Compliance and Satisfaction for 16 months of Adapted Tango vs. Supervised Walking for People with Parkinson’s
Bibliographic record
Abstract
Abstract The present study is an exploratory secondary analysis examining associations between Parkinson’s disease (PD) characteristics and compliance and satisfaction with exercise programs as part of ongoing clinical trial research. 36 participants with PD engaged in either adapted tango (AT; n = 20) or supervised walking (WALK; n = 16) classes for 16 months. This trial was registered at ClinicalTrials.gov (NCT04122690) on October 10, 2019. PD-related metrics, dyskinesia frequency and duration, OFF-time, freezing of gait (FOG), disease duration, Hoehn-Yahr stage, and motor and cognitive function were collected. Linear regression models assessed associations with attendance and satisfaction. Attendance varied widely (range: 1–76; mean ± SD: 39.1 ± 26.0 sessions), with overall satisfaction favorable (4.0 ± 0.8 on a 5-point scale). Dyskinesia metrics showed negative correlations with compliance: percentage of dyskinesia ( β = –0.381, R 2 = 0.145, p = 0.055) and total dyskinesia duration ( β = –0.377, R 2 = 0.142, p = 0.058). Compliance positively predicted satisfaction ( β = 0.378, R 2 = 0.143, p = 0.063). Montreal Cognitive Assessment (MoCA) was the strongest satisfaction predictor ( β = 0.396, R 2 = 0.157, p = 0.050), followed by the Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) scores ( β = –0.343, R 2 = 0.118, p = 0.093). FOG had no significant effect on attendance or satisfaction. Findings indicate dyskinesia limits compliance, while cognitive function enhances satisfaction, emphasizing the need for tailored exercise.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".