Mobile for Mothers mHealth Intervention to Augment Maternal Health Awareness and Behavior of Pregnant Women in Tribal Societies: Randomized Quasi-Controlled Study
Notice bibliographique
Résumé
BACKGROUND: Despite several initiatives taken by government bodies, disparities in maternal health have been noticeable across India's socioeconomic gradient due to poor health awareness. OBJECTIVE: The aim of this study was to implement an easy-to-use mobile health (mHealth) app-Mobile for Mothers (MfM)-as a supporting tool to improve (1) maternal health awareness and (2) maternal health-related behavioral changes among tribal and rural communities in India. METHODS: Pregnant women, aged 18 to 45 years, were selected from two rural villages of Jharkhand, India: (1) the intervention group received government-mandated maternal care through an mHealth app and (2) the control group received the same government-mandated care via traditional means (ie, verbally). A total of 800 accredited social health activists (ASHAs) were involved, of which 400 were allocated to the intervention group. ASHAs used the MfM app to engage with pregnant women during each home visit in the intervention group. The mHealth intervention commenced soon after the baseline survey was completed in February 2014. The end-line data were collected between November 2015 and January 2016. We calculated descriptive statistics related to demographics and the percentage changes for each variable between baseline and end line per group. The baseline preintervention groups were compared to the end-line postintervention groups using Pearson chi-square analyses. Mantel-Haenszel tests for conditional independence were conducted to determine if the pre- to postintervention differences in the intervention group were significantly different from those in the control group. RESULTS: Awareness regarding the five cleans (5Cs) in the intervention group increased (P<.001) from 143 (baseline) to 555 (end line) out of 740 participants. Awareness about tetanus vaccine injections and the fact that pregnant women should receive two shots of tetanus vaccine in the intervention group significantly increased (P<.001) from 73 out of 740 participants (baseline) to 372 out of 555 participants (end line). In the intervention group, awareness regarding the fact that problems like painful or burning urination and itchy genitals during pregnancy are indicative of a reproductive tract infection increased (P<.001) from 15 (baseline) to 608 (end line) out of 740 participants. Similarly, knowledge about HIV testing increased (P<.001) from 39 (baseline) to 572 (end line) out of 740 participants. We also noted that the number of pregnant women in the intervention group who consumed the prescribed dosage of iron tablets increased (P<.001) from 193 (baseline) out of 288 participants to 612 (end line) out of 663 participants. CONCLUSIONS: mHealth interventions can augment awareness of, and persistence in, recommended maternal health behaviors among tribal communities in Jharkhand, India. In addition, mHealth could act as an educational tool to help tribal societies break away from their traditional beliefs about maternal health and take up modern health care recommendations. TRIAL REGISTRATION: OSF Registries 9U8D5; https://doi.org/10.17605/OSF.IO/9U8D5.
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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 | 0,004 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,010 | 0,001 |
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 ».