Experiences Among Patients With Cystic Fibrosis in the MucoExocet Study of Using Connected Devices for the Management of Pulmonary Exacerbations: Grounded Theory Qualitative Research
Bibliographic record
Abstract
BACKGROUND: Early detection of pulmonary exacerbations (PEx) in patients with cystic fibrosis is important to quickly trigger treatment and reduce respiratory damage. An intervention was designed in the frame of the MucoExocet research study providing patients with cystic fibrosis with connected devices and educating them to detect and react to their early signs of PEx. OBJECTIVE: This study aims to identify the contributions and conditions of home monitoring in relation to their care teams from the users' point of view to detect PEx early and treat it. This study focused on the patients' experiences as the first and main users of home monitoring. METHODS: A qualitative study was conducted to explore patients' and professionals' experiences with the intervention. We interviewed patients who completed the 2-year study using semistructured guides and conducted focus groups with the care teams. All the interviews were recorded and transcribed verbatim. Their educational material was collected. A grounded analysis was conducted by 2 researchers. RESULTS: A total of 20 patients completed the study. Three main categories emerged from the patients' verbatim transcripts and were also found in those of the professionals: (1) task technology fit, reflecting reliability, ease of use, accuracy of data, and support of the technology; (2) patient empowerment through technology, grouping patients' learnings, validation of their perception of exacerbation, assessment of treatment efficacy, awareness of healthy behaviors, and ability to react to PEx signs in relation to their care team; (3) use, reflecting a continuous or intermittent use, the perceived usefulness balanced with cumbersome measurements, routinization and personalization of the measurement process, and the way data are shared with the care team. Furthermore, 3 relationships were highlighted between the categories that reflect the necessary conditions for patient empowerment through the use of technology. CONCLUSIONS: We discuss a theorization of the process of patient empowerment through the use of connected devices and call for further research to verify or amend it in the context of other technologies, illnesses, and care organizations. TRIAL REGISTRATION: ClinicalTrials.gov NCT03304028; https://clinicaltrials.gov/ct2/show/results/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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".