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Stepping strategies used by post-stroke individuals to maintain margins of stability during walking
Abstracts / Gait & Posture 39S (2014) S1–S141
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Fig. 1. Sagittal knee and ankle joint moments (Nm/kg) and powers (W/kg) during the initial gait-to-STD (blue line) and final STD-to-gait (black line) transition. * indicates significant differences (p ≤ 0.05).
016 Stepping strategies used by post-stroke individuals to maintain margins of stability during walking L. Hak 1,∗ , H. Houdijk 1,2 , P. Van der Wurff 3 , M.R. Prins 1,3 , A. Mert 3 , P.J. Beek 1 , J.H. Van Dieën 1 1 Research Institute MOVE, Faculty of Human Movement Sciences, VU University Amsterdam, The Netherlands 2 Heliomare Rehabilitation Centre, Wijk aan Zee, The Netherlands 3 Center for Augmented Motor Learning and Training, MRC Aardenburg, Doorn, The Netherlands
Introduction and aim: People who are recovering from a stroke often walk slower, with shorter and slower steps and with a larger step width compared to able-bodied people. Some of these devia-
tions in the gait pattern are explained as strategies to decrease the risk of falling during walking [1]. However, from observational data, it remains unclear whether these differences in the gait pattern represent a strategy to reduce the risk of falling, or are actually causing the increased fall risk. In the current study, we manipulated gait stability and gait adaptability, to investigate how post-stroke patients adapted their gait pattern to withstand these manipulations and whether these adaptations differed from able bodied people. Patients/materials and methods: Ten post-stroke and nine age-matched able-bodied individuals participated in this study. To manipulate gait stability continuous medio-lateral translations of the walking surface were imposed, while subjects walked on a self-paced treadmill. To provoke an adaptive gait pattern, a gait adaptability task (GA-task) was used, in which subjects occasionally had to hit a virtual target with their knees. We measured adaptations in walking speed, step length, step frequency, and step width in response to the manipulations. To investigate whether the manipulations affected the risk of falling we quantified the margins
S14
Abstracts / Gait & Posture 39S (2014) S1–S141
[1] Weerdesteyn V, et al. J Rehabil Res Dev 2008;45(8):1195–213. [2] Hof AL, et al. Gait Posture 2007;25(2):250–8. [3] Espy DD, et al. J Biomech 2010;43(13):2548–53.
Patients/materials and methods: Twenty-nine healthy elderly subjects (24 females, 84 ± 5 years old) participated in the study after signing an informed consent. They were asked to perform one 6MWT, consisting in covering the maximum distance in 6 min (hence walking at maximum speed) while moving back and forth a 30-m rectilinear pathway. An inertial measurement unit (FreeSense, Sensorize s.r.l Rome; fs = 200 sample/s) was positioned over the lower lumbar spine of the subjects, using an elastic belt. Anatomical calibration was obtained from the data recorded while the subjects were in upright posture [4]. Acceleration signals were low-pass filtered (20 Hz) and the beginning of each stride cycle was detected from anteroposterior acceleration peaks [4]. Signals were then segmented into six 1 min windows. Within each window, the first series of 35 strides walked in a straight line was analysed. Gait variability was assessed, within each series, by CVT of the durations (T) of the alternating strides. In addition, interstride variability was assessed by unbiased autocorrelation coefficients (AC) [5] calculated over the trunk acceleration components measured over two neighbouring strides. ANOVA analysis (p = 0.05) was performed to compare gait parameters during each of the six stride series. Intraclass correlation coefficients (ICC) were used to assess the reliability of gait variability between the six stride series. Results: No significant differences were found in gait variability for any of the six stride series. ICCs were 0.919 and 0.939 for CVT and AC, respectively. Inter-series correlation for CVT ranged from 0.396 (3nd–6th) to 0.822 (1st–2nd) (Fig. 1). Discussion and conclusions: The 6MWT is known to induce fatigue in elderly individuals [6], which might have been expected to induce changes in gait variability measurements. Nevertheless, our results showed that gait variability is stable during the six minutes and that results obtained for the first series of strides can be deemed sufficient for its assessment.
http://dx.doi.org/10.1016/j.gaitpost.2014.04.021
Reference
of stability (MoS) in both medio-lateral (ML) and backward (BW) direction [2,3]. Results: Post-stroke participants walked overall slower, with a shorter step length and a larger step width, while step frequency did not differ between both groups. In response to both the manipulations of gait stability and adaptability post-stroke participants further decreased walking speed and step length, while able-bodied subjects kept walking speed constant. The adaptations in step frequency and step width in response to both manipulations did not differ between groups (Fig. 1). Post-stroke participants were able to utilize similar ML MoS as able-bodied people for all conditions. However, the BW MoS tended to be lower for the post-stroke participants, and was significantly lower for the trial in which the GA-task had to be performed. Discussion and conclusions: Post-stroke participants were able to regulate their ML stability, during all experimental conditions, to the same degree as the able-bodied subjects. However, this required a larger step width and a relatively high step frequency compared to able-bodied people [2]. The smaller BW MoS for the post-stroke participants, especially in response to the GA-task, indicates that post-stroke individuals have more difficulties regulating step frequency and step length and walking speed in order to control the risk on making a backward fall while walking in complex environments [3]. Future studies should aim on identifying which impairments cause these limitations.
Reference
017 Analysis of gait variability during a 6-min walk test in older adults E. Grimpampi 1,∗ , S. Oesen 3 , B. Halper 3 , M. Hofmann 2 , B. Wessner 3 , C. Mazzà 1,4 1
Department of Movement, Human, and Health Sciences, University of Rome “Foro Italico”, Roma, Italy 2 Centre for Sport Science and University Sports, University of Vienna, Austria 3 Research Platform Active Ageing, University of Vienna, Austria 4 INSIGNEO Institute, Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK Introduction and aim: Gait variability, described as stride-tostride fluctuations, is related to the underlying neural control of gait [1]. These fluctuations, typically quantified through standard deviation (SD) or coefficient of variation (CV) of chosen quantities, allow identifying changes in the postural control system due to aging, intervention, or pathology. Stride time variability has also been proposed as a fall risk predictor [1]. However, the limited knowledge on gait variability reliability and lack of standardised testing protocols limit its interpretability [2]. The 6-minute walk test (6MWT) is a common tool to assess walking functional capacity, which can be combined with wearable sensors [3] to enhance the understanding of gait pattern related variability. This test may become a standard to this purpose. This study aims to investigate this opportunity in an older healthy population.
[1] [2] [3] [4] [5] [6]
Hausdorff, et al. Arch Phys Med Rehabil 2001;82:1050–6. Lord, et al. Gait Post 2011;34:443–50. Iosa, et al. Stroke Res Treat 2012;810415, 2012. Zijlstra. Eur J Appl Physiol 2004;92:39–44. Moe-Nilssen, et al. J Biomech 2004;37:121–6. Kervio, et al. Med Sci Sports Exerc 2003;35:169–74.
http://dx.doi.org/10.1016/j.gaitpost.2014.04.022 018 Walking with head turns—Comparison of healthy controls with vestibulopathic patients Meldrum Dara 1,∗ , Mcconn-Walsh Rory 2 , Herdman Susan 3 1 School of Physiotherapy, Royal College of Surgeons in Ireland, 123 St. Stephen’s Green, Dublin 2, Ireland 2 Department of Otolaryngology, Beaumont Hospital, Beaumont Road, Dublin 9, Ireland 3 Department of Rehabilitation Medicine, School of Medicine, Emory University, Atlanta, USA
Introduction and aim: Unilateral vestibular loss (UVL) results in significant gait impairment which can be improved with vestibular rehabilitation. Common clinical rating measures include the Dynamic Gait Index [1] or the Functional Gait Assessment [2]. Both of these include rating gait performance during horizontal head turns, on the premise that head turns represent an increased burden on the vestibular system during gait. The aim of this study was to more precisely quantify differences in gait during this task between patients with unilateral vestibular loss and healthy controls using computerised three dimensional gait analysis.
S13
Fig. 1. Sagittal knee and ankle joint moments (Nm/kg) and powers (W/kg) during the initial gait-to-STD (blue line) and final STD-to-gait (black line) transition. * indicates significant differences (p ≤ 0.05).
016 Stepping strategies used by post-stroke individuals to maintain margins of stability during walking L. Hak 1,∗ , H. Houdijk 1,2 , P. Van der Wurff 3 , M.R. Prins 1,3 , A. Mert 3 , P.J. Beek 1 , J.H. Van Dieën 1 1 Research Institute MOVE, Faculty of Human Movement Sciences, VU University Amsterdam, The Netherlands 2 Heliomare Rehabilitation Centre, Wijk aan Zee, The Netherlands 3 Center for Augmented Motor Learning and Training, MRC Aardenburg, Doorn, The Netherlands
Introduction and aim: People who are recovering from a stroke often walk slower, with shorter and slower steps and with a larger step width compared to able-bodied people. Some of these devia-
tions in the gait pattern are explained as strategies to decrease the risk of falling during walking [1]. However, from observational data, it remains unclear whether these differences in the gait pattern represent a strategy to reduce the risk of falling, or are actually causing the increased fall risk. In the current study, we manipulated gait stability and gait adaptability, to investigate how post-stroke patients adapted their gait pattern to withstand these manipulations and whether these adaptations differed from able bodied people. Patients/materials and methods: Ten post-stroke and nine age-matched able-bodied individuals participated in this study. To manipulate gait stability continuous medio-lateral translations of the walking surface were imposed, while subjects walked on a self-paced treadmill. To provoke an adaptive gait pattern, a gait adaptability task (GA-task) was used, in which subjects occasionally had to hit a virtual target with their knees. We measured adaptations in walking speed, step length, step frequency, and step width in response to the manipulations. To investigate whether the manipulations affected the risk of falling we quantified the margins
S14
Abstracts / Gait & Posture 39S (2014) S1–S141
[1] Weerdesteyn V, et al. J Rehabil Res Dev 2008;45(8):1195–213. [2] Hof AL, et al. Gait Posture 2007;25(2):250–8. [3] Espy DD, et al. J Biomech 2010;43(13):2548–53.
Patients/materials and methods: Twenty-nine healthy elderly subjects (24 females, 84 ± 5 years old) participated in the study after signing an informed consent. They were asked to perform one 6MWT, consisting in covering the maximum distance in 6 min (hence walking at maximum speed) while moving back and forth a 30-m rectilinear pathway. An inertial measurement unit (FreeSense, Sensorize s.r.l Rome; fs = 200 sample/s) was positioned over the lower lumbar spine of the subjects, using an elastic belt. Anatomical calibration was obtained from the data recorded while the subjects were in upright posture [4]. Acceleration signals were low-pass filtered (20 Hz) and the beginning of each stride cycle was detected from anteroposterior acceleration peaks [4]. Signals were then segmented into six 1 min windows. Within each window, the first series of 35 strides walked in a straight line was analysed. Gait variability was assessed, within each series, by CVT of the durations (T) of the alternating strides. In addition, interstride variability was assessed by unbiased autocorrelation coefficients (AC) [5] calculated over the trunk acceleration components measured over two neighbouring strides. ANOVA analysis (p = 0.05) was performed to compare gait parameters during each of the six stride series. Intraclass correlation coefficients (ICC) were used to assess the reliability of gait variability between the six stride series. Results: No significant differences were found in gait variability for any of the six stride series. ICCs were 0.919 and 0.939 for CVT and AC, respectively. Inter-series correlation for CVT ranged from 0.396 (3nd–6th) to 0.822 (1st–2nd) (Fig. 1). Discussion and conclusions: The 6MWT is known to induce fatigue in elderly individuals [6], which might have been expected to induce changes in gait variability measurements. Nevertheless, our results showed that gait variability is stable during the six minutes and that results obtained for the first series of strides can be deemed sufficient for its assessment.
http://dx.doi.org/10.1016/j.gaitpost.2014.04.021
Reference
of stability (MoS) in both medio-lateral (ML) and backward (BW) direction [2,3]. Results: Post-stroke participants walked overall slower, with a shorter step length and a larger step width, while step frequency did not differ between both groups. In response to both the manipulations of gait stability and adaptability post-stroke participants further decreased walking speed and step length, while able-bodied subjects kept walking speed constant. The adaptations in step frequency and step width in response to both manipulations did not differ between groups (Fig. 1). Post-stroke participants were able to utilize similar ML MoS as able-bodied people for all conditions. However, the BW MoS tended to be lower for the post-stroke participants, and was significantly lower for the trial in which the GA-task had to be performed. Discussion and conclusions: Post-stroke participants were able to regulate their ML stability, during all experimental conditions, to the same degree as the able-bodied subjects. However, this required a larger step width and a relatively high step frequency compared to able-bodied people [2]. The smaller BW MoS for the post-stroke participants, especially in response to the GA-task, indicates that post-stroke individuals have more difficulties regulating step frequency and step length and walking speed in order to control the risk on making a backward fall while walking in complex environments [3]. Future studies should aim on identifying which impairments cause these limitations.
Reference
017 Analysis of gait variability during a 6-min walk test in older adults E. Grimpampi 1,∗ , S. Oesen 3 , B. Halper 3 , M. Hofmann 2 , B. Wessner 3 , C. Mazzà 1,4 1
Department of Movement, Human, and Health Sciences, University of Rome “Foro Italico”, Roma, Italy 2 Centre for Sport Science and University Sports, University of Vienna, Austria 3 Research Platform Active Ageing, University of Vienna, Austria 4 INSIGNEO Institute, Department of Mechanical Engineering, The University of Sheffield, Sheffield, UK Introduction and aim: Gait variability, described as stride-tostride fluctuations, is related to the underlying neural control of gait [1]. These fluctuations, typically quantified through standard deviation (SD) or coefficient of variation (CV) of chosen quantities, allow identifying changes in the postural control system due to aging, intervention, or pathology. Stride time variability has also been proposed as a fall risk predictor [1]. However, the limited knowledge on gait variability reliability and lack of standardised testing protocols limit its interpretability [2]. The 6-minute walk test (6MWT) is a common tool to assess walking functional capacity, which can be combined with wearable sensors [3] to enhance the understanding of gait pattern related variability. This test may become a standard to this purpose. This study aims to investigate this opportunity in an older healthy population.
[1] [2] [3] [4] [5] [6]
Hausdorff, et al. Arch Phys Med Rehabil 2001;82:1050–6. Lord, et al. Gait Post 2011;34:443–50. Iosa, et al. Stroke Res Treat 2012;810415, 2012. Zijlstra. Eur J Appl Physiol 2004;92:39–44. Moe-Nilssen, et al. J Biomech 2004;37:121–6. Kervio, et al. Med Sci Sports Exerc 2003;35:169–74.
http://dx.doi.org/10.1016/j.gaitpost.2014.04.022 018 Walking with head turns—Comparison of healthy controls with vestibulopathic patients Meldrum Dara 1,∗ , Mcconn-Walsh Rory 2 , Herdman Susan 3 1 School of Physiotherapy, Royal College of Surgeons in Ireland, 123 St. Stephen’s Green, Dublin 2, Ireland 2 Department of Otolaryngology, Beaumont Hospital, Beaumont Road, Dublin 9, Ireland 3 Department of Rehabilitation Medicine, School of Medicine, Emory University, Atlanta, USA
Introduction and aim: Unilateral vestibular loss (UVL) results in significant gait impairment which can be improved with vestibular rehabilitation. Common clinical rating measures include the Dynamic Gait Index [1] or the Functional Gait Assessment [2]. Both of these include rating gait performance during horizontal head turns, on the premise that head turns represent an increased burden on the vestibular system during gait. The aim of this study was to more precisely quantify differences in gait during this task between patients with unilateral vestibular loss and healthy controls using computerised three dimensional gait analysis.