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The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
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Transforming Health and Reducing Perinatal Anxiety Through Virtual Engagement: Protocol for a Randomized Controlled

Carolyn Ponting1, Rebecca J Baer2,3,4, Kacie Blackman5

  • 1Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA, United States.

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This study investigates digital cognitive behavioral therapy (dCBT) for prenatal anxiety in low-income and marginalized pregnant individuals. It aims to assess dCBT

Keywords:
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Area of Science:

  • Perinatal mental health research
  • Digital health interventions
  • Health equity in maternal care

Background:

  • Prenatal anxiety affects 20-30% of pregnant individuals, linked to adverse health and postpartum outcomes.
  • Minority and low-income groups face higher prenatal anxiety risks due to social determinants of health.
  • Digital cognitive behavioral therapy (dCBT) efficacy is understudied in marginalized pregnant populations.

Purpose of the Study:

  • To conduct a randomized controlled trial of a dCBT app (Daylight) for low-income pregnant individuals.
  • To oversample for racial, ethnic, sexual, and gender minority identities within the study population.
  • To evaluate the efficacy and acceptability of dCBT in this understudied group.

Main Methods:

  • 132 participants randomized 1:1 to dCBT intervention or waitlist control.
  • Intervention involves a self-guided app teaching 4 core cognitive behavioral therapy skills.
  • Primary outcome: generalized anxiety symptoms; secondary: depression, stress, pregnancy anxiety, insomnia.

Main Results:

  • Recruitment commenced in June 2024.
  • Data analysis will utilize linear mixed models to assess treatment effects over time.
  • Models will accommodate intent-to-treat and sensitivity analyses for robust findings.

Conclusions:

  • This trial will determine the effectiveness of a self-guided dCBT for prenatal anxiety.
  • It addresses significant barriers to accessing evidence-based mental health care for marginalized pregnant individuals.
  • Findings will inform the use of digital interventions to improve perinatal mental health equity.