Abstract
OBJECTIVE: Healthy life expectancy is conventionally calculated at the population level, with no standardized approach for estimating it for individuals. Despite the increasing availability of personal health records (PHRs), comprehensive personalized health indicators remain scarce. This study aimed to develop a framework for estimating personal healthy life expectancy using PHR data. METHODS: We used the combined dataset of the Comprehensive Survey of Living Conditions and the National Health and Nutrition Survey conducted for randomly sampled general population in Japan, 2019. From the merged dataset, 5552 respondents were included for the analysis. Machine learning models were constructed to predict activity limitation-a key determinant of healthy life expectancy-using age, sex, disease history, blood test results, and lifelog variables (blood pressure, body mass index, waist circumference, daily step counts, and sleep patterns). Subsequently, an equation was derived for estimating personal healthy life expectancy through a mathematical algorithm. RESULTS: The prediction model achieved an area under the receiver operating characteristic curve of 0.84. Personal healthy life expectancy was estimated using a derived equation in which an individual's probability of having no activity limitation, relative to their age group average, was scaled by the population-level healthy life expectancy. CONCLUSION: We developed a novel framework for estimating personal healthy life expectancy from PHR data, incorporating both lifelog data and blood biomarkers, by machine-learning and mathematical approach. The application of this individualized health metric may advance personalized medicine, preventive health strategies, and tailored health guidance, while serving as a behavioral nudge to promote healthier lifestyles. It should be noted, however, that because the framework is derived from cross-sectional data, it does not estimate when future activity limitations may occur.