Impact of mobile health (mHealth) interventions during the perinatal period for mothers in low- and middle-income countries: a systematic review
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,008 | 0,006 |
| Bibliométrie | 0,007 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».