The MoXFo initiative – adherence: Exercise adherence, compliance and sustainability among people with multiple sclerosis: An overview and roadmap for research
Bibliographic record
Abstract
We know very little about exercise adherence, compliance and sustainability in multiple sclerosis (MS), yet adherence is seemingly important for yielding immediate and sustained health benefits. This paper is focused on exercise adherence, compliance and sustainability in the context of informing research and practice involving MS. This focus is critical for clarifying terminology for future research and providing a roadmap guiding clinical research and practice. Our objective was accomplished through a narrative summary of the literature by a panel of experts on exercise adherence from the Moving Exercise Research in Multiple Sclerosis Forward (MoXFo) initiative and a concluding summary of the state of the literature and future research directions. The panel of experts identified three overall themes (Background and Importance; Understanding and Promoting Exercise Adherence, Compliance and Sustainability and Challenges to Exercise Adherence, Compliance and Sustainability) that represented a categorization of nine subthemes. These overall themes and subthemes formed the basis of our recommendations regarding future research broadly involving exercise adherence in MS. Overall, there is limited evidence on rates and determinants of exercise adherence and compliance in MS, and little is known about techniques and interventions for immediate and long-term exercise behaviour change.
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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 | 0.087 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.007 |
| 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".