School of Nursing, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan.
School of Nursing, University of Alabama at Birmingham, Birmingham, USA.
Trials. 2023 Feb 9;24(1):103. doi: 10.1186/s13063-023-07103-8.
Taiwan has a high national caesarean rate coupled with a low vaginal birth after caesarean (VBAC) rate. This study aims to develop and evaluate a web-based decision-aid with communication support tools, to increase shared decision making (SDM) about birth after caesarean.
A quantitative approach will be adopted using a randomized pre-test and post-test experimental design in a medical centre in northern Taiwan. The web-based decision aid consists of five sections. Section 1 provides a two-part video to introduce SDM and how to participate in SDM. Section 2 presents an overview of functions and features of the birth decision-aid. Section 3 presents relevant VBAC information, including definitions, benefits and risks, and an artificial intelligence (AI) calculator for rate and likelihood of VBAC success. Section 4 presents the information regarding elective repeat caesarean delivery (ERCD), involving definitions, benefits, and risks. Section 5 comprises four steps of decision making to meet women's values and preferences. Pregnant women who have had one previous caesarean and are eligible for VBAC, will be recruited at 14-16 weeks. Participants will complete a baseline survey prior to random allocation to either the control group (usual care) or intervention group (usual care plus an AI-decision aid). A follow up survey at 35-38 weeks will measure change in decisional conflict, knowledge, birth mode preference, and decision-aid acceptability. Actual birth outcomes and satisfaction will be assessed one month after birth.
The innovative web-based decision-aid with support tools will help to promote pregnant women's decision-making engagement and communication with their providers and improve opportunities for supportive communication about VBAC SDM in Taiwan. Linking web-based AI data analysis into the medical record will also be assessed for feasibility during implementation in clinical practice.
ClinicalTrials.gov identifier (NCT05091944), Registered on October 24, 2021.
台湾的剖宫产率较高，而阴道分娩率较低。本研究旨在开发和评估一种具有沟通支持工具的基于网络的决策辅助工具，以增加关于剖宫产后续分娩的共同决策（SDM）。
本研究采用定量方法，在台湾北部的一家医疗中心采用随机预测试和后测试实验设计。基于网络的决策辅助工具由五个部分组成。第 1 部分提供两段视频，介绍 SDM 以及如何参与 SDM。第 2 部分介绍了分娩决策辅助工具的功能和特点概述。第 3 部分介绍了相关 VBAC 信息，包括定义、益处和风险，以及用于 VBAC 成功率的人工智能（AI）计算器。第 4 部分介绍了选择性重复剖宫产（ERCD）的信息，包括定义、益处和风险。第 5 部分包括满足女性价值观和偏好的四个决策步骤。有过一次剖宫产且符合 VBAC 条件的孕妇将在 14-16 周时招募。参与者将在随机分配到对照组（常规护理）或干预组（常规护理加 AI 决策辅助）之前完成基线调查。在 35-38 周进行随访调查，以衡量决策冲突、知识、分娩方式偏好和决策辅助可接受性的变化。在分娩后一个月评估实际分娩结果和满意度。
具有支持工具的创新型基于网络的决策辅助工具将有助于促进孕妇的决策参与，并与提供者进行沟通，并改善在台湾进行 VBAC SDM 的支持性沟通机会。在实施过程中，还将评估将基于网络的 AI 数据分析链接到医疗记录的可行性。
ClinicalTrials.gov 标识符（NCT05091944），于 2021 年 10 月 24 日注册。
