MétaCan
Menu
Retour à la cohorte
Enregistrement W4405763799 · doi:10.3389/fpsyt.2024.1512149

Commentary: Relationships between screen time and childhood attention deficit hyperactivity disorder: a Mendelian randomization study

2024· letter· en· W4405763799 sur OpenAlexaboutno aff
Nagahide Takahashi, Kenji J. Tsuchiya

Notice bibliographique

RevueFrontiers in Psychiatry · 2024
Typeletter
Langueen
DomaineSocial Sciences
ThématiqueChild Development and Digital Technology
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMendelian randomizationAttention deficit hyperactivity disorderGeneralizability theoryScreen timePsychologyPopulationAttention deficitClinical psychologyAttention deficit disorderPsychiatryDevelopmental psychologyCausality (physics)Medicine

Résumé

récupéré en direct d'OpenAlex

Screen time has been increasingly linked to several developmental issues in children, including attention deficit hyperactivity disorder (ADHD). The rise of digital technology has significantly increased children's exposure to screens, raising concerns about potential long-term neurodevelopmental effects. A recent study by Meng et al. aimed to investigate the causal relationship between screen time and ADHD using Mendelian randomization (MR), a powerful technique that attempts to infer causality using genetic data. [1]. However, before accepting their conclusions, it is essential to summaries the currently available evidence on this topic and compare it with the assumptions, methodology and generalizability of the results described in the study by Meng et al.The early studies showed that youth with ADHD engaged in more screen time than youth without ADHD [2; 3]. In adolescents with ADHD aged 13-17 years, Becker et al. reported that nighttime media use contributed to sleep problems and comorbid internalizing symptoms. [4].In 2016, Montagni et al reported that there was a dose-dependent associations between screen time and self-perceived levels of attention problems and hyperactivity in French university students. [5] Thoma et al then extended the target to a community sample and reported that media time, sleep deviation and circadian rhythm were correlated with ADHD symptomatology [6]. The Canadian Healthy Infant Longitudinal Development (CHILD) study reported that increased screen time in preschool was associated with worse inattention problems in the general population [7]. However, none of them concluded that there was a causal link between screen time and the diagnosis of ADHD.In order to further investigate the causal relationship between screen time and ADHD, longitudinal studies have been carried out. For example. Levelink et al. examined longitudinal associations between recreational screen time in early childhood (e.g. at ages 2, 4, and 6) and attention-deficit/hyperactivity disorder (ADHD) at ages 8 to 10. They found that screen time in early childhood was not associated with ADHD [8]. Furthermore, using a total of 2511 children from the Brazilian High-Risk Cohort for Psychiatry Disorders, Bado et al found that higher screen use in children and adolescents may be a consequence of psychopathology rather than a cause. However, Soares et al reported that ADHD symptoms at age 22 were positively associated with total screen time at ages 11, 15 and 18 [9]. The Adolescent Brain Cognitive Development (ABCD) study concluded that it could not establish causality, and that increased screen time was unlikely to be directly harmful to 9and 10-year-old children [10].Taken together, a substantial body of evidence suggests an association between screen time and ADHD symptoms, however, no cross-sectional or longitudinal study has conclusively established causality.In 2022, we showed that the Polygenic Risk Score for ADHD (ADHD-PRS) was associated with increased screen time in children [11]. Our study used a long-term observational cohort of 1258 children to monitor screen time at multiple points (from 18 to 40 months), and children's screen time can be categorized into four distinct trajectories: low, medium, high, and increasing. The association between ADHD-PRS and increasing screen time was significant, suggesting that genetic risk for ADHD predisposes children to engage in more screen-based activities over time. Subsequently, Zhang also used ADHD-PRS in the data from the ABCD study and found that ADHD-PRS was associated with longer screen time [12]. They also demonstrated that the association between screen time and attention problems was largely explained by genetic confounders such as ADHD-PRS. Furthermore, more recently, Yang et al. reported an association between longer screen time and reduced fractional anisotropy (FA) values in several white matter tracts in over 11,000 children aged 9-11 years, but they also showed that reduced FA mediated the association between ADHD-PRS and screen time [13]. These findings suggest a unidirectional relationship between screen time and ADHD. Specifically, ADHD appears to predispose children to longer screen time rather than the reverse, since it is unlikely that long screen time affect ADHD-PRS which is calculated utilizing the individual genetic variations.MR is a robust technique for inferring causality from summary GWAS data and, to our knowledge, the study by Meng et al. is the first MR study to examine screen time and ADHD. They did not find a causal relationship between ADHD and screen time, but between screen time and ADHD [1].3) Discussion.What factors contributed to these inconsistent conclusions? One possible reason is the summary data used in the MR analysis. Meng et al. used GWAS summary data on screen time from the adult population. In fact, there are some literature showing that screen time in childhood is not consistently associated with screen time in adulthood, which is influenced by various factors such as lifestyle or partners' attitudes towards media [14]. Second, although Meng et al used sensitivity analyses to check for pleiotropy which justifies an assumption to conduct MR analysis, the small number of instrumental variables (SNPs) used in some categories (e.g., only one SNP for mobile phone usage) weakens the strength of their conclusions. Therefore, readers should be aware that this study may not have used a sample size with adequate statistical power.In conclusion, while the study by Meng et al. provides intriguing insights into the genetic underpinnings of screen time and ADHD, its findings should be interpreted with caution since the limitations of statistical power of Mendelian randomization and the reliance on adult screen time data may have weakened the strength of their conclusions. To date, three independent PRS studies have suggested that possible causal effect of ADHD on screen time, but these findings should also be replicated. Future studies using GWAS summary data on screen time in children may provide clearer conclusion on this topic.

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)0,021
score de la tête « metaresearch » (Gemma)0,177
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: aucune
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,044
Score d'incertitude au seuil0,112

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0210,177
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0030,003
Bibliométrie0,0020,002
Études des sciences et des technologies0,0030,004
Communication savante0,0030,005
Science ouverte0,0100,002
Intégrité de la recherche0,0440,029
Charge utile insuffisante (le modèle a refusé de juger)0,0160,007

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 Opus0,012
Tête enseignante GPT0,254
Écart entre enseignants0,241 · 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
GenreCommentaire

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

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueFrontiers in PsychiatryMême sujetChild Development and Digital TechnologyTravaux en français237 207