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Published in final edited form as: Eat Behav. 2022 Nov 5;47:101682. doi: 10.1016/j.eatbeh.2022.101682

Who is trying to lose weight? Trends and prevalence in past-year weight loss attempts among US adults 1999–2018 at the intersection of race/ethnicity, gender, and weight status

Yongqi Zhong a, F Hunter McGuire b, Alexis E Duncan b,*
PMCID: PMC9973002  NIHMSID: NIHMS1864822  PMID: 36413874

Abstract

Common stereotypes of those who desire or attempt to lose weight often center on the experience of White, thin women. However, prior studies have neglected how systems of oppression at intersection of race/ethnicity, gender, and weight status may interact to place certain subpopulations at elevated risk. Repeated cross-sectional data from the National Health and Nutrition Examination Survey (NHANES) 1999–2018 (n = 53,528), a population-representative sample of US adults, were used to 1) assess trends in past-year weight loss attempts using the Kendall-Mann trend test stratifying by race/ethnicity, gender, and weight status, and 2) estimate the adjusted prevalence of weight loss attempts over the combined 20-year period for combinations of race/ethnicity, gender, and weight status using logistic regression. There were significant monotonic trends from 1999 to 2018 for non-Hispanic Black men (43.8% to 67.8%, FDR adjusted p = .022) with an obese BMI, but not for any other groups. After adjusting for covariates, weight loss attempt prevalence was positively associated with BMI category for all race/ethnicity-gender combinations, although the degree of association differed. These findings underscore the need to use an intersectional lens in weight-related research. Despite limited long-term beneficial health impact, certain population subgroups, particularly Black men with an obese BMI, are increasingly trying to lose weight.

Keywords: Weight control, Gender, Race, Ethnicity, Population-based survey

1. Introduction

Nationally representative surveys of United States (US) adults indicate a steady increase in obesity prevalence since the 1960s (Inoue et al., 2018). Despite ongoing debate over causal relationships between weight and excess morbidity/mortality (Flegal, 2021), weight loss through changes in diet and physical activity has been promoted as a means to improve individual and population health (Centers for Disease Control and Prevention, August 17, 2021). Weight loss attempts, particularly among those of higher weight status, may also be motivated by other factors, including social pressures to achieve standards of thinness/leanness as well as weight-based restrictions for certain medical procedures (Cafri et al., 2005; MacLaughlin & Campbell, 2019). As such, the prevalence of past-year weight loss attempts, in general, has steadily increased over the past two decades (Han et al., 2019). This increase is complicated by associations of dieting/weight loss attempts with depression (Chaitoff et al., 2019; Doan et al., 2021), as well as findings suggesting limited long-term effectiveness of weight loss interventions (Tobias et al., 2015).

Differences in weight loss attempts may exist across racial/ethnic, gender, and weight status groups (Bish et al., 2005). For instance, women, particularly those of higher weight status, may be more likely to report trying to lose weight than men (Bish et al., 2005). Among those of higher weight status, past-year weight loss attempts may be more common among non-Hispanic White people compared to Hispanic and non-Hispanic Black people (Dorsey et al., 2010). Notably, a critical gap remains concerning the degree to which trends in weight loss attempts may have changed over time across mutually exclusive subgroups at the intersection of multiple structural positions, such as race/ethnicity, gender, and weight status.

A recent analysis of data from the National Health and Nutrition Examination Survey (NHANES) 1999–2016 found a general increase in the prevalence of weight loss attempts among US adults (Han et al., 2019); however, this analysis was limited to overall population trends. Additionally, while prevalence estimates in this study were adjusted for race/ethnicity, gender, and self-reported prior year weight (Han et al., 2019), it is unknown whether results may differ by characteristics such as race/ethnicity, gender, and weight status or at the intersection of these factors. Rooted in Black feminist scholarship (Crenshaw, 1989; Crenshaw, 1991), intersectionality refers to the ways in which multifactorial identities and characteristics (e.g., race/ethnicity, gender, weight status) at the individual level interact with systems of oppression (e.g., racism, sexism, fatphobia) to produce systems-level structural inequities (e.g., disparities in health outcomes) (Bowleg, 2012; Bowleg, 2021). Thus, the application of an intersectional framework to epidemiological analysis, such as stratification across multiple dimensions of identity and structural position, offers the opportunity to identify population subgroups that may face a greater burden of weight loss attempts (Bauer, 2014).

To address calls for incorporating an intersectional framework in the analysis of public health data (Bauer, 2014; Bowleg, 2012; Bowleg, 2021), we investigated the prevalence and trends of past-year weight loss attempts stratified by race/ethnicity, gender, and weight status using two decades of data drawn from population-representative samples of US adults. We conceptualized race/ethnicity, gender, and weight status as proxy indicators of structural racism, sexism, and weight stigma/discrimination.

2. Material and methods

2.1. Study design

Data were obtained from the National Health and Nutrition Examination Survey (NHANES), a repeated cross-sectional, US population-representative survey of civilian, non-institutionalized adults and youth conducted every two years since 1999 to acquire information relating to physical, behavioral, and mental health. NHANES data were obtained through interviews and physical examinations conducted by trained staff at participants’ home or in mobile examination centers. For the current study, we identified an analytic sample of 53,528 non-pregnant adults aged 20 years or older who completed NHANES surveys from 1999–2000 to 2017–2018 (10 data collection waves) and responded to NHANES interview questions regarding weight loss attempts. Informed consent was obtained from all participants, and the National Center for Health Statistics Research Ethics Review Board reviewed and approved the NHANES study. Details about NHANES are described elsewhere (Centers for Disease Control and Prevention, 2021a).

2.2. Exposure variables

Race/ethnicity and gender were assessed during in-home interviews. Race/ethnicity was participant-reported and derived from responses to race and Hispanic origin questions, while gender was interviewer-assigned, presumably based on social and visual cues (e.g., gender expression). In the case where gender was “not obvious,” interviewers were instructed to ask if the respondent was “male” or “female.” As this measure more closely captures gender as opposed to sex assigned at birth, we hereafter refer to these categories as “men” and “women.”

In response to calls to disaggregate data by race/ethnicity and gender to better identify disparities at the intersection of these structural positions (Sharpe, 2019), we combined race/ethnicity and gender into a single variable: non-Hispanic White (hereafter White) women, non-Hispanic Black (hereafter Black) women, Hispanic women, Other/Multiracial women, White men, Black men, Hispanic men, and Other/Multiracial men. This operationalization was empirically supported by evidence of additive interaction between gender and race/ethnicity (Section 2.5 and Table S8).

To investigate intersectional experiences of weight loss attempts, our analyses were further stratified by weight status defined by body mass index (BMI). Participant BMI (weight (kg) / [height (m)]) was calculated using objectively measured height and weight, with self-reported height and weight substituted if these measures were unavailable. BMI was categorized as underweight (<18.5), normal weight (18.5–24.9), overweight (25.0–29.9), and obese (≥30.0), according to the CDC criteria (Centers for Disease Control and Prevention, 2021b). Subcategories of obesity (I, 30–34.9; II, 25.0–39.9; III, ≥40.0) were also computed for sensitivity analyses. Due to the small sample size of the underweight category, the underweight and normal weight categories (BMI <25) were combined. In models not stratified by BMI category, BMI was modeled as a set of indicator variables with normal/underweight as the reference category.

2.3. Outcome

Past-year weight loss attempt was the primary outcome of this study. Participants who reported past-year weight loss of >10 pounds were asked “Was the change between your current weight and your weight a year ago intentional?” All other participants were asked “During the past 12 months, have you tried to lose weight?” Those who answered “yes” to either question were coded as having a past-year weight loss attempt.

2.4. Covariates

Weight perception, assessed with the question “Do you consider yourself now to be overweight, underweight, or about the right weight?”, was used as a covariate with a reference category of “about the right weight.” Demographic covariates were age category (20–39 [reference], 40–59, 60–79, 80+), marital status (married/with partner [reference], widowed/divorced/separated, never married, missing/not applicable), and family income (ratio of family income to poverty level).

2.5. Statistical analysis

All analyses were conducted in R (4.2.1) using the survey package to account for the NHANES complex survey design (Lumley, 2021) and weight estimates using 20-year NHANES household interview weights in accordance with the NHANES analytic guidelines (Akinbami et al., 2022). We first examined the trends of past-year weight loss attempts stratified by race/ethnicity, gender, and weight status. NHANES data were treated as repeated cross-sectional studies for estimating prevalence across 10 two-year intervals (1999–2000 to 2017–2018) in accordance with the data collection waves. Mann-Kendall trend tests were conducted to examine whether there was a monotonic change (either increase or decrease) in weight loss attempts over the study period. Difference in prevalence between 1999–2000 (reference) and 2017–2018 was computed with weighted linear regression in which the slope of 2017–2018 represented the difference in prevalence between cycles. Use of this method allows for the calculation of the standard error and 95% confidence interval for the prevalence (risk) difference in a complex survey design. In stratified analyses where the study sample was divided into mutually exclusive subgroups at the intersection of gender, race/ethnicity, and weight status (Table S2), false discovery rate (FDR) p-values were calculated to adjust for multiple comparisons made using independent hypothesis tests.

Second, to examine prevalence differences in weight loss attempts at the intersection of race/ethnicity and gender, the NHANES data were treated as one cross-sectional study by combining all waves. We tested for additive interaction of race/ethnicity with gender using a log likelihood ratio test to compare linear regression models with robust variance estimation and weight loss attempts as the dependent variable. The first model regressed weight loss attempts on race/ethnicity, gender, and the covariates, age, education, marital status, weight status, weight perception, and NHANES survey wave; the second model added an interaction term between the four-category race/ethnicity and two-category gender variables. The log likelihood ratio test comparing models with and without the interaction term was statistically significant (2logLR = 23.40, p < .001, see Table S8), providing additional support for the combined race-ethnicity-gender variable. Five weighted linear regressions with robust variance estimation were used to regress weight loss attempts on the combined race/ethnicity-gender variable and covariates. Model 1 contained all participants and covariates; Model 2 added an interaction term between the eight-category combined race/ethnicity-gender variable and weight status to the variables in Model 1. Given that the log likelihood ratio test indicated that the addition of the interaction term resulted in a significant difference between Models 1 and 2 (p < .001), we estimated three additional regression models (Models 3 to 5) that used all variables from Model 1 except for weight status in separate linear regressions for normal/underweight, overweight, and obese participants, respectively. Post hoc comparisons were conducted for the race/ethnicity-gender variable with FDR p-values for Models 1 to 5. Sensitivity analyses were conducted by repeating the regressions excluding individuals with underweight BMI from the normal BMI category and by stratifying by the obese category into subgroups, respectively.

We computed the crude and adjusted prevalence of weight loss attempts stratified by race/ethnicity, gender, and weight status over 1999–2018. To obtain the prevalence of weight loss attempts adjusting for covariates, we used Models 1 and 3 to 5 to compute the marginal probability of weight loss attempts among categories of the race/ethnicity-gender variable through marginal standardization (Muller & MacLehose, 2014). We calculated 95% confidence intervals of the marginally adjusted prevalence by 1000 bootstrapping using stratified sampling (with replacement) with the sampling package (Tillé & Matei, 2021).

3. Results

3.1. Sample characteristics

Table 1 shows the characteristics of the combined sample of 53,528 adults aged ≥20 years participating in NHANES from 1999 to 2018. Past-year weight loss attempts were reported by 37.7% of participants. Most of the sample was White (68.1%), aged 20–59 years (74.8%), had at least some college (58.4%), and was married or living with a partner (62.3%). Participants were roughly equally divided between the normal weight, overweight, and obese BMI categories, with only 1.8% in the underweight range (<18.5 m/kg2). More than half of participants perceived themselves to be overweight (55.7%).

Table 1.

Characteristics of 53,528 adults aged ≥20 years participating in NHANES 1999–2018.

Overall
N % SE
Past-year weight loss attempt 22,331 45.1 0.3
Race/ethnicity & gender
 Non-Hispanic White women 11,845 34.9 0.6
 Non-Hispanic Black women 5814 6.2 0.3
 Hispanic women 6987 6.7 0.4
 Other/multiracial women 2539 3.6 0.2
 Non-Hispanic White men 11,837 33.2 0.5
 Non-Hispanic Black men 5479 5.1 0.2
 Hispanic men 6559 6.9 0.4
 Other/multiracial men 2468 3.4 0.2
Age
 20–39 17,544 36.9 0.5
 40–59 17,076 37.9 0.4
 60–79 14,724 20.8 0.3
 80+ 4184 4.4 0.1
Education
 Lest than high school 14,646 17.5 0.4
 High school/GED+ 12,432 24.1 0.4
 Some college 14,972 30.7 0.4
 College graduate 11,387 27.7 0.7
Marital status
 Married/with partner 31,360 62.3 0.5
 Widowed/divorced/separated 12,289 18.7 0.3
 Never married 9341 17.8 0.4
 Missing/NA 538 1.2 0.3
Weight status
 Underweight 942 1.8 0.1
 Normal 15,474 30.0 0.4
 Overweight 18,052 33.3 0.3
 Obese 19,060 34.9 0.4
Weight perception
 About the right weight 22,448 39.4 0.4
 Underweight 2996 4.9 0.1
 Overweight 27,966 55.7 0.4
NHANES waves
 1999–2000 4602 8.6 0.3
 2001–2002 5127 9.5 0.4
 2003–2004 4827 9.4 0.6
 2005–2006 4661 9.6 0.6
 2007–2008 5831 9.9 0.5
 2009–2010 6145 10.1 0.5
 2011–2012 5490 10.3 0.6
 2013–2014 5709 10.6 0.5
 2015–2016 5637 10.8 0.5
 2017–2018 5499 11.0 0.4
Overall
Mean SE
Ratio of family income to poverty level 3.0 0.03
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3.2. Trends in weight loss attempts by race/ethnicity, gender, and weight status

Fig. 1 shows the trends of weight loss attempts from 1999 to 2018, stratified by race/ethnicity, gender, and BMI category (see also Tables S1 and S2). Overall, the prevalence of weight loss attempts was greater in higher weight status categories. In each weight status category, women had a higher prevalence of weight loss attempts than men, regardless of racial/ethnic group or survey year. There were no significant trends over the past two decades, except for among Black men with an obese BMI, among whom prevalence increased from 43.8% (SE = 5.1%) in 1999–2000 to 67.8% (SE = 3.2%) in 2017–2018 (FDR-adjusted trend p = .022).

Fig. 1.

Fig. 1.

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Trends in weight loss attempts by gender, race/ethnicity, and weight status.

3.3. Associations of past-year weight loss attempts with race/ethnicity and gender and covariates

All demographic and weight-related covariates were statistically associated with past-year weight loss attempts in the combined sample (see Table 2). Weight loss attempts were more common among women than men within all racial/ethnic categories. Prevalence was highest among persons aged 40–59 years and among those who were married or living with a partner. Weight loss attempt was positively associated with education level, ratio of family income to poverty level, weight status, and weight perception.

Table 2.

Characteristics of NHANES participants aged ≥20 years by past-year weight loss attempt, 1999–2018.

Past-year weight loss attempt
Yes
 No
 p-Value
N % SE N % SE
Overall prevalence 22,331 45.1 0.3 31,197 54.9 0.3 <.001
Race/ethnicity & gender <.001
 Non-Hispanic White women 5590 52.6 0.6 6255 47.4 0.6
 Non-Hispanic Black women 2952 52.4 0.7 2862 47.6 0.7
 Hispanic women 3545 54.2 0.8 3442 45.8 0.8
 Other/multiracial women 1137 47.2 1.4 1402 52.8 1.4
 Non-Hispanic White men 4102 38 0.5 7735 62 0.5
 Non-Hispanic Black men 1834 33.9 0.8 3645 66.1 0.8
 Hispanic men 2268 37 0.8 4291 63 0.8
 Other/multiracial men 903 38.2 1.6 1565 61.8 1.6
Age <.001
 20–39 7715 45.5 0.5 9829 54.5 0.5
 40–59 7984 48.9 0.5 9092 51.1 0.5
 60–79 5850 43 0.7 8874 57 0.7
 80+ 782 19.8 0.8 3402 80.2 0.8
Education <.001
 Lest than high school 4698 33.2 0.7 9948 66.8 0.7
 High school/GED+ 4924 42 0.6 7508 58 0.6
 Some college 7123 49 0.6 7849 51 0.6
 College graduate 5574 51.2 0.7 5813 48.8 0.7
Marital status <.001
 Married/with partner 13,420 46.5 0.4 17,940 53.5 0.4
 Widowed/divorced/separated 4791 43.3 0.7 7498 56.7 0.7
 Never married 3904 42.6 0.6 5437 57.4 0.6
 Missing/NA 216 41 2 322 59 2
Weight status <.001
 Underweight 25 4.2 1.2 917 95.8 1.2
 Normal 3156 25.2 0.6 12,318 74.8 0.6
 Overweight 7459 46.5 0.5 10,593 53.5 0.5
 Obese 11,691 63.1 0.5 7369 36.9 0.5
Weight perception <.001
 About the right weight 4895 24.8 0.5 17,553 75.2 0.5
 Underweight 219 7.8 0.8 2777 92.2 0.8
 Overweight 17,199 62.8 0.4 10,767 37.2 0.4
Past-year weight loss attempt
Yes
 No
 p-Value
Mean SE Mean SE
Ratio of family income to poverty level 3.2 0.03 2.8 0.03 <.001
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3.4. Prevalence differences in past-year weight loss attempts between race/ethnicity, gender, and BMI category

In the base model (overall panel of Fig. 2 or Model 1 column in Table S3), compared to White women, prevalence of past-year weight loss attempt was lower among men of any racial/ethnic category and Black women and modestly elevated among Hispanic women. Given evidence of an interaction between the combined race/ethnicity-gender variable and weight status (Table S3, Model 2; Log Rank Test compared to Model 1, p < .001), we produced regression models stratified by weight status to further investigate associations of gender and race/ethnicity with weight loss attempts among individuals in different weight categories (Models 3–5).

Fig. 2.

Fig. 2.

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Gender and racial/ethnic differences in the adjusted prevalence of weight loss attempts by weight status.

Adjusted prevalence of weight loss attempts were computed using the fitted values from weighted regression models (see Table S3 in the Online supplement document). Confidence Intervals were computed with 1000 bootstrapping using stratified sampling (with replacement). The overall model was adjusted for age, education, marital status, self-perceived weight, NHANES survey wave, ratio of family income to poverty and BMI category. The stratified models were adjusted for aforementioned covariates except for weight status (which was used for stratifications).

In models stratified by weight status (Right three panels of Fig. 2 and Table S3, Models 3–5), men in all racial/ethnic groups had lower prevalence of weight loss attempts than White women in every weight category; however, the magnitude of this difference was attenuated with higher weight status. For example, the prevalence difference (or risk difference, RD) for Black men increased from −0.17 (95% CI −0.19, −0.15) to −0.03 (95% CI −0.06, 0.01). Similarly, in the under/normal weight category Black women were less likely than White women to try to lose weight (RD = −0.08, 95% CI −0.11, −0.06), but the difference was smaller among women with an overweight BMI (RD = −0.05, 95% CI −0.08, −0.02). Among people with an obese BMI, the direction of association reversed: Black women with an obese BMI were more likely to attempt weight loss (RD = 0.03, 95% CI 0.002, 0.05). Although there were no significant differences in the prevalence of weight loss attempts between White and Hispanic women with BMI in the under/normal weight or obese range, Hispanic women had a higher prevalence of weight loss attempts relative to White women with an overweight BMI (RD = 0.06, 95% CI 0.03, 0.09). Post hoc comparisons of FDR-adjusted pairwise differences in ORs within stratified models are presented in Table S4. Sensitivity analyses excluding participants in the underweight BMI category (Table S5) yielded similar results to the main models in Fig. 2 and Table S3. Similarly, analyses using obesity subclasses show a similar magnitude of weight loss attempts among people with an obese BMI (Table S6).

3.5. Adjusted prevalence of past-year weight loss attempts by race/ethnicity, gender, and weight status

In the overall linear regression model adjusted for BMI category (left panel of Fig. 2; Table S7), the prevalence of weight loss attempts was generally lower in men (0.34–0.39) than women (0.46–0.50), but within men and women prevalence was similar for all racial/ethnic groups. In models stratified by weight status (Fig. 2, right three panels), greater weight status had greater adjusted prevalence across race/ethnicity and gender categories. Within each weight status, men had lower adjusted prevalence than women regardless of race/ethnicity. Among both men and women, those in the Other/Multiracial category had the highest adjusted prevalence and widest confidence intervals, indicating heterogeneity within this group. For both men and women, Black people had the lowest adjusted prevalence of past-year weight loss attempt in the under/normal weight category and the second lowest in overweight category but had the second highest adjusted prevalence in the obese category. Crude prevalence estimates were generally higher than adjusted prevalence estimates (Table S7).

4. Discussion

Our study builds upon prior documentation of increasing trends in past-year weight loss attempts among U.S. adults by identifying specific subgroups at the intersection of race/ethnicity, gender, and weight status that may be more or less likely to engage in this behavior (Han et al., 2019). We found that trends in past-year weight loss attempts from 1999 to 2018 increased only among Black men with an obese BMI. In cross-sectional analyses combining data from 1999 to 2018, men in each racial/ethnic group were less likely to report past-year weight loss attempts than women. While this association was statistically significant and in the same direction across respective BMI categories, effect magnitudes were attenuated with higher weight status, indicating that differences were more pronounced at lower weights. Additionally, Black women with a normal/underweight or overweight BMI were less likely to attempt to lose weight compared to White women of a similar weight status. In contrast, among women with an obese BMI, both Hispanic and Black women had similar adjusted prevalence of weight loss attempts relative to White women.

While recent results from NHANES point to overall increasing trends in past-year weight loss attempts among US adults (Han et al., 2019), we found that Black men with an obese BMI were the primary drivers of this increase from 1999 to 2018. Within an intersectional framework, this trend may be due to the impact of interlocking systems of oppression (i. e., racism, sexism, and fatphobia) (Bowleg, 2012; Crenshaw, 1989; Crenshaw, 1991). In particular, those of higher weight status may be driven to lose weight due to weight stigma and discrimination experienced while navigating social systems that privilege those with smaller bodies (Lawrence et al., 2021; Nutter et al., 2019; Spahlholz et al., 2016). Within healthcare, this can manifest as upper BMI limits for certain medical procedures (MacLaughlin & Campbell, 2019), clinical decision-support tools prompting healthcare providers to encourage weight loss (Moschonis et al., 2019), and a tendency among healthcare providers to fixate on weight and attribute non-weight-related health concerns to a patient’s weight status (i.e., “fat broken arm syndrome”) (Paine, 2021; Rathbone et al., 2020). Recent scholarship also highlights the links between white supremacy, fatphobia, racialized body image standards (i.e., the White thin ideal), and the stigmatization of larger Black people in particular as a burden on the public health and medical system (Strings, 2019). As such, an increase in weight loss attempts in this population should be critically viewed in light of this social and structural context.

Compared to men, we found that women at each weight status were more likely to report past-year weight loss attempts. Women may be more likely to experience internalized weight bias and body dissatisfaction (Purton et al., 2019), which may explain increased drive to lose weight. Potential social mechanisms driving this phenomenon include sexual objectification of women in US culture as well as sociocultural influences (i.e., media, family, peers) that promote and reinforce a thin/lean body ideal particularly for women (Cafri et al., 2005; Fredrickson & Roberts, 1997; Thompson et al., 1999). Critically, we also found evidence for an interaction between gender and race/ethnicity for past-year weight loss attempts, and differing body image standards at the intersection of gender and race/ethnicity may help explain this result. In contrast to the thin/lean body ideal, Black and Hispanic women generally report having a larger, curvaceous body ideal (Capodilupo & Forsyth, 2014; Kelch-Oliver & Ancis, 2011), indicating that these groups may be less likely to attempt weight loss relative to White women. In partial support of this hypothesis, we found that among women with a normal/underweight BMI, White women reported similar or greater past-year weight loss attempts compared to women of color. However, among women with an obese BMI, we found that Black and Hispanic women were more likely to try to lose weight, which suggests Black and Hispanic women, relative to White women, may experience equal or greater drive to lose weight with higher weight status.

The idea that higher weight status is linked to adverse health outcomes has been used to promote weight loss at both the population and individual level (Centers for Disease Control and Prevention, 2021). However, this framing of weight status as a modifiable risk factor may promote weight cycling (i.e., repeatedly losing then regain lost weight in relatively short amounts of time). A recent study found that US adults who reported trying to lose weight experienced a lifetime average of 7.8 weight cycling episodes, with increased weight cycling associated with higher depressive symptoms (Quinn et al., 2020). Weight cycling may also place individuals at greater risk for cardiometabolic disease and associated risk factors (Rhee, 2017), which is counterintuitive to the intention of weight loss as a health intervention. Additionally, those who engage in intentional weight loss behaviors may also be at increased risk for developing eating disorder symptoms (Liechty & Lee, 2013; Neumark-Sztainer et al., 2006). Of note, the prevalence of past-year eating disorder diagnosis has been found to be highest among people, particularly women, with a higher BMI (Duncan et al., 2017). Given the current study’s finding that women across racial/ethnic groups with an overweight or obese BMI were most likely to report past-year weight loss attempts, these groups may be particularly at risk for weight cycling as well as poorer cardiometabolic and mental health outcomes, including eating disorders.

4.1. Limitations

This study is not without limitations. First, the NHANES interview did not measure gender in a way that is inclusive of transgender and gender expansive identities. Therefore, it was not possible to include separate categories for transgender and gender expansive individuals. Additionally, gender was interviewer-assigned rather than participant-reported, so gender misclassification may be present. Second, racial/ethnic identity categories in NHANES were broadly defined and findings may not generalize to all subgroups within a given racial/ethnic category. For instance, it is well-documented that there is notable heterogeneity of experiences across diverse Hispanic communities (Guarnaccia et al., 2007). Furthermore, due to low numbers, it was necessary to combine individuals of all racial/ethnic identities other than non-Hispanic Black, non-Hispanic White, and Hispanic (e.g., Asian, Native American) into a single category. Therefore, interpretations of these results within a well-defined social and cultural context are not possible. Third, this study used categories based on body mass index (BMI) as a categorical measure of weight status. The use of BMI as a proxy measure of adiposity to study associations between weight status and health conditions has been critiqued due to its propensity towards measurement error (e.g., inability to distinguish body composition), arbitrary category cutoff points, and lack of generalizability across population subgroups (Rothman, 2008). With these concerns in mind, we used BMI as a proxy measure for experiences (e.g., weight discrimination) and structural factors (e.g., fatphobia) that may be associated with greater weight loss attempts. We also used BMI given its commonality in weight-related literature to allow for comparison across similar studies. Fourth, NHANES is a repeated cross-sectional study. As such, it was not possible to determine temporality regarding weight loss attempts and some covariates.

5. Conclusions

To our knowledge, this is the first study to document the prevalence and trends in weight loss attempts at the intersection of race/ethnicity, gender, and weight status in the US. Using two decades of nationally representative data, our study highlights the importance of using an intersectional lens to identify at-risk subgroups in future eating and weight-related research, as studies analyzing a single characteristic at a time (e.g., stratifying analysis only by gender) may inadvertently mask information that could provide valuable insights for understanding the social patterning of health behaviors and outcomes. As we found that Black men with an obese BMI are increasingly engaging in weight loss attempts, this has implications for intervention development. Additional research is needed to identify specific mechanisms, such as weight stigma (Himmelstein et al., 2017), that may drive increases in weight loss attempts within specific subgroups. Given our findings of differential risk across subgroups, future research and practice should utilize an intersectional framework and empirically measure the impact of interlocking systems of oppression (racism, sexism, and fatphobia) on eating and weight-related behaviors and outcomes.

Supplementary Material

Supplementary Materials

Acknowledgments

The project described was supported by Grant Number T32MH019960 from the National Institute of Mental Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Mental Health or the National Institutes of Health.

Footnotes

Conflict of interest

The authors have no conflicts of interest.

CRediT authorship contribution statement

Yongqi Zhong: Conceptualization, Methodology, Formal analysis, Writing – original draft, Writing – review & editing. F. Hunter McGuire: Writing – original draft, Writing – review & editing. Alexis E. Duncan: Conceptualization, Methodology, Writing – original draft, Writing – review & editing, Supervision.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.eatbeh.2022.101682.

Data availability

Data will be made available on request.

References

  1. Akinbami LJ, Chen TC, Davy O, et al. (2022). National Health and Nutrition Examination Survey, 2017-March 2020 prepandemic file: Sample design, estimation, and analytic guidelines. In, 190. Vital and health statistics Ser 1, programs and collection procedures (pp. 1–36). [PubMed] [Google Scholar]
  2. Bauer GR (2014). Incorporating intersectionality theory into population health research methodology: Challenges and the potential to advance health equity. Soc Sci Med, 110, 10–17. 10.1016/j.socscimed.2014.03.022 [DOI] [PubMed] [Google Scholar]
  3. Bish CL, Blanck HM, Serdula MK, Marcus M, Kohl HW, & Khan LK (2005). Diet and physical activity behaviors among Americans trying to lose weight: 2000 behavioral risk factor surveillance system. Obesity Research, 13(3), 596–607. 10.1038/oby.2005.64 [DOI] [PubMed] [Google Scholar]
  4. Bowleg L (2012). The problem with the phrase women and minorities: Intersectionality—An important theoretical framework for public health. American Journal of Public Health, 102(7), 1267–1273. 10.2105/AJPH.2012.300750 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bowleg L (2021). Evolving intersectionality within public health: From analysis to action. American Journal of Public Health, 111(1), 88–90. 10.2105/AJPH.2020.306031 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Cafri G, Yamamiya Y, Brannick M, & Thompson JK (2005). The influence of sociocultural factors on body image: A meta-analysis. Clinical Psychology: Science and Practice, 12(4), 421–433. 10.1093/clipsy.bpi053 [DOI] [Google Scholar]
  7. Capodilupo CM, & Forsyth JM (2014). Consistently inconsistent: A review of the literature on eating disorders and body image among women of color. In Miville ML, & Ferguson AD (Eds.), Handbook of race-ethnicity and gender in psychology (pp. 343–359). Springer Science + Business Media. [Google Scholar]
  8. Centers for Disease Control and Prevention. (2021). NHANES questionnaires, datasets, and related documentation. https://wwwn.cdc.gov/nchs/nhanes/Default.aspx. (Accessed 8 November 2021).
  9. Centers for Disease Control and Prevention. (2021). Body Mass Index (BMI) - About adult BMI. https://www.cdc.gov/healthyweight/assessing/bmi/adult_bmi/index.html. (Accessed 8 November 2021).
  10. Centers for Disease Control and Prevention. (August 17, 2021). Losing weight. Healthy weight, nutrition, and physical activity. https://www.cdc.gov/healthyweight/losing_weight/index.html. (Accessed 9 September 2021). [Google Scholar]
  11. Chaitoff A, Swetlik C, Ituarte C, et al. (2019). Associations between unhealthy weight-loss strategies and depressive symptoms. American Journal of Preventive Medicine, 56(2), 241–250. 10.1016/j.amepre.2018.09.017 [DOI] [PubMed] [Google Scholar]
  12. Crenshaw K (1989). Demarginalizing the intersection of race and sex: A Black feminist critique of antidiscrimination doctrine, feminist theory and antiracist politics. University of Chicago Legal Forum, 1989(1). [Google Scholar]
  13. Crenshaw K (1991). Mapping the margins: Intersectionality, identity politics, and violence against women of color. Stanford Law Review, 43(6), 1241–1299. 10.2307/1229039 [DOI] [Google Scholar]
  14. Doan N, Romano I, Butler A, Laxer RE, Patte KA, & Leatherdale ST (2021). Weight control intentions and mental health among Canadian adolescents: A gender-based analysis of students in the COMPASS study. Health Promotion and Chronic Disease Prevention in Canada, 41(4), 119–130. 10.24095/hpcdp.41.4.01 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Dorsey RR, Eberhardt MS, & Ogden CL (2010). Racial and ethnic differences in weight management behavior by weight perception status. Ethnicity & Disease, 20(3), 244–250. [PubMed] [Google Scholar]
  16. Duncan AE, Ziobrowski HN, & Nicol G (2017). The prevalence of past 12-month and lifetime DSM-IV eating disorders by BMI category in US men and women. European Eating Disorders Review, 25(3), 165–171. 10.1002/erv.2503 [DOI] [PubMed] [Google Scholar]
  17. Flegal KM (2021). The obesity wars and the education of a researcher: A personal account. Progress in Cardiovascular Diseases, 67, 75–79. 10.1016/j.pcad.2021.06.009 [DOI] [PubMed] [Google Scholar]
  18. Fredrickson BL, & Roberts TA (1997). Objectification theory. Psychology of Women Quarterly, 21(2), 173–206. 10.1111/j.1471-6402.1997.tb00108.x [DOI] [Google Scholar]
  19. Guarnaccia PJ, Martínez Pincay I, Alegría M, Shrout PE, Lewis-Fernández R, & Canino GJ (2007). Assessing diversity among Latinos: Results from the NLAAS. Hispanic Journal of Behavioral Sciences, 29(4), 510–534. 10.1177/0739986307308110 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Han L, You D, Zeng F, et al. (2019). Trends in self-perceived weight status, weight loss attempts, and weight loss strategies among adults in the United States, 1999–2016. JAMA Network Open, 2(11), Article e1915219. 10.1001/jamanetworkopen.2019.15219 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Himmelstein MS, Puhl RM, & Quinn DM (2017). Intersectionality: An understudied framework for addressing weight stigma. American Journal of Preventive Medicine, 53(4), 421–431. 10.1016/j.amepre.2017.04.003 [DOI] [PubMed] [Google Scholar]
  22. Inoue Y, Qin B, Poti J, Sokil R, & Gordon-Larson P (2018). Epidemiology of obesity in adults: Latest trends. Current Obesity Reports, 7(4), 276–288. 10.1007/s13679-018-0317-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Kelch-Oliver K, & Ancis JR (2011). Black women’s body image: An analysis of culture-specific influences. Women & Therapy, 34(4), 345–358. 10.1080/02703149.2011.592065 [DOI] [Google Scholar]
  24. Lawrence BJ, Kerr D, Pollard CM, et al. (September 6, 2021). Weight bias among health care professionals: A systematic review and meta-analysis. Obesity. 10.1002/oby.23266 (Silver Spring). [DOI] [PubMed] [Google Scholar]
  25. Liechty JM, & Lee MJ (2013). Longitudinal predictors of dieting and disordered eating among young adults in the U.S. International Journal of Eating Disorders, 46(8), 790–800. 10.1002/eat.22174 [DOI] [PubMed] [Google Scholar]
  26. Lumley T (2021). Package “survey.”. https://cran.r-project.org/web/packages/survey/survey.pdf. (Accessed 14 January 2022).
  27. MacLaughlin HL, & Campbell KL (2019). Obesity as a barrier to kidney transplantation: Time to eliminate the body weight bias? Seminars in Dialysis, 32(3), 219–222. 10.1111/sdi.12783 [DOI] [PubMed] [Google Scholar]
  28. Moschonis G, Michalopoulou M, Tsoutsoulopoulou K, et al. (2019). Assessment of the effectiveness of a computerised decision-support tool for health professionals for the prevention and treatment of childhood obesity. Results from a randomised controlled trial. Nutrients, 11(3), 706. 10.3390/nu11030706 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Muller CJ, & MacLehose RF (2014). Estimating predicted probabilities from logistic regression: Different methods correspond to different target populations. International Journal of Epidemiology, 43(3), 962–970. 10.1093/ije/dyu029 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Neumark-Sztainer D, Wall M, Guo J, Story M, Haines J, & Eisenberg M (2006). Obesity, disordered eating, and eating disorders in a longitudinal study of adolescents: How do dieters fare 5 years later? Journal of the American Dietetic Association, 106(4), 559–568. 10.1016/j.jada.2006.01.003 [DOI] [PubMed] [Google Scholar]
  31. Nutter S, Ireland A, Alberga AS, et al. (2019). Weight bias in educational settings: A systematic review. Current Obesity Reports, 8(2), 185–200. 10.1007/s13679-019-00330-8 [DOI] [PubMed] [Google Scholar]
  32. Paine EA (2021). “Fat broken arm syndrome”: Negotiating risk, stigma, and weight bias in LGBTQ healthcare. Social Science & Medicine, 270, Article 113609. 10.1016/j.socscimed.2020.113609 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Purton T, Mond J, Cicero D, et al. (2019). Body dissatisfaction, internalized weight bias and quality of life in young men and women. Quality of Life Research, 28(7), 1825–1833. 10.1007/s11136-019-02140-w [DOI] [PubMed] [Google Scholar]
  34. Quinn DM, Puhl RM, & Reinka MA (2020). Trying again (and again): Weight cycling and depressive symptoms in U.S. adults. PLoS One, 15(9), Article e0239004. 10.1371/journal.pone.0239004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Rathbone JA, Cruwys T, Jetten J, & Barlow FK (2020). When stigma is the norm: How weight and social norms influence the healthcare we receive. Journal of Applied Social Psychology. 10.1111/jasp.12689 [DOI] [Google Scholar]
  36. Rhee EJ (2017). Weight cycling and its cardiometabolic impact. Journal of Obesity & Metabolic Syndrome, 26(4), 237–242. 10.7570/jomes.2017.26.4.237 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Rothman KJ (2008). BMI-related errors in the measurement of obesity. International Journal of Obesity, 32(3), S56–S59. 10.1038/ijo.2008.87 [DOI] [PubMed] [Google Scholar]
  38. Sharpe RV (2019). Disaggregating data by race allows for more accurate research. Nature Human Behaviour, 3(12). 10.1038/s41562-019-0696-1, 1240–1240. [DOI] [PubMed] [Google Scholar]
  39. Spahlholz J, Baer N, König HH, Riedel-Heller SG, & Luck-Sikorski C (2016). Obesity and discrimination - A systematic review and meta-analysis of observational studies. Obesity Reviews, 17(1), 43–55. 10.1111/obr.12343 [DOI] [PubMed] [Google Scholar]
  40. Strings S (2019). Fearing the Black body: The racial origins of fat phobia. NYU Press. https://nyupress.org/9781479886753/fearing-the-black-body. (Accessed 14 January 2022). [Google Scholar]
  41. Thompson JK, Heinberg LJ, Altabe M, & Tantleff-Dunn S (1999). Exacting beauty: Theory, assessment and treatment of body image disturbance. American Psychological Association. [Google Scholar]
  42. Tillé Y, & Matei A (2021). Package “sampling.”. https://cran.r-project.org/web/packages/sampling/sampling.pdf. (Accessed 14 January 2022).
  43. Tobias DK, Chen M, Manson JE, Ludwig DS, Willett W, & Hu FB (2015). Effect of low-fat diet interventions versus other diet interventions on long-term weight change in adults: A systematic review and meta-analysis. The Lancet Diabetes and Endocrinology, 3(12), 968–979. 10.1016/S2213-8587(15)00367-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Materials
NIHMS1864822-supplement-Supplementary_Materials.docx (138.8KB, docx)

Data Availability Statement

Data will be made available on request.

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