Integration of a serious video game to positive expiratory pressure respiratory physiotherapy in pediatric patients with cystic fibrosis: a prospective study
Bibliographic record
Abstract
Background: PEP therapy is a cornerstone of the physiotherapeutic treatment in cystic fibrosis (CF), supporting its impaired airway clearance. Multiple studies report poor adherence to the PEP (~50%), mainly due to its time-consuming/monotonous aspect. Technologic advances (e.g. serious games) in health have the potential to change the management of chronic diseases through education and by improving adherence. We will evaluate if this is applicable in children with CF. Aims: 1) to evaluate if a serious game can improve adherence to PEP therapy in children with CF, 2) to further co-create this game with them/their families. Methods: 1-year monocentric, prospective study in the CF clinic at the CHUSJ. Inclusion criteria: age 6-17 years, diagnosed with CF, using PEP therapy for ≥6 months. Thirty participants were enrolled and were able to take home an electronic pressure sensor that follows the number of breaths/repetitions/pressures is connected to the usual PEP setup, enabling them to play a game through their breath and test is over a period of 3 months. Families were regularly contacted for a 15-minutes phone call to gather their feedback, ensuring continuous co-creation with the participants (game usability, visuals and relevance). Results: This innovative serious game was well accepted in our study population. Results show increased adherence to the PEP treatment in comparison to their reported adherence pre-game. Ultimately, this may lead to an improvement in the patients’ lung health due to the better mucus clearing achieved through a more regularly performed PEP treatment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".