Adherence and compliance with exercise training in progressive multiple sclerosis: Rates and correlates from the CogEx trial
Bibliographic record
Abstract
BACKGROUND: There is limited understanding of adherence and compliance rates for exercise training(ET) in people with progressive multiple sclerosis(PMS). OBJECTIVES: This secondary, exploratory data analysis examined rates of adherence and compliance for continuous, moderate intensity training and high-intensity interval training(HIIT) and possible correlates among people with PMS from the CogEx trial. METHODS: CogEx was a multi-site, multi-arm, randomized, double-blinded, and sham-controlled trial undertaken by 11 sites in six different countries. Participants(N = 311) were randomized into one of four conditions with different combinations of ET and cognitive rehabilitation, including respective sham conditions, delivered twice weekly over 12 weeks. The analysis focused on adherence and compliance rates and correlates for participants in the pooled ET intervention conditions who received and attempted the ET intervention(n = 152). RESULTS: The rates of adherence and compliance overall(combined for both training stimuli) were 94 % and 66 %, respectively. The rates of adherence and compliance for continuous, moderate-intensity exercise were 95 % and 73 %, respectively, and for HIIT were 92 % and 58 %, respectively. The multivariable regression indicated that better 6MWT performance predicted higher compliance(particularly with HITT), whereas better CVLT-II performance predicted higher adherence and compliance with continuous training in PMS. CONCLUSION: Our results highlight worse compliance than adherence with ET, particularly for HIIT, in PMS. We further highlight cognitive and physical function as correlates of adherence and compliance for consideration in future clinical trials of ET in PMS.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".