Wearable sensor-derived continuous physical activity dimensions and all-cause mortality in adults with diabetes and hypertension: insights from two population-based accelerometry cohorts.

AIMS/INTRODUCTION: Adults with coexistent diabetes mellitus and hypertension (DM-HTN) have a substantially elevated risk of all-cause mortality. Physical activity (PA) is a key modifiable factor, yet conventional self-reported or categorical measures may fail to capture clinically relevant variation. We investigated associations between sensor-derived continuous PA dimensions and all-cause mortality in DM-HTN. MATERIALS AND METHODS: We analysed 4,294 DM-HTN and 1,482 diabetes-alone adults from the UK Biobank (median follow-up 85.2 months; 731 deaths) and 530 DM-HTN and 235 diabetes-alone participants from NHANES 2011-2014 (mean follow-up 63.5 months; 123 deaths), all with device-measured wrist accelerometry. Continuous sensor-derived metrics included Daily MET/MIMS (volume) and Peak-30 MET/MIMS (peak intensity). Associations with all-cause mortality were examined using multivariable Cox regression, restricted cubic spline modelling, and GBDT models with SHAP attribution. RESULTS: Both continuous PA dimensions were independently associated with all-cause mortality in the DM-HTN group across cohorts. In the UK Biobank, fully adjusted hazard ratios were 0.66 for Daily MET and 0.43 for Peak-30 MET. In NHANES, Peak-30 MIMS showed robust associations (HR = 0.57, p < 0.001), while Daily MIMS attenuated to non-significance (HR = 0.95, p = 0.116) after full adjustment. Non-linear dose-response modelling identified lower inflection points for peak intensity in DM-HTN compared with diabetes-alone individuals. SHAP analyses confirmed that continuous peak intensity maintained stable protective contributions across comorbidity strata. CONCLUSIONS: Wearable sensor-derived continuous PA dimensions capture mortality risk gradients in DM-HTN that are inaccessible to conventional approaches. Population-specific, clinically attainable inflection thresholds identified through continuous monitoring support incorporating sensor-derived PA metrics as digital biomarkers in comorbidity-specific risk stratification frameworks.