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Article

Associations Between Occupational Stress, Disordered Eating, and Obesity Among Police Officers in North Carolina

by
Ya-Ke Wu
1,2,* and
Hanxin Liu
3
1
School of Nursing, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
2
Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
3
Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119077, Singapore
*
Author to whom correspondence should be addressed.
Obesities 2025, 5(3), 65; https://doi.org/10.3390/obesities5030065
Submission received: 14 August 2025 / Revised: 9 September 2025 / Accepted: 12 September 2025 / Published: 15 September 2025
Versions Notes

Abstract

Obesity is a major public health concern among police officers, yet the links between occupational stress, disordered eating, and obesity remain unclear. This cross-sectional study examined 496 North Carolina officers to (1) assess severity of occupational stress (posttraumatic stress disorder [PTSD] symptoms, anti-police sentiment, fear of victimization), disordered eating (binge eating and loss-of-control eating), and obesity by county type, region, and sex; (2) evaluate associations between occupational stress and disordered eating; and (3) explore relationships between disordered eating and weight-related measures. Officers completed online surveys, and trained staff measured body mass index (BMI), waist and hip circumferences, and waist-to-hip ratio. Nearly 60 percent of officers were classified as obese (BMI ≥ 30 kg/m2), and over 20 percent reported moderate to severe binge eating. Rural officers reported higher PTSD symptoms, binge eating, and loss-of-control eating than those in urban or suburban areas. Coastal Plain and Piedmont officers had higher BMI and larger waist and hip circumferences than those in the Mountain region. Higher occupational stress was linked to more severe disordered eating, which was associated with greater BMI and adiposity, although the effect sizes were modest. Findings support targeted interventions addressing occupational stress and disordered eating to prevent obesity and enhance officer well-being.

1. Introduction

Obesity is a significant public health issue among police officers [1]. In fact, United States (U.S.) police officers are the most obese (40.7%, body mass index [BMI] ≥ 30 kg/m2) among all reported occupations [2]. Abdominal obesity (i.e., waist circumferences > 94 cm in men and >80 cm in women) is especially common, affecting 82.04% of police officers [3]. These issues tend to worsen over time, with both overall obesity and abdominal obesity becoming more severe as police officers’ years of service increase [3,4]. This elevated rate of obesity in police officers is linked to a higher prevalence of adverse health conditions such as hypertension [5], metabolic syndrome [6], and type 2 diabetes [5]. The impact of obesity extends beyond health problems. It can also impair a police officer’s ability to perform the demanding physical tasks required by their job [7]. Excess weight, particularly excess fat mass, reduces functional movement, affecting basic movements such as squatting, stepping, and even shoulder mobility [7]. This limitation in physical ability can compromise an officer’s effectiveness on the job, placing both their health and public safety at risk. In short, obesity is not only a personal health concern for police officers but also a professional one, with far-reaching consequences for their well-being and job performance.
Law enforcement is a physically and psychologically demanding profession that exposes police officers to numerous occupational stressors [8,9]. Police officers routinely face high-risk and unpredictable environments, encountering potentially psychologically traumatic events such as investigating crime scenes involving fatalities [10], responding to shooting incidents [11], and handling cases involving illicit substances [12]. These stressors, along with the demanding nature of the job, impose a substantial psychological burden on police officers [13,14]. Repeated exposure to potentially psychologically traumatic events makes police officers especially vulnerable to developing posttraumatic stress disorder (PTSD) [15]. Research indicates that up to 10% of police officers meet the diagnostic criteria for PTSD [16,17,18], with higher prevalence rates observed among male officers compared to female officers [16]. This suggests that gender differences in the nature of police work may contribute to varying levels of PTSD among officers. In addition, there has been a notable rise in anti-police sentiment in recent years, referring to the growing negative attitudes and mistrust towards law enforcement and its practices [19]. This growing mistrust and negative perception of law enforcement have been fueled by public protests following highly publicized cases of police misconduct [20]. Alongside this shift, police officers’ fear of victimization has also risen, partly as a result of the anti-police movement [21]. Officers now face heightened concerns about being injured or falsely accused of misconduct while on duty, which adds another layer of stress to their already challenging roles, particularly in high-crime areas [21,22]. While research has explored PTSD in police officers, few studies have examined the impact of anti-police sentiment and fear of victimization on their well-being. Furthermore, there is a notable gap in understanding how PTSD, anti-police sentiment, and fear of victimization are linked to disordered eating behaviors, such as binge eating and loss-of-control eating, in police officers. Given the high occupational stress and emotional strain police officers face, disordered eating can often be a coping mechanism for managing these pressures [23]. Addressing this gap is crucial for gaining insights into the development of disordered eating in this population.
Binge eating and loss-of-control eating are common disordered eating behaviors, where individuals often engage in excessive eating as a reaction to stress or unpleasant feelings, leading to a temporary sense of relief but ultimately reinforcing unhealthy patterns [24,25]. Binge eating involves the intake of an excessively large quantity of food in a limited timeframe, typically accompanied by a perceived inability to control one’s eating behavior during the episode, but without engaging in compensatory behaviors like self-induced vomiting or fasting [26]. Loss-of-control eating, often referred to as subjective binge eating, describes a situation where an individual feels compelled to eat or struggles to stop eating, even if the quantity consumed is not perceived as large by social standards [27]. However, the person views the amount they have eaten as significant or excessive [27]. To our knowledge, no studies have reported the prevalence of binge eating and loss-of-control eating specifically among police officers. However, research on military personnel shows that the prevalence of binge eating and loss-of-control eating is 4.57% and 6.90%, respectively [28]. Binge eating is more common among men (5.02%) than women (3.80%), while loss-of-control eating is more prevalent in women (10.96%) than men (6.64%) in the military [28]. While binge eating is more easily identifiable due to the large quantities consumed, loss-of-control eating may represent a more hidden tendency toward overeating, one that is harder to detect and may not be as easily recognized by either the individual or those around them [26,27]. This distinction is important because loss-of-control eating (i.e., the quantity of food consumed is not considered objectively excessive) has been found to be a more significant predictor of negative health outcomes than binge eating (i.e., eating an abnormally large quantity of food) [29,30]. Research indicates that individuals who engaged in loss-of-control eating had a greater chance of experiencing persistent eating disorder symptoms and mental health challenges, including weight concerns, depression, anxiety, and social withdrawal [29]. Additionally, individuals who engaged in loss-of-control eating reported greater functional impairment due to eating disorder features (e.g., eating habits affected work performance) and higher rates of psychiatric comorbidities compared to those with binge eating [30].
The relationship between binge eating, loss-of-control eating, and obesity is well-documented [31,32,33]. In a longitudinal study, U.S. veterans who reported binge eating five or more times per week experienced greater weight gain and an increase in waist circumference compared to those who binged less frequently [31]. Loss-of-control eating has been consistently linked to reduced weight loss and increased weight regain in adults following bariatric surgery [34]. Psychological stress is a key factor in the development of binge eating and loss-of-control eating [35]. Research shows that individuals under acute or chronic psychological stress often use disordered eating as a coping mechanism, leading to a cycle of overeating and weight gain [36]. In high-stress occupations, such as law enforcement, prolonged exposure to stressors like traumatic events during work can exacerbate this behavior [37]. For example, research has shown that PTSD is linked to an increased likelihood of binge eating in U.S. veterans [38,39]. However, there are currently no studies investigating the relationship between PTSD and loss-of-control eating in high-stress occupations. Furthermore, no quantitative research has comprehensively examined the relationship among occupational stress, disordered eating, and obesity in police officers. Addressing this gap is crucial, as it could lead to targeted interventions that not only improve the mental and physical well-being of police officers but also enhance their ability to cope with the unique stresses of their profession.
Occupational stress, disordered eating, and obesity are likely to differ based on geographic location, as regional factors can influence both the prevalence and severity of these issues [40,41,42]. North Carolina is among the top 20 states in the U.S. for adult obesity rates [43]. While there is limited specific data on obesity rates among police officers nationwide, the high obesity rates in North Carolina emphasize the need to focus research efforts on the state to tackle this critical issue. North Carolina (NC) is composed of three primary regions: Mountain (Western NC), Piedmont (Central NC), and Coastal Plain (Eastern NC), as well as 100 counties classified by population density into rural (78 counties), suburban (16 counties), and urban (6 counties) [44,45]. The Coastal Plain, with a higher African American population, faces lower incomes, education levels, higher crime, and the highest CVD rates among all regions [46,47]. Piedmont is more urban and diverse, with greater prosperity and education levels, but has pockets of high crime and rising CVD [46,47]. The Mountain region, which has a predominantly white population, is economically mixed, has lower crime rates, but struggles with access to healthcare [46,47]. These geographic and demographic divisions reveal substantial disparities in socioeconomic status, healthcare access, and crime rates. While awareness of these regional disparities is growing, no studies have yet explored the impact of geographic factors, such as region and county type, on occupational stress, disordered eating, and obesity among NC police officers.
Hence, this study examined the associations between occupational stress (i.e., PTSD symptoms, anti-police sentiment, and fear of victimization), disordered eating behaviors (i.e., binge eating and loss-of-control eating), and obesity (measured by weight-related variables, including BMI, circumferences of the waist and hips, and waist-to-hip ratios) among police officers in North Carolina, as this relationship has not been previously examined in this high-stress occupational group. The specific aims of the study were: (1) to assess the severity of occupational stress, disordered eating, and obesity among NC police officers, considering county type, region, and sex; (2) to examine the associations between occupational stress and disordered eating; and (3) to investigate the relationship between disordered eating and weight-related variables. We hypothesized that higher levels of occupational stress would be associated with more severe binge eating and loss-of-control eating. Additionally, we expected that more severe binge eating and loss-of-control eating would be associated with higher BMI and greater waist and hip circumferences. The rationale for not conducting mediation analysis among these variables is that this is a cross-sectional study with a limited sample size, which restricts our ability to establish causal relationships and perform more complex statistical modeling such as mediation analysis. Nevertheless, this study has the potential to inform the development of targeted interventions aimed at reducing obesity in police officers by focusing on modifiable factors, such as binge eating behaviors. The findings could help tailor prevention and treatment strategies to better address the unique health challenges faced by law enforcement personnel, ultimately enhancing their well-being and effectiveness in their roles.

2. Materials and Methods

2.1. Study Design, Setting, and Procedure

Data for this study were drawn from an exploratory cross-sectional study that explored the links between disordered eating, occupational stress, psychosocial distress, and cardiovascular risk among active-duty police officers in NC [48]. Recruitment was conducted through a dedicated project website, social media, and flyers containing QR codes directing participants to an online survey, which included screening items for age, employment status, work location, and coagulation history. Eligible individuals provided electronic informed consent before completing questionnaires on occupational stress, mental health, and eating behaviors. Participants interested in the optional in-person cardiovascular assessments were contacted by email. Assessments took place either at a university laboratory or on-site at their workplace if at least five officers from the same location were enrolled. The study was approved by the Institutional Review Board at the University of North Carolina at Chapel Hill (IRB #22-2052), and data collection occurred between 1 February 2023, and 1 April 2024.

2.2. Participants

Eligible participants were current NC law enforcement officers aged 18 or older, including a range of roles from patrol officers to correctional staff. Individuals with coagulation disorders were excluded due to potential risks during physical assessments. Of the 751 individuals who expressed interest, 496 were included in the final analysis after excluding those ineligible (n = 12, retired; n = 3, from outside NC), nonresponsive (i.e., 100% missing data, n = 198), or duplicate entries (n = 40). Of these, 280 completed both the online and in-person components with data on weight-related measures.

2.3. Measurement

2.3.1. Demographics Variables

Demographic variables include biological sex, age, race (white, black, or other, including mixed races, Asians, Native Alaskans, or American Indians), year of working experience in law enforcement, and the current county in NC where they work. We categorized participants’ work counties into three types based on population density: urban (over 750 people/mi2), suburban (250 to 750 people/mi2), and rural (250 or fewer people/mi2) [49]. We also grouped the counties into three regions based on the geographic location: Mountain, Piedmont, and Coastal Plain [49].

2.3.2. PTSD Symptoms

The severity of PTSD symptoms was assessed using the PTSD Checklist (PCL-5), a 20-item self-report measure that evaluates the 20 symptoms of PTSD over the past month [50]. Each item is scored on a 5-point scale, ranging from 0 (not at all) to 4 (extremely). The PCL-5 can be scored in several ways: (1) a total severity score (range 0–80) by summing the scores for all 20 items, (2) symptom cluster scores by adding items within each cluster (Cluster B: intrusion, items 1–5; Cluster C: avoidance, items 6–7; Cluster D: negative mood/cognitions, items 8–14; Cluster E: arousal/reactivity, items 15–20), and (3) a provisional PTSD diagnosis based on DSM-5 criteria, where a score of 2 or higher on any item is considered endorsed. A diagnosis requires at least one symptom from Cluster B, one from Cluster C, two from Cluster D, and two from Cluster E. For the current study, we utilized the overall severity score, where higher scores correspond to more severe PTSD symptoms. The PCL-5 has been used with U.S. first responders (firefighters, paramedics, and police officers), showing a Cronbach’s α of 0.94 [51]. In our sample, the Cronbach’s α was 0.95.

2.3.3. Anti-Police Sentiment

The level of anti-police sentiment was measured using the Anti-Police Sentiment Scale, a five-item self-report tool that assesses police officers’ perceptions of negative feelings and attitudes toward the police from the community [22]. An example item is, “Over the past few years, there has been an increase in negative feelings towards the police.” Each item is rated on a 5-point scale, ranging from 1 (strongly disagree) to 5 (strongly agree). A higher total score indicates a greater level of anti-police sentiment. In a previous study with police officers, the scale demonstrated reliability with a Cronbach’s α of 0.83 [22]. In our sample, the scale Cronbach’s α was 0.84.

2.3.4. Fear of Victimization

The level of fear of victimization was measured using the Fear of Victimization Scale, a five-item self-report measure that evaluates how concerned police officers are about the possibility of being victimized while on duty [22]. For example, one item asks, “How concerned are you about being injured by a person with a weapon?” Another item asks, “How concerned are you about being falsely accused of misconduct?” Responses are rated on a 5-point scale, from 1 (not at all concerned) to 5 (extremely concerned). A higher total score indicates a greater level of fear of victimization while on duty. The scale showed reliability in a prior study with a Cronbach’s α of 0.80 among police officers [22]. In our sample, the scale’s Cronbach’s α was 0.83.

2.3.5. Binge Eating

The severity of binge eating was assessed using the Binge Eating Scale [52]. The scale contains 16 self-reported items, each featuring three or four statements independently scored on a scale of 0 to 3 or 0 to 4. Participants selected a single statement per item. A sample item with four statements includes the following: (1) “I rarely eat so much food that I feel uncomfortably stuffed afterwards”, (2) “Usually about once a month, I eat such a quantity of food, I end up feeling bad”, (3) “I have regular periods during the month when I eat large amounts of food”, and (4) “I eat so much food that I regularly feel quite uncomfortable after eating and sometimes a bit nauseous” [52]. The total scale score ranges from 0 to 46, with higher scores reflecting more severe binge eating symptoms. The severity of binge eating was also classified into three groups based on the sum scores: (1) <17: non-binge eating, (2) 18–26: moderate binge eating, and (3) >27: severe binge eating [53]. The scale demonstrated reliability in U.S. military populations with a Cronbach’s α of 0.94 [54]. Within our sample, the Cronbach’s α for the scale’s internal consistency was 0.92.

2.3.6. Loss-of-Control Eating

The severity of loss of control over eating was measured using the Loss of Control Over Eating Scale, a 24-item self-report tool assessing the sense of losing control over eating in the past 4 weeks [55]. The scale consists of three subscales: behavioral, which evaluates physical behaviors linked to loss-of-control (seven items); cognitive/dissociative, which examines dissociative experiences and cognitive aspects during eating episodes (four items); and positive/euphoric aspects, which assesses the positive feelings experienced during eating episodes (two items). Responses are scored on a 5-point scale, ranging from 1 (never) to 5 (always). In this study, we utilized the total score, where higher values indicate a greater degree of loss of control over eating. Previous research found a Cronbach’s α of 0.95 for the overall scale in college students [56], while in our sample, the Cronbach’s α was 0.97.

2.3.7. Anthropometric Measurements

The participants’ body weight, body height, BMI, circumferences of the waist and hips, and waist-to-hip ratios were measured by a trained research assistant. All participants were asked to remove all police gear, vest, belt, and shoes before the measures of body height, weight, waist, and hip circumference using standard portable equipment such as a portable Martin stadiometer. Each measurement was taken twice and averaged. BMI was calculated using the standard formula (i.e., BMI = weight (kg)/height (m)2). Participants’ BMI was classified into four groups: (1) underweight: <18.5 kg/m2, (2) healthy weight: 18.5–24 kg/m2, (3) overweight: 25–29.9 kg/m2, and (4) obesity: ≥30 kg/m2 [57]. The circumferences of the waist and hips were measured using standard tape techniques, and the waist-to-hip ratios were calculated accordingly.

2.4. Statistical Analysis

For Aim 1, descriptive statistics, such as means and standard deviations (SD) or frequency and percentage, were calculated for all study variables as appropriate. Independent samples t-tests and one-way ANOVA were conducted to compare various numeric variables, including age, years of experience in law enforcement, levels of PTSD symptoms, anti-police sentiment, fear of victimization, binge eating, loss-of-control eating, BMI, circumferences of the waist and hips, and waist-to-hip ratios, across different sexes, county types, and regions. Additionally, chi-square tests were used to examine differences in binge eating and BMI categories based on sex, county types, and regions. In order to examine bivariate relationships between numeric variables, Pearson’s correlation analysis was conducted.
For Aim 2, the association between occupational stress (including the level of PTSD symptoms, anti-police sentiment, and fear of victimization, as independent variables) and disordered eating (including the levels of binge eating and loss-of-control eating, as dependent variables) was investigated using multiple linear regression models. The levels of PTSD symptoms, anti-police sentiment, and fear of victimization were entered as independent variables in separate models (one for each) to prevent multicollinearity. These models controlled for BMI, year of working experience in law enforcement, sex, county types, and regions. BMI was included as a covariate based on prior research demonstrating its positive association with both binge eating and loss-of-control eating [58,59]. Additionally, years of experience in law enforcement were considered a covariate, as this factor may influence an individual’s PTSD symptoms level, as well as their perceptions of anti-police sentiment and fear of victimization during their police duties.
For Aim 3, the association between disordered eating (including the levels of binge eating and loss-of-control eating, as independent variables) and weight-related outcomes (including BMI, waist circumference, and hip circumference, as dependent variables) was assessed using multiple linear regression models. The levels of binge eating and loss-of-control eating were entered as independent variables in separate models (one for each) to prevent multicollinearity. The rationale for not including the waist-to-hip ratio as a dependent variable is that it is inherently derived from the waist and hip circumference measurements. Since both waist and hip circumference were already included as dependent variables, incorporating the waist-to-hip ratio would introduce redundancy, as the ratio is a simple function of these two variables. These models controlled for sex, county types, and regions, as well as the level of PTSD symptoms and age. The level of PTSD symptoms was included as a covariate because previous research has shown that PTSD symptoms were associated with increased likelihood of obesity [60]. Additionally, age was controlled for, as it is known to influence weight-related outcomes [61]. As individuals age, metabolic changes and shifts in body composition can lead to variations in BMI, waist circumference, and hip circumference [61].
To evaluate the assumptions underlying the regression analyses, we examined histograms to assess whether the variables followed a normal distribution. Additionally, residual-versus-predicted value plots were reviewed to determine if variance was consistent across outcomes. The diagnostic checks confirmed that both normality and homogeneity assumptions were adequately met for all models. The proportion of missing data for the variables varied between 0.4% and 48.6%, with variables related to age, year of working experience in law enforcement, occupational stress, and weight having more than 5% missing data. To address this, the missing values were imputed using the expectation–maximization method before conducting the statistical analyses [62]. SAS 9.3 software was used to analyze all data [63]. p-values smaller than 0.05 were considered statistically significant. Since this study was exploratory in nature, we decided in advance not to apply corrections for multiple testing in our analysis.

3. Results

3.1. Descriptive Statistics and Comparative Analysis Results

The study sample included 68.83% of male and 31.17% of female officers who had a mean (±SD) age of 38.07 ± 9.49 years and a mean BMI of 31.19 ± 6.00 kg/m2 (37.50% overweight, and 57.73% obese). The racial distribution was not provided in Table 1, but the breakdown is as follows: 81.35% White, 13.11% Black, and 5.53% Other. More than 20% of police officers exhibited moderate or severe binge eating behaviors. Overall, waist circumference averages 102.94 ± 15.86 cm, with male officers measuring 106.37 ± 15.33 cm and female officers 96.16 ± 14.88 cm. The average waist-to-hip ratio is 0.93 ± 0.33, with male officers measuring 0.96 ± 0.40 and female officers 0.86 ± 0.08. Male officers reported having more years of experience in law enforcement, higher BMIs, greater percentages of overweight and obesity, larger waist circumferences, and higher waist-to-hip ratios compared to female officers, with the effect sizes small to modest. The results of descriptive statistics can be found in Table 1.
Table 2 presents comparative analysis results by county types in NC. The results show significant differences in the mean level of PTSD symptoms (F (2, 493) = 8.40, p < 0.01), binge eating (F (2, 493) = 5.28, p < 0.01), and loss-of-control eating (F (2, 493) = 3.79, p = 0.02) among the three types of counties. Bonferroni post hoc tests revealed that police officers in rural counties had statistically significantly higher mean levels of PTSD symptoms and binge eating compared to their counterparts in urban and suburban counties. Additionally, officers in rural counties exhibited statistically significantly higher mean levels of loss-of-control eating than those in urban counties, although the effect sizes were small.
Table 3 presents the comparative analysis results by NC regions. The results show significant differences in the mean level of BMI (F (2, 493) = 6.80, p = 0.02), waist circumference (F (2, 493) = 9.14, p < 0.01), and hip circumference (F (2, 493) = 6.85, p < 0.01) among the three NC regions. Bonferroni post hoc tests showed that police officers in the Coastal Plain and Piedmont regions had statistically significantly higher mean BMI, waist circumference, and hip circumference compared to those in the Mountain region. Furthermore, the Piedmont region had the highest proportion of officers classified as obese (BMI ≥ 30 kg/m2), although the effect sizes were small.

3.2. Results of the Correlation Analysis

The levels of PTSD symptoms, anti-police sentiment, and fear of victimization were statistically significantly positively correlated with binge eating, loss-of-control eating, and several weight-related variables (Table 4). Additionally, binge eating showed a significant positive correlation with BMI, as well as with waist and hip circumferences, while loss-of-control eating showed significant positive correlations with all weight-related variables.

3.3. Associations Between Occupational Stress, Disordered Eating, and Weight-Related Variables

Table 5 presents the associations between occupational stress and disordered eating. The findings reveal significant positive associations between the levels of PTSD symptoms, anti-police sentiment, and fear of victimization with both binge eating (βs ranging from 0.24 to 0.54, all p < 0.01) and loss-of-control eating (βs ranging from 0.46 to 0.98, all p < 0.01). These associations remain significant even after adjusting for BMI, years of experience in law enforcement, sex, county type, and region. However, the effect sizes (i.e., the regression coefficients) were small in both unadjusted and adjusted models.
Table 6 presents the associations between disordered eating and weight-related variables. The results show that both the levels of binge eating and loss-of-control eating were statistically significantly positively associated with BMI (βs ranging from 0.12 to 0.25, all p < 0.01), waist circumference (βs ranging from 0.36 to 0.80, all p < 0.01), and hip circumference (βs ranging from 0.19 to 0.41, all p < 0.01). These relationships remained significant after adjusting for the level of PTSD symptoms, age, sex, county type, and region. Similar to the above results, the effect sizes were small in both unadjusted and adjusted models.

4. Discussion

This study aimed to evaluate the severity of occupational stress, disordered eating, and obesity among NC police officers, with a focus on county type, region, and sex differences. Additionally, the research sought to explore the relationships between occupational stress and disordered eating, as well as the link between disordered eating and weight-related variables. Our findings revealed that nearly 60% of the officers were classified as obese, with over 20% reporting moderate or severe binge eating behaviors. Although the effect sizes were relatively small, the results substantiate our hypothesis that, after adjusting for relevant covariates, higher levels of occupational stress were associated with more severe binge eating and loss-of-control eating. Furthermore, the study supports the hypothesis that increased severity in binge eating and loss-of-control eating is linked to higher BMI, as well as greater waist and hip circumferences, even after adjusting for relevant covariates. Collectively, our findings generally align with our hypothesis and underscore the significance of addressing occupational stress in efforts to mitigate disordered eating patterns and prevent obesity within this high-stress occupational group in NC.
Our findings indicate that police officers in rural counties reported statistically significantly higher levels of PTSD symptoms, binge eating, and loss-of-control eating compared to officers in urban or suburban counties in NC. However, these differences were small in magnitude. The possible explanation for the higher level of PTSD symptoms may be due to the unique challenges faced by rural law enforcement personnel [64]. Rural police officers often work in areas with fewer resources and greater geographic isolation, leading to heightened occupational stress levels and limited access to mental health support [64]. In addition, the top 50 counties in NC with the highest crime rates are mostly rural [65], intensifying the strain on officers in these rural areas. This can lead to prolonged working hours, limited backup during critical incidents, and increased pressure to cover larger geographic areas with fewer resources, all of which may link to elevated occupational stress levels and potentially worse individuals’ PTSD symptoms [66]. Higher levels of occupational stress and PTSD symptoms have been shown to be strongly linked to disordered eating behaviors such as binge eating, as individuals may turn to food as a coping mechanism for emotional distress or to manage the physical and mental toll of their work [39,67]. Addressing these issues requires a multifaceted approach that includes offering mental health support and developing targeted interventions such as stress management programs to help mitigate the impact of occupational stress on disordered eating behaviors.
Our result indicates that police officers in the Coastal Plain and Piedmont regions had higher BMI, waist circumference, and hip circumference compared to officers in the Mountain region of NC, though the observed differences were small in effect size. Our findings align with the obesity rates observed in the adult population of NC (see Supplemental Table S1) [68]. Specifically, the percentage of adults aged 18 and older with a BMI greater than or equal to 30 kg/m2 is statistically significantly higher in the Coastal Plain region compared to the Piedmont and Mountain regions [68]. While there is no single explanation for our findings, several lifestyle and environmental factors may help to explain the discrepancy. The Piedmont, located between the Coastal Plain (eastern NC) and the Mountain (western NC) regions, is more urbanized, with top universities, international travel hubs, and corporate headquarters [69]. This urban environment may be linked to a higher prevalence of sedentary behavior, leading to lower levels of physical activity and contributing to the observed health outcomes in our study. Although the Coastal Plain is rural, it offers access to outdoor activities like beach recreation, which could promote physical activity [70]. However, the percentage of physical inactivity (i.e., adults aged 18 and over reporting no leisure-time physical activity) is statistically significantly higher in the Coastal Plain region compared to the Piedmont and Mountain regions (Supplemental Table S1) [68]. In contrast, the Mountain region, with its high elevations and abundant outdoor recreational opportunities in places like the Blue Ridge Parkway and Great Smoky Mountains National Park, might encourage a more active lifestyle, likely leading to lower BMI and waist measurements among police officers in this region [71]. In summary, future research should explore how regional and county-level disparities in PTSD symptoms, binge eating, loss-of-control eating, and obesity can be addressed through targeted interventions.
Our findings reveal that higher levels of PTSD symptoms, anti-police sentiment, and fear of victimization are statistically significantly linked to more severe binge eating and loss-of-control eating. These results align with previous studies examining the relationship between PTSD symptoms, binge eating, and loss-of-control eating in U.S. veterans and the general population [28,39,72]. Specifically, U.S. veterans of the Iraq and Afghanistan wars who met the PTSD screening criteria, as determined by the PTSD Checklist—Military Version, were found to be three times more likely to meet the criteria for binge eating [39]. Additionally, a national survey showed that individuals who met all DSM-5 criteria for PTSD exhibited more binge eating symptoms than those who had experienced trauma but had no or few PTSD symptoms [72]. Furthermore, a longitudinal study reveals that U.S. active military service members who exhibited PTSD symptoms between 2001 and 2003 experienced higher levels of loss-of-control eating from 2004 to 2006 [28]. These findings indicate that PTSD and eating disorder symptoms, such as binge eating and loss-of-control eating, frequently co-occur. One potential explanation for this co-occurrence lies in cognitive behavioral theory, which suggests that both PTSD and eating disorder symptoms may share similar underlying mechanisms, such as traumatic experiences [73]. Trauma experiences may play a role in triggering a cascade of reactions, including the development of PTSD symptoms and disordered eating behaviors like binge eating or loss-of-control eating [73]. In fact, network analysis of PTSD and eating disorder symptoms has revealed that the pathway between binge eating and PTSD symptoms is often mediated by an inability to experience positive emotions [74]. This suggests that binge eating may serve as a maladaptive coping mechanism, helping individuals regulate their negative affect and partially maintain the comorbidity [74].
Moreover, the connection between PTSD symptoms and loss-of-control eating remains largely unexplored. This is particularly important to investigate because loss-of-control eating represents the subjective experience of binge eating urges, which can be a precursor to more severe disordered eating behaviors such as binge-eating disorder [27,75]. Unlike binge eating, which typically involves the consumption of large amounts of food, loss-of-control eating can occur without a significant quantity of food, making it harder to identify but no less harmful [76]. Loss-of-control eating has been linked to a range of negative health outcomes, including emotional distress [77], depressive symptoms [75], and an increased risk of obesity [78]. Understanding this relationship could provide valuable insights into the broader psychological and physiological impacts of PTSD symptoms. Furthermore, to our knowledge, no prior studies have specifically examined the relationship between anti-police sentiment, fear of victimization, and binge eating or loss-of-control eating. However, our findings regarding the link between anti-police sentiment, fear of victimization, and disordered eating behaviors are consistent with our previous analysis [79], which demonstrated that operational police stress (i.e., the psychological strain resulting from the daily responsibilities and duties of police work) is associated with various disordered eating symptoms, including binge eating, in police officers [79]. Taken together, these findings highlight the complex interplay between trauma, psychological distress, and disordered eating behaviors in police officers. The evidence suggests that addressing posttraumatic stress, especially in high-risk populations such as police officers, could reveal potential mechanisms to mitigate disordered eating and improve overall health outcomes.
Our findings show that increased severity in binge eating and loss-of-control eating is linked to higher BMI, as well as greater waist and hip circumferences. These findings align with prior studies conducted in the U.S. [80,81]. Binge eating and loss-of-control eating often lead to excessive calorie intake, which, over time, contributes to weight gain and obesity [82]. Individuals who engage in binge eating may consume large quantities of high-calorie, high-fat foods, leading to an imbalance between energy intake and expenditure [82]. This caloric surplus is stored as adipose tissue, resulting in increases in body weight, BMI, and measurements of central adiposity such as waist circumference [83,84]. It is important to mention that the small effect size indicates that binge eating or loss-of-control eating alone cannot fully account for the elevated weight-related outcomes observed in our study. This suggests that there may be additional factors influencing body composition among police officers that were not included in the current study. Furthermore, it is notable that both male and female participants’ mean waist circumference and waist-to-hip ratio exceeded the healthy range. This indicates a widespread trend of elevated abdominal obesity across biological sex in this population. Given these findings, it is crucial to emphasize that more research is needed to fully understand the complexities of how binge eating, loss-of-control eating, and other factors contribute to abdominal obesity, particularly in occupational settings like law enforcement.
There are several limitations that are worth noting in our study. First, given the exploratory nature of this study, we did not conduct a power analysis to establish a minimum sample size prior to recruitment and data collection in the original study. Second, a notable limitation is the high degree of missing data for several variables, including age, years of law enforcement experience, occupational stress, and weight. Such missingness could have influenced the robustness of our findings. Third, we were unable to establish temporal causality because both the independent variables and dependent variables were measured at a single time point [85]. Fourth, we acknowledge that recruiting participants via flyers, social media, and our website may introduce selection bias, as individuals who are more health-conscious, technologically engaged, or motivated to participate in research may be overrepresented compared with the broader police population. Fifth, the findings may not be generalizable to the entire law enforcement population in NC, as the sample primarily consisted of male law enforcement officers working in the Piedmont region. Sixth, we did not account for other potential factors that could influence occupational stress and weight outcomes, such as the crime rate within each county or the use of weight loss medications. This limits our understanding of the full range of factors that contribute to the observed outcomes, which could affect the generalizability of our findings. Finally, our findings are not generalizable to clinical cases of binge eating or loss-of-control eating, as the Binge Eating Scale and the Loss of Control Over Eating Scale are not intended for clinical diagnosis and only reflect the severity of self-reported behaviors.

5. Conclusions

To the best of our knowledge, no prior research has examined the severity of occupational stress, disordered eating, and obesity among police officers in NC or explored the association among these variables. Research on disordered eating has largely neglected law enforcement as a population of study. Our findings, together with prior evidence, highlight consistent links between occupational stress, binge eating, and loss-of-control eating, connecting these disordered eating behaviors with obesity and emphasizing the importance of incorporating law enforcement professionals into disordered eating and obesity research. Future research should investigate these issues in a more diverse police population, considering factors such as ethnicity, gender, and regional differences. Additionally, it is important to explore broader environmental factors, including organizational culture, workload, and community dynamics, that may influence both the mental health and physical well-being of law enforcement officers. Longitudinal studies could further help identify causal relationships and effective intervention strategies to address these pressing health concerns. Understanding these underlying factors can inform targeted interventions that promote healthier coping strategies and reduce the prevalence of obesity and disordered eating behaviors among police officers.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/obesities5030065/s1. Table S1: The percentages of obesity and physical inactivity among adults in North Carolina regions.

Author Contributions

Conceptualization, Y.-K.W.; Methodology, Y.-K.W.; Software, Y.-K.W.; Validation, Y.-K.W.; Formal analysis, Y.-K.W.; Investigation, Y.-K.W.; Resources, Y.-K.W.; Data curation, Y.-K.W.; Writing—original draft preparation, Y.-K.W. and H.L.; Writing—review and editing, Y.-K.W. and H.L.; Visualization, Y.-K.W.; Supervision, Y.-K.W.; Project administration, Y.-K.W.; Funding acquisition, Y.-K.W. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health under Award Number U24DK132715, and the University of North Carolina at Chapel Hill School of Nursing’s Research, Education, and Quality Improvement Pilot Grant.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board (IRB) of the University of North Carolina at Chapel Hill (IRB number 22-2052, 1 February 2023).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author Dr. Ya-Ke Wu.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; the collection, analyses, or interpretation of data; writing the manuscript; or the decision to publish the results.

References

  1. Stegerhoek, P.; Kooijman, K.; Ziesemer, K.; IJzerman, H.; Kuijer, P.P.F.; Verhagen, E. Risk factors for adverse health in military and law enforcement personnel: An umbrella review. BMC Public Health 2024, 24, 3151. [Google Scholar] [CrossRef]
  2. Luckhaupt, S.E.; Cohen, M.A.; Li, J.; Calvert, G.M. Prevalence of obesity among U.S. workers and associations with occupational factors. Am. J. Prev. Med. 2014, 46, 237–248. [Google Scholar] [CrossRef]
  3. Raju, S.; Tiwari, S.; Verma, N.S.; Kumari, R. Prevalence of generalized and abdominal obesity in police worker—A cross-sectional study. Int. J. Med. Sci. Public Health 2017, 6, 1674–1678. [Google Scholar] [CrossRef]
  4. Boyce, R.; Jones, G.; Lloyd, C.; Boone, E. A longitudinal observation of police: Body composition changes over 12 years with gender and race comparisons. J. Exerc. Physiol. Online 2008, 11, 1. [Google Scholar]
  5. Chaturvedi, D.V.; Gupta, D.; Maheshgauri, D.M.; Yadav, G.E.; Debnath, D.J.; Chaturvedi, A.D. The prevalence and risk factors of diabetes and hypertension among police personnel: A population-based cross-sectional study. J. Fam. Med. Prim. Care 2025, 14, 441–446. [Google Scholar] [CrossRef]
  6. Anderson, A.A.; Yoo, H.; Franke, W.D. Associations of physical activity and obesity with the risk of developing the metabolic syndrome in law enforcement officers. J. Occup. Environ. Med. 2016, 58, 946–951. [Google Scholar] [CrossRef] [PubMed]
  7. Gnacinski, S.L.; Porter, F.J.; Renner, M.N.; Laska, T. Influence of body composition on functional movement among police officers. Int. J. Exerc. Sci. 2024, 17, 418. [Google Scholar] [CrossRef]
  8. Johnson, O.; Russo, C.; Papazoglou, K. Job exposure & occupational challenges: The importance of mindfulness for today’s law enforcement professional. Crisis Stress Hum. Resil. Int. J. 2019, 1, 187–191. [Google Scholar]
  9. Jetelina, K.K.; Molsberry, R.J.; Gonzalez, J.R.; Beauchamp, A.M.; Hall, T. Prevalence of mental illness and mental health care use among police officers. JAMA Netw. Open 2020, 3, e2019658. [Google Scholar] [CrossRef]
  10. Sibisi, N.; Shumba, K.; Ngcece, S.; Gopal, N.D. “Brutal murder scenes are traumatising, and they’re mostly indelible”: Occupational stressors and mental health among South African police service murder detectives at a selected station in Durban, South Africa. Cogent Soc. Sci. 2022, 8, 2123146. [Google Scholar] [CrossRef]
  11. Ellsworth, A.; MacDermott, S.; Scheidler, B. The occupational impact of mass shootings: A qualitative study of survivor accounts. Open J. Occup. Ther. 2022, 10, 1–13. [Google Scholar] [CrossRef]
  12. Attaway, P.R.; Smiley-McDonald, H.M.; Davidson, P.J.; Kral, A.H. Perceived occupational risk of fentanyl exposure among law enforcement. Int. J. Drug Policy 2021, 95, 103303. [Google Scholar] [CrossRef] [PubMed]
  13. Padilla, K.E. Sources and severity of stress in a Southwestern police department. Occup. Med. 2020, 70, 131–134. [Google Scholar] [CrossRef] [PubMed]
  14. Acquadro Maran, D.; Zedda, M.; Varetto, A. Organizational and occupational stressors, their consequences and coping strategies: A questionnaire survey among Italian patrol police officers. Int. J. Environ. Res. Public Health 2018, 15, 166. [Google Scholar] [CrossRef] [PubMed]
  15. Harnett, P.H.; Kelly, M.C.; Gullo, M.J. The impact of posttraumatic stress disorder on the psychological distress, positivity, and well-being of Australian police officers. Psychol. Trauma Theory Res. Pract. Policy 2023, 15, 340. [Google Scholar] [CrossRef]
  16. Brewin, C.R.; Miller, J.K.; Soffia, M.; Peart, A.; Burchell, B. Posttraumatic stress disorder and complex posttraumatic stress disorder in UK police officers. Psychol. Med. 2022, 52, 1287–1295. [Google Scholar] [CrossRef]
  17. Isabirye, R.A.; Namuli, J.D.; Kinyanda, E. Prevalence and factors associated with post traumatic stress disorder among field police patrol officers serving in Kampala Metropolitan region. BMC Psychiatry 2022, 22, 706. [Google Scholar] [CrossRef]
  18. Kyron, M.J.; Rikkers, W.; LaMontagne, A.; Bartlett, J.; Lawrence, D. Work-related and nonwork stressors, PTSD, and psychological distress: Prevalence and attributable burden among Australian police and emergency services employees. Psychol. Trauma Theory Res. Pract. Policy 2022, 14, 1124. [Google Scholar] [CrossRef]
  19. Helfers, R.C.; Reynolds, P.D. Anti-police sentiment, perceived organizational support and perceived danger among police officers in a post-Ferguson era. J. Crim. Justice Pop. Cult. 2022, 22, 20–38. [Google Scholar]
  20. Nix, J.; Wolfe, S.E.; Campbell, B.A. Command-level police officers’ perceptions of the “War on Cops” and de-policing. Justice Q. 2018, 35, 33–54. [Google Scholar] [CrossRef]
  21. Barrick, K.; Hickman, M.J.; Strom, K.J. Representative policing and violence towards the police. Polic. A J. Policy Pract. 2014, 8, 193–204. [Google Scholar] [CrossRef]
  22. Smith, R.; Helfers, R.C.; Reynolds, P. Anti-police sentiment and police fear of victimization in the UK. Polic. A J. Policy Pract. 2022, 16, 1–12. [Google Scholar] [CrossRef]
  23. Gaviria, D.; Ammerman, A. Eating disorders and disordered eating in servicemen and women: A narrative review. J. Clin. Psychol. 2023, 79, 316–373. [Google Scholar] [CrossRef] [PubMed]
  24. Leenaerts, N.; Jongen, D.; Ceccarini, J.; Van Oudenhove, L.; Vrieze, E. The neurobiological reward system and binge eating: A critical systematic review of neuroimaging studies. Int. J. Eat. Disord. 2022, 55, 1421–1458. [Google Scholar] [CrossRef] [PubMed]
  25. Ramalho, S.M.; Conceição, E.; Tavares, A.C.; Freitas, A.L.; Machado, B.C.; Gonçalves, S. Loss of control over eating, inhibitory control, and reward sensitivity in children and adolescents: A systematic review. Nutrients 2023, 15, 2673. [Google Scholar] [CrossRef] [PubMed]
  26. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders: DSM-5; American Psychiatric Association: Washington, DC, USA, 2013. [Google Scholar]
  27. Goldschmidt, A.B. Are loss of control while eating and overeating valid constructs? A critical review of the literature. Obes. Rev. 2017, 18, 412–449. [Google Scholar] [CrossRef]
  28. Mitchell, K.S.; Porter, B.; Boyko, E.J.; Field, A.E. Longitudinal associations among Posttraumatic Stress Disorder, disordered eating, and weight gain in military men and women. Am. J. Epidemiol. 2016, 184, 33–47. [Google Scholar] [CrossRef]
  29. Fitzsimmons-Craft, E.E.; Ciao, A.C.; Accurso, E.C.; Pisetsky, E.M.; Peterson, C.B.; Byrne, C.E.; Le Grange, D. Subjective and objective binge eating in relation to eating disorder symptomatology, depressive symptoms, and self-esteem among treatment-seeking adolescents with bulimia nervosa. Eur. Eat. Disord. Rev. 2014, 22, 230–236. [Google Scholar] [CrossRef]
  30. Vannucci, A.; Theim, K.R.; Kass, A.E.; Trockel, M.; Genkin, B.; Rizk, M.; Weisman, H.; Bailey, J.O.; Sinton, M.M.; Aspen, V. What constitutes clinically significant binge eating? Association between binge features and clinical validators in college-age women. Int. J. Eat. Disord. 2013, 46, 226–232. [Google Scholar] [CrossRef]
  31. Masheb, R.M.; Lutes, L.D.; Myra Kim, H.; Holleman, R.G.; Goodrich, D.E.; Janney, C.A.; Kirsh, S.; Richardson, C.R.; Damschroder, L.J. High-frequency binge eating predicts weight gain among veterans receiving behavioral weight loss treatments. Obesity 2015, 23, 54–61. [Google Scholar] [CrossRef]
  32. Tanofsky-Kraff, M.; Yanovski, S.Z.; Schvey, N.A.; Olsen, C.H.; Gustafson, J.; Yanovski, J.A. A prospective study of loss of control eating for body weight gain in children at high risk for adult obesity. Int. J. Eat. Disord. 2009, 42, 26–30. [Google Scholar] [CrossRef]
  33. Levine, M.D.; Tavernier, R.L.E.; Conlon, R.P.; Grace, J.L.; Sweeny, G.M.; Wang, B.; Cheng, Y. Loss of control eating during pregnancy is associated with excessive gestational weight gain among individuals with overweight and obesity. BMC Pregnancy Childbirth 2023, 23, 340. [Google Scholar] [CrossRef] [PubMed]
  34. Meany, G.; Conceição, E.; Mitchell, J.E. Binge eating, binge eating disorder and loss of control eating: Effects on weight outcomes after bariatric surgery. Eur. Eat. Disord. Rev. J. Eat. Disord. Assoc. 2014, 22, 87–91. [Google Scholar] [CrossRef] [PubMed]
  35. Rosenbaum, D.L.; White, K.S. The relation of anxiety, depression, and stress to binge eating behavior. J. Health Psychol. 2015, 20, 887–898. [Google Scholar] [CrossRef] [PubMed]
  36. Ferriter, C.; Ray, L.A. Binge eating and binge drinking: An integrative review. Eat. Behav. 2011, 12, 99–107. [Google Scholar] [CrossRef]
  37. Jetelina, K.K.; Beauchamp, A.M.; Reingle Gonzalez, J.M.; Molsberry, R.J.; Bishopp, S.A.; Lee, S.C. Cumulative, high-stress calls impacting adverse events among law enforcement and the public. BMC Public Health 2020, 20, 1137. [Google Scholar] [CrossRef]
  38. Litwack, S.D.; Mitchell, K.S.; Sloan, D.M.; Reardon, A.F.; Miller, M.W. Eating disorder symptoms and comorbid psychopathology among male and female veterans. Gen. Hosp. Psychiatry 2014, 36, 406–410. [Google Scholar] [CrossRef]
  39. Hoerster, K.D.; Jakupcak, M.; Hanson, R.; McFall, M.; Reiber, G.; Hall, K.S.; Nelson, K.M. PTSD and depression symptoms are associated with binge eating among US Iraq and Afghanistan veterans. Eat. Behav. 2015, 17, 115–118. [Google Scholar] [CrossRef]
  40. Le, A.; Judd, S.E.; Allison, D.B.; Oza-Frank, R.; Affuso, O.; Safford, M.M.; Howard, V.J.; Howard, G. The geographic distribution of obesity in the US and the potential regional differences in misreporting of obesity. Obesity 2014, 22, 300–306. [Google Scholar] [CrossRef]
  41. Preti, A.; Pinna, C.; Nocco, S.; Pilia, S.; Mulliri, E.; Micheli, V.; Consuleo Casta, M.; Rita Petretto, D.; Masala, C. Rural/urban differences in the distribution of eating disorder symptoms among adolescents from community samples. Aust. J. Psychiatry 2007, 41, 525–535. [Google Scholar] [CrossRef]
  42. Wu, G.; Wen, M. Predicting three dimensions of police officer stress: Does rural or urban setting matter? Polic. Int. J. 2020, 43, 435–449. [Google Scholar] [CrossRef]
  43. American’s Health Rankings. Obesity in North Carolina. Available online: https://www.americashealthrankings.org/explore/measures/obesity/NC (accessed on 4 September 2025).
  44. North Carolina Secretary of State. North Carolina Clinate and Geography. Available online: https://www.sosnc.gov/divisions/publications/kids_page_geography (accessed on 11 May 2024).
  45. Smith, R.M.; Deb, D.; Blizard, Z.; Midgett, R. A Planner’s quest for identifying spatial (in)justice in local communities: A case study of urban census tracts in North Carolina, USA. Appl. Geogr. 2023, 158, 103030. [Google Scholar] [CrossRef]
  46. Patterson, T. Geographic pattern analysis of North Carolina climate division data: 1895–2013. Southeast. Geogr. 2014, 54, 308–322. [Google Scholar] [CrossRef]
  47. Khan, J.A.; Casper, M.; George, M.; Williams, G.; Schieb, L.; Greer, S.; Asimos, A.W.; Clarkson, L.; Fehrs, L.J.; Enright, D. Geographic and sociodemographic disparities in drive times to joint commission–certified primary stroke centers in North Carolina, South Carolina, and Georgia. Prev. Chronic Dis. 2011, 8, A79. [Google Scholar] [CrossRef] [PubMed]
  48. Wu, Y.K.; Pacchioni, T.G.; Gehi, A.K.; Fitzgerald, K.E.; Tailor, D.V. Emotional eating and cardiovascular risk factors in the police force: The Carolina Blue Project. Int. J. Environ. Res. Public Health 2024, 21, 332. [Google Scholar] [CrossRef]
  49. North Carolina Rural Center. North Carolina Counties. Available online: https://www.ncruralcenter.org/about-us/ (accessed on 22 December 2024).
  50. Blevins, C.A.; Weathers, F.W.; Davis, M.T.; Witte, T.K.; Domino, J.L. The posttraumatic stress disorder checklist for DSM-5 (PCL-5): Development and initial psychometric evaluation. J. Trauma. Stress 2015, 28, 489–498. [Google Scholar] [CrossRef]
  51. Boehme, B.A.E.; Shields, R.E.; Carleton, R.N.; Asmundson, G.J.G. Factor structure and factorial invariance of the PTSD checklist for DSM-5 in public safety personnel: Results from a large and diverse sample. Psychol. Trauma Theory Res. Pract. Policy 2023, 17, 576–582. [Google Scholar] [CrossRef]
  52. Gormally, J.; Black, S.; Daston, S.; Rardin, D. The assessment of binge eating severity among obese persons. Addict. Behav. 1982, 7, 47–55. [Google Scholar] [CrossRef]
  53. Grupski, A.E.; Hood, M.M.; Hall, B.J.; Azarbad, L.; Fitzpatrick, S.L.; Corsica, J.A. Examining the Binge Eating Scale in screening for binge eating disorder in bariatric surgery candidates. Obes. Surg. 2013, 23, 1–6. [Google Scholar] [CrossRef]
  54. Ferrell, E.L. Disordered Eating Behavior Among United States Military Personnel. Master’s Thesis, Bowling Green State University, Bowling Green, OH, USA, 2019. [Google Scholar]
  55. Latner, J.D.; Mond, J.M.; Kelly, M.C.; Haynes, S.N.; Hay, P.J. The Loss of Control Over Eating Scale: Development and psychometric evaluation. Int. J. Eat. Disord. 2014, 47, 647–659. [Google Scholar] [CrossRef]
  56. Stefano, E.C.; Wagner, A.F.; Mond, J.M.; Cicero, D.C.; Latner, J.D. Loss of Control Over Eating Scale (LOCES): Validation in undergraduate men and women with and without eating disorder symptoms. Eat. Behav. 2016, 23, 137–140. [Google Scholar] [CrossRef]
  57. Centers for Disease Control and Prevention. Adult BMI Categories. Available online: https://www.cdc.gov/bmi/adult-calculator/bmi-categories.html (accessed on 11 March 2025).
  58. Černelič-Bizjak, M.; Guiné, R.P. Predictors of binge eating: Relevance of BMI, emotional eating and sensivity to environmental food cues. Nutr. Food Sci. 2022, 52, 171–180. [Google Scholar] [CrossRef]
  59. Radin, R.M.; Tanofsky-Kraff, M.; Shomaker, L.B.; Kelly, N.R.; Pickworth, C.K.; Shank, L.M.; Altschul, A.M.; Brady, S.M.; Demidowich, A.P.; Yanovski, S.Z.; et al. Metabolic characteristics of youth with loss of control eating. Eat. Behav. 2015, 19, 86–89. [Google Scholar] [CrossRef] [PubMed]
  60. Pagoto, S.L.; Schneider, K.L.; Bodenlos, J.S.; Appelhans, B.M.; Whited, M.C.; Ma, Y.; Lemon, S.C. Association of post-traumatic stress disorder and obesity in a nationally representative sample. Obesity 2012, 20, 200–205. [Google Scholar] [CrossRef] [PubMed]
  61. Palmer, A.K.; Jensen, M.D. Metabolic changes in aging humans: Current evidence and therapeutic strategies. J. Clin. Investig. 2022, 132, e158451. [Google Scholar] [CrossRef] [PubMed]
  62. Harel, O.; Zhou, X.H. Multiple imputation: Review of theory, implementation and software. Stat. Med. 2007, 26, 3057–3077. [Google Scholar] [CrossRef]
  63. SAS Institute. SAS® 9.4 Statements: Reference; SAS Institute Inc.: Cary, NC, USA, 2013. [Google Scholar]
  64. Oliver, W.M.; Meier, C.A. Stress in small town and rural law enforcement: Testing the assumptions. Am. J. Crim. Justice 2004, 29, 37–56. [Google Scholar] [CrossRef]
  65. NC Office of State Budget and Management. Crime Index in NC Counties. Available online: https://linc.osbm.nc.gov/explore/dataset/crime-index/export/?disjunctive.area_name&disjunctive.year&disjunctive.variable (accessed on 2 May 2024).
  66. Domino, M.E.; Lin, C.-C.; Morrissey, J.; Ellis, A.R.; Fraher, E.; Gaul, K.; Thomas, K. The Psychologist Workforce in North Carolina: Expanding Access for Patients in Rural Areas. Available online: https://www.shepscenter.unc.edu/wp-content/uploads/2016/09/Domino_PsychologistWorkforceInNorthCarolina_June2016.pdf (accessed on 1 July 2025).
  67. Medisauskaite, A.; Kamau, C. Does occupational distress raise the risk of alcohol use, binge-eating, ill health and sleep problems among medical doctors? A UK cross-sectional study. BMJ Open 2019, 9, e027362. [Google Scholar] [CrossRef]
  68. County Health Rankings & Roadmaps. North Carolina Data and Resources. Available online: https://www.countyhealthrankings.org/health-data/north-carolina/data-and-resources (accessed on 26 February 2025).
  69. NCPedia. Our State Geography in A Snap: The Piedmont Region. Available online: https://www.ncpedia.org/geography/region/piedmont (accessed on 2 March 2025).
  70. NCPedia. Our State Geography in A Snap: The Coastal Plain Region. Available online: https://www.ncpedia.org/geography/region/coastal-plain (accessed on 2 March 2025).
  71. NCPedia. Our State Geography in A Snap: The Mountain Region. Available online: https://www.ncpedia.org/our-state-geography-snap-mountain (accessed on 2 March 2025).
  72. Braun, J.; El-Gabalawy, R.; Sommer, J.L.; Pietrzak, R.H.; Mitchell, K.; Mota, N. Trauma exposure, DSM-5 posttraumatic stress, and binge eating symptoms: Results from a nationally representative sample. J. Clin. Psychiatry 2019, 80, 14848. [Google Scholar] [CrossRef]
  73. Mitchell, K.S.; Scioli, E.R.; Galovski, T.; Belfer, P.L.; Cooper, Z. Posttraumatic stress disorder and eating disorders: Maintaining mechanisms and treatment targets. Eat. Disord. 2021, 29, 292–306. [Google Scholar] [CrossRef]
  74. Nelson, J.D.; Cuellar, A.E.; Cheskin, L.J.; Fischer, S. Eating disorders and posttraumatic stress disorder: A network analysis of the comorbidity. Behav. Ther. 2022, 53, 310–322. [Google Scholar] [CrossRef]
  75. Tanofsky-Kraff, M.; Shomaker, L.B.; Olsen, C.; Roza, C.A.; Wolkoff, L.E.; Columbo, K.M.; Raciti, G.; Zocca, J.M.; Wilfley, D.E.; Yanovski, S.Z. A prospective study of pediatric loss of control eating and psychological outcomes. J. Abnorm. Psychol. 2011, 120, 108. [Google Scholar] [CrossRef]
  76. Colles, S.L.; Dixon, J.B.; O’brien, P.E. Loss of control is central to psychological disturbance associated with binge eating disorder. Obesity 2008, 16, 608–614. [Google Scholar] [CrossRef]
  77. Stevenson, B.L.; Dvorak, R.D.; Wonderlich, S.A.; Crosby, R.D.; Gordon, K.H. Emotions before and after loss of control eating. Eat. Disord. 2018, 26, 505–522. [Google Scholar] [CrossRef]
  78. Stojek, M.M.K.; Tanofsky-Kraff, M.; Shomaker, L.B.; Kelly, N.R.; Thompson, K.A.; Mehari, R.D.; Marwitz, S.E.; Demidowich, A.P.; Galescu, O.A.; Brady, S.M.; et al. Associations of adolescent emotional and loss of control eating with 1-year changes in disordered eating, weight, and adiposity. Int. J. Eat. Disord. 2017, 50, 551–560. [Google Scholar] [CrossRef] [PubMed]
  79. Qi, B.; Wu, Y.K. Operational police stress is associated with disordered eating in police officers. Int. J. Eat. Disord. 2024, 58, 531–541. [Google Scholar] [CrossRef] [PubMed]
  80. Higgins, D.M.; Dorflinger, L.; Macgregor, K.L.; Heapy, A.A.; Goulet, J.L.; Ruser, C. Binge eating behavior among a national sample of overweight and obese veterans. Obesity 2013, 21, 900–903. [Google Scholar] [CrossRef] [PubMed]
  81. Kantilafti, M.; Chrysostomou, S.; Yannakoulia, M.; Giannakou, K. The association between binge eating disorder and weight management in overweight and obese adults: A systematic literature review. Nutr. Health 2022, 28, 189–197. [Google Scholar] [CrossRef]
  82. Raymond, N.C.; Neumeyer, B.; Warren, C.S.; Lee, S.S.; Peterson, C.B. Energy intake patterns in obese women with binge eating disorder. Obes. Res. 2003, 11, 869–879. [Google Scholar] [CrossRef]
  83. Prana, V.; Tieri, P.; Palumbo, M.C.; Mancini, E.; Castiglione, F. Modeling the effect of high calorie diet on the interplay between adipose tissue, inflammation, and diabetes. Comput. Math. Methods Med. 2019, 2019, 7525834. [Google Scholar] [CrossRef]
  84. Vosselman, M.J.; Brans, B.; van der Lans, A.A.; Wierts, R.; van Baak, M.A.; Mottaghy, F.M.; Schrauwen, P.; van Marken Lichtenbelt, W.D. Brown adipose tissue activity after a high-calorie meal in humans. Am. J. Clin. Nutr. 2013, 98, 57–64. [Google Scholar] [CrossRef]
  85. Carlson, M.D.; Morrison, R.S. Study design, precision, and validity in observational studies. J. Palliat. Med. 2009, 12, 77–82. [Google Scholar] [CrossRef]
Table 1. Descriptive statistics of all variables for the total sample and by sex groups.
Table 1. Descriptive statistics of all variables for the total sample and by sex groups.
Total Sample
(n = 496)		Male
(n = 340)		Female
(n = 154)		t or χ2p-ValueCohen’s d or
Cramer’s V
VariablesMean ± SD or Number %Mean ± SD or Number %Mean ± SD or Number %
Age 38.07 ± 9.4938.73 ± 9.4836.71 ± 9.422.410.010.23
Years of working experience in law enforcement 12.97 ± 9.1013.64 ± 9.2411.19 ± 8.525.15<0.010.69
PTSD18.94 ± 15.9818.49 ± 16.2420.01 ± 15.50−0.810.410.08
Anti-police sentiment19.72 ± 4.2619.81 ± 4.3419.53 ± 4.100.760.450.07
Fear of victimization14.65 ± 4.8414.60 ± 4.8614.71 ± 4.82−0.170.870.02
Binge eating 10.73 ± 8.0310.81 ± 7.9310.65 ± 8.270.410.680.04
Binge eating categories 2.080.350.06
Non-binge eating 39,079.59%27,480.83%11,476.51%------
Moderate binge eating 8417.14%5315.63%3120.81%------
Severe binge eating 163.27%123.54%42.68%------
Loss-of-control eating42.13 ±16.6842.54 ±16.1641.15 ± 17.781.040.290.10
BMI (kg/m2) 31.19 ± 6.0031.68 ± 5.8530.23 ± 6.272.87<0.010.28
BMI categories 15.77<0.010.18
<18.5 kg/m220.71%10.54%11.09%------
18.5–24 kg/m22910.36%105.38%1920.65%------
25–29.9 kg/m210,537.50%7439.78%3032.61%------
≥30 kg/m214,457.73%10,154.30%4245.65%------
Waist circumference (cm)† 102.94 ± 15.86106.37 ± 15.3396.16 ± 14.887.97<0.010.77
Hip circumference (cm) 112.70 ± 12.83113.17 ± 12.91111.88 ± 12.741.590.110.15
Waist-to-hip ratio ‡0.93 ± 0.330.96 ± 0.400.86 ± 0.084.68<0.010.45
Note. n = number of participants; SD = standard deviation; Cohen’s d = effect size for independent samples t-tests; Cramer’s V = effect size for chi-square (χ2) test; PTSD = the PTSD Checklist for DSM-5 sum score; binge eating = Binge Eating Scale sum score; non-binge eating = sum score less than 17; moderate binge eating = sum score equal to or greater than 18 but less than 26; severe binge eating = sum score equal to or greater than 27; loss-of-control eating = Loss of Control Over Eating Scale sum score; BMI = Body Mass Index; kg = kilogram; m = meter; cm = centimeter. t = independent sample t-test; χ2 = chi-square test. † Healthy range for waist circumference: males = less than or equal to 94 cm, females = less than or equal to 80 cm; ‡ Healthy range for waist-to-hip ratio: males < 0.9, females < 0.8.
Table 2. Descriptive statistics of all variables by county types.
Table 2. Descriptive statistics of all variables by county types.
Urban
(n = 143)		Suburban
(n = 186)		Rural
(n = 165)		F or χ2p-Valueη2 or Cramer’s VPost-Hoc Test
VariablesMean ± SD or Number %Mean ± SD or Number %Mean ± SD or Number %
Age 37.60 ± 10.0437.83 ± 9.0238.73 ± 9.630.340.71<0.01--
Years of working experience in law enforcement 13.76 ± 9.6612.54 ± 8.3512.55 ± 9.180.600.55<0.01--
PTSD15.82 ± 14.2818.24 ± 16.0422.45 ± 16.748.40<0.010.03Rural > Urban **
Rural > Suburban *
Anti-police sentiment19.33 ± 4.6519.82 ± 4.0819.95 ± 4.091.050.35<0.01--
Fear of victimization14.82 ± 4.9414.56 ± 4.8314.61 ± 4.780.080.92<0.01--
Binge eating 9.96 ± 7.479.84 ± 8.0712.34 ± 8.275.28<0.010.02Rural > Urban *
Rural > Suburban *
Binge eating categories 3.710.440.06--
Non-binge eating 11,381.88%15,181.62%12,475.15%------
Moderate binge eating 2215.94%2714.59%3521.21%------
Severe binge eating 32.17%73.78%63.64%------
Loss-of-control eating40.04 ± 15.3441.57 ± 16.8544.57 ± 17.413.790.020.02Rural > Urban *
BMI (kg/m2) 31.11 ± 5.7430.91 ± 5.5531.65 ± 6.850.900.40<0.01--
BMI categories 6.600.350.08--
<18.5 kg/m200%00%22.44%------
18.5–24 kg/m2910.34%1311.71%78.54%------
25–29.9 kg/m23742.53%3935.14%2935.37%------
≥30 kg/m24147.13%5953.15%4453.66%------
Waist circumference (cm) 101.42 ± 14.94103.00 ± 14.26104.47 ± 18.671.360.25<0.01--
Hip circumference (cm) 113.86 ± 11.65112.25 ± 13.14112.06 ± 13.640.360.69<0.01--
Waist-to-hip ratio 0.89 ± 0.080.96 ± 0.520.93 ± 0.110.590.55<0.01--
n = number of participants; SD = standard deviation; η2 = effect size for one-way ANOVA; Cramer’s V = effect size for chi-square (χ2) test; PTSD = the PTSD Checklist for DSM-5 sum score; binge eating = Binge Eating Scale sum score; non-binge eating = sum score less than 17; moderate binge eating = sum score equal to or greater than 18 but less than 26; severe binge eating = sum score equal to or greater than 27; loss-of-control eating = Loss of Control Over Eating Scale sum score; BMI = Body Mass Index; kg = kilogram; m = meter; cm = centimeter. F = one-way ANOVA; Post-hoc Test = Bonferroni test; χ2 = chi-square test; * p < 0.05; ** p < 0.01.
Table 3. Descriptive statistics of all variables by region groups.
Table 3. Descriptive statistics of all variables by region groups.
Mountain
(n = 70)		Piedmont
(n = 299)		Coastal Plain
(n = 124)		F or χ2p-Valueη2 or Cramer’s VPost-Hoc Test
VariablesMean ± SD or Number %Mean ± SD or Number %Mean ± SD or Number %
Age 36.45 ± 9.9738.34 ± 9.7338.40 ± 8.621.440.23<0.01--
Years of working experience in law enforcement 11.35 ± 8.2413.50 ± 9.7312.33 ± 7.940.710.49<0.01--
PTSD20.06 ± 15.8718.32 ± 16.1219.88 ± 15.810.800.44<0.01--
Anti-police sentiment19.76 ± 4.1619.54 ± 4.3720.18 ± 4.041.350.26<0.01--
Fear of victimization14.13 ± 4.8314.84 ± 4.8614.56 ± 4.790.620.54<0.01--
Binge eating 9.91 ± 7.5210.79 ± 8.1111.05 ± 8.230.470.62<0.01--
Binge eating categories 1.690.790.04--
Non-binge eating 5781.43%23,680.27%9476.42%------
Moderate binge eating 1217.14%4816.33%2419.51%------
Severe binge eating 11.43%103.40%54.07%------
Loss-of-control eating40.37 ± 14.8842.26 ± 17.0542.91 ± 16.890.930.39<0.01--
BMI (kg/m2) 29.15 ± 5.8731.92 ± 6.1230.63 ± 5.516.80<0.010.03Coastal Plain > Mountain *
Piedmont > Mountain **
BMI categories 14.600.020.12--
<18.5 kg/m224.55%00%00%------
18.5–24 kg/m2715.91%148.19%812.50%------
25–29.9 kg/m21738.64%6336.84%2539.06%------
≥30 kg/m21840.91%9454.97%3148.44%------
Waist circumference (cm) 96.67 ± 14.01104.61 ± 16.63102.70 ±14.069.14<0.010.04Coastal Plain > Mountain **
Piedmont > Mountain **
Hip circumference (cm) 108.43 ± 10.39113.82 ± 14.03112.60 ± 9.996.85<0.010.03Coastal Plain > Mountain **
Piedmont > Mountain **
Waist-to-hip ratio 0.89 ± 3.990.94 ± 0.420.91 ± 0.081.140.32<0.01--
n = number of participants; SD = standard deviation; η2 = effect size for one-way ANOVA; Cramer’s V = effect size for chi-square (χ2) test; PTSD = the PTSD Checklist for DSM-5 sum score; anti-police sentiment = Anti-Police Sentiment Scale sum score; fear of victimization = Fear of Victimization Scale sum score; binge eating = Binge Eating Scale sum score; non-binge eating = sum score less than 17; moderate binge eating = sum score equal to or greater than 18 but less than 26; severe binge eating = sum score equal to or greater than 27; loss-of-control eating = Loss of Control Over Eating Scale sum score; BMI = Body Mass Index; kg = kilogram; m = meter; cm = centimeter. F = one-way ANOVA; Post-hoc Test = Bonferroni test; χ2 = chi-square test; * p < 0.05; ** p < 0.01.
Table 4. Pearson’s correlation coefficient in the total sample.
Table 4. Pearson’s correlation coefficient in the total sample.
Variables12345678910
1. PTSD--
2. Anti-police sentiment0.25 **--
3. Fear of victimization0.34 **0.32 **--
4. Binge eating0.46 **0.15 **0.31 **--
5. Loss-of-control eating0.43 **0.13 **0.27 **0.83 **--
6. BMI (kg/m2)0.14 **−0.11 *0.040.41 **0.41 **--
7. Waist circumference (cm)0.17 **−0.050.060.48 **0.45 **0.86 **--
8. Hip circumference (cm)0.10 *−0.09−0.020.31 **0.30 **0.82 **0.70 **--
9. Waist-to-hip ratio−0.02−0.050.11 *0.070.10 *0.11 *0.22 **−0.36 **--
10. Age 0.09 *0.10 *0.070.15 **0.16 **0.23 **0.35 **0.19 **0.08--
11. Years of working experience in law enforcement0.090.10 *0.16 **0.18 **0.18 **0.18 **0.31 **0.010.36 **0.85 **
n = number of participants; SD = standard deviation; PTSD = the PTSD Checklist for DSM-5 sum score; anti-police sentiment = Anti-Police Sentiment Scale sum score; fear of victimization = Fear of Victimization Scale sum score; binge eating = Binge Eating Scale sum score; loss-of-control eating = Loss of Control Over Eating Scale sum score; BMI = Body Mass Index; kg = kilogram; m = meter; cm = centimeter; * p < 0.05; ** p < 0.01.
Table 5. Associations between occupational stress and disordered eating.
Table 5. Associations between occupational stress and disordered eating.
Unadjusted ModelsAdjusted Models
βSE95% CIβSE95% CI
DV: Binge eating
PTSD0.24 **0.02(0.20, 0.28)0.21 **0.02(0.17, 0.24)
Anti-police sentiment0.31 **0.09(0.13, 0.48)0.37 **0.08(0.21, 0.53)
Fear of victimization0.54 **0.07(0.39, 0.68)0.49 **0.07(0.36, 0.63)
DV: Loss-of-control eating
PTSD0.46 **0.04(0.38, 0.55)0.39 **0.04(0.31, 0.48)
Anti-police sentiment0.54 **0.18(0.18, 0.90)0.67 **0.17(0.34, 1.00)
Fear of victimization0.98 **0.16(0.67, 1.28)0.88 **0.14(0.60, 1.16)
DV = dependent variable; PTSD = the PTSD Checklist for DSM-5 sum score; anti-police sentiment = Anti-Police Sentiment Scale sum score; fear of victimization = Fear of Victimization Scale sum score; binge eating = Binge Eating Scale sum score; loss-of-control eating = Loss of Control Over Eating Scale sum score; β = standardized parameter estimates; SE = standard error; 95% CI = 95% confidence interval. Adjusted models: models were adjusted for BMI, year of working experience in law enforcement, sex, county types, and regions; ** p < 0.01.
Table 6. Associations between disordered eating and weight-related variables.
Table 6. Associations between disordered eating and weight-related variables.
Unadjusted modelsAdjusted models
βSE95% CIβSE95% CI
DV: BMI (kg/m2)
Binge eating0.25 **0.03(0.20, 0.30)0.25 **0.03(0.19, 0.30)
Loss-of-control eating0.12 **0.01(0.10, 0.15)0.11 **0.01(0.09, 0.14)
DV: Waist circumference (cm)
Binge eating0.80 **0.07(0.67, 0.93)0.77 **0.07(0.64, 0.90)
Loss-of-control eating0.36 **0.03(0.30, 0.43)0.32 **0.03(0.26, 0.38)
DV: Hip circumference (cm)
Binge eating0.41 **0.06(0.30, 0.52)0.41 **0.06(0.29, 0.53)
Loss-of-control eating0.19 **0.03(0.14, 0.24)0.18 **0.03(0.12, 0.24)
DV = dependent variable; BMI = Body Mass Index; kg = kilogram; m = meter; cm = centimeter; binge eating = Binge Eating Scale sum score; loss-of-control eating = Loss of Control Over Eating Scale sum score; β = standardized parameter estimates; SE = standard error; 95% CI = 95% confidence interval. Adjusted models: models were adjusted for PTSD, age, sex, county types, and regions; ** p < 0.01.
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Wu, Y.-K.; Liu, H. Associations Between Occupational Stress, Disordered Eating, and Obesity Among Police Officers in North Carolina. Obesities 2025, 5, 65. https://doi.org/10.3390/obesities5030065

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Wu Y-K, Liu H. Associations Between Occupational Stress, Disordered Eating, and Obesity Among Police Officers in North Carolina. Obesities. 2025; 5(3):65. https://doi.org/10.3390/obesities5030065

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Wu, Ya-Ke, and Hanxin Liu. 2025. "Associations Between Occupational Stress, Disordered Eating, and Obesity Among Police Officers in North Carolina" Obesities 5, no. 3: 65. https://doi.org/10.3390/obesities5030065

APA Style

Wu, Y.-K., & Liu, H. (2025). Associations Between Occupational Stress, Disordered Eating, and Obesity Among Police Officers in North Carolina. Obesities, 5(3), 65. https://doi.org/10.3390/obesities5030065

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