Impact of mobile health (mHealth) interventions during the perinatal period for mothers in low- and middle-income countries: a systematic review
Bibliographic record
Abstract
OBJECTIVE: The primary objective of this review was to determine the impact of mother-targeted mobile health (mHealth) educational interventions available during the perinatal period in low- and middle-income countries (LMICs) on maternal and neonatal outcomes. INTRODUCTION: There has been significant growth of mHealth projects in LMICs. The use of mHealth interventions across the perinatal period offers the ability to share information with mothers about essential newborn care and to encourage mothers to attend perinatal clinics to obtain additional in-person support as needed. The impact of perinatal mHealth educational interventions on maternal behavior change and early neonatal mortality and morbidity outcomes in LMICs is unknown. INCLUSION CRITERIA: This review considered studies that included mHealth educational interventions targeting mothers living in LMICs during the antenatal or postnatal period using mobile devices. The intervention must have been initiated during the antenatal period (conception through birth) through six weeks postnatally. All experimental study designs were included. Outcomes included maternal knowledge, maternal self-efficacy, antenatal/postnatal care attendance and newborn early morbidity and mortality. METHODS: PubMed, Embase and CINAHL were searched on March 19, 2018 for studies published in English. The search was updated on June 7, 2018. Critical appraisal was undertaken by two independent reviewers using standardized critical appraisal instruments. Quantitative data were extracted from included studies independently by two reviewers using a standardized data extraction tool. All conflicts were resolved through consensus with a third reviewer. Quantitative data were, where possible, pooled in statistical meta-analysis. Where statistical pooling was not possible, the findings were reported narratively. RESULTS: A total of 1514 articles were screened, and 71 full-text papers were assessed for eligibility, with 23 articles critically appraised. Following appraisal, three articles were excluded due to poor quality. Of the 20 articles included, 16 were peer reviewed articles and four were gray literature reports. Eight papers targeted antenatal education, eight covered postnatal education and four covered both antenatal and postnatal education. Studies varied in terms of design, country, approach, frequency and content. Mothers who received an mHealth intervention attended a significantly greater number of antenatal care contacts (mean difference = 0.67, 95% confidence interval, 0.35 to 0.99, P = 0.0001) and were significantly more likely to have at least one postnatal care contact between six and eight weeks (odds ratio = 1.36, 95% confidence interval, 1.00 to 1.85, P = 0.05). Maternal knowledge, self-efficacy and neonatal mortality and morbidity were inconsistently reported across studies. CONCLUSIONS: mHealth education interventions are associated with increased maternal contact antenatally and postnatally in LMICs. Due to heterogeneity of studies among country of implementation, approach, frequency and content of the mHealth interventions, the impact on other maternal and neonatal outcomes is inconclusive. Future work using mHealth to target maternal education during the perinatal period should focus on standardization of content and outcome evaluations.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".