Association of daily lung condition in COPD patients with wearable speech and physiological data
Bibliographic record
Abstract
Chronic obstructive pulmonary disease (COPD) is a prevalent condition that imposes significant burden on patients and healthcare systems, with COPD exacerbations being a leading cause of hospitalizations and mortality worldwide. Continuous monitoring of lung function is crucial for effective management, yet traditional methods are often cumbersome and require clinic visits. Wearable technology offers a non-invasive alternative, enabling the monitoring of physiological parameters in real-world settings. In this study, we analyzed free-living speech data collected via smartwatches from 18 COPD patients over an average period of 198.9 ± 122.6 days. Utilizing linear mixed-effects models, we discovered that phonation features are negatively associated with adverse COPD outcomes, while prosodic speech features show a positive correlation with increased exacerbation risk. Further analysis revealed that these associations are significantly moderated by physiological covariates such as heart rate variability and physical activity levels. These findings highlight the complex interplay between respiratory function, autonomic regulation, and vocal production, suggesting that the integration of speech analysis with physiological monitoring through wearables can lead to the development of composite digital biomarkers of impaired lung function.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".