postural variability: an effective way to reduce musculoskeletal discomfort in office work

Objective: This article investigates whether differ-ent interventions aimed at promoting postural change could increase body movement throughout the shift and reduce musculoskeletal discomfort.

Background: Many researchers have reported high levels of discomfort for workers that have rela-tively low-level demands but whose jobs are sedentary in nature. To date, few interventions have been found to be effective in reducing worker discomfort.

Methods: Thirty-seven call center operators were evaluated in four different workstation conditions: con-ventional workstation, sit-stand workstation, conven-tional workstation with reminder software, and sit-stand workstation with break reminder software–prompt to remind workers to take break. The primary outcome variables consisted of productivity, measured by custom software; posture changes, measured by continuous video recording; and discomfort, measured by simple survey. Each condition was evaluated over a 2-week period.

Results: Significant reductions in short-term discom-fort were reported in the shoulders, upper back, and lower back when utilizing reminder software, indepen-dent of workstation type. Although not significant, many productivity indices were found to increase by about 10%.

Conclusions: Posture-altering workstation interven-tions, specifically sit-stand tables or reminder software with traditional tables, were effective in introducing pos-ture variability. Further, postural variability appears to be linked to decreased short-term discomfort at the end of the day without a negative impact on productivity.

Applications: An intervention that can simply induce the worker to move throughout the day, such as a sit-stand table or simple software reminder about making a large posture change, can be effective in reducing discomfort in the worker, while not adversely impacting productivity.

Keywords: sedentary, static postures, musculoskel-etal discomfort, productivity, posture

IntroductIonData entry and long durations of office work

at a computer terminal are common and often very detrimental to the workers (Jensen, Fin-sen, Sogaard, & Chistensen, 2002). Blatter and Bongers (2002) reported that more than 50% of secretarial workers indicated they worked continuously for more than 4 hours using a keyboard and mouse. Call center operators have also been documented to remain in their seats more than 95% of the work shift (Rocha, Glina, Marinho, & Nakasato, 2005). Toomingas, Fors-man, Mathiassen, Heiden, and Nilsson (2012) reported that 80% of the work shift was spent seated with 9% of the workers having periods of more than 1 hour of continuous sitting. The bottom line is that office workers, particularly data entry and call center operators, will work long periods without taking a break.

Incidence rates for neck and shoulder discom-fort were found to be between 10% and 60% in individuals who have extensive computer use (Ekman, Andersson, Hagberg, & Heljm, 2000; Gerr et al., 2002; Korhenon et al., 2003; Rocha et al., 2005; Wahlström, 2005). Prevalence of discomfort in the hand and wrists in call center operators has been reported at similar levels (about 40%) (Rocha et al., 2005). This body dis-comfort has an adverse impact on productivity of the office workers, as reported by Wahlström, Hagberg, Toomingas, and Tornqvist (2004). Another group of researchers have reported about 12% of the computer users indicated pro-ductivity reductions due to discomfort, which resulted in 10% to 20% decreases in perceived productivity (Hagberg, Tornqvist, & Toomingas, 2002). Thus, the opportunity to reduce discom-fort in workers may result in increased produc-tivity and reduce presenteeism.

Although there are many factors that contrib-ute to the discomfort in the shoulders and upper extremities of office workers, one factor that has

528003 HFSXXX10.1177/0018720814528003Month XXXXPostural Variability Reduces MSD Discomfort

Address correspondence to Kermit G. Davis, University of Cincinnati, Low Back Biomechanics and Workplace Stress Laboratory, 3223 Eden Ave, 300 Kettering Lab, Cincinnati, OH 45267-0056, USA; e-mail: [email protected].

Postural Variability: An Effective Way to Reduce Musculoskeletal Discomfort in Office Work

Kermit G. Davis and Susan E. Kotowski, University of Cincinnati, Cincinnati, Ohio

HUMAN FACTORS201X, Vol. XX, No. X, Month XXXX, pp. 1 –13DOI: 10.1177/0018720814528003Copyright © 2014, Human Factors and Ergonomics Society.

at Instituto Nacional de Educ on December 21, 2014hfs.sagepub.comDownloaded from

http://hfs.sagepub.com/

2 Month XXXX - Human Factors

been strongly related to these adverse factors was sitting for long durations without a break (Ariens et al., 2001; Rocha et al., 2005). Rocha et al. (2005) found a strong relationship between symptoms in the neck/shoulder and lack of rest breaks (odds ratio [OR] = 3.17 95%; CI = 1.11–8.97). Some evidence of a dose response effect for prolonged durations of static postures and neck/shoulder discomfort was presented by Blatter and Bongers (2002), who found signifi-cant ORs for 4 to 6 hrs (ORadj = 1.50; 95% CI = 1.11–2.02) and 6 to 8 hrs (ORadj = 2.03; 95% CI = 1.44–2.85) as compared to 0 to 1 hrs of exposure. Jensen et al. (2002) also found a simi-lar dose-response relationship with hours of computer work. Other factors relating to body discomfort and injuries in office workers include mouse activation, low static muscle effort due to lack of postural relief of the arms and keyboard/mouse activation, mental stress, and nonneutral postures of the body while sitting in the chair (Wahlström, 2005).

Although there have been several studies investigating changes in the work environment that target proper ergonomic position such as neutral joint postures, the results are mixed regarding the impact on discomfort and injuries (Cook, Burgess-Limerick, & Chang, 2000; Gerr et al., 2002, 2005; Ketola et al., 2002; Lewis, Krawiec, Confer, Agopsowicz, & Crandall, 2002; Rempel et al., 2006). Gerr, Marcus, and Monteilh (2004) provided extensive literature review that concluded the results linking posture to office worker discomfort are not consistent, further supporting the notion that ergonomics interventions designed to promote neutral work-ing postures may be helpful but might be par-tially the entire solution. One reason why these types of ergonomic interventions may not be effective in reducing discomfort, injuries, and compensation costs might be that although the workers are in better postures, they still remain in fixed postures for long durations (e.g., no breaks). Thus, additional interventions need to be explored.

The premise for the interventions evaluated in the current study was that routinely engaging in postural changes during the work shift will reduce the discomfort and improve productivity. The sit-stand adjustable table may be one option

to introduce postural variability without having to leave the actual work area. The premise of the sit-stand workstation is to promote a dynamic movement by allowing the worker to switch between a sitting and standing work posture throughout the day. In a simulated data entry study, Husemann, Von Mach, Borsotto, Zepf, and Scharnbacher (2009) reported that musculo-skeletal complaints were lower without a detriment to productivity for the sit-stand work-station as compared to a traditional sitting work-station. Furthermore, Alkhajah et al. (2012) found sit-stand workstations reduced sitting time by about 140 minutes per day. However, previous research into sit-stand workstations has shown low levels of usage compliance—work-ers stop adjusting the tables after a few weeks, indicating any benefits could be lost over time (Wilks, Mortimer, & Nylen, 2006).

Another type of postural change intervention is to remind workers to take more routine breaks. A study conducted by Galinsky, Swanson, Sau-ter, Hurrell, and Schleifer (2000) showed that frequent (5 min break/hour) breaks significantly decreased discomfort for data entry workers as compared to a conventional break schedule (two 15 min breaks/day). A follow-up study from this group evaluated routine breaks with stretching (Galinsky et al., 2007). The results indicated that the breaks, but not stretching, had an impact on the discomfort of the workers, possibly due to noncompliance of stretching. Furthermore, the effectiveness of stretching is compounded by compliance issues and appropriateness of the stretching for specific work conditions. A review by Goodman et al. (2012) reported that the few studies investigating rest breaks as an interven-tion found positive results—decreased pain and static awkward postures. Bernaards, Ariëns, Simons, Knol, and Hildebrandt (2008) reported that reminder software was effective in encour-aging the use of breaks. However, two additional reviews (Hoe, Urquhart, Kelsall, & Sim, 2012; Kennedy et al., 2010) reported limited positive results for rest breaks being effective in control-ling musculoskeletal disorders of the neck and shoulder for computer work.

The current study investigated whether inter-ventions aimed at postural change could encour-age adoption of routine dynamic movement of



Postural Variability reduces Msd discoMfort 3

the body, resulting in decreasing musculoskele-tal discomfort and increased productivity. Four different office workstation conditions were evaluated including a conventional workstation, sit-stand workstation, conventional workstation with reminders to take breaks, and sit-stand workstation with reminders to switch table heights and adopt postural changes.

MethodsParticipants

Thirty-seven (29 females and 8 males) call center employees were recruited from a local drug and poison information center that is open 24 hours a day, 7 days a week, and handles regional emergency calls that deal with drug overdose and poison ingestion. The gender mix was representative of the gender ratio in the call center. The participants were full-time (18 employees) and part-time (19 employees) employees at the call center. All participants had worked at the facility for at least 1 year. The participation rate was 76%, with one worker being lost to follow-up due to leaving the place of employment (the partial data were not ana-lyzed in the results). Complete demographic and anthropometric data can be found in Table 1.

experimental design and ApproachA quasi-experimental design study was

adopted with each workstation evaluated for a 1-month period (2-week break-in period and a 2-week observation period) (see Figure 1 for timeline of conditions). During the entire period, video cameras remained up with appear-ance of being active. There were four different workstation conditions: (1) conventional work-station/existing workstation, (2) conventional workstation with reminder software, (3) sit-stand workstation, and (4) sit-stand workstation with reminders. For all conditions, the worksta-tion consisted of a corner unit (see Figure 2) with a flat screen monitor, keyboard, mouse, phone, and reference materials (e.g., books and notes). The outcome variables included the following: productivity—handling calls and computer usage, body discomfort, and postural changes (see Outcome Variables section for actual definitions).

Workstation conditions. The conventional workstation had a fixed height at 71.1 cm from the floor. Although the sit-stand adjustment con-troller was functional, the participants were instructed not to use the function when complet-ing the conventional workstation condition

TAblE 1: Demographic and Anthropometric Data for the Participants Who Enrolled and Completed the Study (Mean and Standard Deviation)

Age (Year)Standing Height

(cm)Body Weight

(kg)Body Mass

Index (kg/cm2)Hours Worked (Hours/Week)

Full-time Male (5) 33.8 181.2 95.4 29.2 44.3 (8.5) (8.2) (23.0) (8.2) (13.8) Female (13) 39.0 165.1 71.4 26.1 33.1 (12.9) (6.0) (15.6) (5.6) (7.9) All (18) 37.3 170.4 79.4 27.2 35.9 (11.5) (10.2) (21.1) (6.4) (10.9)Part-time Male (3) 38.5 180.8 89.0 27.3 19.3 (20.5) (12.4) (4.7) (2.3) (10.0) Female (16) 32.5 161.5 67.7 25.9 13.9 (6.6) (7.0) (14.0) (5.4) (7.1) All (19) 33.5 164.5 71.0 26.1 14.8 (8.9) (10.3) (15.2) (5.0) (7.8)




(compliance was validated through video obser-vation). For the sit-stand workstation condition, participants were instructed how to use the adjustment controller. All participants were encouraged to change the workstation height during the day as they pleased. However, instruc-tions were provided at the beginning of the con-dition with no reminders after the initial interaction. The height of the workstation could

be adjusted between 61.0 cm and 130.8 cm. However, the actual height of the workstation during sit-stand conditions was self-selected and was then specifically measured. The conven-tional workstation with software reminders uti-lized the fixed workstation height, but software was loaded on to the computers to track com-puter activity and request participants to make a postural change at 30-minute intervals. Similarly,

Figure 1. Timeline for evaluation of the conditions: Half of the participants started in normal and half started in sit-stand; the participants switched to other nonsoftware reminder condition; reminder software was activated and half of each group was randomly placed in reminder software and sit-stand with reminder software conditions; and each participant then switched to the last reminder software condition.

Figure 2. Participant sitting at the conventional-height work surface (a) and standing at the high-height work surface (b).




the sit-stand workstation with software remind-ers utilized the adjustable workstation along with tracking software.

The reminder software, which was provided by WorkPace prompted the participants to stand up and move around or adjust the sit-stand table with a simple message (see Figure 3). The prompt would pop up on the screen every 30 minutes but did not require them to stop imme-diately given the potential for handling calls with life and death consequences. Instructions given to each of the participants at the start of every condition stated that the participant should use discretion when taking a break but were encouraged to follow the prompts as much as possible. One suggestion was to continue work-ing, but to stand (or adjust table height) until time permitted a posture change. WorkPace soft-ware monitored the activity including key strokes and mouse movements, total computer usage, and compliance to postural change (e.g., no keyboard or mouse activity).

Each participant selected a chair that was uti-lized throughout the study. Numbers were placed on the back of the chairs for identification by the participants as well as allowing the research team to verify chair number through video capture.

condition Assignment and AssessmentDuring the first 2 weeks for each condition,

participants became familiar with the work con-ditions without any assessments. The break-in period also allowed the participants to establish everyday routines and minimize changes result-ing purely from awareness of being observed (e.g., workers return to routines and habits). The 2-week break-in period was followed by a 2-week assessment period. During the 2-week assessment period, each participant was evalu-ated for productivity levels, presence of body discomfort, and any postural changes during the work shift (see dependent variables for more description of these variables). The participants were aware when the assessment period was underway, as they were required to complete the discomfort survey at the end of each shift. Figure 1 provides the timeline of the conditions.

The order of workstation conditions was counterbalanced with participants who were randomly assigned to workstation condition. At the start of the study, participants were randomly assigned to two groups: half into current work-station condition (e.g., fixed height) and half assigned to sit-stand workstation (e.g., adjust-able height). After the first assessment, the groups switched workstation conditions (e.g., conventional condition switched to sit-stand condition and vice versa). At the midpoint of the study (upon completion of the first two condi-tions), software reminders were activated. Half of each of the original groups was randomly selected to start the conventional workstation with reminder software, whereas the other half were assigned to the sit-stand with reminder software. After the assessment period, the groups switched conditions for the final assessment. The block assessments on reminder software were necessitated by having multiple users on the same computer throughout the assessment period with no way to control the software pop-ups for nonsoftware conditions.

Given the work environment with a 24/7 ser-vice requirement and cubicle layout (two four-cubicle workstations), webcam cameras were set up to monitor each workstation continuously over the 2-week follow-up period. The webcam cameras recorded at a 50 Hz frame rate. Web-cam cameras were present and visible during the

Figure 3. Software reminder that is displayed at the appropriate time (every 30 minutes) to remind the participant to adjust their posture and/or height of the table. The prompt had a simple message with a soft lock on the screen—ignore button. The WorkPace software displayed a status bar for break time/idle time on computer (example, 52 s) and how long computer was used continuously (example, 1.43 hrs).




entire study. Videos were digitally stored on lap-tops connected to the webcam cameras and then downloaded to a data server where they remained until they were viewed and analyzed by a mem-ber of the research team who documented pos-ture changes and postural change compliance.

recruitment of ParticipantsRecruitment consisted of an email announce-

ment from the research group, which obtained email addresses of all current employees from management. The email provided study details and asked for volunteers. Once an email response was returned to the research team, one member of the research team met the individual to com-plete the consent process and obtain a signature on the consent form. The consent form and protocol was approved by the University of Cin-cinnati’s Institutional Review Board. Once a par-ticipant agreed to participate, the individual was assigned an identification number and assigned to an initial workstation group. Although com-plete confidentiality was not achieved because it was impossible to blind study participants from nonparticipants in the call center environment, participant-specific data were coded and kept confidential. Management remained out of the recruitment process and had limited information about who participated.

outcome VariablesBody discomfort. Body discomfort was

assessed at the end of each shift during each assessment period. A short questionnaire uti-lized an 11-point ordinal scale of discomfort (0 = no discomfort to 10 = unbearable discomfort) to document discomfort in the following regions: lower legs and feet, hips, knees, low back, upper back, neck, shoulders, and arms and wrists.

Postural variability. The posture variability included multiple variables that were defined through video analyses. These variables were measured for each participant during every shift during the assessment periods. Each of the digi-tized videos was reviewed by a member of the research team. The following were the posture variables assessed (all measured in minutes): total time on shift, time without postural change, total time sitting, total time standing, total time

sitting at lowered height, total time sitting at raised height, total time standing at lowered height, total time standing at raised height, num-ber of times switching between standing/sitting, number of times switching between lower/raised, total time at lowered height, total time at raised height, and total number of postural changes per hour. A postural change was defined as a major change of the entire body posture such as standing up/sitting down, turning in chair to communicate to coworkers, or walking away from the workstation. The following were not included as a postural change: changes in posture or movements of the upper extremities only, movement in the body while in the chair (e.g., moving leg under body), and reaching for the phone or other documents. Postural changes concentrated on large movements that were eas-ily distinguishable on the video. The research team member would identify key events (e.g., standing and sitting intervals, postural changes) along with the corresponding times using the time stamp on the video and recording them into a database for further calculations. Times between events along with frequency of events were then computed.

Productivity and computer usage. Productiv-ity was tracked by the call center through cus-tom software. The research team was given the data by the management of the call center. The call center reports provided productivity indices relating to average number of calls per hour, average number of calls picked up per hour, average hold time—amount of time placed on hold waiting for call center representative, and percentage of time not available for calls—rela-tive amount of time spent on a call or entering data into computer from previous case. In addi-tion, WorkPace software provided productivity variables that related to input devices and com-puter usage: total number of keystrokes, total number of mouse clicks, and total time using computer. The WorkPace software was installed on all the computers during the entire study and tracked the number of clicks on the keyboard, number of clicks of the mouse (right and left buttons), and time actively using the computer (e.g., when keyboard was being typed on, the mouse was clicked or being moved around the screen). The software provides an accurate




quantitative assessment of these outcomes based on the exact time that the keyboard or mouse was being clicked or moved, with the total time on computer being the summary of keyboard and mouse usage time. Overall, the software provides an accurate measure of computer usage. Douwes, de Kraker, and Blatter (2007) estimated that WorkPace overestimates com-puter usage by 9%, whereas Blangsted, Hansen, and Jensen (2004) reported underestimates of 2% to 8% for computer usage.

statistical AnalysisDescriptive statistics were computed for all

the variables as a function of workstation condi-tion. A Kruskal-Wallis one-way analysis of vari-ance by ranks procedure was utilized to deter-mine significant differences for the discomfort variables. Follow-up post hoc comparisons were computed for significant effects in the form of a Wilcoxon signed rank test with a Bonferroni correction to determine the source of the signifi-cant differences. For the continuous variables (productivity and video outcomes), a repeated measures analysis of variance (ANOVA) was conducted with studentized Tukey post hoc tests. For all statistical analyses, an alpha of 0.05 was utilized to determine significance.

resultsThe results for the productivity and exposure

outcomes are displayed in Table 2. The time worked during all four conditions was very consistent and not significantly different. This indicates that all workstation conditions had equal amounts of exposure to the office work. In general, participants averaged about 25 hrs per week and roughly 8 hrs per shift, indicat-ing a mixture of full-time and part-time work-ers. Furthermore, computer-based productivity indices were also not impacted by the four dif-ferent conditions, although a slight increase in keystrokes and mouse clicks was seen for the conditions with reminder software (Table 2). Over the 2-week evaluation period, the workers utilized the computer keyboard for about 16 hrs and the mouse for about 10 hrs. The number of keystrokes ranged between 40,000 and 45,000 keys with about 12,000 clicks of the mouse

(single and double clicks), independent of inter-vention condition.

Productivity data provided by the call cen-ters’ productivity reports indicated that most of the productivity indices were not affected by workstation condition, including average num-ber of calls picked up per hour, average hold time, and percentage of time not available for calls (Table 2). A couple of variables relating to productivity were found to be significantly impacted by workstation condition. First, although the number of phone calls answered per hour increased with the reminder software, the change was only significant among those using sit-stand workstations. Second, the num-ber of calls completed was not different between conventional and sit-stand. Differences in calls completed were documented for the reminder software (increase of 10%) and sit-stand with reminder software (decrease of 5%) as com-pared to conventional condition.

The video analyses evaluated the time spent sitting, standing, and away from the desk as well as the number of times workers transitioned between sit-stand and low-high workstations (see Table 2). Figure 4 provides a summary of how the different conditions influenced the amount of sitting. During the conventional workstation (with and without reminder soft-ware), the percentage of time standing was vir-tually nothing, whereas sitting approached 90%. The reminder software did reduce the percent-age of time sitting by 8%. The sit-stand condi-tion increased the amount of time standing by approximately 10% to 15%, with and without reminder software, respectively. The amount of time sitting was also significantly reduced by about 20% (for both with and without reminder software). The percentages of time for using the low and high workstations were mirror images of the sitting and standing percentages, potentially indicating an influence of the sit-stand interven-tion. The remaining time was spent away from the work area, which actually increased with the reminder software. The conventional worksta-tion resulted in about 10% of the time away from the desk, whereas the sit-stand and reminder software conditions resulted in 15% of the time not using the computer. The greatest amount of time away from the workstation was for the sit-



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stand with reminder software (about 21% of the time). This time away allowed for breaks and completion of other activities outside the work area.

As expected, the conditions with sit-stand tables had more times transitioning between sit-ting and standing (about four times per shift). The addition of the reminder software resulted in an increase of approximately one switch per shift more. The number of postural changes per shift was impacted (p < 0.05) by condition, with the conventional condition having the least—about seven per 8-hr shift. Both of the sit-stand conditions (with and without reminder software) had approximately eight postural changes per 8-hr shift, whereas the reminder software with conventional workstation had nine postural changes per 8-hr shift. The reminder software appeared to be effective in encouraging postural changes throughout the shift. However, the data from the WorkPace tracking software indicated a 62% compliance of the break prompts (e.g., computer was idle).

The body discomfort (measured at end of shift) across the 2-week intervention period was significantly lower for the shoulders, lower back, and upper back generally in the direction of sit-stand compared to conventional and

reminder software compared to no reminder software (see Table 3). The decrease in discom-fort levels ranged between 22% and 46% (Figure 5). There was no difference (p > 0.05) among the workstation conditions for discom-fort in the neck, elbows, hands and wrists, hips, knees, and lower legs and feet.

dIscussIonAlthough the study population was rela-

tively small and the observation periods short, the results indicated sit-stand tables and break reminder software produce routine postural changes that could impact the discomfort of sedentary office workers. The effect of sit-stand or break reminders appeared to be independent as the synergistic impact on the outcome vari-ables was not significantly different from the individual effects. The breaks/postural changes included in the current study were different than previously investigated by Galinsky et al. (2000, 2007) (e.g., 5 min/hr) and were gener-ally designated (soft break every 30 min) due to the critical nature of the work. This type of break pattern allowed the user to have control of the breaks and to focus on making a large body movement—change in position of the entire body such as standing up, walking away

Figure 4. Percentage of time spent sitting, standing, and away from workstation for the four conditions: (1) conventional, (2) sit-stand, (3) conventional with reminder software, and (4) sit-stand with reminder software.




from the workstation, and turning away from the workstation to communicate with other coworkers. The purpose of the break was to change body posture by altering the sit-stand height or completing productive tasks, which may have lead to the lack of adverse impact on the productivity indices.

As seen by others (Galinsky et al., 2000, 2007; Husemann et al., 2009), breaks and using

an adjustable-height table had no impact on pro-ductivity of the workers. Actually, in the current study, the indices of productivity improved in most cases by about 10% (not statistically sig-nificant). It appeared that the postural changes resulted in the worker feeling better without any negative impact on productivity. One potential explanation might be that when workers felt bet-ter, they were more productive, even though

TAblE 3: Summary of the Body Discomfort at the End of the Shift During Each of the Workstation Conditions (Mean and Standard Deviation)

Outcome Variable Conventional Sit-Stand

Conventional With Reminder

Software

Sit-Stand With Reminder Software p

Neck 1.71 (1.79) 1.19 (1.50) 1.21 (1.61) 1.55 (2.10) 0.10Shoulders 2.14AB (2.01) 1.42BC (1.56) 1.16C (1.52) 1.20C (1.66) 0.02Elbows 0.55 (1.05) 0.24 (0.60) 0.43 (0.92) 0.44 (0.91) 0.33Hands and wrists 1.49 (1.82) 0.98 (1.49) 0.89 (1.24) 1.00 (1.52) 0.11Upper back 2.28B (2.04) 1.33A (1.59) 1.44A (1.61) 1.37A (2.07) 0.004Lower back 2.18A (2.31) 1.37B (2.11) 1.70C (1.77) 1.41B (1.73) 0.04Hips 0.51 (1.06) 0.51 (1.25) 0.80 (1.18) 0.63 (1.29) 0.17Knees 0.78 (1.32) 0.71 (1.20) 0.83 (1.31) 0.83 (1.75) 0.59Lower legs and feet 1.01 (1.40) 0.99 (1.80) 0.88 (1.14) 0.93 (1.58) 0.63

Note. Different alpha characters indicate significant difference. A ≠ B ≠ C.

Figure 5. Discomfort at the end of the shift for the shoulder, upper back, and lower back for the four intervention conditions: (1) conventional, (2) sit-stand, (3) conventional with reminder software, and (4) sit-stand with reminder software.




they were away from their workstations more or paused routinely throughout the shift. Future work needs to better understand how postural changes impact productivity.

The impact of sit-stand on time sitting was slightly lower than what was reported by Alkha-jah et al. (2012), where they reported a decrease of about 140 min (or 29%) in sitting time. The current study found reductions in sitting to be between 15% and 18%. However, the number of postural changes was increased by 40% for the sit-stand conditions, indicating they did more major body changes within their chair rather than standing up entirely.

It appears the frequency of switches in the cur-rent study between sit and stand postures were found to be lower than previous work of Toomin-gas et al. (2012). Toomingas et al. (2012) found approximately 10 changes/hr, whereas sit-stand conditions resulted in four to five per shift and reminder software alone was 1.25 per shift in the current study. These lower numbers of switches also point to dynamic posture changes while seated. For the conventional condition, the aver-age number of switches was less than 0.5 per shift, indicating long durations of sitting. So although there was an increase in the number of times that the workers changed between sit and stand for the intervention studies as compared to the conven-tional workstation, the increases did not equal an average of 2/hr or close to the 10/hr found by Toomingas et al. (2012). The culture, type of emergency situation, and relatively short break-in period (e.g., 2 weeks for each condition) may have contributed to the lower number of actual changes between sitting and standing. Although it appeared from the video analyses that the workers complied with the protocols, more rigid breaks could have had more promising results.

To provide adequate context of the results, the following considerations and limitations need to be discussed. First, the participant population was a sample of convenience that was not selected randomly and was selected from one facility with a given culture and work demands. The sample of convenience was a result of fund-ing considerations and the intensive evaluation of the workers (e.g., monitoring for 2 weeks, 24 hrs and 7 days a week for each condition). Although the sample came from one call center,

the specific one selected offered some unique opportunities such as monitoring continuously for long periods of time (e.g., 2 weeks for each observation period), observing more than one person at a time (e.g., multiple users in multiple cubicle arrangement), and observing workers who were well-educated individuals (e.g., all had a college degree in order to provide the emer-gency services). Although the study was com-pletely voluntary with limited encouragement from management, there were some confidential-ity issues (e.g., some did not complete confidenti-ality of participation) that may have influenced or biased who volunteered for the study. How-ever, the potential bias of a small participant pool and nonparticipation was low, given the overall response from the workers was high (e.g., more than 75% participation). Furthermore, the work environment was relatively unique to this call center in that the calls carried some level of urgency and potential life-threatening risk. As a result, the operators were not able to take breaks immediately on several occasions.

Second, the evaluation was a relatively short-term assessment with only a 2-week break-in period and a 2-week follow-up assessment period. As a result, the health outcomes were limited to discomfort responses rather than actual workplace injuries (e.g., reportable back discomfort, hand/wrist problems, and shoulder problems). The long-term ramifications of postural variability through sit-stand and reminder software remain unexplored. The results provide a more immediate impact of the interventions without any perspec-tive to more long-term impact.

Third, the postural assessment was limited to global movements such as moving away from the computer, transitioning between sitting and standing, and leaving the work area. With the focus on large dynamic movements, the postural changes did not account for microbreaks or small extremity breaks, which could have had some impact on the results. With the length of assessment and time-consuming nature of the video analyses, it was not feasible to quantify the small movement level.

Fourth, the study design was not able to utilize a completely randomized control design due to multiple users sharing a limited number of work-stations and cross-contamination concerns. The




logistics of having reminder software for some participants while others were not to have reminder software was not feasible, so the study was broken into two phases: no reminder software and reminder software. Although not ideal, partici-pants were randomized within these groups, mini-mizing the bias associated with condition ordering effects. Anecdotal data confirmed the issue as nonparticipants complained about the software popping up on the screen, which was resolved by showing them how to ignore the reminders.

Overall, the limitations discussed provide some context when interpreting the results of the current study. However, none of them were felt to provide significant issues with respect to the validity and generalizability of the results. However, the con-cerns above do indicate additional work is needed to better understand the underlying mechanisms of postural changing interventions such as investi-gating (1) muscular responses and changes in fatigue, (2) changes in inflammatory responses in these regions, and (3) more detailed information about postural changes through the use of quanti-tative tools such as goniometers. Future work also needs to expand these results to a long-term pro-spective study that would investigate musculo-skeletal injuries rather than discomfort.

conclusIonThe study results have practical implica-

tions for sedentary office workers who take limited breaks. Based on the overall results, the implementation of sit-stand workstations and/or break reminder software influenced the number of gross postural changes and/or move-ments of the body that a worker will take throughout the shift. A result of the large body changes was a decreased level of discomfort at the end of the day without having any adverse impact on the productivity indices. However, the results should be viewed as preliminary as the long-term impact of these interventions is not fully understood. The study shows promise that reminders and sit-stand workstations leads to short-term reduction of body discomfort without adverse impact on productivity.

AcknowledgMentsWe would like to thank Balaji Sharma, Donald

Herrmann, and Anita P. Krishnan for their contribution

and hard work in analyzing and processing the video data. Also, we would like to thank the Office Ergo-nomics Research Committee for partially funding the project.

key PoInts • The adoption of sit-stand workstations or reminder

software produced significant changes in the pos-tural response during the shifts.

• Sit-stand workstations or reminder software reduced the discomfort in the shoulders, upper back, and lower back that the individuals suffered at the end of the shifts.

• Introduction of postural changing modalities did not adversely impact the productivity of the workers.

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Kermit G. Davis is an associate professor at the Uni-versity of Cincinnati in the College of Medicine, Department of Environmental Health, where he also directs the Low Back Biomechanics and Workplace Stress Laboratory. He received his PhD in occupa-tional ergonomics from the Ohio State University, College of Engineering, Department of Industrial and Systems Engineering. He is a certified profes-sional ergonomist.

Susan E. Kotowski is an assistant professor at the University of Cincinnati in the College of Allied Health Sciences. She is also director of the Gait and Movement Analysis Lab. She received her PhD in occupational ergonomics and safety from the Uni-versity of Cincinnati, College of Medicine. She is also a certified professional ergonomist.

Date received: May 31, 2013Date accepted: February 11, 2014



postural variability: an effective way to reduce musculoskeletal discomfort in office work

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