Multimodal Prodromal Patterns Associated with COPD Exacerbations in Home Telemonitoring: An Exploratory Observational...

Characterizing multi-parameter physiological, acoustic, and environmental changes before acute chronic obstructive pulmonary disease exacerbations (AECOPD) is crucial for improving home telemonitoring and enabling timely clinical intervention. In a 12-month prospective observational exploratory pilot study (N=16), a multimodal Internet of Things (IoT) ecosystem collected daily data on symptoms, lung function, cardiorespiratory dynamics, actigraphy, sleep, fine motor skills, bioacoustics, and indoor air quality. From 427 daily features across 4738 patient-days, collinearity mitigation retained 245 independent features. Linear mixed-effects models (LMM), nonparametric paired tests, and multi-timescale exponential moving average smoothing quantified prodromal trajectories and univariate discrimination during the 7-day pre-exacerbation window. Engagement analysis showed a dual hierarchy: passive background monitoring had high retention (>72 %), while demanding active tasks like peak flow spirometry dropped to 26.9 % compliance. Patients with frequent exacerbations showed higher reactive adherence. LMM frameworks identified a multisystem prodromal footprint marked by coordinated increases in cough frequency, sputum purulence, dyspnea, and resting heart rate. Acoustic measures showed notable pre-event changes in higher-order speech cepstral coefficients, voice-intensity variability, and cough spectral dispersion, providing objective proxies for airway limitation. Indoor environmental signals suggested domestic confinement but weakened after adjustment for macro-seasonal confounders. Multi-temporal kinetics demonstrated that acute physiological symptoms peaked within 72 h, whereas acoustic features required 3-to-7-day temporal smoothing (EMA3-EMA7) to optimize signal-to-noise ratios. In conclusion, these preliminary observations in the exploratory small cohort suggest that the transition from a stable baseline to the prodromal AECOPD phase is marked by a quantifiable, multi-systemic digital footprint across cardiorespiratory, bioacoustic, and symptom domains. Although requiring validation in broader populations, findings highlight the potential of multi-tiered digital health architectures that pair continuous passive sensing with low-friction active reporting to overcome task attrition and secure the uninterrupted longitudinal baselines needed for automated early-warning systems.