Sleeping for two: study protocol for a randomized controlled trial of cognitive behavioral therapy for insomnia in pregnant women
Bibliographic record
Abstract
BACKGROUND: Insomnia and sleep disturbances are common in pregnancy and have potentially significant consequences for both maternal and infant health. There is limited research examining the effectiveness of cognitive behavioral therapy for insomnia (CBT-I) during pregnancy. With increased distress and limited access to services during the COVID-19 pandemic, there is also an unprecedented need for telehealth delivery of treatment programs for pregnant women. The aims of this trial are to evaluate the impact of the Sleeping for Two adaptation of CBT-I in pregnancy (in-person or telehealth) versus treatment as usual (TAU) in reducing symptoms of insomnia (primary outcome), as well as increasing gestational length and reducing symptoms of depression (secondary outcomes). METHODS: A two-arm, single-blinded, parallel group randomized controlled trial (RCT) design with repeated measures will be used to evaluate the impact of CBT-I compared to TAU among a sample of 62 pregnant women, enrolled between 12 and 28 weeks of gestation, who self-identify as experiencing insomnia. Five weekly individual sessions of CBT-I will be delivered in person or via telehealth depending on physical distancing guidelines. Assessment of insomnia diagnosis by structured interview, self-reported insomnia symptom severity and sleep problems, and sleep quantity and quality as measured by a daily diary and actigraphy will occur at 12-28 weeks of pregnancy (T1), 1 week post-treatment (T2), and 6 months postpartum (T3). DISCUSSION: CBT-I delivered in pregnancy has the potential to reduce symptoms of insomnia and depression and could lead to reduced risk of preterm birth, all of which can minimize risk of negative maternal and child health and developmental consequences in the short (e.g., infant death) and long terms (e.g., developmental delays). This RCT builds on a successful open pilot trial conducted by our team and will provide further evaluation of a novel evidence-based treatment for pregnancy-related insomnia, which can be widely disseminated and used to treat individuals that are most in need of intervention. Findings will enhance understanding of pregnancy-related sleep problems, as well as means by which to improve the health and sleep of mothers and their children. TRIAL REGISTRATION: ClinicalTrials.gov NCT03918057. Registered on 17 April 2019.
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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 | 0.036 | 0.037 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.105 | 0.016 |
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".