Home monitoring of breathing rate in people with chronic obstructive pulmonary disease: observational study of feasibility, acceptability, and change after exacerbation
Bibliographic record
Abstract
Telehealth programs to promote early identification and timely self-management of acute exacerbations of chronic obstructive pulmonary diseases (AECOPDs) have yielded disappointing results, in part, because parameters monitored (symptoms, pulse oximetry, and spirometry) are weak predictors of exacerbations. PURPOSE: Breathing rate (BR) rises during AECOPD and may be a promising predictor. Devices suitable for home use to measure BR have recently become available, but their accuracy, acceptability, and ability to detect changes in people with COPD is not known. PATIENTS AND METHODS: , a subsidiary of Becton Dickinson, San Diego, CA, USA). The monitors were validated in 21 stable COPD patients during a 57-min "activities of daily living protocol" in a laboratory setting. The two best performing monitors were then tested in a 14-day trial in a home setting in 23 stable COPD patients to determine patient acceptability and reliability of signal. Acceptability was explored in qualitative interviews. The better performing monitor was then given to 18 patients recruited during an AECOPD who wore the monitor to observe BR during the recovery phase of an AECOPD. RESULTS: While two monitors demonstrated acceptable accuracy compared with the gold standard, some participants found them intrusive particularly when ill with an exacerbation, limiting their potential utility in acute situations. A reduction in resting BR during the recovery from an AECOPD was observed in some, but not in all participants and there was considerable day-to-day individual variation. CONCLUSION: Resting BR shows some promise in identifying exacerbations; however, further prospective study to assess this is required.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".