Personal light exposure is a modifiable component of the health exposome, but its everyday determinants remain poorly understood. We conducted an observational, harmonised, multi-country wearable study of ocular light exposure in 191 adults across nine sites in Sweden, the Netherlands, Germany, Spain, Turkey, Costa Rica, and Ghana, comprising 1,480 participant-days. Participants wore calibrated light loggers near the corneal plane and at chest level and completed repeated contextual assessments. Average daytime exposure remained below recommended melanopic levels, with adherence varying by site, daily phase and photoperiod. Exposure generally increased with latitude despite greater year-round sunlight at lower latitudes, consistent with behavioural filtering through shade-seeking, indoor living or heat avoidance. Individual and activity-level differences explained more variation than site. Daylight access, outdoor activity and micro-environment were the strongest correlates. These findings support personalized, context-sensitive light interventions and establish ocular light exposure as a measurable exposomic factor relevant to health.

Background Light exposure is a key regulator of human circadian physiology and health, yet real-world evidence remains limited, particularly across diverse cultural and geographic contexts. Large-scale, cross-cultural studies require harmonised and linguistically valid tools to assess personal light exposure and its behavioural correlates. Methods Within the MeLiDos multicentre cohort study, we translated and culturally adapted a comprehensive set of English sleep-, circadian-, and light-related questionnaires into five languages (Dutch, German, Spanish, Swedish, and Turkish) using the TRAPD framework, followed by cognitive pretesting. Quantitative analyses were conducted on complete data from 147 participants across five countries (Costa Rica, Ghana, Germany, Spain, and Türkiye) to evaluate potential language effects using one-way ANOVAs, linear mixed-effects models, and principal component analysis (PCA). In addition, language-specific Pearson correlation matrices were computed for momentary mood and alertness data (N = 4,025). Results Questionnaires assessing sleep, light exposure behaviour, light sensitivity, momentary affect and alertness, as well as real-world data collection logs related to device wear and participant experience, were successfully translated and harmonized. The translation and pretesting process identified challenges related to sensitive topics and culturally unfamiliar concepts such as chronotype and light sensitivity , which were addressed through clear, standardized explanations. It was identified that instruments require adaptation to accommodate local norms, technological accessibility, and seasonal interpretation to enhance sociocultural applicability. Quantitatively, robust country effects were observed across multiple scales, whereas language effects were small and inconsistent. Mixed-effects modelling revealed no significant fixed effect of language, with most variance attributable to individual- and country-level factors. PCA showed that multivariate response patterns clustered by country rather than language. Correlation structures of momentary affect and alertness were highly preserved across languages. Conclusions Harmonized multilingual questionnaires can be reliably used in cross-cultural light-exposure research without introducing substantial language-related bias. Observed differences are likely driven by geographic and cultural factors rather than language, supporting the global applicability of subjective light-exposure assessments while underscoring the necessity of culturally diverse, multicentre data collection. This work provides a strong methodological foundation for advancing global research on the interplay between culture, light exposure, and circadian health, with implications for understanding environmental influences on health.

Background:Digital communication device use is changing rapidly among young people, and current research on this topic is limited or outdated. Objective:We aimed to describe the use of digital communication devices by young people from 4 European countries and investigate their socioeconomic and demographic characteristics. Methods:In 2023, we administered an online survey to a convenience sample of 4000 young people aged 16 to 25 years in Italy, Poland, Spain, and Switzerland. Participants reported on their regular use of smartphones, tablets, laptops, cordless phones, and smartwatches or activity trackers. Participants answered which activities they regularly engaged in on their devices, the time spent on these devices and activities, and in what position the device was used with respect to their body over the previous 3 months. We also collected information on participant socioeconomic and demographic characteristics, including age, gender, country of birth, employment status, parental educational level, and urbanicity of the place of residence. Results:Reported prevalence of device use was 90.9% (3635/4000) for smartphones, 33.2% (1329/4000) for tablets, 68.7% (2748/4000) for laptops, 11.6% (462/4000) for cordless phones, and 23.3% (931/4000) for smartwatches or activity trackers. Older age groups and women reported higher use across most devices. The activities reported with the highest engagement for smartphones were voice calls (2553/3635, 70.2%); social media (2693/3635, 74.1%); and texting, emailing, and internet use (2530/3635, 69.6%). For tablets and laptops, they were video streaming (849/1329, 63.9% and 1527/2748, 55.6%, respectively); texting, emailing, and internet use (673/1329, 50.6% and 1218/2748, 44.3%, respectively); and social media (659/1329, 49.6% and 1521/2748, 55.3%, respectively). On average, participants used their smartphones 60.9 (SD 83.1) minutes per day for texting, emailing, and internet use; 85.2 (SD 92.7) minutes per day for social media; 46.9 (SD 70.5) minutes per day for video streaming; and 53.7 (SD 80.3) minutes per day for music streaming. Differences across activities and devices were found among socioeconomic and demographic characteristics. For example, the oldest age groups reported lower duration of smartphone use for voice calls, social media, video streaming, and music streaming compared to the youngest age group but reported higher duration of smartphone use for video calls and texting, emailing, and internet use. Moreover, women reported higher duration of use for most activities on smartphones compared to men, except for online gaming, for which men reported higher duration of use. Conclusions:Our findings provide novel information on digital communication device use by young people. We identified differences between socioeconomic and demographic characteristics that warrant further investigation. These results can be used as a point of reference for digital communication devices in public health research, including health communication strategies and epidemiological research.

To evaluate the implementation of 5G in Europe, we have systematically measured environmental, auto-induced downlink (DL) and uplink (UL) radiofrequency electromagnetic field (RF-EMF) exposure in more than 800 microenvironments in ten European countries. Outdoor, indoor, and public transport microenvironments were measured in two cities and three villages in each country. Exposure was measured during three mobile-phone user scenarios: flight mode (non-user), inducing maximum DL traffic (max DL) or maximum UL traffic (max UL). The mobile phone was carried in a backpack, placed 30 cm from an ExpoM-RF 4 that continuously measured 35 frequency bands (87.5 MHz-6 GHz). For each user scenario, mean exposure levels were calculated. In the non-user scenario, mean exposure levels ranged from 0.33 to 1.72 mW/m2 per country and were lower in Switzerland, Belgium and Italy. RF-EMF levels were, on average, 80 % lower in villages compared to cities, with DL bands contributing the most in this scenario. During max DL, exposure increased mainly due to the 5G band at 3.5 GHz (mean exposure per country 2.61-11.12 mW/m2). However, the time-division nature of this band prevents distinguishing between DL and UL signals with the ExpoM-RF4. Exposure levels were the highest during max UL, particularly in the Netherlands, Italy and Belgium, with 50 % of the mean levels per country above 16 mW/m2. Exposure was, on average, 35 % higher in villages compared to cities. Environmental exposure levels were below international guideline values. Countries with precautionary limits had lower environmental exposure levels but higher auto-induced uplink exposure during data upload.

The auto-induced uplink (a-UL) radio-frequency electromagnetic field (RF-EMF) exposure, often the dominant part of the total RF-EMF exposure, has not been included in previous microenvironmental studies. As 5G exposure depends more on mobile phone usage, monitoring typical transmit power levels is crucial towards more accurate personal exposure assessment. This study describes spatial differences in average mobile phone transmit power and investigates the influence of uplink duty cycles and frequency band usage. A novel methodology using the network monitoring application QualiPoc in fourth-generation (4G) and non-standalone fifth-generation (5G) networks was presented. For the first time, the assessment of 4G and 5G a-UL RF-EMF exposure was conducted simultaneously in a large-scale microenvironmental study in Europe. Measurements were performed along predefined routes in 282 different microenvironments (e.g., parks, residential areas) across seven European countries, during a maximum uplink usage scenario. The Netherlands had the highest average transmit powers per microenvironment (median 20.6 dBm). Transmit powers in villages were 0.6-2.1 dB higher than in big cities. The study suggested that base station density is a key predictor of a-UL exposure. Comparing technologies and frequency bands, average transmit powers for 5G were about 3.3 dB lower than for 4G and lowest for frequency bands with a time division duplexing (TDD) scheme due to the low uplink duty cycle (below 20%). This study provides crucial measurement data for epidemiologists and governments to enhance the understanding of the a-UL component of personal RF-EMF exposure.

In this presented study, we measured in situ the uplink duty cycles of a smartphone for 5G NR and 4G LTE for a total of six use cases covering voice, video, and data applications. The duty cycles were assessed at ten positions near a 4G and 5G base-station site in Belgium. For Twitch, VoLTE, and WhatsApp, the duty cycles ranged between 4% and 22% in time, both for 4G and 5G. For 5G NR, these duty cycles resulted in a higher UL-allotted time due to time division duplexing at the 3.7 GHz frequency band. Ping showed median duty cycles of 2% for 5G NR and 50% for 4G LTE. FTP upload and iPerf resulted in duty cycles close to 100%.

This review offers a detailed examination of the current landscape of radio frequency (RF) electromagnetic field (EMF) assessment tools, ranging from spectrum analyzers and broadband field meters to area monitors and custom-built devices. The discussion encompasses both standardized and non-standardized measurement protocols, shedding light on the various methods employed in this domain. Furthermore, the review highlights the prevalent use of mobile apps for characterizing 5G-NR radio network data. A growing need for low-cost measurement devices is observed, commonly referred to as ”sensors” or ”sensor nodes,” that are capable of enduring diverse environmental conditions. These sensors play a crucial role in both microenvironmental surveys and individual exposures, enabling stationary, mobile, and personal exposure assessments based on body-worn sensors, across wider geographical areas. This review revealed a notable need for cost-effective and long-lasting sensors, whether for individual exposure assessments, mobile (vehicle-integrated) measurements, or incorporation into distributed sensor networks. However, there is a lack of comprehensive information on existing custom-developed RF-EMF measurement tools, especially in terms of measuring uncertainty. Additionally, there is a need for real-time, fast-sampling solutions to understand the highly irregular temporal variations EMF distribution in next-generation networks. Given the diversity of tools and methods, a comprehensive comparison is crucial to determine the necessary statistical tools for aggregating the available measurement data.

This study evaluates the maximum theoretical exposure to radiofrequency (RF) electromagnetic fields (EMFs) from a Fifth-generation (5G) New Radio (NR) base station (BS) while using four commonly used mobile applications: YouTube for video streaming, WhatsApp for voice calls, Instagram for posting pictures and videos, and running a Video game. Three factors that might affect exposure, i.e., distance of the measurement positions from the BS, measurement time, and induced traffic, were examined. Exposure was assessed through both instantaneous and time-averaged extrapolated field strengths using the Maximum Power Extrapolation (MPE) method. The former was calculated for every measured SS-RSRP (Secondary Synchronization Reference Signal Received Power) power sample obtained with a sampling resolution of 1 second, whereas the latter was obtained using a 1-min moving average applied on the applications’ instantaneous extrapolated field strengths datasets. Regarding distance, two measurement positions (MPs) were selected: MP1 at 56 meters and MP2 at 170 meters. Next, considering the measurement time, all mobile application tests were initially set to run for 30 minutes at both MPs, whereas the video streaming test (YouTube) was run for an additional 150 minutes to investigate the temporal evolution of field strengths. Considering the traffic, throughput data vs. both instantaneous and time-averaged extrapolated field strengths were observed for all four mobile applications. In addition, at MP1, a 30-minute test without a User Equipment (UE) device was conducted to analyze exposure levels in the absence of induced traffic. The findings indicated that the estimated field strengths for mobile applications varied. It was observed that distance and time had a more significant impact than the volume of data traffic generated (throughput). Notably, the exposure levels in all tests were considerably lower than the public exposure thresholds set by the ICNIRP guidelines.

AbstractLight 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.

In this work, in situ measurements of the radio frequency electromagnetic field (RF-EMF) exposure have been conducted for an indoor massive MIMO 5G base station (BS) operating at 26-28 GHz. Measurements were performed at six different positions (at distances between 9.94 and 14.32 m from the BS), of which four were in line-of-sight (LOS) and two were in non-line-of-sight (NLOS). A comparison was performed between the measurements conducted with an omnidirectional probe and with a horn antenna, for scenarios with and without a user equipment (UE) used to actively create an antenna traffic beam from the BS towards the measurement location. A maximum exposure of 171.9 mW/m 2 was measured at a distance of 9.94 m from the BS. This is below 2% of the ICNIRP reference level [1]. Moreover, the feasibility to measure the power per resource element of the Synchronization Signal Block - which can be used to extrapolate the maximum exposure level - with a conventional spectrum analyzer was shown by comparison with a network decoder.

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/m 2 , well below the threshold of 10 W/m 2 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.

Technological advances in radiocommunications, such as adaptive antenna arrays, massive MIMO, and the use of ever higher frequencies, have resulted in an enormous boost in mobile data capacity. However, the inconclusive evidence on adverse health effects of our exposure to radiofrequency (RF) electromagnetic fields (EMF) and possible unknown biological mechanisms, as well as the continuing anxiety of the general public concerning the rollout of telecommunication networks urge the need for accurate characterization of the EMF exposure in one's environment as well as over one's lifetime. In general, RF-EMF exposure can be divided in ambient (“involuntary”) and personal (“auto-induced”), and in far-field (EMF from base station radios and most devices not near the body) and near-field components (from devices used on or near the body, usually operated by oneself). These components can be evaluated using different tools and protocols, depending not only on the proximity of the source, but also on other characteristics, such as frequency, power, and modulation use. Even so, efforts have been made to combine them into a single exposure metric (e.g., a dose or a time-averaged metric based on the specific absorption rate (SAR)).

With this contribution, the results of the measurement campaign performed with a synchronously distributed antenna array testbed operating at the center frequency of 3.686 GHz are presented. For the first time, to the best of the authors’ knowledge, the electric field distribution induced by coherent downlink transmission was experimentally assessed in the proximity of the target receiver using a calibrated electromagnetic field probe. This allowed to quantify the actual observed electric field gain and the shape of the electric field enhancement region around the target received. It was found that 16 transmitting antenna elements enhance the root-mean-square electric field level by about a factor of two, in a 2.5λ neighborhood of the target receiver. In addition, a possible disruption of the propagation environment due to the probe movement was assessed through the real-time channel state information feedback.

Of particular interest within fifth generation (5G) cellular networks are the typical levels of radiofrequency (RF) electromagnetic fields (EMFs) emitted by ‘small cells’, low-power base stations, which are installed such that both workers and members of the general public can come in close proximity with them. In this study, RF-EMF measurements were performed near two 5G New Radio (NR) base stations, one with an Advanced Antenna System (AAS) capable of beamforming and the other a traditional microcell. At various positions near the base stations, with distances ranging between 0.5 m and 100 m, both the worst-case and time-averaged field levels under maximized downlink traffic load were assessed. Moreover, from these measurements, estimates were made of the typical exposures for various cases involving users and non-users. Comparison to the maximum permissible exposure limits issued by the International Commission on Non-Ionizing Radiation Protection (ICNIRP) resulted in maximum exposure ratios of 0.15 (occupational, at 0.5 m) and 0.68 (general public, at 1.3 m). The exposure of non-users was potentially much lower, depending on the activity of other users serviced by the base station and its beamforming capabilities: 5 to 30 times lower in the case of an AAS base station compared to barely lower to 30 times lower for a traditional antenna.

This paper compares different low-cost sensors that can measure (5G) RF-EMF exposure. The sensors are either commercially available (off-the-shelf Software Defined Radio (SDR) Adalm Pluto) or constructed by a research institution (i.e., imec-WAVES, Ghent University and Smart Sensor Systems research group (S³R), The Hague University of Applied Sciences). Both in-lab (GTEM cell) and in-situ measurements have been performed for this comparison. The in-lab measurements tested the linearity and sensitivity, which can then be used to calibrate the sensors. The in-situ testing confirmed that the low-cost hardware sensors and SDR can be used to assess the RF-EMF radiation. The variability between the sensors was 1.78 dB on average, with a maximum deviation of 5.26 dB. Values between 0.09 V/m and 2.44 V/m were obtained at a distance of about 50 m from the base station. These devices can be used to provide the general public and governments with temporal and spatial 5G electromagnetic field values.

Future vehicles will be increasingly connected to enable new applications and improve safety, traffic efficiency and comfort, through the use of several wireless access technologies, ranging from vehicle-to-everything (V2X) connectivity to automotive radar sensing and Internet of Things (IoT) technologies for intra-car wireless sensor networks. These technologies span the radiofrequency (RF) range, from a few hundred MHz as in intra-car network of sensors to hundreds of GHz as in automotive radars used for in-vehicle occupant detection and advanced driver assistance systems. Vehicle occupants and road users in the vicinity of the connected vehicle are thus daily immersed in a multi-source and multi-band electromagnetic field (EMF) generated by such technologies. This paper is the first comprehensive and specific survey about EMF exposure generated by the whole ensemble of connectivity technologies in cars. For each technology we describe the main characteristics, relevant standards, the application domain, and the typical deployment in modern cars. We then extensively describe the EMF exposure scenarios resulting from such technologies by resuming and comparing the outcomes from past studies on the exposure in the car. Results from past studies suggested that in no case EMF exposure was above the safe limits for the general population. Finally, open challenges for a more realistic characterization of the EMF exposure scenario in the connected car are discussed.

New measurement methods and equipment for correct 5G New Radio (NR) electromagnetic field (EMF) in-situ exposure assessment of instantaneous time-averaged exposure (E-avg) and maximum extrapolated field exposure (E-max) are proposed. The different options are investigated with in-situ measurements around 5G NR base stations (FR1) in different countries. The maximum electric field values satisfy the ICNIRP 2020 limit (maximum 7.7%). The difference between E-max and E-avg is <3 dB for the different measurement equipment at multiple sites in case there is only self-generated traffic. However, in a more realistic scenario, E-avg cannot be used to assess the exposure correctly due to influence of other users as the spatial distribution of user equipment (UE) influences E-avg, while E-max is not affected. However, when multiple UEs are collocated, there is no influence of the number of UEs. A broadband measurement can give a first impression of the RF-EMF exposure up to 700 m, but is not enough to assess the 5G-NR exposure.

In an increasingly wireless world, spatiotemporal monitoring of the exposure to environmental radiofrequency (RF) electromagnetic fields (EMF) is crucial to appease public uncertainty and anxiety about RF-EMF. However, although the advent of smart city infrastructures allows for dense networks of distributed sensors, the costs of accurate RF sensors remain high, and dedicated RF monitoring networks remain rare. This paper describes a comprehensive study comprising the design of a low-cost RF-EMF sensor node capable of monitoring four frequency bands used by wireless telecommunications with an unparalleled temporal resolution, its application in a small-scale distributed sensor network consisting of both fixed (on building façades) and mobile sensor nodes (on postal vans), and the subsequent analysis of over a year of data between January 2019 and May 2020, during which slightly less than 10 million samples were collected. From the fixed nodes' results, the potential errors were determined that are induced when sampling at lower speeds (e.g., one sample per 15 min) and measuring for shorter periods of time (e.g., a few weeks), as well as an adequate resolution (30 min) for diurnal and weekly temporal profiles which sufficiently preserves short-term variations. Furthermore, based on the correlation between the sensors, an adequate density of 100 sensor nodes per km2 was deduced for future networks. Finally, the mobile sensor nodes were used to identify potential RF-EMF exposure hotspots in a previously unattainable area of more than 60 km2. In summary, through the analysis of a small number of RF-EMF sensor nodes (both fixed and mobile) in an urban area, this study offers invaluable insights applicable to future designs and deployments of distributed RF-EMF sensor networks.

The global rollout of the fifth generation of cellular networks (5G) brings new challenges to the characterization and measurement of their emissions of radio frequency (RF) electromagnetic fields (EMFs). The new generation of telecommunications is focused more than ever on efficiency, flexibility, and adaptability as well as features (among other things) a wide range of carrier frequencies (from 410 MHz up to 52.6 GHz), lean ‘always-on’ (though periodically transmitted) broadcast signaling, and base stations containing advanced antenna systems (AAS) with phased antenna arrays that consist of tens to hundreds of antenna elements.