Early Detection and Remote Care of Frailty for Older Adults Living in Sparsely Populated Rural Areas in Hokkaido,...

The global increase in the number of older people aged 65 and over is causing concern in healthcare and social systems. The lack of health and welfare services and human resources may delay the assessment of frailty and other geriatric syndromes. However, the participation in community-led frailty check-ups remains low, and there are limitations in providing a preventive home visit approach to all residents. Therefore, efficient remote frailty assessment and care systems to support older people in sparsely populated rural areas are needed. This case study investigated the efficacy of an artificial intelligence (AI)-assisted monitoring system coupled with remote multidisciplinary care in addressing frailty among older adults in a depopulated region of Hokkaido, Japan. An 85-year-old woman, residing in a sparsely populated rural city with a significantly high aging rate, was assessed for frailty using the revised Japanese version of the Cardiovascular Health Study criteria. In addition, three aspects of frailty were assessed: physical frailty, cognitive/psychological frailty, and social frailty. Baseline assessments indicated prefrailty, diminished mobility, depressive symptoms, low subjective well-being, and social isolation. An AI-assisted monitoring camera was installed in the participant's living space to provide continuous behavioral analysis. Based on the abovementioned information, multidisciplinary remote care was provided. Over two months, the system identified mobility challenges and prolonged sedentary behaviors, despite no falls or emergencies. In remote care, multidisciplinary teams suggest exercises and environmental adjustments to improve physical activity and activities of daily living, as well as social participation to maintain a sense of purpose and roles in life. Through these interventions, three months after baseline, while her physical frailty progressed, her psychological well-being and social emotional support showed improvements. Notably, she expressed a sense of security with the presence of the monitoring system and appreciated the remote care advice, highlighting its role in alleviating feelings of isolation. This case demonstrates the potential of integrating an AI-assisted monitoring system with remote care to mitigate the multi-dimensional effects of frailty in aging populations, particularly in regions facing disparities in healthcare access. Our findings suggest that such systems can provide valuable insights into daily behaviors, facilitate tailored interventions, and foster a sense of safety among older adults living in sparsely populated rural areas.