Addressing Physical Activity Behavior in Multiple Sclerosis Management
Bibliographic record
Abstract
BACKGROUND: Although physical activity (PA) is considered the most important nonpharmaceutical intervention for persons with multiple sclerosis (MS), less than 20% of people with MS are engaging in sufficient amounts to accrue benefits. Promotion of PA is most effective when combined with additional behavior change strategies, but this is not routinely done in clinical practice. This study aimed to increase our understanding of current practice and perspectives of health care providers (HCPs) in Canada regarding their use of interventions to address PA behavior in MS management. Investigating HCPs' perspectives on implementing PA behavior change with persons with MS will provide insight into this knowledge-to-practice gap. METHODS: Semistructured focus groups were conducted with 31 HCPs working with persons with MS in Saskatchewan, Canada. Based on interpretive description, data were coded individually by three researchers, who then collaboratively developed themes. Analysis was inductive and iterative; triangulation and member reflections were used. RESULTS: Five themes were established: 1) prescribing, promoting, and impacting wellness with PA; 2) coordinating communication and continuity in practice; 3) timely access to relevant care: being proactive rather than reactive; 4) enhancing programming and community-based resources; and 5) reconciling the value of PA with clinical practice. CONCLUSIONS: The HCPs value PA and want more support with application of behavior change strategies to deliver PA behavioral interventions, but due to the acute and reactive nature of health care systems they feel this cannot be prioritized in practice. Individual- and system-level changes are needed to support consistent and effective use of PA behavioral interventions in MS.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".