Assessing recall of personal sun exposure by integrating UV dosimeter and self-reported data with a network flow framework


Autoři: Nabil Alshurafa aff001;  Jayalakshmi Jain aff001;  Tammy K. Stump aff001;  Bonnie Spring aff001;  June K. Robinson aff004
Působiště autorů: Department of Preventive Medicine, Northwestern University, Chicago, Illinois, United States of America aff001;  Department of Computer Science, Northwestern University, Evanston, Illinois, United States of America aff002;  Department of Electrical and Computer Engineering, Northwestern University, Evanston, Illinois, United States of America aff003;  Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, Illinois, United States of America aff004;  Department of Dermatology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America aff005
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0225371

Souhrn

Background

Melanoma 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.

Methods

39 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.

Findings

For 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.

Conclusion

These 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.

Klíčová slova:

Algorithms – Behavior – Cell phones – Melanomas – Memory recall – Overexposure to sun – Ultraviolet radiation – Cluster analysis


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Článek vyšel v časopise

PLOS One


2019 Číslo 12