Self-Reported Sleep Quality and Actigraphic Measures of Sleep in New Mothers and the Relationship to Postpartum Depressive Symptoms
Bibliographic record
Abstract
Objective: This study’s purpose is to examine relationships between self-reported sleep quality, actigraphy data, and depressive symptoms in a sample of women at 6 and 12 weeks postpartum. Methods: This secondary analysis of data from a randomized controlled trial (RCT) of a behavioral sleep intervention measured sleep with actigraphy and self-report. Self-reported measures included the General Sleep Disturbance Scale (GSDS) and mothers’ reports of their sleep as a “small/big/no” problem. Depression was measured with the Edinburgh Postnatal Depression Scale (EPDS). Control variables included group allocation, baseline EPDS, and social support. Logistic regression estimated the association between self-reported and actigraphic measures of sleep and the presence of postpartum depressive symptoms. Separate models estimated the odds of depression according to each sleep variable. Results: In 217 first-time mothers, GSDS scores in the last week of pregnancy were not related to depression; however, GSDS scores at 6 weeks postpartum were associated with > 3 times the odds of depressive symptoms (OR = 3.56; 95% CI = 1.73–7.33) at either 6 or 12 weeks postpartum. The perception that sleep was a “small” or “big” problem at 6 weeks was associated with > 3 (OR = 3.40; 95% CI = 1.54–7.46) and > 8 (OR = 8.29; 95% CI = 2.41–28.59) times the odds of depressive symptoms at either 6 or 12 weeks, respectively. Significant associations between actigraphic sleep measures and depressive symptoms were not found. Conclusion: Self-reported sleep quality is strongly associated with postpartum depressive symptoms. Sleep concerns may be an important clinical indicator of low mood in the postpartum period. Future intervention studies to improve mood could target sleep concerns via cognitive-behavioral strategies.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 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".