Multimodal Prodromal Patterns Associated with COPD Exacerbations in Home Telemonitoring: An Exploratory Observational Study.
- 1Department of Automation Engineering, Electronics and Computer Architecture and Networks, School of Engineering, University of Cadiz, 11519 Puerto Real, Spain.
- 2Instituto de Investigación e Innovación Biomédica de Cádiz (INiBICA), 11009 Cádiz, Spain.
- 3Pulmonology Department, Puerta del Mar University Hospital, 11009 Cádiz, Spain.
Abstract
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.
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BibTeX
@article{sanchezmorillo2026multimodal,
title = {{Multimodal Prodromal Patterns Associated with COPD Exacerbations in Home Telemonitoring: An Exploratory Observational Study.}},
author = {Daniel Sanchez-Morillo and Patricia Camacho-Magriñán and Antonio León-Jiménez},
journal = {Bioengineering (Basel, Switzerland)},
year = {2026},
month = oct,
doi = {10.3390/bioengineering13101195},
url = {https://europepmc.org/article/MED/42939050},
note = {CC BY},
}
RIS
TY - JOUR
TI - Multimodal Prodromal Patterns Associated with COPD Exacerbations in Home Telemonitoring: An Exploratory Observational Study.
AU - Daniel Sanchez-Morillo
AU - Patricia Camacho-Magriñán
AU - Antonio León-Jiménez
PY - 2026
DA - 2026/10/14
JO - Bioengineering (Basel, Switzerland)
DO - 10.3390/bioengineering13101195
UR - https://europepmc.org/article/MED/42939050
AB - 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.
SN - 2306-5354
ER -