Comparing the effectiveness of immersive virtual reality based functional upper limb training and task oriented training in post stroke survivors.

TITLE: Comparing the Effectiveness of Immersive Virtual Reality Based Functional Upper Limb Training and Task-Oriented Training in Post-Stroke Survivors

 BACKGROUND

Upper limb impairment is a common and disabling consequence of stroke, often reducing survivors’ independence and quality of life. Traditional rehabilitation approaches, such as task-oriented training (TOT), focus on practicing meaningful daily activities to restore function and mobility. However, repetitive exercises in TOT can sometimes be monotonous and may not fully engage patients.

Immersive virtual reality (VR) offers a technology-driven alternative. By simulating real-world tasks in engaging, interactive environments, VR can increase patient motivation and adherence to therapy. VR platforms also provide real-time feedback, making learning more effective and enjoyable. As advances in VR technology make it more accessible and realistic, there is growing interest in comparing its effectiveness to conventional TOT for improving upper limb function after stroke. Understanding which approach delivers better outcomes is important for optimizing rehabilitation strategies and enhancing recovery for stroke survivors.

OBJECTIVES

To evaluate the effects of immersive VR-based functional upper limb training versus task-oriented training on motor functions of the upper limb assessed by the Fugl-Meyer Assessment – Upper Extremity (FMA-UE).

To evaluate the effects of immersive VR-based functional upper limb training versus task-oriented training on activities of daily living (ADLs) assessed by the Barthel Index (BI).

To evaluate the effects of immersive VR-based functional upper limb training versus task-oriented training on quality of life assessed by the stroke impact scale.

To assess the effect of the intervention on real-world use of the affected upper limb using the Motor Activity Log.

To assess the effect of the intervention on spasticity of the affected upper limb using the Modified Ashworth Scale.

HYPOTHESIS:

NULL HYPOTHESIS-

HN1: There is no significant difference between immersive virtual reality functional training and task-oriented training in improving upper limb motor function recovery in post-stroke survivors.

HN2: There is no significant difference between immersive virtual reality functional training and task-oriented training in improving upper limb functional ability in post-stroke survivors.

HN3: There will be no statistically significant difference in Motor Activity Log scores between pre- and post-intervention in improving upper limb motor function in post-stroke survivors.

HN4: There will be no statistically significant difference in Modified Ashworth Scale scores between pre- and post-intervention in improving upper limb spasticity in post-stroke survivors.

HN5: There will be no statistically significant difference in Stroke Impact Scale scores between pre- and post-intervention in improving quality of life in post-stroke survivors.

ALTERNATE HYPOTHESIS-

HA1: Immersive virtual reality functional training is more effective than task-oriented training in improving upper limb motor function recovery in post-stroke survivors.

HN2: There will be statistically significant difference between immersive virtual reality functional training and task-oriented training in improving upper limb functional ability in post-stroke survivors.

HN3: There will be statistically significant difference in Motor Activity Log scores between pre- and post-intervention in improving upper limb quality of movement in post-stroke survivors.

HN4: There will be statistically significant difference in Modified Ashworth Scale scores between pre- and post-intervention in improving upper limb spasticity in post-stroke survivors.

HN5: There will be statistically significant difference in Stroke Impact Scale scores between pre- and post-intervention in improving quality of life in post-stroke survivors.

 MATERIALS AND METHODS

STUDY DESIGN: Experimental study design

SAMPLE SIZE:30(15 in each group)

(Outcome measure used is BI)

Huang Q, Jiang X, Jin Y, Wu B, Vigotsky AD, Fan L, Gu P, Tu W, Huang L, Jiang S. Immersive virtual reality-based rehabilitation for subacute stroke: a randomized controlled trial. J Neurol. 2023 Nov 10;271(3):1256–1266. doi: 10.1007/s00415-023-12060-y.

ENROLMENT PERIOD: 6-7 months after RRC and IEC clearance

INCLUSION CRITERIA

Age 40–70 years.

First-ever ischemic stroke with upper limb impairment, onset 1–6 months.

UE spasticity grade 1–1+ (Modified Ashworth Scale).

Brunnstrom stage 4–5 for hand, elbow, and shoulder.

MoCA more than 26 (adequate cognition for VR therapy).

FMA-UE score 25–55 (moderate motor impairment).

 EXCLUSION CRITERIA

Wrist impairments from non-stroke causes (e.g., burns, joint contractures).

Vestibular disorders affecting balance or VR use.

Other neurological conditions causing motor deficits (e.g., Parkinson’s, neuropathy).

Communication difficulties interfering with intervention or assessment.

TOTAL STUDY DURATION: 4 weeks intervention duration.

  STUDY PROCEDURES: Patients will be screened based on inclusion and exclusion criteria. They will be enrolled into 2 groups with the help of random allocation software. (Experimental Group 1) will receive Immersive VR based functional trainingwith 4 sessions per week for 4 weeks. (Experimental Group 2) will receive Task-Oriented Training for 20 sessions over 4 weeks.

  ASSESSMENT TOOLS: Pre- and post-intervention tests will be carried out using-

Fugl-Meyer Assessment for Upper Extremity (FMA-UE) for determining motor functions and functional ability.

Barthel Index (BI), Motor Activity Log (MAL), Modifies Ashworth Scale (MAS), and Stroke Impact Scale for determining quality of movement, spasticity and quality of life and participation.

STATISTICAL ANALYSIS PLAN (SAP):

·       Descriptive statistics will calculate the mean and standard deviation for quantitative variables (e.g. age, BI score) and frequencies for categorical variables (e.g., gender).

·       The Shapiro-Wilk test will assess normality.

·       If the data is normally distributed, an independent t-test will evaluate differences between the groups.

·       Paired t-test will be used to evaluate average difference within the group for normal data.

·       For skewed data, the Wilcoxon signed-rank test will assess within-group changes, and the Mann-Whitney U test will compare between-group differences.

·       Data will be analysed using SPSS 21with Excel for data management.

·       Statistical significance will be set at a 5% alpha level.