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Enregistrement W3138471014 · doi:10.1111/apa.15843

Adolescents’ digital screen time as a concern for health and well‐being? Device type and context matter

2021· letter· en· W3138471014 sur OpenAlexaboutno aff
Stuart J. Fairclough

Notice bibliographique

RevueActa Paediatrica · 2021
Typeletter
Langueen
DomaineSocial Sciences
ThématiqueImpact of Technology on Adolescents
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésScreen timeRecreationContext (archaeology)Physical activityWell-beingPsychologySedentary lifestyleMental healthDevelopmental psychologyGerontologyMedicinePhysical therapyPsychiatry

Résumé

récupéré en direct d'OpenAlex

Digital screen use is highly prevalent among adolescents, most of whom use smartphones, tablets, games consoles, computers and televisions daily. WHO guidelines for physical activity and sedentary behaviour recommend that school-age children and adolescents limit such recreational screen time, while a more definitive recommendation of up to 2 hours/day recreational screen time is provided in Canada, Australia and New Zealand. Compliance with this recommendation is typically low (eg 23% among UK adolescents)1 with 4–5+ hours/day screen use commonplace.2 A widely held view is that excessive screen time is detrimental to adolescents’ health and well-being. Screen time is unfavourably associated with sleep duration and quality, weight status, cardiometabolic risk, physical fitness, mental health and health-related quality of life.3 The magnitude of associations does though vary depending on the specific type of screen in question and the amount of time that adolescents engage with it. The potential negative health and well-being implications of screen time for adolescents may be further exacerbated by the consistently observed decline in physical activity and increase in sedentariness through the teen years. Maturational development drives physical changes that may contribute to reduced motivation for physical activity, particularly among girls, which may be compounded by societal norms and expectations related to physical appearance and accepted behaviours. Furthermore, sedentary and activity habits formed during adolescence can strongly predict those in adulthood. As a consequence, adolescence has been described as a ‘risk factor’ for physical inactivity with adolescent girls at heightened risk.4 The ubiquity of screen-based devices and their prevalence among teenagers only adds to this risk. iPhones and Android smartphones have only been around since 2007 and 2008, respectively, but they and other devices like games consoles, tablets, computers and televisions are now owned and used daily by adolescents across most parts of the world. Devices are central to the lives of adolescent girls and boys with social media, on-demand streaming, interactive gaming and other digital activities firmly embedded in teenagers’ routines. In this issue, Harrington and colleagues5 describe English adolescent girls’ screen use and associations with lifestyle behaviours and psychosocial health. The authors focused on concurrent use of multiple screen-based devices in over 800 adolescent girls who self-reported which screen-devices they used simultaneously during four discrete time periods (after-school, evenings, in bed at night and at weekends). Smartphones were most frequently used at all time periods, followed by televisions and tablets. Between 59% and 68% of girls used concurrent combinations of smartphones, televisions and tablets after school, in the evenings and at weekends, with over one-third using at least two screens in bed at night. At weekends, girls who used three screens simultaneously did 22 min/day less moderate-to-vigorous physical activity (MVPA) overall, 25 min/day less weekend MVPA, and spent an additional 1.5 hours/day in weekend sedentary time than girls who used no screens. Those using two screens concurrently in bed spent around 9 minutes less engaged in MVPA at the weekends, while evening screen use was associated with lower MVPA after-school and in the evening. After-school concurrent screen use was associated with higher body mass index and shorter weekday sleep duration, but interestingly, there were no significant differences in sleep related to bedtime screen use. Harrington and colleague's novel study demonstrates that during discretionary leisure time it is common for adolescent girls to ‘screen-stack’ with at least two devices. This is an important observation for researchers because usually data on simultaneous device use is not gathered, which limits efforts to understand adolescents’ screen behaviours and the potentials implications for health and well-being. The cross-sectional results indicate that concurrent screen use was negatively associated with healthy lifestyle factors like physical activity and sleep, and positively related to sedentary time and body mass index. The study findings raise the question of whether screen time displaces ‘healthy’ behaviours like physical activity, particularly after-school and at weekends when adolescent girls (and boys) have most choice about how they spend their time. While on the face of it this supposition may seem reasonable, the relationship between screen time and physical activity is more nuanced, depending on the type of screen behaviour and mode of physical activity. For example, in a recent analysis of adolescent behaviours during the Covid pandemic, it was found that screen time and habitual physical activity increased, but these changes coincided with a drop in structured sport participation, suggesting that screens do not necessarily compete with all physically active behaviours.6 Conversely, adolescent girls using social media for >2 hours/day are less likely to achieve physical activity and sedentary time guidelines.7 Use of screens for social media is highly prevalent in teen girls and is a significant means of social interaction. During the Covid pandemic, the potential value of screens to offset the limited opportunities for face-to-face peer contact and interaction has doubtless been beneficial in supporting adolescent girls’ social functioning and well-being. Thus, the view that screen use is detrimental to adolescents’ health and well-being is an oversimplification. Although much of the research to date tends to highlight the negative influence of screens, this partly relates to how ‘screen time’ has been defined (ie as television viewing in earlier studies or as total screen time), which screen behaviours were studied, and the contexts for these behaviours. This is illustrated by a large study of Australian adolescents, which found that time spent by girls and boys in passive screen use (ie television) was negatively associated with physical and mental health outcomes, but that educational (ie homework) and interactive (ie video gaming) screen use were positively associated with school achievement. However, when interactive screen time was analysed in relation to physical and mental health, the associations were unfavourable.8 Thus, the interplay between screen type, the context of its use, and the health and well-being outcomes under consideration all need to be accounted when thinking about the role, benefits, and risks of adolescent screen use. Notably, the effect sizes for these associations were very small suggesting that the impact of screen use on adolescents’ health and well-being may be less of an issue than some believe. In circumstances where adolescents’ screen use may impinge on more healthy lifestyle activities, strategies which ironically embrace and integrate screens (eg smartphone apps, social media groups) have potential to support positive behaviour change, for being physically active and helping teens to ‘power-down’ their devices. As screen time is associated with device availability (particularly in bedrooms),9 time spent in bedrooms and being alone after school,10 then families concerned about adolescents’ screen use could agree ways that limit home screen use at specific times of the day to enable better balance with other activities. Adolescents’ use of digital screens is here to stay, and it is incumbent on researchers, health professionals, practitioners and parents with an interest in adolescent health and well-being to be aware of the potential risks and also benefits of screen use. Improving understanding of adolescents’ screen use and promoting active behaviours that are accessible, developmentally appropriate and enjoyable and which can harness the appeal and familiarity of digital screens may be more fruitful endeavours than solely investing effort into limiting screen time. None.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,007
Version: metacan-v3-hybrid-931329e0061cStatut 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: Éditorial · Signal consensuel: aucune
Score de désaccord entre enseignants0,010
Score d'incertitude au seuil0,032

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,007
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0030,002
Science ouverte0,0010,001
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0100,001

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 Opus0,017
Tête enseignante GPT0,294
Écart entre enseignants0,277 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreÉditorial

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

En bref

Citations14
Publié2021
Routes d'admission1
Résumé présentoui

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