Multiple Sclerosis Resource Centre

Welcome to the Multiple Sclerosis Resource Centre. This website is intended for international healthcare professionals with an interest in Multiple Sclerosis. By clicking the link below you are declaring and confirming that you are a healthcare professional

You are here

Determinants of physical activity in minimally impaired people with multiple sclerosis

Clinical Neurology and Neurosurgery, November 2015, Pages 20 - 24

Highlights

 

  • The possible determinants of physical activity are walking speed and gender.
  • Female people with multiple sclerosis have less physical activity.
  • Slower walking speed is associated with less physical activity.
  • More physical activity is associated with being an employee.

Abstract

Objective

Despite the commonly known benefits of physical activity, evidence shows that people with multiple sclerosis (pwMS) are relatively inactive. There are several studies about factors affecting physical activity in pwMS. However, these factors have not investigated in minimally impaired pwMS who do not have remarkable symptoms and walking disturbance. The objective was to determine factors affecting physical activity in minimally impaired pwMS.

Patients and Methods

We recruited 52 minimally impaired pwMS and measured physical activity with Godin Leisure-Time Exercise Questionnaire (GLTEQ) and an accelerometer used for the 7-day period. Demographic data were recorded. Walking (speed, endurance, dexterity, and quality), fatigue, depression, and quality of life were measured.

Methods

We recruited 52 minimally impaired pwMS and measured physical activity with Godin Leisure-Time Exercise Questionnaire (GLTEQ) and an accelerometer used for the 7-day period. Demographic data were recorded. Walking (speed, endurance, dexterity, and quality), fatigue, depression, and quality of life were measured.

Results

The walking speed assessed by the Timed 25-Foot Walk and gender were found the determinants of physical activity level assessed by the GLTEQ and accelerometer, respectively. Walking (speed, endurance, and dexterity), gender, employment status, and quality of life were associated with physical activity. Either female or unemployed participants had significantly less physical activity. There were no significant difference between physical activity levels and the other subgroups.

Conclusion

Either to be a female or to have slower walking speed was associated with less physical activity. Strategies to improve walking should be focused on female pwMS with minimal impairment.

Keywords: Multiple sclerosis, Minimally impaired, Physical activity, Accelerometer, Walking.

1. Introduction

Multiple sclerosis (MS) is a chronic inflammatory demyelinating central nervous system disease that typically strikes adults, especially women [1] . MS affects mainly working-age individuals, obstructing their and their families’ personal and professional life [2] . Walking function is one of the most valuable across levels of disability among people with MS (pwMS), and walking impairment is very challenging and prevalent [3] . The prevalence and impact of walking limitations in MS typically worsen across levels of accumulating disability. Walking limitations have negative consequences for participatory outcomes such as activities of daily living, quality of life, and employment. Both physical and psychological factors have a negative impact on physical activity in pwMS.

There is evidence for the benefits and safety of physical activity among pwMS [4] . Despite the commonly known benefits of exercise and physical activity, evidence shows that pwMS are relatively inactive yet physical activity may be even more important in a population facing functional deterioration [5] . There are several studies about factors affecting physical activity in pwMS [6], [7], [8], [9], and [10]. The levels of disability of pwMS in these studies have a wide range. According to the findings of these studies, walking is impaired with disability progression while physical activity is decreasing. However, it is not clear why minimally impaired pwMS have less physical activity due to the knowledge of pwMS with Expanded Disability Status Scale (EDSS) scores below 4.0 do not have remarkable symptoms and walking disturbance [11] . It motivated us to conduct this study to investigate for variables that can be determinants of physical activity in minimally impaired pwMS.

2. Patients and methods

2.1. Participants

The study was approved by the Ethics Committee of Dokuz Eylul University (Approval number: 2013/03-27) and all participants provided informed consent before participating in the study. The study design was cross-sectional. The recruitment period was between January 2013 and June 2013. The minimum required sample size was calculated 30 based on effect size = 0.32, α = 0.05, power = 0.90 and number of predictors = 14 with G*Power (Version 3.1.9.2). This effect size was selected according to the results of a similar study [12] . We recruited randomly 52 pwMS (43 relapsing-remitting MS and 9 clinically isolated syndrome) from the university MS clinic. The inclusion criteria were a definite diagnosis of MS according to the McDonald criteria [13] , the EDSS score between 0 and 3.5, attack free period for at least one month, and age more than 18 years. The exclusion criteria were to have other disorders which affect the gait, pregnancy and severe cognitive dysfunction.

2.2. Data collection

The demographic data were collected through face-to-face interviews. Age, gender, weight, height, body mass index (BMI), disease duration, and employment and marital status were recorded.

All the participants underwent the neurological examination by the same neurologist for generating an EDSS score. The EDSS is a 10-point scale of disease severity ranging from 0 (no disability) to 10 (death from MS) [11] . In the low range, 0–3.5, it is based on the modest-to-moderate change in one or more of the functional systems. Above 4.0, scoring is based primarily on gait dysfunction. Only pwMS with EDSS scores from 0 to 3.5 participated in the study.

Physical activity was measured with a questionnaire and an accelerometer. The participants wore a Caltrac accelerometer (Muscle Dynamic Fitness Network, Torrance, CA, USA) on an elastic belt around the waist on the non-dominant hip during the waking hours, except while showering, bathing, and swimming, for a 7-day period. Waking hours were defined as the duration from the point of waking out of bed in the morning until the point of going to bed in the evening [14] . The Caltrac accelerometer is a light and pocket instrument that detects vertical movements with a built-in ceramic crystal which transfers kinetic acceleration into electrical impulses [15] . The Caltrac accelerometer estimates physical activity energy expenditure in kilocalories based on the user characteristics (i.e. age, height, weight, and gender). Although the Caltrac accelerometer stored the activity counts in a weekly basis, the participants recorded the counts in a log on a daily basis. The counts of 7 days in the log were summed and compared with the Caltrac information to ensure the data was true. Afterward the average of 1-day activity counts were calculated. There is evidence that accelerometers provide a valid and reliable measure of ambulatory physical activity in the pwMS [14] and [16] and healthy adults [17] . The Godin Leisure Time Exercise Questionnaire (GLTEQ) is a commonly used questionnaire to measure of usual physical activity in pwMS [16], [18], and [19]. The GLTEQ is a two-part measure, but for this study, we only included the first part of the questionnaire. Because the second part contains a single item on number of days per week of physical activity that results in sweating and this sweat index has questionable validity in MS [20] . The first part contains three items that measure the frequency of strenuous, moderate, and mild physical activities for periods of more than 15 min during one's free time within a typical week. Weekly frequencies of strenuous, moderate and mild physical activity were multiplied by 9, 5 and 3 metabolic equivalents, respectively, and were summed to form a measure of the total leisure physical activity [21] . A cut point at 24 units was used to determine the activity level of the participants (i.e. 24 units or more: active – substantial benefits and 23 units or less: insufficiently active – less substantial or any benefits) [22] .

Walking was assessed in multiple aspects included endurance, speed, dexterity and perceived quality. We measured walking endurance with the Six-Minute Walk Test (6MWT). The 6MWT has been used as a tool to measure walking endurance in MS population [23] . The test was performed on a level-surface hallway, controlled for possible obstacles and distractions. Standard instructions and testing guidelines were implemented [23] and [24]. Each participant was instructed to walk as far and as fast as possible for 6 min while traversing 30-meter intervals until the test was completed. Time was recorded using a standard stopwatch. Distance, in meters, was recorded. The walking speed was measured with the Timed 25-Foot Walk (T25FW) which was performed along a clearly marked 25-foot long path on a corridor that was clear of obstructions and foot traffic. We provided standardized instructions and emphasized walking as fast and as safely as possible [25] . The T25FW was performed twice, and the mean of the two trials was included for the analysis. The test is the first component of the MS Functional Composite which is widely used to determine the function in MS [26] . Walking ability is a complex motor task which includes sensorimotor functions, some of which are lower limb strength, spasticity and coordination, as well as balance. The Six Spot Step Test (SSST) was designed to measure these complex arrays of walking in pwMS [27] . Standard instructions and testing guidelines were implemented for the SSST [27] . The test contains a rectangular field with six circles on the floor following a criss-cross course. Five circles contain a block. The starting-point is the first circle, which does not contain a block. From there the patient walks to the other side of the field and kicks the blocks out of the five circles, as quickly as possible. The task is immediately administered again by having the patient walk back the same route. Both the dominant and non-dominant legs were tested twice (i.e. totally 4 trials). Perceived walking quality was measured with the 12-item Multiple Sclerosis Walking Scale (MSWS-12) which is a 12-item self-reported measure of the impact of MS on walking. It was developed using standard methods of test construction and then validated in community and hospital-residing samples of pwMS [28] . Lower scores indicate less perceived walking impairment.

We evaluated the psychological variables such as fatigue, depression, and quality of life. The Fatigue Impact Scale (FIS) is a widely used multidimensional scale measuring the physical, cognitive, and social effects of fatigue [29] . It comprises 40 questions and each question scores between 0 and 4, ranging from minimal to severe degrees. The validation of the Turkish version of the FIS was done on the pwMS [30] . The Beck Depression Inventory (BDI) is a widely used 21-items self-reported measure that assesses the presence and intensity of depressive symptoms reflecting the similar symptoms [31] . Each question has a set of at least four possible answer choices, ranging in intensity. The Multiple Sclerosis International Quality of Life (MusiQoL) questionnaire, a specific, self-administered, multidimensional questionnaire, was co-developed and initially validated in 15 countries including Turkey [32] . The MusiQoL questionnaire comprises 31 questions in 9 subscales: activities of daily living, psychological well-being, symptoms, relationships with friends, relationships with family, sentimental and sexual life, coping, rejection, and relationships with healthcare system. All 9 dimensions and the index score were linearly transformed and standardized on a 0–100 scale, where 0 indicates the worst possible level of quality of life and 100 indicates the best level.

2.3. Procedures

Neurological examinations of all patients were done and the EDSS scores were calculated by the same neurologist. The T25FW (two trials), the SSST (two times for both feet – 4 trials) and the 6MWT were applied. Between the walk tests, the patients rested for 15 min to avoid test-related fatigue. At the breaks, the patients completed the questionnaires in any sequence and were instructed to take the next test. The participants were instructed to wear and use the accelerometer. The participants returned the accelerometer along with the GLTEQ after the 7-day period.

2.4. Statistics

Differences in median values between groups derived from the demographic data were analyzed with the Mann–Whitney U test for comparison of 2 groups and the Kruskal-Wallis test for comparison of more than 2 groups. The difference between the activity levels in weekdays and weekend days was analyzed with paired-samples t test. The significance level was established at 5% (p < 0.05). Two stepwise regression models were defined to evaluate the relation between possible different explanatory variables and physical activity. The dependent variables for each model were the GLTEQ score and accelerometer counts. Independent variables used in the model were all those showing a significant relation in the univariate analysis. The effect size, ƒ2 value was calculated based on the formula, ƒ2 = R2/(1 − R2). The ƒ2 value is interpreted based on guidelines of 0.02, 0.15, and 0.35 as small, moderate, and large, respectively [33] . All data were analyzed using the IBM® SPSS® Statistics (Version 22) software.

3. Results

All the data of 52 participants were analyzed. Table 1 shows the descriptive statistics of the participants. Table 2 shows the differences of demographic characteristics by physical activity variables. No significant differences between the subgroups of BMI, education level, and marital status were detected. Female participants had significantly lower physical activity level measured with the accelerometer (p = 0.005). The unemployed participants had significantly less physical activity (p = 0.028). There was no significant difference physical activity level determined with the GLTEQ between the subgroups of gender, BMI, and employment status. There were 19 participants (36.5%) who had 24 units or more of GLTEQ. The participants had 1444.3 ± 546.4 physical activity counts in weekdays and 1425.9 ± 450.1 counts in weekend days. Physical activity levels did not significantly differ between weekdays and weekend days (p > 0.05).

Table 1 Descriptive statistics of the participants.

  Mean ± SD or frequency
Age (years) 36 ± 8
Body mass index (kg/m2) 25.25 ± 4.98
Disease duration (years) 5 ± 4
MS type (RRMS and CIS) 83% and 17%
Disability level – EDSS 1.5 ± 1.0
Godin Leisure-Time Exercise Questionnaire 21 ± 15
Accelerometer counts 1438.8 ± 501.5
Walking speed – T25FW (seconds) 4.6 ± 0.7
Walking endurance – 6MWT (meters) 485.1 ± 83.3
Walking dexterity – SSST (seconds) 9.6 ± 2.4
Self-reported walking quality – MSWS-12 19 ± 8
Fatigue – FIS 33 ± 30
Quality of life – MusiQoL 75 ± 12
Depression – BDI 12 ± 9

RRMS: Relapsing-Remitting MS, CIS: Clinically Isolated Syndrome, EDSS: Expanded Disability Status Scale, T25FW: Timed 25-Foot Walk, 6MWT: Six-Minute Walk Test, SSST: Six Spot Step Test, MSWS-12: 12-Item Multiple Sclerosis Walking Scale, FIS: Fatigue Impact Scale, MusiQoL: Multiple Sclerosis International Quality of Life, BDI: Beck Depression Inventory.

Table 2 Demographic characteristics differences of the participants by physical activity variables.

  Godin leisure-time exercise questionnaire Accelerometer
  Number Median p Median p
Gender
 Female 35 15.0 0.433 1215.0 0.005 *
 Male 17 20.0 1578.0
Body mass index (kg/m2)
 18.5–24.9 (normal weight) 28 19.5 0.327 1152.5 0.088
 25.0–29.9 (overweight) 15 15.0 1571.0
 ≥30.0 (obesity) 9 11.0 1369.0
Education (years)
 0–8 8 14.5 0.299 1177.0 0.455
 9–12 14 15.5 1519.5
 >12 30 16.5 1385.0
Marital status
 Single 18 21.5 0.231 1373.5 0.623
 Married 28 15.0 1418.0
 Divorced 6 9.0 1414.0
Employment status
 Employed 37 16.0 0.212 1542.0 0.028 *
 Unemployed 12 11.0 1045.0
 Student 3 24.0 1601.0

* p < 0.05. The results are based on the Mann–Whitney U test for comparison of 2 groups and the Kruskal–Wallis test for comparison of more than 2 groups.

The model used to study the dependent variable the GLTEQ ( Table 3 ) explained 16% of the variance (adjusted R2 = 0.16). The T25FW was found the predictor of the GLTEQ. In this model which four variables were entered in the last step, the 6MWT and MusiQoL were positively correlated with the GLTEQ, and the T25FW and SSST were negatively correlated. The ƒ2 value of 0.19 is interpreted as a moderate-to-large effect size. Table 4 shows the model used for the dependent variable the accelerometer, which predicted 16% of the variance (adjusted R2 = 0.16). Gender was the predictor of accelerometer. The ƒ2 value of 0.19 was calculated and interpreted as a moderate-to-large effect size. Explanatory variables entered in the last step were the BMI, employment status, 6MWT, and gender.

Table 3 Stepwise linear regression model of the Godin Leisure-Time Exercise Questionnaire as dependent variable against possible explanatory variables.

Independent variables B Beta t p
Constant 59.26 4.99 <0.001 *
Walking speed – T25FW −8.41 −0.42 −3.29 0.002 *

* p < 0.05. R = 0.42, adjusted R2 = 0.16. T25FWT: Timed 25-Foot Walk. Variables were excluded from the final model because of lack of significance at p < 0.05. These variables were the Six Spot Step Test, Six-Minute Walt Test, and Multiple Sclerosis International Quality of Life.

Table 4 Stepwise linear regression model of the accelerometer as dependent variable against possible explanatory variables.

Independent variables B Beta t p
Constant 1293.28 16.65 <0.001 *
Gender – male 445.01 −0.42 3.27 0.002 *

* p < 0.05. R = 0.42, adjusted R2 = 0.16. Variables were excluded from the final model because of lack of significance at p < 0.05. These variables were the body mass index, employment status, and Six-Minute Walt Test.

4. Discussion

In this study, we examined the possible factors affecting physical activity in the minimally impaired pwMS and identified walking speed, endurance and dexterity, gender, employment status, and quality of life were associated with physical activity in the minimally impaired pwMS. Either to be a female or to have slower walking speed was associated with less physical activity.

Choquette et al. determined the activity level of the older adults by using a Caltract accelerometer as 1629.4 ± 360.1 for females and 2187.7 ± 289.1 for males [34] . The minimally impaired pwMS, both females and males, in our study had less physical activity levels compared with the older adults. When the GLTEQ scores were considered, only 36.5% pwMS were physically active and 63.5% had low physical activity level. The International Prevalence Study on physical activity contacted in 52,746 participants from 20 countries indicated that the prevalence of low physical activity levels ranged from 6.9% to 43.3% [35] . Gender differences were also noted, especially for younger adults, with males more active than females in most countries. Our results suggests that the pwMS are less physically active than healthy subjects and even older adults according to the previous studies. The female pwMS were less physically active as their healthy counterparts.

Examining the demographic characteristics of pwMS that are related with physical activity is important because it will help identify specific subgroups of the MS population that are in greatest need of an intervention that is designed to increase physical activity participation [12] . Especially, it is more important for minimally impaired pwMS. Our results showed there is no self-reported physical activity difference between the demographic subgroups. On the other hand, male and employed pwMS had more physical activity measured with accelerometer. To support our results, a study showed that healthy, employed male individuals had significantly higher physical activity than the non-working population [36] . As in the healthy population, it seems that working can increase physical activity in pwMS. However most of the pwMS prefer not to do physically active works, and they want to retire very early. Although our aim was not to investigate the relationship between the work characteristics and physical activity, we believe that work characteristics can influence physical activity level (i.e. some works may lead to less physical activity). Additionally, upon the analysis to examine the difference between weekdays and weekend days in terms of physical activity counts, we found no significantly difference. This result suggests that working was an independent predictor of physical activity. According to the results of our study, health professionals should encourage eligible pwMS about sustaining physical actively working. It should be also noted that pwMS should keep in balance between working and the other important life domains. If a person suffers from fatigue, she or he may need to change her or his workload to be able to do the other important activities of daily living. The fatigue must be always kept in mind in deciding the appropriate workload. Additionally, the female and unemployed pwMS are in a need of interventions to increase physical activity.

Walking impairment is one of the most important factors restricting the activities of daily living in pwMS. Because, walking is a complex motor activity, the use of a single measurement method is inadequate for the evaluation. Therefore, multiple assessment methods are needed to better determine the walking in pwMS [37] . Walking endurance, speed and dexterity were correlated with self-reported physical activity in this study. In the literature, there are several studies showed the correlation between physical activity and the self-reported walking quality, the MSWS-12 [38] and [39]. However, participants in these studies had wide disability levels not only minimal disability level as our participants. Only walking endurance was correlated with accelerometer detected physical activity in four walking measurements. This result suggests that walking endurance is the most important for physical activity and the 6MWT can better measure physical activity in minimally impaired pwMS.

Our finding of significant correlations with the self-reported physical activity and the fatigue, depression and quality of life is consistent with the other studies [40], [41], [42], [43], [44], and [45]. However, there was no correlation between the accelerometer, and fatigue and depression unlike the results of other studies [7] and [46]. Our study showed that the psychological factors such as fatigue, depression and quality of life have a direct relationship with the self-reported physical activity. Even if patients have objectively more physical activity, they report much less physical activity under the influence of psychological factors. These factors should be taken into consideration for increasing physical activity in minimally impaired pwMS.

There are several limitations of this study. The cross-sectional design of the study precludes inferences about the direction of causality among variables. Experimental and longitudinal researches are needed to determine the extent to which systematic changes in physical activity impact outcomes in pwMS. We did not measure physical activity in a sample of age- and gender-matched healthy controls that would allow for direct comparisons between people with and without MS. However, published data available in the literature permitted to draw some inferences about our sample. Although we recruited more than the minimum required sample size for our first aim (i.e. investigating the possible predictors), the sample size would not be enough to do sub-group analysis. This could be affected our secondary results.

Either to be a female or to have slower walking speed was associated with less physical activity. Such kind of pwMS are in a need of interventions to increase physical activity levels. Strategies to improve walking should be focused on female pwMS with minimal impairment. Additionally, pwMS should be assisted to maintain their employment status with an appropriate workload in order to prevent inactive lifestyles in the future.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Conflict of interest

The authors report no declarations of interest.

References

  • [1] D.M. Wingerchuk, J.L. Carter. Multiple sclerosis: current and emerging disease-modifying therapies and treatment strategies. Mayo Clin. Proc.. 2014;89:225-240 Crossref
  • [2] K. Karampampa, A. Gustavsson, E.T. van Munster, et al. Treatment experience, burden, and unmet needs (TRIBUNE) in Multiple Sclerosis study: the costs and utilities of MS patients in The Netherlands. J. Med. Econ.. 2013;16:939-950 Crossref
  • [3] C. Heesen, J. Böhm, C. Reich, et al. Patient perception of bodily functions in multiple sclerosis: Gait and visual function are the most valuable. Mult. Scler.. 2008;14:988-991 Crossref
  • [4] R.W. Motl. Lifestyle physical activity in persons with multiple sclerosis: the new kid on the MS block. Mult. Scler.. 2014;20:1025-1029 Crossref
  • [5] N.E. Mayo, M. Bayley, P. Duquette, et al. The role of exercise in modifying outcomes for people with multiple sclerosis: a randomized trial. BMC Neurol.. 2013;13:69 Crossref
  • [6] R.W. Motl, B. Fernhall, E. McAuley, G. Cutter. Physical activity and self-reported cardiovascular comorbidities in persons with multiple sclerosis: evidence from a cross-sectional analysis. Neuroepidemiology. 2011;36:183-191 Crossref
  • [7] R.W. Motl, E. McAuley. Symptom cluster as a predictor of physical activity in multiple sclerosis: preliminary evidence. J. Pain Symptom Manage.. 2009;38:270-280 Crossref
  • [8] R.W. Motl, E. McAuley, S. Doerksen, et al. Preliminary evidence that self-efficacy predicts physical activity in multiple sclerosis. Int. J. Rehabil. Res.. 2009;32:260-263 Crossref
  • [9] H. Beckerman, V. de Groot, M.A. Scholten, et al. Physical activity behavior of people with multiple sclerosis: understanding how they can become more physically active. Phys. Ther.. 2010;90:1001-1013 Crossref
  • [10] N.M. Kayes, K.M. McPherson, P. Schluter, et al. Exploring the facilitators and barriers to engagement in physical activity for people with multiple sclerosis. Disabil. Rehabil.. 2011;33:1043-1053 Crossref
  • [11] J.F. Kurtzke. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology. 1983;33:1444-1452
  • [12] R.W. Motl, E.M. Snook, E. McAuley, et al. Demographic correlates of physical activity in individuals with multiple sclerosis. Disabil. Rehabil.. 2007;29:1301-1304 Crossref
  • [13] W.I. McDonald, A. Compston, G. Edan, et al. Recommended diagnostic criteria for multiple sclerosis: guidelines from the International Panel on the diagnosis of multiple sclerosis. Ann. Neurol.. 2001;50:121-127 Crossref
  • [14] S. Balantrapu, J.J. Sosnoff, J.H. Pula, et al. Leg spasticity and ambulation in multiple sclerosis. Mult. Scler. Int.. 2014;2014:649390
  • [15] E. Sigmund, W. El Ansari, D. Sigmundová. Does school-based physical activity decrease overweight and obesity in children aged 6–9 years? A two-year non-randomized longitudinal intervention study in the Czech Republic. BMC Public Health. 2012;12:570 Crossref
  • [16] R.W. Motl, E. McAuley, R. Klaren. Reliability of physical-activity measures over six months in adults with multiple sclerosis: implications for designing behavioral interventions. Behav. Med.. 2014;40:29-33 Crossref
  • [17] M. Saglam, H. Arikan, S. Savci, et al. International physical activity questionnaire: reliability and validity of the Turkish version. Percept. Mot. Skills. 2010;111:278-284 Crossref
  • [18] D.E. Rosenberg, C.H. Bombardier, S. Artherholt, et al. Self-reported depression and physical activity in adults with mobility impairments. Arch. Phys. Med. Rehabil.. 2013;94:731-736 Crossref
  • [19] R.W. Motl, M. Goldman. Physical inactivity, neurological disability, and cardiorespiratory fitness in multiple sclerosis. Acta Neurol. Scand.. 2011;123:98-104 Crossref
  • [20] M. Weikert, Y. Suh, A. Lane, et al. Accelerometry is associated with walking mobility, not physical activity, in persons with multiple sclerosis. Med. Eng. Phys.. 2012;34:590-597 Crossref
  • [21] G. Godin, R. Shephard. Godin leisure-time exercise questionnaire. Med. Sci. Sports Exerc.. 1997;29:S36
  • [22] G. Godin, R.J. Shephard. A simple method to assess exercise behavior in the community. Can. J. Appl. Sport Sci.. 1985;10:141-146
  • [23] M.D. Goldman, R.W. Motl, R.A. Rudick. Possible clinical outcome measures for clinical trials in patients with multiple sclerosis. Ther. Adv. Neurol. Disord.. 2010;3:229-239 Crossref
  • [24] ATS statement: guidelines for the six-minute walk test. Am. J. Respir. Crit. Care Med.. 2002;166:111-117
  • [25] J.S. Fischer, R.A. Rudick, G.R. Cutter, S.C. Reingold. The Multiple Sclerosis Functional Composite Measure (MSFC): an integrated approach to MS clinical outcome assessment. National MS Society Clinical Outcomes Assessment Task Force. Mult. Scler.. 1999;5:244-250
  • [26] J. Fischer, R. Rudick, G. Cutter, S. Reingold. The Multiple Sclerosis Functional Composite measure (MSFC): an integrated approach to MS clinical outcome assessment. Mult. Scler.. 1999;5:244-250
  • [27] M.M. Nieuwenhuis, H. Van Tongeren, P.S. Sorensen, M. Ravnborg. The six spot step test: a new measurement for walking ability in multiple sclerosis. Mult. Scler.. 2006;12:495-500 Crossref
  • [28] J.C. Hobart, A. Riazi, D.L. Lamping, et al. Measuring the impact of MS on walking ability: the 12-Item MS Walking Scale (MSWS-12). Neurology. 2003;60:31-36 Crossref
  • [29] J.D. Fisk, P.G. Ritvo, L. Ross, et al. Measuring the functional impact of fatigue: initial validation of the fatigue impact scale. Clin. Infect. Dis.. 1994;18(SUPPL 1):S79-S83 Crossref
  • [30] K. Armutlu, I. Keser, N. Korkmaz, et al. Psychometric study of Turkish version of Fatigue Impact Scale in multiple sclerosis patients. J. Neurol. Sci.. 2007;255:64-68 Crossref
  • [31] A.T. Beck, R.A. Steer, R. Ball, W. Ranieri. Comparison of Beck Depression Inventories -IA and -II in psychiatric outpatients. J. Pers. Assess.. 1996;67:588-597 Crossref
  • [32] M. Simeoni, P. Auquier, O. Fernandez, et al. Validation of the Multiple Sclerosis International Quality of Life questionnaire. Mult. Scler.. 2008;14:219-230
  • [33] J. Cohen. Statistical Power Analysis for the Behavioral Sciences. (Academic Press, New York, NY, 1988)
  • [34] S. Choquette, A. Chuin, D.A. Lalancette, et al. Predicting energy expenditure in elders with the metabolic cost of activities. Med. Sci. Sports Exerc.. 2009;41:1915-1920 Crossref
  • [35] A. Bauman, F. Bull, T. Chey, et al. The International Prevalence Study on Physical Activity: results from 20 countries. Int. J. Behav. Nutr. Phys. Act.. 2009;6:21 Crossref
  • [36] D.R. Van Domelen, A. Koster, P. Caserotti, et al. Employment and physical activity in the U.S.. Am. J. Prev. Med.. 2011;41:136-145 Crossref
  • [37] R. Phan-Ba, P. Calay, P. Grodent, et al. Motor fatigue measurement by distance-induced slow down of walking speed in multiple sclerosis. PLoS ONE. 2012;7:e34744 Crossref
  • [38] R.W. Motl, D. Dlugonski, Y. Suh, et al. Accelerometry and its association with objective markers of walking limitations in ambulatory adults with multiple sclerosis. Arch. Phys. Med. Rehabil.. 2010;91:1942-1947 Crossref
  • [39] E.M. Snook, R.W. Motl, R.C. Gliottoni. The effect of walking mobility on the measurement of physical activity using accelerometry in multiple sclerosis. Clin. Rehabil.. 2009;23:248-258 Crossref
  • [40] M.B. Rietberg, E.E. van Wegen, B.M. Uitdehaag, G. Kwakkel. The association between perceived fatigue and actual level of physical activity in multiple sclerosis. Mult. Scler.. 2011;17:1231-1237 Crossref
  • [41] R.W. Motl, E. McAuley, D. Wynn, et al. Effects of change in fatigue and depression on physical activity over time in relapsing-remitting multiple sclerosis. Psychol. Health Med.. 2011;16:1-11 Crossref
  • [42] Y. Suh, M. Weikert, D. Dlugonski, et al. Physical activity, social support, and depression: possible independent and indirect associations in persons with multiple sclerosis. Psychol. Health Med.. 2012;17:196-206 Crossref
  • [43] M. Asano, P. Duquette, R. Andersen, et al. Exercise barriers and preferences among women and men with multiple sclerosis. Disabil. Rehabil.. 2013;35:353-361 Crossref
  • [44] R.W. Motl, E. McAuley, D. Wynn, et al. Physical activity, self-efficacy, and health-related quality of life in persons with multiple sclerosis: analysis of associations between individual-level changes over one year. Qual. Life Res.. 2013;22:253-261 Crossref
  • [45] R.W. Motl, E.M. Snook. Physical Activity. Self-Efficacy, and Quality of Life in Multiple Sclerosis. Ann. Behav. Med.. 2008;35:111-115 Crossref
  • [46] Y. Suh, R.W. Motl, D.C. Mohr. Physical activity, disability, and mood in the early stage of multiple sclerosis. Disabil. Health J.. 2010;3:93-98 Crossref

Footnotes

a Dokuz Eylül University, School of Physical Therapy and Rehabilitation, Saglik Yerleskesi, Inciralti, Izmir TR 35340, Turkey

b Dokuz Eylül University, Faculty of Medicine, Department of Neurology, Saglik Yerleskesi, Inciralti, Izmir, Turkey

Corresponding author. Tel.: +90 5557219849; fax: +90 2324124946.


Search this site

Stay up-to-date with our monthly e-alert

If you want to regularly receive information on what is happening in MS research sign up to our e-alert.

Subscribe »

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...

This online Resource Centre has been made possible by a donation from EMD Serono, Inc., a business of Merck KGaA, Darmstadt, Germany.

Note that EMD Serono, Inc., has no editorial control or influence over the content of this Resource Centre. The Resource Centre and all content therein are subject to an independent editorial review.

The Grant for Multiple Sclerosis Innovation
supports promising translational research projects by academic researchers to improve understanding of multiple sclerosis (MS) for the ultimate benefit of patients.  For full information and application details, please click here

Journal Editor's choice

Recommended by Prof. Brenda Banwell

Causes of death among persons with multiple sclerosis

Gary R. Cutter, Jeffrey Zimmerman, Amber R. Salter, et al.

Multiple Sclerosis and Related Disorders, September 2015, Vol 4 Issue 5