Motor performance improvement through virtual reality task is related to fatigue and cognition in people with multiple sclerosis


Background and Purpose: People with multiple sclerosis (MS) who undergo reha- bilitation need to perform new motor skills or relearn old motor skills. It is not clear whether people with MS retain the ability to improve motor performance or learning. Furthermore, factors that influence motor performance in people with MS need to be investigated. This study explored motor performance in people with MS using virtual reality (VR). The effect of fatigue and cognitive function on motor performance improvement in people with MS was investigated.
Methods: Twenty MS participants and 20 controls were recruited into the study. To assess motor performance, each participant was asked to perform a VR game for five times (blocks). The main outcome was time to complete the VR game and number of recorded errors. To assess fatigue level and cognitive function, participants were asked to complete the Arabic versions of the Modified Fatigue Impact Scale (MFIS) and the Montréal Cognitive Assessment (MOCA), respectively.

Results: MS participants and controls demonstrated a practice‐related improve- ment in performance as shown by the main effect of block for each of the outcome measures (p < .001, time required to complete VR game; p < .001, errors recorded). Strong and significant negative correlations between recorded errors and MOCA (r = .75, p < .001) and between recorded errors and MFIS (r = .55, p = .011) were found in people with MS. Conclusions: Ability to improve motor performance in people with MS is preserved and related to cognitive function and fatigue impact. Health‐care professionals should be made aware of the negative impact of cognitive function and fatigue on motor performance. A multicomponent intervention that targets these factors is advisable. Future research, however, is required to determine the content and potential benefits of such an intervention in the MS population. KEYWORDS : multiple sclerosis, motor performance, rehabilitation, virtual reality 1 | INTRODUCTION Multiple sclerosis (MS) is a chronic inflammatory disease characterized by the demyelination of neurons of the central nervous system (Newland, Naismith, & Ullione, 2009). People with MS often present with a wide variety of neurological signs and symptoms including def- icits in ambulation, cognitive, and fatigue (Newland et al., 2009). As there is currently no cure for the MS disease and the symptoms worsen with disease progression, motor rehabilitation is important to delay the loss of motor functions (Burks, Bigley, & Hill, 2009). Conse- quently, effective interventions are needed to promote motor perfor- mance and learning in people with MS who undergo rehabilitation (Muratori, Lamberg, Quinn, & Duff, 2013). Activity‐focused motor interventions for individuals with disabilities emphasize the need for practice and repetition of purposeful motor actions in challenging envi- ronments, which increase the individual's participation in daily routines (i.e., motor performance). Whereas motor performance is relatively defined as a temporary change in the execution of a motor task, motor learning can be defined as an internal process that results in a relatively permanent change in an individual's capability to perform a motor task (Magill, 2004; Schmidt & Wrisberg, 2004). In that sense, the best para- digm for practitioners to achieve motor learning is to initially assess people's performance on motor tasks. Motor task performance and learning play major role in preserving the functional quality of life in people with MS as long as possible (Muratori et al., 2013). Studies investigating motor performance and learning in people with MS have offered mixed conclusions (Gera et al., 2016; Leocani et al., 2007; Tabrizi et al., 2013; Tacchino et al., 2014; Tomassini et al., 2011). Gera et al. (2016) reported that both control and MS par- ticipants demonstrated similar improvements in acquiring and retaining changes in the temporal control after practising postural con- trol training. On the other hand, Tacchino et al. (2014) found selective impairments of motor sequence learning in MS with minimal disability. Other studies detected impaired motor learning in this population (Leocani et al., 2007; Tabrizi et al., 2013). Leocani et al. (2007) have demonstrated impaired motor learning in MS for task features requir- ing complex integration of visual and sensory–motor information. The use of different motor tasks may explain the discrepancy between the findings of the above‐mentioned studies. Most of these studies have utilized relatively simple motor tasks (Gera et al., 2016; Tabrizi et al., 2013; Tacchino et al., 2014) to examine motor performance and learn- ing. To transfer motor learning into everyday life, tasks practised should be more complex, longer in duration, and require coordination between different body parts for correct responses to sensory and environmental challenges. Thus, it is important to utilize tasks that are functional, complex, and related to daily life activities in MS motor learning research. Many studies have examined factors that might affect motor per- formance and learning. Feedback (Wulf, Shea, & Lewthwaite, 2010), self‐controlled practice (Wulf et al., 2010), physical fatigue (Williams, Daniell‐Smith, & Gunson, 1976), and cognitive abilities (Wulf et al., 2010) are shown to affect motor learning. In MS, no studies have investigated factors that affect motor performance in MS. Motor learning consists of three stages, which are cognitive phase, associative phase, and autonomous phase. The first stage depends on the cognitive abilities. It requires general understanding of the requested skills, specify the goals, and determine the surrounding psy- chological and environmental variables that influence the general per- formance. Cognitive impairments are common in people with MS (Claesson, Ytterberg, Johansson, Almkvist, & von Koch, 2007). Deficits in cognitive domains associated with attention and executive func- tioning (Rosti, Hamalainen, Koivisto, & Hokkanen, 2007) memory and learning (Cerezo Garcia, Martin Plasencia, Aladro Benito, Balseiro Gomez, & Rueda Marcos, 2009) have repeatedly been found. All of these cognitive functions are important for successful motor skill per- formance. Despite the fact that cognitive abilities are important for motor performance, no studies have examined how cognitive impair- ments affect motor performance in MS. In addition to cognitive impairments, fatigue is considered to be one of the most common symptoms in MS affecting up to 90% of these individuals (MacAllister et al., 2009). Fatigue in MS comes on easily and spontaneously even with small amount of effort and does not decline sufficiently with rest (Freal, Kraft, & Coryell, 1984). Many studies have reported the effect of fatigue on motor learning (Alder- man, 1965; Carron, 1969; Masters, Poolton, & Maxwell, 2008). How- ever, no studies have examined the impact of fatigue on motor performance in people with MS. In recent years, researchers have shown great interest in using vir- tual reality (VR) as an assessment and treatment tool in rehabilitation (Lange et al., 2010; Saposnik et al., 2010). Its uniqueness arises from the opportunities that users have to engage in environments, which appear similar to real‐world events (Yalon‐Chamovitz & Weiss, 2008). Therefore, participants can practice tasks that are more com- plex and resembling tasks in their daily life. Furthermore, VR inher- ently offers three important cornerstones of motor performance and learning: repetition, motivation, and feedback (Weiss, Bialik, & Kizony, 2003; Yalon‐Chamovitz & Weiss, 2008). To date, no previous study utilized VR as a tool to assess motor performance in people with MS. Therefore, the primary aim of this study was to examine whether people with MS are able to improve their performance of new motor tasks through VR. A secondary aim of this study was to investigate the effect of fatigue and cognitive function on motor task perfor- mance in people with MS. 2 | METHODS 2.1 | Setting and participants The study used an observational cross‐sectional design in which 20 MS participants and 20 age‐ and gender‐matched controls were recruited into the study. Sequential MS individuals attending routine neurology clinics between August 2016 and August 2017 at a Univer- sity Hospital were screened for eligibility by a neurology consultant. Eligible participants were invited to participate in this study. In addi- tion, brochures and adverts about the study were distributed through Multiple Sclerosis Society. Participants who were willing to participate were also screened for eligibility by a neurology consultant. Inclusion criteria were as follows: (a) neurologist‐confirmed diagnosis of relapsing–remitting MS according to the revised McDonald criteria (Polman et al., 2011), (b) ability to walk independently with or without walking aid, (c) no exacerbation of symptoms 30 days prior to com- pleting testing, (d) age above 18 years, (e) capacity to give informed consent, and (f) participants had no experience with Wii Balance Board. Exclusion criteria were as follows: (a) the presence of additional neurological disorders that may affect balance and gait (e.g., head injury, stroke, vestibular dysfunction, or peripheral neuropathy), (b) the presence of orthopaedic conditions that may affect mobility, and (c) the presence of severe cognitive deficits or behavioural disorders preventing safe participation. All participants gave a written informed consent approved from the Institutional Research Committees. 2.2 | Task description Central for this study is the use of the nonimmersive VR system, which consists of the Nintendo Wii Plus and Balance Board, large standard LCD monitor, and its software. The novel VR scenario was developed as part of a previous conducted study. Details are pub- lished elsewhere (Khalil, Al‐Sharman, Kazaaleh, & El‐Salem, 2016). In brief, the aim of the game that was used in this study was to steer an avatar skiing down a track to collect coins and avoid obstacles. The number of collected coins plus obstacles that participants man- aged successfully to avoid determined the game score. Participants could control the avatar skiing by moving from left to right to shift their body weight accordingly while they are standing on the Wii Bal- ance Board. To assess motor skill performance, each participant was asked to perform the game for five times (blocks). Each block lasted for 2 min with total practice of 10 min. In order to familiarize the participants with the game, a baseline block was performed and discarded from analysis. Participants were allowed to rest between blocks if needed. 2.3 | Other outcome measures Personal data including age and gender were collected. The Expanded Disability Status Scale was conducted to determine the severity of the disease. All tests were administered in a standardized manner. To assess the relationship between fatigue and cognitive function and motor performance, participants also were asked to complete questionnaires to assess their fatigue and cognitive function. The Arabic version of the Modified Fatigue Impact Scale (MFIS), a valid 21‐item questionnaire, was used to evaluate fatigue. The MFIS is a reliable, valid, and responsive tool to assess the impact of fatigue in MS. It contains 9 “physical” items, 10 “cognitive” items, and 2 “psycho- social” items. Cognitive status was determined using the Arabic version of the Montréal Cognitive Assessment (MOCA) total score. MOCA is a brief and comprehensive cognitive screening tool that is useful for detect- ing cognitive dysfunctions in patients with MS, even those with mild functional disability (Dagenais et al., 2013; Kaur, Kumar, & Singh, 2013). The MOCA test is designed to assess eight cognitive function domains, including attention and concentration, executive functions, memory, language, visuoconstructional skills, conceptual thinking, cal- culations, and time and place orientation. 2.4 | Data analysis and statistics The main outcome variables of interest for this study were time to complete the VR game and recorded errors. Both outcome measures were recorded from the virtual game itself (the system) for each block performed by the participant. Individual data were averaged by group for each of the outcome measures to represent performance for Blocks 1–5 during acquisition practice. To reflect practice‐related improvement in performance, we calculated the difference in values for Block 5 versus Block 1 for both outcome measures. The total score of the MFIS was used in data analysis; the maxi- mum possible score of MFIS is 84, with higher scores indicating a greater impact on quality of life. Additionally, the total score of MOCA was used; the maximum possible score of MOCA is 30, with a higher score that indicates a better cognitive function. Statistical analyses were performed with Statistical Package for the Social Sciences software (SPSS 20.00). One‐way ANOVAs were used to assess differences in participants' characteristics between groups. Acquisition performance was examined using a two‐factor [Group (MS, healthy control) × Block (1, 2, 3, 4, 5)] repeated measures ANOVAs with time to complete VR game and number of errors recorded as dependent variables. The associations between fatigue, cognitive function, and motor performance (differences in perfor- mance between Block 5 and Block 1 for time to complete VR game and number of errors recorded) were assessed using the nonparamet- ric Spearman rank‐order correlation coefficient. In general, r values <.10 are considered to be a small effect; >.10 to <.50, a moderate effect; and >.50, a strong effect (Cohen, 1992).


3.1 | Subject characteristics

Table 1 shows the characteristics of participants.

3.2 | Motor skill performance

Individuals with MS and healthy adults demonstrated a practice‐ related improvement in performance as shown by the significant main effect of block for each of the outcome measures (p < .001, time required to complete VR game; p < .001, errors recorded; Figures 1 and 2, respectively). The extent of improvement in performance across blocks revealed no significant difference between the MS and the healthy control as indicated by the no main effect of group for time to complete VR game (p = .08) and errors recorded (p = .44). There was no interaction effect between block and group for both of the out- come measures (time to complete VR game, p = .07; errors recorded, p = .27). FIGURE 1 Practice‐related improvement in performance (time required to complete virtual reality game in seconds) in multiple sclerosis (MS) participants and healthy control. FIGURE 2 Practice‐related improvement in performance (errors recorded) in multiple sclerosis (MS) participants and healthy control. FIGURE 3 Correlation between recorded errors (calculated by subtracting errors in Block 5 from errors in Block 1) and fatigue measured by the Modified Fatigue Impact Scale. Higher Modified Fatigue Impact Scale scores indicate a greater impact of fatigue on quality of life. Negative recorded errors indicate better performance in Block 5 relative to Block 1. The results indicated strong and significant correlations between performance as indicated by change in number of errors and MOCA score (ρ = −.75, p < .001) and MFIS score (ρ = .55, p = .011) in the MS participants. Note that lower score on MFIS is positively corre- lated with fewer errors in Block 5 compared with Block 1, indicating that better performance is related to less fatigue. On the other hand,MOCA score is negatively correlated with decrease in number of errors, indicating that better performance is related to better cogni- tion. Interestingly, no relation was found between time to complete VR game, fatigue, and cognitive (Figures 3 and 4). FIGURE 4 Correlation between recorded errors (calculated by subtracting errors in Block 1 from Block 5) and cognitive function measured by the Montréal Cognitive Assessment (MOCA). Higher MOCA scores indicate better cognitive function. Negative recorded errors indicate better performance in Block 5 relative to Block 1. 4 | DISCUSSION The results of this study have demonstrated that the potential to improve performance of a new motor skill is preserved in people with MS. Regardless of the presence of the disease, performance improved similarly in both groups with practice. However, in people with MS, lower cognitive status and higher fatigue levels were found to be neg- atively associated with drop in errors after practice on this VR task, thus indicating the negative effect of cognitive impairments and fatigue on motor skill performance in this population. Understanding improvement in motor performance in people with MS has important practical implications for rehabilitation because the acquisition and reacquisition of motor skills are important parts of functional motor rehabilitation. The lack of consistency between our findings and the results from earlier studies by Tacchino et al. (2014) and Leocani et al. (2007), who found that people with MS failed to perform and learn motor skills, may be explained by the type of the motor task used. Evidence sug- gests that motor performance is dependent on task characteristics. Many studies have investigated the influence of task complexity on motor skill performance and learning. A review by Wulf and Shea (2002) states that learning complex skills does not always follow the same principles as learning simple skills. Therefore, in order to under- stand the process of motor skill performance and learning, complex motor tasks should be utilized in motor learning research. In this pres- ent study, the VR system was used to assess improvement in motor skill performance in people with MS. The VR system is considered complex, as it requires coordination between different body parts and response to environmental stimuli (such as obstacles) with sensory challenges (visual) that are presented in the VR environment. Future studies can further utilize the VR game used in the current study to explore motor learning in people with MS by assessing long‐term per- formance changes in the motor tasks performed and the ability to transfer the motor skills learned into other complex functional daily activities. The lack of consistency between our findings and these earlier studies might be explained through the advantages the VR environ- ment provides for the participants compared with other motor tasks that were used in the previous studies. The inherent feedback (visual and auditory stimuli) and augmented feedback (through game correct scores and performance errors) provided to the user have been found to enhance motor performance (Grecco et al., 2013). Grecco et al. (2013) reported that augmented feedback provided by the image cre- ates positive reinforcement, thus facilitating the practice and perfec- tion of the exercises. Gordon, Roopchand‐Martin, and Gregg (2012) and Straker et al. (2011) reported that the VR devices provide high level of visual and special integration that may have a positive impact on motor performance and learning. The results of this study indicated strong relationship between cognitive function and motor performance improvement in people with MS. Our results confirm the results from a study by O'Neil, Skeel, and Ustinova (2013) who have found that the cognitive decline that occurred after traumatic brain injury affects the performance and the learning of motor tasks in virtual environments. This is a very impor- tant finding considering that cognitive impairments are known to be highly prevalent in MS (Chiaravalloti & DeLuca, 2008). Across settings of motor learning practices, teaching patients complex skills initially requires full attention and understanding. Therefore, to enhance the acquisition and the performance of new motor skills, cognitive assess- ment and cognitive rehabilitation programmes may need to be inte- grated for people with MS. Future studies are needed to explore the effects of cognitive rehabilitation on motor performance and learning in people with MS. The current study reported strong and significant correlation between motor performance and how fatigue impacts quality of life. These results are in line with previous findings that demonstrated the relationship between fatigue and motor performance in healthy individuals (Cochran, 1975). Our findings are the first to explore this issue in people with MS. These findings are very important as it might influence motor task practice sessions during MS rehabilitation. For many people with MS, fatigue is considered to be one of the most incapacitating symptoms, exceeding pain and physical disability. Therefore, fatigue might be an important factor to influence improve- ment in motor skill performance in people with MS. In this regard, Schmidt and Wrisberg (2004) distinguish two types of practice. In “massed practice,” the greater proportion of the sessions is dedicated to training, whereas in “distributed practice,” the duration of rest is equal to or greater than that of practice. We believe that in order to maximize motor performance improvement in people with MS, distrib- uted practice should be emphasized during the rehabilitation session. However, to date, the effects of alternating these two training methods in people with MS still require further investigation. In addi- tion, dividing the motor task during initial practice into several parts (part practice) with no variation in practising the task (constant prac- tice), instead of practising the whole task (whole practice) with varia- tions (variable practice), may decrease the fatigue level as well as cognitive load and attention needed from the individual to perform the task. This work is not without limitations. This pilot study was with a cross‐sectional design focusing on motor skill performance. Future research with a longitudinal design examining the ability of people with MS to learn, retain, and transfer is required (i.e., motor learning). Additionally, the sample size was low, and only relapsing remitting MS individuals with minimal disability participated in the study. This restricts generalizability and interpretations of the current results. A larger cohort of different MS types with the inclusion of a larger num- ber of MS participants is needed in future studies. Moreover, involving a more extensive battery of cognitive testing in which the contribution of each cognitive domain to motor learning is warranted. In this study, we focused on cognitive and fatigue as factors that might influence motor learning. However, other factors that might influence motor learning such as sleep disturbances, depression, and anxiety have not been assessed. Future studies that include these fac- tors are needed to confirm these results. 4.1 | Implications for physiotherapy practice Understanding motor performance improvement in people with MS has important practical implications for rehabilitation because the reacquisition of motor skills is an important part of functional motor performance. Health‐care professionals are prompted to be aware of the impact of cognitive functions and fatigue on motor learning. Better cognitive function and less impact of fatigue on quality of life have positive impact on improving motor performance. Intervention that targets these factors is advisable. VR can be utilized as an assessment and treatment tool in rehabilitation to improve motor performance as it gives the users the opportunities to engage in environments, which appear similar to real‐world events. ACKNOWLEDGEMENTS The authors would like to acknowledge all the participants of the study. Acknowledgement for funding support is to Jordan University of Science and Technology (Grant 2016l64 to A. A. A.) and to EU Commission for funding support through Support to Research and Technological Development & Innovation Initiatives and Strategies in Jordan Scheme (Grant AR‐42). CONFLICT OF INTEREST The authors declare no conflict of interest. This original manuscript has not been published and is not being considered for publication elsewhere. All research conducted in this manuscript was approved by and in accordance with the Human Subjects Committee at the Jor- dan University of Science and technology. All authors have made important contributions to, as well as read and approved, this paper. REFERENCES Alderman, R. B. (1965). Influence of local fatigue on speed and accuracy in motor learning. Research Quarterly, 36, 131–140. Burks, J. S., Bigley, G. K., & Hill, H. H. (2009). Rehabilitation challenges in multiple sclerosis. Annals of Indian Academy of Neurology, 12(4), 296–306.‐2327.58273 Carron, A. V. (1969). Physical fatigue and motor learning. Research Quarterly of the American Association for Health, Physical Education, and Recreation, 40(4), 682–686. Cerezo Garcia, M., Martin Plasencia, P., Aladro Benito, Y., Balseiro Gomez, J. J., & Rueda Marcos, A. (2009). Executive function and memory in patients with relapsing–remitting multiple sclerosis. Psicothema, 21(3), 416–420. Chiaravalloti, N. D., & DeLuca, J. (2008). Cognitive impairment in multiple sclerosis. Lancet Neurology, 7(12), 1139–1151. 10.1016/S1474‐4422(08)70259‐X Claesson, I. M., Ytterberg, C., Johansson, S., Almkvist, O., & von Koch, L. (2007). Rapid cognitive screening in multiple sclerosis accomplished by the Free Recall and Recognition Test. Multiple Sclerosis, 13(2), 272–274. Cochran, B. J. (1975). Effect of physical fatigue on learning to perform a novel motor task. Research Quarterly, 46(2), 243–249. Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159.‐2909.112.1.155 Dagenais, E., Rouleau, I., Demers, M., Jobin, C., Roger, É., Chamelian, L., & Duquette, P. (2013). Value of the MoCA test as a screening instrument in multiple sclerosis. The Canadian Journal of Neurological Sciences, 40(03), 410–415. Freal, J. E., Kraft, G. H., & Coryell, J. K. (1984). Symptomatic fatigue in mul- tiple sclerosis. Archives of Physical Medicine and Rehabilitation, 65(3), 135–138. Gera, G., Fling, B. W., Van Ooteghem, K., Cameron, M., Frank, J. S., & Horak, F. B. (2016). Postural motor learning deficits in people with MS in spatial but not temporal control of center of mass. Neurorehabilitation and Neural Repair, 30(8), 722–730. 10.1177/1545968315619700 Gordon, C., Roopchand‐Martin, S., & Gregg, A. (2012). Potential of the Nintendo Wii as a rehabilitation tool for children with cerebral palsy in a developing country: A pilot study. Physiotherapy, 98(3), 238–242. Grecco, L. A., Duarte Nde, A., de Mendonca, M. E., Pasini, H., Lima, V. L., Franco, R. C., … Oliveira, C. S. (2013). Effect of transcranial direct cur- rent stimulation combined with gait and mobility training on functionality in children with cerebral palsy: Study protocol for a double‐blind randomized controlled clinical trial. BMC Pediatrics, 13, 168.‐2431‐13‐168 Khalil, H., Al‐Sharman, A., Kazaaleh, S., El‐Salem, K. (2016). The develop- ment of virtual reality balance scenarios to improve balance in people with multiple sclerosis (MS) [abstract]. ECTRIMS Online Library; 145683. 2016. Kaur, D., Kumar, G., & Singh, A. K. (2013). Quick screening of cognitive function in Indian multiple sclerosis patients using Montreal Cognitive Assessment Test—Short Version. Annals of Indian Academy of Neurol- ogy, 16(4), 585–589.‐2327.120478 Lange, B. S., Requejo, P., Flynn, S. M., Rizzo, A. A., Valero‐Cuevas, F. J., Baker, L., & Winstein, C. (2010). The potential of virtual reality and gaming to assist successful aging with disability. Physical Medicine and Rehabilitation Clinics of North America, 21(2), 339–356. https://doi. org/10.1016/j.pmr.2009.12.007 Leocani, L., Comi, E., Annovazzi, P., Rovaris, M., Rossi, P., Cursi, M., … Comi, G. (2007). Impaired short‐term motor learning in multiple sclerosis: Evi- dence from virtual reality. Neurorehabilitation and Neural Repair, 21(3), 273–278. MacAllister, W. S., Christodoulou, C., Troxell, R., Milazzo, M., Block, P., Preston, T. E., … Krupp, L. B. (2009). Fatigue and quality of life in pedi- atric multiple sclerosis. Multiple Sclerosis, 15(12), 1502–1508. https:// Magill, R. A. (2004). Motor learning and control: Concepts and applications. Masters, R. S., Poolton, J. M., & Maxwell, J. P. (2008). Stable implicit motor processes despite aerobic locomotor fatigue. Consciousness and Cogni- tion, 17(1), 335–338. Muratori, L. M., Lamberg, E. M., Quinn, L., & Duff, S. V. (2013). Applying principles of motor learning and control to upper extremity rehabilita- tion. Journal of Hand Therapy, 26(2), 94–102; quiz 103. https://doi. org/10.1016/j.jht.2012.12.007 Newland, P. K., Naismith, R. T., & Ullione, M. (2009). The impact of pain and other symptoms on quality of life in women with relapsing–remitting multiple sclerosis. The Journal of Neuroscience Nursing, 41(6), 322–328. O'Neil, R. L., Skeel, R. L., & Ustinova, K. I. (2013). Cognitive ability predicts motor learning on a virtual reality game in patients with TBI. NeuroRehabilitation, 33(4), 667–680.‐ 130985 Polman, C. H., Reingold, S. C., Banwell, B., Clanet, M., Cohen, J. A., Filippi, M., … Lublin, F. D. (2011). Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Annals of Neurology, 69(2), 292–302. Rosti, E., Hamalainen, P., Koivisto, K., & Hokkanen, L. (2007). PASAT in detecting cognitive impairment in relapsing–remitting MS. Applied Neu- ropsychology, 14(2), 101–112. doi: 09084280701319938. Saposnik, G., Teasell, R., Mamdani, M., Hall, J., McIlroy, W., Cheung, D., … Bayley, M. (2010). Effectiveness of virtual reality using Wii gaming technology in stroke rehabilitation: A pilot randomized clinical trial and proof of principle. Stroke, 41(7), 1477–1484. 10.1161/strokeaha.110.584979 Schmidt, R. A., & Wrisberg, C. A. (2004). Motor learning and performance. Straker, L. M., Campbell, A. C., Jensen, L. M., Metcalf, D. R., Smith, A. J., Abbott, R. A., … Piek, J. P. (2011). Rationale, design and methods for a randomised and controlled trial of the impact of virtual reality games on motor competence, physical activity, and mental health in children with developmental coordination disorder. BMC Public Health, 11, 654.‐2458‐11‐654 Tabrizi, Y. M., Mazhari, S., Nazari, M. A., Zangiabadi, N., Sheibani, V., & Azarang, S. (2013). Compromised motor imagery ability in individuals with multiple sclerosis and mild physical disability: An ERP study. Clin- ical Neurology and Neurosurgery, 115(9), 1738–1744. 10.1016/j.clineuro.2013.04.002 Tacchino, A., Bove, M., Roccatagliata, L., Luigi Mancardi, G., Uccelli, A., & Bonzano, L. (2014). Selective impairments of motor sequence learning in multiple sclerosis patients with minimal disability. Brain Research, 1585, 91–98. Tomassini, V., Johansen‐Berg, H., Leonardi, L., Paixao, L., Jbabdi, S., Palace, J., … Matthews, P. M. (2011). Preservation of motor skill learning in patients with multiple sclerosis. Multiple Sclerosis, 17(1), 103–115. Weiss, P. L., Bialik, P., & Kizony, R. (2003). Virtual reality provides leisure time opportunities for young adults with physical and intellectual dis- abilities. Cyberpsychology & Behavior, 6(3), 335–342. 10.1089/109493103322011650 Williams, L. R., Daniell‐Smith, J. H., & Gunson, L. K. (1976). Specificity of training for motor skill under physical fatigue. Medicine and Science in Sports, 8(3), 162–167. Wulf, G., Shea, C., & Lewthwaite, R. (2010). Motor skill learning and perfor- mance: A review of influential factors. Medical Education, 44(1), 75–84.‐2923.2009.03421.x Wulf, G., & Shea, C. H. (2002). Principles derived from the study of simple skills do not generalize to complex skill learning. Psychonomic Bulletin & Review, 9(2), 185–211. Yalon‐Chamovitz, S., & Weiss, P. L. (2008). Virtual reality as a leisure activ- ity for young adults with physical and intellectual disabilities. Research in Developmental Disabilities,AR-42 29(3), 273–287. 10.1016/j.ridd.2007.05.004