Exposure-Based Intervention in Virtual Reality to Address Kinesiophobia in Parkinson’s Disease: A Narrative Review
Bibliographic record
Abstract
Background/Objectives: Physical activity alleviates symptoms and may slow Parkinson’s disease (PD) progression, yet many individuals with PD remain sedentary. Kinesiophobia, the fear of movement, may represent a significant but underexplored psychological barrier to physical activity in this population. Virtual reality (VR), already effective in phobias, may represent a promising approach to address this challenge. This review initially aimed to systematically examine exposure-based interventions in VR (E-IVR) directly targeting kinesiophobia in PD. Methods: Database searches using keywords such as “kinesiophobia,” “fear of movement,” and “VR” combined with “PD” yielded no eligible studies. Consequently, the scope was broadened to include populations with neurological or musculoskeletal conditions, and a narrative review format was adopted to synthesize the available evidence. Furthermore, relevant studies of interventions in VR applied in PD, although not specifically addressing kinesiophobia, are detailed to provide evidence of efficacy and feasibility of VR interventions in PD. Finally, directions are offered to support the creation of E-IVR targeting kinesiophobia in individuals with PD. Results: Meta-analyses in neurological and musculoskeletal populations demonstrate moderate to large reductions in kinesiophobia following VR interventions, although effects vary depending on assessment tools, degree of immersion, and exposure design. In PD, VR has been applied to rehabilitation, anxiety reduction, and quality of life enhancement. These interventions achieved high adherence (≥90%), were well tolerated, and reported no major adverse events. Conclusions: Kinesiophobia is prevalent in PD and could contribute to physical inactivity. E-IVR appears feasible, safe, and innovative for addressing kinesiophobia in people living with PD.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".