Physical Activity and Fatigue in Breast Cancer and Multiple Sclerosis: Psychosocial Mechanisms
Bibliographic record
Abstract
OBJECTIVE: To examine the role of self-efficacy and depression as potential pathways from physical activity to fatigue in two study samples: breast cancer survivors (BCS) (n = 192) and individuals with multiple sclerosis (MS) (n = 292). METHODS: We hypothesized that physical activity would be associated indirectly with fatigue through its influence on self-efficacy and depressive symptomatology. A cross-sectional path analysis (BCS) and a longitudinal panel model (MS) were conducted within a covariance modeling framework. RESULTS: Physical activity had a direct effect on self-efficacy and, in turn, self-efficacy had both a direct effect on fatigue and an indirect effect through depressive symptomatology in both samples. In the MS sample, physical activity also had a direct effect on fatigue. All model fit indices were excellent. These associations remained significant when controlling for demographics and health status indicators. CONCLUSIONS: Our findings suggest support for at least one set of psychosocial pathways from physical activity to fatigue, an important concern in chronic disease. Subsequent work might replicate such associations in other diseased populations and attempt to determine whether model relations change with physical activity interventions, and the extent to which other known correlates of fatigue, such as impaired sleep and inflammation, can be incorporated into this model.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".