A scoping review of rehabilitation interventions that reduce fatigue among adults with multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: To identify and summarize available research literature about rehabilitation interventions for multiple sclerosis (MS) fatigue management. METHODS: Database searches (PubMed, EMBASE, CINAHL and PsychINFO) were conducted in October 2011 and last updated in July 2013. To be included, studies must have been published in a peer-reviewed scientific journal, written in English and included an intervention to manage MS fatigue. Effect size (ES) were calculated for the quantitative studies to estimate intervention effects, and major themes were summarized for the qualitative studies. RESULTS: Thirty-eight studies were included in this review. A variety of exercise and behavior change interventions were prescribed to adults with MS. The two most common interventions were progressive resistive training and fatigue management programs. Three exercise intervention studies and nine behavior change intervention studies with quantitative data presented significant ES. Four studies with qualitative data supported the positive impact of certain exercise and behavior change interventions. CONCLUSIONS: This review identified a variety of exercise and behavior change interventions for MS fatigue management. While the findings may provide helpful information to inform practice, future researchers need to develop and evaluate knowledge translation strategies to facilitate the application of this evidence to daily practice to advance MS rehabilitation care. IMPLICATIONS FOR REHABILITATION: Both exercise and behavior change interventions demonstrate some degree of effectiveness for managing MS fatigue. Effect sizes for exercise and behavior change interventions are similar, although the populations examined are different. Overall, evidence for exercise focuses on people who are less disabled, while evidence for behavior change interventions includes a broader population. Future researchers need to develop and evaluate knowledge translation strategies that facilitate application of evidence in daily practice in order to advance MS rehabilitation.
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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 | 0.016 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".