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Generalised cognitive motor interference in multiple sclerosis

Gait & Posture

Highlights

 

  • Cognitive motor interference (CMI) occurs in the upper and lower limb of those with MS and controls.
  • CMI is generalised and present across the disease spectrum in MS.
  • MS interventions must address CMI in the upper and lower limb of persons with MS.

Abstract

Researchers have examined cognitive motor interference (CMI) for lower extremity function in MS, but have not examined this in the upper extremity. This study examined CMI for both lower and upper extremity motor tasks in persons with MS and without MS. Eighty-two persons walked on a GAITRite electronic walkway (velocity) and performed the nine-hole peg test (NHPT, seconds) without (single task) and with a cognitive challenge (dual task). The data were analysed with mixed-factor ANOVA and Pearson correlations. When comparing MS and controls, there were statistical significant and exceptionally large Task main effects on gait velocity (ηp2 = .41;F1,60 = 55.78;p < .005) and NHPT performance (ηp2 = .62;F1,60 = 127.8;p < .005). When considering disability status among those with MS, there were statistically significant and large Task main effects on velocity (ηp2 = .38;F1,60 = 37.3;p < .005) and NHPT test (ηp2 = .62;F1,60 = 95.7;p < .005). The dual task cost of walking and performing the NHPT were significantly correlated in the entire sample, those with MS and controls, and in those with MS who had mild, moderate, and severe disability (all |r| > .450). CMI occurs in both the lower and upper extremities, and is comparable between persons with and without MS and across MS disability level.

Keywords: Cognitive motor interference, Dual task, Multiple sclerosis, Lower extremity, Upper extremity.

1. Introduction

Multiple sclerosis (MS) presents with cognitive and motor dysfunction[1] and [2]. There is an association between cognitive function and motor performance for both the upper and lower extremities (i.e., cognitive-motor coupling) [3] . Researchers have experimentally examined cognitive-motor coupling in MS based on the dual task paradigm resulting in cognitive motor interference (CMI) of lower extremity functioning [4] . Concurrently performing an alternate letter alphabet cognitive task while walking reduced gait velocity by 15% [5] and gait initiation time by 18% [6] on an electronic walkway compared with walking alone in persons with MS. A similar phenomenon has been observed in other populations using an alternate letter alphabet task[7], [8], [9], [10], [11], and [12].

Researchers have compared CMI between ambulatory persons with MS and healthy controls across different walking and cognitive conditions of the dual task paradigm. Overall, walking performance consistently declines in people with Clinically Isolated Syndrome (CIS), MS, and healthy controls under dual task conditions based on the dual task cost (DTC) metric[13], [14], [15], and [16]. There is some inconsistency in the literature regarding whether DTC differs between persons with MS and healthy controls. Two research groups have reported small, yet statistically significant differences[13] and [14], whereas other researchers have reported small, non-significant difference in DTC[15] and [16].

To date, there is no research applying the dual task paradigm for upper extremity motor functioning in MS (for example, does performing the alternate letter alphabet cognitive task reduce performance on an upper extremity motor functioning task such as the nine-hole peg test (NHPT) [17] ?). Such an examination is important for multiple reasons. The presence of cognitive-motor coupling based on the dual task paradigm for both upper and lower extremity tasks would indicate, in part, the generality of this phenomenon across motor domains [3] . The co-occurrence for both upper and lower extremity motor function might further direct efforts towards approaches for rehabilitation [18] . If the effect exists for upper and lower extremity functioning, researchers might globally target the co-occurrence or interaction of cognition and motor functioning for reducing CMI. The examination of CMI in the upper extremity further permits an investigation and extension of this phenomenon and its correlates among non-ambulatory persons with MS.

This study examined the presence of CMI for both upper and lower extremity motor tasks in persons with MS. The study adopted an established and refined alternate letter alphabet task [7] , recently applied in MS[5] and [6], and examined the influence on performance of upper and lower extremity tasks measured by the Nine Hole Peg Test [17] and GAITrite electronic walkway. The hypotheses were that (a) there would be a reduction in performance for both upper and lower extremity tasks when concurrently performing a cognitive task; (b) the magnitude of reduction for upper and lower extremity tasks would be slightly larger in persons with MS compared with controls; and (c) disability status would not influence CMI in upper and lower extremity tasks, based on previous research in MS [5] .

2. Methods

2.1. Sample

The protocol was approved by a University institutional review board and all participants provided written informed consent prior to participation. The protocol was an adjuvant part of a cross-sectional, comparative study of approaches for fitness assessments in therapeutic interventions for MS [19] .

Persons with MS were recruited through flyers distributed within the North American Research Committee on Multiple Sclerosis (NARCOMS) registry. Flyers were distributed to participants from previous research studies in our laboratory who had expressed interest in future opportunities. Criteria for inclusion were (a) diagnosis of MS; (b) Expanded Disability Status Score (EDSS) score <8.0; (c) age 18–64 years; (d) able to visit our laboratory on two testing occasions; (e) minimal risk for engaging in physical activity (i.e., reported ‘yes’ to less than two questions on the Physical Activity Readiness Questionnaire); and (f) physician approval for undertaking exercise testing. Of note, an equivalent number of participants with mild, moderate, and severe MS disability were recruited, this facilitated comparison of outcomes across the MS disability spectrum; disability was initially based on a self-reported EDSS performed over the phone and then confirmed with a neurological examination in person. Persons without MS were recruited through university wide recruitment emails. Inclusion criteria involved items c-f listed above, and control participants were age- (within 5 years), sex-, height- (within 3″) and weight- (within 5 lbs) matched with one of the MS participants. The flow of participants through stages of the research is presented in Fig. 1 . Sixty-two participants with MS were enrolled in the study and completed testing. Eleven persons with MS completed the walking task using an assistive device (i.e., cane or walking-frame); gait data were not collected from two participants with MS due to severe ambulatory impairment. Fifty-two individuals without MS contacted our research coordinator, 29 were screened for inclusion. Twenty participants without MS completed all testing.

gr1

Fig. 1 Flow diagram of participant recruitment and enrollment.

2.2. Disability status

Disability status was based on the Expanded Disability Status Scale (EDSS) [20] score determined through a clinical examination that was performed by Neurostatus certified examiners. Disability groups were represented as mild (EDSS score of 1.0–3.5), moderate (EDSS score of 4.0–5.5), and severe (EDSS score of 6.0–7.5) disability.

2.3. Dual task (DT) paradigm

Cognitive motor interference (CMI) was determined during one lower extremity (i.e., walking) and one upper extremity (i.e., NHPT) motor task. To reduce task familiarisation, participants completed the walking task and NHPT on 2 separate days separated by 7 days, and the order of walking task and NHPT test administration was randomised.

2.4. Lower extremity dual task (DT) paradigm

Participants completed four walking trials at a self-selected, comfortable pace across a 4.6 m GAITRite walkway (CIR systems, Havertown, PA, USA) as a measure of lower extremity function. Participants started walking 1.5 m in front of the mat and ending 1.5 m past the end of the mat. The GAITRite automatically collected data on walking velocity (cm/s).

Single-task (ST) walking (i.e., only walking) was performed during the first two walking trials, with executive attention challenged in the second dual-task (DT) walking trials (i.e., walking with a cognitive task). The DT involved all participants reciting alternate letters of the alphabet while walking following established protocol [8] . In brief, after the ST walks, a DT example was provided by a researcher and participants then completed a practice of the cognitive task only. During the DT walk, participants were asked to recite alternate letters of the alphabet starting with the letter M for the first walk and N for the second; participants were asked to pay equal attention to reciting alternate letters and walking. A mean value for velocity for the ST and DT walks independently was computed, as done in previous research [21] .

CMI is expressed during walking as the dual task cost (DTC). This was calculated as the percent change in velocity between ST and DT conditions, such that DTC = 100 × ((ST − DT)/ST), as done in previous research [14] .

2.5. Upper extremity dual task (DT) paradigm

Participant completed eight trials of the NHPT as a measure of upper extremity function. The NHPT was performed according to standardised instructions [22] . The test was timed (s) from when the participants touched the first peg until when the last peg had been removed and hit the table (i.e., movement of 18 pegs per trial). Two consecutive trials were performed on the dominant hand and then two consecutive trials were performed on the non-dominant hand. The mean score for the two dominant hand and two non-dominant trials were expressed as speed based on pegs/s, and the summary score of both the dominant and non-dominant hand together was determined.

The single-task (ST) NHPT (i.e., only completing the motor task) was performed during the first two NHPT trials for each hand, with executive attention challenged in the second dual-task (DT) NHPT trials for each hand (i.e., completing the task with a cognitive task). The DT involved participants reciting alternate letters of the alphabet and was administered in a similar manner to the lower extremity task. To minimise task-familiarisation during the DT NHPT, participants were asked to recite alternate letters of the alphabet starting with specified letters different from those of the lower extremity DT (i.e., C and D, for the two dominant hand trials and K and J for the two non-dominant hand trials).

CMI during the NHPT as the DTC was computed as previously described.

2.6. Statistical analysis

The analysis was performed using PASW Statistics 22 (SPSS Inc. Chicago, IL) Descriptive statistics are reported as mean and standard deviation (SD). Skewness and kurtosis estimates are provided for describing the distributions of variables. A mixed-factor ANOVA was adopted for examining CMI during walking measured as velocity and hand/arm function based on NHPT performance. Task (i.e., ST and DT for velocity and NHPT performance) served as the within-subjects factor, whereas Group (e.g., MS and control, or mild, moderate, and severe disability status) served as the between-subjects factor. Partial eta-squared (ηp2) estimates are provided for establishing the magnitude of CMI on walking velocity and NHPT performance. Values forηp2of .01, .06, and .14 reflected small, moderate, and large effects, respectively [23] . A one-way ANOVA on DTC with Group as a between-subjects factor was performed. This analysis accounts for percent change and seems less likely to be biased by the initial starting value in the ST conditions. Pearson product-moment (r) correlations between the DTC of walking and the DTC of the NHPT task in the whole sample, MS and control samples separately, and across disability status in the MS sample were computed.

3. Results

3.1. Description of sample

There were 62 participants with MS in the study, and 20 individuals without MS as a comparison control group. Participant with MS had a mean age of 52.1 years (SD = 7.8) and the majority were female (73%) ( Table 1 ). The control group had a mean age of 51.7 years (SD = 10.6) and the majority were female (80%). There were no significant differences between the groups in age (p = .115) and sex (p = .160).

Table 1 Characteristics of participants with MS overall and by level of disability.

Characteristic Total MS (n = 62) Mild MS (n = 20) Moderate (n = 22) Severe (n = 20) Control (n = 20)
Age (years) 52.1 (7.8) 50.1 (9.5) 51.8 (6.9) 54.5 (6.5) 51.7 (10.6)
Sex (% female) 73.0% 60.0% 72.7% 85.0% 80.0%
Height (cm) 169.7 (10.2) 173.1 (12.6) 170.4 (7.9) 165.4 (8.7) 170.5 (9.2)
Weight (kg) 77.7 (19.7) 77.4 (13.9) 81.1 (19.6) 74.5 (24.5) 74.3 (18.2)
EDSS (mdn (IQR)) 4.25 (2.5) 3.00 (1.5) 4.25 (.5) 6.25 (.5)
Disease duration (years) 13.2 (8.8) 5.9 (1.1) 14.5 (8.9) 16.6 (9.3)
Disease course (% RRMS) 76.2% 95.0% 86.4% 45.0%

Note: EDSS = Expanded Disability Status Scale; IQR = interquartile range; mdn = median; RRMS = relapsing-remitting multiple sclerosis. Values are mean (SD), unless otherwise noted.

3.2. Comparison between task conditions for MS vs. control participants

Walking performance (i.e., velocity) and upper extremity performance (i.e., pegs/s) for all participants during ST and DT conditions is reported in Table 2 . There was a statistically significant and moderate Group by Task interaction on velocity (ηp2 = .09;F1,60 = 7.7;p = .007) such that there was a larger reduction in velocity between ST and DT conditions for controls compared to persons with MS. The one-way ANOVA on DTC indicated no Group main effect (ηp2 = .02;F1,60 = 1.4;p = .247) suggesting that the interaction was largely explained by the higher velocity for the ST condition in the control group. The walking DTC was 12.5% in MS and 17.5% in controls. There was a statistically significant and large Task main effect on velocity (ηp2 = .41;F1,60 = 55.8;p < .005), indicating an effect of the DT on velocity for the entire sample. There further was a statistically significant and exceptionally large Group main effect on velocity (ηp2 = .21;F1,60 = 20.7;p < .005), indicating that velocity was slower in the MS group.

Table 2 Performance for single and dual tasks of walking and NHPT, and dual task cost in the 62 participants with MS and 20 control participants.

  ST

Mean (SD, skewness, kurtosis)
DT

Mean (SD, skewness, kurtosis)
DTC %

Mean (SD, skewness, kurtosis)
  MS Controls MS Controls MS Controls
Velocity a (cm/s) 99.6 (37.7, .1, .8) 144.7 (17, .1, .1) 87.5 (36, .2, .7) 118.3 (26.6, .3, .8) 12.5 (15.7, .2, .1) 17.5 (18.6, .9, .1)
NHPT (pegs/s) 1.4 (.4, .6, .1) 1.8 (.2, .2, .9) 1.1 (.4, .1, .1) 1.4 (.3, .6, .9) 20.1 (15.6, .4, .1) 23.1 (13.0, .4, .6)

a Missing data from two participants with severe MS.

Note: ST; single task, DT; dual task, SD; standard deviation, DTC; dual task cost.

There was not a significant Group by Task interaction on NHPT (ηp2 < .01;F1,60 = 3.3;p = .07). The one-way ANOVA further indicated no statistically significant Group main effect on DTC (ηp2 = .05;F1,60 = .6;p = .44), and the DTC for the NHPT was 20% in MS and 23% in controls. There was a statistically significant and large Task main effect on NHPT (ηp2 = .62;F1,60 = 127.8;p < .005), indicating an effect of the DT on NHPT performance for the entire sample. The Group main effect on NHPT was statistically significant (ηp2 = .17;F1,60 = 16.6;p < .05), providing evidence for worse performance on the NHPT for those with MS.

3.3. Comparison between task conditions for participants with MS based on disability status

Walking performance (i.e., velocity) and upper extremity performance (i.e., pegs/s) for those with MS based on disability status (i.e., mild, moderate and severe MS) during ST and DT conditions is reported in Table 3 . There was no significant Group by Task interaction on velocity (ηp2 = .04;F1,60 = 1.3;p = .27). The one-way ANOVA further indicated no Group main effect on DTC (ηp2 = .01;F1,60 = .3;p = .75). There was a velocity DTC of 10.9% in the mild MS group, 12% in the moderate MS group, and 14.7% in the severe MS group. There was a statistically significant and large Task main effect (ηp2 = .39;F1,60 = 37.3;p < .005), indicating an effect of DT on velocity for the entire MS sample. The Group main effect on velocity (ηp2 = .67;F1,60 = 58.6;p < .005) indicated that velocity was slowed in participants with MS as a function of worsening of disability.

Table 3 Performance for single and dual tasks of walking and NHPT, and dual task cost in the three disability groups of 62 participants with MS.

  ST

Mean (SD, skewness, kurtosis)
DT

Mean (SD, skewness, kurtosis)
DTC %

Mean (SD, skewness, kurtosis)
  Mild Moderate Severe Mild Moderate Severe Mild Moderate Severe
Velocity a (cm/s) 135.5 (17.6, .9, 2.5) 103.6 (24, .6, .9) 59.3 (23.3, .7, .3) 119.8 (20, .4, .1) 90.8 (23.7, .3, .7) 51.6 (25.7, .7, .7) 10.9 (14.4, .5, .1) 12 (13.4, .5, .9) 14.7 (19.4, .1, .6)
NHPT (pegs/s) 1.7 (.2, .3, 1.2) 1.4 (.3, 1.4, 2.6) 1.1 (.3, .7, .1) 1.4 (.3, .8, .9) 1.1 (.3, .2, 1.1) .9 (.3, .6, .9) 21.0 (15, .1, .8) 21.8 (18.1, .5, .6) 17.4 (13.7, .6, .9)

a Missing data from two participants with severe MS.

Note: ST; single task, DT; dual task, SD; standard deviation, DTC; dual task cost.

There was a significant Group by Task interaction on NHPT (ηp2 = .10;F1,60 = 3.36;p = .04) with a larger reduction in NHPT speed between ST and DT conditions for those with more severe MS than moderate MS. The one-way ANOVA further indicated no group main effect on DTC (ηp2 = .02;F1,60 = .60;p = .46), indicating the interaction was largely explained by the faster initial NHPT speed in those with mild and moderate disability. The NHPT DTC was 21% for those in the mild MS group, 22% for the moderate MS group, and 17% in the severe MS group. Notably, there was a statistically significant and large Task main effect (ηp2 = .62;F1,60 = 95.7;p < .005), suggesting an effect of DT on NHPT for the entire sample of persons with MS. There was a Group main effect on NHPT (ηp2 = .47;F1,60 = 26.0;p < .005), indicating worsening performance on the NHPT with increasing disability in the MS sample.

3.4. Relationship between velocity and NHPT performance

The DTC of walking and NHPT were significantly correlated in the entire sample (r = .452,p = .01). There further was a relationship between the DTC of walking and performing the NHPT in those with MS (r = .450,p = .01) and control participants (r = .489,p = .05). There was a relationship between the DTC of walking and performing the NHPT in those with MS who had mild (r = .496,p = .03), moderate (r = .619,p = .02), and severe (r = .514,p = .02) disability.

4. Discussion

This study examined the presence of CMI for both upper and lower extremity motor tasks in persons with MS and healthy controls. Evidence is provided for the effect of CMI in the lower extremities of persons with MS, irrespective of disability level [4] , and novel evidence is reported that this effect is comparable with a control sample when expressed as the DTC. There is additional evidence regarding the presence of CMI in the upper extremities of persons with MS across the disability spectrum and in controls without MS. There was a consistent association between the DTC of lower and upper extremity tasks. Our results suggest that CMI is likely a generalised phenomenon that occurs across upper and lower extremities, persons with MS and controls, and MS disability status when using the alternate letter alphabet task.

Results confirm [5] that there is a large degree of CMI during walking in persons with MS, irrespective of disability level. There was a 12.5% deterioration in walking velocity compared with 15% in previous research [5] . Importantly, results highlight that CMI is not specific to lower extremity function in those with MS. The current results indicate that CMI is present when performing upper extremity tasks; there was a 20% deterioration in the NHPT. Of note, CMI is not specific among those with MS, as our results indicate no difference in the decline of walking and upper extremity performance between MS and healthy controls when considering the DTC (e.g., 12.5% and 17.5% deterioration in walking velocity). Our results are partially consistent with previous research. Two studies reported significant differences in DTC between MS and healthy controls[13] and [14], whereas two reported no significant differences[15] and [16]. One research group reported a 7% DTC during walking in 52 persons with CIS compared with a 1% DTC in controls [13] , other researchers reported a 8.5% DTC during walking in persons with MS compared with a 2% change in controls [14] ; non-significant differences have also been reported, for example a 6% DTC in persons with MS and a 2.5% change in controls [15] , and 17.6% in persons with MS compared with 15.3% in controls [16] . Methodological variation (i.e., cognitive task and walking task) across studies may explain these differences, including cognitive tasks and duration of walking trials. Overall, the differences between persons with MS and controls in DTC are generally small, and no strong evidence indicates that DTC is substantially greater in those with MS.

To date, knowledge of CMI in MS has been limited to the lower limb[4], [14], [15], [16], and [24]and this restricts our understanding of CMI amongst ambulatory individuals. Upwards of 10% of persons with MS will require mobility devices (i.e., wheelchair) within 10–15 years after MS diagnosis [25] ; therefore, there is a substantial proportion of the MS population in whom assessment and rehabilitation of CMI may be restricted to the upper extremities. CMI in upper extremities has important real-world implications such as for driving, self-care or maintaining employment. Investigation of upper limb CMI and applicable interventions are warranted and should be an important focus in MS rehabilitation.

CMI is present among persons with MS regardless of disability status and generalised in both the upper and lower extremities. This finding may have implications for future rehabilitation of CMI in MS. With a focus on global CMI rehabilitation strategies, rather than focusing separately on cognitive and motor task rehabilitation to optimise rehabilitation in MS.

Our study is not without important limitations. The order of ST and DT testing within the upper and lower extremity tasks was not randomised. The cross-sectional nature of the study restricted our ability to adequately determine variability within our sample and limits our understanding as to how CMI may change over time. The short nature of the walking condition may not have yielded a reliable motor task compared with a longer walking test [14] , but our protocol duration is consistent with the majority of research in this area. The use of an upper limb measure of disability, may improve future understanding of DTC in the upper extremities. Determining the variability over time will further provide details on the interpretability of DTC in persons with MS.

Overall, the results indicate that CMI is present for both upper and lower motor domains of functioning among those with MS, across the disability spectrum, and occurs comparably in healthy controls. It is recommended that assessment of CMI be adopted in MS research to monitor progression and the impact of rehabilitation interventions on CMI. Such rehabilitation interventions should adopt a generalised approach to training of CMI rather than focusing on the separate components of CMI.

Conflict of interest statement

The authors declare that there is no conflict of interest.

Acknowledgements

The authors wish to thank all research participants and all staff and students involved in data collection and analysis. This work was supported, in part, by a mentor-based post-doctoral fellowship from the NMSS (MB 029) and a pilot grant from NMSS (IL 0003).

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  • [25] C. Hirst, G. Ingram, R. Swingler, D.A.S. Compston, T. Pickersgill, N.P. Robertson. Change in disability in patients with multiple sclerosis: a 20-year prospective population-based analysis. J Neurol Neurosurg Psychiatry. 2008;79:1137-1143 10.1136/jnnp.2007.133785 Crossref

Footnotes

Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, 233 Freer Hall, Urbana, IL 61820, USA

lowast Corresponding author. Tel.: +1 217 265 0886; fax: +1 217 333 3124.


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About the Editors

  • Prof Timothy Vartanian

    Timothy Vartanian, Professor at the Brain and Mind Research Institute and the Department of Neurology, Weill Cornell Medical College, Cornell...
  • Dr Claire S. Riley

    Claire S. Riley, MD is an assistant attending neurologist and assistant professor of neurology in the Neurological Institute, Columbia University,...
  • Dr Rebecca Farber

    Rebecca Farber, MD is an attending neurologist and assistant professor of neurology at the Neurological Institute, Columbia University, in New...

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