Mindfulness-Based Cognitive Therapy in Pregnancy and Sleep Quality: Secondary Analysis from a Randomized Controlled Trial
Bibliographic record
Abstract
Poor sleep quality during pregnancy is prevalent, commonly persists into the postpartum period, and has been associated with poor maternal and child outcomes including risk of preterm birth and postpartum depression. Pregnant individuals are hesitant to take medication leaving many without evidence-based treatments for sleep problems. Mindfulness-based interventions have been shown to improve sleep in adults, but their impact on sleep in pregnancy has rarely been investigated. The current investigation comprises secondary analysis of a randomized controlled trial (RCT) of an 8-week modified Mindfulness-Based Cognitive Therapy for Perinatal Depression (MBCT-PD) group intervention for pregnant individuals experiencing psychological distress. A community sample of pregnant individuals who self-identified as experiencing high levels of psychological distress were randomized to MBCT-PD (n=28) or treatment as usual (n=32) conditions. Assessments comprised a sleep quality questionnaire, a sleep diary and actigraphy, at enrolment, post- intervention, and follow-up at 3 months postpartum. Multilevel modeling revealed a significant effect of MBCT-PD on overall sleep quality across time moderated by baseline levels, such that participants with initial worse sleep quality who received treatment had greater improvement from post intervention to follow-up. Sleep efficiency assessed by diary improved significantly among those who received MBCT-PD, but no other differential changes were observed in diary or actigraphy parameters. Among pregnant individuals with high levels of psychological distress, training in mindfulness was associated with secondary benefits for sleep. Further research is warranted to examine what MBCT-PD components are necessary and could be tailored to address sleep problems during pregnancy.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".