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The characteristics of gait disturbance and its relationship with posterior tibial somatosensory evoked potentials in patients with cervical myelopathy
ESMAC 2012 abstract / Gait & Posture 38 (2013) S1–S116
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P66
Reference
Gait impairment in cervical spondylotic myelopathy: analysis of muscle activation timing
[1] Malone A, Meldrum D, Gleeson J, Bolger C. Journal of Electromyography & Kinesiology 2011;21:1004–10.
Ailish Malone 1 , Dara Meldrum 2 , John Gleeson 3 , Ciaran Bolger 4 1
Beaumont Hospital, Physiotherapy Department, Dublin, Ireland 2 Royal College of Surgeons in Ireland, School of Physiotherapy, Dublin, Ireland 3 Royal College of Surgeons in Ireland, Department of Anatomy, Dublin, Ireland 4 Beaumont Hospital, Department of Neurosurgery, Dublin, Ireland Introduction: Gait impairment is a primary symptom of cervical spondylotic myelopathy (CSM). Analysis of the timing of muscle activation during gait, using surface electromyography (SEMG), can improve the understanding of a gait deficit. The aim of this study was to compare muscle activation timing during gait in people with untreated CSM and in age- and gender- matched healthy controls. Patients/Materials and Methods: Ethical approval was obtained from a hospital Ethics Committee. Sixteen people with untreated CSM were recruited consecutively from a neurosurgery clinic, and matched to healthy controls of the same age (±5 years) and gender. Participants from both groups completed at least ten walking trials at comfortable gait speed along a 10-metre walkway. Healthy controls then completed a second set of ten walking trials at the same speed as the CSM participants to whom they were matched. SEMG signals were recorded during gait from rectus femoris, biceps femoris, tibialis anterior and medial gastrocnemius using standard electrode recording and placement procedures. Signals were processed using MATLAB® and the timing of activation was extracted using a previously validated reliable method [1]. The duration of activation for each muscle was expressed as a percentage of gait cycle time. Data for CSM participants and healthy controls were compared using paired t-tests. Results: The mean comfortable walking speed of CSM participants was 1.12 m/s, while healthy controls walked at a mean of 1.49 m/s. Comparing both groups at comfortable walking speed, CSM participants demonstrated significantly prolonged activation of rectus femoris (33% gait cycle duration compared to 22% in healthy controls, p = 0.04), biceps femoris (32% vs 22%, p = 0.001) and tibialis anterior (42% vs 30%, p = 0.007), but not medial gastrocnemius (32% vs 29%, p = 0.4). Co-activation of rectus and biceps femoris was also prolonged in CSM (14% vs 9%, p = 0.04). No differences were found in the co-activation time of tibialis anterior and medial gastrocnemius (7% vs 4%, p = 0.17). The results of the matched speed analysis mirrored the findings at comfortable speed, with the same muscles showing statistically significant differences between CSM participants and controls. Discussion and conclusions: This study was the first to identify and quantify abnormally prolonged duration of activation of the lower limb muscles as a feature of gait in CSM. The persistence of these findings at matched gait speed confirmed that these were features of abnormal neuromuscular control, and not a consequence of slower gait speed. Prolonged duration of activation may be a strategy to compensate for lack of stability or weakness of lower limb muscles during gait.
http://dx.doi.org/10.1016/j.gaitpost.2013.07.217 P68 The characteristics of gait disturbance and its relationship with posterior tibial somatosensory evoked potentials in patients with cervical myelopathy Jung Hwan Lee 1 , Sang-Ho Lee 2 1
Wooridul Spine Hospital, Physical Medicine and Rehabilitation, Seoul, South Korea 2 Wooridul Spine Hospital, Neurosurgery, Seoul, South Korea Introduction: Many of patients with cervical myelopathy (CM) suffer from gait disturbance so that the assessment of walking ability and its restoration are one of main concerns. This study is to investigate the gait characteristics of CM and to assess the relationship between presence of abnormality of posterior tibial somatosensory evoked potentials (PTSEP) and gait parameters. Patients/materials and methods: The patients were recruited who had suffered from gait disturbance and were diagnosed as CM by cervical magnetic resonance image (MRI). All subjects underwent three dimensional gait analysis and PTSEP. Normal person were recruited as control groups and underwent gait analysis The CM patients were divided into two groups such as normal and abnormal SEP groups and two groups were compared as to presence of signal change in MRI and gait parameters. Results: CM groups revealed significantly decreased gait velocity, step length and stride length, as well as increased double support time. They showed significantly decreased maximal knee flexion angle in swing phase, the decreased plantarflexion angle at push off, and the increased maximal dorsiflexion angle at swing phase in comparison with control group. Abnormal SEP group demonstrated decreased gait velocity and cadence, decreased plantarflexion angle at push off and increased maximal dorsiflexion angle at swing phase in comparison with normal SEP group. There was no significant relationship between presence of SEP abnormality and signal change of MRI. Discussion and conclusions: CM Patients with PTSEP abnormality showed gait characteristics of CM patients more prominently than those without PTSEP abnormality. These results supported that the gait deviation of CM was attributed to impaired proprioception of lower limbs and poor stability. Damage of the posterior columns by CM interrupted the delivery of information about joint position and movement and consequently often causes gait disturbance. CM patients compensated stabilizing balance by decreasing gait velocity and step length as well as increasing step width and double support time. Step length was shortened by decreasing ankle plantarflexion at push off stage at the end of preswing in gait cycle. Decreased ankle plantarflexion at push off might lead to decreased knee flexion and increased ankle dorsiflexion at swing stage.
Further reading [1] Dennis GC, Dehkordi O, Millis RM, et al. Somatosensory evoked potential, neurological examination and magnetic resonance imaging for assessment of cervical spinal cord decompression. Life Science 2000;66:389–97. [2] Takayama H, Muratsu H, Doita M, et al. Impaired joint proprioception in patients with cervical myelopathy. Spine (Phila Pa 1976) 2005;30:83–6.
S108
ESMAC 2012 abstract / Gait & Posture 38 (2013) S1–S116
[3] Maezawa Y, Uchida K, Baba H. Gait analysis of spastic walking in patients with cervical compressive myelopathy. The Journal of Orthopaedic Science 2001;6:378–84.
http://dx.doi.org/10.1016/j.gaitpost.2013.07.218 P70 A thorough analysis of the coefficient of multiple correlation (Cmc) shows multiple problems Jo
Røislien 1 ,
Linda
Rennie 2 ,
Arve
Opheim 2
1
University of Oslo, Department of Biostatistics, Oslo, Norway 2 Sunnaas Rehabilitation Hospital, Nesoddtangen, Norway Introduction: The question of reliability is essential for any measurement method, and when investigating reliability of 3DGA kinematic curves the coefficient of multiple correlations (CMC) [1] is frequently used, despite the reports of methodological issues [2]. The aim of the current study was to perform a systematic evaluation of the CMC using stochastic simulations. The results are exemplified on an inter-rater reliability study of 3DGA data related to marker placement. Patients/materials and methods: Synthetic gait curves were generated from a stochastic model where amplitude, vertical offset and a possible horizontal shift due to random error and timing issues were included as stochastic variables. The model was used hierarchically. First a “true” curve for each subject was sampled; then curves from each of the different test situations, e.g. different tester teams, was sampled using this “true” curve as the mean. The CMC is a measure of similarity of waveforms, e.g. curve data [1], comparing multiple curves from several subjects across test situations, e.g. testers. The CMC was calculated based on the synthetic data with systematic variations in curve amplitude, frequency, vertical curve offset, and number of subjects and number of test situations. Confidence intervals were estimated using bootstrapping. Seven healthy adult volunteers gave written informed consent to take part in the accompanying inter-rater reliability study. The subjects were tested on two consecutive days by two different tester teams. 3DGA data was recorded during bare feet, level walking along a 10m walkway at self-selected comfortable walking speed, using six Vicon MX13 cameras, and the Plug- inGait model. For each subject, one left cycle from each test situation was selected, based on similarity in walking speed. Results: Joints with large amplitudes, i.e. above 15◦ , resulted in CMC > 0.9. High sampling frequencies, i.e. above 20 points per gait cycle, also resulted in CMC > 0.9. Increasing offsets resulted in decreasing CMC values. For two test situations 20 subjects resulted in wider CIs for the CMC than five subjects on five test situations. In the inter-rater reliability study, the CMC calculations consistently broke down, i.e. could not be computed, for joints with a low amplitude. In curves with a visually apparent systematic vertical offset, the CMC was still approximately 0.9. Discussion and conclusions: The CMC is sensitive to the amplitude of the curve, demonstrating how the signal-to-noise ratio is a problem issue. As the CMC does not adjust for the inter-gait-curve correlation between data points, higher sample frequency leads to high CMCs, merely due to high correlation between data in the calculations. The frequently used reliability design of two tester teams was demonstrated to show comparably high CIs. In conclusion, in our study the CMC did not show the statistical properties that are needed for it to be an overall measurement of curve similarity. Other methods than the CMC must be used to assess reliability of 3DGA data.
References [1] Kadaba MP, Ramakrishnan HK, Wootten ME, Gainey J, Gorton G, Cochran GVB. Repeatability of kinematic, kinetic, and electromyographic data in normal adult gait. Journal of Orthopaedic Research 1989;7:849–60. [2] McGinley JL, Baker R, Wolfe R, Morris ME. The reliability of threedimensional kinematic gait measurements: a systematic review. Gait & Posture 2009;29:360–9.
http://dx.doi.org/10.1016/j.gaitpost.2013.07.219 P72 Determination of reaction forces and moments in the foot joints using multibody dynamics and Fe analysis—A clinical application Christian Wyss, Reinald Brunner Laboratory for Movement Analysis, University Children’s Hospital Basel, University of Basel, Basel, Switzerland Introduction: Daily routine does not allow determining joint reaction forces and moments or stress and strain in vivo. These data of mechanics, however, help understanding foot function and may guide treatment decision making. Finite element analysis (FE) offers one way to calculate the intra-articular forces and moments in the foot. Patients/materials and methods: A full 3D gait analysis was performed including one EMED SF pressure measurement platform. The “GaitLowerExtremity” model from the ANYBODY Repository AMMRV1.4.1 was used to determine the muscle forces of the lower leg during gait. The muscle forces from the ANYBODY model, the regional reaction forces from the EMED SF platform and the 3D kinematics were used as boundary conditions for the FE-model. A “standard foot model” was created from a CT scan of one healthy foot. This model was imported in ANSYS Workbench V.14.0 and scaled according to the subject’s standard x-rays. For ethical reasons true normals were not available for reasons of irradiation. Thus 10 patients with moderate hallux valgus deformity were considered close to normals. Their data were analysed by this model using a multi body dynamic analysis with 100 load steps and taken as reference (normative) values. The data of one patient with hindfoot instability were analysed in respect of the normative data. Results: Mean and standard deviation of the reaction force and the reaction moment of every joint in the foot were calculated. The data from a patient with hindfoot instability were compared with the normal values (Fig. 1). The red curves show the mean and the confidence interval in x direction in the subtalar joint gained from the 10 patients. The blue curve represents the data of the patient. A deviation of more than one confidence interval from the normative values is considered significant. The analysis shows an increased load at the subtalar joint in this case. The mathematical principle described can be applied for every joint in the foot.
Figure 1. Red=normal (mean and confidence interval),blue=patient.
S107
P66
Reference
Gait impairment in cervical spondylotic myelopathy: analysis of muscle activation timing
[1] Malone A, Meldrum D, Gleeson J, Bolger C. Journal of Electromyography & Kinesiology 2011;21:1004–10.
Ailish Malone 1 , Dara Meldrum 2 , John Gleeson 3 , Ciaran Bolger 4 1
Beaumont Hospital, Physiotherapy Department, Dublin, Ireland 2 Royal College of Surgeons in Ireland, School of Physiotherapy, Dublin, Ireland 3 Royal College of Surgeons in Ireland, Department of Anatomy, Dublin, Ireland 4 Beaumont Hospital, Department of Neurosurgery, Dublin, Ireland Introduction: Gait impairment is a primary symptom of cervical spondylotic myelopathy (CSM). Analysis of the timing of muscle activation during gait, using surface electromyography (SEMG), can improve the understanding of a gait deficit. The aim of this study was to compare muscle activation timing during gait in people with untreated CSM and in age- and gender- matched healthy controls. Patients/Materials and Methods: Ethical approval was obtained from a hospital Ethics Committee. Sixteen people with untreated CSM were recruited consecutively from a neurosurgery clinic, and matched to healthy controls of the same age (±5 years) and gender. Participants from both groups completed at least ten walking trials at comfortable gait speed along a 10-metre walkway. Healthy controls then completed a second set of ten walking trials at the same speed as the CSM participants to whom they were matched. SEMG signals were recorded during gait from rectus femoris, biceps femoris, tibialis anterior and medial gastrocnemius using standard electrode recording and placement procedures. Signals were processed using MATLAB® and the timing of activation was extracted using a previously validated reliable method [1]. The duration of activation for each muscle was expressed as a percentage of gait cycle time. Data for CSM participants and healthy controls were compared using paired t-tests. Results: The mean comfortable walking speed of CSM participants was 1.12 m/s, while healthy controls walked at a mean of 1.49 m/s. Comparing both groups at comfortable walking speed, CSM participants demonstrated significantly prolonged activation of rectus femoris (33% gait cycle duration compared to 22% in healthy controls, p = 0.04), biceps femoris (32% vs 22%, p = 0.001) and tibialis anterior (42% vs 30%, p = 0.007), but not medial gastrocnemius (32% vs 29%, p = 0.4). Co-activation of rectus and biceps femoris was also prolonged in CSM (14% vs 9%, p = 0.04). No differences were found in the co-activation time of tibialis anterior and medial gastrocnemius (7% vs 4%, p = 0.17). The results of the matched speed analysis mirrored the findings at comfortable speed, with the same muscles showing statistically significant differences between CSM participants and controls. Discussion and conclusions: This study was the first to identify and quantify abnormally prolonged duration of activation of the lower limb muscles as a feature of gait in CSM. The persistence of these findings at matched gait speed confirmed that these were features of abnormal neuromuscular control, and not a consequence of slower gait speed. Prolonged duration of activation may be a strategy to compensate for lack of stability or weakness of lower limb muscles during gait.
http://dx.doi.org/10.1016/j.gaitpost.2013.07.217 P68 The characteristics of gait disturbance and its relationship with posterior tibial somatosensory evoked potentials in patients with cervical myelopathy Jung Hwan Lee 1 , Sang-Ho Lee 2 1
Wooridul Spine Hospital, Physical Medicine and Rehabilitation, Seoul, South Korea 2 Wooridul Spine Hospital, Neurosurgery, Seoul, South Korea Introduction: Many of patients with cervical myelopathy (CM) suffer from gait disturbance so that the assessment of walking ability and its restoration are one of main concerns. This study is to investigate the gait characteristics of CM and to assess the relationship between presence of abnormality of posterior tibial somatosensory evoked potentials (PTSEP) and gait parameters. Patients/materials and methods: The patients were recruited who had suffered from gait disturbance and were diagnosed as CM by cervical magnetic resonance image (MRI). All subjects underwent three dimensional gait analysis and PTSEP. Normal person were recruited as control groups and underwent gait analysis The CM patients were divided into two groups such as normal and abnormal SEP groups and two groups were compared as to presence of signal change in MRI and gait parameters. Results: CM groups revealed significantly decreased gait velocity, step length and stride length, as well as increased double support time. They showed significantly decreased maximal knee flexion angle in swing phase, the decreased plantarflexion angle at push off, and the increased maximal dorsiflexion angle at swing phase in comparison with control group. Abnormal SEP group demonstrated decreased gait velocity and cadence, decreased plantarflexion angle at push off and increased maximal dorsiflexion angle at swing phase in comparison with normal SEP group. There was no significant relationship between presence of SEP abnormality and signal change of MRI. Discussion and conclusions: CM Patients with PTSEP abnormality showed gait characteristics of CM patients more prominently than those without PTSEP abnormality. These results supported that the gait deviation of CM was attributed to impaired proprioception of lower limbs and poor stability. Damage of the posterior columns by CM interrupted the delivery of information about joint position and movement and consequently often causes gait disturbance. CM patients compensated stabilizing balance by decreasing gait velocity and step length as well as increasing step width and double support time. Step length was shortened by decreasing ankle plantarflexion at push off stage at the end of preswing in gait cycle. Decreased ankle plantarflexion at push off might lead to decreased knee flexion and increased ankle dorsiflexion at swing stage.
Further reading [1] Dennis GC, Dehkordi O, Millis RM, et al. Somatosensory evoked potential, neurological examination and magnetic resonance imaging for assessment of cervical spinal cord decompression. Life Science 2000;66:389–97. [2] Takayama H, Muratsu H, Doita M, et al. Impaired joint proprioception in patients with cervical myelopathy. Spine (Phila Pa 1976) 2005;30:83–6.
S108
ESMAC 2012 abstract / Gait & Posture 38 (2013) S1–S116
[3] Maezawa Y, Uchida K, Baba H. Gait analysis of spastic walking in patients with cervical compressive myelopathy. The Journal of Orthopaedic Science 2001;6:378–84.
http://dx.doi.org/10.1016/j.gaitpost.2013.07.218 P70 A thorough analysis of the coefficient of multiple correlation (Cmc) shows multiple problems Jo
Røislien 1 ,
Linda
Rennie 2 ,
Arve
Opheim 2
1
University of Oslo, Department of Biostatistics, Oslo, Norway 2 Sunnaas Rehabilitation Hospital, Nesoddtangen, Norway Introduction: The question of reliability is essential for any measurement method, and when investigating reliability of 3DGA kinematic curves the coefficient of multiple correlations (CMC) [1] is frequently used, despite the reports of methodological issues [2]. The aim of the current study was to perform a systematic evaluation of the CMC using stochastic simulations. The results are exemplified on an inter-rater reliability study of 3DGA data related to marker placement. Patients/materials and methods: Synthetic gait curves were generated from a stochastic model where amplitude, vertical offset and a possible horizontal shift due to random error and timing issues were included as stochastic variables. The model was used hierarchically. First a “true” curve for each subject was sampled; then curves from each of the different test situations, e.g. different tester teams, was sampled using this “true” curve as the mean. The CMC is a measure of similarity of waveforms, e.g. curve data [1], comparing multiple curves from several subjects across test situations, e.g. testers. The CMC was calculated based on the synthetic data with systematic variations in curve amplitude, frequency, vertical curve offset, and number of subjects and number of test situations. Confidence intervals were estimated using bootstrapping. Seven healthy adult volunteers gave written informed consent to take part in the accompanying inter-rater reliability study. The subjects were tested on two consecutive days by two different tester teams. 3DGA data was recorded during bare feet, level walking along a 10m walkway at self-selected comfortable walking speed, using six Vicon MX13 cameras, and the Plug- inGait model. For each subject, one left cycle from each test situation was selected, based on similarity in walking speed. Results: Joints with large amplitudes, i.e. above 15◦ , resulted in CMC > 0.9. High sampling frequencies, i.e. above 20 points per gait cycle, also resulted in CMC > 0.9. Increasing offsets resulted in decreasing CMC values. For two test situations 20 subjects resulted in wider CIs for the CMC than five subjects on five test situations. In the inter-rater reliability study, the CMC calculations consistently broke down, i.e. could not be computed, for joints with a low amplitude. In curves with a visually apparent systematic vertical offset, the CMC was still approximately 0.9. Discussion and conclusions: The CMC is sensitive to the amplitude of the curve, demonstrating how the signal-to-noise ratio is a problem issue. As the CMC does not adjust for the inter-gait-curve correlation between data points, higher sample frequency leads to high CMCs, merely due to high correlation between data in the calculations. The frequently used reliability design of two tester teams was demonstrated to show comparably high CIs. In conclusion, in our study the CMC did not show the statistical properties that are needed for it to be an overall measurement of curve similarity. Other methods than the CMC must be used to assess reliability of 3DGA data.
References [1] Kadaba MP, Ramakrishnan HK, Wootten ME, Gainey J, Gorton G, Cochran GVB. Repeatability of kinematic, kinetic, and electromyographic data in normal adult gait. Journal of Orthopaedic Research 1989;7:849–60. [2] McGinley JL, Baker R, Wolfe R, Morris ME. The reliability of threedimensional kinematic gait measurements: a systematic review. Gait & Posture 2009;29:360–9.
http://dx.doi.org/10.1016/j.gaitpost.2013.07.219 P72 Determination of reaction forces and moments in the foot joints using multibody dynamics and Fe analysis—A clinical application Christian Wyss, Reinald Brunner Laboratory for Movement Analysis, University Children’s Hospital Basel, University of Basel, Basel, Switzerland Introduction: Daily routine does not allow determining joint reaction forces and moments or stress and strain in vivo. These data of mechanics, however, help understanding foot function and may guide treatment decision making. Finite element analysis (FE) offers one way to calculate the intra-articular forces and moments in the foot. Patients/materials and methods: A full 3D gait analysis was performed including one EMED SF pressure measurement platform. The “GaitLowerExtremity” model from the ANYBODY Repository AMMRV1.4.1 was used to determine the muscle forces of the lower leg during gait. The muscle forces from the ANYBODY model, the regional reaction forces from the EMED SF platform and the 3D kinematics were used as boundary conditions for the FE-model. A “standard foot model” was created from a CT scan of one healthy foot. This model was imported in ANSYS Workbench V.14.0 and scaled according to the subject’s standard x-rays. For ethical reasons true normals were not available for reasons of irradiation. Thus 10 patients with moderate hallux valgus deformity were considered close to normals. Their data were analysed by this model using a multi body dynamic analysis with 100 load steps and taken as reference (normative) values. The data of one patient with hindfoot instability were analysed in respect of the normative data. Results: Mean and standard deviation of the reaction force and the reaction moment of every joint in the foot were calculated. The data from a patient with hindfoot instability were compared with the normal values (Fig. 1). The red curves show the mean and the confidence interval in x direction in the subtalar joint gained from the 10 patients. The blue curve represents the data of the patient. A deviation of more than one confidence interval from the normative values is considered significant. The analysis shows an increased load at the subtalar joint in this case. The mathematical principle described can be applied for every joint in the foot.
Figure 1. Red=normal (mean and confidence interval),blue=patient.