A Graded Exposure, Locomotion-Enabled Virtual Reality App During Walking and Reaching for Individuals With Chronic Low Back Pain: Cohort Gaming Design
Bibliographic record
Abstract
BACKGROUND: Chronic low back pain (cLBP) can interfere with daily activities, and individuals with elevated pain-related fear (also known as kinesiophobia or the fear of injury due to movement) can develop worse long-term disability. Graded exposure (GEXP) protocols use successive participation in avoided activities to help individuals overcome fearful movement appraisals and encourage activity. We sought to develop a series of GEXP virtual reality (VR) walking and reaching scenarios to increase the exposure and engagement of people with high kinesiophobia and cLBP. OBJECTIVE: This study aims to (1) determine GEXP content validity of the VR application and (2) determine the feasibility of individuals with cLBP performing locomotion-enabled physical activities. METHODS: We recruited 13 individuals with cLBP and high pain-related fear to experience six VR modules, which provide progressive movement exposure over three sessions in a 1 week period. At session 1, participants ranked each module by likelihood to avoid and assigned an expected pain and concern for harming their back rating to each module. Participants provided a rating of perceived exertion (RPE) after experiencing each module. To test feasibility, we administered the system usability scale (SUS) and treatment evaluation inventory (TEI) following the final session. In addition, we measured pain and pain-related fear at baseline and follow-up. RESULTS: The 12 participants who completed the study period assigned higher avoidance (P=.002), expected pain (P=.002), and expected concern (P=.002) for session 3 modules compared with session 1 modules. RPE significantly increased from session 1 (mean 14.8, SD 2.3) to session 3 (mean 16.8, SD 2.2; P=.009). The VR application showed positive feasibility for individuals with cLBP through acceptable SUS (mean 76.7, SD 13.0) and TEI (mean 32.5, SD 4.9) scores. Neither pain (P=.20) nor pain-related fear (P=.58) changed significantly across sessions. CONCLUSIONS: The GEXP VR modules provided progressive exposure to physical challenges, and participants found the VR application acceptable and usable as a potential treatment option. Furthermore, the lack of significant change for pain and pain-related fear reflects that participants were able to complete the modules safely.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".