Percussion Hero: A Chest Physical Therapy Game for People with Cystic Fibrosis and their Caregivers
Bibliographic record
Abstract
Chest physical therapy—including chest percussion, vibration, and postural drainage—is an important part of cystic fibrosis (CF) treatment. Chest percussion and vibration are exercises that require coordinated effort between patient and caregiver, during which the caregiver performs manual, rhythmic blows to the patient's chest and back. When practiced regularly alongside postural drainage techniques, percussion therapy mobilizes and removes fluid buildup in the lungs, reducing inflammation and risks of infection that could lead to hospitalization. Despite the importance of chest physical therapy for those with CF, adherence is often low. Low adherence to at-home therapies is common to the treatment of many diseases, yet one distinct challenge of chest physical therapy is the caregiver intervention—a role often assumed by a family member or loved one. The caregiver role is challenging for chest therapy because it is active, focused, and strenuous. While research has proposed many solutions for increasing patient engagement during similar airway clearance exercises, the critical role of the caregiver has often been overlooked. In this paper, we present Percussion Hero, a cooperative, rhythm-based game designed to improve chest physical therapy adherence by actively targeting both patients and caregivers during therapeutic exercises.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".