Active Play in a Digital Age, Exploring Children’s (Aged 8-13 Years) Views of a Physical Activity App: Qualitative Formative Study
Notice bibliographique
Résumé
Background: The use of smartphones and interest in mobile health (mHealth) has grown in recent years with physical activity apps demonstrating potential to facilitate behavior change. However, there remains limited understanding of what specifically motivates children to engage meaningfully with these tools. Objective: This qualitative formative study aimed to determine children's perceptions of a bespoke physical activity mHealth app (Bestlife; Dubbit). It sought to explore the app's appeal, functionality, and potential to support behavior change among children aged 8-13 years. Methods: A total of 68 Young Citizen Scientists (YCSs) aged 8-13 years were recruited from 5 schools (3 primary and 2 secondary) in Bradford, United Kingdom, through purposive sampling as part of a whole-system physical activity program (Join Us: Move. Play; JU:MP). Recruitment procedures were school-led, incorporating consented whole-class involvement at primary level and teacher-nominated groups at secondary level. YCSs were asked to download and explore the Bestlife app 1-2 weeks before the school-based research session, completing a booklet to capture their experiences and those of their families. A total of 13 focus groups were conducted across 5 schools to explore children's views in depth. The focus groups were designed to investigate children's perceptions of the app. Qualitative data were analyzed inductively and deductively: An initial inductive analysis identified emerging themes, which were then mapped onto a framework of feasibility, usability, acceptability, and behavior change. Results: A total of 68 children (60 from primary schools and 8 from secondary schools) participated in the study. The study identified key factors influencing the feasibility, acceptability, usability, and behavior change potential of the Bestlife app among children. Feasibility was hindered by the parental email requirement during registration, which limited autonomy for older children. Acceptability was driven by gamified features, proportional rewards, and avatar customization, though participants requested more personalization to promote cultural inclusion and dynamic updates, linked to seasonal themes. Usability findings showed the interface was intuitive, with features promoting social interaction and competition enhancing engagement. However, younger users experienced navigational challenges, underscoring the need for clearer guidance. The app effectively incorporated behavior change techniques, including goal-setting, self-monitoring, and social collaboration, but required adjustments, such as reducing the frequency of emotional tracking prompts. Conclusions: The Bestlife app shows potential as an mHealth intervention for promoting physical activity in children. Enhancing cultural representation, simplifying onboarding processes, and refining engagement strategies could strengthen both uptake and sustained use. These findings highlight the importance of integrating user feedback into the iterative design process to optimize digital health tools for young populations. Further longitudinal research is recommended to evaluate longer-term engagement with the app, its impact on physical activity levels, and behavior change sustainability.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,011 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,007 | 0,006 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».