Machine Learning for the Analysis of Healthy Lifestyle Data: Scoping Review and Guidelines
Abstract
Background: Advances in data science and technology have transformed lifestyle research by enabling the integration of multimodal information and the generation of large-scale datasets. Despite the growing interest in machine learning (ML) within health behavior research, significant methodological gaps remain.
Objective: The study aims to systematically review the applications of supervised ML algorithms in the analysis of healthy lifestyle data, with a particular focus on the methodological approaches used. The specific objectives are to explore the types and sources of data used for health outcomes, examine the ML processes used, including explainable artificial intelligence (XAI) methods, and review the software tools used. Additionally, this review aims to provide practical guidelines to enhance the quality and transparency of future ML research in health.
Methods: Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) recommendations, the search was conducted across PubMed, PsycINFO, and Web of Science, yielding 65 studies that met the inclusion criteria.
Results: Most studies (48/65, 74%) integrated multidomain data from physical activity, diet, sleep, and stress. Data sources were split between self-acquired data (33/65, 51%) and health repositories (32/65, 49%). Single-item measurements were common, particularly for physical activity, diet, and sleep. Although 40 of 65 studies used a multimodel…
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