Multiple Sclerosis
Read Time: 9 mins

Disease Progression in Multiple Sclerosis II. Methods for the Determination of Walking Impairment and Its Impact on Activities and Social Participation

Copy Link
Published Online: Jun 4th 2011 European Neurological Review, 2010;5(1):61-68 DOI:
Authors: Jürg Kesselring
Quick Links:
Article Information

Walking ability is a vital component of validated test procedures to assess mobility impairment in multiple sclerosis (MS). The methods used to assess walking ability vary widely between treatment centres, and the accuracy of the methods used and numbers of parameters determined to analyse specific aspects of walking and gait are often limited. The questionnaire- and task-based methods used to assess walking in MS can be divided into different categories. First, there are the general-purpose tests such as the Expanded Disability Status Scale (EDSS), the Multiple Sclerosis Functional Composite (MSFC), the Family Assessment of Multiple Sclerosis Trial Outcome Index (FAMS-TOI) and the Short Form-36 (SF-36). These, particularly EDSS, are widely used in MS to assess limitations of all activities and social participation, of which walking is only a part. Others, such as SF-36, assess health-related quality of life (HRQoL). Second, there are methods designed to specifically assess walking or gait, including the timed 25-foot walk (T25FW), the Dynamic Gait Index (DGI), the 12-Item MS Walking Scale (MSWS-12) and the Timed Up and Go Test (TUGT). These test methods require minimal equipment to perform such as a stopwatch, a hallway or a chair, and can be completed at a medical centre in a few minutes. Most of these tests provide reliable and valid data but some lack accurate assessment of gait and some require clinician training. Third, there are tests that specifically measure balance, such as the Berg Balance Test, in which the patient completes a series of balance exercises while being observed. A recent development is the use of accelerometers to monitor MS patients over extended periods; these can provide more accurate data than patient self-report tools. In future, it is likely that more specific tests of walking ability will be more widely used as an important part of MS diagnosis and to more precisely monitor disease progression and assess patient needs.

Acknowledgement: Editorial assistance was provided by James Gilbart at Touch Briefings.

Support: The publication of this article was funded by Biogen Idec, Inc. The views and opinions expressed are those of the author and not necessarily those of Biogen Idec, Inc.


Multiple sclerosis, walking impairment, ambulation tests, gait, balance, mobility determination methods

Overview of Disability and Walking Impairment in Multiple Sclerosis

Overview of Disability and Walking Impairment in Multiple Sclerosis

In multiple sclerosis (MS), walking ability is an important component of a variety of validated measures of mobility impairment.1–3 The methods used to assess the degree of impairment vary widely between studies, investigation groups and treatment centres, and few of the more commonly used approaches determine walking impairment with sufficient precision. In fact, subtle changes in walking ability can indicate early stages of neurodegeneration, but these signs are not used as a central part of the diagnostic process in MS. Moreover, detailed changes over time are rarely monitored in sufficient detail at any disease stage. The continued accurate monitoring of mobility is important in determining both the treatments and support needs of patients. A variety of methods have been used to assess outcome measures in patients with MS in many studies.4 The most frequently used scales determine disability and mobility only as a component of overall disease assessment, but many neurologists and rehabilitation specialists argue that these do not provide an adequate assessment of mobility and certainly fail to capture small changes that can indicate the gradual accumulation of neuronal loss.5–7 The purpose of this article is to outline the more commonly used methods of general disability assessment in MS and also the methods for specifically analysing walking ability, gait, balance and the likelihood of falling. It will also discuss the advantages of some of these test methods and consider the clinical studies in which they have been used.

Methods for General Assessment of Activities and Social Participation in Multiple Sclerosis

In clinical studies of MS, and in regular practice, a variety of methods are used for the general assessment of activities and social participation. Many of these methods were designed for application in different diseases or across a general health spectrum; some were designed to assess overall health-related quality of life (HRQoL) and, therefore, determination of mobility is only a component or subscale within a larger set of assessments. Thus, the detail these methods provide in determining mobility is limited, as they address many aspects of the disease. An overview of the more frequently used general methods for such assessment in MS is given in Table 1.
The most frequently used scale in MS mobility assessment is the Expanded Disability Status Scale (EDSS),8,9 which rates disability progression on a range of 0.0–10.0 in increments of 0.5. The rating is usually performed by a neurologist. The criteria used to define the EDSS score are given in Table 2. EDSS is considered a ‘standard’ method and is almost universally recognised by neurologists. It has therefore often been used as part of the inclusion criteria for numerous MS clinical trials and is also commonly used to rate patients in clinical practice.7 Despite its wide utilisation, EDSS has also been much criticised because it defines ambulation only in terms of the distance a patient can walk and assistance needed; qualitative changes are not assessed and it is considered by some to be insufficient to fully assess disability.7 Further criticisms include poor psychometric properties,10 a modest inter-rater reliability and lack of linearity.
Increasing disability, as indicated by rising EDSS scores, is closely associated with the degree of neurological pathology (particularly the extent of lesions and decreased brain volume) as detected by magnetic resonance imaging (MRI).11–13 In an analysis of multiple studies in MS, a cross-sectional correlation was reported between MRI T2 parameters and EDSS scores in MS patients at different disease stages (correlation coefficients were 0.15 for secondary progressive MS (SPMS), 0.55 for CIS and 0.60 for relapsing–remitting MS [RRMS]). In a study of SPMS patients, a correlation of up to 0.81 between the presence of MRI black holes and EDSS scores was reported.11 In other studies, the correlation between disability and MRI lesions in MS patients was considered to be weak, most likely owing to the unpredictable consequences of damage at different brain sites and the variable effects seen on neurological function.12
Another study in The Netherlands showed that during 12 years of follow-up of 46 patients with confirmed MS, increasing lesion loads, atrophy and axonal loss were associated with disease severity as determined by the MS Severity Score (MSSS).13 The MSSS method is based on the EDSS, but uses an algorithm that incorporates the distribution of disability in patients with similar disease durations.
Consequently, MSSS provides information about disease progression as well as current disability status14 (see Table 1).
Another assessment of outcome is the MS Functional Composite (MSFC).15,16 This test involves three quantitative components: a timed 25-foot walk (T25FW) to measure leg function and ambulation, a nine-hole peg test (9HPT) to measure arm and hand function and the Paced Auditory Serial Addition Test (PASAT) to measure cognitive function. These three components are used to produce a combined Z-score that indicates the overall relative difference from the mean of a non-diseased population. The reliability of the MSFC was demonstrated in a small study of 10 MS patients at a treatment centre in the US.17 Repeated tests conducted by two technicians showed that the MSFC provided excellent reproducibility in terms of intra- and inter-rater variability. In another study, the T25FW and 9HPT were repeated for five consecutive days in 63 patients with MS from four different university treatment centres in the US. The results showed a <20% variation in individual mean scores.18 It was concluded that changes >20% in MSFC scores were needed to reliably indicate a true change in function for a patient. This represents a substantial change in status and a weakness of this scoring system, which might not detect smaller or more subtle deteriorations in a patient’s condition and may thus fail to alert the clinician to the need for improved treatments or support.
There are many other tests for assessing general MS status. Some of these are designed to assess HRQoL, e.g. the Family Assessment of MS Trial Outcome Index (FAMS-TOI), comprising the dimensions mobility, symptoms, emotional wellbeing, general contentment, thinking and fatigue, family and social wellbeing and additional concerns.19 Specifically designed for assessing patients diagnosed with MS, this scale provides a comprehensive determination of disease status; the mobility subscale is highly predictive of EDSS and has been used in large-scale MS trials.20 Other tests used in MS populations were designed to be used in various diseases affecting neurological abilities: the Health Utilities Index Mark 3 (HUI3), which assesses eight aspects of disease effects;21 the MS Impact Scale-29 (MSIS-29), which addresses physical and psychological impact in two separate subscales;22 and the Short Form-36 (SF-36).23,24 The latter was primarily designed to assess various aspects of QoL in many different diseases and populations, with 25% (nine out of 36) of the questions relating to mobility (see Table 1). While these instruments provide good overall assessments of disease, the determination of mobility in each is inherently limited and the tests are considered by many to be insufficiently precise, or inappropriate, for accurately monitoring progression of mobility in MS patients. The perceived shortcomings in general disability test methods to specifically assess mobility in MS patients led some neurologists to call for improved methods and assessment scales to more precisely determine the parameters associated with walking ability, and for mobility to be more generally recognised as a major indicator of MS progression.7,25

Methods for Assessing Mobility and Gait in Multiple Sclerosis

Mobility tests are mostly simple, requiring minimal equipment or facilities, and can be completed within a few minutes. An overview of the more frequently used tests is given in Table 3. Some of these tests include an assessment of gait, a vital and complex factor influencing walking ability. Gait is affected by strength, motor control, range of motion and sensation. In MS, there is no one gait type that is characteristic of the disease, although some frequent gait features have been observed.26,27
A consensus meeting sponsored by the Consortium of MS Centers (CMSC) in 2009 developed recommendations for the determination of gait in MS.4 It was agreed that this complex function can only be assessed by measuring a range of parameters. The participants recommended a set of five mobility tests considered to form a useful preliminary measure of gait in MS: T25FW, Dynamic Gait Index (DGI), 12-Item MS Walking Scale (MSWS-12), Timed Up and Go Test (TUGT) and the Six-Minute Walk (6MW). All of these assessments are easy to perform, require minimal equipment and provide reliable and valid data; some lack accurate assessment of gait and some require clinician training.
The MSWS-12 is a prominent example of a walking-ability test specifically developed for MS patients.1 The test consists of 12 questions related to walking and running ability and the requirement for support. Responses are graded from 1 (ability not limited at all) to 5 (extremely limited ability). The test method was evaluated in a group of 78 patients with primary progressive MS (PPMS) and a separate group of 54 patients with PPMS (n=1), SPMS (n=16) or RRMS (n=37) who were receiving steroid treatment for relapses. The MSWS-12 findings in all patients were highly reproducible, and relative efficiency determinations showed the MSWS-12 to be more responsive (relative efficiency [RE] 1.0) than the FAMS-TOI mobility scale (RE 0.76), the SF-36 (RE 0.48), the EDSS (RE 0.31) and the T25FW (RE 0.64).
The DGI is a frequently used test, originally developed to assess the risk of falling, which comprises eight sets of tasks to assess various facets of gait. Performance is graded on a scale of 0–3. The neurologist or technician observes the patient performing tasks that include walking at different speeds for fixed time periods, walking while their head is turned and ability to avoid obstacles while walking. The DGI has been shown to be a reliable and valid method, the results of which are inversely correlated with results from a timed walk over a 6.1m distance.28
The TUGT was primarily designed as a means of assessing the risk of falling in the elderly, but it also addresses wider aspects of mobility.29–34 This timed test requires subjects to rise from an armchair, walk three metres, return and sit down as quickly as they are able. Subjects with higher scores were generally less mobile and at greater risk of falling. A study of 413 community-dwelling and 78 institutionalised elderly women showed that reduced levels of physical activity and residence in an institution were strongly associated with poorer performance on the TUGT (p<0.0001 for both criteria).31 The TUGT method is a useful assessment of mobility but evaluation in MS patients has been limited.35,36 The T25FT is often used as a stand-alone test of mobility (see Table 3). As a measure of mobility it is limited in scope, as it only gauges walking speed and not other specific characteristics of gait or balance. The T25FT is an integral part of the MSFC test, which was discussed above under methods for general assessment of disability in MS.37,38
In addition to the five preliminary tests listed by the consensus group, the Six Spot Step Test (SSST) has recently been used to assess MS patients.39 Patients are instructed to walk as quickly as possible between marked circles in a rectangular floor area measuring 1x5m following diagonal paths and knocking wooden blocks out of each circle using the same foot (see Figure 1). A study in Denmark and The Netherlands of 151 patients with MS showed that the SSST performed better than the T25FW in terms of dynamic range, floor effect and discriminatory power. It was suggested by the authors that this might be a better test to use as part of the MSFC described above, but further research is required.
There are many more tests of mobility that have been used for limited numbers of investigations in MS patients (see Table 3). The choice of test usually depends on investigator preference or established practice at each treatment centre. A simple alternative to these tests is observation in a controlled clinical setting; however, this approach requires experience and a thorough understanding of normal and abnormal gait and has poor inter-rater reliability.4 Given the multiplicity of test methods available, it may be necessary to establish new guidelines that recommend particular methods and attempt to standardise assessments used at different treatment centres and in clinical trials.

Methods for the Assessment of Balance and Prediction of Falling in Multiple Sclerosis

Several different tests have been used in MS patients to specifically assess balance or assess it as part of a wider determination of mobility (see Table 4). Some of these tests, such as the Berg Balance Test, require no special equipment. The patient completes a questionnaire and is then asked to make a number of defined movements while initially sitting and then moving to a standing position and then finally balancing on one leg.40 Results using this method showed that use of an assistive device was a strong predictor of performance and that a score of 45 (out of 56) was a fairly reliable cut-off value that discriminated those susceptible to falling from those who were not. Another such test instrument is the Tinetti Performance Overall Mobility Assessment (POMA), which has two subscales addressing balance and gait. In the balance section, the patient completes nine timed tests that involve movements associated with sitting, rising, standing and turning. Only limited data are available for the use of this test in MS. Using POMA to retrospectively analyse records from 126 patients with Parkinson’s disease showed good to excellent intra- and inter-rater reliability with an intra-class correlation coefficient of >0.80; the sensitivity and specificity of the test to identify fallers were 76 and 66%, respectively.41
Some other balance tests involve computer-controlled instrumentation, e.g. the Modified Clinical Test for Sensory Interaction on Balance.42 The patient stands on a platform that has either a firm or a foam surface, and the platform is rocked following a pre-set programme. The patient’s ability to balance is then tested with the eyes either open or closed to determine postural sway in different conditions. Using this equipment, the test–retest reliability was shown to be high, but the perception of imbalance of the patients did not correlate well with assessments of postural stability.43 Methods requiring specialist equipment are confined to neurology centres and would not be available for assessing most MS patients.

Use of Devices to Monitor Mobility and Activity

When studying mobility in MS patients, it is necessary to monitor movement and exertion over extended periods. To achieve this, some investigators have conducted trials using accelerometers worn by MS patients to continuously monitor activity. These devices provide a good measure of both physical activity and walking compared with patient self-report methods, which tend to be more restricted in the range of parameters reported and for various reasons are likely to be less accurate.44,45 In a study including 269 patients with RRMS, self-report questionnaires were effective at assessing either walking (using MSWS-12 and Patient-Determined Disease Steps) or physical activity (using the Godin Leisure-Time Exercise Questionnaire [GLTEQ] and International Physical Activity Questionnaire). However, no questionnaire was effective at assessing both aspects.45 The use of accelerometers in the same set of patients provided an accurate assessment of both walking and physical activity.
A new tool for assessing gait in MS patients is the GaitRite® system, which consists of a portable walkway mat with an active area measuring 366x61cm containing a total of 13,820 pressure sensors.46 When a patient walks over the walkway, the system is able to capture the geometry of each footfall and computes multiple temporal and spatial parameters. This enables a rapid and detailed analysis of gait, monitors change in parameters and compares them with normal performance. The system can also assess muscle weakness and determine risk of falling. This type of system for monitoring walking is new and is currently restricted to a limited number of treatment centres, but it has the potential to monitor patient walking performance more accurately than most of the currently used tests.

Future Developments in Mobility Assessment and Management in Multiple Sclerosis

Existing measures of disability are mostly elements of other tests that assess wider aspects of MS status and are too ‘general-purpose’ for an accurate assessment of walking ability and specific aspects of gait. The limitations of the more frequently used test methods may become more widely recognised, causing many neurologists to make greater use of more specific tests and questionnaires and to develop new instruments including wider-ranging questionnaires that will more accurately determine mobility and gait.
As mobility tests are inexpensive and require fewer resources than MRI or laboratory investigations, these new tests are likely to be increasingly used in clinical trials and routine monitoring of MS patients in clinical practice at MS centres and general clinics. These tests will be valuable when used in initial diagnosis, but their importance in monitoring pathological progression and for assessing the requirement for support and assistance of patients should not be underestimated.
Currently, the methods used both for general assessment of limitations of activities and social participation and for the more specific determination of walking ability, gait and balance are diverse and vary widely between treatment centres. As our understanding of the applicability of these tests to disease progression increases, we should begin to develop guidelines for the better assessment of mobility and gait in MS over time. This would also help to standardise methods used in clinical trials, making results more readily comparable, and would help, ensure that patient management and treatment are based on current best practice. ■

Article Information:

Jürg Kesselring serves or has served on data safety monitoring and advisory boards of clinical trials in multiple sclerosis sponsored by Biogen, Novartis, Serono, Schering and Wyeth.


Jürg Kesselring, Head, Department of Neurology and Neurorehabilitation, Rehabilitation Centre, Neuroscience Centre Zurich, CH 7317 Valens, Switzerland. E:




  1. Hobart JC, Riazi A, Lamping DL, et al., Measuring the impact of MS on walking ability: the 12-Item MS Walking Scale (MSWS-12), Neurology, 2003;60:31–6.
  2. Hobart JC, Riazi A, Thompson AJ, et al., Getting the measure of spasticity in multiple sclerosis: the Multiple Sclerosis Spasticity Scale (MSSS-88), Brain, 2006;129:224–34.
  3. McGuigan C, Hutchinson M, Confirming the validity and responsiveness of the Multiple Sclerosis Walking Scale- 12 (MSWS-12), Neurology, 2004;62:2103–5.
  4. Hutchinson B, Forwell SJ, Bennett S, et al., Toward a Consensus on Rehabilitation Outcomes in MS: Gait and Fatigue Report of a CMSC Consensus Conference, 28–29 November 2007, Int J MS Care, 2009;11:67–78.
  5. D'Souza M, Kappos L, Czaplinski A, Reconsidering clinical outcomes in Multiple Sclerosis: relapses, impairment, disability and beyond, J Neurol Sci, 2008;274:76–9.
  6. Hobart J, Kalkers N, Barkhof F, et al., Outcome measures for multiple sclerosis clinical trials: relative measurement precision of the Expanded Disability Status Scale and Multiple Sclerosis Functional Composite, Mult Scler, 2004;10:41–6.
  7. Thompson AJ, Hobart JC, Multiple sclerosis: assessment of disability and disability scales, J Neurol, 1998;245:189–96.
  8. Kurtzke JF, Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS), Neurology, 1983;33:1444–52.
  9. Kurtzke JF, Historical and clinical perspectives of the expanded disability status scale, Neuroepidemiology, 2008;31:1–9.
  10. Hobart J, Freeman J, Thompson A, Kurtzke scales revisited: the application of psychometric methods to clinical intuition, Brain, 2000;123(Pt 5):1027–40.
  11. Barkhof F, MRI in multiple sclerosis: correlation with expanded disability status scale (EDSS), Mult Scler, 1999;5:283–6.
  12. Goodin DS, Magnetic resonance imaging as a surrogate outcome measure of disability in multiple sclerosis: have we been overly harsh in our assessment?, Ann Neurol, 2006;59:597–605.
  13. Minneboo A, Uitdehaag BM, Jongen P, et al., Association between MRI parameters and the MS severity scale: a 12 year follow-up study, Mult Scler, 2009;15:632–7.
  14. Roxburgh RH, Seaman SR, Masterman T, et al., Multiple Sclerosis Severity Score: using disability and disease duration to rate disease severity, Neurology, 2005;64: 1144–51.
  15. Fischer JS, Rudick RA, Cutter GR, et al., 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;5:244–50.
  16. Polman CH, Rudick RA, The multiple sclerosis functional composite: a clinically meaningful measure of disability, Neurology, 2010;74(Suppl. 3):S8–15.
  17. Cohen JA, Fischer JS, Bolibrush DM, et al., Intrarater and interrater reliability of the MS functional composite outcome measure, Neurology, 2000;54:802–6.
  18. Schwid SR, Goodman AD, McDermott MP, et al., Quantitative functional measures in MS: what is a reliable change?, Neurology, 2002;58:1294–6.
  19. Cella DF, Dineen K, Arnason B, et al., Validation of the functional assessment of multiple sclerosis quality of life instrument, Neurology, 1996;47:129–39.
  20. Kappos L, Freedman MS, Polman CH, et al., Effect of early versus delayed interferon beta-1b treatment on disability after a first clinical event suggestive of multiple sclerosis: a 3-year follow-up analysis of the BENEFIT study, Lancet, 2007;370:389–97.
  21. Feeny D, Furlong W, Boyle M, et al., Multi-attribute health status classification systems. Health Utilities Index, Pharmacoeconomics, 1995;7:490–502.
  22. Ramp M, Khan F, Misajon RA, et al., Rasch analysis of the Multiple Sclerosis Impact Scale MSIS-29, Health Qual Life Outcomes, 2009;7:58.
  23. Freeman JA, Hobart JC, Langdon DW, et al., Clinical appropriateness: a key factor in outcome measure selection: the 36 item short form health survey in multiple sclerosis, J Neurol Neurosurg Psychiatry, 2000;68:150–56.
  24. Hobart J, Freeman J, Lamping D, et al., The SF-36 in multiple sclerosis: why basic assumptions must be tested, J Neurol Neurosurg Psychiatry, 2001;71:363–70.
  25. Zwibel HL, Contribution of impaired mobility and general symptoms to the burden of multiple sclerosis, Adv Ther, 2010;26:1043–57.
  26. Kelleher KJ, Spence W, Solomonidis S, et al., The characterisation of gait patterns of people with multiple sclerosis, Disabil Rehabil, 2010;32(15):1242–50.
  27. Martin CL, Phillips BA, Kilpatrick TJ, et al., Gait and balance impairment in early multiple sclerosis in the absence of clinical disability, Mult Scler, 2006;12:620–28.
  28. McConvey J, Bennett SE, Reliability of the Dynamic Gait Index in individuals with multiple sclerosis, Arch Phys Med Rehabil, 2005;86:130–33.
  29. Arnold CM, Faulkner RA, The history of falls and the association of the timed up and go test to falls and nearfalls in older adults with hip osteoarthritis, BMC Geriatr, 2007;7:17.
  30. Berg KO, Maki BE, Williams JI, et al., Clinical and laboratory measures of postural balance in an elderly population, Arch Phys Med Rehabil, 1992;73:1073–80.
  31. Bischoff HA, Stahelin HB, Monsch AU, et al., Identifying a cut-off point for normal mobility: a comparison of the timed 'up and go' test in community-dwelling and institutionalised elderly women, Age Ageing, 2003;32:315–20.
  32. Botolfsen P, Helbostad JL, Moe-Nilssen R, et al., Reliability and concurrent validity of the Expanded Timed Up-and-Go test in older people with impaired mobility, Physiother Res Int, 2008;13:94–106.
  33. Boulgarides LK, McGinty SM, Willett JA, et al., Use of clinical and impairment-based tests to predict falls by communitydwelling older adults, Phys Ther, 2003;83:328–39.
  34. Dibble LE, Lange M, Predicting falls in individuals with Parkinson disease: a reconsideration of clinical balance measures, J Neurol Phys Ther, 2006;30:60–67.
  35. Nilsagard Y, Lundholm C, Denison E, et al., Predicting accidental falls in people with multiple sclerosis – a longitudinal study, Clin Rehabil, 2009;23:259–69.
  36. Nilsagard Y, Lundholm C, Gunnarsson LG, et al., Clinical relevance using timed walk tests and ‘timed up and go’ testing in persons with multiple sclerosis, Physiother Res Int, 2007;12:105–14.
  37. Kaufman M, Moyer D, Norton J, The significant change for the Timed 25-foot Walk in the multiple sclerosis functional composite, Mult Scler, 2000;6:286–90.
  38. Rudick RA, Cutter G, Reingold S, The multiple sclerosis functional composite: a new clinical outcome measure for multiple sclerosis trials, Mult Scler, 2002;8:359–65.
  39. Nieuwenhuis MM, Van Tongeren H, Sorensen PS, et al., The six spot step test: a new measurement for walking ability in multiple sclerosis, Mult Scler, 2006;12:495–500.
  40. Bogle Thorbahn LD, Newton RA, Use of the Berg Balance Test to predict falls in elderly persons, Phys Ther, 1996;76: 576–83, discussion 84–5.
  41. Kegelmeyer DA, Kloos AD, Thomas KM, et al., Reliability and validity of the Tinetti Mobility Test for individuals with Parkinson disease, Phys Ther, 2007;87:1369–78.
  42. Hageman PA, Leibowitz JM, Blanke D, Age and gender effects on postural control measures, Arch Phys Med Rehabil, 1995;76:961–5.
  43. Loughran S, Gatehouse S, Kishore A, et al., Does patientperceived handicap correspond to the modified clinical test for the sensory interaction on balance?, Otol Neurotol, 2006;27:86–91.
  44. Snook EM, Motl RW, Gliottoni RC, The effect of walking mobility on the measurement of physical activity using accelerometry in multiple sclerosis, Clin Rehabil, 2009;23: 248–58.
  45. Weikert M, Motl RW, Suh Y, et al., Accelerometry in persons with multiple sclerosis: measurement of physical activity or walking mobility?, J Neurol Sci, 2010;290:6–11.
  46. Sacco R, Bussman R, Oesch P, et al., Change of gait parameters and fatigue in MS patients during inpatient rehabilitation, J Neurol, 2010; in press.
  47. Cutter GR, Baier ML, Rudick RA, et al., Development of a multiple sclerosis functional composite as a clinical trial outcome measure, Brain, 1999;122(Pt 5):871–82.
  48. Ravnborg M, Blinkenberg M, Sellebjerg F, et al., Responsiveness of the Multiple Sclerosis Impairment Scale in comparison with the Expanded Disability Status Scale, Mult Scler, 2005;11:81–4.
  49. Riazi A, Thompson AJ, Hobart JC, Self-efficacy predicts self-reported health status in multiple sclerosis, Mult Scler, 2004;10:61–6.
  50. Koziol JA, Lucero A, Sipe JC, et al., Responsiveness of the Scripps neurologic rating scale during a multiple sclerosis clinical trial, Can J Neurol Sci, 1999;26:283–9.
  51. Sipe JC, Knobler RL, Braheny SL, et al., A neurologic rating scale (NRS) for use in multiple sclerosis, Neurology, 1984;34:1368–72.
  52. Krokavcova M, van Dijk JP, Nagyova I, et al., Perceived health status as measured by the SF-36 in patients with multiple sclerosis: a review, Scand J Caring Sci, 2009;23:529–38.
  53. van der Putten JJ, Hobart JC, Freeman JA, et al., Measuring change in disability after inpatient rehabilitation: comparison of the responsiveness of the Barthel index and the Functional Independence Measure, J Neurol Neurosurg Psychiatry, 1999;66:480–84.
  54. Goldman MD, Marrie RA, Cohen JA, Evaluation of the six-minute walk in multiple sclerosis subjects and healthy controls, Mult Scler, 2008;14:383–90.
  55. Hauser SL, Dawson DM, Lehrich JR, et al., Intensive immunosuppression in progressive multiple sclerosis. A randomized, three-arm study of high-dose intravenous cyclophosphamide, plasma exchange, and ACTH, N Engl J Med, 1983;308:173–80.
  56. Krebs DE, Edelstein JE, Fishman S, Reliability of observational kinematic gait analysis, Phys Ther, 1985;65:1027–33.
  57. Brousseau L, Wolfson C, The inter-rater reliability and construct validity of the Functional Independence Measure for multiple sclerosis subjects, Clin Rehabil, 1994;8:107–15.
  58. Granger CV, Fielder RC, The reliability of the FIM: a quantitative review, Arch Phys Med Rehabil, 1996;77:1226–32.
  59. Collen FM, Wade DT, Robb GF, et al., The Rivermead Mobility Index: a further development of the Rivermead Motor Assessment, Int Disabil Stud, 1991;13:50–54.
  60. Hobart J, Cano S, Improving the evaluation of therapeutic interventions in multiple sclerosis: the role of new psychometric methods, Health Technol Assess, 2009;13:iii, ix–x:1–177.

Further Resources

Share this Article
Related Content In Multiple Sclerosis
  • Copied to clipboard!
    accredited arrow-down-editablearrow-downarrow_leftarrow-right-bluearrow-right-dark-bluearrow-right-greenarrow-right-greyarrow-right-orangearrow-right-whitearrow-right-bluearrow-up-orangeavatarcalendarchevron-down consultant-pathologist-nurseconsultant-pathologistcrosscrossdownloademailexclaimationfeedbackfiltergraph-arrowinterviewslinkmdt_iconmenumore_dots nurse-consultantpadlock patient-advocate-pathologistpatient-consultantpatientperson pharmacist-nurseplay_buttonplay-colour-tmcplay-colourAsset 1podcastprinter scenerysearch share single-doctor social_facebooksocial_googleplussocial_instagramsocial_linkedin_altsocial_linkedin_altsocial_pinterestlogo-twitter-glyph-32social_youtubeshape-star (1)tick-bluetick-orangetick-red tick-whiteticktimetranscriptup-arrowwebinar Sponsored Department Location NEW TMM Corporate Services Icons-07NEW TMM Corporate Services Icons-08NEW TMM Corporate Services Icons-09NEW TMM Corporate Services Icons-10NEW TMM Corporate Services Icons-11NEW TMM Corporate Services Icons-12Salary £ TMM-Corp-Site-Icons-01TMM-Corp-Site-Icons-02TMM-Corp-Site-Icons-03TMM-Corp-Site-Icons-04TMM-Corp-Site-Icons-05TMM-Corp-Site-Icons-06TMM-Corp-Site-Icons-07TMM-Corp-Site-Icons-08TMM-Corp-Site-Icons-09TMM-Corp-Site-Icons-10TMM-Corp-Site-Icons-11TMM-Corp-Site-Icons-12TMM-Corp-Site-Icons-13TMM-Corp-Site-Icons-14TMM-Corp-Site-Icons-15TMM-Corp-Site-Icons-16TMM-Corp-Site-Icons-17TMM-Corp-Site-Icons-18TMM-Corp-Site-Icons-19TMM-Corp-Site-Icons-20TMM-Corp-Site-Icons-21TMM-Corp-Site-Icons-22TMM-Corp-Site-Icons-23TMM-Corp-Site-Icons-24TMM-Corp-Site-Icons-25TMM-Corp-Site-Icons-26TMM-Corp-Site-Icons-27TMM-Corp-Site-Icons-28TMM-Corp-Site-Icons-29TMM-Corp-Site-Icons-30TMM-Corp-Site-Icons-31TMM-Corp-Site-Icons-32TMM-Corp-Site-Icons-33TMM-Corp-Site-Icons-34TMM-Corp-Site-Icons-35TMM-Corp-Site-Icons-36TMM-Corp-Site-Icons-37TMM-Corp-Site-Icons-38TMM-Corp-Site-Icons-39TMM-Corp-Site-Icons-40TMM-Corp-Site-Icons-41TMM-Corp-Site-Icons-42TMM-Corp-Site-Icons-43TMM-Corp-Site-Icons-44TMM-Corp-Site-Icons-45TMM-Corp-Site-Icons-46TMM-Corp-Site-Icons-47TMM-Corp-Site-Icons-48TMM-Corp-Site-Icons-49TMM-Corp-Site-Icons-50TMM-Corp-Site-Icons-51TMM-Corp-Site-Icons-52TMM-Corp-Site-Icons-53TMM-Corp-Site-Icons-54TMM-Corp-Site-Icons-55TMM-Corp-Site-Icons-56TMM-Corp-Site-Icons-57TMM-Corp-Site-Icons-58TMM-Corp-Site-Icons-59TMM-Corp-Site-Icons-60TMM-Corp-Site-Icons-61TMM-Corp-Site-Icons-62TMM-Corp-Site-Icons-63TMM-Corp-Site-Icons-64TMM-Corp-Site-Icons-65TMM-Corp-Site-Icons-66TMM-Corp-Site-Icons-67TMM-Corp-Site-Icons-68TMM-Corp-Site-Icons-69TMM-Corp-Site-Icons-70TMM-Corp-Site-Icons-71TMM-Corp-Site-Icons-72