Potentially Modifiable Factors Associated With Physical Activity in Individuals With Multiple Sclerosis
Bibliographic record
Abstract
Physical activity levels among persons with multiple sclerosis (MS) are worryingly low. We aimed to identify the factors associated with physical activity for people with MS, with an emphasis on factors that have not been studied previously (bladder and hand dysfunction) and are potentially modifiable. This study was a secondary analysis of data collected in the spring of 2012 during the North American Research Committee on Multiple Sclerosis (NARCOMS) Registry. NARCOMS participants were surveyed regarding smoking using questions from the Behavioral Risk Factor Surveillance Survey; disability using the Patient Determined Disease Steps; fatigue, cognition, spasticity, sensory, bladder, vision and hand function using self-reported Performance Scales; health literacy using the Medical Term Recognition Test; and physical activity using questions from the Health Information National Trends Survey. We used a forward binary logistic regression to develop a predictive model in which physical activity was the outcome variable. Of 8,755 respondents, 1,707 (19.5%) were classified as active and 7,068 (80.5%) as inactive. In logistic regression, being a current smoker, moderate or severe level of disability, depression, fatigue, hand, or bladder dysfunction and minimal to mild spasticity were associated with lower odds of meeting physical activity guidelines. MS type was not linked to activity level. Several modifiable clinical and lifestyle factors influenced physical activity in MS. Prospective studies are needed to evaluate whether modification of these factors can increase physical activity participation in persons with MS. © 2016 Wiley Periodicals, Inc.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".