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Assessing recall of personal sun exposure by integrating UV dosimeter and self-reported data with a network flow framework
- Source :
- PLoS ONE, PLoS ONE, Vol 14, Iss 12, p e0225371 (2019)
- Publication Year :
- 2019
- Publisher :
- Public Library of Science (PLoS), 2019.
-
Abstract
- BackgroundMelanoma survivors often do not engage in adequate sun protection, leading to sunburn and increasing their risk of future melanomas. Melanoma survivors do not accurately recall the extent of sun exposure they have received, thus, they may be unaware of their personal UV exposure, and this lack of awareness may contribute towards failure to change behavior. As a means of determining behavioral accuracy of recall of sun exposure, this study compared subjective self-reports of time outdoors to an objective wearable sensor. Analysis of the meaningful discrepancies between the self-report and sensor measures of time outdoors was made possible by using a network flow algorithm to align sun exposure events recorded by both measures. Aligning the two measures provides the opportunity to more accurately evaluate false positive and false negative self-reports of behavior and understand participant tendencies to over- and under-report behavior.Methods39 melanoma survivors wore an ultraviolet light (UV) sensor on their chest while outdoors for 10 consecutive summer days and provided an end-of-day subjective self-report of their behavior while outdoors. A Network Flow Alignment framework was used to align self-report and objective UV sensor data to correct misalignment. The frequency and time of day of under- and over-reporting were identified.FindingsFor the 269 days assessed, the proposed framework showed a significant increase in the Jaccard coefficient (i.e. a measure of similarity between self-report and UV sensor data) by 63.64% (p < .001), and significant reduction in false negative minutes by 34.43% (p < .001). Following alignment of the measures, under-reporting of sun exposure time occurred on 51% of the days analyzed and more participants tended to under-report than to over-report sun exposure time. Rates of under-reporting of sun exposure were highest for events that began from 12-1pm, and second-highest from 5-6pm.ConclusionThese discrepancies may reflect lack of accurate recall of sun exposure during times of peak sun intensity (10am-2pm) that could ultimately increase the risk of developing melanoma. This research provides technical contributions to the field of wearable computing, activity recognition, and identifies actionable times to improve participants' perception of their sun exposure.
- Subjects :
- Melanomas
Light
Epidemiology
Sun protection
Social Sciences
Sunburn
Audiology
Mathematical and Statistical Techniques
Cognition
Learning and Memory
0302 clinical medicine
Time of day
Medicine and Health Sciences
Ultraviolet light
Cluster Analysis
Psychology
030212 general & internal medicine
Melanoma
Multidisciplinary
Cancer Risk Factors
Physics
Electromagnetic Radiation
Applied Mathematics
Simulation and Modeling
Environmental Causes of Cancer
Oncology
030220 oncology & carcinogenesis
Physical Sciences
Memory Recall
Sunlight
Medicine
Engineering and Technology
Sun exposure
Algorithms
Research Article
Medical Device Recalls
medicine.medical_specialty
Ultraviolet Rays
Science
Equipment
Research and Analysis Methods
Sensitivity and Specificity
03 medical and health sciences
Memory
Radiation Monitoring
Ultraviolet Radiation
medicine
Network flow algorithms
Communication Equipment
Behavior
Dosimeter
Recall
Radiation Dosimeters
business.industry
Cancers and Neoplasms
Biology and Life Sciences
Reproducibility of Results
Overexposure to Sun
medicine.disease
Medical Risk Factors
Cognitive Science
Cell Phones
business
Mathematics
Neuroscience
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 14
- Database :
- OpenAIRE
- Journal :
- PLOS ONE
- Accession number :
- edsair.doi.dedup.....2ea66bc85d8037ea5793e35cf2215eda