Predictors of exercise participation are different depending on ambulatory status among older people with multiple sclerosis
Bibliographic record
Abstract
Background: Exercise at moderate intensity may confer neuroprotective benefits in multiple sclerosis (MS), however it has been reported that people with MS (PwMS) exercise less than national guideline recommendations. We aimed to determine predictors of moderate to vigorous exercise among a sample of older Canadians with MS who were divided into ambulatory (less disabled) and non-ambulatory (more disabled) groups. Methods: We analysed data collected as part of a national survey of health, lifestyle and aging with MS. Participants (n=743) were Canadians over 55 years of age with MS for 20 or more years. We identified ‘a priori’ variables (demographic, personal, socioeconomic, physical health, exercise history and health care support) that may predict exercise at moderate to vigorous intensity (>6.75 metabolic equivalent hours/week). Predictive variables were entered into stepwise logistic regression, adding and deleting correlated variables until best fit was achieved for each of the two groups (ambulatory and non-ambulatory). Results: Seventy-seven percent (77%) of participants in the ambulatory group (n=351) and 35% of the non-ambulatory group (n=392) were classified as exercisers. In the ambulatory group, exercise predictors included degree of disability (OR 1.95, 95%CI 1.18-3.25), depressive symptoms (OR 0.51, 95%CI 0.29-0.89) and perseverance (OR 1.8, 95%CI 1.04-3.10). In the non-ambulatory group, exercise predictors included degree of disability (OR 5.3, 95%CI 3.22-8.71) and perseverance (OR 2.1, 95%CI 1.27-3.54). It was also notable that the factors, age, gender, years with MS, co-morbid conditions, social support, health care support and financial status were not predictive of exercise. Conclusions: This is the first examination of exercise and exercise predictors among older, more disabled PwMS. Disability and perseverance are major predictors of exercise participation (at moderate to vigorous levels) in both ambulatory and non-ambulatory groups. Presence of depressive symptoms was only predictive in the ambulatory group. Our results suggest that more exercise options must be developed for people with greater disability. Perseverance and depression are both characteristics that are modifiable and are potential targets for exercise adherence interventions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".