Experiencing Immersive Virtual Nature for Well-Being, Restoration, Performance, and Nature Connectedness: A Scoping Review
Bibliographic record
Abstract
This paper presents a scoping review of immersive virtual nature experiences delivered via head-mounted displays (HMDs) and their role in promoting well-being, psychological restoration, cognitive performance, and nature connectedness. As access to natural environments becomes increasingly constrained by urbanization, technological lifestyles, and environmental change, immersive technologies offer a scalable and accessible alternative for engaging with nature. Guided by three core research questions, this review explores how HMD-mediated immersive technologies have been used to promote nature connectedness and well-being, what trends and outcomes have been observed across applications, and what methodological gaps or limitations exist in this growing body of work. Fifty-five peer-reviewed studies were analyzed and categorized into six key implication areas: emotional well-being, stress reduction, cognitive performance, attention recovery, restorative benefits, and nature connectedness. The review identifies immersive virtual nature as a promising application of extended reality (XR) technologies, with potential across healthcare, education, and daily life, while also emphasizing the need for more consistent methodologies and long-term research.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".