Mental health status and leisure‐time physical activity contribute to fatigue intensity in patients with spondylarthropathy
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between disease-related variables, leisure-time physical activity (LTPA), and mental health status with fatigue severity in patients with spondylarthropathy (SpA). METHODS: Sixty-six SpA patients completed questionnaires assessing disease activity (Bath Ankylosing Spondylitis Disease Activity Index [BASDAI]), functional ability (Bath Ankylosing Spondylitis Functional Index), and health-related quality of life (Short Form 36). LTPA patterns, demographics, and disease-related data were obtained by interview. A clinical examination determined tender point count. Fatigue was assessed with the BASDAI fatigue item. RESULTS: The mean BASDAI fatigue score was 5.5 (SD=2.7) with 59% of the sample obtaining a score > or =5. Disease activity, functional disability, and worse mental health contributed to greater fatigue (R2=0.56). The relationship between exercise duration and fatigue intensity was moderated by mental health status. For patients with poorer mental health scores, exercise did not influence fatigue severity. However, for patients reporting better mental health status, engaging in more LTPA decreased fatigue severity. CONCLUSION: In addition to increased disease activity and functional disability, greater fatigue severity in SpA is associated with poorer mental health status. Integrating regular leisure physical activity into the comprehensive treatment of SpA may be useful for modulating 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 | 0.000 | 0.003 |
| 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) | 0.002 | 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".