Virtual, Augmented, and Mixed Reality Interventions for Physical Activity: A Systematic Review
Bibliographic record
Abstract
The emergence of Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MR) technologies have made it possible to actualize many things in virtual space that may have cost more to explore in real-life settings. Such technologies have significantly contributed to human health and well-being over the years and they recently gained research attention in promoting physical activities across all age groups. This paper presents a systematic review of VR/AR/MR applications for promoting physical activity (PA). We reviewed 39 papers from 5 databases that met the inclusion criteria. The review results show that (1) VR-driven interventions were more common than AR and MR interventions, (2) more interventions were targeted toward the general public, (3) most interventions adopted exergames as the approach for designing their systems, (4) the self-monitoring strategy emerged as the most used persuasive strategy while tailoring was the least used strategy, and (5) the majority of the interventions were perceived to be effective in promoting PA. We propose that future AR/VR/MR interventions should investigate skill-building goals through systems that provide dynamic feedback to the target audience. This will allow participants to gain experience and skills to promote their physical health.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".