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

Abstract

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.

INTRODUCTION

Approximately 42% of adults in the United States have obesity, ¹ and rural residents are more likely to have obesity compared to their urban counterparts. ² , ³ , ⁴ , ⁵ Based on data from the 2013–2016 National Health and Nutrition Examination Survey (NHANES) in which height and weight were objectively measured, the prevalence of obesity was approximately 43% in rural nonmetropolitan areas compared to 35% in large urban metropolitan areas. ⁶ The higher prevalence of obesity in rural areas represents an important clinical and public health concern. Obesity is associated with multiple chronic health conditions, ⁷ and certain obesity‐related chronic health conditions such as heart disease, diabetes, and hypertension are more prevalent in rural areas. ⁸ , ⁹ , ¹⁰ Obesity in the rural US South is especially concerning due to rural‐urban differences in obesity prevalence being highest in the South census region ¹¹ and evidence that rural health disparities are worse in the South compared to other US regions. ¹²

Health care access challenges and lack of environmental support for healthy dietary habits and physical activity, among other factors, likely contribute to the higher prevalence of obesity in rural areas. ¹³ , ¹⁴ Inaccurate weight perceptions may also play a role in the higher obesity prevalence among rural adults, but few studies to date have explored rural residents’ weight perceptions and how those perceptions may affect obesity. In one study involving 305 Black and White adults living in the rural US South, approximately 51% of men and 41% of Black adults misclassified their weight status compared to approximately 35% of women and 36% of White adults. ¹⁵ Age, education level, and poverty were not associated with weight misperceptions. Inaccurate perceptions of body weight among rural residents may serve as a weight loss barrier, as weight underestimation has been associated with lower weight loss intentions and weight loss attempts across multiple adult samples, ¹⁶ , ¹⁷ , ¹⁸ Moreover, other research conducted among adults with overweight and obesity suggest that accurate weight perceptions are linked to a greater likelihood of engaging in weight loss behaviors. ¹⁹ People who have accurate weight perceptions or overestimate their weight status are more likely to attempt dieting and order food with fewer calories compared to those who underestimate their weight. ²⁰ Despite evidence that weight perceptions have important clinical implications for addressing obesity, research on the factors associated with weight underestimation has been predominantly conducted with urban samples.

There are several reasons why rural residents may be at risk for underestimating their weight status and why improving the accuracy of weight perceptions may be needed to better address obesity in rural areas. Rural residents often experience barriers accessing primary care including transportation and financial challenges, ²¹ and weight loss counseling is often not provided in health care settings at all. For example, evidence from the NHANES survey suggests that only about 40% of adults with overweight or obesity are counseled about losing weight. ²² Several other studies have provided evidence that patients are not adequately informed on weight maintenance in primary care settings, with weight counsel often overlooked or under addressed. ²³ , ²⁴ , ²⁵ Moreover, evidence from the Behavioral Risk Factor and Surveillance System suggests that rural patients are less likely than urban patients to receive a weight loss recommendation from a health care provider. ²⁶ Rural residents’ greater travel burden can negatively affect clinic visits for weight loss counseling, ²⁷ which is important because patients who are counseled about their weight are less likely to underestimate their weight and are more likely to attempt losing weight. ²⁸ In addition to the multiple challenges with addressing obesity in rural health care settings, rural areas are much less likely to have access to intensive behavioral change programs that focus on weight management. ²⁹ Even in areas where weight management programs are available, inaccurate weight perceptions may serve as a barrier to participation. Finally, evidence suggests that rural residents are less likely than urban residents to have broadband internet access, particularly in the South and in areas with high proportions of Black residents. ³⁰ Lower access to broadband internet in rural areas can limit access to both education and health‐enhancing resources, ³¹ which could have implications for weight management.

The purposes of this study were to (1) examine weight status underestimation in the rural US South and to explore relationships among sociodemographic factors, health indicators, and weight status underestimation and (2) examine the relationship between weight status underestimation and weight loss as a weight management goal among rural residents with overweight and obesity. Findings from this study can provide insight on factors that may increase the risk of weight status underestimation in rural areas and whether efforts may be warranted to increase the accuracy of weight status perceptions as part of weight management strategies.

METHODS

Design, setting, and sample

This cross‐sectional study involved an anonymous, multimodal survey (i.e., paper and electronic) to examine weight perceptions and health programming needs and preferences among adult residents in rural, South Carolina. Surveys were distributed at six rural primary care clinics and two churches located across two rural South Carolina counties. The two counties in this study had US Department of Agriculture Rural‐Urban Continuum Codes of 8, ³² and codes 4 or higher are nonmetropolitan counties that are commonly considered rural. Population sizes for the two counties were approximately 20,000 and 8000 residents, and recruitment occurred at four primary care clinics and one church in the larger county and two primary care clinics and one church in the smaller county. All adult residents of the target rural counties were eligible to participate.

Procedure

Data collection occurred from October 2021 to March 2022, and the PI made in‐person visits to the target rural communities every 2–3 weeks to recruit participants, to collect paper surveys, and to meet with community partners (e.g., churches and health care clinics) to promote the study. The PI was born and raised in one of the target rural communities and therefore had established relationships with several community partners. The PI also formed new community relationships during his in‐person visits to the target rural communities.

At primary care facilities, participants were recruited by reception staff who provided paper surveys directly to patients. REDCap was used for the online survey, and flyers containing a QR code that directed patients to REDCap were also posted in waiting rooms and could be completed on patients’ personal mobile devices. One flyer was placed at a local grocery store. At churches, a member of the study team (HW) set up a table to recruit participants. Participants could choose to complete the study survey at a convenient or private location of their choice. Paper surveys were returned to reception staff or study team member depending on site. Surveys were retrieved from participating health care facilities at the end of two three‐month intervals. Data from paper surveys were manually entered into Microsoft Excel and merged with the online survey Excel file. The Excel file was later opened in SPSS for data analysis.

Measures

Self‐reported height and weight

Survey participants were asked to self‐report their height in feet and inches and weight in pounds. Self‐reported weight was converted to kilograms for calculating body mass index (BMI). Self‐reported height was converted to centimeters for calculating BMI.

Body mass index category and perceived weight status

Body mass index (kg/m²) was calculated from self‐reported height and weight. We used standard BMI categories for determining weight status: underweight (< 18.5 kg/m²), normal weight (18.5–24.9 kg/m²), overweight (25–29.9 kg/m²), obese (≥30 kg/m²). Survey participants were also asked to describe their perceived weight status using the standard BMI categories of underweight, normal weight, overweight, and obese.

Weight management goal

Survey participants were asked “Which of the following below best reflects your weight management goal?” Response options included “to lose weight,” “to maintain weight,” or “to gain weight.”

Other variables of interest

Survey participants also provided information on age, sex, race/ethnicity, education, relationship status, and internet access. Participants were asked to rate the perception of their general health with the response options of excellent, very good, good, fair, or poor.

Data analysis

Surveys were completed by 188 participants. Of these, two were under 18 years of age and were excluded from analyses. An additional respondent indicated they were attending high school but did not report their age. This respondent was also excluded from analyses.

Weight status underestimation was determined by examining the discrepancy between perceived weight status and BMI category based on self‐reported height and weight. For example, participants who self‐reported perceived weight status as “overweight” but whose calculated BMI indicated they had obesity were classified as having underestimated their weight status. Chi‐square analyses were used to examine differences in weight status underestimation by sociodemographic factors, perceived health status (excellent/very good/good vs. fair/poor), and BMI categories. For each bivariate test, no cells had expected counts less than 5. Multivariable logistic regression analyses were used to determine factors independently associated with weight status underestimation. Model 1 included the sociodemographic factors of race/ethnicity, age, sex, relationship status, education level, and internet access. Model 2 included the same sociodemographic factors and the health factors of perceived health status and BMI. Similar logistic regression models were used to examine the relationship between weight status underestimation and weight management goals. An alpha level of 0.05 was used to determine statistical significance, and listwise deletion was used for variables with missing data. Multicollinearity was assessed for each logistic regression model. Variance inflation factors were less than 2.0 for all predictor variables indicating low collinearity (values >10 are suggestive of multicollinearity). SPSS version 24 was used for data analyses.

RESULTS

Sample characteristics

Table 1 presents the general characteristics of the overall survey sample. About 76% of participants were women, 51% identified as non‐Hispanic White, 40% identified as being non‐Hispanic Black, and about 8% identified as Hispanic (Black, White, and other). Approximately 53% of participants identified as being married or a member of an unmarried couple compared to 26% who were single, 9% who were divorce or separated, and 11% who were widowed. Nearly 45% of participants reported having a high school education of less, 37% reported some college or an associate degree, and 16% reported having a bachelor's degree or higher. Nearly 80% of participants reported having internet access. About 3% of participants perceived themselves as underweight, 45% perceived themselves as normal weight, 43% perceived themselves as overweight, and 9% perceived themselves as obese. Based on self‐reported height and weight, approximately 2% of participants would be classified as underweight, 21% normal weight, 28% overweight, and 49% obese. Slightly more than half the sample (56.9%) reported that their weight management goal was to lose weight.

TABLE 1.

Sample characteristics (n = 185).

Variable	Frequency (%) or mean ± SD
Age	50 ± 17
Sex
Male	43 (23.9)
Female	137 (76.1)
Race/ethnicity
Non‐Hispanic Black	70 (39.5)
Non‐Hispanic White	91 (51.4)
Hispanic Black	3 (1.7)
Hispanic White	3 (1.7)
Non‐Hispanic other	1 (0.6)
Hispanic other	9 (5.1)
Relationship status
Single	47 (25.5)
Married/unmarried couple	98 (53.3)
Divorced/separated	17 (9.2)
Widowed	21 (11.4)
Other	1 (0.5)
Education
Less than high school	8 (4.4)
Diploma/GED	75 (41.0)
Some college	39 (21.3)
Associate	28 (15.3)
Bachelors	22 (12.0)
Masters	6 (3.3)
Professional/doctoral	2 (1.1)
Other	3 (1.6)
Internet access
Yes	145 (79.7)
No	37 (20.3)
Perceived weight status
Underweight	5 (2.7)
Normal	82 (44.8)
Overweight	79 (43.2)
Obese	17 (9.3)
BMI	31.3 ± 8.6
BMI category
Underweight	4 (2.4)
Normal weight	34 (20.6)
Overweight	46 (27.9)
Obese	81 (49.1)
Weight management goals
Lose weight	99 (56.9)
Maintain weight	66 (37.9)
Gain weight	9 (5.2)
Perceived health status
Excellent	10 (5.5)
Very good	45 (24.6)
Good	99 (54.1)
Fair	27 (14.8)
Poor	2 (1.1)

Note: Due to missing data, n = 174 for age and n = 165 for BMI. Percentages of missing data were approximately 11% for BMI category and less than 5% for the other categorical variables. Race and ethnicity were asked separately. Most respondents who selected “Other” for race wrote‐in “Hispanic” or “Spanish” while also selecting “Hispanic” for ethnicity.

Weight status underestimation by sample characteristics

Table 2 presents the proportion of the sample that underestimated their weight status by various sociodemographic factors, Internet access, perceived health status, and BMI category. Approximately 67% of non‐Hispanic Black adults underestimated their weight status compared to 54% of non‐Hispanic White adults and 33% of Hispanic adults. Approximately 80% of participants with obesity underestimated their weight status compared to 63% of those who were overweight and 2.6% of those who were normal or underweight (p < 0.01). Differences in the proportion of those who underestimated their weight status by other sociodemographic factors, Internet access, and perceived health status were not statistically significant.

TABLE 2.

Weight status underestimation by sample characteristics.

Variable	Over/accurate n (%)	Underestimate n (%)	p value
Sex			0.23
Male	12 (33.3)	24 (66.7)
Female	56 (44.4)	70 (55.6)
Race/ethnicity			0.05
Non‐Hispanic Black	22 (32.8)	45 (67.2)
Non‐Hispanic White	38 (46.3)	44 (53.7)
Hispanic	8 (66.7)	4 (33.3)
Relationship status			0.07
Single	22 (52.4)	20 (47.6)
Married/unmarried couple	40 (44)	51 (56)
Divorced/separated/widowed	8 (25.8)	23 (74.2)
Education			0.18
Less than HS, diploma/GED, other	27 (37)	46 (63)
Some college/associate	32 (51.6)	30 (48.4)
Bachelor or higher	11 (36.7)	19 (63.3)
Internet access			0.14
Yes	60 (44.8)	74 (55.2)
No	9 (30)	21 (70)
Categories of general health status			0.40
Excellent, very good, good	57 (40.7)	83 (59.3)
Fair and poor	12 (50)	12 (50)
BMI categories			<0.01
Under/normal weight	37 (97.4)	1 (2.6)
Overweight	17 (37)	29 (63)
Obese	16 (19.8)	65 (80.2)

Factors associated with weight status underestimation

Table 3 presents the results of logistic regression models examining associations between sociodemographic factors, BMI, and health status and weight status underestimation. In the first model that includes only sociodemographic factors, no statistically significant associations with weight status underestimation were observed. In the second model, after controlling for covariates, BMI was positively associated with weight underestimation, such that for every one‐point increase in BMI, the odds of weight underestimation increased 10% (OR = 1.10, 95% CI = 1.04–1.15). Self‐perception of fair or poor health was associated with lower odds of weight status underestimation compared to perceptions of being in excellent, very good, or good health, after adjusting for covariates (OR = 0.21, 95% CI = 0.06–0.66).

TABLE 3.

Factors associated with weight status underestimation (n = 150 for Model 1 and n = 149 for Model 2).

	Model 1		Model 2
Variable	OR (95% CI)	p value	OR (95% CI)	p value
Race/ethnicity
Non‐Hispanic White (ref)
Non‐Hispanic Black/other	1.44 (0.65–3.19)	0.37	1.65 (0.70–3.92)	0.26
Hispanic	0.31 (0.07–1.35)	0.12	0.36 (0.08–1.72)	0.20
Education
<HS/diploma/GED	1.03 (0.38–2.81)	0.96	1.30 (0.44–3.83)	0.64
Some college/associate	0.53 (0.19–1.47)	0.22	0.54 (0.18–1.66)	0.28
Bachelor or higher (ref)
Relationship status
Single (ref)
Married/unmarried couple	1.11 (0.45–2.77)	0.82	1.14 (0.43–3.82)	0.64
Divorced/separated/widowed	2.32 (0.66–8.20)	0.19	2.70 (0.70–10.38)	0.15
Age	1.00 (0.98–1.03)	0.93	1.01 (0.98–1.04)	0.60
Sex
Male (ref)
Female	0.80 (0.34–1.92)	0.62	0.86 (0.33–2.22)	0.75
Internet access
Yes (ref)
No	1.94 (0.70–5.37)	0.21	2.38 (0.79–7.10)	0.12
Perceived health status
Good, very good, excellent (ref)
Fair/poor	N/A	N/A	0.21 (0.06–0.66)	0.01
BMI	N/A	N/A	1.10 (1.04–1.15)	<0.01

Model 1: Model Χ² = 15.89, df = 9, p = 0.07; Nagelkerke R ² = 0.14; Hosmer and Lemeshow Test p = 0.88.

Model 2: Model Χ² = 31.47, df = 11, p < 0.001; Nagelkerke R ² = 0.26; Hosmer and Lemshow Test p = 0.02.

Factors associated with having weight loss as a weight management goal

Table 4 presents the results of the binary logistic regression to examine associations of sociodemographic factors, perceived health, and weight status underestimation with weight loss as a weight management goal among those with overweight or obesity. When considering only sociodemographic factors (Model 1), being married or in an unmarried couple was associated with higher odds of having weight loss as a weight management goal relative to being single (OR = 5.89, 95% CI = 1.45–23.86). In contrast, participants identifying as non‐Hispanic Black had lower odds of having weight loss as a weight management goal (OR = 0.27, 95% CI = 0.09–0.76). While not statistically significant at a p < 0.05 level, individuals who underestimated their weight status had 80% lower odds of identifying weight loss as a weight management goal (OR = 0.20, 95% CI = 0.04–1.04) compared to individuals who did not underestimate their weight status, after adjusting for all other covariates (Model 2). Associations of being in a married or unmarried couple and race/ethnicity with weight loss as a weight management goal were similar to associations observed in the first model.

TABLE 4.

Factors associated with having weight loss as a weight management goal for those with overweight/obesity (n = 108).

	Model 1		Model 2
Variable	OR (95% CI)	p value	OR (95% CI)	p value
Race/ethnicity
Non‐Hispanic White (ref)	–		–
Non‐Hispanic Black	0.27 (0.09–0.76)	0.01	0.28 (0.10–0.85)	0.02
Education
<HS/diploma/GED	0.34 (0.09–1.26)	0.11	0.30 (0.07–1.25)	0.10
Some college/associate	2.38 (0.55–10.26)	0.25	1.97 (0.42–9.32)	0.39
Bachelor or higher (ref)	–
Relationship status
Single (ref)	–		–
Married/unmarried couple	5.89 (1.45–23.86)	0.01	5.89 (1.40–24.72)	0.02
Divorced/separated/widowed	2.04 (0.39–10.66)	0.40	2.19 (0.40–11.92)	0.37
Age	0.97 (0.93–1.01)	0.10	0.97 (0.93–1.01)	0.09
Sex
Male (ref)	–
Female	2.69 (0.78–9.22)	0.12	2.61 (0.70–9.68)	0.15
Internet access
Yes (ref)
No	3.33 (0.70–15.75)	0.13	4.43 (0.87–22.58)	0.07
Perceived health status
Good, very good, excellent (ref)			–
Fair/poor	N/A	N/A	1.01 (0.23–4.54)	0.99
Weight status estimation
Overestimated/accurate (ref)			–
Underestimated	N/A	N/A	0.20 (0.04–1.04)	0.06

Model 1: Model Χ² = 39.88, df = 8, p <0.01; Nagelkerke R ² = 0.43; Hosmer and Lemeshow Test p = 0.87.

Model 2: Model Χ² = 44.61, df = 10, p <0.01; Nagelkerke R ² = 0.47; Hosmer and Lemeshow Test p = 0.70.

Notes: Of the 6 Hispanic respondents with overweight or obesity, all selected that their weight management goal was to lose weight. These 6 respondents were excluded from analysis. By comparison, 80% of non‐Hispanic White respondents with overweight or obesity (n = 64) and 47% of non‐Hispanic Black/Other respondents (n = 51) selected weight loss as their weight management goal.

DISCUSSION

The purpose of this study was to examine weight status underestimation among rural residents living in the US South and to examine the relationship between weight status underestimation and weight management goals among residents living in the rural South. Our primary findings revealed that nearly 60% of rural residents underestimated their weight status, that increasing BMI was associated with higher odds of weight status underestimation, and perception of being in fair or poor health was associated with lower odds of weight status underestimation. Among Southern rural residents with overweight or obesity, weight status underestimation was strongly associated with lower odds of identifying weight loss as a weight management goal. However, this finding was not statistically significant. Findings from this study may have practical clinical, public health, and research implications for addressing obesity in rural areas.

The finding in this study that higher BMI was associated with an increased odds of underestimating weight status is consistent with other research in which having overweight or obesity was associated with weight misperceptions. ³³ , ³⁴ For example, Garett et al. found that 43% of people with overweight and 69% with obesity had inaccurate weight perceptions. ³⁴ However, Robinson and Oldham found that less than 10% of people with obesity underestimated their weight status, ³³ which is substantially lower than the 80% of Southern rural residents with obesity in the current study who underestimated their weight status. These differences may reflect methodological differences in assessing weight underestimation, as Robinson and Oldham asked participants to rate their weight status as too light, about right, or too heavy. The high proportion of participants with overweight and obesity who underestimated their weight status in our study suggests a need to properly teach rural residents of their BMI, weight status, and the health risks associated with excess body weight. Patients in health care settings may benefit from education about BMI and weight categories as part of obesity‐related counseling. Incorporating similar education into high school physical education or biology curriculums may also be of benefit. However, research is needed to determine the extent to which these educational approaches may be effective in changing inaccurate weight perceptions.

In our study, differences in the proportions of participants who underestimated their weight status were not statistically significant by sex but were statistically significant by race such that proportionally more non‐Hispanic Black adults underestimated their weight status compared to non‐Hispanic White and Hispanic adults. Findings from prior research conducted with youth has suggested there may be limited differences in weight perceptions by race and sex and some research involving African American adults has shown there were no differences in weight misperceptions by sex. ³⁵ However, our findings regarding racial/ethnic differences in weight status underestimation are consistent with research conducted by Smalley et al. ¹⁵ who found that Black rural residents are more likely to misperceive weight than White rural residents. Racial differences in weight perceptions may potentially be at least partially attributed to African Americans being more accepting of larger body sizes, which has been speculated to facilitate setting realistic weight management goals in this population. ³⁶ Unlike Smalley et al. who found that rural men are more likely to misperceive their weight compared to rural women, there were no statistically significant differences in weight status underestimation by sex in this study. Although differences by sex were not statistically significant in our study, 67% of rural men and 56% of rural females underestimated their weight status. These findings underscore that weight status underestimation is widespread in the rural South.

Education is an important social determinant of health linked to obesity with research suggesting that higher education is inversely related to obesity rates among White and Hispanic adults ³⁷ and more accurate weight perceptions among African American adults. ³⁵ However, our data did not show significant differences in weight status underestimation by education levels and is similar to other research involving Southern rural residents in which education was not associated with weight status misperception. ¹⁵ One reason for this finding could be that the relationship between education and obesity varies by racial/ethnic backgrounds, and our sample was well represented by White and Black adults. Among White adults, the prevalence of obesity is lowest among college graduates but differences in the obesity prevalence by education among Black adults is not statistically significant. ³⁷ Another potential explanation is that obesity may be more normalized in rural settings due to the higher obesity prevalence compared to urban areas areas, ² , ³ , ⁴ , ⁵ which might therefore affect the relationship between education levels and weight perceptions. One study found that in parts of rural South Africa where bigger body weights are socially desirable, many individuals may perceive having larger bodies but do not perceive themselves as overweight or obese. ³⁸ Irrespective of rurality, however, evidence suggests that fewer US adults who have excess body weight perceive themselves as overweight despite an increasing prevalence of overweight and obesity. ³⁹ Other evidence suggests that the prevalence of more severe obesity (BMI >35) is higher in rural compared to urban areas. ⁴⁰ Because evidence suggests that the severity of obesity is greater in rural areas, rural residents with BMIs closer to 30 may be more likely to underestimate their weight status due to being thinner relative to those with greater obesity severity.

Although the proportion of adults who underestimated their weight status did not differ by perceived health status in the bivariate analysis, multivariable analysis revealed that odds of weight status underestimation were lower among those who perceived themselves in fair or poor health. This finding may be due to health care providers addressing obesity among those who have already developed obesity‐related chronic health conditions given that obesity is associated with multiple chronic health conditions that are more prevalent in rural areas, ⁸ , ⁹ , ¹⁰ that obesity severity is greater in rural areas, ⁴⁰ and that nearly 80% of our sample received the survey in primary care settings. This rationale would be consistent with evidence suggesting that patients with severe obesity and those in poorer health are more likely to receive weight‐related advice. ⁴¹ In addition, participants in this study who perceived being in fair or poor health may have developed obesity‐related health problems and therefore have a greater self‐awareness of their weight status compared to those who perceive being in good health and have not developed obesity‐related conditions. Similarly, participants who perceived they were in good health may have been more likely to underestimate their weight status because they did not perceive that weight has adversely affected their health. These potential explanations would be consistent with the Health Belief Model ⁴² in which the accuracy of weight perceptions may be influenced by the extent to which people perceive that excess weight increases their risk for and severity of weight‐related health outcomes.

While slightly above the threshold for being statistically significant, we found that underestimating weight status was strongly associated with lower odds of identifying weight loss as a weight management goal among Southern rural adults with overweight and obesity. While this finding aligns with literature suggesting that perceptions of having excess body weight are associated with weight loss attempts, perceptions of being overweight have been linked to both healthy and unhealthy weight loss behaviors and weight gain. ¹⁷ Challenges in implementing effective weight loss strategies may include societal or psychological influences, such as experiences of social exclusion or stigma of overweight leading to more extreme weight loss behaviors. ¹⁷ To more effectively promote weight loss through behavior change, primary care providers should offer or refer patients with obesity to high intensity multicomponent behavioral interventions ⁴³ , ⁴⁴ as opposed to approaches such as motivational interviewing that have shown limited effectiveness. ⁴⁵ Recommendations that clinicians offer or refer patients with obesity to high‐intensity multicomponent behavioral interventions also align with rural residents’ preference for specialized obesity treatment rather than treatment in primary care settings. ⁴⁶ While rural primary care providers may lack the time, specialized knowledge, or ability to provide obesity services due to limited reimbursement and lack of community resources to promote weight loss, ⁴⁷ evidence supports the effectiveness of using digital technologies to deliver high‐intensity behavioral obesity interventions. ⁴⁴ In addition, evidence suggests rural residents with obesity are open to using technology to promote behavior change and improve their health. ⁴⁸ However, many rural residents experience various technology‐related barriers such as lacking electronic devices, poor Internet connectivity, and less access to broadband Internet. ³⁰ , ³¹ , ⁴⁸ Health care providers should therefore assess their patients’ resources for using digital‐based high‐intensity behavioral obesity interventions when exploring options for obesity treatment.

One strength of this paper is its focus on rural counties in the Southern United States, which tend to experience a higher prevalence of obesity and poorer health outcomes compared to other rural and urban counties across the United States. ¹¹ , ¹² There are a few limitations to note regarding this study. This was a cross‐sectional survey in which the temporal nature of the relationship between BMI, perceived health status, and weight status underestimation cannot be determined and findings are subject to reverse causality. For example, while it is possible that rural residents with higher BMIs are more likely to underestimate their weight, it is also possible that rural residents who underestimate their weight are less likely to engage in weight management behaviors and therefore have higher BMIs. Further, height and weight were self‐reported potentially leading to inaccuracy in determining participants’ BMI categories. Although a strength of this study was the focus on rural counties in the Southern United States, findings may not be generalizable across other diverse rural areas and regions. Finally, findings reported in this study should be interpreted with caution given the relatively small sample size. Nonetheless, findings from this study provide valuable insight relevant to the rural US South in which obesity and poor health outcomes are of particular concern. ¹²

CONCLUSIONS

Our study suggests that underestimating weight status is prevalent among adults living in the rural US South and that adults with higher BMIs and who perceive themselves as being healthy are more likely to underestimate their weight status. Among Southern rural adults with overweight and obesity, underestimating weight status may serve as a barrier to having weight loss as a weight management goal, but additional research is needed to understand to how the accuracy of weight perceptions influences weight management in this population. Given evidence that perceptions of having excess body weight is one of the strongest predictors of weight loss efforts, ⁴⁹ improving the accuracy of weight perceptions among adults with overweight and obesity could potentially be an important strategy as part of efforts to prevent and manage obesity in rural areas. Health care professionals in rural areas can play an important role in teaching patients about obesity and addressing weight misperceptions.

CONFLICT OF INTEREST STATEMENT

The authors report no conflicts of interest.

Waraich HA, Wirth MD, Wandji S, Graves JM, Abshire DA. Weight status underestimation and weight management goals among adults in the rural South of the United States. J Rural Health. 2025;41:e12892. 10.1111/jrh.12892