0383 Associations Between Digital Technology and Sleep Health by Country, Age, and Sex
Notice bibliographique
Résumé
Abstract Introduction We investigated whether the widely-observed association between digital technology use and sleep health varied by country, age, and/or sex in a global sample of adults. Methods We used 2023 survey data from 35 countries (n=35,018, ~1000/country) to characterize the self-reported effects of digital technology on physical health, sleep quality, and tiredness (see https://sync.ithra.com/research). We examined whether responses varied by country, age, and sex. Results Participants from 35 countries (52.2% male) ranged from 18-99 years old (mean=38); 18.7% of respondents were between 18-24, and 8.0% were 65+. Unadjusted analyses showed that across all participants, 31.7% reported that digital technology reduced their physical health. Respondents in China had the lowest prevalence (11.8%) of digital media worsening physical health, while respondents in Estonia had the highest prevalence (56.4%). Younger respondents (18-24) were more likely to report that digital technology worsened physical health than older (65+) respondents (38.5% vs. 21.9%). Females were slightly more likely (33.7%) than males (30.0%) to report that digital media worsened physical health. When asked which physical conditions were experienced after using digital technology for longer than usual, 40.5% reported tiredness, and 39.0% reported decreased sleep quality. Out of all 35 countries, prevalence was lowest in Italy for both the symptoms of tiredness (22.1%) and decreased sleep quality (19.7%), while they were highest in Ghana (60.6%) for tiredness and Malaysia (57.3%) for decreased sleep quality. Among the youngest age group (18-24-year-olds), 48.4% and 47.1% reported tiredness and decreased sleep quality, respectively, compared to 22.4% and 16.1% for 65+. Females were more likely to report tiredness (42.3%) and decreased sleep quality (40.9%) as symptoms compared to males (38.8% and 37.8%, respectively). Conclusion These novel global results show that over one-third of adult respondents believe heavy use of digital technology leads to sleep-related symptoms, with larger effects for younger and female adults. Variation by country suggests that cultural factors may affect the association between digital technology use and sleep health. Support (if any) Aramco
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 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.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».