Treatment seeking as a mechanism of change in a randomized controlled trial of a mobile health intervention to support recovery from alcohol use disorders.

BACKGROUND: We estimated the efficacy of the Addiction-Comprehensive Health Enhancement Support System (A-CHESS) in increasing the use of services for addiction and examined the extent to which this use of services mediated the effects of A-CHESS on risky drinking days and abstinence from drinking. METHODS: We conducted secondary data analyses of the A-CHESS randomized controlled trial. Recruitment occurred in five residential treatment programs operated by two addiction treatment organizations. Participants were 349 adults with alcohol use disorders recruited two weeks before discharge from residential treatment. We provided intervention arm participants with a smartphone, the A-CHESS application, and an 8-month service plan. Control arm participants received treatment as usual. Telephone interviews at 4, 8, and 12-month follow-ups assessed past-month risky drinking days, past-month abstinence, and post-discharge service utilization (past-month outpatient addiction treatment and past-week mutual help including Alcoholics Anonymous and Narcotics Anonymous). Using mixed effects latent variable models, we estimated the indirect effects of A-CHESS on drinking outcomes, as mediated by post-discharge service utilization. RESULTS: Approximately 50.5% of participants reported outpatient addiction treatment and 75.5% reported mutual help at any follow-up interview in the year following randomization. Assignment to the A-CHESS intervention was associated with an increased odds of outpatient addiction treatment across follow-ups, but not mutual help. This use of outpatient addiction treatment mediated the effect of A-CHESS on risky drinking days, but not abstinence. The effect of A-CHESS through outpatient addiction treatment appeared to reduce the expected number of risky drinking days across follow-ups by 11%. CONCLUSIONS: The mobile health (mHealth) intervention promoted the use of outpatient addiction treatment, which appeared to contribute to its efficacy in reducing risky drinking. Future research should investigate how mHealth interventions could link patients to needed treatment services and promote the sustained use of these services.

Using mobile health technology to improve behavioral skill implementation through homework in evidence-based parenting intervention for disruptive behavior disorders in youth: study protocol for intervention development and evaluation.

BACKGROUND: Disruptive behavior disorders (DBDs) (oppositional defiant disorder (ODD) and conduct disorder (CD)) are prevalent, costly, and oftentimes chronic psychiatric disorders of childhood. Evidence-based interventions that focus on assisting parents to utilize effective skills to modify children's problematic behaviors are first-line interventions for the treatment of DBDs. Although efficacious, the effects of these interventions are often attenuated by poor implementation of the skills learned during treatment by parents, often referred to as between-session homework. The multiple family group (MFG) model is an evidence-based, skills-based intervention model for the treatment of DBDs in school-age youth residing in urban, socio-economically disadvantaged communities. While data suggest benefits of MFG on DBD behaviors, similar to other skill-based interventions, the effects of MFG are mitigated by the poor homework implementation, despite considerable efforts to support parents in homework implementation. This paper focuses on the study protocol for the development and preliminary evaluation of a theory-based, smartphone mobile health (mHealth) application (My MFG) to support homework implementation by parents participating in MFG. METHODS/DESIGN: This paper describes a study design proposal that begins with a theoretical model, uses iterative design processes to develop My MFG to support homework implementation in MFG through a series of pilot studies, and a small-scale pilot randomised controlled trial to determine if the intervention can demonstrate change (preliminary efficacy) of My MFG in outpatient mental health settings in socioeconomically disadvantaged communities. DISCUSSION: This preliminary study aims to understand the implementation of mHealth methods to improve the effectiveness of evidence-based interventions in routine outpatient mental health care settings for youth with disruptive behavior and their families. Developing methods to augment the benefits of evidence-based interventions, such as MFG, where homework implementation is an essential mediator of treatment benefits is critical to full adoption/implementation of these intervention in routine practice settings and maximizing benefits for youth with DBDs and their families. TRIAL REGISTRATION: ClinicalTrials.gov NCT01917838.

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 ).

Using the NIATx Model to Implement User-Centered Design of Technology for Older Adults.

What models can effectively guide the creation of eHealth and mHealth technologies? This paper describes the use of the NIATx model as a framework for the user-centered design of a new technology for older adults. The NIATx model is a simple framework of process improvement based on the following principles derived from an analysis of decades of research from various industries about why some projects fail and others succeed: (1) Understand and involve the customer; (2) fix key problems; (3) pick an influential change leader; (4) get ideas from outside the field; (5) use rapid-cycle testing. This paper describes the use of these principles in technology development, the strengths and challenges of using this approach in this context, and lessons learned from the process. Overall, the NIATx model enabled us to produce a user-focused technology that the anecdotal evidence available so far suggests is engaging and useful to older adults. The first and fourth principles were especially important in developing the technology; the fourth proved the most challenging to use.

The effect of an information and communication technology (ICT) on older adults' quality of life: study protocol for a randomized control trial.

BACKGROUND: This study investigates the use of an information and communication technology (Elder Tree) designed for older adults and their informal caregivers to improve older adult quality of life and address challenges older adults face in maintaining their independence (for example, loneliness and isolation, falling, managing medications, driving and transportation). METHODS/DESIGN: This study, an unblinded randomized controlled trial, will evaluate the effectiveness and cost of Elder Tree. Older adults who are at risk for losing their independence - along with their informal caregivers, if they name them - are randomized to two groups. The intervention group has access to their usual sources of information and communication as well as to Elder Tree for 18 months while the control group uses only their usual sources of information and communication. The primary outcome of the study is older adult quality of life. Secondary outcomes are cost per Quality-Adjusted Life Year and the impact of the technology on independence, loneliness, falls, medication management, driving and transportation, and caregiver appraisal and mastery. We will also examine the mediating effect of self-determination theory. We will evaluate the effectiveness of Elder Tree by comparing intervention- and control-group participants at baseline and months 6, 12, and 18. We will use mixed-effect models to evaluate the primary and secondary outcomes, where pretest score functions as a covariate, treatment condition is a between-subjects factor, and the multivariate outcome reflects scores for a given assessment at the three time points. Separate analyses will be conducted for each outcome. Cost per Quality-Adjusted Life Year will be compared between the intervention and control groups. Additional analyses will examine the mediating effect of self-determination theory on each outcome. DISCUSSION: Elder Tree is a multifaceted intervention, making it a challenge to assess which services or combinations of services account for outcomes in which subsets of older adults. If Elder Tree can improve quality of life and reduce healthcare costs among older adults, it could suggest a promising way to ease the burden that advancing age can place on older adults, their families, and the healthcare system. TRIAL REGISTRATION: ClinicalTrials.gov NCT02128789 . Registered on 26 March 2014.

A smartphone application to support recovery from alcoholism: a randomized clinical trial.

IMPORTANCE: Patients leaving residential treatment for alcohol use disorders are not typically offered evidence-based continuing care, although research suggests that continuing care is associated with better outcomes. A smartphone-based application could provide effective continuing care. OBJECTIVE: To determine whether patients leaving residential treatment for alcohol use disorders with a smartphone application to support recovery have fewer risky drinking days than control patients. DESIGN, SETTING, AND PARTICIPANTS: An unmasked randomized clinical trial involving 3 residential programs operated by 1 nonprofit treatment organization in the Midwestern United States and 2 residential programs operated by 1 nonprofit organization in the Northeastern United States. In total, 349 patients who met the criteria for DSM-IV alcohol dependence when they entered residential treatment were randomized to treatment as usual (n = 179) or treatment as usual plus a smartphone (n = 170) with the Addiction-Comprehensive Health Enhancement Support System (A-CHESS), an application designed to improve continuing care for alcohol use disorders. INTERVENTIONS: Treatment as usual varied across programs; none offered patients coordinated continuing care after discharge. A-CHESS provides monitoring, information, communication, and support services to patients, including ways for patients and counselors to stay in contact. The intervention and follow-up period lasted 8 and 4 months, respectively. MAIN OUTCOMES AND MEASURES: Risky drinking days--the number of days during which a patient's drinking in a 2-hour period exceeded 4 standard drinks for men and 3 standard drinks for women, with standard drink defined as one that contains roughly 14 g of pure alcohol (12 oz of regular beer, 5 oz of wine, or 1.5 oz of distilled spirits). Patients were asked to report their risky drinking days in the previous 30 days on surveys taken 4, 8, and 12 months after discharge from residential treatment. RESULTS: For the 8 months of the intervention and 4 months of follow-up, patients in the A-CHESS group reported significantly fewer risky drinking days than did patients in the control group, with a mean of 1.39 vs 2.75 days (mean difference, 1.37; 95% CI, 0.46-2.27; P = .003). CONCLUSIONS AND RELEVANCE: The findings suggest that a multifeatured smartphone application may have significant benefit to patients in continuing care for alcohol use disorders. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT01003119.

Predictive modeling of addiction lapses in a mobile health application.

The chronically relapsing nature of alcoholism leads to substantial personal, family, and societal costs. Addiction-comprehensive health enhancement support system (A-CHESS) is a smartphone application that aims to reduce relapse. To offer targeted support to patients who are at risk of lapses within the coming week, a Bayesian network model to predict such events was constructed using responses on 2,934 weekly surveys (called the Weekly Check-in) from 152 alcohol-dependent individuals who recently completed residential treatment. The Weekly Check-in is a self-monitoring service, provided in A-CHESS, to track patients' recovery progress. The model showed good predictability, with the area under receiver operating characteristic curve of 0.829 in the 10-fold cross-validation and 0.912 in the external validation. The sensitivity/specificity table assists the tradeoff decisions necessary to apply the model in practice. This study moves us closer to the goal of providing lapse prediction so that patients might receive more targeted and timely support.

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.

Potential roles for new communication technologies in treatment of addiction.

Information and communication technologies offer clinicians the opportunity to work with patients to manage chronic conditions, including addiction. The early research on the efficacy of electronic treatment and support tools is promising. Sensors have recently received increased attention as key components of electronic treatment and recovery management systems. Although results of the research are very promising, concerns at the clinical and policy level must be addressed before widespread adoption of these technologies can become practical. First, clinicians must adapt their practices to incorporate a continuing flow of patient information. Second, payment and regulatory systems must make adjustments far beyond what telemedicine and electronic medical records have required. This paper examines potential roles of information and communication technologies as well as process and regulatory challenges.

An e-health solution for people with alcohol problems.

Self-management of chronic diseases has been a research focus for years. Information and communication technologies (ICTs) have played a significant role in aiding patients and their families with that management task. The recent dramatic increase in smartphone capabilities has expanded the potential of these technologies by facilitating the integration of features specific to cell phones with advanced capabilities that extend the reach of what type of information can be assessed and which services can be provided. A recent review of the literature covering the use of ICTs in managing chronic diseases, including addiction, has examined the effectiveness of ICTs, with an emphasis on technologies tested in randomized controlled trials. One example of an addiction-relapse prevention system currently being tested is the Alcohol Comprehensive Health Enhancement Support System (A-CHESS) Program.

Explicating an evidence-based, theoretically informed, mobile technology-based system to improve outcomes for people in recovery for alcohol dependence.

Post-treatment relapse to uncontrolled alcohol use is common. Currently available communication technology can use existing models for relapse prevention to cost-effectively improve long-term relapse prevention. This paper describes: (1) research-based elements of alcohol consumption-related relapse prevention and how they can be encompassed in self-determination theory (SDT) and Marlatt's cognitive behavioral relapse prevention model, (2) how technology could help address the needs of people seeking recovery, (3) a technology-based prototype, organized around sexual transmitted disease and Marlatt's model, and (4) how we are testing a system based on the ideas in this article and related ethical and operational considerations.