Virtual street-crossing performance in persons with multiple sclerosis: Feasibility and task performance characteristics

ABSTRACT Objectives: Multiple sclerosis (MS) is a neurological disease that commonly results in physical and cognitive dysfunction. Accordingly, MS might impact the ability to safely cross the street. The purpose of this study was to examine the feasibility of a simulated street-crossing task in persons with MS and to determine differences in street-crossing performance between persons with MS and non-MS controls. Methods: 26 participants with MS (median Expanded Disability Status Scale [EDSS] score = 3.5) and 19 controls completed 40 trials of a virtual street-crossing task. There were 2 crossing conditions (i.e., no distraction and phone conversation), and participants performed 20 trials per condition. Participants were instructed that the goal of the task was to cross the street successfully (i.e., without being hit be a vehicle). The primary outcome was task feasibility, assessed as completion and adverse events. Secondary outcomes were measures of street-crossing performance. Results: Overall, the simulated street-crossing task was feasible (i.e., 90% completion, no adverse events) in participants with MS. Participants with MS waited longer and were less attentive to traffic before entering the street compared with controls (all P < .05). Participants with MS also took longer to cross the street and were closer to oncoming vehicles when exiting the street compared to controls (all P < .05). When distracted, all participants took longer to initiate crossing, took longer to cross the street, and made more head turns while crossing (all P < .05). There were no significant group by condition interaction effects (all P > .05). Conclusions: A virtual street-crossing task is feasible for studying street-crossing behavior in persons with mild MS and most individuals with moderate MS. Virtual street-crossing performance is impaired in persons with MS compared to controls; however, persons with MS do not appear to be more vulnerable to a distracting condition. The virtual reality environment presents a safe and useful setting for understanding pedestrian behavior in persons with MS.

[1]  Aurélie Dommes,et al.  Is Mild Dementia Related to Unsafe Street-Crossing Decisions? , 2015, Alzheimer disease and associated disorders.

[2]  Despina Stavrinos,et al.  Distraction and pedestrian safety: how talking on the phone, texting, and listening to music impact crossing the street. , 2012, Accident; analysis and prevention.

[3]  Jacob Cohen.  Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[4]  Fabrice Vienne,et al.  Crossing a two-way street: comparison of young and old pedestrians. , 2014, Journal of safety research.

[5]  Philip Stoker,et al.  Pedestrian Safety and the Built Environment , 2015 .

[6]  Virginia Sisiopiku,et al.  Community-based pedestrian safety training in virtual reality: A pragmatic trial. , 2016, Accident; analysis and prevention.

[7]  N. Larocca.  Impact of Walking Impairment in Multiple Sclerosis , 2011, The patient.

[8]  John G. Gaspar,et al.  Walking and talking: dual-task effects on street crossing behavior in older adults. , 2011, Psychology and aging.

[9]  Nguyen-Thong Dang,et al.  Towards an explanation of age-related difficulties in crossing a two-way street. , 2015, Accident; analysis and prevention.

[10]  Aurélie Dommes,et al.  The role of perceptual, cognitive, and motor abilities in street‐crossing decisions of young and older pedestrians , 2011, Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians.

[11]  Massimo Filippi,et al.  Cognition in multiple sclerosis , 2018, Neurology.

[12]  Arthur F Kramer,et al.  Pedestrians, vehicles, and cell phones. , 2010, Accident; analysis and prevention.

[13]  David C Schwebel,et al.  Effects of mobile Internet use on college student pedestrian injury risk. , 2013, Accident; analysis and prevention.

[14]  Lauren B. Krupp,et al.  Fatigue in Multiple Sclerosis , 2003, CNS drugs.

[15]  Ross H Day,et al.  Crossing roads safely: an experimental study of age differences in gap selection by pedestrians. , 2005, Accident; analysis and prevention.

[16]  Arthur F. Kramer,et al.  Are Gamers Better Crossers? An Examination of Action Video Game Experience and Dual Task Effects in a Simulated Street Crossing Task , 2014, Hum. Factors.

[17]  M. Piepoli,et al.  Effect of Aerobic Training on Walking Capacity and Maximal Exercise Tolerance in Patients With Multiple Sclerosis: A Randomized Crossover Controlled Study , 2007, Physical Therapy.

[18]  Hirofumi Ochi,et al.  [Cognitive impairment in multiple sclerosis]. , 2014, Brain and nerve = Shinkei kenkyu no shinpo.

[19]  B Fildes,et al.  Differences in traffic judgements between young and old adult pedestrians. , 1997, Accident; analysis and prevention.

[20]  R. Klaren,et al.  Physical Fitness Assessment Across the Disability Spectrum in Persons With Multiple Sclerosis: A Comparison of Testing Modalities , 2015, Journal of neurologic physical therapy : JNPT.

[21]  Arthur F. Kramer,et al.  The Effects of Cell Phone and Text Message Conversations on Simulated Street Crossing , 2016, Hum. Factors.

[22]  J. Kurtzke.  Rating neurologic impairment in multiple sclerosis , 1983, Neurology.

[23]  J. Burridge,et al.  Can aerobic treadmill training reduce the effort of walking and fatigue in people with multiple sclerosis: a pilot study , 2007, Multiple sclerosis.

[24]  Chin-Hsien Lin,et al.  Predictors of road crossing safety in pedestrians with Parkinson's disease. , 2013, Accident; analysis and prevention.

[25]  Fabrice Vienne,et al.  Age-related differences in street-crossing safety before and after training of older pedestrians. , 2012, Accident; analysis and prevention.

[26]  Katherine W. Byington,et al.  Effect of Cell Phone Distraction on Pediatric Pedestrian Injury Risk , 2009, Pediatrics.

[27]  Aurélie Dommes,et al.  Functional declines as predictors of risky street-crossing decisions in older pedestrians. , 2013, Accident; analysis and prevention.

[28]  Robert W. Motl,et al.  Effect of Exercise Training on Walking Mobility in Multiple Sclerosis: A Meta-Analysis , 2009, Neurorehabilitation and neural repair.

[29]  P. Lachenbruch.  Statistical Power Analysis for the Behavioral Sciences (2nd ed.) , 1989 .

[30]  R J Shephard,et al.  Revision of the Physical Activity Readiness Questionnaire (PAR-Q). , 1992, Canadian journal of sport sciences = Journal canadien des sciences du sport.

[31]  Charles H Hillman,et al.  Role of childhood aerobic fitness in successful street crossing. , 2012, Medicine and science in sports and exercise.

[32]  Carol Holland,et al.  Gender differences in factors predicting unsafe crossing decisions in adult pedestrians across the lifespan: a simulation study. , 2010, Accident; analysis and prevention.

[33]  M. Weiner,et al.  Strength, skeletal muscle composition, and enzyme activity in multiple sclerosis. , 1997, Journal of applied physiology.

[34]  Joan Severson,et al.  Validation of virtual reality as a tool to understand and prevent child pedestrian injury. , 2008, Accident; analysis and prevention.

[35]  B. Phillips,et al.  Gait and balance impairment in early multiple sclerosis in the absence of clinical disability , 2006, Multiple sclerosis.

[36]  K. Dodd,et al.  Progressive resistance exercise for people with multiple sclerosis , 2006, Disability and rehabilitation.