Trajectories of sleep quality and depressive symptoms in women from pregnancy to 3 months postpartum: a prospective cohort study
Bibliographic record
Abstract
Sleep quality and depression during pregnancy often affect women's adaptation to motherhood and are linked with adverse maternal and neonatal outcomes. Using a prospective cohort study comprising 190 pregnant women in central Taiwan, we investigated the trajectories of sleep quality and depressive symptoms and their associated predictors in perinatal women from pregnancy to postpartum. Sleep and depressive symptoms were assessed using the Pittsburgh Sleep Quality Index and the Edinburgh Postnatal Depression Scale, respectively, from mid-pregnancy to 3 months postpartum. We used group-based trajectory modelling and logistic regression modelling to analyse the data collected from the structured questionnaires. Pregnant women (50.5% primipara) with a mean (standard deviation) age of 32.3 (4.1) years were included. We identified three distinctive classes of sleep quality trajectories during the perinatal period: 'stable good' (18.4%), 'increasing poor' (48.9%), and 'stable poor' (32.6%). We further detected three stable trajectories of depressive symptoms: 'stable low' (36.3%), 'stable mild' (42.1%), and 'stable high' (21.6%). A significant association between sleep quality and depression trajectories was evident (p < 0.001). High fatigue symptoms and low social support predicted the high trajectories of poor sleep and depressive symptoms. Distinctive dynamic sleep quality and stable depression trajectories were characterised. Our findings revealed that both the sleep and depression trajectories were closely associated with one another, with common predictors of fatigue symptoms and social support. The early assessment of maternal sleep and depression status is important for identifying at-risk women and initiating interventions tailored to perinatal women to improve their sleep and mental health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".