increasing smoking cessation. Screening and brief interventions delivered via an mHealth modality enhance the accessibility and equity of clinical encounters and lower many of the aforementioned barriers. There are data which demonstrate this approach does increase reported use in women otherwise reluctant, alleviates pressure of transport or inaccessibility in rural areas, and circumvents the dilemma

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presented by limited access to drug and alcohol treatment services [2].

With increased use of technology in the treatment of substance use in pregnancy, epidemiologic data can be collected through avenues such as clinical applications (e.g. electronic medical record systems) as patients register for digital portals and participate in electronic surveys. Through collection of information, public health policy and education can have a more precise target in decreasing treatment disparities and lessening the prevalence and impact of substance use in pregnancy.

ity of E-sbi or E-sbirt in the management of alcohol and illicit substance use in pregnant and post-partum women. Front Psychiatry. 2021;12:634805. 3. Forray A, Martino S, Gilstad-Hayden K, Kershaw T, Ondersma S, Olmstead T, et  al. Assessment of an electronic and clinician-delivered brief intervention on cigarette, alcohol and illicit drug use among women in a reproductive healthcare clinic. Addict Behav. 2019;96:156–63. 4. Centers for Disease Control and Prevention. Polysubstance use in pregnancy. Atlanta, GA: Centers for Disease Control and Prevention; 2020. https:// www.cdc.gov/pregnancy/polysubstance-­u se-­i n-­ pregnancy.html. Accessed 22 Jun 2022. 5. Rothschild CW, Dublin S, Brown JS, Klasnja P, Herzig-Marx C, Reynolds JS, et al. Use of a mobile app to capture supplemental health information during pregnancy: implications for clinical research. Pharmacoepidemiol Drug Saf. 2021;31(1):37–45. 6. Browne FA, Wechsberg WM, Kizakevich PN, Zule WA, Bonner CP, Madison AN, et al. Mhealth versus face-to-face: study protocol for a randomized trial to test a gender-focused intervention for young African American women at risk for HIV in North Carolina. BMC Public Health. 2018;18(1):982. 7. Cordova D, Munoz-Velazquez J, Mendoza Lua F, Fessler K, Warner S, Delva J, et  al. Pilot study of a multilevel mobile health app for substance use, sexual risk behaviors, and testing for sexually transmitted infections and HIV among youth: randomized controlled trial. JMIR Mhealth Uhealth. 2020;8(3):e16251. 8. Oostingh EC, Koster MPH, van Dijk MR, Willemsen SP, Broekmans FJM, Hoek A, et  al. First effective mHealth nutrition and lifestyle coaching program for subfertile couples undergoing in  vitro fertilization treatment: a single-blinded multicenter randomized controlled trial. Fertil Steril. 2020;114(5):945–54. 9. Blessing E, Virani S, Rotrosen J.  Clinical trials for opioid use disorder. Handb Exp Pharmacol. 2019;258:167–202. 10. Martino S, Ondersma SJ, Forray A, Olmstead TA, Gilstad-Hayden K, Howell HB, et  al. A randomized controlled trial of screening and brief interventions for substance misuse in reproductive health. Am J Obstet Gynecol. 2018;218(3):322.e1. 11. Phelan N, Behan LA, Owens L.  The impact of the COVID-19 pandemic on women’s reproductive health. Front Endocrinol. 2021;12:642755.

References

Further Reading

Considerations in the COVID-19 Pandemic Modalities discussed, such as screening, brief interventions, and referral, are scalable and can be more equitably distributed with technology, particularly in populations which were previously difficult to access. This is especially true in the context of the COVID-19 global pandemic, during which use of technology in healthcare has greatly expanded. The effects of the pandemic are further reaching than the use of mHealth though. A digital survey conducted showed that there was a significant increase in excess alcohol intake in women of reproductive age in 2020. The pandemic had an effect on reproductive health specifically as well: women reported changes in menstrual cycles including variability and missed periods, new incidence of menorrhagia and dysmenorrhea, and reduced libido [11].

Conclusion

1. Revised April 2020 substance use in women research report. 2020. https://nida.nih.gov/. https://nida.nih. gov/download/18910/substance-­u se-­i n-­w omen-­ research-­report.pdf?v=b802679e27577e5e53650924 66ac42e8. Accessed 12 Apr 2022. 2. Wouldes TA, Crawford A, Stevens S, Stasiak K.  Evidence for the effectiveness and acceptabil-

Gance-Cleveland B, Leiferman J, Aldrich H, Nodine P, Anderson J, Nacht A, et  al. Using the technology acceptance model to develop “startsmart”: Mhealth for screening, brief intervention, and referral for risk and protective factors in pregnancy. J Midwifery Womens Health. 2019;64(5):630–40.

Technology-Assisted Therapies for Substance Use Disorders in LGBTQIA

10

Nia Harris

Abbreviations LGBTQIA+ lesbian gay bisexual transgender queer/questioning intersex asexual/agender+ MSM men who have sex with men SUD substance sue disorders

Introduction Even prior to the start of the pandemic, concerns about mental health and substance use were on the incline. The need for access to mental health and substance use treatment is as imperative now as ever. This holds especially true for marginalized communities who at baseline have a higher incidence of mental health issues and substance misuse [1]. This chapter will focus on one such population, the lesbian, gay, bisexual, transgender, queer/questioning, intersex, asexual/agender+ (LGBTQIA+) community. Importantly, studies have shown that members of the LGBTQIA+ community have a greater likelihood of substance misuse and substance use disorders [1, 2]. Perhaps this is in part because of the unique discrimination and stigma that this popula-

N. Harris (*) Psychiatry Resident, New York, NY, USA e-mail: [email protected]

tion faces compared to their heterosexual and cisgender counterparts. It has been postulated that substance misuse in this community may serve as a type of maladaptive coping strategy [3]. Increasing access to targeted substance use treatment is imperative for all, especially for such a high-risk population as the LGBTQIA+ community. One way to increase access is through technology-­assisted therapies. Many of us utilize technology from the moment we wake up to the moment we go to bed. Technology is our alarm clock, a mechanism for enjoyment, our calendar, and our workspace, among other things. Its access is widespread. Utilization of technology provides a significant opportunity for therapeutic intervention. Moreover, addiction research has demonstrated the importance of factoring in demographic factors, such as sexual orientation and gender identity, in substance use disorder treatment [4]. Here, we explore substance use disorders (SUD) in the LGBTQIA+ community as well as the impact of technology-assisted therapy in this population.

SUD in LGBTGIA+ Communities A variety of factors have been postulated to increase risk for substance use disorders within the LGBTQIA+ community. Literature suggests that this is in part because of the expectation that substances enhance sexual experiences within

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Avery, M. Khan (eds.), Technology-Assisted Interventions for Substance Use Disorders, https://doi.org/10.1007/978-3-031-26445-0_10

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this community [5]. Methamphetamines and inhalant nitrites, specifically, have been some of the documented substances commonly utilized for enhancement of pleasure and to aid in sense of belongingness within this population [6]. It is perhaps not surprising that these are among substances that are commonly misused in the LGBTQIA+ community. This is supported through data from the 2017–2019 National Survey on Drug Use and Health (NSDUH) [7] that when analyzed revealed that gay and bisexual persons have higher prevalence of methamphetamine use and abuse (Fig. 10.1a, b), as well as hallucinogen abuse (Fig. 10.1c), than their heterosexual counterparts. It is worth noting that while the NSDUH data collected reports on “abuse,” we prefer the term “misuse”.

% met hamphet amine abuse in past year

a

Methamphetamine abuse in past year 1.5 1 0.5 0 Heterosexual

Gay or Lesbian

Bisexual

There are several additional reasons that research suggests as possible explanations for heightened substance use within the LGBTQIA+ population. These include victimization related to prejudice and discrimination, internalized stigma, anticipation of maltreatment, and comorbid mental health and other conditions [8, 9]. This raises some questions. Does substance use serve as a maladaptive coping strategy in response to anticipated maltreatment and discrimination? Are substances used as a form of self-medication to treat under treated and untreated mental health conditions that are common in this population? Both are certainly plausible. It is perhaps also not surprising in light of this discussion that data from the 2017–2019 National Survey on Drug Use and Health (NSDUH) has found LGBTQIA+ b

Methamphetamine Use

120 100 80 60 40 20 0

No Yes

Sexual Orientation

Hallucinogen abuse in past year 10 0 HeterosexualGay or Lesbian Bisexual

Sexual Orientation

Fig. 10.1  Prevalence of methamphetamine abuse (a—top left), methamphetamine use (b—top right), hallucinogen abuse (c—bottom left), and young age initiation of alcohol consumption (d—bottom right) among gay, bisexual, and heterosexual persons in the USA, 2017–2019. (a–c) shows that use and abuse are higher in gay lesbian and bisexual persons compared to heterosexual counterparts. (d) shows that gay, lesbian, and bisexual persons initiate alcohol use

d % age alcohol use 14 (or younger)

& hallucinogen abuse in past year

c

Sexual Orientation

Young initiation of alcohol consumption (age 14 or younger) 40 20 0 Heterosexual

Gay or Lesbian

Bisexual

Sexual Orientation

at an earlier age than their heterosexual counterparts. Data (% of total population surveyed) are from the Substance Abuse and Mental Health Services Administration, National Survey on Drug Use and Health (NSDUH), for survey years 2017–2019 (https://datafiles.samhsa.gov/ dataset/national-­s urvey-­d rug-­u se-­a nd-­h ealth-­2 019-­ nsduh-­2019-­ds0001/) [7]

10  Technology-Assisted Therapies for Substance Use Disorders in LGBTQIA

population has earlier onset of substance use initiation when compared to heterosexual persons (see Fig. 10.1d). This is all to say that it is imperative that the substance use in this population be addressed.

LGBTQIA+ Specific SUD TX Substance use disorders disproportionately affect larger numbers of the LGBTQIA+ community. Moreover, research has found that LGBTQIA+ clients who enter substance use treatment enter with more severe substance use problems and psychopathology [1]. Notably, research has also revealed that treatment programs with specialized groups for gay and bisexual clients have better outcomes compared to gay and bisexual clients in non-specialized programs [10]. However, despite this, substance use recovery has been largely targeted toward cisgendered and heterosexual populations. According to one analysis, only about 7% of the nation’s substance use disorder treatment agencies offer LGBT-specialized in-person services [11]. Moreover, another study found that among SUD programs that were advertised for LGBTspecific clients, only 20% were found to offer LGBTQIA+-specific treatments when reviewed in the SAMHSA Treatment locator [12]. This highlights an important SUD treatment gap. Notably, this treatment gap has also been found within technology-assisted therapies for SUD.  One literature review examined how technology-­based therapy integrates sexual orientation and gender identity into their substance treatment interventions [4]. This study conducted a review of over 5000 papers. Despite this extensive review, it was found that only one study, [13], examined SUD therapy within the LGBTQIA+ population. This means that the other 4999 articles did not evaluate substance use within the LGBTQIA+ community as part of their study. It is surprising that a population that is well documented to have a high prevalence of substance use and substance use disorders was only mentioned once in such an extensive review.

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What is perhaps additionally unfortunate is that of the already minority of studies that investigate technology-assisted therapy in treatment of SUDs in LGBTQIA+, the majority are descriptive in nature and lack evidence of impact. For example, the above-mentioned article [13] recruited LGBTQIA+ teens and young adults who were current or former smokers and asked them to participate in focused groups pertaining to thoughts on mobile applications targeted toward smoking cessation. This study found that most participants expressed positive feelings toward a mobile smoking cessation application and thought it was vital that smoking cessation applications be tailored to the LGBTQIA+ community. However, this study did not investigate implementation of the described application within this population. Another study evaluated technology-­ assisted therapy for SUD in the LGBTQIA+ community, however, this too was descriptive in nature [14]. This study investigated implementation of text messaging risk reduction in a population of men who have sex with men (MSM) who used methamphetamines and were not currently in substance treatment. In this study, four-hundred pre-written text messages tailored toward MSM were created with assistance from community-­based organizations who serve MSM populations. Specifically, the messages were targeted toward providing informational support, emotional support, instrumental support, health and health risk information, self-regulation skills, and self-efficacy. However, utilization of the created messages was not evaluated as part of this study. While this and the other studies highlighted did not investigate outcomes following utilization of LGBTQIA+ specific such as abstinence from substance, etc., they highlight the importance of tailored therapies toward LGGBTQIA+ populations.

Technology-Assisted Therapy Members of the LGBTQIA+ community are thought to be among the earliest adopters of technology-­based communications [15]. This is speculated to be in part because of the major role

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that technology plays in dating culture for this population, likely even more so than their heterosexual counterparts. Given this, as well as what was previously discussed regarding substance use in this community, this population, perhaps above others even, would be an ideal candidate for technology-assisted SUD therapy. The remainder of this chapter seeks to identify applications (or lack thereof) being utilized for therapeutic interventions within the LGBTQIA+ community. The American Addiction Recovery Center cites six applications as the most utilized for treatment of substance use disorders: Pear reSET, SoberGrid, SoberTool, Nomo-Sobriety Clocks, WEconnect, and r-Tribe [16]. Use of these six applications was investigated in persons with SUD through PubMed review (see Table  10.1). Per this review, four of the six applications have been evaluated in persons with SUD (Pear reSET,

Sober Grid, Nomo-Sobriety Clocks, and WEconnect). The remaining two applications, however, were not mentioned in the reviewed literature (r-Tribe, SoberTool). There are many commonalities among the listed applications. Importantly, they are all easily accessible and free to download. Moreover, they all target a specific substance (reSET-O) or range of substances utilizing CBT (reSET/reSETO), coaching, and peer support, among other ­ techniques. One unfortunate common factor is that none of the listed applications investigated sexual orientation or gender identity demographics within the participants. Importantly, no research that this writer was able to locate has investigated the utilization of these applications by members of the LGBTQIA+ community. This highlights a missed opportunity, and also a possible avenue for future research.

Table 10.1  SUD applications

reSET

Target substance(s) Alcohol, cocaine, marijuana, and stimulants

reSET-O

Opioids

Sober grid

Non-specific

Nomo— Sobriety clock WEconnect

Non-specific

r-tribe SoberTool

Non-specific Non-specific

Non-specific

Cost References Special comments Free [17] •  The first prescription digital therapeutic (PDT) to receive authorization from FDA for SUD • 90 days • Adjunctive therapy • Utilizes CBT Free [18, 19] • 84 days • FDA approved • Utilizes CBT • Adjunctive therapy Free [20] • Peer support: connects sober people with those looking to get sober Free [21] • Sobriety tracking • Connects people to sobriety partners • Provides journaling space Free [21] • One-one-one peer support • Incentives to support recovery • Free online meetings Free N/A • Counseling and coaching Free N/A • Counseling • Message board • Motivational messages

Mention of LGBTQIA+ participants? No

No

No No

No

N/A N/A

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Conclusion

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national sample of heterosexual and sexual minority women and men: sexual identity, victimization and SUDs. Addiction. 2010;105(12):2130–40. The LGBTQIA+ population has been well-­ 10. Senreich E.  Are specialized LGBT program components helpful for gay and bisexual men in documented to have earlier and more severe substance abuse treatment? Subst Use Misuse. substance use compared to other populations. 2010;45(7–8):1077–96. Despite this, substance use treatments, both in- 11. Cochran BN, Peavy KM, Robohm JS. Do specialized person and through technology-­ associated services exist for LGBT individuals seeking treatment for substance misuse? A study of available treatment therapies, lack adequate tailored treatment programs. Subst Use Misuse. 2007;42(1):161–76. toward this population. Moreover, while tech12. Mericle AA, de Guzman R, Hemberg J, Yette E, nology-assisted therapy has evidence in treatDrabble L, Trocki K. Delivering LGBT-sensitive subing substance use disorders, there is little to no stance use treatment to sexual minority women. J Gay Lesbian Soc Serv. 2018;30(4):393–408. evidence in persons within the LGBTQIA+ 13. Baskerville NB, Dash D, Wong K, Shuh A, community. This warrants further research. Abramowicz A. Perceptions toward a smoking cessation app targeting LGBTQ+ youth and young adults: a qualitative framework analysis of focus groups. JMIR Public Health Surveill. 2016;2(2):e165. References 14. Reback CJ. Developing a text messaging risk reduction intervention for methamphetamine-using MSM: 1. Cochran BN, Cauce AM.  Characteristics of lesbian, research note. Open AIDS J. 2010;4(1):116–22. gay, bisexual, and transgender individuals enter- 15. Wong N, Gullo K, Stafford, J.  Gays lead non-gays ing substance abuse treatment. J Subst Abuse Treat. in cell phone use, cable TV and HDTV viewership. 2006;30(2):135–46. Technology use and preferences of gay and non-gay 2. McCabe SE, West BT, Hughes TL, Boyd CJ. Sexual consumers. [updated date – n.d.2004, Nov 20; 2021, orientation and substance abuse treatment utilization Dec 6]. http://hispanicad.com/blog/news-­article/had/ in the United States: results from a national survey. J research/gays-­lead-­non-­gays-­cell-­phone-­use-­cable-­ Subst Abuse Treat. 2013;44(1):4–12. tv-­hdtv-­viewership. 3. Mereish EH, O’Cleirigh C, Bradford 16. Villa L.  American Addiction Centers. Top 6 JB.  Interrelationships between LGBT-based vicSmartphone Addiction Recovery Apps. American timization, suicide, and substance use problems in Addiction Centers. 2021. [updated date—2021, a diverse sample of sexual and gender minorities. cited date -Oct 13; 2021]. https://rehabs.com/ Psychol Health Med. 2014;19(1):1–13. smartphone-­apps-­for-­recovery/. 4. Stinson J, Wolfson L, Poole N.  Technology-based 17. Campbell ANC, Nunes EV, Matthews AG, Stitzer M, substance use interventions: opportunities for Miele GM, Polsky D, et  al. Internet-delivered treat­gender-­transformative health promotion. Int J Environ ment for substance abuse: a multisite randomized conRes Public Health. 2020;17(3):992. trolled trial. Am J Psychiatry. 2014;171(6):683–90. 5. Szymanski DM, Kashubeck-West S, Meyer 18. Wang W, Gellings Lowe N, Jalali A, Murphy J.  Internalized heterosexism: measurement, psySM. Economic modeling of reSET-O, a prescription chosocial correlates, and research directions. Couns digital therapeutic for patients with opioid use disorPsychol. 2008;36(4):525–74. der. J Med Econ. 2021;24(1):61–8. 6. Zhang Z, Zhang L, Zhou F, Li Z, Yang J. Knowledge, 19. Christensen DR, Landes RD, Jackson L, Marsch LA, attitude, and status of nitrite inhalant use among men Mancino MJ, Chopra MP, et  al. Adding an internet-­ who have sex with men in Tianjin, China. BMC Public delivered treatment to an efficacious treatment packHealth. 2017;17(1):690. https://doi.org/10.1186/ age for opioid dependence. J Consult Clin Psychol. s12889-­017-­4696-­7. 2014;82(6):964–72. 7. National Survey on Drug Use and Health (NSDUH), 20. Ashford RD, Giorgi S, Mann B, Pesce C, Sherritt L, for survey years 2017 through 2019. https://datafiles. Ungar L, et al. Digital recovery networks: charactersamhsa.gov/dataset/national-­survey-­drug-­use-­and-­ izing user participation, engagement, and outcomes of health-­2019-­nsduh-­2019-­ds0001/. a novel recovery social network smartphone applica8. Mays VM, Cochran SD.  Mental health correlates of tion. J Subst Abuse Treat. 2020;109:50–5. perceived discrimination among lesbian, gay, and 21. Schmitt Z, Yarosh S.  Participatory design of techbisexual adults in the United States. Am J Public nologies to support recovery from substance Health. 2001;91(11):1869–76. use disorders. Proc ACM Hum Comput Interact. 9. Hughes T, McCabe SE, Wilsnack SC, West BT, Boyd 2018;2(CSCW):1–27. CJ.  Victimization and substance use disorders in a

Technology-Assisted Interventions for SUDs with Racial/Ethnic Minorities in the United States

11

Stephanie Chiao, Ariella Dagi, and Derek Iwamoto

Intro Health disparities and substance use treatment underutilization experienced by racial/ethnic minorities in the USA are well-documented [1, 2]. Non-Whites generally have similar or lower rates of substance use disorders, with the exception of American Indians and Alaska Natives (Graphs 11.1 and 11.2), but they are more likely to experience negative consequences [3–11]. Various factors contributing to these disparities have been identified, including structural barriers, attitudinal differences, and varying perceptions about need for treatment [12]. Additionally, the COVID-19 pandemic has created new challenges, including the need to provide alternative treatment modalities to reduce infectious spread risks associated with in-person care [13]. To

S. Chiao (*) Department of Psychiatry, NewYork-Presbyterian Weill Cornell Medicine, New York, NY, USA e-mail: [email protected] A. Dagi Department of Psychiatry, NewYork-Presbyterian Weill Cornell Medicine, New York, NY, USA Weill Cornell Medical College of Cornell University, New York, New York, USA e-mail: [email protected] D. Iwamoto Department of Psychology, University of Maryland, College Park, MD, USA e-mail: [email protected]

address these disparities and pandemic-related challenges, there has been a call to the field to develop novel interventions that are culturally responsive, cost-effective, and can be disseminated widely. Technology-assisted interventions including web-based, mobile, telehealth, and video-telehealth interventions for substance use disorders are viewed as promising approaches that can possibly reduce health disparities and increase access to care for underserved populations. This chapter will focus on the use of such interventions for racial and ethnic minority populations (Table 11.1). The possibility of significantly increasing access to treatment using technology-assisted interventions is supported by the widespread ownership of technological devices in the USA. Over 90% of individuals in the USA have at least one type of computer, which includes tablets and smartphones [14], and the rate of ownership of at least 1 electronic device is similar in Whites and non-Whites [15]. There are, however, some important differences by race/ethnicity. Racial/ ethnic minorities are more likely to be “smartphone dependent,” meaning their internet access is exclusively through smartphones (12% of Blacks and 13% of Hispanics/Latinos versus only 4% of Whites). The smartphone-dependent population has more tenuous internet access, with nearly half (48%) needing to forgo their cell phone service at times because of financial hardship. One way in which this obstacle has been

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Avery, M. Khan (eds.), Technology-Assisted Interventions for Substance Use Disorders, https://doi.org/10.1007/978-3-031-26445-0_11

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88 Rates of substance use disorders and treatment in the past year among age 18+ population by race 30 25 20 15 10 5 0 Non-Hispanic or Latino White

Hispanic or Latino

Black or African American

Substance use disorder

Asian

Two or more races

American Native Indians and Hawaiian and Alaska Natives Other Pacific Islander

Received treatment for substance use disorder

Graph 11.1  Rates of substance use disorder and treatment in past year among age 18+ population in percentages, adapted from the 2020 National Survey on Drug Use and Health: Detailed Tables United States Population Estimates by Race in 2021

Hispanic or Latino 18.1%

Two or more races 2.7% Asian 5.8% Native Hawaiian and Other Pacific Islander 0.2% American Indians and Alaska Natives 1.3% Black or African

Non-Hispanic or Latino White 58.8%

American 13.1%

Graph 11.2  Population estimates by race as of 2021, based on the United States Census Bureau’s Population Estimates Program

overcome in some settings is via reverse telehealth, in which patients access telemedicine services via technology located within medical

facilities that do not have the necessary mental health staffing on site or in which patients use other services such as Wi-Fi hotspots or phones

11  Technology-Assisted Interventions for SUDs with Racial/Ethnic Minorities in the United States Table 11.1 Technology-assisted interventions with empiric evidence in racial/ethnic minority populations Type of technology-­ assisted intervention Web-based interventions

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Web-Based Interventions

Web-based programs are standardized interventions that are accessed online, often consisting of Examples of technology-assisted psychoeducation, symptom monitoring, and skill interventions practice using approaches such as motivational Smoking cessation websites, therapeutic education system (TES), interviewing, cognitive behavioral therapy, or CBT4CBT contingency management. Interventions can be Mobile-based CASA-CHESS, computer-delivered stand-alone or add-on, and the literature suggests interventions screening and brief intervention that both of these approaches can have a small (e-SBI) for postpartum drug use, tobacco telephone quitlines but significant clinical effect [19, 20]. Less is Telehealth Videoconference or phone call known about the effectiveness of these interveninterventions directly with a mental health tions in racial/ethnic minorities. Recent meta-­ professional such as a physician, analyses published on these interventions psychologist, or clinical social surprisingly did not include data on race [19, 20]. worker, either one-on-one or in a group format However, a small number of individual studies suggest that these interventions can be similarly and minutes provided by a health care facility for effective and that race is not a significant moderuse in telehealth appointments [16]. However, ating factor [1]. There have been a handful of studies on there has been concern that some minority groups online, self-help, tobacco-use interventions for including Latinos may have lower acceptance rates of technology-assisted interventions due to a non-English speakers that suggest that these cultural emphasis on personal relationships [17]. interventions can be well received by patients Many racial/ethnic minorities in the USA and effective. Munoz and colleagues examined have low levels of income and education and are various technology-based online tobacco cessaless likely to have health insurance. This puts tion interventions for Spanish speakers living in affordability of substance use disorder treatment California, which yielded a number of important at a premium and preferences lower-cost options, insights [21–23]. In one study, the team comwhich technology-assisted treatment may be able pared a static online smoking cessation guide to offer [15]. Studies on the utilization rates of alone versus the guide combined with additional technology-assisted interventions by race have elements, including email reminders to visit the been mixed, and trends are likely evolving with website with the guide, an online cognitive the many changes brought on by the COVID-19 behavioral mood management course with eight pandemic [18]. Access to necessary electronics, lessons, and a “virtual group” which consisted of internet connectivity, and privacy to be able to a bulletin board for mutual support [22]. The engage in technology-assisted interventions will study found that overall abstinence rates (20%) were comparable to similar online interventions need to be continually assessed. While there are limited data on technology-­ for English speakers, demonstrating that online assisted interventions for racial/ethnic minority interventions can be effectively tailored for populations with substance use disorders relative racial/ethnic minority populations. In addition, to racial/ethnic minorities with other psychiatric there were no significant differences in absticonditions, collectively, these studies suggest nence rates between those who received the static that these modalities may be acceptable and intervention alone (the guide) versus those who effective in racial/ethnic minorities who use sub- had the guide and additional interactive elements. stances and therefore warrant further exploration. However, given there was no control group, it is This chapter will review the existing literature on unclear whether the relatively high abstinence promising web- and mobile-based as well as tele- levels for the website alone represent the website’s effectiveness or if it represents high levels health interventions among minorities.

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of motivation to quit tobacco in this sample. This finding highlighted the need for a no-intervention control group in future trials, which is a recurrent weakness in this area of scholarship [24]. Lastly, although initially intended for Spanish speakers in California, the smoking cessation website was ultimately accessed by Spanish speakers around the world, demonstrating the potential for rapid scalability and wide dissemination with online interventions. Over the course of 10 years, more than 735,000 Spanish speakers from the Americas and 139,000 from Spain visited the website, of which 41,970 consented to participate in one of their studies. There are fewer studies of online interventions for alcohol and illicit substance use disorders. One exception is the Therapeutic Education System (TES), which is a web-based intervention that uses the principles of the Community Reinforcement Approach (CRA), Contingency Management Behavior Therapy, and HIV Prevention and consists of 62 internet-delivered, interactive modules. In prior studies, the TES was shown to be comparable to therapist-delivered CRA and significantly better than treatment as usual, but there had been no consideration of race as a potential moderating factor [25]. [26] addressed this gap by studying outcomes with the TES in a racially diverse sample. The study recruited more than 500 patients with racially diverse backgrounds from 10 outpatient substance use treatment programs located in different regions of the USA and found that TES yielded similar rates of abstinence, treatment retention, social functioning, and craving in Black, Hispanic/Latino, and White participants. Acceptability measures were also high across races and were even higher in Black and Hispanic/ Latino participants than White participants. These findings were particularly significant given that face-to-face interventions often yield different outcomes according to race and lend strong support for the potential of technology-assisted interventions in reducing disparities. Another notable example is CBT4CBT, which uses a cognitive behavioral therapy approach and has been shown to be effective in racially diverse populations, although race has not been evalu-

ated specifically as a potential moderator. CBT4CBT is a web-based program with seven modules that uses movies and interactive exercises to teach cognitive and behavioral skills to those struggling with substance use [27, 28]. In multiple randomized trials, Kiluk et  al. [29] showed that CBT4CBT was effective with medium sized effects as an add-on or stand-alone treatment when compared with treatment as usual. In addition to having decreased substance use, participants in the CBT4CBT group also had higher rates of satisfaction and treatment engagement [29]. Recent studies have examined a culturally and linguistically adapted version of CBT4CBT in Spanish-speaking samples and found evidence of efficacy [30, 31]. The efficacy of this intervention strongly supports greater use of these technologies and further exploration of adaptations for different difficult-to-reach populations.

Mobile-Based Interventions Interventions can be considered mobile-based if they are based around delivery using mobile devices, including phones, tablets, and wearables. Examples include telephone quitlines and app-based interventions, and some prior studies also included brief telephone counseling. However, audio-only encounters with professionally trained providers such as psychotherapists and psychiatrists are increasingly used interchangeably with videoconferencing and traditional clinic visits, so these interventions will be discussed in the telehealth section. Many apps have been created for substance use disorders, but only a few have been studied and shown to be effective [1]. A recent meta-­ analysis found 12 unique apps for adults using alcohol, only one of which had a randomized controlled trial that showed a significant reduction in alcohol use in a sample with alcohol use disorder [32]. The app, Addiction-Comprehensive Health Enhancement Support System (A-CHESS), was developed in 2008 for alcohol use disorder using principles of self-­determination theory and has a range of features, including a

11  Technology-Assisted Interventions for SUDs with Racial/Ethnic Minorities in the United States

locator that prompts users if they are in a high-­ risk location and a panic button for users to notify others that they need help. The app is currently being studied in a number of different populations, including racial/ethnic minority populations and populations using drugs besides alcohol. A-CHESS was adapted for Spanish-­ speaking Latinos with alcohol and other substance use disorders (CASA-CHESS), and early studies have shown that app usage correlates with less substance use, but a randomized controlled trial has not yet been conducted [33, 34]. Another body of literature has focused on technology-based interventions for postpartum substance use, and many of these studies have focused on tablet-based interventions in minority populations. A recent meta-analysis by Hai et al. (2019) reported that most of the studies in this area primarily focused on alcohol use, and more than half used tablet-delivered interventions, with the remainder using text-messaging and online interventions. Notably, most of the studies were conducted among racial/ethnic minority samples: eight studies included predominately Black participants, one study included predominately Latina participants, and one study included American Indian/Alaska Native participants only. Generally, these interventions were efficacious in preventing and reducing substance use and importantly, they found that efficacy did not vary by race. Taken together, these studies lend strong support for the use of technology-based interventions in all postpartum women, including racial/ethnic minorities. Among the interventions for postpartum population, one of the most well-studied technology-­ based treatment modalities has been a tablet-delivered intervention. Ondersma and colleagues conducted a series of studies evaluating the use of a 20 min, single-session brief motivational interview intervention. The intervention was interactive and included a talking character who was non-judgmental, empathic, and reflective. Material focused on eliciting participants’ thoughts about making changes, providing feedback about the participant’s drug use compared to that of others, and optional goal setting. Importantly, the intervention was well-received

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in studies, with 61% reporting that they were more likely to change because of the intervention and 56% reporting a preference for the software over talking with medical staff about substance use [35]. In a series of studies, the tablet-­delivered intervention led to short-term, small decreases in illicit substance use that were not durable [35, 36]. Although effect sizes were small, these studies suggest that these interventions can still have very significant role, particularly when weighing the possibility of wide dissemination in a population in which many do not receive any substance use care. Finally, another area of inquiry has been the use of telephone counseling (“quitlines”) for tobacco cessation. For the last several decades, quitlines have been used by state governments to provide counseling and nicotine replacement therapy free of charge to those trying to quit tobacco [37]. One particularly notable study demonstrated the effectiveness of quitlines for Chinese, Korean, and Vietnamese speaking immigrant sample [38]. The quitlines increased the 6-month abstinence rate for each language group with an overall 6-month abstinence rate of 16.4% vs. 8% in the control group which received only self-help materials. The study was the first large, randomized trial to test and show the effectiveness of quitlines among Asian American immigrants. This was a particularly significant finding, as there has been low enthusiasm for quitlines for Asian Americans based on narratives that “talk therapy” would be ineffective for members of a culture where therapy is less widely used [24, 38]. The study demonstrated the importance and necessity of evaluating treatment efficacy in diverse groups, as results may be different from expectations.

Telehealth Telehealth consists of the delivery of treatment by a mental health professional via regular phone calls or videoconferencing. Studies that look at telehealth outcomes have often not included diverse samples or addressed cultural-specific factors, making it difficult to discern associations

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or relative outcomes for these groups [39]. There is a growing body of literature that has begun to explore the effectiveness of culturally adapted interventions with diverse populations. Kim et al. [40] conducted a randomized smoking cessation intervention delivered via telephone or videoconferencing to Korean American women. The 8-week intervention consisted of nicotine patches and eight 30-min weekly individualized counseling sessions that incorporated “deep” cultural adaptations, meaning they reflected core cultural values and a social and historical context for the smoking, as opposed to mere language translation. This was the first study that evaluated videoconferencing for smoking cessation among Asian American women, and they found that videoconferencing was acceptable and feasible for Korean American women less than 50 years old. Abstinence rates were similar for both treatment arms (33% for videoconferencing and 28% for telephone using salivary cotinine test), and these rates were better than those achieved by standard in-person standard cessation treatments and multilingual quitlines, suggesting the added value of deep cultural adaptations [40]. A retrospective study from Anchorage, Alaska investigated a residential substance use treatment program for Alaska Native communities with telepsychiatry support. Legha et  al. [41] completed a chart review study using a matched case control design comparing those who received telepsychiatry services with those who did not. They found that those who received telepsychiatry treatment had more severe psychosocial stressors, remained engaged in treatment longer, had fewer discharges against medical advice, and were more likely to complete treatment. Although study participants were not randomized, the study supports the real-world effectiveness of telepsychiatry and again highlights the potential to traverse geographic barriers in order to deliver high quality, culturally appropriate care [41]. A study conducted in Baltimore, Maryland by King et al. [42] assessed the effectiveness of videoconferencing for delivering group counseling to methadone-maintenance partial responders in

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the course of opioid use disorder treatment. While the study was not specifically designed to consider the treatment of minorities, 44% of the fifty patients enrolled in the study were of minority status and outcomes were not reported to be different from the non-minorities in the study though were not explicitly discussed. Patients who tested positive for an illicit substance were randomly assigned to an in-person or live videoconferencing-­ based group counseling for 6  weeks in addition to ongoing methadone-­ maintenance treatment. Both groups responded well to this intensification of the treatment. In both groups, approximately 70% of the participants achieved at least two consecutive weeks of abstinence and all returned to less intensive treatment successfully, as measured by continued attendance. There were no significant outcome differences between the two groups, and in fact many participants indicated a preference for the videoconference group predominantly due to reasons of convenience. There was a 26% drop-out rate from the study, with a greater percentage of those dropping out being racial/ethnic minorities, but more dropouts occurred in the in-person than videoconferencing group, suggesting there may be trend toward greater use and accessibility for racial/ethnic minorities, though again, this was not explicitly reported [42]. This study suggests telehealth group counseling could be an acceptable and effective part of substance use treatment in inner-city predominantly racial/ethnic minority settings. Similarly, an intensified treatment protocol for smoking cessation that used telephone-based counseling was found to improve outcomes in a study of a predominantly African American and Latinx, economically disadvantaged, HIV-­ positive clinical sample [43]. Vidrine et  al. randomized 95 participants to a cell phone intervention, which consisted of proactive counseling calls made by a trained smoking cessation counselor around the planned quit date or were continued in usual care alone. The counseling sessions focused on increasing social support and teaching coping strategies using cognitive behavioral strategies. The study found that the cell

11  Technology-Assisted Interventions for SUDs with Racial/Ethnic Minorities in the United States

phone intervention plus usual care was more effective; abstinence rates were 10.3% in the usual care group and 36.8% for the cellular telephone group, and those who received the cellular telephone intervention were 3.6 times more likely to quit smoking compared with participants in the usual care group [43]. Prior to this study, proactive counseling calls had been shown to be effective, but this study is one of the few to demonstrate its effectiveness with African American and Latinx Americans experiencing economic hardship. Given the deleterious impact of the COVID-­19 pandemic on treatment utilization especially among racial/ethnic minority populations, Yeo and colleagues (2021) examined the audio-only telehealth treatment effects for low-threshold medication-assisted treatment of opioid use disorder serving a predominantly Black population (91% of 277 participants). Low-threshold refers to the use of medication-assisted treatment regardless of ongoing use of substances, without a requirement for regular urine drug screens or bloodwork, and without penalty for missing appointments, with the idea that a harm reduction model increases the likelihood that patients will enter into, continue, or return to treatment with buprenorphine and that any diverted medication would ultimately be non-harmful and potentially therapeutically beneficial to the community at large. The findings indicated that the standard care arm pre-pandemic versus audioonly arm early pandemic had a 94% vs. 68% 90-day retention rate and a 92% vs. 52% 180day retention rate in the program. Though the audio-only arm had substantially lower retention rates, these findings are limited by the lack of randomization, major differences in the financial and other psychosocial circumstances for patients pre- versus during-pandemic, and a reported change in staffing that occurred during the study period. These results encourage the pursuit of randomized treatment arms and close scrutiny of longer term follow-up differences that may exist in in-person versus telehealth care in future studies.

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Conclusions Racial/ethnic minorities remain underserved in the field of substance use treatment. With increasing availability of technological devices and internet connectivity and a growing population of people comfortable with using technology in their daily lives, this is an opportune moment to invest in the development of culturally competent substance use treatment resources in a variety of languages to provide greater access and encourage more uptake of well-tailored substance use treatment. By the same token, further study of technology-based treatments is critical to ascertain which approaches are effective, feasible, and desirable in order to reach target communities and achieve better outcomes across a diverse community of racial/ethnic minorities in the USA.

References 1. Ramos G, Chavira DA. Use of technology to provide mental health care for racial and ethnic minorities: evidence, promise, and challenges. Cogn Behav Pract. 2019;29(1):15–40. 2. Wu L-T, Zhu H, Swartz MS.  Treatment utilization among persons with opioid use disorder in the United States. Drug Alcohol Depend. 2016;169:117–27. 3. Acevedo A, Garnick DW, Lee MT, Horgan CM, Ritter G, Panas L, et al. Racial and ethnic differences in substance abuse treatment initiation and engagement. J Ethn Subst Abuse. 2012;11(1):1–21. 4. Acevedo A, Miles J, Panas L, Ritter G, Campbell K, Garnick D.  Disparities in criminal justice outcomes after beginning treatment for substance use disorders: the influence of race/ethnicity and place. J Stud Alcohol Drugs. 2019;80(2):220–9. 5. Alegria M, Carson NJ, Goncalves M, Keefe K.  Disparities in treatment for substance use disorders and co-occurring disorders for ethnic/racial minority youth. J Am Acad Child Adolesc Psychiatry. 2011;50(1):22–31. 6. Austin A, Wagner EF.  Correlates of treatment retention among multi-ethnic youth with substance use problems: initial examination of ethnic group differences. J Child Adolesc Subst Abuse. 2006;15(3):105–28. 7. Lagisetty PA, Ross R, Bohnert A, Clay M, Maust DT. Buprenorphine treatment divide by race/ethnicity and payment. JAMA Psychiat. 2019;76(9):979–81.

94 8. McCaul ME, Svikis DS, Moore RD.  Predictors of outpatient treatment retention: patient versus ­substance use characteristics. Drug Alcohol Depend. 2001;62(1):9–17. 9. Mennis J, Stahler GJ. Racial and ethnic disparities in outpatient substance use disorder treatment episode completion for different substances. J Subst Abuse Treat. 2016;63:25–33. 10. Satre DD, Palzes VA, Young-Wolff KC, Parthasarathy S, Weisner C, Guydish J, Campbell CI.  Healthcare utilization of individuals with substance use disorders following affordable care act implementation in a California healthcare system. J Subst Abuse Treat. 2020;118:108097. 11. Vilsaint CL, NeMoyer A, Fillbrunn M, Sadikova E, Kessler RC, Sampson NA, et  al. Racial/ethnic differences in 12-month prevalence and persistence of mood, anxiety, and substance use disorders: variation by nativity and socioeconomic status. Compr Psychiatry. 2019;89:52–60. 12. Green JG, McLaughlin KA, Fillbrunn M, Fukuda M, Jackson JS, Kessler RC, et  al. Barriers to mental health service use and predictors of treatment drop out: racial/ethnic variation in a population-based study. Adm Policy Ment Health. 2020;47(4):606–16. 13. Yang J, Landrum MB, Zhou L, Busch AB. Disparities in outpatient visits for mental health and/or substance use disorders during the COVID surge and partial reopening in Massachusetts. Gen Hosp Psychiatry. 2020;67:100–6. 14. Martin M.  Computer and internet use in the United States: 2018. (ACS-49). 2021. census.gov. 15. Smith A, McGeeney K, Duggan M.  US smartphone use in 2015. 2015. http://www.pewinternet. org/2015/04/01/us-­smartphone-­use-­in-­2015/. 16. Kleinman MB, Felton JW, Johnson A, Magidson JF. “I have to be around people that are doing what I'm doing”: the importance of expanding the peer recovery coach role in treatment of opioid use disorder in the face of COVID-19 health disparities. J Subst Abuse Treat. 2021;122:108182. 17. Nieves JE, Stack KM.  Hispanics and telepsychiatry. Psychiatr Serv. 2007;58(6):877. 18. Campos-Castillo C, Anthony D.  Racial and ethnic differences in self-reported telehealth use during the COVID-19 pandemic: a secondary analysis of a US survey of internet users from late march. J Am Med Inform Assoc. 2021;28(1):119–25. 19. Boumparis N, Karyotaki E, Schaub MP, Cuijpers P, Riper H.  Internet interventions for adult illicit substance users: a meta-analysis. Addiction. 2017;112(9):1521–32. 20. Tait RJ, Spijkerman R, Riper H. Internet and computer based interventions for cannabis use: a meta-analysis. Drug Alcohol Depend. 2013;133(2):295–304. 21. Muñoz RF, Aguilera A, Schueller SM, Leykin Y, Pérez-Stable EJ. From online randomized controlled trials to participant preference studies: morphing the San Francisco stop smoking site into a worldwide

S. Chiao et al. smoking cessation resource. J Med Internet Res. 2012;14(3):e64. 22. Muñoz RF, Barrera AZ, Delucchi K, Penilla C, Torres LD, Pérez-Stable EJ.  International Spanish/ English internet smoking cessation trial yields 20% abstinence rates at 1 year. Nicotine Tob Res. 2009;11(9):1025–34. 23. Muñoz RF, Chen K, Bunge EL, Bravin JI, Shaughnessy EA, Pérez-Stable EJ.  Reaching Spanish-speaking smokers online: a 10-year worldwide research program. Rev Panam Salud Publica. 2014;35:407–14. 24. McDonnell DD, Kazinets G, Lee H-J, Moskowitz JM. An internet-based smoking cessation program for Korean Americans: results from a randomized controlled trial. Nicotine Tob Res. 2011;13(5):336–43. 25. Bickel WK, Marsch LA, Buchhalter AR, Badger GJ.  Computerized behavior therapy for opioid-­ dependent outpatients: a randomized controlled trial. Exp Clin Psychopharmacol. 2008;16(2):132. 26. Campbell AN, Montgomery L, Sanchez K, Pavlicova M, Hu M, Newville H, Nunes EV. Racial/ethnic subgroup differences in outcomes and acceptability of an Internet-delivered intervention for substance use disorders. Journal of ethnicity in substance abuse. 2017;16(4):460–478. 27. Carroll KM, Ball SA, Martino S, Nich C, Babuscio TA, Nuro KF, et  al. Computer-assisted delivery of cognitive-behavioral therapy for addiction: a randomized trial of CBT4CBT.  Am J Psychiatry. 2008;165(7):881–8. 28. Carroll KM, Kiluk BD, Nich C, Gordon MA, Portnoy GA, Marino DR, Ball SA. Computer-assisted delivery of cognitive-behavioral therapy: efficacy and durability of CBT4CBT among cocaine-dependent individuals maintained on methadone. Am J Psychiatry. 2014;171(4):436–44. 29. Kiluk BD, Nich C, Buck MB, Devore KA, Frankforter TL, LaPaglia DM, et  al. Randomized clinical trial of computerized cognitive behavioral therapy and clinician-­delivered CBT in comparison with standard outpatient treatment for substance use disorders: primary within-treatment and follow-up outcomes. Am J Psychiatry. 2018;175(9):853. 30. Paris M, Silva M, Añez-Nava L, Jaramillo Y, Kiluk BD, Gordon MA, et  al. Culturally adapted, web-­ based cognitive behavioral therapy for Spanish-­ speaking individuals with substance use disorders: a randomized clinical trial. Am J Public Health. 2018;108(11):1535–42. 31. Silva MA, Jaramillo Y, Paris M Jr, Añez-Nava L, Frankforter TL, Kiluk BD. Changes in DSM criteria following a culturally-adapted computerized CBT for Spanish-speaking individuals with substance use disorders. J Subst Abuse Treat. 2020;110:42–8. 32. Colbert S, Thornton L, Richmond R.  Smartphone apps for managing alcohol consumption: a literature review. Addict Sci Clin Pract. 2020;15:1–16. 33. Muroff J, Robinson W, Chassler D, López LM, Gaitan E, Lundgren L, et al. Use of a smartphone recovery tool for Latinos with co-occurring alcohol and other

11  Technology-Assisted Interventions for SUDs with Racial/Ethnic Minorities in the United States drug disorders and mental disorders. J Dual Diagn. 2017;13(4):280–90. 34. Muroff J, Robinson W, Chassler D, López LM, Lundgren L, Guauque C, et al. An outcome study of the CASA-CHESS smartphone relapse prevention tool for Latinx Spanish-speakers with substance use disorders. Subst Use Misuse. 2019;54(9):1438–49. 35. Ondersma SJ, Svikis DS, Thacker LR, Beatty JR, Lockhart N. Computer-delivered screening and brief intervention (e-SBI) for postpartum drug use: a randomized trial. J Subst Abuse Treat. 2014;46(1):52–9. 36. Ondersma SJ, Svikis DS, Schuster CR.  Computer-­ based brief intervention: a randomized trial with postpartum women. Am J Prev Med. 2007;32(3):231–8. 37. Nash CM, Vickerman KA, Kellogg ES, Zbikowski SM. Utilization of a web-based vs integrated phone/ web cessation program among 140,000 tobacco users: an evaluation across 10 free state quitlines. J Med Internet Res. 2015;17(2):e36. 38. Zhu S-H, Cummins SE, Wong S, Gamst AC, Tedeschi GJ, Reyes-Nocon J. The effects of a multilingual telephone quitline for Asian smokers: a randomized controlled trial. J Natl Cancer Inst. 2012;104(4):299–310.

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39. Lin LA, Casteel D, Shigekawa E, Weyrich MS, Roby DH, McMenamin SB. Telemedicine-delivered treatment interventions for substance use disorders: a systematic review. J Subst Abuse Treat. 2019;101:38–49. 40. Kim SS, Sitthisongkram S, Bernstein K, Fang H, Choi WS, Ziedonis D.  A randomized controlled trial of a videoconferencing smoking cessation intervention for Korean American women: preliminary findings. Int J Womens Health. 2016;8:453. 41. Legha RK, Moore L, Ling R, Novins D, Shore J. Telepsychiatry in an Alaska native residential substance abuse treatment program. Telemed J E Health. 2020;26(7):905–11. 42. King VL, Stoller KB, Kidorf M, Kindbom K, Hursh S, Brady T, Brooner RK. Assessing the effectiveness of an internet-based videoconferencing platform for delivering intensified substance abuse counseling. J Subst Abuse Treat. 2009;36(3):331–8. 43. Vidrine DJ, Arduino RC, Lazev AB, Gritz ER. A randomized trial of a proactive cellular telephone intervention for smokers living with HIV/AIDS.  AIDS. 2006;20(2):253–60.

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12

Charalambia Louka

Introduction Traditionally, media has been subdivided into three types: print, broadcast, and support (Table  12.1). With the advent of new technologies, the sphere of media has expanded to include new media: social networks, podcasts, blogs, and online forums to name a few. Media has affected the way we engage in our communities [1], maintain interpersonal relationships [2], and make important decisions [3]. The media has affected the way that the public perceives illnesses and makes healthcare decisions. When HIV first appeared in the USA in the early 1980s, the media sensationalized reports of

cases and perpetuated the socioeconomic, ethnic, and sexual orientation stereotypes associated with HIV/AIDS [4]. As time has moved on, the news and entertainment industries have been essential in de-stigmatizing HIV/AIDS, educating the masses, and popularizing treatment [4]. Since the beginning of the COVID-19 pandemic in March 2020, the media has played a major role in the politicization of the disease and its ramifications. This has resulted in a schism among political parties on wearing masks, social distancing, and adhering to other healthcare-­ mandated recommendations [5]. Other examples of the media influencing healthcare include the decreased use of aspirin in children after wide-

Table 12.1  Types of traditional and modern media Traditional media Print  Newspapers, magazines, books, newsletters Broadcast  Television, radio, cinema, music Support  Billboards, transit advertising

Modern media Online/mobile apps  Websites, blogs, discussion forums Streaming  Podcasts, TV/film, music, digital text Social media  Social networks, media sharing, messaging services Other  Virtual and augmented reality, gaming media, e-commerce

C. Louka (*) Weill Cornell Medicine/NewYork-Presbyterian, New York, NY, USA e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Avery, M. Khan (eds.), Technology-Assisted Interventions for Substance Use Disorders, https://doi.org/10.1007/978-3-031-26445-0_12

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spread media reports on Reye’s syndrome [6], increased popularity of genetic testing as a result of “hyping” in media coverage [7], and a heightened perceived risk of breast cancer in women due to its over-representation in the news [8]. In psychiatry, the media has been influential in the stigmatization of mental illness. Mental illness has been portrayed in a largely negative light in the news for decades. This has resulted in deeply rooted stereotypes and biases [9]. Popular horror films of the twenty-first century have often exploited mental illness as a trope for maniacal, demonic, and criminal characters. This has contributed to perpetuating stereotypes of individuals with mental illness in the public sphere [10]. Today, efforts are being made to address mental illness without stigmatizing disease via the media. Some examples include the sharing of personal mental illness stories on social media [11] and commitment to responsible reporting by journalists [12]. In this chapter, we will explore these themes with regard to substance use: depiction in the media, glamorization and stigmatization, and media as a tool to de-mystify biases against addiction.

 resence of Substance Use P in Traditional Media Drugs and alcohol began to appear in the media centuries ago. In 1789—just 100  years after the first American newspaper was published—the first print ad for tobacco snuff was printed by Lorillard Tobacco Company in a New York paper (Fig. 12.1) [14]. When silent films began to appear on the big screen in the early 1900s, they too exhibited substance use. Charlie Chaplin used morphine in Easy Street (1917), Harry Langdon smoked opium in The Hansom Cabman (1924), and Larry Semon took cocaine in The Flying Cop (1917) [15]. Over time, the presence of substances in the media has only increased. A 1999 study analyz-

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ing substance use in the 200 most popular movie rentals from 1996 to 1997 found that more than 90% of the movies contained alcohol/tobacco use and 22% contained illicit drug use [16]. Another study analyzed the top 20 Billboard songs from 2009 to 2013 and found that out of 600 songs, 29% mentioned alcohol, 12% mentioned marijuana, and 9% mentioned other drugs [17]. Of all the music genres that were looked at, substances were mentioned most frequently in rap and R&B/ hip-hop. Drugs have also been at the forefront of many bestselling books and novels through the decades: Beatrice Sparks’s Go Ask Alice, Hunter S. Thompson’s Fear and Loathing in Las Vegas, and Jack Kerouac’s On the Road, to name a few. In novels, drugs have often served as literary devices to symbolize transcendence, destruction, euphoria, and power. To achieve this, authors have been known to go so far as to invent fictional drugs in their books. Some examples include “melange” in Dune, that allows users to travel through space, “soma” in Brave New World, a calming, pacifying substance, and “moloko plus’‘in Clockwork Orange, that fuels the protagonists’ violent rampages [18]. The history of substances in traditional media cannot be discussed without mentioning one of the oldest and classic examples at the intersection of substance use and the media: the marketing of Big Tobacco. As mentioned above, the history of tobacco advertising dates back to the late eighteenth century. In 2019, the four biggest tobacco companies in North America spent $7.62 billion dollars in cigarette advertising [19]. While the most popular streams of promotion involve TV spot ads, magazine spreads, and billboards, other forms of media have been involved as well. Tobacco company Philip Morris paid hundreds of thousands of dollars to place their cigarettes and branding in blockbuster movies such as Superman II (1980), License to Kill (1989), and Beverly Hills Cop (1984) [20].

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Fig. 12.1  Lorillard advertisement, 1789 [public domain] [13]

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 resence of Substance Use P in Modern Media With the rise of social media, online sites, and mobile apps, the presence of drugs in the media has become even more widespread. This has in turn been correlated with younger drug users, who tend to be robust users of social media. A study in 2020 found a rise in social media posts related to the popular nicotine e-cigarette JUUL, many involving promotional advertisements [21]. One-half of the posts in the study were associated with youth-related content. Between 2015 and 2017, JUUL spent $2.1 million in advertising, with the largest marketing category being internet display [22]. Advertisements touted e-­cigarette smoking as “cool” and “safer” than smoking cigarettes and promoted fruity, sweet flavors (e.g. creme brulee, mango, cucumber) that appeal to younger audiences [23]. It has been shown that this kind of promotional content has increased both initiation and continued usage of e-cigarettes in teenagers and young adults (Fig.  12.2) [24, 25]. This heavy promotion of e-cigarettes has led to an e-cigarette epidemic in American youth. In 2018, it was found that 27.1% of American high school students (an estimated 4.04 million) and 7.2% of middle school students (an estimated 840,000) were using either combustible tobacco or e-cigarette products [26, 27]. While the long-term effects of e-cigarette use are

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Fig. 12.2 As expenditures on e-cigarette advertising increased from 2011 to 2014, so did e-cigarette use among middle and high school students. (Adapted from (Centers for Disease Control and Prevention (CDC) 2016) and (Office on Smoking and Health, CDC 2014))

still being studied, the propylene glycol, flavorants, and other chemicals found in such products are known to cause acute lung injury, COPD, lung cancer, and cardiovascular disease [28]. In addition to this, exposure to e-cigarette use in youth has been associated with subsequent cigarette use and ongoing nicotine addiction even after cessation of e-cigarette use [27]. After facing pressures from the FDA, JUUL pulled its flavored nicotine pod flavors from the market in 2019; the FDA banned the sale of many fruit and mint flavored e-cigarette products shortly after [29]. While e-cigarette among teenagers dropped somewhat in 2019, the epidemic continued. Beyond the presence and advertising of substances on modern media, there have been reports that social media networking services and crypto markets alike have become new avenues for buying and selling illicit drugs [30]. A 2019 study analyzing online drug buying behaviors on social media by surveying 358 drug users found that the most popular trafficked drug was marijuana (64.5% of users), followed by LSD (7.9%), MDMA (6.5%), and mushrooms (4.7%) [31]. These transactions were made mostly on Snapchat (used by 76.1% of buyers), Instagram (21.6%), Wickr (16.7%), and Kik (12.6%) [31]. Buying and selling drugs online comes with greater anonymity, ease of use, and a larger audience reach, which also makes regulating these types of exchanges more difficult.

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In September of 2021, DEA Administrator Anne Milgram called on TikTok and Snapchat to make greater efforts to stop the exchange of these drugs [32]. In official statements, both TikTok and Snapchat vowed to take down any drug-­ related content and cooperate with law enforcement investigations. Other sites like Facebook have banned the use of hashtags such as #oxy, #percocet, #painpills, and others [32]. Today, a search for the hashtags #drugs, #cocaine, on Instagram yields the message “This May Be Associated with the Sale of Drugs: The sale, purchase, or trade of illicit drugs can cause harm to yourself and others and is illegal in most countries. If you or someone you know struggles with substance abuse, you can get help through confidential treatment referrals, prevention, and recovery support.” A link to “Get Help” directs users to the SAMHSA’s (Substance Abuse and Mental Health Services Administration) National Helpline.

Stigmatization of Substance Use Substance use in the media is often portrayed negatively. A 2003 review article analyzing the stigmatization of substance use in movies found that four main stereotypes were the most prevalent: the tragic hero, the rebellious free spirit, the demonized addict/homicidal maniac, and the humorous/comedic user [33]. They noted that the “demonized addict” was the most damaging of all the stereotypes, found in films such as David Lynch’s Blue Velvet (1986), Spike Lee’s Jungle Fever (1991), and Gary Oldman’s Nil By Mouth (1998). This phenomenon dates back to the 1936 propaganda film Reefer Madness, which was financed by a church group as a warning against the dangers of using marijuana [34]. The film depicted youth being driven to commit violent crimes such as manslaughter, rape, and grand larceny as a result of being under the influence of marijuana. While the film was inaccurate and biased in its portrayal of marijuana use, it contributed to the passing of the 1937 Marihuana Tax Act, which made marijuana possession virtually illegal. In the news, substance use disorders con-

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tinue to be often depicted as being criminal, immoral, and associated with flawed character. A report looking at US print and TV news coverage of opioid analgesic misuse between 1998 and 2012 found that messaging was largely negative. Of 673 news stories, 53% depicted individuals engaged in criminal activity, yet only 3% mentioned substance use treatment and 1% mentioned harm-reduction policies [35]. Often these negative portrayals of substance use are disproportionately attributed to people of color, ethnic minority groups, and individuals with low socioeconomic status. A 2016 content analysis of popular press articles from 2001 and 2011 on heroin and prescription opioid misuse found that individuals who were black, Latino, or otherwise described as “urban” were more likely to be associated with criminal activity [36]. Conversely, white, “suburban” users of drugs were cast as victims of addiction whose substance use was shocking, unexpected, and blameless [36]. Framing substance use with racial biases and sociopolitical qualifiers leads to lasting effects on public perceptions of substance use. A study looking at this phenomenon exposed groups of participants to a short text narrative that assessed perceptions of a pregnant woman with opioid use disorder with either low or high socioeconomic status [37]. Those reading the narrative of the woman with high socioeconomic status were less likely to exhibit stigma toward opioid use disorders, less likely to support punitive policies against drug use, and less likely to blame the pregnant woman for her drug use.

Glamorization of Substance Use In other instances—often in films, popular culture, and celebrity news—substance use is glamorized. In the 1990s, high fashion photographers captured supermodels styled with dark eye circles, disheveled clothing, and a waif-like thinness. They called this new fashion wave “heroin-chic,” perhaps inspired by the grunge music of famous musicians, Courtney Love and Kurt Cobain to name a few, who were associated with heroin use. While the trend did not appear to

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be correlated with increased heroin use in the public, it still glamorized the use of drugs [38, 39]. This was compounded by the fact that “heroin-­chic” models were often photographed by paparazzi smoking cigarettes and were reported to use alcohol and cocaine. In the early 2000s, substance use was rampant in tabloid stories about young Hollywood starlets. A 2009 qualitative analysis on US media coverage of four female celebrities (Lindsay Lohan, Michelle Rodriguez, Paris Hilton, and Nicole Richie) in the year following their DUI arrests found that most accounts included glitzy photographs and spoke about the starlets’ actions in a routine and inconsequential manner [40]. Very little of the media coverage addressed the public health implications or negative consequences of these events. Studies have shown that glamorization of drug use is not without its dangers. Increased visibility of alcohol, tobacco, and illicit drugs—especially when presented in a positive light—has been shown time and time again to lead to increased drug use in media consumers. A 2007 longitudinal study of 6522 adolescents aged 10–14 years old found that increased exposure to on-screen smoking in blockbuster movies by famous actors significantly increased the hazard ratio of established smoking [41]. Various systematic reviews have consistently found that the same effect is observed with exposure to alcohol content in media and marketing and the rates of drinking in adolescents and young adults [42].

Using the Media as a Tool Ultimately, understanding how the media presents substance use can allow us to use it as a powerful tool in studying, monitoring, and treating substance use disorders. Social media is a map of the way individuals think, feel, behave, and interact. Big Data analytics already use online behavioral patterns to gain insights in fields such as e-commerce, communications, and banking [43]. The field of healthcare should be no different. In 2014, the National Institute of Health (NIH) allocated $11 million to support research studying

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social media as a tool to better understand substance use. Grants were awarded to researchers studying how to mine information on substance use patterns, use social networks to target behavioral change, and create online interventions for parents of young substance users [44]. One approach to this is studying the framing of substance-related media content. Media-­ related messaging to the public is shaped by language, tone, and story framing [45]. Multiple efforts have been made to reduce stigmatization of substance use in journalism and news media by encouraging responsible reporting. In the May 2017 Associated Press Stylebook, a mainstay writing guide for journalists, the Associated Press advised against the use of addiction-related terms with negative connotations. These included words such as “addict,” “junkie,” “crackhead,” “drunk,” and “alcoholic” [46]. A 2020 paper looking at opioid-related articles in New York Times, Los Angeles Times, and USA Today found that there was no significant decrease in the use of these terms before and after the release of the stylebook [47]. Still, these efforts are in their early days and may still help improve news portrayals of substance use in the years to come. The content of substance-related media is also important to study and streamline, given that many people receive information and form opinions based on such content. There are already many apps that deliver iCBT (internet-based cognitive behavioral therapy) and other interventions that have been found to be largely effective in individuals with major depression and anxiety [48]. A March 2018 review article found that there were 904 apps on the iTunes and Google Play app stores that matched keywords for “sobriety” and “recovery”; of these, 74 were specifically focused on reducing use [49]. Most apps involved tracking substance use, setting personal goals, providing online peer support, and helping users locate nearby recovery meetings [49]. A few involved delivery of online evidence-based psychotherapeutic interventions such as iCBT, motivational interviewing, and pharmacological treatment [49]. One app called 24 Hours A Day created by the Hazelden Betty Ford Foundation provides users with a digitized version of The 12

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Steps of Alcoholics Anonymous, as well as daily affirmations and meditations. Another called Sober Grid allows users to connect with nearby peers and post on supportive message boards. Social media interventions can be particularly helpful in younger audiences. It has been shown that youth respond well to online media containing health information because of its anonymity, relatability, and ease of use [50]. Pilot studies testing social media content to educate people about substance use, eradicate stigma, and encourage safe practices have been promising. A combined social media-educational intervention on moderating alcohol use in teenagers significantly reduced intentions of drinking among teenage girls [51]. Another media format that can be utilized to target substance use recovery is podcasts. The Hazelden Betty Ford Foundation has a series of podcasts available on Spotify, YouTube, Apple, and Google that feature personal stories, interviews, and research lectures by clinicians and people in recovery alike. There are other podcasts created by individuals who have been through substance addiction and recovery that aim to guide and inspire listeners. Some include DJ Jessica Jeboult’s A Sober Girl’s Guide, Shane Ramer’s That Sober Guy, and Jean McCarthy’s The Bubble Hour. Social media can also be utilized on a clinical level. In an editorial to the Journal of Adolescent Health, it was proposed that social media use be discussed in appointments as routinely and systematically as schooling, friendships, and exercise [52]. The article suggests questions such as “What do your friends post on social media about alcohol use?” Questions like these can give clinicians insight into how the media affects their patients’ substance use and how they can better prevent and treat substance use disorders.

Conclusion Despite its omnipresence, there is still much research to be done on substance use in the media. What is known already is that depictions of substance use affect public perceptions: sometimes in a negative light, and other times in a positive light.

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Writers, producers, and content creators alike must be cognizant of the social and structural factors that are associated with the way they depict substance use; these insights are important in framing substance use and reducing both stigma and glamorization of addiction. This is not only important because of the way the media sways public opinion, but because it has been shown to affect how the public acquires and consumes substances. More research is needed to fully understand the downstream effects of presenting substances in the media especially as new forms of media proliferate. New insights can guide us on responsible representation of substance use, and on how to use media as a tool in the future.

References 1. Zhang Y, Trusov M, Stephen AT, Jamal Z.  Online shopping and social media: friends or foes? J Mark. 2017;81(6):24–41. 2. Parks MR.  Embracing the challenges and opportunities of mixed-media relationships. Hum Commun Res. 2017;43:505–17. 3. Li Y, Shi H, Zhou Y.  The influence of the media on government decisions: evidence from IPOs in China. Finance. 2021;70:102056. 4. Gouda NK. Mass media coverage of HIV/AIDS: the unintended consequences. J Med Com. 2019;3:1–16. 5. Christos M.  Politics is wrecking America’s pandemic response. Washington, DC: Brookings Up Front; 2020. https://www.brookings.edu/blog/up-­ front/2020/09/17/politics-­i s-­w recking-­a mericas-­ pandemic-­response/. Accessed 28 Nov 2021. 6. Soumerai SB, Ross-Degnan D, Kahn JS.  Effects of professional and media warnings about the association between aspirin use in children and Reye’s syndrome. Milbank Q. 1992;70:155–82. 7. Bubela TM.  Do the print media “hype” genetic research? A comparison of newspaper stories and peer-reviewed research papers. CMAJ. 2004;170:1399–407. 8. Blanchard D, Erblich J, Montgomery GH, Bovbjerg DH.  Read all about it: the over-representation of breast cancer in popular magazines. Prev Med. 2002;35:343–8. 9. Williams M, Taylor J. Mental illness: media perpetuation of stigma. Contemp Nurse. 1995;4:41–6. 10. Goodwin J.  The horror of stigma: psychosis and mental health care environments in twenty-first-­ century horror film (part II). Perspect Psychiatr Care. 2014;50:224–34. 11. Betton V, Borschmann R, Docherty M, Coleman S, Brown M, Henderson C. The role of social media in

104 reducing stigma and discrimination. Br J Psychiatry. 2015;206:443–4. 12. Hocking B. Reducing mental illness stigma and discrimination—everybody’s business. Med J Aust. 2003;178:S47–8. 13. Lorillard P&G. “Tobacco & Snuff Advertisement.” The center for the study of tobacco and society, The University of Alabama, csts.ua.edu. 14. James R.  A brief history of cigarette advertising. Time. 2009. http://content.time.com/time/subscriber/ article/0,33009,1905530,00.html. Accessed 23 Nov 2021. 15. O’Dell C.  The Cokey comedies of the silent screen era. Roger Ebert. 2015. https://www.rogerebert.com/ features/the-­cokey-­comedies-­of-­the-­silent-­screen-­era. Accessed 23 Nov 2021. 16. Roberts PG, Henriksen L, Christenson PG.  Substance use in popular movies and music. Rockville, MD: Substance Abuse and Mental Health Services Administration; 1999. https://eric. ed.gov/?id=ED449404. 17. Holody KJ, Anderson C, Craig C, Flynn M. “Drunk in love”: the portrayal of risk behavior in music lyrics. J Health Commun. 2016;21:1098–106. 18. Noon J.  Top 10 imaginary drugs in fiction. The Guardian. 2007. https://www.theguardian.com/ books/2017/oct/18/top-­1 0-­i maginary-­d rugs-­i n-­ fiction. Accessed 26 Nov 2021. 19. U.S.  Federal Trade Commission (FTC). Cigarette report for 2019. Washington, DC: Federal Trade Commission; 2021. 20. Chapman S, Davis RM.  Smoking in movies: is it a problem? Tob Control. 1997;6:269–71. 21. Czaplicki L, Kostygina G, Kim Y, Perks SN, Szczypka G, Emery SL, et  al. Characterising JUUL-related posts on instagram. Tob Control. 2020;29:612–7. 22. Huang J, Duan Z, Kwok J, Binns S, Vera LE, Kim Y, et  al. Vaping versus JUULing: how the extraordinary growth and marketing of JUUL transformed the US retail e-cigarette market. Tob Control. 2019;28:146–51. 23. Jackler R, Chau C, Getachew B, Whitcomb M, Lee-Heidenreich J.  JUUL advertising over its first three years on the market. Stanford, CA: Stanford University School of Medicine: Stanford Research into the Impact of Tobacco Advertising; 2019. 24. Hammig B, Daniel-Dobbs P, Blunt-Vinti H. Electronic cigarette initiation among minority youth in the United States. Am J Drug Alcohol Abuse. 2017;43:306–10. 25. Mantey DS, Cooper MR, Clendennen SL, Pasch KE, Perry CL.  E-cigarette marketing exposure is associated with e-cigarette use among US youth. J Adolesc Health. 2016;58:686–90. 26. Gentzke AS, Creamer M, Cullen KA, Ambrose BK, Willis G, Jamal A, et al. Vital signs: tobacco product use among middle and high school students—United States, 2011-2018. MMWR Morb Mortal Wkly Rep. 2019;68:157–64. 27. Soneji S, Barrington-Trimis JL, Wills TA, Leventhal AM, Unger JB, Gibson LA, et al. Association between

C. Louka initial use of e-cigarettes and subsequent cigarette smoking among adolescents and young adults: a systematic review and meta-analysis. JAMA Pediatr. 2017;171:788–97. 28. Bracken-Clarke D, Kapoor D, Baird AM, Buchanan PJ, Gately K, Cuffe S, et al. Vaping and lung cancer— a review of current data and recommendations. Lung Cancer. 2021;153:11–20. 29. Lucas A.  FDA says it won’t complete review of e-cigarette products by thursday deadline. Englewood Cliffs, NJ: CNBC; 2021. https://www.cnbc. com/2021/09/09/fda-­w ill-­r eportedly-­s eek-­m ore-­ time-­before-­deciding-­if-­juul-­can-­keep-­selling-­its-­e-­ cigarettes.html. Accessed 12 Dec 2021. 30. Oksanen A, Miller BL, Savolainen I, et  al. Social media and access to drugs online: a Nationwide study in the United States and Spain among adolescents and young adults. Eur J Psychol Appl Leg Context. 2020;13:29–36. 31. Moyle L, Childs A, Coomber R, Barratt MJ. #Drugsforsale: an exploration of the use of social media and encrypted messaging apps to supply and access drugs. Int J Drug Policy. 2019;63:101–10. 32. Barrett D, Dwoskin E.  DEA’s anne milgram issues stark warning on fentanyl-laced pills. Washington, DC: The Washington Post; 2021. https://www. washingtonpost.com/national-­security/dea-­warning-­ counterfeit-­drugs/2021/09/27/448fcb18-­1f27-­11ec-­ b3d6-­8cdebe60d3e2_story.html. Accessed 22 Nov 2021. 33. Cape GS.  Addiction, stigma and movies. Acta Psychiatr Scand. 2003;107:163–9. 34. Hall W, Yeates S.  Reefer madness: an undeserved classic movie. Addiction. 2021;116:963–9. 35. McGinty EE, Kennedy-Hendricks A, Baller J, Niederdeppe J, Gollust S, Barry CL. Criminal activity or treatable health condition? News media framing of opioid analgesic abuse in the United States, 1998-­ 2012. Psychiatr Serv. 2016;67:405–11. 36. Netherland J, Hansen HB.  The war on drugs that wasn’t: wasted whiteness, “dirty doctors,” and race in media coverage of prescription opioid misuse. Cult Med Psychiatry. 2016;40:664–86. 37. Kennedy-Hendricks A, McGinty EE, Barry CL.  Effects of competing narratives on public perceptions of opioid pain reliever addiction during pregnancy. J Health Polit Policy Law. 2016;41:873–916. 38. Denham BE. Folk devils, news icons and the construction of moral panics. J Lesbian Stud. 2008;9:945–61. 39. Hickman TA. Heroin chic: the visual culture of narcotic addiction. Third Text. 2002;16:119–36. 40. Smith KC, Twum D, Gielen AC. Media coverage of celebrity DUIs: teachable moments or problematic social modeling? Alcohol Alcohol. 2009;44:256–60. 41. Sargent JD, Stoolmiller M, Worth KA, Dal Cin S, Wills TA, Gibbons FX, et  al. Exposure to smoking depictions in movies: its association with established adolescent smoking. Arch Pediatr Adolesc Med. 2007;161:849–56.

12  The Media and Substance Use Disorders 42. Gupta H, Pettigrew S, Lam T, Tait RJ.  A systematic review of the impact of exposure to internet-based alcohol-related content on young people’s alcohol use behaviours. Alcohol Alcohol. 2016;51:763–71. 43. Crosier BS, Marsch LA. Harnessing social media for substance use research and treatment. J Alcohol Drug Depend. 2016;4(3):1000238. 44. Using social media to better understand, prevent, and treat substance use. National Institutes of Health (NIH): News Releases. 2014. https://www.nih.gov/ news-­e vents/news-­r eleases/using-­s ocial-­m edia-­ better-­u nderstand-­p revent-­t reat-­s ubstance-­u se. Accessed 28 Nov 2021. 45. Stigmatization & Media. Dianova International. 2018. https://www.dianova.org/wp-­content/ uploads/2019/07/QuitStigma-­R ecommendations-­ Media-­en.pdf. 46. Aliferis L.  In stylebook, AP directs its reporters: addiction is a ‘disease’. Oakland, CA: California Health Care Foundation; 2017. https://www.chcf.org/ blog/in-­stylebook-­ap-­directs-­its-­reporters-­addiction-­ is-­a-­disease. Accessed 28 Nov 2021.

105 47. Bessette LG, Hauc SC, Danckers H, Atayde A, Saitz R.  The associated press stylebook changes and the use of addiction-related stigmatizing terms in news media. Subst Abus. 2020;43:1–4. 48. Karyotaki E, Efthimiou O, Miguel C, Bermpohl FMG, Furukawa TA, Cuijpers P, et al. Internet-based cognitive behavioral therapy for depression: a s­ystematic review and individual patient data network meta-­ analysis. JAMA Psychiat. 2021;78:361–71. 49. Tofighi B, Chemi C, Ruiz-Valcarcel J, Hein P, Hu L. Smartphone apps targeting alcohol and illicit substance use: systematic search in in commercial app stores and critical content analysis. JMIR Mhealth Uhealth. 2019;7:e11831. 50. Fergie G, Hunt K, Hilton S. What young people want from health-related online resources: a focus group study. J Youth Stud. 2013;16:579–96. 51. Rundle-Thiele S, Russell-Bennett R, Leo C, Dietrich T. Moderating teen drinking: combining social marketing and education. Health Educ. 2013;113:392–406. 52. Costello CR, Ramo DE. Social media and substance use: what should we be recommending to teens and their parents? J Adolesc Health. 2017;60:629–30.

Legal Technologies in Substance Use Disorders Sanya Virani

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and Patricia Ryan Recupero

Introduction

currently utilized or recognized by the criminal justice system have also been mentioned (Table 13.1). The internet makes the sale of illegal drugs very easy and provides anonymity for both dealers and users. Several sources of data exist to monitor and track patterns of drug use, in addition to events like overdose-related deaths. Yet there seems to be a lack of properly established

Legal technology broadly refers to technology that can be used to provide legal services. In the context of substance use, it finds most applicability for surveillance and tracking, and sometimes for the purpose of treatment. The law routinely deploys technology for the purposes of diagnosis and detection wherever possible to obtain an objective measure when the substance user in question is facing criminal charges. With this Table 13.1  Key points broad definition in mind, we write about the vari- 1. Legal technology is technology broadly used to provide legal services and is utilized in substance ous devices and technologies that currently exist use disorders for the purpose of detection, and lend themselves to the practice of the law, surveillance and sometimes encourage retention in well beyond the scope and needs of a clinician treatment programs diagnosing and treating their patient on a one-to-­ 2. Many platforms and interventions can lend one basis. Of note, there are many interventions themselves to machine learning and analyses to detect trends of substance use on a population level that fall under the umbrella of digital technology 3.   This chapter divides legal technologies as follows: for substance use disorders, developed with the  (a) Technologies currently in use aim to improved adherence to treatment, absti-         – National data and tracking systems/agencies nence or for harm reduction, but unless they are         – Smartphones (reminders/texts/surveys) and applications (e.g., reSET-O) directly linked to or utilized by the legal system for purposes of detection, monitoring, or surveil-         – Electronic pillboxes         – Prescription drug monitoring programs lance, they have not been included in this chapter. (PDMP) Some technologies that have potential but are not         – Electronic monitoring devices

S. Virani (*) · P. R. Recupero Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, Providence, RI, USA e-mail: [email protected]; [email protected]

 (b) Technologies with untapped potential         – Newly developed breathalyzers         – Social media platforms/social networking sites         – PREDOSE platform         – Machine learning using internet sites, algorithms for state data

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Avery, M. Khan (eds.), Technology-Assisted Interventions for Substance Use Disorders, https://doi.org/10.1007/978-3-031-26445-0_13

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and consolidated surveillance platforms that legally harness technology at our disposal to inform stakeholders about drug use on individual and population levels on a regular basis. Even the systems that are currently in place struggle with delayed reporting, inadequate notification, and an inefficient detection of new or emerging illicit drug use. The most commonly used technologies and some unrecognized ones (in infant stages of development but with great potential) lending themselves to future application in the criminal justice system have been summarized below.

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this data is currently being analyzed for, it may be useful as early warnings of specific illicit drug outbreaks .

Smartphones (Reminders/Texts/ Surveys) and Applications

The SmokefreeTXT program was developed as a text messaging intervention by the National Cancer Institute in 2011 for non-daily smokers [1]. Real-time cigarette reports were passively collected at baseline and during the first 2 weeks after the quit day. It is based on the knowledge that non-daily smoking usually arises from cue Technologies Currently in Use exposure or positive reinforcement rather than needing to maintain blood nicotine levels to avoid National Data and Tracking Systems/ withdrawal symptoms. It targets mood and cravAgencies ing and offers a just-in-time “rescue” messaging for moments of temptation due to a mood or cravThe Drug Abuse Warning Network (DAWN), ing and inquires specifically about it. It caters to established in 1972 and discontinued in 2011 was this hard-to-reach group of individuals who are operated by the Substance Abuse and Mental much less likely to initiate treatment compared Health Services Administration (SAMHSA) and with daily smokers. Researchers at Massachusetts the Department of Health and Human Services General Hospital collected data from the (HHS). It collected data on substance use and SmokefreeTXT messages and sought feedback abuse from non-federal hospital Emergency from participants to determine the degree of helpDepartments from 37 metropolitan states. fulness of the texts [2]. The results-based levels The Research Abuse Diversion and Addiction-­ of participation based on engagement were quite Related Surveillance (RADARS) is a monitoring high. Reduced craving, increased abstinence, and system operated by the Center for Applied less favorable perceptions of the pros of smoking Research on Substance Use and Health Disparities were the outcomes of the study. Researchers (ARSH) and the National Drug Control Strategy opined that these kinds of services are being (NDCS). RADARS collects quarterly and annual underutilized. data from multiple police agencies, poison cenSelf-administered electronic platform surters, pharmaceutical boards, and health depart- veys: Surveys have found some utility in adolesments in 49 US states. Data collection takes place cent substance use surveillance because of the through field surveys, personal interviews, tele- ubiquitous nature of smartphones in this populaphone and mail surveys, computer-assisted inter- tion group. And while there is acknowledgment views and focus groups, as well as operations that that not much data has collected for this group, a involve tracing hard-to-find interviewees. The study [3] evaluated completion rates, times, and Web Monitoring system, started by RADARS in responses of high school students and used sur2014 for the purposes of surveillance tracks posts veys to get information regarding adolescent attion the internet, which are then organized by tudes, beliefs, and use trends around substance trained team coders to review and code the data use. There is not much novelty to the technique of on illicit drug use. In addition to what markers surveillance that relies on self-reporting, but the

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study gleaned that there were opportunities to increase engagement in these methods using smartphones for behavioral health surveillance. reSET-O: The National Institute on Drug Abuse sponsored a 12-week clinical trial involving 170 patients to evaluate a version of the reSET-O application [4]. Clinically supervised delivery of buprenorphine was made possible through the app, which was designed to deliver cognitive behavioral therapy in conjunction with transmucosal buprenorphine and contingency management. Incentives were used to reinforce certain behaviors. And while the FDA noted that reSET-O did not decrease illicit drug use or improve abstinence, it became known that more of the patients that stayed in the program used it.

Electronic Pillboxes The utility of an electronic and cellular-enabled pillbox was studied for the purpose of safe dispensing of methadone with multiple layers of checks and monitoring, alerting providers regularly [5]. The dimensions of the MedMinder “Jon” electronic pillbox are 14″  ×  11″  ×  2″; it weighs approximately 5.5 pounds when empty and contains 28 cells (using a 7 × 4 grid) that lock independently to secure a medication cup that can be filled with several tablets/capsules and it can be open during preprogrammed time windows, though assigned staff can reprogram individual cells to open outside of programmed times when clinically indicated [5]. Each cell is remotely unlocked automatically for a brief period, at a time that the patient and staff member predetermine, to allow the patient to remove the medication cup and consume his or her medication. Staff members can remotely reprogram the time at which cells are unlocked to accommodate unexpected changes in patient schedules. The pillbox recorded all instances when the medication cup is removed (e.g., medication dispensing, participant consumption) and replaced in real-­ time and sends a series of real-time alerts to predetermined staff members when any activity

Table 13.2  Nonpatient/medication events of MedMinder “Jon” electronic pillbox 1. Notifications that the box had been registered/ deregistered to patients 2. Staff confirmations of changes in assigned dosing periods 3. Notifications of low batteries on the box 4. Temporary loss/reconnection of wireless connection to the server 5. Standard weekly reports

(expected or unexpected) is registered. In this proof-of-concept study, staff received real-time alerts from the pillbox via phone and email. Alerts were designated into categories that signified patient-related events (e.g., nursing’s removal/return of the medication cup during the dispensing process, patient’s removal/return of the medication cup, or patient’s failure to return/ remove the medication cup) or nonpatient/medication events. Staff contacted participants promptly in response to any unexpected patient-­ related events (e.g., failure to take medication at predetermined time) (Table 13.2).

 rescription Drug Monitoring P Programs (PDMP) A PDMP is a state-operated database of patient prescriptions for controlled substances. Authorized providers can access the PDMP database to identify the inappropriate use of pain medications. Bokyung Kim extensively studied the associations of heroin use and overdose deaths with access to PDMPs [6]. Must access PDMPs were associated with an increased heroin death rate. A year after the implementation of PDMPs, heroin mortality in the half year period went up by 0.42 and an additional 0.9 per 100,000 over 2  years compared with control states. And while increases were seen with illicit opioids, prescription opioid use seemed to decrease, thereby showing what spillover effects that the implementation of new technology of PDMPs had on patterns of opioid use that followed.

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Electronic Monitoring Devices Electronic monitors were designed to reduce recidivism and technical violations among probationers and parolees. GPS-tracking ankle monitors have proven to be an alternative to jail by affording a way to mandate supervision. In Drug and Specialty Court settings, electronic monitors are used commonly as a sanction for rule infractions and are often used in combination with house arrest for those on parole or intensive supervision. However, the results of monitors for recidivism rates are mixed in that several studies indicated that these monitors had only a minimal effect. Overall, a study by Gonzalez et al. found that controlling for criminogenic risk factors, the odds of re-arrest with use of these monitors were in fact 3.13 greater than without [7]. Further, electronic monitoring devices were not associated with the completion of substance use treatment.

Technologies with Untapped Potential Newly Developed Breathalyzers Various remote breathalyzers have either received or are on their way receiving premarket clearance from the FDA, one of which has been developed by Soberlink working in conjunction with the “Sober Sky Web Portal” to transmit blood alcohol content (BAC) results in real time to treatment providers and/or family and loved ones. Furthermore, facial recognition technology is built into the device, helping confirm the identity of the tester. Remote testing provides another option from the conventional form of alcohol monitoring through a urinalysis test. Fairbairn et al. [8] studied transdermal sensors providing repeated breathalyzer readings for alcohol (BrAC). To date the most widely researched transdermal device is the Secure Continuous Remote Alcohol Monitor (SCRAM), relying on a pump to actively generate airflow and because it is an older generation device, it is bulky and often a source of embarrassment for those that wore it around their ankles. A newer

S. Virani and P. R. Recupero

and sleeker version, the BACtrack Skyn monitor, weighing one-sixth of the bulkier SCRAM device became available, relying on passive rather than active airflow and thereby permitting total alcohol level to be measured as rapidly as every 20 s became available.

 ocial Media Platforms/Social S Networking Sites Kazemi et al. [9] demonstrated the utility of utilizing social media platforms to track patterns of drug use, given the commonality in the age group that most uses illicit drugs (18–25-yearolds) and accesses social networking sites (SNS) very frequently. The authors of this review alluded to futuristic public health approaches to tracking drug use by pointing out the parallels with syndromic surveillance for flu, internet pharmacy pricing, and illicit drug sales. Worldwide the demographics (age, gender, socio-economic status, geographic location, etc.) of those who use illicit drugs can fluctuate dynamically over time. Every year new types of drugs and drug combinations entice millions of young people to use drugs. Increasingly, epidemiological surveillance of websites such as blogs or Twitter messages are used as sources to collect global health data. All studies in this review reported that illicit drug surveillance from SNS (e.g., Twitter, LiveJournal, and Facebook profiles) was a positive pursuit. However, there were issues of sampling bias and limiting generalizability. In the last decade, social media has been used to assess the motivations behind the posting of images and videos of substance use. Five of the 14 studies reviewed used Twitter to track trends in substance use. Cavazos-Rehg et al. [10] investigated Twitter sources and found that offline and online social networks influenced health behaviors. Facebook was found to have a potential impact on perceptions of peer alcohol use. A qualitative study of focus groups showed similar results, i.e., SNSs may influence adolescents’ positive attitude toward alcohol consumption. Information in SM can be used by teachers and parents to track how youths interact relative to alcohol use.

13  Legal Technologies in Substance Use Disorders

PREDOSE Platform The PREscription Drug Abuse Online Surveillance and Epidemiology (PREDOSE) Platform is capable of extracting entities, relationships, triples, and sentiments from unstructured texts [11]. This platform facilitates web-based research on the illicit use of pharmaceutical drugs to help researchers gain knowledge of the attitudes and behaviors of drug abusers related to the illicit use of pharmaceutical opioids such as buprenorphine.

 achine Learning Using Internet M Sites, Algorithms for State Data There is often discussion of “doping substances” and suppliers on internet forums. Researchers [12] opine that a comprehensive understanding of products and sellers could lead to an operational monitoring of the online doping market. It is known that internet sales of illicit substances can take place through the surface web (indexed sites), non-indexed sites (deep web), or overlay networks (darknet) which can only be accessed with specific software, configurations, or authorization and often uses a unique customized communication protocol. Sellers utilize these platforms to promote, advertise, and sell, whereas (potential) buyers share information and reviews about products. Importantly, the authors state that “doping” is not considered as high of a priority as compared to child pornography, for example, and that research strategies need to be appropriately set up to open the various layers of the internet to regular surveillance. The method of “intelligence-led crime scene processing” proposed by Ribaux et al. [13] to guide the collection of forensic traces on a crime scene based on knowledge was suggested for online drug market surveillance by collecting data, integrating it into specific memory, detection of specific problems through pattern recognition and its analysis, using the intelligence gained to make decisions and then study the impact of those decisions. Researchers of this study found that anabolic steroids were the most frequently mentioned drug on internet platforms.

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One enterprising way to deploy state data like the Adoption and Foster Care Analysis and Reporting System (AFCARS) federally mandated for collection to measure outcomes in the juvenile justice system (e.g., time to reunification with parents) as it relates to monitoring of substance use and could be an extension of the research. A study [14] highlighted the racial and ethnic differences in family reunification: foster care non-Hispanic black children who lived in states which adopted criminal justice oriented policies had lower chances of reunification with parents. As of 2017, the District of Columbia and 24 other states, Alabama, Arizona, Arkansas, Colorado, Florida, Illinois, Indiana, Iowa, Louisiana, Maryland, Minnesota, Missouri, Nevada, North Dakota, Ohio, Oklahoma, Rhode Island, South Carolina, South Dakota, Texas, Utah, Virginia, Washington, and Wisconsin had adopted these policies.

 imitations of Currently Available L Technologies Despite recent efforts toward more sophisticated web-based monitoring (e.g., RADARS), many of the current surveillance methods have limitations, including difficulties linking data, reliance on retrospective cross-sectional surveys, and reports from interviewees/patients themselves. As a result, regional illicit drug use may remain unknown to healthcare professionals until overdoses or deaths are encountered in the emergency room. Further, as it pertains to pregnant substance users interfacing with the criminal justice systems, states which implemented punitive prenatal substance use policies intended to reduce prenatal substance use. However, pregnant women have often found this as a deterrent to seeking care because of fear of criminal charges and loss of their child/children to the child welfare system. In any case, women that seek care are known to be less likely to provide information associated with their substance use and those who received prenatal care and honestly shared their experiences with medical professional acknowledged poor treatment. Several organizations have thus advocated against the establishment of criminal charges for substance use during pregnancy.

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Acceptability of Technological Solutions for Overdose Monitoring Tsang et  al. [15] investigated the factors that make it suitable for certain subgroups of populations in Vancouver, British Columbia to accept and utilize technology as a means of monitoring their safety. Thirty people who used drugs were recruited from the downtown area of Vancouver notable for homelessness, drug use, and poverty. The authors found that the stigma associated with substance use reporting and showing up at consumption sites might deter people due to lack of privacy or just for the sake of having different personal preferences. Novel technologies that incorporate automated monitoring and response might provide an alternative. Regarding accessibility, most participants indicated dissatisfaction with the long wait times and crowded facilities. On the flip side, many participants shared that they would only have access to a cellphone for half a month and would sell them when they got desperate for drugs, with some concluding that technological monitoring might be fruitful for those that had money. Further, mention was made about concerns regarding battery life, lack of access to Wi-Fi or data needing to support applications on phones and the extra caution that those who have been in prison might exercise around cameras and machine “monitoring” of their behaviors and that they would be “reported” if they did anything out of line. Despite these concerns, many still supported the proposed applications as an additional means to stay safe and were more inclined to utilize them in special circumstances—if they were alone, had a new substance, a new dealer, or when injecting as opposed to smoking or snorting. Regular users however indicated that they might try technological devices simply out of curiosity one time or for monetary gain. Importantly, regarding police interactions with the mentally ill and substance users, serious concerns were raised about contacting the police, based on prior experiences of their lack of ability with managing medical situations well and in fact being aggravating or attacking those that came to them asking for help. The trust between people who use drugs and their dealers is also a deterring

factor to seek out and use technology voluntarily, even for their own safety. Many users are familiar with how to respond to an overdose situation and clients assume that someone within close proximity on the streets would likely carry a naloxone kit. These devices could reduce the number of individuals who use in private and overdose alone.

Conclusion Legal technologies though available in many formats are often underutilized for substance use disorders monitoring (surveillance/tracking) and have variable results for treatment completion. Even so, these promising technologies are not without limitations and will need ongoing research for implementation. If deployed strategically for use by select groups of substance users, legal technology can lend itself to wider uptake by groups of substance users most likely to view them as helpful instead of punitive strategies.

References 1. Squiers L, Brown D, Parvanta S, Dolina S, Kelly B, Dever J, Southwell BG, Sanders A, Augustson E. The SmokefreeTXT (SFTXT) study: web and mobile data collection to evaluate smoking cessation for young adults. JMIR Research Protocols. 2016;5(2):e134. https://doi.org/10.2196/resprot.5653. 2. Baggett TP, McGlave C, Kruse GR, Yaqubi A, Chang Y, Rigotti NA.  SmokefreeTXT for homeless smokers: pilot randomized controlled trial. JMIR Mhealth Uhealth. 2019;7(6):e13162. https://doi. org/10.2196/13162. 3. Riccio KK, Rawlins DB, Talbot JN, Boyd CJ.  Demonstration of smartphones as viable tools for adolescent substance use surveillance. Subst Use Misuse. 2020;55(5):693–6. https://doi.org/10.1080/1 0826084.2019.1696822. 4. Maricich YA, Bickel WK, Marsch LA, Gatchalian K, Botbyl J, Luderer HF.  Safety and efficacy of a prescription digital therapeutic as an adjunct to buprenorphine for treatment of opioid use disorder. Curr Med Res Opin. 2021;37(2):167–73. https://doi.org/10.108 0/03007995.2020.1846022. 5. Dunn KE, Brooner RK, Stoller KB.  Technology-­ assisted methadone take-home dosing for dispensing methadone to persons with opioid use disorder

13  Legal Technologies in Substance Use Disorders during the Covid-19 pandemic. J Subst Abuse Treat. 2021;121:108197. https://doi.org/10.1016/j. jsat.2020.108197. 6. Kim B.  Must-access prescription drug monitoring programs and the opioid overdose epidemic: the unintended consequences. J Health Econ. 2021;75:102408. https://doi.org/10.1016/j.jhealeco.2020.102408. 7. Reingle Gonzalez J, Johansson-Love J, Edmonds L, Jetelina K.  Electronic monitoring devices during substance use treatment are associated with increased arrests among women in specialty courts. Am J Drug Alcohol Abuse. 2020;46(5):632–41. https://doi.org/1 0.1080/00952990.2020.1771722. 8. Fairbairn CE, Kang D, Bosch N.  Using machine learning for real-time BAC estimation from a new-­ generation transdermal biosensor in the laboratory. Drug Alcohol Depend. 2020;216:108205. https://doi. org/10.1016/j.drugalcdep.2020.108205. 9. Kazemi DM, Borsari B, Levine MJ, Dooley B. Systematic review of surveillance by social media platforms for illicit drug use. J Public Health (Oxf). 2017;39(4):763–76. https://doi.org/10.1093/pubmed/ fdx020. 10. Cavazos-Rehg P, Krauss M, Grucza R, Bierut L.  Characterizing the followers and tweets of a marijuana-­focused twitter handle. J Med Internet Res. 2014;16(6):e3247. https://doi.org/10.2196/jmir.3247.

113 11. Cameron D, Smith GA, Daniulaityte R, Sheth AP, Dave D, Chen L, Anand G, Carlson R, Watkins KZ, Falck R. PREDOSE: a semantic web platform for drug abuse epidemiology using social media. J Biomed Inform. 2013;46(6):985–97. https://doi.org/10.1016/j. jbi.2013.07.007. 12. Pineau T, Schopfer A, Grossrieder L, Broséus J, Esseiva P, Rossy Q. The study of doping market: how to produce intelligence from internet forums. Forensic Sci Int. 2016;268:103–15. https://doi.org/10.1016/j. forsciint.2016.09.017. 13. Ribaux O, Baylon A, Roux C, Delémont O, Lock E, Zingg C, Margot P.  Intelligence-led crime scene processing. Part I: forensic intelligence. Forensic Sci Int. 2010;195(1–3):10–6. https://doi.org/10.1016/j. forsciint.2009.10.027. 14. Sanmartin MX, Ali MM, Lynch S, Aktas A.  Association between state-level criminal justice-­ focused prenatal substance use policies in the US and substance use-related foster care admissions and family reunification. JAMA Pediatr. 2020;174(8):782–8. https://doi.org/10.1001/jamapediatrics.2020.1027. 15. Tsang VWL, Papamihali K, Crabtree A, Buxton JA. Acceptability of technological solutions for overdose monitoring: perspectives of people who use drugs. Subst Abus. 2021;42(3):284–93. https://doi. org/10.1080/08897077.2019.1680479.

Technology-Assisted Therapies in Healthcare Professionals

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Adeolu Ilesanmi

Introduction Substance abuse is typically defined as the use of drugs for nonmedical reasons to reach a psychoactive effect (e.g., euphoria, sedation), while misuse is usually defined as the incorrect use of prescribed and over the counter drugs for a legitimate medical reason [1]. Approximately 10–15% of all HCPs misuse drugs or alcohol during their careers [2]. Rates of substance abuse and dependence in HCPs resemble those of the general population, which is concerning given the high degree of responsibility that HCPs have in caring for the general population. Misuse and abuse of opiates and benzodiazepines are high among HCPs, and specialties such as psychiatry, emergency medicine, and anesthesia exhibit higher rates of drug abuse compared to their peers. This phenomenon is likely a result of the high-risk environments associated with these specialties, the personalities that self-select into these specialties, and the ease of access to drugs of abuse in these fields [2]. Substance abuse among HCPs is high, and risk is higher among those who practice independently due to the lack of accountability [3]. Current literature presents conflicting reports concerning the true prevalence of SUDs among HCPs. This problem in part stems from A. Ilesanmi (*) New York Presbyterian Hospital/Weill Cornell Medical College, New York, NY, USA e-mail: [email protected]

the reality that many of the reported statistics regarding inappropriate substance use and chemical dependency are measured within the context of illegal or inappropriate behaviors, such as medication diversion, which complicates accurately measuring a variable as sensitive as substance use. Medication diversion is a primary means by which HCPs abuse nonprescribed drugs; though, the actual scope of this phenomenon is unknown because the data are largely retrospective. The absence of any consistent empirical data regarding rates of substance abuse and dependence among HCPs also makes it difficult to properly identify risk factors for this phenomenon. Many studies that have attempted to explore this question are limited by their lack of generalizability to different HCPs or to different regions throughout the USA.  Additionally, as within any population, stigma surrounding drug and alcohol use leads to gross underreporting and underestimations of problem use in HCPs and acts as a significant barrier help-seeking behaviors among HCPs [4]. Those HCPs who do seek and receive appropriate care have better outcomes when they are also held accountable to existing professional monitoring bodies that ultimately work to protect their licensure and the safety of patients. Due to increasing incorporation of telehealth practices into mental healthcare, and specifically the treatment of SUDs, there presents a heightened challenge of balancing the privacy of HCPs seeking treatment for

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Avery, M. Khan (eds.), Technology-Assisted Interventions for Substance Use Disorders, https://doi.org/10.1007/978-3-031-26445-0_14

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their SUDs and the safety of their patients. Here, I attempt to consolidate and summarize what is known about the prevalence of SUDs among major HCP groups, risk factors for SUDs in these groups, barriers to treatment, existing treatments and related technological advances, and the ethical and legal implications for technology-assisted treatment for HCPs with SUDs.

Nurses

Many studies have demonstrated that nurses engage in more average monthly alcohol use than pharmacists and physicians, as well as more past-­ year binge drinking than the general population over 35  years of age. In one comparative study among HCP groups, nurses reported higher rates of family history of addiction than pharmacists but not physicians, and nurses reported higher Patterns of Substance Use rates of benzodiazepine abuse compared with in Different Populations physicians [10]. Cigarette use is another primary of Healthcare Professionals substance use concern among nurses compared with other HCPs, and some research suggests Physicians that smoking in the nursing profession was astoundingly higher than any other HCP group Physicians appear to be less likely to use alcohol, [11]. More recent research reports that past-­ tobacco, and other drugs—with the exception of month cigarette use continues to be highest anxiolytics—than other HCPs or the general pop- among nurses compared to other HCPs but is less ulation. Physicians’ lifetime prevalence of alco- than half the rate in age-matched peers among the hol use and past-year binge drinking was general population [6, 7]. Illicit drug use is also comparable to that of the general population; prominent among many nurses, with one study though, past-month binge drinking was much reporting significant rates of lifetime and past-­ lower [5]. Alcohol continues to be offered and year marijuana use by nurses [12]. Nonprescribed served by pharmaceutical companies at continu- prescription medication use is also said to be ing education seminars as part of marketing tac- extensive among nurses, with one study demontics, and evidence shows that physicians are strating that 14% of nurses in its sample had at offered alcohol in these settings significantly least 61 nonprescribed medication-taking more so than other HCPs [6, 7]. Unlike with alco- instances, which was the most of any other HCP hol, the prevalence of marijuana use by physi- surveyed [5]. Iatrogenic drug exposure may also cians exceeded rates in the general population be a contributing factor to rates of inappropriate [8], while the lifetime prevalence of cocaine and minor and major opioid use among nurses, a hallucinogen use among physicians was the high- notion supported by this same study, which found est compared to other HCP groups and the gen- that prescribed drug use was much higher in eral population [5]. The same pattern was true for nurses than other HCPs [5]. Psychiatric comoruse of anxiolytics among physicians compared to bidities may play a role in these rates of substance other HCPs and the general population [9]. use in nurses, as indicated in a study that found Physicians generally tend to self-treat their medi- nurses to be more likely than their peer HCP cal conditions and self-prescribe their drugs groups to have psychiatric disorders, particularly rather than consulting other physicians. Self-­ major depressive disorder followed by anxiety. In prescribed minor opioid use by physicians, how- fact, 10% of nurses in this sample reported expeever, was reported as far lower than among riencing suicidal ideation in the past 30 days of pharmacists [5]; though, among anesthesiolo- being surveyed. Nurses were also more likely to gists, opioids are reported as the main drug of be taking psychiatric medications compared to abuse [4]. Anticholinergic drugs have also been other HCPs [10]. Findings from an institutional identified as drugs of abuse among some physi- ethnography suggest that substance use is intencians because of the hallucinogenic and euphoric tionally leveraged to numb physical pain and effects they can produce [1]. emotional distress of punishing work conditions

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[13]. While lifetime, past-year, and past-month substance use rates are higher among nurses compared to the general population, a reduced rate of drug use has been observed among nurses over time [14]. One explanation for this trend may be the changes in how prescription medications are accessed in medical facilities over this time period. Now, in order to meet the Joint Commission’s requirements to maintain strict controls over medications and to promote patient safety, most hospitals use automated systems to maintain accurate counts of controlled drugs when dispensing to nurses to give to patients [15]. That these counts must be verified at each shift change makes it easier to detect potentially unauthorized access to these medications.

Pharmacists Pharmacists are no more likely to drink alcohol than other HCPs according to existing data; however, qualitative studies suggest that alcohol remains one of the major drugs of choice for substance-impaired pharmacists; though, it is rarely the sole choice [6, 7]. For instance, one study found that only 21% of pharmacists were addicted to alcohol alone; however, 77% of them were addicted to a combination of alcohol and other drugs, almost always prescription medications [16]. This data suggests that the pathway to addiction for most pharmacists who do become impaired is through polysubstance use, specifically, alcohol combined with minor opioids and anxiolytics. While no studies suggest that total lifetime or past-year drug use by pharmacists exceeds that of other major HCP groups or the general populations, pharmacists may still be at greater risk to use prescription medication than the general population [5]. Specifically, they are at higher risk of nonmedical opioid, stimulant, and anxiolytic drug use due to easy access [17]. One study found that 40% of pharmacists surveyed used prescription medications without a physician’s authorization, while 20% reported having done so five or more times in their lifetime [14]. In another study, pharmacists reported higher rates of opioid abuse compared with

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nurses and physicians [6, 7]. Diversion is thought to be the primary source of obtaining these medications and, thus, access to medications is theorized to be a prerequisite to use for many pharmacists [18]. Access, knowledge of drugs, minimal education around how addiction develops, and influences (peer, academic, occupational) that do not discourage substance use appear to be the primary contributors to illicit prescription medication use among pharmacists [14].

Dentists Alcohol use and misuse appear to be the most prevalent substance use issue facing dentistry. Dentists are significantly more likely than other HCPs to self-report use and misuse of alcohol by nearly every major alcohol use standard assessed [19]. Dentists report a higher lifetime prevalence, significantly greater past 30-day quantity and frequency, greater past-year and past-month binge drinking (five or more drinks for men, four or more drinks for women at one time), and greater daily use of alcohol compared to other HCPs [9]. Specific evidence as to why alcohol use and misuse are so prevalent among dentists is limited but is likely similar to risk factors found among the general population, including gender (greater number of male dentists than female), family history, income (though most data suggests that alcohol use is independent of socioeconomic status among HCPs), and social influences.

Social Workers The first study to empirically examine SUDs in social workers sampled and surveyed 50 social workers who were members of Alcoholics Anonymous and had been sober for at least 1 year [20]. Among those surveyed, there were 63 arrests and 120 inpatient hospitalizations related to substance use; however, only 42% received any sanction from supervisors or employers. According to another study by Fewell et al. [21], 8% of social workers sampled drank almost daily,

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4% drank daily, 5.7% were reported to be “problem drinkers,” while 36% knew 1–3 colleagues struggling with abuse of alcohol or other drugs, including benzodiazepines, marijuana, sedatives, narcotics, hallucinogens, and cocaine in order of prevalence of use [3]. Furthermore, Siebert [22] surveyed social workers who were defined as being at moderate risk of substance abuse, and 40% of them agreed or strongly agreed that they had worked when too distressed to be effective, while 37% reported experiencing negative consequences in the workplace, and 22% reported at least three workplace incidents. Similar trends were reported in social workers at serious risk of substance abuse. The study concluded that only 9% of social workers at moderate and high risk of substance abuse felt they had a substance abuse problem and experienced high levels of denial, which is likely true among most groups of HCPs.

 isk Factors for Substance Use R in Healthcare Professionals A well-acknowledged factor that contributes to substance use in HCPs is the ease of access to prescription medications, which is a likely cause of reported higher rates of misuse of these drugs among this population compared to the general population [12]. Considerable evidence suggests that HCPs are at greater risk specifically because of their work environments, and that this risk is directly related to their roles as HCPs. For instance, a pilot study measuring three workplace dimensions (availability, frequency of administration, and workplace controls) summed as an index in a sample of almost 4000 nurses found that nurses with easy access on all dimensions were most likely to misuse prescription drugs in the preceding year. Many studies have shown that a strong family history of substance use is a major risk factor for SUDs in HCPs, as well. For example, a positive family history of alcoholism was the most predictive factor for alcoholism in physicians in a retrospective review of addiction and substance use in medical students, residents, and

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physicians [23]. Additionally, Hankes and Bissel [24] referred to “MDeity” as the sense of invincibility physicians have toward their ability to fall prey to substance use, misuse, and dependence due to their education, intelligence, and knowledge of pharmacology. Many HCPs, in fact, adopt an attitude of denial of their susceptibility to substance-related impairment, which makes intervention more difficult to initiate [25]. HCPs are particularly adept at hiding any signs of dependence by walking what is called a “pharmacological tightrope,” marked heavily by their education in drug titration [26]. HCPs often use greater amounts of drugs for longer periods and can go undetected by their ability to use various drugs to cover drug side effects. However, this perceived recognition of boundaries between healthy and unhealthy use of substances may be misleading, and there are telltale signs of impairment that can be observed in the workplace (see Table 14.1). Finally, both quantitative and qualitative studies demonstrate that older HCPs drink significantly more alcohol than younger HCPs. One study, for instance, found that alcohol use increased with age among physicians, while heavy alcohol use declined only slightly after peaking between ages 31 and 40 among pharmacists. Notably, drinking habits among doctors are not associated with medical specialty or type of practice, but correlate positively to age, with older doctors being more likely to be heavy drinkers (i.e., have or more drinks at one time on five or more days during the past 30 days) [27]. Table 14.1  Potential signs of chemical impairment in a coworker (adapted from [4]) Odor of alcohol, mouthwash, or mints (to mask the alcohol) on breath Tremulous hands (could indicate alcohol withdrawal) Hyperhidrosis Frequent absenteeism, emergencies, reports of illness, or lateness Frequent bathroom breaks Volunteering overtime or being at work at unscheduled times Heavy drug waste or drug shortages Frequent mood swings or personality changes

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 arriers to Recovery Among B Healthcare Professionals with Substance Use Disorders Early intervention and treatment for HCPs with SUDs is highly desirable because it would decrease the risk of errors in patient care due to substance impairment and help HCPs prevent death by overdose or suicide, as well as other adverse drug reactions. There are myriad reasons why many HCPs struggling with SUDs cannot or will not seek help for their conditions or do not maintain effective recovery. Denial is often greatest for those HCPs who are most addicted. Those who identify as “natural helpers” have a hard time asking for help and a high likelihood of minimizing and denying their own substance abuse problems [3]. Another concern is how HCPs are sometimes placed on a pedestal in society with certain expectations that make HCPs hesitant to seek help lest they appear to fall short of society’s ideals both within and outside of the workplace. For instance, there is stigma among some nurses that SUDs are viewed as a character flaw, which can lead to othering practices in the workplace [13]. Warren et al. [3] also assert that social workers are glorified in the public eye and are expected to have the answers to all of life’s problems. Many find it hard to cope with such expectations and believe that participating in support groups like AA could tarnish their image. Moreover, many HCPs are concerned about risks related to stereotypes, stigma, confidentiality, and ruining the hard-fought professional ethos of care and collegiality in their workplaces [28]. Cost is also a critical barrier for some HCPs, particularly those who are younger and earlier in their careers. Rojas et al. [10], for instance, found that of the HCPs sampled in their study, nurses were, on average, younger and had less work experience than the providers with prescriptive authority and pharmacists studied. These nurses were significantly undertreated compared to the pharmacists but not the providers with prescriptive authority, and the authors theorized that finding was due to financial costs of long-term treatment that is the standard of care for HCPs. Relapse is also more likely in HCPs who have neglected, untreated, or

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unmet healthcare needs, especially nonmalignant pain, and 71% of those in Rojas et al. [10] sample reported major physical health issues. The work environment is a less critiqued barrier to change in HCPs with SUDs. Ross et al. [13] argue that substance use practices provide institutions with compliant workers at the cost of workers’ health and well-being. They go on to assert that working more hours and shifts than scheduled to access drugs on which they eventually become dependent can be mistaken as “professionalism,” leading to unintentional encouragement of SUDs. Confidentiality is also a major consideration when exploring barriers to recovery in HCPs. Privacy-versus-safety issues often prevent HCPs from seeking help [29], and the growing use of telehealth in assisting treatments for SUDs further complicates this issue. Additionally, fear of punishment is one of the more prevalent barriers to seeking treatment among HCPs. Multiple studies demonstrate that nurses and pharmacists reported fear of punishment as reasons for not seeking help, and other studies indicate that nurses identifying with punitive responses related to SUD from workplaces are less likely to enter treatment [30]. Many HCPs are unaware that those who pursue treatment on their own volition are also protected by confidentiality laws that keep their identities hidden and, thus, prevent the loss of privileges or licensure, which would ultimately result from the alternative of becoming known to the Drug Enforcement Administration or state licensure boards. Early intervention in a HCPs career may result in better outcomes. Most of the time, HCPs are taken off work initially for a period of intensive treatment. Poor outcomes, such as relapse and inability to keep employment, result when there is an absence of appropriate structure and follow-up. Physician Health Programs (PHPs) for physicians or similar programs for other providers, also known as alternative-­to-discipline programs (ATDs), are an extremely effective means of achieving and maintaining this necessary structure. ATDs serve as ways to provide confidential treatment without discipline to nurses, physicians, pharmacists, and other licensed HCPs with SUD that

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is available to the public. The premise of these programs is to reduce stigma and damage to professional reputations and to offer the opportunity to seek help and support to safely return to practice [30]. States have legislation for special treatment programs for SUDs among HCPs, and programs are voluntary as long as there is no legal or malpractice consequence associated with the SUD.  These programs are paid for through fees needed from all HCPs each year to renew their licenses [3]. Ultimately, ATDs are meant to protect the HCP and patients with early intervention by providing specific terms and conditions such that if the provider successfully completes the ATD probation, there is no licensure discipline and no public report [30]. Physicians in PHP for SUDs have impressive rates of recovery [31], with the overwhelming majority remaining abstinent from alcohol and nonprescribed drugs. A multistate study found that only 22% of physicians in PHP tested positive for alcohol or other drugs of abuse during the 5-year monitoring period, and 71% stayed licensed and in practice 5 years after completing treatment [30]. Outcomes for physicians with opioid use disorder (OUD) were as good as for those with alcohol use disorder (AUD) and other drug use disorders. The added accountability, especially with the continued use of random drug screens and leverage with boards, and many treatment requirements contribute to good results for physicians in PHPs [30].

Technology-Assisted Treatment of Substance Use Disorders in Healthcare Professionals Federal and state agencies have loosened restrictions on telehealth to provide care in the wake of the COVID-19 pandemic [32]. There is no current consensus as to best practice recommendations for medical visits focused on addiction treatment via telehealth, but the American Psychiatric Association and the American Telemedicine Association developed a guide for general clinical videoconferencing in mental health. This guide represents a model based on

A. Ilesanmi

research evidence, expert consensus, patient needs, and available resources and aims to help provide safe, effective medical care [33]. This guide is helpful for general mental health considerations but not necessarily sensitive to the needs of SUD treatment. Generally, the recommended best practices for initial synchronized video-­ based telehealth assessment for SUD should contain all the elements typically obtained in an in-person visit. The four most common modes of telehealth in SUD treatment programs are computerized assessments (45%), telephone-based recovery support (29%), telephone-based therapy (28%), and video-based therapy (20%). Lesser used tools include texting, smartphone applications, and virtual reality (VR) interventions [32] (see Fig. 14.1). Computerized and web-based assessments and treatments with no live interaction are asynchronous, meaning patients can access them at any time. Benefits include improved ease of access to assessments and patients’ ability to use them at critical times in recovery. HCPs would especially benefit from this form of treatment after returning to work considering the often unpredictable nature of workflow for most specialties and types of HCPs. Common features include screening assessments, cognitive behavioral therapy (CBT) modules, motivational therapy sessions, psychoeducation, behavioral skill building, links to self-help recovery groups, and computerized brief interventions. Most studies show consistently positive effects and few adverse outcomes of these tools in addiction treatment when they focus on delivering evidence-­based strategies [32], which HCPs are more likely to embrace given that their work is heavily reliant on evidence-based research. One study of 84 alcoholic patients assessed at 3, 6, and 12  months noted improvement in the percentage of days abstinent, reduced mean drinks per drinking day, and reduced alcohol-related problems with results being similar to traditional face-to-face interventions; no safety concerns identified [34]. Such findings are promising for physicians and dentists, who experience more AUD than their peer HCP groups. Conversely, several reviews of online asynchronous smoking

Fig. 14.1 Common telehealth modalities in SUD treatment (adapted from [32])

Percentage Used in SUD Treatment Programs (%)

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50 45 38 29

25

28 20

13

10 0

CA

TBRS

TBT

VBT

Telehealth Modalities

Other

CA = Computerized Assessments, TBRS = Telephone-based Recovery Supports, TBT = Telephone-based Therapy, VBT = Video-based Therapy, Other = Texting/Smartphone Applications/VR

cessation resources showed that most computerized programs were of mediocre quality and that the highest quality websites attracted few people [35], which could create a risk if people are trying to apply mediocre tools without consulting a physician for advice on quality. Given that smoking is especially prevalent among nurses, these findings underscore a gap in much needed technology-­ assisted treatment for this population. Stewart and Lam’s [36] randomized control trial (RCT) of patients with SUDs showed that participation in computer-assisted cognitive rehab (CACR) led to better treatment engagement and commitment compared to the equally intensive attention control treatment (CATT). CACR recipients also had better post-treatment outcomes regarding substance use frequency reduction and other indications of addiction severity. Additionally, Shulman et  al. [37] conducted a large RCT examining the association between cognitive functioning and treatment outcomes in Therapeutic Education System (TES), an internet-based psychosocial intervention for SUDs that can provide enhanced treatment for people with cognitive deficits, compared to treatment as usual (TAU) in outpatient programs at National Drug Abuse Treatment Clinical Trials Network. Their study did not produce evidence suggesting that TES differs in effectiveness

across levels of cognitive function, suggesting it would be an appropriate treatment for people with cognitive impairment. The implications of these findings for HCPs are salient for those who would be receiving treatment for OUD and undergoing medication-assisted treatment. Concerns about cognitive impairments from opioid substitution therapy could be mitigated if TES or similar programming is incorporated into treatment. Telephone-based recovery supports are synchronous because they require real-time contact between the patient and the clinician. Phone calls offer support, link patients to resources, and deliver brief interventions. They are considered minimally resource intensive with low infrastructural costs; however, cost efficiency is limited by low reimbursement rates that vary geographically and by payer type [32]. Current evidence only supports use of telephone-based medicine in continuing care after completion of traditional addiction treatment and may be specific to alcohol [38, 39]. For HCPs undergoing treatment, phone-­ based care would likely only be possible outside of normal work hours unless specific guaranteed protected time could be carved out. Such a luxury is typically only afforded to specialist or attending-­level HCPs or those working in a private practice setting. This issue points to the need for healthcare institutions and training programs

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to offer protected time for therapeutic endeavors such as this, which would likely only improve HCPs’ health and, thus, efficiency while bolstering patient safety. Compared with in-person treatment, videoconferencing to administer treatments of SUDs is no less effective and is associated with significant patient satisfaction and safety. Use for treatment of AUD is associated with reduced dropout, reduced alcohol consumption, higher abstinence rates, and high patient satisfaction compared to TAU.  Similar results are seen for treatment of OUD with videoconferencing alongside buprenorphine and methadone. With smoking cessation, videoconferencing has shown similar 12-month abstinence rates compared to in-person treatment. Improved 1-year retention with videoconferencing compared with in-person treatment is owed in part to ease of access, perception of reduced stigma, and reduced burden of travel to appointments [40–44]. Given that stigma has been identified as a major barrier to recovery for many HCPs, videoconferencing is an understandably appealing option for this population. Many HCPs are also naïve to the fact that groups specifically geared toward HCPs with SUDs meet both in-person and now more so online because of the COVID-19 pandemic. Greater ability to access such sources of community and support and more education that such groups exist would likely reduce the perceived stigma among HCPs by creating a sense of normalcy, solidarity, and validation. Healthcare organizations are increasingly using text messages to support healthcare delivery, most often with appointment reminders, which have reduced the frequency of missed appointments [41]. Text-delivered care also includes craving helplines, automated CBT, relapse prevention skills support, personalized messages delivery based on stage of change, and personalized motivational reminders. This modality can also be used in vivo at moments of critical decision-making and is shown to have improved long-term abstinence rates in 11 RCTs when used for smoking cessation either as a

A. Ilesanmi

stand-alone treatment or with traditional treatment [32]. This finding suggests specific utility of text message delivery in addressing the prevalence of smoking among the nursing population specifically, as well as indirectly impacting how HCPs in general cope with stressors in the workplace and, thus, reducing use of various substances. What is clear is that text message delivery of SUD treatment is often underused given its simplicity and cost-effectiveness. Smartphone applications share features of web-based tools and offer personalized push notifications, direct connections to support people, in vivo assessments, real-time craving interventions, contingency management-based rewards, and global positioning system-tracking that alert patients when they are approaching high-risk location [45]. They reduce hazardous drinking and drinks per day [46], and some use predictive models to identify patients at high risk for relapse and to deliver personalized interventions [47]. AA and NA have also developed free applications that provide a consolidated source of local meeting locations and guidelines, daily reflections, news, and other information. These applications are commonly used to enhance TAU rather than as stand-alone forms of treatment. The major concern for HCPs using this form of treatment is the protection of confidentiality and anonymity, which would vary based on the level of security involved in the encryption services used in each app. VR for SUDs offers the possibility of both asynchronous and synchronous environments. Asynchronous environments are designed to simulate reality for patients to test reactions to environmental cues, while synchronous ones allow patients to make digital avatars to interact in real time with peers and clinicians. Studies show that VR can reliably recreate cravings; though, no studies have evaluated effects of synchronous virtual world on SUD treatment [32]. If such technology could be applied to SUD effectively, one could argue that HCPs would benefit greatly from being able to simulate their respective work environments, associated stress-

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ors, and cravings that they might encounter to mitigate the risk associated with returning to work in recovery. At up to $100,000 per session [32], the cost of VR is prohibitive for most individuals regardless of socioeconomic status or profession. If enough data is accumulated over time suggesting so, there may be a role for healthcare institutions in funding VR treatment for employees or lobbying for insurance reimbursements for such treatments in their contracts with insurance companies. Many limitations exist surrounding technology-­ assisted treatment of SUDs as it stands. Notably, many patients take ill to the lack of fluidity of virtual interactions compared to ­in-­person ones [32]. There is also likely wide variability in the degree of internet and equipment access that different healthcare institutions and HCPs have depending on geographic location and resources. Moreover, less than 1% of SUD treatment centers adopted telemedicine technologies as of 2012; though, this number may have changed in the wake of the pandemic [48]. The added difficulty of obtaining vitals, performing physical exams, urine drug screening, and observation for signs of intoxication suggests that there should be new considerations for what the standard of care could mean in this telehealth setting. At the very least, HCPs can still access labs for urine samples, and some labs may even be located at HCPs’ workplaces. Remote options for monitoring substance use also include oral fluid and hair analysis. There is, however, a high risk for tampering with unobserved methods outside of clinic, which HCPs may be more adept at achieving compared to the general population. To help address this problem, observed oral fluid testing has been integrated into applications where patients are observed placing fluids into testing cups. Urine testing in labs remains the modality of choice because it is the only option reimbursed by most insurance companies at this time. Home monitoring kits, such as a Bluetooth-­enabled breathalyzer, can be used to assess acute intoxication, and while they are not widely available for most patients, HCPs would likely have access to them through ADTs.

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Medication-Assisted Treatment and Telehealth for Healthcare Professionals Prescribing controlled substances like buprenorphine for patients seen exclusively virtually was previously restricted but is now possible due to temporary emergency legislative changes from the COVID-19 pandemic, but methadone induction still requires in-person visits given the increased risk of overdose early in induction, concern for stacking with other opioids, and need for a lab workup and electrocardiogram monitoring [32]. Medications for OUD need the greatest supervision due to risk of misuse and diversion ([49–51]; American Academy of Addiction Psychotherapy). The risk of diversion is particularly high in certain populations of HCPs and should be considered when determining the best medication to assist in treatment for their SUDs. For new OUD patients via telehealth, buprenorphine has advantages over methadone or injectable naltrexone because it has greater prescribing flexibility and a better safety profile than methadone and does not require the monthly office visits that injectable naltrexone does [32]. This flexibility is more likely to align with the scheduling needs of most HCPs. Buprenorphine home induction via telehealth should follow most of same steps as process would be in person (see Table  14.2), and most HCPs would be adept in following those steps at home. More flexible Table 14.2 Buprenorphine home induction (adapted from [32]) Start with a visit to establish: (a) DSM-5 diagnosis, (b) complete history of substance use, (c) full medial, social, and psychiatric history; (d) current depression or suicidal ideation; (e) review the prescription monitoring program Provide medications for breakthrough withdrawal symptoms including but not limited to insomnia, nausea, and abdominal cramping Warn patient of precipitated withdrawal Initial prescription must last patient completion of induction phase, stabilization, and 1-week follow-up Most patients stabilize on 8–16 mg of buprenorphine After-hours clinical contact information should be provided Provision of naloxone kit to all OUD patients

A. Ilesanmi

124

take-home methadone dosing is available in light of the pandemic, but considerations of the “pharmacological tightrope” are critical when deciding how to proceed with treatment of certain HCPs with OUD.

limitations of occupational evaluations and how technology-assisted treatment may impact their protections under GINA.

Psychosocial and Ethico-Legal Considerations of Treating Healthcare Professionals with Substance Use Disorders

Much more research is needed to investigate the role of telehealth in treatment of SUDs specifically in HCPs given the potential advantages. Many privacy considerations exist for HCPs that do not exist for the general population. Considerations about medication-assisted treatment also hinge on the specific skill sets required by certain HCPs. Treatment for SUDs needs to be individualized to each type of HCPs role and work environment needs. It is important to note that work environments for many HCPs as they stand are not conducive to smooth and stable recovery, and better safeguards should be employed to protect HCPs and to encourage them to seek treatment for their SUDs without fear of punishment or disclosure of their SUDs via technological breaches. Better educational resources regarding the multifactorial nature of how addiction develops, the internal and external factors that lead to substance among HCPs, and the availability and effectiveness of monitoring programs in promoting recovery are also imperative.

Many patients and providers have concerns around the quality of video-based interactions compared to in-person. Studies have shown that group-based treatment by videoconference can provide safe intervention, high patient satisfaction, and have similar outcomes to in-person [32]; but few studies of group treatment by videoconference looking at PTSD in inmates show possible reduction in patient-reported group cohesion and treatment alliance. Still, virtual groups offer practical alternative to face-to-face treatment that has become more limited due to social distancing, and HCPs could benefit from Caduceus meetings and other support groups specifically geared toward HCPs, as mentioned before. HCPs with SUDs often prefer to keep their medical concerns private so as to not jeopardize their careers, which underscores the need for sound institutional policies and state laws promoting HCPs getting proper help while maintaining their privacy, particularly if technology-assisted treatment is to become more prevalent in this population. Such a trend raises the stakes and increases the legal risks, especially to naïve providers that do not understand the risks. Occupational Health Providers (OHPs) are likely to be incredibly helpful in addressing this conundrum [29]. OHPs are also likely the most knowledgeable among all HCPs of the details of the Genetic Information Nondiscrimination Act (GINA), which preserves employee privacy potentially at the unintended expense of public safety. Their specialized knowledge of GINA obligates OHPs to educate their employee-­ patients about the

Conclusion

References 1. Al-Maaz A, Abu-Dahabb R, Shawagfeh M, et  al. Prevalence and pattern of substance use and misuse among anesthesia health-care personnel in Jordan. J Subst Abuse. 2019;24(3):317–22. 2. Baldisseri M.  Impaired healthcare professional. Crit Care Med. 2007;35(2):108. 3. Warren L, Feit M, Wells J.  Substance use among professionals: limited research on substance-­ abusing social workers. J Hum Behav Soc Environ. 2011;21:803–12. 4. Baldwin J, Trinkoff A, Lewis D. Substance use disorders in health care professionals. Addic Med Sci and Prac. 2010;68:921–34. 5. Kenna G, Wood M.  Relations between social influence factors, pharmaco-expectancy drug use and problems in healthcare students. In: Presented at the 72nd Annual Eastern Psychological Association Convention, Washington, DC; 2001.

14  Technology-Assisted Therapies in Healthcare Professionals 6. Kenna G, Wood M. Alcohol use by healthcare professionals. Drug Alcohol Depend. 2004a;75:107–16. 7. Kenna G, Wood M.  Substance use by pharmacists and other health professionals. J Am Pharm Assoc. 2004b;44:684–93. 8. Hughes P, Brandenburg N, DeWitt B, et al. Prevalence of substance use among US physicians. JAMA. 1992;267:2333–9. 9. National Household Survey on Drug Abuse Series: H-14. Summary of findings from the 2001 National Household Survey on Drug Abuse. Washington, DC: Substance Abuse and Mental Health Services Administration; Office of Applied Studies, DHHS Pub No. (SMA) 01-3549; 2002. 10. Rojas J, Haekyung J, Bran M, et al. Substance abuse patterns and psychiatric symptomatology among three healthcare provider groups evaluated in an out-­ patient program for impaired healthcare professionals. J Addict Dis. 2013;32(1):99–107. 11. Padula C. Nurses and smoking: review and implications. J Prof Nurs. 1992;8:120–32. 12. Trinkoff A, Storr C.  Incorporating auxiliary variables into probability sampling designs. Nurs Res. 1997;46:182–5. 13. Ross C, Jakubec S, Berry N, et  al. “A two glass of wine shift”: dominant discourses and the social organization of nurses’ substance use. Glob Qual Nurs Res. 2018;5:1–12. 14. Dabney D.  Response to kenna. J Am Pharm Assoc. 2001;41:790. 15. Joint Commission on Accreditation of Health Care Organizations. 2002;3:6. 16. Bissel L, Haberman P, Williams R. Pharmacists recovering from alcohol and other addictions: an interview study. Am Pharm. 1989;NS29:19–30. 17. McAuliffe W.  Non-therapeutic opioid addiction in health professionals: a new form of impairment. Am J Drug Alcohol Abuse. 1983;10:1–22. 18. Baldwin J, Thibault E.  Substance abuse by pharmacists: stopping the insanity. J Am Pharm Assoc. 2001;41:373–5. 19. Kenna G, Wood M.  Family history of alcohol and drug use in healthcare professionals. J Subst Use. 2005;10:225–38. 20. Pooler D, Sheheen F, Davidson J. Professional impairment: a history and one state’s response. J Addict Dis. 2009;28:113–23. 21. Fewell C, King B, Weinstein D. Alcohol and other drug abuse among social work colleagues and their families: impact on practice. Soc Work. 1993;38:565–70. 22. Siebert D. Work and well being: a survey of distress and impairment among North Carolina social workers (Doctoral dissertation). Dissertation Abstracts International; 2001. 23. Flaherty J, Richman J.  Substance use and addiction among medical students, residents, and physicians. Psychiatr Clin North Am. 1993;16:189–97. 24. Hankes L, Bissel L.  Health professionals. In: Substance abuse: a comprehensive textbook; 1992. p. 897–907.

125

25. Skipper G. Treating the chemically dependent health professional. J Addict Dis. 1997;16:67–73. 26. Coombs R.  Addicted health professionals. J Subst Misuse. 1996;1:187–94. 27. McAuliffe W, Rohman M, Breer P, et al. Alcohol use and abuse in random samples of physicians and medical students. Am J Public Health. 1991;81:177–82. 28. Kunyk D, Inness M, Reisdorfer E, et al. Healp seeking by health professionals for addiction: a mixed studies review. Int J Nurs Stud. 2016;60:202–15. 29. Couser G.  Return to work for healthcare professionals with substance use disorders: clinical, legal, and ethical challenges. Psychol Inj Law. 2013;6:238–49. 30. Eisenmann N.  Differences between licensed healthcare professionals with substance use-related licensure discipline. Subst Use Misuse. 2020;55(12):2035–42. 31. DuPont R, McLellan A, White W, et al. Setting the standard for recovery: Physicians’ Health Programs. J Subst Abuse Treat. 2009;36:159–71. 32. Oesterle M, Kolla B, Risma C, et  al. Substance use disorders and telehealth in the COVID-19 pandemic era: a new outlook. Mayo Clin Proc. 2020;95(12):2709–18. 33. Shore J, Yellowlees P, Caudill R, et  al. Best practices in videoconferencing-based telemental health. Telemed J E Health. 2018;24(11):827–32. 34. Hester R, Squires D, Delaney H. The Drinker’s check­up: 12-month outcomes of a controlled clinical trial of a stand-alone software program for problem drinkers. J Subst Abuse Treat. 2005;28(2):159–69. 35. Etter J. A list of the most popular smoking cessation web sites and a comparison of their quality. Nicotine Tob Res. 2006;8(1):27–34. 36. Fals-Stewart W, Lam WK. Computer-assisted cognitive rehabilitation for the treatment of patients with substance use disorders: a randomized clinical trial. Exp Clin Psychopharmacol. 2010;18(1):87–98. 37. Shulman M, Campbell A, Pavlicova M, et  al. Cognitive functioning and treatment outcomes in a randomized controlled trial of internet-delivered drug and alcohol treatment. Am J Addict. 2018;27:509–15. 38. Cacciola J, Camilleri A, Carise D, et  al. Extending residential care through telephone counseling: initial results from the betty ford center focused continuing care protocol. Addict Behav. 2008;33(9):1208–16. 39. McKay J, Van Horn D, Oslin D, et  al. Extended telephone-based continuing care for alcohol dependence: 24-month outcomes and subgroup analyses. Addiction. 2011;106(10):1760–9. 40. Eibl J, Gauthier G, Pellegrini D, et al. The effectiveness of telemedicine-delivered opioid agonist therapy in a supervised clinical setting. Drug Alcohol Depend. 2017;176:133–8. 41. Free C, Phillips G, Watson L, et al. The effectiveness of mobile-health technologies to improve health care service delivery processes: a systematic review and meta-analysis. PLoS Med. 2013;10(1):e1001363.

126 42. Leo J, Lamb K, LaRowe S, et al. A brief behavioral telehealth intervention for veterans with alcohol misuse problems in VA primary care. Drug Alcohol Depend. 2014;140:45. 43. Staton-Tindall M, Havens J, Webster J, et  al. METelemedicine: a pilot study with rural alcohol users on community supervision. J Rural Health. 2014;30(4):422–32. 44. Tarp K, Bojesen A, Mejldal A, et al. Effectiveness of optional videoconferencing-based treatment of alcohol use disorders: randomized controlled trial. J Ment Health. 2017;4(3):38. 45. Giroux D, Bacon S, King D, et  al. Examining perceptions of a smartphone-based intervention system for alcohol use disorders. Telemed J E Health. 2014;20(10):923–9. 46. Dulin P, Gonzalez V, Campbell K. Results of a pilot test of a self-administered smartphone-based treatment system for alcohol use disorders: usability and early outcomes. Subst Abus. 2014;35(2):168–75.

A. Ilesanmi 47. Chih M, Patton T, McTavish F, et al. Predictive modeling of addiction lapses in a mobile health application. J Subst Abuse Treat. 2014;46(1):29–35. 48. Molfenter T, Brown R, O’Neill A, et al. Use of telemedicine in addiction treatment: current practices and organizational implementation characteristics. Int J Telemed Appl. 2018;2018:3932643. 49. Hilty D, Crawford A, Teshima J, et al. A framework for telepsychiatric training and e-health: competency-­ based education, evaluation and implications. Int Rev Psychiatry. 2015;27(6):569–92. 50. Maheu MM, Drude KP, Hertlein KM, et al. A framework of interprofessional telebehavioral health competencies: implementation and challenges moving forward. Acad Psychiatry. 2018;42(6):825–33. 51. American Academy of Addiction Psychotherapy. System PCS. Telehealth tip sheet. PCSS FAQ-treating opioid use disorder via telehealth tips for primary care providers. 2020. https://www.aaap.org/clinicians/ covid-­19-­resources/. Accessed 25 Apr 2022.

Index

A Addiction treatment program (ATP), 18 Addiction-Comprehensive Health Enhancement Support System (A-CHESS), 43, 44, 90 Addictive disorders, 24 Adoption and Foster Care Analysis and Reporting System (AFCARS), 111 Alcohol, 69 Alcohol use and misuse, 117 Alcohol use disorder (AUD), 7, 23 rTMS for, 52 tDCS for, 50 Alcoholics Anonymous (AA), 43 American Addiction Recovery Center, 84 Applied Research on Substance Use and Health Disparities (ARSH), 108 Asynchronous telemedicine, 14 Audio-only arm, 93 B Behavioral addictions, 61 target groups identification, 62 technology-based interventions for gambling disorder, 63, 64 for internet gaming disorder, 62, 63 for problematic pornography use, 64 Big data analytics, 102 Big tobacco, 98 Bilateral tDCS, 50 Blood alcohol content (BAC), 110 Brain stimulation methods deep brain stimulation, 54 nerve stimulation, 54, 55 rTMS, 52–54 tDCS, 49–51 Breathalyzer readings for alcohol (BrAC), 110 Buprenorphine, 1, 15, 16, 18, 19, 111, 123 Buprenorphine home induction, 16, 123

C Cannabis use disorder, 7 rTMS in, 54 tDCS in, 51 CASA-CHESS, 91 CATT, 121 CBT 4 CBT, 27, 43, 90 Center on addiction and substance abuse, 69 Co-occurring mental illness epidemiology, 24, 25 technology-assisted treatments, 26–28 Cocaine use disorder, 7, 23 rTMS for, 53 tDCS for, 51 Cognitive behavioral therapy (CBT), 27, 43, 61, 63, 89, 109 Community Reinforcement Approach (CRA), 90 Computer assisted-therapy programs, 5 Computer-assisted cognitive rehab (CACR), 121 Contingency management behavior therapy, 90 Continuous theta burst stimulation (cTBS), 52 COVID-19 Isolation and Quarantine (I&Q) sites, 18 COVID-19 pandemic, 13, 14, 18, 26, 32, 36, 37, 70, 71 and mutual aid, 33–35 beyond, 19, 20 patient and provider experience, 19 pregnancy, 80 PRS, 35 removal of barriers, 18, 19 Ryan Haight Act, 16 SUPPORT for Patients and Communities Act, 16, 17 telemedicine, 13 with PUI, 62 Criminal justice system, 108 D Deep brain stimulation (DBS), 6–8 description of, 54 in SUD, 54

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 J. Avery, M. Khan (eds.), Technology-Assisted Interventions for Substance Use Disorders, https://doi.org/10.1007/978-3-031-26445-0

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Index

128 Department of Health and Human Services (HHS), 108 Digital portals, 80 Doping substances, 111 Dorsolateral prefrontal cortex (DLPFC), 7, 49, 50, 52 Drug Abuse Resistance Education (D.A.R.E.), 42 Drug Abuse Warning Network (DAWN), 108 Drug Enforcement Agency (DEA), 16 E E-cigarettes, 8, 79, 100 Electronic and cellular-enabled pillbox, 109 Electronic delivery, 79 Electronic medical record (EMR), 43 technology, 78 Electronic monitors, 110 Electronic nicotine delivery systems (ENDS), 6, 8 Evidence-based “SBIRT” model (eSBIRT), 79 F Facebook, 110 Films, 101 G Gambling disorder (GD), 63, 64 Genetic Information Nondiscrimination Act (GINA), 124 Google play, 102 H Hallucinogen abuse, 82 Health disparities population estimates by race, 88 racial/ethnic minority populations, 89 rates of substance use disorder and treatment, 88 and SUD treatment underutilization attitudinal differences, 87 COVID-19 pandemic, 89 scalability and wide dissemination with online interventions, 90 structural barriers, 87 technology-assisted interventions, 87 Healthcare organizations, 122 Healthcare professionals (HCPs), 115, 116, 118–120, 122 Health Insurance Portability and Accountability Act (HIPAA), 3, 18 HIV prevention, 90 Home-based technology-assisted intervention, 42 I In-person versus telehealth care, 93 Instagram, 100 Integrative voice recognition (IVR), 3 Intermittent theta burst stimulation (iTBS), 52 Internet, 107 Internet addiction (IA), 61

Internet-based cognitive behavioral therapy (iCBT), 102 Internet gaming disorder (IGD), 62, 63 Internet Treatment for Alcohol and Depression (iTreAD) study, 27 Interpersonal modalities, 78 iTunes, 102 J Journal of Adolescent Health, 103 JUUL, 100 K Kik, 100 L Legal technology, 107, 112 Lesbian gay bisexual transgender queer/questioning intersex asexual/agender+ (LGBTQIA+), 81–85 Lifestyle coaching, 78 Lorillard advertisement, 1789, 99 Lorillard Tobacco Company in a New York paper, 98 M Machine learning, 111 MAOs, see Mutual aid organizations (MAOs) Marijuana, 69 MDeity, 118 Media, SUD broadcast, 97 COVID-19 pandemic, 97 drugs, 98 glamorization, 98, 101, 102 negative portrayals, 101 print, 97 social and structural factors, 103 socioeconomic status, 101 socioeconomic, ethnic, and sexual orientation stereotypes associated with HIV/AIDS, 97 stereotypes, 101 stigmatization, 98, 101 support, 97 types, 97 Media-related messaging to the public, 102 Medicaid, 14 Medicare, 14 Medicare Provision to Address the Opioid Crisis, 16 Medication-assisted treatment (MAT), 49 Medication-assisted treatment and telehealth for healthcare professionals, 123 MedMinder “Jon” Electronic Pillbox, 109 Men who have sex with men (MSM), 83 Mental health, 24, 45 Mental health professionals, 23 Methadone, 15 Methamphetamine abuse, ss, 82

Index MSM, 83 rTMS in, 53 tDCS in, 51 use, 7, 82 Mobile App Rating Scale (MARS), 5 Mobile health (mHealth) apps, 2, 78, 79, 100 applications, 79 modality, 79 for substance use disorders, 3, 4 objective data collection by, 3 quality assessment, 5 text messaging and integrative voice recognition, 2 Mobile-based interventions, 90, 91 Modern media, 100 Monitoring programs in promoting recovery, 124 Motivational interviewing, 89 mu-opioid receptor, 15 Mutual aid organizations (MAO), 31, 33, 37 MyChart, 71 N Naltrexone, 1, 15, 18 Narcotics Anonymous (NA), 43 National data and tracking systems/agencies, 108 National Drug Abuse Treatment Clinical Trials Network, 121 National Drug Control Strategy (NDCS), 108 National Institute on Drug Abuse (NIDA), 5 National State of Emergency due to COVID-19 pandemic, 17 National Survey on Drug Use and Health (NSDUH), 82 Nerve stimulation, 54, 55 Nicotine vaping products, 69 Nomo-Sobriety Clocks, 84 Non-invasive brain stimulation (NIBS), 6 O Occupational Health Providers (OHPs), 124 One Mind Psyberguide, 5 Online Ambulatory Service for Internet Addicts (OASIS), 63 Online-based motivational intervention to reduce PUI (OMPRIS), 63 Online cognitive-behavioral mood management, 89 Online sites, 100 Opiates, 14 Opioid treatment programs (OTP), 18 Opioid use disorders (OUD) epidemiology, 14 medication treatments, 15 buprenorphine, 15 at home buprenorphine induction, 16 methadone, 15 naltrexone, 15 pertinent legislation prior to COVID-19 Ryan Haight Act, 16 SUPPORT for Patients and Communities Act, 16, 17 rTMS in, 53

129 tDCS in, 51 telemedicine (see Telemedicine) in United States, 13 with videoconferencing, 122 P Pear reSET, 84 Peer coaching, 77 Peer recovery specialist (PRS), 31, 33, 35, 37 benefits and limitations of, 35, 36 vs. MAOs, 35 Personal technology, 72 Pharmacological tightrope, 124 Phone-based care, 121 Polysubstance dependence, 75 Pregnancy addictive substances, 75 barriers and practical applications, 77 clinical applications, 75, 76 clinical barriers, 77 electronic medical records, 77 mobile applications, 77 optimizing health, 76 research, and public health initiatives, 75 technology assisted treatment, 77 Prenatal care, 111 Prenatal counseling, 77 PREscription Drug Abuse Online Surveillance and Epidemiology (PREDOSE) Platform, 111 Prescription drug monitoring programs (PDMP), 109 Prescription monitoring system (PMP), 16 Primary prevention, 41, 42 Private internet, 72 Problematic pornography use (PPU), 64 Problematic use of the internet (PUI), 61, 64 Psychosocial and ethico-legal considerations, 124 Public health application, 78, 79 Public health policy and education, 80 Q Quaternary prevention, 43, 44 Quitlines, 91 R Randomized control trial (RCT), 121 Recreational drugs, 79 Reefer Madness, 101 Remote breathalyzers, 110 Remote testing, 110 Repetitive transcranial magnetic stimulation (rTMS), 6, 7, 52 for alcohol use disorder, 7, 52 in cannabis use disorder, 7, 54 in cocaine use disorder, 7, 53 in methamphetamine, 7, 53 in opioid use disorder, 53 in tobacco use disorder, 53

130 Reproductive health, 80 The Research Abuse Diversion and Addiction-Related Surveillance (RADARS), 108 reSET/reSET-O, 43, 84, 109 Reverse telemedicine solutions, 37 r-Tribe, 84 Ryan Haight Act, 16 Ryan Haight Online Pharmacy Consumer Protection Act, 16, 17 S Screening, Brief Intervention, and Referral to Treatment (SBIRT), 42 Secondary prevention, 42, 43 Secure continuous remote alcohol monitor (SCRAM), 110 Self-administered electronic platform surveys, 108 Self-help for alcohol and other drug use and depression (SHADE), 27, 28 Smartphones, 108, 109, 122 SmokefreeTXT messages, 108 SmokefreeTXT program, 108 Snapchat, 100, 101 Sober Sky Web Portal, 110 SoberGrid, 84 Soberlink, 110 SoberTool, 84 Social anxiety, 35 Social media, 31–33, 35, 37, 44, 45, 100, 103 interventions, 103 platforms, 110 Social networking sites (SNS), 110 Specific, Measurable, Achievable, Relevant, Time-bound (SMART) Recovery, 43 Spotify, 103 Store and forward approach, 14 Substance Abuse and Mental Health Services Administration (SAMHSA), 5, 101, 108 Substance specific adaptations, 77, 78 Substance Use Disorder Prevention that Promotes Opioid Recovery and Treatment for Patients and Communities Act (SUPPORT for Patients and Communities Act), 16, 17 Substance use disorders (SUD), 13, 19, 49 accessibility, technological uses for adolescents, 70 applications, 84, 85 brain stimulation methods (see Brain stimulation methods) child welfare and public health standpoint, 69 epidemiology, 24, 25 first line pharmacologic treatments for, 1 interaction with technology, 70 mobile phone technologies for, 1, 2 applications for, 3, 4 objective data collection by, 3 quality assessment, 5 text messaging and integrative voice recognition, 2 online peer support for, 31

Index benefits, 32 COVID-19 pandemic and mutual aid, 33–35 future directions, 32 limitations, 32 online mutual aid services, 32, 33 online peer recovery specialist services, benefits and limitations, 35, 36 virtual peer recovery specialist services, 35 outpatient procedures and devices for DBS, 7, 8 ENDS, 8 NIBS, 6 rTMS, 6, 7 tDCS, 6, 7 parental involvement, 71 pregnancy, 75–79 prevention interventions for primary prevention, 41, 42 pro’s and con’s of, 45 quaternary prevention, 43, 44 secondary prevention, 42, 43 social media, networking within addiction community, 44, 45 tertiary prevention, 43, 44 risk factors, 69 technology-assisted therapies, 26–28, 70, 71 web-based technologies for treatment of, 5, 6 SUPPORT for Patients and Communities Act, 16, 17 Synchronous telemedicine, 14 T Technologic integration, 70, 71 Technological solutions for overdose monitoring, 112 Technology access, 75 Technology assisted treatments of substance use disorders, 78, 123 Technology-based therapy, 83 Telehealth, 14, 19, 37, 42, 91–93 appointments, 89 logistical barriers, 17 Telemedicine, 14–17, 44 asynchronous, 14 with buprenorphine, 15 definition of, 14 effectiveness, existing literature, 17, 18 modalities, 14 synchronous, 14 Telephone quitlines and app-based interventions, 90 Telephone-based recovery, 121 Telepsychiatry, 15, 36 Tertiary prevention, 43, 44 Therapeutic education system (TES), 5, 90 TikTok, 101 Tobacco use disorder rTMS for, 53 tDCS for, 50, 51 Tobacco-use interventions, 89 Traditional media, 98

Index Transcranial direct current stimulation (tDCS), 6, 7, 49, 50 for AUS, 50 in cannabis use disorders, 51 for cocaine use disorder, 51 in methamphetamine, 51 in opioid use disorders, 51 for tobacco use disorder, 50, 51 Transcranial magnetic stimulation (TMS), 6 Transcutaneous auricular nerve stimulation, 55 Treatment as usual (TAU) in outpatient programs, 121 Treatment Episode Data Set (TEDs), 14 Twitter, 110 U U.S. Preventive Services Task Force, 79

131 V Vagus nerve stimulation, 54 VetChange study, 28 Virtual buprenorphine clinic (VBC), 44 Virtual reality therapy (VRT), 63 VR cue-exposure therapy (VRCET), 63 W Web based interventions and applications, 78 Web-based monitoring, 108, 111 Web-based programs, 89 Web-based technologies for treatment of SUD, 5, 6 WEconnect, 84 Wickr, 100 Wise IT-use (WIT), 63 World Health Organization (WHO), 61, 78