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Enregistrement W3031640782 · doi:10.1542/peds.2020-1242

Using Mobile Device Sampling to Objectively Measure Screen Use in Clinical Care

2020· letter· en· W3031640782 sur OpenAlexaff
Libby Matile Milkovich, Sheri Madigan

Notice bibliographique

RevuePEDIATRICS · 2020
Typeletter
Langueen
DomaineSocial Sciences
ThématiqueChild Development and Digital Technology
Établissements canadiensAlberta Children's HospitalUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésMedicineMobile deviceAndroid (operating system)Screen timeModalitiesThe InternetHealth careInternet privacyMultimediaComputer scienceWorld Wide Web

Résumé

récupéré en direct d'OpenAlex

The widespread cultural adoption of media devices (eg, smartphones and tablets) in the last decade has changed the nature of screen time and consumption in young children. The single-user platforms of media devices promote solitary use1 and allow access to streaming videos, gaming, and the Internet: anytime and anywhere. Moreover, unlike traditional television viewing, mobile devices use gamification and persuasive design to grab and maintain the user’s attention.Media viewing is an environmental factor that has a direct impact on young children’s health and development.2–7 Unfortunately, the large majority of preschoolers are exceeding screen time guidelines8 at a time when their learning capacity is vast. Accordingly, pediatricians have a role in guiding families in adopting healthy device habits.9 Traditionally, clinicians have relied on caregiver reports of child screen use to guide clinical recommendations, but these reports may be inaccurate,10 casting doubt on their utility for evaluating problematic media habits. What if clinicians could more accurately capture children’s mobile device use to help families gain awareness on how digital media use may be impinging on children’s health and development?In this issue of Pediatrics, Radesky et al11 are the first to trial an objective measurement of young children’s mobile device use, referred to as “mobile device sampling.” They used 2 modalities based on the device’s operating system to gather data on device use by children between the ages of 3 and 5 years: a passive monitoring app for Android users and the battery feature in iOS devices, which captured the amount of time the device was used as well as apps used over a 7- to 10-day period. Comparing the mobile device sampling to retrospective reports from the caregivers about children’s mobile device use, they concluded that caregiver-reported duration of children’s unshared mobile device use had low accuracy. Specifically, approximately one-third (29%) of caregivers were accurate reporters of child screen use, whereas one-third of caregivers either over- (35%) or underreported (36%) screen usage by ±60 minutes a day.An important caveat before a fuller discussion of the clinical implications of these findings is that the typical home has 5 Internet-connected devices (eg, tablet, smartphone, computer, etc),12,13 possibly allowing for discrepancy in reporting use when only 1 device is monitored. Thus, although passively collecting data on mobile devices is a momentous step forward methodologically for accurately collecting screen use data, it likely does not capture the full breadth of exposure in the child’s digital media ecology.14In addition to revealing the inaccuracy of caregiver self-report, several additional clinically relevant trends were revealed in the study by Radesky et al.11 First, ∼15% of children were on their device ≥4 hours per day (not accounting for other device use), which far exceeds the screen time guidelines of ≤1 hour per day of high-quality programming for preschoolers.15 Second, unregulated video streaming services (YouTube and YouTube Kids) were the most commonly used apps (∼44–113 minutes per day on average). Video streaming apps are not recommended for this age group,16–18 making the high duration of use and popularity of these apps clinically concerning. Third, the authors report that some preschoolers were accessing and using gaming apps, as well as apps with violent content (eg, Terrorist Shooter and Flip the Gun), which have been correlated with increased aggression and interest in guns.19,20 Taken together, these findings point to the need for pediatricians to be aware of their pediatric patients’ device use.Methodologically, the study by Radesky et al11 moves the field forward to more accurately understanding children’s mobile device use data. But what does mobile device sampling mean for pediatricians who seek to accurately understand their patients’ media use to facilitate guidance on managing their digital media ecology?Mobile device sampling can accurately reveal trends in a patient’s duration and content of media use, which could help a pediatrician identify specific targets for intervention. Removing reporting bias improves the clinician’s ability to formulate relevant interventions, creating opportunities for conversations about the reality of implementing technology use interventions. For example, if the content of app use is gaming related, violent, or developmentally inappropriate, the pediatrician can guide caregivers and children to access and use age-appropriate content. Similarly, when the duration of use exceeds the screen use guidelines, pediatricians can work with caregivers in developing and sustaining a family media plan.21 Mobile device sampling could also be used to accurately track intervention progress over time.The potential benefits of mobile device sampling need to be balanced with the caregiver’s acceptance of this potentially useful clinical method. Moreover, the feasibility of adopting this method clinically needs further consideration given the pediatrician’s time constraints with patients. Therefore, future research should be used to address the acceptability and feasibility of mobile device sampling to elucidate its appropriateness for clinical use.

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)0,003
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,569
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,002
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,002
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 Opus0,196
Tête enseignante GPT0,395
Écart entre enseignants0,199 · 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.

Devis d'étudeSans objet
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

Citations7
Publié2020
Routes d'admission1
Résumé présentoui

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