Active Play in a Digital Age, Exploring Children’s (Aged 8-13 Years) Views of a Physical Activity App: Qualitative Formative Study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".