Applying machine learning to predict future adherence to physical activity programs.

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

SUBMITTER: Zhou M 

PROVIDER: S-EPMC6704548 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature