Decreasing screen time and/or increasing exercise only helps in certain situations for young adults
Notice bibliographique
Résumé
There seems to be a lack of consensus about whether greater screen time is highly correlated to greater body mass index (BMI) (and lower physical exercise). What has been proven is that the vast majority of children and adolescents spend a "lot of time" indulging in screen-based leisure. The aim of this study was to investigate, among young adults, screen time and physical activity/fitness. A questionnaire was developed and circulated to young adults via media networks (i.e. email, social media platforms, etc.). There was no geographic restriction, and the survey was designed in English. Two people did not consent to the study, while 262 consented and completed the survey. The vast majority of participants resided in Canada, with a noticeable minority living in the United Kingdom and the United States. Of the participants, 46% were 18 or 19 years old, 30% were between the ages of 20 and 22 years and the remaining 24% were split evenly between the age cohorts of under 18 years and over 22 years. Four of 262 (2%) participants did not disclose their sex, 66% reported as female and 32% noted they were male. The BMI ranged from 14.4525 to 39.5325, and had a mean of 22.8155 and standard deviation of 4.1939. Among people who spent less than 4 h of exercise a week, those who spent more than 5 h on screen time based activities had a higher BMI (p = 0.0032) of 23.8151 vs. 21.7879 for those who spent less than 5 h. There was no relation between screen time and BMI among people who spent more than 4 h of exercise a week (p = 0.6209). Between exercise groups who spent less than 5 h of screen time a day, there was no relation between hours of exercise and BMI (p = 0.1242). There seems to exist a trend that among those who spend more than 5 h of screen time a day, less exercise is related to higher BMI (p = 0.0510) - 23.8151 vs. 22.4361. Healthy lifestyle choices such as fewer screen time hours and more exercise can be beneficial to young adults. Among certain groups, such as those who spend a lot of time on screens and those who do not exercise regularly, the benefits of more exercise and less screen time, respectively, are much more noticeable.
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 | 0,001 | 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 | 0,000 | 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 ».