Real-time Associations between Digital Indicators and Daily Pain and Depressive Symptoms among Korean Older Adults

Abstract Despite the importance of personalized digital healthcare services for addressing pain and emotional health in older adults, the development of digital healthcare interventions has been limited by a lack of evidence on real-time predictors of pain and emotional health among older adults. This study examined whether digital sensing data on heart rate variability, sleep quality, and physical activity could predict same-day or next-day pain and depressive symptoms among socially vulnerable older adults. As part of a larger project evaluating the efficacy of a digital healthcare platform integrated with a public community-based care service, older adult care recipients (n = 35; Mean age = 78.03, SD = 4.10; 80% women) and their community caregivers (n = 16) participated in a 6-week trial. Depressive symptoms were assessed daily using the 9-item Patient Health Questionnaire via scripted chatbot interviews, while pain was measured using a Visual Analogue Scale. Digital biomarkers—including heart rate variability, sleep, and physical activity—were collected via a continuously worn wearable sensor (Fitbit). Multilevel modeling revealed that within-person fluctuations in heart rate variability, mean of normal-to-normal intervals (Mean NNI; p = .016), and moderate physical activity (p = .001) were associated with same-day pain. Changes in sleep duration (p = .031) and efficiency (p = .053) were linked to same-day depressive symptoms. Lagged models further showed that Mean NNI (p = .031) and sleep fragmentation (p = .027) predicted next-day pain. These findings highlight the potential of real-time wearable data to inform personalized pain and mental health management for older adults.