Characterization of personal solar ultraviolet radiation exposure using detrended fluctuation analysis
Date
2020Author
Blesić, Susana M.
Ramotsehoa, M. Cynthia
Du Preez, D. Jean
Stratimirović, Djordje I.
Ajtić, Jelena V.
Metadata
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Studies of personal solar ultraviolet radiation (pUVR) exposure are important to identify populations at-risk of excess and insufficient exposure given the negative and positive health impacts, respectively, of time spent in the sun. Electronic UVR dosimeters measure personal solar UVR exposure at high frequency intervals generating large datasets. Sophisticated methods are needed to analyze these data. Previously, wavelet transform (WT)analysis was applied to high-frequency personal recordings collected by electronic UVR dosimeters. Those findings showed scaling behavior in the datasets that changed from uncorrelated to long-range correlated with increasing duration of time spent in the sun. We hypothesized that the WT slope would be influenced by the duration of time that a person spends in continuum outside. In this study, we address this hypothesis by using an experimental study approach. We aimed to corroborate this hypothesis and to characterize the extent and nature of influence time a person spends outside has on the shape of statistical functions that we used to analyze individual UVR exposure patterns. Detrended fluctuation analysis (DFA) was applied to personal sun exposure data. We analyzed sun exposure recordings from skiers (on snow) and hikers in Europe, golfers in New Zealand and outdoor workers in South Africa. Results confirmed validity of the DFA superposition rule for assessment ofpUVR data and showed that pUVR scaling is determined by personal patterns of exposure on lower scales. We also showed that this dominance ends at the range of time scales comparable to the maximal duration of con-tinuous exposure to solar UVR during the day; in this way the superposition rule can be used to quantify behavioral patterns, particularly accurate if it is determined on WT curves. These findings confirm a novel way in which large datasets of personal UVR data may be analyzed to inform messaging regarding safe sun exposure for human health
URI
http://hdl.handle.net/10394/33949https://www.sciencedirect.com/science/article/pii/S001393511930773X
https://doi.org/10.1016/j.envres.2019.108976
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- Faculty of Health Sciences [2404]