How does exercise influence fatigue in people with multiple sclerosis?
Bibliographic record
Abstract
PURPOSE: This study explored the influence of an 8-week exercise programme on fatigue perceptions in people with multiple sclerosis (MS). METHOD: Eight women and two men with a confirmed diagnosis of MS participated three times a week in an 8-week exercise programme at a physiotherapy gymnasium. Participants were interviewed at three defined time points. Interviews were transcribed, analysed, and emergent categories were subject to verification by three independent sources. RESULTS: Five interrelated categories were identified from the data. The category, 'listening to your body' evolved from the participants' 'perceived control over fatigue', which subsequently defined the 'reaching the edge'; a critical point at which the 'nature of tiredness' perceived by participants following exercise was either healthy or unhealthy. This critical point consequently explained either perceived positive 'exercise outcomes' outcomes of physical improvement and wellbeing or perceived physical deterioration and negative feelings. CONCLUSION: This study details the positive and negative influences of exercise on fatigue perceptions in people with MS. Healthcare professionals therefore, need to be cognisant of strategies which may enhance 'perceived control over fatigue' and promote 'listening to your body', in order to maximise the benefits of exercise intervention for individuals with MS-related fatigue.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".