The effects of e-health care on health outcomes and psychological distress in patients undergoing assisted reproductive technology: A systematic review and meta-analysis of randomized controlled trials

Abstract

Background

Many studies have reported that electronic health (e-health) care helps health professionals manage patients undergoing assisted reproductive technology (ART) and improves their reproductive outcomes and psychological distress. However, little is known about the effectiveness of e-health care on the health outcomes of patients undergoing ART.

Objectives

This study aimed to evaluate the effectiveness of e-health care on patient-centered health outcomes, such as live birth rate, pregnancy rate, time to pregnancy, etc. as well as psychological distress (i.e., infertility distress and anxiety) among individuals undergoing ART.

Design

A systematic review with random-effects or fixed-effects meta-analysis was conducted to compare e-health interventions with usual care in patients undergoing ART.

Method

Electronic database, including Medline, EMBASE, Web of Science, CINHAL, and CENTRAL, were systematically searched from the inception to December 01, 2023. The authors independently reviewed the articles based on inclusion and exclusion criteria, extracted data, and assessed risk of bias used the Cochrane Risk of Bias tool version 2.0. Heterogeneity was evaluated with I² and Chi-square. We pooled data from each study using fixed-effects meta-analysis if heterogeneity was low. Random-effects meta-analysis was used to pool data with high heterogeneity. Subgroup analysis (i.e., data collection time point) and sensitivity analysis was performed.

Result

Data were synthesized from 21 articles covering a total of 6,749 participants (female:6,227; male:522). Pooled analysis showed that e-health care may not increase live birth rate (RR 1.44 95 %CI0.78–2.67, P = 0.25). The clinical pregnancy rate was increased to 1.57 times in the e-health care group compared with the control group (Z = 2.19, P = 0.03) and the e-health care group had an increase in time to pregnancy by 17.40 days than that of the control group (Z = 2.13, P = 0.03). Lower score of Fertility Problem Inventory-social subscale was found in the e-health care group. Subgroup analysis showed that the risk ratio of clinical pregnancy was 3.07 (95 %CI 1.60–5.89) in < 3 months group, 1.21 (95 %CI 0.93–1.59) in ≥3 months group. The fertility-related knowledge level in e-health group was higher than that of the control group (Z = 2.01, P = 0.04).

Conclusion

Low certainty evidence suggests that e-health care increases the clinical pregnancy rate after the intervention. Additionally, e-health care benefits in improving perceived infertility-related stress specific to social and the level of infertility-related knowledge. Future studies are needed to establish core outcome measures for e-health intervention targeting infertile individuals.