University of Michigan
The aim of this study is to evaluate the efficacy of using a reinforcement learning algorithm to determine the optimal content of a mobile health intervention (message delivered via smartphone) for improving the mood, physical activity, and sleep of medical interns.
Due to their high workloads, less sleep and physical activity and other stressors, medical interns suffer from depression at higher rates than the general population. The goal of this study is to evaluate the efficacy of a mobile health intervention intending to help prevent the degradation of health behaviors and the development of depression. The intervention sends mobile phone notifications which aim to help interns improve their mood, maintain physical activity, and obtain adequate sleep during their internship year. A reinforcement learning algorithm will use prior survey, daily mood, and…
Inclusion Criteria: * Medical intern during the 2025-2026 internship year * iPhone or Android phone user * Completed the Intern Health Study consent and baseline survey by June 20 prior to the start of intern year Exclusion Criteria: * None
The study's mobile app will be used to deliver push notifications. The notifications appear on the participant's phone lock screen. The notifications include 3 categories: mood notifications, activity notifications, sleep notifications. Mood notifications aim to increase the participant's mood. Activity notifications aim to increase the participant's physical activity. Sleep notifications aim to increase the participant's sleep duration. All notifications are categorized as one of five therapeutic approaches: 1) CBT-Behavioral, 2) CBT-Cognitive, 3) Distanced Self-Talk, 4) Mindfulness, 5) Motivational Interviewing.
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