Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activity

With the recent proliferation of mobile health technologies, health scientists are increasingly interested in developing just-in-time adaptive interventions (JITAIs), typically delivered via notifications on mobile devices and designed to help users prevent negative health outcomes and to promote th...

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Vydáno v:Proceedings of ACM on interactive, mobile, wearable and ubiquitous technologies Ročník 4; číslo 1
Hlavní autoři: Liao, Peng, Greenewald, Kristjan, Klasnja, Predrag, Murphy, Susan
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States 01.03.2020
Témata:
Mobile Health
Reinforcement Learning
Applied computing → Health care information systems
Computing methodologies → Machine learning algorithms
Just-in-Time Adaptive Intervention
ISSN:2474-9567, 2474-9567
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Shrnutí:With the recent proliferation of mobile health technologies, health scientists are increasingly interested in developing just-in-time adaptive interventions (JITAIs), typically delivered via notifications on mobile devices and designed to help users prevent negative health outcomes and to promote the adoption and maintenance of healthy behaviors. A JITAI involves a sequence of decision rules (i.e., treatment policies) that take the user's current context as input and specify whether and what type of intervention should be provided at the moment. In this work, we describe a reinforcement learning (RL) algorithm that continuously learns and improves the treatment policy embedded in the JITAI as data is being collected from the user. This work is motivated by our collaboration on designing an RL algorithm for HeartSteps V2 based on data collected HeartSteps V1. HeartSteps is a physical activity mobile health application. The RL algorithm developed in this work is being used in HeartSteps V2 to decide, five times per day, whether to deliver a context-tailored activity suggestion.
Bibliografie:ObjectType-Article-1
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ISSN:2474-9567
2474-9567
DOI:10.1145/3381007