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Record W3190025885 · doi:10.2196/29694

Reactivity to UV Radiation Exposure Monitoring Using Personal Exposure Devices for Skin Cancer Prevention: Longitudinal Observational Study

2021· article· en· W3190025885 on OpenAlexvenueno aff
Bridget G. Parsons, Elizabeth Nagelhout, Ali P. Wankier, Nan Hu, Riley Lensink, Angela Zhu, Katy Nottingham, Douglas Grossman, Jakob Solgaard Jensen, Yelena P. Wu

Bibliographic record

VenueJMIR mhealth and uhealth · 2021
Typearticle
Languageen
FieldMedicine
TopicSkin Protection and Aging
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesNational Center for Research ResourcesNational Institute of Biomedical Imaging and BioengineeringNational Cancer InstituteNational Institutes of HealthHuntsman Cancer InstituteAmerican Cancer Society
KeywordsSkin cancerWorryPsychological interventionSunburnContext (archaeology)MedicineObservational studyCancer preventionEnvironmental healthPsychologyClinical psychologyCancerAnxietyNursingInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Emerging UV radiation (UVR) monitoring devices may present an opportunity to integrate such technology into skin cancer prevention interventions. However, little is known about the effects of using a wearable UVR monitor on adults' and children's sun protection-related behaviors and attitudes (eg, cancer worry and perceived risk). Understanding the potential role of reactivity and seasonal effects will help inform the use of objective monitors in the context of skin cancer prevention research, including intervention studies. OBJECTIVE: The aim of this study is to examine the potential reactivity associated with a wearable personal UVR monitor, specifically the effects associated with reported sun-protective behaviors and skin cancer-related attitudes, which are often the targets of skin cancer preventive interventions. METHODS: Child-parent dyads (n=97 dyads) were asked to wear a UVR monitoring device during waking hours for 2 weeks. Participants were asked to sync the device daily with a smartphone app that stored the UVR exposure data. Participants were blinded to their UVR exposure data during the 2-week period; thus, the smartphone app provided no feedback to the participants on their UVR exposure. Participants completed self-report questionnaires assessing sun-protective behaviors, sunburn, tanning, skin self-examination, skin cancer-related knowledge, perceived risk, cancer worry, response efficacy, and intentions to change behaviors over the 2-week period. Linear regressions were conducted to investigate changes in the outcomes over time and to account for the role of the season of study participation. RESULTS: Regression results revealed that there was a significant decrease over time for several sun protection outcomes in children, including time spent outdoors on weekends (P=.02) and weekdays (P=.008), sunscreen use (P=.03), reapplication (P<.001), and unintentional tanning (P<.001). There were no significant changes over time in children's and parents' UVR exposure, sunburn occurrence, or sun protection attitudes. Season of participation was associated with several outcomes, including lower sunscreen use (P<.001), reapplication (P<.001), sunburns (P=.01), intentions to change sun-protective behaviors (P=.02), and intentional (P=.008) and unintentional tanning (P=.01) for participants who participated in the fall versus the summer. CONCLUSIONS: The findings from this study suggest that daily use of a UVR monitoring device over a 2-week period may result in changes in certain sun-protective behaviors. These results highlight the importance of identifying and addressing potential reactivity to UVR monitoring devices, especially in the context of skin cancer preventive intervention research. Ultimately, objectively assessed UVR exposure could be integrated into the outcome assessment for future testing of skin cancer prevention interventions.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.245
GPT teacher head0.471
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2021
Admission routes1
Has abstractyes

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