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Enregistrement W2738374831 · doi:10.1186/s12966-017-0547-2

The use of entertainment and communication technologies before sleep could affect sleep and weight status: a population-based study among children

2017· article· en· W2738374831 sur OpenAlexafffundabout
Nomathemba Dube, Kaviul Khan, Sarah Loehr, Yen Li Chu, Paul J. Veugelers

Notice bibliographique

RevueInternational Journal of Behavioral Nutrition and Physical Activity · 2017
Typearticle
Langueen
DomainePsychology
ThématiqueSleep and related disorders
Établissements canadiensCentre for Advancing Health OutcomesUniversity of British ColumbiaPublic Health OntarioUniversity of TorontoUniversity of Alberta
Organismes subventionnairesWomen and Children's Health Research InstituteAlberta Health Services
Mots-clésBedtimeSleep (system call)BedroomChildhood obesitySleep hygieneMedicinePopulationPsychologyObesityOverweightGerontologyPsychiatryInsomniaSleep qualityEnvironmental healthInternal medicine

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Short sleep duration and poor sleep quality have been demonstrated to be associated with childhood obesity. It has been suggested that electronic entertainment and communication devices (EECDs) including TVs, computers, tablets, video games and cell phones interfere with sleep in children and youth. The aim of this study was to assess the impact that the use of EECDs in the hour before bedtime has on sleep and weight status to inform sleep promotion interventions and programs to prevent childhood obesity. METHODS: A provincially representative sample of 2334 grade 5 children and their parents in Alberta, Canada was surveyed. Parents reported their child's bedtime and wake-up time along with how often their child snored, felt sleepy during the day, woke-up at night and woke-up in the morning feeling unrefreshed. Sleep duration, sleep quality and sleep efficiency were derived from these indicators. Parents also reported on the presence of EECDs in their child's bedroom, while children reported use of EECDs during the day and frequency of using each of these devices during the hour before sleep. The height and weight of children were measured. Multivariable mixed effect linear and logistic regression models were used to determine how sleep duration, sleep quality, sleep efficiency and weight status are influenced by (i) access to EECDs in children's bedrooms, (ii) use of EECDs during the hour before sleep, and (iii) calming activities specifically reading during the hour before sleep. RESULTS: Sleep duration was shorter by -10.8 min (cell phone), -10.2 min (computer) and -7.8 min (TV) for those with bedroom access to and used these EECDs during the hour before sleep compared to no access and no use. Good sleep quality was hindered by bedroom access to and use of all EECDs investigated during the hour before sleep, particularly among users of cell phones (OR = 0.64, 95% CI: 0.58-0.71) and computers (OR = 0.72, 95% CI: 0.65-0.80). Very good sleep efficiency was decreased by access to and frequent use of a TV (54%), cell phone (52%), tablet (51%) and video games (51%). Odds of obesity were doubled by bedroom access to and use of a TV and computer during the hour before sleep. Children who rarely read a printed book in the bedroom during the hour before sleep had a shorter sleep duration and poorer sleep quality and sleep efficiency compared to their peers. Having access to an EECD in the bedroom was associated with increased obesity despite frequently reading during the hour before sleep. CONCLUSIONS: Our findings suggest that sleep duration, sleep quality, sleep efficiency and weight status are better among children who do not have EECDs in the bedroom and frequently read a book during the hour before sleep as opposed to those who use EECDs during this hour. Education of limits against EECD use by parents may improve sleep outcomes. These findings will inform health promotion messages and may give rise to national recommendations regarding EECD use. TRIAL REGISTRATION: ClinicalTrials.gov NCT01914185 . Registered 31 July 2013 Retrospectively registered.

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)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
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 enseignants0,099
Score d'incertitude au seuil0,305

Scores Codex et Gemma par catégorie

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

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 Opus0,029
Tête enseignante GPT0,351
Écart entre enseignants0,323 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
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

Citations90
Publié2017
Routes d'admission3
Résumé présentoui

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