IF 5.8 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Jianing Wang, Nu Tang, Congcong Jin, Jianxue Yang, Xiangpeng Zheng, Qiujing Jiang, Shengping Li, Nian Xiao, Xiaojun Zhou
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引用次数: 0

摘要

背景:妊娠体重增加(GWG)对孕产妇和新生儿的健康至关重要,但许多妇女未能达到推荐的标准,从而增加了并发症的风险。数字健康干预措施提供了有前景的解决方案,但其效果仍不确定。本研究评估了此类干预措施对 GWG 及其他孕产妇和新生儿结局的影响:本研究旨在调查数字健康干预对孕妇和新生儿的影响:共有 2 名独立研究人员在 PubMed、Embase、Web of Science 和 Cochrane Library 数据库中进行了电子文献检索,以确定从开始到 2024 年 2 月发表的符合条件的研究;2024 年 8 月进行了更新检索。这些研究包括与孕产妇和新生儿临床结局相关的随机对照试验(RCT)。随机试验的修订版 Cochrane 偏倚风险工具用于检查发表偏倚风险。使用 Stata(15.1 版;StataCorp)分析数据:我们纳入了 42 项相关的 RCT,涉及 148,866 名参与者。与常规护理组相比,干预组的 GWG 显著降低(标准化平均差-0.19,95% CI -0.25 至 -0.13;P.05):研究结果证实了我们的假设,即数字化医疗保健可以改善孕产妇和新生儿的某些预后,尤其是通过减轻体重过重和将个体体重维持在合理的增长范围内。因此,鼓励妇女加入数字医疗团队听起来既可行又有帮助:ProCORD42024564331; https://tinyurl.com/5n6bshjt.
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Association of Digital Health Interventions With Maternal and Neonatal Outcomes: Systematic Review and Meta-Analysis.

Background: Gestational weight gain (GWG) is crucial to maternal and neonatal health, yet many women fail to meet recommended guidelines, increasing the risk of complications. Digital health interventions offer promising solutions, but their effectiveness remains uncertain. This study evaluates the impact of such interventions on GWG and other maternal and neonatal outcomes.

Objective: This study aimed to investigate the effect of digital health interventions among pregnant women and newborns.

Methods: A total of 2 independent researchers performed electronic literature searches in the PubMed, Embase, Web of Science, and Cochrane Library databases to identify eligible studies published from their inception until February 2024; an updated search was conducted in August 2024. The studies included randomized controlled trials (RCTs) related to maternal and neonatal clinical outcomes. The Revised Cochrane risk-of-bias tool for randomized trials was used to examine the risk of publication bias. Stata (version 15.1; StataCorp) was used to analyze the data.

Results: We incorporated 42 pertinent RCTs involving 148,866 participants. In comparison to the routine care group, GWG was markedly reduced in the intervention group (standardized mean difference-0.19, 95% CI -0.25 to -0.13; P<.001). A significant reduction was observed in the proportion of women with excessive weight gain (odds ratio [OR] 0.79, 95% CI 0.69-0.91; P=.001), along with an increase in the proportion of women with adequate weight gain (OR 1.33, 95% CI 1.10-1.64; P=.003). Although no significant difference was reported for the proportion of individuals below standardized weight gain, there is a significant reduction in the risk of miscarriage (OR 0.66, 95% CI 0.46-0.95; P=.03), preterm birth (OR 0.8, 95% CI 0.75-0.86; P<.001), as well as complex neonatal outcomes (OR 0.93, 95% CI 0.87-0.99; P=.02). Other maternal and fetal outcomes were not significantly different between the 2 groups (all P>.05).

Conclusions: The findings corroborate our hypothesis that digitally facilitated health care can enhance certain facets of maternal and neonatal outcomes, particularly by mitigating excessive weight and maintaining individuals within a reasonable weight gain range. Therefore, encouraging women to join the digital health team sounds feasible and helpful.

Trial registration: PROSPERO CRD42024564331; https://tinyurl.com/5n6bshjt.

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来源期刊
CiteScore
14.40
自引率
5.40%
发文量
654
审稿时长
1 months
期刊介绍: The Journal of Medical Internet Research (JMIR) is a highly respected publication in the field of health informatics and health services. With a founding date in 1999, JMIR has been a pioneer in the field for over two decades. As a leader in the industry, the journal focuses on digital health, data science, health informatics, and emerging technologies for health, medicine, and biomedical research. It is recognized as a top publication in these disciplines, ranking in the first quartile (Q1) by Impact Factor. Notably, JMIR holds the prestigious position of being ranked #1 on Google Scholar within the "Medical Informatics" discipline.
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