The Value of Mobile Health in Improving Breastfeeding Outcomes Among Perinatal or Postpartum Women: Systematic Review and Meta-analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Background 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. Objective The main objective of this study was to analyze the existing evidence to determine whether mHealth-based interventions can improve the status of breastfeeding. Methods 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. Results 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; P<.001), enhanced breastfeeding self-efficacy (mean difference [MD] 8.15, 95% CI 3.79-12.51; P=.002; I2=88%), reduced health problems in infants (OR 0.62, 95% CI 0.43-0.90; P=.01; I2=0%), and improved participants’ attitudes toward breastfeeding compared with usual care (MD 3.94, 95% CI 1.95-5.92; P<.001; I2=0%). There was no significant difference in the initiation of breastfeeding within an hour of birth between the intervention group and the usual care group (OR 1.26, 95% CI 0.55-2.90; P=.59). In addition, subgroup analysis was carried out according to different subjects and publication times. The results showed that the breastfeeding rate was not limited by the types of subjects. The breastfeeding rate based on mHealth at 1 month and 2 months after delivery did not change over the time of publication (2009 to 2020), and the breastfeeding rate based on mHealth at 3 months and 6 months after delivery gradually increased with time (2009 to 2020). Conclusions 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.066 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.036 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".