Note: This study found no significant biological effects under its experimental conditions. We include all studies for scientific completeness.
A forecasting method to reduce estimation bias in self-reported cell phone data.
Redmayne M, Smith E, Abramson MJ. · 2012
View Original AbstractPoor memory of phone usage in health studies may be hiding real EMF risks by making heavy users appear safer than they are.
Plain English Summary
Researchers developed a new statistical method to improve the accuracy of cell phone usage estimates in health studies. They found that people are very poor at remembering how much they actually use their phones, leading to significant errors that could make health risks appear smaller than they really are. This mathematical approach could help future studies better identify real health effects by correcting for these memory biases.
Study Details
There is ongoing concern that extended exposure to cell phone electromagnetic radiation could be related to an increased risk of negative health effects. Epidemiological studies seek to assess this risk, usually relying on participants’ recalled use, but recall is notoriously poor. Our objectives were primarily to produce a forecast method, for use by such studies, to reduce estimation bias in the recalled extent of cell phone use.
The method we developed, using Bayes’ rule, is modelled with data we collected in a cross-sectional ...
Participants recalled their recent extent of SMS-texting and retrieved from their provider the curre...
Show BibTeX
@article{m_2012_a_forecasting_method_to_3323,
author = {Redmayne M and Smith E and Abramson MJ. },
title = {A forecasting method to reduce estimation bias in self-reported cell phone data.},
year = {2012},
url = {https://www.nature.com/articles/jes201270},
}