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Obesity and Presenteeism: The Impact of Body Mass Index on Workplace Productivity

Donna M. Gates, EdD, RN, FAAN Paul Succop, PhD Bonnie J. Brehm, PhD, RD Gordon L. Gillespie, MSN, APRN, BC Benjamin D. Sommers, MD, PhD

Learning Objectives • Relate presenteeism, as reflected by scores in four dimensions of work and

percentage productivity lost on the Work Limitations Questionnaire, to body mass index (BMI) in this study of 341 manufacturing employees.

• Identify any associations between BMI grouping and absenteeism. • Estimate annual per-worker costs of health-related productivity losses and

absenteeism as related to BMI.

Abstract Objective: To examine whether obesity is associated with increased presen-

teeism (health-related limitations at work). Methods: Randomly selected manu- facturing employees (n � 341) were assessed via height and weight measures, demographic survey, wage data, and the Work Limitations Questionnaire. The Work Limitations Questionnaire measures productivity on four dimensions. Analyses of variance and analyses of covariance were computed to identify productivity differences based on body mass index (BMI). Results: Moderately or extremely obese workers (BMI �35) experienced the greatest health-related work limitations, specifically regarding time needed to complete tasks and ability to perform physical job demands. These workers experienced a 4.2% health-related loss in productivity, 1.18% more than all other employees, which equates to an additional $506 annually in lost productivity per worker. Conclusions: The relationship between BMI and presenteeism is characterized by a threshold effect, where extremely or moderately obese workers are significantly less productive than mildly obese workers. ( J Occup Environ Med. 2008;50:39 – 45)

I n the early 1970s an editorial in the Lancet1 identified obesity as the most important nutritional disease affecting affluent countries. Yet, over 30 years later, the US prevalence of obesity has increased dramatically among children, adolescents, and adults. Human obesity has serious consequences on health, in- cluding increased risks for depression,2

noninsulin-dependent diabetes melli- tus,3,4 cancer,5,6 rheumatoid and osteoar- thritis,7,8 hypertension,9,10 and heart disease.11,12 In addition, obesity has been found to reduce the quality of life for both men and women2,4,13,14 and markedly reduces life expectancy,15,16

The risks associated with overweight and obesity are alarming because ap- proximately 66% of US adults are over- weight or obese (body mass index [BMI] �25), with 32% being obese (BMI �30).17

The obesity-related costs to soci- ety are astounding. Finkelstein et al18

recently estimated obesity-attribut- able medical expenditures in the United States to be $75 billion in 2003, with one half of these expen- ditures financed by Medicaid and Medicare. Employers are struggling with increasing costs related to health care and absenteeism. US em- ployers are spending in excess of $900 billion per year for medical expenditures.19 Researchers have es- timated that costs attributed to obe- sity represent between 2% and 7.8% of the total health care expenditures of US businesses,20,21 and that obe- sity is positively related to health care costs and absenteeism.19,22–26

Schmier et al27 reviewed eight studies and found that overweight or obese employees had higher sick

From the College of Nursing (Drs Gates and Brehm and Mr Gillespie) and the Department of Environmental Health (Dr Succop), University of Cincinnati, Cincinnati, Ohio; Cincinnati Children’s Hospital Medical Center (Mr Gillespie), Cincinnati, Ohio; and the Division of General Internal Medicine (Dr Sommers), Brigham & Women’s Hospital, Boston, Mass.

Donna M. Gates and co authors received grant support from the Centers for Disease Control and Prevention. All authors were supported by this grant funding from the CDC. Everyone but Benjamin Sommers had a role as an Investigator (either principal or co-investigator). Dr. Sommers served as a consultant. His consulting fee was paid using grant funds from CDC.

Address correspondence to: Donna M. Gates, EdD, RN, 212 Procter Hall ML 0038, 3110 Vine Street, University of Cincinnati, Cincinnati, OH 45221-0038; E-mail: [email protected].

Copyright © 2008 by American College of Occupational and Environmental Medicine

DOI: 10.1097/JOM.0b013e31815d8db2

CME Available for this Article at ACOEM.org

JOEM • Volume 50, Number 1, January 2008 39

leave and disability use, and work- place injuries were higher for em- ployees with higher BMIs. The authors concluded that obesity is an important driver of costs in the workplace.

Although previous studies have examined the relationship between absenteeism and obesity, there is a lack of published research about the effect of obesity and related diseases on presenteeism, or the degree to which workers are on the job but are not fully functioning because of medical or psychological conditions. The purpose of this study is to help fill that gap by identifying whether employees with obesity experience greater work limitations than those with lower BMIs.

Materials and Methods

Subjects This study was conducted at eight

manufacturing companies in Ken- tucky with workforces ranging from 150 to 350 employees. The investi- gators assigned unique numbers to employees of the eight participating companies. Using a software pro- gram (www.randomizer.com), em- ployees were randomly selected to receive letters of invitation that de- scribed the study, selection criteria, and benefits of participation. Non– English-speaking persons and tem- porary or agency workers were excluded. Of the 622 employees in- vited to participate, 341 subjects (55%) consented and completed the baseline surveys and anthropometric measures. Participants represented both office- based and plant-based workers, with job titles including labor, safety, oper- ations, human resources, engineering, and management.

Data Collection and Measurements

Participants completed an investi- gator-developed demographic and employment survey and the Work- place Limitations Questionnaire (WLQ).28 The demographic and em- ployment survey asked participants

to provide information about their race, gender, age, job title, and ab- sences. The WLQ is a self-adminis- tered survey measuring the degree to which health problems interfered with the respondent’s ability to per- form job activities during the previ- ous 2 weeks.28 The WLQ was designed by researchers at Tufts Uni- versity and has been shown to be valid and reliable in previous stud- ies.28 –30 Responses to the question- naire’s 25 items are combined into four dimensions of work: time de- mands, physical demands, mental or interpersonal demands, and output demands. The time scale addresses difficulties with meeting job expec- tations and scheduling demands. The physical scale focuses on workers’ ability to perform their normal job tasks as influenced by bodily strength, movement, endurance, coordination, and flexibility. The mental-interper- sonal scale examines cognitive tasks, sensory input, and interactions with others. The output scale focuses on the quantity, quality, and timeliness of meeting job demands. Scale scores range from 0 (ie, limited none of the time) to 100 (ie, limited all of the time). In addition, the WLQ allows for the calculation of a com- posite index score that reflects the overall percent productivity loss be- cause of health limitations, relative to a healthy worker.31

Height and body weight were measured according to standard pro- tocol by trained nurses, dietitians, and health educators. BMI was cal- culated using the formula of weight in kilograms divided by height in meters squared (kg/m2). Employee wages were obtained by company hu- man resource personnel for seven of the companies. One company chose not to divulge wage information.

Statistical Analysis Participants were grouped into

four categories based on their BMIs in keeping with guidelines from the National Institutes of Health: under- weight and normal weight (BMI �25.0), overweight (BMI 25.0 –

29.9), mildly obese (BMI 30.0 – 34.9), and moderately or extremely obese (BMI �35.0). Demographic characteristics of gender, race, age, worker status (plant vs office), and income were compared across each group using analyses of variance (ANOVA) for continuous variables and chi-squared tests for categori- cal variables.

Worker presenteeism for the four WLQ subscales and percent produc- tivity loss and absenteeism were then analyzed across the four BMI groups using univariate ANOVAs. Percent productivity loss was calculated us- ing methods outlined by Lerner et al31 in their technical report for the WLQ. Hours absent for the last 6 months were divided into those per- taining to the worker’s own health and all other causes such as a family member’s health or child care. The 6-month absenteeism data were an- nualized by doubling the reported number of absences. Arithmetic and geometric means were then calcu- lated. Geometric means were used to describe the central tendency of rightly skewed data.32

The ANOVAs were followed by Student-Newman-Keuls (SNK) tests to compare each of the four obesity groups in a pairwise fashion. Analy- ses of covariance (ANCOVAs) were performed to test for differences in these outcomes across BMI groups, controlling for the following covari- ates: gender, race and ethnicity, age, plant versus office workers, and in- come quartile. The strategy of back- ward elimination was used to remove insignificant covariates from the sta- tistical models, which resulted in es- timating final models that included only the obesity group variable and significant covariates.

Annual costs of productivity loss were calculated by multiplying the percent of productivity loss times the sample’s mean hourly wage, then multiplying by a standard 40-hour workweek over 50 weeks a year (2000 hours). Annual absenteeism costs for personal health-related rea- sons were calculated by multiplying

40 Obesity and Presenteeism • Gates et al

the sample’s mean hourly wage times the arithmetic mean. Annual costs of productivity loss and absen- teeism for the moderately or ex- tremely obese were then compared with all other workers. Wage data were available for 309 participants.

Results

Description of Employees Table 1 presents descriptive statis-

tics for the sample. Approximately 91.5% of the 341 participants were white, 5.5% were black, and 1% or less each were Alaskan-American Indian, Hispanic, Asian, or other. The mean age of the participants was 43.6 � 10.0 years (range 19 –72). The mean BMI was 29.0 � 5.5 (range 17.2–52.9). Overall, the ma- jority of the participants were over- weight or obese; only 22.3% workers had a BMI �25, whereas 41.9% workers were overweight, 23.2% were mildly obese, and 12.6% were moderately or extremely obese. In the lowest BMI group, only three workers were underweight (BMI �18.5), and the results were essen- tially unchanged when these three participants were excluded from the analysis.

There were significant differences across BMI groups in terms of gen- der, age, and ethnicity. Gender was related to BMI group in a nonlinear fashion, with women representing the highest proportion in the normal or underweight group (47 of 76 or 62%) and the lowest proportion among overweight workers (43 of 143 or 30%). Over half of the male workers were overweight and an ad- ditional third were obese. Two thirds of female workers were overweight or obese. Overweight workers were disproportionately white whereas obese workers were disproportion- ately black and had lower annual incomes. Age was also related to BMI group in a nonlinear fashion with the highest and lowest BMI groups being younger than the mid- dle two BMI groups.

Presenteeism and Absenteeism Table 2 describes the results for

presenteeism and absenteeism, strat- ified by BMI. There were significant differences across the four BMI groups for the WLQ time subscale scores (F(3,330) � 3.13, P � 0.03). The SNK tests showed that partici- pants whose BMI was 35.0 or greater experienced significantly more diffi-

culty in completing work demands on time than participants in all other BMI groups. Marginal significance was found among the groups for the mean physical subscale scores (F(3,334) � 2.53, P � 0.057). The SNK comparisons showed that par- ticipants whose BMI was 35.0 or greater experienced significantly more difficulty with job-related physical tasks than did participants who were either overweight or mildly obese. Participants with a BMI �25 did not differ significantly from any other group in the physical subscale scores. There were no signif- icant differences between group means on either the mental-interpersonal sub- scale scores (F(3,335) � 1.48, P � 0.22) or the output subscale scores (F(3,332) � 1.17, P � 0.32).

For the WLQ index of percentage productivity lost, the overall test comparing the four BMI groups was only marginally significant with a P � 0.10 (F(3,326) � 2.21, P � 0.09). Nevertheless, the percentage productivity lost for the moderately or extremely obese group (4.16%) was significantly higher than the mildly obese group (2.45%) at P � 0.05. Productivity loss for over- weight and underweight or normal

TABLE 1 Participant Characteristics According to BMI (N � 341)

Variable

BMI Group

Total

Underweight/Normal (n � 76)

BMI < 25

Overweight (n � 143)

BMI 25–29.9

Mildly Obese (n � 79)

BMI 30 –34.9

Moderately/Extremely Obese (n � 43)

BMI > 35

Gender* Male 197 29 (14.7%) 100 (50.7%) 46 (23.4%) 22 (11.2%) Female 144 47 (32.6%) 43 (29.9%) 33 (22.9%) 21 (14.6%)

Race* White, non-Hispanic 311 67 (21.5%) 136 (43.7%) 73 (23.5%) 35 (11.3%) Black, non-Hispanic 19 4 (21.1%) 4 (21.1%) 3 (15.8%) 8 (42.1%)

Age** 341 41.89 � 10.81 43.35 � 9.66 46.35 � 9.58 41.93 � 9.81 Worker status

Plant 193 44 (22.8%) 78 (40.4%) 42 (21.8%) 29 (15.0%) Office 146 32 (21.9%) 63 (43.2%) 37 (25.3%) 14 (9.6%)

Income (n � 319) �$32,635 79 14 (17.7%) 33 (41.8%) 20 (25.3%) 12 (15.2%) $32,635– 41,084 80 21 (26.3%) 25 (31.3%) 19 (23.8%) 15 (18.8%) $41,085–59,062 81 15 (18.5%) 36 (44.4%) 21 (25.9%) 9 (11.1%) �$59,062 79 23 (29.1%) 39 (49.4%) 13 (16.5%) 4 (5.1%)

*P � 0.01; **P � 0.05. BMI, body mass index is calculated as weight (kg)/height (m)2.

JOEM • Volume 50, Number 1, January 2008 41

weight BMI workers did not differ significantly from any other group.

The total sample had an arithmetic mean of 55.84 missed hours of work because of personal health absentee- ism, with a range of 0 to 160 days of missed work leading to right-skewed absenteeism data. The mildly obese and moderately or extremely obese groups had the greatest average num- ber of annual hours absent for per- sonal health reasons, 91.08 and 73.48 hours, respectively. The overweight group averaged 26.86 hours absent.

Because of right skewing of per- sonal health-related absenteeism data, geometric means were calcu- lated for the groups and an ANOVA revealed that the groups were signif- icantly different (F(3,337) � 5.12, P � 0.002). The overweight group was absent significantly fewer hours than all other groups (SNK compar- isons). Interestingly, the underweight or normal weight group had the high- est number of hours absent on the geometric scale at 16.14, more than double that of the overweight group.

Twelve percent of the total sample missed more than 2 weeks (80 hours) of work time because of personal health reasons and 6.2% missed more than 4 weeks (160 hours) of work during the previous year. The overweight group had the lowest per-

centage (7.7%) of workers who missed more than 2 weeks of work and lowest percentage of those who missed more than 4 weeks of work (2.8%). The mildly obese and the moderately or extremely obese groups had the highest percentages of workers who missed more than 2 weeks of work (16.5% and 11.6%, respectively), as well as those who missed over 4 weeks of work (8.9% and 9.3%, respectively).

Repeating the comparisons in Ta- ble 2 using ANCOVAs, none of the covariates (gender, race and ethnic- ity, age, plant vs office worker, in- come quartile) were significant in any of the models except the physi- cal subscale, in which the plant ver- sus office worker comparison was significant (F(1,331) � 11.93, P � 0.0006). The mean was 18.36 for plant and 9.82 for office workers. This indicates that, with the excep- tion of the physical subscale, the results in Table 2 were unaffected by multivariate adjustment. After con- trolling for plant versus office worker, there was no significant dif- ference among the BMI groups for the physical subscale.

Costs Annual costs per worker because

of health-related productivity losses

and absenteeism were calculated based on the sample-wide mean hourly wage of $21.44. The mean per-person cost for presenteeism was $1337.86 compared with $1197.21 for absenteeism.

The moderately or extremely obese group had the greatest loss of productivity for each of the subscales with a mean of 4.16% compared with the mean for all other workers at 2.98%. Overall, the moderately or extremely obese workers experi- enced a health-related loss in produc- tivity 1.18% higher than the mean percentage for all other workers. An- nual costs per moderately or ex- tremely obese worker for loss of productivity and absenteeism were calculated using the sample-wide mean hourly wage of $21.44. The annual presenteeism cost of $1783.81 for the moderately or extremely obese worker was $506 above the annual presenteeism cost of $1277.82 for all other workers in the study. The annual absenteeism cost of $1575.41 for the moderately or extremely obese worker was $433 above the absenteeism cost of $1142.76 for all other workers.

Discussion Extreme obesity was associated

with significantly greater health-

TABLE 2 Mean Values of Work-Related Variables in Manufacturing Employees According to BMI (N � 341)

Outcome Variable Total

(N � 341)

Underweight/ Normal* (n � 76)

Overweight* (n � 143)

Mildly Obese* (n � 79)

Moderately/ Extremely

Obese* (n� 43) F and P

WLQ scales Time scale 12.26 12.04a 12.14a 9.32a 18.54b F(3,330) � 3.13, P � 0.03 Physical scale 13.45 14.81ab 11.49a 11.27a 21.67b F(3,334) � 2.53, P � 0.06 Mental/interpersonal 10.95 11.67a 11.31a 8.38a 13.22a F(3,335) � 1.48, P � 0.22 Output scale 10.45 10.59a 11.14a 7.79a 12.86a F(3,332) � 1.17, P � 0.32

Productivity loss (%) 3.12 3.25ab 3.13ab 2.45a 4.16b F(3,326) � 2.21, P � 0.09 Absenteeism (hr)

Arithmetic mean 55.84 63.76 26.86 91.08 73.48 Geometric mean 10.50 16.14a 6.10b 14.34a 14.82a F(3,337) � 5.12, P � 0.002

High absenteeism �2 wk/yr, n (%) 41 (12.0) 12 (15.8) 11 (7.7) 13 (16.5) 5 (11.6) �4 wk/yr, n (%) 21 (6.2) 6 (7.9) 4 (2.8) 7 (8.9) 4 (9.3)

*Means that do not share a common superscripted letter (a,b) are significantly different according to the Student-Newman-Keuls test at P � 0.05.

BMI, body mass index is calculated as weight (kg)/height (m)2; WLQ, Work Limitations Questionnaire.

42 Obesity and Presenteeism • Gates et al

related limitations in the workplace. The job limitations most affected by obesity were those with time and physical demands, whereas mental or interpersonal and overall output- related demands were not affected by obesity. The WLQs time and physi- cal demand subscale items identified, respectively, the subject’s difficulty to move as necessary to perform essential job functions and the sub- ject’s difficulty to complete work in the expected amount of time. There are several plausible explanations for these results. Moderately or ex- tremely obese people often have dif- ficulty moving because of their body size and the large amount of weight they carry.4,33 In addition, pain has been found to be prevalent in obese persons33 and is often associated with musculoskeletal or joint-related pain in the feet, knees, ankles, and back.34 Obesity has been found to be related to the development of osteoar- thritis and rheumatoid arthritis7,8,34

and carpal tunnel syndrome.35,36 All of these physical conditions are likely to have an impact on the worker’s ability to move without pain and could result in a decrease in productivity in those job functions that are physically de- manding.37,38 Another possible reason for the difference in the physical scale across BMI groups may be the de- creased balance and coordination seen in obese versus normal weight per- sons.39 Obesity has also been found to be a risk factor for sleep apnea40 and heart disease.11,12 Workers with these conditions may experience weakness and shortness of breath, making the worker tired or slow and thus more likely to have greater difficulty in meeting time demands for completing job tasks.

It is not surprising that plant work- ers had a significantly higher mean for the physical demand scale than office workers had. Working in the plant areas of the eight manufactur- ing companies requires workers to be able to adapt to the work environ- ment and to engage in various phys- ical movements, including bending, stretching, squatting, pushing, and

walking. In contrast, office workers often sit for long periods and have the capability of adjusting their work environment to meet their physical abilities.

The effect of obesity on productiv- ity was nonlinear and appears to exhibit a threshold effect, in which limitations were concentrated among moderately or extremely obese indi- viduals (BMI �35), whereas over- weight (BMI 25–29.9) and mildly obese individuals (BMI 30 –34.9) did not experience any adverse produc- tivity effects. This threshold effect suggests that workplace interven- tions for reducing obesity may have a significant economic benefit to em- ployers, even if they only produce modest weight changes that enable workers to move from the category of moderate or extreme obesity to mild obesity.

The study’s results support other research that has indicated that a weight loss of 10% can yield sub- stantial health and economic bene- fits. These results have important implications to those individuals and companies who are overwhelmed with the belief that dramatic weight loss is needed to achieve positive health and productivity outcomes for the majority of their workers.

The dollar value cost of presentee- ism among moderately or extremely obese workers was $506 per worker each year compared with $433 per worker for health-related absentee- ism above that of the other workers. Thus, health-related limitation on the job may be even more economically significant than health-related time off the job. Furthermore, absences do not necessarily cost the employer the full value of the worker’s time to the extent that these are unpaid absences and that other workers are able to cover the missing shifts. Meanwhile, presenteeism is always a cost to employers because the worker is re- ceiving a full paycheck despite re- ductions in productivity.

Given the prevalence of workers with BMI �35 in our sample (12.6%), the impaired productivity

of moderate or extreme obesity translates into an overall annual pre- senteeism cost of $6376 for a com- pany with 100 workers, compared with a firm without any moderately or extremely obese workers. This suggests that workplace interven- tions targeting extreme obesity could easily produce overall cost savings to employers—without even factoring in possible reductions in health care costs paid by the employer-provided health insurance plans. Decreasing absenteeism because of improved health would lead to further costs savings.

It is important to note that 12% of the workers missed 2 or more weeks of work and that 6.2% missed over 4 weeks of work be- cause of personal health reasons. Many of the workers had additional absences due to nonhealth reasons including child care or a family member’s health. This high rate of absenteeism may lead to produc- tion delays in manufacturing plants as well as place workers at risk for loss of employment.41 It is interest- ing to note that the overweight group missed the least number of days because of personal health reasons. Because this overweight group represented the greatest pro- portion of workers (41.9%), this represents a positive finding for companies who are becoming in- creasingly concerned about the growing epidemic of overweight and obesity and the effects on ill- ness, health care costs, and absen- teeism.

The worksite offers unique oppor- tunities to reach a large number of individuals at a relatively low cost by providing information, activities, and social support to promote healthier lifestyles. Environmental and policy interventions can support improved diet and physical activity habits among workers. Healthy lifestyles are likely to result in lowered costs related to medical care, absenteeism, and productivity, which will have an impact on corporate profits.

JOEM • Volume 50, Number 1, January 2008 43

Limitations There were limitations in this

study that should be addressed. First, there may be differences in produc- tivity caused by other factors, rather than truly being caused by obesity. Nevertheless, we used ANCOVA to adjust for many possibly confound- ing variables, and found that the results were largely unchanged. One unobserved feature that is perhaps most concerning would be other un- derlying diseases that are affecting both BMI and worker productivity; however, such differences (such as cancer leading to weight loss and decreased productivity) would likely lead us to underestimate the true productivity loss because of obesity.

Another limitation is that partici- pation in our study may not have been random. Though subjects were selected randomly, only 55% of those selected ultimately partici- pated, which may have introduced a selection bias. In particular, it is pos- sible that workers who were sensitive about their weight or overwhelmed with work-related difficulties chose not to participate—in which case, our results again may underestimate the true adverse effects of obesity on productivity.

Lastly, the sample used in this study is certainly not representative of all companies in the United States. The results are most applicable to other manufacturing companies. Fur- thermore, our results may be specific to the geographic region of the coun- try in which our companies were clustered. Future research exploring different workplace populations is needed to determine the generaliz- ability of our results to other settings.

Conclusions Obesity can have a negative im-

pact on workers not only through absenteeism but also presenteeism, that is, a reduced productivity on the job. In particular, health effects on productivity are concentrated among the most obese workers with BMIs of 35.0 and greater, suggesting that

employers should consider work- place interventions targeting obesity. Even modest weight loss could result in hundreds of dollars of improved productivity costs per worker each year.

Future research should be con- ducted to determine if workers who are missing the greatest number of days because of personal health rea- sons are more productive than work- ers who come to work sick and demonstrate presenteeism. Further- more, research should be conducted to determine the effectiveness of workplace interventions targeting a reduction in obesity.

Acknowledgment The study described in this article is sup-

ported by Cooperative Agreement (R01 DP000113-01) by the Centers for Disease Control and Prevention.

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