Willingness to Adopt mHealth Among Chinese Parents During the COVID-19 Outbreak: Cross-sectional Questionnaire Study
Notice bibliographique
Résumé
Background Parental involvement in mobile health (mHealth) to consult with medical professionals appears to be prevalent in China with the rapid development of the internet. More parents with busy jobs have chosen to use mHealth. During the ongoing COVID-19 outbreak, mHealth can assist with health promotion, directions for medication use, and disease diagnosis via online chat and video consultation without contacting others. To our knowledge, no studies have been performed to explore the role of mHealth in parents’ attitudes toward child health care at home during the COVID-19 outbreak. Objective This study aims to identify the associated factors of willingness to adopt mHealth among Chinese parents during the COVID-19 outbreak and to explore the correlation between the frequency of adopting mHealth and parents’ attitudes toward child health care at home. Methods Chinese parents were asked to complete an online survey from January 25 to February 15, 2020. The questionnaire comprised of two parts with a total of 16 items, including parents’ demographic variables and attitudes toward child health care at home. By multivariate logistic regression, we explored factors associated with parents’ willingness to adopt mHealth during the COVID-19 outbreak. Pearson chi-square tests were used to reveal the correlation between the frequency of adopting mHealth and parents’ attitudes toward child health care at home. Results A total of 254 parents enrolled, and 202 (79.5%) parents were willing to adopt mHealth during the COVID-19 outbreak. Parents’ age (26-35 years: adjusted odds ratio [AOR] 8.114, 95% CI 1.471-44.764), parents’ interest in the COVID-19 pandemic (moderate: AOR 8.753, 95% CI 2.009-38.127; high: AOR 22.194, 95% CI 5.509-89.411), the source that recommended mHealth (medical health providers: AOR 4.257, 95% CI 1.439-12.596), the presence of chronic disease in their children (yes: AOR 20.844, 95% CI 4.600-94.443), parents’ duration of daily internet use (4-6 hours: AOR 6.487, 95% CI 1.870-22.495; >6 hours: AOR 8.766, 95% CI 1.883-40.804), and adoption of mHealth before the COVID-19 outbreak (yes: AOR 3.413, 95% CI 1.234-9.444) were significantly correlated with the parents’ willingness to adopt mHealth during the COVID-19 outbreak. The frequency of mHealth use among parents was correlated with their behaviors in regard to handwashing (χ26=18.967, P=.004), mask wearing (χ26=45.364, P<.001), frequency of leaving the home (χ26=16.767, P=.01), room disinfection and ventilation (χ26=19.515, P=.003), temperature checking (χ26=17.47, P=.007), and mental health care of children (χ26=63.810, P<.001) during the COVID-19 pandemic. Conclusions We found various objective factors that were associated with parents’ willingness to adopt mHealth during the COVID-19 outbreak. Overall, parents’ willingness to adopt mHealth was high. The frequency of mHealth use among parents was correlated with their attitudes toward child health care at home. The option of mHealth to patients at home during the COVID-19 outbreak would be beneficial for education and improvement in self-management of child health care at home.
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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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 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 ».