Longitudinal Associations Between Adolescents’ mHealth App Use, Body Dissatisfaction, and Physical Self-Worth: Random Intercept Cross-Lagged Panel Study
Notice bibliographique
Résumé
BACKGROUND: Longitudinal investigation of the association between mobile health (mHealth) app use and attitudes toward one's body during adolescence is scarce. mHealth apps might shape adolescents' body image perceptions by influencing their attitudes toward their bodies. Adolescents might also use mHealth apps based on how they feel and think about their bodies. OBJECTIVE: This prospective study examined the longitudinal within-person associations between mHealth app use, body dissatisfaction, and physical self-worth during adolescence. METHODS: The data were gathered from a nationally representative sample of Czech adolescents aged between 11 and 16 years (N=2500; n=1250, 50% girls; mean age 13.43, SD 1.69 years) in 3 waves with 6-month intervals. Participants completed online questionnaires assessing their mHealth app use, physical self-worth, and body dissatisfaction at each wave. The mHealth app use was determined by the frequency of using sports, weight management, and nutritional intake apps. Physical self-worth was assessed using the physical self-worth subscale of the Physical Self Inventory-Short Form. Body dissatisfaction was measured with the items from the body dissatisfaction subscale of the Eating Disorder Inventory-3. The random intercept cross-lagged panel model examined longitudinal within-person associations between the variables. A multigroup design was used to compare genders. Due to the missing values, the final analyses used data from 2232 adolescents (n=1089, 48.8% girls; mean age 13.43, SD 1.69 years). RESULTS: The results revealed a positive within-person effect of mHealth app use on the physical self-worth of girls: increased mHealth app use predicted higher physical self-worth 6 months later (β=.199, P=.04). However, this effect was not consistent from the 6th to the 12th month: a within-person increase in using apps in the 6th month did not predict changes in girls' physical self-worth in the 12th month (β=.161, P=.07). Regardless of gender, the within-person changes in the frequency of using apps did not influence adolescents' body dissatisfaction. In addition, neither body dissatisfaction nor physical self-worth predicted app use frequency at the within-person level. CONCLUSIONS: This study highlighted that within-person changes in using mHealth apps were differentially associated with adolescents' body-related attitudes. While increased use of mHealth apps did not influence body dissatisfaction across genders, it significantly predicted higher physical self-worth in adolescent girls 6 months later. A similar association was not observed among boys after 6 months. These findings indicate that using mHealth apps is unlikely to have a detrimental impact on adolescents' body dissatisfaction and physical self-worth; instead, they may have a positive influence, particularly in boosting the physical self-worth of adolescent girls.
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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».