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Record W1999859936 · doi:10.1002/art.21470

Maintenance of exercise in women with fibromyalgia

2005· article· en· W1999859936 on OpenAlexaff
Patricia L. Dobkin, Michał Abrahamowicz, Mary‐Ann Fitzcharles, Maria Dritsa, Deborah Da Costa

Bibliographic record

VenueArthritis Care & Research · 2005
Typearticle
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsFibromyalgiaPhysical therapyMedicineRandomized controlled trialAerobic exercisePhysical medicine and rehabilitationInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To identify predictors of maintenance of exercise for women with fibromyalgia (FM). METHODS: Women with FM who had been randomized to the exercise arm of a clinical trial were studied prospectively during and 3 months following treatment. Subjects completed exercise logs weekly and returned the data via postal mail. Outcome variables were duration of aerobic and stretching exercises. Two separate multivariate models for longitudinal data were built with adjustment for in-treatment adherence and time. Pretreatment characteristics (self efficacy, pain, disability, stress, exercise barriers and benefits, and age) and changes during treatment (pain, disability, stress, and exercise barriers and benefits) were considered potential predictors of exercise maintenance. RESULTS: Stretching significantly decreased in the 3 months following treatment. High stress at baseline and increases in stress during treatment were associated with poor maintenance of stretching. Disability at baseline (measured with the Fibromyalgia Impact Questionnaire), an increase in barriers to exercise during treatment, and increases in upper-body pain during treatment were associated with worse maintenance of aerobic exercise in the 3 months following treatment. CONCLUSION: The maintenance of an exercise program in women with FM appears to be contingent on being able to deal with stress, pain, barriers to exercise, and disability.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.826
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.316
Teacher spread0.296 · 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 designOther design
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

Citations74
Published2005
Admission routes1
Has abstractyes

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