TY - JOUR AU - Lee, Young Soon AU - Kim, Yejin AU - Kim, Bomgyeol AU - Lee, Gyu Sang AU - Jang, Suk-Yong AU - Kim, Hyun Tae PY - 2025/6/5 TI - Digital Literacy and Its Association With Subjective Health Status and Healthy Lifestyle Behaviors Among Korean Older Adults: Cross-Sectional Study JO - JMIR Aging SP - e64974 VL - 8 KW - digital literacy KW - healthy lifestyle behaviors KW - older adults KW - subjective health status KW - quality of life N2 - Background: With an aging population driven by advances in medical technology, digital literacy has become essential for improving the quality of life of older adults, enhancing access to health information, and promoting healthy lifestyles. Furthermore, the COVID-19 pandemic may have influenced the subjective health perceptions and healthy lifestyle behaviors of older adults. However, there is limited research exploring the relationship between digital literacy, subjective health perceptions, and healthy lifestyle behaviors in Korea. Objective: This study aimed to investigate digital literacy?s impact on Korean older adults? subjective health status and healthy lifestyle behaviors. Methods: Data of 8664 respondents (aged 65 years and older) from the 2020 National Survey of the Older Koreans were analyzed. Digital literacy was measured based on the use of IT devices (ITDs), difficulty using online information, and inconvenience of ITDs. Statistical analyses, such as the Rao-Scott chi-square test, Wilcoxon rank sum test, and multiple regression analysis, were conducted. Results: Respondents with above-average ITD use (adjusted odds ratio [aOR] 1.73, 95% CI 1.50?1.99) and less difficulty using online information (aOR 1.41, 95% CI 1.24?1.61) had higher odds of perceiving themselves as healthy. Conversely, high difficulty using ITDs was associated with lower odds of respondents perceiving themselves as healthy (aOR 0.84, 95% CI 0.82?0.87). Furthermore, high ITD use predicted engagement in healthy lifestyle behaviors (aOR 1.51, 95% CI 1.33?1.72), whereas high difficulty using ITDs predicted lower odds of engagement (aOR 0.94, 95% CI 0.92?0.97). In contrast, there was no difference in the odds of engaging in healthy lifestyle behaviors regardless of difficulty using online information (aOR 1.03, 95% CI 0.92?1.15). Conclusions: This study underscores the significant association between digital literacy and improved health outcomes among older adults. Promotion of digital literacy and relevant policies is essential to help older adults effectively obtain health information online, thereby improving their quality of life and overall health. UR - https://aging.jmir.org/2025/1/e64974 UR - http://dx.doi.org/10.2196/64974 ID - info:doi/10.2196/64974 ER - TY - JOUR AU - Jeong, Chang-Uk AU - Leiby, S. Jacob AU - Kim, Dokyoon AU - Choe, Kyung Eun PY - 2025/4/11 TI - Artificial Intelligence-Driven Biological Age Prediction Model Using Comprehensive Health Checkup Data: Development and Validation Study JO - JMIR Aging SP - e64473 VL - 8 KW - biological age KW - aging clock KW - mortality KW - artificial intelligence KW - machine learning KW - record KW - history KW - health checkup KW - clinical relevance KW - gerontology KW - geriatric KW - older KW - elderly KW - aging KW - prediction KW - predictive KW - life expectancy KW - AI N2 - Background: The global increase in life expectancy has not shown a similar rise in healthy life expectancy. Accurate assessment of biological aging is crucial for mitigating diseases and socioeconomic burdens associated with aging. Current biological age prediction models are limited by their reliance on conventional statistical methods and constrained clinical information. Objective: This study aimed to develop and validate an aging clock model using artificial intelligence, based on comprehensive health check-up data, to predict biological age and assess its clinical relevance. Methods: We used data from Koreans who underwent health checkups at the Seoul National University Hospital Gangnam Center as well as from the Korean Genome and Epidemiology Study. Our model incorporated 27 clinical factors and employed machine learning algorithms, including linear regression, least absolute shrinkage and selection operator, ridge regression, elastic net, random forest, support vector machine, gradient boosting, and K-nearest neighbors. Model performance was evaluated using adjusted R2 and the mean squared error (MSE) values. Shapley Additive exPlanation (SHAP) analysis was conducted to interpret the model?s predictions. Results: The Gradient Boosting model achieved the best performance with a mean (SE) MSE of 4.219 (0.14) and a mean (SE) R2 of 0.967 (0.001). SHAP analysis identified significant predictors of biological age, including kidney function markers, gender, glycated hemoglobin level, liver function markers, and anthropometric measurements. After adjusting for the chronological age, the predicted biological age showed strong associations with multiple clinical factors, such as metabolic status, body compositions, fatty liver, smoking status, and pulmonary function. Conclusions: Our aging clock model demonstrates a high predictive accuracy and clinical relevance, offering a valuable tool for personalized health monitoring and intervention. The model?s applicability in routine health checkups could enhance health management and promote regular health evaluations. UR - https://aging.jmir.org/2025/1/e64473 UR - http://dx.doi.org/10.2196/64473 ID - info:doi/10.2196/64473 ER -