Abstract P3-08-04: Improving sleep to reduce breast cancer risk in shift workers
Bibliographic record
Abstract
Abstract BACKGROUND: Shiftwork that involves circadian disruption has been designated as a 2A (probable) breast cancer carcinogen by the International Agency for Research on Cancer. Night shift workers experience many lifestyle disturbances including disrupted sleep. While the specific biological mechanisms that confer increased breast cancer risk are not yet clear, sleep disruption is hypothesized to have both direct (lowering melatonin levels) and indirect (obesity status) impacts on risk status. However, few interventions of any type have been reported for shift workers to potentially reduce their risk for breast cancer. An effective intervention to improve sleep quality would be one way to potentially reduce breast cancer risks in these women. METHODS: 47 female shift workers aged 40-65 who had experienced high circadian disruption (rotating or permanent night shifts) at least 3 times per month for at least 2 years participated in a single arm study examining the impact of a sleep intervention on health behaviours and breast cancer risk. Over the course of 10 months, women received with a 10-session (plus 2 booster sessions), telephone-delivered sleep hygiene intervention. The program was adapted from a hospital-based sleep clinic protocol based on cognitive behaviour therapy (CBT) principles and aimed to improve sleep quality and quantity. Sleep quality was assessed by the Pittsburgh Sleep Quality Index (PSQI). Data were assessed at baseline and 6 and 12 months. RESULTS: Mean age was 47 years, 68% were partnered, and 78% had diploma level education or higher. Participants were nurses (49%), emergency communications personnel (17%) and paramedics (13%), and others. At baseline, 79% had "poor" sleep quality (score above 5 on the PSQI), decreasing to 54% at 6 months and 49% at 12 months (p<0.001 for both changes between baseline and 6 months, and between baseline and 12 months, based on McNemar's test). Significant correlates of better sleep included younger age, being married, and having more education, but not obesity (measured by body mass index). We also investigated chronotype, which characterizes sleep time preferences that reflect underlying circadian rhythms; individuals may be "morning types" or larks (preferring early awakenings and bedtimes), "evening types" or owls (preferring late nights and mornings), or intermediate. We found that our intervention was more significantly effective for larks (where 11% reported good sleep at baseline and 67% at 12 months) and intermediates (where 28% reported good sleep at baseline and 62% at 12 months) than for owls (where 15% reported good sleep at baseline and 18% at 12 months). DISCUSSION: Most female shift workers in this study report impaired sleep quality. Our CBT-based sleep intervention led to significant improvements in sleep quality in 6 months, and these improvements were maintained at one year. This approach was more effective for some chronotypes than others, and "night owls" may require a different intervention. Sleep is a modifiable risk factor that is increasingly linked with cancer-related outcomes, including cancer incidence. Interventions to improve sleep quality such as the program used here offer a novel approach with the potential to reduce breast cancer risk. Citation Format: Gotay C, Aronson K, Campbell K, Demers P, Fleming J, Gelmon K, Goodfellow E, Munoz C, Neil-Sztramko S, Pollak M, Shen H, Spinelli J. Improving sleep to reduce breast cancer risk in shift workers. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P3-08-04.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".