Individual and Parental Factors of Adolescents’ mHealth App Use: Nationally Representative Cross-sectional Study
Notice bibliographique
Résumé
BACKGROUND: Knowledge of the characteristics of adolescents who use mobile health (mHealth) apps to monitor health and how these characteristics differ from those of app nonusers is limited. OBJECTIVE: We aimed to determine mHealth app use based on adolescent and parental factors, including sociodemographics, digital skills, and health indicators, in a nationally representative sample of Czech adolescents (N=2500). METHODS: Adolescents aged 11 to 16 years and one of their parents participated in an online survey in 2021. A professional research agency recruited the participants. Quotas were used to ensure the sample's representativeness. The sociodemographic factors were the adolescents' age, gender, and parental perceived financial security. The adolescents also provided information about their screen time, eHealth literacy, BMI, health anxiety, physical activity, and sleep quality. Parents reported their digital skills, mobile phone attitudes, and the mediation of their children's online health information-seeking behaviors. We evaluated the differences between the users and nonusers of mHealth apps and identified the significant predictors of mHealth app use. Next, we separately examined how these factors were associated with the use of mHealth apps that track calorie intake or expenditure, number of steps, weight, or sports activity (eg, exercise, running, and working out), as well as other mHealth apps (eg, those that track sleep and heart rate). RESULTS: More than half of the adolescents (1429/2455, 58.21%) reported using mHealth apps. App users were relatively older and, more often, girls. Apps that counted the number of steps were used most frequently, and adolescents whose parents reported higher perceived financial security used them more regularly. Overall, being older and physically active and having higher eHealth literacy skills were associated with using mHealth apps. Adolescents with higher BMI, health anxiety, and lower sleep quality more frequently used mHealth apps to track calorie intake or expenditure, weight, and health indicators. mHealth apps to track physical activity were used more regularly by girls. There was a positive association between parental mediation of online health information-seeking behaviors and adolescents' mHealth app use. CONCLUSIONS: These findings demonstrated that older age, physical activity, and eHealth literacy skills were the common underlying factors of adolescents' mHealth app use. We initially showed parents as significant role models for their children's adoption of, and engagement with, mHealth apps when they actively mediate their online health information-seeking behaviors. Improving the eHealth literacy skills of adolescents through parental guidance might enhance health technology use in this population. Tracking eating behaviors, weight, and health were more prevalent for adolescents who reported higher BMI, health anxiety, and lower sleep quality. Future research studies should examine the determinants and health outcomes of adolescents' mHealth app use longitudinally.
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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 | 0,001 |
| 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 | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| 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 ».