Augmented and Virtual Reality-Driven Interventions for Healthy Behavior Change: A Systematic Review
Bibliographic record
Abstract
Augmented Reality (AR) and Virtual Reality (VR) have shown potential benefits in managing healthy behavior. This paper presents a systematic review of AR- or VR-driven interventions for promoting healthy behaviors. The review investigates the design, implementations of the intervention, persuasive strategies, intervention platforms, underlying technologies, current trends, and research gaps. Our review of the past 10-years' work in the area reveals that 1) the considered papers focused on seven main healthy behaviors, where “alcohol use” emerged as the most commonly considered behavior; 2) trustworthiness emerged as the most commonly used persuasive strategy; 3) youth are the most targeted audience; 4) VR is more common than AR; and 5) most AR- or VR-driven interventions are perceived to be effective in motivating healthy behavior in people. We also uncover how they use Artificial Intelligence and Object Tracking in this space. Finally, we identify gaps and offer recommendations for advancing research in this area.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".