Adding morning biometric data to prior-day symptom reports significantly improved the prediction of evening symptom exacerbations, increasing model AUC from 0.73-0.83 to 0.82-0.85.
Observational (n=4,244)
Does daily morning biometric monitoring improve the prediction of evening symptom exacerbations in people with complex chronic illnesses?
Daily morning biometric monitoring of heart rate and heart rate variability using mobile health tools provides incremental, statistically significant improvements in predicting real-time symptom exacerbations in individuals with complex chronic illnesses.
Effect estimate: AUC 0.82-0.85
p-value: p=<0.001
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.
Aitken et al. (Tue,) conducted a observational in Complex chronic illnesses (Long COVID, ME/CFS) (n=4,244). Morning biometric monitoring (HR, HRV, RR) vs. Prior-day symptom reports alone was evaluated on Prediction of evening symptom severity (crash, fatigue, brain fog) (AUC 0.82-0.85, p=<0.001). Adding morning biometric data to prior-day symptom reports significantly improved the prediction of evening symptom exacerbations, increasing model AUC from 0.73-0.83 to 0.82-0.85.