Paradigm. Aging is conceptualized as a total chronic disease (TCD) affecting all multicellular animals and humans from the moment of conception, consistent with ICD-11 codes XT9T “Ageing-related” (2018) and MG2A “Ageing associated decline in intrinsic capacity” (2025). Problem. Current medicine primarily addresses acute exacerbations without continuous monitoring of the primary disease activity. Wearable devices collect physiological signals but lack a theoretical framework linking these signals to fundamental aging processes. Solution—BioSense. We present BioSense, a wearable platform that: (1) estimates aging activity via the χ_Ze index—derived from Ze Vectors Theory—computed from EEG, HRV, respiration, and sleep patterns; (2) provides preliminary estimates of 30-day exacerbation risk (bootstrap-corrected AUC 0.76–0.88 in a prospective pilot cohort, N=150, 9 months; note: this sample size is underpowered for the declared α=0.00025; results are exploratory); (3) follows an open-science framework (preregistration planned at OSF, α=0.00025 requires N≥500 per power analysis); (4) is embedded within the FCLC data infrastructure with five-layer differential privacy (ε=2.0, k-anonymity k≥7). External validation. On All of Us Fitbit data (N=2,222), χ_Ze correlates with PhenoAge (r=0.67, 95% CI: 0.64–0.70, R²=0.45) and detects accelerated aging with AUC=0.81 (95% CI: 0.78–0.84). Note: This is an exploratory analysis, not confirmatory validation, due to absence of pre-registration.