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Women sleep differently and report differently about sleep disturbances than men. However, it is unclear whether the sleep of women and men is affected differently by traffic noise exposure. We aimed to address gender specific noise effects by using objective and subjective exposure measures as well as objective and subjective outcome data. In a questionnaire survey conducted in 2008 including 733 women and 533 men from Basel, Switzerland, with follow-up 1 year later, we collected data on subjective sleep disturbances and annoyance to road traffic noise. Objective noise exposure data was obtained using validated propagation models. In a nested diary study with 119 participants, objective sleep efficiency and sleep duration was measured by means of actigraphic devices for 1551 nights. Data were analyzed using random intercept mixed-effects multilevel regression models adjusted for relevant confounding factors. Objectively measured sleep duration in highly exposed men (>55 dB) was reduced by 1.5 h (95% confidence interval [CI]: 2.3-0.8 h) compared with low exposed men (<30 dB). No noise effect on sleep duration was observed in women. The association of modeled noise exposure with self-reported sleep quality rating was also more pronounced in men (-0.8 unit, 95% CI: -1.4 to -0.2) than in women (-0.3 unit, 95% CI: -0.8 to 0.2). However, in highly annoyed women reduction in sleep quality and well-being rating tended to be stronger than in highly annoyed men. Our study provides some indications that noise exposure affects men's sleep differently than women's sleep, which may have distinct long-term health consequences.

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BACKGROUND: Various epidemiological studies have found an association between noise exposure and sleep quality, but the mediating role of annoyance is unclear for this association. OBJECTIVES: To investigate the effects of both objectively modeled road traffic noise exposure as well as noise annoyance on subjective and objective sleep quality measures. METHODS: 1375 randomly selected participants from Basel, Switzerland, were enrolled in a questionnaire survey in 2008 with follow-up one year later (1122 participants). We assessed sleep quality by using a standardized sleep disturbance score, as well as the level of annoyance with road traffic noise at home. Objective sleep efficiency data was collected in a nested diary study by means of actigraphy from 119 subjects for 1551 nights. Residential nocturnal exposure to road traffic noise was modeled using validated models. Data were analyzed with random intercept mixed-effects regression models. RESULTS: In the main study, self-reported sleep quality was strongly related to noise annoyance (p for trend<0.001) but only moderately correlated with modeled noise exposure (p=0.07). In the nested diary study objectively measured sleep efficiency was not related to annoyance (p=0.25) but correlated with modeled noise exposure (p=0.02). Strikingly, noise induced decreased sleep efficiency was even more significant for study participants who were not annoyed with traffic noise (p=0.001). CONCLUSIONS: This study indicates that effects of nocturnal traffic noise on objective sleep quality are independent of perceived noise annoyance, whereas the association between self-reported sleep quality and noise is mediated by noise annoyance.

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In five countries (Belgium, Switzerland, Slovenia, Hungary, and the Netherlands), personal radio frequency electromagnetic field measurements were performed in different microenvironments such as homes, public transports, or outdoors using the same exposure meters. From the mean personal field exposure levels (excluding mobile phone exposure), whole-body absorption values in a 1-year-old child and adult male model were calculated using a statistical multipath exposure method and compared for the five countries. All mean absorptions (maximal total absorption of 3.4 µW/kg for the child and 1.8 µW/kg for the adult) were well below the International Commission on Non-Ionizing Radiation Protection (ICNIRP) basic restriction of 0.08 W/kg for the general public. Generally, incident field exposure levels were well correlated with whole-body absorptions (SAR(wb) ), although the type of microenvironment, frequency of the signals, and dimensions of the considered phantom modify the relationship between these exposure measures. Exposure to the television and Digital Audio Broadcasting band caused relatively higher SAR(wb) values (up to 65%) for the 1-year-old child than signals at higher frequencies due to the body size-dependent absorption rates. Frequency Modulation (FM) caused relatively higher absorptions (up to 80%) in the adult male.

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BACKGROUND: There is persistent public concern about sleep disturbances due to radiofrequency electromagnetic field (RF-EMF) exposure. The aim of this prospective cohort study was to investigate whether sleep quality is affected by mobile phone use or by other RF-EMF sources in the everyday environment. METHODS: We conducted a prospective cohort study with 955 study participants aged between 30 and 60 years. Sleep quality and daytime sleepiness was assessed by means of standardized questionnaires in May 2008 (baseline) and May 2009 (follow-up). We also asked about mobile and cordless phone use and asked study participants for consent to obtain their mobile phone connection data from the mobile phone operators. Exposure to environmental RF-EMF was computed for each study participant using a previously developed and validated prediction model. In a nested sample of 119 study participants, RF-EMF exposure was measured in the bedroom and data on sleep behavior was collected by means of actigraphy during two weeks. Data were analyzed using multivariable regression models adjusted for relevant confounders. RESULTS: In the longitudinal analyses neither operator-recorded nor self-reported mobile phone use was associated with sleep disturbances or daytime sleepiness. Also, exposure to environmental RF-EMF did not affect self-reported sleep quality. The results from the longitudinal analyses were confirmed in the nested sleep study with objectively recorded exposure and measured sleep behavior data. CONCLUSIONS: We did not find evidence for adverse effects on sleep quality from RF-EMF exposure in our everyday environment.

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Body-worn radiofrequency electromagnetic field (RF-EMF) personal exposure meters (PEMs) have been increasingly used for exposure assessment in epidemiological research. However, little research on the measurement accuracy of these devices is available. In this article a novel measurement setup and a measurement protocol are presented for characterizing and testing PEMs. The whole setup and procedure is tested using two EME SPY 120 devices. The performance of the PEM was analyzed for absolute measurements in an anechoic chamber. Modulated signals representing the different services as real signals generated by appropriate testers were used. Measurement results were evaluated with respect to a root mean square detector. We found that measurement accuracy depends strongly on the carrier frequency and also on the number of occupied time slots for Time Division Multiple Access (TDMA)-based services. Thus, correction factors can only be derived if the distribution of the network configuration over the measurement time for all measurement points is available. As a result of the simplicity of the measurement setup and the straightforward measurement protocol, the possibility of fast validation leads to a higher accuracy in the characterization and testing of PEMs.

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BACKGROUND: There is public concern regarding potential health effects of radio frequency electromagnetic fields (RF-EMF) exposure, as produced by mobile phones or broadcast transmitters. The objective of this study was to investigate the association between RF-EMF exposure and non-specific symptoms and tinnitus in a prospective cohort study. METHODS: In 2008, 1375 randomly selected participants from Basel, Switzerland, were enrolled in a questionnaire survey with follow-up after one year (participation rate 82%). A score for somatic complaints (von Zerssen list) and headache (HIT-6) was assessed. Far-field environmental RF-EMF exposure was predicted using a validated prediction model. Regarding near-field exposure, self-reported mobile and cordless phone use as well as mobile phone operator data were collected. In multivariate regression models, we investigated whether exposure at baseline (cohort analysis) or changes in exposure between baseline and follow-up (change analysis) were related to changes in health scores. RESULTS: For participants in the top decile of environmental far-field RF-EMF exposure at baseline, in comparison to participants exposed below the median value, the change in the von Zerssen- and HIT-6-scores between baseline and follow-up was -0.12 (95%-CI: -1.79 to 1.56) and -0.37 (95%-CI: -1.80 to 1.07) units, respectively. Exposure to near-field sources and a change in exposure between baseline and follow-up were not related to non-specific symptoms. Similarly, no association between RF-EMF exposure and tinnitus was observed. CONCLUSIONS: In this first cohort study using objective and well-validated RF-EMF exposure measures, we did not observe an association between RF-EMF exposure and non-specific symptoms or tinnitus.

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During increased ventilation, inspiratory rib cage muscles have been suggested to take over part of diaphragmatic work after the diaphragm fatigues. We investigated the extent to which this proposed change in muscle recruitment is associated with changes in the relative contribution of chest wall compartments to tidal volume (V(T)). Thirteen healthy subjects performed 1 h of fatiguing normocapnic hyperpnoea. Chest wall volumes were assessed by optoelectronic plethysmography. While breathing frequency increased (43±3 to 56±5 breaths min(-1), p=0.006) and V(T) decreased during normocapnic hyperpnoea (2.6±0.2 to 1.9±0.1l, p<0.001), the relative contribution of chest wall compartments to V(T) remained unchanged (pulmonary rib cage: 48±9 versus 51±14%; abdominal rib cage: 24±4 versus 23±9%; abdomen: 28±8 versus 26±9%; all p>0.05). In conclusion, fatiguing respiratory work is not associated with a change in compartmental contribution to V(T), even in the presence of a change in breathing pattern.

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We assessed the effect of inspiratory loaded breathing (ILB) on respiratory muscle strength and investigated the extent to which respiratory muscle fatigue is associated with chest wall volume changes during ILB. Twelve healthy subjects performed ILB at 76 ± 11% of maximal inspiratory mouth pressure (MIP) for 1h. MIP and breathing pattern during 3 min of normocapnic hyperpnea (NH) were measured before and after ILB. Breathing pattern and chest wall volume changes were assessed by optoelectronic plethysmography. After ILB, six subjects decreased MIP significantly (-16 ± 10%; p < 0.05), while the other six subjects did not (0 ± 7%, p = 0.916). Only subjects with decreased MIP after ILB lowered end-expiratory rib cage volume (volume at which inspiration is initiated) below resting values during ILB. During NH after ILB, tidal volume was smaller in subjects with decreased MIP (-19 ± 16%, p < 0.05), while it remained unchanged in the other group (-3 ± 11%, p = 0.463). These results suggest that respiratory muscle fatigue depends on the lung volume from which inspiratory efforts are made during ILB.

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OBJECTIVE: to review and evaluate the recent literature on the health effects of exposure to mobile phone base station (MPBS) radiation. METHODS: we performed a systematic review of randomized human trials conducted in laboratory settings and of epidemiological studies that investigated the health effects of MPBS radiation in the everyday environment. FINDINGS: we included in the analysis 17 articles that met our basic quality criteria: 5 randomized human laboratory trials and 12 epidemiological studies. The majority of the papers (14) examined self-reported non-specific symptoms of ill-health. Most of the randomized trials did not detect any association between MPBS radiation and the development of acute symptoms during or shortly after exposure. The sporadically observed associations did not show a consistent pattern with regard to symptoms or types of exposure. We also found that the more sophisticated the exposure assessment, the less likely it was that an effect would be reported. Studies on health effects other than non-specific symptoms and studies on MPBS exposure in children were scarce. CONCLUSION: the evidence for a missing relationship between MPBS exposure up to 10 volts per metre and acute symptom development can be considered strong because it is based on randomized, blinded human laboratory trials. At present, there is insufficient data to draw firm conclusions about health effects from long-term low-level exposure typically occurring in the everyday environment.

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The aim of this cross-sectional study was to investigate the association between exposure to various sources of radiofrequency electromagnetic fields (RF EMFs) in the everyday environment and sleep quality, which is a common public health concern. We assessed self-reported sleep disturbances and daytime sleepiness in a random population sample of 1,375 inhabitants from the area of Basel, Switzerland. Exposure to environmental far-field RF EMFs was predicted for each individual using a prediction model that had been developed and validated previously. Self-reported cordless and mobile phone use as well as objective mobile phone operator data for the previous 6 months were also considered in the analyses. In multivariable regression models, adjusted for relevant confounders, no associations between environmental far-field RF EMF exposure and sleep disturbances or excessive daytime sleepiness were observed. The 10% most exposed participants had an estimated risk for sleep disturbances of 1.11 (95% CI: 0.50 to 2.44) and for excessive daytime sleepiness of 0.58 (95% CI: 0.31 to 1.05). Neither mobile phone use nor cordless phone use was associated with decreased sleep quality. The results of this large cross-sectional study did not indicate an impairment of subjective sleep quality due to exposure from various sources of RF EMFs in everyday life.

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BACKGROUND: Only limited data are available on personal radio frequency electromagnetic field (RF-EMF) exposure in everyday life. Several European countries performed measurement studies in this area of research. However, a comparison between countries regarding typical exposure levels is lacking. OBJECTIVES: To compare for the first time mean exposure levels and contributions of different sources in specific environments between different European countries. METHODS: In five countries (Belgium, Switzerland, Slovenia, Hungary, and the Netherlands), measurement studies were performed using the same personal exposure meters. The pooled data were analyzed using the robust regression on order statistics (ROS) method in order to allow for data below the detection limit. Mean exposure levels were compared between different microenvironments such as homes, public transports, or outdoor. RESULTS: Exposure levels were of the same order of magnitude in all countries and well below the international exposure limits. In all countries except for the Netherlands, the highest total exposure was measured in transport vehicles (trains, car, and busses), mainly due to radiation from mobile phone handsets (up to 97%). Exposure levels were in general lower in private houses or flats than in offices and outdoors. At home, contributions from various sources were quite different between countries. CONCLUSIONS: Highest total personal RF-EMF exposure was measured inside transport vehicles and was well below international exposure limits. This is mainly due to mobile phone handsets. Mobile telecommunication can be considered to be the main contribution to total RF-EMF exposure in all microenvironments.

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The use of personal exposure meters (exposimeters) has been recommended for measuring personal exposure to radio frequency electromagnetic fields (RF-EMF) from environmental far-field sources in everyday life. However, it is unclear to what extent exposimeter readings are affected by measurements taken when personal mobile and cordless phones are used. In addition, the use of exposimeters in large epidemiological studies is limited due to high costs and large effort for study participants. In the current analysis we aimed to investigate the impact of personal phone use on exposimeter readings and to evaluate different exposure assessment methods potentially useful in epidemiological studies. We collected personal exposimeter measurements during one week and diary data from 166 study participants. Moreover, we collected spot measurements in the participants' bedrooms and data on self-estimated exposure, assessed residential exposure to fixed site transmitters by calculating the geo-coded distance and mean RF-EMF from a geospatial propagation model, and developed an exposure prediction model based on the propagation model and exposure relevant behavior. The mean personal exposure was 0.13 mW/m(2), when measurements during personal phone calls were excluded and 0.15 mW/m(2), when such measurements were included. The Spearman correlation with personal exposure (without personal phone calls) was 0.42 (95%-CI: 0.29 to 0.55) for the spot measurements, -0.03 (95%-CI: -0.18 to 0.12) for the geo-coded distance, 0.28 (95%-CI: 0.14 to 0.42) for the geospatial propagation model, 0.50 (95%-CI: 0.37 to 0.61) for the full exposure prediction model and 0.06 (95%-CI: -0.10 to 0.21) for self-estimated exposure. In conclusion, personal exposure measured with exposimeters correlated best with the full exposure prediction model and spot measurements. Self-estimated exposure and geo-coded distance turned out to be poor surrogates for personal exposure.

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BACKGROUND: The development of new wireless communication technologies that emit radio frequency electromagnetic fields (RF-EMF) is ongoing, but little is known about the RF-EMF exposure distribution in the general population. Previous attempts to measure personal exposure to RF-EMF have used different measurement protocols and analysis methods making comparisons between exposure situations across different study populations very difficult. As a result, observed differences in exposure levels between study populations may not reflect real exposure differences but may be in part, or wholly due to methodological differences. METHODS: The aim of this paper is to develop a study protocol for future personal RF-EMF exposure studies based on experience drawn from previous research. Using the current knowledge base, we propose procedures for the measurement of personal exposure to RF-EMF, data collection, data management and analysis, and methods for the selection and instruction of study participants. RESULTS: We have identified two basic types of personal RF-EMF measurement studies: population surveys and microenvironmental measurements. In the case of a population survey, the unit of observation is the individual and a randomly selected representative sample of the population is needed to obtain reliable results. For microenvironmental measurements, study participants are selected in order to represent typical behaviours in different microenvironments. These two study types require different methods and procedures. CONCLUSION: Applying our proposed common core procedures in future personal measurement studies will allow direct comparisons of personal RF-EMF exposures in different populations and study areas.

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We present a geospatial model to predict the radiofrequency electromagnetic field from fixed site transmitters for use in epidemiological exposure assessment. The proposed model extends an existing model toward the prediction of indoor exposure, that is, at the homes of potential study participants. The model is based on accurate operation parameters of all stationary transmitters of mobile communication base stations, and radio broadcast and television transmitters for an extended urban and suburban region in the Basel area (Switzerland). The model was evaluated by calculating Spearman rank correlations and weighted Cohen's kappa (kappa) statistics between the model predictions and measurements obtained at street level, in the homes of volunteers, and in front of the windows of these homes. The correlation coefficients of the numerical predictions with street level measurements were 0.64, with indoor measurements 0.66, and with window measurements 0.67. The kappa coefficients were 0.48 (95%-confidence interval: 0.35-0.61) for street level measurements, 0.44 (95%-CI: 0.32-0.57) for indoor measurements, and 0.53 (95%-CI: 0.42-0.65) for window measurements. Although the modeling of shielding effects by walls and roofs requires considerable simplifications of a complex environment, we found a comparable accuracy of the model for indoor and outdoor points.

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Radio frequency electromagnetic fields (RF-EMF) in our daily life are caused by numerous sources such as fixed site transmitters (e.g. mobile phone base stations) or indoor devices (e.g. cordless phones). The objective of this study was to develop a prediction model which can be used to predict mean RF-EMF exposure from different sources for a large study population in epidemiological research. We collected personal RF-EMF exposure measurements of 166 volunteers from Basel, Switzerland, by means of portable exposure meters, which were carried during one week. For a validation study we repeated exposure measurements of 31 study participants 21 weeks after the measurements of the first week on average. These second measurements were not used for the model development. We used two data sources as exposure predictors: 1) a questionnaire on potentially exposure relevant characteristics and behaviors and 2) modeled RF-EMF from fixed site transmitters (mobile phone base stations, broadcast transmitters) at the participants' place of residence using a geospatial propagation model. Relevant exposure predictors, which were identified by means of multiple regression analysis, were the modeled RF-EMF at the participants' home from the propagation model, housing characteristics, ownership of communication devices (wireless LAN, mobile and cordless phones) and behavioral aspects such as amount of time spent in public transports. The proportion of variance explained (R2) by the final model was 0.52. The analysis of the agreement between calculated and measured RF-EMF showed a sensitivity of 0.56 and a specificity of 0.95 (cut-off: 90th percentile). In the validation study, the sensitivity and specificity of the model were 0.67 and 0.96, respectively. We could demonstrate that it is feasible to model personal RF-EMF exposure. Most importantly, our validation study suggests that the model can be used to assess average exposure over several months.

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BACKGROUND: Little is known about the population's exposure to radio frequency electromagnetic fields (RF-EMF) in industrialized countries. OBJECTIVES: To examine levels of exposure and the importance of different RF-EMF sources and settings in a sample of volunteers living in a Swiss city. METHODS: RF-EMF exposure of 166 volunteers from Basel, Switzerland, was measured with personal exposure meters (exposimeters). Participants carried an exposimeter for 1 week (two separate weeks in 32 participants) and completed an activity diary. Mean values were calculated using the robust regression on order statistics (ROS) method. RESULTS: Mean weekly exposure to all RF-EMF sources was 0.13 mW/m(2) (0.22 V/m) (range of individual means 0.014-0.881 mW/m(2)). Exposure was mainly due to mobile phone base stations (32.0%), mobile phone handsets (29.1%) and digital enhanced cordless telecommunications (DECT) phones (22.7%). Persons owning a DECT phone (total mean 0.15 mW/m(2)) or mobile phone (0.14 mW/m(2)) were exposed more than those not owning a DECT or mobile phone (0.10 mW/m(2)). Mean values were highest in trains (1.16 mW/m(2)), airports (0.74 mW/m(2)) and tramways or buses (0.36 mW/m(2)), and higher during daytime (0.16 mW/m(2)) than nighttime (0.08 mW/m(2)). The Spearman correlation coefficient between mean exposure in the first and second week was 0.61. CONCLUSIONS: Exposure to RF-EMF varied considerably between persons and locations but was fairly consistent within persons. Mobile phone handsets, mobile phone base stations and cordless phones were important sources of exposure in urban Switzerland.

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Exposimeters are increasingly applied in bioelectromagnetic research to determine personal radiofrequency electromagnetic field (RF-EMF) exposure. The main advantages of exposimeter measurements are their convenient handling for study participants and the large amount of personal exposure data, which can be obtained for several RF-EMF sources. However, the large proportion of measurements below the detection limit is a challenge for data analysis. With the robust ROS (regression on order statistics) method, summary statistics can be calculated by fitting an assumed distribution to the observed data. We used a preliminary sample of 109 weekly exposimeter measurements from the QUALIFEX study to compare summary statistics computed by robust ROS with a naïve approach, where values below the detection limit were replaced by the value of the detection limit. For the total RF-EMF exposure, differences between the naïve approach and the robust ROS were moderate for the 90th percentile and the arithmetic mean. However, exposure contributions from minor RF-EMF sources were considerably overestimated with the naïve approach. This results in an underestimation of the exposure range in the population, which may bias the evaluation of potential exposure-response associations. We conclude from our analyses that summary statistics of exposimeter data calculated by robust ROS are more reliable and more informative than estimates based on a naïve approach. Nevertheless, estimates of source-specific medians or even lower percentiles depend on the assumed data distribution and should be considered with caution.

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