1053 Sleep Duration And Timing Associated With History Of Breast Prostate And Skin Cancer: Data From A Nationally-representative Sample
Bibliographic record
Abstract
Abstract Introduction Sleep disturbances are a common problem among cancer survivors. Also, cancer patients can have altered circadian rhythms and these changes can continue to affect the patient long after the conclusion of their treatment. This analysis aims to investigate how the sleep and wake times of cancer survivors differ from the rest of the population, depending on the type of cancer. Methods Data from the 2015-2016 National Health and Nutrition Examination Survey were used. Population-weighted data on N=5,581 individuals provided complete data. History of breast, prostate, and skin cancer (melanoma or other) was self-reported. Sleep duration was self-reported in half-hour increments, and typical bedtime and waketime was self-reported. Covariates included age, sex, and race/ethnicity. Weighted linear regressions with sleep duration, bedtime and waketime were examined, with each cancer type as predictor. Results Prevalence was 1.7% for prostate cancer, 1.5% for breast cancer, 2.3% for non-melanoma skin cancer, and 0.8% for melanoma. In adjusted analyses, prostate cancer was associated with an additional 26.5 minutes of average total sleep (95%CI 2.2,50.9, p=0.03), a 23.1 bedtime minutes earlier (95%CI -40.4,-5.8, p=0.009), and no difference in waketime. Breast cancer was associated with a bedtime that was 41.1 minutes later (95%CI 10.3,72.0, p=0.009) and a waketime that was 48.7 minutes later (95%CI 12.5,84.9, p=0.008), but no difference in sleep duration. No statistically significant effects were seen for either type of skin cancer, melanoma or non-melanoma. Conclusion Prostate cancer was associated with an earlier bedtime and associated increased sleep time. Breast cancer, on the other hand, was associated with a phase delay of the sleep period but no change in sleep duration. Skin cancer was not associated with differences in sleep duration or timing. These findings may have implications for not only treatment of sleep problems in different types of cancer, but also possible circadian mechanisms. Support Dr. Grandner is supported by R01MD011600
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".