We study the relationship among real, self-perceived, and desired body mass index (BMI) in 21,288 adults from the Mexican National Health and Nutrition Survey 2012, analyzing the effect of sex and diagnosis of obesity/overweight by a healthcare professional. Self-perceived and desired BMI are analyzed via a figure rating scale question and compared to real BMI. Only 8.8 and 6.1% of the diagnosed and non-diagnosed obese, respectively, correctly identify themselves as such. For the obese, 20.2% of non-diagnosed and 12.7% of diagnosed perceive themselves as normal or underweight, while 49.1 and 37% of these are satisfied with their perceived BMI. Only 7.8% of the obese, whose real and perceived BMI coincide, have a desired BMI equal to their perceived one. In contrast, 43.2% of the obese, whose perceived BMI is normal, have a desired BMI the same as their perceived one. Although the average desired body figure corresponds to the normal BMI range, misperceptions of BMI correlate strongly with the degree of satisfaction associated with perceived BMI, with larger misperceptions indicating a higher degree of satisfaction. Hypothesizing that the differences between real, perceived, and desired weight are a motivator for weight change, one potential intervention could be the periodic assessment of real, perceived, and desired BMI in order to correct misleading weight misperceptions that could potentially obstruct positive behavioral change.
BackgroundThis study analysed the relationship between perceived and actual Body Mass Index (BMI) and the effect of a prior identification of obesity by a medical professional for adults using difference in response for two distinct BMI self-perception questions. Typically, self-perception studies only investigate the relation with current weight, whereas here the focus is on the self-perception of weight differences.MethodsA statistical approach was used to assess responses to the Mexican ENSANUT 2006 survey. Adults in the range of BMI from 13 to 60 were tested on responses to a categorical question and a figure rating scale self-perception question. Differences in response by gender and identification of obesity by a medical professional were analysed using linear regression.ResultsResults indicated that regardless of current BMI and gender, a verbal intervention by a medical professional will increase perceived BMI independently of actual BMI but does not necessarily make the identified obese more accurate in their BMI estimates. A shift in the average self-perception was seen with a higher response for the identified obese. A linear increase in perceived BMI as a function of actual BMI was observed in the range BMI < 35 but with a rate of increase much less than expected if weight differences were perceived accurately.ConclusionsObese and overweight Mexican adults not only underestimated their weight, but also, could not accurately judge changes in weight. For example, an increase of 5 kg is imagined, in terms of self-image, to be considerably less. It was seen that an identification of obesity by a health care professional did not improve ability to judge weight but, rather, served as a new anchor from which the identified obese judge their weight, suggesting that even those identified obese who have lost weight, perceive their weight to be greater than it actually is. We believe that these results can be explained in terms of two cognitive biases; the self-serving bias and the anchoring bias.
Aim: To investigate whether eating patterns of specific food groups can be used to predict and classify Mexican adults who have been diagnosed as having obesity, diabetes or both, when compared to those without a diagnosis. Additionally, we aim to show the benefit of data mining techniques in nutritional studies. Methods: Statistical analysis of self-reported eating patterns based on designated food groups is conducted. Predictive models for health status based on dietary patterns are built using a naïve Bayes classifier. Results: Clear patterns emerge in the model building where adults are categorised as having obesity, diabetes or both. The model for diabetics showed the greatest degree of predictability, producing sensitivity results 2.4 times higher than the average, using score decile testing. The models for people with obesity and for those with both obesity and diabetes both reported sensitivity doubling the average. Coverage also showed greatest response for the diabetic model, the first decile containing 24% of all diabetics. Conclusions: Classifier models using dietary habits as inputs succeed in subcategorising Mexican adults based on health status. Diabetics are associated with a very different, and more appropriate dietary pattern (significantly less sugar consumption) for their condition, relative to the non-diagnosed group. Adults with obesity are also associated with a very different, but inappropriate (higher overall consumption), dietary pattern. We hypothesise that obesity, unlike diabetes, is not seen as a sufficiently serious condition, leading to an inadequate response to the diagnosis. Furthermore, data mining techniques can provide new results in nutritional studies.
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