@Article{info:doi/10.2196/57255,
author="Kumar, Devender
and Haag, David
and Blechert, Jens
and Niebauer, Josef
and Smeddinck, Jan David",
title="Feature Selection for Physical Activity Prediction Using Ecological Momentary Assessments to Personalize Intervention Timing: Longitudinal Observational Study",
journal="JMIR Mhealth Uhealth",
year="2025",
month="Jan",
day="24",
volume="13",
pages="e57255",
keywords="digital health; behavior change; tailoring; personalization; adaptive systems; ecological momentary assessments; sensing; questionnaires; machine learning; feature selection; situated research; physical activity; implementation intentions; barriers; intention-behavior gap; artificial intelligence; AI; well-being; user assessment; survey; self-efficacy; stress; mood; emotions; mobile phone",
abstract="Background: There has been a surge in the development of apps that aim to improve health, physical activity (PA), and well-being through behavior change. These apps often focus on creating a long-term and sustainable impact on the user. Just-in-time adaptive interventions (JITAIs) that are based on passive sensing of the user's current context (eg, via smartphones and wearables) have been devised to enhance the effectiveness of these apps and foster PA. JITAIs aim to provide personalized support and interventions such as encouraging messages in a context-aware manner. However, the limited range of passive sensing capabilities often make it challenging to determine the timing and context for delivering well-accepted and effective interventions. Ecological momentary assessment (EMA) can provide personal context by directly capturing user assessments (eg, moods and emotions). Thus, EMA might be a useful complement to passive sensing in determining when JITAIs are triggered. However, extensive EMA schedules need to be scrutinized, as they can increase user burden. Objective: The aim of the study was to use machine learning to balance the feature set size of EMA questions with the prediction accuracy regarding of enacting PA. Methods: A total of 43 healthy participants (aged 19‐67 years) completed 4 EMA surveys daily over 3 weeks. These surveys prospectively assessed various states, including both motivational and volitional variables related to PA preparation (eg, intrinsic motivation, self-efficacy, and perceived barriers) alongside stress and mood or emotions. PA enactment was assessed retrospectively via EMA and served as the outcome variable. Results: The best-performing machine learning models predicted PA engagement with a mean area under the curve score of 0.87 (SD 0.02) in 5-fold cross-validation and 0.87 on the test set. Particularly strong predictors included self-efficacy, stress, planning, and perceived barriers, indicating that a small set of EMA predictors can yield accurate PA prediction for these participants. Conclusions: A small set of EMA-based features like self-efficacy, stress, planning, and perceived barriers can be enough to predict PA reasonably well and can thus be used to meaningfully tailor JITAIs such as sending well-timed and context-aware support messages. ",
issn="2291-5222",
doi="10.2196/57255",
url="https://mhealth.jmir.org/2025/1/e57255",
url="https://doi.org/10.2196/57255"
}