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Machine Learning for the Analysis of Healthy Lifestyle Data: Scoping Review and Guidelines
Tony Estrella1,2, Lluis Capdevila1,2, Carla Alfonso1,2
1Sport Research Institute, Universitat Autònoma de Barcelona, Bellaterra, Spain.
JMIR Human Factors
|March 3, 2026
Summary
This review highlights gaps in applying machine learning (ML) to healthy lifestyle data, emphasizing the need for rigorous data acquisition and explainability. Recommendations include using multidomain data and explainable AI (XAI) for personalized health insights.
Area of Science:
- Data Science & Health Behavior Research
- Machine Learning Applications in Lifestyle Studies
Background:
- Advances in data science enable multimodal information integration for large-scale lifestyle research.
- Despite growing interest, significant methodological gaps persist in applying machine learning (ML) to health behavior research.
