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

Effects of a medication adherence app among medically underserved adults with chronic illness: a randomized controlled trial

For individuals living with a chronic illness who require use of long-term medications, adherence is a vital aspect of successful symptom management and outcomes. This study investigated the effect of a smartphone app on adherence, self-efficacy, knowledge, and medication social support in a medically underserved adult population with various chronic illnesses. Participants were randomized to a group who used the app for one month or a control group provided with a printed medication list. Compared to the control group, participants receiving the intervention had significantly greater medication adherence (Cohen's d = -0.52, p = .014) and medication self-efficacy (Cohen's d = 0.43, p = .035). No significant effects were observed related to knowledge or social support. The findings suggest use of the app could positively impact chronic disease management in a medically underserved population in the United States.

Implementation and workflow strategies for integrating digital therapeutics for alcohol use disorders into primary care: a qualitative study

BACKGROUND

Alcohol use disorders (AUD) are prevalent and often go untreated. Patients are commonly screened for AUD in primary care, but existing treatment programs are failing to meet demand. Digital therapeutics include novel mobile app-based treatment approaches which may be cost-effective treatment options to help fill treatment gaps. The goal of this study was to identify implementation needs and workflow design considerations for integrating digital therapeutics for AUD into primary care.

METHODS

We conducted qualitative interviews with clinicians, care delivery leaders, and implementation staff (n = 16) in an integrated healthcare delivery system in the United States. All participants had experience implementing digital therapeutics for depression or substance use disorders in primary care. Interviews were designed to gain insights into adaptations needed to optimize existing clinical processes, workflows, and implementation strategies for use with alcohol-focused digital therapeutics. Interviews were recorded and transcribed and then analyzed using a rapid analysis process and affinity diagramming.

RESULTS

Qualitative themes were well represented across health system staff roles. Participants were enthusiastic about digital therapeutics for AUD, anticipated high patient demand for such a resource, and made suggestions for successful implementation. Key insights regarding the implementation of digital therapeutics for AUD and unhealthy alcohol use from our data include: (1) implementation strategy selection must be driven by digital therapeutic design and target population characteristics, (2) implementation strategies should seek to minimize burden on clinicians given the large numbers of patients with AUD who are likely to be interested in and eligible for digital therapeutics, and (3) digital therapeutics should be offered alongside many other treatment options to accommodate individual patients' AUD severity and treatment goals. Participants also expressed confidence that previous implementation strategies used with other digital therapeutics such as clinician training, electronic health record supports, health coaching, and practice facilitation would be effective for the implementation of digital therapeutics for AUD.

CONCLUSIONS

The implementation of digital therapeutics for AUD would benefit from careful consideration of the target population. Optimal integration requires tailoring workflows to meet anticipated patient volume and designing workflow and implementation strategies to meet the unique needs of patients with varying AUD severity.

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.

Digital Facilitation to Support Patient Access to Web-Based Primary Care Services: Scoping Literature Review

BACKGROUND

The use of web-based services within primary care (PC) in the National Health Service in England is increasing, with medically underserved populations being less likely to engage with web-based services than other patient groups. Digital facilitation-referring to a range of processes, procedures, and personnel that seek to support patients in the uptake and use of web-based services-may be a way of addressing these challenges. However, the models and impact of digital facilitation currently in use are unclear.

OBJECTIVE

This study aimed to identify, characterize, and differentiate between different approaches to digital facilitation in PC; establish what is known about the effectiveness of different approaches; and understand the enablers of digital facilitation.

METHODS

Adopting scoping review methodology, we searched academic databases (PubMed, EMBASE, CINAHL, Web of Science, and Cochrane Library) and gray literature published between 2015 and 2020. We conducted snowball searches of reference lists of included articles and articles identified during screening as relevant to digital facilitation, but which did not meet the inclusion criteria because of article type restrictions. Titles and abstracts were independently screened by 2 reviewers. Data from eligible studies were analyzed using a narrative synthesis approach.

RESULTS

A total of 85 publications were included. Most (71/85, 84%) were concerned with digital facilitation approaches targeted at patients (promotion of services, training patients to improve their technical skills, or other guidance and support). Further identified approaches targeted PC staff to help patients (eg, improving staff knowledge of web-based services and enhancing their technical or communication skills). Qualitative evidence suggests that some digital facilitation may be effective in promoting the uptake and use of web-based services by patients (eg, recommendation of web-based services by practice staff and coaching). We found little evidence that providing patients with initial assistance in registering for or accessing web-based services leads to increased long-term use. Few studies have addressed the effects of digital facilitation on health care inequalities. Those that addressed this suggested that providing technical training for patients could be effective, at least in part, in reducing inequalities, although not entirely. Factors affecting the success of digital facilitation include perceptions of the usefulness of the web-based service, trust in the service, patients' trust in providers, the capacity of PC staff, guidelines or regulations supporting facilitation efforts, and staff buy-in and motivation.

CONCLUSIONS

Digital facilitation has the potential to increase the uptake and use of web-based services by PC patients. Understanding the approaches that are most effective and cost-effective, for whom, and under what circumstances requires further research, including rigorous evaluations of longer-term impacts. As efforts continue to increase the use of web-based services in PC in England and elsewhere, we offer an early typology to inform conceptual development and evaluations.

TRIAL REGISTRATION

PROSPERO International Prospective Register of Systematic Reviews CRD42020189019; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=189019.

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.

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.

Smartphone use by health professionals: A review

Introduction

On the world stage unlimited internet access by smartphones have made in social, cultural and economic relations, has transformed the world to be faster and more efficient. In this context, health care requires more attention. Health professionals must be concerned about the majority of the patient’s care on the use of smartphone’s in this process. Smartphones can be cause distractions and cause poor patient care. Behavioral and psychological changes related to abusive and uncontrolled use in this population may create severe impacts not only on the user’s life but also on the community health care.

Method

A careful research was performed through PubMed, Web of Science and Psycho Info databases using the terms: “Smartphone addiction”, “Smartphone dependence”, “Smartphone abuse”, “physicians”, “nurses” and ‘‘health professionals”. The search covered the past 5 years up to August 2019. Articles that examine abusive use on smartphones in health professionals were included. We analyzed how this concept evolved over the last five years and hope to contribute to the better understanding of the issue and its impacts on this population and on the health care.

Results

There is a lack of specific trial instruments on screening of smartphone addiction or abusive use, tending to adopt different diagnostic criteria by the reports included. There are also a lack of studies discussing etiology of this compulsive behavior. Although known risks, published reports show there is no consciousness of abusive use and possible damages in healthcare by some health professionals. Instead, there are others that can affirm there are unpredictable risks in patient care and tend to stimulate conscience use politics in health settings. Most of them point out smartphone’s app benefits.

Conclusion

Smartphones and its functionalities became part of everyone's life by the various benefits guaranteed. However, an addictive behavior can cause damage, principally in the Health Care setting and health professional’s abusive use must be monitored. Further investigation is needed to determine the motivations of this addictive behavior and if abusive smartphone usage is a new psychiatric classification or merely the substrate of other disorders.

Health workers' perceptions and experiences of using mHealth technologies to deliver primary healthcare services: a qualitative evidence synthesis

BACKGROUND

Mobile health (mHealth), refers to healthcare practices supported by mobile devices, such as mobile phones and tablets. Within primary care, health workers often use mobile devices to register clients, track their health, and make decisions about care, as well as to communicate with clients and other health workers. An understanding of how health workers relate to, and experience mHealth, can help in its implementation.

OBJECTIVES

To synthesise qualitative research evidence on health workers' perceptions and experiences of using mHealth technologies to deliver primary healthcare services, and to develop hypotheses about why some technologies are more effective than others.

SEARCH METHODS

We searched MEDLINE, Embase, CINAHL, Science Citation Index and Social Sciences Citation Index in January 2018. We searched Global Health in December 2015. We screened the reference lists of included studies and key references and searched seven sources for grey literature (16 February to 5 March 2018). We re-ran the search strategies in February 2020. We screened these records and any studies that we identified as potentially relevant are awaiting classification.

SELECTION CRITERIA

We included studies that used qualitative data collection and analysis methods. We included studies of mHealth programmes that were part of primary healthcare services. These services could be implemented in public or private primary healthcare facilities, community and workplace, or the homes of clients. We included all categories of health workers, as well as those persons who supported the delivery and management of the mHealth programmes. We excluded participants identified as technical staff who developed and maintained the mHealth technology, without otherwise being involved in the programme delivery. We included studies conducted in any country.

DATA COLLECTION AND ANALYSIS

We assessed abstracts, titles and full-text papers according to the inclusion criteria. We found 53 studies that met the inclusion criteria and sampled 43 of these for our analysis. For the 43 sampled studies, we extracted information, such as country, health worker category, and the mHealth technology. We used a thematic analysis process. We used GRADE-CERQual to assess our confidence in the findings.

MAIN RESULTS

Most of the 43 included sample studies were from low- or middle-income countries. In many of the studies, the mobile devices had decision support software loaded onto them, which showed the steps the health workers had to follow when they provided health care. Other uses included in-person and/or text message communication, and recording clients' health information. Almost half of the studies looked at health workers' use of mobile devices for mother, child, and newborn health. We have moderate or high confidence in the following findings. mHealth changed how health workers worked with each other: health workers appreciated being more connected to colleagues, and thought that this improved co-ordination and quality of care. However, some described problems when senior colleagues did not respond or responded in anger. Some preferred face-to-face connection with colleagues. Some believed that mHealth improved their reporting, while others compared it to "big brother watching". mHealth changed how health workers delivered care: health workers appreciated how mHealth let them take on new tasks, work flexibly, and reach clients in difficult-to-reach areas. They appreciated mHealth when it improved feedback, speed and workflow, but not when it was slow or time consuming. Some health workers found decision support software useful; others thought it threatened their clinical skills. Most health workers saw mHealth as better than paper, but some preferred paper. Some health workers saw mHealth as creating more work. mHealth led to new forms of engagement and relationships with clients and communities: health workers felt that communicating with clients by mobile phone improved care and their relationships with clients, but felt that some clients needed face-to-face contact. Health workers were aware of the importance of protecting confidential client information when using mobile devices. Some health workers did not mind being contacted by clients outside working hours, while others wanted boundaries. Health workers described how some community members trusted health workers that used mHealth while others were sceptical. Health workers pointed to problems when clients needed to own their own phones. Health workers' use and perceptions of mHealth could be influenced by factors tied to costs, the health worker, the technology, the health system and society, poor network access, and poor access to electricity: some health workers did not mind covering extra costs. Others complained that phone credit was not delivered on time. Health workers who were accustomed to using mobile phones were sometimes more positive towards mHealth. Others with less experience, were sometimes embarrassed about making mistakes in front of clients or worried about job security. Health workers wanted training, technical support, user-friendly devices, and systems that were integrated into existing electronic health systems. The main challenges health workers experienced were poor network connections, access to electricity, and the cost of recharging phones. Other problems included damaged phones. Factors outside the health system also influenced how health workers experienced mHealth, including language, gender, and poverty issues. Health workers felt that their commitment to clients helped them cope with these challenges.

AUTHORS' CONCLUSIONS

Our findings propose a nuanced view about mHealth programmes. The complexities of healthcare delivery and human interactions defy simplistic conclusions on how health workers will perceive and experience their use of mHealth. Perceptions reflect the interplay between the technology, contexts, and human attributes. Detailed descriptions of the programme, implementation processes and contexts, alongside effectiveness studies, will help to unravel this interplay to formulate hypotheses regarding the effectiveness of mHealth.

Review and Implementation of Self-Help and Automated Tools in Mental Health Care

Self-help and automated technologies can be useful for behavioral and mental health education and interventions. These technologies include interactive media, online courses, artificial intelligence-powered chatbots, voice assistants, and video games. Self-help media can include books, videos, audible media like podcasts, blog and print articles, and self-contained Internet sites. Social media, online courses, and mass-market mobile apps also can include such media. These technologies serve to decrease geospatial, temporal, and financial barriers. This article describes different self-help and automated technologies, how to implement such technologies in existing clinical services, and how to implement according to patient needs.

The utility and impact of the addiction comprehensive health enhancement support system (ACHESS) on substance abuse treatment adherence among youth in an intensive outpatient program

Youth experiencing substance use disorders often are susceptible to relapse because traditional support systems can be expensive, geographically dispersed, operated on limited schedules and lacking in peer support. The continuity of care offered via the digital Addiction Comprehensive Health Enhancement Support System (ACHESS) system holds promise in preventing relapse because of its portability and capability to foster virtually anytime/anywhere, cost-effective access to supportive interventions. The aim of this mixed-methods study was to evaluate the utility and impact of ACHESS on treatment adherence among youth with substance use disorders in an intensive outpatient program in the US Midwest. Data on 28 clients using ACHESS during 2016-17 were compared to retrospective data on 28 carefully-matched others treated without ACHESS during 2014-16. Fifty-four percent of the study group successfully completed treatment as opposed to 42.9% of those in the comparison group. Staff focus group findings highlighted how some features of ACHESS were effectively integrated into the care model and appeared to positively impact outcomes, while other elements of the application offered little utility. We suggest further study of ACHESS among larger samples of youth with substance use disorders in intensive outpatient programs to assess its efficacy in supporting adherence to treatment.

Measuring the Implementation of Behavioral Intervention Technologies: Recharacterization of Established Outcomes

Behavioral intervention technologies (BITs) are websites, software, mobile apps, and sensors designed to help users address or change behaviors, cognitions, and emotional states. BITs have the potential to transform health care delivery, and early research has produced promising findings of efficacy. BITs also favor new models of health care delivery and provide novel data sources for measurement. However, there are few examples of successful BIT implementation and a lack of consensus on as well as inadequate descriptions of BIT implementation measurement. The aim of this viewpoint paper is to provide an overview and characterization of implementation outcomes for the study of BIT use in routine practice settings. Eight outcomes for the evaluation of implementation have been previously described: acceptability, adoption, appropriateness, feasibility, fidelity, implementation cost, penetration, and sustainability. In a proposed recharacterization of these outcomes with respect to BIT implementation, definitions are clarified, expansions to the level of analysis are identified, and unique measurement characteristics are discussed. Differences between BIT development and implementation, an increased focus on consumer-level outcomes, the expansion of providers who support BIT use, and the blending of BITs with traditional health care services are specifically discussed. BITs have the potential to transform health care delivery. Realizing this potential, however, will hinge on high-quality research that consistently and accurately measures how well such technologies have been integrated into health services. This overview and characterization of implementation outcomes support BIT research by identifying and proposing solutions for key theoretical and practical measurement challenges.

Measuring Psychiatric Symptoms Remotely: a Systematic Review of Remote Measurement-Based Care

PURPOSE OF REVIEW

This article systematically reviews studies examining remote measurement-based care (RMBC), defined as using technology to measure patients' psychiatric symptoms outside the context of a clinical encounter.

RECENT FINDINGS

Thirty-six studies were identified that measured patients' psychiatric symptoms remotely and provided feedback to treatment providers. The majority were single group designs. There was evidence supporting the short-term feasibility and acceptability of RMBC, although long-term sustainability was less clear. Thirteen randomized controlled trials were identified. RMBC was typically implemented as part of a multicomponent intervention (e.g., internet-based cognitive behavioral therapy with feedback to provider). Three studies experimentally isolated the clinical effects of RMBC, with two reporting no statistically significant differences between the RMBC and control conditions and one reporting greater symptom improvement associated with RMBC. RMBC appears feasible and acceptable and may be a promising intervention for improving mental health care, but additional experimental studies are needed.

Detecting Recovery Problems Just in Time: Application of Automated Linguistic Analysis and Supervised Machine Learning to an Online Substance Abuse Forum

Background

Online discussion forums allow those in addiction recovery to seek help through text-based messages, including when facing triggers to drink or use drugs. Trained staff (or “moderators”) may participate within these forums to offer guidance and support when participants are struggling but must expend considerable effort to continually review new content. Demands on moderators limit the scalability of evidence-based digital health interventions.

Objective

Automated identification of recovery problems could allow moderators to engage in more timely and efficient ways with participants who are struggling. This paper aimed to investigate whether computational linguistics and supervised machine learning can be applied to successfully flag, in real time, those discussion forum messages that moderators find most concerning.

Methods

Training data came from a trial of a mobile phone-based health intervention for individuals in recovery from alcohol use disorder, with human coders labeling discussion forum messages according to whether or not authors mentioned problems in their recovery process. Linguistic features of these messages were extracted via several computational techniques: (1) a Bag-of-Words approach, (2) the dictionary-based Linguistic Inquiry and Word Count program, and (3) a hybrid approach combining the most important features from both Bag-of-Words and Linguistic Inquiry and Word Count. These features were applied within binary classifiers leveraging several methods of supervised machine learning: support vector machines, decision trees, and boosted decision trees. Classifiers were evaluated in data from a later deployment of the recovery support intervention.

Results

To distinguish recovery problem disclosures, the Bag-of-Words approach relied on domain-specific language, including words explicitly linked to substance use and mental health (“drink,” “relapse,” “depression,” and so on), whereas the Linguistic Inquiry and Word Count approach relied on language characteristics such as tone, affect, insight, and presence of quantifiers and time references, as well as pronouns. A boosted decision tree classifier, utilizing features from both Bag-of-Words and Linguistic Inquiry and Word Count performed best in identifying problems disclosed within the discussion forum, achieving 88% sensitivity and 82% specificity in a separate cohort of patients in recovery.

Conclusions

Differences in language use can distinguish messages disclosing recovery problems from other message types. Incorporating machine learning models based on language use allows real-time flagging of concerning content such that trained staff may engage more efficiently and focus their attention on time-sensitive issues.

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.

Review of Use and Integration of Mobile Apps Into Psychiatric Treatments

PURPOSE OF REVIEW

Mental health practitioners should understand the features of current, publicly available apps; the features of novel, research apps; and issues behind the integration of mobile apps and digital health services into clinical workflows.

RECENT FINDINGS

The review is based on a research literature and the authors' clinical and healthcare administration experiences. Articles searched-on telepsychiatry, telemental health, mobile mental health, informatics, cellular phone, ambulatory monitoring, telemetry, and algorithms-were restricted to 2016 and 2017. Technologies are used in a variety of clinical settings, including patients with varying mental illness severity, social supports, and technological literacy. Good practices for evaluating apps, understanding user needs, and training and educating users can increase success rates. Ethics and risk management should be considered. Mobile apps are versatile. Integrating apps into psychiatric treatment requires addressing both patient and clinical workflows, design and usability principles, accessibility, social concerns, and digital health literacy.

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.