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UNSTRUCTURED Background: Alcohol use disorder (AUD), associated with significant morbidity and mortality, continues to be a major public health problem. The COVID pandemic exacerbated the impact of AUD, with a 25% increase in alcohol-related mortality from 2019 to 2020. As such, innovative treatments for AUD are urgently needed. While inpatient alcohol withdrawal management (i.e., detoxification) is often an entry point for recovery, most do not successfully link to ongoing treatment. Transitions between inpatient and outpatient treatment pose many challenges to successful treatment continuation. Peer recovery coaches, who are individuals with the “lived experience” of recovery who obtain training to be coaches, are increasingly utilized to assist individuals with AUD and may provide a degree of continuity during this transition. We evaluated the feasibility of using an existing care coordination app (“Lifeguard”) to assist peer recovery coaches in supporting patients after discharge and facilitating linkage to care. Methods: The study was conducted on an ASAM Level IV inpatient withdrawal management unit within an academic medical center in Boston, MA. After providing informed consent, participants were contacted by the coach through the app, and after discharge received daily prompts to complete a modified version of the Brief Addiction Monitor (BAM). The BAM inquired about alcohol use, risky (e.g., craving) and protective (e.g., 12-step attendance) factors. The coach sent daily motivational texts, appointment reminders, and checked-in if BAM responses were concerning. Follow-up continued for 30 days post-discharge. The following feasibility outcomes were evaluated: 1) proportion of participants engaging with the coach before discharge, 2) proportion of participants and the number of days engaging with the coach after discharge, 3) proportion of participants and the number of days responding to BAM prompts, and 4) the proportion of participants successfully linking with addiction treatment by 30-day follow-up. Results: The 10 participants were all men, averaged 50.5 years old, mostly white (60%), non-Hispanic (90%), and single (80%). Overall, 8 participants (80%) successfully engaged with the coach prior to discharge. Following discharge, 6 participants (60%) continued to engage with the coach, doing so on average 5.3 days (SD 7.3, range 0-20). Five (50%) participants responded to the BAM prompts during the follow-up, doing so on average 4.6 days (SD 6.9, range 0-21). Half (50%) successfully linked with ongoing addiction treatment during the follow-up. Participants who engaged with the coach post-discharge, compared to those who did not, were significantly more likely to link with treatment (83.0% vs 0%, χ2=6.67, P=.01). Conclusions: The results from this proof-of-concept pilot trial demonstrated that a digitally assisted peer recovery coach may be feasible in facilitating linkage to care following discharge from inpatient withdrawal management treatment. Further research is warranted to evaluate the potential role for peer recovery coaches in improving post-discharge outcomes. Keywords: Alcohol use disorder; inpatient detoxification; peer recovery coach; smartphone app

BACKGROUND In recent years, there has been increasing interest in implementing digital technologies to diagnose, monitor, and intervene in substance use disorders. Smartphones are now a vehicle for facilitating telepsychiatry visits, measuring health metrics, and communicating with health care professionals. In light of the COVID-19 pandemic and the movement toward web-based and hybrid clinic visits and meetings, it has become especially salient to assess phone ownership among individuals with substance use disorders and their comfort in navigating phone functionality and using phones for mental health purposes. OBJECTIVE The aims of this study were to summarize the current literature around smartphone ownership, smartphone utilization, and the acceptability of using smartphones for mental health purposes and assess these variables across two disparate substance use treatment sites. METHODS We performed a focused literature review via a search of two academic databases (PubMed and Google Scholar) for publications since 2007 on the topics of smartphone ownership, smartphone utilization, and the acceptability of using mobile apps for mental health purposes among the substance use population. Additionally, we conducted a cross-sectional survey study that included 51 participants across two sites in New England—an inpatient detoxification unit that predominantly treats patients with alcohol use disorder and an outpatient methadone maintenance treatment clinic. RESULTS Prior studies indicated that mobile phone ownership among the substance use population between 2013 and 2019 ranged from 83% to 94%, while smartphone ownership ranged from 57% to 94%. The results from our study across the two sites indicated 96% (49/51) mobile phone ownership and 92% (47/51) smartphone ownership among the substance use population. Although most (43/49, 88%) patients across both sites reported currently using apps on their phone, a minority (19/48, 40%) reported previously using any apps for mental health purposes. More than half of the participants reported feeling at least neutrally comfortable with a mental health app gathering information regarding appointment reminders (32/48, 67%), medication reminders (33/48, 69%), and symptom surveys (26/45, 58%). Most patients were concerned about privacy (34/51, 67%) and felt uncomfortable with an app gathering location (29/47, 62%) and social (27/47, 57%) information for health care purposes. CONCLUSIONS The majority of respondents reported owning a mobile phone (49/51, 96%) and smartphone (47/51, 92%), consistent with prior studies. Many respondents felt comfortable with mental health apps gathering most forms of personal information and with communicating with their clinician about their mental health. The differential results from the two sites, namely greater concerns about the cost of mental health apps among the methadone maintenance treatment cohort and less experience with downloading apps among the older inpatient detoxification cohort, may indicate that clinicians should tailor technological interventions based on local demographics and practice sites and that there is likely not a one-size-fits-all digital psychiatry solution.

UNSTRUCTURED Due to the COVID-19 pandemic, many clinical addiction treatment programs have been required to transition to telephonic or virtual visits. Novel solutions are needed to enhance substance use treatment during a time when many patients are disconnected from clinical care and social support. Digital phenotyping, which leverages the unique functionality of smartphone sensors (GPS, social behavior, and typing patterns), can buttress clinical treatment in a remote, scalable fashion. Specifically, digital phenotyping has the potential to improve relapse prediction and intervention, relapse detection, and overdose intervention. Digital phenotyping may enhance relapse prediction through coupling machine learning algorithms with the enormous amount of collected behavioral data. Activity-based analysis in real time can potentially be used to prevent relapse by warning substance users when they approach locational triggers such as bars or liquor stores. Wearable devices detect when a person has relapsed to substances through measuring physiological changes such as electrodermal activity and locomotion. Despite the initial promise of this approach, privacy, security, and barriers to access are important issues to address.

Due to the COVID-19 pandemic, many clinical addiction treatment programs have been required to transition to telephonic or virtual visits. Novel solutions are needed to enhance substance use treatment during a time when many patients are disconnected from clinical care and social support. Digital phenotyping, which leverages the unique functionality of smartphone sensors (GPS, social behavior, and typing patterns), can buttress clinical treatment in a remote, scalable fashion. Specifically, digital phenotyping has the potential to improve relapse prediction and intervention, relapse detection, and overdose intervention. Digital phenotyping may enhance relapse prediction through coupling machine learning algorithms with the enormous amount of collected behavioral data. Activity-based analysis in real time can potentially be used to prevent relapse by warning substance users when they approach locational triggers such as bars or liquor stores. Wearable devices detect when a person has relapsed to substances through measuring physiological changes such as electrodermal activity and locomotion. Despite the initial promise of this approach, privacy, security, and barriers to access are important issues to address.