Probation officers are a subset of law enforcement officers who contribute to public safety by supervising offenders on probation (a period of court ordered supervision), monitoring the compliance of offenders, and addressing those who violate their terms of probation. Despite similar public safety occupations (i.e., firefighters, police officers) having seen a rise in research recently (1–6), probation officers remain significantly understudied. Furthermore, given that probation officers have similar job demands (i.e., unpredictable bursts of strenuous activity) and experience similar work-related events (i.e., witnessing or experiencing violent acts) as traditional police officers, it is possible they may also have very poor health profiles as seen in police officers (7).
Obesity (body mass index [BMI] ≥ 30.0 kg·m−2) is a disease and remains a significant public health concern which is associated with numerous health issues (e.g., cardiovascular disease, diabetes mellitus) (8–10). Furthermore, the influence of obesity in the workplace has grown (11), with previous studies indicating obese workers miss more work days (absenteeism) compared with their normal weight colleagues, incurring a substantial financial burden on the employer (5). Although select public safety personnel (i.e., firefighters, law enforcement officers) are expected to perform strenuous duties, the prevalence of overweight and obesity in these occupational groups is higher than the general public (7) and has been shown to negatively impact occupational performance (12). Although the contributors to obesity are multifaceted (11), physical activity (PA) status is consistently cited as a key contributor (13,14). Thus, future studies are needed to determine the prevalence of overweight and obesity in probation officers, in addition to their current PA status.
Public safety occupations have one of the highest rates of work-related musculoskeletal injuries (15). Although some injury risk factors are inherent to all public safety personnel, each public safety profession may have unique risk factors (16). For example, probation officer duties are unique and include both law enforcement (i.e., arresting offenders) and social work (i.e., counseling offenders) responsibilities, which may influence injury patterns. However, we are aware of no previous studies investigating factors contributing to work-related musculoskeletal injury history in probation officers. The goal of the current study is to characterize the current health status of probation officers which may help to inform future worksite health-related decisions for public safety administrators. Thus, the purpose of the present investigation was to identify 1) the prevalence of overweight and obesity and 2) factors associated with previous work-related musculoskeletal injury and PA levels in North Carolina probation officers.
Sample and data collection
The current study used a questionnaire administered to all North Carolina probation officers in 2015 (N = 1866). The questionnaire collected information regarding a variety of factors related to general health history and job-related tasks. Probation officers were sent an email with a link to the questionnaire and invited to participate in the study. A follow-up reminder email was sent 2 wk later. This study and its procedures were approved by the University Institutional Review Board for the protection of human subjects (IRB 18-0668).
The health history questionnaire obtained information on demographics (age, sex, body mass, and stature), injury history, PA, geographical location of work (i.e., rural or urban), and years of employment as a probation officer. Body mass index (BMI) was calculated from stature and body mass and then classified in accordance with the National Heart, Lung, and Blood Institute’s (NHLBI) guidelines: normal weight (BMI, 18.5–24.9 kg·m−2), overweight (25.0–29.9 kg·m−2), and obesity (BMI ≥ 30.0 kg·m−2) (17). Obesity was further classified into class I obese (BMI, 30.0–34.9 kg·m−2), class II obese (BMI, 35–39.9 kg·m−2), and class III obese (BMI, ≥ 40.0 kg·m−2; severe obesity) (17) for the regression analyses (see below). Because of the relatively low amount of underweight individuals (n = 4; mean ± SD BMI, 18.1 ± 0.4 kg·m−2), participants with a BMI < 18.5 kg·m−2 were included with the normal weight group for all analyses.
Participants were asked to indicate if, over the past year, they had sustained a musculoskeletal injury (i.e., strain, sprain, tear, dislocation, bruise, or fracture) while at work for which they had completed an accident report for the department, reported to workers compensation, or received medical care (by a physician or other medical professional). A similar questionnaire was used previously by Jahnke et al. (6).
To assess PA habits, specifically exercise-related PA, participants selected a single value (0–7) which best represented their PA over the past month using the NASA PA scale (18). Previous research (19) has reported the concurrent validity of the scale with the NASA PA scale values related to aerobic fitness. The values in this questionnaire were as follows: 0, do not participate regularly in PA; 1, walk for pleasure, occasionally exercise causing perspiration; 2, 10 to 60 min of moderate PA per week; 3, participate in over 1 h of moderate PA per week; 4, participate in heavy physical exercise less than 30 min·wk−1; 5, participate in heavy physical exercise 30 to 60 min·wk−1; 6, participate in heavy physical exercise 1 to 3 h·wk−1; 7, participate in heavy physical exercise greater than 3 h·wk−1. Participants who had a score of 0 or 1 were placed into a lower PA group, participants with a score between 2 and 4 were placed in a moderate PA group, whereas participants who had a score of 5 or above were placed into a higher PA group.
Data were analyzed using SAS software (version 9.4, SAS Institute Inc., Cary, North Carolina). Descriptive analyses were presented as mean ± SD. The prevalence of overweight and obesity (BMI ≥ 25 kg·m−2) combined, and obesity (BMI ≥ 30 kg·m−2) only were calculated according to the NHLBI guidelines (17). To assess the factors associated with previous work-related musculoskeletal injury and PA levels, two multivariable models were conducted. First, a logistic regression model estimated the odds of work-related musculoskeletal injury history within the past year. Second, an ordinal logistic regression model estimated the odds of PA status within the past month. In this model, the ordering of the categories within the outcome variable assessed the odds of having a lower level of PA (i.e., low vs moderate and high combined; and low and moderate combined vs high). Predictor variables in both models included: age (in years; discrete variable); sex (male vs female); employment history (in years; discrete variable); geographical location (urban vs rural); and BMI classification (per NHLBI guidelines). Physical activity status was included as a predictor in the work-related musculoskeletal injury history model, and vice-versa. We reanalyzed the multivariable models stratified by sex to assess potential effect measure modification. Odds ratios (OR) with 95% confidence intervals (CI) were calculated, with those not including 1.00 being deemed statistically significant.
Demographic and work characteristics
Of the total North Carolina probation officers (N = 1866) employed, there were 1323 (70.9%) who completed the entire survey and the mean ± SD age and employment history was 39.9 ± 10.0 and 10.2 ± 8.4 yr, respectively (Table 1). Additionally, the majority of participants were male (53.5%), from rural areas (62.3%), and overweight/obese (BMI ≥ 25.0 kg·m−2; 80.8%), with 49.9% classified as obese (BMI ≥ 30 kg·m−2). When examining the individual obesity categories, 26.0% of participants were classified as class I obese (30.0–34.9 kg·m−2), 14.3% as class II obese (35–39.9 kg·m−2), and 9.5% as class III obese (BMI ≥ 40.0 kg·m−2). The largest proportion of participants had moderate levels of PA within the past month (40.3%) and 7.3% reported sustaining a work-related musculoskeletal injury within the past year. When examining work-related musculoskeletal injury history and PA level, respondents classified as obese were the most likely to have had an injury within the past year (Fig. 1) and had the largest proportions of low PA within the past month (Fig. 2).
Odds of work-related musculoskeletal injury within the past year
In the multivariable logistic regression model, age and obesity were the only factors associated with sustaining a work-related musculoskeletal injury within the past year. Controlling for all covariates, older probation officers displayed higher odds of having sustained a work-related musculoskeletal injury within the past year (1-yr increase; OR = 1.06; 95% CI = 1.04–1.09; Table 2). Additionally, probation officers categorized as class III obese had a higher odds of sustaining a work-related musculoskeletal injury within the past year than their normal weight colleagues (OR = 2.56; 95% CI = 1.19–5.51). Similar associations between each variable and work-related musculoskeletal injury within the past year were found in the sex-specific models (Table 2).
Odds of lower PA within the past month
In the multivariable ordinal logistic model, the assumption of proportional odds was met (P = 0.20). Sex, employment history, and BMI classification were found to be associated with the odds of lower PA within the past month (Table 3). Controlling for all covariates, the odds of having a lower level of PA within the past month was lower in males than females (OR, 0.38; 95% CI, 0.31–0.47). Additionally, the odds of lower PA within the past month increased with more years of employment (1-yr increase; OR, 1.03; 95% CI, 1.01–1.04). Furthermore, compared with normal weight officers, the odds of lower PA within the past month were higher among those classified as overweight (OR, 1.57; 95% CI, 1.16–2.13), class I obese (OR, 2.38; 95% CI, 1.74–3.27), class II obese (OR, 3.22; 95% CI, 2.24–4.64), and class III obese (OR, 2.98; 95% CI, 1.98–4.49). Similar patterns were found when analyses were restricted to males (proportional odds assumption P = 0.15) and females only (proportional odds assumption P = 0.39).
The findings of the present study indicated that greater than 80% of respondents were overweight or obese (BMI ≥ 25 kg·m−2), whereas nearly 50% were obese (BMI ≥ 30 kg·m−2). Surprisingly, these estimates from our sample of probation officers were higher than the current estimates in US adults (69.5% of adults with BMI ≥ 25 kg·m−2; 36.4% of adults with BMI ≥ 30 kg·m−2) (20). Furthermore, it is important to note that 9.5% of sampled probation officers were classed as severe or morbidly obese (BMI ≥ 40 kg·m−2), which poses significant health concerns (9). When compared with other public safety occupations (i.e., firefighters, law enforcement officers), the prevalence of overweight and obesity is similar, whereas the prevalence of obesity alone was higher in our sample of probation officers. For example, previous studies with similar age groups as the current study (range of mean values; 37.6–42.3 yr) have reported the prevalence of overweight and obesity to range from 79.5% to 80.4%, whereas the prevalence of obesity ranged from 23.6% to 37.5% (4,5,21–23). It is possible that the prevalence of obesity in the current study may be due to the timing of the data collection period. For instance, all the data collected in the current study occurred in 2015, whereas the data from these previous studies occurred between 2004 and 2013 (i.e., more recent data may show the prevalence of obesity is higher than in previous years) (4,5,21–23). This was noted by Hales, et al. (24) who reported that the prevalence of obesity among US adults increased from 33.7% in 2007 to 2008 to 39.6% in 2015 to 2016.
Obesity has been linked to a significant number of risk factors associated with various adverse health conditions (e.g., cardiovascular disease, diabetes mellitus) (8–10) and increased health care costs (8). The large (~10%) prevalence of severe obesity (class III) in probation officers is particularly concerning as previous studies have suggested that severe obesity is associated with a larger number of comorbidities which occur at an earlier age of onset, and includes a higher risk of premature death (25). Furthermore, the negative impacts of overweight or obesity (e.g., high BMI or percent fat) may also influence occupational performance, which is critical in probation officers who encounter potentially dangerous and physically demanding confrontations. For example, previous studies have reported that increases in BMI or percent body fat are associated with poorer performance during various tasks specific to public safety occupations (e.g., simulated defensive tactics, firefighter ability tests, functional balance assessment) (2,26,27). While the cause of the high prevalence of obesity seen in public safety occupations is multifactorial, it is commonly linked to low levels of PA (13,14), poor dietary habits (28), and various work-related factors (e.g., long working hours, overnight shift work) (11). These are supported in part by our findings which demonstrated that overweight and obese probation officers were more likely to participate in lower amounts of PA in the past month than their normal weight colleagues (Fig. 2). In addition, our findings suggest that female probation officers were more likely to perform lower amounts of PA than their male colleagues (Table 3), which is similar to previous reports in US adults (29). These findings may highlight the need for intensified efforts from management to improve PA levels to help combat the high prevalence of obesity.
Factors associated with work-related musculoskeletal injury within the past year
Work-related musculoskeletal injuries account for one third of all occupational injuries each year and are the primary cause of nonfatal injuries in public safety occupations in the United States (15,30). Approximately 7% of probation officers in the current study (n = 96) reported a previous work-related musculoskeletal injury within the past year, and of those, the majority were severely obese (Fig. 1). Furthermore, participants who were severely obese were 2.5 times as likely (OR, 2.56; 95% CI, 1.19–5.51) to sustain an injury within the past year than their normal weight colleagues (Table 2). Although previous studies did not categorize participants into similar groups as used in the current study (i.e., class I, II, and III obesity), authors have reported that obese workers are more likely to sustain an occupational musculoskeletal injury than their normal weight counterparts (31–33). In a similar public safety occupation, Jahnke et al. (1) reported that obese firefighters (BMI ≥ 30 kg·m−2) were over five times as likely to experience a musculoskeletal injury than their normal weight colleagues. Additionally, Poston et al. (5) reported that class II and III obese (BMI ≥ 35 kg·m−2) firefighters missed five times the amount of work due to injury than their normal weight colleagues. These studies, in conjunction with the results of the current study, may demonstrate that severe obesity is a significant risk factor associated with work-related injuries and subsequent absenteeism in public safety occupations. Although the cause(s) of work-related musculoskeletal injuries in our sample of probation officers is unknown, it is possible that excessive body weight (i.e., obesity) played a significant role. In addition to being associated with musculoskeletal pain (34), obesity has been suggested to negatively impact work-related postures and motions, which may influence biomechanical demands (12) and alter skeletal muscle function (i.e., impaired fatigue resistance) (35). Furthermore, recent authors have reported obesity is associated with a higher incidence of occupational injuries caused by slips, trips, and falls compared to their normal weight colleagues (31), which may be due to a reduced ability to maintain balance in obese workers (36).
When controlling for all other variables, a 1-yr increase in age was associated with a 6% increase in the odds of sustaining an injury in the previous year (OR, 1.06; 95% CI, 1.04–1.09). Some previous studies have reported similar results (37); however, contrasting findings (younger adults more likely to sustain a work-related injury) have also been presented in the literature (38,39). The discrepancy between these findings may be due to differences in the physical demands of specific occupations (40), as blue collar workers (e.g., manual laborers) may be more likely to have sustained occupational injuries than white collar workers (e.g., office administrators) (38,39). Additionally, although it is likely that age-related occupational injuries are a function of job-related experience (37), the combined impacts of the age-related decline in physical abilities (e.g., muscular strength, aerobic capacity) (41) and an increase in the frequency of physical impairments (e.g., sensory or cognitive disabilities) (42) may place older workers at an increased risk of injury. Interestingly, our findings suggest that a 1-yr increase in employment was associated with increased odds of participating in lower amounts of PA in the past month (OR, 1.03; 95% CI, 1.01–1.04). Thus, it is possible that the reduced levels of PA with increasing years of employment may exacerbate the age-related changes in physical abilities (3,26) and increase the likelihood of work-related musculoskeletal injury in probation officers. Collectively, these findings may highlight the need for workers to maintain or increase PA levels during their tenure as a probation officer to counteract the age-related increases in work-related musculoskeletal injures.
Limitations of the current study include examining data from probation officers in North Carolina. Thus, our sample may be biased and our findings may not be generalizable to all probation officers in the United States. It is important to note that additional demographic (e.g. race, education history, smoking), work-related characteristics (e.g., long working hours, overnight shift work), and health-related (e.g., dietary habits) variables were not collected in the current study which may be related to PA habits and/or musculoskeletal injury. Further, our current injury questionnaire did not specify details on the injury (e.g., type, location, activity performed). Future research is needed to identify more specific injury characteristics as reported in other public safety personnel (6). Our findings may also be limited by typical biases related to survey research, such as recall and social desirability bias. Lastly, it is important to note that although a small percentage (0.3%) of our sample was classified as underweight, these participants were grouped in with our normal weight group for all analyses and may have unique health concerns when compared with their normal weight colleagues (43).
In summary, this study reports findings from a questionnaire of 70.9% of all North Carolina probation officers. Results from our study suggest that nearly 50% were obese, with approximately 10% classified as severely obese. Additionally, older and severely obese participants demonstrated a greater odds of sustaining a work-related musculoskeletal injury in the past year, when compared with their younger and normal weight colleagues, respectively. Furthermore, years of employment, BMI classification, and sex influenced the odds of participating in lower amounts of PA in the past month. These findings may highlight key areas of concern for an understudied subset of law enforcement officers and may help to inform future worksite health-related decisions for public safety administrators. Future studies are needed to determine the impact of feasible, worksite-specific exercise and nutritional strategies, and the influence of implementing physical employment standards for probation officers.
The authors would like to thank the North Carolina Department of Public Safety for supporting this project and all the probation officers who participated in this study.
E. D. R. was the principle investigator of a service contract with the North Carolina Department of Public Safety during the time of data collection. All results in this study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation. Results of the present study do not constitute endorsement by the American College of Sports Medicine.
1. Jahnke SA, Poston WS, Haddock CK, Jitnarin N. Obesity and incident injury among career firefighters in the central United States. Obesity
2. Dawes JJ, Kornhauser C, Crespo D, Elder CL, Lindsay K, Holmes RJ. Does body mass index influence the physiological and perceptual demands associated with defensive tactics training in state patrol officers? Int J Exerc Sci
3. Dawes JJ, Orr RM, Flores RR, Lockie RG, Kornhauser C, Holmes R. A physical fitness profile of state highway patrol officers by gender and age. Ann Occup Environ Med
4. Poston W, Jitnarin N, Haddock C, Jahnke S, Day R. Accuracy of self-reported weight, height and BMI in US firefighters. Occup Med
5. Poston W, Jitnarin N, Haddock CK, Jahnke SA, Tuley BC. Obesity and injury-related absenteeism in a population-based firefighter cohort. Obesity
6. Jahnke SA, Poston WSC, Haddock CK, Jitnarin N. Injury among a population based sample of career firefighters in the central USA. Inj Prev
7. Hartley TA, Burchfiel CM, Fekedulegn D, Andrew ME, Violanti JM. Health disparities in police officers: comparisons to the U.S. general population. Int J Emerg Ment Health
8. Hruby A, Hu FB. The epidemiology of obesity: a big picture. Pharmacoeconomics
9. Prospective Studies Collaboration. Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies. The Lancet
10. Abdullah A, Peeters A, de Courten M, Stoelwinder J. The magnitude of association between overweight and obesity and the risk of diabetes: a meta-analysis of prospective cohort studies. Diabetes Res Clin Pract
11. Yarborough CM, Brethauer S, Burton WN, et al. Obesity in the workplace: impact, outcomes, and recommendations. J Occup Environ Med
12. Cavuoto LA, Nussbaum MA. Influences of obesity on job demands and worker capacity. Curr Obes Rep
13. Choi B, Schnall PL, Yang H, et al. Sedentary work, low physical job demand, and obesity in US workers. Am J Ind Med
14. Church TS, Thomas DM, Tudor-Locke C, et al. Trends over 5 decades in US occupation-related physical activity
and their associations with obesity. PLoS One
15. U.S. Bureau of Labor Statistics. Survey of Occupational Injuries and Illnesses. 2017.
16. Suyama J, Rittenberger JC, Patterson PD, Hostler D. Comparison of public safety
provider injury rates. Prehosp Emerg Care
17. Expert Panel on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults. Executive summary of the clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults. Arch Intern Med
18. Ross RM, Jackson AS. Exercise Concepts, Calculations, and Computer Applications
. Benchmark Press, Incorporated; 1990.
19. Jackson AS, Blair SN, Mahar MT, Wier LT, Ross RM. Prediction of functional aerobic capacity without exercise testing. Med Sci Sports Exerc
20. National Center for Health Statistics. Health, United States, 2016: with chartbook on long-term trends. Health
21. Gu JK, Charles LE, Burchfiel CM, et al. Associations between psychological distress and body mass index among law enforcement
officers: the National Health Interview Survey 2004-2010. Saf Health Work
22. Anderson AA, Yoo H, Franke WD. Associations of physical activity
and obesity with the risk of developing the metabolic syndrome in law enforcement
officers. J Occup Environ Med
23. Choi B, Steiss D, Garcia-Rivas J, et al. Comparison of body mass index with waist circumference and skinfold-based percent body fat in firefighters: adiposity classification and associations with cardiovascular disease risk factors. Int Arch Occup Environ Health
24. Hales CM, Fryar CD, Carroll MD, Freedman DS, Ogden CL. Trends in obesity and severe obesity prevalence in US youth and adults by sex and age, 2007–2008 to 2015–2016. JAMA
25. McTigue K, Larson JC, Valoski A, et al. Mortality and cardiac and vascular outcomes in extremely obese women. JAMA
26. Michaelides MA, Parpa KM, Henry LJ, Thompson GB, Brown BS. Assessment of physical fitness aspects and their relationship to firefighters’ job abilities. J Strength Cond Res
27. Mota JA, Barnette TJ, Gerstner GR, et al. Relationships between neuromuscular function and functional balance performance in firefighters. Sci Rep
28. Jahnke SA, Poston WS, Jitnarin N, Haddock CK. Health concerns of the US fire service: perspectives from the firehouse. Am J Health Promot
29. Caspersen CJ, Pereira MA, Curran KM. Changes in physical activity
patterns in the United States, by sex and cross-sectional age. Med Sci Sports Exerc
30. Marsh SM, Gwilliam M, Konda S, Tiesman HM, Fahy R. Nonfatal injuries to Firefighters treated in US emergency departments, 2003–2014. Am J Prev Med
31. Kouvonen A, Kivimäki M, Oksanen T, et al. Obesity and occupational injury: a prospective cohort study of 69,515 public sector employees. PLoS One
32. Gu JK, Charles LE, Fekedulegn D, Ma CC, Andrew ME, Burchfiel CM. Prevalence of injury in occupation and industry: role of obesity in the National Health Interview Survey 2004 to 2013. J Occup Environ Med
33. Gu JK, Charles LE, Andrew ME, et al. Prevalence of work-site injuries and relationship between obesity and injury among US workers: NHIS 2004–2012. J Safety Res
34. Wearing SC, Hennig EM, Byrne NM, Steele JR, Hills AP. Musculoskeletal disorders associated with obesity: a biomechanical perspective. Obes Rev
35. Cavuoto LA, Nussbaum MA. The influences of obesity and age on functional performance during intermittent upper extremity tasks. J Occup Environ Hyg
36. Allin LJ, Wu XF, Nussbaum MA, Madigan ML. Falls resulting from a laboratory-induced slip occur at a higher rate among individuals who are obese. J Biomech
37. Chau N, Gauchard GC, Dehaene D, et al. Contributions of occupational hazards and human factors in occupational injuries and their associations with job, age and type of injuries in railway workers. Int Arch Occup Environ Health
38. Smith TD, DeJoy DM. Occupational injury in America: an analysis of risk factors using data from the General Social Survey (GSS). J Safety Res
39. Simpson SA, Wadsworth EJ, Moss SC, Smith AP. Minor injuries, cognitive failures and accidents at work: incidence and associated features. Occup Med
40. Chau N, Bhattacherjee A, Kunar B; Group L. Relationship between job, lifestyle, age and occupational injuries. Occup Med
41. de Zwart BC, Frings-Dresen MH, van Dijk FJ. Physical workload and the ageing worker: a review of the literature. Int Arch Occup Environ Health
42. Gauchard GC, Deviterne D, Guillemin F, et al. Prevalence of sensory and cognitive disabilities and falls, and their relationships: a community-based study. Neuroepidemiology
43. Bhaskaran K, dos-Santos-Silva I, Leon DA, Douglas IJ, Smeeth L. Association of BMI with overall and cause-specific mortality: a population-based cohort study of 3· 6 million adults in the UK. The Lancet Diabetes & Endocrinology