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Validity over time of self-reported anthropometric variables during follow-up of a large cohort of UK women

Overview of attention for article published in BMC Medical Research Methodology, October 2015
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Title
Validity over time of self-reported anthropometric variables during follow-up of a large cohort of UK women
Published in
BMC Medical Research Methodology, October 2015
DOI 10.1186/s12874-015-0075-1
Pubmed ID
Authors

F. Lucy Wright, Jane Green, Gillian Reeves, Valerie Beral, Benjamin J. Cairns

Abstract

In prospective epidemiological studies, anthropometry is often self-reported and may be subject to reporting errors. Self-reported anthropometric data are reasonably accurate when compared with measurements made at the same time, but reporting errors and changes over time in anthropometric characteristics could potentially generate time-dependent biases in disease-exposure associations. In a sample of about 4000 middle-aged UK women from a large prospective cohort study, we compared repeated self-reports of weight, height, derived body mass index, and waist and hip circumferences, obtained between 1999 and 2008, with clinical measurements taken in 2008. For self-reported and measured values of each variable, mean differences, correlation coefficients, and regression dilution ratios (which measure relative bias in estimates of linear association) were compared over time. For most variables, the differences between self-reported and measured values were small. On average, reported values tended to be lower than measured values (i.e. under-reported) for all variables except height; under-reporting was greatest for waist circumference. As expected, the greater the elapsed time between self-report and measurement, the larger the mean differences between them (each P < 0.001 for trend), and the weaker their correlations (each P < 0.004 for trend). Regression dilution ratios were in general close to 1.0 and did not vary greatly over time. Reporting errors in anthropometric variables may result in small biases to estimates of associations with disease outcomes. Weaker correlations between self-reported and measured values would result in some loss of study power over time. Overall, however, our results provide new evidence that self-reported anthropometric variables remain suitable for use in analyses of associations with disease outcomes in cohort studies over at least a decade of follow-up.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 53 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Unknown 52 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 21%
Student > Bachelor 9 17%
Student > Master 9 17%
Student > Doctoral Student 4 8%
Student > Postgraduate 3 6%
Other 6 11%
Unknown 11 21%
Readers by discipline Count As %
Medicine and Dentistry 16 30%
Agricultural and Biological Sciences 5 9%
Social Sciences 5 9%
Nursing and Health Professions 5 9%
Biochemistry, Genetics and Molecular Biology 3 6%
Other 6 11%
Unknown 13 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 November 2015.
All research outputs
#4,845,130
of 6,549,866 outputs
Outputs from BMC Medical Research Methodology
#582
of 708 outputs
Outputs of similar age
#144,340
of 210,194 outputs
Outputs of similar age from BMC Medical Research Methodology
#17
of 21 outputs
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