Influence of Preferred versus Prescribed Exercise on Pain in Fibromyalgia
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to examine the influence of a preferred- versus a prescribed-intensity exercise session on pain in women with fibromyalgia (FM). METHODS: Twenty-one women with FM (mean age = 44 yr) completed two randomly assigned exercise sessions consisting of 20 min of cycle ergometry at a self-selected intensity and a prescribed intensity. Experimental pain perception was assessed before and after aerobic exercise. During exercise, HR, watts, RPE, and muscle pain were assessed every 5 min. Clinical pain was assessed with the Short-Form McGill Pain Questionnaire (SF-MPQ) immediately and 24, 48, 72, and 96 h after exercise. Data were analyzed with repeated-measures ANOVA. RESULTS: Women with FM preferred a lower intensity of exercise than what was prescribed as indicated by significantly lower HR, watts, and RPE responses (P < 0.05). Muscle pain in the legs, however, was similar in the two conditions and significantly increased during exercise (P < 0.05). Pain thresholds and pain tolerances increased significantly after exercise, whereas peak pain ratings decreased after exercise (P < 0.05). Furthermore, pain (SF-MPQ) in the follow-up period was found to be lower than baseline (P < 0.05). CONCLUSIONS: It is concluded that the women with FM who participated in this study experienced significant improvements in pain after exercise. The results from this study are novel and indicate that recommendations for exercise prescription for individuals with FM should consider the preferred-intensity exercise model as a strategy to reduce pain.
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 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".