Assessing Sunscreen Protection Using UV Photography: Descriptive Study
Bibliographic record
Abstract
BACKGROUND: Photography using a UV transmitting filter allows UV light to pass and can be used to illuminate UV blocking lotions such as sunscreens. OBJECTIVE: The aim of this study is to compare currently available UV photography cameras and assess whether these devices can be used as visualization tools for adequate coverage of sun protection lotions. METHODS: This study was conducted in 3 parts: in phase 1, 3 different UV cameras were tested; in phase 2, we explored whether UV photography could work on a range of sun protection products; and in phase 3, a UV webcam was developed and was field-tested in a beach setting. In phase 1, volunteers were recruited, and researchers applied 3 sun protection products (ranging from sun protection factor [SPF] 15 to 50+) to the participants' faces and arms. UV photography was performed using 3 UV cameras, and the subsequent images were compared. In phase 2, volunteers were recruited and asked to apply their own SPF products to their faces in their usual manner. UV photographs were collected in the morning and afternoon to assess whether the coverage remained over time. Qualitative interviews were conducted to assess the participants' level of satisfaction with the UV image. In phase 3, a small portable UV webcam was designed using a plug-and-play approach to enable the viewing of UV images on a larger screen. The developed webcam was deployed at a public beach setting for use by the public for 7 days. RESULTS: having lower levels of coverage. Participants in phase 2 reported high satisfaction with the UV photography images, with 83% (29/35) of participants likely to use UV photography in the future. During phase 2, it was noted that many participants used tinted SPF-containing cosmetics, and several tinted products were further tested. However, it was observed that UV photography could not identify the areas missed for all tinted products. During phase 3, the electrical components of the UV webcam remained operational, and the camera was used 233 times by the public during field-testing. CONCLUSIONS: In this study, we found that UV photography could identify the areas missed by sun protection lotions with chemical filters, and participants were engaged with personalized feedback. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12619000975190; http://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=377089 ; Australian New Zealand Clinical Trials Registry (ANZCTR) ACTRN12619000145101; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=376672.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".