Younger Adolescents’ Perceptions of Physical Activity, Exergaming, and Virtual Reality: Qualitative Intervention Development Study
Bibliographic record
Abstract
BACKGROUND: Novel strategies to promote physical activity (PA) in adolescence are required. The vEngage study aims to test whether a virtual reality (VR) exergaming intervention can engage younger adolescents (aged 13 to 15 years) with PA. OBJECTIVE: This study aimed to gather adolescents' views of using VR to encourage PA and identify the key features they would like to see in a VR exergaming intervention via interviews. METHODS: Participants were recruited through 2 schools in London, United Kingdom. Semistructured interviews were conducted with adolescents about their views on PA and what might work to increase PA, technology, knowledge and experience of VR, and desired features in a VR exergaming intervention. Data were analyzed using Framework Analysis. RESULTS: A total of 31 participants aged between 13 and 15 years (58% female, 62% from nonwhite ethnicities) participated in this interview study. The vast majority had no awareness of government PA recommendations but felt they should be more thoroughly informed. All participants were positive about the use of VR in PA promotion. Rewards, increasing challenges, and a social or multiplayer aspect were identified by participants as crucial aspects to include in a VR exercise game. Barriers were related to cost of high-end systems. Being able to exercise at home was very appealing. VR exergaming was viewed as a way to overcome multiple perceived social and cultural barriers to PA, particularly for girls. CONCLUSIONS: Key elements that should be incorporated into a VR game for health intervention were identified and described. These also included the use of rewards, novelty and enjoyment in immersive game play, multiplayer options, and real-world elements, as well as continual updates and new challenge levels. The use of VR to promote PA in adolescents is promising, but some barriers were raised.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".