Development of a Web App to Enhance Physical Activity in People With Cystic Fibrosis: Co-Design and Acceptability Evaluation by Patients and Health Professionals
Bibliographic record
Abstract
BACKGROUND: Cystic fibrosis (CF) is a genetic disease affecting the respiratory and digestive systems, with recent treatment advances improving life expectancy. However, many people with CF lack adequate physical activity (PA). PA can enhance lung function and quality of life, but barriers exist. The Cystic Fibrosis Decisional Balance of Physical Activity questionnaire assesses the decisional balance for PA in adults with CF, but it is not optimal for clinical use. A digital app might overcome this limitation by improving the efficiency of administration, interpretation of results, and communication between patients and health care professionals. OBJECTIVE: This paper presents the development process and reports on the acceptability of a web app designed to measure and monitor the decisional balance for PA in people with CF. METHODS: This study comprised two stages: (1) the co-design of a digital app and (2) the evaluation of its acceptability among health care professionals and people with CF. A participatory approach engaged stakeholders in the app's creation. The app's acceptability, based on factors outlined in the Unified Theory of Acceptance and Use of Technology 2, is vital for its successful adoption. Participants volunteered, gave informed consent, and were aged >18 years and fluent in French. Data collection was performed through qualitative interviews, video presentations, surveys, and individual semistructured interviews, followed by quantitative and qualitative data analyses. RESULTS: In total, 11 health care professionals, 6 people with CF, and 5 researchers were involved in the co-design phase. Results of this phase led to the coconstruction of an app named MUCO_BALAD, designed for people with CF aged ≥18 years, health care professionals, and researchers to monitor the decisional balance for PA in people with CF. In the acceptability evaluation phase, the sample included 47 health care professionals, 44 people with CF, and 12 researchers. The analysis revealed that the acceptability measures were positive and that app acceptability did not differ according to user types. Semistructured interviews helped identify positive and negative perceptions of the app and the interface, as well as missing functionalities. CONCLUSIONS: This study assessed the acceptability of an app and demonstrated promising qualitative and quantitative results. The digital tool for measuring the decisional balance in PA for people with CF is encouraging for health care professionals, people with CF, and researchers, according to the valuable insights gained from this study.
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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 | 0.028 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".