Wearable-Derived Physiological Features Associated with Caregiver Burden: A Fitbit-Based Observational Study Toward Remote Monitoring of Caregiver Health Risk

Caregiver burden is a critical public health issue among individuals providing long-term care for family members with cognitive impairment and is closely associated with physiological stress. However, conventional assessments primarily rely on self-reported measures, which provide limited insight into underlying physiological processes and temporal dynamics. To address this limitation, we investigate the use of wearable-derived physiological and behavioral signals for the objective assessment of caregiver burden. We analyzed multimodal data collected from 78 informal caregivers using Fitbit devices, including heart rate, activity patterns, sleep architecture, and variability-based features aggregated over a 7-day window. Associations with caregiver burden, measured by the Zarit Burden Interview (ZBI), were evaluated using Spearman correlation with false discovery rate correction and multivariable regression models adjusted for demographic and clinical covariates. Variability in REM sleep duration showed an exploratory negative association with caregiver burden (ρ = −0.32, p = 0.004), with similar but weaker nominal patterns observed for light and total sleep variability. In multivariable analysis, maximum daily heart rate was positively associated with caregiver burden (β = 2.59, p = 0.005), although overall model performance was modest (R2 = 0.32, adjusted R2 = 0.08) and the full model did not reach statistical significance. These findings demonstrate that multimodal wearable signals may provide complementary objective insights into caregiver burden and support the evaluation of sleep variability and heart rate measures as candidate indicators.