Individuals with multiple sclerosis (MS) appear to be less physically active than their nondiseased, relatively sedentary peers.1,2 Such profound inactivity is alarming for its health implications3-5 and its association with worsening disability.6 Although evidence suggests that MS symptoms contribute to inactivity,7 there remains much to be learned about specific physical activity limitations that may result from disease progression or may respond positively to intervention.
Ambulatory activity refers to a subset of naturally occurring physical activity behaviors that require stepping (eg, walking, jogging, climbing stairs, mowing the lawn). In contrast to clinical gait measures (eg, timed walk), ambulatory activity measures reflect the life situations of individuals interacting with their customary environment. In this context, ambulatory activity measures can be considered to fall within the “Participation” domain of the International Classification of Functioning, Disability and Health (ICF) model.8
Studies of ambulatory activity in MS are few. In the most detailed report available, pedometer methodology was used to determine that a sample of 193 individuals with MS accumulated on average 5902 (confidence interval: 5442-6362) steps per day over a 1-week period.9 Such data provided preliminary insight into the activity behavior of individuals with MS and revealed important directions for further study. For example, although the relationship between physical activity in general and MS disability has been documented,10 the extent to which daily step counts in particular are impacted by ambulatory limitation remains unclear. The proportion of ambulatory individuals with MS who meet recommended daily physical activity guidelines for healthy adults3 by stepping at the rate of 100 steps per minute for 30 consecutive minutes11 is unknown. Furthermore, patterns of ambulatory activity, such as those that characterize cycles of work and rest and peak activity levels have not been quantified. Clinical variables that might predict ambulatory activity have yet to be determined.
A step activity monitor (Orthocare Innovations, Mountlake Terrace, Washington) is a device that specifically records free-living walking behavior. In contrast to pedometers, which simply record the total accumulation of steps recorded over time and which may not be accurate for individuals with reduced gait speed,12 a step activity monitor accurately records walking in brief intervals to create a temporal series of step counts synchronized to a 24-hour clock. The resulting data can be analyzed in a variety of ways to identify ambulatory activity characteristics that may otherwise not be apparent. Although step activity monitoring was used previously in a study that included individuals with MS, the findings were nonspecific because participants with other forms of neuropathology were also included.13
The broad objective of this study was to use step activity monitoring to examine in detail the patterns of daily ambulatory activity of individuals with MS. In doing so, we specifically sought to explore characteristics of physical activity limitations that had not been previously studied. In our primary analysis, we examined the impact of MS-related physical disability, operationalized as the presence of ambulatory limitation, on parameters characterizing overall activity, the upper limits of activity output, and activity work-rest cycles. In a secondary analysis, we sought to (1) enhance the interpretation of the data by exploring relationships among ambulatory activity parameters and (2) begin the process of identifying clinical variables that might predict ambulatory activity by exploring the bivariate relationships between daily step count and common clinical measures. We hypothesized that in comparison to individuals without ambulatory limitation, individuals with limitation would accumulate fewer daily steps, spend a greater percentage of the day inactive, generate reduced upper limits of activity output, engage in fewer and shorter bouts of activity, and display less variable day-to-day activity. We also hypothesized that greater daily step accumulations would be associated with better ambulatory functioning, better balance, and less fatigue.
Data were collected from individuals with MS referred to a Rehabilitation and Wellness Program at the University of Utah Health Sciences Center. As part of the program, each individual underwent a baseline medical evaluation of physical status and function to determine his or her specific exercise prescription. Evaluations were conducted by trained clinical staff and included a comprehensive neuromuscular examination, functional mobility testing, ambulatory activity assessment, and various health status and quality of life questionnaires. Baseline data from consenting individuals were used for subsequent analysis. The University of Utah institutional review board approved the study protocol.
Study participants were ambulatory community-dwelling women (12) and men (9) with a diagnosis of MS (aged 57.6 ± 12.7 years, body mass index = 28.8 ± 6.6 kg/m2, years since diagnosis = 14.0 ± 10.3). Potential participants were excluded if they (1) were unable to walk inside their residence without assistance from another person; (2) were living in assisted living or skilled nursing facility; (3) had active, unstable angina or uncontrolled hypertension; (4) had a medical or postsurgical order to purposefully restrict walking activity; (5) were participating in other research that might influence walking activity; (6) would not be living continuously in a solitary residence throughout their participation; or (7) because of cognitive impairment, required the assistance of a proxy to complete informed consent or study surveys.
Clinical measures were used to determine disability status and to evaluate ambulatory functioning, balance, and fatigue. Gait and fatigue measures were selected based on recent consensus among health care professionals with expertise in MS.14 Balance measures were selected for their concurrent validity and relevance to MS clinical rehabilitation.15
Expanded Disability Status Scale
The Expanded Disability Status Scale (EDSS)16 is a reliable indicator of disability in MS.17 Scale scores range from 0 to 10; individuals who score at or less than 4.5 are considered to be fully ambulatory without aid, individuals who score more than 4.5 but less than 8.0 are considered to be ambulatory but with limitations, and individuals who score more than 8.0 are considered to be nonambulatory. Scores were determined using the “Neurostatus” standardized neurological examination protocol.18
Multiple Sclerosis Walking Scale
The Multiple Sclerosis Walking Scale (MSWS) is a validated 12-item survey designed to evaluate the limiting impact of MS on walking during the previous 2 weeks.19 Each item is rated on a 5-point ordinal scale; higher values indicate greater walking difficulty. Raw scores were summed to generate a total score ranging from 12 to 60. The MSWS was added to the clinical measurement battery after data collection had already commenced.
Timed 25-foot Walk
The time to walk 25 ft is a known indicator of ambulatory impairment20 and a component of the Multiple Sclerosis Functional Composite.21 Higher values reflect greater limitation. Participants were instructed to walk along a clearly marked 25-ft course as quickly and safely as possible. Time was recorded in seconds. Two trials were conducted, with the fastest value used for analysis.
Timed Up and Go
The Timed Up and Go (TUG) measures the time required for an individual to stand from a chair, walk 3 m at a comfortable pace, return to the chair, and sit down. Higher values reflect greater limitation and indicate greater risk of falls. The test has been validated previously for use in MS.15,22 Participants completed 1 practice and 2 test trials, with the fastest trial value used for analysis.
The distance walked in 6 minutes (Six-Minute Walk [6MW]) is a valid and reliable measure of locomotor ability in populations with a variety of chronic diseases including MS.23,24 Higher values reflect greater ability. Participants circled a 25-m course continually for 6 minutes according to the standardized protocol described by the American Thoracic Society.25
Dynamic Gait Index
The Dynamic Gait Index (DGI) is an 8-item battery of walking tasks that involve obstacle negotiation, head movement, and speed changes. Each task is rated on an ordinal scale from 0 to 3. Summary scores range from 0 to 24, with higher scores indicating better functioning. The DGI has previously been validated and determined to be reliable for individuals with MS.15,26
Berg Balance Scale
The Berg Balance Scale (BBS) is a clinical instrument consisting of 14 items for evaluation of balance in sitting and standing. Performance is rated from 0 (cannot perform) to 4 (normal performance). Scores range from 0 to 56. Higher values indicate better balance. The scale has been validated for use in individuals with MS27 and was found to have high test-retest and interrater reliability.24
Activities-Specific Balance Confidence Scale
The Activities-Specific Balance Confidence (ABC) is a 16-item survey of falls-related self-efficacy.28 Each item is rated from 0% (no confidence) to 100% (complete confidence); self-efficacy is represented as the mean item score on a scale from 0 to 100. Previously, the ABC has demonstrated good test-retest reliability27 and clinical utility for predicting fall status in individuals with MS.15
Modified Fatigue Impact Scale
Fatigue was assessed using the abbreviated 5-item version of the Modified Fatigue Impact Scale (MFIS).29 The scale contains 5 statements that describe how fatigue may impact an individual with MS during the previous 4 weeks. Each item is rated on a 5-point ordinal scale; total scores range from 0 to 20, and lower scores indicate less fatigue. The MFIS has been validated previously as a measure of fatigue in individuals with MS.30
Ambulatory Activity Measures
Participants wore a step activity monitor during all waking hours for up to 7 days, except when bathing, swimming, or sleeping. During monitoring, participants were instructed simply to engage in their normal everyday activities. The monitor is the size of a pager, weighs 38 g, attaches at the ankle using Velcro closures, and requires no maintenance by the user. It uses a combination of acceleration, position, and timing to detect strides taken by the leg to which it is attached. Using manufacturer software, study personnel configured monitors to record stride counts in 1-minute intervals, such that each 24-hour period produced a time series of 1440 values. Optimal accuracy was verified during the first minutes of recording by comparing monitor step counts, identified via flashing indicator light, with visual observation. Data were subsequently downloaded to a personal computer for reduction and analysis. The monitor has previously demonstrated good test-retest reliability and accuracy and has been particularly useful in accurately quantifying steps in populations characterized by slow and/or shuffling gait.13,31-35
Mean daily values for activity variables were calculated based on previous work13,35,36 using either manufacturer software or analysis programs written in MATLAB (Mathworks, Natick, Massachusetts). Overall activity was measured as (a) total number of steps (STEPS), defined in biomechanical terms as twice the total stride count and (b) the percentage of the day spent in activity, operationalized as 100 × number of minutes in which 0 steps were recorded.
To understand the extent to which participants met minimum physical activity guidelines, we examined 3 different measures that characterized participants’ upper limits of activity output during a 30-minute interval. We employed (a) Peak Activity Index (PEAK) as a measure of capacity8 to indicate the mean step rate (ie, steps per minute) during the 30 most active minutes of the day, regardless of when they occurred; (b) Maximum Output (MAX) as a measure of performance8 to indicate the mean step rate during the 30 most active consecutive minutes of the day; and (c) number of minutes during which participants accumulated more than 100 steps (INTENSITY). The latter variable was based on the previous determination that 100 steps per minute represents the absolute minimal value for moderate-intensity walking in adults.11
To explore the impact of disability on work-rest cycle characteristics, we examined the (a) mean number of daily activity bouts (BOUTS), defined as the number of 1-minute intervals for which the participant switched from inactivity (stride count = 0) to activity (stride count > 0), (b) mean duration of daily activity bouts, measured in minutes, and (c) day-to-day variability in mean daily steps (VARIABILITY), operationalized using the coefficient of variation (CV = 100 × [standard deviation/mean]).
Participants were separated into 2 groups based on the absence (EDSS score ≤ 4.5) or presence (EDSS score > 4.5) of ambulatory limitation. Because of the small sample size and nonnormal distributions of some variables, between-group differences in mean age, body mass index, and ambulatory activity parameter values were evaluated using separate nonparametric Mann-Whitney U tests (α = .05). Spearman rho was used to describe associations between dependent variables. Statistical procedures were performed using SPSS 16.0 (SPSS Inc, Chicago, Illinois).
Expanded Disability Status Scale scores among participants ranged from 3.5 to 7.5, with group membership distributed nearly evenly (Table 1). Expanded Disability Status Scale groups were similar in age, body mass index, and years since diagnosis. Step activity monitor–wearing compliance ranged from 3 to 7 days, with 19 (90%) of the participants completing at least 6 days of recording. One participant (EDSS score = 7.5) did not complete the Timed 25-foot walk test; another participant did not complete the MFIS. Because of its late addition to the study protocol, MSWS scores were not collected from the first 6 participants.
Daily step counts ranged from 123 to 15 300 (mean = 6154 ± 3718). Percentage of the day spent inactive ranged from 67.4 to 98.5 (mean = 80.2 ± 7.9). Only 1 participant (EDSS score = 3.5) accumulated on average at least 30 minutes of daily ambulatory activity at a rate of 100 or more steps per minute. Multiple Sclerosis Walking Scale scores ranged from 25 to 59 (mean = 41.5 ± 11.0); Timed 25-foot Walk Time ranged from 4.3 to 35.7 seconds (mean = 7.7 ± 6.6); TUG values ranged from 5.2 to 69.1 seconds (mean = 11.8 ± 1.4); 6MW distances ranged from 32 to 579 m (mean = 356 ± 126); DGI scores ranged from 12 to 24 (mean = 19.0 ± 3.4); BBS scores ranged from 6 to 56 (mean = 43.0 ± 16.1); ABC scores ranged from 2.5 to 95.6 (mean = 64.8 ± 2.2); MFIS scores ranged from 6 to 18 (mean = 11.7 ± 2.9).
Between-group differences in many ambulatory activity characteristics were significant (Table 2). Compared with the EDSS score > 4.5 group, the EDSS score ≤ 4.5 group accumulated significantly more steps, spent less of the day inactive, engaged in longer bouts of activity, accumulated more minutes of activity at rates greater than 100 steps per minute, and had higher stepping rates during peak activity and during maximum output (see Figures 1 and 2, Supplemental Digital Content 1, http://links.lww.com/JNPT/A8 for representative 7-day plots of stepping activity produced by a participant from each group). Ninety-one percent of participants in the EDSS score > 4.5 group and 9% of participants in the EDSS score ≤ 4.5 group were sedentary (ie, accumulated less than 5000 steps per day).37 Only the number of daily activity bouts and the day-to-day variability in STEPS were similar between groups.
To enhance our interpretation of the data, we analyzed bivariate correlations among various combinations of activity parameters (Table 3). All combinations involving activity parameters that significantly distinguished EDSS groups (ie, STEPS, the percentage of the day spent inactive, mean duration of daily activity bouts, INTENSITY, PEAK, and MAX) were strongly related. Participants who accumulated more daily steps tended to engage in longer bouts, accumulated more intense minutes of activity, had higher step rates at peak activity and maximum output, and spent less of the day inactive.
On closer examination, the relationship between STEPS and VARIABILITY appeared nonlinear (Figures 1 and 2). Whereas the association across the sample was moderately negative (rho = −0.44, P = 0.05), a marked difference in the strength and direction of the association was evident between groups. For participants with EDSS scores ≤ 4.5, STEPS and VARIABILITY were not significantly related (rho = 0.46, P = 0.16). In contrast, for participants with EDSS scores more than 4.5, lower step accumulations were significantly associated with greater day-to-day step count variability (rho = −0.70, P = 0.03).
Relationships varied between daily step counts and clinical measures (Table 4). Total number of steps was most strongly related to EDSS score (rho = −0.90, P < 0.01), such that higher daily step count was associated with lower amount of disability. Higher step counts also were associated with lower MSWS score (rho = −0.83, P < 0.01), lower Times 25-foot walk value (rho = −0.64, P < 0.01), lower TUG time (rho = −0.51, P = 0.02), longer 6MW distance (rho = 0.67, P ≤ 0.01), higher DGI score (rho = 0.62, P < 0.01), higher BBS score (rho = 0.58, P < 0.01), and higher ABC score (rho = 0.47, P = 0.03).
Relationships also varied between MFIS score and ambulatory activity parameters (Table 3). Relatively weaker relationships were found between MFIS and STEPS (rho = −0.42, P = 0.07), the percentage of the day spent inactive (rho = 0.35, P = 0.13), and the activity work cycle characterizations (BOUTS [rho = −0.18, P = 0.45], mean duration of daily activity bouts [rho = −0.38, P = 0.10], VARIABILITY [rho = 0.03, P = 0.90]). In contrast, MFIS was somewhat more strongly related to the upper limits of ambulatory activity output, such that greater fatigue was associated with less intense activity output (INTENSITY [rho = − 0.57, P = 0.01], PEAK [rho = −0.47, P = 0.04], and MAX [rho = −0.45, P = 0.05]).
Impact of Ambulatory Limitation on Activity
Consistent with previous reports,9 participants in our study accumulated on average approximately 6000 steps per day; those with greater MS-related disability tended to be less active.10 Importantly, however, our findings offer insight into the impact of ambulatory limitation on natural walking behavior. Participants with limitation (EDSS score > 4.5) accumulated daily step counts consistent with sedentary community-dwelling individuals living with chronic illness.38,39 In contrast, participants without limitation (EDSS score ≤ 4.5) accumulated daily step counts consistent with levels recorded in the “somewhat active” general adult population.37 The finding supports the idea that the presence of mild MS-related disability does not necessarily preclude engagement in healthy active lifestyles. Furthermore, given that nearly all sedentary participants in our sample were ambulatory with limitations, our findings suggest that an EDSS score of 4.5 may serve as a useful cut point for distinguishing, in ICF terms, between restricted and unrestricted levels of participation. Previous efforts to use the ICF model to describe participation restrictions in MS have not considered ambulatory activity measures.40-42
Expanding on previous research,13 our findings also revealed that measures reflecting the upper limits of activity output distinguished individuals with and without ambulatory limitation. Notably, however, even for the group with EDSS score of 4.5 or less, PEAK and MAX step rates were well less than 100 steps per minute; on average, participants engaged in moderate-intensity walking activity for only a few minutes per day, and only one individual (EDSS score = 3.5) managed to meet recommended physical activity guidelines.11 The findings raise further concern regarding the problem of inactivity among individuals with MS: even among the individuals with mild disability, who as described earlier may be categorized as “somewhat active,” few may be achieving intensity levels necessary for optimal health maintenance.
Our measures reflecting the upper limits of activity output also provided preliminary insight into the extent to which MS disability might impact the use of ambulatory functional reserve. In this context, we viewed PEAK as a measure of capacity, reflecting the 30 most active individual minutes of the day regardless of when they occurred. We viewed MAX as a measure of performance, reflecting the 30 most active consecutive minutes.8 For participants with EDSS score of 4.5 or less, the percent error between PEAK and MAX values was 37.4%; for participants with EDSS scores greater than 4.5, the percent error was 48.4%. Thus, it appeared that individuals with less disability generated maximum output relatively closer to their capacity to produce it. In other words, the finding suggested that individuals with greater disability used less of their available ambulatory functional reserve. Whether the finding represented an adaptive energy conservation behavior,43 cognitive impairment,44 neurophysiologic manifestation of fatigue,45 or other factor(s) remains unclear and warrants further investigation.
Sedentarism is a well-known contributor to comorbidities that are likely to have a detrimental effect on mobility and disability among persons with MS.6,46-50 Our data were consistent with this premise: participants with ambulatory limitation were more likely to be sedentary (ie, to accumulate on average less than 5000 steps per day).37 In addition, participants with limitation had relatively shorter bouts of activity and spent approximately 10% more of the day inactive. The latter difference translated into an estimated 140 fewer active min/d, and by extension, 850 fewer active h/y. Of these individuals, participants with the greatest sedentarism also appeared to have the greatest day-to-day variability in accumulated step counts (Figures 1 and 2). Unfortunately, the extent to which other factors (eg, EDSS scores) may have uniquely influenced VARIABILITY values could not be adequately determined, although fatigue did not appear to be a substantial contributor (Table 3). The result was in contrast to our initial hypothesis, which had been based on the idea that lower-functioning individuals with MS would tend to have fewer meaningful daily occupations among which to choose,51 and therefore, less variable day-to-day activity. Although we believe that this idea continues to have merit, its potential implication for daily activity patterns will require further investigation. Nonetheless, our data suggested that profound sedentarism, such as that associated with higher levels of physical disability, may reflect a reduction in both overall amount of activity and the ability to sustain activity from day to day.
Taken together, the results of our primary analysis reinforced the idea that determinants of physical inactivity in MS may not be homogeneous. Reduced activity levels among individuals without limitation may be more likely to result, for example, from lifestyle choice or reduced self-efficacy; sedentarism among individuals with limitation may be more likely the result of interactions between impairments of either body structure or function and the physical environment.7 To be optimally effective, therefore, it seems likely that health-promoting physical activity interventions will need to be designed to match specific disability profiles. Further investigation of this proposition is warranted.
Relationships Among Measures
Our secondary analysis revealed that step count was related in varying degrees to common clinical measures of disability, ambulatory functioning, balance, and fatigue. Most strikingly, EDSS score explained more than 80% of the variance in STEPS (Table 4). Multiple Sclerosis Walking Scale score also explained a substantial, albeit somewhat lesser, proportion of step count variance. These findings imply that among the clinical measures that we studied, EDSS and MSWS scores may be the best predictors of actual walking behavior outside the clinic spotlight. In this sense, the MSWS may make a particularly useful outcome measure for physical therapy intervention, especially when objective ambulatory activity–monitoring instruments are unavailable.
To a lesser extent, step count was related to physical performance measures of ambulatory functioning and balance (ie, Timed 25-foot Walk, TUG, 6MW, DGI, BBS) and to ABC score (Table 4). We interpreted the relatively lower strength of these relationships in the context of the ICF model. As a direct, ecologically valid measure of “life situations,” we considered daily step counts to reflect the ICF Participation domain.8 The physical performance measures, all of which were at least moderately related to one another (Table 4), and all of which focused on the execution of a task or action in a controlled environment, were considered to represent the ICF “Activity” domain.40,41 The ABC score, as a self-report measure of balance self-efficacy, was considered to be an ICF Personal Factor that contributed indirectly to functioning rather than constituting a physical performance measure of balance per se. Thus, as representatives of distinct theoretical domains, it may be that clinical measures of physical performance and balance self-efficacy were less likely to produce values consistent with step accumulation during “life situations.” This interpretation suggests that predictions of ambulatory activity based on physical performance test results or reported balance self-efficacy should be undertaken cautiously.
Recent reports using accelerometer-based meth-odology52-54 have confirmed previous survey-based research55 that individuals with MS who experience greater fatigue tend to be less physically active. Our study expanded this line of inquiry with 2 distinct findings. The first involved a methodological issue; namely, that the relationship between fatigue and ambulatory activity appeared modestly stronger when intensity of activity was considered (Table 3). In our sample, this idea was supported by a significant relationship between each intensity-related measure (ie, INTENSITY, PEAK, MAX) and MFIS that was not present between STEPS and MFIS. Although the difference in strength of relationship was relatively small, the finding highlighted the importance of considering intensity of activity, rather than step counts alone, when evaluating the impact of fatigue on individuals with MS.
The second finding regarding fatigue involved its potential impact on patterns of activity. Our data revealed that fatigue did not appear to influence work-rest cycles, such that participants with greater fatigue did not tend to display patterns of activity involving (1) fewer and shorter bouts of activity or (2) relatively greater day-to-day fluctuations in activity. In other words, our findings did not support the idea that individuals with MS necessarily need relatively long periods of rest following active periods involving walking.
The strong correlations found between daily step counts and most of the remaining ambulatory activity parameters have implications for future MS research. For studies seeking to capture average amount of daily ambulatory activity, step counts alone may be sufficient. Toward this end, an accurate pedometer is an important, valuable, and inexpensive tool, especially applied to individuals without ambulatory limitation. Step activity monitors, however, remain important tools for quantifying patterns or intensities of activity not apparent in total step counts yet indicative of general health behavior, functional reserve, or the impact of MS symptoms (eg, fatigue) on physical function. Monitors also may be more accurate when measuring activity in individuals with mobility impairment.13,31-35
The study was preliminary and had limitations. The sample was not necessarily representative of the population of individuals with MS; it was small in size comprising individuals actively participating in a community-based wellness program. Likewise, the distribution of males and females in the 2 groups was markedly different, raising the possibility that gender differences in activities of daily living may have confounded the results. External factors that might have influenced ambulatory activity (eg, weather, suitability of outdoor environment, family composition, and accessibility to transportation and indoor walking facilities) were not considered. We acknowledge that global physical activity could not be inferred from step counts alone; some inactive participants, for example, may have preferred nonambulatory types of exercise activities such as swimming and weight training. Similarly, participants engaging in other forms of nonambulatory exercise (eg, bicycling) may have generated spurious step counts. Multiple sclerosis disability related to other impairments (eg, upper extremity or cognitive) was not well captured by the EDSS; thus, the impact of such impairments on activity remains unknown. Finally, the strong correlations among the clinical gait measures suggested that they captured a similar facet of ambulatory functioning, and therefore, were somewhat redundant. Future investigations should consider these issues when seeking to validate a set of optimal outcome measurement tools to be used for individuals with MS.14
Ambulatory limitation in individuals with MS appears to contribute to lower daily accumulations of steps, a greater percentage of the day spent inactive, reduced upper limits of activity output, and shorter bouts of activity. Individuals with MS who have greater disability may be profoundly sedentary and have difficulty sustaining optimal levels of activity from day to day. Even individuals with mild disability may not achieve recommended levels of daily physical activity. Although various ambulatory activity parameters can serve as useful descriptors, mean daily step count appeared to best reflect gait and balance abilities among participants. Of the clinical measures studied, disability status and self-reported walking limitation showed the greatest promise for predicting ambulatory activity levels beyond the clinic spotlight. Study findings suggest that disability status should direct efforts to develop appropriate physical activity interventions.
The authors thank the participants of the study and Patricia J. Manns, PT, PhD, of the University of Alberta, Edmonton, Canada, for her assistance in calculating ambulatory activity characteristics.
Using PEAK as the theoretical maximum value, Percent Error was calculated as 100 × [(PEAK-MAX)/PEAK)]
1. Ng AV, Kent-Braun JA. Quantitation of lower physical activity in persons with multiple sclerosis. Med Sci Sports Exerc. 1997; 29(4):517–523.
2. Motl RW, McAuley E, Snook EM. Physical activity and multiple sclerosis: a meta-analysis. Mult Scler. 2005; 11(4):459–463.
3. US Department of Health and Human Services. Physical Activity and Health: A Report of the Surgeon General. Atlanta (GA): National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Department of Health and Human Services; 1996.
4. Woolf K, Reese CE, Mason MP, Beaird LC, Tudor-Locke C, Vaughan LA. Physical activity is associated with risk factors for chronic disease across adult women's life cycle. J Am Diet Assoc. 2008; 108(6):948–959.
5. Macera CA, Ham SA, Yore MM, et al.. Prevalence of physical activity in the United States: Behavioral Risk Factor Surveillance System, 2001. Prev Chronic Dis. 2005;2(2):A17.
6. Dallmeijer AJ, Beckerman H, de Groot V, van de Port IG, Lankhorst GJ, Dekker J. Long-term effect of comorbidity on the course of physical functioning in patients after stroke and with multiple sclerosis. J Rehabil Med. 2009; 41(5):322–326.
7. Motl RW. Physical activity and its measurement and determinants in multiple sclerosis. Minerva Med. 2008; 99(2):157–165.
8. Bowden MG, Hannold EM, Nair PM, Fuller LB, Behrman AL. Beyond gait speed: a case report of a multidimensional approach to locomotor rehabilitation outcomes in incomplete spinal cord injury. J Neurol Phys Ther. 2008; 32(3):129–138.
9. Motl RW, Zhu W, Park Y, McAuley E, Scott JA, Snook EM. Reliability of scores from physical activity monitors in adults with multiple sclerosis. Adapt Phys Activ Q. 2007; 24(3):245–253.
10. 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(3):248–258.
11. Tudor-Locke C, Sisson SB, Collova T, Lee SM, Swan PD. Pedometer-determined step count guidelines for classifying walking intensity in a young ostensibly healthy population. Can J Appl Physiol. 2005; 30(6):666–676.
12. Elsworth C, Dawes H, Winward C, et al.. Pedometer step counts in individuals with neurological conditions. Clin Rehabil. 2009; 23(2):171–175.
13. Busse ME, Pearson OR, Van Deursen R, Wiles CM. Quantified measurement of activity provides insight into motor function and recovery in neurological disease. J Neurol Neurosurg Psychiatry. 2004; 75(6):884–888.
14. Hutchinson B, Forwell SJ, Bennett S, Brown T, Karpatkin H, Miler D. Toward a consensus on rehabilitation outcomes in MS: gait and fatigue. Int J MS Care. 2009; 11:67–78.
15. Cattaneo D, Regola A, Meotti M. Validity of six balance disorders scales in persons with multiple sclerosis. Disabil Rehabil. 2006;30,28(12):789–795.
16. Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology. 1983; 33(11):1444–1452.
17. Goodkin DE, Cookfair D, Wende K, et al.. Inter- and intrarater scoring agreement using grades 1.0 to 3.5 of the Kurtzke Expanded Disability Status Scale (EDSS). Multiple Sclerosis Collaborative Research Group. Neurology. 1992; 42(4):859–863.
18. Lechner-Scott J, Huber S, Kappos L. Expanded Disability Status Scale (EDSS) training for MS-multicenter trials. J Neurol. 1997;244(suppl 3):S25.
19. Hobart JC, Riazi A, Lamping DL, Fitzpatrick R, Thompson AJ. Measuring the impact of MS on walking ability: the 12-Item MS Walking Scale (MSWS-12). Neurology. 2003;14,60(1):31–36.
20. Schwid SR, Goodman AD, Mattson DH, et al.. The measurement of ambulatory impairment in multiple sclerosis. Neurology. 1997; 49(5):1419–1424.
21. Fischer JS, Rudick RA, Cutter GR, Reingold SC. 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(4):244–250.
22. Nilsagard Y, Lundholm C, Denison E, Gunnarsson LG. Predicting accidental falls in people with multiple sclerosis—a longitudinal study. Clin Rehabil. 2009; 23(3):259–269.
23. Goldman MD, Marrie RA, Cohen JA. Evaluation of the six-minute walk in multiple sclerosis subjects and healthy controls. Mult Scler. 2008; 14(3):383–390.
24. Paltamaa J, West H, Sarasoja T, Wikstrom J, Malkia E. Reliability of physical functioning measures in ambulatory subjects with MS. Physiother Res Int. 2005; 10(2):93–109.
25. ATS Committee on Proficiency Standards for Clinical Pulmonary Function Laboratories. ATS statement: guidelines for the six-minute walk test. Am J Respir Crit Care Med. 2002; 166(1):111–117.
26. McConvey J, Bennett SE. Reliability of the Dynamic Gait Index in individuals with multiple sclerosis. Arch Phys Med Rehabil. 2005; 86(1):130–133.
27. Cattaneo D, Jonsdottir J, Repetti S. Reliability of four scales on balance disorders in persons with multiple sclerosis. Disabil Rehabil. 2007;30,29(24):1920–1925.
28. Powell LE, Myers AM. The Activities-specific Balance Confidence (ABC) Scale. J Gerontol A Biol Sci Med Sci. 1995;50A(1):M28–M34.
29. Ritvo PG, Fischer JS, Miller DM, Andrews H, Paty DW, LaRocca NG. Multiple Sclerosis Quality of Life Inventory: A User's Manual. New York, NY: National Multiple Sclerosis Society; 1977.
30. Fisk JD, Ritvo PG, Ross L, Haase DA, Marrie TJ, Schlech WF. Measuring the functional impact of fatigue: initial validation of the fatigue impact scale. Clin Infect Dis. 1994;18(suppl 1):S79–S83.
31. Behrman AL, Lawless-Dixon AR, Davis SB, et al.. Locomotor training progression and outcomes after incomplete spinal cord injury. Phys Ther. 2005; 85(12):1356–1371.
32. Bergman RJ, Bassett DR Jr, Muthukrishnan S, Klein DA. Validity of 2 devices for measuring steps taken by older adults in assisted-living facilities. J Phys Act Health. 2008;5(suppl 1):S166–S175.
33. Macko R, Haeuber E, Shaughnessy M, et al.. Microprocessor-based ambulatory activity monitoring in stroke patients. Med Sci Sports Exerc. 2002; 34(3):394–399.
34. Hartsell H, Fitzpatrick D, Brand R, Frantz R, Saltzman C. Accuracy of a custom-designed activity monitor: implications for diabetic foot ulcer healing. J Rehabil Res Dev. 2002; 39(3):395–400.
35. Manns PJ, Baldwin E. Ambulatory activity of stroke survivors: measurement options for dose, intensity, and variability of activity. Stroke. 2009; 40(3):864–867.
36. Cavanaugh JT, Coleman KL, Gaines JM, Laing L, Morey MC. Using step activity monitoring to characterize ambulatory activity in community-dwelling older adults. J Am Geriatr Soc. 2007; 55(1):120–124.
37. Tudor-Locke C, Hatano Y, Pangrazi RP, Kang M. Revisiting “how many steps are enough?” Med Sci Sports Exerc. 2008;40(7) (suppl):S537–S543.
38. Tudor-Locke C, Washington TL, Hart TL. Expected values for steps/day in special populations. Prev Med. 2009; 49(1):3–11.
39. Tudor-Locke CE, Myers AM. Methodological considerations for researchers and practitioners using pedometers to measure physical (ambulatory) activity. Res Q Exerc Sport. 2001; 72(1):1–12.
40. Paltamaa J, Sarasoja T, Leskinen E, Wikstrom J, Malkia E. Measures of physical functioning predict self-reported performance in self-care, mobility, and domestic life in ambulatory persons with multiple sclerosis. Arch Phys Med Rehabil. 2007; 88(12):1649–1657.
41. Paltamaa J, Sarasoja T, Wikstrom J, Malkia E. Physical functioning in multiple sclerosis: a population-based study in central Finland. J Rehabil Med. 2006; 38(6):339–345.
42. Khan F, Pallant JF. Use of International Classification of Functioning, Disability and Health (ICF) to describe patient-reported disability in multiple sclerosis and identification of relevant environmental factors. J Rehabil Med. 2007; 39(1):63–70.
43. Holberg C, Finlayson M. Factors influencing the use of energy conservation strategies by persons with multiple sclerosis. Am J Occup Ther. 2007; 61(1):96–107.
44. Rocca MA, Absinta M, Ghezzi A, Moiola L, Comi G, Filippi M. Is a preserved functional reserve a mechanism limiting clinical impairment in pediatric MS patients?. Hum Brain Mapp. 2009; 30(9):2844–2851.
45. Vucic S, Burke D, Kiernan MC. Fatigue in multiple sclerosis: mechanisms and management. Clin Neurophysiol. 2010; 121(6):809–817.
46. Marrie R, Horwitz R, Cutter G, Tyry T, Campagnolo D, Vollmer T. High frequency of adverse health behaviors in multiple sclerosis. Mult Scler. 2009; 15(1):105–113.
47. Marrie RA, Horwitz R, Cutter G, Tyry T, Campagnolo D, Vollmer T. Comorbidity delays diagnosis and increases disability at diagnosis in MS. Neurology. 2009; 72(2):117–124.
48. Redelings MD, McCoy L, Sorvillo F. Multiple sclerosis mortality and patterns of comorbidity in the United States from 1990 to 2001. Neuroepidemiology. 2006; 26(2):102–107.
49. Stuifbergen A, Brown A, Phillips L. Predictors and moderators of the disablement process in persons with multiple sclerosis. Neuro Rehabil. 2009; 24(2):119–129.
50. Turner AP, Kivlahan DR, Haselkorn JK. Exercise and quality of life among people with multiple sclerosis: looking beyond physical functioning to mental health and participation in life. Arch Phys Med Rehabil. 2009; 90(3):420–428.
51. Lexell EM, Lund ML, Iwarsson S. Constantly changing lives: experiences of people with multiple sclerosis. Am J Occup Ther. 2009; 63(6):772–781.
52. Motl RW, McAuley E. Pathways between physical activity and quality of life in adults with multiple sclerosis. Health Psychol. 2009; 28(6):682–689.
53. Motl RW, McAuley E, Snook EM, Gliottoni RC. Physical activity and quality of life in multiple sclerosis: intermediary roles of disability, fatigue, mood, pain, self-efficacy and social support. Psychol Health Med. 2009; 14(1):111–124.
54. Motl RW, McAuley E, Wynn D, Suh Y, Weikert M, Dlugonski D. Symptoms and physical activity among adults with relapsing-remitting multiple sclerosis. J Nerv Ment Dis. 2010; 198(3):213–219.
55. Motl RW, Snook EM, Schapiro RT. Symptoms and physical activity behavior in individuals with multiple sclerosis. Res Nurs Health. 2008; 31(5):466–475.
Keywords:© 2011 Neurology Section, APTA
Ambulation; balance; fatigue; measurement; mobility; physical activity; steps