The Relationship between Fatigue and Actigraphy-Derived Sleep and Rest–Activity Patterns in Cancer Survivors
Bibliographic record
Abstract
Cancer-related fatigue can continue long after curative cancer treatment. The aim of this study was to investigate sleep and rest–activity cycles in fatigued and non-fatigued cancer survivors. We hypothesized that sleep and rest–activity cycles would be more disturbed in people experiencing clinically-relevant fatigue, and that objective measures of sleep would be associated with the severity of fatigue in cancer survivors. Cancer survivors (n = 87) completed a 14-day wrist actigraphy measurement to estimate their sleep and rest–activity cycles. Fatigue was measured using the Functional Assessment of Chronic Illness Therapy Fatigue Scale (FACIT-F). Participants were dichotomised into two groups using a previously validated score (fatigued n = 51 and non-fatigued n = 36). The participant’s perception of sleep was measured using the Insomnia Severity Index (ISI). FACIT-F score was correlated with wake after sleep onset (r = −0.28; p = 0.010), sleep efficiency (r = 0.26; p = 0.016), sleep onset latency (r = −0.31; p = 0.044) and Insomnia Severity Index (ISI) score (r = −0.56; p < 0.001). The relative amplitude of the rest–activity cycles was lower in the fatigued vs. the non-fatigued group (p = 0.017; d = 0.58). After treatment for cancer, the severity of cancer-related fatigue is correlated with specific objective measures of sleep, and there is evidence of rest–activity cycle disruption in people experiencing clinically-relevant 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 | 0.002 |
| 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".