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Unplanned gait termination in individuals with multiple sclerosis
Gait & Posture 53 (2017) 168–172
Contents lists available at ScienceDirect
Gait & Posture journal homepage: www.elsevier.com/locate/gaitpost
Full length article
Unplanned gait termination in individuals with multiple sclerosis Kathleen L. Roeing, Yaejin Moon, Jacob J. Sosnoff* Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, United States1
A R T I C L E I N F O
Article history: Received 20 September 2016 Received in revised form 13 December 2016 Accepted 22 January 2017 Keywords: Multiple sclerosis Unplanned gait termination Gait
A B S T R A C T
Despite the pervasive nature of gait impairment in multiple sclerosis (MS), there is limited information concerning the control of gait termination in individuals with MS. The purpose of this investigation was to examine unplanned gait termination with and without cognitive distractors in individuals with MS compared to healthy controls. Thirty-one individuals with MS and 14 healthy controls completed a series of unplanned gait termination tasks over a pressure sensitive walkway under distracting and nondistracting conditions. Individuals with MS were further broken down into groups based on assistive device use: (no assistive device (MSnoAD) n = 18; and assistive device (MSAD) n = 13). Individuals with MS who walked with an assistive device (MSAD: 67.8 15.1 cm/s) walked slower than individuals without an assistive device (MSnoAD: 110.4 32.3 cm/s, p < 0.01) and controls (120.0 30.0 cm/s; p < 0.01). There was a significant reduction in velocity in the cognitively distracting condition (93.4 32.1 cm/s) compared to the normal condition [108.8 36.2 cm/s; F(1,43) = 3.4, p = 0.04]. All participants took longer to stop during the distracting condition (1.70.6 s) than the non-distracting condition (1.4 0.4 s; U = 673.0 p < 0.01). After controlling for gait velocity, post-hoc analysis revealed the MSAD group took significantly longer to stop compared to the control group (p = 0.05). Further research investigating the control of unplanned gait termination in MS is warranted. © 2017 Elsevier B.V. All rights reserved.
1. Introduction Multiple sclerosis is the leading cause of neurological disability in young adults [1]. It is estimated that there are nearly 400,000 individuals in US living with MS and over 2.5 million worldwide [2,3]. The exact cause and mechanism of MS is a subject of ongoing scientific investigation. It is suggested that MS is an autoimmune disorder that results in axonal degeneration and widespread neuronal damage [2]. Due to the dispersed nature of the neurological damage individuals with MS face a wide variety of symptoms including impairments in sensorimotor functioning, cognition, balance, and gait [4–7]. Gait impairment is prevalent in 75% of the MS population and significantly interferes with activities of daily living [8]. Traditionally, gait is studied under continuous walking conditions. However, successful community ambulation includes numerous other aspects of gait including initiation, turning, and termination. Additionally, transitions (e.g. sit to stand) and ambulation-related
* Corresponding author at: University of Illinois at Urbana-Champaign, Department of Kinesiology and Community Health, 906 South Goodwin Ave. Urbana, IL, 61801, United States. E-mail address: [email protected] (J.J. Sosnoff). 1 http://publish.illinois.edu/motorcontrol/. http://dx.doi.org/10.1016/j.gaitpost.2017.01.016 0966-6362/© 2017 Elsevier B.V. All rights reserved.
activities are the two most cited activities being performed during a fall for individuals with MS [9]. When transitioning from walking to a stop (or vice versa), a dynamic postural transition takes place and the body experiences higher destabilizing forces than during walking itself [10,11]. Indeed, step time during gait initiation has been linked to physiological fall risk as well as history of falls in individuals with MS [12]. Gait termination occurs in everyday life under differing environmental conditions resulting in either planned or unplanned gait termination. Planned gait termination is preplanned and occurs at the desired location determined by an interaction between an individual and environmental constraints. Walking to the end of a driveway and stopping at the mailbox is an example of planned gait termination. In contrast, unplanned gait termination occurs as a response to an unexpected external stimulus. Suddenly having to stop walking in order to avoid a speeding bicyclist on a sidewalk is an example of unplanned gait termination. Planned and unplanned gait termination require different control processes. Planned gait termination relies on feedforward planning to stop at the desired target as well as the continuous control of the body’s center of mass within its stability boundaries. Unplanned gait termination relies on this continuous control of the center of mass as well as feedback control [13,14].
K.L. Roeing et al. / Gait & Posture 53 (2017) 168–172
During feedback control, the sensorimotor system processes and responds to external stimuli and adjusts the ongoing movement to meet environmental demands [14,15]. Individuals with MS have demonstrated impairments in feedback control during an upper limb aiming task [16] and a turning task [17]. In addition to the impairments in feedback control, individuals with MS also experience muscle weakness [18], balance impairment [19], and impaired somatosensory processing [20]. These processes also contribute to the control of unplanned stopping and might negatively impact unplanned stopping performance in individuals with MS. Feedback and feedforward control of gait are dependent in part on an individual’s ability to attend to the environment. Indeed, individuals with MS have demonstrated impairments in various aspects of gait including steady state gait, gait initiation, and planned gait termination and these impairments are often greater when performed under cognitively distracting conditions [12,13,21–23]. Consequently, if individuals with MS demonstrate impairments in unplanned stopping, those deficits could increase under cognitively distracting conditions. Recently, it was demonstrated that individuals with MS were more unstable than healthy age matched controls during planned gait termination [13]. Additionally, a cognitive distractor led to a 10-fold increase in gait termination failure rates [13]. It was proposed that attentional impairments, muscle weakness, balance impairment, impaired somatosensory processing, and delayed anticipatory postural adjustments contributed to the observed impairments. Based on this collective research it is hypothesized that persons with MS will have imapirments in unplanned gait termination. The purpose of this investigation was to examine unplanned gait termination in individuals with MS with and without walking impairment and healthy age matched controls under normal and cognitively distracting conditions. We hypothesized that individuals with MS, particularly those with walking impairment, would take longer to stop in response to a visual cue than healthy controls. Additionally, we hypothesized that stopping times would be greater under the cognitively distracting condition with the MS group, particularly those with a greater walking impairment, demonstrating a greater decrement than controls. 2. Methods 2.1. Participants The investigation included 32 individuals with MS and 14 healthy controls. Controls were screened to insure the absence of any neurologic conditions that would impact gait function. Inclusion criteria for the MS group included a neurologist confirmed diagnosis of MS, the ability to walk for 6 min with or without an aid, and relapse free for the previous 30 days. All participants had normal or corrected to normal vision. Participants were recruited through fliers and email advertisements to the local community. 2.2. Procedures The current investigation was completed in a single visit to the Motor Control Research Laboratory at the University of Illinois at Urbana-Champaign. All study procedures were approved by the institutions internal review board. Upon arriving at the testing facility, a verbal explanation of procedures was given to each participant. Participants were given the opportunity to ask questions about the study procedures and provided written informed consent once all queries were addressed.
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All participants provided demographic information including age and gender. Individuals with MS also provided MS sub-type, year since diagnosis, and disability level as measured by the selfreported Expanded Disability Status Scale (EDSSSR) [24]. Individuals with MS were broken into two groups. Individuals who did not use of an assistive device (e.g. cane; walker, etc) during walking were placed into the MSnoAD (no assistive device) group while individuals who utilized an assistive device were placed into the MSAD (assistive device) group. Regardless of grouping, participants completed a series of eight walking trials on a 6 m ZenoTM pressure sensitive walkway. Participants were instructed to walk at their normal comfortable pace down the walkway and stop as quickly but safely as possible if a visual cue was presented. The visual cue was a blue light that was affixed on the wall at eye level at the end of the 6 m walkway. The blue light was synchronized with the data received from the Zeno TM walkway through the ProtoKineticsTM Movement Analysis Software. Participants were provided a practice trial in which the visual cue was provided to ensure proper understanding of the task. No participants required more than a single practice trial. The remaining eight trials were completed in blocks of four. The first block consisted of walking at a comfortable place down the mat with no additional task. The second block of trials consisted of participants walking at a comfortable pace while performing a cognitive task. For the cognitively distracting condition, participants recited alternate letters of the alphabet (e.g. N, P, R) [25]. 75% of trials in each block included a stop cue while the remaining 25% did not contain a stop cue (i.e. catch trial). The catch trial randomly occurred within the second, third or fourth trial of each block. To further minimize expectations of the stop cue, it was presented at either the 2nd, 3rd, or 4th step of each trial. Participants were aware that a stop cue would or would not be presented but were not told the ratio of catch trails. 2.3. Data analysis Main outcome measures included gait termination time (GTT), gait velocity on the catch trial, and GTT normalized to gait velocity. GTT was calculated as the time from stop cue onset until the participant’s anterior-posterior (AP) center of pressure (COP) velocity falls below a baseline measure following the first of their two terminal steps. Fig. 1 demonstrates how GTT was calculated from AP COP measures. Baseline AP COP velocity was the average AP COP velocity during four seconds of quiet standing on the pressure sensitive walkway. Baseline AP COP velocities were collected under both normal and cognitively distracting conditions. GTT was averaged across the three trials for each participant within each condition. COP measurements were determined by the ProtoKineticsTM Movement Analysis Software based on the shift patterns of footfalls and values of pressure sensor activations over time as described in the software’s measurements manual. Participants who required the use of an assistive device walked across the mat with their device. The data was then processed to only reflect footfall pressure changes (e.g. all pressure readings of assistive devices were removed). These procedures have been previously used to examine planned gait termination in individuals with MS and healthy controls [13]. Given the impact of gait velocity on GTT and the expected slower gait speed of the MS groups [26], GTT was normalized to gait velocity (GTTnorm) of the catch trial for the corresponding condition [14,16,27,28]. 2.4. Statistics All statistical analyses were performed in SPSS Statistics 22.0 (IBM Inc., Armonk, NY). The Shapiro-Wilk test was utilized to
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K.L. Roeing et al. / Gait & Posture 53 (2017) 168–172
Fig. 1. Sample Gait Termination Time. Note: S represents the onset of the visual cue, T1 represents the beginning of the first terminal step, and T2 represents the time to stop following the first of the two terminal steps. Gait termination time is equal to T2 minus S.
examine the normality of the outcome measures. Descriptive statistics were computed for all demographic and stopping outcome measures. A one-way Analysis of Variance (ANOVA) and chi-squared tests were used to determine differences in age and gender respectively between groups. Due to the nonnormality of GTT and GTTnorm, Kruskal-Wallis tests were utilized to analyze differences between groups and Mann Whitney U tests were used to analyze differences between conditions. Gait velocity in the catch trial was normally distributed and placed into a 3 2 repeated measures ANOVA with group (MS-noAD/MS-AD/control) as the between subject factor and task (normal/cognitive distraction) as the within subject factor. The significance level for statistical tests was set at p 0.05. Outliers who were 2 standard deviations away from the mean on any main outcome measure were removed from analysis. This resulted in one individual from the MSnoAD group being removed from analysis. GTT was not calculated for three trials from three different participants with MS under the dual task condition due to their failure to stop after the presentation of the stop cue. These participants kept walking to the end of the walkway although the stop cue was presented. 3. Results Demographic information for the MS and control groups are presented in Table 1. The MS group consisted of 20 females and 11 males with an average age of 57.0 11.1 years. The control group consisted of 10 females and 4 males with an average age of 57.6 13.1 years. The participants with MS were split into groups based on assistive device usage: 18 individuals in the MSnoAD group and 13 individuals in the MSAD group. Eleven individuals used unilateral support and two individuals used bilateral support. There were no differences in age [F(2,42) = 0.965, p = 0.38] or gender [x2(2,N = 45) = 0.297, p = 0.86] between groups. Descriptive statistics for gait velocity and GTT variables are reported in Table 2. As expected, there was a significant effect of group [F(1,42) = 15.5, p < 0.01] on gait velocity. Post-hoc analysis of
the group effect demonstrated that the MSAD group walked significantly slower (67.8 15.1 cm/s) than the MSnoAD (110.4 32.3 cm/s, p < 0.01) and control groups (120.0 30.0, p < 0.01). There was no difference between controls and the MSnoAD group (p = 0.31). There was also a significant effect of condition [F(1,42) = 60.6, p < 0.01] with participants walking slower in the cognitively distracting condition (93.4 32.1 cm/s) compared to the nondistracting condition (108.8 36.2 cm/s). There was a significant group by condition interaction [F(1,43) = 3.4, p = 0.04]. The Kruskal Wallis tests revealed a significant difference between groups on GTTnorm in the normal condition [x2(2) = 5.9, p = 0.05] with MSAD taking longer to terminate their gait than the other groups. No other significant group differences were observed. A Mann-Whitney U test revealed a significant difference across conditions in GTT (U = 673.0 p < 0.01) with longer GTT times in the cognitively distracting condition (1.7 0.6 s) compared to the normal condition (1.4 0.4 s). There was also a significant difference across conditions in GTTnorm (U = 514.0, p < 0.01) with longer GTTnorm times under the cognitively distracting condition (2.0 0.9 s) compared to the normal condition (1.4 2.0 s). 4. Discussion The purpose of this investigation was to examine unplanned gait termination behavior in a diverse population of individuals with MS and healthy controls under cognitively distracting and non-distracting conditions. The results partially support our hypotheses and are consistent with previously reported impairments in gait in individuals with MS. Individuals in the MSAD group demonstrated an increase in GTTnorm compared to the MSAD and control groups. As expected, individuals with MS walked slower than controls and all groups slowed down during the cognitively distracting condition. There are several mechanisms that could contribute to
Table 1 Demographic information as a function of group.
Age (years) Gender Year Since Diagnosis MS Subtype EDSSSR
MSnoAD (n = 18)
MSAD (n = 13)
Combined (n = 31)
Controls (n = 14)
54.6 12.3 (29–72) 13 = F, 6 = M 16.1 9.3 (1–37) 15 = RR, 1 = SP, 2 = B 3.5 (2.6 3.5)
60.4 8.5 (48–79) 8 = F, 5 = M 22.0 10.5 (5–41) 5 = RR, 6 = SP, 2 = PP 6.2 0.3 (6–7)
56.8 11.0 (29–79) 20 = F, 11 = M 18.3 10.0 (1–41) 21 = RR, 7 = SP, 2 = PP, 2 = B 4.4 1.8 (0–7)
57.6 13.1 (37–73) 10 = F, 4 = M N/A N/A N/A
Note: Age and year since diagnosis values are reported as mean SD (range). EDSSSR is reported as median(IQR). F = female, M = Male; RR = relapse remitting MS, SP = secondary progressive MS, PP = primary progressive MS, B = Benign MS, EDSSSR = Self-Reported Expanded Disability Status Score
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Table 2 Main outcome measures as a function of group and task.
Normal Condition
Gait Velocity (cm/s)* GTT (s)b GTTnorm (s2/m)b
Cognitively Distracting Condition
Gait Velocity (cm/s)* GTT (s)b GTTnorm (s2/m)b
MSnoAD (n = 18)
MSAD (n = 13)
Combined (n = 31)
Controls (n = 14)
117.4 34.7 (53.6–195.8) 1.4 0.4 (1.1–2.6) 1.3 0.5 (0.6–2.2) 103.4 28.9 (41.0–166.6) 1.9 0.6 (1.2–3.3) 1.9 0.9 (1.0–3.5)
72.6 13.4 (54.3–100.2) 1.3 0.5 (0.7–2.3) 1.9 0.9 (0.9–4.1)a 63.0 15.8 (45.6–90.7) 1.5 0.5 (0.7–2.2) 2.4 2.0 (1.2–4.6)
98.6 35.5 (53.6–195.8) 1.4 0.4 (0.7–2.6) 1.5 0.7 (0.6–4.1) 86.5 31.4 (41.0–166.6) 1.7 0.6 (0.7–3.3) 2.1 0.9 (1.0–4.6)
131.3 27.4 (97.1–188.1) 1.5 0.3 (1.0–2.2) 1.2 0.3 (0.8–2.1) 108.8 29.1 (66.2–172.6) 1.8 0.8 (1.1–3.7) 1.8 0.7 (0.7–3.4)
Note: Values are reported as mean SD (range). * Indicates a significant group and condition effect (p < 0.05). a Indicates a significant group difference (p < 0.05). b Indicates a significant difference across conditions (p < 0.05).
the slower gait speed observed in the MS group. Individuals with MS tend to experience muscle weakness, spasticity, impaired balance, decreased coordination, and sensory impairments which could all contribute to gait dysfunction [26]. Additionally, cognitive motor interference occurs during the simultaneous performance of a gait and cognitive task and typically results in a slowing of gait in healthy individuals [29] and individuals with MS [30]. Individuals with MS with walking impairment took longer to stop compared to controls after normalizing to gait velocity. This is consistent with unplanned stopping behavior in individuals with MS [13]. Unplanned gait termination requires feedback control as participants first process the visual cue and then generate a motor response to adjust their current movement pattern [14,15]. Processing sensory information and generating a new motor task during mobility based tasks have been shown to be impaired in individuals with MS [16,20,31,32]. Additionally, well established impairments in dynamic balance could be contributing to the observed deficit in unplanned stopping [33]. This combination of increased response time to a stimulus and impairments in dynamic stability most likely compound to produce the observed impairments in unplanned stopping in the MSAD group. The observed impairments occurred in individuals with MS who utilized an assistive device during walking. Individuals in this group demonstrate higher level of neurologic and gait impairment suggesting that a compounding of deficits is needed prior to this impairment being observable. Further research exploring possible mechanisms underlying impairments in unplanned gait perturbation is necessary. Unplanned stopping is a necessary task for community ambulation and therefore is important to evaluate. The task requires the use of feedback control as well as overcoming large destabilizing forces as the body shifts from dynamic to static balance [10,11]. Failure to complete an unplanned stop when necessary could have life threatening consequences. Additionally, the observed impairments in individuals with MS who require an assistive device could be related to other adverse outcomes, such as falls. Recent reports have shown that in individuals with MS, stepping behavior can be improved with targeted interventions [34]. It remains to be seen if this type of approach could be replicated in unplanned stopping and improvements in community ambulation. Possible limitations of the current investigation include the relatively small and unequal sample sizes between the groups and limited power. Therefore, the current findings may not be generalizable to the overall MS population. Additionally, the ProtoKineticsTM software was used to analyze changes in posture, which is not the traditional method of analyzing small changes in
COP. However, similar methods have been used previously to analyze planned stopping in individuals with MS [13]. It is also possible that impairments in vision could have posed additional challenges to participants with MS. However, efforts were made during recruiting to screen out all individuals with any form of visual impairment. 5. Conclusion Overall, this investigation demonstrated impairments in unplanned gait termination in individuals with MS who require the use of an assistive device. This was demonstrated by increases in GTTnorm from the MSAD group compared to the MSnoAD and control groups. Further investigation into the ecological validity and performance over time of unplanned gait termination in individuals with MS is warranted. Conflict of interest The authors declare they have no conflict of interest. No author received payment for work on any stage of manuscript preparation. We confirm that the manuscript has been read and approved by all named authors. Acknowledgements JJS was supported in part by grants from MC10 Inc., National MS Society, and Consortium of Multiple Sclerosis Centers. References [1] R. Dutta, B.D. Trapp, Pathogenesis of axonal and neuronal damage in multiple sclerosis, Neurology 68 (Suppl. 3 (22)) (2007) S22–S31. [2] A. Compston, A. Coles, Multiple sclerosis, Lancet 359 (9313) (2002) 1221–1231. [3] H.L. Zwibel, J. Smrtka, Improving quality of life in multiple sclerosis: an unmet need, Am. J. Manag. care 17 (2011) S139–45. [4] N.D. Chiaravalloti, J. DeLuca, Cognitive impairment in multiple sclerosis, Lancet Neurol. 7 (12) (2008) 1139–1151. [5] C. Forn, et al., Analysis of task-positive and task-negative functional networks during the performance of the Symbol Digit Modalities Test in patients at presentation with clinically isolated syndrome suggestive of multiple sclerosis, Exp. Brain Res. 225 (3) (2013) 399–407. [6] R. Kalb, N. Reitman, Overview of Multiple Sclerosis, National Multiple Sclerosis Society, 2012. [7] R.W. Motl, Ambulation and multiple sclerosis, Phys. Med. Rehabil. Clin. N. Am. 24 (2) (2013) 325–336. [8] N. LaRocca, Impact of walking impairment in multiple sclerosis, Patient: Patient-Center. Outcomes Res. 4 (3) (2011) 189–201. [9] P.N. Matsuda, et al., Falls in multiple sclerosis, PM&R 3 (7) (2011) 624–632. [10] L.D. Hedman, et al., Locomotor requirements for bipedal locomotion: a Delphi survey, Phys. Ther. 94 (1) (2014) 52–67.
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[11] Y. Jian, et al., Trajectory of the body COG and COP during initiation and termination of gait, Gait Posture 1 (1) (1993) 9–22. [12] D.A. Wajda, et al., Preliminary investigation of gait initiation and falls in multiple sclerosis, Arch. Phys. Med. Rehabil. (2015). [13] K.L. Roeing, et al., Gait termination in individuals with multiple sclerosis, Gait Posture 42 (3) (2015) 335–339. [14] W.A. Sparrow, O. Tirosh, Gait termination: a review of experimental methods and the effects of ageing and gait pathologies, Gait Posture 22 (4) (2005) 362– 371. [15] C. Cao, et al., Effects of age: available response time and gender on ability to stop suddenly when walking, Gait Posture 8 (2) (1998) 103–109. [16] A.M. Ternes, et al., Movement planning and online control in multiple sclerosis: assessment using a Fitts law reciprocal aiming task, Cogn. Behav. Neurol. 27 (3) (2014) 139–147. [17] L. Denommé, P. Mandalfino, M. Cinelli, Strategies used by individuals with multiple sclerosis and with mild disability to maintain dynamic stability during a steering task, Exp. Brain Res. 232 (6) (2014) 1811–1822. [18] P. Thoumie, et al., Motor determinants of gait in 100 ambulatory patients with multiple sclerosis, Mult. Scler. 11 (4) (2005) 485–491. [19] J.M. Huisinga, et al., Postural control strategy during standing is altered in patients with multiple sclerosis, Neurosci. Lett. 524 (2) (2012) 124–128. [20] M.H. Cameron, et al., Imbalance in multiple sclerosis: a result of slowed spinal somatosensory conduction, Somatosens. Mot. Res. 25 (2) (2008) 113–122. [21] D.A. Wajda, J.J. Sosnoff, Cognitive-motor interference in multiple sclerosis: a systematic review of evidence correlates, and consequences, BioMed Res. Int. (2015). [22] J.V. Jacobs, S.L. Kasser, Effects of dual tasking on the postural performance of people with and without multiple sclerosis: a pilot study, J. Neurol. 259 (6) (2012) 1166–1176.
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Contents lists available at ScienceDirect
Gait & Posture journal homepage: www.elsevier.com/locate/gaitpost
Full length article
Unplanned gait termination in individuals with multiple sclerosis Kathleen L. Roeing, Yaejin Moon, Jacob J. Sosnoff* Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, United States1
A R T I C L E I N F O
Article history: Received 20 September 2016 Received in revised form 13 December 2016 Accepted 22 January 2017 Keywords: Multiple sclerosis Unplanned gait termination Gait
A B S T R A C T
Despite the pervasive nature of gait impairment in multiple sclerosis (MS), there is limited information concerning the control of gait termination in individuals with MS. The purpose of this investigation was to examine unplanned gait termination with and without cognitive distractors in individuals with MS compared to healthy controls. Thirty-one individuals with MS and 14 healthy controls completed a series of unplanned gait termination tasks over a pressure sensitive walkway under distracting and nondistracting conditions. Individuals with MS were further broken down into groups based on assistive device use: (no assistive device (MSnoAD) n = 18; and assistive device (MSAD) n = 13). Individuals with MS who walked with an assistive device (MSAD: 67.8 15.1 cm/s) walked slower than individuals without an assistive device (MSnoAD: 110.4 32.3 cm/s, p < 0.01) and controls (120.0 30.0 cm/s; p < 0.01). There was a significant reduction in velocity in the cognitively distracting condition (93.4 32.1 cm/s) compared to the normal condition [108.8 36.2 cm/s; F(1,43) = 3.4, p = 0.04]. All participants took longer to stop during the distracting condition (1.70.6 s) than the non-distracting condition (1.4 0.4 s; U = 673.0 p < 0.01). After controlling for gait velocity, post-hoc analysis revealed the MSAD group took significantly longer to stop compared to the control group (p = 0.05). Further research investigating the control of unplanned gait termination in MS is warranted. © 2017 Elsevier B.V. All rights reserved.
1. Introduction Multiple sclerosis is the leading cause of neurological disability in young adults [1]. It is estimated that there are nearly 400,000 individuals in US living with MS and over 2.5 million worldwide [2,3]. The exact cause and mechanism of MS is a subject of ongoing scientific investigation. It is suggested that MS is an autoimmune disorder that results in axonal degeneration and widespread neuronal damage [2]. Due to the dispersed nature of the neurological damage individuals with MS face a wide variety of symptoms including impairments in sensorimotor functioning, cognition, balance, and gait [4–7]. Gait impairment is prevalent in 75% of the MS population and significantly interferes with activities of daily living [8]. Traditionally, gait is studied under continuous walking conditions. However, successful community ambulation includes numerous other aspects of gait including initiation, turning, and termination. Additionally, transitions (e.g. sit to stand) and ambulation-related
* Corresponding author at: University of Illinois at Urbana-Champaign, Department of Kinesiology and Community Health, 906 South Goodwin Ave. Urbana, IL, 61801, United States. E-mail address: [email protected] (J.J. Sosnoff). 1 http://publish.illinois.edu/motorcontrol/. http://dx.doi.org/10.1016/j.gaitpost.2017.01.016 0966-6362/© 2017 Elsevier B.V. All rights reserved.
activities are the two most cited activities being performed during a fall for individuals with MS [9]. When transitioning from walking to a stop (or vice versa), a dynamic postural transition takes place and the body experiences higher destabilizing forces than during walking itself [10,11]. Indeed, step time during gait initiation has been linked to physiological fall risk as well as history of falls in individuals with MS [12]. Gait termination occurs in everyday life under differing environmental conditions resulting in either planned or unplanned gait termination. Planned gait termination is preplanned and occurs at the desired location determined by an interaction between an individual and environmental constraints. Walking to the end of a driveway and stopping at the mailbox is an example of planned gait termination. In contrast, unplanned gait termination occurs as a response to an unexpected external stimulus. Suddenly having to stop walking in order to avoid a speeding bicyclist on a sidewalk is an example of unplanned gait termination. Planned and unplanned gait termination require different control processes. Planned gait termination relies on feedforward planning to stop at the desired target as well as the continuous control of the body’s center of mass within its stability boundaries. Unplanned gait termination relies on this continuous control of the center of mass as well as feedback control [13,14].
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During feedback control, the sensorimotor system processes and responds to external stimuli and adjusts the ongoing movement to meet environmental demands [14,15]. Individuals with MS have demonstrated impairments in feedback control during an upper limb aiming task [16] and a turning task [17]. In addition to the impairments in feedback control, individuals with MS also experience muscle weakness [18], balance impairment [19], and impaired somatosensory processing [20]. These processes also contribute to the control of unplanned stopping and might negatively impact unplanned stopping performance in individuals with MS. Feedback and feedforward control of gait are dependent in part on an individual’s ability to attend to the environment. Indeed, individuals with MS have demonstrated impairments in various aspects of gait including steady state gait, gait initiation, and planned gait termination and these impairments are often greater when performed under cognitively distracting conditions [12,13,21–23]. Consequently, if individuals with MS demonstrate impairments in unplanned stopping, those deficits could increase under cognitively distracting conditions. Recently, it was demonstrated that individuals with MS were more unstable than healthy age matched controls during planned gait termination [13]. Additionally, a cognitive distractor led to a 10-fold increase in gait termination failure rates [13]. It was proposed that attentional impairments, muscle weakness, balance impairment, impaired somatosensory processing, and delayed anticipatory postural adjustments contributed to the observed impairments. Based on this collective research it is hypothesized that persons with MS will have imapirments in unplanned gait termination. The purpose of this investigation was to examine unplanned gait termination in individuals with MS with and without walking impairment and healthy age matched controls under normal and cognitively distracting conditions. We hypothesized that individuals with MS, particularly those with walking impairment, would take longer to stop in response to a visual cue than healthy controls. Additionally, we hypothesized that stopping times would be greater under the cognitively distracting condition with the MS group, particularly those with a greater walking impairment, demonstrating a greater decrement than controls. 2. Methods 2.1. Participants The investigation included 32 individuals with MS and 14 healthy controls. Controls were screened to insure the absence of any neurologic conditions that would impact gait function. Inclusion criteria for the MS group included a neurologist confirmed diagnosis of MS, the ability to walk for 6 min with or without an aid, and relapse free for the previous 30 days. All participants had normal or corrected to normal vision. Participants were recruited through fliers and email advertisements to the local community. 2.2. Procedures The current investigation was completed in a single visit to the Motor Control Research Laboratory at the University of Illinois at Urbana-Champaign. All study procedures were approved by the institutions internal review board. Upon arriving at the testing facility, a verbal explanation of procedures was given to each participant. Participants were given the opportunity to ask questions about the study procedures and provided written informed consent once all queries were addressed.
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All participants provided demographic information including age and gender. Individuals with MS also provided MS sub-type, year since diagnosis, and disability level as measured by the selfreported Expanded Disability Status Scale (EDSSSR) [24]. Individuals with MS were broken into two groups. Individuals who did not use of an assistive device (e.g. cane; walker, etc) during walking were placed into the MSnoAD (no assistive device) group while individuals who utilized an assistive device were placed into the MSAD (assistive device) group. Regardless of grouping, participants completed a series of eight walking trials on a 6 m ZenoTM pressure sensitive walkway. Participants were instructed to walk at their normal comfortable pace down the walkway and stop as quickly but safely as possible if a visual cue was presented. The visual cue was a blue light that was affixed on the wall at eye level at the end of the 6 m walkway. The blue light was synchronized with the data received from the Zeno TM walkway through the ProtoKineticsTM Movement Analysis Software. Participants were provided a practice trial in which the visual cue was provided to ensure proper understanding of the task. No participants required more than a single practice trial. The remaining eight trials were completed in blocks of four. The first block consisted of walking at a comfortable place down the mat with no additional task. The second block of trials consisted of participants walking at a comfortable pace while performing a cognitive task. For the cognitively distracting condition, participants recited alternate letters of the alphabet (e.g. N, P, R) [25]. 75% of trials in each block included a stop cue while the remaining 25% did not contain a stop cue (i.e. catch trial). The catch trial randomly occurred within the second, third or fourth trial of each block. To further minimize expectations of the stop cue, it was presented at either the 2nd, 3rd, or 4th step of each trial. Participants were aware that a stop cue would or would not be presented but were not told the ratio of catch trails. 2.3. Data analysis Main outcome measures included gait termination time (GTT), gait velocity on the catch trial, and GTT normalized to gait velocity. GTT was calculated as the time from stop cue onset until the participant’s anterior-posterior (AP) center of pressure (COP) velocity falls below a baseline measure following the first of their two terminal steps. Fig. 1 demonstrates how GTT was calculated from AP COP measures. Baseline AP COP velocity was the average AP COP velocity during four seconds of quiet standing on the pressure sensitive walkway. Baseline AP COP velocities were collected under both normal and cognitively distracting conditions. GTT was averaged across the three trials for each participant within each condition. COP measurements were determined by the ProtoKineticsTM Movement Analysis Software based on the shift patterns of footfalls and values of pressure sensor activations over time as described in the software’s measurements manual. Participants who required the use of an assistive device walked across the mat with their device. The data was then processed to only reflect footfall pressure changes (e.g. all pressure readings of assistive devices were removed). These procedures have been previously used to examine planned gait termination in individuals with MS and healthy controls [13]. Given the impact of gait velocity on GTT and the expected slower gait speed of the MS groups [26], GTT was normalized to gait velocity (GTTnorm) of the catch trial for the corresponding condition [14,16,27,28]. 2.4. Statistics All statistical analyses were performed in SPSS Statistics 22.0 (IBM Inc., Armonk, NY). The Shapiro-Wilk test was utilized to
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Fig. 1. Sample Gait Termination Time. Note: S represents the onset of the visual cue, T1 represents the beginning of the first terminal step, and T2 represents the time to stop following the first of the two terminal steps. Gait termination time is equal to T2 minus S.
examine the normality of the outcome measures. Descriptive statistics were computed for all demographic and stopping outcome measures. A one-way Analysis of Variance (ANOVA) and chi-squared tests were used to determine differences in age and gender respectively between groups. Due to the nonnormality of GTT and GTTnorm, Kruskal-Wallis tests were utilized to analyze differences between groups and Mann Whitney U tests were used to analyze differences between conditions. Gait velocity in the catch trial was normally distributed and placed into a 3 2 repeated measures ANOVA with group (MS-noAD/MS-AD/control) as the between subject factor and task (normal/cognitive distraction) as the within subject factor. The significance level for statistical tests was set at p 0.05. Outliers who were 2 standard deviations away from the mean on any main outcome measure were removed from analysis. This resulted in one individual from the MSnoAD group being removed from analysis. GTT was not calculated for three trials from three different participants with MS under the dual task condition due to their failure to stop after the presentation of the stop cue. These participants kept walking to the end of the walkway although the stop cue was presented. 3. Results Demographic information for the MS and control groups are presented in Table 1. The MS group consisted of 20 females and 11 males with an average age of 57.0 11.1 years. The control group consisted of 10 females and 4 males with an average age of 57.6 13.1 years. The participants with MS were split into groups based on assistive device usage: 18 individuals in the MSnoAD group and 13 individuals in the MSAD group. Eleven individuals used unilateral support and two individuals used bilateral support. There were no differences in age [F(2,42) = 0.965, p = 0.38] or gender [x2(2,N = 45) = 0.297, p = 0.86] between groups. Descriptive statistics for gait velocity and GTT variables are reported in Table 2. As expected, there was a significant effect of group [F(1,42) = 15.5, p < 0.01] on gait velocity. Post-hoc analysis of
the group effect demonstrated that the MSAD group walked significantly slower (67.8 15.1 cm/s) than the MSnoAD (110.4 32.3 cm/s, p < 0.01) and control groups (120.0 30.0, p < 0.01). There was no difference between controls and the MSnoAD group (p = 0.31). There was also a significant effect of condition [F(1,42) = 60.6, p < 0.01] with participants walking slower in the cognitively distracting condition (93.4 32.1 cm/s) compared to the nondistracting condition (108.8 36.2 cm/s). There was a significant group by condition interaction [F(1,43) = 3.4, p = 0.04]. The Kruskal Wallis tests revealed a significant difference between groups on GTTnorm in the normal condition [x2(2) = 5.9, p = 0.05] with MSAD taking longer to terminate their gait than the other groups. No other significant group differences were observed. A Mann-Whitney U test revealed a significant difference across conditions in GTT (U = 673.0 p < 0.01) with longer GTT times in the cognitively distracting condition (1.7 0.6 s) compared to the normal condition (1.4 0.4 s). There was also a significant difference across conditions in GTTnorm (U = 514.0, p < 0.01) with longer GTTnorm times under the cognitively distracting condition (2.0 0.9 s) compared to the normal condition (1.4 2.0 s). 4. Discussion The purpose of this investigation was to examine unplanned gait termination behavior in a diverse population of individuals with MS and healthy controls under cognitively distracting and non-distracting conditions. The results partially support our hypotheses and are consistent with previously reported impairments in gait in individuals with MS. Individuals in the MSAD group demonstrated an increase in GTTnorm compared to the MSAD and control groups. As expected, individuals with MS walked slower than controls and all groups slowed down during the cognitively distracting condition. There are several mechanisms that could contribute to
Table 1 Demographic information as a function of group.
Age (years) Gender Year Since Diagnosis MS Subtype EDSSSR
MSnoAD (n = 18)
MSAD (n = 13)
Combined (n = 31)
Controls (n = 14)
54.6 12.3 (29–72) 13 = F, 6 = M 16.1 9.3 (1–37) 15 = RR, 1 = SP, 2 = B 3.5 (2.6 3.5)
60.4 8.5 (48–79) 8 = F, 5 = M 22.0 10.5 (5–41) 5 = RR, 6 = SP, 2 = PP 6.2 0.3 (6–7)
56.8 11.0 (29–79) 20 = F, 11 = M 18.3 10.0 (1–41) 21 = RR, 7 = SP, 2 = PP, 2 = B 4.4 1.8 (0–7)
57.6 13.1 (37–73) 10 = F, 4 = M N/A N/A N/A
Note: Age and year since diagnosis values are reported as mean SD (range). EDSSSR is reported as median(IQR). F = female, M = Male; RR = relapse remitting MS, SP = secondary progressive MS, PP = primary progressive MS, B = Benign MS, EDSSSR = Self-Reported Expanded Disability Status Score
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Table 2 Main outcome measures as a function of group and task.
Normal Condition
Gait Velocity (cm/s)* GTT (s)b GTTnorm (s2/m)b
Cognitively Distracting Condition
Gait Velocity (cm/s)* GTT (s)b GTTnorm (s2/m)b
MSnoAD (n = 18)
MSAD (n = 13)
Combined (n = 31)
Controls (n = 14)
117.4 34.7 (53.6–195.8) 1.4 0.4 (1.1–2.6) 1.3 0.5 (0.6–2.2) 103.4 28.9 (41.0–166.6) 1.9 0.6 (1.2–3.3) 1.9 0.9 (1.0–3.5)
72.6 13.4 (54.3–100.2) 1.3 0.5 (0.7–2.3) 1.9 0.9 (0.9–4.1)a 63.0 15.8 (45.6–90.7) 1.5 0.5 (0.7–2.2) 2.4 2.0 (1.2–4.6)
98.6 35.5 (53.6–195.8) 1.4 0.4 (0.7–2.6) 1.5 0.7 (0.6–4.1) 86.5 31.4 (41.0–166.6) 1.7 0.6 (0.7–3.3) 2.1 0.9 (1.0–4.6)
131.3 27.4 (97.1–188.1) 1.5 0.3 (1.0–2.2) 1.2 0.3 (0.8–2.1) 108.8 29.1 (66.2–172.6) 1.8 0.8 (1.1–3.7) 1.8 0.7 (0.7–3.4)
Note: Values are reported as mean SD (range). * Indicates a significant group and condition effect (p < 0.05). a Indicates a significant group difference (p < 0.05). b Indicates a significant difference across conditions (p < 0.05).
the slower gait speed observed in the MS group. Individuals with MS tend to experience muscle weakness, spasticity, impaired balance, decreased coordination, and sensory impairments which could all contribute to gait dysfunction [26]. Additionally, cognitive motor interference occurs during the simultaneous performance of a gait and cognitive task and typically results in a slowing of gait in healthy individuals [29] and individuals with MS [30]. Individuals with MS with walking impairment took longer to stop compared to controls after normalizing to gait velocity. This is consistent with unplanned stopping behavior in individuals with MS [13]. Unplanned gait termination requires feedback control as participants first process the visual cue and then generate a motor response to adjust their current movement pattern [14,15]. Processing sensory information and generating a new motor task during mobility based tasks have been shown to be impaired in individuals with MS [16,20,31,32]. Additionally, well established impairments in dynamic balance could be contributing to the observed deficit in unplanned stopping [33]. This combination of increased response time to a stimulus and impairments in dynamic stability most likely compound to produce the observed impairments in unplanned stopping in the MSAD group. The observed impairments occurred in individuals with MS who utilized an assistive device during walking. Individuals in this group demonstrate higher level of neurologic and gait impairment suggesting that a compounding of deficits is needed prior to this impairment being observable. Further research exploring possible mechanisms underlying impairments in unplanned gait perturbation is necessary. Unplanned stopping is a necessary task for community ambulation and therefore is important to evaluate. The task requires the use of feedback control as well as overcoming large destabilizing forces as the body shifts from dynamic to static balance [10,11]. Failure to complete an unplanned stop when necessary could have life threatening consequences. Additionally, the observed impairments in individuals with MS who require an assistive device could be related to other adverse outcomes, such as falls. Recent reports have shown that in individuals with MS, stepping behavior can be improved with targeted interventions [34]. It remains to be seen if this type of approach could be replicated in unplanned stopping and improvements in community ambulation. Possible limitations of the current investigation include the relatively small and unequal sample sizes between the groups and limited power. Therefore, the current findings may not be generalizable to the overall MS population. Additionally, the ProtoKineticsTM software was used to analyze changes in posture, which is not the traditional method of analyzing small changes in
COP. However, similar methods have been used previously to analyze planned stopping in individuals with MS [13]. It is also possible that impairments in vision could have posed additional challenges to participants with MS. However, efforts were made during recruiting to screen out all individuals with any form of visual impairment. 5. Conclusion Overall, this investigation demonstrated impairments in unplanned gait termination in individuals with MS who require the use of an assistive device. This was demonstrated by increases in GTTnorm from the MSAD group compared to the MSnoAD and control groups. Further investigation into the ecological validity and performance over time of unplanned gait termination in individuals with MS is warranted. Conflict of interest The authors declare they have no conflict of interest. No author received payment for work on any stage of manuscript preparation. We confirm that the manuscript has been read and approved by all named authors. Acknowledgements JJS was supported in part by grants from MC10 Inc., National MS Society, and Consortium of Multiple Sclerosis Centers. References [1] R. Dutta, B.D. Trapp, Pathogenesis of axonal and neuronal damage in multiple sclerosis, Neurology 68 (Suppl. 3 (22)) (2007) S22–S31. [2] A. Compston, A. Coles, Multiple sclerosis, Lancet 359 (9313) (2002) 1221–1231. [3] H.L. Zwibel, J. Smrtka, Improving quality of life in multiple sclerosis: an unmet need, Am. J. Manag. care 17 (2011) S139–45. [4] N.D. Chiaravalloti, J. DeLuca, Cognitive impairment in multiple sclerosis, Lancet Neurol. 7 (12) (2008) 1139–1151. [5] C. Forn, et al., Analysis of task-positive and task-negative functional networks during the performance of the Symbol Digit Modalities Test in patients at presentation with clinically isolated syndrome suggestive of multiple sclerosis, Exp. Brain Res. 225 (3) (2013) 399–407. [6] R. Kalb, N. Reitman, Overview of Multiple Sclerosis, National Multiple Sclerosis Society, 2012. [7] R.W. Motl, Ambulation and multiple sclerosis, Phys. Med. Rehabil. Clin. N. Am. 24 (2) (2013) 325–336. [8] N. LaRocca, Impact of walking impairment in multiple sclerosis, Patient: Patient-Center. Outcomes Res. 4 (3) (2011) 189–201. [9] P.N. Matsuda, et al., Falls in multiple sclerosis, PM&R 3 (7) (2011) 624–632. [10] L.D. Hedman, et al., Locomotor requirements for bipedal locomotion: a Delphi survey, Phys. Ther. 94 (1) (2014) 52–67.
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