A Review of Behavioral, Dietary, and Fitness Data Analyses in Health-Related Research

Behavioral, dietary, and physical activity patterns are quantifiable determinants of morbidity and mortality. This review synthesizes peer-reviewed evidence on the collection, processing, and analysis of lifestyle-related data in health research, with emphasis on population-level surveillance and individual-level monitoring. Data modalities include digital behavioral traces (web queries, social media, mobility records), dietary assessments (diet quality indices, image-assisted food records, intervention trials), and objectively measured physical activity (wearable sensors, accelerometry). Empirical findings demonstrate associations between modifiable lifestyle factors and health outcomes, quantified through large-scale observational studies and meta-analyses. Specific case studies include infodemiological analyses of collective online behavior during the COVID-19 outbreak, ketogenic diet trials for type~2 diabetes glycemic control, and machine learning–based modeling of fitness tracker outputs for training effect prediction. The review integrates methodological advances in data acquisition, analytical modeling, and cross-domain evidence synthesis, providing a consolidated academic perspective on the role of multi-source lifestyle data in health-related research.