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Record W2030510629 · doi:10.1080/09638280802273473

How does exercise influence fatigue in people with multiple sclerosis?

2008· article· en· W2030510629 on OpenAlexaff
Cath Smith, Leigh Hale, Kärin Olson, Anthony G. Schneiders

Bibliographic record

VenueDisability and Rehabilitation · 2008
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPhysical medicine and rehabilitationMultiple sclerosisPhysical therapyMedicinePsychologyPsychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.679

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.283
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations58
Published2008
Admission routes1
Has abstractyes

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