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The Effects of Lokomat Virtual Reality Applications on Balance and Gait in Stroke Patients

The Effects of Lokomat Virtual Reality Applications on Balance and Gait in Stroke Patients

Status
Completed
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT05419791
Enrollment
56
Registered
2022-06-15
Start date
2021-11-11
Completion date
2022-06-01
Last updated
2022-11-21

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Stroke

Keywords

Augmented performance feedback, Lokomat, Stroke, Rehabilitation, Virtual Reality

Brief summary

The effectiveness of applying robot-assisted walking training together with conventional physiotherapy is widely accepted. Virtual reality is another component and its contribution to rehabilitation is important. When we look at the literature on virtual reality applications on balance and walking, it is seen that some systems are beneficial while some systems have no effect. This raises the possibility that virtual reality applications may produce different results due to the task in the exercise, patient motivation or any other reason. There is a need to investigate whether virtual reality applications have different effects, if any, what is the source of this and what care should be taken in the development of these applications in the future. Therefore, we focused on investigating the different effects of Lokomat virtual reality applications.

Detailed description

Stroke is an important health problem worldwide. Emotional, cognitive, sensory and motor problems occur with stroke. Motor problems are especially balance and walking problems and they are related to each other. So balance and gait rehabilitation are primary goals in stroke. Although the first study to support the efficacy of Lokomat is that Lokomat is superior to conventional physiotherapy in walking, there are also studies reporting that there is no difference between the effects of Lokomat and conventional physiotherapy and that conventional physiotherapy is superior. In the light of all these studies, the effectiveness of applying robotic systems together with conventional physiotherapy is widely accepted. Robot Assisted Walking Training has many components such as guiding force, walking speed, body weight support. Virtual reality is one of these components and there is literature in which Lokomat virtual reality applications are effective on balance and walking. Although the virtual reality effect is emphasized for Lokomat, there is a lack of literature on the specific effect of different virtual reality applications. In our study, it was aimed to examine the changes caused by different Lokomat virtual reality applications in the spatiotemporal parameters of balance and gait. Method: This study was conducted to examine the effects of Lokomat VR applications on balance and spatiotemporal parameters of gait in patients with chronic stroke; It is a prospective, randomized controlled, single-blind study. The study will be carried out in a single-blind manner, and the evaluator will not know which group the individual is in. 56 individuals with chronic stroke included our study. All individuals have been informed about the study and read and signed the consent form stating that they voluntarily participated in the study. For balance evaluation we used Berg Balance Scale and Huber 360 device, which measures postural stability and limits of stability. And for gait evaluation we used 10 MWT, 6 MWT and spatiotemporal gait analysis for C-Mill VR+.

Interventions

OTHERRobot Asisted Gait Training with Lokomat (Endurance)

* Faster application (using for Endurance) : 3 days in a week for 6 weeks,very season was 40 min. * Conventional Physiotherapy : 5 days in a week for 6 weeks, every seanson was 40 min.

OTHERRobot Asisted Gait Training with Lokomat (Attention and Motivation)

* Smile application (using for Attetion and Motivation) : 3 days in a week for 6 weeks,very season was 20 min. * Gabarello application (using for Attetion and Motivation) : 3 days in a week for 6 weeks,very season was 20 min. * Conventional Physiotherapy : 5 days in a week for 6 weeks, every seanson was 40 min.

OTHERRobot Asisted Gait Training with Lokomat (Activity Timing)

* High Flyer application (using for Activity Timing) : 3 days in a week for 6 weeks,very season was 20 min. * Treasures application (using for Activity Timing) : 3 days in a week for 6 weeks,very season was 10 min. * Curve Pursuit application (using for Activity Timing) : 3 days in a week for 6 weeks,very season was 10 min. * Conventional Physiotherapy : 5 days in a week for 6 weeks, every seanson was 40 min.

OTHERConventional Physiotherapy

-Conventional Physiotherapy : 5 days in a week for 6 weeks, every seanson was 40 min.

Sponsors

Ankara City Hospital Bilkent
CollaboratorOTHER
Ankara Yildirim Beyazıt University
Lead SponsorOTHER

Study design

Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
TREATMENT
Masking
SINGLE (Outcomes Assessor)

Masking description

The assessor has been masked.

Intervention model description

Individuals have been assigned to groups by Research Randomizer program.

Eligibility

Sex/Gender
MALE
Age
18 Years to 80 Years
Healthy volunteers
No

Inclusion criteria

* Inclusion Criteria: * Clinical diagnosis is stroke * The time period have to be chronic period (+6 months) * The patient must have the ability to walk with or without support * The patiens should be able to understand Lokomat exercises *

Exclusion criteria

* Any condition that may prevent walking with Lokomat * Not volunteering to participate in the study

Design outcomes

Primary

MeasureTime frameDescription
Change in Spatiotemporal Gait Analysis6 weeks after treatmentSpatiotemporal gait analysis gives spatial (distance) parameters along with temporal (time) parameters. * Spatial parameters: * Step Length * Step Width * Stride Length * Temporal Parameters * Step Time * Stride Time * Stance Time * Swing Time * Double Support Time We used C-Mill device to evaluate the spatiotemporal parameters.
Change in 10 MWT6 weeks after treatment10 MWT is a test using for evaluate the gait speed. Gait speed is a spatiotemporal parameter.
Change in BBS6 weeks after treatmentBerg Balance Scale used for balance assesment. The scale consists of 14 items and each item gets a score between 0-4. As a result, the person evaluated gets a score between 0-56 on the berg balance scale. A higher score means the person has better balance skills.
Change in Limits of Stability6 weeks after treatmentLimits of Stability (LoS) are defined as the points at which the center of gravity (CoG) approaches the limits of the base of support (BoS) and a correction strategy is required to return the center of mass (CoM) to within the BoS. In other words, LoS is the amount of maximum excursion an individual is able to intentionally cover in any direction without losing his/her balance or taking a step. We used Huber 360 device to evaluate the LoS.
Change in 6 MWT6 weeks after treatment6 Minutes Walk Test is a test using for walking distance. We evaluated the change in walking distance with 6 MWT.

Secondary

MeasureTime frameDescription
Change in Temporal Symmetry6 weeks after treatmentFor temporal symmetry we used the description Gait Symmetry. Gait symmetry is calculated by the ratio of the hemiplegic side sway phase to the hemiplegic side stance phase ratio and the ratio of the non-hemiplegic side sway phase to the non-hemiplegic side stance phase ratio.
Change in Postural Stability6 weeks after treatmentPostural stability refers to the ability to maintain your body in a position to effectively complete a task or demand, using large muscle groups at the shoulders, trunk, and hips. We used Huber 360 device to evaluate the postural staiblity. The assesments were combination of opened and closed eyes. We recorded the COP vellocity and area.
Change in Spatial Symmetry6 weeks after treatmentIt is the ratio of the hemiplegic side step lenght to the non-hemiplegic side step lenght.

Countries

Turkey (Türkiye)

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026