Long-term impact of a community-based adapted boxing program on physical functioning and quality of life of individuals with Parkinson’s disease
Bibliographic record
Abstract
BACKGROUND: Adapted boxing can help improve the physical functioning and health-related quality of life (HRQoL) of individuals with Parkinson's disease (PD). Whether these benefits persist longitudinally is unclear. OBJECTIVE: The purpose of this retrospective study was to evaluate the impact of a community-based adapted boxing program on the physical functioning and HRQoL of individuals with PD over 1-1.5 years. METHODS: Twenty-six individuals with PD agreed to share their results on tests administered upon enrollment in the program (PRE) and ∼431 days later (POST). The tests included the Fullerton Advanced Balance scale, (FAB), the Timed Up-and-Go test (TUG), the 30-second Sit-to-Stand test (30-STS), and the PD questionnaire-39 (PDQ-39). RESULTS: From PRE to POST, performance significantly improved on the TUG and 30-STS tests (both p < 0.001), but not on the FAB (p = 0.79). Over the same period, PDQ-39 scores significantly increased (p = 0.05). No PRE to POST changes surpassed the minimal detectable change threshold. CONCLUSION: The results of this study suggest that adapted boxing is at worst non-detrimental and at best potentially beneficial for muscle strength, endurance, and functional mobility in individuals with PD. However, adapted boxing probably cannot fully counteract the HRQoL decrements that accompany PD progression.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".