Analysis of physiological data from long- term physical exercise: A basis for improving the health of rural residents of Korea

BACKGROUND: Analysis of the change pattern of a user’s physiological signals collected over a long time and user health information prediction technology based on personal history data can provide the user with accurate health information. OBJECTIVE: In this study, we analyzed the change pattern of health information that was altered by performing personal training (PT) at the beginning of the study and subsequently quitting exercise and only performing activities of daily living (ADL). METHODS: The subject performed PT for 75 days, and then consistently performed relatively simple ADL, such as standing, sitting, and walking, for 325 days that followed. After 400 days, the health information was analyzed through the change patterns of various physiological signals. Personal exercise information and heart rate data were collected through a smart watch (Apple watch Nike [Formula: see text] , Apple Inc.) and the obesity parameters (i.e. information on the weight, body fat fate, and body muscle rate) were measured using a smart scale (PICOOC S1 Pro, EasySolar). RESULTS: We found that the weight and body fat rate linearly decreased and the body muscle rate increased for the first 75 days when hard training was performed whereas they repeatedly increased or decreased when only ADL were performed. As the daily exercise distance increased, weight and body fat rate tended to decrease while body muscle rate tended to increase. There was no significant change in the weight, body fat rate, and body muscle rate while daily activities were performed, but continuous exercise is expected to improve cardiac function by increasing the cardiac output of the heart. CONCLUSION: We expected that the simple physiological data measured with smart watches and smart scales can be used for the health management of the elderly living in rural areas in the future.