0109 Working Memory across Sleep and the Menstrual Cycle in Young and Midlife Women
Bibliographic record
Abstract
Abstract Introduction The menses phase of a woman’s menstrual cycle, compared to other phases, is more likely to be associated with poorer sleep quality and alterations in cognitive performance, specifically impaired working memory. However, the relationship among these factors has been poorly investigated, and how age impacts these relationships is currently unknown. The present study examines the effect of menstrual cycle phase and sleep on working memory performance in young and midlife women. Methods Fifty-five young and midlife women (n = 29, 18 – 35 years; n = 26, 45 – 56 years) completed four remote assessments at different phases of their menstrual cycle: menses, late-follicular, mid-luteal, and late-luteal, defined based on days of menses and ovulation. On each visit, participants completed the operation span (OSPAN) working memory task in the evening and were re-tested for sleep-related performance change in the morning. In addition, participants wore an Oura ring, a multi-sensor wearable sleep tracker, throughout the night. Mixed linear regression, correlation models, and paired t-tests were used to determine the relationship between menstrual phase, sleep, and OSPAN outcomes in both groups. Results In midlife women only, OSPAN performance improvement significantly changed across menstrual cycle phases (p < .05). The greatest post-sleep improvement in OSPAN performance was detected during the mid-luteal and late-follicular phases of the cycle, while lower performance gains were detected during menses and late-luteal phases. Post-hoc paired t-tests confirmed that post-sleep performance was significantly worst during menses compared to each of the other phases (p < .05). Additionally, during the mid-luteal phase, time spent in deep sleep positively correlated with post-sleep performance in midlife women (r = .55, p < .05). No significant effects were detected in young women. Conclusion These findings suggest a complex interaction between sleep, menstrual cycle phase, and cognitive performance in midlife women. Our data suggest that deep sleep may mediate post-sleep performance during specific cycle phases. Reasons why these results are not evident in younger women are yet to be determined. Support (If Any) Supported by: RF1AG061355 (Baker/Mednick)
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 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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".