TY  - JOUR
AU  - Kumar, Devender
AU  - Haag, David
AU  - Blechert, Jens
AU  - Niebauer, Josef
AU  - Smeddinck, Jan David
PY  - 2025
DA  - 2025/1/24
TI  - Feature Selection for Physical Activity Prediction Using Ecological Momentary Assessments to Personalize Intervention Timing: Longitudinal Observational Study
JO  - JMIR Mhealth Uhealth
SP  - e57255
VL  - 13
KW  - digital health
KW  - behavior change
KW  - tailoring
KW  - personalization
KW  - adaptive systems
KW  - ecological momentary assessments
KW  - sensing
KW  - questionnaires
KW  - machine learning
KW  - feature selection
KW  - situated research
KW  - physical activity
KW  - implementation intentions
KW  - barriers
KW  - intention-behavior gap
KW  - artificial intelligence
KW  - AI
KW  - well-being
KW  - user assessment
KW  - survey
KW  - self-efficacy
KW  - stress
KW  - mood
KW  - emotions
KW  - mobile phone
AB  - 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. 
SN  - 2291-5222
UR  - https://mhealth.jmir.org/2025/1/e57255
UR  - https://doi.org/10.2196/57255
DO  - 10.2196/57255
ID  - info:doi/10.2196/57255
ER  -