EMF-protection sleep study near mobile phone base ... - Springer Link

Somnologie 12:234–243 (2008) DOI 10.1007/s11818-008-0353-9

Norbert Leitgeb Jörg Schröttner Roman Cech Reinhold Kerbl

ORIG IN AL ART IC LE

EMF-protection sleep study near mobile phone base stations

Prof. Dr. N. Leitgeb (✉) · J. Schröttner · R. Cech Institute of Health Care Engineering Graz University of Technology Inffeldgasse 18 8010 Graz, Austria Tel.: +43-316/873-7397 Fax: +43-316/873-4412 E-Mail: [email protected]

Schirmqualität überprüfen zu können, die Auswertung erfolgte doppelblind durch ein unabhängiges Auswerteteam. Die hochfrequenten Immissionen wurden kontinuierlich und frequenzselektiv aufgezeichnet. Insgesamt wurden 465 Nächte durch Morgenfragebögen und polysomnographische Aufzeichnungen erfasst. Die gepoolte Analyse zeigte keine statistisch signifikanten EMFabhängigen Veränderungen der Schlafparameter, weder von der gesamten HF-EMF-Immission noch vom Mobilfunkanteil. Die Probanden-spezifische Auswertung zeigte bei der überwiegenden Mehrheit der Probanden keine statistisch signifikanten Effekte. Bei sieben Probanden (16 %) zeigten sich signifikante Placebo-Effekte bei subjektiven Schlafparametern, vier Probanden (9 %) zeigten jedoch konsistent statistisch signifikante Verlängerungen von Latenzzeiten, für die Einschlaflatenz zum Stadium 1 bis zu 36,8 min (Median der ungeschirmten Nächte 4,7 min), die Latenz vom Stadium 1 bis Stadium 2 bis 3,8 min (Median 2,3 min), vom Stadium 2 bis Stadium 3 bis 18,0 min (Median 8,0 min). Die Verlängerung der REM-Latenz betrug bis zu 160,0 min (Median 85,8 min).

R. Kerbl Sleep Laboratory University Paediatric Clinic Graz, Austria

왘 Schlüsselwörter Mobilkommunikation – nicht-thermische Effekte – Schlafstudie - Elektrosensibilität

EMF-Protektions-Schlafstudie in der Nähe von Mobilfunk- Basisstationen 왘 Zusammenfassung In einer Crossover-Studie wurde die mögliche kausale Rolle hochfrequenter (HF) elektromagnetischer Felder (EMF) für Schlafstörungen in den Schlafzimmern von 43 elektrosensiblen Probanden (26 Frauen und 17 Männer) untersucht. Dabei wurde mit einem neuartigen Studienansatz und mobilen Schirmen Schlafparameteränderungen nicht in Bezug auf zusätzliche, sondern auf den Schutz vor vorhandenen Immissionen untersucht. In zufälliger Reihenfolge wurden dazu drei Bedingungen (Kontrolle, ShamSchirm und Verum-Schirm) getestet. Die Schirmbedingungen waren einfach-blind, um jeweils die

Received: 18 January 2008 Accepted: 23 July 2008

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왘 Summary In a crossover field study the potential role of radio frequency (RF) electromagnetic fields (EMF) in causing sleep disturbances was investigated in sleeping rooms of 43 volunteers (26 women and 17 men) attributing their sleep problems to RF-EMF from mobile telecommunication base stations. With a new approach of protection from rather than provocation to RF-EMF exposure potential sleep parameter changes were investigated. With mobile shields three conditions (trueshield, sham-shield and control) were tested in random order. Shielding conditions were singleblind to allow controlling shielding efficiency while data analysis was performed double-blind by an independent team. RF-EMF immissions were continuously recorded frequency-selectively. In total, 465 nights were assessed by morning questionnaires and polysomnographic recordings. Pooled analysis did not exhibit statistically significant EMF-dependent sleep parameters, neither on total RF-EMF immissions nor on base station signals. Volunteer-specific analysis mostly did not show any significant effect on sleep parameters. Subjective sleep parameters of seven volunteers (16 %) exhibited significant placebo effects. However, four volunteers (9 %) showed consistent statistical significant prolongations

Sleep near mobile phone base stations

of sleep latencies: for sleep onset latency to sleep stage 1 it was up to 36.8 min (median of unshielded nights 4.7 min), from sleep stage 1 to sleep stage 2 up to 3.8 min

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(median 2.3 min) and from sleep stage 2 to 3 up to 18.0 min (median 8.0 min). REM latency was prolonged up to 160.0 min (median 85.8 min).

Introduction Concern about adverse health effects of radio frequency (RF) mobile telecommunication electromagnetic fields (EMF) and, consequently, the number of persons with sleep disturbances associating their sleep problems with nocturnal exposures to such fields is increasing [9]. In the meantime, numerous petitions have been filed demanding removal of already existing base stations and/ or opposing building new ones. This makes the problem a public issue and justified specific research at environmental field levels. Particular attention was given to people attributing non-specific health symptoms to their exposure to electromagnetic fields. Frequently, such persons claim to be electromagnetic hypersensitive (EHS) and to have a considerably increased sensitivity to EMF compared with the general population. Since EHS resembles multiple chemical sensitivity the world health organisation (WHO) proposed to replace this term by “Idiopathic Environmental Incompatibility associated to EMF” (IEI-EMF) [34]. However, this term has not become widely used nor was it accepted by the public or media. The potential impact of RF-EMF on sleep quality was first investigated by questionnaire-based epidemiological studies showing increasing prevalence of sleep disorders with decreasing distance to short-wave broadcasting antennae [3, 4] and mobile phone base stations [1, 17, 26, 30]. Associations of sleep disturbances were also reported with mobile phone use [2] but not confirmed by Herr et al. [11]. Laboratory sleep studies, all with provocation to increased RF-EMF-levels, most of them simulated GSM mobile phone signals, found inconclusive effects on sleep parameters.All-night continuous exposures resulted in statistical significantly reduced sleep onset latencies and reduced REM-sleep [24]. In a second study only similar trends but no statistical significant results were reported [31]. Other studies reported on non-significant decreases of sleep onset latencies but found no change in REM sleep [33] or significant effects [10]. Studies with intermitted exposure to simulated GSM fields found changes in EEG power in α- and β-frequency bands but no changes of sleep onset latencies [6, 13–15]. Studies with RF-EMF exposures prior to sleep exhibited decreased REM latency [23] and significantly prolonged sleep onset latency [16] or prolonged latency time to stage 3 sleep [5]. Increased EEG power was re-

왘 Key words mobile telecommunication – non-thermal effects – sleep study – electromagnetic hypersensitivity

ported in the 11.5–12.5 Hz frequency range by Loughran et al. [23] and in the EEG spindle frequency range by Regel et al. [28]. Overall, it is still open whether RF-EMF affects sleep quality and whether differences in study outcomes could be explained by differences in signals, exposures and/or exposure parameters. This paper presents the results from a crossover study applying a new design for investigating potential EMFrelated sleep parameter changes using protection from rather than provocation to RF-EMF. It was initiated to respond to public concerns about RF-EMF from mobile phone base stations.

Method The study was conducted in the homes of volunteers under real environmental field conditions and levels. The investigated regions included each of the 9 provinces of Austria and extended up to Northrhine-Westphalia, Germany. Due to the sequential order investigations were made from January 2004 to February 2006. For each volunteer sleep was assessed during 10 consecutive nights with random order (generated by a random generator) of unshielded control, true-shield and shamshield conditions. The first night was allowed for accommodation. Test conditions were equally distributed over the nine investigated nights. To protect from RF-EMF exposure a mobile Faraday cage of electric conductive tissue was mounted around all sides of the bed, including the bottom [21]. Volunteers were blinded to shield conditions by using two optically and tactually indistinguishable tissues which differed only by their electric conductivity. To minimize the risk of detecting differences tissues were taken away each morning and brought back in the evening, if necessary. They were carried by an adjustable cubic support with approximately 2.2 m edge length to ensure a sufficiently large air volume to avoid/minimize unintended side effects due to impaired air exchange (Fig. 1). The study was approved by the Ethics Committee of the Medical University Graz (approval 19–174). Volunteers participated with informed and written consent. If weak RF-EMF could affect sleep, an effect should be seen most clearly in persons attributing their sleep disturbances to RF-EMF. Recruitment of volunteers was made from more than 600 applicants responding to media reports (triggered by a press release) on the planned project which offered the possibility of free participa-

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Fig. 1 On-site bedstead covered by a shield tissue carried by a mobile adjustable cubic support with RF measurement probe inside (the measurement equipment was in a locked box outside the shield)

tion. A first selection was made by initial telephone interviews. Apart from time restrictions exclusion criteria were neurological and psychiatric disorders, somatic reasons for sleep disturbances such as sleep apnea syndrome, drug consumption and actual medical treatment. Final acceptance was based on personal conviction on a causal role of EMF indicated by activities reducing fields, having exposure measured, etc., and the outcome of anamnestic inquiries based on standardized questionnaires such as the Pittsburg Sleep Quality Index (PSQI) [7] and Freiburger Personality Inventory (FPI) [8] to assess personal data, health history and sleep status of volunteers as well as potential cofactors such as alcohol consumption, smoking, exposure to EMF, lifestyle, etc. From answers to 19 standardized questions of the PSQI, seven component sub-scores were generated (subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication and daytime dysfunction) which are summed to generate PSQI total scores. As an additional inclusion criterium PSQI total scores had to be higher than 5 indicating above-normal sleep impairment or disturbance. Selected volunteers reported on daily difficulties to fall asleep and/or maintain sleep. As a consequence of exclusion criteria volunteers were not under medical treatment and had to abstain from any drugs at least two weeks before starting the investigation. During the study an inquiry was performed each evening on potential daytime sleep-relevant cofactors like stress, psychological problems, alcohol consumption, meals etc. using the evening protocol recommended by

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DGSM [12] amended by additional questions. Each morning, the individual condition and subjective rating of sleep was assessed by the standardized questionnaire “subjective self rating scale of sleep and awaking quality” (SSA) [29]. All together, from 20 individual questions three component sub-scores were generated (sleep quality, awaking quality and somatic complaints) and summed to obtain the SSA total score. In case data of volunteers were compared among each other they were related to normative values accounting for different gender and age [29]. Sleep was monitored by adhesive electrodes and a body-worn polysomnographic system (Somnomedics Somnoscreen®) recording EEG, ECG, EOG and body position changes. A combined EEG electrode incorporating the reference was mounted at the forehead, EOG electrodes in the corner of the eyes and ECG electrodes at the chest (simplified Wilson derivative). Biosignal sampling rate was 256 samples/s for EEG and ECG, 128 samples/s for EOG, and 4 samples/s for body position. Signal frequency bandwidths were 0.2–35 Hz for EEG and EOG and 0.07–128 Hz for ECG. Polysomnographic data were analyzed by the commercial sleep analysis software Somnomedics Domino®. For improved reliability results of software-generated sleep classifications were verified by visual scoring based on EEG, EOG and ECG recordings and the criteria of Rechtschaffen and Kales [27] (without differentiation between sleep stages 3 and 4) by two scorers experienced with bipolar frontal EEG derivatives (while Rechtschaffen and Kales proposed using C4-A1 and C3-A2 EEG signals). ECG-related parameters such as heart rate and heart rate variability were registered for control reasons to identify unexpected sleep-related events. Environmental RF-EMFs were recorded frequencyselectively by a broadband spectral analyzer (Rhode & Schwartz TS-EMF®). An isotropic probe with 100 cm² sensing area was placed above the bed inside the shield (Fig. 1). The frequency range was 80 MHz to 2.5 GHz. RFEMFs were monitored continuously during evenings and nights. To assure volunteers are blinded for shielding conditions the equipment was placed into a locked black box outside the shield. In addition, physical cofactors were recorded such as temperature, whether conditions and environmental extremely low frequency (ELF) magnetic fields.

■ Data analysis Each morning subjective sleep quality was assessed by subjective SSA sleep parameters: sleep quality, awakening quality, sleep efficiency and SSA total score. Objective sleep parameters were derived from polysomnographic recordings, namely sleep onset latencies (starting from light-off to first sleep stage 1, 2 or 3, respectively,


provided they lasted at least two epochs), latencies between sleep stages, percentages of sleep stages, sleep efficiency (related to total sleep time) and number of awakenings. To assure almost double-blind conditions and avoid any bias, data analysis was made by a blinded team independent from the investigators on-site. Therefore, neither volunteers nor analyzers were informed about test conditions. Statistical analysis was made with anonymous (coded) data. MANOVA (multivariate analysis of variances) was applied for identification of differences between the three test conditions (independent variables) and sleep parameters (dependent variables). In a first step, MANOVA was performed for pooled data with three “independent variables” (control, sham-shield and true-shield condition) and 15 “dependent variables” (data sets of 43 × 9 values of each sleep parameter). In addition, MANOVA was applied also to individual data of each volunteer. This time the “dependent variables” were data sets of 9 values per sleep parameter (of all investigated nights per person). Prior application of MANOVA it was checked whether all preconditions were fulfilled. These were a) independence of the “independent variables” (assured by the random selection of test conditions), b) normal distributions of dependent variables (checked with the Kolmogorov-Smirnov test) and c) homogeneity of variances of dependent variables (checked with the Levene test). In cases where MANOVA showed significant differences within test conditions, additional post hoc analyses were performed with Duncan test, Student-Newman-Keuls (S-N-K) test, Tukey-B test and Dunnett test to identify to which specific test conditions differences were related. This allowed identification of effects (see below). In cases where the preconditions for MANOVA were not fulfilled (and, therefore, it could not be applied), non-parametric tests (Kolmogorov-Smirnov and MannWhitney-U) were performed to check for differences. In principle, it could be expected to find effects (positive or negative), and side-effects (positive due to placebo or negative because of methodological reasons such as impaired air exchange). To identify sleep parameters exhibiting such effects a statistically significant difference to one of the test conditions was not considered sufficient. Based on post hoc tests a combined statistical criterion was defined requiring a combination of statistical results simultaneously meeting the following conditions: Effects (positive if shielding improved sleep or negative in case of impaired sleep) required statistical significant differences between the test condition trueshield and both sham-shield and control, however, at the same time with no significant difference between sham-shield und control. Side-effects required significant differences between control and both true-shield and sham-shield, howe-

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ver, at the same time with no significant difference between sham-shield and true-shield. Therefore, three statistical conditions had to be met simultaneously for identifying an effect. It was required that a) no statistical significant difference had to exist between two test conditions (e.g., control and shamshield) and b) each of the two other test conditions needed to exhibit statistical significant differences to the third one (e.g., true-shield). With the significance level p of one single statistical test the significance level pc of these combined criteria could be determined by (1) pc = p²(1-p) Based on the significance level p = 0.05 for single statistical tests the combined criterium for clustered significances allowed identifying effects at the significance level p = 0.0024. This is even tougher than the Bonferroni-corrected level for multiple independent testing of 15 parameters which would be p = 0.05/15 = 0.0033. Potential influences of cofactors were investigated by univariate (ANOVA) and multivariate variance analysis (MANOVA). This allowed identifying whether the results could have been influenced by cofactors such as workday/weekend, weather conditions, mobile phone use, ELF magnetic fields, eating, drinking, smoking, menstruation, daytime stress etc. In addition, non-parametric tests (Mann-Whitney-U, Kolmogorov-SmirnovZ) were performed for each volunteer and cofactor to check for differences between test conditions.

Results Overall, 97 volunteers had been accepted for participation. Finally, 43 volunteers (17 men, 26 women) were investigated in total during 465 nights (35 additional nights were necessary because of different reasons such as electrode/biosignal artifacts, sudden illness and unexpected nocturnal events). This does not include investigated nights of 7 additional volunteers quitting investigation prematurely. For four of them the burden (required time for inquiries and measurements each morning and evening, restriction of evening activities) was too high, two became ill. One had expected free EMF measurements to be obtained already after the first night. In addition, 25 volunteers cancelled their participation on short notice and 20 persons on-site right before the intended start. The age distributions were similar for both genders, namely 56.0 ± 10.6 years (mean ± standard deviation) for women and 55.0 ± 10.5 years for men. (In the remain-

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ing text the term “volunteer” will be used both for men and women.) Among the volunteers 20 (47 %) were at least high school graduates. With regard to profession, 18 (42 %) were retirees, seven (16 %) homemakers and 9 (20 %) employees, from civil servant to company manager. The majority of the 29 persons (67 %) declared living healthy, only two (5 %) were occasional smokers, there was no heavy smoker. Twenty volunteers (46 %) lived in cities, eleven (26 %) in urban areas, the remaining twelve (28 %) lived somewhere in between. Thirtytwo volunteers (74 %) declared suffering from more than 5 health symptoms (including sleep problems). Forty (92 %) attributed their sleep problem to RF-EMF from mobile telecommunication base stations because of their own observations and partly because of reassurance by physicians (about 96 % of Austrians general practitioners believe, at least partly, that EMF can cause health problems [20]). Almost all volunteers declared sleep problems occurred shortly after EMF exposure onset. All exhibited a PSQI total score higher than normal (mean 11.8, standard deviation 4.1) (Fig. 2). Frequency-selective RF-EMF measurement allowed monitoring shielding conditions and identifying unusual EMF-related situations (Fig. 3). Results showed that environmental RF-EMFs were not unusually high. Typical levels were less than 0.5 % of ICNIRP’s reference criterion [18], the worst-case was 3.5 %. In most cases immissions were dominated by broadcasting rather than by mobile telecommunication fields. The maximum GSM level was 0.82 %. A potential role of encountered RF-EMF levels was tested by regression analysis of control nights only. Restriction to control nights should avoid bias due to potential psychosomatic reactions to shields. Pooled data of all volunteers (with different EMF levels in their sleeping rooms) did not exhibit statistically significant dependences of sleep quality on RF-EMF immission. In contrast to volunteer’s beliefs on a causal role of EMF, sleep parameters such as SSA total score exhibited even Fig. 2 Total PSQI scores of 43 volunteers (for privacy identification number ID does not fit with sequence of investigation)

a slight non-significant tendency to improved sleep quality with increasing GSM-EMF. The linear regression coefficient was only 0.017. The results of the combined statistical investigation showed that 24 volunteers (56 %) did not exhibit any significant effect. However, 19 volunteers (44 %) exhibited one or more subjective and/or objective sleep parameters with significant effects, in total 45. Fig. 4 shows examples of boxplots exhibiting a significant placebo-effect (P) on subjective sleep quality (SSA total score) and a negative effect on sleep onset latency (Δt2) to sleep stage 2. Table 1 summarizes the results of volunteers exhibiting at least one significant effect. It contains the largest (most unfavorable) p values for differences of each of two test conditions required for concluding on an effect. It is important to note that significant effects are not randomly distributed among subjective and objective sleep parameters as would be expected in case of random influences such as from cofactors. Among subjective sleep parameters 24 effects were found. Most of them (18) were placebos and 6 positive effects. However, there were neither negative effects nor unintended side effects. Objective sleep parameters exhibited 21 effects. Ten were negative effects, 5 positive, and 5 unintended side effects. There was only 1 placebo effect among this group (Table 1). The results were not randomly distributed among volunteers but clustered. This allowed identifying different groups of volunteers: 앫 The first group comprised 3 volunteers with only positive effects (sleep improvement at true-shield condition) mainly restricted to subjective sleep parameters. One of the volunteers accumulated 6 sleep parameters with significant effects. 앫 A second group of 6 volunteers exhibited negative effects (sleep impairment at true-shield condition) mainly restricted to prolonged sleep latencies. Significant prolongations amounted up 36.8 min (median

15

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PSQI

mean 10

–σ

impaired sleep

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normal

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E

Fig. 3 RF-EMF frequency spectra recorded from the erection of the shield in the evening until the demounting in the following morning exhibiting clearly visible reductions of the spectral contributions during the time in bed when the shield was closed

[dBμV]

100 95 90 85 80 75 70 65

t [h]

60 55 500 1000 1500

2000 2500

Fig. 4 Examples of boxplots showing a significant placebo-effect (P) on subjective sleep quality (SSA total score) and a negative effect on sleep onset latency (Δt2) to sleep stage 2 (NE). TS true-shield; SS sham-shield; C control; p-values from MANOVA analysis

20

21

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1

2

3

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f [MHz]

Δt2

SSA

60

60 P

p=0.017

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45

50

p=0.035 40

30

30

15

20

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TS

of unshielded nights 4.7 min) for sleep onset latency to sleep stage 1. Latency from sleep stage 1 to sleep stage 2 was prolonged by up to 3.8 min (median of unshielded nights 2.3 min), latency from sleep stage 2 to sleep stage 3 by up to 18.0 min (median of uns-

SS

C

NE TS

SS

C

hielded nights 8.0 min) and REM latency by up to 160.0 min (median of unshielded nights 85.8 min). Two volunteers exhibited increased numbers of awakenings and sleep stage changes. 앫 The third and largest group of seven volunteers exhi-

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0.001 E

0.001 E

0.077 e

0.114

0.139

0.714

0.317

0.574

0.432

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0.047 E*)

0.837

0.585

0.047

subj. sleep quality

subj. awakening quality

subj. sleep efficiency

onset latency to sleep stage 1



REM latency

latency diff. sleep stage 1–2

latency diff. sleep stage 2–3

sleep stage changes per test

awakenings per total sleep time

percentage of REM sleep

percentage of sleep stages 3+4

objective sleep efficiency

0.254

0.322

0.225

0.264

0.263

0.512

0.326

0.505

0.675

0.507

0.580

0.050 E*)

0.185

0.110

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B

0.105

0.913

0.285

0.819

0.493

0.209

1.000

0.405

0.174

0.066

0.325

0.855

0.741

ne

0.608

0.797

0.696

0.910

0.999

0.817

0.310

0.605

0.071 ne

0.035 nE

0.058 ne

0.414

0.234

0.053

0.081

0.050 E*) 0.378

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C

0.745

0.237

0.005 E

0.292

0.306

0.032 nE

0.023 nE

0.857

0.265

0.316

0.313

0.745

0.565

0.729

0.975

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1.000

0.326

0.466

0.082 p

0.766

0.05 nE*)

0.055 se

0.663

0.134

0.801

0.459

0.373

0.114

0.834

0.085

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0.432

0.734

0.666

0.895

0.264

0.147

0.956

0.010 nE

0.793

0.489

0.357

0.057

0.694

0.729

0.607

G

0.054

0.799

0.339

0.038 nE

0.030 nE

0.926

0.178

0.436

0.960

0.084 ne

0.192

0.983

0.506

0.183

0.281

H

0.193

0.911

0.901

0.007 nE

0.329

0.995

0.476

0.539

0.984

0.920

0.412

0.528

0.717

0.080 b

0.685

I

0.511

0.544

0.313

0.249

0.012 E

0.772

0.435

0.431

0.673

0.479

0.798

0.050 P*)

0.001 P

0.000 P

0.000 P

J

0.772

0.575

0.785

0.149

0.609

0.637

0.708

0.591

0.699

0.743

0.781

0.005 P

0.000 P

0.043 P*)

0.050 P*)

K

0.291

0.485

0.685

0.360

0.957

0.582

0.166

0.489

0.528

0.468

0.377

0.050 P*)

0.008 P

0.001 P

0.003 P

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0.438

0.429

0.073

0.017 nE

0.691

0.267

0.508

0.406

0.384

0.514

0.100 p

0.123

0.001 P

0.001 P

0.000 P

M

0.974

0.039 SE

0.850

0.852

0.526

0.593

0.140

0.847

0.804

0.199

0.812

0.285

0.101

0.001 P

0.017 P

N

0.465

0.251

0.118

0.265

0.391

0.717

0.722

0.612

0.863

0.796

0.781

0.358

0.033 P

0.354

0.158

O

0.803

0.753

0.218

0.632

0.848

0.324

0.239

0.027 P

0.672

0.520

0.451

0.063 p

0.368

0.161

0.283

P

0.257

0.691

0.031 SE

0.003 SE

0.381

0.454

0.399

0.223

0.709

0.098 p

0.055 p

0.965

0.397

0.500

0.479

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0.367

0.067

0.419

0.105

0.150

0.206

0.166

0.401

0.050 SE*)

0.150

0.700

0.794

0.788

0.586

0.590

R

0.041

0.988

0.003 SE

0.058 b

0.366

0.464

0.352

0.747

0.766

0.274

0.146

0.246

0.714

0.054

0.237

S

E effect (sleep improvement by true-shield); nE negative effect (sleep impairment by true-shield); P placebo effect (sleep improvement by any shield); SE side-effect (sleep impairment by any shield); capital letters (grey elements) = differences with p < 0.05; small letters = differences with p < 0.1

E*)

0.001 E

A

volunteer

SSA total score

sleep parameter

Table 1 P-values resulting from MANOVA analysis for volunteers (capital letters A–S) with at least one sleep parameter exhibiting statistical significant changes (p < 0.05). In case where MANOVA-preconditions were not met, p-values resulting from nonparametric testing (Kolmogorov-Smirnov-Z test and Mann-Whitney-U test) are given (marked with *))

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bited placebo effects. It should to be noted that they were mainly restricted to subjective sleep parameters. This is in good agreement with beliefs in shielding. 앫 The fourth group of three volunteers exhibited mainly unintended side effects. Analysis of potential cofactors showed no influence in the majority of investigated parameters. In a few cases, statistical analysis identified cofactors which were significantly different between test conditions: Such cofactors included the following: fatigue before true-shield nights (4 volunteers), late dinner before control night (1 volunteer) and before true-shield nights (2 volunteers), stress before true shield (1 volunteer), increased liquid consumption before control night (1 volunteer), watching a thriller before true-shield night (1 volunteer). In a second step it was checked whether identified cofactors had a potential impact on MANOVA – identified differences between test conditions. As a result, no statistical significant difference could be explained by these cofactors. This applies also to workday/weekends. As expected, for workday/weekends influences were encountered in pooled data. However, no significant differences were found between test conditions. This had been prevented by their random order.

Discussion The study deliberately investigated volunteers characterized by strong convictions on a causal role of RF-EMF on sleep impairment. It could be expected that an effect, if existent, should be encountered most likely in this group. It is interesting to note that most volunteers reported on more than 5 subjective health symptoms. This is in good agreement with investigations on frequency of self-reported complaints where it was found that EHS volunteers were characterized by an elevated degree of somatization [9]. Subjective influences such as belief in shielding or checking for shielding conditions could bias individual ratings. When sleep quality parameters were pooled such as for checking for a potential dependence of sleep quality on RF immission levels, only results of control nights were used to avoid any bias due to shielding. However, it is known that individual sensitivities can vary considerably. It could be demonstrated that the ability of perceiving electric currents varies by 2 orders of magnitudes within the general population [19, 22]. Therefore, if an RF-EMF related effect existed it could not be expected that all of the volunteers would react in the same way. Therefore, data pooling could mask individual reactions. Consequently, such an approach would be not sufficient in case of suspected exceptional individual reactions. Besides this, pooled results are criti-

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cally dependent on the volunteer recruitment strategy and homogeneity of the investigated group [31]. These were the reasons why a crossover study design and a tough combined statistical criterion had been chosen requiring three conditions to be met: simultaneous statistical significant results for two test conditions and lack of significance for the third one. The chosen combined statistical significance level pc = 0.00238 was even stricter than the Bonferroni criterion (0.05/15 = 0.0033). Since 15 sleep parameters of 43 volunteers were subjected to the combined significance criterion at the probability level pc only 1.5 significant (clustered) results should be expected by chance. In fact, 45 significant effects were encountered (Table 1) demonstrating that this cannot be explained just by chance. (Of course, many more significant results than expected by chance were encountered on the basis of just paired statistical testing.) It could be argued that crossover analysis could be inadequate or that number of significances be elevated because of cofactors. However, if relevant, cofactors could act in either way and not only enhance but also reduce the number of significancies due to masking effects. Besides this, if relevant, cofactors should cause effects randomly distributed among sleep parameters and/or volunteers. This was not the case. On contrary, effects were clearly clustered both regarding sleep parameters and volunteers. This does not support the argument. Finally, a potential influence of cofactors on encountered effects was tested and could not be verified. On the other hand, it could not be excluded that cofactors could have masked additional effects. The crossover approach allowed each volunteer to be his/her own reference and identifying reactions on an individual basis. Results show that there were responders and non-responders and different reaction groups. The positive-effect group needs further discussion. One important condition to avoid bias was to assure that volunteers were blinded with regard to test conditions. Since this could not be assured a priori, in the study design indicators had been implemented allowing at least subsequently detecting whether volunteers had checked shielding conditions. One means was examining continuous RF-EMF records for suspicious openings/closings of the true-shield, in particular in the evening. Another means were analyses of suspicious test-condition-dependent differences between ratings of subjective sleep quality parameters and/or RF immission. These checks were made for every volunteer irrespective of investigation result. These precautionary measures allowed proving that all three volunteers exhibiting positive effects on subjective sleep parameters had checked shielding. In addition, two of them were identified giving biased sleep ratings. It is consistent with faking that influences should be seen mainly in subjective rather than objective sleep parameters. In pooled analysis such

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volunteers would have had to be excluded from analysis (except for control nights). One advantage of individual analysis was that faking does not have any crossover effect on the whole study. However, the consequence was that the results of these faking volunteers could not be considered as evidence for anything, in particular not for potential causal effects of RF immissions on sleep. It could be expected faking could enhance a positive effect, if existent, or mask a negative effect or a placebo effect. Therefore, no reliable conclusion can be drawn from results of these three volunteers. Overall, it could be shown that 25 volunteers (58 %) did not exhibit any statistically significant effect of any sleep parameter associated with changing environmental RF-EMF exposure. Adding another seven (16 %) who clearly exhibited statistical significant placebo effects, it can be concluded that convictions of the overwhelming majority of the investigated volunteers (74 %) could not be verified. The relevance of three de-blinded volunteers (7 %) with biased subjective ratings remains open but would not change the overall conclusion. The significant prolongations of sleep latencies found in four volunteers were consistent: none of them exhibited any other kind of effect. Their subjective ratings were correct. Checks did not indicate biased responses. It could not be expected that an effect, if it existed, would affect all investigated sleep parameters nor was it possible to conclude a priori on parameters specifically sensitive to RF-EMF. However, the observed effect on sleep onset latencies is in agreement with several other studies reporting changes of latencies during RF-EMF provocation. Some studies [24, 25, 31, 33] report on decrease of sleep latencies under continuous elevated exposure others found prolonged sleep latencies after reducing exposure from provocation to environmental levels [5], [16]. It is not necessarily contradictory that persistent night-time provocation to EMF was associated with the contrary (reduction of sleep onset latencies). It is yet an open question to which extent the strat-

egy of selecting volunteers [31] might explain the different outcomes of sleep studies. Effects on sleep latencies did not affect subjective sleep quality ratings. However, health relevance of identified RF-EMF effects, if existent, needs to be verified. In any case it cannot be explained by present interaction mechanisms, in particular in view to low exposure levels. This would merit further investigations replicating these results. Further analyses are on the way to identify common similarities and/or differences of the reacting group compared to non-responders.

Conclusion Pooled sleep parameter analysis did not exhibit statistical significant dependencies on RF-EMF immissions. Although all volunteers were convinced on a causal role of RF-EMF from mobile phone base stations, sleep analysis did not indicate adverse health effects on sleep quality, neither from mobile telecommunication fields nor from total RF-EMF immission. Since the existing exposure limits [18] preventing (only) health-relevant effects rather than any measurable biological response to RFEMF exposure, the observed effects on sleep onset latencies do not challenge limits. However, they are of particular interest in regard to potential interaction mechanisms of weak (non-thermal) radiofrequency electromagnetic fields and merit further investigation. ■ Conflict of Interest We declare that we have no conflict of interest of any kind that could bias performance and/or outcome of this study. ■ Acknowledgement The study was supported by the Austrian Federal Ministry of Agriculture, Forestry, Environment and Water Management, the Austrian Federal Ministry of Traffic, Innovation and Technology, the Austrian Province Government of Vorarlberg and the German Federal Office of Radiation Protection (within the German Mobile Telecommunication Research Program).

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