Stooping, crouching, and standing – Characterizing balance control strategies across postures

Author’s Accepted Manuscript Stooping, crouching, and standing – characterizing balance control strategies across postures Tyler B. Weaver, Michal N. Glinka, Andrew C. Laing www.elsevier.com/locate/jbiomech

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S0021-9290(17)30004-0 http://dx.doi.org/10.1016/j.jbiomech.2017.01.003 BM8075

To appear in: Journal of Biomechanics Accepted date: 2 January 2017 Cite this article as: Tyler B. Weaver, Michal N. Glinka and Andrew C. Laing, Stooping, crouching, and standing – characterizing balance control strategies across postures, Journal of Biomechanics, http://dx.doi.org/10.1016/j.jbiomech.2017.01.003 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Stooping, crouching, and standing – characterizing balance control strategies across postures Tyler B. Weaver, Michal N. Glinka, Andrew C. Laing* Injury Biomechanics and Aging Laboratory, Department of Kinesiology, University of Waterloo, Waterloo, ON, Canada * Correspondence to: Department of Kinesiology, Faculty of Applied Health Sciences, University of Waterloo, 200 University Avenue West, Waterloo, ON, Canada N2L 3G1. Tel.: +519 888 4567, ext. 38947. [email protected] Abstract Background While stooping and crouching postures are critical for many activities of daily living, little is known about the balance control mechanisms employed during these postures. Accordingly, the purpose of this study was to characterize the mechanisms driving net center of pressure (COPNet) movement across three postures (standing, stooping, and crouching) and to investigate if control in each posture was influenced by time. Methods Ten young adults performed the three postures for 60 seconds each. Kinetic signals were collected via a force platform under each foot. To quantify mechanisms of control, correlations (CorrelLR) were calculated between the left and right COP trajectories in the anterior-posterior (AP) and medio-lateral (ML) directions. To examine the potential effects of time on balance control strategies, outcomes during the first 30 seconds were compared to the last 30 seconds. Results CorrelLR values did not differ across postures (AP: p=0.395; ML: p=0.647). Further, there were no main effects of time on CorrelLR (AP: p=0.976; ML: p=0.105). A significant posture-time interaction was observed in the ML direction (p=0.045) characterized by 35% decreases in CorrelLR over time for stooping (p=0.022).

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Conclusion The dominant controllers of sway (i.e., AP: ankle plantar/dorsi flexors; ML: hip load/unload mechanism) are similar across quiet stance stooping, and crouching. Changes in ML control strategies over time suggests that fatigue could affect prolonged stooping more so than crouching or standing. Keywords Center of pressure; Force plates; Postural sway; Bending; Squatting 1. Introduction Stooping and crouching postures are required to perform important daily tasks such as reaching for items on the floor\low shelves, and gardening (Long and Pavalko, 2004). These postures involve musculoskeletal configurations that differ greatly from upright standing (Gallagher et al., 2011; Glinka et al., 2015). Stooping consists of flexing the trunk forward and rotating the head downward while keeping the legs relatively straight. Crouching requires significant flexion at the hip, knee, and ankle while maintaining a vertically oriented trunk. While the challenges associated with stooping and crouching have been examined in a number of recent studies (Bhattacharya et al., 2009; Dionisio et al., 2008; Gallagher et al., 2011; Glinka et al., 2015; Hemmerich et al., 2006; Hernandez et al., 2013; Hernandez et al., 2010; Kuo et al., 2011), little is known about how balance is actually maintained once an individual is in these postures. It is possible that the mechanisms we employ to control the body’s net centre of pressure (COPNet) while standing (Winter et al., 1993) are different from those used during stooping and crouching. Understanding how these mechanisms may differ could contribute to our knowledge of balance-related challenges associated with tasks that require stooping and crouching postures, and ultimately inform intervention strategies aimed at improving stooping and crouching performance. 2

The body’s COPNet is typically quantified from kinetic signals recorded using a single force platform under an individual’s feet (Mackey and Robinovitch, 2005; Panzer et al., 1995; Prieto et al., 1996). However, toward understanding the separate mechanisms responsible for controlling COPNet movement in the anterior-posterior (AP) and medial-lateral (ML) directions, two force platforms (one under each foot) are required (Winter et al., 1993). Using two platforms facilitated the pioneering work of Winter et al (1993) who identified that COPNet movement in the AP direction is achieved primarily by varying the stiffness of the ankle plantar flexors and dorsiflexors, while ML control relies on the hip ab/adductors to transfer weight from one foot to the other (Winter et al., 1998; Winter et al., 1993). This early work laid the foundation for more recent studies investigating how directional (AP vs ML) balance control during upright standing can be affected either by experimentally manipulating sensory modalities, or naturally by aging and pathological processes (Morrison et al., 2016; Peterka, 2002; Termoz et al., 2008). As stooping and crouching postures involve body configurations that differ substantially compared to standing (e.g., joint angles, muscle lengths, joint loads), the directional control mechanisms employed (i.e., ankle plantar/dorsi flexors for AP; hip load/unload for ML) might also change (Weaver et al., 2014). It is well known that when muscles act outside of their optimal operating lengths, their ability to generate force is diminished (Edman, 1966; Zajac, 1989). During stooping, the lower back and posterior leg muscles are stretched (BurgessLimerick, 2003). Crouching involves similar length changes, particularly to the quadriceps and ankle plantar flexor muscles, as a consequence of the significant flexion occurring at the knee and ankle joints (Burgess-Limerick, 2003; Glinka et al., 2015). Further, in the crouching posture, activating only the hip musculature may be insufficient to load/unload bodyweight from each foot. Unlike standing, in a crouch the hip and knee joints are all fully flexed, potentially

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necessitating activation of the quadriceps and calf musculature – in addition to the hip ab/adductors – to lift the entire lower limb and initiate a weight shift between legs. It is possible that some of these postural constraints prompt a reorganization of the motor strategies utilized to maintain balance during upright stance. It is also of interest to determine whether time spent in stooping and crouching postures influences the control strategies employed. Borrowing from the vast literature on the lower back, prolonged trunk flexion has been shown to induce viscoelastic tissue creep (McGill and Brown, 1992; Shin et al., 2009; Shin and Mirka, 2007; Solomonow et al., 2003; Toosizadeh and Nussbaum, 2013). Accordingly, fatigue of the low back extensor muscles may also occur during stooping, as these muscles must generate more active force to compensate for the reduced extension moment contribution of creep-deformed passive tissues (Shin et al., 2009; Shin and Mirka, 2007). Research has also shown that in the lower back, muscle onset can be delayed due to tissue creep, which may in turn influence sensorimotor function (Sanchez-Zuriaga et al., 2010). Therefore, it is conceivable that creep and fatigue-related changes could also occur in the lower-limbs during periods of quasi-static stooping and crouching, resulting in changes in the control of postural sway. Despite potential drivers of between-posture differences in control strategies (as detailed above), Weaver et al. (2014) recently demonstrated that balance control during both stooping and crouching can be well-represented using the inverted-pendulum model originally developed to characterize control strategies during quiet stance (Winter et al., 1998). Specifically, all three postures demonstrated similar relationships between centre of mass (COM) acceleration and the COPNet-COM signal (Weaver et al., 2014). However, it is currently unknown which strategies are used to control postural sway while stooping and crouching. Such information would have

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functional relevance as stooping and crouching postures are common during activities of daily living (ADLs) and in many occupational tasks, but performing these tasks becomes more difficult for older adults (Glinka et al., 2015; Hernandez et al., 2013; Kuo et al., 2011). Knowledge of underlying control strategies across these postures could provide evidence to support generic intervention approaches that are robust across postures, or alternatively, the need to develop separate intervention approaches tailored to each specific task. Accordingly, the purpose of this study was to determine if known strategies of COPNet control for upright standing are applicable to stooping and crouching postures. Specifically, we hypothesized that posture would not influence: 1) the synchronicity between the COP of the left and right feet, in the (a) AP or (b) ML direction, assessed via Pearson product-moment correlations; 2) the degree of synchronicity between the COPNet and COP components controlled by either the ankle plantar/dorsi flexor or hip load/unload mechanisms, in the (a) AP or (b) ML direction, also assessed via Pearson product-moment correlations; and 3) the magnitude of each proposed mechanism’s (ankle plantar/dorsi flexion versus hip load/unloading) contribution to balance control in the (a) AP or (b) ML direction, assessed using root mean square amplitudes. Additionally, as a secondary objective, we investigated whether extended time spent in each posture influenced balance control characteristics. Specifically, we hypothesized that no main effects of time or interaction effects between posture and time would be observed in the metrics calculated for hypotheses 1-3 (referred to as hypothesis 4 in the remainder of the document).

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2. Methods 2.1 Experimental Protocol Ten healthy young adults (five females) participated in the study (mean (SD) age = 23.1 (2.3) years; body height = 1.71 (0.10) m; body mass = 70.6 (10.9) kg). This sample size is based on the work of Termoz et al. (Termoz et al., 2008) who assessed control mechanisms during upright standing. A sample of healthy, young adults was chosen as a first step in quantifying the control mechanisms during stooping and crouching postures, allowing for a baseline of control to be established. Prior to study commencement, informed consent was obtained from all participants. The study protocol was approved by the Office of Research Ethics at the University of Waterloo. Balance was assessed during standing, stooping and crouching (Figure 1). Participants were instructed to stay as still as possible within each posture while ground reaction forces and moments were collected from individual force platforms (OR6, AMTI, Watertown, MA) positioned under each foot. Force data was collected at 1024 Hz for 60 seconds (Carpenter et al., 2001; van der Kooij et al., 2011). Each posture was performed once. During stooping, participants were also instructed to minimize knee flexion and to let their arms hang freely in front of them (but avoid touching the floor). During crouching, participants were instructed to support their weight on their forefeet and to rest their elbows/forearms on their thighs (Figure 1). No further instruction was provided to participants about how to perform each posture. Postural standardization (via joint angles) was avoided to limit the differential task demands which may have arose between participants, due to varying levels of strength, range-of-motion, and

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anthropometrics. Nonetheless, all participants were able to achieve the postures depicted in Figure 1. 2.2 Mathematical Derivation The following equations were used to asses the control mechanisms for standing, stooping and crouching, and to evaluate hypotheses 1-4. et

(

eft

)

ight

(

)

(1)

where COPLeft and COPRight represent the COP responses of the left and right feet, while RVL and RVR are the vertical reaction forces under the left and right feet, respectively. Theoretically, if there is no change in vertical loading/unloading between the limbs, each foot will support half of total body weight. Therefore, the following equation can be used to isolate the COPC, which is the component of COPNet directly associated with combined left and right COP changes (i.e., excluding the effect of weight shifting between the feet): (

eft

)

ight

(

) (2)

The COPC is essentially the average of the COPLeft and COPRight contributions. Therefore, if the COPC is subtracted from COPNet, the resulting value will reflect the loading/unloading contribution due to only the vertical reaction forces, COPV: et

-

(3)

Using these equations, researchers (Winter et al., 1996; 1993) found that for quiet stance, in the AP direction, the COPLeft and COPRight were strongly positively correlated and that the COPC was the dominant contribution to COPNet. Thus, the authors suggested that AP balance was driven by in-phase COPLeft and COPRight changes, likely via the ankle plantar/dorsiflexors. In contrast, for the ML direction, COPLeft and COPRight were strongly negatively correlated, where the COPLeft and COPRight contributions due to the ankle invertors/evertors tended to cancel each 7

other out. Further, the dominant contribution to the COPNet was the COPV, meaning that ML balance was controlled primarily by a vertical loading/unloading response, via the hip abductors/adductors. Subsequent research has confirmed these distinct AP and ML control mechanisms in both young and older adults (Termoz et al., 2008). In the current study similar experimental approaches were used to investigate whether these control strategies (validated during quiet stance) were applicable to stooping and crouching postures. Data Analysis: All kinetic data was low-pass filtered using a 2nd order, dual-pass filter, with a 5 Hz cutoff frequency (Warnica et al., 2014). The COPLeft and COPRight signals were computed so that the COPNet, COPC and COPV could be calculated for each participant, posture, and direction, using equations 1-3. In accordance with Winter et al. (1993), Pearson product-moment correlations (CorrelLR) between the COPLeft and COPRight were calculated for each posture in both the AP (hypothesis 1a) and ML (1b) directions. Towards testing hypothesis two, for each posture, correlations (CorrelNet) were also calculated between the COPNet and the COPC for the AP direction (2a), and the COPNet and COPV for the ML direction (2b) (Termoz et al., 2008). The root mean square (RMS) amplitudes of both the COPC (3a) and COPV (3b) were also calculated for each posture and direction. The RMS calculation allowed for the magnitude of the COPC and COPV contributions to COPNet movement to be assessed. Finally, these calculations were completed for the entire 60 second duration (hypotheses 1-3), as well as the first and last 30 seconds of each trial. The latter durations allowed for the influence of time to be investigated (hypothesis 4).

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2.3 Statistical Analysis To determine if CorrelLR and CorrelNet (hypotheses 1 and 2) or RMS amplitudes (i.e., hypothesis 3) were influenced by posture, a one-factor repeated measures analysis of variance (ANOVA) was conducted for each variable (Termoz et al., 2008) utilizing the data from the full 60 s trial. Additionally, to examine the effect of time on CorrelLR, CorrelNet and the RMS amplitudes (hypothesis 4), a two-factor repeated measures ANOVA was conducted for each variable with posture (quiet stance vs. stooping vs. crouching) and time (first 30 sec. vs. last 30 sec.) as within-subject factors. Correlation strength was interpreted according to guidelines suggested by Evans (1996): moderate (.40-59); strong (.60-.79); and very strong (.80-1.0) (Evans, 1996). Where appropriate, Fisher’s t-tests were used for post-hoc analysis. In cases where sphericity assumptions were violated, a Greenhouse-Geisser correction was applied. All statistical analyses were completed with software (SPSS Version 22.0, SPSS Inc., Chicago, IL) using an alpha of 0.05. All values are displayed as means (standard deviations).

3. Results Overall, the magnitude and direction of CorrelLR values supported previously proposed control mechanisms. For all three postures, the COPLeft and COPRight were strongly positively correlated in the AP direction (0.719 (0.219)), and strongly negatively correlated in the ML direction (-0.623 (0.216)). This trend is demonstrated by time-varying data from a representative participant in Figures 2 and 3. The ANOVA analyses of the full 60 second trial data indicated that CorrelLR values were not affected by posture in either the AP (F(2,18)=0.978; p=0.395) or ML (F(2,18)=0.447;

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p=0.647) directions (Figure 4). Further, AP CorrelNet values between the COPNet and COPC were also not influenced by posture (F(2,18)=0.282; p=0.758) (Table 1). However, posture significantly influenced ML CorrelNet values between COPNet and COPV (F(2,18)=15.05; p=0.002). Post-hoc analysis revealed that this was driven by small differences in the crouching posture, with CorrelNet values that were 2.0% lower than standing (p=0.001) and 1.7% lower than stooping (p=0.007). There was no difference in CorrelNet between standing and stooping (p=0.167). The magnitude of COP movement was influenced by posture. Specifically, a main effect of posture was found for the COPC RMS amplitude in the AP direction (F(2,18)=22.93; p<0.001). On average, the values for stooping were 43% and 52% greater than quiet stance (p=0.001) and crouching (p<0.001), respectively. COPC RMS did not differ between quiet stance and crouching (p=0.216). No effect of posture was observed for COPV RMS in the ML direction (F(2,18)=2.13; p=0.148) (Figure 5). Similar to the values from the full 60 s trial, all CorrelLR values were moderate to strong regardless of whether the first (AP: 0.765(0.146); ML: -0.668(0.202)) or last (AP: 0.766(0.161); ML: -0.565(0.260)) 30 seconds were analyzed. Additionally, very strong (i.e., >0.95) CorrelNet values between the COPNet and COPC in the AP direction, and COPNet and COPV in the ML direction, were observed for all three postures, regardless of time. Figures 2 (AP) and 3 (ML) display the COPLeft, COPRight and COPNet time-series data from a representative participant for the entire 60 seconds in each direction. Regarding CorrelLR, there were no main effects of time in either the AP (F(1,9)=0.001; p=0.976) or ML (F(1,9)=3.26; p=0.105) directions, and no posture-time interaction in the AP direction (F(2,18)=1.29; p=0.301). However, a significant interaction existed in the ML direction (F(2,18)=3.70;

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p=0.045), characterized by a 35% decrease in CorrelLR over time for the stooping posture (p=0.022) (Figure 4). Regarding CorrelNet, no main effects of time (F(1,9)=0.006-3.45; p=0.0960.942) or posture-time interactions (F(2,18)=0.660-1.75; p=0.216-0.529) were observed in either the AP or ML direction (Table 1). Lastly, a main effect of time was found for the RMS amplitudes of ML COPV (F(1,9)=5.99; p=0.037), whereby values were 15% larger during the first 30 seconds compared to the last 30 seconds. No main effect of time was observed for the RMS amplitude of AP COPC (F(1,9)= 4.39; p=0.066). Further, no posture-time interactions were found for RMS amplitudes of AP COPC (F(2,18)=1.51; p=0.249) or ML COPV (F(2,18)=2.84; p=0.116) (Figure 5).

4. Discussion This study explored the AP and ML control of balance during quiet stance, stooping and crouching postures. In line with our first three hypotheses, COPNet control during stooping and crouching postures is similar to that described for upright standing, with the ankle plantar/dorsi flexor mechanism controlling AP balance, and the hip abductor/adductor mechanism controlling ML balance (Winter et al., 1996; Winter et al., 1993). This was evidenced by positive AP and negative ML correlations (i.e., CorrelLR) between the COP under each foot as well as large and dominant contributions from the ankle plantar/dorsi flexors and the hip load/unload mechanism in the control of postural sway in the AP and ML directions, respectively (i.e., CorrelNet and RMS values). Interestingly, time was found to influence the ML control of stooping (but not quiet stance or crouching), demonstrated through time-dependent changes in CorrelLR (i.e., hypothesis four).

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Effect of Posture on Correlations and RMS Amplitudes: No effects of posture were found on the correlation-based metrics of balance control for the AP direction (Figure 4; Table 1). This suggests that the ankle plantar/dorsi flexor mechanism previously proposed for quiet standing AP control (Winter et al., 1996; Winter et al., 1993) – is applicable to both stooping and crouching. However, in the ML direction, CorrelNet values between the COPNet and COPV were greater for standing and stooping than crouching for the full trial duration analysis. While the COPV represents hip loading/unloading contributions to the COPNet during standing, it is possible that during crouching we are forced to rely more on our ankle invertors/evertors to control the anti-phase dynamics between the ML COP of the left and right feet (Winter et al., 1996; Winter et al., 1993). Further, both stooping and crouching likely require an adjustment in the control of the muscles at the hip, knee and ankle compared to standing (Weaver et al., 2014). This may indeed promote a more mixed-control strategy, noted to occur previously for stance types other than feet side-by-side (Winter et al., 1996). The RMS amplitudes lend support to this notion. Recall that COPC reflects individual COP changes under the left and right feet, while COPV describes the contribution due to loading and unloading (Winter, 1995; Winter et al., 1996; Winter et al., 1993). In the current study, COPC was not hypothesized to be the dominant ML control mechanism for standing, stooping or crouching. However, secondary analyses, which arose based on results from our initial hypotheses, revealed a trend due to posture on the COPC in the ML direction (F(2,18)=3.25; p=0.062). Post-hoc paired sample t-tests showed that the ML COPC contribution to crouching was greater than for standing (p=0.041) (Figure 5). No other differences were observed between postures. This suggests that

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left and right COP changes may play a larger role in ML control during crouching compared to standing. The stooping posture was associated with the greatest sway magnitude (Figure 5; AP direction). While stooping, the forward pitched trunk, increased postural sway and downward head orientation may expose individuals to a greater fall-risk. Furthermore, information from sensorimotor control pathways such as the vestibulo-ocular, cervicocollic, and vestibulocollic reflexes may be more difficult to interpret with the head and neck tilted forward (Allum et al., 1997). Interestingly, despite the inherent control challenges associated with this stooping posture, it is often preferred over the crouch; particularly by older adults who may lack the knee extensor strength required to safely transition into and out of the high knee flexion crouching posture (Glinka et al., 2015; Hernandez et al., 2010; Hernandez et al., 2008). Effect of Time on Correlations and RMS Amplitudes: This study demonstrated direction-dependent effects of time on the dominant balance control mechanisms. Specifically, time had little influence on the dominant mechanism of AP balance control (i.e., in-phase ankle plantar/dorsi flexion; CorrelLR values). However, in the ML direction for stooping CorrelLR values were 35% greater during the first 30 seconds, compared to the last 30 seconds (Figure 4). One explanation may be that the hip load/unload mechanism proposed for quiet standing ML balance control (Winter et al., 1996; Winter et al., 1993) is sensitive to time during stooping. As discussed above for the back (Shin et al., 2009; Shin and Mirka, 2007) perhaps during stooping, passive tissue creep (e.g., of tendons and ligaments that provide extensor moments) occurs in the hips and/or lower back (resulting in increased hip/lumbar flexion), requiring greater extensor moments from the hip and lower back muscles, thus contributing to muscle fatigue. Consequently, as muscles slowly begin to fatigue during

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sustained stooping, it is possible that participants are less able to control their COPLeft and COPRight in the highly anti-phase manner, as noted previously for standing (Winter et al., 1996; Winter et al., 1993). Regarding the magnitude of the control response (i.e., RMS amplitudes), the ML COPV was influenced by a main effect of time, where RMS amplitudes were 15% larger during the first 30 seconds versus last 30 seconds (Figure 5). The reduction of the ML COPV RMS amplitudes over time may reflect a shift to an alternate control strategy where participants were not relying as heavily on the load/unload mechanism to control their ML COPNet. A similar mechanism may explain why COP CorrelLR values in the ML direction decreased over time for stooping. However, kinematic data is needed to explore this alternate control hypothesis in detail. This study had several limitations. First, this study did not measure joint kinematics. While stooping and crouching kinematics have individually been characterized previously (Acker et al., 2011; Hemmerich et al., 2006; Hwang et al., 2009), kinematic data could provide indirect insights into tissue creep during these tasks. The second and third limitations relate to our secondary objective of investigating whether balance control characteristics in these postures are time-dependent. Specifically, extending the trial duration to a minimum of 120 s would potentially elicit more fatigue-related effects (if they exist), and meet the recommendations of previous literature related to minimum time required to adequately capture low frequency COP components (Carpenter et al., 2001; van der Kooij et al., 2011). Collecting muscle activation data would provide details related to potential fatigue onset, into addition to the specific activation strategies employed during these postures. Fourth, the current study only evaluated ten participants, this sample size was sufficient for examining our specific research questions and was similar to research in the area (Termoz et al., 2008). Finally, while only young healthy adults

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were included in this study, the intent was to assess these control strategy questions in baseline, healthy participants. Limitations aside, this study is the first to investigate specific AP and ML mechanisms of balance control during stooping and crouching postures. In conclusion, this study provides evidence that control of postural sway is similar during standing, stooping and crouching. This suggests that interventions aimed at improving task performance in these postures may be able to focus on a single, robust strategy rather than on separate approaches specific to each posture. Specifically, across all postures, the ankle plantar/dorsi flexors were the dominant controllers of sway in the AP direction, while a hip load/unload mechanism controlled the ML direction. Interestingly, the control of postural sway in the ML direction was influenced by time during the task of stooping. Future research should investigate whether different posture-specific control strategies exist in older adults and pathological groups, whether similar time-related ML control strategies exist for these populations during stooping and crouching. Additionally, future research should explore the source of potential control strategy changes that occur over time (e.g., tissue creep, muscle activation patterns), by incorporating longer trial durations and collecting electromyographic data. Overall, these data provide an evidence base to assist in the development of interventions to improve balance control in stooping or crouching postures during occupational tasks and activities of daily living.

Conflict of Interest Statement: The authors verify that no conflicts of interest exist.

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Acknowledgements This research was funded in part by an operating grant from the Natural Sciences and Engineering Research Council of Canada (grant # 386544), and infrastructure grants from the Canadian Foundation for Innovation and the Ontario Ministry of Research and Innovation.

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Figure captions Figure 1: Illustrations of the standing (left), stooping (middle) and crouching postures (right) employed in this study. Each participant completed each posture with their feet aligned in the sagittal plane and shoulder width apart. The order of posture completion was randomized for each participant.

Figure 2: Representative time-varying AP values of COPRight (dashed black line), COPLeft (solid black line), and COPNet (gray line) for standing (top), stooping (middle) and crouching postures (bottom) for the full 60 second trial. AP = anterior-posterior.

Figure 3: Representative time-varying ML values of COPRight (dashed black line), COPLeft (solid black line), and COPNet (gray line) for standing (top), stooping (middle) and crouching postures (bottom) for the full 60 second trial. ML = medio-lateral.

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Figure 4: Mean (SD) correlation coefficients between the time-varying centres of pressure for the left and right feet (in the text referred to as CorrelLR) for each posture and direction. Data is presented based on the entire 60 seconds trial duration, the first 30 seconds and the last 30 seconds. The vertical dashed lines show the separate statistical analyses which were conducted in the study (i.e., (1) the entire 60 seconds; (2) the first vs. last 30 seconds). AP = anterior-posterior; ML = medio-lateral; *# = posture x time interaction (p≤ )

Figure 5: Mean root-mean squared (RMS) amplitudes of the dominant (left column) and nondominant (right column) COP contributions to AP (top row) and ML (bottom row) control. Data is presented based on the entire 60 seconds trial duration, the first 30 seconds and the last 30 seconds. The vertical dashed lines show the separate statistical analyses which were conducted in the study (i.e., (1) the entire 60 seconds; (2) the first vs. last 30 seconds). AP = anterior-posterior; ML = medio-lateral; * = posture main effect; # = time main effect (p≤ ); % trend due to posture (p<0.10).

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Table 1 Mean correlation coefficients between the net COP response (COPNet) and 1) the left and right contributions (COPC; AP) and 2) the loading/unloading contributions (COP V; ML) for each posture. Data is included based on analysis of the entire 60 seconds, the first 30 seconds and the last 30 seconds. AP = anterior-posterior; ML = mediolateral. Correlat ed Variable s COPNet & COPC Correlat ed Variable s COPNet & COPV

Standi ng Mea n SD

0.9997 2 0.0003 3 Standi ng

Mea n SD

0.9923 6 0.0097 3

AP - 60 s. Stoopi Crouchi ng ng 0.9996 0.99982 8 0.0005 0.00029 7 ML - 60 s. Stoopi Crouchi ng ng 0.9895 4 0.0129 3

0.97280 0.01400

Standi ng

AP - First 30 s. Stoopi Crouchi ng ng

0.9997 2 0.0004 1

Standi ng

0.9997 0.99989 7 0.0003 0.00013 7 ML - First 30 s. Standi Stoopi Crouchi ng ng ng

0.9998 6 0.0002 1

0.9924 9 0.0073 7

0.9865 7 0.0167 4

0.9919 4 0.0079 0

0.97191 0.02114

AP - Last 30 s. Stoopi Crouchi ng ng

0.9997 0.99980 1 0.0003 0.00038 7 ML - Last 30 s. Standi Stoopi Crouchi ng ng ng 0.9919 4 0.0073 3

0.95463 0.03888

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