Background/Objectives: Comprehensive Geriatric Assessment (CGA) is essential for maintaining quality of life (QOL) and independence in older adults. Still, its implementation is labor-intensive and difficult to sustain in aging societies such as Japan. Digital technologies may enable continuous, scalable CGA in daily living environments. This study aimed to develop and preliminarily evaluate a digital CGA (D-CGA) framework by integrating data from multiple monitoring devices, as a preparatory step toward Artificial Intelligence (AI)-supported personalized care planning. Methods: Four devices (Handy, Apple Watch, Withings Sleep, and Vieureka) were selected. Due to ethical constraints in Japan, a pilot study was conducted with graduate students. Participants underwent continuous monitoring for five weekdays. Common and device-specific measurement items were extracted, visualized, and compared across devices. Heart rate data were examined using correlation-based analyses. Baseline CGA was conducted before monitoring. Results: Distributional and temporal characteristics of physiological measures were explored separately for daytime and nocturnal periods. Continuous heart rate and respiratory rate data were successfully collected across monitoring days, demonstrating the feasibility of real-life data acquisition using the selected devices. Heart and respiratory rates showed distinct distributional patterns between daytime and nocturnal periods, supporting context-specific physiological characterization. Conclusions: This pilot study demonstrates the feasibility of integrating multi-device data for D-CGA and provides foundational reference data for future studies of older adults. The results support the potential of D-CGA to inform personalized care and guide subsequent large-scale and clinical investigations.
Park et al. (Thu,) studied this question.