Look to the Future and SMILE: Feasibility of Interactive Voice Assistant Technology to Support Maternal Infant Health
Notice bibliographique
Résumé
Background Both maternal and infant mortality rates serve as indicators of population health and are unacceptably high worldwide. Voice assistant (VA) technologies present a potential new modality to support maternal child health. We developed an interactive VA intervention app (SMILE) to deliver brief, maternal-infant education and management skills (eg, perinatal care, stress management, breast feeding, infant-care) using evidence-based content. Objective The objective was to understand the feasibility and usability of an interactive VA intervention to support maternal and infant health among a group of pregnant women. Methods We employed a mixed methods study design. Pregnant women were recruited via email and word of mouth. Participants completed a baseline demographic and technology-use survey and were asked to use the intervention over the course of two weeks. Postintervention, participants were invited to participate in an individual or group interview. Interviews were conducted to elicit feedback regarding thoughts and attitudes towards VA technology to support the health of mothers and infants. Descriptive analysis was used to summarize quantitative data (ie, survey responses, app logs) and thematic analysis was used for qualitative data (ie, transcriptions of voice recordings collected from SMILE, transcriptions of follow-up interviews). Results Out of 46 respondents, 19 participants were consented, completed baseline surveys and used SMILE. Approximately 63% (n=12) of participants participated in exit interviews. The sample was predominantly 25-34 years old (n=16, 84%), part of a two-parent household (n=19, 100%), white (n=15, 79%), and pregnant with their first child (n=12, 63%). Nine participants (47.4%) reported that they practice stress management, and favorable stress-management activities were mainly comprised of exercise activities, yoga, and outdoor activities without technology involvement. Over half of the participants reported using technology to support pregnancy self-management (n=10, 53%). However, participants preferred mobile apps for education and self-management support during pregnancy and relied on the Internet to access health-related information. More than half of participants reported using default VAs on their phone (n=11, 58%) and on smart speakers (n=10, 53%). Yet, VA technology was mainly reported as being used for basic tasks, such as setting a timer or reminder, checking the weather, turning on/off the lights, or playing music. Postintervention, participants verbalized that VA technology was a potential medium for receiving health information, pregnancy-related information, and could be a strategy to engage other family members in the process. Major concerns revolved around security, privacy, trust, and concerns regarding interacting via voice when in public. Conclusions Although this research is limited by the small and predominantly white sample size, this research represents one of the first studies to explore perceptions and attitudes towards VA to promote maternal-infant health. As VA technology increases in popularity, adoption and utility to support health and well-being among pregnant women is nascent. While VA technology offers some benefits (eg, reduce literacy barriers, hands-free), familiarity and trust of nonvoice digital health tools (eg, mobile apps, Web-based content) remain important in supporting maternal-child health. Digital health solutions that incorporate multiple platforms (eg, mobile apps, Internet, voice) warrant further exploration to optimize support for maternal child health.
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 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 | 0,012 | 0,034 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| 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 ».