The Mediating Role of Exercise in Depression and Fatigue in Patients with Advanced Cancer
Bibliographic record
Abstract
This study explored the interconnections between sociodemographic elements, depression, fatigue, and exercise in patients suffering from incurable neoplasm, particularly emphasizing the mediating influence of exercise on the relationship between depression and fatigue This was a prospective, multicenter, observational study involving 15 hospitals across Spain. After three months of systemic cancer treatment, participants completed the Brief Symptom Inventory (BSI), the Godin-Shephard Leisure-Time Physical Activity Questionnaire (GSLTPAQ) and the Fatigue Assessment Scale (FAS) to measure levels of depression, fatigue, and exercise, respectively. A total of 616 subjects participated in this study. Activity levels differed markedly according to educational attainment, marital, and work status. There was a negative correlation between physical activity and depression, and a positive correlation between depression and fatigue (β = -0.18, and β = 0.46, respectively). Additionally, physical activity inversely influenced fatigue levels (β = 0.21). Physical activity served as a partial intermediary in the link between depression and fatigue among patients with advanced, unresectable cancer. Healthcare providers are urged to consider both the physical and emotional dimensions of cancer treatment, implementing physical activity programs to enhance overall patient quality of life and mental health.
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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 | 0.003 |
| 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) | 0.001 | 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".