Physical activity promotion in children using a novel smartphone game: a pilot randomized controlled trial
Bibliographic record
Abstract
Background: Regular physical activity (PA) is critical for children’s health and wellbeing. Despite the numerous health benefits, most Canadian children do not meet the Canadian PA guidelines. The emergence of the COVID-19 pandemic and social restrictions added new challenges to meeting the PA guidelines. Mobile health (mHealth) technology can be leveraged to promote PA among children. Combining gamification with mHealth interventions has the potential to further improve program effectiveness. Thus, “Draco” was developed as a virtual pet smartphone app to increase PA in children using self-determination theory as a framework to promote intrinsic motivation for PA. Objective: The primary objective is to evaluate the satisfaction and acceptability of the Draco app after four weeks. Secondary objectives include evaluating the preliminary effectiveness of the Draco app to improve average daily steps, average daily MVPA, perceived autonomy for PA, perceived competence for PA, and perceived relatedness to the app. Methods: 43 Canadian children, aged 8-14 years old, not meeting the Canadian PA guidelines of 60min of MVPA per day were randomly allocated to an intervention or control group. Participants in the control group used a step-tracking app for four weeks. Intervention participants were instructed to use the Draco app. Participants completed a baseline and follow-up questionnaire. PA outcomes were tracked using a Fitbit provided to each participant. Intrinsic PA motivation was assessed using an adapted version of the Intrinsic Motivation Inventory (IMI). Intrinsic motivation was assessed using the satisfaction subscale. Exit interviews were completed to determine app acceptability. Results: Participants demonstrated high levels of satisfaction and acceptability with the Draco app 2.83 (1.29). Intervention participants increased their average daily steps by 909 (1701). The control group increased their steps by 46 (1507). The Draco app had a small effect on promoting steps, MVPA, relatedness and small effects at increasing autonomy and competence. Conclusion: Participants demonstrated high levels of satisfaction and acceptability with the app. Participants in the intervention group showed greater increases in PA with small effect sizes. Preliminary evidence highlights the importance of tailoring game design to the users. Technical limitations impacted recruitment and user experiences. Additional development time should be taken to stabilize the app and add new game features for a definitive RCT.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".