The main objective of the study is to determine if non-specific physical symptoms (NSPS) in people with self-declared sensitivity to radiofrequency electromagnetic fields (RF EMF) can be explained (across subjects) by exposure to RF EMF. Furthermore, we pioneered whether analysis at the individual level or at the group level may lead to different conclusions. By our knowledge, this is the first longitudinal study exploring the data at the individual level. A group of 57 participants was equipped with a measurement set for five consecutive days. The measurement set consisted of a body worn exposimeter measuring the radiofrequency electromagnetic field in twelve frequency bands used for communication, a GPS logger, and an electronic diary giving cues at random intervals within a two to three hour interval. At every cue, a questionnaire on the most important health complaint and nine NSPS had to be filled out. We analysed the (time-lagged) associations between RF-EMF exposure in the included frequency bands and the total number of NSPS and self-rated severity of the most important health complaint. The manifestation of NSPS was studied during two different time lags - 0–1 h, and 1–4 h - after exposure and for different exposure metrics of RF EMF. The exposure was characterised by exposure metrics describing the central tendency and the intermittency of the signal, i.e. the time-weighted average exposure, the time above an exposure level or the rate of change metric. At group level, there was no statistically significant and relevant (fixed effect) association between the measured personal exposure to RF EMF and NSPS. At individual level, after correction for multiple testing and confounding, we found significant within-person associations between WiFi (the self-declared most important source) exposure metrics and the total NSPS score and severity of the most important complaint in one participant. However, it cannot be ruled out that this association is explained by residual confounding due to imperfect control for location or activities. Therefore, the outcomes have to be regarded very prudently. The significant associations were found for the short and the long time lag, but not always concurrently, so both provide complementary information. We also conclude that analyses at the individual level can lead to different findings when compared to an analysis at group level. https://doi.org/10.1016/j.envint.2019.104948 LinkedIn: https://www.linkedin.com/in/john-bolte-0856134/
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As Vehicle-to-Everything (V2X) communication technologies gain prominence, ensuring human safety from radiofrequency (RF) electromagnetic fields (EMF) becomes paramount. This study critically examines human RF exposure in the context of ITS-5.9 GHz V2X connectivity, employing a combination of numerical dosimetry simulations and targeted experimental measurements. The focus extends across Road-Side Units (RSUs), On-Board Units (OBUs), and, notably, the advanced vehicular technologies within a Tesla Model S, which includes Bluetooth, Long Term Evolution (LTE) modules, and millimeter-wave (mmWave) radar systems. Key findings indicate that RF exposure levels for RSUs and OBUs, as well as from Tesla’s integrated technologies, consistently remain below the International Commission on Non-Ionizing Radiation Protection (ICNIRP) exposure guidelines by a significant margin. Specifically, the maximum exposure level around RSUs was observed to be 10 times lower than ICNIRP reference level, and Tesla’s mmWave radar exposure did not exceed 0.29 W/m2, well below the threshold of 10 W/m2 set for the general public. This comprehensive analysis not only corroborates the effectiveness of numerical dosimetry in accurately predicting RF exposure but also underscores the compliance of current V2X communication technologies with exposure guidelines, thereby facilitating the protective advancement of intelligent transportation systems against potential health risks.
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Light profoundly impacts many aspects of human physiology and behaviour, including the synchronization of the circadian clock, the production of melatonin, and cognition. These effects of light, termed the non-visual effects of light, have been primarily investigated in laboratory settings, where light intensity, spectrum and timing can be carefully controlled to draw associations with physiological outcomes of interest. Recently, the increasing availability of wearable light loggers has opened the possibility of studying personal light exposure in free-living conditions where people engage in activities of daily living, yielding findings associating aspects of light exposure and health outcomes, supporting the importance of adequate light exposure at appropriate times for human health. However, comprehensive protocols capturing environmental (e.g., geographical location, season, climate, photoperiod) and individual factors (e.g., culture, personal habits, behaviour, commute type, profession) contributing to the measured light exposure are currently lacking. Here, we present a protocol that combines smartphone-based experience sampling (experience sampling implementing Karolinska Sleepiness Scale, KSS ratings) and high-quality light exposure data collection at three body sites (near-corneal plane between the two eyes mounted on spectacle, neck-worn pendant/badge, and wrist-worn watch-like design) to capture daily factors related to individuals’ light exposure. We will implement the protocol in an international multi-centre study to investigate the environmental and socio-cultural factors influencing light exposure patterns in Germany, Ghana, Netherlands, Spain, Sweden, and Turkey (minimum n = 15, target n = 30 per site, minimum n = 90, target n = 180 across all sites). With the resulting dataset, lifestyle and context-specific factors that contribute to healthy light exposure will be identified. This information is essential in designing effective public health interventions.
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