Light therapy for insomnia symptoms in fatigued cancer survivors: a secondary analysis of a randomized controlled trial
Bibliographic record
Abstract
Abstract Background: Cancer-related fatigue (CRF) and insomnia are highly comorbid. This association implies that treatment aimed at improving one may also simultaneously target the other. This secondary analysis examined whether severity of insomnia affects sleep outcomes in response to a light therapy intervention in cancer survivors with CRF. Methods: This 4-week trial recruited cancer survivors who met ICD-10 criteria for CRF. Participants were randomized to receive bright white light (BWL) or dim red light (DRL). Light devices were used daily for 30 minutes over 28 days. Participants completed the Insomnia Severity Index (ISI) at baseline, weeks 2 and 4, and the Pittsburgh Sleep Quality Index, daily sleep diaries and actigraphy at baseline and week 4. ISI scores 0 to 14 represent no/mild symptoms and scores 15+ correspond to moderate-to-severe (clinically significant) insomnia symptoms. Results: Of the 81 participants (BWL: n = 42; DRL: n = 39), 49% had ISI scores ≥15 at baseline. There was a significant group-by-time interaction for insomnia severity (F[2, 75.55] = 4.15, P = .020) and sleep quality (F [1, 39.25] = 7.66, P = .009), favoring the BWL condition for those individuals with no or mild insomnia symptoms. In the moderate-to-severe group, there were time effects for insomnia severity and sleep quality, diary measured wake after sleep onset and sleep efficiency, and actigraphy measured wake after sleep onset and sleep efficiency; however, participants remained in the clinical range for insomnia symptoms on the ISI and poor sleep quality on the PSQI. Effect sizes were larger, regardless of intervention condition, in participants with moderate-to-severe insomnia symptoms. Conclusions: The severity of insomnia symptoms may impact sleep outcomes in response to a light therapy intervention in cancer survivors with clinical levels of fatigue, but light therapy alone does not appear to be adequate to produce clinically meaningful change in sleep outcomes. Trial registration number: Clinicaltrials.gov identifier NCT01780623.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".