A prediction model for personal radio frequency electromagnetic field exposure.

The Science of the total environment · 2009 · Unclear · Moderate evidence

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UnclearModerate evidencePaper 4939

Study snapshot

PublicationThe Science of the total environment · 2009

Study typeexposure_assessment

Reported directionUnclear

Evidence ratingModerate

Abstract

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.

Machine Enhanced Logic

AI evidence extraction

Structured fields below were generated from the paper’s abstract and metadata to make the evidence easier to inspect. Automated extraction may be incomplete or incorrect; verify important details against the original paper.

Study typeexposure_assessment

Effect directionunclear

Population166 volunteers from Basel, Switzerland

Sample size166

ExposureRF · 1 week personal measurements; validation repeat ~21 weeks later

Evidence strengthModerateConfidence: 78% · Peer reviewed: yes

Main findings

Using questionnaire variables and modeled RF-EMF from fixed site transmitters at residence, the final multiple regression model explained 52% of variance in mean personal RF-EMF exposure (R2=0.52). Agreement analysis reported sensitivity 0.56 and specificity 0.95 (90th percentile cut-off); in a validation subset with repeat measurements (~21 weeks later), sensitivity was 0.67 and specificity 0.96. The authors conclude it is feasible to model personal RF-EMF exposure and that the model may assess average exposure over several months.

Outcomes measured

Prediction model performance for mean personal RF-EMF exposure from different sources (R2)
Agreement between calculated and measured RF-EMF (sensitivity, specificity; 90th percentile cut-off)
Validation performance over several months (repeat measurements ~21 weeks later)

View raw extracted JSON

{
  "study_type": "exposure_assessment",
  "exposure": {
    "band": "RF",
    "source": "personal exposure from multiple sources (fixed site transmitters and indoor devices)",
    "frequency_mhz": null,
    "sar_wkg": null,
    "duration": "1 week personal measurements; validation repeat ~21 weeks later"
  },
  "population": "166 volunteers from Basel, Switzerland",
  "sample_size": 166,
  "outcomes": [
    "Prediction model performance for mean personal RF-EMF exposure from different sources (R2)",
    "Agreement between calculated and measured RF-EMF (sensitivity, specificity; 90th percentile cut-off)",
    "Validation performance over several months (repeat measurements ~21 weeks later)"
  ],
  "main_findings": "Using questionnaire variables and modeled RF-EMF from fixed site transmitters at residence, the final multiple regression model explained 52% of variance in mean personal RF-EMF exposure (R2=0.52). Agreement analysis reported sensitivity 0.56 and specificity 0.95 (90th percentile cut-off); in a validation subset with repeat measurements (~21 weeks later), sensitivity was 0.67 and specificity 0.96. The authors conclude it is feasible to model personal RF-EMF exposure and that the model may assess average exposure over several months.",
  "effect_direction": "unclear",
  "limitations": [],
  "evidence_strength": "moderate",
  "confidence": 0.78,
  "peer_reviewed_likely": "yes",
  "keywords": [
    "RF-EMF",
    "radio frequency electromagnetic fields",
    "personal exposure",
    "exposure prediction model",
    "portable exposure meters",
    "questionnaire predictors",
    "geospatial propagation model",
    "mobile phone base stations",
    "broadcast transmitters",
    "cordless phones",
    "wireless LAN",
    "public transport",
    "validation study"
  ],
  "suggested_hubs": [
    {
      "slug": "occupational-exposure",
      "weight": 0.1,
      "reason": "Includes behavioral predictors such as time spent in public transport, but not clearly occupational."
    }
  ]
}

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AI-extracted fields are generated from the abstract and metadata and may be incomplete or incorrect. This material is for informational purposes and is not medical advice.

Identifiers and source

DOI: 10.1016/j.scitotenv.2009.09.023
PubMed: 19819523

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