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Journal of Occupational & Environmental Medicine:
doi: 10.1097/JOM.0b013e31829178bf
Original Articles

Objectively Measured Sedentary Behavior and Physical Activity in Office Employees: Relationships With Presenteeism

Brown, Helen Elizabeth MSc; Ryde, Gemma C. BSc; Gilson, Nicholas D. PhD; Burton, Nicola W. PhD; Brown, Wendy J. PhD

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Author Information

From the School of Human Movement Studies, The University of Queensland, St Lucia Campus, Brisbane, Queensland, Australia.

Address correspondence to: Helen Elizabeth Brown, MSc, School of Human Movement Studies, The University of Queensland, Brisbane, Queensland 4072, Australia (heb56@medschl.cam.ac.uk).

The authors declare no conflicts of interest.

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Abstract

Objective: Employee presenteeism is the extent to which health conditions adversely affect at-work productivity. Given the links between health and activity, this study examined associations between objectively measured physical activity, sedentary behavior, and presenteeism.

Methods: Participants were 108 office employees (70% women, mean age 40.7 ± 11.2 years). Activity was measured using ActiGraph GT3X+ accelerometers to determine sedentary (≤150 counts) and light (151 to 1689 counts) activity; presenteeism with the Work Limitations Questionnaire.

Results: Fifty-seven percent of time was spent in sedentary behavior and 38% in light activity. The median Work Limitations Questionnaire Index was 4.38; 6% of participants reported at least moderate impairment. Significant associations were reported for time spent in sedentary behavior before/after work (odds ratio [OR] = 2.58; 95% CI: 1.08 to 6.20) and in light activity, overall (OR = 0.43; 95% CI: 0.19 to 0.97) and during workday lunch hours (OR = 0.34; 95% CI: 0.15 to 0.77), and presenteeism.

Conclusions: Future studies should seek greater variation in employee levels of activity and presenteeism to confirm these relationships.

Employee presenteeism, a relatively new concept, is the extent to which physical or psychosocial symptoms or conditions adversely affect the work productivity of individuals who choose to remain at work.1 Conceptualizations of presenteeism indicate that it is not simply the opposite of absenteeism, but rather, a reduced ability to work productively.2 A recent policy paper3 indicated that the costs of presenteeism are between 1.9 and 5.1 times more than those incurred for absenteeism.3 These costs are associated with reduced work output, errors on the job, and failure to meet company standards.4 To support the development of intervention strategies in this area, there is a need, therefore, to understand the factors associated with employee presenteeism.

Physical health conditions associated with employee presenteeism include hypertension and cardiovascular disease, arthritis, diabetes and other metabolic disorders, migraines/headaches, cancer and respiratory tract infections/asthma, and allergies.1 Psychosocial conditions include anxiety, chronic fatigue, depression, nervousness, panic attacks, and low energy levels.1 Some of these psychosocial conditions are reported to be among the most frequent causes of occupational disability while at work.5 As physical activity (PA) has a well-established inverse association with many of these physical6–10 and psychosocial conditions,11–16 it could, therefore, also be inversely associated with presenteeism.

The first study reviewed extant evidence on the relationship between PA and sedentary behavior, and EP, identifying 13 intervention trials (8 randomized controlled trials, 5 comparison trials) and seven observational studies (three cohort and four cross-sectional) that met inclusion criteria. Findings were inconclusive for EP, with mixed evidence for associated well-being outcomes (eg, workplace stress). A standardized definition of EP and an appropriate evaluation tool were identified as key research priorities if complex relationships between movement patterns and EP are to be better understood.

Cross-sectional analyses have shown significant weak positive relationships between pedometer-measured, total daily step counts, and employees' ability to meet demands for quantity, quality, and timeliness of completed work.17 Two intervention studies that demonstrated an increase in self-reported18 and objectively measured19 PA also showed a small decline in employee presenteeism. Other intervention studies have demonstrated concurrent improvements in PA and constructs related to presenteeism, such as work performance,20 and reductions in work stress21,22 and job burnout.23 Demonstrating the growing interest in this research, a recent systematic review also identified studies of PA and employee presenteeism (and associated well-being indicators), citing mixed but encouraging evidence for outcomes.24 Despite these emerging results, no studies have specifically assessed relationships between objectively measured PA and employee presenteeism. Studies have also not differentiated between work time and nonwork time activity.

The first aim of this study was, therefore, to examine associations between objectively measured PA, during work and nonwork time, and employee presenteeism. In light of the recent evidence suggesting associations between sedentary behavior (SB) and adverse physical25–35 and psychosocial36,37 health outcomes, the second aim of this study was to examine associations between objectively measured SB, during work and nonwork time, and employee presenteeism. Given previous literature indicating sex differences in the prevalence and burden of many of the conditions typically associated with employee presenteeism,38 and in levels of PA and SB,39,40 the third aim of this study was to compare these patterns in male and female employees.

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METHODS

The study procedure was approved by the Human Research Ethics Committee of the University of Queensland, Brisbane, Australia.

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Participant Recruitment

Full-time employees from five office-based organizations (comprising approximately 2500 employees, located at 12 different worksites) in urban South East Queensland, Australia, received information about the study in early 2012. On the basis of worksite access, equipment availability, and rolling data collection procedures, we aimed to recruit a maximum of 200 employees over a 6-month period. Recruitment methods were tailored to each worksite, typically through internal distribution channels such as e-mail notifications, staff newsletters, and researcher-led information sessions. The study was presented as an opportunity for employees to receive objective feedback about their health and movement patterns. Interested employees were invited to contact project staff via e-mail and were provided with full study information. Individual assessments were then scheduled at worksites, during which written and verbal information was provided about the purpose of the study, participants' questions were answered, and written consent was obtained.

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Study Procedures and Measures
Health Indicators

Measurements were conducted between 7:30 AM and 11:30 AM, and participants were asked to refrain from smoking, exercising vigorously, or drinking caffeine at least 1 hour prior to assessment. Trained research staff measured height to the nearest 0.1 cm, using the stretch stature method with a portable stadiometer (SECA 213, SECA, Birmingham, United Kingdom), and weight to the nearest 0.1 kg, using digital scales (NuWeigh LOG842, Newcastle Weighing Services, Newcastle, United Kingdom). Waist circumference was measured twice to the nearest 0.1 cm, at the narrowest point between the lower costal border and the iliac crest at exhalation, using a flexible steel tape (Lufkin W606PM, Lufkin, OH). A third measure was taken if the results of the first two differed by more than 5%. Blood pressure was taken three times while seated, with an automatic monitor (Omron Ultra Premium, Omron Corporation, East Sussex, Australia).

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Objectively Measured PA and Sedentary Behavior

Each participant was provided with an ActiGraph GT3X+ accelerometer (and elastic belt), which were initialized to sample acceleration at a rate of 30 Hz for a period of 10 days, to allow for at least seven consecutive days of wear time. Participants were advised (both during a one-on-one demonstration by a researcher and with printed instructions for later reference) to wear the device during all waking hours over a continuous 7-day period, maintaining a consistent position on their right hip. Participants were asked to remove the device when in water, or during any contact sports. Participants were also provided with a daily wear-time log, in which they were asked to record their waking time and work time for each day.

Accelerometers and logbooks were collected by research staff, who then e-mailed an electronic survey (created using LimeSurvey version 1.92; Build 120330) to each participant. Participants were asked to fill this out immediately so as to ensure a recall period that was consistent with the accelerometer wear period. Participants were contacted via e-mail to thank them for their involvement and to provide feedback on their individual health assessment and movement patterns.

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Demographic Characteristics

Items in the electronic survey were used to record sex, age, country of birth, education level, annual personal income, and self-rated health.

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Self-Reported Employee Presenteeism

The Work Limitations Questionnaire (WLQ)41 was included in the electronic survey to assess employee presenteeism. The WLQ was previously identified by the research team as one of the most favorable measures in terms of key instrument characteristics (such as the number of items), range of conceptual foci covered, and psychometric properties.42 The WLQ is a 25-item survey that gives an overall WLQ Index relating to percent lost productivity, and four subscale scores reflecting time management (five items addressing difficulty in scheduling demands), physical demands (five items addressing the ability to perform job tasks that involve bodily strength, movement, endurance, coordination, and flexibility), mental–interpersonal demands (six items addressing difficulty performing cognitive tasks and interacting with people “on the job”) and output (nine items addressing decrements in the ability to meet demands for quantity, quality, and timeliness of completed work). Responses are given using a five-point Likert scale to indicate proportion of work time impaired. The WLQ Index is derived by summing all items and transforming the total mathematically to a 0 (limited none of the time) to 100% (limited all of the time) continuum, representing the reported proportion of time spent impaired.43

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Data Treatment and Analysis

Body mass index was calculated (kg/m2) and categorized as follows: 18.5 kg/m2 or less, underweight; 18.5 to 25 kg/m2, healthy weight; 25 to 30 kg/m2, overweight; or 30 kg/m2 or greater, obese.44 The mean of the two waist circumference values was calculated (where three values were taken, the median was calculated). “Healthy” waist circumference was defined as 80 cm or greater for women and 94 or less for men;45 values higher than this were categorized as high risk. The mean of the second and third blood pressure values was calculated and used as a continuous variable. Spurious or implausible values were removed after consensus discussion involving at least two authors.

Data from the ActiGraph GT3X accelerometer were downloaded using ActiLife software (version 5.7.4; Full Edition) and saved as 60-second epochs. Nonwear time was removed using STATA software (version 11.0; StataCorp. 2009, TX), using a criterion of consecutive runs of zero counts per minute (vector magnitude) for a minimum duration of 90 uninterrupted minutes.46,47 Device malfunctions were identified as consecutive constant values greater than zero or implausible values greater than 15,000 counts per minute.48 Data were included if accelerometer wear time was at least 10 hours per day on at least three workdays.

STATA software was also used to identify time spent in activities categorized as sedentary (150 or fewer counts per minute)49, light intensity (151 to 2689 counts per minute), and moderate-vigorous intensity (2690 or more counts per minute).50 Time spent (minutes) in work time, before and after work, at lunchtime and on nonwork days was identified using data from the log books. All activity variables were dichotomized on the median and categorized as “high” or “low,” with low used as the referent group for analyses.

Data from the electronic survey were downloaded using the LimeSurvey software and entered into an Excel spreadsheet. Self-reported health status was dichotomized as “low” (poor or fair) or “high” (good, very good or excellent), with low used as the referent group for analyses.

Employees were categorized according to their WLQ Index score, using cutoffs from the WLQ scoring documentation: less than 5% as no impairment, 5% to 10.9% as “mild impairment,” 11% to 16.9% as “moderate impairment,” and 17% to 100% as “severe impairment.”51 Given the small proportion of participants across moderate and severe conditions (<6%), this variable was dichotomized into “no impairment” (WLQ Index score less than 5%) and “impairment” (WLQ Index score 5% or greater) for analyses. WLQ subscale scores were derived as an average of the responses to relevant items and then dichotomized on the median into “high” or “low,” with “low” used as the referent group for analyses.

All data were analyzed using SPSS (version 20.0.0; IBM SPSS Statistics, Portsmouth, United Kingdom). Descriptive statistics were used with continuous data presented as means ± standard deviations and categorical data as sample percentages. To compare differences between male and female employees, one-way ANOVA was conducted for continuous variables, and Pearson's chi-square test for categorical variables. Binary logistic regression was conducted to examine associations between PA and SB, and employee presenteeism, with adjustment for sex and age. Odds ratios and 95% confidence intervals were reported.

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RESULTS

Recruitment and assessment compliance information are presented in Fig. 1. Of 180 employees who expressed an interest in the study, 157 were eligible and consented to participate, and 108 met inclusion criteria for data analyses.

Figure 1
Figure 1
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Demographic characteristics of the sample are presented in Table 1. Participants (mean age 40.7 ± 11.2 years; 70% female) were predominantly Australians (86%) and educated to at least certificate/diploma level (82%) with an annual income of more than $60,000 (71%). Almost half rated their health as very good or excellent (48%). More than half the participants were overweight or obese (57%), and 51% were in the increased risk category for waist circumference. Significant differences were found between men and women for body mass index and waist circumference (P > 0.01).

Table 1
Table 1
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Objectively Measured Sedentary Behavior and PA

Times (minutes) spent in sedentary behavior and PA for the 108 employees who provided workday accelerometer data are presented in Table 1. The mean total accelerometer wear time was 903.6 ± 66.1 minutes per day (range, 705.7 to 1046.2 minutes per day). Of total wear time, 57.4% was spent in sedentary behavior, 37.5% in light activity, and 5.1% in moderate-to-vigorous physical activity (MVPA). Sedentary behavior was also the predominant activity during work time (65.6%), lunchtime on a workday (60.1%), and nonwork time (53.0%). Men had significantly more sedentary time than women for total wear time (P = 0.001), before and after work (P = 0.000), and during lunchtime (P = 0.003). Valid data for nonworkdays (mostly weekend days) were collected for 105 employees. More than half of all nonwork time was spent in sedentary activity (52.3%), with 42.4% spent in light-intensity activity, and 5.3% in MVPA.

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Self-Reported Employee Presenteeism

WLQ Index data are also presented in Table 1. The mean WLQ Index was 4.7% ± 3.85, with participants' scores ranging from 0.0% to 20.0%. The majority of employees (58%) were categorized as no impairment, 36% were classified as having mild impairment, 4% moderate impairment, and 2% severe impairment. Median scores for the subscales were as follows: time management, 10%; physical demands, 5%; mental–interpersonal, 30%; and output demands, 9.5%. Approximately 7% of all survey respondents reported some difficulty (for at least half of their work time) with time management, 56% with physical demands, 4% with mental–interpersonal concerns, and 3% with output demands. Items frequently endorsed as causing difficulty included completing work without taking breaks (time management subscale), remaining in one position for extended periods (physical demands subscale), keeping one's mind on work (mental–interpersonal subscale), and “feeling that you've completed what you're capable of” (output demands subscale). No sex differences were observed for WLQ Index scores or WLQ subscales, so the relationships with movement patterns was not further explored.

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Associations Between Movement Patterns and Employee Presenteeism

Associations between movement patterns and WLQ index and subscale scores are presented in Tables 2 and 3, respectively. There was a significant positive association between time spent in sedentary behavior before and after work and WLQ Index score (OR = 2.58; 95% CI: 1.08 to 6.20), even after adjustment for age and sex. Difficulties with time management contributed most significantly to this relationship (OR = 2.29; 95% CI: 1.05 to 4.97). The relationships between the WLQ Index score and total sedentary time (OR = 2.15; 95% CI: 0.99 to 4.69), and sedentary time on a nonworkday (OR = 2.07; 95% CI: 0.92 to 4.66), approached significance. There was a significant inverse association between time in light-intensity activity, both overall (OR = 0.43; 95% CI: 0.19 to 0.97) and during workday lunch hours (OR = 0.34; 95% CI: 0.15 to 0.77), and WLQ Index score.

Table 2
Table 2
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TABLE 3-a. Odds Rati...
TABLE 3-a. Odds Rati...
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There were significant and positive associations between total sedentary time and reduced output (OR = 2.30; 95% CI: 1.06 to 5.00), between sedentary time before and after work and time management (OR = 2.29; 95% CI: 1.05 to 4.97), and between sedentary time during workday lunch hours and mental–interpersonal difficulties (OR = 2.47; 95% CI: 1.14 to 5.35). Time spent in light-intensity activity on a nonworkday was significantly and inversely associated with poor time management (OR = 0.44; 95% CI: 0.20 to 0.97) and mental–interpersonal difficulties (OR = 0.41; 95% CI: 0.19 to 0.90).

TABLE 3-b. Odds Rati...
TABLE 3-b. Odds Rati...
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DISCUSSION

This study aimed to objectively measure movement patterns of typical office employees, to explore relationships between PA and sedentary behavior, and employee presenteeism. Significant associations were found between employee presenteeism and sedentary time (before and after work) and light-intensity PA (total, and during workday lunch hours).

Employees who spent more time sedentary and less time in light-intensity activity were also more likely to report higher levels of presenteeism; this is consistent with the theoretical basis of this study. Those spending more time sedentary before and after work were more than twice as likely to report impairment, particularly in regard to time management. Contexts in which employees are sedentary before and after work include watching television, computer use, and in transit to and from work.52 Of these contexts, encouraging active transport to and from work (eg, cycling, walking) may be particularly beneficial for health-related productivity of employees. The “Walk In to Work Out” intervention conducted in Scottish workplaces53 demonstrated the feasibility and efficacy of schemes to promote active transport and may, therefore, be a useful template for further workplace interventions that target gains in employee productivity. Other strategies could target reductions in screen time before and after work.

Light activity at lunchtime was also associated with reduced impairment. Instructor-led lunchtime walking groups have been successful in increasing light PA levels in sedentary workers.54 Other strategies for including incidental movement in the workday, for example, holding walking meetings,55 incorporating “booster breaks,”56 or using active workstations,57 may also provide opportunity for light activity and may be an important strategy for reducing presenteeism in office employees.

No significant associations were found between MVPA and employee presenteeism (neither WLQ Index score nor WLQ subscales). Reasons for this warrant further investigation to fully understand the factors associated with employee presenteeism.

The third aim of this study was to compare sex differences in movement patterns and employee presenteeism. Men had significantly more sedentary time than women overall, before and after work, and during lunchtime. This reflects previous work demonstrating significantly greater sedentary time for travel, at work, and in leisure in men than women.58 Nevertheless, no sex differences were observed for WLQ Index scores or WLQ subscales, so the relationships with movement patterns were not further explored. A study with almost 13,000 Danish employees also indicated no sex differences in employee presenteeism.59

This study provides new insights into PA and SB, during work and nonwork time, and relationships with employee presenteeism. Previous studies have used self-reported PA or pedometer-measured total daily step counts to explore associations between movement patterns and constructs associated with productivity.17 The present study was the first to use ActiGraph GT3X accelerometer data to examine associations between objectively measured movement patterns, in and outside work time, and employee presenteeism. The ActiGraph GT3X accelerometer provides a reliable measure of time spent sedentary, and in light- and moderate- to vigorous-intensity activity during different time periods (eg, during work hours; at lunchtime; on a nonworkday), which, combined with the well-validated WLQ, enabled rigorous assessment. Accelerometers have been shown to be a reliable and valid way to determine activity levels of office employees,50,60 and overcome the difficulties of self-reported movement patterns such as recall inaccuracy. Participants in this study were highly compliant with the assessment protocol, with less than 5% not completing the measurement. Another strength of this study was that the sample size was larger than that of previous work in this area.17

Nevertheless, there are several limitations to this study. The cross-sectional design, while appropriate considering the novel and exploratory nature of this study, does not allow for causality to be examined; we are, therefore, unable to comment on whether there is a temporal relationship between the patterns observed and the impairment scores reported. Notably, only 6.4% of employees in the worksites approached volunteered to participate, which may limit applicability to the general population.61 The variability among employees in this study was constrained, with high levels of sedentary behavior and MVPA, and low levels of presenteeism. Work time was predominantly sedentary, as expected considering the nature of the worksites targeted, with 57.4% of time spent sitting or in activity intensity of less than 100 accelerometer counts per minute. The median time spent in MVPA was more than 39 minutes per day, which is higher than other accelerometer studies with working-age adults in the United States, Canada, and Australia that have reported 20 to 34 minutes per day (National Health and Nutrition Examination Survey62), 21 to 27 minutes per day (Canadian Health Measures Survey63), and 36 minutes per day (AusDiab64).

In addition, those recruited showed little variability in WLQ Index scores, with 58% reporting no impairment and only 6% reporting at least moderate impairment. Given the nature of presenteeism, it may be that the more productive employees were recruited, whereas those who would have scored highly on the WLQ Index declined to participate. Although low active, high presenteeism employees may be a challenging group to engage, further research should aim to recruit employees across a range of levels of PA, SB, and impairment, to further explore relationships between movement patterns and employee presenteeism.

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CONCLUSIONS

This study examined associations between objectively measured movement patterns and employee presenteeism. Results indicated that there are associations between nonoccupational sedentary behavior (positive) and light-intensity activity (inverse) and presenteeism, but not between MVPA and presenteeism. Further studies should seek to recruit employees with a greater variation in both activity and employee presenteeism, to fully explore relationships between movement patterns and employee presenteeism. This information could guide the development of interventions to positively influence patterns of PA and sedentary behavior to improve employee productivity.

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