As the use of self-reported data to classify obesity continues, the sources of the reporting errors remain unclear. These data from three nationally representative population surveys show that self-reported height bias is stable over time regardless of gender, age or clinically measured BMI category. Self-reported weight bias is continuing to increase regardless of gender or age or knowledge that weight would be measured after self-reporting. These data further show that when classified by clinical BMI measurement, across time, the increase in self-reported weight bias is evidenced only in the obese category for males and the obese and overweight categories for females.

Comparison with Other Studies

A focused search of the BMI literature revealed only one study which focused on height and weight across time [25], but this study did not look at these biases in the context of BMI category. The search identified many research articles devoted to the validity of self-reported height and weight [3], [7], [8], [10]–[18], [22], [31], [32]. While not all focus on both height and weight, all express a similar opinion, i.e., an underestimated numerator and an overestimated denominator lead to a pattern of underestimation when self-reported height and weight are used to calculate BMI. The fact that older adults have been shown to systematically overestimate their height [7], [8], [10]–[14], and women tend to underreport their weight and men tend to overreport their weight [6], [11], [15], [17], [18], may have led researchers to accept that self-report height and weight bias both contribute equally to an underestimation of BMI. Two recent articles focusing on ethnic differences in self-reported and measured obesity add weight to this conclusion [22], [31]. The most recent publication on temporal trends in BMI bias [26] also comes to the same erroneous conclusion. Many studies have focused on trying to correct for this underestimation to provide a more accurate estimation of BMI, and particularly obesity prevalence, in populations [8], [25], [33], [34]. But the generalisation of these equations depends on the stability of the self-reporting bias over time and populations. Connor Gorber and Tremblay [21] recently suggested that if we can establish if the bias is constant or changing systematically over time, then self-reported estimates may still be valuable for monitoring trends and could be statistically adjusted to increase their accuracy. Our conclusions that self-report height bias remains stable over a 10 year period, and that self-report weight bias is increasing, but for the main in the obese category, will now allow researchers to apply imputation methods more accurately.

Our recent finding, using three population surveys from Ireland with both clinically measured and self-reported BMI [1], a decline in the sensitivity of the obese category across time (79.5%→64%→53.4%), led us to question if in fact both an overestimation of height and an underestimation in weight contribute equally to this decline in sensitivity over time. Our present study using all three BMI classifications and three cross sectional studies at three time points shows that the declining sensitivity in the obese category is caused by an increasing underestimation of weight, while the effect of height overestimation remains stable over time. On one hand, such inaccuracies could be understood as the consequence of a lack of information regarding one’s own height and weight. It is also possible that this group are in denial of their unhealthy weight, or don’t want to be labelled as obese. On the other hand, a more plausible explanation, given the rising clinically measured overweight and obesity levels in Ireland between SLÁN 1998 and SLÁN 2007 (60% to 64%) [1] is that increases in the adiposity levels of the general population may have normalised obesity. Recent literature suggests that there is a shift in the social norm of what is regarded as overweight or obese [9], [35]. Thus the height and weight errors evident here may be as a result of a cognitive distortion affecting the individuals’ perception of their own body shape [32]. This would explain why older individuals show a greater unawareness of their actual height and weight compared to other age groups. Survey context should also be given consideration. Just as prior knowledge of height and weight measurement leads to more accurate self-report estimates [36], giving information on the importance of accurate estimates to participants before they self-report their height and weight, may lead to more accurate responses.

A recent 2011 study based their study objectives on the assumption that social norms regarding what constitutes an ideal body weight also affect individuals’ self-reporting decisions when answering anthropometric questions on health questionnaires [32]. They report that, the greater the average “ideal” weight shared by the reference group, the lower the weight bias or the less inclined sample individuals are to under-report their weight. An important finding to support this theory in the current study is that while self-reported weight bias is evident in all three BMI categories, it is most notably on the increase in the obese category for both males and females (Figures 3a and 3b).

As in existing literature, we identified that female height reporting bias is not consistent across all age groups and that older women tend to overestimate their height more than younger women [8], [10]–[14]. An important additional finding is that this overestimation in height is consistent across time and also across clinically measured BMI category.

The underestimation of weight in the normal category is consistent across time. A recent British study identified a decline in the sensitivity of self-diagnosis of overweight [9]. A disadvantage of the study was the inability to distinguish between the overweight and obese BMI categories. An important finding in the current study, is the establishment that the underestimation of weight is stable across time for males in the overweight category but not so for females. While the underestimation of female weight in the overweight category was stable from 1998 to 2002, we observed an increase to 2007. As overweight levels, as well as obese levels, in the general population are on the increase, females that measure as overweight are finding it difficult to recognise they are overweight. It is important to monitor this trend in future population surveys.

The recent paper by Stommel and Osier [26] on temporal trends in BMI bias across time in the USA reported that the bias in self-reported height and weight has declined, leading to more accurate BMI categorisations based on self-report. This finding appears to be at odds with the findings of our study. However, the Stommel and Osier paper have examined BMI bias, not height bias or weight bias, and incorrectly assume improved accuracy in reporting of both height and weight. Nevertheless, if we focus on the improvement in BMI categorisations, as outlined by Stommel and Osier, what is calculated is the sensitivity of six BMI categories, underweight <18.5 kg m−2, normal weight 18.5<25 kg m−2, overweight 25<30 kg m−2, obese I 30<35 kg m−2, obese II 35<40 kg m−2 and obese III 40+ kg m−2. The sensitivity of the underweight, normal weight and overweight categories remains the same between the two time points. Improvements in the sensitivity of the obese categories are evident, and this improvement is greatest for those in obese category III, or >40 kg m−2 (54.4%→71.7%). We would argue that these patients are most likely engaged with the health service given their extremely high BMI, and their height and weight are therefore monitored regularly. Consequently, they will know their height and weight and this will result in a smaller self-report BMI bias. This is likely the source of the improvement in BMI categorisation, rather than a population wide improvement in self-reported height and weight. There are some limitations to our own findings, all three are relatively small samples and the two older surveys have modest response rates. Nonetheless we have established that in socio-demographic terms all three surveys are reasonably representative of the main datasets and all three surveys used a clustered random selection strategy countrywide, using standardised measurement protocols.