Digital Technology in Skin Cancer Prevention and Early Detection
Bibliographic record
Abstract
Background Mobile teledermatology is increasingly being used in clinical practice and offers the opportunity to counsel the general public about sun protection and skin cancer early detection. Growing evidence suggests that SMS text messaging interventions are an effective way to reach a large number of people and promote sun protection behaviors. Many medical practices already have SMS text message systems in place to communicate with patients, especially for appointment reminders and information. However, could we use these systems for even better outcomes? If so, how? Objective This presentation will outline the results of the SunText study, a theory-based SMS text messaging intervention designed to evaluate how often and in what way we could communicate with people at risk of skin cancer to have a beneficial effect on sun protection behaviors, sunburn, and participant engagement. Methods The SunText study was conducted between February-July 2019 in Queensland, Australia. Volunteer participants aged 18 to 40 years were randomized to 4 different intervention schedules using a Latin square design. The schedules included personalized or interactive messages with constant frequency and personalized and interactive messages with either increasing or decreasing frequency. Outcomes measured were reduction in sunburn and engagement with interactive messages, defined as responding to messages by return text. Results Compared to baseline, the self-reported sun protection habits index was significantly higher in all 4 interventions (P<.01). Overall, sunburn rates decreased from baseline to the end of the intervention (40.3% to 7.0%), and remained significantly below baseline levels (23.5%) at the 6-month follow-up (P<.01). All 4 interventions achieved reductions in sunburn rates (18%-48% reduction) during the intervention period. The overall engagement rate with interactive messages was 71%. The intervention involving interactive messages with constant frequency achieved the highest engagement rate. The intervention with personalized and interactive messages with increasing frequency had the lowest engagement rate. Conclusions This study adds to the evidence that text messages targeting sun protection are effective in improving sun protection behaviors and reducing sunburn. Results also suggest higher engagement with constant or decreasing message frequency. Although many clinics already use SMS text messaging for scheduling, this presentation may encourage its extended use to raise awareness of sun protection. Interactive messages could also be integrated into sun protection mobile health apps, and provide an opportunity for engaging in health promotion content. Acknowledgments This study was funded by a research grant from the Harry J Lloyd Charitable Trust. Conflicts of Interest HPS is a shareholder of MoleMap NZ Limited and e-derm consult GmbH, and undertakes regular teledermatological reporting for both companies. HPS is a Medical Consultant for Canfield Scientific Inc, MoleMap Australia Pty Ltd, Blaze Bioscience Inc, Revenio Research Oy and a Medical Advisor for First Derm. All other authors declare no conflicts of interest.
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 | 0.000 |
| 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".