Digital Health Interventions for Mental Health, Substance Use, and Co-occurring Disorders in the Criminal Justice Population: A Scoping Review

Methodological approaches in developing and implementing digital health interventions amongst underserved women

BACKGROUND

Minority populations are utilizing mobile health applications more frequently to access health information. One group that may benefit from using mHealth technology is underserved women, specifically those on community supervision.

OBJECTIVE

Discuss methodological approaches for navigating digital health strategies to address underserved women's health disparities.

DESCRIPTION OF THE INNOVATIVE METHOD

Using an intersectional lens, we identified strategies for conducting research using digital health technology and artificial intelligence amongst the underserved, particularly those with community supervision.

DESCRIPTION OF ITS EFFECTIVENESS

We explore (1) methodological approaches that combine traditional research methods with precision medicine, digital phenotyping, and ecological momentary assessment; (2) implications for artificial intelligence; and (3) ethical considerations with data collection, storage, and engagement.

DISCUSSION

Researchers must address gendered differences related to health, social, and economic disparities concurrently with an unwavering focus on the protection of human subjects when addressing the unique needs of underserved women while utilizing digital health methodologies.

PUBLIC CONTRIBUTION

Women on community supervision in South Central Texas helped inform the design of JUN, the mHealth app we reported in the case exemplar. JUN is named after the Junonia shell, a native shell to South Texas, which means strength, power, and self-sufficiency, like the participants in our preliminary studies.

Comprehensive synthesis of mHealth interventions in psychiatry: insights from systematic, scoping, narrative reviews and content analysis

INTRODUCTION

Mobile health (mHealth) technologies, including smartphone apps and wearables, have improved health care by providing innovative solutions for monitoring, education and treatment, particularly in mental health.

METHOD

This review synthesises findings from a series of reviews on mHealth interventions in psychiatry. Publications were systematically searched in PubMed, MEDLINE, PsycINFO, ScienceDirect, Scopus, Web of Science and Cochrane Library.

RESULTS

Out of 2147 records, 111 studies from 2014 to 2024 focusing on anxiety and depression were included. These studies highlight the effectiveness of mHealth interventions in reducing symptoms through cognitive-behavioural therapy, mindfulness and psychoeducation, benefitting adolescents, perinatal women and marginalised groups. Additionally, mHealth shows promise in managing substance use disorders and severe mental illnesses like schizophrenia, bipolar disorder and psychosis.

CONCLUSION

Despite positive outcomes, challenges such as data privacy, user engagement and healthcare integration persist. Further robust trials and evidence-based research are needed to validate the efficacy of mHealth technologies.

Use of Digital Health and Digital Therapeutics to Treat SUD in Criminal Justice Settings: a Review

Purpose of Review

The purpose of this review is to investigate the use of digital health technologies and/or digital therapeutics (DTx) products in the treatment of substance use disorders (SUDs) in the general population and among criminal justice-involved individuals.

Recent Findings

Despite an expanding evidence base, only three SUD DTxs have received federal regulatory approval. Across studies, DTx products have proven successful in engaging patients in SUD treatment and reducing healthcare costs and resource utilizations. Findings for emerging SUD DTx products show similar results. Still, there is a paucity of evidence regarding the use of digital health technologies and/or DTx among criminal justice populations.

Summary

DTxs have proven effective for treating multiple SUD types (e.g., nicotine and opioids) among the general population. DTx shows similar promise among justice-involved populations, but additional efficacy and implementation research is needed to address barriers such as cost, cultural resistance, and infrastructure.

The role of co-occurring disorders in criminal recidivism and psychiatric recovery among adults with opioid use disorder and criminal-legal involvement: A statewide retrospective cohort study

INTRODUCTION

Individuals with opioid use disorder (OUD) in the criminal-legal system commonly present co-occurring mental health disorders. However, evidence-based treatment for high-risk populations such as those with co-occurring disorders is often unavailable within jails and prisons. Coordination of timely and affordable access to behavioral health treatment following incarceration is critical to address the multidimensional needs of people with co-occurring needs. However, the role of co-occurring disorders among adults with OUD and criminal-legal involvement who are accessing community-based treatment is understudied.

METHODS

This retrospective cohort study investigated community and recovery outcomes among 2039 adults with OUD and criminal-legal involvement enrolled in a statewide forensic treatment initiative between October 2015 to March 2018. Using court records and clinical data, we assessed the impact of co-occurring OUD and mental health disorders on criminal recidivism and psychiatric recovery and the moderating role of co-occurring disorders on the relationship between community-based treatment and these outcomes.

RESULTS

We found that 47 % of those with OUD also had an underlying mental health disorder. Co-occurring OUD and mental health disorders predicted higher rates of recidivism during the early stages of treatment. Furthermore, group and individual therapy services were associated with lower odds of recidivism. A co-occurring disorder was an important predictor of more severe behavioral health needs when exiting community-based services and did moderate the relationship between service utilization-specifically group therapy and substance use outpatient services-and psychiatric recovery (i.e., behavioral health needs at exit).

CONCLUSIONS

Co-occurring mental health disorders are highly prevalent among adults with OUD who have criminal-legal involvement, but it appears that they can benefit from social support services in the community. Given the multidimensional needs of this high-risk population, criminal-legal stakeholders and community-based clinicians must work in tandem to develop tailored treatment plans that give individuals with co-occurring OUD and mental health disorders the best chance for success post-incarceration rather than a siloed approach to overlapping disorders.

Study protocol paper for the multi-site feasibility evaluation of mobile and technology-assisted aftercare services for crisis stabilization units

BACKGROUND

Law enforcement frequently responds to substance abuse and mental health crises. Crisis stabilization units (CSUs) operate as a public-receiving facility to provide short-term stabilization services for individuals experiencing these crises and offer law enforcement an important alternative to arrest. However, there is limited understanding about how and when law enforcement decides to use CSUs. There is also the challenge of retaining individuals in treatment after CSU stabilization to prevent future crises and persistent engagement with police. This study will respond to these gaps by exploring CSU procedures and examining the feasibility and acceptability of a technology-assisted mobile aftercare intervention designed for individuals brought to a CSU by law enforcement.

METHODS

This study will consist of three aims. Aim 1 will include qualitative interviews with law enforcement and CSU-affiliated mental health staff (n=80) regarding CSU utilization and collaboration logistics between the groups. Findings from Aim 1 will be synthesized for the development of an implementation guide of our intervention, mobile, and technology-assisted aftercare, designed for individuals brought to a CSU by law enforcement, during Aim 2. During Aim 2, intervention services will be pilot-tested for 6 months through a small sample (n=24), randomized control trial (RCT). Control participants will receive standard services available for individuals discharging from a CSU. Treatment participants will receive the mobile aftercare intervention. Qualitative and quantitative data will be collected at 2 weeks, 3 months, and 6 months post-recruitment for all study participants. Aims 1 and 2 will inform the design of a multi-site RCT to compare CSUs with and without mobile and technology-assisted aftercare (Aim 3).

DISCUSSION

The study will offer decision making and procedural insight into law enforcement use of CSUs as an alternative to jail and provide opportunities to inform that process. This research will provide outcome trends for those who go through standard CSU services compared to those who receive mobile and technology-assisted aftercare services. The current study will inform a larger RCT efficacy study of CSUs with and without technology-assisted aftercare services.

TRIAL REGISTRATION

This study was registered on ClinicalTrials.gov (reference #NCT04899934) on May 25, 2021.

The adoption and sustainability of digital therapeutics in justice systems: A pilot feasibility study

BACKGROUND

This study explored whether participants with substance use disorder (SUD) would adopt and use a smart-phone app with a cognitive behavioral therapy program, weekly Brief Addiction Monitor (BAM) assessments, daily check-ins, tools to track sobriety and treatment, and other patient-centered resources. In addition, participants with SUD could access a social worker and peer support specialists.

METHODS

The study sought participants from two groups: those referred by a justice-related agency and participants who responded to outreach from the Addiction Policy Forum (APF). The Connections smart-phone app was offered to both groups. The study examined use of the app and social worker/peer recovery support services by participants who downloaded and used the app; those referred by a justice-related agency and those who self-referred through APF. The app provided primary data, including socio-demographics, referral status, dates of use, activities completed, and BAM scores.

RESULTS

The app was offered to 1973 participants, 40% of whom downloaded it. Three groups emerged from among the 350 who used the app: those who used only the cognitive behavioral aspects of the app, those who used only the recovery support services offered, and those who used both the app and recovery support services. Looking at the two referral groups, the justice-referred group preferred telehealth recovery support services with the social worker; the self-referred group used the app and the app plus the recovery support services equally. Scores on the BAM improved across time. Justice-referred participants' protective behaviors improved more than those of the self-referred participants while self-referred participants' risk behaviors improved more than those of justice-referred participants. Older participants were more likely to use the app, and to report fewer risky behaviors, as measured by the BAM.

CONCLUSIONS

Use of a digital therapeutic appears to support recovery of participants with SUD although many clients need and want the integration of social worker-driven recovery support services. Basically, the app can be an extension to personal services, but many people with SUD (particularly during COVID-19) crave human interaction. It also appears that those who seek assistance on their own, rather than being referred by a justice-related agency, may be more likely to benefit from digital therapeutics such as the Connections app.

Can a Single Variable Predict Early Dropout From Digital Health Interventions? Comparison of Predictive Models From Two Large Randomized Trials

BACKGROUND

A single generalizable metric that accurately predicts early dropout from digital health interventions has the potential to readily inform intervention targets and treatment augmentations that could boost retention and intervention outcomes. We recently identified a type of early dropout from digital health interventions for smoking cessation, specifically, users who logged in during the first week of the intervention and had little to no activity thereafter. These users also had a substantially lower smoking cessation rate with our iCanQuit smoking cessation app compared with users who used the app for longer periods.

OBJECTIVE

This study aimed to explore whether log-in count data, using standard statistical methods, can precisely predict whether an individual will become an iCanQuit early dropout while validating the approach using other statistical methods and randomized trial data from 3 other digital interventions for smoking cessation (combined randomized N=4529).

METHODS

Standard logistic regression models were used to predict early dropouts for individuals receiving the iCanQuit smoking cessation intervention app, the National Cancer Institute QuitGuide smoking cessation intervention app, the WebQuit.org smoking cessation intervention website, and the Smokefree.gov smoking cessation intervention website. The main predictors were the number of times a participant logged in per day during the first 7 days following randomization. The area under the curve (AUC) assessed the performance of the logistic regression models, which were compared with decision trees, support vector machine, and neural network models. We also examined whether 13 baseline variables that included a variety of demographics (eg, race and ethnicity, gender, and age) and smoking characteristics (eg, use of e-cigarettes and confidence in being smoke free) might improve this prediction.

RESULTS

The AUC for each logistic regression model using only the first 7 days of log-in count variables was 0.94 (95% CI 0.90-0.97) for iCanQuit, 0.88 (95% CI 0.83-0.93) for QuitGuide, 0.85 (95% CI 0.80-0.88) for WebQuit.org, and 0.60 (95% CI 0.54-0.66) for Smokefree.gov. Replacing logistic regression models with more complex decision trees, support vector machines, or neural network models did not significantly increase the AUC, nor did including additional baseline variables as predictors. The sensitivity and specificity were generally good, and they were excellent for iCanQuit (ie, 0.91 and 0.85, respectively, at the 0.5 classification threshold).

CONCLUSIONS

Logistic regression models using only the first 7 days of log-in count data were generally good at predicting early dropouts. These models performed well when using simple, automated, and readily available log-in count data, whereas including self-reported baseline variables did not improve the prediction. The results will inform the early identification of people at risk of early dropout from digital health interventions with the goal of intervening further by providing them with augmented treatments to increase their retention and, ultimately, their intervention outcomes.

The impact of COVID-19 on the treatment of opioid use disorder in carceral facilities: a cross-sectional study

While the COVID-19 pandemic disrupted healthcare delivery everywhere, persons with carceral system involvement and opioid use disorder (OUD) were disproportionately impacted and vulnerable to severe COVID-associated illness. Carceral settings and community treatment programs (CTPs) rapidly developed protocols to sustain healthcare delivery while reducing risk of COVID-19 transmission. This survey study assessed changes to OUD treatment, telemedicine use, and re-entry support services among carceral and CTPs participating in the National Institute on Drug Abuse (NIDA)-funded study, Long-Acting Buprenorphine vs. Naltrexone Opioid Treatments in Criminal Justice System-Involved Adults (EXIT-CJS) study. In December 2020, carceral sites (n = 6; median pre-COVID 2020 monthly census = 3468 people) and CTPs (n = 7; median pre-COVID 2020 monthly census = 550 patients) participating in EXIT-CJS completed a cross-sectional web-based survey. The survey assessed changes pre- (January-March 2020) and post- (April-September 2020) COVID-19 in OUD treatment, telemedicine use, re-entry supports and referral practices. Compared to January-March 2020, half of carceral sites (n = 3) increased the total number of persons initiating medication for opioid use disorder (MOUD) from April-September 2020, while a third (n = 2) decreased the number of persons initiated. Most CTPs (n = 4) reported a decrease in the number of new admissions from April-September 2020, with two programs stopping or pausing MOUD programs due to COVID-19. All carceral sites with pre-COVID telemedicine use (n = 5) increased or maintained telemedicine use, and all CTPs providing MOUD (n = 6) increased telemedicine use. While expansion of telemedicine services supported MOUD service delivery, the majority of sites experienced challenges providing community support post-release, including referrals to housing, employment, and transportation services. During the COVID-19 pandemic, this small sample of carceral and CTP sites innovated to continue delivery of treatment for OUD. Expansion of telemedicine services was critical to support MOUD service delivery. Despite these innovations, sites experienced challenges providing reintegration supports for persons in the community. Pre-COVID strategies for identifying and engaging individuals while incarcerated may be less effective since the pandemic. In addition to expanding research on the most effective telemedicine practices for carceral settings, research exploring strategies to expand housing and employment support during reintegration are critical.