Integrating addiction treatment into primary care using mobile health technology: protocol for an implementation research study

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.

Acceptability of Personal Sensing Among People With Alcohol Use Disorder: Observational Study

BACKGROUND

Personal sensing may improve digital therapeutics for mental health care by facilitating early screening, symptom monitoring, risk prediction, and personalized adaptive interventions. However, further development and the use of personal sensing requires a better understanding of its acceptability to people targeted for these applications.

OBJECTIVE

We aimed to assess the acceptability of active and passive personal sensing methods in a sample of people with moderate to severe alcohol use disorder using both behavioral and self-report measures. This sample was recruited as part of a larger grant-funded project to develop a machine learning algorithm to predict lapses.

METHODS

Participants (N=154; n=77, 50% female; mean age 41, SD 11.9 years; n=134, 87% White and n=150, 97% non-Hispanic) in early recovery (1-8 weeks of abstinence) were recruited to participate in a 3-month longitudinal study. Participants were modestly compensated for engaging with active (eg, ecological momentary assessment [EMA], audio check-in, and sleep quality) and passive (eg, geolocation, cellular communication logs, and SMS text message content) sensing methods that were selected to tap into constructs from the Relapse Prevention model by Marlatt. We assessed 3 behavioral indicators of acceptability: participants' choices about their participation in the study at various stages in the procedure, their choice to opt in to provide data for each sensing method, and their adherence to a subset of the active methods (EMA and audio check-in). We also assessed 3 self-report measures of acceptability (interference, dislike, and willingness to use for 1 year) for each method.

RESULTS

Of the 192 eligible individuals screened, 191 consented to personal sensing. Most of these individuals (169/191, 88.5%) also returned 1 week later to formally enroll, and 154 participated through the first month follow-up visit. All participants in our analysis sample opted in to provide data for EMA, sleep quality, geolocation, and cellular communication logs. Out of 154 participants, 1 (0.6%) did not provide SMS text message content and 3 (1.9%) did not provide any audio check-ins. The average adherence rate for the 4 times daily EMA was .80. The adherence rate for the daily audio check-in was .54. Aggregate participant ratings indicated that all personal sensing methods were significantly more acceptable (all P<.001) compared with neutral across subjective measures of interference, dislike, and willingness to use for 1 year. Participants did not significantly differ in their dislike of active methods compared with passive methods (P=.23). However, participants reported a higher willingness to use passive (vs active) methods for 1 year (P=.04).

CONCLUSIONS

These results suggest that active and passive sensing methods are acceptable for people with alcohol use disorder over a longer period than has previously been assessed. Important individual differences were observed across people and methods, indicating opportunities for future improvement.

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.

A Comparison of Mathematical and Statistical Modeling with Longitudinal Data: An Application to Ecological Momentary Assessment of Behavior Change in Individuals with Alcohol Use Disorder

Ecological momentary assessment (EMA) has been broadly used to collect real-time longitudinal data in behavioral research. Several analytic methods have been applied to EMA data to understand the changes of motivation, behavior, and emotions on a daily or within-day basis. One challenge when utilizing those methods on intensive datasets in the behavioral field is to understand when and why the methods are appropriate to investigate particular research questions. In this manuscript, we compared two widely used methods (generalized estimating equations and generalized linear mixed models) in behavioral research with three other less frequently used methods (Markov models, generalized linear mixed-effects Markov models, and differential equations) in behavioral research but widely used in other fields. The purpose of this manuscript is to illustrate the application of five distinct analytic methods to one dataset of intensive longitudinal data on drinking behavior, highlighting the utility of each method.

A comparison of cognitive behavioral therapy for insomnia to standard of care in an outpatient substance use disorder clinic embedded within a therapeutic community: a RE-AIM framework evaluation

Background

Rates of substance use disorders (SUDs) continue to rise in the USA with parallel rises in admissions to outpatient SUD treatment programs. Insomnia symptoms reduce treatment adherence, trigger relapse, and generally undermine SUD recovery efforts. Cognitive-behavioral therapy for insomnia (CBT-I) is the first-line treatment recommended for chronic insomnia. No study has examined the effectiveness of CBT-I for individuals who recently entered an outpatient SUD treatment program embedded within a therapeutic community (i.e., long-term drug-free residential setting).

Methods

A randomized controlled trial conducted at a SUD program embedded in a therapeutic community aimed to compare group-based CBT-I (gCBT-I) (N = 10) with the standard of care (SOC) (N = 11) among individuals who have SUDs and comorbid insomnia. We present a RE-AIM (reach, effectiveness, adoption, implementation, and maintenance) framework evaluation to provide empirical data on gCBT-I feasibility and facilitators and barriers of conducting an insomnia-focused clinical effectiveness study within a therapeutic community.

Results

Participants in both study arms reported moderately severe insomnia symptoms at admission and reductions in insomnia symptoms over time. Among participants who completed the Insomnia Severity Index (ISI) beyond admission, ISI decreased to ≤ 8 (the clinical cutoff for mild insomnia) in 80% of individuals in the gCBT-I group compared with 25% of individuals in the SOC group. A RE-AIM framework evaluation showed initial success with Reach and Adoption while Implementation, and Maintenance were limited. Effectiveness was inconclusive because of challenges with recruitment, intervention integrity, and missing data that precluded meeting the planned recruitment and study aims and led to study termination. Coordination and communication with staff and leadership facilitated gCBT-I implementation, yet well-known CBT-I barriers including time- and resource-intensive sleep medicine training for interventionalists and maintenance of treatment integrity during an 8-week intervention limited gCBT-I sustainability.

Conclusions

This analysis supports the feasibility of conducting behavioral sleep medicine research in outpatient SUD treatment programs embedded within therapeutic communities. Implementation of an insomnia-focused intervention was widely accepted by patients and providers and has potential to address insomnia symptoms in early SUD recovery. Addressing patient- and organizational-level implementation barriers may enhance the sustainability and scalability of sleep interventions and provide new hope to effectively treat insomnia among people living with SUDs.

Trial registration

Clinicaltrials.gov: NCT03208855. Registered July 6, 2017https://clinicaltrials.gov/ct2/show/NCT03208855?term=NCT03208855&draw=2&rank=1

Intraindividual, Dyadic, and Network Communication in a Digital Health Intervention: Distinguishing Message Exposure from Message Production

Communicating within digital health interventions involves a range of behaviors that may contribute to the management of chronic illnesses in different ways. This study examines whether communication within a smartphone-based application for addiction recovery produces distinct effects depending on 1) the "level" of communication, defined as intraindividual communication (e.g., journal entries to oneself); dyadic communication (e.g., private messaging to other individuals); or network communication (e.g., discussion forum posts to all group members), and 2) whether individuals produce or are exposed to messages. We operationalize these communication levels and behaviors based on system use logs as the number of clicks dedicated to each activity and assess how each category of system use relates to changes in group bonding and substance use after 6 months with the mobile intervention. Our findings show that (1) intraindividual exposure to one's own past posts marginally predicts decreased drug use; (2) dyadic production predicts greater perceived bonding; while dyadic exposure marginally predicts reduced drug use; (3) network production predicts decreased risky drinking. Implications for digital health interventions are discussed.

Approaches for implementing digital interventions for alcohol use disorders in primary care: A qualitative, user-centered design study

Background

Digital interventions, such as smartphone apps, can be effective in treating alcohol use disorders (AUD). However, efforts to integrate digital interventions into primary care have been challenging. To inform successful implementation, we sought to understand how patients and clinicians preferred to use apps in routine primary care.

Methods

This study combined user-centered design and qualitative research methods, interviewing 18 primary care patients with AUD and nine primary care clinicians on topics such as prior experiences with digital tools, and design preferences regarding approaches for offering apps for AUD in primary care. Interviews were recorded and transcribed for template analysis whereby a priori codes were based on interview topics and refined through iterative coding. New codes and cross-cutting themes emerged from the data.

Results

Patient participants with AUD indicated they would be more likely to engage in treatment if primary care team members were involved in their use of apps. They also preferred to see clinicians “invested” and recommended that clinicians ask about app use and progress during follow-up appointments or check-ins. Clinician participants valued the opportunity to offer apps to their patients but noted that workflows would need to be tailored to individual patient needs. Time pressures, implementation complexity, and lack of appropriate staffing were cited as barriers. Clinicians proposed concrete solutions (e.g., education, tools, and staffing models) that could improve their ability to use apps within the constraints of primary care and suggested that some patients could potentially use apps without clinician support.

Conclusions

A user-centered approach to engaging patients in digital alcohol interventions in primary care may require personalized support for both initiation and follow-up. Meeting patients’ needs likely require increased staffing and efficient workflows in primary care. Health systems should consider offering multiple pathways for enrolling patients in apps to accommodate individual preferences and contextual barriers.

Plain Language Summary

Healthcare systems have begun using app-based treatments to help patients manage their health conditions, including alcohol use disorders. Some apps have been tested in research studies and appear to be effective. However, it is difficult for healthcare teams to offer apps to patients. Clinicians must engage in new activities that they have not done before, such as “teaching” patients to use apps and checking in on their use of the apps. Identifying how to use apps in routine healthcare is critical to their successful implementation. This study interviewed 27 people, including healthcare providers and patients in primary care, to uncover the most optimal ways to offer apps to patients with alcohol use disorders. The interviews combined the use of qualitative research methods and user-centered design. Results suggest that to use to address alcohol use disorders, primary care teams should be prepared to offer personalized support to patients. Both patient and clinician interviewees said that the steps required to use apps must be intuitive and simple. Patients could gain more benefits if clinicians introduced the apps and guided patients to use them, as opposed to making apps available for patients to download and use on their own. However, the exact approach to offering apps would depend on a given patient’s preferences and the extent that staffing was available in the clinic to support patients. Health systems should be prepared to offer and support patients in their use of apps, which should accommodate patient preferences and the constraints of the clinic.

What helps implement smartphone systems designed to improve quality of life for people with substance use disorder: an interim report on a randomized controlled trial with SUD providers in Iowa (Preprint)

Background

Researchers have conducted numerous studies seeking to understand how to improve the implementation of changes in health care organizations, but less focus has been given to applying lessons already learned from implementation science. Finding innovative ways to apply these findings efficiently and consistently will improve current research on implementation strategies and allow organizations utilizing these techniques to make changes more effectively.

Objective

This research aims to compare a practical implementation approach that uses principles from prior implementation studies to more traditional ways of implementing change.

Methods

A total of 43 addiction treatment sites in Iowa were randomly assigned to 2 different implementation strategies in a randomized comparative effectiveness trial studying the implementation of an eHealth substance use disorder treatment technology. One strategy used an adaptation of the Network for the Improvement of Addiction Treatment (NIATx) improvement approach, while the other used a traditional product training model. This paper discusses lessons learned about implementation.

Results

This midterm report indicates that use of the NIATx approach appears to be leading to improved outcomes on several measures, including initial and sustained use of new technology by both counselors and patients. Additionally, this research indicates that seamlessly integrating organizational changes into existing workflows and using coaching to overcome hurdles and assess progress are important to improve implementation projects.

Conclusions

At this interim point in the study, it appears that the use of the NIATx improvement process leads to better outcomes in implementation of changes within health care organizations. Moreover, some strategies used in this improvement process are particularly useful and should be drawn on more heavily in future implementation efforts.

Trial Registration

ClinicalTrials.gov NCT03954184; https://clinicaltrials.gov/ct2/show/NCT03954184

Approaches for Implementing App-Based Digital Treatments for Drug Use Disorders Into Primary Care: A Qualitative, User-Centered Design Study of Patient Perspectives

Background

Digital interventions, such as websites and smartphone apps, can be effective in treating drug use disorders (DUDs). However, their implementation in primary care is hindered, in part, by a lack of knowledge on how patients might like these treatments delivered to them.

Objective

This study aims to increase the understanding of how patients with DUDs prefer to receive app-based treatments to inform the implementation of these treatments in primary care.

Methods

The methods of user-centered design were combined with qualitative research methods to inform the design of workflows for offering app-based treatments in primary care. Adult patients (n=14) with past-year cannabis, stimulant, or opioid use disorder from 5 primary care clinics of Kaiser Permanente Washington in the Seattle area participated in this study. Semistructured interviews were recorded, transcribed, and analyzed using qualitative template analysis. The coding scheme included deductive codes based on interview topics, which primarily focused on workflow design. Inductive codes emerged from the data.

Results

Participants wanted to learn about apps during visits where drug use was discussed and felt that app-related conversations should be incorporated into the existing care whenever possible, as opposed to creating new health care visits to facilitate the use of the app. Nearly all participants preferred receiving clinician support for using apps over using them without support. They desired a trusting, supportive relationship with a clinician who could guide them as they used the app. Participants wanted follow-up support via phone calls or secure messaging because these modes of communication were perceived as a convenient and low burden (eg, no copays or appointment travel).

Conclusions

A user-centered implementation of treatment apps for DUDs in primary care will require health systems to design workflows that account for patients’ needs for structure, support in and outside of visits, and desire for convenience.

Implementing a peer recovery coach model to reach low-income, minority individuals not engaged in substance use treatment

Background: Low-income, racial/ethnic minority individuals face significant barriers in access to substance use (SU) treatment. Peer recovery coaches (PRCs), individuals with lived experience with substance use disorder (SUD), may be uniquely well suited to assist those encountering barriers to treatment. PRCs can also help reach those not engaged in treatment to promote harm reduction and support linkage-to-care when embedded in community rather than clinical settings. This study evaluated a community-based program in which a PRC facilitated linkage to and supported retention in SU treatment. Methods: Guided by the RE-AIM framework, we evaluated implementation of the intervention in a community resource center (CRC) serving homeless and low-income residents of Baltimore City. We examined the reach, effectiveness, adoption, and implementation of this PRC model. Results: Of 199 clients approached by or referred to the PRC, 39 were interested in addressing their SU. Of those interested in addressing SU, the PRC linked 64.1% (n = 25) to treatment and was able to follow up with 59.0% (n = 23) at prespecified time points after linkage (24-48 hours, 2 weeks, and 1 month). Fifty-two percent (n = 13) of clients linked to SU treatment remained in treatment at 30 days post-linkage. Of clients who did not remain in treatment, 77% (n = 10) continued contact with the PRC. Conclusions: Results indicate the utility of the CRC's approach in linking people to treatment for SU and addressing barriers to care through work with a PRC. Findings also highlight important barriers and facilitators to implementation of this model, including the need for adaptation based on individual goals and fluctuations in readiness for treatment.

Giving and receiving social support in online substance use disorder forums: How self-efficacy moderates effects on relapse

OBJECTIVE

Individuals in recovery for substance use disorders (SUDs) increasingly use online social support forums, necessitating research on how communicating through these forums can affect recovery. This study examines how giving and receiving support within an SUDs recovery forum predict substance use, and considers whether effects vary according to participants' self-efficacy.

METHODS

We applied content analysis to 3440 messages that were posted by 231 participants in an online SUDs forum. Surveys assessed social support reception and substance use at three timepoints. We assessed relationships between giving and receiving support and substance use (risky drinking days, illicit drug use days), and the interactions between self-efficacy and social support in predicting substance use outcomes.

RESULTS

Receiving more emotional support was associated with reduced illicit drug use at 6 and 12 months. For those with low self-efficacy, giving more emotional support predicted less risky drinking at month 12, whereas giving more informational support predicted more risky drinking at month 12.

CONCLUSION

These results suggest conditional benefits of exchanging support in an online SUDs forum, depending upon type of support (informational versus emotional), the participants' role (giver or receiver), and their self-efficacy.

PRACTICE IMPLICATIONS

We discuss implications for designing and using peer-to-peer support platforms.

How mobile health affects primary healthcare? Questionnaire design and attitude assessment

OBJECTIVES

The current research aimed to develop a questionnaire for the evaluation of the staff viewpoints in mobile phone use in the delivery of their services and then to assess the primary health center staff attitudes toward this area.

METHODS

This was a two-stage cross-sectional study. In the initial stage, a questionnaire was constructed that tested their reliability and validity through Cronbach's alpha coefficient, multitrait/multi-item correlation matrix and multivariate method of factor analysis. In the second phase, we computed the raw score of each construct which was calculated by taking the mean of the responses of all the items in a particular construct. The normality of the scores for each construct was tested via Kolmogorov-Smirnov and various parametric/non-parametric statistical tests were applied to compare the responses of the subjects. After statistical tests, the final questionnaire was confirmed, including 28 items.

RESULTS

The final questionnaires' five main axes consisted of health services efficiency, education, notices, consultation, as well as follow-up. Personnel perspective assessment indicates that there is no difference of view among individuals coming from various demographic features, including gender, age, work experience, as well as education level, to mobile phone use in their services.

CONCLUSION

The attitude of public health center staff to mobile phone use in providing health services was positive in general, which would be an influential context for the effective application of mobile phones in public health; such a context would result in users' intentions to use and accept m-Health.

Conducting Psychological Intervention Research in the Information Age: Reconsidering the "State of the Field"

Enthusiasm for technology in mental health has evolved as a function of its promise to increase the reach and impact of services, particularly for traditionally at-risk and underserved groups. Preliminary findings suggest that technology-enhanced interventions indeed hold promise for increasing engagement in and outcomes of evidence-based treatment approaches. The time- and resourceintensive nature of traditional randomized control trials, however, may be even more of a challenge for further advancement in this area, given the rapid innovation of consumer driven new product development. Accordingly, this review aims to summarize how a broader range of scientific designs and analyses may be necessary in order to further advance and optimize the reach and impact of technology-enhanced psychological practice. Examples of various approaches are provided and recommendations are provided for future work in this area.

An Overlapping Systems Conceptual Framework to Evaluate Implementation of a Behavioral Health Intervention for Justice-Involved Youth

Background:

Nearly 65% of justice–involved youth have a substance use and/or mental health disorder. Although evidence–based practices have been established for adolescents with co–occurring mental health and substance use disorders, these practices are not widely used in juvenile justice agencies due to environmental and organizational complexities.

Methods:

Our study builds on Juvenile Justice—Translational Research on Interventions for Adolescents in the Legal System (JJ–TRIALS), a multi–site cooperative research initiative of juvenile justice and partnering behavioral health agencies. We also integrate state and county–level data to support broader assessment of key drivers of implementation success.

Results:

We present an economics/systems conceptual model describing how the environmental context, systems organization, and economic costs of implementation can affect implementation outcomes. Comparison of intervention condition (Core vs Enhanced) and pre–implementation costs (High vs Low) found differences in insurance reimbursements and types, as well as agency staffing characteristics.

Discussion:

Implementing new procedures or policies at a systems level must consider implementation outcomes in a broad context. Factors such as population demographics, primary care and behavioral health treatment capacity, unemployment rates, and public funding for treatment and other services are important in determining intervention success and sustainability.

Smartphone Apps Targeting Alcohol and Illicit Substance Use: Systematic Search in in Commercial App Stores and Critical Content Analysis

BACKGROUND

Smartphone apps promise to enhance the reach of evidence-based interventions (cognitive behavior therapy, contingency management and therapeutic education system) for populations with substance use disorders, with minimal disruption to health systems. However, further studies are needed to systematically evaluate smartphone apps targeting alcohol and illicit substances.

OBJECTIVE

The aim of this study was to evaluate the functionality, aesthetics, and quality of information of free or low-cost apps claiming to target alcohol, benzodiazepine, cocaine, crack/cocaine, crystal methamphetamine, and heroin use using the validated Mobile App Rating Scale (MARS) and critical content analysis.

METHODS

A systematic search of iTunes and Google Play app stores for free or low-cost apps facilitating recovery was conducted in March 2018 and yielded 904 apps using the keywords described in previous studies (eg, recovery, sobriety, sober, alcohol, and heroin). An interdisciplinary team of clinicians, behavioral informatics, and public health reviewers trained in substance use disorders conducted a descriptive analysis of 74 apps categorized as reducing use. In addition to the MARS scale, a descriptive analysis of relevant apps was conducted by the study team to assess for quality indicators emphasized by expert guidelines and review articles.

RESULTS

Most apps (n=74) claimed to reduce use or promote abstinence and yielded an overall low median MARS score of 2.82 (0.55) and a wide range of scores (1.64, 4.20). Ratings were also low for engagement (2.75 (0.72)), functionality (3.64 (0.78)), aesthetics (3.03 (0.87)), information (2.82 (0.62)), and satisfaction (1.76 (0.67)) subdomains. Innovative design and content features elicited in the review included initial assessments of substance use following app download, tracking substance use, and related consequences (eg, cost or calorie intake), remote and proximate peer support per geospatial positioning, and allowing users and family members of individuals with substance use disorders to locate 12-step meetings, treatment programs, and mental health services. Few apps integrated evidence-based psychotherapeutic (eg, cognitive behavioral therapy [CBT] or motivational interviewing) and pharmacologic interventions (eg, naloxone or buprenorphine).

CONCLUSIONS

Few commercially available apps yielded in our search integrated evidence-based interventions (eg, extended-release naltrexone, buprenorphine, naloxone, Self-Management and Recovery Training recovery, or CBT), and a concerning number of apps promoted harmful drinking and illicit substance use.

Technology-Based Alcohol Interventions in Primary Care: Systematic Review

BACKGROUND

Primary care settings are uniquely positioned to reach individuals at risk of alcohol use disorder through technology-delivered behavioral health interventions. Despite emerging effectiveness data, few efforts have been made to summarize the collective findings from these delivery approaches.

OBJECTIVE

The aim of this study was to review recent literature on the use of technology to deliver, enhance, or support the implementation of alcohol-related interventions in primary care. We focused on addressing questions related to (1) categorization or target of the intervention, (2) descriptive characteristics and context of delivery, (3) reported efficacy, and (4) factors influencing efficacy.

METHODS

We conducted a comprehensive search and systematic review of completed studies at the intersection of primary care, technology, and alcohol-related problems published from January 2000 to December 2018 within EBSCO databases, ProQuest Dissertations, and Cochrane Reviews. Of 2307 initial records, 42 were included and coded independently by 2 investigators.

RESULTS

Compared with the years of 2000 to 2009, published studies on technology-based alcohol interventions in primary care nearly tripled during the years of 2010 to 2018. Of the 42 included studies, 28 (64%) were randomized controlled trials. Furthermore, studies were rated on risk of bias and found to be predominantly low risk (n=18), followed by moderate risk (n=16), and high risk (n=8). Of the 24 studies with primary or secondary efficacy outcomes related to drinking and drinking-related harms, 17 (71%) reported reduced drinking or harm in all primary and secondary efficacy outcomes. Furthermore, of the 31 studies with direct comparisons with treatment as usual (TAU), 13 (42%) reported that at least half of the primary and secondary efficacy outcomes of the technology-based interventions were superior to TAU. High efficacy was associated with provider involvement and the reported use of an implementation strategy to deliver the technology-based intervention.

CONCLUSIONS

Our systematic review has highlighted a pattern of growth in the number of studies evaluating technology-based alcohol interventions in primary care. Although these interventions appear to be largely beneficial in primary care, outcomes may be enhanced by provider involvement and implementation strategy use. This review enables better understanding of the typologies and efficacy of these interventions and informs recommendations for those developing and implementing technology-based alcohol interventions in primary care settings.

Innovative Implementation Studies Conducted in US Safety Net Health Care Settings: A Systematic Review

Little is known about dissemination and implementation in safety net settings. The authors conducted a literature review of innovation/implementation studies in US safety net health care settings between 2008 and 2017. Each article was coded for (1) intervention characteristics, (2) implementation stage, (3) internal versus external ownership, and (4) prespecified implementation outcomes (eg, acceptability and fidelity). Twenty studies were identified; the majority were implemented within community clinics or integrated safety net systems (15 articles), most involved care process improvements (13 articles), and most were internally developed (13 articles). The internally developed innovations reported fewer barriers to acceptability among staff/providers, higher leadership involvement and organizational alignment, greater amounts of customization to the local setting, and better sustainment. Future work should harness the high levels of alignment and acceptability in implementation research within safety net settings, with an eye toward maintaining fidelity to facilitate dissemination across sites.

Implementing a Mobile Health System to Integrate the Treatment of Addiction Into Primary Care: A Hybrid Implementation-Effectiveness Study

BACKGROUND

Despite the near ubiquity of mobile phones, little research has been conducted on the implementation of mobile health (mHealth) apps to treat patients in primary care. Although primary care clinicians routinely treat chronic conditions such as asthma and diabetes, they rarely treat addiction, a common chronic condition. Instead, addiction is most often treated in the US health care system, if it is treated at all, in a separate behavioral health system. mHealth could help integrate addiction treatment in primary care.

OBJECTIVE

The objective of this paper was to report the effects of implementing an mHealth system for addiction in primary care on both patients and clinicians.

METHODS

In this implementation research trial, an evidence-based mHealth system named Seva was introduced sequentially over 36 months to a maximum of 100 patients with substance use disorders (SUDs) in each of three federally qualified health centers (FQHCs; primary care clinics that serve patients regardless of their ability to pay). This paper reports on patient and clinician outcomes organized according to the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework.

RESULTS

The outcomes according to the RE-AIM framework are as follows: Reach-Seva reached 8.31% (268/3226) of appropriate patients. Reach was limited by our ability to pay for phones and data plans for a maximum of 100 patients per clinic. Effectiveness-Patients who were given Seva had significant improvements in their risky drinking days (44% reduction, (0.7-1.25)/1.25, P=.04), illicit drug-use days (34% reduction, (2.14-3.22)/3.22, P=.01), quality of life, human immunodeficiency virus screening rates, and number of hospitalizations. Through Seva, patients also provided peer support to one another in ways that are novel in primary care settings. Adoption-Patients sustained high levels of Seva use-between 53% and 60% of the patients at the 3 sites accessed Seva during the last week of the 12-month implementation period. Among clinicians, use of the technology was less robust than use by patients, with only a handful of clinicians using Seva in each clinic and behavioral health providers making most referrals to Seva in 2 of the 3 clinics. Implementation-At 2 sites, implementation plans were realized successfully; they were delayed in the third. Maintenance-Use of Seva dropped when grant funding stopped paying for the mobile phones and data plans. Two of the 3 clinics wanted to maintain the use of Seva, but they struggled to find funding to support this.

CONCLUSIONS

Implementing an mHealth system can improve care among primary care patients with SUDs, and patients using the system can support one another in their recovery. Among clinicians, however, implementation requires figuring out how information from the mHealth system will be used and making mHealth data available in the electronic health (eHealth) record. In addition, paying for an mHealth system remains a challenge.

Stage-Based Mobile Intervention for Substance Use Disorders in Primary Care: Development and Test of Acceptability

Background

In 2016, 21 million Americans aged 12 years and older needed treatment for a substance use disorder (SUD). However, only 10% to 11% of individuals requiring SUD treatment received it. Given their access to patients, primary care providers are in a unique position to perform universal Screening, Brief Intervention, and Referral to Treatment (SBIRT) to identify individuals at risk, fill gaps in services, and make referrals to specialty treatment when indicated. Major barriers to SBIRT include limited time among providers and low motivation to change among many patients.

Objective

The objective of this study was to develop and test the acceptability of a prototype of a mobile-delivered substance use risk intervention (SURI) for primary care patients and a clinical dashboard for providers that can address major barriers to SBIRT for risky drug use. The SURI delivers screening and feedback on SUD risk via mobile tools to patients at home or in the waiting room; for patients at risk, it also delivers a brief intervention based on the transtheoretical model of behavior change (TTM) to facilitate progress through the stages of change for quitting the most problematic drug and for seeking treatment if indicated. The prototype also delivers 30 days of stage-matched text messages and 4 Web-based activities addressing key topics. For providers, the clinical dashboard summarizes the patient’s SUD risk scores and stage of change data, and provides stage-matched scripts to guide in-person sessions.

Methods

A total of 4 providers from 2 federally qualified health centers (FQHCs) were recruited for the pilot test, and they in turn recruited 5 patients with a known SUD. Furthermore, 3 providers delivered dashboard-guided SBIRT sessions and completed a brief acceptability survey. A total of 4 patients completed a Web-based SURI session and in-person SBIRT session, accessed other program components, and completed 3 acceptability surveys over 30 days. Questions in the surveys were adapted from the National Cancer Institute’s Education Materials Review Form. Response options ranged from 1=strongly disagree to 5=strongly agree. The criterion for establishing acceptability was an overall rating of 4.0 or higher across items.

Results

For providers, the overall mean acceptability rating was 4.4 (standard deviation [SD] 0.4). Notably, all providers gave a rating of 5.0 for the item, “The program can give me helpful information about my patient.” For patients, the overall mean acceptability rating was 4.5 (SD 0.3) for the mobile- and provider-delivered SBIRT sessions and 4.0 (SD 0.4) for the text messages and Web-based activities. One highly rated item was “The program could help me make some positive changes” (4.5).

Conclusions

The SURI program and clinical dashboard, developed to reduce barriers to SBIRT in primary care, were well received by providers and patients.

Use of technology for care coordination initiatives for patients with mental health issues: a systematic literature review

This systematic literature review investigates the use of technology for the coordination and management of mental health care with an emphasis on outcomes. Searches of MEDLINE/PubMed, Scopus, and EMBASE were conducted between January 1, 2003, and January 4, 2018, to identify articles that assessed patient outcomes associated with care coordination, evaluated technology to improve care, or discussed management of mental health care using technology. A total of 21 articles were included in a qualitative review based on the recommendations set forth by the PRISMA statement. Among the various health technologies, electronic health records were most commonly used for care coordination, with primary care being the most frequent setting. Care coordination was shown to provide easier patient access to health care providers and to improve communication between caregiver and patient, especially in cases where geographic location or distance is a challenge. Barriers to coordinated care included, but were not limited to, insufficient funding for health information technology, deficient reimbursement plans, limited access to technologies, cultural barriers, and underperforming electronic health record templates. In conclusion, many studies showed the benefit of coordinated and collaborative care through the use of technology; however, further research and development efforts are needed to continue technological innovation for advanced patient care.

Use of a Smartphone Recovery Tool for Latinos with Co-Occurring Alcohol and Other Drug Disorders and Mental Disorders

OBJECTIVE

Addressing alcohol and other drug disorders and other mental disorders among adult Hispanics/Latinos is of critical concern, as they are one of the fastest-growing ethnic groups with a disproportionate rate of disease, mental disorders, and poverty. Although improvement in outcomes is associated with sustained participation in ongoing treatment for co-occurring alcohol and other drug disorders/mental disorders, continuing care is rare for these chronic conditions, especially for Latinos with more limited access to culturally and linguistically competent services.

METHODS

The evidence-based smartphone recovery application Addiction-Comprehensive Health Enhancement Support System (A-CHESS) was translated and adapted for Spanish-speaking Latinos with alcohol and other drug disorders/mental disorders, thus developing CASA-CHESS to address a high level of need for services, high rates of relapse, and lack of existing culturally competent services for Latinos.

RESULTS

Of the 79 Latino clients who completed residential treatment and received a smartphone equipped with CASA-CHESS, 26.6% discontinued using CASA-CHESS and 73.4% remained active for four or more months. CASA-CHESS usage was sustained over the four months across all three tenets of self-determination theory (competence, relatedness, and autonomy), with the most commonly utilized services being relevant to relatedness (e.g., messaging, discussion boards). CASA-CHESS clients demonstrated a similar pattern of usage to A-CHESS clients.

CONCLUSIONS

Findings illustrate that Spanish-speaking Latinos with alcohol and other drug disorders/mental disorders will use a smartphone application to assist with their recovery, continuing their access to resources, case management, and quality information after leaving residential treatment. Consistent with previous findings, our results also emphasize the importance of social support during the four months post-discharge. Such evidence-based, theory-driven digital interventions may extend access to culturally and linguistically competent services.

The effect of bundling medication-assisted treatment for opioid addiction with mHealth: study protocol for a randomized clinical trial

BACKGROUND

Opioid dependence has devastating and increasingly widespread consequences and costs, and the most common outcome of treatment is early relapse. People who inject opioids are also at disproportionate risk for contracting the human immunodeficiency virus (HIV) and hepatitis C virus (HCV). This study tests an approach that has been shown to improve recovery rates: medication along with other supportive services (medication-assisted treatment, or MAT) against MAT combined with a smartphone innovation called A-CHESS (MAT + A-CHESS).

METHODS/DESIGN

This unblinded study will randomly assign 440 patients to receive MAT + A-CHESS or MAT alone. Eligible patients will meet criteria for having an opioid use disorder of at least moderate severity and will be taking methadone, injectable naltrexone, or buprenorphine. Patients with A-CHESS will have smartphones for 16 months; all patients will be followed for 24 months. The primary outcome is the difference between patients in the two arms in percentage of days using illicit opioids during the 24-month intervention. Secondary outcomes are differences between patients receiving MAT + A-CHESS versus MAT in other substance use, quality of life, retention in treatment, health service use, and, related to HIV and HCV, screening and testing rates, medication adherence, risk behaviors, and links to care. We will also examine mediators and moderators of the effects of MAT + A-CHESS. We will measure variables at baseline and months 4, 8, 12, 16, 20, and 24. At each point, patients will respond to a 20- to 30-min phone survey; urine screens will be collected at baseline and up to twice a month thereafter. We will use mixed-effects to evaluate the primary and secondary outcomes, with baseline scores functioning as covariates, treatment condition as a between-subject factor, and the outcomes reflecting scores for a given assessment at the six time points. Separate analyses will be conducted for each outcome.

DISCUSSION

A-CHESS has been shown to improve recovery for people with alcohol dependence. It offers an adaptive and extensive menu of services and can attend to patients nearly as constantly as addiction does. This suggests the possibility of increasing both the effectiveness of, and access to, treatment for opioid dependence.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT02712034 . Registered on 14 March 2016.

Implementing an mHealth system for substance use disorders in primary care: a mixed methods study of clinicians' initial expectations and first year experiences

Background

Millions of Americans need but don’t receive treatment for substance use, and evidence suggests that addiction-focused interventions on smart phones could support their recovery. There is little research on implementation of addiction-related interventions in primary care, particularly in Federally Qualified Health Centers (FQHCs) that provide primary care to underserved populations. We used mixed methods to examine three FQHCs’ implementation of Seva, a smart-phone app that offers patients online support/discussion, health-tracking, and tools for coping with cravings, and offers clinicians information about patients’ health tracking and relapses. We examined (a) clinicians’ initial perspectives about implementing Seva, and (b) the first year of implementation at Site 1.

Methods

Prior to staggered implementation at three FQHCs (Midwest city in WI vs. rural town in MT vs. metropolitan NY), interviews, meetings, and focus groups were conducted with 53 clinicians to identify core themes of initial expectations about implementation. One year into implementation at Site 1, clinicians there were re-interviewed. Their reports were supplemented by quantitative data on clinician and patient use of Seva.

Results

Clinicians anticipated that Seva could help patients and make behavioral health appointments more efficient, but they were skeptical that physicians would engage with Seva (given high caseloads), and they were uncertain whether patients would use Seva. They were concerned about legal obligations for monitoring patients’ interactions online, including possible “cries for help” or inappropriate interactions. One year later at Site 1, behavioral health care providers, rather than physicians, had incorporated Seva into patient care, primarily by discussing it during appointments. Given workflow/load concerns, only a few key clinicians monitored health tracking/relapses and prompted outreach when needed; two researchers monitored the discussion board and alerted the clinic as needed. Clinician turnover/leave complicated this approach. Contrary to clinicians’ initial concerns, patients showed sustained, mutually supportive use of Seva, with few instances of misuse.

Conclusions

Results suggest the value of (a) focusing implementation on behavioral health care providers rather than physicians, (b) assigning a few individuals (not necessarily clinicians) to monitor health tracking, relapses, and the discussion board, (c) anticipating turnover/leave and having designated replacements. Patients showed sustained, positive use of Seva.

Trial registration

ClinicalTrials.gov (NCT01963234).

A mixed-methods evaluation of the feasibility, acceptability, and preliminary efficacy of a mobile intervention for methadone maintenance clients

Despite the recent explosion of behavioral health interventions delivered on mobile devices, little is known about factors that make such applications practical, engaging and useful to their target audience. This study reports on the feasibility, acceptability and preliminary efficacy of a prototype of a novel, interactive mobile psychosocial intervention to reduce problematic drug use among clients in methadone maintenance treatment (MMT). A mixed-methods pilot study with new MMT clients (n = 25) indicated that the mobile intervention approach was feasible, and that participants found the intervention highly acceptable and useful. On 100-point visual analog scale (VAS) items, participants reported high levels of liking the program (M = 75.6), and endorsed it as useful (M = 77.5), easy to use (M = 80.7), and containing a significant amount of new information (M = 74.8). When compared with 25 study participants who received standard MMT alone, pilot participants rated their treatment significantly higher in interestingness and usefulness, and were significantly more satisfied with their treatment. In qualitative interviews, participants reported using the mobile intervention in a range of settings, including during times of heightened risk for substance use, and finding it helpful in managing drug cravings. Additionally, pilot participants showed evidence of increased treatment retention and abstinence from illicit opioids (in terms of effect size) over a 3-month period relative to those in standard MMT, suggesting the application's potential to enhance treatment outcomes. These promising findings suggest that an evidence-based mobile therapeutic tool addressing substance use may appeal to drug treatment clients and have clinical utility as an adjunct to formal treatment.

Sample size calculations for micro-randomized trials in mHealth

The use and development of mobile interventions are experiencing rapid growth. In "just-in-time" mobile interventions, treatments are provided via a mobile device, and they are intended to help an individual make healthy decisions 'in the moment,' and thus have a proximal, near future impact. Currently, the development of mobile interventions is proceeding at a much faster pace than that of associated data science methods. A first step toward developing data-based methods is to provide an experimental design for testing the proximal effects of these just-in-time treatments. In this paper, we propose a 'micro-randomized' trial design for this purpose. In a micro-randomized trial, treatments are sequentially randomized throughout the conduct of the study, with the result that each participant may be randomized at the 100s or 1000s of occasions at which a treatment might be provided. Further, we develop a test statistic for assessing the proximal effect of a treatment as well as an associated sample size calculator. We conduct simulation evaluations of the sample size calculator in various settings. Rules of thumb that might be used in designing a micro-randomized trial are discussed. This work is motivated by our collaboration on the HeartSteps mobile application designed to increase physical activity. Copyright © 2015 John Wiley & Sons, Ltd.

Using e-technologies in clinical trials

Clinical trials have been slow to incorporate e-technology (digital and electronic technology that utilizes mobile devices or the Internet) into the design and execution of studies. In the meantime, individuals and corporations are relying more on electronic platforms and most have incorporated such technology into their daily lives. This paper provides a general overview of the use of e-technologies in clinical trials research, specifically within the last decade, marked by rapid growth of mobile and Internet-based tools. Benefits of and challenges to the use of e-technologies in data collection, recruitment and retention, delivery of interventions, and dissemination are provided, as well as a description of the current status of regulatory oversight of e-technologies in clinical trials research. As an example of ways in which e-technologies can be used for intervention delivery, a summary of e-technologies for treatment of substance use disorders is presented. Using e-technologies to design and implement clinical trials has the potential to reach a wide audience, making trials more efficient while also reducing costs; however, researchers should be cautious when adopting these tools given the many challenges in using new technologies, as well as threats to participant privacy/confidentiality. Challenges of using e-technologies can be overcome with careful planning, useful partnerships, and forethought. The role of web- and smartphone-based applications is expanding, and the increasing use of those platforms by scientists and the public alike make them tools that cannot be ignored.

Mobile Delivery of Treatment for Alcohol Use Disorders: A Review of the Literature

Several systems for treating alcohol-use disorders (AUDs) exist that operate on mobile phones. These systems are categorized into four groups: text-messaging monitoring and reminder systems, text-messaging intervention systems, comprehensive recovery management systems, and game-based systems. Text-messaging monitoring and reminder systems deliver reminders and prompt reporting of alcohol consumption, enabling continuous monitoring of alcohol use. Text-messaging intervention systems additionally deliver text messages designed to promote abstinence and recovery. Comprehensive recovery management systems use the capabilities of smart-phones to provide a variety of tools and services that can be tailored to individuals, including in-the-moment assessments and access to peer discussion groups. Game-based systems engage the user using video games. Although many commercial applications for treatment of AUDs exist, few (if any) have empirical evidence of effectiveness. The available evidence suggests that although texting-based applications may have beneficial effects, they are probably insufficient as interventions for AUDs. Comprehensive recovery management systems have the strongest theoretical base and have yielded the strongest and longest-lasting effects, but challenges remain, including cost, understanding which features account for effects, and keeping up with technological advances.