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A novel approach to the clinical evaluation of differential kinematics of the lumbar spine
Manual Therapy 18 (2013) 130e135
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Original article
A novel approach to the clinical evaluation of differential kinematics of the lumbar spineq Jonathan Mark Williams a, *, Inam Haq b, Raymond Y. Lee c a
School of Health and Social Care, Bournemouth University, Royal London House, Christchurch Road, Bournemouth, Dorset BH1 3LT, UK Brighton and Sussex Medical School, UK c Department of Life Sciences, University of Roehampton, UK b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 6 April 2012 Received in revised form 1 August 2012 Accepted 30 August 2012
Clinical measurement of lumbar motion has traditionally been limited to range of motion (ROM). Despite this, deficits in angular velocities and accelerations are more pronounced compared to ROM in low back pain (LBP) sufferers. There is increasing interest in movement quality among manual therapists and therefore the ability to measure angular velocities and accelerations within the clinical environment is becoming increasingly important. The aims of this study were to (1) investigate the reliability of a clinic based inertial sensor system to measure ROM along with angular velocities and accelerations in low back pain sufferers; (2) introduce the feasibility and reliability of using the relationship between ROM and velocity to investigate movement trajectory and irregularity. Forty LBP sufferers completed three trials of spinal movements and lifting. The ROM curve was differentiated and double differentiated to yield angular velocities and accelerations. Repeated measures reliabilities were determined by comparisons of kinematic curves as well as peak values. ROM and angular velocity relationships were investigated for their use in describing the movement trajectory and irregularity. Results show excellent similarities of ROM and angular velocity curves and moderate-to-good similarities for angular acceleration curves. Peak value similarities were excellent with small error measurements for all variables. The quantification of ROM-angular velocity plots was reliable with small mean absolute differences in motion irregularity scores. Such a method was able to demonstrate differences in movement irregularity. This method provides clinicians with the ability to yield important additional movement related information including angular velocity, acceleration and movement irregularity. Ó 2012 Elsevier Ltd. All rights reserved.
Keywords: Lumbar spine Kinematics Reliability Velocity
1. Introduction Measurement of lumbar motion is common in the assessment of low back pain (LBP) (Cocchiarella and Andersson, 2000). Clinical measures including skin distension (MacRae and Wright, 1969; Dolan et al., 1995), finger tip-to-floor (Frost et al., 1982; Battie et al., 1987; Gauvin et al., 1990) and inclinometers (Burdett et al., 1986; Saur et al., 1996; Bierma-Zeinstra et al., 2001; Kachingwe and Phillips, 2005) are unable to provide continuous data. This means they are unable to provide information about patterns of
q This work is affiliated to the Department of Life Sciences, University of Roehampton. * Corresponding author. Tel.: þ44 (0) 7766141620. E-mail addresses: [email protected], [email protected] (J.M. Williams). 1356-689X/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.math.2012.08.003
movement. Some laboratory methods such as electromagnetic or opto-electronic video based systems can provide this extra information, but often the reporting has been limited to range of motion (ROM) only. It is well documented that LBP sufferers display alterations in spinal kinematics (Shum et al., 2007a, 2007b), but this is most pronounced in the differential kinematics of the movement, i.e. angular velocities and accelerations (Marras and Wongsam, 1986; Marras et al., 1995, 1999; Novy et al., 1999). Angular velocities and accelerations have been able to accurately discriminate between LBP and non-LBP sufferers (Marras et al., 2000) as well as distinguish risk of LBP within the workplace (Marras et al., 1993). However, the reliability of the measurements of angular velocity and acceleration need to be further established. A few studies (Shum et al., 2005a, 2005b) have looked at the ROM-angular velocity relationship. This relationship, known as the
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spatial relationship, can be visualised by a ROM-velocity plot. The diagram provides a useful clinical picture of examining the spine from a dynamical perspective, in which the emphasis is on representing the kinematic control of the system (Burgess-Limerick et al., 1993; Li et al., 1999). Visualising motion control is important because various studies have shown that low back pain is associated with altered control (Van Dillen et al., 2009; Hodges et al., 2009; Xu et al., 2010). One novel use of this visualisation is to examine the degree of movement irregularity, which cannot be revealed by routine ROM assessment or the more familiar ROMtime plot. In the clinic the above approach can be easily achieved with the use of inertial sensors. Similar sensors have been used to study gait, movements of the upper and lower limb as well as the spine (Tong and Granat, 1999; Lee et al., 2003; Zhou et al., 2008; Saber-Sheikh et al., 2010; Cuesta-Vargas et al., 2010; Theobald et al., 2012; Ha et al., in press). However before such sensors are recommended for clinical use it is imperative to determine the reliability in LBP sufferers within a clinical setting, including the angular velocities and accelerations. Furthermore the feasibility of analysing and quantifying movement irregularity through spatial relationship plots should be established. The aim of this study was to (1) investigate the similarity between repeated movements, known as repeated measures reliability, of an inertial sensor system to measure ROM, angular velocity and acceleration of LBP sufferers; (2) to describe the use of spatial plots to study the movement irregularity of spinal movements. 2. Methods 2.1. Participants Forty LBP sufferers were initially recruited (twenty acute and twenty chronic) from general practitioner referrals to local therapy departments. Referrals were screened for inclusion and exclusion criteria. Inclusion criteria included pain confined to between the lower ribs and inferior gluteal folds, 18e55 years old, seeking healthcare for their LBP and evoked pain on at least 3 of the test movements. Exclusion criteria included a history of tumours, spinal fractures, surgery, rheumatological or neurological diseases and any neurological signs or symptoms. Acute pain was pain present for less than three weeks and chronic defined as pain present on at least three days per week for greater than twelve months. Four acute low back pain (ALBP) and eight chronic low back pain (CLBP) sufferers were excluded as they did not demonstrate three or more movements which evoked pain. Sixteen acute LBP sufferers (male 10; age 42.6 7.2 years; BMI 27.7 3.5 kg/m2; duration 12.0 7.3 days) and twelve chronic LBP sufferers (male 7; age 34.5 10.0 years; BMI 27.4 4.8 kg/m2; duration 4030.2 2992.2 days) recorded their mean pain scores from the week preceding data collection, using a visual analogue scale (VAS) (61.5 18.9 mm ALBP; 45.7 22.1 mm CLBP) and a tampa scale of kinesiophobia questionnaire (39.3 4.1 ALBP; 38.3 7.5 CLBP). This study was approved by the National Research Ethics Service of the National Health Service and written informed consent was obtained following explanation of procedures and risks. 2.2. Instrumentation Two wired inertial sensors were used to measure lumbar kinematics (3DM-GX3-25, Microstrain, VT, USA). Each sensor contained tri-axial gyroscopes (300 s1), accelerometers (5 g) and magnetometers. Each sensor was cased with dimensions 44 mm (h) 25 mm (w) 11 mm (d), and weight of 18 g. The sensors have
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a reported accuracy of 0.5 during static testing and 2.0 during dynamic testing. One sensor was placed over the S1 spinous process and the second over the L1 spinous process. The sensors were attached using double sided tape with the wires secured to the trunk so as not to move the sensors erroneously. The sensors were connected to a purpose built datalogger and software (ThetaMetrix, UK) and data were collected at 100 Hz. The relative orientations between the L1 and S1 sensor were determined from the direction cosine matrices (Grood and Suntay, 1983; Burnett et al., 1998; Lee et al., 2003). Flexion, left side bending and left rotation were considered positive and the opposite movements negative. 2.3. Procedure Participants stood upright and completed three trials of flexion, extension, left side bending, right side bending, left rotation, right rotation and a box lift. The box (460 260 300 mm) was positioned on markers ensuring identical placement and weighed 3 kg. The movement order was identical for all participants and the sensors remained attached throughout. 2.4. Data analysis All raw data were transferred to Matlab for processing (Mathworks, R2008b). The ROM data for each movement were determined and smoothed using a fourth order zero-lag Butterworth filter with a cut off frequency of 1 Hz (Tsang et al., 2011). The ROM data were differentiated to yield the velocity and double differentiated to calculate acceleration. The ROM curves were timenormalised so that each movement had the same time base from 0% to 100%. This normalisation process takes into account the differing rates at which the participants completed the trials and allows the direct comparison of the kinematic pattern. The coefficient of multiple correlation (CMC) (Li and Caldwell, 1999; Williams et al., 2010) and root mean square error (RMSE) were calculated from the normalised range of motion, angular velocity and acceleration curves to determine the similarities between the three movement trials. The peak ROM, peak angular velocity and peak angular acceleration values were obtained and intra-class coefficients (ICC3,1) calculated along with the mean absolute differences between peak values for the three trials. Two peak values were obtained, positive and negative. Positive velocity and acceleration relate to flexion, left side bending and left rotation movement, whereas negative velocity and acceleration relate to movement in the opposite direction. Angular velocity-ROM plots were used to reveal the spatial relationship where the overall shape of the plot describes the trajectory of the movement and dynamic control. Movement irregularity was determined from the plots by separating the plot into quartiles (see Fig. 2). The quartiles were defined as the region from motion onset to peak angular velocity (quartile 1); peak angular velocity to end ROM or maximum angular displacement (quartile 2); maximum angular displacement to minimum angular velocity (quartile 3) and minimum angular velocity to minimum angular displacement or return to upright standing (quartile 4). Each quartile was fitted with a 4th order polynomial and the beginning and end of the polynomial was fixed to match the collected data. The polynomial was used to determine the RMS difference between the actual data and the polynomial, providing quantification of motion irregularity for each quartile. The RMS was normalised to the maximum velocity of each individual to provide a value representing the percentage of maximum velocity. Scores of motion irregularity (RMS) were determined for each quartile across
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J.M. Williams et al. / Manual Therapy 18 (2013) 130e135
Table 1 Mean (sd) values of kinematic variables recorded by the inertial system. þve Vel ( s1)
Disp ( )
Flex Ext LSF RSF LRot RRot Lift
ALBP
CLBP
35.1 11.8 11.5 15.0 7.1 10.6 29.9
48.1 16.5 14.6 15.9 4.4 8.3 43.8
(14.5) (8.8) (3.7) (4.1) (3.5) (4.5) (15.0)
(11.4) (5.1) (6.9) (4.6) (3.6) (4.1) (12.6)
ve Vel ( s1)
ALBP
CLBP
20.1 10.6 8.2 13.4 5.0 7.0 21.5
28.2 15.0 9.6 13.6 4.5 6.1 33.8
(15.8) (6.3) (4.4) (4.8) (2.6) (3.0) (14.0)
(9.6) (6.3) (5.8) (5.9) (3.1) (4.2) (13.9)
þve Acc ( s2)
ALBP
CLBP
20.1 6.2 11.2 8.8 5.1 5.6 26.6
29.0 10.0 11.6 10.2 5.0 6.4 36.3
(11.8) (3.0) (4.3) (3.7) (2.6) (2.5) (13.5)
(10.0) (4.3) (5.8) (5.1) (3.9) (4.8) (14.5)
ve Acc ( s2)
ALBP
CLBP
29.8 18.9 15.3 20.9 11.5 12.2 44.1
34.1 25.1 18.9 20.6 13.5 14.9 59.7
(18.8) (10.1) (8.5) (9.3) (4.5) (7.0) (23.3)
(28.7) (12.0) (11.4) (11.8) (9.2) (10.1) (29.8)
ALBP
CLBP
26.6 14.6 17.5 17.2 10.6 12.7 42.8
41.3 26.1 17.6 20.7 13.5 14.2 58.5
(16.7) (8.1) (7.6) (8.2) (4.6) (5.7) (24.6)
(21.0) (14.5) (10.8) (11.3) (8.9) (9.9) (30.5)
Flex, Flexion; Ext, Extension; LSF, Left side flexion; RSF, Right side flexion; LRot, Left rotation; RRot, Right rotation; Disp, Displacement; þve, positive; ve, negative; Vel, Velocity; Acc, Acceleration.
each movement and ICC3,1 and mean absolute difference between trials were used to determine reliability of such a method. 3. Results Mean (sd) for displacement, velocity and acceleration for each movement can be found in Table 1. The mean CMC values were found to be good across all repeated range of motion curves for the ALBP (0.83e0.97 0.02e0.09) and CLBP (0.72e0.94 0.03e0.26) groups. This was also seen for angular velocity curves (0.83e0.94 0.04e0.10 ALBP and 0.71e 0.91 0.05e0.18 CLBP). The mean CMC values for angular acceleration curves show moderate consistency across the different movements tested (0.61e0.77 0.10e0.18 ALBP and 0.52e0.73 0.10e0.26 CLBP). The RMSE magnitudes across all kinematic variables were small for all repeated movements (Fig. 1). Mean ICC values were shown to be good across all movements for both groups (0.71e0.99) and mean absolute differences of repeated peak measures were small, <3.7 and <5.6 s1 for ROM and angular velocity and moderate <13.5 s2 for angular acceleration (Fig. 1). The similarity in irregularity scores between repeated trials was moderate-to-good for all the movements investigated (0.49e0.86 ALBP and 0.50e0.83 CLBP group). Mean absolute differences between repeated movements were small (1.2e5.5) suggesting good reliability of such a method. Fig. 2 shows a typical spatial plot exhibiting motion irregularity. It can be visualised from graph 2(a) that the second quartile displays greater irregularity especially at more than 40 ROM. It is also around this region that more irregularity is evident for quartile 3 resulting in a score of 2.6. If this graph is compared to 2(b) then it is evident that quartiles 1 and 4 are again those with fewest irregularities, as mirrored by the group results, however the second quartile demonstrates much greater irregularity (score ¼ 8.5). This is in part due to some jerkiness visualised in the final 5 of the movement. Quartile 3 also scores highly due to motion irregularity in the first 5 of returning from flexion. The mean numerical motion irregularity score for each quartile for each group as a whole is presented in Table 2, and shows that significant between group difference in motion irregularity values during extension, left and right side flexion, left rotation and lifting. The quartile with the greatest irregularity was consistently the second for all movements except left rotation and lifting in the CLBP group. 4. Discussion The results of this study show that inertial sensors are capable of measuring lumbar kinematics including ROM, angular velocity,
angular acceleration and movement irregularity through spatial plot analysis. The ROM and angular-velocity plots, as well as peak values demonstrate good reliability and small errors, however the results for angular-acceleration plots show slightly lower results in comparison. This may suggest greater acceleration movement variation from the participants; however it may also reflect the sensitivity of the double derivative technique to yield accelerations. The peak acceleration ICC values were good suggesting this variable is highly reliable for use in quantifying lumbar acceleration in LBP sufferers. Moreover the mean absolute difference in peak value was small, especially for movements other than lifting.
Fig. 1. Root mean square error values for displacement-, velocity- and acceleratione time curves along with absolute mean difference in peak values of displacement, velocity and acceleration (Flex, Flexion; Ext, Extension; LSF, Left Side-Flexion; RSF, Right Side-Flexion; Lrot, Left Rotation; Rrot, Right Rotation).
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Fig. 2. Quantification of spatial plots. (a) Flexion trial of individual participant. (b) Flexion trial of different individual.
The results of this study are comparable to those available in the literature (Marras and Wongsam, 1986; Esola et al., 1996; Granata et al., 1997; Marras and Granata, 1997; McClure et al., 1997; Marras et al., 2000, 2001; Pal et al., 2007; Shum et al., 2007a; Milosavljevic et al., 2008). The differences that do exist may be due to the different characteristics in the participants, with non-LBP participants displaying greater angular velocities and accelerations. These differences may be due to greater intra-participant movement variation possibly due to having or anticipating movement evoked pain. This may be especially true for angular velocities and accelerations which would be significantly affected by less smooth movements. The results of this study demonstrate that quantification of motion irregularity using spatial plots is possible and the results, as a score of movement irregularity, are reliable. This is the first time such movement profiles have been quantified and applied to LBP sufferers and it is evident that this new information enables the identification of which section of the movement is affected. The ability of the spatial plots to display movement coordination and control is of great use to the clinician. The quantification of movement irregularity demonstrates a consistent pattern across the LBP sufferers where more irregular motions are evident in the quartile leading up to end of range. This may possibly be due to attempts to minimise provocation of pain, as the individual adjusts or ‘explores’ the movement close to the terminal range in an attempt to find the most comfortable path. This results in deviations in velocity behaviour and causes greater scores in the irregularity quantification. However, it may also be the case that this loss of movement smoothness represents an impairment of spinal function due to perhaps alteration in proprioceptive input and
therefore represents an attempt to increase afferent information to guide the movement pattern. Previous research has already provided extensive information about how manual therapy may improve spinal ROM (Goodsell et al., 2000; Lee et al., 2005; Powers et al., 2008). However, it has been established that ROM does not correlate well with function in LBP sufferers (Parks et al., 2003), and that LBP sufferers have large deficits in angular-velocities and accelerations (Marras and Wongsam, 1986; Marras et al., 1995, 1999; Novy et al., 1999; Shum et al., 2007a, 2007b). Furthermore clinicians are increasingly interested in movement coordination and control (O’Sullivan, 2000, 2005; Shum et al., 2005a; Dankaerts et al., 2007). These methods of quantifying movement behaviour will enhance the understanding of movement coordination and control as well as enabling clinicians to study, in detail, the effect of interventions targeting movement control. The current work allows further research to look at the effects of manual therapy with new perspectives. For example, it would be able to show how manual therapy affects the angular velocity and acceleration of the spine. Using spatial plots, it would be possible to visualise how manual therapy affects control or movement irregularities in different quartiles of spine motion. The inertial sensors are easy to use and provide information that could be analysed immediately after clinical assessment. Although the current method involves some complexities in data processing, the algorithms can easily be incorporated into carefully designed computer software providing the clinician with immediate information about ROM, angular velocity, angular accelerations and movement irregularity through spatial plot analysis. Indeed this is currently being explored in on-going development.
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J.M. Williams et al. / Manual Therapy 18 (2013) 130e135 Table 2 Quantification of movement irregularity per quartile.
Flexion 1 2 3 4 Extension 1 2 3 4 LSF 1 2 3 4 RSF 1 2 3 4 LRot 1 2 3 4 RRot 1 2 3 4 Lift 1 2 3 4
ALBP
CLBP
3.1 6.4 4.4 3.5
3.7 4.9 3.7 2.4
6.0 13.1 7.2 3.1
6.1 6.3* 5.5 4.0
2.0 8.0 5.5 1.3
5.6* 5.8 3.6 2.6
1.4 7.1 4.1 1.1
3.5* 7.2 4.2 2.9*
6.8 11.4 9.3 11.4
16.7* 14.3 13.3 11.5
5.1 10.5 7.4 5.6
6.4 14.3 11.5 8.8
3.4 4.7 4.1 2.2
2.9 4.3 3.8 4.5*
*P ¼ 0.05 or less.
5. Conclusion The results of this study show that the inertial sensor system is capable of providing a method of measuring ROM, angular velocities and accelerations in LBP sufferers within a clinical environment. The additional kinematic information can be incorporated to study the spatial relationships providing a measure of movement irregularity. These data processing methods can be simplified by their inclusion into automated computer software. Therefore it is possible to study detailed kinematics of the lumbar region which may yield important clinical information regarding movement quality and control.
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Manual Therapy journal homepage: www.elsevier.com/math
Original article
A novel approach to the clinical evaluation of differential kinematics of the lumbar spineq Jonathan Mark Williams a, *, Inam Haq b, Raymond Y. Lee c a
School of Health and Social Care, Bournemouth University, Royal London House, Christchurch Road, Bournemouth, Dorset BH1 3LT, UK Brighton and Sussex Medical School, UK c Department of Life Sciences, University of Roehampton, UK b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 6 April 2012 Received in revised form 1 August 2012 Accepted 30 August 2012
Clinical measurement of lumbar motion has traditionally been limited to range of motion (ROM). Despite this, deficits in angular velocities and accelerations are more pronounced compared to ROM in low back pain (LBP) sufferers. There is increasing interest in movement quality among manual therapists and therefore the ability to measure angular velocities and accelerations within the clinical environment is becoming increasingly important. The aims of this study were to (1) investigate the reliability of a clinic based inertial sensor system to measure ROM along with angular velocities and accelerations in low back pain sufferers; (2) introduce the feasibility and reliability of using the relationship between ROM and velocity to investigate movement trajectory and irregularity. Forty LBP sufferers completed three trials of spinal movements and lifting. The ROM curve was differentiated and double differentiated to yield angular velocities and accelerations. Repeated measures reliabilities were determined by comparisons of kinematic curves as well as peak values. ROM and angular velocity relationships were investigated for their use in describing the movement trajectory and irregularity. Results show excellent similarities of ROM and angular velocity curves and moderate-to-good similarities for angular acceleration curves. Peak value similarities were excellent with small error measurements for all variables. The quantification of ROM-angular velocity plots was reliable with small mean absolute differences in motion irregularity scores. Such a method was able to demonstrate differences in movement irregularity. This method provides clinicians with the ability to yield important additional movement related information including angular velocity, acceleration and movement irregularity. Ó 2012 Elsevier Ltd. All rights reserved.
Keywords: Lumbar spine Kinematics Reliability Velocity
1. Introduction Measurement of lumbar motion is common in the assessment of low back pain (LBP) (Cocchiarella and Andersson, 2000). Clinical measures including skin distension (MacRae and Wright, 1969; Dolan et al., 1995), finger tip-to-floor (Frost et al., 1982; Battie et al., 1987; Gauvin et al., 1990) and inclinometers (Burdett et al., 1986; Saur et al., 1996; Bierma-Zeinstra et al., 2001; Kachingwe and Phillips, 2005) are unable to provide continuous data. This means they are unable to provide information about patterns of
q This work is affiliated to the Department of Life Sciences, University of Roehampton. * Corresponding author. Tel.: þ44 (0) 7766141620. E-mail addresses: [email protected], [email protected] (J.M. Williams). 1356-689X/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.math.2012.08.003
movement. Some laboratory methods such as electromagnetic or opto-electronic video based systems can provide this extra information, but often the reporting has been limited to range of motion (ROM) only. It is well documented that LBP sufferers display alterations in spinal kinematics (Shum et al., 2007a, 2007b), but this is most pronounced in the differential kinematics of the movement, i.e. angular velocities and accelerations (Marras and Wongsam, 1986; Marras et al., 1995, 1999; Novy et al., 1999). Angular velocities and accelerations have been able to accurately discriminate between LBP and non-LBP sufferers (Marras et al., 2000) as well as distinguish risk of LBP within the workplace (Marras et al., 1993). However, the reliability of the measurements of angular velocity and acceleration need to be further established. A few studies (Shum et al., 2005a, 2005b) have looked at the ROM-angular velocity relationship. This relationship, known as the
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spatial relationship, can be visualised by a ROM-velocity plot. The diagram provides a useful clinical picture of examining the spine from a dynamical perspective, in which the emphasis is on representing the kinematic control of the system (Burgess-Limerick et al., 1993; Li et al., 1999). Visualising motion control is important because various studies have shown that low back pain is associated with altered control (Van Dillen et al., 2009; Hodges et al., 2009; Xu et al., 2010). One novel use of this visualisation is to examine the degree of movement irregularity, which cannot be revealed by routine ROM assessment or the more familiar ROMtime plot. In the clinic the above approach can be easily achieved with the use of inertial sensors. Similar sensors have been used to study gait, movements of the upper and lower limb as well as the spine (Tong and Granat, 1999; Lee et al., 2003; Zhou et al., 2008; Saber-Sheikh et al., 2010; Cuesta-Vargas et al., 2010; Theobald et al., 2012; Ha et al., in press). However before such sensors are recommended for clinical use it is imperative to determine the reliability in LBP sufferers within a clinical setting, including the angular velocities and accelerations. Furthermore the feasibility of analysing and quantifying movement irregularity through spatial relationship plots should be established. The aim of this study was to (1) investigate the similarity between repeated movements, known as repeated measures reliability, of an inertial sensor system to measure ROM, angular velocity and acceleration of LBP sufferers; (2) to describe the use of spatial plots to study the movement irregularity of spinal movements. 2. Methods 2.1. Participants Forty LBP sufferers were initially recruited (twenty acute and twenty chronic) from general practitioner referrals to local therapy departments. Referrals were screened for inclusion and exclusion criteria. Inclusion criteria included pain confined to between the lower ribs and inferior gluteal folds, 18e55 years old, seeking healthcare for their LBP and evoked pain on at least 3 of the test movements. Exclusion criteria included a history of tumours, spinal fractures, surgery, rheumatological or neurological diseases and any neurological signs or symptoms. Acute pain was pain present for less than three weeks and chronic defined as pain present on at least three days per week for greater than twelve months. Four acute low back pain (ALBP) and eight chronic low back pain (CLBP) sufferers were excluded as they did not demonstrate three or more movements which evoked pain. Sixteen acute LBP sufferers (male 10; age 42.6 7.2 years; BMI 27.7 3.5 kg/m2; duration 12.0 7.3 days) and twelve chronic LBP sufferers (male 7; age 34.5 10.0 years; BMI 27.4 4.8 kg/m2; duration 4030.2 2992.2 days) recorded their mean pain scores from the week preceding data collection, using a visual analogue scale (VAS) (61.5 18.9 mm ALBP; 45.7 22.1 mm CLBP) and a tampa scale of kinesiophobia questionnaire (39.3 4.1 ALBP; 38.3 7.5 CLBP). This study was approved by the National Research Ethics Service of the National Health Service and written informed consent was obtained following explanation of procedures and risks. 2.2. Instrumentation Two wired inertial sensors were used to measure lumbar kinematics (3DM-GX3-25, Microstrain, VT, USA). Each sensor contained tri-axial gyroscopes (300 s1), accelerometers (5 g) and magnetometers. Each sensor was cased with dimensions 44 mm (h) 25 mm (w) 11 mm (d), and weight of 18 g. The sensors have
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a reported accuracy of 0.5 during static testing and 2.0 during dynamic testing. One sensor was placed over the S1 spinous process and the second over the L1 spinous process. The sensors were attached using double sided tape with the wires secured to the trunk so as not to move the sensors erroneously. The sensors were connected to a purpose built datalogger and software (ThetaMetrix, UK) and data were collected at 100 Hz. The relative orientations between the L1 and S1 sensor were determined from the direction cosine matrices (Grood and Suntay, 1983; Burnett et al., 1998; Lee et al., 2003). Flexion, left side bending and left rotation were considered positive and the opposite movements negative. 2.3. Procedure Participants stood upright and completed three trials of flexion, extension, left side bending, right side bending, left rotation, right rotation and a box lift. The box (460 260 300 mm) was positioned on markers ensuring identical placement and weighed 3 kg. The movement order was identical for all participants and the sensors remained attached throughout. 2.4. Data analysis All raw data were transferred to Matlab for processing (Mathworks, R2008b). The ROM data for each movement were determined and smoothed using a fourth order zero-lag Butterworth filter with a cut off frequency of 1 Hz (Tsang et al., 2011). The ROM data were differentiated to yield the velocity and double differentiated to calculate acceleration. The ROM curves were timenormalised so that each movement had the same time base from 0% to 100%. This normalisation process takes into account the differing rates at which the participants completed the trials and allows the direct comparison of the kinematic pattern. The coefficient of multiple correlation (CMC) (Li and Caldwell, 1999; Williams et al., 2010) and root mean square error (RMSE) were calculated from the normalised range of motion, angular velocity and acceleration curves to determine the similarities between the three movement trials. The peak ROM, peak angular velocity and peak angular acceleration values were obtained and intra-class coefficients (ICC3,1) calculated along with the mean absolute differences between peak values for the three trials. Two peak values were obtained, positive and negative. Positive velocity and acceleration relate to flexion, left side bending and left rotation movement, whereas negative velocity and acceleration relate to movement in the opposite direction. Angular velocity-ROM plots were used to reveal the spatial relationship where the overall shape of the plot describes the trajectory of the movement and dynamic control. Movement irregularity was determined from the plots by separating the plot into quartiles (see Fig. 2). The quartiles were defined as the region from motion onset to peak angular velocity (quartile 1); peak angular velocity to end ROM or maximum angular displacement (quartile 2); maximum angular displacement to minimum angular velocity (quartile 3) and minimum angular velocity to minimum angular displacement or return to upright standing (quartile 4). Each quartile was fitted with a 4th order polynomial and the beginning and end of the polynomial was fixed to match the collected data. The polynomial was used to determine the RMS difference between the actual data and the polynomial, providing quantification of motion irregularity for each quartile. The RMS was normalised to the maximum velocity of each individual to provide a value representing the percentage of maximum velocity. Scores of motion irregularity (RMS) were determined for each quartile across
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Table 1 Mean (sd) values of kinematic variables recorded by the inertial system. þve Vel ( s1)
Disp ( )
Flex Ext LSF RSF LRot RRot Lift
ALBP
CLBP
35.1 11.8 11.5 15.0 7.1 10.6 29.9
48.1 16.5 14.6 15.9 4.4 8.3 43.8
(14.5) (8.8) (3.7) (4.1) (3.5) (4.5) (15.0)
(11.4) (5.1) (6.9) (4.6) (3.6) (4.1) (12.6)
ve Vel ( s1)
ALBP
CLBP
20.1 10.6 8.2 13.4 5.0 7.0 21.5
28.2 15.0 9.6 13.6 4.5 6.1 33.8
(15.8) (6.3) (4.4) (4.8) (2.6) (3.0) (14.0)
(9.6) (6.3) (5.8) (5.9) (3.1) (4.2) (13.9)
þve Acc ( s2)
ALBP
CLBP
20.1 6.2 11.2 8.8 5.1 5.6 26.6
29.0 10.0 11.6 10.2 5.0 6.4 36.3
(11.8) (3.0) (4.3) (3.7) (2.6) (2.5) (13.5)
(10.0) (4.3) (5.8) (5.1) (3.9) (4.8) (14.5)
ve Acc ( s2)
ALBP
CLBP
29.8 18.9 15.3 20.9 11.5 12.2 44.1
34.1 25.1 18.9 20.6 13.5 14.9 59.7
(18.8) (10.1) (8.5) (9.3) (4.5) (7.0) (23.3)
(28.7) (12.0) (11.4) (11.8) (9.2) (10.1) (29.8)
ALBP
CLBP
26.6 14.6 17.5 17.2 10.6 12.7 42.8
41.3 26.1 17.6 20.7 13.5 14.2 58.5
(16.7) (8.1) (7.6) (8.2) (4.6) (5.7) (24.6)
(21.0) (14.5) (10.8) (11.3) (8.9) (9.9) (30.5)
Flex, Flexion; Ext, Extension; LSF, Left side flexion; RSF, Right side flexion; LRot, Left rotation; RRot, Right rotation; Disp, Displacement; þve, positive; ve, negative; Vel, Velocity; Acc, Acceleration.
each movement and ICC3,1 and mean absolute difference between trials were used to determine reliability of such a method. 3. Results Mean (sd) for displacement, velocity and acceleration for each movement can be found in Table 1. The mean CMC values were found to be good across all repeated range of motion curves for the ALBP (0.83e0.97 0.02e0.09) and CLBP (0.72e0.94 0.03e0.26) groups. This was also seen for angular velocity curves (0.83e0.94 0.04e0.10 ALBP and 0.71e 0.91 0.05e0.18 CLBP). The mean CMC values for angular acceleration curves show moderate consistency across the different movements tested (0.61e0.77 0.10e0.18 ALBP and 0.52e0.73 0.10e0.26 CLBP). The RMSE magnitudes across all kinematic variables were small for all repeated movements (Fig. 1). Mean ICC values were shown to be good across all movements for both groups (0.71e0.99) and mean absolute differences of repeated peak measures were small, <3.7 and <5.6 s1 for ROM and angular velocity and moderate <13.5 s2 for angular acceleration (Fig. 1). The similarity in irregularity scores between repeated trials was moderate-to-good for all the movements investigated (0.49e0.86 ALBP and 0.50e0.83 CLBP group). Mean absolute differences between repeated movements were small (1.2e5.5) suggesting good reliability of such a method. Fig. 2 shows a typical spatial plot exhibiting motion irregularity. It can be visualised from graph 2(a) that the second quartile displays greater irregularity especially at more than 40 ROM. It is also around this region that more irregularity is evident for quartile 3 resulting in a score of 2.6. If this graph is compared to 2(b) then it is evident that quartiles 1 and 4 are again those with fewest irregularities, as mirrored by the group results, however the second quartile demonstrates much greater irregularity (score ¼ 8.5). This is in part due to some jerkiness visualised in the final 5 of the movement. Quartile 3 also scores highly due to motion irregularity in the first 5 of returning from flexion. The mean numerical motion irregularity score for each quartile for each group as a whole is presented in Table 2, and shows that significant between group difference in motion irregularity values during extension, left and right side flexion, left rotation and lifting. The quartile with the greatest irregularity was consistently the second for all movements except left rotation and lifting in the CLBP group. 4. Discussion The results of this study show that inertial sensors are capable of measuring lumbar kinematics including ROM, angular velocity,
angular acceleration and movement irregularity through spatial plot analysis. The ROM and angular-velocity plots, as well as peak values demonstrate good reliability and small errors, however the results for angular-acceleration plots show slightly lower results in comparison. This may suggest greater acceleration movement variation from the participants; however it may also reflect the sensitivity of the double derivative technique to yield accelerations. The peak acceleration ICC values were good suggesting this variable is highly reliable for use in quantifying lumbar acceleration in LBP sufferers. Moreover the mean absolute difference in peak value was small, especially for movements other than lifting.
Fig. 1. Root mean square error values for displacement-, velocity- and acceleratione time curves along with absolute mean difference in peak values of displacement, velocity and acceleration (Flex, Flexion; Ext, Extension; LSF, Left Side-Flexion; RSF, Right Side-Flexion; Lrot, Left Rotation; Rrot, Right Rotation).
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Fig. 2. Quantification of spatial plots. (a) Flexion trial of individual participant. (b) Flexion trial of different individual.
The results of this study are comparable to those available in the literature (Marras and Wongsam, 1986; Esola et al., 1996; Granata et al., 1997; Marras and Granata, 1997; McClure et al., 1997; Marras et al., 2000, 2001; Pal et al., 2007; Shum et al., 2007a; Milosavljevic et al., 2008). The differences that do exist may be due to the different characteristics in the participants, with non-LBP participants displaying greater angular velocities and accelerations. These differences may be due to greater intra-participant movement variation possibly due to having or anticipating movement evoked pain. This may be especially true for angular velocities and accelerations which would be significantly affected by less smooth movements. The results of this study demonstrate that quantification of motion irregularity using spatial plots is possible and the results, as a score of movement irregularity, are reliable. This is the first time such movement profiles have been quantified and applied to LBP sufferers and it is evident that this new information enables the identification of which section of the movement is affected. The ability of the spatial plots to display movement coordination and control is of great use to the clinician. The quantification of movement irregularity demonstrates a consistent pattern across the LBP sufferers where more irregular motions are evident in the quartile leading up to end of range. This may possibly be due to attempts to minimise provocation of pain, as the individual adjusts or ‘explores’ the movement close to the terminal range in an attempt to find the most comfortable path. This results in deviations in velocity behaviour and causes greater scores in the irregularity quantification. However, it may also be the case that this loss of movement smoothness represents an impairment of spinal function due to perhaps alteration in proprioceptive input and
therefore represents an attempt to increase afferent information to guide the movement pattern. Previous research has already provided extensive information about how manual therapy may improve spinal ROM (Goodsell et al., 2000; Lee et al., 2005; Powers et al., 2008). However, it has been established that ROM does not correlate well with function in LBP sufferers (Parks et al., 2003), and that LBP sufferers have large deficits in angular-velocities and accelerations (Marras and Wongsam, 1986; Marras et al., 1995, 1999; Novy et al., 1999; Shum et al., 2007a, 2007b). Furthermore clinicians are increasingly interested in movement coordination and control (O’Sullivan, 2000, 2005; Shum et al., 2005a; Dankaerts et al., 2007). These methods of quantifying movement behaviour will enhance the understanding of movement coordination and control as well as enabling clinicians to study, in detail, the effect of interventions targeting movement control. The current work allows further research to look at the effects of manual therapy with new perspectives. For example, it would be able to show how manual therapy affects the angular velocity and acceleration of the spine. Using spatial plots, it would be possible to visualise how manual therapy affects control or movement irregularities in different quartiles of spine motion. The inertial sensors are easy to use and provide information that could be analysed immediately after clinical assessment. Although the current method involves some complexities in data processing, the algorithms can easily be incorporated into carefully designed computer software providing the clinician with immediate information about ROM, angular velocity, angular accelerations and movement irregularity through spatial plot analysis. Indeed this is currently being explored in on-going development.
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J.M. Williams et al. / Manual Therapy 18 (2013) 130e135 Table 2 Quantification of movement irregularity per quartile.
Flexion 1 2 3 4 Extension 1 2 3 4 LSF 1 2 3 4 RSF 1 2 3 4 LRot 1 2 3 4 RRot 1 2 3 4 Lift 1 2 3 4
ALBP
CLBP
3.1 6.4 4.4 3.5
3.7 4.9 3.7 2.4
6.0 13.1 7.2 3.1
6.1 6.3* 5.5 4.0
2.0 8.0 5.5 1.3
5.6* 5.8 3.6 2.6
1.4 7.1 4.1 1.1
3.5* 7.2 4.2 2.9*
6.8 11.4 9.3 11.4
16.7* 14.3 13.3 11.5
5.1 10.5 7.4 5.6
6.4 14.3 11.5 8.8
3.4 4.7 4.1 2.2
2.9 4.3 3.8 4.5*
*P ¼ 0.05 or less.
5. Conclusion The results of this study show that the inertial sensor system is capable of providing a method of measuring ROM, angular velocities and accelerations in LBP sufferers within a clinical environment. The additional kinematic information can be incorporated to study the spatial relationships providing a measure of movement irregularity. These data processing methods can be simplified by their inclusion into automated computer software. Therefore it is possible to study detailed kinematics of the lumbar region which may yield important clinical information regarding movement quality and control.
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