ObjectiveTo systematically review the efficacy of non-pharmacological interventions to reduce fear of childbirth. MethodsThe Cochrane Library, PubMed, EMbase, Web of Science, CNKI, WanFang Data, VIP, and CBM databases were electronically searched to collect randomized controlled trials (RCTs) of the efficacy of non-pharmacological interventions to reduce fear of childbirth from inception to December 2021. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias of the included studies; then, a network meta-analysis was performed using Stata 15.0 software. ResultsA total of 19 RCTs involving 3 409 patients were included. Ten non-pharmacological interventions (prenatal education, scenario-based health education, psychological guidance, yoga training, hypnosis, mobile learning education, cognitive behavioral therapy, physical relaxation guidance, breathing guidance, and usual care) were included. The results of the reticulated meta-analysis of the Wijma Delivery Expectancy Questionnaire (W-DEQ-A) showed that the rankings of the interventions were as follows: prenatal education > yoga training > cognitive behavioral therapy > situational simulation health education > psychological guidance > physical relaxation guidance > conventional care. The results of the Wijma Experience of Childbirth Questionnaire (W-DEQ-B) mesh meta-analysis showed that the rankings of the interventions were as follows: mobile learning education > prenatal education > scenario-based health education > cognitive behavioral therapy > breathing instruction > hypnosis > psychological instruction > physical relaxation instruction > usual care. ConclusionThe current evidence suggests that prenatal education, mobile learning education, situational simulation health education, and yoga training may be effective interventions in improving maternal fear of childbirth. Due to the limited quality and quantity of the included studies, more high-quality studies are needed to verify the above conclusion.
ObjectiveTo construct and verify the nomogram prediction model of pregnant women's fear of childbirth. MethodsA convenient sampling method was used to select 675 pregnant women in tertiary hospital in Tangshan City, Hebei Province from July to September 2022 as the modeling group, and 290 pregnant women in secondary hospital in Tangshan City from October to December 2022 as the verification group. The risk factors were determined by logistic regression analysis, and the nomogram was drawn by R 4.1.2 software. ResultsSix predictors were entered into the model: prenatal education, education level, depression, pregnancy complications, anxiety and preference for delivery mode. The areas under the ROC curves of the modeling group and the verification group were 0.834 and 0.806, respectively. The optimal critical values were 0.113 and 0.200, respectively, with sensitivities of 67.2% and 77.1%, the specificities were 87.3% and 74.0%, and the Jordan indices were 0.545 and 0.511, respectively. The calibration charts of the modeling group and the verification group showed that the coincidence degree between the actual curve and the ideal curve was good. The results of Hosmer-Lemeshow goodness of fit test were χ2=6.541 (P=0.685) and χ2=5.797 (P=0.760), and Brier scores were 0.096 and 0.117, respectively. DCA in modeling group and verification group showed that when the threshold probability of fear of childbirth were 0.00 to 0.70 and 0.00 to 0.70, it had clinical practical value. ConclusionThe nomogram model has good discrimination, calibration and clinical applicability, which can effectively predict the risk of pregnant women's fear of childbirth and provide references for early clinical identification of high-risk pregnant women and targeted intervention.