Maintenance of exercise in women with fibromyalgia
Bibliographic record
Abstract
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.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".