Bactericidal Efficacy of Ultraviolet-C Light on Virtual Reality Devices: In Vitro Assessment of Bacterial Killing
Bibliographic record
Abstract
Background: Virtual reality (VR) headsets are increasingly used in health care settings for a variety of clinical indications, yet processes to ensure safe use between patients are not well-established. Centers vary in how these processes are performed. Most use disinfection wipes that require manual contact with VR devices for a specified dwell time to allow for sufficient pathogen killing, which may introduce manual error and device degradation over time. Ultraviolet-C light (UV-C) devices offer a no-touch, low-cost, and passive method to achieve pathogen killing without the harms of chemical contact-based disinfectants. The use of UV-C for disinfection has been studied for some medical devices but its efficacy for microbe killing on VR headsets is not well-established. Objective: This study aims to determine the bactericidal efficacy of UV-C on VR headsets through quantifying UV-C irradiance and bacterial killing of 3 commercially available UV-C devices. Methods: Three commercially available, low-cost UV-C devices were tested for UV-C energy output at multiple positions, angles, and times across the devices' zone of disinfection. The top and lens of a VR headset, the Meta Oculus Quest 2, were artificially inoculated with high quantities of 3 different strains of bacteria (Staphylococcus aureus, Pseudomonas aeruginosa, and Staphylococcus epidermidis) and subjected to UV-C light according to each device's manufacturer's instructions for use. The primary outcome was the amount of bacterial killing after exposure to UV-C light. Results: All 3 UV-C devices produced a UV-C dose that ranged from 3.57 to 195.37 mJ/cm2, depending on proximity, angle, irradiance, and time the sensor received. At least 3-log10 killing of all 3 strains of bacteria was achieved for each of the tested UV-C devices; however, there was variability by organism with respect to UV-C device and VR headset location within the device, notably with the proximity of the bacteria to the bulb. S aureus and P aeruginosa were more readily killed than S epidermidis, with increased bacterial killing occurring with increased UV-C exposure doses. There was no experiment in which all bacteria were killed. Conclusions: UV-C dosage increased with exposure irradiance, time, proximity, and angle to the bulb for all 3 UV-C devices. Bacterial killing on the top and lens of a VR headset occurred in all 3 UV-C devices when run according to their manufacturer's instructions for use, although full bacterial killing did not occur in any experiment. UV-C may be an effective method for microbial killing on VR equipment with low-level contamination.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".