The purpose of this proposal is develop, and bring to fruition, methods for using data to optimize mobile interventions aimed at preventing, treating and supporting the recovery from alcohol use disorders. Social and personal costs of alcohol use disorders in the US are very high, yet relatively few Americans with alcohol use disorders receive treatment. Interventions delivered via mobile devices, such as smartphones, can increase the availability and decrease the cost of both preventing, treating and providing support for individuals in recovery. At this time data has seen little use in optimizing the timing and selection of behavioral interventions that can be delivered via mobile devices. The goal of this project is (1) to develop and evaluate data analysis methods and optimization algorithms that can reside on the mobile device and that, as an individual experiences the mobile intervention and provides responses, will optimize the timing and selection of the behavioral intervention to the individual; (2) to develop data analysis methods and optimization algorithms that can be used following a clinical study involving the mobile intervention to further optimize the intervention; and (3) to disseminate and illustrate the developed methods and algorithms to the clinical science community so as to maximize clinical impact. We propose to conduct the technical development of the data analysis methods/algorithms and to test and refine the developments in a virtual environment using simulators that mimic the data from three different clinical studies involving prevention, treatment and recovery from alcohol use disorders. We will collaborate with clinical scientists on tutorials for these methods as well as disseminate the data analysis methods by conducting symposia and workshops. We will develop initial versions of the data analysis/algorithm software and make the software freely accessible.
The proposed research involves the development and evaluation of data analysis methods that can be used to optimize the selection of interventions as well as the timing of the delivery of the interventions via mobile devices such as smartphones. The goal of these methods is to facilitate improvements in the ability of individuals to combat their problematic excessive drinking and to recover from such drinking behavior. The research team, from the University of Michigan and North Carolina State University, includes experts in statistical methods for using data to optimize treatment sequences (Susan Murphy, Ph.D. and Eric Laber, Ph.D.), experts in computer science methods for using real time data to optimize treatment sequences (Ambuj Tewari, Ph.D.) and in coping, health behavior change and alcohol use (Inbal Nahum-Shani, Ph.D.).
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