Robotic-Assisted Upper Limb Rehabilitation After Stroke Using the ReHand System: A Randomized Controlled Trial
Phase: NA
Conditions: Stroke, Rehabilitation, Robotic Exoskeleton
Sponsor: Karaganda Medical University

 
 
 
 
 
Robotic-Assisted Upper Limb Rehabilitation 
After Stroke Using the ReHand System: A 
Randomized Controlled Trial 
 
 
 
 
NCT number: NCT06937346 
Document Date: 27.02.2024 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Introduction 
Stroke remains one of the leading causes of disability and mortality worldwide. With 
an aging global population and the widespread prevalence of risk factors, the 
incidence of stroke continues to rise annually. A significant proportion of stroke 
survivors experience upper limb dysfunction, particularly impairments in fine motor 
skills, which substantially limit daily activities and reduce quality of life (1,2). 
Traditional rehabilitation methods—such as physical therapy, occupational therapy, 
and mirror therapy—require substantial time and human resources and may not 
always provide sufficient intensity and repetition of movements (3,4). Robotic 
technologies offer a promising alternative by enabling precise, repetitive, and 
patient-tailored execution of movements (5). 
Among these, soft wearable robotic devices—such as rehabilitative gloves—have 
shown potential for restoring fine motor function. These systems can be used in both 
clinical and home-based settings, which is particularly advantageous for long-term 
post-stroke rehabilitation (6,7). 
This study aims to evaluate the effectiveness of the ReHand robotic system for upper 
limb recovery in post-stroke patients. 
Study Design 
This is a single-center, randomized, controlled trial with parallel groups and a single-
blind design (blinded assessor and statistician). Participants are allocated in a 1:1 
ratio to either the intervention group or the control group. Stratified block 
randomization is used based on age and baseline upper limb motor deficit. 
Participants 
Eligible participants are adults aged 18 years and older who have experienced an 
ischemic or hemorrhagic stroke within the past six months, present with upper limb 
motor impairment, and are capable of understanding instructions. Patients are 
excluded if they have severe somatic or cognitive impairments, acute medical 
conditions, or fail to provide informed consent. 
Randomization and Allocation 
A total of 120 participants will be randomized (60 per group). Stratified block 
randomization (based on age and Fugl-Meyer Assessment for Upper Extremity 
[FMA-UE] score) will be used, with the random sequence generated using Random 
Allocation Software. Allocation will be managed by an independent coordinator 
who is not involved in the intervention or assessment processes. 
Blinding 

Single blinding will be implemented: outcome assessors and the statistician will be 
blinded to group assignments. Patients will not be blinded due to ethical 
considerations and the nature of the intervention. 
Interventions 
The intervention group will receive robotic therapy using the ReHand system (five 
45-minute sessions per week over eight weeks) in addition to standard rehabilitation. 
The control group will receive standard therapy only. The ReHand device employs 
a mirror-motion method and supports both active and passive hand training. 
Outcome Measures 
The primary outcome is the Fugl-Meyer Assessment of Upper Extremity (FMA-
UE). 
Secondary outcomes include the Functional Independence Measure (FIM), Wolf 
Motor Function Test (WMFT), National Institutes of Health Stroke Scale (NIHSS), 
Barthel Index (BI), Disabilities of the Arm, Shoulder and Hand Questionnaire 
(DASH), 
and 
the 
Hospital 
Anxiety 
and 
Depression 
Scale 
(HADS). 
Assessments will be conducted at two time points: baseline (T0) and post-
intervention (T1). 
Sample Size Calculation 
The sample size will be calculated using G*Power software based on the analysis of 
the primary outcome measure (FMA-UE). According to published clinical data, the 
minimal clinically important difference (MCID) between groups will guide the 
expected mean difference and standard deviation, enabling the estimation of the 
anticipated 
effect 
size 
(Cohen’s 
d). 
To achieve the desired statistical power at the predefined significance level, the 
minimum number of participants per group will be determined. To account for 
potential dropout, the total sample size will be increased accordingly to maintain 
statistical power. 
Statistical Analysis Plan 
Software and Libraries: 
All statistical analyses will be conducted using Python (version 3.11) in the Google 
Colab environment. The following libraries will be used: 
 pandas (v2.2.2) – data structuring and management 
 numpy (v1.26.4) – numerical computations 
 scikit-learn (v1.4.2) – data preprocessing 
 statsmodels (v0.14.2) – statistical testing and modeling 
Data Preprocessing: 

 Categorical variables (e.g., sex, stroke type, treatment group) will be encoded 
using one-hot encoding. 
 Numerical variables (e.g., age, days post-stroke) will be standardized using 
StandardScaler. 
Population and Analytical Approach: 
All analyses will follow the intention-to-treat (ITT) principle, including all 
randomized participants regardless of protocol adherence or dropout. 
Descriptive Statistics: 
 The Shapiro–Wilk test will assess the normality of continuous variables. 
 Non-normally distributed data will be summarized using medians and 
interquartile ranges (Q1–Q3). 
 Categorical variables will be reported as absolute and relative frequencies (%) 
with 95% confidence intervals (CI). 
Statistical Methods: 
 The Wilcoxon signed-rank test will assess within-group changes (T0 vs. T1). 
 The Mann–Whitney U test will compare between-group changes (Δ values). 
 Pearson’s chi-squared test with Yates’ correction will compare categorical 
data. 
 All tests will be two-sided with a significance threshold of p < 0.05. 
Effect Size Estimation: 
To evaluate the magnitude of between-group differences, Cliff’s Delta will be 
calculated. This non-parametric measure is suitable for ordinal and non-normally 
distributed data. 
Additional Analysis: 
A sensitivity analysis using rank-based ANCOVA (Quade’s test) will be performed 
with baseline scores as covariates. This will help adjust for initial differences and 
assess the robustness of the results. 
 
 
 
 
 

References 
1. GBD 2019 Stroke Collaborators. (2021). Global burden of stroke and risk 
factors in 2019: A systematic analysis. The Lancet Neurology, 20(10), 795–
820. 
2. Langhorne, P., Bernhardt, J., & Kwakkel, G. (2011). Stroke rehabilitation. 
The Lancet, 377(9778), 1693–1702. 
3. Pollock, A., Baer, G., Campbell, P., Choo, P. L., Forster, A., Morris, J., ... & 
Langhorne, P. (2014). Physical rehabilitation approaches for the recovery of 
function and mobility following stroke. Cochrane Database of Systematic 
Reviews, (4), CD001920. 
4. Kwakkel, G., van Peppen, R., Wagenaar, R. C., Dauphinee, S. W., Richards, 
C., Ashburn, A., ... & Langhorne, P. (2004). Effects of augmented exercise 
therapy time after stroke: a meta-analysis. Stroke, 35(11), 2529–2539. 
5. Cramer, S. C., et al. (2011). Harnessing neuroplasticity for clinical 
applications. Brain, 134(6), 1591–1609. 
6. Qian, Q., et al. (2020). Effectiveness of a soft robotic glove for hand function 
recovery in stroke patients. Journal of NeuroEngineering and Rehabilitation, 
17(1), 1–10. 
7. Liao, W. W., et al. (2021). Wearable robotic gloves: a systematic review for 
stroke rehabilitation. Journal of Rehabilitation Research and Development, 
58(2), 213–226.