Publication
Research Article
Background: In the absence of gait automaticity, the need to allocate cognitive resources to walking has implications for everyday mobility and community participation. The purpose of this study was to examine the effects of different attentionally demanding conditions on gait metrics in people with multiple sclerosis (MS).
Methods: Twenty-one individuals (11 with MS, 10 without MS) wore wireless inertial sensors and completed 1-minute walking trials under 4 different attentional conditions: no-attentional-instruction, focus on gait quality, attention to a narrative discourse task, and attentional switching. Walking trials were performed at a self-selected and fast-paced walking speed. A 3-way linear mixed model including conditions, groups, speeds, and interactions was used to evaluate the effects on gait speed and stride length variability.
Results: Participants with MS demonstrated slower gait speed and greater gait variability in all attentional conditions than participants without MS. During self-paced walking, participants with MS showed the greatest decline in mobility metrics during the attentional switch condition. When walking fast, significant differences were observed for both the divided attention and attentional switching conditions, with divided attention having the greatest effect on gait speed and attentional switching resulting in the greatest decrease in gait variability.
Conclusions: Difficult walking conditions, such as attentional switching, elicit greater gait deficits in people with MS. Evaluating attentional switching may be useful for mobility assessment in people with MS, and incorporating this attentional technique in future mobility training may improve stability and prevent falls.
From the Department of Pharmacy and Health Sciences, Wayne State University, Detroit, Michigan (MV) and the Department of Rehabilitation and Movement Science, University of Vermont, Burlington, Vermont (MB, SLK). Correspondence: Michael VanNostrand, PhD, 259 Mack Avenue, #2305, Detroit, MI, 48201; email: michael.vannostrand@wayne.edu.
Well-controlled and coordinated walking is essential for safe and successful community mobility, with automaticity—defined as the ability to walk with minimal reliance on attention-demanding cognitive resources—serving as a critical component of efficient gait.1 Automaticity allows individuals to walk without the need for continuous attentional monitoring, preserving cognitive capacity for navigating complex and dynamic environments.1,2 When automaticity is compromised, walking becomes increasingly dependent on cognitive control, resulting in competition for attentional resources and contributing to gait difficulties and impaired performance of concurrent tasks.3 This is particularly relevant for individuals with multiple sclerosis (MS), who often exhibit reduced walking automaticity4 and report needing to consciously direct attention to their walking in order to maintain stability.4-6 Such compensatory strategies are concerning, as attentional difficulties have been shown to negatively impact walking7 and are associated with increased fall risk in this population.7-9 Accordingly, understanding how attentional deficits influence gait has important real-world implications.
While research has reported that between 20% and 50% of adults with MS have specific attentional difficulties,10 much of the attentional research regarding gait has focused on walking while dividing attention (ie, dual tasking).11-13 Yet, to walk safely and successfully, individuals not only have to divide attention but must also be able to flexibly prioritize and switch attention in accordance with the demands of everyday conditions and circumstances.14 Attentional switching denotes the ability to flexibly shift back and forth between multiple tasks or mental sets.15 Recent research has sought to examine outcomes associated with flexible and adaptive attentional control in individuals with MS through task-switching paradigms. Migliore et al16 found that participants with MS demonstrated greater deficits in attentional switching compared with the control group when completing a computer-based cued-switching task while seated. However, research by Shiri and colleagues17 found no relationship between cognitive flexibility and static balance in study participants when attentional switching was assessed via common neuropsychological measures. Notwithstanding the methodological differences and inconsistencies across these 2 studies, no study to date has specifically investigated whether impaired attentional switching influences walking in people with MS.
During walking, competition for attentional resources is influenced not only by the timing of concurrent tasks but also by their complexity—that is, tasks that demand greater cognitive load or involve more advanced mental processes.18 Individuals with MS tend to have a reduced capacity to recruit additional cognitive resources in response to increasing task complexity.19 Moreover, gait impairments can often be exacerbated at faster walking speeds,20 highlighting the importance of understanding how individuals with MS manage attentional switching across varying gait speeds. This is particularly important given the demands of real-world ambulation, where navigating busy or unpredictable environments often requires rapid shifts in attention and adaptive motor responses.
Given the importance of cognitive resources for optimal gait performance, isolating the effects of cognitive flexibility from other cognitive processes and under differential cognitive loads may further the development of novel therapeutic interventions designed to enhance attentional switching and facilitate gait automaticity in people with MS. As such, the purposes of this study were to compare the effects of divided attention and attentional switching on gait in adults with and without MS and to explore the differential effects of these attentional conditions when walking at self-selected or fast-paced speeds.
Participants
A convenience sample of 11 individuals with MS and 10 without MS (NoMS) was recruited. An email invitation was sent to individuals with MS listed in the authors’ research database who had participated in previous research. Additionally, a flyer was posted in the multiple sclerosis center of the local medical center. Those with MS had to self-report (1) physician-diagnosed MS and (2) minimal to moderate disability (a score of 1-4 on the Patient-Determined Disease Steps [PDDS] scale).21 Individuals with MS were excluded if they had (1) a severe exacerbation of symptoms requiring medical intervention in the previous month or (2) any health condition or diagnosis besides MS that could influence balance or ambulation. Participants without MS were matched by age (±3 years) and sex, and also had to report no health condition or diagnoses affecting balance or ambulation. This study was approved by the Institutional Review Board of the University of Vermont, and all participants provided written consent prior to data collection.
Measures
Demographic data included age, sex, years post diagnosis, and MS phenotype. For additional insight into the sample, cognitive functioning was assessed using the Symbol Digit Modalities Test (SDMT) and the Montreal Cognitive Assessment (MoCA). The SDMT, a reliable and valid measure of cognitive impairment in MS,22 evaluates constructs of attention, perceptual processing, working memory, and psychomotor speed through a timed task of matching abstract symbols to numbers. The MoCA is a sensitive and useful cognitive screening tool used to detect early and mild cognitive impairment in people with MS.23,24 It assesses function across the cognitive domains of attention, executive functions, memory, language, visuo-constructional skills, conceptual thinking, calculations, and orientation.
Mobility impairment was measured with the 12-item Multiple Sclerosis Walking Scale, a reliable and valid instrument,25 whereby participants self-report how much their MS impacts their ability to walk under various circumstances. The Falls Efficacy Scale–International26 was used to assess participant concerns about falling during 16 activities of daily living.
Measurements of continuous walking were derived using APDM Opals, small wireless inertial motion sensors, and its related Mobility Lab software (Clario). Sensors were placed on a participant’s lumbar spine, sternum, and both feet. Data from the Opal sensors were collected wirelessly at 128 Hz. Measurements included gait speed (m/s) and stride length variability, which was expressed as the coefficient of variation (CV [%]) and calculated by stride length SD/stride length mean to represent relative gait variability.27 These metrics have been identified as sensitive measures of dual-task mobility impairment in individuals with MS.20
Procedures
Participants completed eight 1-minute trials walking between 2 lines 25 feet apart. The 8 walking bouts consisted of 4 different attentional conditions at both a self-paced (SP) and a fast-paced (FP) walking speed. For the no-attentional-instruction (NI) condition, participants were simply asked to walk back and forth. For the internal-focus (IF) condition, participants were instructed to focus their attention on their gait throughout the walking bout, a task selected based on research highlighting the impact of internally focused attention on daily walking in individuals with MS.5 In the divided-attention (DA) condition, participants were asked to walk while simultaneously completing a narrative discourse task, which required them to verbally produce a detailed and coherent story in response to an open-ended prompt (eg, “trip to New York City”) while walking, or a similarly structured alternate form (eg, “moving to a new city”).28 This task has been shown to be a cognitively demanding task of natural language formulation, planning skills, and discourse production requiring substantial attentional resources.29 In the attentional-switching (AS) condition, participants were instructed to switch their attention every 15 seconds between the IF and DA tasks throughout the 1-minute bout, allowing for an assessment of their ability to flexibly shift attention between internally and externally oriented tasks—an ability that mirrors the dynamic attentional demands of real-world environments (Figure). All walking trials and discourse topics were randomized across participants to minimize order effects. Participants were instructed to rest, as needed, between trials to minimize the impact of fatigue from both the mobility and cognitive components of the testing paradigm.
Figure. Description of Walking Trials
Analysis
Descriptive statistics were performed to examine group differences with independent t test, Wilcoxon rank-sum test, or Fisher exact test after inspection of normality. To evaluate the effects of attentional conditions and walking speeds on gait metrics (gait speed and stride length variability), we employed a 3-way linear mixed model including attentional conditions (NI, IF, DA, AS), groups (MS, NoMS), speeds (SP, FP), and interaction terms (group × condition, group × speed, condition × speed, group × condition × speed) as fixed effects with participants as random effects to allow for the entry point to vary across participants. A linear mixed model was selected, as it accounts for the heterogeneity and correlation of repeated measures under different conditions and retains participants with partially missing data. Effect sizes and 95% CIs of the linear mixed model were calculated using fixed-effects regression coefficients. All analyses were performed using Stata 17 (StataCorp), and statistical significance was set at P < .05.
There were no significant differences in age (P = .27) or sex (P = .99) between participants with and without MS. Participants with MS had greater concerns about falling (P < .001) than those without MS and lower cognitive functioning than the NoMS group, as indicated by SDMT (P = .005) and MoCA (P < .001) scores. The MS group reported a median PDDS score of 3 (ie, gait disability) and average years post diagnosis of 24.1 ± 12.8 (Table 1).
Table 1. Participant Demographics
The 3-way linear mixed model found significant group effects (P < .05) in both gait metrics. Participants with MS showed significantly lower gait speed and greater stride length variability than participants without MS in all attentional conditions, regardless of speed instructions, except for stride length variability in the fast-paced internal-focus condition (Table 2).
Table 2. Within- and Between-Group Differences in Gait Metrics
We also observed significant speed main effects (P < .05) in both groups. Multiple post hoc contrast comparisons indicated that participants significantly increased their gait speed when instructed to walk at a faster speed. However, no significant differences in gait speed were found between self-paced and fast-paced walking in the divided-attention condition for participants with MS. Unlike gait speed, stride length variability did not differ among walking speeds across attentional conditions (Table 2).
Results revealed different patterns of attentional condition main effects based on speed instruction and group. In self-paced walking, there were significant attentional condition main effects on gait speed in the NoMS group, but not in participants with MS (Figure S1). No significant gait speed differences were observed across any of the attentional conditions in participants with MS. The NoMS group, on the other hand, showed the greatest decline in gait speed in the attentional-switching condition compared to the no-attentional-instruction condition (B = –0.13 m/s; 95% CI, –0.20 to –0.06; P < .001) and the divided-attention condition (B = –0.09 m/s; 95% CI, –0.17 to –0.01; P = .024). Compared to gait speed, however, we observed that the attentional-switching condition resulted in the greatest stride length variability in both groups at the self-selected pace. Participants with MS demonstrated significantly greater gait variability in the attentional-switching condition than in the no-attentional-instruction condition (B = 1.28%; 95% CI, 0.18-2.37; P = .022) and the internal-focus conditions (B = 1.67%; 95% CI, 0.03-3.31; P = .043), whereas the NoMS group had greater variability in the attentional-switching condition compared to the divided-attention condition (B = 1.23%; 95% CI, 0.08-2.38; P = .036).
Figure S1. Self-Paced Walking by Attentional Condition
In fast-paced walking, both groups demonstrated the slowest gait speed in the divided-attention condition (P < .01) (Figure S2). For the MS group, the divided-attention condition (B = –0.20 m/s; 95% CI, –0.28 to –0.12; P < .001) resulted in slower walking speed than in the attentional-switching condition (B = –0.16 m/s; 95% CI, –0.23 to –0.08; P < .001) when compared to the no-attentional-instruction condition. For the NoMS group, both the divided-attention and attentional-switching conditions significantly differed from the other conditions and one another (B = –0.08 m/s; 95% CI, –0.16 to –0.001; P = .047), although the divided-attention condition had the greatest effect on speed. For the MS group, while the divided-attention condition elicited greater stride length variability than the no-attentional-instruction (B = 1.47%; 95% CI, 0.30-2.63; P = .013) and the internal-focus conditions (B = 1.41%; 95% CI, 0.23-2.59; P = .019) during fast-paced walking, attentional switching had the greater impact on this outcome than did the divided-attention condition. Consistent with the MS group, the attentional-switching condition also revealed the greatest stride length variability across the attentional tasks for the NoMS group, with significant differences observed between the attentional switching and the no-attentional-instruction conditions (B = 1.69%; 95% CI, 0.50-2.87; P = .004) as well as the attentional-switching and divided-attention conditions (B = 1.61%; 95% CI, 0.46-2.75; P = .006).
Figure S2. Fast-Paced Walking by Attentional Condition
Despite significant group and condition main effects, there were no significant group × condition interaction effects (P > .05) in either gait metric, as both groups showed similar patterns across attentional conditions. This insignificant finding indicated that the participants who did and did not have MS responded similarly to the varying attentional contexts.
Multiple contrasts analyses for the significant condition × speed interaction effect on gait speed (MS: P = .002; NoMS: P = .005) revealed significant differences between the no-attentional-instruction and the divided-attention conditions in both groups (Figure S3). Specifically, participants with and without MS maintained gait speed in the divided-attention condition compared to the no-attentional-instruction condition while self-paced walking. However, participants in both groups had decreased gait speed while fast-paced walking in the divided-attention condition compared to the no-attentional-instruction condition, contributing to the significant interaction effects. There was no significant condition × speed interaction effect for stride length variability.
Figure S3. Task x Speed Interaction Effects
Significant group × speed interactions and multiple contrast comparisons in gait speed (NI: P = .009; IF: P < .001; DA: P = .007; AS: P < .001) showed that the magnitude of gait speed differences between groups increased when walking at a fast pace compared to the self-selected pace under all attentional conditions (Figure S4). There were no significant group × speed interaction effects for stride length variability. Additionally, no significant group × condition × speed interaction effects were seen in either gait metric.
Figure S4. Group x Speed Interaction Effects
Moving through daily life requires the ability to flexibly switch attention between walking and other cognitive tasks or across varied environmental situations. For individuals with MS, walking requires greater attentional resources,30 and this can present a considerable challenge and an increased fall risk. The results of this study indicate that alterations in gait speed and gait variability are sensitive to varying attentional demands; the greatest decrements in gait quality occurred when participants walked faster and engaged in attentional switching. These findings extend prior research highlighting the critical role of attention in mobility among individuals with MS.31 Importantly, our results offer novel insights into mobility decline specifically related to attentional switching—an ecologically valid cognitive demand that reflects real-world challenges in everyday ambulation.
Consistent with previous MS research,20 participants with MS had slower gait speed and greater stride length variability compared to their counterparts without MS. As has been observed previously,32 participants with MS demonstrated twice as much variability in their gait as the participants without MS. Our MS sample also had slower cognitive processing speed, known to correlate with greater gait variability.33,34 Gait speed differences between groups were greater at a fast pace than at a self-selected pace, aligning with prior findings that fast walking reveals more pronounced deficits in individuals with MS.20 Collectively, participants with MS showed worse gait deficits than those without MS, regardless of attentional condition, and greater gait decrements were elicited in their fast-paced walking in this population.
When examining between-group differences, results were significant for all conditions except for internal focus while walking fast. While both groups showed variability, the underlying cause may differ. Whereas the gait variability observed in the MS group could be attributable to neuromotor and cognitive deficits, the variability seen in the NoMS group may have been a function of redirecting unnecessary attentional resources inward to their walking. Research findings show that internal focus on one’s movement pattern, thus bringing it under more conscious control, can interfere with already automatized movement control processes.35 Given that motor function was not directly assessed in the adults without MS, it is unclear if the observed variability in gait was indeed a function of attentional focus or of preexisting motor deficits. Future research is needed to more clearly elucidate these differences.
Although participants with MS exhibited worse gait speed and stride length variability than participants without MS, both groups showed relatively similar patterns of reductions in gait in response to attentional condition, resulting in insignificant group x condition interactions. Our finding is supported by recent evidence that suggests that there is only a minimal difference in cognitive-motor interference between individuals with and without MS.36 This further indicates that attentional conditions, particularly DA and AS, required effort from both groups and led to similar levels of cognitive-motor interference on gait. However, this should be interpreted with caution, given that this study did not assess cognitive performance during the DA and AS tasks to determine if there was a greater cost to the cognitive task in individuals with MS.
When examining within-group differences across the attentional conditions at a self-selected pace, the MS group exhibited no significant differences in gait speed. While speculative, previous research supports the idea that these participants may have employed increased cortical activity needed to maintain gait speed under the various attentional conditions or that the cost of dividing or switching attention may have negatively impacted the unmeasured cognitive task rather than the observed motor task.29,30 However, for participants without MS, when walking at a self-selected speed, the slowest gait speed occurred when engaging in attentional switching. It has been shown that as individuals age and mobility becomes more dependent on executive control, attentional switching produces high cognitive-motor interference and is a detriment to gait speed.37 These observed gait speed patterns, however, differed for both groups when walking faster, with the slowest gait speed occurring when participants divided their attention. For those with MS, this may have been due to the constant attention, and thus higher cognitive-motor demand, necessitated by both the concurrent cognitive task and the more challenging task of walking fast. Although there was also a significant difference in speed during the AS condition, attentional switching likely allowed at least some attention to be intermittently redirected back to an internal focus on walking with less cost to speed. For the NoMS group, the divided-attention condition also had the greatest impact on gait speed compared to all other attentional conditions, further supporting the increased need for executive control of gait when walking in more challenging situations, such as at a faster pace.
The differences regarding gait variability were greater when participants walked at either their self-selected or fast pace. Most noteworthy was the fact that the greatest variability for both groups was observed with attention switching rather than dividing attention. For those with MS, only attentional switching resulted in a significantly more variable gait when walking at a self-chosen pace. Whereas both divided attention and attention switching significantly impacted gait variability at the faster pace, the greatest decrease in gait variability occurred during the attention switching condition. Gait variability was also impacted by attentional switching in the NoMS group and was also the most pronounced in the fast-pace condition. This is in contrast to current research involving healthy older adults,12,38 which found that pace-related parameters, such as gait speed, were more closely associated with attention switching than variability-related parameters. The difference could be due to how and when attention switching was assessed. Our study observed the effects of attentional switching during gait and may reveal more ecological and important insights, as an inconsistent and unstable gait pattern has been shown to be a more sensitive indicator of fall risk than other gait metrics.39 Nevertheless, our results offer insight into the underpinnings of this previous research and advance support for the increasing challenge of attentional switching while walking, especially at a fast pace and in terms of gait variability.
Strengths and Limitations
The effects of different attentional conditions at varying walking speeds have important ecological and practical implications. The gait parameters examined, especially stride length variability, offer insight into the differential consequences underpinning gait deficits during attentionally demanding tasks in people with MS. Additionally, employing ecologically valid cognitive tasks may contribute to the generalizability of our findings beyond those traditionally obtained from
laboratory-based cognitive assessments.40
Nonetheless, some limitations exist. The relatively small sample with a narrow range of disability may not be representative of the larger MS population. Our study did not assess performance on a discourse task, leaving the cognitive cost of multitasking unknown. It was difficult to accurately assess where participants actually directed their attention when given no instruction or asked to internally focus. Finally, although this study aimed to enhance ecological validity within lab-based assessments, prior research has highlighted the constraints of laboratory walking tasks in capturing the complexity of real-world ambulation41 that is influenced by multiple interacting factors.42 While our attentional conditions were designed to reflect common cognitive demands, they may not fully replicate the dynamic and unpredictable nature of attentional challenges encountered during everyday walking.
Our data show that participants with MS had slower gait speed and greater gait variability than those without MS in all attentional conditions. Notably, we found that difficult walking conditions, such as attentional switching, elicited greater gait deficits, especially related to gait variability in persons with MS, and these deficits became profound in fast-paced walking. Thus, this study demonstrates the value of evaluating attentional switching for mobility assessment in people with MS and the need to incorporate this attentional technique in future mobility training to improve stability and prevent falls.
Conflicts of Interest: The authors declare no conflicts of interest for
this study.
Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Previous Presentation: This work, in part, was presented previously as 2 posters at the North American Federation of Adapted Physical Activity Conference; October 11-13, 2022; St. Catharines, Ontario, Canada.
Clark DJ. Automaticity of walking: functional significance, mechanisms, measurement and rehabilitation strategies. Front Hum Neurosci. 2015;9:246. doi:10.3389/fnhum.2015.00246
Takakusaki K. Functional neuroanatomy for posture and gait control. J Mov Disord. 2017;10(1):1-17. doi:10.14802/jmd.16062
Clark DJ, Rose DK, Ring SA, Porges EC. Utilization of central nervous system resources for preparation and performance of complex walking tasks in older adults. Front Aging Neurosci. 2014;6:217. doi:10.3389/fnagi.2014.00217
Santinelli FB, Veldkamp R, Vitório R, et al. Hemodynamics of the frontopolar and dorsolateral pre-frontal cortex in people with multiple sclerosis during walking, cognitive subtraction, and cognitive-motor dual-task. Neurorehabil Neural Repair. 2024;38(11-12):820-831. doi:10.1177/15459683241279066
VanNostrand M, Emberley K, Cairns E, Shanahan K, Kasser SL. Mobility and dual tasking in the everyday lives of adults with multiple sclerosis: a qualitative exploration. Adapt Phys Activ Q. 2023;40(4):723-738. doi:10.1123/apaq.2022-0161
Carling A, Forsberg A, Nilsagård Y. Falls in people with multiple sclerosis: experiences of 115 fall situations. Clin Rehabil. 2018;32(4):526-535. doi:10.1177/0269215517730597
Mofateh R, Salehi R, Negahban H, Mehravar M, Tajali S. Effects of cognitive versus motor dual-task on spatiotemporal gait parameters in healthy controls and multiple sclerosis patients with and without fall history. Mult Scler Relat Disord. 2017;18:8-14. doi:10.1016/j.msard.2017.09.002
Etemadi Y. Dual task cost of cognition is related to fall risk in patients with multiple sclerosis: a prospective study. Clin Rehabil. 2017;31(2):278-284. doi:10.1177/0269215516637201
VanNostrand M, Sogoloff B, Giroux C, Bergmans L, Kasser SL. Predicting falls in adults with multiple sclerosis using patient-reported measures: are perceptions of dual-tasking missing? Mult Scler Relat Disord. 2022;68:104115. doi:10.1016/j.msard.2022.104115
Oreja-Guevara C, Ayuso Blanco T, Brieva Ruiz L, Hernández Pérez MÁ, Meca-Lallana V, Ramió-Torrentà L. Cognitive dysfunctions and assessments in multiple sclerosis. Front Neurol. 2019;10:581. doi:10.3389/fneur.2019.00581
Leone C, Patti F, Feys P. Measuring the cost of cognitive-motor dual tasking during walking in multiple sclerosis. Mult Scler. 2015;21(2):123-131. doi:10.1177/1352458514547408
Postigo-Alonso B, Galvao-Carmona A, Benitez I, et al. Cognitive-motor interference during gait in patients with multiple sclerosis: a mixed methods systematic review. Neurosci Biobehav Rev. 2018;94:126-148. doi:10.1016/j.neubiorev.2018.08.016
Wallin A, Franzén E, Bezuidenhout L, Ekman U, Piehl F, Johansson S. Cognitive-motor interference in people with mild to moderate multiple sclerosis, in comparison with healthy controls. Mult Scler Relat Disord. 2022;67:104181. doi:10.1016/j.msard.2022.104181
Hawkes TD, Siu KC, Silsupadol P, Woollacott MH. Why does older adults’ balance become less stable when walking and performing a secondary task? examination of attentional switching abilities. Gait Posture. 2012;35(1):159-163. doi:10.1016/j.gaitpost.2011.09.001
Miyake A, Friedman NP, Emerson MJ, Witzki AH, Howerter A, Wager TD. The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: a latent variable analysis. Cogn Psychol. 2000;41(1):49-100. doi:10.1006/cogp.1999.0734
Migliore S, Curcio G, Couyoumdjian A, et al. Executive functioning in relapsing-remitting multiple sclerosis patients without cognitive impairment: a task-switching protocol. Mult Scler. 2018;24(10):1328-1336. doi:10.1177/1352458517719149
Shiri V, Emami M, Shiri E. Investigating the relationship between selective attention and cognitive flexibility with balance in patients with relapsing-remitting multiple sclerosis. Arch Rehabil. 2018;18(4):296-305.
Esmaeili Bijarsari S. A current view on dual-task paradigms and their limitations to capture cognitive load. Front Psychol. 2021;12:648586. doi:10.3389/fpsyg.2021.648586
Santinelli FB, Sebastiao E, Palucci LH, Kuroda MH, Barbieri FA. Loss in gait automaticity reflects in higher frontal cortical activity and gait impairments during obstacle crossing in people with multiple sclerosis. Braz J Mot Behav. 2020;14(4):67-68.
Comber L, Galvin R, Coote S. Gait deficits in people with multiple sclerosis: a systematic review and meta-analysis. Gait Posture. 2017;51:25-35. doi:10.1016/j.gaitpost.2016.09.026
Learmonth YC, Motl RW, Sandroff BM, Pula JH, Cadavid D. Validation of patient determined disease steps (PDDS) scale scores in persons with multiple sclerosis. BMC Neurol. 2013;13:37. doi:10.1186/1471-2377-13-37
Parmenter BA, Weinstock-Guttman B, Garg N, Munschauer F, Benedict RH. Screening for cognitive impairment in multiple sclerosis using the Symbol Digit Modalities Test. Mult Scler. 2007;13(1):52-57. doi:10.1177/1352458506070750
Nasreddine ZS, Phillips NA, Bédirian V, et al. The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc. 2005;53(4):695-699. doi:10.1111/j.1532-5415.2005.53221.x
Freitas S, Batista S, Afonso AC, et al. The Montreal Cognitive Assessment (MoCA) as a screening test for cognitive dysfunction in multiple sclerosis. Appl Neuropsychol Adult. 2018;25(1):57-70. doi:10.1080/23279095.2016.1243108
McGuigan C, Hutchinson M. Confirming the validity and responsiveness of the Multiple Sclerosis Walking Scale-12 (MSWS-12). Neurology. 2004;62(11):2103-2105. doi:10.1212/01.wnl.0000127604.84575.0d
van Vliet R, Hoang P, Lord S, Gandevia S, Delbaere K. Falls efficacy scale-international: a cross-sectional validation in people with multiple sclerosis. Arch Phys Med Rehabil. 2013;94(5):883-889. doi:10.1016/j.apmr.2012.10.034
Kalron A. Gait variability across the disability spectrum in people with multiple sclerosis. J Neurol Sci. 2016;361:1-6. doi:10.1016/j.jns.2015.12.012
Fleming VB, Harris JL. Complex discourse production in mild cognitive impairment: detecting subtle changes. Aphasiology. 2008;22(7-8):729-740. doi:10.1080/02687030701803762
VanNostrand M, Belanger B, Purin G, Kasser SL, Cannizzaro M. Cognitive-motor interference and cortical activation while walking in individuals with multiple sclerosis. Motor Control. 2022;26(4):677-693. doi:10.1123/mc.2021-0051
Hernandez ME, Holtzer R, Chaparro G, et al. Brain activation changes during locomotion in middle-aged to older adults with multiple sclerosis. J Neurol Sci. 2016;370:277-283. doi:10.1016/j.jns.2016.10.002
Shafizadeh M, Platt GK, Mohammadi B. Effects of different focus of attention rehabilitative training on gait performance in multiple sclerosis patients. J Bodyw Mov Ther. 2013;17(1):28-34. doi:10.1016/j.jbmt.2012.04.005
Malcay O, Grinberg Y, Berkowitz S, Hershkovitz L, Kalron A. Cognitive-motor interference in multiple sclerosis: What happens when the gait speed is fixed? Gait Posture. 2017;57:211-216. doi:10.1016/j.gaitpost.2017.06.024
Kalron A, Aloni R, Dolev M, Frid L, Givon U, Menascu S. The relationship between gait variability and cognitive functions differs between fallers and non-fallers in MS. J Neural Transm (Vienna). 2018;125(6):945-952. doi:10.1007/s00702-018-1843-y
Hsieh KL, Sun R, Sosnoff JJ. Cognition is associated with gait variability in individuals with multiple sclerosis. J Neural Transm (Vienna). 2017;124(12):1503-1508. doi:10.1007/s00702-017-1801-0
Kal EC, van der Kamp J, Houdijk H. External attentional focus enhances movement automatization: a comprehensive test of the constrained action hypothesis. Hum Mov Sci. 2013;32(4):527-539. doi:10.1016/j.humov.2013.04.001
Learmonth YC, Ensari I, Motl RW. Cognitive motor interference in multiple sclerosis: insights from a systematic quantitative review. Arch Phys Med Rehabil. 2017;98(6):1229-1240. doi:10.1016/j.apmr.2016.07.018
Langeard A, Torre MM, Temprado JJ. A dual-task paradigm using the oral trail making test while walking to study cognitive-motor interactions in older adults. Front Aging Neurosci. 2021;13:712463. doi:10.3389/fnagi.2021.712463
Killane I, Donoghue OA, Savva GM, Cronin H, Kenny RA, Reilly RB. Relative association of processing speed, short-term memory and sustained attention with task on gait speed: a study of community-dwelling people 50 years and older. J Gerontol A Biol Sci Med Sci. 2014;69(11):1407-1414. doi:10.1093/gerona/glu140
Kalron A. Association between gait variability, falls and mobility in people with multiple sclerosis: a specific observation on the EDSS 4.0-4.5 level. NeuroRehabilitation. 2017;40(4):579-585. doi:10.3233/NRE-171445
Hobart JC, Riazi A, Lamping DL, Fitzpatrick R, Thompson AJ. Measuring the impact of MS on walking ability: the 12-Item MS Walking Scale (MSWS-12). Neurology. 2003;60(1):31-36. doi:10.1212/wnl.60.1.31
Shema-Shiratzky S, Hillel I, Mirelman A, et al. A wearable sensor identifies alterations in community ambulation in multiple sclerosis: contributors to real-world gait quality and physical activity. J Neurol. 2020;267(7):1912-1921. doi:10.1007/s00415-020-09759-7
VanNostrand M, Bae M, Ramsdell JC, Kasser SL. Information processing speed and disease severity predict real-world ambulation in persons with multiple sclerosis. Gait Posture. 2024;111:99-104. doi:10.1016/j.gaitpost.2024.04.018
