mHealth-assisted expiratory muscle strength training in Parkinson's disease patients: A proof-of-concept study
Bibliographic record
Abstract
BACKGROUND: Expiratory muscle strength training (EMST) is acknowledged for its therapeutic benefits in Parkinson's disease (PD), yet long-term adherence remains a challenge. OBJECTIVE: The primary aim of this study was to assess the preliminary effects of EMST coupled with a mobile health app (SpiroGym) on self-efficacy and exercise adherence in PD patients. The secondary aim was to assess the usability of the SpiroGym app. METHODS: This single-group, multicenter, multinational proof-of-concept study involved 63 PD patients across four tertiary PD centers. Participants were enrolled in either a 1-week (n = 35) or 24-week (n = 28) EMST program coupled with SpiroGym app. Self-efficacy was assessed using the Self-Efficacy for Home Exercise Program scale (SEHEPS) and exercise adherence was monitored by SpiroGym app. Usability was evaluated using the System Usability Scale. RESULTS: Post-intervention, significant improvements in SEHEPS were observed in 1-week group (d = 0.48; p = 0.02) and 24-week group (d = 0.57; p = 0.002). Adherence rates in the 24-week PD patient group were high throughout the course of the study. Post-training SEHEPS was found to correlate (rho = 0.55; adjusted p = 0.016) with adherence to EMST during the non-supervised maintenance phase. The SpiroGym app exhibited high usability (>85th percentile score), with no significant differences noted between short-term and long-term use, indicating sustained user satisfaction. CONCLUSIONS: The results of our study suggest a promising role for SpiroGym app in supporting adherence to home-based EMST in PD patients. Nevertheless, future comparative studies are required to confirm SpiroGym's effectiveness.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".