Variability of radiofrequency exposure across days of the week: A population-based study

• Published in: Environmental research Vol. 111; no. 4; pp. 510 - 513
• Main Authors: Viel, J.-F., Tiv, M., Moissonnier, M., Cardis, E., Hours, M.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Inc 01.05.2011; Elsevier
• Subjects: Assessments; Biological and medical sciences; Cell Phone - statistics & numerical data; Day of the week; Effects of various physical factors on living matter (vibrations, electric field, ultrasound, sound...); Environmental Exposure - analysis; Environmental Exposure - statistics & numerical data; Exposure assessment; Exposure meters; Fundamental and applied biological sciences. Psychology; Humans; Inclusions; Mathematical models; Prediction model; Radiation Dosage; Radio frequencies; Radio Waves; Radiofrequency; Statistical methods; Statistics; Surveys and Questionnaires; Tissues, organs and organisms biophysics; France; Europe; Day of the week; Radiofrequency; Exposure assessment; Prediction model; Human; Measurement; Evaluation; Methodology; Toxicity; Variability; Prediction; Exposure; Models; Biological effect; Predictive factor; Day to day variation; Public health
• ISSN: 0013-9351; 1096-0953
• DOI: 10.1016/j.envres.2011.02.015

Although measurement of the radiofrequency (RF) exposure can today be performed with personal exposure meters, this approach would be very expensive and time-consuming for large studies, and long term measurements would require considerable commitment of the study participants. Thus, there is a need for validated exposure assessment methods that do not require individual measurements for each study participant. Among the potential predictors, one of the most amenable to being recorded adequately is the day of the week. Drawing upon an existing population-based study, our goal was therefore to assess variability of individual RF exposure across days of the week. The random sample consisted of 34 people who were supplied with a personal exposure meter for seven consecutive days, and kept a time–location–activity diary. A total of 225,414 electric field strength measurements were recorded in 12 different RF bands. Summary statistics were calculated with the robust regression on order statistics method. We found evidence for statistically significant variability of individual RF exposure across days of the week, though the relative magnitude of the differences observed was small. Larger studies are needed to validate these results and determine whether day of the week is an important determinant for inclusion in individual RF exposure prediction models that remain urgently needed to conduct epidemiological studies on potential health effects. ► Individual radiofrequency exposure prediction models remain urgently needed. ► One of the most amenable potential predictors to collect is the day of the week. ► We found evidence for statistically significant variability across days of the week. ► The magnitude of differences in mean radiofrequency fields was, however, small.