Integrating IoT and advanced sensors has the potential to transform digital health management. A main problem in digital health management is fragmented device interoperability and the absence of standardized frameworks that simultaneously account for physiological adaptations and behavioral adherence. To address these limitations, a multi‑parametric simulation framework was implemented in aerobic exercise to evaluate across 1000 heterogeneous individual profiles over 24 sessions. The results of the framework implementation showed that the IoT‑assisted program achieved a 14.8% higher cumulative energy expenditure (8.81 million kcal, 7.67 million kcal in the traditional program) and an adjusted exercise intensity of 4.8–8.4 metabolic equivalents, and sustained a mean adherence rate of 88.4% (76.1% in the traditional program). A Kolmogorov–Smirnov test showed that the IoT‑assisted program constrained cardiovascular exertion within the optimal 60–80% of the maximum heart rate. The established closed‑loop data‑fusion architecture that integrates multimodal sensor data of electrocardiogram, photoplethysmography, and inertial measurement units with adaptive load‑scaling algorithms enables scalable and safe personalization. Although this study was conducted on the basis of secondary and simulated data, and long‑term physiological markers were not included, the results provide a reference for advancing sensor accuracy, interoperability, and AI‑driven analytics in digital health and fitness applications.