Importance: Patients leaving treatment for alcohol-use disorders (AUDs) 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.
Post treatment relapse to uncontrolled alcohol use is common. More cost-effective approaches are needed. We believe 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 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 Self Determination Theory 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.
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.
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.
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