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Record W4394856419 · doi:10.3233/nre-230382

Long-term impact of a community-based adapted boxing program on physical functioning and quality of life of individuals with Parkinson’s disease

2024· article· en· W4394856419 on OpenAlexaff
Félix-Antoine Savoie, Simon Benoît, Éléonor Riesco, Andréanne Tanguay

Bibliographic record

VenueNeurorehabilitation · 2024
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversité du Québec à RimouskiUniversité de Sherbrooke
Fundersnot available
KeywordsQuality of life (healthcare)Term (time)Physical medicine and rehabilitationGerontologyParkinson's diseasePsychologyDiseasePhysical therapyMedicinePsychotherapist

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.063
GPT teacher head0.420
Teacher spread0.357 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2024
Admission routes1
Has abstractyes

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