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A First Step Towards Behavioral Coaching for Managing Stress: A Case Study on Optimal Policy Estimation with Multi-stage Threshold Q-learning

Xinyu Hu; Pei-Yun S. Hsueh; Ching-Hua Chen; Keith Diaz; Ying Kuen Cheung; Min Qian

Title:
A First Step Towards Behavioral Coaching for Managing Stress: A Case Study on Optimal Policy Estimation with Multi-stage Threshold Q-learning
Author(s):
Hu, Xinyu
Hsueh, Pei-Yun S.
Chen, Ching-Hua
Diaz, Keith
Cheung, Ying Kuen
Qian, Min
Date:
Type:
Conference objects
Department(s):
Center for Behavioral Cardiovascular Health
Biostatistics
Volume:
2017
Persistent URL:
Book/Journal Title:
AMIA Annual Symposium Proceedings
Abstract:
Psychological stress is a major contributor to the adoption of unhealthy behaviors, which in turn accounts for 41% of global cardiovascular disease burden. While the proliferation of mobile health apps has offered promise to stress management, these apps do not provide micro-level feedback with regard to how to adjust one's behaviors to achieve a desired health outcome. In this paper, we formulate the task of multi-stage stress management as a sequential decision-making problem and explore the application of reinforcement learning to provide micro-level feedback for stress reduction. Specifically, we incorporate a multi-stage threshold selection into Q-learning to derive an interpretable form of a recommendation policy for behavioral coaching. We apply this method on an observational dataset that contains Fitbit ActiGraph measurements and self-reported stress levels. The estimated policy is then used to understand how exercise patterns may affect users' psychological stress levels and to perform coaching more effectively.
Subject(s):
Stress management
Cardiovascular system--Diseases--Prevention
Health
Item views
10
Metadata:
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Suggested Citation:
Xinyu Hu, Pei-Yun S. Hsueh, Ching-Hua Chen, Keith Diaz, Ying Kuen Cheung, Min Qian, , A First Step Towards Behavioral Coaching for Managing Stress: A Case Study on Optimal Policy Estimation with Multi-stage Threshold Q-learning, Columbia University Academic Commons, .

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