Digital Health Needs and Preferences During Pregnancy and the Postpartum Period: Mixed Methods Study
Notice bibliographique
Résumé
BACKGROUND: Digital health is increasingly used to meet the needs of perinatal people, with estimates of pregnancy-related internet use ranging from 90% to 97% of pregnant people. As digital health takes on greater importance during the perinatal period, it is essential that providers and developers of digital health content understand why perinatal people use these resources and the features that enhance their experience. However, gaps remain in understanding the content that is most helpful and how the platforms are navigated. Learning directly from perinatal people about their needs will help ensure alignment between perinatal needs and available content. OBJECTIVE: This formative study aims to identify the reasons why perinatal people use digital health resources; the features of the digital health platforms that are of greatest importance to them; and how these differ by perinatal stage (pregnancy vs post partum), mental health conditions, parity, and demographics (race and ethnicity). METHODS: This mixed methods study used interviews; surveys; and secondary data on demographic, health, and pregnancy characteristics to identify the digital health needs and preferences of pregnant and postpartum people who used the Maven digital health platform in the United States during their pregnancy or postpartum period. The interviews informed the content of the surveys and provided additional insights and examples for interpreting the survey results. The surveys were used to collect data from a sample of Maven users, and the results were linked to the secondary data set. The interviews were thematically analyzed, and survey data were analyzed using descriptive statistics and stratified by parity, race, and mental health status. RESULTS: Overall, 13 people were interviewed (including n=4, 31% pilot interviews), and 147 pregnancy and 110 postpartum survey respondents completed the surveys and had linkable secondary data. Top reasons for using digital health resources during pregnancy were to (1) know what is normal or typical during pregnancy, (2) have access to a health care provider when needed, and (3) know how the baby is developing. Top reasons for postpartum use were to (1) help with breastfeeding, (2) know what normal baby development is, and (3) help with the baby's health issues. Top platform features during pregnancy and the postpartum period were (1) credible and trustworthy information and providers, (2) nonjudgmental information and support, and (3) no cost to the user. In general, more reasons for using digital resources were identified as extremely important during pregnancy compared with post partum. The results showed minor variations across strata. CONCLUSIONS: This formative research found minor differences in digital resource needs and preferences across user characteristics among perinatal people in the United States. Future work should examine whether there are variations in interests within topics by user characteristics, which may provide additional opportunities to better meet user needs.
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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 | 0,010 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».