Augmented Reality Playgrounds to Promote Physical Activity in Young Children: Feasibility Study Using a Repeated Measures Laboratory Design
Bibliographic record
Abstract
Background: Only 10% of Australian children meet the recommended daily physical activity guidelines. Augmented reality (AR) is increasingly being used in primary education and clinical rehabilitation, with high enjoyment and motivation to participate frequently reported. AR has increased physical activity participation in adult populations, but whether AR can increase physical activity in young children has yet to be investigated. Objective: This study aimed to determine if an indoor AR-enhanced playground is enjoyed by young children and prompts physical activity in both low- and high-structured scavenger hunts. Methods: Seventeen pairs of 5- to 8-year-olds participated in 2 animal search tasks (ie, AR and non-AR) in 2 activity structure levels (ie, low-structured and high-structured) in a 2×2 repeated measures design in an indoor laboratory playground. Children searched for either AR animals (a custom AR app on a smartphone) or toy animals and followed a set obstacle course route (high-structured) or moved wherever they wished (low-structured). Questionnaires assessed child enjoyment, perceived physical activity, and caregiver perception of enjoyment. Thigh-worn accelerometers (SENS; SENS Innovation ApS) assessed postures and movements, and a video camera recorded engagement time. Results: Children rated AR conditions (low-structured: mean 4.4, SD 1.0, and high-structured: mean 4.5, SD 0.9) as more enjoyable than high-structured non-AR (mean 4.1, SD 1.0; P=.03). When asked which condition was the most enjoyable, 15 chose the low-structured AR, followed by the high-structured AR (n=11) and low-structured non-AR (n=8). Caregiver perception of children's enjoyment ratings generally aligned. Ratings of perceived physical activity level were the same in all conditions (mean 4.3, SD 0.7; P>.05). Accelerometry showed that a greater percentage of time was spent in low-intensity postures and movements during AR conditions (AR: mean 50%, SD 13% vs non-AR: mean 35%, SD 14%; P<.001), namely in sitting and standing, and in high-intensity movements during non-AR (AR: mean 21%, SD 12% vs non-AR: mean 32%, SD 18%; P<.001). During low-structured conditions, engagement time was significantly longer with the AR animals compared to the toy animals (AR: mean 263.1, SD 65.7 seconds vs non-AR: mean 197.3, SD 76.5 seconds; P=.002). Conclusions: While the intensity of physical activity was lower during AR, the greater enjoyment and longer engagement time may lead to greater overall accumulation of active play by motivating young children to go to and stay longer at playgrounds. The high-structured AR conditions resulted in higher-intensity physical activity compared to low-structured AR conditions; however, enjoyment ratings from children and caregivers were generally higher in the low-structured AR. Therefore, AR may be suitable to implement in both low- and high-structured play environments. Future research should investigate whether these findings hold true at outdoor playgrounds and examine the impact of novelty over time.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".