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.

Public Health Relevance

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.).

Agency
National Institute of Health (NIH)
Institute
National Institute on Alcohol Abuse and Alcoholism (NIAAA)
Type
Research Project (R01)
Project #
5R01AA023187-05
Application #
9757592
Study Section
National Institute on Alcohol Abuse and Alcoholism Initial Review Group (AA)
Program Officer
Hagman, Brett Thomas
Project Start
2015-09-01
Project End
2020-08-31
Budget Start
2019-09-01
Budget End
2020-08-31
Support Year
5
Fiscal Year
2019
Total Cost
Indirect Cost
Name
Harvard University
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
082359691
City
Cambridge
State
MA
Country
United States
Zip Code
02138
Nahum-Shani, Inbal; Smith, Shawna N; Spring, Bonnie J et al. (2018) Just-in-Time Adaptive Interventions (JITAIs) in Mobile Health: Key Components and Design Principles for Ongoing Health Behavior Support. Ann Behav Med 52:446-462
Walton, Ashley; Nahum-Shani, Inbal; Crosby, Lori et al. (2018) Optimizing Digital Integrated Care via Micro-Randomized Trials. Clin Pharmacol Ther 104:53-58
Nahum-Shani, Inbal; Dziak, John J; Collins, Linda M (2018) Multilevel factorial designs with experiment-induced clustering. Psychol Methods 23:458-479
Almirall, Daniel; Kasari, Connie; McCaffrey, Daniel F et al. (2018) Developing Optimized Adaptive Interventions in Education. J Res Educ Eff 11:27-34
Bidargaddi, Niranjan; Almirall, Daniel; Murphy, Susan et al. (2018) To Prompt or Not to Prompt? A Microrandomized Trial of Time-Varying Push Notifications to Increase Proximal Engagement With a Mobile Health App. JMIR Mhealth Uhealth 6:e10123
Rabbi, Mashfiqui; Aung, Min Sh; Gay, Geri et al. (2018) Feasibility and Acceptability of Mobile Phone-Based Auto-Personalized Physical Activity Recommendations for Chronic Pain Self-Management: Pilot Study on Adults. J Med Internet Res 20:e10147
Boruvka, Audrey; Almirall, Daniel; Witkiewitz, Katie et al. (2018) Assessing Time-Varying Causal Effect Moderation in Mobile Health. J Am Stat Assoc 113:1112-1121
Crane, Harry; Dempsey, Walter (2018) EDGE EXCHANGEABLE MODELS FOR INTERACTION NETWORKS. J Am Stat Assoc 113:1311-1326
Rabbi, Mashfiqui; Philyaw Kotov, Meredith; Cunningham, Rebecca et al. (2018) Toward Increasing Engagement in Substance Use Data Collection: Development of the Substance Abuse Research Assistant App and Protocol for a Microrandomized Trial Using Adolescents and Emerging Adults. JMIR Res Protoc 7:e166
Klasnja, Predrag; Smith, Shawna; Seewald, Nicholas J et al. (2018) Efficacy of Contextually Tailored Suggestions for Physical Activity: A Micro-randomized Optimization Trial of HeartSteps. Ann Behav Med :

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