Smartphone addiction and well-being in adolescents: testing the mediating role of self-regulation and attention
Notice bibliographique
Résumé
Background: Smartphone addiction can have negative consequences such as anxiety, depression, insomnia, and a loss of social connectivity. Understanding smartphone addiction is still in its early stages, but self-regulation and attention deficit hyperactivity disorder (ADHD) symptoms are two established risk factors. Exploring these risk factors and their impact on individuals’ well-being may help prevent smartphone addiction. Objective: This study aims to (1) explore the relationship between smartphone addiction and psychological and social well-being (e.g., friendship validation and caring, and friendship and intimate exchange) among adolescents. (2) Examine whether self-regulation mediates the relationship between smartphone addiction and psychological well-being and social well-being. (3) Examine whether attention mediates the relationship between smartphone addiction and psychological well-being and social well-being. Methods: This was a cross-sectional study conducted in middle school in Victoria, British Columbia, Canada. Students (Grade 6-8) completed an online survey that measured smartphone addiction, attention, self-regulation, and psychological and social well-being. A bivariate correlational analysis was used to examine the relationship between smartphone addiction, self-regulation, attention psychological well-being, and social well-being. Multiple mediation analyses were used to perform the mediation between smartphone addiction, attention, self-regulation, and psychological and social well-being. Results: The bivariate correlation showed significant negative associations between smartphone addiction and attention, self-regulation, psychological well-being, and friendship validation and caring. Smartphone addiction did not have a significant relationship with friendship intimate exchange. The mediation analysis showed that attention was a significant mediator between smartphone addiction and psychological well-being (indirect effect= -.102; 95% CI -.142, -.066) and between smartphone addiction and friendship validation and caring (indirect effect= -.056; 95% CI -.093, -.024; direct effect= -.071; 95% CI -.155, .013). Attention did not significantly mediate the relationship between smartphone addiction and the friendship intimate exchange aspect of social well-being (indirect effect= -.005; 95% CI -.026, .016). Self-regulation showed a significant partial mediation between smartphone addiction and psychological well-being (indirect effect= -.016; 95% CI -.034, -.002). Self-regulation did not significantly mediate the relationship between smartphone addiction and friendship validation and caring (indirect effect=-.014; 95% CI -.034, .001) and friendship intimate exchange (indirect effect=-.001; 95% CI -.007, .007). Conclusion: The results indicated that the negative relationship between smartphone addiction and psychological well-being can be partially explained by adolescents’ attention and self-regulation abilities. The negative relationship between smartphone addiction and social well-being (validation and caring) can be partially explained by adolescents’ attention. However, both aspects of social well-being (validation and caring and intimate exchange) were not impacted by self-regulation. This study identified potential mediators that may be used for future interventions to prevent smartphone addiction and promote wellbeing.
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 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 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) | 0,004 | 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 ».