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Breastfeeding is essential for maintaining the health of mothers and babies. Breastfeeding can reduce the infection rate and mortality in newborns, and can reduce the chances of overweight and obesity in children and adolescents. For mothers, a longer duration of breastfeeding can reduce the risk of breast cancer, ovarian cancer, and type 2 diabetes. Although breastfeeding has many benefits, the global breastfeeding rate is low. With the progress of time, the popularity of mobile devices has increased rapidly, and interventions based on mobile health (mHealth) may have the potential to facilitate the improvement of the breastfeeding status.
The main objective of this study was to analyze the existing evidence to determine whether mHealth-based interventions can improve the status of breastfeeding.
We systematically searched multiple electronic databases (PubMed, Web of Science, The Cochrane Library, Embase, CNKI, WanFang, and Vip ) to identify eligible studies published from 1966 to October 29, 2020. Included studies were randomized controlled trials (RCTs) studying the influence of mHealth on breastfeeding. The Cochrane Collaboration Risk of Bias tool was used to examine the risk of publication bias. RevMan 5.3 was used to analyze the data.
A total of 15 RCTs with a total sample size of 4366 participates met the inclusion criteria. Compared with usual care, interventions based on mHealth significantly increased the postpartum exclusive breastfeeding rate (odds ratio [OR] 3.18, 95% CI 2.20-4.59;
Interventions based on mHealth can significantly improve the rate of postpartum exclusive breastfeeding, breastfeeding efficacy, and participants’ attitudes toward breastfeeding, and reduce health problems in infants. Therefore, encouraging women to join the mHealth team is feasible, and breastfeeding-related information can be provided through simple measures, such as text messages, phone calls, and the internet, to improve the health of postpartum women and their babies.
Proper feeding is a prerequisite for the healthy growth of babies. The World Health Organization (WHO) recommends starting exclusive breastfeeding within an hour of birth and continuing it for at least 6 months after delivery. However, maintaining breastfeeding to 2 years or longer can be beneficial to the health of both infants and mothers. For babies, early initiation and exclusive breastfeeding within 6 months can reduce the infection rate and mortality in newborns, and continuous breastfeeding for 2 years or longer can reduce the chances of overweight and obesity in children and adolescents. For mothers, a longer duration of breastfeeding can reduce the risk of breast cancer, ovarian cancer, and type 2 diabetes [
Although breastfeeding has many benefits, the rate of exclusive breastfeeding within 6 months in low-income countries and middle-income countries is only 37%, and in high-income countries, the duration of exclusive breastfeeding is shorter than that in low-income and middle-income countries [
Many factors have been identified as having an impact on breastfeeding outcomes, and a key to solving the problem of the low breastfeeding rate is to improve awareness among pregnant women and mothers, as well as perform regular follow-ups [
Previous research into the effectiveness of mHealth-based interventions for promoting breastfeeding have been inconclusive. Therefore, the purpose of this study was to integrate the best evidence to clarify whether these interventions can improve the current breastfeeding status.
A systematic search of databases (PubMed, Embase, The Cochrane Library, and Web of Science) was conducted to identify eligible studies published from 1966 to October 29, 2020. The retrieval strategy of the PubMed database was as follows: ((“breastfeeding” OR “exclusive breastfeeding”) AND (“Mobile Applications” OR “Telemedicine” OR “Text Messaging” OR “Cell Phone” OR “Smartphone” OR “mHealth” OR “eHealth” OR “Mobile” OR “Portable Software Application” OR “Tele*” OR “e-Health” OR “m-Health” OR “?phone*” OR “Text*” OR “Short Message” OR “SMS” OR “App” OR “Apps” OR “App-based” OR “Electronic” OR “Message*” OR “Web” OR “Web-based” OR “Internet*” OR “Digital*”) AND (“randomized controlled trial” OR “controlled clinical trial” OR “randomized” OR “placebo” OR “clinical trials as topic” OR “randomly” OR “trial”) NOT (“animals”) NOT (“humans” AND “animals)). The detailed search strategy for each database is presented in
We included all studies that met the following requirements: (1) research subjects were pregnant or postpartum women; (2) the intervention group included studies that involved mHealth interventions, such as phone calls, text messages, and interactive computer systems, and the control group received usual care; (3) the study was a randomized controlled trial (RCT); (4) the definition of breastfeeding conformed to the WHO definition; and (5) the study mentioned the calculation of sample size and reported enough data to calculate the effect size.
Studies were excluded from the meta-analysis if (1) both the intervention and control groups accepted mHealth treatment; (2) the data could not be obtained, or the extracted data could be combined with other data; and (3) the study was not published in English.
Literature screening first involved reading the title and abstract to determine if the study met the inclusion criteria and then reading the full text before finally determining whether it should be included. The main data extracted were (1) the name of the first author and the date of publication; (2) research characteristics, such as the mean sample age, interventions, and sample size; and (3) outcomes, including exclusive breastfeeding rate, breastfeeding self-efficacy, health problems of infants, rate of initiation of breastfeeding within an hour of birth, and maternal attitude to breastfeeding. Data extraction was performed independently by two reviewers. Any discrepancies were resolved by discussion or by a third investigator.
The bias of RCTs included in the systematic review was assessed using the Cochrane tool [
The meta-analysis was performed using RevMan 5.3. The odds ratios (ORs), mean differences (MDs), 95% CIs, and
A total of 1368 papers were found, and further screening yielded 35 papers for the full-text search. Of these, 20 papers were excluded owing to irrelevant content, failure to meet the inclusion criteria, and qualitative results. The screening process is shown in
Screening flowchart.
A total of 15 RCTs were included in this study, and the basic characteristics of the included studies are shown in
Characteristics of the clinical trials included in this study.
First author and year |
Mode of intervention | Location | Type of participant | Intervention subjects, n | Control subjects, n | Outcomes |
Sari, 2020 [ |
Web-based program | Turkey | Pregnant women | 35 | 36 | 1. Infant prevalence |
Wen, 2020 [ |
Telephone support + SMS support | Australia | Pregnant women | 770 | 385 | 1. Exclusive breastfeeding rate |
Uscher-Pines, 2019 [ |
Video call | North Central Pennsylvania | Postpartum women | 94 | 93 | 1. Exclusive breastfeeding rate |
Puharic, 2019 [ |
Telephone support + booklet | Split Dalmatia County | Pregnant women | 232 | 123 | 1. Exclusive breastfeeding rate |
Cavalcanti, 2019 [ |
Online social network | Northeast Brazil | Postpartum women | 123 | 128 | 1. Exclusive breastfeeding rate |
Patel, 2018 [ |
Telephone support + SMS support + standard management | Rural India | Women in the third trimester | 519 | 518 | 1. Exclusive breastfeeding rate |
Araban, 2018 [ |
SMS support + courses + standard management | Iran | Pregnant women | 56 | 54 | 1. Exclusive breastfeeding rate |
Ahmed, 2017 [ |
Breastfeeding monitoring system | Midwestern Hospital | Postpartum women | 49 | 57 | 1. Exclusive breastfeeding rate |
Efrat, 2016 [ |
Telephone support + standard management | Spain | Pregnant women | 111 | 109 | 1. Exclusive breastfeeding rate |
Flax, 2014 [ |
Telephone support + courses | Nigeria | Pregnant women | 196 | 194 | 1. Exclusive breastfeeding rate |
Bonuck, 2014 [ |
E-prompt | Bronx | First or second trimester of a singleton pregnancy | 236 | 77 | 1. Exclusive breastfeeding rate |
Scott, 2013 [ |
Web-based program | United States | Pregnant women | 49 | 50 | 1. ⅡFAS |
Tahir, 2012 [ |
Telephone support + standard management | Malaysia | Postpartum women | 179 | 178 | 1. Exclusive breastfeeding rate |
Simonetti, 2011 [ |
Telephone support | Italy | Postpartum women | 55 | 59 | 1. Exclusive breastfeeding rate |
Pate, 2009 [ |
Web-based program | United States | Pregnant women | 23 | 23 | 1. BSES, BSES-SF |
aⅡFAS: Infant Feeding Attitude Scale (17-item 5-point scale).
bBSES: Breastfeeding Self-Efficacy Scale, a mother’s confidence in her ability to breastfeed.
cBSES-SF: Breastfeeding Self-Efficacy Scale-Short Form, a measurement of exclusive breastfeeding self-efficacy (14-item 5-point scale).
The bias of RCTs included in the systematic review was assessed using the Cochrane tool, and the results are shown in
Bias risk assessment chart.
Summary of risk of bias.
A total of seven studies [
Forest plot of exclusive breastfeeding rates.
A total of three studies [
A total of nine studies [
A total of eight studies [
In order to explore whether a different starting time of the intervention has an effect on the rate of exclusive breastfeeding, a subgroup analysis was carried out according to different types of subjects. The results of the study showed that there was no significant difference between the pregnancy group and the postpartum group for the increase in the rate of exclusive breastfeeding at 1, 2, 3, and 6 months after delivery, indicating that the time to start the intervention had no effect on the increase in the breastfeeding rate. The forest plots are shown in Figures S1-S4 in
We also conducted a subgroup analysis of the publication year. We found that the publication time of the study did not influence the breastfeeding rate at 1 and 2 months after delivery, and the reason may be that people generally think exclusive breastfeeding in the short term after delivery is very important. Therefore, it does not show a significant time effect. However, with extension of the follow-up, the publication time of the study had an impact on the breastfeeding rate. The possible reason is that with the extension of time, people stop exclusive breastfeeding due to lack of corresponding knowledge. However, with the comprehensive popularization of mobile devices in recent years, people’s perceptions have changed in all directions. They are paying more attention to breastfeeding, and there are increasing number of ways to obtain breastfeeding knowledge. Thus, the breastfeeding rate at 3 months after delivery has gradually increased with time. The forest plots are shown in Figures S5-S8 in
A total of three [
Forest plot of breastfeeding self-efficacy.
Forest plot of the breastfeeding self-efficacy subgroup.
A total of three [
Forest plot of health problems of infants.
A total of two [
Forest plot of breastfeeding attitudes.
Two studies [
Forest plot of initiation of breastfeeding within an hour of birth.
In this meta-analysis, we included 15 RCTs comprising 4293 patients. The purpose of this meta-analysis was to evaluate whether mHealth-based interventions can improve the current breastfeeding situation compared with usual care. The meta-analysis showed that these interventions could improve the rate of exclusive breastfeeding at 1, 2, 3, and 6 months after delivery, improve breastfeeding efficacy, and reduce health problems in infants. Since breastfeeding efficacy has a great impact on postpartum breastfeeding, using mHealth interventions to enhance breastfeeding efficacy could greatly improve the breastfeeding status. As for breastfeeding attitude and the proportion of rapid initiation of breastfeeding, there was no significant difference between the groups. Thus, interventions based on mHealth are effective for improving the breastfeeding status.
In terms of health problems in infants, sensitivity analysis showed that the results were unstable. This may be related to the inconsistent follow-up duration. One paper assessed the rate over 3 months, one assessed the rate from 3 to 6 months, and one assessed the rate in the first 6 months. It may also be related to the different intervention modes used, which were telephone support and other interventions, telephone and SMS support, and internet-based support.
In terms of the exclusive breastfeeding rate, this study found that mHealth-based interventions increased the rate, and this is consistent with a study by Lee et al [
The two existing measures to improve the breastfeeding status have their own advantages and limitations. One way to effectively convey health information to mothers who wish to breastfeed is mHealth-based interventions. The verbal and nonverbal communication behaviors of mHealth used by the provider can be used to build trust with the patients to improve satisfaction and adherence to the treatment plan [
Breastfeeding can not only reduce the risk of breast cancer and ovarian cancer, but also promote the healthy growth of babies [
The study has several limitations. First, there was insufficient literature on several outcomes, which may lead to bias. For example, the outcome of rapid initiation of breastfeeding requires more data to obtain more reliable results. Second, the results of the sensitivity analysis of some outcomes were not stable, which may lead to bias, and they need to be further verified by more studies. Third, several articles were not highly representative. For example, the research subjects in the study by Flax et al [
Our study found that interventions based on mHealth can improve the rate of exclusive breastfeeding, the breastfeeding attitude of mothers, and breastfeeding efficiency, and reduce health problems in infants. In view of the universality of mobile devices, mHealth can be used to promote the health of pregnant mothers and infants. The meta-analysis found limited improvement in rapid initiation of breastfeeding with mHealth interventions. More clinical studies are needed to confirm this view. In general, interventions based on mHealth can improve the breastfeeding status.
Search strategy of the databases.
Forest plots of subgroup analysis.
odds ratio
mean difference
mobile health
randomized controlled trial
World Health Organization
This study was funded by the Natural Science Foundation of Fujian Province, China (2018Y0037) and Fujian Provincial Health Technology Project, China (2019-CX-19).
JZ initiated the study. JQ, TW, and ML performed data extraction and analyses. JQ drafted the first version of the manuscript. JZ and TW critically reviewed the manuscript and revised it. All authors made substantial contributions to the concept and design of the study, interpreted the data, and reviewed the manuscript.
None declared.