The reciprocal relationship between daily fatigue and catastrophizing following cancer treatment: Affect and physical activity as potential mediators
Bibliographic record
Abstract
OBJECTIVE: Fatigue is a distressing symptom many cancer patients experience even after completion of treatment. Although theory and empirical evidence indicate that negative cognitions perpetuate fatigue after completion of treatment, insight into how this process unfolds in daily life is limited. This study used an intensive longitudinal design to investigate the reciprocal relationship between catastrophizing and fatigue in daily life and whether affective and behavioral processes mediate these relationships. METHODS: Post-treatment colorectal cancer patients (n = 101) completed daily diaries (14 days, 3 times daily) regarding their fatigue, catastrophizing, positive and negative affect, and physical activity. Multilevel modeling was applied to investigate within-person associations within days. RESULTS: Analyses revealed a positive reciprocal relationship between fatigue and catastrophizing throughout the day. That is, high levels of catastrophizing were associated with increases in fatigue within patients. In turn, but to a lesser extent, high levels of fatigue predicted increases in catastrophizing at the next assessment. Low positive affect and high negative affect mediated the effect of catastrophizing on increases in fatigue. Only negative affect mediated the reverse relationship. Physical activity did not mediate either relationship. CONCLUSIONS: This study provides evidence for a mutually reinforcing relationship between catastrophizing and fatigue in daily life, which might explain the perpetuation of fatigue after completion of cancer treatment. Fatigue-specific cognitive behavior therapy could be improved by educating patients about this daily reciprocal relationship, train them to quickly replace catastrophizing thoughts in daily life, and help them to cope with affective changes induced by 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".