Fig. 1: Study framework for physical activity patterns and mortality risk analysis in people with epilepsy.

a Study population including 1167 people with epilepsy and 97,394 controls with 7-day accelerometer data. b Mortality ascertainment for all-cause and cause-specific deaths including cardiovascular, cancer, and epilepsy-related mortality. c Comprehensive demographic and clinical characteristics collection including age, education, smoking status, alcohol use, BMI, overall health, disability status, and Charlson Comorbidity Index. d Physical activity exposure measurements including daily sedentary duration, light PA duration, and MVPA duration (min/day). e Primary analysis incorporating temporal distribution analysis and standardized mortality ratio analysis across groups. f In-depth statistical analysis including correlation analysis of PA patterns with mortality outcomes, proportional hazards regression for all-cause and cause-specific mortality, dose-response analysis, and optimal PA threshold determination across demographic subgroups. g Machine learning framework for mortality prediction using GridSearchCV for kernel SVM with feature extraction, model development and interpretable analysis incorporating PA pattern features (sedentary, light PA, and MVPA duration) and signal features (statistical, time domain, and frequency domain features). h EpiActivity Manager design for PA management and health monitoring in people with epilepsy using smartwatch technology.