Performance, Movement, Posture, and Perceived Discomfort in Active vs. Static Seating

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Performance, Movement, Posture, and Perceived Discomfort in Active vs. Static Seating

Jeremy D. Faulk¹, Cameron C. McKee¹, Heather Bazille¹, Michael Brigham¹, Jasmine Daniel¹, Julia G.

Jaffe¹, JeeEun Lee¹, Elena Sabinson¹, Yaoyi Zhou¹, & Yige Zhu¹, Yoo jin Chung²,and Alan Hedge¹ 1. Department of Design & Environmental Analysis, Cornell University, Ithaca, NY 14853, USA 2. Department of Fiber Science & Apparel Design, Cornell University, Ithaca, NY 14853, USA Active seating designs may enable users to move more frequently, thereby decreasing physiological risks associated with a sedentary lifestyle. In this preliminary study, two active seating designs (QOR360, Ariel;

QOR360, Newton) were compared to a static chair (Herman Miller, Aeron) to understand how active vs.

static seating may affect task performance, movement, posture, and perceived discomfort. This within-sub- jects experiment involved n = 11 student participants who sat upon each of the three chairs for 20 minutes while performing a series of computer-based tasks. Participants showed increased trunk movement while also reporting higher levels of perceived discomfort in the two active chair conditions. There was no significant difference in either posture or fine motor task performance between the active and static conditions.

Future research may benefit from additional physiological measurements along with a wider variety of tasks that require seated users to make postural adjustments.

Introduction

Much of the global workforce spends days on end completing computer-based tasks while sitting at a desk or table.

This habitual sedentary behavior poses a range of health risks to employees, including an increased risk of diabetes, cardiovascular disease, low mood, and mortality (Hamilton, Healy, Dunstan, Zderic, & Owen, 2008; Katzmarzyk & Lee, 2012;

Mummery, Schofield, Steele, Eakin, & Brown, 2005; Stubbs et al., 2017). The pathogenic mechanisms that link inactivity to these health risks lead to an accumulation of visceral fat and systemic inflammation in the lower body, and can also impair the function of skeletal muscles, organs, and body tissue (Ekblom-Bak & Ekblom, 2012).

Chair designs that promote ‘active’ seating represent a recent attempt at a design-oriented solution to this health prob- lem. Active seating makes use of dynamic or unstable surfaces to increase movement, activate muscles, and to promote a healthier range of postures and seating angles (Ellegast et al., 2012). In most standard office chairs, the decreased trunk-thigh angle experienced by the user has been found to flatten the lumbar curve, which in turn decreases lower back support and makes the user more vulnerable to lower back pain (Frymoyer

& Cats-Baril, 1991; Keegan, 1953). Yet some studies have found that an active engagement of the trunk and adjacent muscles can help one to achieve a more neutral lumbar spine posture and more stable upper body alignment when sitting. This outcome is thought to both decrease excess pressure on the lower back while also mitigating health risks caused by the lack of movement and muscle activation (Bendix, Poulsen, Klausen, &

Jensen, 1996; Grooten, Condradsson, Äng, & Franzén, 2013).

The evidence is mixed regarding active chair designs and their effect on user movement and seating angles. Wang and colleagues (2014) found significantly increased trunk muscle movement in active seating conditions compared to static seating; Hiemstra-van Mastrigt and colleagues (2015) reported similar findings of active-seating in automobiles. Yet these findings run counter to previous studies that observed no impact of

active seating on measures of user movement (Gregor, Dunk,

& Callaghan, 2006; Jensen & Bendix, 1992; O’Sullivan et al., 2006).

For those who have found that active seating affords increased in-chair movement (ICM), the mediating cause of this ICM warrants further investigation. Although increased ICM may confer health benefits (e.g., through more frequent muscle activity), multiple studies suggest that these movements are driven primarily by user discomfort (Cascioli, Liu, Heusch, &

McCarthy, 2016; Søndergaard, Olesen, Søndergaard, De Zee,

& Madeleine, 2010; Vergara & Page, 2002). The ‘movement because of discomfort’ argument complicates the evaluation of active seating designs and, furthermore, may introduce a hurdle to their acceptance.

Regarding the effects of active seating on posture, two studies (Grooten et al., 2013; Gregor et al., 2006) showed a ben- eficial reduction in posterior pelvic tilt in active seating conditions while a more recent study (Hamaoui, Hassaïne, & Zanone, 2015) found that even a 15-degree forward tilt (1) produced no significant trunk or pelvic angle changes, (2) did not lead to im- proved posture, and (3) furthermore increased lower-limb muscle strain. Clearly, there is still room to explore the postural outcomes of active seating designs.

This preliminary study examined the effect of seating type (active vs. static) on trunk and lower-limb ICM, fine motor performance, seating angle and posture, and perceived discomfort during several short-duration computer tasks. It was hypothesized that (H1) fine motor performance would not differ between active and static seating conditions. (H2) Participants’

ICM was hypothesized to be greater in the active seating condition when compared to the static seating conditions. (H3) It was predicted that participants would report lower levels of back pain but higher levels of lower-limb pain in the active seating chairs when compared to the static seat design. (H4) The average leg-to-torso posture angle for each of the two active seating conditions was predicted be closer to Keegan’s normal posture (1953; i.e., 135°) than in the static seating condition.

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Methods Participants

The study was conducted with a convenience sample of 11 graduate students (3 males, 8 females) who were completing a graduate-level course in ergonomics at a large research university in the northeastern United States. Institutional Review Board approval was obtained on the basis of archival data analysis. The participants were on average 30.27 (s = + 5.08) years old. The sample group had an average height of 167.78 (s = + 7.69) cm, an average weight of 69.39 (s = + 23.41) kg, and an average BMI of 24.42 (s = + 6.84).

Experimental Design

This study utilized a repeated-measures, within-subject design. Each participant performed three computer-based tasks across three seating conditions: The Newton (C1) (active); the Ariel (C2) (active); and Aeron Chair (C3) (static) (see Figure 1). The order of the seating conditions was counterbalanced across participants.

Apparatus

A 24-inch LCD computer monitor (Dell UltraSharp 2407WFP), 104-key keyboard (Kensington 64332 Comfort Type Slim), and wireless optical mouse (Perixx PERIDUO-712 2.4G) were used with a desktop computer (Dell Optiplex 790) for completing the computer tasks. The desk (Quickstand, Hu- manscale, USA) used in the study was height-adjustable.

Three seating conditions were investigated: The Newton (C1) (QOR360, Burlington, VT, USA) is an active stool-type chair with a star shaped base, an adjustable shaft, and a flat seat pan (Figure 1, left). The Ariel (C2) (QOR360, Burlington, VT, USA) is identical to the Newton except for the wider seat pan (Figure 1, middle). The Aeron Chair (C3) (Herman Miller, Zee- land, MI, USA) is static in design and is highly adjustable to individual preference. The Aeron has a star shaped base, an adjustable shaft, seat pan, an adjustable back support, and pad- ded arm rests (Figure 1, right). No chairs had movable wheels.

Figure 1. Seating conditions

(left: The Newton, C1; middle: The Ariel, C2; right: Aeron Chair, C3) (QOR360, 2018; Herman Miller, 2018).

Tasks and Assessments

Participants’ fine motor performance was assessed using two computerizeds tasks: a Homing Task (Liu, 2018) and Fitts’

Spiral Task (Ren, 2018). The Homing Task, a standardized measure of fine motor performance, is a clicking-and-typing task in which participants click on the correct highlighted bar and then must correctly type the appeared text. Twenty-four trials of the high-frequency, short-word option were run for each participant. Performance was based on four timed

measures (milliseconds): pointing (elapsed time between the start click and first click on the highlighted bar), m2k (elapsed time between clicking and typing), typing (elapsed time for a correctly typed word), and k2m (elapsed time between the typing and the next click).

As a second measure of fine motor performance, the Fitts’

Spiral Task was used. This is a mousing task in which the participant must complete one continuous trace of a screen- viewed spiral without going outside the spiral boundaries. The participant must complete several traces to progress with the size of the spiral changing after each progressive task completion. Participants completed four trials with combinations of 4- and 20 pixel track widths and followed by 1-2 rotations of the spiral track. The tests varied depending on the width (w), turns (t), and difficulty (d) respectively; type A (w 40, t 1, d 5), B (w 40, t 2, d 14), C (w 20, t 1, d 5), and D (w 20, t 2, d 14). The output of these collective spiral task results was measured using five parameters: target track width, number of turns of the spiral track, ID (Index of Difficulty), MT (average movement time in milliseconds), and number of errors.

Figure 2. Postural Angle Measurements using CAD software at 13, 15 and 18 minutes.

Movements were analyzed by two reviewers who were trained to code visible movements from a video recording of the participant. Any changes in postural position of the trunk was counted; this included leaning forward/backward, side-to-side, shifting position in the seat, or turning. Any movement of the legs or feet was counted, including stretching of legs, re- positioning of feet, crossing legs, and any other visible movements. Shaking of the leg within a 2-second interval was counted as one movement. If shaking persisted, counts of the movement increased once every 2 seconds. Movements above the neck and hand/arm were disregarded.

Perceived discomfort was measured using a paper Visual Analog Discomfort Scale (Kar & Hedge, 2016). For 13 indicated body parts, participants rated their discomfort by marking along a 100mm line ranging from ‘no discomfort’ to

‘extreme discomfort.’ Discomfort data were collected at two points: after the participant was seated for 5 minutes, and again after 20 minutes.

Procedures

Participants entered the experiment in pairs and were asked to familiarize themselves with the protocols. They were informed that the study would be video recorded. Each

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participant completed the three, 20-minute trials, one for each chair. During the experiment, the experimenter-participant pairs alternated roles [as this was a class project], with the experimenter monitoring the video camera and administering the tasks and discomfort questionnaire at the appropriate times.

Participants read the instructions and performed the Homing task for the first five minutes of each trial. At the five-minute mark, participants were instructed to complete the discomfort questionnaire. Following this, participants completed Fitts’

Spiral Task, and then were told they could surf the internet until the 18-minute mark, at which point they were to fill out the second discomfort questionnaire. After each trial, the participant and experimenter switched roles.

Data Analysis

Statistical analyses were performed using JMP version 13.1 for Mac. Significance was set at p < 0.05 for all statistical tests. Paired sample t-tests were used to compare the mean difference of perceived discomfort, movements, mousing and typing speed, and posture angle between three pairs of datasets:

C1–C2, C1–C3, and C2–C3. A multivariate regression analysis was used to test the effect of both treatment and time on perceived discomfort. For fine motor task performance, a multivariate regression analysis was conducted to test the effect of seating condition on the completion speed and number of errors for Fitts’ Spiral task.

Results Discomfort

Seating Conditions. Perceived discomfort was measured in three different seating conditions (C1, C2, and C3) at the five-minute mark and again at the end of each trial (i.e., 20- minute mark). Mean discomfort scores across the three chair conditions at the 20-minute mark were analyzed using one-way analysis of variance (ANOVA) repeated-measures design.

When controlling for same body parts being measured repeatedly across all three chairs, there was a significant effect of chair condition on discomfort (F = 9.316; p < 0.0001).

Employing a Tukey-Kramer HSD correction, a post-hoc comparison of estimated discomfort means across all chairs indicated that the only significant post hoc difference was between C1 and C3. On average, C3 was 7.75 points less uncomfortable than C1 (p <0.028). Estimated means along with a connecting letters report are provided in Table 1 below.

Table 1. Mean Discomfort X Chair Comparison with Tukey-Kramer HSD Connecting Letters Report

Level Mean

C1 A 24.2169

C2 A B 21.3078

C3 B 16.4721

Levels not connected by the same letter are significant at a = 0.05

To test these relationships in a different way, a paired sample t-test comparing the difference of means revealed a statistically significant difference in perceived discomfort for the C1–C3 and C2–C3 datasets, but no significant difference for C1–C2 datasets. For this and all similar tests, the Bonferroni correction was used to account for multiple comparisons. There

was a statistically significant difference between C1 (M = 22.11, s = + 28.25) and C3 (M = 14.75, s = + 21.16; p

<0.0003). There was also a statistically significant difference between C2 and C3 (M = 20.34, s = + 27.91, p <0.0003) (DF = 329). The average discomfort across all body parts was lowest in C3 (M = 14.75), followed by C2 (M = 20.35), and C1 (M = 22.11).

A paired sample t-test comparing difference of means across chairs revealed a significant difference in overall dis- comfort intensity between the 5-minute (M =17.48, s = + 24.78) and 20-minute marks (M = 20.67, s = + 27.46, p <.0001.

Table 2. Paired Samples Statistics Discomfort 20 – Discomfort 5 Discomfort_20 20.6656 t-Ratio 4.03461

Discomfort_5 17.4779 DF 494

Mean Difference 3.18767 Prob > |t| <.0001*

Std Error 0.79008 Prob > t <.0001*

Upper 95% 4.74 Prob < t 1

Lower 95% 1.63533

N 495

Correlation 0.77827

A multivariate analysis revealed a significant main effect of chair type on discomfort (p = 0.008). The average discomfort across all body parts was lowest in C3 (M = 14.75, s = + 21.16), followed by C2 (M = 20.35, s = + 27.91), and C1 (M = 22.11, s = + 28.25). When including both time and chair type in the model, there was no significant effect of time on discom- fort (p = 0.542).

Performance Measures

Homing Task: Mousing-and-Typing. The homing tasks performance results were compared in three pairs of datasets (C1-C2, C2-C3, and C1-C3) using paired sample t-tests.

No significant differences emerged in the mousing-and-typing task performance across conditions.

Fitts’ Spiral Test. Multivariate analysis was conducted to test the main effects of both seating conditions and the test var- iation on the time of task completion. The results indicated a significant difference between the four test variations (p <

0.001), but no significant differences between seating conditions. The distribution of task completion times was not per- fectly normalized. A multivariate analysis was conducted to test the main effect of seating conditions and the test variations on errors in Fitts’ Spiral Test, with the results suggesting no main effect of Treatment, Conditions, and Condition x Treat- ment on the task error rate.

Movement

Paired sample t-tests were conducted to compare the differences in trunk and leg movement between C1–C3, C2–C3, C1–C2 datasets. For trunk movement, there was a statistically significant difference between C1 (M=39.00) and C3 (M=13.81) (p = 0.0074). There was also a statistically signifi- cant difference between C2 (M=40.55) and C3 (p = 0.0267). However, a comparison of the trunk movement for the

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C1-C2, revealed no statistically significant difference in movement. For leg movement, a paired sample t-test of trunk and leg movement between C1-C3, C2-C3, C1-C2 datasets suggests that there is no statistically significant difference for all three pairs.

Posture

Posture angle was calculated by reviewing video snap- shots at the 13-minute, 15-minute, and 18-minute marks of each experimental session. Overall means indicated relatively simi- lar angles across conditions (C1 M= 99.91, s = + 3.4; C2 M=

101.76; s = + 5.08; C3 M= 98.30, s = + 13.51). A paired sample t-test was conducted comparing the average posture angle between C1–C3, C2–C3, C1-C2 datasets with the results indi- cating no significant differences for all three pairings.

Discussion

This study aimed to evaluate the potential mediating effects of active vs. static seating designs on discomfort, task performance, posture, and movement. While the majority of the hypotheses were not supported, the results highlight some differences between the active vs. static chair designs.

H1: The results supported this hypothesis. The performance on fine motor tasks did not differ as a function of the active vs. static seating design. This outcome provides preliminary evidence that fine motor tasks (e.g., tattooing, dentistry, surgical procedures, painting, etc.) may not suffer as a result of using the C1 and C2 active chair designs. These findings alingn with others who propose light physical activity while working does not negatively impact fine motor task performance (Funk et al., 2012; Commissaris et al., 2014).

Further research is needed to test the external validity of these findings in real-world settings and with other fine motor tasks.

H2: This hypothesis was partially supported by the results.

There was a higher frequency of trunk movement for both active chairs when compared to the static chair. These findings are consistent with others who have established that trunk muscle movement increases with active seating designs (Wang et al., 2014; Hiemstra-van Mastrigt et al., 2015). Unlike Hiemstra-van Mastright et al. (2015), this study did not find a corresponding increase in lower-limb movement in the active seating conditions. The lack of lower limb movement in the active seating conditions may stem from one of the following factors: (a.) the chair designs (which may require greater stabilization using the legs), (b.) the nature of the tasks (computer-based work rather than driving), or (c.) differences between movement measurement tools. Further research should add physiologic measurements of movement.

H3: This hypothesis was partially supported by the results.

As predicted, participants reported greater levels of lower-limb discomfort in the active seating conditions, however, the prediction about reduced lower back pain in the active chairs was not supported. This result of increased lower-limb discomfort in the active chair conditions is consistent with others who have found that chairs with a forward pelvic tilt led to lower-limb discomfort. This is most likely explained by the increase in gravitational force on the legs due to the forward tilt of the seat pan (Hamaoui et al., 2015). The finding that lower

back pain did not differ between active and static seating conditions is unexpected. We hypothesized that the active seating conditions would result in reduced lower back pain due to the increased lordosis of the spine from the forward tilt of the seat pan. It is possible some participants did not take advantage of the affordance for forward pelvic tilt in the active seat design.

Further studies should examine deviations from lordosis of the spine due to the presence (or lack) of pelvic tilt and the resulting effect on perceived lower back discomfort.

H4: This hypothesis was not supported by the results of this study; the mean posture angles for all three conditions were nearly equivalent and the forward pelvic tilt capability of the two active seating designs (C1, C2) did not encourage the body to conform to Keegan’s recommended leg-to-torso posture angle of 135°. All three conditions resulted in an average angle that deviated a minimum of 33° from this standard.

Overall discomfort increased over the course of all trials, regardless of chair design, which suggests a general discomfort related to prolonged sitting. The active seating conditions had a moderating effect on perceived discomfort by exacerbating the experience of discomfort over time. The increased discomfort may have been caused by improper use of the chair or by the vigilance required to maintain balance. Because of the high variance across chair features, future research should isolate aspects of active chair designs to better understand the underlying cause of user-reported discomfort.

Active and static chair designs appear to have the greatest effect on trunk movement as well as overall discomfort. The two active chair types (C1, C2) resulted in both a higher frequency of trunk movement and increased discomfort when compared to the static chair (C3). Therefore, the findings of the current study appear to align with previous authors who proposed that perceived discomfort may mediate increased ICM resulting from active seating designs (Vergara & Page, 2002; Søndergard et al. 2010; Cascioli et al., 2016). Future research should explore the causal direction of this relationship.

Limitations

There were a fair number of limitations to this study.

Because it was originally conducted as a course exercise, the experimenters also served as participants, which may have resulted in unintended behavioral effects and introduced threats to construct validity, such as hypothesis guessing. There may be limited ecological validity to the results given the short length of the experimental trials (20 minutes).

Because experimental trials were conducted two-at-a-time with one participant and one experimenter alternating roles, these sessions lasted a minimum of two hours. Also, when participants were acting as the proctor, some were observed to sit in chairs that were not part of the study. The above two factors likely increased error variance in the performance and discomfort measures.

In reference to experimental apparatus, some participants were aware that they were permitted to adjust the table height to suit their preference, while others later expressed they were not aware of this possibility. In addition, observations of the trunk movement were more challenging in the C3 condi-

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tion because of the chair’s high seat back (which reduced visi- bility). Disparate levels of seat padding may have imbalanced discomfort measures across conditions due to non-uniform seat pan pressures. Finally, the sample was comprised entirely of graduate students (mean age = 30.27 years) with an unequal number of male and female participants. For these reasons, the generalizability of the results is limited.

Conclusion

Active seating remains a potentially important design-me- diated answer to the harmful health effects of prolonged sedentary work. From this paradigm, the current preliminary study examined how active vs. static seating affects the task performance, movement, posture, and perceived discomfort of the user. While the authors acknowledge that a set of 20-minute sessions is not representative of an 8-hour workday, this study may supply a helpful framework for comparing the effects of seating design on a variety of possible outcomes. Findings from this study may also guide hypothesis generation for subsequent tests of active seating in sedentary work environments. As a separate direction for further research, it may be worthwhile to examine this active seating design in a context that either (a.) requires prolonged sitting accompanied by task-oriented postural adjustments, or (b.) incites more ICM in general; these cir- cumstances may better highlight the strengths of this active chair design.

Acknowledgements

The authors acknowledge support from QOR360 and Herman Miller, Inc. for their donation of the seating equipment used in this research.

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