Testing Wearable UV Sensors to Improve Sun Protection in Young Adults at an Outdoor Festival: Field Study
Bibliographic record
Abstract
BACKGROUND: Australia and New Zealand have the highest skin cancer incidence rates worldwide, and sun exposure is the main risk factor for developing skin cancer. Sun exposure during childhood and adolescence is a critical factor in developing skin cancer later in life. OBJECTIVE: This study aims to test the effectiveness of wearable UV sensors to increase sun protection habits (SPH) and prevent sunburn in adolescents. METHODS: During the weeklong school leavers outdoor festival (November 2019) at the Gold Coast, Australia, registered attendees aged 15-19 years were recruited into the field study. Participants were provided with a wearable UV sensor and free sunscreen. The primary outcome was sun exposure practices using the SPH index. Secondary outcomes were self-reported sunburns, sunscreen use, and satisfaction with the wearable UV sensor. RESULTS: A total of 663 participants were enrolled in the study, and complete data were available for 188 participants (188/663, 28.4% response rate). Participants provided with a wearable UV sensor significantly improved their use of sunglasses (P=.004) and sunscreen use both on the face (P<.001) and on other parts of the body (P=.005). However, the use of long-sleeve shirts (P<.001) and the use of a hat (P<.001) decreased. During the study period, 31.4% (59/188) of the participants reported receiving one or more sunburns. Satisfaction with the wearable UV sensor was high, with 73.4% (138/188) of participants reporting the UV sensor was helpful to remind them to use sun protection. CONCLUSIONS: Devices that target health behaviors when outdoors, such as wearable UV sensors, may improve use of sunscreen and sunglasses in adolescents.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".