The Relationship between Bodyweight Status and Weight Perception Explains Differences in Calories Ordered in a Food Choice Exercise

Weight status underestimation and weight management goals among adults in the rural South of the United States

PURPOSE

To examine factors associated with weight status underestimation and the relationship between weight status underestimation and weight loss as a weight management goal among adults living in the rural South.

METHODS

An anonymous survey was distributed at six primary care clinics and two churches in rural, South Carolina counties. Weight status underestimation was determined based on the difference between perceived weight status using standard body mass index (BMI) categories (underweight, normal weight, overweight, and obese) and BMI category from self-reported height and weight. Participants reported whether their weight management goal was to lose, maintain, or gain weight. Chi-square and binary logistic regressions were used for data analysis.

FINDINGS

A total of 185 respondents (76% female) at least partially completed the survey. Nearly 60% underestimated their weight status. Increasing BMI was associated with higher odds of weight status underestimation (OR: 1.10, 95%: 1.04, 1.15) and perceptions of being in fair or poor health was associated with lower odds of weight status underestimation relative to perceiving health as good or better (OR: 0.21, 95% CI: 0.06, 0.66). Among those with overweight and obesity, the association between underestimating weight status and having weight loss as a weight management goal was strong but not statistically significant (OR: 0.20, 95% CI: 0.04, 1.04).

CONCLUSIONS

Underestimating weight status was common among adults in the rural US South and was related to BMI and health status. Research is needed to determine if improving the accuracy of weight perceptions can promote weight management in the rural South.

The effect of perceived weight status and BMI perception on food attitudes and food relationships

CONTEXT

Obesity has been a national epidemic throughout the United States due to the increasingly sedentary western lifestyle, paired with calorically dense abundant low-nutritional food options. Discussing weight necessitates the conversation of not only the numerical value (body mass index [BMI]) associated with obesity but also the perceived weight or how an individual categorizes their weight, irrespective of their calculated BMI classification. Perceived weight can influence food relationships, overall health, and lifestyle habits.

OBJECTIVES

The purpose of this study was to identify differences in dietary habits, lifestyle habits, and food attitudes among three groups: those correctly identifying as "obese" with a BMI >30 (BMI Corrects [BCs]), those incorrectly identifying as "obese" with a BMI <30 (BMI Low Incorrect [BLI]), and those incorrectly identifying as "nonobese" with a BMI >30 (BMI High Incorrect [BHI]).

METHODS

An online cross-sectional study was conducted from May 2021 to July 2021. Participants (n=104) responded to a 58-item questionnaire regarding demographics (n=9), health information (n=8), lifestyle habits (n=7), dietary habits (n=28), and food attitudes (n=6). Frequency counts and percentages were tabulated, and analysis of variance (ANOVA) testing was conducted to examine the associations utilizing SPSS V28 at a statistical significance level of p<0.05.

RESULTS

Participants incorrectly identifying as "obese" with a BMI <30 (BLI) had higher food attitude scores, indicating worse food attitudes, behaviors, and relationships with food compared to participants correctly identifying as "obese" with a BMI >30 (BC) and incorrectly identifying as "non-obese" with a BMI >30 (BHI). When comparing BC, BLI, and BHI participants, no statistically significant differences were found in dietary habit scores, lifestyle habit scores, weight change, or nutritional supplement or diet started. However, overall, BLI participants had worse food attitude scores and consumption habits when compared to BC and BHI participants. Even though dietary habit scores were not significant, an examination of specific food items revealed significant findings, in which BLI participants had higher consumption of potato chips/snacks, milk, and olive oil/sunflower oil, compared to BHI participants. BLI participants had higher beer and wine consumption compared to BC participants. Additionally, BLI participants had higher carbonated beverages, low-calorie beverages, and margarine and butter consumption compared to BHI and BC participants. BHI participants had the lowest hard liquor consumption, BC had the second lowest hard liquor consumption, and BLI participants had the highest consumption of hard liquor products.

CONCLUSIONS

The findings of this study have shed light on the intricate relationship that exists between perceived weight status from a "nonobese/obese" perspective and attitudes toward food and the overconsumption of particular food items. Participants who perceived their weight status as "obese" despite having a calculated BMI below the CDC threshold and classification for "obesity" had poorer relationships with food, consumption behaviors, and on average consumed food items that were detrimental to overall health. Comprehending a patient's weight status perception and conducting a thorough history of their food intake could play a crucial role in addressing the patient's overall health and medically managing this population.

High BMI Predicts Attention to Less Healthy Product Sets: Can a Prompt Lead to Consideration of Healthier Sets of Products?

While the food environment has been implicated in diet-related health disparities, individuals’ ability to shape the food environment by limiting attention to a subset of products has not been studied. We examine the relationship between BMI category and consideration set—the products the individual considers before making a final choice—in an online hypothetical shopping experiment. Specifically, we focus on the healthiness of the consideration set the individual selected. Secondly, we examined the interaction of a health prompt (versus a no-prompt control) with BMI category on the healthiness of the consideration set. We used linear probability models to document the relationship between weight status and consideration set, between prompt and consideration set, and the effect of the interaction between prompt and weight status on consideration set. We found that (1) obese individuals are 10% less likely to shop from a consideration set that includes the healthy options, (2) viewing the prompt increased the probability of choosing a healthy consideration set by 9%, and (3) exposure to the prompt affected individuals in different BMI categories equally. While obese individuals are more likely to ignore healthier product options, a health-focused prompt increases consideration of healthy options across all BMI categories.