Clinical Biomechanics 30 (2015) 726–731

Contents lists available at ScienceDirect

Clinical Biomechanics journal homepage: www.elsevier.com/locate/clinbiomech

Passive lumbar tissue loading during trunk bending at three speeds: An in vivo study Xiaopeng Ning a,⁎, Maury A. Nussbaum b a b

Industrial and Management Systems Engineering, West Virginia University, P.O. Box 6070, Morgantown, WV 26506, USA Industrial and Systems Engineering, Virginia Tech, 521 Whittemore Hall (0118), Blacksburg, VA 24061, USA

a r t i c l e

i n f o

Article history: Received 14 January 2015 Accepted 28 April 2015 Keywords: Low back pain Lumbar passive tissue loading Viscoelasticity

a b s t r a c t Background: Low back disorders are closely related with the magnitude of mechanical loading on human spine. However, spinal loading contributed by the lumbar passive tissues is still not well understood. In this study, the effect of motion speed on lumbar passive moment output was investigated. In addition, the increase of lumbar passive moment during trunk bending was modeled. Methods: Twelve volunteers performed trunk-bending motions at three different speeds. Trunk kinematics and muscle activities were collected and used to estimate instantaneous spinal loading and the corresponding lumbar passive moment. The lumbar passive moments at different ranges of trunk motion were compared at different speed levels and the relationship between lumbar passive moment lumbar flexion was modeled. Findings: A non-linear, two-stage pattern of increase in lumbar passive moment was evident during trunk flexion. However, the effect of motion speed was not significant on lumbar passive moments or any of the model parameters. Interpretation: As reported previously, distinct lumbar ligaments may begin to generate tension at differing extents of trunk flexion, and this could be the cause of the observed two-stage increasing pattern of lumbar passive moment. The current results also suggest that changes in tissue strain rate may not have a significant impact on the total passive moment output at the relatively slow trunk motions examined here. © 2015 Elsevier Ltd. All rights reserved.

1. Introduction Low Back Disorders (LBDs) continue to be among the most prevalent global health problems (Hoy et al., 2012), and which lead to a substantial financial burden to individuals, families, and society (Maetzel and Li, 2002). Each year in the United States, for example, more than 10 million people will experience a LBD, and the annual costs of LBD-related conditions is more than 90 billion dollars (Luo et al., 2003) including both direct and indirect costs. The risk of a LBD is closely associated with the magnitude of mechanical loading on the spine (Bakker et al., 2007), and these disorders likely occur when spinal loading exceeds tissue tolerance limits (McGill, 1997). Therefore, a detailed understanding of spinal loading during task performance is critical for aiding in prevention of this common condition. During task performance, both muscle contraction and the deformation (especially elongation) of lumbar soft tissues generate forces and moments to initiate, maintain, or terminate trunk motions. Because spinal tissues have relatively short lever arms, high forces are often generated by spinal tissues to counterbalance external loading ⁎ Corresponding author. E-mail addresses: [email protected] (X. Ning), [email protected] (M.A. Nussbaum).

http://dx.doi.org/10.1016/j.clinbiomech.2015.04.014 0268-0033/© 2015 Elsevier Ltd. All rights reserved.

(e.g., hand load), and which consequently result in high spinal loading (e.g., intervertebral disc compression and shear forces). To quantify loadings on spinal tissues, several biomechanical models using electromyography (EMG) have been developed and used to quantify muscle contractile forces (e.g., Davis et al., 1998; Granata and Marras, 1995; Marras and Granata, 1997; McGill and Norman, 1986; Nussbaum and Chaffin, 1998). These models have been applied to a range of tasks, and have demonstrated relatively high accuracy and reliability in predicting muscle-generated active moments in comparison to measured moment values. However, estimation of elastic forces due to tissue deformation (e.g. elongation) remains challenging, largely due to the lack of non-invasive sensing technologies that could quantify tissue loadings in vivo. Early studies described a relatively rapid cessation of lumbar extensor muscle activities when trunk flexion approaches the end range-of-motion (Floyd and Silver, 1951, 1955). Subsequent literature has confirmed that with approximately 65° of trunk flexion more than 3/4 of the external moment (due to body segment weights) is counterbalanced by passive lumbar components (Arjmand and Shirazi-Adl, 2005), while 90% or more of the external moment is equilibrated by passive lumbar tissues in full flexion (Nussbaum and Chaffin, 1996). For the purpose of assessing spinal loading, it is thus important to understand the contributions of passive lumbar

X. Ning, M.A. Nussbaum / Clinical Biomechanics 30 (2015) 726–731

tissues (i.e., forces and moments resulting from tissue elongation), especially for tasks requiring substantial trunk flexion. Due to the complex lumbar geometry and the large number of passive elastic tissues that are involved (e.g. passive component of muscles, as well as ligaments, tendons, fascia, and discs), the net contribution of passive tissues typically demonstrates a nonlinear response with increasing trunk flexion. A recent study (Ning et al., 2012) described a clear boundary during trunk flexion, and was termed the Active Region Boundary (ARB). When trunk flexion passes this boundary, passive lumbar tissues quickly become the dominant load bearer. This work further demonstrated that passive lumbar tissue loading increases in a biphasic pattern during trunk flexion. Such a pattern was determined by the net contribution of all passive lumbar tissues. This work, however, found the ARB and passive contribution during essentially static loading conditions. The viscoelastic characteristics of the total (i.e., lumped) contributions of passive lumbar tissues during trunk motions are still not well understood. Viscoelastic properties of lumbar posterior tissues, in-vivo, have been described in prior work (Olson et al., 2009; Toosizadeh et al., 2012), and a number of in-vitro studies have investigated the viscoelastic properties of individual lumbar posterior ligaments (Ambrosetti-Giudici et al., 2009; Lucas et al., 2009; Provenzano et al., 2001; Yahia et al., 1991). Results of such studies indicate that both tissue strain and strain rate affect stress levels on those tissues. This sensitivity of forces to the rate of tissue deformation indicates the existence of viscoelastic components in lumbar ligaments (i.e., in addition to purely elastic behaviors), and such components are also present in passive muscle tissues (Linke and Leake, 2004; Magnusson et al., 1995; Ryan et al., 2010) and intervertebral discs (Chaudhry et al., 2009; Wang et al., 2000). At a systems level, the presence of viscoelasticity suggests that the speed of trunk motion (and consequent rate of tissue deformation) may substantially influence the overall lumbar passive tissue loading. However, direct in vivo evidence for such an influence is lacking. In addition, and likely due to relatively large individual differences (e.g., age) and distinct sampling techniques, there is substantial variability in existing in vitro studies regarding human lumbar tissue properties such as ligamentous strain– stress relationships and failure thresholds (Bogduk, 1980; Macintosh and Bogduk, 1987; Pintar et al., 1992; Skipor et al., 1985). The current study had two objectives. The first was to assess the patterns of moments generated by the lumped lumbar passive tissues during in vivo trunk bending motions and to quantify these moments as a function of lumbar flexion. According to previous work (Ning et al., 2012) a biphasic pattern of lumbar passive moments during trunk flexion was expected. The second objective was to determine the effect of motion speed on lumbar passive tissue loading during trunk flexion motion. Based on existing evidence for viscoelasticity in lumbar passive tissues, we hypothesized that an increase of trunk motion speed would increase the magnitude of lumped lumbar passive tissue loading. 2. Method 2.1. Participants Twelve male volunteers from the student population of West Virginia University completed the study, whose mean age, height, and body mass were 26.5 (SD 2.2) years, 174 (SD 4) cm and 70 (SD 8) kg. All participants were free from any self-reported back or lower extremity injuries. Signed informed consent forms were obtained from all participants prior to their participation, and the experimental procedures were approved by the Research Integrity and Compliance Committee of West Virginia University. 2.2. Experimental design and procedures A repeated-measures design was used, in which participants completed trunk flexion tasks (Fig. 1) at each of several SPEED conditions.

727

Fig. 1. An illustration of the experiment setup and the performance of a trunk flexion task.

These different speeds were used to identify the expected changes in contributions of lumbar passive tissues. SPEED was controlled at three levels, based on the time allotted to complete a motion from upright to full, passive trunk flexion. The specific times were 3, 5, and 7 s, intended to qualitatively represent “normal”, “slow” and “pseudo-static” trunk motions, respectively. Initially during the experiment, a brief (5 min) warm up period was provided, during which participants stretched and warmed up their lower back muscles. Electromyographic (EMG) electrodes were placed to monitor the activity of four bilateral pairs of muscles: erector spinae (4 cm lateral distance from L3 spinous process); multifidus (2 cm lateral distance from L4 spinous process); rectus abdominis (2 cm above the umbilicus and 3 cm to the midline of the abdomen); and external obliques (10 cm to the midline of the abdomen and 4 cm above the ilium with an angle of 45° to the midline of the abdomen). A commercial system (Model: Bagnoli, Delsys Inc, Boston, MA, USA) was used to amplify EMG. Subsequently, participants performed maximum voluntary contraction (MVC) trials in a back flexion–extension module that attached to a commercial dynamometer (Humac Norm, CSMi, MA, USA). Participants performed three replications of isometric maximum trunk extension/ flexion exertions against a static resistance with a 20° forward trunk flexion posture at which the resting lengths of the back extensor muscles are reached (Chaffin et al., 2006). Each MVC exertion lasted for 5 s, and 1-min of rest was provided between each exertion to avoid muscle fatigue. The maximal EMG signals obtained during the MVC trials were later used as inputs to an EMG-assisted biomechanical model (described in detail in the “Data processing” section). After completing the MVC trials, participants were fitted with three magnetic sensors attached to the skin surface over the vertebral processes of C7, T12 and L5 levels (Fig. 1a). Using these sensors, a motion tracking system (Model Motion Star, Ascension Burlington VT, USA) was used to record trunk kinematics. MotionMonitor software (Innovative Sports Training Chicago IL, USA) was used to synchronize EMG and trunk kinematics data, both of which were sampled at 1024 Hz. Participants then performed 12 trunk flexion tasks (three SPEED levels, with four replications of each), in a completely randomized order. In each task, participants were instructed to flex their trunk from an upright standing posture to a full flexion posture at the different assigned motion speeds and while keeping their knees straight and

X. Ning, M.A. Nussbaum / Clinical Biomechanics 30 (2015) 726–731

2.3.1. Modeling External moments at the L5/S1 joint were estimated by using a multi-segment dynamic model described previously (Mirka et al., 1998; Zhou et al., 2013). Model kinematics were specified using the motion tracking system, while the mass and center of mass of related body segments were estimated according to early work (Pheasant, 1986). The centers of mass of both arms were assumed to align vertically with their respective shoulder joints. The model was calibrated by adjusting upright standing posture to a near 0° trunk flexion angle, which corresponds to a near 0 Nm external moment. Raw EMG data were band-pass filtered between 10 Hz and 500 Hz, and notch filtered at 60 Hz and its aliases. Subsequently, EMG signals for each muscle recorded during the flexion trials were normalized with respect to corresponding maximal values obtained from MVC trials. These normalized EMG signals were then used as input to an EMG-assisted model (Marras and Granata, 1997; Ning et al., 2012; Zhou et al., 2013) to estimate instantaneous muscle forces and the “active” internal moments about the L5/S1 joint (i.e., moments generated by active muscle contraction). Parameters within the model, such as the moment arms and cross sectional areas of the trunk muscles, were obtained from early studies (Jorgensen et al., 2001; Marras et al., 2001). Parameters describing the force–length and force–velocity relationships were also obtained from previous studies (Davis et al., 1998; Marras and Granata, 1997). The mechanical equilibrium between the external moment and the internal moment (i.e. summation of active and passive moments) about the L5/S1 joint was satisfied in a three-dimensional space for all trunk postures. This was achieved by calibrating the model parameters (e.g. muscle cross-sectional areas, moment arms and gain) base on anthropometric measures and task performance (Marras and Granata, 1997; Ning et al., 2012). 2.3.2. Dependent variables Three categories of dependent variables were investigated in the current study: 1) boundary conditions (BC); 2) divided lumbar passive moment (DLPM); and 3) passive moment profile parameters (PMPP). BC included two dependent variables related to the Active Region Boundary (ARB), which were the lumbar angle at the ARB (LARB) and the passive moment at the ARB (PMARB). Lumbar angle was defined as the angular difference between the T12 and L5 motion sensors (Hu and Ning, 2015; Hu et al., 2013; Ning et al., 2011, 2012); in an upright standing posture the natural lordosis of the lumbar spine generates negative lumbar angle readings, in a trunk full flexion posture, the kyphosis of the lumbar spine generates positive values. The ARB was defined as the point during trunk flexion that the internal active moment reached a peak. Beyond this peak, lumbar passive tissue loading begins to increase substantially and quickly becomes the dominant load bearer (Ning et al., 2012). The location of ARB was calculated using previously described methods (Ning et al., 2012), and the corresponding lumbar angle at ARB was defined as the LARB (Fig. 2, bottom panel). The instantaneous lumbar passive tissue moment (lumped contribution of passive lumbar tissues including the passive component of lumbar muscles) during trunk flexion was calculated as the difference between the total external moment and the active internal moment about the L5/S1 joint, and the corresponding passive tissue moment at ARB was defined as PMARB (Fig. 2, top panel). DLPM included nine dependent variables, which were the lumbar passive tissue moments at nine consecutive intervals of lumbar flexion magnitude (from an upright standing posture to a full flexion posture). For each trunk bending trial, mean lumbar passive tissue moments were obtained within each of nine ‘5-degree’ lumbar

Moment (Nm)

2.3. Data processing

140

External moment Active moment

120 100

Passive tissue moment at ARB (PMARB)

80 60 40 20 0 120

Angle (degrees)

arms down (Fig. 1b). Training was provided at the beginning of the data collection to help participants practice different motion speeds. Timing of the motions was controlled using a metronome.

Active region boundary (ARB)

Trunk angle Lumbar angle

90 60

Lumbar angle at ARB (LARB)

30 0

0

2

4

-30

6

8

10

Time (s)

Fig. 2. Illustration of the ARB and the corresponding LARB and PMARB, derived from a typical trunk flexion motion.

angle intervals (starting from the upright standing posture): 0–5 degrees, 6–10 degrees, … , 41–45 degrees. These values were then compared between different SPEED levels to assess the relationship between trunk motion speed and total lumbar passive tissue moment. PMPP included four variables, described below, which together describe the pattern of increasing lumbar passive moment relative to lumbar flexion. Passive tissue stress is directly associated with tissue elongation, and in the lumbar region tissue elongation is determined by the degree of rotation in lumbar spine. Therefore, the relationship between lumbar passive moments and lumbar flexion during trunk flexion motions was modeled. Our initial analysis, consistent with earlier evidence (Ning et al., 2012), revealed a distinct non-linear, two-stage increasing pattern (i.e., pre-ARB and post-ARB). To reflect this pattern, the data were divided into two subsets: 1) from the upright standing posture to the ARB; and 2) from the ARB to the full trunk flexion posture (Fig. 3). Eq. (1) was used to model the increase of lumbar passive moment in both subsets during trunk flexion, wherein LPM represents lumbar passive moment; a is the lumbar angle; c is a participant-specific constant that represents the initial lumbar angle in the upright standing posture; and σi and βi are model parameters for subset i. The latter two parameters determine the shape of the profile (Fig. 4). A custom computer program was used to calculate the σ and β parameters for each

Lumbar passive tissue moment (Nm)

728

140

ARB

120 100 80 60 40 20 0 -30

-20

-10

0

10

20

30

40

Lumbar angle (degrees) Fig. 3. Demonstration of the two-stage increasing pattern of lumbar passive moment vs. lumbar angle, from a typical trunk flexion motion.

X. Ning, M.A. Nussbaum / Clinical Biomechanics 30 (2015) 726–731

SPEED

σ increased

3s 5s 7s

Moment

3s 5s 7s

β increased

Moment

c increased

Lumbar flexion angle Fig. 4. An illustration of how the three model parameters (i.e., σ, β and c in Eq. (1)) each change the predicted lumbar passive moment.

subset in each trial, using a least-squares approach. Coefficients of determination (R2) were obtained for the best-fit results for each subset. 

eβi ða−cÞ −1 eβi −1

 ð1Þ

2.4. Statistical analysis Repeated-measures Analyses of Variance (ANOVA) was used to assess the effects of SPEED on the dependent variables in the BC and PMPP categories (i.e., LARB, PMARB, σ1, β1, σ2 and β2). Tukey–Kramer post hoc tests were used to compare between different SPEED levels as relevant. Because dependent variables in the DLPM category likely reflect similar underlying phenomena and are correlated, a multivariate Analysis of Variance (MANOVA) was conducted to determine the effects of SPEED on these variables (Montgomery, 2005). If the effect of SPEED was significant in the MANOVA, then separate repeated-measures ANOVAs were used to test for effects of SPEED on each variable. A p value of 0.05 was set as the criteria for significance in all analyses, and assumptions of the parametric models were verified. 3. Results Results for all dependent variables in the three categories (i.e., BC, DLPM and PMPP) are summarized in Tables 1 and 2. None of the Table 1 Mean (SD) values of dependent variables in BC and PMPP at each SPEED level. SPEED

LARB (degrees) PMARB (Nm) σ1 β1 R21 σ2 β2 R22

Passive moment (SD) (Nm) 0–5 deg

6–10 deg

11–15 deg

16–20 deg

21–25 deg

10.5 (4.7) 11.1 (5.4) 11.7 (4.9) 26–30 deg 63.2 (4.3) 62.6 (4.8) 62.7 (4.3)

26.3 (4.9) 27.8 (3.9) 27.9 (4.2) 31–35 deg 72.2 (4.3) 72.1 (4.3) 72.2 (4.2)

37.1 (4.6) 37.8 (3.7) 38.9 (3.8) 36–40 deg 84.5 (5.1) 84.2 (4.6) 84.3 (3.3)

45.7 (4.5) 45.5 (3.7) 47.3 (3.3) 41–45 deg 97.1 (5.6) 96.8 (5.2) 96.0 (5.2)

53.9 (4.7) 53.9 (4.6) 54.5 (4.1)

dependent variables in any of the three categories were significantly affected by SPEED (Table 1). Mean coefficients of determination were 0.95 (SD 0.08) and 0.96 (SD 0.04) for subsets 1 and 2, respectively. Mean values of the model parameters σ1, β1, σ2 and β2 were 4.2, − 0.067, 1.8 and 0.019, respectively, and the mean initial lumbar angle was − 26.8 degrees. Using these parameters, a predicted profile for lumbar passive moment during trunk flexion motion was obtained (Fig. 5). This profile represents the typical pattern of increasing passive moment across all participants.

Lumbar flexion angle

3s

5s

7s

p-Value

−2.5 (10.5) 50.0 (12.9) 4.11(1.52) −0.063 (0.060) 0.94 (0.09) 1.81 (1.06) 0.019 (0.027) 0.95 (0.05)

−2.5 (9.7) 49.2 (12.0) 4.29 (1.27) −0.069 (0.044) 0.93(0.12) 1.82 (1.05) 0.019 (0.028) 0.95 (0.04)

−1.5 (9.3) 52.1 (10.5) 4.30 (1.41) −0.069 (0.058) 0.95 (0.05) 1.83 (0.90) 0.017 (0.027) 0.96 (0.04)

0.13 0.27 0.58 0.98 N/A 0.99 0.84 N/A

4. Discussion In the current study, the total passive moment at trunk full flexion reached around 120 Nm, they are similar to the results of previous invivo studies (Hu et al., 2014a; Ning et al., 2012) and comparable to those reported using finite element models (Bazrgari et al., 2007, 2008). The first objective of this study was to understand the increasing pattern of global lumbar passive moment as a function of lumbar flexion during trunk bending motions. Previous studies have reported the nonlinear increasing nature of lumbar passive moment during trunk bending and twisting motions (McGill et al., 1994; Parkinson et al., 2004), however, a clear biphasic lumbar passive moment increasing pattern in relation to lumbar rotation has not been discovered before. The results of the current study showed that, as expected based on earlier work, there was a clear two-stage increase in lumbar passive moment with increasing lumbar angle. These patterns were modeled with relatively high accuracy (R2 values ~ 0.95) using the proposed nonlinear function, with the ARB appearing to be a boundary between two distinct patterns in the relationship between lumbar moment and flexion angle (Figs. 2, 3, and 5). Previous in vitro evidence suggests that the lumbar posterior ligaments generate substantial passive force at differing degrees of lumbar flexion. For instance, the ligamentum flavum is at

Lumbar passive tissue moment (Nm)

Moment

Table 2 Mean (SD) values of dependent variables in DLPM at each SPEED level.

Lumbar flexion angle

LPMðaÞ ¼ σ i 

729

160 140

ARB

120 100 80 60 40 20 0 -30

-20

-10

0

10

20

30

Lumbar angle (degree) Fig. 5. Overall profile of passive lumbar tissue moment vs. lumbar angle, using model parameters summarized from all participants.

730

X. Ning, M.A. Nussbaum / Clinical Biomechanics 30 (2015) 726–731

resting length near the upright standing posture, and therefore tension will be generated over the entire range of lumbar flexion (Anderson et al., 1985). Other soft tissues, however, such as the supraspinous and interspinous ligaments, begin to generate tension near the mid-range of trunk flexion (Anderson et al., 1985), and the articular ligament begins to generate tension at about 4 degrees of L4/L5 flexion angle (Abouhossein et al., 2011). Based on this, differences in the contributions of ligament tension at different degrees of lumbar flexion are considered the main reason that the global lumbar passive tissue moment output demonstrated the noted two-stage increasing pattern. The second objective of the current study was to understand the effect of motion speed on passive lumbar tissue loading. Previous in vivo studies have provided evidence for viscoelasticity of lumbar posterior ligaments (Olson et al., 2009; Toosizadeh et al., 2012). And, earlier in vitro studies have shown that the stress levels of the lumbar posterior ligaments are affected by strain rate (Hukins et al., 1990; Lucas et al., 2009), a result of viscoelasticity (vs. purely elastic behaviors). Results of a modeling study also suggested that an increase in lumbar sagittal rotational speed can increase the passive moment (Wang et al., 2000). Therefore, it was hypothesized that an increase of motion speed will generate larger total lumbar passive tissue moment due to elevated lumbar ligament loading. However, this effect was not found here, and instead changes in trunk motion speed did not significantly affect either the magnitudes or the patterns of lumbar passive moment. One possible reason for this lack of a speed effect is that the total moment contribution of lumbar posterior ligaments only accounts for a relatively small portion of the total passive moment. A substantial portion of the remaining passive moment is likely generated by the elongation of muscle fibers, which could be less influenced by the rate of strain. Another possible explanation is that the relatively slow trunk motion speeds tested in the current study only generated small changes in force through the viscous component of lumbar posterior ligaments. Such small changes may have been difficult to detect given the relatively large forces generated by the elastic component of lumbar tissues. Further, and despite previous evidence that different trunk motion speeds can alter the flexion–relaxation response during trunk flexion motion (Sarti et al., 2001), the current results indicate that the global lumbar active and passive load-sharing boundary, the ARB, was not significantly affected by the speed of motion. This boundary is determined by the structure of the bony and soft tissues of lumbar spine, and according to recent investigations the ARB often remains unchanged across different conditions (Hu et al., 2014a; Ning et al., 2012). Despite its potential influence on lumbar ligament loadings, the current results suggest that the speed of trunk motion did not change the global loading sharing mechanism between active muscles and lumbar passive tissues. An important potential limitation of the current study is that the accuracy of estimated lumbar passive tissue moments depends on how well active muscle forces and moments were predicted. Although the EMG-driven model used here has been extensively assessed in prior work, and based on this was considered to be relatively accurate, inherent limitations of this modeling approach should be noted. First, EMG signals were only sampled from superficial muscle fibers; deeper fibers or muscles were not sampled. Second, a proportional linear relationship was assumed between active muscle force components and EMG signal magnitudes. As such, an alternative EMG-driven approach may have yielded different passive moments than those reported. Several other limitations of this study should also be noted. First, we tested three specific motion speeds levels, which were considered to represent a reasonable distribution of normal to slow trunk motions. Faster trunk motion speeds may generate higher viscous forces from ligaments, and such motions therefore warrant further investigation. Second, no hand loads were used in the current design to reduce the possibility of lumbar muscle fatigue. Previous work, though, has shown that hand loads may alter lumbar and pelvis motion patterns and change passive tissue loading (Hu et al., 2014b),

and thus should also be addressed in future studies. Third, only male participants were involved in the current data collection. Females may have slightly different lumbar tissue structure and soft tissue viscoelastic properties, and future studies should thus investigate the effect of speed motion on the lumbar passive moment output among females. 5. Conclusions During trunk flexion, the global lumbar passive tissue loading consistently demonstrated a two-stage pattern of increase with trunk flexion. These two stages were separated by a previously defined lumbar active and passive tissue load-sharing boundary. Although viscoelastic behaviors of individual lumbar posterior ligaments were observed from prior in vitro studies, results of the current study suggest that, when assessing the global lumbar passive tissue loading in vivo (e.g., during trunk bending motions), the viscous component of the lumbar tissues does not make a substantial contribution for the tasks (speeds) investigated here. References Abouhossein, A., Weisse, B., Ferguson, S.J., 2011. A multibody modeling approach to determine load sharing between passive elements of the lumbar spine. Comput. Meth. Biomech. Biomed. Eng. 14 (6), 527–537. Ambrosetti-Giudici, S., Gedet, P., Ferguson, S.J., Chegini, S., Burger, J., 2009. Viscoelastic properties of the ovine posterior spinal ligaments are strain dependent. Clin. Biomech. 25 (2), 97–102. Anderson, C.K., Chaffin, D.B., Herrin, G.D., Matthews, L.S., 1985. A biomechanical model of the lumbosacral joint during lifting activities. J. Biomech. 18 (8), 571–584. Arjmand, N., Shirazi-Adl, A., 2005. Biomechanics of changes in lumbar posture in static lifting. Spine 30 (23), 2637–2648. Bakker, E.W., Verhagen, A.P., Lucas, C., Koning, H.J., de Haan, R.J., Koes, B.W., 2007. Daily spinal mechanical loading as a risk factor for acute non-specific low back pain: a case–control study using the 24-Hour Schedule. Eur. Spine J. 16 (1), 107–113. Bazrgari, B., Shirazi-Adl, A., Arjmand, N., 2007. Analysis of squat and stoop dynamic liftings: muscle forces and internal spinal loads. Eur. Spine J. 16 (5), 687–699. Bazrgari, B., Shirazi-Adl, A., Trottier, M., Mathieu, P., 2008. Computation of trunk equilibrium and stability in free flexion–extension movements at different velocities. J. Biomech. 41 (2), 412–421. Bogduk, N., 1980. A reappraisal of the anatomy of the human lumbar erector spinae. J. Anat. 131 (3), 525–540. Chaffin, D.B., Andersson, G.B.J., Martin, B.J., 2006. Occupational Biomechanics. Fourth ed. John Wiley & Sons, New York, NY. Chaudhry, H., Ji, Z., Shenoy, N., Findley, T., 2009. Viscoelastic stresses on anisotropic annulus fibrosus of lumbar disk under compression, rotation and flexion in manual treatment. J. Bodyw. Mov. Ther. 13 (2), 182–191. Davis, K.G., Marras, W.S., Waters, T.R., 1998. Evaluation of spinal loading during lowering and lifting. Clin. Biomech. 13 (3), 141–152. Floyd, W.F., Silver, P.H.S., 1951. Function of the erectores spinae in flexion of the trunk. Lancet 260, 133–134. Floyd, W.F., Silver, P.H.S., 1955. The function of the erector spinae muscles in certain movements and postures in man. J. Physiol. 129, 184–203. Granata, K.P., Marras, W.S., 1995. An EMG-assisted model of trunk loading during freedynamic lifting. J. Biomech. 28 (11), 1309–1317. Hoy, D., Bain, C., Williams, G., March, L., Brooks, P., Blyth, F., Woolf, A., Vos, T., Buchbinder, R., 2012. A systematic review of the global prevalence of low back pain. Arthritis Rheum. 64 (6), 2028–2037. Hu, B., Ning, X., 2015. The changes of trunk motion rhythm and spinal loading during trunk flexion and extension motions caused by lumbar muscle fatigue. Ann. Biomed. Eng. http://dx.doi.org/10.1007/s10439-015-1248-0. Hu, B., Ning, X., Nimbarte, A.D., 2013. The changes of lumbar muscle flexion–relaxation response due to laterally slanted ground surfaces. Ergonomics 56 (8), 1295–1303. Hu, B., Shan, X., Zhou, J., Ning, X., 2014a. The effects of stance width and foot posture on lumbar muscle flexion–relaxation phenomenon. Clin. Biomech. 29 (3), 311–316. Hu, B., Ning, X., Nussbaum, M.A., 2014b. The influence of hand load on lumbar–pelvic coordination during lifting task. International Annual Meeting of the Human Factors and Ergonomics Society, Chicago, IL, USA October 27–31. Hukins, D.W.L., Kirby, M.C., Sikoryn, T.A., Aspden, R.M., Cox, A.J., 1990. Comparison of structure, mechanical properties, and functions of lumbar spinal ligaments. Spine 15, 787–795. Jorgensen, W.J., Marras, W.S., Granata, K.P., Wiand, J.W., 2001. MRI-derived moment-arms of the female and male spine loading muscles. Clin. Biomech. 16, 182–193. Linke, W.A., Leake, M.C., 2004. Multiple sources of passive stress relaxation in muscle fibres. Phys. Med. Biol. 49 (16), 3613–3627. Lucas, S.R., Bass, C.R., Crandall, J.R., Kent, R.W., Shen, F.H., Salzar, R.S., 2009. Viscoelastic and failure properties of spine ligament collagen fascicles. Biomech. Model. Mechanobiol. 8 (6), 487–498.

X. Ning, M.A. Nussbaum / Clinical Biomechanics 30 (2015) 726–731 Luo, X., Pietrobon, R., Sun, S.X., Liu, G.G., 2003. Estimates and patterns of direct health care expenditures among individuals with back pain in the United States. Spine 29, 79–86. Macintosh, J.E., Bogduk, N., 1987. The morphology of the lumbar erector spinae. Spine 12 (7), 658–668. Maetzel, A., Li, L., 2002. The economic burden of low back pain: a review of studies published between 1996 and 2001. Best Pract. Res. Clin. Rheumatol. 16 (1), 23–30. Magnusson, S.P., Simonsen, E.B., Aagaard, P., Gleim, G.W., McHugh, M.P., Kjaer, M., 1995. Viscoelastic response to repeated static stretching in the human hamstring muscle. Scand. J. Med. Sci. Sports 5 (6), 342–347. Marras, W.S., Granata, K.P., 1997. The development of an EMG-assisted model to assess spine loading during whole-body free-dynamic lifting. J. Electromyogr. Kinesiol. 7 (4), 259–268. Marras, W.S., Jorgensen, W.J., Granata, K.P., Wiand, B., 2001. Female and male trunk geometry: size and prediction of the spine loading trunk muscle derived from MRI. Clin. Biomech. 16, 38–46. McGill, S.M., 1997. The biomechanics of low back injury: implications on current practice in industry and the clinic. J. Biomech. 30 (5), 465–475. McGill, S.M., Norman, R.W., 1986. Partitioning of the L4–L5 dynamic moment into disc, ligamentous, and muscular components during lifting. Spine 11 (7), 666–678. McGill, S.M., Seguin, J., Bennett, G., 1994. Passive stiffness of the lumbar torso in flexion extension lateral bending and axial rotation. Effect of belt wearing and breath holding. Spine 19 (6), 696–704. Mirka, G.A., Baker, A., Harrison, A., Kelaher, D., 1998. The interaction between load and coupling during dynamic manual materials handling tasks. Occup. Ergon. 1 (1), 3–11. Montgomery, D.C., 2005. Design and Analysis of Experiments. Sixth ed. John Wiley & Sons, New York, NY. Ning, X., Haddad, O., Jin, S., Mirka, G.A., 2011. Influence of asymmetry on the flexion relaxation response of the low back musculature. Clin. Biomech. 26 (1), 35–39. Ning, X., Jin, S., Mirka, G.A., 2012. Describing the active region boundary of EMG-assisted biomechanical models of the low back. Clin. Biomech. 27 (5), 422–427. Nussbaum, M.A., Chaffin, D.B., 1996. Development and evaluation of a scalable and deformable geometric model of the human torso. Clin. Biomech. 11 (1), 25–34.

731

Nussbaum, M.A., Chaffin, D.B., 1998. Lumbar muscle force estimation using a subjectinvariant 5-parameter EMG-based model. J. Biomech. 31 (7), 667–672. Olson, M.W., Li, L., Solomonow, M., 2009. Interaction of viscoelastic tissue compliance with lumbar muscles during passive cyclic flexion–extension. J. Electromyogr. Kinesiol. 19 (1), 30–38. Parkinson, R.J., Beach, T.A.C., Callaghan, J.P., 2004. The time-varying response of the in vivo lumbar spine to dynamic repetitive flexion. Clin. Biomech. 19 (4), 330–336. Pheasant, S., 1986. Bodyspace: Anthropometry, Ergonomics and Design. Taylor & Francis, London, pp. 131–133. Pintar, F.A., Yoganandan, N., Myers, T., Elhagediab, A., Sances, A., 1992. Biomechanical properties of human lumbar spine ligaments. J. Biomech. 25 (11), 1351–1356. Provenzano, P., Lakes, R., Keenan, T., Vanderby, R., 2001. Nonlinear ligament viscoelasticity. Ann. Biomed. Eng. 29 (10), 908–914. Ryan, E.D., Herda, T.J., Costa, P.B., Walter, A.A., Hoge, K.M., Stout, J.R., Cramer, J.T., 2010. Viscoelastic creep in the human skeletal muscle–tendon unit. Eur. J. Appl. Physiol. 108 (1), 207–211. Sarti, M.A., Lison, J.F., Monfort, M., Fuster, M.A., 2001. Response of the flexion–relaxation phenomenon relative to the lumbar motion to load and speed. Spine 26, E421–E426. Skipor, A.F., Miller, J.A.A., Spencer, D.A., Schultz, A.B., 1985. Stiffness properties and geometry of lumbar spine posterior elements. J. Biomech. 18 (11), 821–830. Toosizadeh, N., Nussbaum, M.A., Bazrgari, B., Madigan, M.L., 2012. Load–relaxation properties of the human trunk in response to prolonged flexion: measuring and modeling the effect of flexion angle. PLoS One 7 (11), e48625. Wang, J.L., Parnianpour, M., Shirazi-Adl, A., Engin, A.E., 2000. Viscoelastic finite-element analysis of a lumbar motion segment in combined compression and sagittal flexion. Effect of loading rate. Spine 25 (3), 310–318. Yahia, L.H., Audet, J., Drouin, G., 1991. Rheological properties of the human lumbar spine ligaments. J. Biomed. Eng. 13 (5), 399–406. Zhou, J., Dai, B., Ning, X., 2013. The assessment of material handling strategies in dealing with sudden loading: influences of foot placement on trunk biomechanics. Ergonomics 56 (10), 1569–1576.

Passive lumbar tissue loading during trunk bending at three speeds: An in vivo study.

Low back disorders are closely related with the magnitude of mechanical loading on human spine. However, spinal loading contributed by the lumbar pass...
510KB Sizes 0 Downloads 6 Views