Internet-delivered treatment for substance abuse: a multisite randomized controlled trial

A phase I pilot study of a mobile education tool for supporting pregnant women with opioid use disorder

Background

Perinatal opioid use disorder (OUD) and neonatal abstinence syndrome (NAS) require targeted interventions to address gaps in maternal education and support. Maternal involvement in non-pharmacological NAS care is essential for improving neonatal outcomes, yet many mothers lack accessible resources to manage NAS symptoms and to navigate social and healthcare challenges. Mobile health applications offer a promising solution, but few cater specifically to the needs of perinatal women with OUD.

Objective

We assessed the usability, acceptability, and feasibility of a new mobile educational tool for pregnant women with OUD, focusing on the perinatal period and NAS care.

Results

Six perinatal women with OUD (n = 1 pregnant, n = 5 postpartum; mean age 31) found the tool highly acceptable (modified CSQ-8 mean=28.8 out of 32) and usable (modified SUS mean=45.0 out of 50). Most were likely to use the tool during pregnancy and postpartum, citing improved preparedness for advocating for themselves, managing NAS, and navigating CPS. Feedback suggested expanding content on infant withdrawal medications.

Conclusions

This mobile tool shows promise in empowering perinatal women with OUD. Further research is needed to evaluate its impact on clinical and neonatal outcomes.

Monitoring Opioid-Use-Disorder Treatment Adherence Using Smartwatch Gesture Recognition

The opioid epidemic in the United States has significantly impacted pregnant women with opioid use disorder (OUD), leading to increased health and social complications. This study explores the feasibility of using machine learning algorithms with consumer-grade smartwatches to identify medication-taking gestures. The research specifically focuses on treatments for OUD, investigating methadone and buprenorphine taking gestures. Participants (n = 16, all female university students) simulated medication-taking gestures in a controlled lab environment over two weeks, with data collected via Ticwatch E and E3 smartwatches running custom ASPIRE software. The study employed a RegNet-style 1D ResNet model to analyze gesture data, achieving high performance in three classification scenarios: distinguishing between medication types, separating medication gestures from daily activities, and detecting any medication-taking gesture. The model's overall F1 scores were 0.89, 0.88, and 0.96 for each scenario, respectively. These findings suggest that smartwatch-based gesture recognition could enhance real-time monitoring and adherence to medication regimens for OUD treatment. Limitations include the use of simulated gestures and a small, homogeneous participant pool, warranting further real-world validation. This approach has the potential to improve patient outcomes and management strategies.

Drug Overdose Death among Residents of Urban Census Tracts: How Granular Geographical Analyses Uncover Socioenvironmental Correlates in Cuyahoga County, Ohio

Geostatistical data aggregated at state, county, municipality, or ZIP code levels often are utilized for assessing drug overdose epidemic impact and planning resource distribution. Data aggregated at these levels may obscure critical disparities among populations experiencing high rates of drug-related mortality (DRM), especially in densely populated urban areas. Our research was centered on Cuyahoga County (Cleveland), OH, which ranks 15th in the USA for drug-related mortality. This study built on recent efforts that adopted a finer geographical lens by examining DRM rates at the census tract level. Our investigation used Cuyahoga County census tracts with high and low DRM rates and compared them with Cuyahoga County census tracts with high and low levels of opportunity as developed by a publicly available, statewide opportunity index. Analyzing DRM data from 2014 to 2022, we found that the odds of an individual experiencing DRM in low-opportunity areas were quadruple the odds for someone in high-opportunity zones. Our findings highlight the critical need for more granular geographic analysis in urban areas, where heterogenous socioenvironmental conditions appear to correlate with significant heterogeneity in the ways in which residents experience the risk of dying from a drug overdose. By focusing on smaller areas, this approach provides a clearer understanding of the DRM landscape that could facilitate the prioritization of more targeted, culturally centered, public health interventions.

A framework for designing hybrid effectiveness-implementation trials for digital health interventions

This article proposes methods for designing randomized controlled trials studying the implementation and effectiveness of digital interventions, meaning websites or applications ("apps") that patients use in healthcare. Deploying digital interventions for behavioral health differs from implementing traditional interventions such as medications or human-delivered therapy. Prior trial design guidance has ignored the existence of international governmental evidence standards, has paid insufficient attention to implementation reporting guidelines, and has not described methods for empirically testing the approach for organizing the delivery of digital interventions. This framework for designing hybrid effectiveness-implementation trials of digital behavioral health interventions helps researchers articulate research questions that matter to decision-makers and meaningfully contribute to implementation. The framework outlines three phases: 1) frame effectiveness and implementation questions in terms of the digital intervention components, types of clinical support for the digital intervention, and specific strategies for implementing the digital intervention; 2) define and delineate actors, activities, action targets, dose, temporality, and outcomes to maximize inference and reproducibility; and 3) specify trial design features used for hybrid classification. We illustrate the utility of this framework with two effectiveness-implementation studies of digital interventions for substance use. This framework can help researchers decide on appropriate methodology and help decision-makers apply findings.

Treatment of Substance Use Disorders With a Mobile Phone App Within Rural Collaborative Care Management (Senyo Health): Protocol for a Mixed Methods Randomized Controlled Trial

BACKGROUND

COVID-19 worsened an already existing problem in substance use disorder (SUD) treatment. However, it helped transform the use of telehealth, which particularly benefits rural America. The lack of specialty addiction treatment in rural areas places the onus on primary care providers. Screening, brief intervention, and referral to treatment (SBIRT) is an evidenced-based strategy commonly used in primary care settings to target SUD outcomes and related behaviors. The integration of telehealth tools within the SBIRT pathway may better sustain the program in primary care. Building on Mayo Clinic's experience with collaborative care management (CoCM) for mental health treatment, we built a digitally native, integrated, behavioral health CoCM platform using a novel mobile app and web-based provider platform called Senyo Health.

OBJECTIVE

This protocol describes a novel use of the SBIRT pathway using Senyo Health to complement existing CoCM integration within primary care to deliver SUD treatment to rural patients lacking other access. We hypothesize that this approach will improve SUD-related outcomes within rural primary care clinics.

METHODS

Senyo Health is a digital tool to facilitate the use of SBIRT in primary care. It contains a web-based platform for clinician and staff use and a patient-facing mobile phone app. The app includes 16 learning modules along with data collection tools and a chat function for communicating directly with a licensed drug counselor. Beta-testing is currently underway to examine opportunities to improve Senyo Health prior to the start of the trial. We describe the development of Senyo Health and its therapeutic content and data collection instruments. We also describe our evaluation strategy including our measurement plan to assess implementation through a process guided by Consolidated Framework for Implementation Research methods and effectiveness through a waitlist control trial. A randomized controlled trial will occur where 30 participants are randomly assigned to immediately start the Senyo intervention compared to a waitlist control group of 30 participants who will start the active intervention after a 12-week delay.

RESULTS

The Senyo Health app was launched in May 2023, and the most recent update was in August 2024. Our funding period began in September 2023 and will conclude in July 2027. This protocol defines a novel implementation strategy for leveraging a digitally native, clinical platform that enables the delivery of CoCM to target an SUD-specific patient population. Our trial will begin in June 2025.

CONCLUSIONS

We present a theory of change and study design to assess the impact of a novel and patient-centered mobile app to support the SBIRT approach to SUD in primary care settings.

TRIAL REGISTRATION

ClinicalTrials.gov NCT06743282; http://clinicaltrials.gov/ct2/show/NCT06743282.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/65693.

What smartphone apps exist to support recovery from opioid use disorder? A content analysis of publicly available opioid-related smartphone apps

BACKGROUND

An estimated 84,181 people died due to opioid overdose in 2022 alone [1]. Mobile technologies may offer an additional pathway to provide support to people seeking recovery from opioid use disorder (OUD). To this end, we conducted a content analysis of opioid-related apps to determine to what extent apps exist that provide support to people seeking or in recovery from OUD. For apps specifically targeting OUD recovery, we identified the tools these apps offer to users seeking support in their recovery.

METHODS

Our team conducted a content analysis of publicly available opioid-related apps identified via web-scraping in the Apple and Google app stores. Using a two-step qualitative coding process, we first identified which apps were meaningfully related to OUD recovery and second identified what tools, if any, these apps provided.

RESULTS

Web-scraping identified 1,136 apps from the Apple App Store (n = 247) and Google Play (n = 889). Of those, 290 apps were specific to OUD recovery (65% of iOS apps, 35% of Android apps). Of those, 161 apps were included in our final analysis. The most common type of tools provided support for motivation (65.2%) and accountability (65.8%). Many apps (53%) also supported linkage to recovery support (e.g., meeting finder, telehealth). Surprisingly, fewer apps provided information about OUD recovery (43.5%) or tools for cravings (33.5%). 42.9% of apps had limited accessibility (e.g., paywalls, private invite).

CONCLUSIONS

Our results show a substantial increase in the number of apps designed to support OUD recovery. Nevertheless, there remains a need for apps that provide empirically supported information and tools. Furthermore, restrictions in accessibility (i.e., findability, cost, private) may limit the impact of available apps.

A randomized controlled trial to assess whether a telehealth-based contingency management intervention reduces alcohol use for individuals with alcohol use disorder

BACKGROUND

Contingency management (CM) is an intervention for alcohol use disorder (AUD) that reinforces abstinence, as confirmed by alcohol biomarkers. CM is usually brief (12-16 weeks) despite evidence that longer interventions have better long-term outcomes. Most CM models are in-person which can also be a barrier for treatment. Studies of longer duration telehealth-based CM models are needed.

AIMS

To determine if a telehealth-based CM model that utilizes phosphatidylethanol (PEth) to confirm abstinence is effective at reducing alcohol use during a 26-week intervention and 12-month follow-up. We will evaluate the impact of CM on alcohol-related outcomes, determine if Addiction Neuroclinical Assessment variables are associated with outcomes in follow-up, and whether savings related to decreased alcohol use offset intervention costs.

METHODS

Adults with AUD residing in the United States will be recruited via online advertising. Research procedures will be conducted virtually. Participants who submit a PEth-positive blood sample (≥20 ng/mL) at enrollment will be randomized to 26 weeks of either 1) online cognitive behavior therapy (CBT4CBT) with rewards not contingent on PEth results (Control group) or 2) CBT4CBT with a maximum of $1,820 of rewards contingent on PEth results (CM group). Efficacy outcomes of PEth-negative tests (primary) and PEth-defined excessive drinking (≥200 ng/mL; secondary) will be assessed. Predictors of intervention outcomes and economic viability will also be investigated.

DISCUSSION

If this telehealth-delivered PEth-based CM intervention reduces alcohol use and is cost-effective, it could be used to provide effective treatment to millions of individuals with AUD who do not receive in-person care.

Application of Causal Forest Model to Examine Treatment Effect Heterogeneity in Substance Use Disorder Psychosocial Treatments

OBJECTIVES

Heterogeneity of treatment effect (HTE) is a concern in substance use disorder (SUD) treatments but has not been rigorously examined. This exploratory study applied a causal forest approach to examine HTE in psychosocial SUD treatments, considering multiple covariates simultaneously.

METHODS

Data from 12 randomized controlled trials of nine psychosocial treatments were obtained from the National Institute on Drug Abuse Clinical Trials Network. Using causal forests, we estimated the conditional average treatment effect (CATE) on drug abstinence. To assess HTE, we compared CATE variance against total outcome variability, conducted an omnibus test, and applied the Rank-Weighted Average Treatment Effect (RATE).

RESULTS

Across nine interventions, CATE variance was lower than total outcome variability, indicating lack of strong evidence of HTE with respect to the baseline covariates considered. The omnibus test and RATE analysis generally support this finding. However, the RATE analysis identified potential HTE in a motivational interviewing trial; this could be a false positive given the multiple analyses; replication is needed to confirm this.

CONCLUSIONS

While causal forests show utility in exploring HTE in SUD interventions, limited baseline assessments in most trials suggest a cautious interpretation. The RATE findings for motivational interviewing highlight potential subgroup-specific treatment benefits, warranting further research.

An Assertive Community Intervention to Engage Youth with Opioid Use Disorder and Their Families

Medications for opioid use disorder (MOUD) are the most effective treatment for OUD. Many patients struggle with adherence, but young adults face unique developmental barriers and experience higher relapse rates. The Youth Opioid Recovery Support (YORS) intervention is a developmentally informed behavioral approach to increase medication adherence through assertive outreach, family involvement, low-barrier access to extended-release MOUD, and contingency management. Early studies have shown promising results, and a randomized controlled trial is underway. Here we describe the implementation of YORS using case examples, offer guidance on adapting YORS to real-world clinical settings, and explore future directions for research and practice.

Machine learning models for temporally precise lapse prediction in alcohol use disorder

We developed three machine learning models that predict hour-by-hour probabilities of a future lapse back to alcohol use with increasing temporal precision (i.e., lapses in the next week, next day, and next hour). Model features were based on raw scores and longitudinal change in theoretically implicated risk factors collected through ecological momentary assessment. Participants (N = 151, 51% male, Mage = 41, 87% White, 97% non-Hispanic) in early recovery (1-8 weeks of abstinence) from alcohol use disorder provided 4 × daily ecological momentary assessment for up to 3 months. We used grouped, nested cross-validation to select the best models and evaluate the performance of those best models. Models yielded median areas under the receiver operating curves of 0.89, 0.90, and 0.93 in the 30 held-out test sets for week-, day-, and hour-level models, respectively. Some feature categories consistently emerged as being globally important to lapse prediction across our week-, day-, and hour-level models (i.e., past use, future self-efficacy). However, most of the more punctate, time-varying constructs (e.g., craving, past stressful events, arousal) appear to have a greater impact within the next-hour prediction model. This research represents an important step toward the development of a smart (machine learning guided) sensing system that can both identify periods of peak lapse risk and recommend specific supports to address factors contributing to this risk. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Reducing mental health emergency visits: population-level strategies from participatory modelling

BACKGROUND

Emergency departments (EDs) are often the front door for urgent mental health care, especially when demand exceeds capacity. Long waits in EDs exert strain on hospital resources and worsen distress for individuals experiencing a mental health crisis. We used as a test case the Australian Capital Territory (ACT), with a population surge of over 27% across 2011-2021 and a lagging increase in mental health care capacity, to evaluate population-based approaches to reduce mental health-related ED presentations.

METHODS

We developed a system dynamics model for the ACT region using a participatory approach involving local stakeholders, including health planners, health providers and young people with lived experience of mental health disorders. Outcomes were projected over 2023-2032 for youth (aged 15-24) and for the general population.

RESULTS

Improving the overall mental health care system through strategies such as doubling the annual capacity growth rate of mental health services or leveraging digital technologies for triage and care coordination is projected to decrease youth mental health-related ED visits by 4.3% and 4.8% respectively. Implementation of mobile crisis response teams (consisting of a mental health nurse accompanying police or ambulance officers) is projected to reduce youth mental health-related ED visits by 10.2% by de-escalating some emergency situations and directly transferring selected individuals to community mental health centres. Other effective interventions include limiting re-presentations to ED by screening for suicide risk and following up with calls post-discharge (6.4% reduction), and limiting presentations of frequent users of ED by providing psychosocial education to families of people with schizophrenia (5.1% reduction). Finally, combining these five approaches is projected to reduce youth mental health-related ED presentations by 26.6% by the end of 2032.

CONCLUSIONS

Policies to decrease youth mental health-related ED presentations should not be limited to increasing mental health care capacity, but also include structural reforms.

Predicting Long-Term Engagement in mHealth Apps: Comparative Study of Engagement Indices

BACKGROUND

Digital health care apps, including digital therapeutics, have the potential to increase accessibility and improve patient engagement by overcoming the limitations of traditional facility-based medical treatments. However, there are no established tools capable of quantitatively measuring long-term engagement at present.

OBJECTIVE

This study aimed to evaluate an existing engagement index (EI) in a commercial health management app for long-term use and compare it with a newly developed EI.

METHODS

Participants were recruited from cancer survivors enrolled in a randomized controlled trial that evaluated the impact of mobile health apps on recovery. Of these patients, 240 were included in the study and randomly assigned to the Noom app (Noom Inc). The newly developed EI was compared with the existing EI, and a long-term use analysis was conducted. Furthermore, the new EI was evaluated based on adapted measurements from the Web Matrix Visitor Index, focusing on click depth, recency, and loyalty indices.

RESULTS

The newly developed EI model outperformed the existing EI model in terms of predicting EI of a 6- to 9-month period based on the EI of a 3- to 6-month period. The existing model had a mean squared error of 0.096, a root mean squared error of 0.310, and an R2 of 0.053. Meanwhile, the newly developed EI models showed improved performance, with the best one achieving a mean squared error of 0.025, root mean squared error of 0.157, and R2 of 0.610. The existing EI exhibited significant associations: the click depth index (hazard ratio [HR] 0.49, 95% CI 0.29-0.84; P<.001) and loyalty index (HR 0.17, 95% CI 0.09-0.31; P<.001) were significantly associated with improved survival, whereas the recency index exhibited no significant association (HR 1.30, 95% CI 1.70-2.42; P=.41). Among the new EI models, the EI with a menu combination of menus available in the app's free version yielded the most promising result. Furthermore, it exhibited significant associations with the loyalty index (HR 0.32, 95% CI 0.16-0.62; P<.001) and the recency index (HR 0.47, 95% CI 0.30-0.75; P<.001).

CONCLUSIONS

The newly developed EI model outperformed the existing model in terms of the prediction of long-term user engagement and compliance in a mobile health app context. We emphasized the importance of log data and suggested avenues for future research to address the subjectivity of the EI and incorporate a broader range of indices for comprehensive evaluation.

Prescription Digital Therapeutics for Substance Use Disorder in Primary Care: Mixed Methods Evaluation of a Pilot Implementation Study

BACKGROUND

Delivering prescription digital therapeutics (ie, evidence-based interventions designed to treat, manage, or prevent disorders via websites or smartphone apps) in primary care could increase patient access to substance use disorder (SUD) treatments. However, the optimal approach to implementing prescription digital therapeutics in primary care remains unknown.

OBJECTIVE

This pilot study is a precursor to a larger trial designed to test whether implementation strategies (practice facilitation [PF] and health coaching [HC]) improve the delivery of prescription digital therapeutics for SUDs in primary care. This mixed methods study describes outcomes among patients in the 2 pilot clinics and presents qualitative findings on implementation.

METHODS

From February 10 to August 6, 2021, a total of 3 mental health specialists embedded in 2 primary care practices of the same integrated health system were tasked with offering app-based prescription digital therapeutics to patients with SUD. In the first half of the pilot, implementation activities included training and supportive tools. PF (at 1 clinic) and HC (at 2 clinics) were added in the second half. All study analyses relied on secondary data, including electronic health records and digital therapeutic vendor data. Primary outcomes were the proportion of patients reached by the prescription digital therapeutics and fidelity related to ideal use. We used qualitative methods to assess the adherence to planned activities and the barriers and facilitators to implementing prescription digital therapeutics.

RESULTS

Of all 18 patients prescribed the apps, 10 (56%) downloaded the app and activated their prescription, and 8 (44%) completed at least 1 module of content. Patients who activated the app completed 1 module per week on average. Ideal use (fidelity) was defined as completing 4 modules per week and having a monthly SUD-related visit; 1 (6%) patient met these criteria for 10 weeks (of the 12-week prescription period). A total of 5 (28%) patients had prescriptions while HC was available, 2 (11%) were successfully contacted, and both declined coaching. Clinicians reported competing clinical priorities, technical challenges, and logistically complex workflows in part because the apps required a prescription. Some pilot activities were impacted by staff turnover that coincided with the COVID-19 pandemic. The facilitators to implementation were high engagement and the perception that the apps could meet patient needs.

CONCLUSIONS

The pilot study encountered the barriers to implementing prescription digital therapeutics in a real-world primary care setting, especially staffing shortages, turnover, and competing priorities for clinic teams. The larger randomized trial will clarify the extent to which PF and HC improve the implementation of digital therapeutics.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04907045; https://clinicaltrials.gov/study/NCT04907045.

Electronic interventions in primary care to address substance use: A systematic review

The present systematic review aims to identify electronic interventions for addressing substance use and understand their effectiveness in primary care settings. A systematic search was carried out in the Web of Science, PubMed/MEDLINE, Scopus, and Cochrane Library. The search included the keywords "electronic intervention", "substance use", "primary care" and synonyms. To determine the quality and recommendation of the analyzed interventions, the efficacy results reported by the studies were considered, as well as the Mixed Methods Appraisal Tool (MMAT) assessment and the GRADE Evidence Assessment. Twenty-one electronic interventions in Primary Care were identified: internet, mobile or tablet applications, text messages, emails, automated telephone calls, or electronic self-reports. These interventions had diverse components, incorporating theories that have proven effective in face-to-face interventions as their theoretical frameworks. Some of them were complementary to in-person treatment, while others replaced it. Six interventions (28.5 %) displayed high quality: HealthCall, AB-CASI, Quit Genius, eCHECKUP-TOGO, CBI, and TES. Another nine interventions (42.8 %) were found to have moderate-high quality: Alcohol y Salud, IVR-BI, Program of Wallace et al., Let's Talk About Smoking, SMSalud, ESCAPE, AAC-ASPIRE, iQuit, and Programa VIH. One intervention (4.7 %) had moderate-low quality: Vive sin tabaco ¡Decídete! The remaining five interventions (23.8 %) were found to have very low quality: Connection to health, cSBI, Teen Well Check, the program of Helzer et al. (2008), and Down your drink. The programs with the highest recommendation for addressing alcohol-related issues are HealthCall and AB-CASI; for tobacco use, it is Quit Genius; for cannabis use, it is eCHECKUP-TOGO; for addressing both legal and illegal substances, it is CBI and TES. Finally, for specific illicit drug use, the only recommended program is CBI. This last intervention, CBI, is of the highest quality and, therefore, can be considered a model intervention for dissemination in the primary care setting.

Evaluating the Efficacy of a Digital Therapeutic (CT-152) as an Adjunct to Antidepressant Treatment in Adults With Major Depressive Disorder: Protocol for the MIRAI Remote Study

BACKGROUND

Major depressive disorder (MDD) is common worldwide and can be highly disabling. People with MDD face many barriers to treatment and may not experience full symptom relief even when treated. Therefore, new treatment modalities are needed for MDD. Digital therapeutics (DTx) may provide people with MDD an additional treatment option.

OBJECTIVE

This study aimed to describe a phase 3 remote, multicenter, randomized, masked, sham-controlled trial evaluating the efficacy of a smartphone app-based DTx (CT-152) in adult participants diagnosed with MDD, used as an adjunct to antidepressant therapy (ADT).

METHODS

Participants aged 22-64 years with a current primary diagnosis of MDD and an inadequate response to ADT were included. Participants were randomized 1:1 to CT-152 or a sham DTx. CT-152 is a smartphone app-based DTx that delivers a cognitive-emotional and behavioral therapeutic intervention. The core components of CT-152 are the Emotional Faces Memory Task exercises, brief lessons to learn and apply key therapeutic skills, and SMS text messaging to reinforce lessons and encourage engagement with the app. The sham DTx is a digital working memory exercise with emotionally neutral stimuli designed to match CT-152 for time and attention. Participants took part in the trial for up to 13 weeks. The trial included a screening period of up to 3 weeks, a treatment period of 6 weeks, and an extension period of 4 weeks to assess the durability of the effect. Sites and participants had the option of an in-person or remote screening visit; the remaining trial visits were remote. Efficacy was evaluated using the Montgomery-Åsberg Depression Rating Scale, the Generalized Anxiety Disorder-7, Clinical Global Impression-Severity scale, the Patient Health Questionnaire-9, and the World Health Organization Disability Assessment Schedule 2.0. The durability of the effect was evaluated with the Montgomery-Åsberg Depression Rating Scale and Generalized Anxiety Disorder-7 scale. Adverse events were also assessed. Satisfaction, measured by the Participant and Healthcare Professional Satisfaction Scales, and health status, measured by the EQ-5D-5L, were summarized using descriptive statistics.

RESULTS

This study was initiated in February 2021 and had a primary completion date in October 2022.

CONCLUSIONS

This represents the methodological design for the first evaluation of CT-152 as an adjunct to ADT. This study protocol is methodologically robust and incorporates many aspects of conventional pivotal pharmaceutical phase 3 trial design, such as randomization and safety end points. Novel considerations included the use of a sham comparator, masking considerations for visible app content, and outcome measures relevant to DTx. The rigor of this methodology will provide a more comprehensive understanding of the effectiveness of CT-152.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04770285; https://clinicaltrials.gov/study/NCT04770285.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR1-10.2196/56960.

Predictors of urine toxicology and other biologic specimen missingness in randomized trials of substance use disorders

BACKGROUND

High levels of missing outcome data for biologically confirmed substance use (BCSU) threaten the validity of substance use disorder (SUD) clinical trials. Underlying attributes of clinical trials could explain BCSU missingness and identify targets for improved trial design.

METHODS

We reviewed 21 clinical trials funded by the NIDA National Drug Abuse Treatment Clinical Trials Network (CTN) and published from 2005 to 2018 that examined pharmacologic and psychosocial interventions for SUD. We used configurational analysis-a Boolean algebra approach that identifies an attribute or combination of attributes predictive of an outcome-to identify trial design features and participant characteristics associated with high levels of BCSU missingness. Associations were identified by configuration complexity, consistency, coverage, and robustness. We limited results using a consistency threshold of 0.75 and summarized model fit using the product of consistency and coverage.

RESULTS

For trial design features, the final solution consisted of two pathways: psychosocial treatment as a trial intervention OR larger trial arm size (complexity=2, consistency=0.79, coverage=0.93, robustness score=0.71). For participant characteristics, the final solution consisted of two pathways: interventions targeting individuals with poly- or nonspecific substance use OR younger age (complexity=2, consistency=0.75, coverage=0.86, robustness score=1.00).

CONCLUSIONS

Psychosocial treatments, larger trial arm size, interventions targeting individuals with poly- or nonspecific substance use, and younger age among trial participants were predictive of missing BCSU data in SUD clinical trials. Interventions to mitigate missing data that focus on these attributes may reduce threats to validity and improve utility of SUD clinical trials.

Peril and Promise: Teens, Tech, and America's Mental Health Crisis

Advances in Internet technologies have implications for the health and development of children and adolescents with potential for both beneficial and harmful outcomes. Similar technological advances also impact how psychiatrists deliver mental health care in clinical settings. Internet tech adds complexities to psychiatric practice in the form of electronic health records, patient portals, and virtual patient contact, which clinicians must understand and successfully incorporate into practice. Digital therapeutics and virtual mental health endeavors offer new treatment delivery options for patients and providers. Some have proven benefits, such as improved accessibility for patients, but all require provider expertise to utilize.

Therapeutic Content of Mobile Phone Applications for Substance Use Disorders: An Umbrella Review

Mobile phone applications (MPAs) for substance use disorder (SUD) treatment are increasingly used by patients. Although pilot studies have shown promising results, multiple previous systematic reviews noted insufficient evidence for MPA use in SUD treatment-many of the previously published reviews evaluated different trials. Subsequently, we aimed to conduct an umbrella review of previously published reviews investigating the efficacy of MPAs for SUD treatment, excluding nicotine/tobacco because umbrella reviews have been done in this population and the nicotine/tobacco MPA approach often differs from SUD-focused MPAs. No previous reviews have included a statistical meta-analysis of clinical trials to quantify an estimated overall effect. Seven reviews met inclusion criteria, and 17 unique studies with available data were taken from those reviews for the meta-analysis. Overall, reviews reported a lack of evidence for recommending MPAs for SUD treatment. However, MPA-delivered recovery support services, cognitive behavioral therapy, and contingency management were identified across multiple reviews as having promising evidence for SUD treatment. Hedges g effect size for an MPA reduction in substance use-related outcomes relative to the control arm was insignificant (0.137; 95% CI, -0.056 to 0.330; P=.16). In subgroup analysis, contingency management (1.29; 95% CI, 1.088-1.482; τ 2=0; k=2) and cognitive behavioral therapy (0.02; 95% CI, 0.001-0.030; τ 2=0; k=2) were significant. Although contingency management's effect was large, both trials were small (samples of 40 and 30). This review includes an adapted framework for the American Psychiatric Association's MPA guidelines that clinicians can implement to review MPAs critically with patients.

Causal dynamics of sleep, circadian rhythm, and mood symptoms in patients with major depression and bipolar disorder: insights from longitudinal wearable device data

BACKGROUND

Sleep and circadian rhythm disruptions are common in patients with mood disorders. The intricate relationship between these disruptions and mood has been investigated, but their causal dynamics remain unknown.

METHODS

We analysed data from 139 patients (76 female, mean age = 23.5 ± 3.64 years) with mood disorders who participated in a prospective observational study in South Korea. The patients wore wearable devices to monitor sleep and engaged in smartphone-delivered ecological momentary assessment of mood symptoms. Using a mathematical model, we estimated their daily circadian phase based on sleep data. Subsequently, we obtained daily time series for sleep/circadian phase estimates and mood symptoms spanning >40,000 days. We analysed the causal relationship between the time series using transfer entropy, a non-linear causal inference method.

FINDINGS

The transfer entropy analysis suggested causality from circadian phase disturbance to mood symptoms in both patients with MDD (n = 45) and BD type I (n = 35), as 66.7% and 85.7% of the patients with a large dataset (>600 days) showed causality, but not in patients with BD type II (n = 59). Surprisingly, no causal relationship was suggested between sleep phase disturbances and mood symptoms.

INTERPRETATION

Our findings suggest that in patients with mood disorders, circadian phase disturbances directly precede mood symptoms. This underscores the potential of targeting circadian rhythms in digital medicine, such as sleep or light exposure interventions, to restore circadian phase and thereby manage mood disorders effectively.

FUNDING

Institute for Basic Science, the Human Frontiers Science Program Organization, the National Research Foundation of Korea, and the Ministry of Health & Welfare of South Korea.

The ASAM/AAAP Clinical Practice Guideline on the Management of Stimulant Use Disorder

The American Society of Addiction Medicine/American Academy of Addiction Psychiatry (ASAM/AAAP) Clinical Practice Guideline on the Management of Stimulant Use Disorder provides guidance on evidence-based strategies for the treatment of stimulant use disorders (StUDs), stimulant intoxication, and stimulant withdrawal, as well as secondary and tertiary prevention of harms associated with stimulant use. The Clinical Guideline Committee (CGC) comprised experts from ASAM and AAAP representing a range of clinical settings and patient populations. The guideline was developed following modified GRADE methodology. The process included a systematic literature review as well as several targeted supplemental searches. The CGC utilized Evidence to Decision tables to review available evidence and rate the strength of each recommendation. The clinical practice guideline was revised based on external stakeholder review. Key takeaways included: Contingency management represents the current standard of care for treatment of StUDs; Pharmacotherapies may be utilized off-label to treat StUDs; Acute stimulant intoxication can result in life-threatening complications that should be addressed in an appropriate level of care; Secondary and tertiary prevention strategies should be used to reduce harms related to risky stimulant use.

Optimizing Contingency Management with Reinforcement Learning

Contingency Management (CM) is a psychological treatment that aims to change behavior with financial incentives. In substance use disorders (SUDs), deployment of CM has been enriched by longstanding discussions around the cost-effectiveness of prized-based and voucher-based approaches. In prize-based CM, participants earn draws to win prizes, including small incentives to reduce costs, and the number of draws escalates depending on the duration of maintenance of abstinence. In voucher-based CM, participants receive a predetermined voucher amount based on specific substance test results. While both types have enhanced treatment outcomes, there is room for improvement in their cost-effectiveness: the voucher-based system requires enduring financial investment; the prize-based system might sacrifice efficacy. Previous work in computational psychiatry of SUDs typically employs frameworks wherein participants make decisions to maximize their expected compensation. In contrast, we developed new frameworks that clinical decision-makers choose actions, CM structures, to reinforce the substance abstinence behavior of participants. We consider the choice of the voucher or prize to be a sequential decision, where there are two pivotal parameters: the prize probability for each draw and the escalation rule determining the number of draws. Recent advancements in Reinforcement Learning, more specifically, in off-policy evaluation, afforded techniques to estimate outcomes for different CM decision scenarios from observed clinical trial data. We searched CM schemas that maximized treatment outcomes with budget constraints. Using this framework, we analyzed data from the Clinical Trials Network to construct unbiased estimators on the effects of new CM schemas. Our results indicated that the optimal CM schema would be to strengthen reinforcement rapidly in the middle of the treatment course. Our estimated optimal CM policy improved treatment outcomes by 32% while maintaining costs. Our methods and results have broad applications in future clinical trial planning and translational investigations on the neurobiological basis of SUDs.

Psychosocial interventions for stimulant use disorder

BACKGROUND

Stimulant use disorder is a continuously growing medical and social burden without approved medications available for its treatment. Psychosocial interventions could be a valid approach to help people reduce or cease stimulant consumption. This is an update of a Cochrane review first published in 2016.

OBJECTIVES

To assess the efficacy and safety of psychosocial interventions for stimulant use disorder in adults.

SEARCH METHODS

We searched the Cochrane Drugs and Alcohol Group Specialised Register, Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, three other databases, and two trials registers in September 2023. All searches included non-English language literature. We handsearched the references of topic-related systematic reviews and the included studies.

SELECTION CRITERIA

We included randomised controlled trials (RCTs) comparing any psychosocial intervention with no intervention, treatment as usual (TAU), or a different intervention in adults with stimulant use disorder.

DATA COLLECTION AND ANALYSIS

We used the standard methodological procedures expected by Cochrane.

MAIN RESULTS

We included a total of 64 RCTs (8241 participants). Seventy-three percent of studies included participants with cocaine or crack cocaine use disorder; 3.1% included participants with amphetamine use disorder; 10.9% included participants with methamphetamine use disorder; and 12.5% included participants with any stimulant use disorder. In 18 studies, all participants were in methadone maintenance treatment. In our primary comparison of any psychosocial treatment to no intervention, we included studies which compared a psychosocial intervention plus TAU to TAU alone. In this comparison, 12 studies evaluated cognitive behavioural therapy (CBT), 27 contingency management, three motivational interviewing, one study looked at psychodynamic therapy, and one study evaluated CBT plus contingency management. We also compared any psychosocial intervention to TAU. In this comparison, seven studies evaluated CBT, two contingency management, two motivational interviewing, and one evaluated a combination of CBT plus motivational interviewing. Seven studies compared contingency management reinforcement related to abstinence versus contingency management not related to abstinence. Finally, seven studies compared two different psychosocial approaches. We judged 65.6% of the studies to be at low risk of bias for random sequence generation and 19% at low risk for allocation concealment. Blinding of personnel and participants was not possible for the type of intervention, so we judged all the studies to be at high risk of performance bias for subjective outcomes but at low risk for objective outcomes. We judged 22% of the studies to be at low risk of detection bias for subjective outcomes. We judged most of the studies (69%) to be at low risk of attrition bias. When compared to no intervention, we found that psychosocial treatments: reduce the dropout rate (risk ratio (RR) 0.82, 95% confidence interval (CI) 0.74 to 0.91; 30 studies, 4078 participants; high-certainty evidence); make little to no difference to point abstinence at the end of treatment (RR 1.15, 95% CI 0.94 to 1.41; 12 studies, 1293 participants; high-certainty evidence); make little to no difference to point abstinence at the longest follow-up (RR 1.22, 95% CI 0.91 to 1.62; 9 studies, 1187 participants; high-certainty evidence); probably increase continuous abstinence at the end of treatment (RR 1.89, 95% CI 1.20 to 2.97; 12 studies, 1770 participants; moderate-certainty evidence); may make little to no difference in continuous abstinence at the longest follow-up (RR 1.14, 95% CI 0.89 to 1.46; 4 studies, 295 participants; low-certainty evidence); reduce the frequency of drug intake at the end of treatment (standardised mean difference (SMD) -0.35, 95% CI -0.50 to -0.19; 10 studies, 1215 participants; high-certainty evidence); and increase the longest period of abstinence (SMD 0.54, 95% CI 0.41 to 0.68; 17 studies, 2118 participants; high-certainty evidence). When compared to TAU, we found that psychosocial treatments reduce the dropout rate (RR 0.79, 95% CI 0.65 to 0.97; 9 studies, 735 participants; high-certainty evidence) and may make little to no difference in point abstinence at the end of treatment (RR 1.67, 95% CI 0.64 to 4.31; 1 study, 128 participants; low-certainty evidence). We are uncertain whether they make any difference in point abstinence at the longest follow-up (RR 1.31, 95% CI 0.86 to 1.99; 2 studies, 124 participants; very low-certainty evidence). Compared to TAU, psychosocial treatments may make little to no difference in continuous abstinence at the end of treatment (RR 1.18, 95% CI 0.92 to 1.53; 1 study, 128 participants; low-certainty evidence); probably make little to no difference in the frequency of drug intake at the end of treatment (SMD -1.17, 95% CI -2.81 to 0.47, 4 studies, 479 participants, moderate-certainty evidence); and may make little to no difference in the longest period of abstinence (SMD -0.16, 95% CI -0.54 to 0.21; 1 study, 110 participants; low-certainty evidence). None of the studies for this comparison assessed continuous abstinence at the longest follow-up. Only five studies reported harms related to psychosocial interventions; four of them stated that no adverse events occurred.

AUTHORS' CONCLUSIONS

This review's findings indicate that psychosocial treatments can help people with stimulant use disorder by reducing dropout rates. This conclusion is based on high-certainty evidence from comparisons of psychosocial interventions with both no treatment and TAU. This is an important finding because many people with stimulant use disorders leave treatment prematurely. Stimulant use disorders are chronic, lifelong, relapsing mental disorders, which require substantial therapeutic efforts to achieve abstinence. For those who are not yet able to achieve complete abstinence, retention in treatment may help to reduce the risks associated with stimulant use. In addition, psychosocial interventions reduce stimulant use compared to no treatment, but they may make little to no difference to stimulant use when compared to TAU. The most studied and promising psychosocial approach is contingency management. Relatively few studies explored the other approaches, so we cannot rule out the possibility that the results were imprecise due to small sample sizes.

How effective are digital/e-health interventions for supporting prisoners with mental ill-health? An integrative review

PURPOSE

There has been a move towards the implementation of digital/e-health interventions for some time. Digital/e-health interventions have demonstrable efficacy in increasing individual empowerment, providing timely access to psychological interventions for those experiencing mental ill-health and improving outcomes for those using them. This study aims to determine the efficacy of digital/e-health interventions for individuals detained in prison who experience mental ill-health.

DESIGN/METHODOLOGY/APPROACH

A systematic search of five academic databases - CINAHL, ASSIA, PsycINFO, Embase and Medline - was completed in December 2020 and updated in February 2022. The review was guided by the Whittemore and Knafl (2005) framework for integrative reviews. A total of 6,255 studies were returned and screened by title and abstract. A full-text screening of nine (n = 9) studies was conducted.

FINDINGS

No study met the inclusion criteria for the clinical efficacy of digital/e-health interventions in a prison setting. Subsequently, a review of the literature that made it to the full-text review stage was conducted, and gaps in the literature were identified to inform policy, practice and future research.

ORIGINALITY/VALUE

To the best of the authors' knowledge, this is the first integrative review conducted on the efficacy of digital/e-health interventions for mental ill-health in prison settings.

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.

Formative evaluation of the implementation of digital therapeutics for opioids and other substance use disorders in primary care (DIGITS trial)

Background

Substance use disorders (SUDs) result in individual and societal burden. However, most individuals with SUD receive no treatment. Implementing SUD interventions in primary care could address this population's treatment needs. In the USA, reSET® and reSET-O® were the first prescription digital therapeutics (PDTs) for SUDs and opioid use disorder (OUD), respectively. The Digital Treatments for Substance Use Disorder (DIGITS) study tested the effectiveness of practice facilitation and health coaching strategies to support reSET and reSET-O implementation into primary care. A formative evaluation was conducted to monitor implementation, inform adaptations, and learn what promotes PDT sustainment.

Method

The Dynamic Sustainability Framework and the Framework for Reporting Adaptations and Modifications to Evidence-based Implementation Strategies guided the evaluation. Using rapid qualitative methods, we collected and analyzed observational fieldnotes, key informant interviews, and document sources (e.g., meeting minutes) for synthesis and dissemination to clinical partners and the study team via formative reports. We analyzed the reports to generate evaluation results.

Results

Twenty-four primary care clinics participated. Evaluation data included 98 observational fieldnotes, 16 interviews, and 253 document sources. We produced nine formative reports. The study encountered barriers and facilitators in each DSF domain (ecological system, practice setting, and intervention). In the ecological system, the PDT vendor enabled the study, but the COVID-19 pandemic, laws, regulations, and contracting delayed implementation. In the practice setting, staff shortages and low clinic capacity were implementation challenges, while electronic health record capabilities were both barriers and facilitators. At the intervention level, non-routine workflows, clinician burden, and low patient engagement were barriers despite clinicians' efforts.

Conclusions

Digital therapeutics are promising SUD and OUD treatments, but integration into primary care requires conducive laws and regulations, organizational capacity, and patient and clinician engagement. Formative evaluation identified important lessons for future PDT implementation.

Culturally tailored digital therapeutic for substance use disorders with urban Indigenous people in the United States: A randomized controlled study

INTRODUCTION

Indigenous people experience health disparities, including higher rates of substance use disorders (SUDs). Digital therapeutics are a growing platform for treatment services and have the potential to expand access to culturally responsive interventions for Indigenous people. As one of the first randomized controlled trials for SUDs for American Indian and Alaska Native (AI/AN) adults, the aim of this study was to pilot test the efficacy of a culturally tailored intervention among urban Indigenous adults.

METHODS

The study used a randomized controlled parallel design of 12 weeks of treatment-as-usual (TAU) (n = 26) versus TAU + Therapeutic Education System-Native Version (TES-NAV) (n = 27) with follow-up assessments at end of treatment and week 24 in an urban outpatient addiction treatment program for Native American adults. TAU consisted of individual/group counseling and cultural activities. The TES-NAV arm comprised TAU + 26 self-directed culturally tailored digital skills-based modules grounded in the community reinforcement approach with contingency management for abstinence and module completion. Primary outcome was longest consecutive weeks of abstinence from drugs and heavy drinking measured using self-report (Timeline Followback) and urine alcohol and drug toxicology screen during 12 weeks of treatment. Secondary outcomes were percent days abstinence during and posttreatment, coping strategies, social connectedness, and substance use and sexual risk behaviors.

RESULTS

The study enrolled fifty-three (52.8 % male) AI/AN adults seeking treatment for a SUD. Although the study did not detect a benefit of TAU+TES-NAV over TAU on the primary outcome (Median = 2 consecutive weeks of abstinence for both arms) at end of treatment (treatment effect: Z = -0.78, p = 0.437), TAU+TES-NAV participants did demonstrate significantly greater percent days of abstinence at the week 24 follow-up (69.3 % versus 49.0 % for TAU; t = 2.08, p = 0.045) and significantly greater change in social connectedness mean score, baseline to week 12 (Z = -2.66, p = 0.011), compared to TAU. The study detected no differences between treatment arms for coping strategies or risk behaviors.

CONCLUSION

The addition of TES-NAV to TAU did not significantly improve consecutive weeks of abstinence from drugs or heavy drinking; however, several secondary findings suggest promise for a culturally tailored digital therapeutic SUD intervention among urban Indigenous people.

CLINICAL TRIALS

GOV REGISTRATION: #NCT03363256.

Collection and Analysis of Adherence Information for Software as a Medical Device Clinical Trials: Systematic Review

BACKGROUND

The rapid growth of digital health apps has necessitated new regulatory approaches to ensure compliance with safety and effectiveness standards. Nonadherence and heterogeneous user engagement with digital health apps can lead to trial estimates that overestimate or underestimate an app's effectiveness. However, there are no current standards for how researchers should measure adherence or address the risk of bias imposed by nonadherence through efficacy analyses.

OBJECTIVE

This systematic review aims to address 2 critical questions regarding clinical trials of software as a medical device (SaMD) apps: How well do researchers report adherence and engagement metrics for studies of effectiveness and efficacy? and What efficacy analyses do researchers use to account for nonadherence and how appropriate are their methods?

METHODS

We searched the Food and Drug Administration's registration database for registrations of repeated-use, patient-facing SaMD therapeutics. For each such registration, we searched ClinicalTrials.gov, company websites, and MEDLINE for the corresponding clinical trial and study articles through March 2022. Adherence and engagement data were summarized for each of the 24 identified articles, corresponding to 10 SaMD therapeutics. Each article was analyzed with a framework developed using the Cochrane risk-of-bias questions to estimate the potential effects of imperfect adherence on SaMD effectiveness. This review, funded by the Richard King Mellon Foundation, is registered on the Open Science Framework.

RESULTS

We found that although most articles (23/24, 96%) reported collecting information about SaMD therapeutic engagement, of the 20 articles for apps with prescribed use, only 9 (45%) reported adherence information across all aspects of prescribed use: 15 (75%) reported metrics for the initiation of therapeutic use, 16 (80%) reported metrics reporting adherence between the initiation and discontinuation of the therapeutic (implementation), and 4 (20%) reported the discontinuation of the therapeutic (persistence). The articles varied in the reported metrics. For trials that reported adherence or engagement, there were 4 definitions of initiation, 8 definitions of implementation, and 4 definitions of persistence. All articles studying a therapeutic with a prescribed use reported effectiveness estimates that might have been affected by nonadherence; only a few (2/20, 10%) used methods appropriate to evaluate efficacy.

CONCLUSIONS

This review identifies 5 areas for improving future SaMD trials and studies: use consistent metrics for reporting adherence, use reliable adherence metrics, preregister analyses for observational studies, use less biased efficacy analysis methods, and fully report statistical methods and assumptions.

Pair-switching rerandomization

Rerandomization discards assignments with covariates unbalanced in the treatment and control groups to improve estimation and inference efficiency. However, the acceptance-rejection sampling method used in rerandomization is computationally inefficient. As a result, it is time-consuming for rerandomization to draw numerous independent assignments, which are necessary for performing Fisher randomization tests and constructing randomization-based confidence intervals. To address this problem, we propose a pair-switching rerandomization (PSRR) method to draw balanced assignments efficiently. We obtain the unbiasedness and variance reduction of the difference-in-means estimator and show that the Fisher randomization tests are valid under PSRR. Moreover, we propose an exact approach to invert Fisher randomization tests to confidence intervals, which is faster than the existing methods. In addition, our method is applicable to both nonsequentially and sequentially randomized experiments. We conduct comprehensive simulation studies to compare the finite-sample performance of the proposed method with that of classical rerandomization. Simulation results indicate that PSRR leads to comparable power of Fisher randomization tests and is 3-23 times faster than classical rerandomization. Finally, we apply the PSRR method to analyze two clinical trial datasets, both of which demonstrate the advantages of our method.

How does the COVID-19 pandemic impact medication adherence of patients with chronic disease?: A systematic review

OBJECTIVE

To determine how the COVID-19 pandemic impacts patients with chronic disease medication adherence.

METHODS

Four electronic databases, PubMed, MEDLINE, Web of Science, and CINAHL Plus Full Text, were searched for literature between 2019 and 2021. Abstracts and later full texts were independently screened by the authors of this review using inclusion and exclusion criteria to determine relevance to our study. Joanna Briggs Institute (JBI) critical appraisal tools were used to assess the quality of included texts. Relevant information and data from the included texts were extracted into tables for data synthesis and analysis.

RESULTS

Ten studies met the study criteria, the most popular study design was cross-sectional design (n = 9, 90.0%), others were case series (n = 1, 10.0%). Barriers to medication adherence and facilitators of medication adherence were the major two themes that participants reported regarding the impact of COVID-19 on medication adherence. Moreover, these two main themes have been organized in sub-themes that are dealt with in-depth.

DISCUSSION

Our results could heighten healthcare providers, stakeholders, and policy leaders' awareness of providing appropriate support for chronic disease patients, especially regarding medication adherence. Future research incorporating programs that support patients' needs is recommended.

Digital Therapeutics as a New Therapeutic Modality: A Review from the Perspective of Clinical Pharmacology

The promise of transforming digital technologies into treatments is what drives the development of digital therapeutics (DTx), generally known as software applications embedded within accessible technologies-such as smartphones-to treat, manage, or prevent a pathological condition. Whereas DTx solutions that successfully demonstrate effectiveness and safety could drastically improve the life of patients in multiple therapeutic areas, there is a general consensus that generating therapeutic evidence for DTx presents challenges and open questions. We believe there are three main areas where the application of clinical pharmacology principles from the drug development field could benefit DTx development: the characterization of the mechanism of action, the optimization of the intervention, and, finally, its dosing. We reviewed DTx studies to explore how the field is approaching these topics and to better characterize the challenges associated with them. This leads us to emphasize the role that the application of clinical pharmacology principles could play in the development of DTx and to advocate for a development approach that merges such principles from development of traditional therapeutics with important considerations from the highly attractive and fast-paced world of digital solutions.

A Digital Counselor-Delivered Intervention for Substance Use Among People With HIV: Development and Usability Study

BACKGROUND

Substance use disorders are prevalent and undertreated among people with HIV. Computer-delivered interventions (CDIs) show promise in expanding reach, delivering evidence-based care, and offering anonymity. Use in HIV clinic settings may overcome access barriers. Incorporating digital counselors may increase CDI engagement, and thereby improve health outcomes.

OBJECTIVE

We aim to develop and pilot a digital counselor-delivered brief intervention for people with HIV who use drugs, called "C-Raven," which is theory grounded and uses evidence-based practices for behavior change.

METHODS

Intervention mapping was used to develop the CDI including a review of the behavior change research in substance use, HIV, and digital counselors. We conducted in-depth interviews applying the situated-information, motivation, and behavior skills model and culturally adapting the content for local use with people with HIV. With a user interaction designer, we created various digital counselors and CDI interfaces. Finally, a mixed methods approach using in-depth interviews and quantitative assessments was used to assess the usability, acceptability, and cultural relevance of the intervention content and the digital counselor.

RESULTS

Participants found CDI easy to use, useful, relevant, and motivating. A consistent suggestion was to provide more information about the negative impacts of drug use and the interaction of drug use with HIV. Participants also reported that they learned new information about drug use and its health effects. The CDI was delivered by a "Raven," digital counselor, programmed to interact in a motivational interviewing style. The Raven was perceived to be nonjudgmental, understanding, and emotionally responsive. The appearance and images in the intervention were perceived as relevant and acceptable. Participants noted that they could be more truthful with a digital counselor, however, it was not unanimously endorsed as a replacement for a human counselor. The C-Raven Satisfaction Scale showed that all participants rated their satisfaction at either a 4 (n=2) or a 5 (n=8) on a 5-point Likert scale and all endorsed using the C-Raven program again.

CONCLUSIONS

CDIs show promise in extending access to care and improving health outcomes but their development necessarily requires integration from multiple disciplines including behavioral medicine and computer science. We developed a cross-platform compatible CDI led by a digital counselor that interacts in a motivational interviewing style and (1) uses evidence-based behavioral change methods, (2) is culturally adapted to people with HIV who use drugs, (3) has an engaging and interactive user interface, and (4) presents personalized content based on participants' ongoing responses to a series of menu-driven conversations. To advance the continued development of this and other CDIs, we recommend expanded testing, standardized measures to evaluate user experience, integration with clinician-delivered substance use treatment, and if effective, implementation into HIV clinical care.

Exploring the performance of during-treatment substance use outcome measures in predicting longer-term psychosocial functioning and post-treatment abstinence

BACKGROUND

The selection of appropriate efficacy endpoints in clinical trials has been a long-standing challenge for the substance use disorder field. Using data from a large, multi-site National Drug Abuse Treatment Clinical Trials Network trial (CTN-0044; n=474), this secondary data analysis aimed to explore whether specific proximal (during-treatment) substance use outcome measures predict longer-term improvements in psychosocial functioning and post-treatment abstinence, and whether predictions vary depending on the specific substance (cannabis, cocaine/stimulants, opioids, and alcohol).

METHODS

Generalized linear mixed models examined associations between six during-treatment substance use outcome measures and social functioning impairment (Social Adjustment Scale Self-Report) and severity of psychiatric symptoms (Brief Symptom Inventory-18) at end-of-treatment, and 3- and 6-months after treatment as well as post-treatment abstinence.

RESULTS

Maximum days of consecutive abstinence, proportion of days abstinent, ≥3 weeks of continuous abstinence, and the proportion of urine specimens negative for the primary substance were associated with post-treatment psychiatric and social functioning improvement and abstinence. However, only the effects of abstinence during the last 4 weeks of the treatment period on all three post-treatment outcomes was stable over time and did not differ between primary substance groups. In contrast, complete abstinence during the 12-week treatment period was not consistently associated with functioning improvements.

CONCLUSIONS

Substance use outcome measures capturing the duration of primary substance abstinence during treatment are suitable predictors of post-treatment abstinence and longer-term psychosocial functioning improvement. Binary outcomes, such as end-of-treatment abstinence, may be particularly stable predictors and attractive given their ease of computation and straightforward clinical interpretability.

The Evolution of Telepsychiatry for Substance Use Disorders During COVID-19: a Narrative Review

Purpose of Review

This article aims to review and synthesize the current research evidence regarding the efficacy of telepsychiatry-delivered substance use disorder treatment using a narrative review with a focus on the effects of remote healthcare delivery within the substance abuse treatment space.

Recent Findings

The COVID-19 pandemic exerted substantial pressures on all levels of society. Social isolation, loss of employment, stress, physical illness, overburdened health services, unmet medical needs, and rapidly changing pandemic restrictions had particularly severe consequences for people with mental health issues and substance use disorders. Since the start of the pandemic, addiction treatment (and medical treatment overall) using remote health platforms has significantly expanded to different platforms and delivery systems. The USA, in particular, reported transformational policy developments to enable the delivery of telehealth during the COVID-19 pandemic. However, systemic barriers such as a widespread lack of internet access and insufficient patient and provider digital skills remain.

Summary

Overall, telepsychiatry is a promising approach for the treatment of substance use disorders, but more randomized controlled trials are needed in the future to assess the evidence base of available interventions.

Leveraging Digital Technology to Support Pregnant and Early Parenting Women in Recovery from Addictive Substances: A Scoping Review

Little is known about digital health interventions used to support treatment for pregnant and early parenting women (PEPW) with substance use disorders (SUD).

METHODS

Guided by the Arksey and O'Malley's Scoping Review Framework, empirical studies were identified within the CINAHL, PsycInfo, PubMed, and ProQuest databases using subject headings and free-text keywords. Studies were selected based on a priori inclusion/exclusion criteria, and data extraction and descriptive analysis were performed.

RESULTS

A total of 27 original studies and 30 articles were included. Varying study designs were used, including several feasibility and acceptability studies. However, efficacious findings on abstinence and other clinically important outcomes were reported in several studies. Most studies focused on digital interventions for pregnant women (89.7%), suggesting a dearth of research on how digital technologies may support early parenting women with SUD. No studies included PEPW family members or involved PEPW women in the intervention design.

CONCLUSIONS

The science of digital interventions to support treatment for PEPW is in an early stage, but feasibility and efficacy results are promising. Future research should explore community-based participatory partnerships with PEPW to develop or tailor digital interventions and include family or external support systems to engage in the intervention alongside PEPW.

Project BETTER: A Family-Centered, Technology-Delivered Intervention for Pregnant People with Opioid Use Disorder

Birthing people with opioid use disorder (OUD) face unique stressors during the transition from pregnancy to postpartum that can negatively impact the maternal-infant dyad. This study aimed to describe the development of a family-centered, technology-delivered intervention tailored to help pregnant people receiving medication for OUD (MOUD) prepare for this transition. Formative data from patients and providers identified intervention content: (1) recovery-oriented strategies for the pregnancy-to-postpartum transition; (2) guidance around caring for an infant with opioid withdrawal symptoms; and (3) preparation for child welfare interactions. The content was reviewed in successive rounds by an expert panel and modified. Pregnant and postpartum people receiving MOUD pre-tested the intervention modules and provided feedback in semi-structured interviews. The multidisciplinary expert panel members (n = 15) identified strengths and areas for improvement. Primary areas for improvement included adding content, providing more structure to help participants navigate the intervention more easily, and revising language. Pre-testing participants (n = 9) highlighted four themes: reactions to intervention content, navigability of the intervention, feasibility of the intervention, and recommendation of the intervention. All iterative feedback was incorporated into the final intervention modules for the prospective randomized clinical trial. Family-centered interventions tailored for pregnant people receiving MOUD should be informed by patient-reported needs and multidisciplinary perspectives.

Study protocol for a factorial-randomized controlled trial evaluating the implementation, costs, effectiveness, and sustainment of digital therapeutics for substance use disorder in primary care (DIGITS Trial)

BACKGROUND

Experts recommend that treatment for substance use disorder (SUD) be integrated into primary care. The Digital Therapeutics for Opioids and Other SUD (DIGITS) Trial tests strategies for implementing reSET® and reSET-O®, which are prescription digital therapeutics for SUD and opioid use disorder, respectively, that include the community reinforcement approach, contingency management, and fluency training to reinforce concept mastery. This purpose of this trial is to test whether two implementation strategies improve implementation success (Aim 1) and achieve better population-level cost effectiveness (Aim 2) over a standard implementation approach.

METHODS/DESIGN

The DIGITS Trial is a hybrid type III cluster-randomized trial. It examines outcomes of implementation strategies, rather than studying clinical outcomes of a digital therapeutic. It includes 22 primary care clinics from a healthcare system in Washington State and patients with unhealthy substance use who visit clinics during an active implementation period (up to one year). Primary care clinics implemented reSET and reSET-O using a multifaceted implementation strategy previously used by clinical leaders to roll-out smartphone apps ("standard implementation" including discrete strategies such as clinician training, electronic health record tools). Clinics were randomized as 21 sites in a 2x2 factorial design to receive up to two added implementation strategies: (1) practice facilitation, and/or (2) health coaching. Outcome data are derived from electronic health records and logs of digital therapeutic usage. Aim 1's primary outcomes include reach of the digital therapeutics to patients and fidelity of patients' use of the digital therapeutics to clinical recommendations. Substance use and engagement in SUD care are additional outcomes. In Aim 2, population-level cost effectiveness analysis will inform the economic benefit of the implementation strategies compared to standard implementation. Implementation is monitored using formative evaluation, and sustainment will be studied for up to one year using qualitative and quantitative research methods.

DISCUSSION

The DIGITS Trial uses an experimental design to test whether implementation strategies increase and improve the delivery of digital therapeutics for SUDs when embedded in a large healthcare system. It will provide data on the potential benefits and cost-effectiveness of alternative implementation strategies.

CLINICALTRIALS

gov Identifier: NCT05160233 (Submitted 12/3/2021). https://clinicaltrials.gov/ct2/show/NCT05160233.

Comprehensive Analysis of Clinical Studies and Regulations of Therapeutic Applications in the United States and Japan

BACKGROUND

Digital therapeutics (DTx), the provision of treatment through mobile devices such as smartphones, have attracted great interest as a new medical modality. However, the number of authorized therapeutic applications in the US and Japan is low. Understanding the obstacles in obtaining regulatory authorizations will be the key in promoting timely development of therapeutic applications. Thus, we conducted a comprehensive analysis of the clinical study designs of therapeutic applications authorized in the US and Japan.

METHODS

Data on authorized therapeutic applications and the regulations involved were collated from the databases of the Food and Drug Administration (USA), Ministry of Health, Labour and Welfare (Japan), and Pharmaceuticals and Medical Devices Agency (Japan).

RESULTS

Most therapeutic applications authorized targeted neuropsychiatric disorders and used cognitive behavioral therapy (CBT)-based treatments. All the involved clinical trials were randomized-controlled studies. Various types of controls-such as standard care, sham application, digital control, and therapies delivered by healthcare providers-were used. Both subjective and objective indices were acceptable as the primary endpoints. Long-term efficacy was evaluated, and all adverse events were assessed comprehensively. The setting up of controls and the need to study long-term efficacy depend heavily on the applications functionality and the target disease characteristics.

CONCLUSIONS

This study reveals the points to be considered in planning clinical studies and regulatory strategies for authorizing therapeutic applications. Therapeutic applications can provide new therapy and have potential to solve unmet clinical needs. Our findings shed a light on efficient development and rapid commercialization of therapeutic applications.

Evaluation of real-world outcomes associated with use of a prescription digital therapeutic to treat substance use disorders

BACKGROUND AND OBJECTIVES

Digital therapeutics can expand the reach and fidelity of behavioral treatment for substance use disorders (SUDs). This analysis evaluated real-world engagement and clinical outcomes in patients diagnosed with SUD who were prescribed reSET®, an FDA-authorized prescription digital therapeutic (PDT).

METHODS

Patients were prescribed a 12-week PDT comprising 61 therapy lessons (31 "core" and 30 "keep learning" lessons) and contingency management rewards (positive reinforcement message or monetary gift cards) based on lesson completion and negative urine drug screens. Engagement (defined as any activity in the PDT), retention (any activity in Weeks 9-12), and substance use data were collected automatically by the PDT and analyzed descriptively. Associations between early lesson completion and end-of-treatment outcomes were assessed.

RESULTS

Six hundred and fifty-eight patients filled their prescription. Evaluated were 602 patients who were exposed to therapeutic content by completing at least one lesson (median age 37 years, 33% female, 41% male, 26% unreported sex). Median lessons completed was 33 (out of 61 possible), and 52% of patients completed all core modules. Retention in treatment during the last 4 weeks of treatment was 74%, and 62% were abstinent (missing data considered positive). [Correction added on 13 December 2022, after first online publication: In the preceding sentence, the treatment percentage values were revised from 74.6% to 74%.] DISCUSSION AND CONCLUSIONS: Patients with SUD exhibited robust engagement with a PDT, high rates of retention through 12 weeks, and substantial rates of abstinence at end of treatment when the therapeutic was used in a real-world setting. PDT's hold promise as a new way to access effective SUD treatment.

SCIENTIFIC SIGNIFICANCE

This study is the first to report real-world PDT engagement and clinical outcomes data from a large, geographically diverse population of patients with SUDs.

Adherence to e-health interventions for substance use and the factors influencing it: Systematic Review, meta-analysis, and meta-regression

Background

Substance use disorders affect 36 million people globally, but only a small proportion of them receive the necessary treatment. E-health interventions have been developed to address this issue by improving access to substance use treatment. However, concerns about participant engagement and adherence to these interventions remain. This review aimed to evaluate adherence to e-health interventions targeting substance use and identify hypothesized predictors of adherence.

Methods

A systematic review of literature published between 2009 and 2020 was conducted, and data on adherence measures and hypothesized predictors were extracted. Meta-analysis and meta-regression were used to analyze the data. The two adherence measures were (a) the mean proportion of modules completed across the intervention groups and (b) the proportion of participants that completed all modules. Four meta-regression models assessed each covariate including guidance, blended treatment, intervention duration and recruitment strategy.

Results

The overall pooled adherence rate was 0.60 (95%-CI: 0.52-0.67) for the mean proportion of modules completed across 30 intervention arms and 0.47 (95%-CI: 0.35-0.59) for the proportion of participants that completed all modules across 9 intervention arms. Guidance, blended treatment, and recruitment were significant predictors of adherence, while treatment duration was not.

Conclusion

The study suggests that more research is needed to identify predictors of adherence, in order to determine specific aspects that contribute to better exposure to intervention content. Reporting adherence and predictors in future studies can lead to improved meta-analyses and the development of more engaging interventions. Identifying predictors can aid in designing effective interventions for substance use disorders, with important implications for e-health interventions targeting substance use.

Mobile Digital Health Intervention to Promote Nutrition and Physical Activity Behaviors Among Long-term Unemployed in Rural Areas: Protocol for a Randomized Controlled Trial

BACKGROUND

Long-term unemployed have poor nutritional and physical activity statuses, and, therefore, special health promotion needs. Particularly in rural areas, however, they often do not have access to health promotion service. Thus, new promising strategies to improve the health of long-term unemployed are needed. Hence, a digital health intervention to promote nutritional and physical health behaviors was conceived, and the effectiveness of the intervention in combination with face-to-face sessions will be evaluated in a randomized controlled trial.

OBJECTIVE

The aim of this study is to elucidate the effectiveness of a mobile digital health intervention to promote the nutritional and physical activity behaviors of long-term unemployed in the rural areas of Germany.

METHODS

The 9-week intervention aims to promote nutritional or physical activity behavior by improving drinking habits, increasing the consumption of fruits, vegetables, and whole grains, increasing daily step count, strengthening muscles, and improving endurance. The intervention design is based on the transtheoretical model and is implemented in a mobile app using the MobileCoach open-source platform. The effectiveness of the intervention will be elucidated by a 9-week, 2-armed, parallel-designed trial. Therefore, long-term unemployed will be recruited by employees of the German social sector institutions and randomized either to receive information brochures; the digital intervention in the form of a mobile app; and 3 face-to-face sessions regarding technical support, healthy eating, and physical activity (n=100) or to receive a control treatment consisting of solely the hand over of information brochures (n=100). The effectiveness of the intervention will be assessed using questionnaires at baseline, after 9 weeks in face-to-face appointments, and after a 3-month follow-up period by postal contact. The use of the mobile app will be monitored, and qualitative interviews or focus groups with the participants will be conducted. Incentives of €50 (US $49.7) will be paid to the participants and are tied to the completion of the questionnaires and not to the use of the mobile app or progress in the intervention.

RESULTS

The effectiveness of the intervention in promoting the nutritional and physical activity behaviors of long-term unemployed participants will be elucidated. The adherence of the participants to and the acceptance and usability of the mobile device app will be evaluated. Recruitment started in March 2022, and the final publication of the results is expected in the first half of 2023.

CONCLUSIONS

Positive health-related changes made by the intervention would display the potency of digital health interventions to promote nutritional and physical activity behaviors among long-term unemployed in the rural areas of Germany, which would also contribute to an improved health status of the German population in general.

TRIAL REGISTRATION

German Clinical Trials Register DRKS00024805; https://www.drks.de/DRKS00024805.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/40321.

Predicting non-initiation of care and dropout in a blended care CBT intervention: Impact of early digital engagement, sociodemographic, and clinical factors

Objective

This study examines predictors of non-initiation of care and dropout in a blended care CBT intervention, with a focus on early digital engagement and sociodemographic and clinical factors.

Methods

This retrospective cohort analysis included 3566 US-based individuals who presented with clinical levels of anxiety and depression and enrolled in a blended-care CBT (BC-CBT) program. The treatment program consisted of face-to-face therapy sessions via videoconference and provider-assigned digital activities that were personalized to the client's presentation. Multinomial logistic regression and Cox proportional hazard survival analysis were used to identify predictors of an increased likelihood of non-initiation of therapy and dropout.

Results

Individuals were more likely to cancel and/or no-show to their first therapy session if they were female, did not disclose their ethnicity, reported poor financial status, did not have a college degree, endorsed more presenting issues during the onboarding triage assessment, reported taking antidepressants, and had a longer wait time to their first appointment. Of those who started care, clients were significantly more likely to drop out if they did not complete the digital activities assigned by their provider early in treatment, were female, reported more severe depressive symptoms at baseline, reported taking antidepressants, and did not disclose their ethnicity.

Conclusions

Various sociodemographic and clinical predictors emerged for both non-initiation of care and for dropout, suggesting that clients with these characteristics may benefit from additional attention and support (especially those with poor early digital engagement). Future research areas include targeted mitigation efforts to improve initiation rates and curb dropout.

Practical Technology for Expanding and Improving Substance Use Disorder Treatment: Telehealth, Remote Monitoring, and Digital Health Interventions

The US opioid crisis and the COVID-19 pandemic have sparked innovation in substance use disorder (SUD) treatment such that telehealth, remote monitoring, and digital health interventions are increasingly feasible and effective. These technologies can increase SUD treatment access and acceptability, even for nontreatment seeking, remote, and underserved populations, and can be used to reduce health disparities. Overall, digital tools will likely overcome many barriers to delivery of evidence-based behavioral treatments such as cognitive behavioral therapy and contingency management, that, along with appropriate medications, constitute the foundation of treatment of SUDs.

Real-World Reductions in Healthcare Resource Utilization over 6 Months in Patients with Substance Use Disorders Treated with a Prescription Digital Therapeutic

BACKGROUND AND AIMS

Substance use disorders (SUDs) affect approximately 40.3 million people in the USA, yet only approximately 19% receive evidence-based treatment each year. reSET® is a prescription digital therapeutic (PDT) and the only FDA-authorized treatment for patients with cocaine, cannabis, and stimulant use disorders. This study evaluated real-world healthcare resource utilization (HCRU) and associated costs 6 months after initiation of reSET in patients with SUD.

METHODS

A retrospective analysis of HealthVerity PrivateSource20 data compared the 6-month incidence of all-cause hospital facility encounters and clinician services in patients treated with reSET (re-SET cohort) before (pre-index period) and after (post-index period) reSET initiation (index). Incidence was compared using incidence rate ratios (IRR). HCRU-related costs were also assessed.

RESULTS

The sample included 101 patients (median age 37 years, 50.5% female, 54.5% Medicaid-insured). A statistically significant decrease of 50% was observed in overall hospital encounters from pre-index to post-index (IRR 0.50; 95% CI 0.37-0.67; P < 0.001), which included inpatient stays (56% decrease; IRR 0.44; 95% CI 0.26-0.76; P = 0.003), partial hospitalizations (57% decrease; IRR 0.43; 95% CI 0.21-0.88; P = 0.021), and emergency department visits (45% decrease; IRR 0.55; 95% CI 0.38-0.80; P < 0.004). Additionally, some clinician services declined significantly including pathology and laboratory services: other (54% decrease; IRR 0.46; 95% CI 0.28-0.76; P = 0.003); pathology and laboratory services: drug assays prior to opioid medication prescription (37% decrease; IRR 0.63; 95% CI 0.41-0.96; P = 0.031); and alcohol and drug abuse: medication services (46% decrease; IRR 0.54; 95% CI 0.41-0.70; P < 0.001). Reductions in facility-encounters drove 6-month reSET per-patient cost reductions of $3591 post-index compared to pre-index.

CONCLUSIONS

Use of reSET by patients with SUD is associated with durable reductions in HCRU and lower healthcare costs over 6 months compared to the 6 months before PDT treatment, after adjusting for covariates, providing an economic benefit to the healthcare system.

Appropriate controls for digital therapeutic clinical trials: A narrative review of control conditions in clinical trials of digital therapeutics (DTx) deploying psychosocial, cognitive, or behavioral content

Digital therapeutics (DTx) are software programs that treat a disease or condition. Increasingly, DTx are part of medical care, and in the US healthcare system they are regulated by the FDA as Software as a Medical Device (SaMD). Randomized controlled trials (RCT) remain a key evidence generation step for most DTx. However, developing a unified approach to the design of appropriate control conditions has been a challenge for two main reasons: (1) inheriting control condition definitions from pharmacotherapy and medical device RCT that may not directly apply, and (2) challenges in establishing control conditions for psychosocial interventions that build the core of many DTx. In our critical review we summarize different approaches to control conditions and patient blinding in RCT evaluating DTx with psychosocial, cognitive or behavioral content. We identify control condition choices, ranging from very minimal digital controls to more complex and stringent digital applications that contain aspects of “fake” therapy, general wellness content or games. Our review of RCTs reveals room for improvement in describing and naming control conditions more consistently. We further discuss challenges in defining placebo controls for DTx and ways in which control choices may have a therapeutic effect. While no one-size-fits-all control conditions and study designs will apply to all DTx, we propose points to consider for defining appropriate digital control conditions. At the same time, given the rapid iterative development and optimization of DTx, treatments with low risk profile may be evaluated with minimal digital controls followed by extensive real-world effectiveness trials.

Clinical Evaluation of Digital Therapeutics: Present and Future

Objectives

Digital therapeutics (DTx) are software-based therapeutic interventions based on clinical evidence. Randomized clinical trials (RCTs) are often the source of clinical evidence, similar to conventional drugs or medical devices. However, novel approaches such as the use of real-world data or digital biomarkers are also utilized. This article aimed to review how DTx products have been clinically evaluated.

Methods

DTx products approved by the US Food and Drug Administration as of 2020 were reviewed and products with sufficient published information were selected. Pivotal clinical trials were analyzed according to the elements of the Consolidated Standards of Reporting Trials (CONSORT) guideline. Case reviews were presented for other clinical evaluation strategies, considering the small number of publications.

Results

Most approved DTx products used RCTs for clinical evaluations. Similar to conventional RCTs, parallel-group designs with statistical hypothesis testing were adopted. However, DTx trials were often not blinded due to practical issues and involved various comparator groups. In addition, DTx products could be readily evaluated in home-based settings and delivered through the internet. Other evaluation approaches included retrospective analyses using insurance claims data or usage data, which enabled long-term evaluations of effectiveness. Digital biomarkers obtained from real-time and continuous log data were also used to improve the objectiveness of endpoints.

Conclusions

RCTs accounted for the majority of DTx evaluations. The designs of DTx trials were comparable to those of drug or device trials, but blinding and comparator elements were often different. Furthermore, the use of real-world data and digital biomarkers are also being tried.

Contingency management and cognitive behavior therapy for smoking cessation among veterans with posttraumatic stress disorder: Design and methodology of a randomized clinical trial

BACKGROUND

Smoking is a prevalent concern among Veterans, especially those with posttraumatic stress disorder (PTSD). Despite the availability of interventions for smoking cessation, these treatments have not been as effective among Veteran populations, particularly Veterans with PTSD. The present study seeks to describe the methods of a randomized clinical trial examining the efficacy of CPT-SMART, a multidimensional treatment combining cognitive processing therapy (CPT) for PTSD, smoking cessation counseling, pharmacotherapy, and contingency management (CM) compared to a yoked comparison group.

METHODS

One hundred twenty Veterans with PTSD who smoke cigarettes will be enrolled. All participants will receive CPT in addition to counseling and pharmacotherapy for smoking cessation. Participants will be randomized to the CPT-SMART condition, which includes monetary reinforcement that is contingent on bioverification of smoking abstinence (i.e., contingency management), or a yoked comparison with monetary reinforcement matched to the participant to whom they are yoked. The primary outcome is bioverified smoking abstinence at the 6-month follow-up appointment.

CONCLUSION

If shown efficacious, a combined PTSD and smoking treatment plus incentive-based approach for smoking could be implemented into specialty PTSD programs. The positive public health impact of reducing smoking among Veterans with PTSD could be enormous as it would prevent significant smoking-related morbidity and mortality.

Addiction and COVID: Issues, Challenges, and New Telehealth Approaches

In recent decades, the United States has seen a substantial increase in the number of people diagnosed with substance use disorder (SUD). Both SUDs and COVID-19 separately have had, and continue to have, a widespread impact on our society. While they are two distinct entities, they are intricately related and have been shown to influence one another. Lockdown mandates intended to enhance public safety produced unintended consequences for people with SUDs by decreasing access to treatment and disrupting their current care. Telehealth could offer a solution to this disruption as its utilization expands the provider’s reach and increases access to treatment in underserved populations, including those with SUDs. The use of telemedicine seems to result in higher rates of patient satisfaction, compliance, and treatment retention rates while maintaining the need for social distancing. Even when pandemic restrictions resolve, telehealth can continue to provide invaluable benefits to individuals with addiction, particularly those in rural America. In summary, ongoing research regarding telehealth delivery and the expansion of telehealth is a byproduct of the pandemic and can advance the American healthcare system beyond the days of COVID-19. This manuscript will review studies regarding the use of telehealth in SUD with the hope that further research within and beyond the COVID-19 pandemic will lead to the increased use of telehealth by those involved in and those receiving care for SUDs.

Blended Treatment for Alcohol Use Disorder (Blend-A): Explorative Mixed Methods Pilot and Feasibility Study

Background

In Denmark, approximately 150,000 people have alcohol use disorder (AUD). However, only approximately 10% seek AUD treatment, preferably outside conventional health care settings and opening hours. The AUD treatment area experiences low adherence to treatment, as well as high numbers of no-show and premature dropouts.

Objective

The purpose of the Blend-A (Blended Treatment for Alcohol Use Disorder) feasibility and pilot study was to describe the process of translating and adapting the Dutch treatment protocol into Danish and Danish culture with a high amount of user involvement and to report how patients and therapists perceived the adapted version, when trying it out.

Methods

The settings were 3 Danish public municipal outpatient alcohol clinics. Study participants were patients and therapists from the 3 settings. Data consisted of survey data from the System Usability Scale, individual patient interviews, and therapist group interviews. Statistical analyses were conducted using the Stata software and Excel. Qualitative analysis was conducted using a theoretical thematic analysis.

Results

The usability of the treatment platform was rated above average. The patients chose to use the blended treatment format because it ensured anonymity and had a flexible design. Platform use formed the basis of face-to-face sessions. The use of the self-determined platform resulted in a more thorough process. Patient involvement qualified development of a feasible system. Managerial support for time use was essential. Guidance from an experienced peer was useful.

Conclusions

This study indicates that, during the processes of translating, adapting, and implementing blended, guided, internet-based, and face-to-face AUD treatment, it is relevant to focus on patient involvement, managerial support, and guidance from experienced peers. Owing to the discrete and flexible design of the blended offer, it appears that it may reach patient groups who would not otherwise have sought treatment. Therefore, blended treatment may increase access to treatment and contribute to reaching people affected by excessive alcohol use, who would not otherwise have sought treatment. In addition, it seems that the blended offer may enhance the participants’ perceived satisfaction and the effect of the treatment course. Thus, it appears that Blend-A may be able to contribute to existing treatment offers. Such findings highlight the need to determine the actual effect of the Blend-A offer; therefore, an effectiveness study with a controlled design is warranted.