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Enregistrement W4285610179 · doi:10.2196/38370

An Observational Report of Screen Time Use Among Young Adults (Ages 18-28 Years) During the COVID-19 Pandemic and Correlations With Mental Health and Wellness: International, Online, Cross-sectional Study

2022· article· en· W4285610179 sur OpenAlexvenueno aff
Michelle Teresa Wiciak, Omar Shazley, Daphne Santhosh

Notice bibliographique

RevueJMIR Formative Research · 2022
Typearticle
Langueen
DomainePsychology
ThématiqueCOVID-19 and Mental Health
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPandemicCross-sectional studyAnxietyMedicineMental healthObservational studyYoung adultScreen timeDepression (economics)Coronavirus disease 2019 (COVID-19)DemographyGerontologyPsychiatryPhysical therapyPhysical activityInternal medicineDisease

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Screen time (ST) drastically increased during the COVID-19 pandemic, but there is little research on the specific type of ST use, degree of change from before COVID-19, and possible associations with other factors. Young adults are a particular interest since previous studies have shown the detriment ST has on a young person's health. With the combination of a life-changing pandemic, there are unreached depths regarding ST and young adults. This study aims to provide insight into these unknowns. OBJECTIVE: This study aims to assess ST in 3 domains (entertainment, social media [SM], and educational/professional) in young adults early in the COVID-19 pandemic; identify trends; and identify any correlations with demographics, mental health, substance abuse, and overall wellness. METHODS: An online, cross-sectional observational study was performed from September 2020 to January 2021 with 183 eligible respondents. Data were collected on ST, trauma from COVID-19, anxiety, depression, substance use, BMI, and sleep. RESULTS: The average total ST during COVID-19 was 23.26 hours/week, entertainment ST was 7.98 hours/week, SM ST was 6.79 hours/week, and ST for educational or professional purposes was 8.49 hours/week. For all categories, the average ST during COVID-19 was higher than before COVID-19 (P<.001). We found ST differences between genders, student status, and continent of location. Increased well-being scores during COVID-19 were correlated with greater change in total ST (P=.01). Poorer sleep quality (P=.01) and longer sleep duration (P=.03) were associated with a greater change in entertainment ST (P=.01). More severe depression and more severe anxiety was associated with the amount of entertainment ST (P=.047, P=.03, respectively) and greater percent change in SM (P=.007, P=.002, respectively). Greater stress from COVID-19 was associated with the amount of ST for educational/professional purposes (P=.05), change in total ST (P=.006), change in entertainment ST (P=.01), and change in ST for educational/professional purposes (P=.02). Higher Alcohol, Smoking, and Substance Involvement Screening Test (ASSIST) tobacco scores were associated with greater change in total ST (P=.004), and higher pack-years were associated with greater change in SM ST (P=.003). Higher alcohol scores (P=.004) and servings of alcohol per week (P=.003) were associated with greater change in entertainment ST. Quarantining did not negatively impact these variables. CONCLUSIONS: There is no doubt ST and worsening mental health increased during COVID-19 in young adults. However, these findings indicate there are many significant associations between ST use and mental health. These associations are more complex than originally thought, especially since we found quarantining is not associated with mental health. Although other factors need to be further investigated, this study emphasizes different types of ST and degree of change in ST affect various groups of people in discrete ways. Acknowledging these findings can help young adults optimize their mental health during pandemics.

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 distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,017
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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.

Tête enseignante Opus0,221
Tête enseignante GPT0,529
Écart entre enseignants0,308 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations18
Publié2022
Routes d'admission1
Résumé présentoui

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