The Development and Evaluation of a Text Message Program to Prevent Perceived Insufficient Milk Among First-Time Mothers: Retrospective Analysis of a Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Several recent trials have examined the feasibility and efficacy of automated SMS text messaging to provide remote breastfeeding support to mothers, but these texting systems vary in terms of design features and outcomes examined. OBJECTIVE: This study examined user engagement with and feedback on a theory-grounded SMS text messaging intervention intended to prevent perceived insufficient milk (PIM)-the single, leading modifiable cause of unintended breastfeeding reduction and cessation. METHODS: We recruited 250 nulliparous individuals intending to breastfeed between 13 and 25 weeks of pregnancy in southwestern Pennsylvania. Participants were randomly assigned with equal allocation to either an SMS intervention to prevent PIM and unintended breastfeeding reduction or cessation (MILK, a Mobile, semiautomated text message-based Intervention to prevent perceived Low or insufficient milK supply; n=126) or a control group receiving general perinatal SMS text messaging-based support via the national, free Text4Baby system (n=124). Participants in both groups received SMS text messages 3 to 7 times per week from 25 weeks of pregnancy to 8 weeks postpartum. The MILK intervention incorporated several automated interactivity and personalization features (eg, keyword texting for more detailed information on topics and branched response logic) as well as an option to receive one-on-one assistance from an on-call study lactation consultant. We examined participant interactions with the MILK system, including response rates to SMS text messaging queries. We also sought participant feedback on MILK content, delivery preferences, and overall satisfaction with the system via interviews and a remote survey at 8 weeks postpartum. RESULTS: Participants randomized to MILK (87/124, 70.2% white and 84/124, 67.7% college educated) reported that MILK texts increased their breastfeeding confidence and helped them persevere through breastfeeding problems. Of 124 participants, 9 (7.3%) elected to stop MILK messages, and 3 (2.4%) opted to reduce message frequency during the course of the study. There were 46 texts through the MILK system for individualized assistance from the study lactation consultant (25/46, 54% on weekends or after-hours). The most commonly texted keywords for more detailed information occurred during weeks 4 to 6 postpartum and addressed milk volume intake and breastfeeding and sleep patterns. MILK participants stated a preference for anticipatory guidance on potential breastfeeding issues and less content addressing the benefits of breastfeeding. Suggested improvements included extending messaging past 8 weeks, providing access to messaging for partners, and tailoring content based on participants' pre-existing breastfeeding knowledge and unique breastfeeding trajectory. CONCLUSIONS: Prenatal and postpartum evidence-based breastfeeding support delivered via semiautomated SMS text messaging is a feasible and an acceptable intervention for first-time mothers. To optimize engagement with digital breastfeeding interventions, enhanced customization features should be considered. TRIAL REGISTRATION: ClinicalTrials.gov NCT02724969; https://clinicaltrials.gov/ct2/show/NCT02724969.
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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 | 0,037 | 0,081 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,005 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».