A Mobile App, KhunLook, to Support Thai Parents and Caregivers With Child Health Supervision: Development, Validation, and Acceptability Study
Notice bibliographique
Résumé
BACKGROUND: In Thailand, children born in government hospitals receive a maternal and child health handbook (MCHH). However, when a new MCHH edition is released, those with the previous editions do not have access to the updated information. A mobile app is an appealing platform to fill this gap. We developed a mobile app called "KhunLook" as an interactive electronic MCHH intended to assist parents in child health supervision. OBJECTIVE: This study describes the user requirements and development of the KhunLook mobile app, validity of parents' growth assessments, and parents' evaluation of feasibility and acceptability of the app. METHODS: Phase 1 was a qualitative study using individual interviews. The interview data were used to revise the prototype. In phase 2, parents were randomly assigned to assess their children's growth with the app or the MCHH. The outcomes were compared to those of the physician's assessment, and congruence was determined. In phase 3, parents evaluated the feasibility and acceptability of the app in comparison to the MCHH through a web-based survey. RESULTS: Four health care providers and 8 parents participated in phase 1. Two themes were identified: (1) the mobile app potentially counters parents' infrequent use of the MCHH with accuracy, attractiveness, convenience, and simplicity, and (2) health supervision needs to be standard, up-to-date, and understandable. KhunLook was publicly launched with a family page and 7 key features: growth and nutrition, development, immunizations, oral health, reminders for the next appointment, memories, and health advice. In phase 2, 56 parents participated in the growth parameter assessments; 34 were in the App group and 22 in the MCHH group. The outcomes of the growth parameter assessments between parents and physicians in both the App and MCHH groups were not significantly different. The congruence proportions were higher in the App group for weight and head circumference, but the differences were not statistically significant. In phase 3, 356 parents from all over Thailand participated in a web-based survey. Parents rated the app feasibility as "very easy to easy" to use at higher proportions than the MCHH in all health assessment domains (growth, development, and immunizations) and ease-of-use domains with statistical significance (P<.001). The KhunLook app received a significantly higher mean score (8.59/10) than the MCHH (7.6/10) (P<.001). Most parents (317/356, 89.0%) preferred the app over MCHH. Further, 93.5% (333/356) of the parents stated that they would continue to use the app and 96.9% (345/356) would recommend others to use it. CONCLUSIONS: KhunLook, a Thai mobile app for child health supervision, was developed, validated for growth assessments, and was well accepted for ease-of-use by parents. Further studies should be conducted with a large scale of users, and the impact of this app on health behaviors and health outcomes must be evaluated.
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 | 0,009 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».