The Impact of Virtual Reality (VR) Gaming and Casual/Social Gaming on the Quality of Life, Depression, and Dialysis Tolerance in Patients With Chronic Kidney Disease: A Narrative Review
Bibliographic record
Abstract
This comprehensive narrative review aims to investigate the impact of virtual reality (VR) and social gaming on the quality of life, depression, and dialysis tolerance in patients with chronic kidney disease (CKD), a growing global health concern affecting the quality of life and increasing mortality rates. Through a combination of exercise and video games, exergaming, and digital game therapy (DGT), sedentary risks can be mitigated and therapy adherence enhanced. Despite potential side effects such as exhaustion and nausea, research indicates that virtual therapeutic games improve motivation and dialysis tolerance, and even reduce brain activity in pain-associated regions during procedures. These methods are superior to traditional distraction techniques like music, movies, and toys. Exergaming, social gaming, and VR gaming can improve various health factors including depressive symptoms, functional capacity, muscle strength, fatigue, physical activity, mental health, motivation, engagement, cognition, and overall quality of life for CKD patients. Representing a novel approach to CKD management, these interventions promote physical activity, cognitive stimulation, and social interaction. Continued research and innovation will facilitate the integration of VR-based therapies into routine CKD care.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".