Home-Based Connected Devices Combined With Statistical Process Control for the Early Detection of Respiratory Exacerbations by Patients With Cystic Fibrosis: Pilot Interventional Study With a Pre-Post Design
Bibliographic record
Abstract
BACKGROUND: Currently, patients with cystic fibrosis do not routinely monitor their respiratory function at home. OBJECTIVE: This study aims to assess the clinical validity of using different connected health devices at home to measure 5 physiological parameters to help prevent exacerbations on a personalized basis from the perspective of patient empowerment. METHODS: A multicenter interventional pilot study including 36 patients was conducted. Statistical process control-the cumulative sum control chart (CUSUM)-was used with connected health device measures with the objective of sending patients alerts at a relevant time in order to identify their individual risk of exacerbations. Associated patient education was delivered. Quantitative and qualitative data were collected. RESULTS: One-half (18/36) of the patients completed the protocol through the end of the study. During the 12-month intervention, 6162 measures were collected with connected health devices, 387 alerts were sent, and 33 exacerbations were reported. The precision of alerts to detect exacerbations was weak for all parameters, which may be partly related to the low compliance of patients with the measurements. However, a decrease in the median number of exacerbations from 12 months before the study to after the 12-month intervention was observed for patients. CONCLUSIONS: The use of connected health devices associated with statistical process control showed that it was not acceptable for all patients, especially because of the burden related to measurements. However, the results suggest that it may be promising, after adaptations, for early identification and better management of exacerbations. TRIAL REGISTRATION: ClinicalTrials.gov NCT03304028; https://clinicaltrials.gov/study/NCT03304028.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".