Designing a Virtual Reality Game for Promoting Empathy Toward Patients With Chronic Pain: Feasibility and Usability Study
Bibliographic record
Abstract
BACKGROUND: Many researchers have been evaluating how digital media may impact the emotional and perspective taking aspects of empathy in both clinical and nonclinical settings. Despite the growing interest in using virtual reality (VR) and VR games to motivate empathy, few studies have focused on empathy for people who live with chronic pain. OBJECTIVE: Chronic pain affects, by conservative estimates, 1 in 5 people in industrialized countries. Despite this prevalence, public awareness of chronic pain was remarkably low until the recent opioid crisis; as a result, stigma remains a problem frequently faced by people who live with this condition. To address this, the VR game AS IF was developed to increase nonpatients' empathy toward the growing number of people who live with long-term chronic pain. On the basis of our prior work, we overhauled our approach, designed and built a VR prototype and evaluated it, and offered design suggestions for future research. METHODS: We introduced the design features of the VR game AS IF and described the study we devised to evaluate its effectiveness. We adopted a mixed methods approach and compared the empathy-related outcomes in both pre- and posttesting. A total of 19 participants were recruited. RESULTS: The findings of this study suggest that the VR game was effective in improving implicit and explicit empathy as well as its emotional and perspective taking aspects. More specifically, for the Empathy Scale, the total pretest scores (mean 47.33, SD 4.24) and posttest scores (mean 59.22, SD 4.33) did not reach statistical significance (P=.08). However, we did find differences in the subscales. The kindness subscale showed a statistically significant increase in the posttest score (mean 15.61, SD 2.85) compared with the pretest score (mean 17.06, SD 2.65;P=.001). For the Willingness to Help Scale, a significant increase was observed from a t test analysis (P<.001) of scores before (mean 7.17, SD 2.28) and after (mean 8.33, SD 2.03) the gameplay. The effect size for this analysis was large (d=-1.063). CONCLUSIONS: The contributions of this research are as follows: AS IF provides a promising approach for designing VR games to motivate people's empathy toward patients with chronic pain, the study evaluates the potential effectiveness of such a VR approach, and the general design suggestions devised from this study could shed light on future VR game systems.
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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) | 0.000 | 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".