1036 Does Diary and Actigraphy Measured Sleep Differ Between Good and Poor Sleepers During Breast Cancer Treatment?
Bibliographic record
Abstract
Abstract Introduction Women may enter in breast cancer (BCa) treatment with poor sleep, or it may begin during treatment. We assessed how subjective and objective sleep changes during the first year of treatment for women with BCa. Further, we examined whether this differs between previously good and poor sleepers and whether there was agreement between subjective and objective measures of sleep. Methods Sleep onset latency (SOL), wake after sleep onset (WASO), total sleep time (TST), and sleep efficiency (SE) were measured among 100 patients with newly diagnosed, non-metastatic BCa using 7 days of diary and actigraphy collected at 4 time points: pre-treatment, 4, 8, and 12 months. Women with a score ≥5 on the Pittsburgh Sleep Quality Index at treatment onset were classified as poor sleepers. A 4 (time: 0-, 4-, 8-, 12-months) by 2 (sleep measure: sleep diary, actigraphy) by 2 (group: good, poor sleepers) mixed model ANOVAs was performed for each sleep parameter. Results There was a time by sleep measure by group interaction for TST, [F(3,294)= 3.014, p = .03). Good sleepers reported greater TST on diaries- than actigraphy at pre-treatment and 12 months, whereas there were no differences in poor sleepers. There was a group by time effect for good vs. poor sleepers [F(3,294)= 2.909, p = .035]. Good sleepers experienced decreased TST and SE from pre-treatment through 4-mo, followed by increases. Poor sleepers showed the opposite pattern. Neither group returned to pre-treatment levels. Sleep diaries and actigraphy are concordant over time for TST, but not SOL, WASO, or SE. Conclusion Sleep parameters worsen during the first year following onset of BCa and concordance between sleep diaries and actigraphy differ between good or poor sleepers. Support Dr. Garland is supported by a Scotiabank New Investigator Award and seed funding from the Beatrice Hunter Cancer Research Institute (BHCRI).
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) | 0.001 | 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 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".