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BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2019

Open Access 01-12-2019 | Research article

Applying machine learning to predict future adherence to physical activity programs

Authors: Mo Zhou, Yoshimi Fukuoka, Ken Goldberg, Eric Vittinghoff, Anil Aswani

Published in: BMC Medical Informatics and Decision Making | Issue 1/2019

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Abstract

Background

Identifying individuals who are unlikely to adhere to a physical exercise regime has potential to improve physical activity interventions. The aim of this paper is to develop and test adherence prediction models using objectively measured physical activity data in the Mobile Phone-Based Physical Activity Education program (mPED) trial. To the best of our knowledge, this is the first to apply Machine Learning methods to predict exercise relapse using accelerometer-recorded physical activity data.

Methods

We use logistic regression and support vector machine methods to design two versions of a Discontinuation Prediction Score (DiPS), which uses objectively measured past data (e.g., steps and goal achievement) to provide a numerical quantity indicating the likelihood of exercise relapse in the upcoming week. The respective prediction accuracy of these two versions of DiPS are compared, and then numerical simulation is performed to explore the potential of using DiPS to selectively allocate financial incentives to participants to encourage them to increase physical activity.

Results

we had access to a physical activity trial data that were continuously collected every 60 sec every day for 9 months in 210 participants. By using the first 15 weeks of data as training and test on weeks 16–30, we show that both versions of DiPS have a test AUC of 0.9 with high sensitivity and specificity in predicting the probability of exercise adherence. Simulation results assuming different intervention regimes suggest the potential benefit of using DiPS as a score to allocate resources in physical activity intervention programs in reducing costs over other allocation schemes.

Conclusions

DiPS is capable of making accurate and robust predictions for future weeks. The most predictive features are steps and physical activity intensity. Furthermore, the use of DiPS scores can be a promising approach to determine when or if to provide just-in-time messages and step goal adjustments to improve compliance. Further studies on the use of DiPS in the design of physical activity promotion programs are warranted.

Trial registration

ClinicalTrials.gov NCT01280812 Registered on January 21, 2011.
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Metadata
Title
Applying machine learning to predict future adherence to physical activity programs
Authors
Mo Zhou
Yoshimi Fukuoka
Ken Goldberg
Eric Vittinghoff
Anil Aswani
Publication date
01-12-2019
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2019
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-019-0890-0

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