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通过虚拟参与改善健康状况并减轻围产期焦虑:一项随机对照试验方案

Transforming Health and Reducing Perinatal Anxiety Through Virtual Engagement: Protocol for a Randomized Controlled Trial.

作者信息

Ponting Carolyn, Baer Rebecca J, Blackman Kacie, Blebu Bridgette, Felder Jennifer N, Oltman Scott, Tabb Karen M, Jelliffe Pawlowski Laura

机构信息

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

Department of Obstetrics, Gynecology, and Reproductive Health Sciences, University of California San Francisco, San Francisco, CA, United States.

出版信息

JMIR Res Protoc. 2025 May 30;14:e70627. doi: 10.2196/70627.

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.

摘要

背景

产前焦虑影响20%至30%的孕妇,与不良的产前健康状况、分娩结局及产后心理健康挑战相关。来自种族和族裔少数群体、性取向和性别少数群体以及低收入人群,由于过多暴露于不良的健康社会决定因素,均面临更高的产前焦虑风险。数字认知行为疗法(dCBT)已被证明能可靠地减轻焦虑,主要针对白人和中高收入人群样本,但对低收入及边缘化孕妇的疗效研究不足。

目的

我们提议在一个因种族、族裔、性取向和性别少数群体身份而被过度抽样的低收入孕妇样本中,对一种dCBT(Daylight应用程序,Big Health有限公司)进行随机对照试验。

方法

参与者(N = 132)将以1:1的分配比例随机分为干预组或等待列表对照组。干预措施为一款自助式应用程序,它利用在线治疗师传授并鼓励实践4种关键的认知行为疗法技能(例如,识别灾难性思维并增强身体放松),这些技能可减轻焦虑。主要结局将是广泛性焦虑症状;次要结局将包括抑郁症状、压力、特定于妊娠的焦虑及失眠症状。对部分参与者进行焦点小组访谈,将提供有关dCBT可接受性的定性数据。

结果

招募工作于2024年6月开始。数据将使用线性混合模型进行分析,该模型将以治疗条件(dCBT组和等待列表对照组)作为组间因素,时间(基线、随机分组后3周、6周和10周)作为组内因素,并纳入组×时间交互作用。在线性混合模型中,当每条记录的观察数量不同时,会产生无偏参数估计,并将纳入意向性分析和敏感性分析。

结论

这项临床试验将评估一种自助式dCBT对低收入及边缘化孕妇产前焦虑的疗效和可接受性,这一群体在获得面对面的循证心理治疗方面仍面临重大障碍。

试验注册

ClinicalTrials.gov NCT06404450;https://clinicaltrials.gov/study/NCT06404450。

国际注册报告标识符(IRRID):DERR1-10.2196/70627。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c54a/12166326/6722e9a6c070/resprot_v14i1e70627_fig1.jpg

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