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However, we identified various participant-specific race effects from the multi-variate results. For example, we found that AA participants were slightly better at categorizing Obese male bodies relative to CA participants. AA participants also over-estimated Underweight female bodies, as they had consistently mis-categorized Underweight female bodies as Normal size and this effect was not present in CA participants.
For normal male bodies, AA performed better than CA, but AA made more over-estimation errors than CA. Interestingly, some stimuli-specific race effects were also identified.
Overall, categorization performance was slightly but significantly better for Avatar stimuli in terms of percent accuracy.
Multi-variate analysis further revealed that during categorization of female overweight stimuli, participants showed higher accuracy for Avatar and CA bodies than for AA Fig.
A recent study 20 had used visual adaptation to study the after-effect following repeated exposure of Asian or Caucasian female bodies, and their results seemed to be consistent to our findings. People are better at recalling or recognizing faces of their own gender relative to faces of the opposite gender 41 , Limited work has been conducted regarding gender-biases in body perception.
Multi-variate analysis in the current study has identified significant differences in performance between viewing female and male body images. There is a stimuli- and participant-specific gender effect that is particularly prominent for Obese bodies. Specifically, a marked difference was found between male and female participants, where male participants showed significantly higher accuracy for female Obese bodies.
For male Overweight bodies, male participants performed better than female, while female participants showed more under-estimation errors. This suggests that under-estimation bias for Overweight male bodies was primarily driven by female participants. A stimuli-specific gender effect was also observed whereby, consistent with the univariate analysis, participants performed more accurately for Underweight male bodies than female Underweight bodies.
Overall performance for Normal weight images was also better for male than female bodies. For Obese bodies, performance was better for female bodies, and there were also more under-estimation errors for male Obese bodies.
These findings demonstrated that, by increasing the diversity in the stimuli and participants tested and by adopting a multi-variate approach, a more complex categorization pattern can be revealed.
Furthermore, our observations of behavioural biases for higher BMI male stimuli and for lower BMI female stimuli seem to be consistent with the idea that partial overlapping or multiple gender-specific neural mechanisms may be at play during body size perception 24 , While it is important to recognize that people have different body sizes, shapes, and other physical characteristics 19 , and that even BMI cut-off points may not capture variations in physiological measurements across cultures 45 , our current approach aims demonstrated that it is possible to capture and quantify some of the multi-dimensional visual characteristics, and it is critical that future work should also harness similar approaches.
Our findings here certainly do not attempt to capture categorization patterns for all types of bodies, and despite the constraints in our well-controlled paradigm in real life, people with the same BMI may have different body shapes, and we see bodies from many different viewpoints other than straight-on , we have taken an important first step to quantify complex patterns in body weight perception.
Finally, we believe that providing a careful characterization of perceptual biases in body weight here may lead to better diagnostic decision-making and development of personalized intervention programmes in both clinical and non-clinical settings.
Sixty participants were recruited for this study age 20— Specifically, four groups of participants were tested, 15 participants per group with equal numbers of female and male participants, equal numbers of participants who had identified themselves as African American and Caucasian American.
The current protocol was approved by the Institutional Review Board of the University of Tennessee Health Science Center Protocol no.
The current research had been performed in accordance with the Declaration of Helsinki. All participants gave written informed consent and were compensated for their participation. Each condition consisted of 5 computer-generated individual identities see Fig. These stimuli were computer-generated images, polygon meshes Dyna Models created by Pons-Moll and colleagues 46 using 4D cameras to capture images of actors with a range of BMI from underweight to obese.
These meshes were exported to Poser Pro Smith Micro Software , where they were then customized in order to generate additional race types and identities within each BMI category. Also see Supplementary Figure 1 for body stimuli weight categories and the standard BMI boundaries. Experimental paradigm for the visual categorization task.
Each condition consisted of 5 computer-generated individual identities. B Example trial, where each body stimulus was presented for 2 s, followed by a 3-s response window. Participants were asked to judge whether each stimulus was underweight, normal, overweight, or obese by pressing the corresponding button on the keyboard.
The experiment was presented to the participants on a computer screen, one stimulus at a time. This was an event-related design, where stimuli and position of the stimuli were randomized using Optseq2 47 , and the position of each image was slightly jittered from the center of the screen to minimize estimation using low-level visual differences between images.
Participants were asked to estimate if the body is underweight, normal, overweight, or obese by pressing the corresponding key. Both accuracy and error rates were measured Fig. We measured the correct responses and calculated percent accuracy for each condition and for each participant.
Percent accuracy was then submitted to a repeated measures factorial ANOVA, with the Stimulus Gender Female, Male , Stimulus Race Types African American, Caucasian American, Green Avatar , and Stimulus Weight Categories Underweight, Normal, Overweight, Obese as within-subject factors, and Participant Genders Female, Male and Participant Race Types African American, Caucasian American as between-subject factors.
To gauge the decision-making patterns, we had not only measured the accuracy rates correct-estimation but also the direction of biases under-estimation, over-estimation of the weight categories. To expose the specific observant and stimuli profiles leading to correct classification, under-estimation, or over-estimation, we implemented a classification tree analysis using a Chi-Squared Automatic Interaction Detector CHAID algorithm.
The CHAID algorithm splits parent nodes into children nodes using the predictor yielding the minimum p-value by chi-squared test that is lower than the splitting criteria 0. CHAID uses Bonferroni-adjusted p-values since the selection of the predictor with the smallest p-value is a multiple testing task.
The algorithm is terminated when there is no Bonferroni-adjusted p-value lower than the determined significance level. In addition, we also set the minimum size of parent nodes to 50, the minimum size of children nodes to 25, and maximum depth max number of splits to 3 As a non-parametric classification method, the main concern about classification trees is over-fitting, leading to a lack of generalizability of the model.
To control overfitting, we implemented fivefold cross-validation. In fivefold cross-validation, the cohort was divided into five equal-size subgroups. The associated risk for each case in the test data was calculated for each of the 5 subgroups; the average of the risk across the 5 test samples were presented as the cross-validation risk.
Smaller values of cross-validation risk indicate that the produced classification model is generalizable.
The final tree represented was the one built on the full cohort. This study was approved by University of Tennessee Health Science Center Institutional Review Board. Protocol no. Burke, M. Evolving societal norms of obesity: What is the appropriate response?.
JAMA 3 , — Article PubMed Google Scholar. Fitzgibbon, M. The relationship between body image discrepancy and body mass index across ethnic groups. Article CAS PubMed Google Scholar.
Fletcher, J. The interplay between gender, race and weight status: Self perceptions and social consequences. Ruiz, A. True believers?
Religion, physiology, and perceived body weight in Texas. Health 54 4 , — Oldham, M. Visual weight status misperceptions of men: Why overweight can look like a healthy weight. Health Psychol. Visual body size norms and the under-detection of overweight and obesity.
Glasser, C. Sex Roles 61 1—2 , 14—33 Article PubMed PubMed Central Google Scholar. Perez, M. Body image dissatisfaction and disordered eating in black and white women.
Piryankova, I. et al. Owning an overweight or underweight body: Distinguishing the physical, experienced and virtual body. PLoS ONE 9 8 , e Article ADS PubMed PubMed Central CAS Google Scholar. Cornelissen, K. Visual biases in judging body weight. Freedman, R. Do men hold African—American and Caucasian women to different standards of beauty?.
Eat Behav. Thaler, A. Body size estimation of self and others in females varying in BMI. PLoS ONE 13 2 , e Article PubMed PubMed Central CAS Google Scholar. Fixation patterns, not clinical diagnosis, predict body size over-estimation in eating disordered women and healthy controls.
Smith, D. Body image among men and women in a biracial cohort: The CARDIA Study. Gledhill, L. Harris, C. BMI-based body size guides for women and men: Development and validation of a novel pictorial method to assess weight-related concepts.
Article MathSciNet CAS Google Scholar. Lynch, E. Body size perception among African American women. Robinson, E.
Visual identification of obesity by healthcare professionals: An experimental study of trainee and qualified GPs. Brooks, K. Muscle and fat aftereffects and the role of gender: Implications for body image disturbance.
Article MathSciNet PubMed Google Scholar. Gould-Fensom, L. The thin white line: Adaptation suggests a common neural mechanism for judgments of Asian and Caucasian body size.
Stephen, I. Visual attention mediates the relationship between body satisfaction and susceptibility to the body size adaptation effect. PLoS ONE 13 1 , e Challinor, K. Body size and shape misperception and visual adaptation: An overview of an emerging research paradigm.
Body image distortion and exposure to extreme body types: Contingent adaptation and cross adaptation for self and other. Looking at the figures: Visual adaptation as a mechanism for body-size and -shape misperception.
Gender and the body size aftereffect: Implications for neural processing. Stunkard, A. Nutrition, aging and obesity: A critical review of a complex relationship.
CAS PubMed Google Scholar. Use of the Danish Adoption Register for the study of obesity and thinness. Cachelin, F. Does ethnicity influence body-size preference? A comparison of body image and body size.
Visual perceptions of male obesity: A cross-cultural study examining male and female lay perceptions of obesity in Caucasian males.
BMC Public Health 15 , Akbilgic, O. Classification trees aided mixed regression model. Article MathSciNet MATH Google Scholar. Zhang, H. RCircos: An R package for Circos 2D track plots. BMC Bioinform.
Google Scholar. Anzures, G. Developmental origins of the other-race effect. Two other major studies narrowed the prescription slightly, concluding that replacing saturated fat with polyunsaturated fats like vegetable oils or high-fiber carbohydrates is the best bet for reducing the risk of heart disease, but replacing saturated fat with highly processed carbohydrates could do the opposite.
Good fats come mainly from vegetables, nuts, seeds, and fish. They differ from saturated fats by having fewer hydrogen atoms bonded to their carbon chains. Healthy fats are liquid at room temperature, not solid.
There are two broad categories of beneficial fats: monounsaturated and polyunsaturated fats. Monounsaturated fats. When you dip your bread in olive oil at an Italian restaurant, you're getting mostly monounsaturated fat. Monounsaturated fats have a single carbon-to-carbon double bond.
The result is that it has two fewer hydrogen atoms than a saturated fat and a bend at the double bond. This structure keeps monounsaturated fats liquid at room temperature. Good sources of monounsaturated fats are olive oil, peanut oil, canola oil, avocados, and most nuts, as well as high-oleic safflower and sunflower oils.
The discovery that monounsaturated fat could be healthful came from the Seven Countries Study during the s. It revealed that people in Greece and other parts of the Mediterranean region enjoyed a low rate of heart disease despite a high-fat diet.
The main fat in their diet, though, was not the saturated animal fat common in countries with higher rates of heart disease. It was olive oil, which contains mainly monounsaturated fat. This finding produced a surge of interest in olive oil and the " Mediterranean diet ," a style of eating regarded as a healthful choice today.
Although there's no recommended daily intake of monounsaturated fats, the National Academy of Medicine recommends using them as much as possible along with polyunsaturated fats to replace saturated and trans fats. Polyunsaturated fats. When you pour liquid cooking oil into a pan, there's a good chance you're using polyunsaturated fat.
Corn oil, sunflower oil, and safflower oil are common examples. Polyunsaturated fats are essential fats. That means they're required for normal body functions, but your body can't make them. So, you must get them from food.
Polyunsaturated fats are used to build cell membranes and the covering of nerves. They are needed for blood clotting, muscle movement, and inflammation. A polyunsaturated fat has two or more double bonds in its carbon chain. There are two main types of polyunsaturated fats: omega-3 fatty acids and omega-6 fatty acids.
The numbers refer to the distance between the beginning of the carbon chain and the first double bond. Both types offer health benefits. Eating polyunsaturated fats in place of saturated fats or highly refined carbohydrates reduces harmful LDL cholesterol and improves the cholesterol profile.
It also lowers triglycerides. Good sources of omega-3 fatty acids include fatty fish such as salmon, mackerel, and sardines, flaxseeds, walnuts, canola oil, and un-hydrogenated soybean oil. Foods rich in linoleic acid and other omega-6 fatty acids include vegetable oils such as safflower, soybean, sunflower, walnut, and corn oils.
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Our knowledge of nutrition has come full circle, back to eating food that is as close as possible to the way nature made it. Working with a therapist you resonate with can make a huge difference. Wishing you all the best. Not to mention the huge food bill this entails. Do you have any advice for me?
Thank you. I hear you. Stick to your guns. You are not being selfish at all! Entertaining can be stressful and time-consuming. Your husband may not understand but so be it. Save my name, email, and website in this browser for the next time I comment.
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