Prevalence of Walking-Related Motor Fatigue in Persons With Multiple Sclerosis

Abstract

Objective. To investigate the individual occurrence of walking-related motor fatigue in persons with multiple sclerosis (PwMS), according to disability level and disease phenotype. Study design. This was a cross-sectional, multinational study. Participants. They were 208 PwMS from 11 centers with Expanded Disability Status Scale (EDSS) scores up to 6.5. Methods. The percentage change in distance walked (distance walked index, DWI) was calculated between minute 6 and 1 (DWI_6-1) of the 6-Minute Walk Test (6MWT). Its magnitude was used to classify participants into 4 subgroups: (1) DWI_6-1[≥5%], (2) DWI_6-1[5%; –5%], (3) DWI_6-1[–5%; > –15%], and (4) DWI_6-1[≤−15%]. The latter group was labeled as having walking-related motor fatigue. PwMS were stratified into 5 subgroups based on the EDSS (0-2.5, 3-4, 4.5-5.5, 6, 6.5) and 3 subgroups based on MS phenotype (relapsing remitting [RR], primary progressive [PP], and secondary progressive [SP]). Results. The DWI_6-1 was ≥5% in 16 PwMS (7.7%), between 5% and −5% in 70 PwMS (33.6%), between −5% and −15% in 58 PwMS (24%), and ≤−15% in 64 PwMS (30.8%). The prevalence of walking-related motor fatigue (DWI_6-1[≤−15%]) was significantly higher among the progressive phenotype (PP = 50% and SP = 39%; RR = 15.6%) and PwMS with higher disability level (EDSS 4.5-5.5 = 48.3%, 6 = 46.3% and 6.5 = 51.5%, compared with EDSS 0-2.5 = 7.8% and 3-4 = 16.7%; P < .05). Stepwise multiple regression analysis indicated that EDSS, but not MS phenotype, explained a significant part of the variance in DWI_6-1 (R² = 0.086; P < .001). Conclusion. More than one-third of PwMS showed walking-related motor fatigue during the 6MWT, with its prevalence greatest in more disabled persons (up to 51%) and in those with progressive MS phenotype (up to 50%). Identification of walking-related motor fatigue may lead to better-tailored interventions.

Keywords

walking 6-Minute Walk Test motor fatigue distance walked index deceleration index

Introduction

Multiple sclerosis (MS) is a chronic inflammatory and neurodegenerative disease of the central nervous system, with a negative impact on physical functioning and activity.^1,2Ambulation is frequently reported by persons with MS (PwMS) as an important bodily function.^2-4 From the beginning of the disease,^5-7 decreased walking capacity and physical activity levels^2,8,9 are present, even in the absence of clinical signs of pyramidal dysfunction.⁶ Walking dysfunction can be primarily related to the occurrence of symptoms such as muscle weakness, spasticity, and ataxia.⁷ Besides these well-known motor symptoms that affect walking speed, MS-related motor fatigue may occur and affect walking endurance and the related action radius,^10-12 in turn affecting participative social behavior¹³ (eg, walking from a parking area to a store, going out for a walk with the partner or friends¹³) and occupation.¹⁴

Generally, motor fatigue refers to the term fatigability, defined as the magnitude of change in a performance relative to a reference value over a given time.^15,16 The occurrence of enhanced levels of motor fatigue has been shown in MS during repeated maximal muscle contractions.^10,12 The functional impact of motor fatigue has been mainly addressed by investigating whether PwMS slowed down during prolonged walking tests.^11,12 Multiple studies have investigated the pacing pattern during the 6-Minute Walk Test (6MWT), a validated and reliable measure of walking endurance and motor function that is widely applied in MS.^17-24 Overall, it was found that PwMS with mild to moderate disability reduce walking speed in the first 2 minutes, followed by a stable walking speed starting from the third minute, whereas some accelerate during the last minute.^18,22,24,25 PwMS with more pronounced disability, as expressed by the Expanded Disability Status Scale (EDSS) scores above 3.5²⁵ and 4.5,²² showed, on average, a consistent decrease in the mean walking speed over 6 minutes as well as during longer tests of up to 12 minutes.²⁵ Previous studies, however, were not confirmed by Dalgas et al,²⁴ who reported a constant average walking speed from minutes 3 to 6, which was similar for all subgroups and independent of disability, general fatigue, and MS phenotype.

All the above studies reported (changes in) walking distance/speed per minute on the group level without identifying the percentage of individual occurrence of walking-related motor fatigue observed during long walk tests according to disability level and MS phenotype. Within this framework, Sehle et al²⁶ as well as Phan-Ba et al¹¹ proposed calculation methods for quantifying the magnitude of walking-related motor fatigue in PwMS during treadmill and overground walking, respectively. The latter study expressed walking speed at the end of the timed 500-m walk test (T500MW) as a fraction of the walking speed obtained from a short walking test (Timed 25-foot Walk Test, T25WT) to derive a deceleration index (DI).¹¹ However, no individual occurrence of walking-related motor fatigue was determined, and also, none of the existing studies have expressed walking-related motor fatigue from the 6MWT (gold standard) in PwMS.

The objective of the present study was, therefore, to determine the individual occurrence of walking-related motor fatigue according to the MS phenotype and disability level in PwMS. Based on the suggestion that diffuse demyelination and axonal loss, associated with reduced nerve conduction velocity and a prolonged refractory period, may produce fatigue in MS,²⁷ we hypothesized that walking-related motor fatigue would occur more frequently in progressive forms of MS and in PwMS with higher disability levels.

Methods

The current study presents post hoc analyses based on baseline data from a previously published multinational study²⁸ performed within the European Rehabilitation In MS (RIMS) network for best practice and research (http://www.eurims.org).

Participants

A convenience sample of 208 PwMS was recruited from 10 rehabilitation and research centers in Europe (n = 9) and the United States (n = 1). The included participants had a confirmed diagnosis of MS according to the McDonald Criteria²⁹ and a maximal EDSS³⁰ score of 6.5. PwMS who needed more than 30 s to perform a 10-m walk (so with a mean walking speed below 0.3 m/s) were not required to perform the 6MWT, given ethical constraints.

The centers were the following: REVAL Rehabilitation Research Center, Hasselt (Belgium; n = 30 participants); Rehabilitation and MS Center Overpelt, Overpelt (Belgium; n = 22); National MS Center, Melsbroek (Belgium; n = 15); Centre Neurologique et de Réadaptation Fonctionelle, Faiture-en-Condroz (Belgium; n = 14); Charles University and General Faculty Hospital Prague (Czech Republic; n = 33); Danish MS Hospitals in Haslev and Ry (Denmark; n = 27); West-Tallinn Central Hospital, Tallinn (Estonia; n = 10); Masku Neurological Rehabilitation Center, Masku (Finland; n = 19); Hospital de Dia de Barcelona CEMCat, Barcelona (Spain; n = 29); The Mellen Center for MS Treatment and Research, Cleveland (OH, US; n = 9). Compared with the included patient sample in the previously published multinational study,²⁸ 7 participants were excluded for this study because there were no minute-to-minute values for the 6MWT needed for computing the percentage change of distance walked each minute, starting from the second to the sixth minute of the 6MWT (see paragraph below for more details). An additional 26 PwMS were enrolled to increase the number of more disabled participants in the current study.

Participants were excluded for the following reasons: (1) if a MS relapse had occurred within 30 days before the testing date or when the participant had not yet stabilized from a previous relapse; a relapse was defined as an episode of neurological symptoms that occurs in the absence of an infection or fever, lasts at least 24 hours, and is not attributable to another cause³¹; (2) if other medical conditions that could interfere with the motor performance were present at the time of enrolment (ie, any orthopedic or traumatic injury, arthritis, cerebral vascular accidents). Each participant signed the informed consent form, and each center obtained approval from the local ethical committee.

Study Design and Clinical Outcomes

A cross-sectional multinational study design was applied. Demographic and clinical characteristics were collected (age, gender, body mass index [BMI], disease duration, MS phenotype, EDSS). Walking-related outcome measures were the following: Timed 25-foot Walk at Fast Speed (T25FW),³² 6MWT,²² and Multiple Sclerosis Walking Scale-12 (MSWS-12).³³ The Modified Fatigue Impact Scale (MFIS)³⁴ questionnaire was used to assess the subjective impact of fatigue on physical, cognitive, and psychosocial domains.

To ensure sufficient standardization across the participating centers, an instruction booklet with details on test procedures, verbal instructions, and level of encouragement was made and distributed. Participants were permitted to use habitual assistive devices during testing. The Multiple Sclerosis Functional Composite (MSFC) guidelines³² were used for the T25FWT, indicating that participants were instructed “to walk at fastest but safe speed” over a 25-foot (7.62 m) course. A static start was used, and participants were timed using a handheld stopwatch, which was started when the lead foot crossed the starting line and stopped when the lead foot crossed the finishing line. Participants were also instructed to complete the 6MWT “at fastest speed, aiming to cover as much distance as possible,” according to the protocol of Goldman et al.²² Participants walked back and forth in a 30-m hallway turning around cones at each end and were notified, without further encouragement, about each expired minute. Distances walked per minute and total distance were registered and expressed in meters.

To measure the decline in distance walked from the first to all the other minutes of the 6MWT, we calculated, starting from the second minute, the percentage change in distance walked (distance walked index, DWI). The DWI was calculated using the following equation:

DWI = ([Distance walked at minute n - Distance walked at minute 1] / Distance walked at minute 1) \times 100 .

Based on the DWI calculated between minutes 6 and 1 (DWI_6-1), participants were categorized into 4 subgroups: (1) DWI_6-1[≥5%] (acceleration ≥5%), (2) DWI_6-1[5%; –5%] (acceleration or deceleration <5%), (3) DWI_6-1[–5%; –15%] (deceleration ≥5% but less than 15%), and (4) DWI_6-1[≤−15%] (deceleration ≥15%).

A threshold of −15% was chosen to categorize walking-related motor fatigue, based on studies in other populations with neurological issues (spinal muscular atrophy³⁵ and postpolio syndrome³⁶) that showed an average decline in walking distance per minute, during the 6MWT, of between 14% and 17%.^35,36 To investigate the concurrent validity of our DWI_6-1, we calculated a proxy of the DI proposed by Phan-Ba et al.¹¹ This proxy DI used the walking speed at the sixth minute instead of the lowest speed of the finishing part of the T500MW to be compared with the walking speed during the T25FW. The lower the DWI and the DI values, the greater is the slowing down over a longer walking distance.

Statistical Analyses

The Shapiro-Wilk test was used to verify the normal distribution of the continuous variables. Descriptive statistics were calculated in the total MS sample and in the 4 subgroups based on the DWI_6-1 categories. All statistical tests were applied with a 2-tailed analysis and a level of significance of .05. The statistical software SPSS version 20 was used for all analyses (IBM SPSS Statistics 20, ©IBM, Armonk, NY). Descriptive characteristics and clinical outcome measures were compared across the 4 subgroups. This was done by 1-way between-group ANOVA for parametric variables (age, BMI, disease duration, T25WT, 6MWT) and the χ² or Kruskal-Wallis test ANOVA for nonparametric variables (gender, MS phenotype, EDSS, MSWS-12, MFIS).

To detect differences in DWI_n-1 (percentage of deceleration in minute n, compared with the first minute, starting from the second minute) across subgroups, a 2-way repeated-measures ANOVA was applied, with the within-subjects factor being DWI_n-1 (from the second to the sixth minute) and the between-subjects factor being subgroups based on DWI_6-1. Honestly significant difference Tukey post hoc tests were applied for contrast analysis when appropriate.

A Pearson product correlation test was used to examine associations between the DWI_6-1 and the DI, T25WT, and 6MWT. A Spearman product correlation was used to correlate the DWI_6-1 with the MSWS-12, MFIS total, and MFIS subscores. This was done in both the total MS sample and in the 4 DWI_6-1 subgroups. Correlation coefficients of <.19 were considered very weak, between 0.20 and 0.39 weak, between 0.40 and 0.59 moderate, and between 0.60 and 0.79 strong; values of 0.80 or more were considered as demonstrating a very strong relationship.³⁷ We corrected the significance level for the number of variables by means of α adjustment (α = .05/number of variables).

To investigate the impact of disability and MS phenotype on walking-related motor fatigue, participants were categorized into 5 subgroups according to the EDSS score—0-2.5, 3-4, 4.5-5.5, 6, 6.5—and into 3 subgroups according to the MS phenotype—relapsing-remitting (RR) MS, secondary progressive (SP) MS, and primary progressive (PP) MS. The frequency distribution of walking-related motor fatigue was calculated and compared with the χ² test. Univariate linear regression analyses were performed and a stepwise multivariate linear regression model was used in the total group to examine predictors of the DWI_6-1. Univariate simple linear regression analyses were applied to examine which variables were univariate predictors for the DWI_6-1. Independent variables were demographic (age, gender, BMI) and clinical MS related (disease duration, MS phenotype, EDSS). To examine if a combination of predictors would explain more variance in the DWI_6-1, these variables were entered in a multivariate stepwise analysis. The probability of the F value for entry of variables into the model was set at .05, whereas the probability of the F value for removal of variables was set at .10.

Results

Table 1 shows the demographic and clinical characteristics for the total MS sample and for the 4 subgroups based on the DWI_6-1. Demographic characteristics were similar across the 4 DWI_6-1 subgroups. Table 2 shows the walking and fatigue outcome measures for both the whole sample and the subgroups. The 4 subgroups differed significantly with respect to all variables except for the MFIS total, cognitive, and psychosocial score. Post hoc testing showed consistently significant differences between PwMS who decelerated at least 15% and all the other DWI_6-1 subgroups for all clinical variables (P < .05). For both the DWI_6-1 and the DI, a consistently significant difference was found between all subgroups (P < .05).

Table 1.

Demographic and Clinical Characteristics (Mean ± SD and Median ± IQR^ù or n [%]) of the Total Study Sample and Subgroups Based on DWI_6-1.

	Total Sample	Subgroups Based on DWI_6-1
	(n = 208)	[≥5%] (n = 16)	[5%; −5%] (n = 70)	[−5%; >−15%] (n = 58)	[≤−15%] (n = 64)	P Value
Age (years)	47.9 ± 10.7	48.8 ± 10.6	46.7 ± 11.4	49.9 ± 9.8	47.9 ± 10.7	NS^a
Gender
F/M (n)	122/86	8/8	41/29	38/20	35/29	NS^b
BMI (kg/m²)	25.4 ± 5.3	25.1 ± 5.7	24.7 ± 4.4	25.5 ± 4.9	26.1 ± 6.4	NS^a
DD	11.3 ± 37.8	13.1 ± 8.4	11.4 ± 7.4	9.9 ± 8.3	12.1 ± 7.5	NS^a
MS phenotype, n (%)
RR	90 (43.3)	6 (6.7)	33 (36.7)	37 (41.1)	14 (15.6)^c,d	<.001^b
SP	82 (39.4)	6 (7.3)	30 (36.6)	14 (17.1)	32 (39)^d	<.001^b
PP	36 (17.3)	4 (11.1)	7 (19.4)	7 (19.4)	18 (50)^c	<.001^b
EDSS^ù	4.2 ± 3.6	3 ± 3	3.5 ± 3.5	3.5 ± 2.8	6 ± 2^c,d	<.001^e
EDSS category, n (%)
0-2.5	51 (24.5)	4 (7.8)	21 (41.2)	21 (41.2)	5 (9.8)^c,d	<.001^b
3-4	54 (26)	6 (11.1)	17 (31.5)	22 (40.7)	9 (16.7)^d	<.001^b
4.5-5.5	29 (13.9)	0	10 (34.5)	5 (17.2)	14 (48.3)	NS^b
6	41 (19.7)	2 (4.9)	15 (36.6.)	5 (12.2)	19 (46.3)^d	<.001^b
6.5	33 (15.9)	4 (12.1)	7 (21.2)	5 (12.2)	17 (51.5)	NS^b

Abbreviations: BMI, body mass index; DD, disease duration; DWI, distance walked index; EDSS, Expanded Disability Status Scale; F, female; IQR, interquartile range; M, male; MS, multiple sclerosis; PP, primary progressive MS; RR, relapsing-remitting MS; SP, secondary progressive MS.

One-way analysis of variance.

χ² Pearson test.

Post hoc test: significantly different from DWI_6-1[5%; –5%].

Post hoc test: significantly different from group DWI_6-1[–5%; > –15%].

Kruskal-Wallis analysis of variance.

Median + Interquartile range.

Table 2.

Walking- and Fatigue-Related Outcomes (Mean ± SD and Median ± IQR^ù) of the Total Study Sample and Subgroups Based on the DWI_6-1.

Clinical Outcomes	Total Sample	Subgroups Based on DWI_6-1
Clinical Outcomes	(n = 208)	[≥5%] (n = 16)	[5%; −5%] (n = 70)	[−5%; >−15%] (n = 58)	[≤−15%] (n = 64)	P Value
T25FW fast (m/s)	1.2 ± 0.53	1.3 ± 0.5	1.3 ± 0.6	1.4 ± 0.4	0.9 ± 0.5^a	<.001^b
6MWT (m)	361.2 ± 169.8	439 ± 158	395.2 ± 175.9	429.6 ± 128.6	242.6 ± 136^a	<.001^b
MSWS-12 total score^ù	38.5 ± 20	32 ± 29	31.5 ± 22	37.5 ± 19	47 ± 10^a	<.001^c
DWI_6-1	−11 ± 18	16.5 ± 11.7	−0.4 ± 2.6	−9.5 ± 2.6	−30.8 ± 17.2^a	<.001^b
DI	0.8 ± 0.2	1.1 ± 0.2	0.9 ± 0.1	0.8 ± 0.1	0.6 ± 0.1^a	<.001^b
MFIS total score^ù	40 ± 20	44 ± 24	37 ± 26	42 ± 20	40 ± 17	NS
MFIS cognitive subscore^ù	16 ± 14	18 ± 16	15 ± 14	18 ± 14	15 ± 13	NS
MFIS psyschosocial subscore^ù	4 ± 3	4 ± 4	3 ± 3	4 ± 3	4 ± 3	NS
MFIS physical subscore^ù	20 ± 10	21 ± 14	17 ± 12	21 ± 10	22 ± 8^d	<.05^c

Abbreviations: 6MWT, 6-Minute Walk Test; DI, deceleration index; DWI, distance walked index; IQR, interquartile range; MFIS, Modified Fatigue Impact Scale; MS, multiple sclerosis; MSWS-12, MS Walking Scale-12 items; T25FW, Timed 25-Foot Walk Test.

Post hoc test: significantly different from every other group.

One-way analysis of variance.

Kruskal-Wallis analysis of variance.

Post hoc test: significantly different from group with DWI_6-1[5%; −5%].

Median + Interquartile range.

Prevalence of Walking-Related Motor Fatigue (DWI_6-1[≤−15%]) According to MS Phenotype and EDSS

The 4 subgroups differed significantly with respect to distribution according to MS phenotype and EDSS scores. The percentage with a DWI_6-1[≤−15%] was significantly higher among PwMS with the progressive forms of MS (SP = 39% > RR = 15.6% and PP = 50% > RR = 15.6%; both P < .05; see Figure 1) and among those with higher disability levels (walking-related motor fatigue in PwMS with EDSS 6.5 = 51.5%, EDSS 6 = 46.3%, EDSS 4.5-5.5 = 48.3%, compared with EDSS 0-2.5 = 7.8% and 3-4 = 16.7%, respectively, P < .05 for all comparisons; see Figure 2).

Figure 1.

Frequency distribution of the subgroups based on the distance walked index [DWI_6-1] according to multiple sclerosis (MS) phenotype; number of persons with MS in the relapsing-remitting (RR) group = 90, secondary progressive (SP) group = 82, and primary progressive (PP) group = 36.

Figure 2.

Frequency distribution of the subgroups based on the distance walked index [DWI_6-1] according to 5 categories of disability level (Expanded Disability Status Scale [EDSS]). Number of persons with MS in the EDSS category “0-2.5” = 51, “3.0-4.0” = 54, “4.5-5.5” = 29, “6.0” = 41, and “6.5” = 33.

The Percentage Change in Distance Walked (DWI)

Out of 208 PwMS, 16 (7.7%) showed a DWI_6-1[≥5%], 70 (33.7%) a DWI_6-1[5%; –5%], 58 (27.9%) a DWI_6-1[–5%; > –15%], and 64 (30.8%) a DWI_6-1[≤−15%] (see Table 1). The DWI_n-1 significantly decreased (F = 8.1; P < .001) over time from the second to the sixth minute, throughout the 6MWT, and is shown in Figure 3. The Subgroup × Time interaction reached significance (F = 12.1; P < .001), showing that all subgroups based on DWI showed a different pattern of DWI change during the 6 minutes. When focusing on the last 2 minutes of the 6MWT, post hoc tests showed significant differences between DWI_5-1 and DWI_6-1 in all subgroups except for the subgroup with DWI_6-1[–5%; > –15%]. An acceleration during the last minute was found in the subgroup with DWI_6-1[≥5%] and a DWI_6-1[5%; –5%](see Figure 3). In the subgroup with DWI_6-1[≤−15%], significant slowing down was found between each consecutive DWI_n-1 except between DWI_4-1 and DWI_5-1, where only a trend was found. This is illustrated in Figure 3.

Figure 3.

The percentage distance walked index [DWI_n-1] for each consecutive minute compared with the first minute of the 6-Minute Walk Test (6MWT).^a

Correlation Analysis

Table 3 shows the results of the specified correlations. A very strong positive correlation was found between the DWI_6-1 and the DI in the total group (r = 0.84; P < .001) as well as in people with walking-related motor fatigue (r = 0.92; P < .001).

Table 3.

Pearson and Spearman Correlation Coefficients Between DWI_6-1 and Clinical Variables for the Total Group and the Subgroup Based on the DWI_6-1.

	Total Sample (n = 208)	P	[≥5%], n = 16	P	[5%; –5%], n = 70	P	[−5%; >−15%], n = 58	P	[≤−15%], n = 64	P
Age (years)^a	−0.05	NS	0.24	NS	0.19	NS	−0.13	NS	0.05	NS
Gender^b	0.06	NS	−0.16	NS	0.00	NS	0.51	<.001	0.19	NS
DD^a	−0.02	NS	0.33	NS	0.03	NS	−0.00	NS	−0.09	NS
MS phenotype^b	−0.18	<.05	0.30	NS	0.18	NS	−0.16	NS	−0.31	<.05
EDSS^b	0.28	<.001	0.42	NS	−0.02	NS	−0.26	<.05	0.48	<.001
DI^a	0.84	<.001	0.41	NS	0.14	NS	0.36	<.05	0.92	<.001

Abbreviations: DD, disease duration; DI, deceleration index; DWI, distance walked index; EDSS, Expanded Disability Status Scale; MS, multiple sclerosis; NS, not significant.

Pearson correlation coefficients.

Spearman correlation coefficients.

In the total group, a very weak correlation was found between DWI_6-1 and the MS phenotype and a weak correlation was found with the EDSS. In the subgroup of patients with DWI_6-1[≤−15%], we found a weak correlation between the DWI_6-1 and the MS phenotype and a moderate correlation with the EDSS (see Table 3). The stepwise multiple linear regression analysis only retained the EDSS in the equation (β = 0.29; P < .001) and independently explained 8.6% of the variance in the DWI_6-1: F(1, 206) = 19.9; P < .001; R² = 0.086; adjusted R² = 0.081. Please consult Table S1 in the supplemental material for more detailed results on univariate regression analyses performed for each parameter (age, gender, BMI, disease duration, MS phenotype, and EDSS) and the stepwise multivariate regression analysis.

Discussion

The present study documented the prevalence of walking-related motor fatigue by calculating the DWI as percentage change from the first to the sixth minute (DWI_6-1) during the 6MWT in the 208 participants. PwMS who declined more than 15% in DWI_6-1 were labeled as showing walking-related motor fatigue. These PwMS were found in each EDSS-based category and in each MS phenotype–based category. The prevalence of walking-related motor fatigue increased from 10% in mildly disabled patients and was present in up to almost half of the more disabled patients and in those having progressive MS.

So far, there is no gold standard measure that quantifies motor fatigue during walking. A previous study proposed a DI based on comparison of walking speed during a long (T500MW) and short (T25FW) walking test.¹¹ Inspired by previous work, the present study proposed a method that can be obtained during a single walking test—in this case the gold standard measure of walking endurance, the 6MWT. Furthermore, the index is easy to calculate based on 2 walking distances.¹⁷ The DWI_6-1 correlated highly with a proxy of the DI, supporting concurrent validity. A DWI_6-1 of 15% was used as threshold, based on the average decline in walking speed reported in previous studies carried out in other neurological diseases. The present study revealed that the 15% threshold can be considered a valid criterion to identify walking-related motor fatigue because the patients in this subgroup showed a continuous slowing down throughout the 6MWT. The fact that patients in this subgroup with a DWI_1-6[≤−15%] also showed a significant decline in the last minute of the 6MWT compared with minute 5 indicates that these patients were actually showing increased motor impairment. This is in contrast to patients in the other subgroups who showed no further decline in distance walked between minute 5 and 6 or even managed to accelerate when being notified by the evaluator about entering the last minute (end-of-task effect). This suggests that these patients used a pacing pattern strategy, with some slowing down during the 6MWT, which does not reflect walking-related motor fatigue but rather reflects a conservation strategy.

Patients who slowed down more than 15% during the 6MWT were labeled as having walking-related motor fatigue because they showed a worsening in performance over time. This could indicate that the slowing down is caused by peripheral muscle fatigue. However, a continuous slowing down may also be related to other motor symptoms as well as to central factors that can be unmasked during prolonged walking. In this regard, McLoughlin et al³⁸ documented, after the performance of a 6MWT, reduced lower-limb muscle strength and increased postural sway in people with mild-to-moderate disability level (mean EDSS = 3.7). Further studies incorporating kinematic characteristics during prolonged overground walking may help in understanding the mechanisms underlying walking-related motor fatigue. In this regard, when calculating gait changes during walking, one can build on the previous work of Sehle et al²⁶ who applied kinematic gait analyses to capture walking-related motor fatigue after exhausting treadmill walking.

The present study documented the individual occurrence of walking-related motor fatigue according to 5 EDSS subgroups. The prevalence increased from about 10% to 15% in mild EDSS (<4) to 45% in moderate-to-severe EDSS (≥4). As hypothesized, the prevalence of walking-related motor fatigue was higher in the more disabled groups. This was in line with previous research also demonstrating an average decline of walking speed in the more disabled groups.²² Nevertheless, the present study shows that one must be careful with generalizing group data because results showed that this phenomenon was only present in half of the PwMS. Furthermore, there was a small albeit significant percentage of variance of the DWI_6-1 (8.6%) explained by the EDSS, which may indicate that walking-related motor fatigue is not solely dependent on overall disability, with important determination of ambulatory function, as measured by EDSS. Other factors need to be identified to explain a larger proportion of the decline in walking distance. One may argue that walking-related motor fatigue can be related to muscle weakness; however, previous research showed that walking-related motor fatigue is not fully explained by baseline muscle weakness and could thus be considered as a distinct symptom.^12,22 In support of this view, Schwid et al¹² reported that specific motor fatigue indexes on impairment level, based on sustained and repeated maximal muscle contractions, did not correlate with the degree of muscle weakness. Identifying explaining factors is a relevant question for clinical practice, where one aims to reduce walking-related motor fatigue. Future studies should include a broader assessment, with direct measures of not only muscle strength, but also other MS-related symptoms such as level of spasticity, cerebellar signs, cognitive impairment, and cardiopulmonary capacity as well as subjective fatigue.

The continuous slowing down observed in the group with a DWI_6-1[≤−15%] could be attributed to both central and peripheral factors.^39,40 Peripheral factors intrinsic to the muscle tissue itself can be involved.⁴⁰ However, central mechanisms may also influence the slowing down during walking. It is shown that PwMS have to use more central adaptive mechanisms to preserve task performance.⁴¹ In the group of PwMS who slowed down continuously, it can be hypothesized that these central mechanisms fail in programming the required muscle work and coordination. This has already been shown after fatiguing exercises in the upper limb.⁴² In this framework, we also documented the prevalence of walking-related motor fatigue according to the MS phenotype to take into account a potential differential impact of the neuropathophysiology of MS. A higher frequency of walking-related motor fatigue was found among SP and PP phenotypes (up to 50%) compared with RR (~18%), and the univariate linear regression indicated significant association between MS phenotype and DWI_6-1. One explanation for this could be that these patients generally have accumulated greater axonal degeneration, leading them to have less central motor drive for sustained muscle contractions and coordination during the 6MWT.⁴³ It is, however, noted that patients with progressive MS showed more pronounced disability⁴⁴ impeding certainty on associations with MS phenotype. In fact, the multiple regression analysis showed that only the EDSS was statistically significantly associated with the DWI_6-1, whereas the MS phenotype was not.

The present study has clinical implications. We propose a method to individually identify walking-related motor fatigue that shows face and concurrent validity. A threshold of 15% change from the first to the last minute of the 6MWT was applied as the cutoff value describing walking-related motor fatigue. The DWI_6-1 index has high clinical utility because it is easy to calculate by any clinician and can be applied during a single, standard, long walking capacity test. Further research should, however, provide normative data based on performance of healthy reference persons and investigate psychometric properties such as test-retest reliability of the DW_6-1 index and ecological validity of the 15% threshold based on patient’s perception and the relation to real-world performance. It is hypothesized that absolute walking speed capacity is not the only determining factor for PwMS to engage in a physically active lifestyle and enhance social participation. It is thought to be equally important that PwMS should be able to confidently cover a large distance during activities such as shopping and recreational hiking without experiencing a decline in physical resources. There are also clinical implementations on treatment level. The identification of PwMS showing walking-related motor fatigue as well as understanding the phenomenon (eg, reduction of muscle strength, coordination, or balance over time) may help in tailoring rehabilitation interventions and/or medication. Rehabilitation may focus more on muscular and walking endurance, rather than maximal strength or maximal walking speed alone. It can also prompt for the prescription of appropriate assistive devices and functional electrical stimulation.⁴⁵ Finally, it would be interesting to verify the impact of fampridine, which was shown to be able to improve walking speed in some PwMS.^46,47

The study has several limitations. One may argue that the lack of familiarization to the 6MWT might affect pacing patterns. However, Feys et al⁴⁸ previously reported that 6MWT results were unchanged when comparing multiple tests throughout the day and Dalgas et al²⁴ confirmed unchanged pacing patterns across time of day. Another limitation is the lack of complete functional system scores (eg, pyramidal) of the EDSS, which hampered our evaluation of potential covariates of the DWI_6-1 and, thereby, prevented us from gaining a better understanding of the underlying factors contributing to walking-related motor fatigue. We also lack complete records of the type of symptomatic drugs (eg, spasticity treatment) that were used by the participants. Specifically, one may comment that the use of fampridine could have affected the results. Data were indeed collected in different countries, with differences in pharmacological availability and reimbursement. However, at the time of data collection, fampridine was not yet widely available in Europe and was only reported in a few patients. Future studies need to rigorously document drug use. Finally, as mentioned above, further research into psychometric properties is needed before including the DWI_6-1 as an experimental outcome measure in clinical trials.

Conclusion

The DWI_6-1 with a 15% threshold identified about one-third of all PwMS showing walking-related motor fatigue during the 6MWT, with individual occurrence increasing markedly in more disabled PwMS.

Footnotes

Acknowledgements

Coordination of this study was partially funded via an unrestricted educational grant from Novartis Pharma AG to the European RIMS network (), which is acknowledged for facilitating inter-European consultation and testing.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The coordination of this study and data processing was partially funded via an unrestricted educational grant from Novartis Pharma AG to the European RIMS network (), which is acknowledged for facilitating inter-European consultation and testing. Data collection was performed voluntarily and without external funding in the participating centers. Kamila Rasova was partially funded via grant 260168/SVV/2015 and PRVOUK P34.

Supplemental Material

Supplemental material for this article is available on the Neurorehabilitation & Neural Repair website at .

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Supplementary Material

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Prevalence of Walking-Related Motor Fatigue in Persons With Multiple Sclerosis

Abstract

Keywords

Introduction

Methods

Participants

Study Design and Clinical Outcomes

Statistical Analyses

Results

Prevalence of Walking-Related Motor Fatigue (DWI6-1[≤−15%]) According to MS Phenotype and EDSS

The Percentage Change in Distance Walked (DWI)

Correlation Analysis

Discussion

Conclusion

Footnotes

Acknowledgements

Declaration of Conflicting Interests

Funding

Supplemental Material

References

Supplementary Material

Prevalence of Walking-Related Motor Fatigue (DWI_6-1[≤−15%]) According to MS Phenotype and EDSS