Loneliness unlocked: Associations with smartphone use and personality
Notice bibliographique
Résumé
Communication and relationships have been dramatically altered among emerging adults thanks to the rapid adoption of the smartphone in just over a decade. Studying the effects of evolving personal technology helps researchers understand both the detriments of widespread adoption and the benefits that accompany the technology. One such area of concern is the relationship of technology with loneliness. Emerging adulthood is described as the period of transition from adolescence to adulthood, taking place from age 18–25. This period is characterized by change, exploration, but also a vulnerability to psychological distress. Young adults are not only at greater risk of loneliness compared to other developmental stages, but report greater distress about being lonely (Rokach, 2000). Previous research has found support for the hypothesis that use of social communication on the Internet has a bidirectional relationship with loneliness (Nowland et al., 2018); use of the Internet can support relationships and decrease loneliness, but if used as a compensation for social skill deficits, the Internet can also displace quality time spent in relationships, and thereby increase loneliness. This study examines loneliness and its relationship with smartphone use, while also accounting for individual differences in facets of neuroticism, communication apprehension, emotional support, and nomophobia for emerging adults. Participants (N = 302; MAGE = 18.85) completed self-report measures of loneliness and the individual differences variables. They also reported average daily smartphone data of screen time, pickups, and application (app) use, which was measured by their personal devices. Correlations indicated loneliness was positively associated with screen time, social media app use, neuroticism, social recognition, communication anxiety, and nomophobia. Loneliness was negatively associated with smartphone pickups, communication application use, need for affiliation, and emotional support. A regression analysis revealed that neuroticism, need for affiliation, social recognition, emotional support, and smartphone pickups were significant predictors of loneliness, when taking into account all the individual difference and smartphone use variables. Neuroticism and loneliness have a strong relationship, but a hierarchical regression showed that over and above neuroticism and its facets, smartphone screen time and pickups predict loneliness. Overall, the results for this sample of emerging adults supported the hypotheses by Nowland et al. (2018) about social use of the Internet, but applied to smartphone use. More time spent on one's smartphone and on social media apps is related to increased loneliness, and is discussed in context of identity development. More frequent use (pickups) and use of communication apps is related to decreased loneliness and is discussed with respect to development of relationship intimacy. These results suggest that loneliness in young adults is related to different types of smartphone use, even when accounting for stable characteristics such as personality. Finally, neuroticism remains a significant variable in understanding loneliness, and further examination of lower-order facets help define a more nuanced profile in individual differences.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, pas un consensus.
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