Transforming Health and Reducing Perinatal Anxiety Through Virtual Engagement: Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Prenatal anxiety affects between 20% and 30% of pregnant people and is associated with adverse prenatal health conditions, birth outcomes, and postpartum mental health challenges. Individuals from racial and ethnic minority groups, sexual and gender minority groups, and those with low income are all at heightened risk for prenatal anxiety due to disproportionate exposure to adverse social determinants of health. Digital cognitive behavioral therapy (dCBT) has been shown to reliably reduce anxiety in mostly White and middle- to higher-income samples, but its efficacy in low-income and marginalized pregnant people is understudied. OBJECTIVE: We propose a randomized controlled trial of a dCBT (Daylight app, Big Health, Ltd) in a sample of low-income pregnant people oversampled for racial, ethnic, sexual, and gender minority identity. METHODS: Participants (N=132) will be randomized to the intervention or waitlist control group using a 1:1 allocation ratio. The intervention will be a self-guided application that uses an online therapist to teach and encourage the practice of 4 key cognitive behavioral therapy skills (eg, identifying catastrophic thinking and increasing physical relaxation) that can reduce anxiety. The primary outcome will be generalized anxiety symptoms; secondary outcomes will include depressive symptoms, stress, pregnancy-specific anxiety, and insomnia symptoms. Focus groups with a subset of participants will provide qualitative data about the acceptability of dCBT. RESULTS: Recruitment began in June 2024. Data will be analyzed using linear mixed models, which will be fit with treatment condition (dCBT and waitlist control group) as the between-group factor, time (baseline, 3, 6, and 10 weeks post randomization) as a within-group factor, and a group-by-time interaction. Linear mixed models produce unbiased parameter estimates in situations where there are different numbers of observations per record and will accommodate intent-to-treat and sensitivity analyses. CONCLUSIONS: This clinical trial will evaluate the efficacy and acceptability of a self-guided dCBT for prenatal anxiety among low-income and marginalized pregnant people, a group that continues to experience substantial barriers to accessing in-person evidence-based psychotherapy. TRIAL REGISTRATION: ClinicalTrials.gov NCT06404450; https://clinicaltrials.gov/study/NCT06404450. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/70627.
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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 | 0.035 | 0.038 |
| Meta-epidemiology (narrow) | 0.007 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.153 | 0.019 |
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".