MétaCan
Menu
Retour à la cohorte
Enregistrement W2999912403 · doi:10.11124/jbisrir-d-19-00191

Impact of mobile health interventions during the perinatal period on maternal psychosocial outcomes: a systematic review

2020· review· en· W2999912403 sur OpenAlexafffund
Justine Dol, Brianna Hughes, Gail Tomblin Murphy, Megan Aston, Douglas McMillan, Marsha Campbell‐Yeo

Notice bibliographique

RevueJBI Evidence Synthesis · 2020
Typereview
Langueen
DomaineHealth Professions
ThématiqueMobile Health and mHealth Applications
Établissements canadiensIzaak Walton Killam Health CentreNova Scotia Health AuthorityDalhousie University
Organismes subventionnairesCanadian Institutes of Health Research
Mots-clésCINAHLPsychological interventionmHealthPsychosocialCritical appraisalPostpartum periodPsycINFOMedicineMEDLINEFamily medicineNursingPsychiatryPregnancyAlternative medicine

Résumé

récupéré en direct d'OpenAlex

OBJECTIVE: The objective of this review was to evaluate the effectiveness of mother-targeted mobile health (mHealth) education interventions during the perinatal period on maternal psychosocial outcomes in high-income countries. INTRODUCTION: The perinatal period is an exciting yet challenging period for mothers that requires physical, emotional and social adjustment to new norms and expectations. In recent years, there has been an increase in the use of mHealth by new mothers who are seeking health information through online or mobile applications. While there have been systematic reviews on the impact of mHealth interventions on maternal and newborn health in low- and middle-income countries, the impact of these interventions on maternal psychosocial health outcomes in high-income countries remains uncertain. INCLUSION CRITERIA: This review considered studies of mHealth education interventions targeting mothers in high-income countries (as defined by the World Bank) during the perinatal period. Interventions must have started between the antenatal period (conception through birth) through six weeks postpartum. All experimental study designs were included. Outcomes included self-efficacy, social support, postpartum anxiety and postpartum depression. METHODS: PubMed, CINAHL, PsycINFO and Embase were searched for published studies in English on December 16, 2018. Gray literature was also searched for non-peer reviewed articles, including Google Scholar, mHealth intelligence and clinical trials databases. Critical appraisal was undertaken by two independent reviewers using standardized critical appraisal instruments from JBI. Quantitative data were extracted from included studies independently by two reviewers using the standardized data extraction tool from JBI. All conflicts were solved through consensus with a third reviewer. Quantitative data were, where possible, pooled in statistical meta-analysis using RevMan. Where statistical pooling was not possible, findings were reported narratively. RESULTS: Of the 1,607 unique articles identified, 106 full-text papers were screened and 24 articles were critically appraised, with 21 included in the final review. Eleven were quasi-experimental and 10 were randomized controlled trials. The mHealth intervention approach varied, with text message and mobile applications being the most common. Length of intervention ranged from four weeks to six months. The topics of the mHealth intervention varied widely, with the most common topic being postpartum depression. Mothers who received an mHealth intervention targeting postpartum depression showed a decreased score on the Edinburgh Postnatal Depression Scale when measured post-intervention (odds ratio = -6.01, 95% confidence interval = -8.34 to -3.67, p < 0.00001). The outcomes related to self-efficacy, social support and anxiety showed mixed findings of effectiveness (beneficial and no change) across the studies identified. CONCLUSIONS: This review provides insight into the effectiveness of mHealth interventions targeting mothers in high-income countries in the perinatal period to enhance four psychosocial outcomes: self-efficacy, social support, anxiety and depression. Despite a wide variety of outcome measurements used, the predominant findings suggest that there are insufficient data to conclude that mHealth interventions can improve self-efficacy and anxiety outcomes. Potential benefits on social support were related to interventions targeting postnatal behaviors. Postpartum depression was the mostly commonly reported outcome. Findings related to the comparison of pre-post outcomes and intervention versus control demonstrated that mHealth interventions targeting postpartum depression were associated with a reduction in postpartum depression.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,008
score de la tête « metaresearch » (Gemma)0,038
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,008
Score d'incertitude au seuil0,040

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0080,038
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0070,006
Bibliométrie0,0060,007
Études des sciences et des technologies0,0010,001
Communication savante0,0030,002
Science ouverte0,0020,002
Intégrité de la recherche0,0020,001
Charge utile insuffisante (le modèle a refusé de juger)0,0050,000

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.

Tête enseignante Opus0,078
Tête enseignante GPT0,525
Écart entre enseignants0,447 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeRevue systématique
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations80
Publié2020
Routes d'admission2
Résumé présentoui

Explorer davantage

Même revueJBI Evidence SynthesisMême sujetMobile Health and mHealth ApplicationsTravaux en français237 207