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Physical activity is associated with cognitive processing speed in persons with multiple sclerosis
Multiple Sclerosis and Related Disorders, 1, 3, pages 123 - 128
The impairment of cognitive processing speed is common, disabling, and poorly managed in multiple sclerosis (MS). This study examined the association between objectively-measured physical activity and cognitive processing speed (CPS) in a large sample of persons with MS. Patients (N=212) underwent two valid neuropsychological tests of CPS, completed the Timed 25-Foot Walk (T25FW), and wore an ActiGraph model GT3X accelerometer during the waking hours of a 7-day period for objectively measuring physical activity as steps/day. Physical activity was significantly associated with CPS (r=.39,p<.01), even when controlling for age, sex, and education (pr=.26,p<.01). This association was attenuated, but still significant after further controlling for T25FW performance (pr=.13,p=.03). Physical activity behavior is positively and independently, albeit weakly, associated with CPS in persons with MS, and may play an important role in managing this aspect of cognition as it does in other outcomes in MS.
- Physical activity and cognitive processing speed were measured in persons with MS.
- Higher physical activity levels were associated with better cognitive processing speed.
- The association was independent of age, sex, education, and walking performance.
Keywords: Multiple sclerosis, Cognitive processing speed, Physical activity, Cognition, Accelerometer, Walking performance.
Multiple sclerosis (MS) is a prevalent neurologic disease characterized by disseminated areas of demyelination ( Trapp and Nave, 2008 ) and atrophy (Trapp and Nave, 2008 and Vollmer, 2007) within the central nervous system (CNS). The CNS damage results in cognitive impairment ( Benedict et al., 2006 ) with 45–65% of persons who have MS demonstrating impaired cognitive function (Benedict and Zivadinov, 2011, Bobholz and Rao, 2003, and Rao et al, 1991), particularly slowed cognitive processing speed ( Bobholz and Rao, 2003 ). Importantly, cognitive processing speed has been linked with unemployment, reduced social interaction, inability to drive, and compromised quality of life ( Benedict et al., 2005 ). There are no approved pharmacological treatments for cognitive impairment in MS ( Benedict and Zivadinov, 2011 ), and studies involving cognitive rehabilitation have been conflicting and disappointing (Amato et al, 2006 and O'Brien et al, 2008). Collectively, this highlights the importance of considering other approaches that could be integrated into the comprehensive management of cognitive impairment in MS.
Physical activity has been suggested to be an important behavioral factor for preventing or even treating cognitive dysfunction in MS ( Motl et al., 2011c ). This hypothesis is based, in part, on the wealth of evidence for positive physical activity effects on cognitive function in older adults ( Ratey and Loehr, 2011 ). For example, one meta-analysis indicated that exercise training resulted in a .478 standard deviation unit increase in cognitive domains among older adults, whereas the control conditions resulted in a .164 standard deviation unit change ( Colcombe and Kramer, 2003 ). Such positive effects of exercise training have been confirmed in subsequent meta-analyses of more recent literature ( Smith et al., 2010 ).
To date, there is very limited research on physical activity and cognition in persons with MS. We are aware of two cross-sectional studies with small samples that reported an association between aerobic fitness, based on peak aerobic capacity, and cognitive processing speed, but not learning and memory, in persons with MS (Prakash et al, 2007 and Prakash et al, 2010). Importantly, aerobic fitness represents a characteristic of a person rather than a behavior itself, and has demonstrated different associations with outcomes than physical activity in the general population ( McMurray et al., 1998 ). To that end, one pilot study examined the association between physical activity and cognition using neuropsychological testing in 33 persons with MS ( Motl et al., 2011b ). Physical activity, based on steps per day, was significantly correlated with cognitive processing speed, but not learning and memory, after controlling for age, sex, and education; the lack of an association between physical activity, based on counts per day from an accelerometer, and performance on neuropsychological tests of learning and memory was further reported in a recent study of 45 persons with MS ( Prakash et al., 2011 ). Importantly, the association between physical activity and cognitive processing speed might be the product of a relatively small sample size and the possibility of a small number of influential data points (i.e., outliers) driving the association between variables ( Rousselet and Pernet, 2012 ).
Another limitation is that pervious research did not account for walking impairment as a possible confounding variable. Walking impairment is a primary feature of MS that is both life-altering and disabling ( Heesen et al., 2006 ). Indeed, the Timed 25-Foot Walk (T25FW) has been characterized as the best objective measure of walking disability in persons with MS ( Kieseier and Pozzilli, 2012 ), and T25FW performance has been associated with both cognition ( Benedict et al., 2011 ) and physical activity ( Motl et al., 2012 ) in this population. Hence, the association between physical activity and cognition might be confounded by impairment in walking disability.
The current study examined the association between physical activity, objectively-measured as steps per day, and cognitive processing speed, measured by neuropsychological examination, in a large sample of MS patients, controlling for age, sex, education, and T25FW performance. Such an examination is important for replicating and extending previous research in MS before investing considerable time, effort, and resources into a longitudinal or interventional trial on physical activity and cognition.
2. Material and methods
Persons with MS who resided within an approximately 90-min drive of a Midwestern neurology practice were recruited via local media outlets, promotional flyers, and medical records. The inclusion criteria for participants involved (a) having a clinically definite diagnosis of MS; (b) being ambulatory either with or without use of an assistive device (i.e., unassisted, cane/crutch/walker use, but not confined to wheelchair); and relapse free during the past 30 day period. We contacted 482 persons with MS and screened and scheduled 295 of them. Of the 295 who were screened, 83 persons canceled the testing appointment and were not available to reschedule, resulting in a final sample of 212 persons with MS who completed testing.
2.2. Primary measurements
Physical activity was objectively-measured as steps per day by ActiGraph (Health One Technology, Fort Walton Beach, FL) model GT3X accelerometers. This model of accelerometer is small (3.8×3.7×1.8 cm) and light weight (27 g) and contains a solid state, digital accelerometer that generates an electrical signal proportional to the force acting on it, which is digitized by a 12-bit analog-to-digital converter at a rate of 30 Hz and numerically integrated over a pre-programmed epoch interval; the epoch was 1 min in this study. The integrated value is stored in memory as activity counts as well as step counts and the integrator is reset at the end of each interval. The data were retrieved from the accelerometer via a direct USB 2.0 connection with a personal computer and then imported into MeterPlus for validity check (i.e.,≥10 h per day wear time) and processing of total daily step counts. The average of total daily step counts across the 7 days was the main outcome from the accelerometer and this outcome has been validated in MS (Busse et al, 2004 and Motl et al, 2011d).
The Paced Auditory Serial Addition Test (PASAT) and Symbol Digit Modalities Test (SDMT) were included as a neuropsychological measures of cognitive processing speed as both are relatively quick assessment and valid in persons with MS (Benedict et al, 2002, Parmenter et al, 2007, and Rao et al, 1989). The Rao adaptation of the PASAT 3-s version provides a measure of cognitive processing speed and flexi-bility, and is a component of the Multiple Sclerosis Functional Composite (MSFC;Rao et al, 1989 and Fisher et al, 2001). The specific procedure for the Rao adaptation of the PASAT is described elsewhere ( Rao et al., 1989 ). The main outcome measure of the PASAT was the number of correct responses given out of a possible 60. For the SDMT, participants were presented a series of 9 geometric symbols, each paired with a single digit number in a key at the top of an 8.5×11-in. sheet of paper. The remainder of the page included a pseudo-randomized list of 110 symbols for which the participant was instructed to provide the digit associated with each corresponding symbol. Participants were then given 90 s to provide a digit associated with as many symbols as possible, in order, without skipping any items. The main outcome measure of the SDMT was the number of correctly provided numbers in the 90 s period ( Smith, 1982 ).
The T25FW was administered as a measure of walking performance. The T25FW is a component of the Multiple Sclerosis Functional Composite ( Fisher et al., 2001 ) and consists of the participant walking 25 ft as quickly and safely as possible in a hallway clear of debris. Two trials were performed, and the main outcome measure was the mean time taken to complete the T25FW ( Benedict et al., 2011 ).
The protocol for this study was approved by a University Institutional Review Board, and all participants provided written informed consent. The protocol included a single testing session. Participants initially completed a demographics questionnaire, followed by administration of the PASAT and SDMT. Following neuropsychological testing, participants undertook 2 trials of the T25FW. Participants then wore an ActiGraph model GT3X accelerometer on an elastic belt around the waist on the side of the body in-line with the non-dominant hip during the waking hours, except while showering, bathing, and swimming, over a 7-day period. After the 7-day period, participants returned the accelerometer through the US Postal Service using pre-stamped, pre-addressed envelopes. Participants were given a $15 gift card covering travel expenses upon returning the materials.
2.4. Data analysis
The data were analyzed using PASW version 18.0 (SPSS Inc, Chicago, IL). We examined the extent of impairment in cognitive processing speed by comparing the scores on the PASAT and SDMT from the present study with data from a normative control sample ( Strober et al., 2009 ) and creatingz-scores (i.e., score from present study minus mean of control divided by controlSD). Thez-score indicated the magnitude of cognitive impairment inSDunits compared with normative controls. Based on precedence of previous work (e.g.,Motl et al, 2011b, Prakash et al, 2007, and Prakash et al, 2010) and improved representation of the cognitive processing speed domain, we subsequently created a compositez-score based on the mean of the individualz-scores for the PASAT and SDMT. We then examined the associations between the PASAT, SDMT, and compositez-score with physical activity using Pearson product-moment correlation coefficients (r). Values for correlation coefficients of .1, .3, and .5 were interpreted as small, moderate, and large, respectively ( Cohen, 1988 ). We included Spearman rho non-parametric rank-order correlation coefficients (ρ) as an approach to check if outliers or non-linearity were driving the correlations. We then conducted additional partial correlation (pr) analyses controlling for age, sex, and education as covariates of cognitive impairment and physical activity (Prakash et al, 2008 and Motl et al, 2006), as well as controlling for T25FW performance ( Benedict et al., 2011 ).
3.1. Demographic and clinical characteristics
The demographic and clinical characteristics of the sample are presented in Table 1 . Briefly, the sample consisted of 212 persons with a definite diagnosis of MS. The sample was largely female (n=170/212; 80%) with an average age of 50.0 (SD=10.3) years. Of the 212 participants, 47 (22%) reported a high school education, 53 (25%) attended some college, and 112 (53%) were university/college graduates. Regarding clinical MS course, 173 (82%) of participants had relapsing–remitting MS, 22 (10%) had secondary progressive MS, 12 (6%) had primary progressive MS, and 5 (2%) parti-cipants were unaware of MS subtype. The mean duration of MS was 11.4 (SD=8.8) years, and the mean time to complete the T25FW was 6.9 s (SD=6.7).
|Variable||MS ( n =212)|
|Age (years)||50.0 (10.3)|
|Sex (n, % female)||170/212, 80%|
|Education (n, %)|
|High school||47/212, 22%|
|Some college||53/212, 25%|
|University/college graduate||112/212, 53%|
|Clinical course (n, % RRMS)||173/212, 83%|
|Disease duration (years)||11.4 (8.8)|
|T25FW performance (s)||6.9 (6.7)|
|PASAT performance||42.6 (12.2)|
|SDMT performance||46.3 (11.6)|
|Average steps per day||4385 (2432)|
Note: Data are presented as mean (SD) unless noted otherwise; RRMS=relapsing–remitting multiple sclerosis; T25FW=Timed 25-Foot Walk; PASAT=Paced Auditory Serial Addition Test; SDMT=Symbol Digit Modalities Test.
3.2. Cognitive and physical activity characteristics
The mean scores for the PASAT and SDMT in this study were 42.6 (SD=12.2) and 46.3 (SD=11.6), respectively ( Table 1 ), and these were .7 and 1.5SDunits below normative values for controls without MS ( Strober et al., 2009 ). The mean score from the GT3X accelerometer was 4385 steps per day (SD=2432) ( Table 1 ), and this value is lesser than average steps per day reported in other samples of persons with MS (average steps per day=7698; Sandroff et al., 2012 ).
3.3. Correlations among physical activity and cognition
Average steps per day was significantly correlated with scores on the PASAT (r=.27,p<.01), SDMT (r=.42,p<.01), and com-posite cognitive processing speed outcome (r=.39,p<.01); the scatter plots for these associations are presented in Fig. 1 . Using non-parametric correlations, average steps per day was significantly and similarly associated with PASAT (ρ=.30,p<.01), SDMT (ρ=.45,p<.01), and composite cognitive processing speed (ρ=.43,p<.01) scores, respectively. The differential associations of the SDMT and PASAT were expected, as the SDMT has had slightly stronger correlations than the PASAT with regard to clinical outcomes (e.g., diagnosis, disease course, and work disability; Drake et al., 2010 ). As an additional check for outliers, we temporarily removed scores of 0 on the PASAT from analyses, and the correlations did not change.
We further note that the composite cognitive processing speed outcome and average steps per day were correlated with age (r=−.34,p<.01 &r=−.38,p<.01, respectively), sex (r=−.21,p=<.01 &r=−.16,p=.01, respectively), education (r=.30,p<.01 &r=.21,p<.01, respectively), and T25FW performance (r=−.16,p=.01 &r=−.49,p<.01, respectively). After controlling for age, sex, and education, average steps per day was still significantly correlated with composite cognitive processing speed (pr=.26,p<.01). After controlling for age, sex, education, and T25FW performance, this association was attenuated, but remained statistically significant (pr=.13,p=.03).
This study indicated that objectively-measured physical activity in steps per day was positively and independently, albeit weakly, associated with cognitive processing speed among a large sample of persons with MS. There are two primary implications of such results. This study replicates and extends previous research (Motl et al, 2011b and Prakash et al, 2011) using a substantially larger sample and analyses that would indicate if the associations were driven by extreme cases or outliers ( Rousselet and Pernet, 2012 ). This study further indicates that the association between physical activity and cognitive processing speed is preserved when accounting for walking disability, although the association was attenuated in analyses that controlled for T25FW performance. Collectively, our cross-sectional data indicate that general physical activity, perhaps in the form of walking, might be an important target of a subsequent behavioral intervention to be developed and tested for managing cognitive processing speed impairments in persons with MS. This recommendation is not to infer causality, but to suggest that we need to design longitudinal investigations and clinical trials that can test the nature of causality between variables in persons with MS.
Our results are similar to those of a recent pilot investigation ( Motl et al., 2011b ). The study reported that physical activity, measured as average steps per day, was significantly associated with a composite cognitive processing speedz-score (r=.39) in 33 persons with MS ( Motl et al., 2011b ). The current investigation had a six times larger sample of 212 persons with MS and physical activity measured as steps per day was significantly and similarly associated with the composite cognitive processing speedz-score (r=.39). The relationship remained statistically significant after controlling for age, sex, and education as covariates in the current and previous research ( Motl et al., 2011b ). This would suggest that neither age, sex, nor education status are driving the relationship between physical activity and cognition in persons with MS. Interestingly, age is associated with slowed cognitive processing speed ( Ratey and Loehr, 2011 ) even in the absence of neurological disease, as reported in the gerontology literature. The current study further controlled for T25FW performance, as previous investigations have failed to account for this potential confound ( Motl et al., 2011b ). After controlling for age, sex, education, and T25FW performance, the association between physical activity and cognition became attenuated, but remained statistically significant. This suggests that walking performance is indeed contributing to the association between physical activity and cognitive processing speed in MS ( Benedict et al., 2011 ). Nevertheless, physical activity was still significantly associated with cognition, after controlling for age, sex, education, and T25FW performance, and this suggests that physical activity, independent of walking performance, is associated with cognitive processing speed.
Collectively, our results strengthen the evidence for a link between physical activity and cognitive function in persons with MS. This body of evidence on a relationship between physical activity and cognitive function is only cross-sectional and would encourage the development and testing of physical activity interventions that target cognitive impairment in persons with MS. Such an intervention would be necessary for establishing causality between variables. The proposed intervention is practically important as there are currently no FDA-approved pharmacological treatments for cognitive impairment in MS ( Benedict and Zivadinov, 2011 ) and studies involving alternative approaches, such as cognitive rehabilitation, have been conflicting and disappointing in this population (Amato et al, 2006 and O'Brien et al, 2008).
We see considerable promise in physical activity and cognition in MS as recently highlighted in a literature review ( Motl et al., 2011c ) and editorial ( Benito-Leon, 2011 ; Feinstein, 2011 ). To date, we are aware of only one randomized controlled trial (RCT) of exercise training and cognitive function in persons with MS ( Oken et al., 2004 ). That RCT examined the effect of 6 months of yoga, aerobic exercise, or a wait-list control on cognition in persons with MS ( Oken et al., 2004 ). Overall, neither yoga nor aerobic exercise resulted in significant improvements in cognition compared with the wait-list control ( Oken et al., 2004 ). The most salient concern with that study is that the exercise sessions took place only once per week and this amount of physical activity that occurred might not be sufficient for improving cognitive function.
An alternative approach for increasing physical activity and examining the effect on cognition in persons with MS is a behavioral intervention. Behavioral interventions involve the systematic instruction on skills and techniques for modifying health-related behaviors including physical activity. Such behavioral interventions have been successful for increasing physical activity behavior among persons with MS (McAuley et al, 2007 and Motl et al, 2011a), and lifestyle physical activity has been associated with quality of life, walking mobility, and other disease-related outcomes in this population (Coote et al, 2009 and Motl and Snook, 2008). Accordingly, researchers might consider delivering a behavioral intervention alone or in combination with exercise training for increasing physical activity and examining the resulting influence on cognition in persons with MS.
Strengths of this study include the relatively large sample size; control for age, sex, education, and T25FW performance; inclusion of valid neuropsychological tests of cognitive processing speed; and administration of a valid and objective measure of physical activity. One limitation of the study is the cross-sectional nature of this investigation as it does not indicate causality between measures of physical activity and cognitive function. Indeed, cognitive impairment might influence physical activity participation as much as physical activity influences cognition. We did not control for disability status (i.e., Expanded Disability Status Scale; EDSS), but rather cont-rolled for walking performance (i.e., T25FW) as an indication of disability ( Kieseier and Pozzilli, 2012 ). Although the T25FW is not a direct measure of overall disability that is assessed by a clinician (e.g., EDSS), previous research has indicated that the T25FW has been able to differentiate between persons with MS and healthy controls, and individuals with varying levels of MS disability ( Phan-Ba et al., 2011 ). Subsequent research should take this into consideration and include a clinical measure of overall disability, as presumably, physical activity and cognition have joint relationships with disability as well as walking performance. Another limitation is that we did not control for depression as a potential confounding factor, as depression might be jointly associated with physical activity and cognition in persons with MS ( Motl et al., 2010 ). Lastly, our study did not include normal controls matched by age, education, and sex for direct comparison of physical activity and cognitive function and the magnitude of association between variables.
Overall, physical activity, measured as steps per day, was associated with faster cognitive processing speed, assessed by neuropsychological testing, in 212 persons with MS, independent of age, sex, education, and T25FW performance. These findings are consistent with previous pilot research examining this relationship in a smaller sample of persons with MS ( Motl et al., 2011b ) and suggest that (a) physical activity and cognition are indeed related, and (b) even after controlling for age, sex, education, and walking performance, physical activity is still associated with cognition. Collectively, this meaningful association of physical activity and cognition should prompt future examinations of the effect of physical activity interventions on cognitive impairment in MS as an approach for establishing causality between variables and informing therapeutic approaches.
Conflict of interest
The authors report no conflicts of interest. This project was funded by grants no. PP 1695, from the National Multiple Sclerosis Society and OSF Saint Francis Medical Center Foundation.
- Amato et al., 2006 M.P. Amato, E. Portaccio, V. Zipoli. Are there protective treatments for cognitive decline in MS?. Journal of the Neurological Sciences. 2006;245:183-186
- Benedict and Zivadinov, 2011 R.H.B. Benedict, R. Zivadinov. Risk factors for and management of cognitive dysfunction in multiple sclerosis. Nature Reviews Neuroscience. 2011;7(6):332-342
- Benedict et al., 2002 R.H.B. Benedict, J. Fischer, C.J. Archibald, P. Arnett, W.W. Beatty, J. Bobholz, et al. Minimal neuropsychological assessment of MS patients: a consensus approach. Clinical Neuropsychology. 2002;16:381-397
- Benedict et al., 2005 R.H.B. Benedict, E. Wahlig, R. Bakshi, I. Fishman, F. Munschauer, R. Zivadinov, et al. Predicting quality of life in multiple sclerosis: accounting for physical disability, fatigue, cognition, mood disorder, personality, and behavior change. Journal of the Neurological Sciences. 2005;231(1–2):29-34
- Benedict et al., 2006 R.H.B. Benedict, J.M. Bruce, M.G. Dwyer, N. Abdelrahman, S. Hussein, B. Weinstock-Guttman, et al. Neocortical atrophy, third ventricular width, and cognitive dysfunction in multiple sclerosis. Archives of Neurology. 2006;63(9):1301-1306
- Benedict et al., 2011 R.H.B. Benedict, R. Holtzer, R.W. Motl, F.W. Foley, S. Kaur, D. Hojnacki, et al. Upper and lower extremity motor function and cognitive impairment in multiple sclerosis. Journal of the International Neuropsychological Society. 2011;17(4):643-653
- Benito-Leon, 2011 J. Benito-Leon. Physical activity in multiple sclerosis: the missing prescription. Neuroepidemiology. 2011;36(3):192-193
- Bobholz and Rao, 2003 J.A. Bobholz, S.M. Rao. Cognitive dysfunction in multiple sclerosis: a review of recent developments. Current Opinion in Neurology. 2003;16(3):283-288
- Busse et al., 2004 M.E. Busse, O.R. Pearson, R. Van Deursen, C.M. Wiles. Quantified measurement of activity provides insight into motor function and recovery in neurological disease. Journal of Neurology, Neurosurgery, and Psychiatry. 2004;75:884-888
- Cohen, 1988 J. Cohen. Statistical power analysis for the behavioral sciences. Second edition (Lawrence Erlbaum Associates, Hillsdale, NJ, 1988)
- Colcombe and Kramer, 2003 S. Colcombe, A.F. Kramer. Fitness effects on the cognitive function of older adults: a meta-analytic study. Psychological Science. 2003;14(2):125-130
- Coote et al., 2009 S. Coote, M. Garrett, N. Hogan, A. Larkin, J. Saunders. Getting the balance right: a randomized controlled trial of physiotherapy and exercise interventions for ambulatory people with multiple sclerosis. BMC Neurology. 2009;9:34
- Drake et al., 2010 A.S. Drake, B. Weinstock-Guttman, S.A. Morrow, D. Hojnacki, F.E. Munschauer, R.H.B. Benedict. Psychometrics and normative data for the Multiple Sclerosis Functional Composite: replacing the PASAT with the Symbol Digit Modalities Test. Multiple Sclerosis. 2010;16:228-237
- Feinstein, 2011 A. Feinstein. Multiple sclerosis, cognitive dysfunction and the potential benefits of exercise. Mult Scler. 2011;17(9):1032-1033
- Fisher et al., 2001 J.S. Fisher, A.J. Jak, J.E. Kniker, R.A. Rudick, G. Cutter. Multiple sclerosis functional composite (MSFC). Administration and Scoring Manual. 2001;:1-44
- Heesen et al., 2006 C. Heesen, J. Segal, C. Reich, P. Hamalainen, F. Broemel, S. Niemann, et al. Patient information on cognitive symptoms in multiple sclerosis—acceptability in relation to disease duration. Acta Neurologica Scandinavica. 2006;114(4):268-272
- Kieseier and Pozzilli, 2012 B.C. Kieseier, C. Pozzilli. Assessing walking disability in multiple sclerosis. Multiple Sclerosis. 2012;18(7):914-924
- McAuley et al., 2007 E. McAuley, R.W. Motl, K.S. Morris, L. Hu, S.E. Doerksen, S. Elavsky, et al. Enhancing physical activity adherence and well-being in multiple sclerosis: a randomized controlled trial. Multiple Sclerosis. 2007;13:652-659
- McMurray et al., 1998 R.G. McMurray, B.E. Ainsworth, J.S. Harrell, T.R. Griggs, O.D. Williams. Is physical activity of aerobic power more influential on reducing cardiovascular disease risk factors?. Medicine & Science in Sports & Exercise. 1998;30(10):1521-1529
- Motl and Snook, 2008 R.W. Motl, E.M. Snook. Physical activity, self-efficacy, and quality of life in multiple sclerosis. Annals of Behavioral Medicine. 2008;35(1):11-15
- Motl et al., 2006 R.W. Motl, E. McAuley, E.M. Snook, J.A. Scott. Validity of physical activity measures in ambulatory individuals with multiple sclerosis. Disability and Rehabilitation. 2006;28:1151-1156
- Motl et al., 2010 R.W. Motl, M. Weikert, Y. Suh, D. Dlugonski. Symptom cluster and physical activity in relapsing–remitting multiple sclerosis. Research in Nursing & Health. 2010;33(5):398-412
- Motl et al., 2011a R.W. Motl, D. Dlugonski, T.R. Wojcicki, E. McAuley, D.C. Mohr. Internet intervention for increasing physical activity in persons with multiple sclerosis. Multiple Sclerosis. 2011;17(1):116-128
- Motl et al., 2011b R.W. Motl, E. Gappmaier, K. Nelson, R.H.B. Benedict. Physical activity and cognitive function in multiple sclerosis. Journal of Sport and Exercise Psychology. 2011;33(5):734-741
- Motl et al., 2011c R.W. Motl, B.M. Sandroff, R.H.B. Benedict. Cognitive dysfunction and multiple sclerosis: developing a rationale for considering the efficacy of exercise training. Multiple Sclerosis. 2011;17(9):1034-1040
- Motl et al., 2011d R.W. Motl, E.M. Snook, S. Agiovlasitas. Does an accelerometer accurately measure steps taken under controlled conditions in adults with mild multiple sclerosis?. Disability and Health Journal. 2011;4(1):52-57
- Motl et al., 2012 R.W. Motl, L. Pilutti, B.M. Sandroff, D. Dlugonski, J.J. Sosnoff, J.H. Pula. Accelerometry as a measure of walking behavior in multiple sclerosis. Acta Neurologica Scandinavica. 2012;
- Oken et al., 2004 B.S. Oken, S. Kishiyama, D. Zajdel, D. Bourdette, J. Carlsen, M. Haas, et al. Randomized controlled trial of yoga and exercise in multiple sclerosis. Neurology. 2004;62:2058-2064
- O'Brien et al., 2008 A.R. O'Brien, N. Chiaravalloti, Y. Goverover, J. DeLuca. Evidence-based cognitive rehabilitation for persons with multiple sclerosis: a review of the literature. Archives of Physical Medicine and Rehabilitation. 2008;89:761-769
- Parmenter et al., 2007 B.A. Parmenter, B. Weinstock-Guttman, N. Garg, F. Munschauer, R.H.B. Benedict. Screening for cognitive impairment in multiple sclerosis using the Symbol Digit Modalities Test. Multiple Sclerosis. 2007;13:52-57
- Phan-Ba et al., 2011 R. Phan-Ba, A. Pace, P. Calay, P. Grodent, F. Douchamps, R. Hyde, et al. Comparison of the timed 25-ft and the 100-m walk as performance measures in multiple sclerosis. Neurorehabilitation and Neural Repair. 2011;25(7):672-679
- Prakash et al., 2007 R.S. Prakash, E.M. Snook, K.I. Erickson, S.J. Colcombe, M.W. Voss, R.W. Motl, et al. Cardiorespiratory fitness: a predictor of cortical plasticity in multiple sclerosis. NeuroImage. 2007;34:1238-1244
- Prakash et al., 2008 R.S. Prakash, E.M. Snook, J.M. Lewis, R.W. Motl, A.F. Kramer. Cognitive impairments in relapsing-remitting multiple sclerosis: a meta-analysis. Multiple Sclerosis. 2008;14:1250-1261
- Prakash et al., 2010 R.S. Prakash, E.M. Snook, R.W. Motl, AF. Kramer. Aerobic fitness is associated with gray matter volume and white matter integrity in multiple sclerosis. Brain Research. 2010;1341:41-51
- Prakash et al., 2011 R.S. Prakash, B. Patterson, A. Janssen, A. Abduljalil, A. Boster. Physical activity associated with increased resting-state functional connectivity in multiple sclerosis. Journal of the International Neuropsychological Society. 2011;17:1-12
- Rao et al., 1989 S.M. Rao, G.J. Leo, V.M. Haughton, P. St. Aubin-Faubert, L. Bernardin. Correlation of magnetic resonance imaging with neuropsychological testing in multiple sclerosis. Neurology. 1989;39:161-166
- Rao et al., 1991 S.M. Rao, G.J. Leo, L. Bernarndin, F. Unverzagt. Cognitive dysfunction in multiple sclerosis. I. Frequency, patterns, and prediction. Neurology. 1991;41:685-691
- Ratey and Loehr, 2011 J.J. Ratey, J.E. Loehr. The positive impact of physical activity on cognition during adulthood: a review of underlying mechanisms, evidence and recommendations. Reviews in the Neurosciences. 2011;22(2):171-185
- Rousselet and Pernet, 2012 G.A. Rousselet, C.R. Pernet. Improving standards in brain–behavior correlation analyses. Frontiers in Human Neuroscience. 2012;6:119
- Sandroff et al., 2012 B.M. Sandroff, D. Dlugonski, M. Weikert, Y. Suh, S. Balantrapu, R.W. Motl. Physical activity and multiple sclerosis: new insights regarding inactivity. Acta Neurologica Scandinavica. 2012;126(4):256-262
- Smith, 1982 A. Smith. Symbol Digit Modalities Test: Manual. (Western Psychological Services, Los Angeles, 1982)
- Smith et al., 2010 P.J. Smith, J.A. Blumenthal, B.M. Hoffman, H. Cooper, T.A. Strauman, K. Welsh-Bohmer, et al. Aerobic exercise and neurocognitive performance: a meta-analytic review of randomized controlled trials. Psychosomatic Medicine. 2010;72(3):239-252
- Strober et al., 2009 L. Strober, J. Englert, F. Munschauer, B. Weinstock-Guttman, S. Rao, R.H.B. Benedict. Sensitivity of conventional memory tests in multiple sclerosis: comparing the Rao Brief Repeatable Neuropsychological Battery and the Minimal Assessment of Cognitive Function in MS. Multiple Sclerosis. 2009;15:1077-1084
- Trapp and Nave, 2008 B.D. Trapp, K. Nave. Multiple sclerosis: an immune or neurodegenerative disorder?. Annual Review of Neuroscience. 2008;31:247-269
- Vollmer, 2007 T. Vollmer. The natural history of relapses in multiple sclerosis. Journal of the Neurological Sciences. 2007;256:S5-S13
a Department of Kinesiology and Community Health, University of Illinois at Urbana-Champaign, 233 Freer Hall, 906 South Goodwin Avenue, Urbana 61801, IL, United States
b Department of Neuro-Ophthalmology, University of Illinois College of Medicine at Peoria, United States
c Illinois Neurologic Institute, United States
d Department of Neurology, SUNY Buffalo School of Medicine, United States
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