Towards Smart Wearables as Digital Metabolic Twins: Algorithmic Estimation of Nutrient Depletion During Exercise

Wearable devices and health applications have transformed the monitoring of exercise performance, providing metrics such as heart rate, step count, distance travelled, calories burned, and recovery time. While current algorithms primarily estimate energy expenditure, long-standing evidence suggests that exercise induces systematic depletion of electrolytes, glycogen, vitamins, and other nutrients. Integrating physiological models with wearable sensor data could enable the estimation of these metabolic parameters, effectively creating a digital metabolic twin. This opinion explores the current advances in wearable monitoring, highlights the physiological basis for nutrient depletion, and discusses the feasibility of algorithmic approximation of broader metabolic changes. Incorporating these capabilities into wearable technology offers a new avenue for precision exercise physiology, with potential applications in personalized nutrition, training optimization, and disease prevention.