Clinical trial NCT06874764: Effect of Training With Wireless Lightning Reaction Systems on Cognitive Functions in Individuals With Multiple Sclerosis
Official title: Effects of Agility and Cognitive Training With Reaction System on Cognitive Functions and Walking in Individuals With Multiple Sclerosis
Study type: INTERVENTIONAL
Overall status: NOT_YET_RECRUITING
Phase: NA
Conditions: Multiple Sclerosis
Keywords: Multiple sclerosis
Cognitive
Virtual reality
Agility
Training
Gait
Lead sponsor: Ege University
Sponsor class: OTHER
First posted: 2025-03-13
Enrollment: 40
Sex: ALL
Minimum age (years): 18
Maximum age (years): 55
Standard ages: ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: SUPPORTIVE_CARE

Brief summary: When the literature is examined, balance disorders, walking disorders and cognitive problems are frequently observed in Multiple Sclerosis(MS) patients. Using technologically supported equipment such as reaction systems, cognitive function measurements have been successfully performed in the elderly, and improvements in reaction times have been detected in athletes when used for training purposes. The primary aim of the study is to provide improvement in agility and cognitive functions in patients using a technology-supported reaction device. In addition, it aims to improve balance function assessed with static posturography and gait parameters assessed with gait analysis by increasing sensory input and shortening reaction time. As a result of the agility and cognitive training performed, we aim to provide improvement in MS-related quality of life and decrease in disability.

Detailed description: Multiple Sclerosis (MS) is an immune-mediated central nervous system disease characterized by inflammation, demyelination, and axonal damage. Myelin sheaths, oligodendrocytes, and to a lesser extent the axon and the nerve cell itself are damaged.Balance problems can be seen even in the early stages of MS. In the later stages of the disease, they are the primary cause of falls. Balance is defined as the body's ability to maintain its center of gravity with minimal sway. Approximately two-thirds of MS patients report balance or coordination problems that affect their daily lives.Gait impairment is common in MS patients. Walking speed, quality and endurance are the components of the Expanded Disability Status Scale (EDSS), which is most commonly used to monitor MS-related disability and disease progression.Inflammation and demyelination that develop during the MS process have significant effects on central nervous system functions, especially cognitive performance. Approximately 40-70% of people with multiple sclerosis have cognitive impairments such as slowed processing speed, impaired learning and memory functions, and deficits in executive functions. These symptoms affect patients' emotional well-being, work capacity, and quality of life (QoL).Reaction systems (Witty SEM, Microgate, Bolzano, Italia) are devices with computer-aided wireless lighting feature that evaluate and train various visual, cognitive and sensory-motor skills. It consists of an light emitting diode(LED) screen that displays different colors, numbers and characters. This technology is used for evaluation and special training for reactivity, agility and coordination.A total of 40 male and female individuals between the ages of 18-55 will be included in the study. Participants will be divided into two different groups as intervention and control in equal numbers. In both groups, the Brief International Cognitive Assessment for MS (BICAMS) battery, Berg Balance Scale, Hospital Anxiety and Depression Scale (HADS), Fatigue Severity Scale (FSS) and Short Form-36 (SF-36) health questionnaire will be applied to the participants before the study. BICAMS is a battery consisting of the California Verbal Learning Test II (CVLT II), Symbol Digit Modalities Test (SDMT) and Brief Visuospatial Memory Test-Revised (BVMTR) subscales used in the assessment of cognitive status in MS patients.In both groups, before the study, static posturography (HUR SMART BALANCE BTG4) will be used and the patients' balance-related measurements such as forward-backward and sideways swings, swing speeds during pressure, and measurements of the static balance scores provided by the device will be noted, respectively, in the eyes open normal ground - eyes closed normal ground - eyes open soft mat - eyes closed soft mat ground.In addition, using the pressure-gait analysis system (DIERS International GmbH, Schlangenbad, Germany), the participants will undergo pressure analysis and walking evaluation on a treadmill at a walking speed chosen by the patient between 2-5 km/h for approximately 20 seconds.The intervention group will be trained at a difficulty level appropriate to their individual levels to improve agility and cognitive functions using the reaction system 2 days a week for 2 months, and if the participant can easily perform the relevant training, the difficulty will be increased by one level. Each training session will last approximately 30 minutes. There will be no intervention in the control group. At the end of the 8 weeks, the scales, questionnaires and measurements made before the study will be repeated in both groups.

Eligibility criteria: Inclusion Criteria:

* Being between the ages of 18-55
* Having a definite MS diagnosis according to the 2017 McDonald criteria.
* EDSS score of 4 or below
* Being able to communicate (speaking and understanding Turkish, no speech disorders)

Exclusion Criteria:

* Regular exercise in the last 6 months
* Having an MS attack in the last month
* Having an MS attack during the study period
* Presence of additional accompanying neurological diseases
* Use of dalfampridine
* Use of antipsychotic and psychostimulant drugs
* Drug and alcohol addiction
* Pregnancy, breastfeeding
* No chronic systemic disease preventing exercise
* Patients who cannot reach the targeted exercise level

Primary outcomes:
measure: Cognitive Functions
timeFrame: 8th week
Secondary outcomes:
measure: Balance assessment
timeFrame: 8th week
measure: Anxiety and Depression Status
timeFrame: 8th week
measure: Fatigue Assessment
timeFrame: 8th week
measure: MS Quality of Life
timeFrame: 8th week
measure: Static Posture Assessment(Static Balance)
timeFrame: 8th week
measure: Static Posture Assessment(Visual Dependency)
timeFrame: 8th week
measure: Static Posture Assessment(Proprioception Disturbance)
timeFrame: 8th week
measure: Static Posture Assessment(Vestibular Dominant)
timeFrame: 8th week
measure: Gait Analysis(Step length)
timeFrame: 8th week
measure: Gait Analysis(Stride length)
timeFrame: 8th week
measure: Gait Analysis(Step time)
timeFrame: 8th week
measure: Gait Analysis(Step width)
timeFrame: 8th week
measure: Gait Analysis(Cadence)
timeFrame: 8th week
measure: Gait Analysis(Percentage distribution of walking phases)
timeFrame: 8th week
Other outcomes:
Locations:
city: İzmir
country: Turkey
facility: Ege University Sport Medicine Clinic
state: Bornova
Study officials:
References:
citation: Matsuda PN, Shumway-Cook A, Ciol MA, Bombardier CH, Kartin DA. Understanding falls in multiple sclerosis: association of mobility status, concerns about falling, and accumulated impairments. Phys Ther. 2012 Mar;92(3):407-15. doi: 10.2522/ptj.20100380. Epub 2011 Dec 1.
citation: Martin CL, Phillips BA, Kilpatrick TJ, Butzkueven H, Tubridy N, McDonald E, Galea MP. Gait and balance impairment in early multiple sclerosis in the absence of clinical disability. Mult Scler. 2006 Oct;12(5):620-8. doi: 10.1177/1352458506070658.
citation: Pollock AS, Durward BR, Rowe PJ, Paul JP. What is balance? Clin Rehabil. 2000 Aug;14(4):402-6. doi: 10.1191/0269215500cr342oa.
citation: Prosperini L, Castelli L. Spotlight on postural control in patients with multiple sclerosis. Degener Neurol Neuromuscul Dis. 2018 Apr 3;8:25-34. doi: 10.2147/DNND.S135755. eCollection 2018.
citation: Iqbal K. Mechanisms and models of postural stability and control. Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:7837-40. doi: 10.1109/IEMBS.2011.6091931.
citation: Creath R, Kiemel T, Horak F, Jeka JJ. The role of vestibular and somatosensory systems in intersegmental control of upright stance. J Vestib Res. 2008;18(1):39-49.
citation: Melillo F, Di Sapio A, Martire S, Malentacchi M, Matta M, Bertolotto A. Computerized posturography is more sensitive than clinical Romberg Test in detecting postural control impairment in minimally impaired Multiple Sclerosis patients. Mult Scler Relat Disord. 2017 May;14:51-55. doi: 10.1016/j.msard.2017.03.008. Epub 2017 Mar 23.
citation: Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology. 1983 Nov;33(11):1444-52. doi: 10.1212/wnl.33.11.1444.
citation: Givon U, Zeilig G, Achiron A. Gait analysis in multiple sclerosis: characterization of temporal-spatial parameters using GAITRite functional ambulation system. Gait Posture. 2009 Jan;29(1):138-42. doi: 10.1016/j.gaitpost.2008.07.011. Epub 2008 Oct 31.
citation: Thoumie P, Lamotte D, Cantalloube S, Faucher M, Amarenco G. Motor determinants of gait in 100 ambulatory patients with multiple sclerosis. Mult Scler. 2005 Aug;11(4):485-91. doi: 10.1191/1352458505ms1176oa.
citation: Crenshaw SJ, Royer TD, Richards JG, Hudson DJ. Gait variability in people with multiple sclerosis. Mult Scler. 2006 Oct;12(5):613-9. doi: 10.1177/1352458505070609.
citation: Gehlsen G, Beekman K, Assmann N, Winant D, Seidle M, Carter A. Gait characteristics in multiple sclerosis: progressive changes and effects of exercise on parameters. Arch Phys Med Rehabil. 1986 Aug;67(8):536-9.
citation: Savci S, Inal-Ince D, Arikan H, Guclu-Gunduz A, Cetisli-Korkmaz N, Armutlu K, Karabudak R. Six-minute walk distance as a measure of functional exercise capacity in multiple sclerosis. Disabil Rehabil. 2005 Nov 30;27(22):1365-71. doi: 10.1080/09638280500164479.
citation: Franceschini M, Rampello A, Bovolenta F, Aiello M, Tzani P, Chetta A. Cost of walking, exertional dyspnoea and fatigue in individuals with multiple sclerosis not requiring assistive devices. J Rehabil Med. 2010 Sep;42(8):719-23. doi: 10.2340/16501977-0600.
citation: Gijbels D, Alders G, Van Hoof E, Charlier C, Roelants M, Broekmans T, Eijnde BO, Feys P. Predicting habitual walking performance in multiple sclerosis: relevance of capacity and self-report measures. Mult Scler. 2010 May;16(5):618-26. doi: 10.1177/1352458510361357. Epub 2010 Mar 5.
citation: Hamilton F, Rochester L, Paul L, Rafferty D, O'Leary CP, Evans JJ. Walking and talking: an investigation of cognitive-motor dual tasking in multiple sclerosis. Mult Scler. 2009 Oct;15(10):1215-27. doi: 10.1177/1352458509106712. Epub 2009 Aug 10.
citation: Langdon DW, Amato MP, Boringa J, Brochet B, Foley F, Fredrikson S, Hamalainen P, Hartung HP, Krupp L, Penner IK, Reder AT, Benedict RH. Recommendations for a Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS). Mult Scler. 2012 Jun;18(6):891-8. doi: 10.1177/1352458511431076. Epub 2011 Dec 21.
citation: Chiaravalloti ND, DeLuca J. Cognitive impairment in multiple sclerosis. Lancet Neurol. 2008 Dec;7(12):1139-51. doi: 10.1016/S1474-4422(08)70259-X.
citation: Strober LB, Rao SM, Lee JC, Fischer E, Rudick R. Cognitive impairment in multiple sclerosis: An 18 year follow-up study. Mult Scler Relat Disord. 2014 Jul;3(4):473-81. doi: 10.1016/j.msard.2014.03.004. Epub 2014 Apr 13.
citation: Opara JA, Jaracz K, Brola W. Quality of life in multiple sclerosis. J Med Life. 2010 Oct-Dec;3(4):352-8.
citation: das Nair R, Martin KJ, Lincoln NB. Memory rehabilitation for people with multiple sclerosis. Cochrane Database Syst Rev. 2016 Mar 23;3:CD008754. doi: 10.1002/14651858.CD008754.pub3.
citation: Nasios G, Bakirtzis C, Messinis L. Cognitive Impairment and Brain Reorganization in MS: Underlying Mechanisms and the Role of Neurorehabilitation. Front Neurol. 2020 Mar 6;11:147. doi: 10.3389/fneur.2020.00147. eCollection 2020.
citation: Motl RW, Sandroff BM, Benedict RH. Cognitive dysfunction and multiple sclerosis: developing a rationale for considering the efficacy of exercise training. Mult Scler. 2011 Sep;17(9):1034-40. doi: 10.1177/1352458511409612. Epub 2011 Jun 17.
citation: Laver K, George S, Ratcliffe J, Crotty M. Virtual reality stroke rehabilitation--hype or hope? Aust Occup Ther J. 2011 Jun;58(3):215-9. doi: 10.1111/j.1440-1630.2010.00897.x. Epub 2011 Jan 9. No abstract available.
citation: Lehrer N, Attygalle S, Wolf SL, Rikakis T. Exploring the bases for a mixed reality stroke rehabilitation system, part I: a unified approach for representing action, quantitative evaluation, and interactive feedback. J Neuroeng Rehabil. 2011 Aug 30;8:51. doi: 10.1186/1743-0003-8-51.
citation: Beraud-Peigne N, Perrot A, Maillot P. Wireless Lighting System: A New Tool for Assessing Cognitive Functions in the Elderly. Behav Sci (Basel). 2023 Nov 17;13(11):943. doi: 10.3390/bs13110943.
citation: Vasile AI, Stanescu MI. Strobe training as a visual training method that improves performance in climbing. Front Sports Act Living. 2024 May 20;6:1366448. doi: 10.3389/fspor.2024.1366448. eCollection 2024.
citation: Ozakbas S, Yigit P, Cinar BP, Limoncu H, Kahraman T, Kosehasanogullari G. The Turkish validation of the Brief International Cognitive Assessment for Multiple Sclerosis (BICAMS) battery. BMC Neurol. 2017 Dec 6;17(1):208. doi: 10.1186/s12883-017-0993-0.
citation: Nortvedt MW, Riise T, Myhr KM, Nyland HI. Performance of the SF-36, SF-12, and RAND-36 summary scales in a multiple sclerosis population. Med Care. 2000 Oct;38(10):1022-8. doi: 10.1097/00005650-200010000-00006.
citation: Sahin F, Yilmaz F, Ozmaden A, Kotevolu N, Sahin T, Kuran B. Reliability and validity of the Turkish version of the Berg Balance Scale. J Geriatr Phys Ther. 2008;31(1):32-7. doi: 10.1519/00139143-200831010-00006.
citation: Armutlu K, Korkmaz NC, Keser I, Sumbuloglu V, Akbiyik DI, Guney Z, Karabudak R. The validity and reliability of the Fatigue Severity Scale in Turkish multiple sclerosis patients. Int J Rehabil Res. 2007 Mar;30(1):81-5. doi: 10.1097/MRR.0b013e3280146ec4.
citation: Ware JE Jr. SF-36 health survey update. Spine (Phila Pa 1976). 2000 Dec 15;25(24):3130-9. doi: 10.1097/00007632-200012150-00008. No abstract available.
citation: Trentzsch K, Schumann P, Sliwinski G, Bartscht P, Haase R, Schriefer D, Zink A, Heinke A, Jochim T, Malberg H, Ziemssen T. Using Machine Learning Algorithms for Identifying Gait Parameters Suitable to Evaluate Subtle Changes in Gait in People with Multiple Sclerosis. Brain Sci. 2021 Aug 7;11(8):1049. doi: 10.3390/brainsci11081049.