孕产妇基本服务覆盖率低,例如产前护理(ANC)和熟练的分娩护理利用,在低收入和中等收入国家(LMICs)中,孕产妇和婴儿死亡率较高。尽管移动健康(mHealth)干预措施可能会在资源有限的环境中提高服务利用率,其有效性尚不清楚。
这篇综述旨在总结mHealth干预措施对改善ANC就诊率的影响,分娩时熟练的接生,以及中低收入国家孕妇的设施分娩。
从成立到2020年10月,我们对9个电子数据库和其他资源进行了全面搜索。我们纳入了个体随机对照试验和集群随机对照试验,这些试验评估了mHealth干预措施对改善LMIC中健康孕妇围产期保健利用的有效性。我们进行了随机效应荟萃分析,并使用95%CI的风险比(RR)估计了合并效应大小。此外,2名评审员通过使用Cochrane偏差风险工具独立评估纳入研究的偏差风险,并通过使用建议分级评估证据的确定性,评估,开发和评估方法。
共有9项研究(10篇)将10,348名孕妇(干预组n=6254,60.44%;对照组n=4094,39.56%)随机分组。汇总估计显示,mHealth干预措施对改善LMIC孕妇的4个或更多ANC访问利用率具有积极作用,无论干预方向如何(单向沟通:RR2.14,95%CI1.76-2.60,I2=36%,2研究,中等确定性;双向通信:RR1.17,95%CI1.08-1.27,I2=59%,3研究,低确定性)。只有双向m健康干预措施可有效改善分娩期间熟练分娩的使用(RR1.23,95%CI1.14-1.33,I2=0%,2研究,适度的确定性),但单向m健康干预的效果尚不清楚(RR1.04,95%CI0.97-1.10,I2=73%,3研究,与标准护理相比,确定性非常低)。对于设施交付,干预措施在较少孕妇使用机构分娩的情况下有效(RR1.68,95%CI1.30-2.19,I2=36%,2研究,适度的确定性);然而,在大多数孕妇已经使用机构分娩的情况下,效果尚不清楚(RR1.01,95%CI0.97-1.04,I2=0%,1研究,低确定性)。
mHealth干预措施可能有助于改善LMIC孕妇的ANC和熟练分娩护理利用率。然而,由于在荟萃分析中观察到相当大的异质性,因此需要更多的研究来提高其可重复性和效率或加强不同形式的mHealth干预措施的证据.
PROSPEROCRD42020210813;https://tinyurl.com/2n7ny9a7。
The poor coverage of essential maternal services, such as antenatal care (
ANC) and skilled delivery care utilization, accounts for higher maternal and infant mortality in low- and middle-income countries (LMICs). Although mobile health (mHealth) interventions could potentially improve the service utilization in resource-limited settings, their effectiveness remains unclear.
This
review aimed to summarize the effect of mHealth interventions on improving the uptake of
ANC visits, skilled birth attendance at the time of delivery, and facility delivery among pregnant women in LMICs.
We conducted a comprehensive search on 9 electronic databases and other resources from inception to October 2020. We included individual randomized controlled trials and cluster randomized controlled trials that assessed the effectiveness of mHealth interventions for improving perinatal health care utilization among healthy pregnant women in LMICs. We performed a random-effects meta-analysis and estimated the pooled effect size by using risk ratios (RRs) with 95% CIs. In addition, 2 reviewers independently assessed the risk of bias of the included studies by using the Cochrane risk of bias tool and the certainty of the evidence by using the Grading of Recommendation, Assessment, Development and Evaluation approach.
A total of 9 studies (10 articles) that randomized 10,348 pregnant women (n=6254, 60.44% in the intervention group; n=4094, 39.56% in the control group) were included in this synthesis. The pooled estimates showed a positive effect of mHealth interventions on improving 4 or more
ANC visit utilizations among pregnant women in LMICs, irrespective of the direction of interventions (1-way communications: RR 2.14, 95% CI 1.76-2.60, I2=36%, 2 studies, moderate certainty; 2-way communications: RR 1.17, 95% CI 1.08-1.27, I2=59%, 3 studies, low certainty). Only 2-way mHealth interventions were effective in improving the use of skilled birth attendance during delivery (RR 1.23, 95% CI 1.14-1.33, I2=0%, 2 studies, moderate certainty), but the effects were unclear for 1-way mHealth interventions (RR 1.04, 95% CI 0.97-1.10, I2=73%, 3 studies, very low certainty) when compared with standard care. For facility delivery, the interventions were effective in settings where fewer pregnant women used facility delivery (RR 1.68, 95% CI 1.30-2.19, I2=36%, 2 studies, moderate certainty); however, the effects were unclear in settings where most pregnant women already used facility delivery (RR 1.01, 95% CI 0.97-1.04, I2=0%, 1 study, low certainty).
mHealth interventions may contribute to improving
ANC and skilled delivery care utilization among pregnant women in LMICs. However, more studies are required to improve their reproducibility and efficiency or strengthen the evidence of different forms of mHealth interventions because of the considerable heterogeneity observed in the meta-analyses.
PROSPERO CRD42020210813; https://tinyurl.com/2n7ny9a7.