Digital physiological biomarkers predict within-person symptom changes in complex chronic illness.

Abstract

Altered heart‑rate variability (HRV) and resting heart rate (HR) are common in many complex chronic conditions. Mobile and wearable technologies now provide real-time, valid measurements of HRV and HR, advancing symptom monitoring and management. The current study integrates a 60-s morning PPG assessment with evening symptom severity reports, yielding a high-density mobile health dataset (n = 4244) with an average of 125 biometric observations per participant. We examined whether within-person fluctuations in HR, HRV, and respiratory rate predicted daily changes in crash, fatigue, and brain fog symptoms and secondarily evaluated model predictive performance. Model fit and variance explained were highest in models that included morning biometrics in addition to prior-day symptom reports and covariates. Within-person increases in HR and decreases in HRV in the morning were associated with worsening symptom reports in the evening. Walk-forward cross-validation showed a statistically significant improvement in model performance when morning biometrics were added to prior-day symptom reports (AUC = 0.82-0.85 vs. 0.73-0.83). These findings represent the prospective utility of mobile health tools for precision monitoring and prediction of real-time symptom exacerbations in complex chronic illness.