Skip to main content
NCBI home page
Brain & NeuroRehabilitation logoLink to Brain & NeuroRehabilitation
. 2025 Jan 27;18(1):e2. doi: 10.12786/bn.2025.18.e2

The Effects of Virtual Reality Training on Post-Stroke Upper and Lower Limb Function: A Meta-Analysis

Woo-Kyoung Yoo 1, Yongkyun Jung 2, Gyu Jin Kim 1, Shahid Bashir 3, Jihyun Park 2,✉
PMCID: PMC11966005  PMID: 40191224

Abstract

This meta-analysis presents an updated comparison between virtual reality (VR) training and conventional training (CT) in post-stroke rehabilitation by incorporating recent studies based on prior meta-analyses. We searched 3 international electronic databases (MEDLINE, Embase, and the Cochrane Library) and a Korean database (KoreaMed) to identify relevant studies. Out of 5,218 studies, 30 randomized controlled trials were selected through the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method. Among these, 21 focused on upper limb training and 14 on lower limb training. A subgroup meta-analysis was conducted based on the VR type. The risk of bias (RoB) was assessed using Cochrane’s RoB tool. The certainty of the evidence was assessed using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) method. The outcomes were categorized into upper limb motor function, fine motor function, and activities of daily living (ADLs) for upper limb training, and lower limb motor function, balance, and gait velocity for lower limb training. A random-effects model for the meta-analysis indicated that VR training showed significant superiority over CT in improving upper limb motor function, ADL, and balance. This study provides low- to moderate-certainty evidence supporting the superiority of VR training over CT. Clinicians and therapists should consider individual rehabilitation needs, goals, patient preferences, and available resources when selecting VR for post-stroke functional recovery.

Keywords: Virtual Reality, Randomized Controlled Trials as Topic, Recovery of Function, Stroke Rehabilitation

Highlights

  • • Virtual reality (VR) training is gaining attention in stroke rehabilitation.

  • • VR training significantly improves arm motor function over conventional training.

  • • VR training enhances balance but not lower limb motor or gait function.

INTRODUCTION

Stroke is one of the leading causes of death and long-term disability globally, with numerous survivors facing severe motor impairments in their upper and lower limbs [1,2,3]. More than one-third of chronic stroke patients experience upper limb dysfunction, while over one-fifth have impaired walking abilities. Therefore, stroke rehabilitation is essential for restoring limb function and increasing physical independence [4,5].

Virtual reality (VR) training employs a range of technological tools to support rehabilitation exercises. VR is defined as an interactive simulation created by computer hardware and software, which allows users to engage with environments that mimic real-world scenarios [6]. In recent years, VR training has been refined to improve therapeutic rehabilitation, aiming to achieve diverse goals through various approaches [7,8]. This training promotes functional recovery by facilitating repetitive practice of voluntary movements in the upper or lower limbs [9]. The exercises focus on training either whole tasks or specific components to strengthen functional movement. Over time, these methods have expanded to include robotic rehabilitation with electrical stimulation and task-based training using VR. The interfaces have evolved from easily accessible, commercially available non-immersive VR to more sophisticated immersive VR, which addresses spatial and temporal limitations and has further developed into augmented reality (AR) [10]. VR training serves multiple therapeutic purposes and fosters motor learning through multisensory feedback, including visual, tactile, auditory, and balance inputs, underscoring its significance in stroke rehabilitation [6,11,12,13,14]. Numerous comparative studies have assessed the effects of VR training compared to traditional therapies, and their results have been integrated into national clinical guidelines [15,16,17,18]. Additionally, systematic literature reviews and analyses are essential to further inform clinical guidelines.

Recent reviews, including meta-analyses, have limitations such as high heterogeneity [19,20] or findings that focus solely on either upper or lower limb function [21,22]. Therefore, our objective was to evaluate the impact of VR training on the recovery of both upper and lower limb function in post-stroke patients by conducting a meta-analysis that includes the latest research.

MATERIALS AND METHODS

The protocol used in this study followed the method specifically designed for meta-analysis for the development of the Clinical Practice Guideline for Stroke Rehabilitation in Korea [23].

Criteria for this review (PICO)

1) Patients (P): Adult post-stroke patients (age 18 and older, with either cerebral hemorrhage or cerebral infarction)

2) Intervention (I): VR training for post-stroke patients with disabilities in upper and lower limb function (various types of VR, including non-immersive, immersive, and AR)

3) Comparison (C): Comparisons between the effects of VR training and the effects of conventional training (CT)

4) Outcomes (O): Improvement in upper and lower limb function

  • 4-1) Outcome domains for the upper extremity: Upper limb motor function, fine motor function

  • We assessed upper limb motor function using the Fugl-Meyer Assessment-Upper Extremities (FMA-UE) and measured grip strength in kilograms. Fine motor function was evaluated with the Wolf Motor Function Test (WMFT), Action Research Arm Test (ARAT), and Box and Block Test (BBT). Additionally, the measures for activities of daily living (ADLs) included in the meta-analysis were the Functional Independence Measure (FIM), Modified Barthel Index (MBI), and Barthel Index (BI).

  • 4-2) Outcome domains for the lower extremity: Lower limb motor function, balance, and gait function

  • We assessed lower limb motor function using the Fugl-Meyer Assessment-Lower Extremities (FMA-LE). Balance evaluations were conducted with the Berg Balance Scale (BBS), Functional Reach Test (FRT), FMA-balance, and Timed Up and Go (TUG) test. To measure gait function, we included assessments that estimate walking speed, such as gait velocity, the 6-Minute Walk Test (6MWT), and the 10-Meter Walk Test (10MWT).

Evidence was analyzed only from randomized controlled trials (RCTs) that directly compared different forms of VR training (including non-immersive VR, immersive VR, and AR) with CT, all conducted at the same intensity and frequency.

Studies that combined VR training with other treatments—such as robotic-assisted or treadmill gait training, which involve controlling patient movements through an interface—were excluded from the meta-analysis.

Search and selection

We searched online international databases (MEDLINE, Embase, and the Cochrane Library) and a Korean database (KoreaMed). The search query was based on a review of the VR training sections in the latest clinical practice guidelines for stroke from the U.S. [15,18] and Canada [17]. The specific search terms and strategies are detailed in Supplementary Table 1. After a comprehensive literature search, we refined our search queries related to VR training and eliminated duplicate entries, yielding a total of 5,218 studies. Two reviewers independently screened these studies in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, as illustrated in Fig. 1.

Fig. 1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow chart.

Fig. 1

Open in a new tab

Given the distinct purposes of VR training interfaces, separate meta-analyses were conducted for upper and lower limb VR training. A total of 21 studies focused on upper limb VR training, while 14 addressed lower limb VR training. Within each category, subgroup meta-analyses were performed based on different outcome measures. Participants were divided into 2 groups: those who underwent VR training and those who received CT. Outcome measures for the upper extremity included upper limb motor function, fine motor function, and ADL. For the lower extremity, the measures encompassed lower limb motor function, balance, and gait velocity. These outcomes were assessed by comparing results before and after the interventions.

Risk of bias (RoB) assessment

Two reviewers (Y.W.K. and P.J.) independently evaluated the methodological quality of the included RCTs using the Cochrane RoB 1.0 tool and resolved any disagreements by discussion.

Data extraction and transformation

The outcome measures for motor function of the upper and lower extremities were categorized into the following domains: motor function, fine motor function, balance, gait velocity, and ADL. Each domain was ranked based on a predefined list, with the highest-ranking outcome selected for the meta-analysis. Two reviewers (Y.W.K. and P.J.) conducted the meta-analysis, agreeing to use the difference between pre- and post-intervention results (delta values) for each domain. When studies did not provide delta values, the pre-intervention (baseline) and post-intervention means and standard deviations were used to calculate the mean change and standard deviation, following the formula provided in the Cochrane Handbook [24]. For studies with more than one intervention group or those not presenting continuous outcomes as means and standard deviations, valid methods from the handbook were applied to convert the data into a format suitable for the meta-analysis. Studies were excluded if the outcomes required for meta-analysis could not be obtained using the aforementioned methods.

Assessment of certainty of evidence

The certainty of the evidence was evaluated using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach and rated as high, moderate, low, or very low [25]. Two reviewers independently assessed the selected RCTs for RoB, inconsistency, indirectness, imprecision, and publication bias. They then reached a consensus to assign the GRADE level of evidence based on the results of these assessments.

Statistical analysis

Review Manager Software 5.4 (Cochrane Collaboration, Oxford, UK) was utilized to analyze the evidence. Statistical significance was established at p < 0.05. Heterogeneity was evaluated using the I2 statistic, with values of ≥ 50% indicating significant statistical heterogeneity. When significant heterogeneity was detected, subgroup analyses were performed. These analyses categorized VR training types into non-immersive (commercial game console, computer display) and immersive (head-mounted display) VR groups, following criteria similar to those used in Cochrane reviews [13]. In light of recent technological advancements and the growing use of immersive VR devices, we also analyzed immersive VR training, which had not been previously included in meta-analyses. Due to the variety of outcome measures, a random-effects model was employed for the meta-analysis.

RESULTS

Study selection

Following a comprehensive literature search and screening, 30 RCTs were selected from a pool of 5,218 studies using the PRISMA method [19,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54]. Detailed information about the included studies can be found in Supplementary Table 2. In the study by Ögün et al. (2019) [50], patients with hemorrhagic stroke were excluded during recruitment, leading to a potential bias in patient characteristics of the recruited population and increasing heterogeneity in the meta-analysis. However, since this exclusion did not significantly impact the statistical of the results, the study was included in the meta-analysis.

Study characteristics

The RoB evaluation of the 30 RCTs included in this review is presented in Fig. 2. For the upper extremity, 19 RCTs were analyzed for upper limb motor function, 10 for fine motor function, and 8 for ADL. For the lower extremity, 5 were analyzed for lower limb motor function, 13 for balance, and 6 for gait velocity.

Fig. 2. Risk of Bias for included studies in the meta-analysis.

Fig. 2

Open in a new tab

Meta-analysis for the effects of VR training

The evidence summaries and certainty of evidence according to GRADE are presented in Table 1. The primary outcome measures of VR training—motor function for upper and lower limbs, fine motor function, balance, and gait velocity—were considered crucial, while ADLs were deemed important but not essential. According to the GRADE approach, no significant RoB was detected across any of the outcome measures. However, inconsistencies were noted in the measures of lower limb motor function, fine motor function, and balance due to the inclusion of studies with high heterogeneity. Measures of lower limb motor function and gait velocity demonstrated imprecision, which was attributed to wide confidence intervals (CIs) resulting from small sample sizes. Publication bias, along with heterogeneity, was evident in the measures of upper limb motor function, fine motor function, and balance, although no issues of indirectness were identified.

Table 1. Evidence summary and GRADE evaluations.

Outcomes Importance No. of participants (studies) Certainty of evidence (GRADE) Statistical method Effect estimate
1.1 Motor function (upper extremity) 9 790 (19) Moderate SMD (IV, random, 95% CI) 0.57 (0.28, 0.86)
1.1.1 Non-immersive VR 641 (15) SMD (IV, random, 95% CI) 0.48 (0.17, 0.78)
1.1.2 Immersive VR 149 (4) SMD (IV, random, 95% CI) 0.95 (0.26, 1.64)
1.2 Fine motor function 9 486 (10) Moderate SMD (IV, random, 95% CI) 0.31 (−0.15, 0.77)
1.2.1 Non-immersive VR 421 (9) SMD (IV, random, 95% CI) 0.05 (−0.19, 0.29)
1.2.2 Immersive VR 65 (1) SMD (IV, random, 95% CI) 2.10 (1.49, 2.72)
1.3 Activities of daily living 4 513 (8) Moderate SMD (IV, random, 95% CI) 0.33 (0.08, 0.58)
1.3.1 Non-immersive VR 425 (6) SMD (IV, random, 95% CI) 0.26 (0.04, 0.48)
1.3.2 Immersive VR 88 (2) SMD (IV, random, 95% CI) 0.54 (−0.38, 1.45)
2.1 Motor function (lower extremity) 9 225 (5) Moderate SMD (IV, random, 95% CI) 0.51 (−0.49, 1.52)
2.1.1 Non-immersive VR 225 (5) SMD (IV, random, 95% CI) 0.51 (−0.49, 1.52)
2.1.2 Immersive VR 0
2.2 Balance 9 493 (13) Low SMD (IV, random, 95% CI) 0.66 (0.21, 1.11)
2.2.1 Non-immersive VR 391 (10) SMD (IV, random, 95% CI) 0.77 (0.21, 1.33)
2.2.2 Immersive VR 102 (3) SMD (IV, random, 95% CI) 0.25 (−0.24, 0.75)
2.3 Gait velocity 8 254 (6) Low SMD (IV, random, 95% CI) 0.03 (−0.30, 0.35)
2.3.1 Non-immersive VR 182 (4) SMD (IV, random, 95% CI) 0.02 (−0.27, 0.31)
2.3.2 Immersive VR 72 (2) SMD (IV, random, 95% CI) 0.19 (−1.14, 1.51)
Open in a new tab

GRADE, Grading of Recommendations, Assessment, Development, and Evaluations; SMD, standardized mean difference; IV, inverse variance; CI, confidence interval; VR, virtual reality.

Figs. 3, 4, 5, 6, 7, 8 present the forest plots from the meta-analysis. When comparing VR training (experimental group) to control group using standardized mean difference (SMD) with 95% CIs, VR training demonstrated statistically significant improvements in motor function and ADLs of the upper extremities, as well as balance.

Fig. 3. Forest plot of the meta-analysis for virtual reality training: motor function of the upper extremity.

Fig. 3

Open in a new tab

SD, standard deviation; Std., standardized; IV, inverse variance; CI, confidence interval.

Fig. 4. Forest plot of the meta-analysis for virtual reality training: fine motor function of the upper extremity.

Fig. 4

Open in a new tab

SD, standard deviation; Std., standardized; IV, inverse variance; CI, confidence interval.

Fig. 5. Forest plot of the meta-analysis for virtual reality training: activities of daily living of the upper extremity.

Fig. 5

Open in a new tab

SD, standard deviation; Std., standardized; IV, inverse variance; CI, confidence interval.

Fig. 6. Forest plot of the meta-analysis for virtual reality training: motor function of the lower extremity.

Fig. 6

Open in a new tab

SD, standard deviation; Std., standardized; IV, inverse variance; CI, confidence interval.

Fig. 7. Forest plot of the meta-analysis for virtual reality training: balance of the lower extremity.

Fig. 7

Open in a new tab

SD, standard deviation; Std., standardized; IV, inverse variance; CI, confidence interval.

Fig. 8. Forest plot of the meta-analysis for virtual reality training: gait velocity of the lower extremity.

Fig. 8

Open in a new tab

SD, standard deviation; Std., standardized; IV, inverse variance; CI, confidence interval.

Motor function (upper extremity)

The meta-analysis for upper limb motor function included 19 RCTs. The outcome measures used were the FMA-UE and grip strength. The effect size calculated as the SMD was 0.57 (0.28, 0.86).

A subgroup analysis revealed that the non-immersive VR group (15 RCTs, n = 641) had an effect size (SMD) of 0.48 (0.17, 0.78), while the immersive VR group (4 RCTs, n = 149) had an effect size (SMD) of 0.95 (0.26, 1.64) (Fig. 3).

Fine motor function

For fine motor function, 10 RCTs were included in the meta-analysis. These trials measured WMFT, ARAT, and BBT. The effect size (SMD) was 0.31 (−0.15, 0.77).

Subgroup analysis showed that the effect size (SMD) was 0.05 (−0.19, 0.29) for the non-immersive VR group (9 RCTs, n = 421), while for the immersive VR group (1 RCT, n = 65), it was 2.10 (1.49, 2.72) (Fig. 4).

ADLs

Eight RCTs were included in the analysis of ADL. These studies reported results for the FIM, BI, and MBI. The effect size (SMD) was 0.33 (0.08, 0.58).

A subgroup analysis for the non-immersive VR group (6 RCTs, n = 425) showed an effect size (SMD) of 0.26 (0.04, 0.48), while the immersive VR group (2 RCTs, n = 88) had an effect size (SMD) of 0.54 (−0.38, 1.45) (Fig. 5).

Motor function (lower extremity)

For lower limb motor function, the meta-analysis included 5 RCTs, which used the FMA-LE and weight balance ratio as outcome measures. The effect size (SMD) was 0.51 (−0.49, 1.52), with all studies using non-immersive VR as intervention (Fig. 6).

Balance

Balance was assessed in 13 RCTs, using the BBS, FRT, FMA-balance, and TUG test. The effect size (SMD) was 0.66 (0.21, 1.11).

A subgroup analysis for the non-immersive VR group (10 RCTs, n = 391) revealed an effect size (SMD) of 0.77 (0.21, 1.33), while for the immersive VR group (3 RCTs, n = 102), the effect size (SMD) was 0.25 (−0.24, 0.75) (Fig. 7).

Gait velocity

For gait velocity, 6 RCTs were included, measuring gait velocity, gait speed, the 6MWT, and the 10MWT. The effect size (SMD) was 0.03 (−0.30, 0.35).

A subgroup analysis showed that the non-immersive VR group (4 RCTs, n = 182) had an effect size (SMD) of 0.02 (−0.27, 0.31), while the immersive VR group (2 RCTs, n = 72) had an effect size (SMD) of 0.19 (−1.14, 1.51) (Fig. 8).

DISCUSSION

This meta-analysis was designed to compare the effects of VR training with those of CT in stroke patients, drawing on recent studies and data from a Cochrane review. The findings revealed that VR training significantly outperformed CT in enhancing upper limb motor function, ADL, and balance, when administered with equivalent intensity and frequency.

The primary difference between the findings of this meta-analysis and those of earlier meta-analyses [13] relates to the scope and quality of the included studies. Previous analyses frequently included preliminary studies that employed with less rigorous methodologies or focused primarily on non-immersive VR. In contrast, this meta-analysis excluded preliminary RCTs and incorporated newer studies featuring immersive VR training.

For the meta-analysis of upper limb function, we conducted a subgroup analysis to differentiate between immersive and non-immersive VR training. Previous studies encountered analytical challenges due to small sample sizes and a limited number of RCTs focusing on immersive VR or newly developed VR technologies for rehabilitation therapy. In this meta-analysis, we performed subgroup analyses to ascertain if outcomes varied across domains based on the type of VR employed.

In terms of upper limb motor function, VR training demonstrated better outcomes compared to CT, with both non-immersive and immersive VR training showing benefits. When examining fine motor function, no significant difference was found between VR training and CT overall. However, the sole study using immersive VR training, conducted by Ögün et al. (2019) [50], showed significant improvement. VR training also demonstrated better improvement in ADL performance, though this superiority was observed only in non-immersive VR training under subgroup analysis. Moreover, 2 studies found that immersive VR training did not perform significantly better than CT. Regarding stroke type, while most studies did not specify stroke subtype or exclude hemorrhagic stroke patients during recruitment, the study by Ögün et al. (2019) [50] did exclude hemorrhagic stroke patients. This exclusion led to a notable increase in heterogeneity in the meta-analysis. Nevertheless, the statistical significance of the overall results remained unaffected by this inclusion.

The review conducted by Laver et al. [13] indicated that VR training did not significantly enhance any aspects of upper limb function, with the exception of upper limb motor function. However, this meta-analysis, incorporating recent RCTs and studies on newly developed VR training methods, has shown significant improvements in upper limb motor function following VR training—improvements that were not observed in earlier meta-analyses. Specifically, recent studies have suggested through subgroup analysis that immersive VR training leads to significantly greater improvements than non-immersive VR in this domain.

In terms of balance, VR training was found to be more advantageous than CT. The study conducted by Cano-Mañas et al. (2020) [33] initially showed that the experimental group had significantly higher cognitive scores than the control group, indicating a bias in baseline characteristics. This bias notably increased the heterogeneity in the meta-analysis, although it did not impact the statistical significance of the results. Regarding lower limb motor function and gait velocity, the SMD for the effect of VR training included zero within the 95% CI, confirming that there was no significant difference compared to CT.

In the subgroup analysis concerning balance, immersive VR training did not show a significant difference compared to CT, and, notably, this analysis did not exhibit the heterogeneity observed in the overall analysis. In contrast, non-immersive VR training demonstrated a statistically significant advantage over CT in improving balance. For lower limb motor function, only non-immersive VR training was utilized across all studies, leaving no data available to evaluate the effectiveness of immersive VR training in this area. In terms of gait velocity, the subgroup analysis revealed no significant differences between either immersive or non-immersive VR training and CT. The study conducted by Anwar et al. (2021) [29] involved participants with an average age of 51 years, which is younger than the average age reported in previous studies. This younger cohort may have experienced a more pronounced effect from VR training, thereby contributing to increased heterogeneity in the meta-analysis. However, this variation did not affect the statistical significance of the overall results for lower limb motor function, allowing for the inclusion of this study in the analysis. Similar to the findings of the previous Cochrane review [13], this meta-analysis also determined that neither immersive nor non-immersive VR training significantly outperformed CT in enhancing lower limb function, with the exception of balance. However, balance showed a statistically significant improvement with non-immersive VR training. This outcome is slightly different from that of the earlier Cochrane review’s meta-analysis, which only observed significant improvements in balance when VR as an additional intervention was incorporated into CT. The distinctions in this meta-analysis compared to the prior one include the incorporation of more recent studies and the omission of studies involving direct therapist intervention in VR training.

In summary, VR training has been shown to be a safe and effective intervention that offers improvements in motor function and balance for stroke patients. Its accessibility and potential for remote implementation, particularly during unique situations such as the coronavirus disease 2019 pandemic, have led to a growing interest in VR. However, there is still a need for more substantial evidence to fully affirm its safety. Additionally, potential side effects like photosensitivity seizures, nausea, and blurred vision need to be closely monitored by clinicians and therapists [55]. Despite these concerns, VR training is considered a relatively safe alternative to CT, with no additional risks expected when it is prescribed correctly and used with appropriate patients. Clinicians and therapists can decide whether to use CT or prioritize VR training based on the patient’s goals, accessibility, personal interests, and any constraints related to time or space. Furthermore, VR training can be used in conjunction with CT to create a more comprehensive therapeutic strategy.

Footnotes

Funding: None.

Conflict of Interest: The authors have no potential conflicts of interest to disclose.

SUPPLEMENTARY MATERIALS

Supplementary Table 1

Search terms and strategies

bn-18-e2-s001.xls (59KB, xls)
Supplementary Table 2

Characteristics of included studies

bn-18-e2-s002.xls (64.5KB, xls)

References

  • 1.GBD 2013 DALYs and HALE Collaborators. Murray CJ, Barber RM, Foreman KJ, Abbasoglu Ozgoren A, Abd-Allah F, Abera SF, Aboyans V, Abraham JP, Abubakar I, Abu-Raddad LJ, Abu-Rmeileh NM, Achoki T, Ackerman IN, Ademi Z, Adou AK, Adsuar JC, Afshin A, Agardh EE, Alam SS, Alasfoor D, Albittar MI, Alegretti MA, Alemu ZA, Alfonso-Cristancho R, Alhabib S, Ali R, Alla F, Allebeck P, Almazroa MA, Alsharif U, Alvarez E, Alvis-Guzman N, Amare AT, Ameh EA, Amini H, Ammar W, Anderson HR, Anderson BO, Antonio CA, Anwari P, Arnlöv J, Arsic Arsenijevic VS, Artaman A, Asghar RJ, Assadi R, Atkins LS, Avila MA, Awuah B, Bachman VF, Badawi A, Bahit MC, Balakrishnan K, Banerjee A, Barker-Collo SL, Barquera S, Barregard L, Barrero LH, Basu A, Basu S, Basulaiman MO, Beardsley J, Bedi N, Beghi E, Bekele T, Bell ML, Benjet C, Bennett DA, Bensenor IM, Benzian H, Bernabé E, Bertozzi-Villa A, Beyene TJ, Bhala N, Bhalla A, Bhutta ZA, Bienhoff K, Bikbov B, Biryukov S, Blore JD, Blosser CD, Blyth FM, Bohensky MA, Bolliger IW, Bora Başara B, Bornstein NM, Bose D, Boufous S, Bourne RR, Boyers LN, Brainin M, Brayne CE, Brazinova A, Breitborde NJ, Brenner H, Briggs AD, Brooks PM, Brown JC, Brugha TS, Buchbinder R, Buckle GC, Budke CM, Bulchis A, Bulloch AG, Campos-Nonato IR, Carabin H, Carapetis JR, Cárdenas R, Carpenter DO, Caso V, Castañeda-Orjuela CA, Castro RE, Catalá-López F, Cavalleri F, Çavlin A, Chadha VK, Chang JC, Charlson FJ, Chen H, Chen W, Chiang PP, Chimed-Ochir O, Chowdhury R, Christensen H, Christophi CA, Cirillo M, Coates MM, Coffeng LE, Coggeshall MS, Colistro V, Colquhoun SM, Cooke GS, Cooper C, Cooper LT, Coppola LM, Cortinovis M, Criqui MH, Crump JA, Cuevas-Nasu L, Danawi H, Dandona L, Dandona R, Dansereau E, Dargan PI, Davey G, Davis A, Davitoiu DV, Dayama A, De Leo D, Degenhardt L, Del Pozo-Cruz B, Dellavalle RP, Deribe K, Derrett S, Des Jarlais DC, Dessalegn M, Dharmaratne SD, Dherani MK, Diaz-Torné C, Dicker D, Ding EL, Dokova K, Dorsey ER, Driscoll TR, Duan L, Duber HC, Ebel BE, Edmond KM, Elshrek YM, Endres M, Ermakov SP, Erskine HE, Eshrati B, Esteghamati A, Estep K, Faraon EJ, Farzadfar F, Fay DF, Feigin VL, Felson DT, Fereshtehnejad SM, Fernandes JG, Ferrari AJ, Fitzmaurice C, Flaxman AD, Fleming TD, Foigt N, Forouzanfar MH, Fowkes FG, Paleo UF, Franklin RC, Fürst T, Gabbe B, Gaffikin L, Gankpé FG, Geleijnse JM, Gessner BD, Gething P, Gibney KB, Giroud M, Giussani G, Gomez Dantes H, Gona P, González-Medina D, Gosselin RA, Gotay CC, Goto A, Gouda HN, Graetz N, Gugnani HC, Gupta R, Gupta R, Gutiérrez RA, Haagsma J, Hafezi-Nejad N, Hagan H, Halasa YA, Hamadeh RR, Hamavid H, Hammami M, Hancock J, Hankey GJ, Hansen GM, Hao Y, Harb HL, Haro JM, Havmoeller R, Hay SI, Hay RJ, Heredia-Pi IB, Heuton KR, Heydarpour P, Higashi H, Hijar M, Hoek HW, Hoffman HJ, Hosgood HD, Hossain M, Hotez PJ, Hoy DG, Hsairi M, Hu G, Huang C, Huang JJ, Husseini A, Huynh C, Iannarone ML, Iburg KM, Innos K, Inoue M, Islami F, Jacobsen KH, Jarvis DL, Jassal SK, Jee SH, Jeemon P, Jensen PN, Jha V, Jiang G, Jiang Y, Jonas JB, Juel K, Kan H, Karch A, Karema CK, Karimkhani C, Karthikeyan G, Kassebaum NJ, Kaul A, Kawakami N, Kazanjan K, Kemp AH, Kengne AP, Keren A, Khader YS, Khalifa SE, Khan EA, Khan G, Khang YH, Kieling C, Kim D, Kim S, Kim Y, Kinfu Y, Kinge JM, Kivipelto M, Knibbs LD, Knudsen AK, Kokubo Y, Kosen S, Krishnaswami S, Kuate Defo B, Kucuk Bicer B, Kuipers EJ, Kulkarni C, Kulkarni VS, Kumar GA, Kyu HH, Lai T, Lalloo R, Lallukka T, Lam H, Lan Q, Lansingh VC, Larsson A, Lawrynowicz AE, Leasher JL, Leigh J, Leung R, Levitz CE, Li B, Li Y, Li Y, Lim SS, Lind M, Lipshultz SE, Liu S, Liu Y, Lloyd BK, Lofgren KT, Logroscino G, Looker KJ, Lortet-Tieulent J, Lotufo PA, Lozano R, Lucas RM, Lunevicius R, Lyons RA, Ma S, Macintyre MF, Mackay MT, Majdan M, Malekzadeh R, Marcenes W, Margolis DJ, Margono C, Marzan MB, Masci JR, Mashal MT, Matzopoulos R, Mayosi BM, Mazorodze TT, Mcgill NW, Mcgrath JJ, Mckee M, Mclain A, Meaney PA, Medina C, Mehndiratta MM, Mekonnen W, Melaku YA, Meltzer M, Memish ZA, Mensah GA, Meretoja A, Mhimbira FA, Micha R, Miller TR, Mills EJ, Mitchell PB, Mock CN, Mohamed Ibrahim N, Mohammad KA, Mokdad AH, Mola GL, Monasta L, Montañez Hernandez JC, Montico M, Montine TJ, Mooney MD, Moore AR, Moradi-Lakeh M, Moran AE, Mori R, Moschandreas J, Moturi WN, Moyer ML, Mozaffarian D, Msemburi WT, Mueller UO, Mukaigawara M, Mullany EC, Murdoch ME, Murray J, Murthy KS, Naghavi M, Naheed A, Naidoo KS, Naldi L, Nand D, Nangia V, Narayan KM, Nejjari C, Neupane SP, Newton CR, Ng M, Ngalesoni FN, Nguyen G, Nisar MI, Nolte S, Norheim OF, Norman RE, Norrving B, Nyakarahuka L, Oh IH, Ohkubo T, Ohno SL, Olusanya BO, Opio JN, Ortblad K, Ortiz A, Pain AW, Pandian JD, Panelo CI, Papachristou C, Park EK, Park JH, Patten SB, Patton GC, Paul VK, Pavlin BI, Pearce N, Pereira DM, Perez-Padilla R, Perez-Ruiz F, Perico N, Pervaiz A, Pesudovs K, Peterson CB, Petzold M, Phillips MR, Phillips BK, Phillips DE, Piel FB, Plass D, Poenaru D, Polinder S, Pope D, Popova S, Poulton RG, Pourmalek F, Prabhakaran D, Prasad NM, Pullan RL, Qato DM, Quistberg DA, Rafay A, Rahimi K, Rahman SU, Raju M, Rana SM, Razavi H, Reddy KS, Refaat A, Remuzzi G, Resnikoff S, Ribeiro AL, Richardson L, Richardus JH, Roberts DA, Rojas-Rueda D, Ronfani L, Roth GA, Rothenbacher D, Rothstein DH, Rowley JT, Roy N, Ruhago GM, Saeedi MY, Saha S, Sahraian MA, Sampson UK, Sanabria JR, Sandar L, Santos IS, Satpathy M, Sawhney M, Scarborough P, Schneider IJ, Schöttker B, Schumacher AE, Schwebel DC, Scott JG, Seedat S, Sepanlou SG, Serina PT, Servan-Mori EE, Shackelford KA, Shaheen A, Shahraz S, Shamah Levy T, Shangguan S, She J, Sheikhbahaei S, Shi P, Shibuya K, Shinohara Y, Shiri R, Shishani K, Shiue I, Shrime MG, Sigfusdottir ID, Silberberg DH, Simard EP, Sindi S, Singh A, Singh JA, Singh L, Skirbekk V, Slepak EL, Sliwa K, Soneji S, Søreide K, Soshnikov S, Sposato LA, Sreeramareddy CT, Stanaway JD, Stathopoulou V, Stein DJ, Stein MB, Steiner C, Steiner TJ, Stevens A, Stewart A, Stovner LJ, Stroumpoulis K, Sunguya BF, Swaminathan S, Swaroop M, Sykes BL, Tabb KM, Takahashi K, Tandon N, Tanne D, Tanner M, Tavakkoli M, Taylor HR, Te Ao BJ, Tediosi F, Temesgen AM, Templin T, Ten Have M, Tenkorang EY, Terkawi AS, Thomson B, Thorne-Lyman AL, Thrift AG, Thurston GD, Tillmann T, Tonelli M, Topouzis F, Toyoshima H, Traebert J, Tran BX, Trillini M, Truelsen T, Tsilimbaris M, Tuzcu EM, Uchendu US, Ukwaja KN, Undurraga EA, Uzun SB, Van Brakel WH, Van De Vijver S, van Gool CH, Van Os J, Vasankari TJ, Venketasubramanian N, Violante FS, Vlassov VV, Vollset SE, Wagner GR, Wagner J, Waller SG, Wan X, Wang H, Wang J, Wang L, Warouw TS, Weichenthal S, Weiderpass E, Weintraub RG, Wenzhi W, Werdecker A, Westerman R, Whiteford HA, Wilkinson JD, Williams TN, Wolfe CD, Wolock TM, Woolf AD, Wulf S, Wurtz B, Xu G, Yan LL, Yano Y, Ye P, Yentür GK, Yip P, Yonemoto N, Yoon SJ, Younis MZ, Yu C, Zaki ME, Zhao Y, Zheng Y, Zonies D, Zou X, Salomon JA, Lopez AD, Vos T. Global, regional, and national disability-adjusted life years (DALYs) for 306 diseases and injuries and healthy life expectancy (HALE) for 188 countries, 1990-2013: quantifying the epidemiological transition. Lancet. 2015;386:2145–2191. doi: 10.1016/S0140-6736(15)61340-X. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Morovatdar N, Avan A, Azarpazhooh MR, Di Napoli M, Stranges S, Kapral MK, Rezayat AA, Shariatzadeh A, Abootalebi S, Mokhber N, Spence JD, Hachinski V. Secular trends of ischaemic heart disease, stroke, and dementia in high-income countries from 1990 to 2017: the Global Burden of Disease Study 2017. Neurol Sci. 2022;43:255–264. doi: 10.1007/s10072-021-05259-2. [DOI] [PubMed] [Google Scholar]
  • 3.Martin SS, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, Baker-Smith CM, Barone Gibbs B, Beaton AZ, Boehme AK, Commodore-Mensah Y, Currie ME, Elkind MSV, Evenson KR, Generoso G, Heard DG, Hiremath S, Johansen MC, Kalani R, Kazi DS, Ko D, Liu J, Magnani JW, Michos ED, Mussolino ME, Navaneethan SD, Parikh NI, Perman SM, Poudel R, Rezk-Hanna M, Roth GA, Shah NS, St-Onge MP, Thacker EL, Tsao CW, Urbut SM, Van Spall HGC, Voeks JH, Wang NY, Wong ND, Wong SS, Yaffe K, Palaniappan LP American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee. 2024 Heart disease and stroke statistics: a report of US and global data from the American Heart Association. Circulation. 2024;149:e347–e913. doi: 10.1161/CIR.0000000000001209. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Pollock A, Baer G, Campbell P, Choo PL, Forster A, Morris J, Pomeroy VM, Langhorne P. Physical rehabilitation approaches for the recovery of function and mobility following stroke. Cochrane Database Syst Rev. 2014;2014:CD001920. doi: 10.1002/14651858.CD001920.pub3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Pollock A, Farmer SE, Brady MC, Langhorne P, Mead GE, Mehrholz J, van Wijck F. Interventions for improving upper limb function after stroke. Cochrane Database Syst Rev. 2014;2014:CD010820. doi: 10.1002/14651858.CD010820.pub2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Weiss PL, Kizony R, Feintuch U, Katz N. In: Textbook of neural repair and rehabilitation. Selzer M, Clarke S, Cohen L, Duncan P, Gage F, editors. Cambridge: Cambridge University Press; 2006. Virtual reality in neurorehabilitation; pp. 182–197. [Google Scholar]
  • 7.Fan T, Wang X, Song X, Zhao G, Zhang Z. Research status and emerging trends in virtual reality rehabilitation: bibliometric and knowledge graph study. JMIR Serious Games. 2023;11:e41091. doi: 10.2196/41091. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Lange B, Koenig S, Chang CY, McConnell E, Suma E, Bolas M, Rizzo A. Designing informed game-based rehabilitation tasks leveraging advances in virtual reality. Disabil Rehabil. 2012;34:1863–1870. doi: 10.3109/09638288.2012.670029. [DOI] [PubMed] [Google Scholar]
  • 9.de Sousa DG, Harvey LA, Dorsch S, Glinsky JV. Interventions involving repetitive practice improve strength after stroke: a systematic review. J Physiother. 2018;64:210–221. doi: 10.1016/j.jphys.2018.08.004. [DOI] [PubMed] [Google Scholar]
  • 10.Keshner EA, Weiss PT, Geifman D, Raban D. Tracking the evolution of virtual reality applications to rehabilitation as a field of study. J Neuroeng Rehabil. 2019;16:76. doi: 10.1186/s12984-019-0552-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Langhorne P, Bernhardt J, Kwakkel G. Stroke rehabilitation. Lancet. 2011;377:1693–1702. doi: 10.1016/S0140-6736(11)60325-5. [DOI] [PubMed] [Google Scholar]
  • 12.Veerbeek JM, van Wegen E, van Peppen R, van der Wees PJ, Hendriks E, Rietberg M, Kwakkel G. What is the evidence for physical therapy poststroke? A systematic review and meta-analysis. PLoS One. 2014;9:e87987. doi: 10.1371/journal.pone.0087987. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Laver KE, Lange B, George S, Deutsch JE, Saposnik G, Crotty M. Virtual reality for stroke rehabilitation. Cochrane Database Syst Rev. 2017;11:CD008349. doi: 10.1002/14651858.CD008349.pub4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Demeco A, Zola L, Frizziero A, Martini C, Palumbo A, Foresti R, Buccino G, Costantino C. Immersive virtual reality in post-stroke rehabilitation: a systematic review. Sensors (Basel) 2023;23:1712. doi: 10.3390/s23031712. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Winstein CJ, Stein J, Arena R, Bates B, Cherney LR, Cramer SC, Deruyter F, Eng JJ, Fisher B, Harvey RL, Lang CE, MacKay-Lyons M, Ottenbacher KJ, Pugh S, Reeves MJ, Richards LG, Stiers W, Zorowitz RD American Heart Association Stroke Council, Council on Cardiovascular and Stroke Nursing, Council on Clinical Cardiology, and Council on Quality of Care and Outcomes Research. Guidelines for adult stroke rehabilitation and recovery: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke. 2016;47:e98–e169. doi: 10.1161/STR.0000000000000098. [DOI] [PubMed] [Google Scholar]
  • 16.Stroke Foundation. Clinical guidelines for stroke management 2017. Melbourne: Stroke Foundation; 2017. [Google Scholar]
  • 17.Teasell R, Salbach NM, Foley N, Mountain A, Cameron JI, Jong A, Acerra NE, Bastasi D, Carter SL, Fung J, Halabi ML, Iruthayarajah J, Harris J, Kim E, Noland A, Pooyania S, Rochette A, Stack BD, Symcox E, Timpson D, Varghese S, Verrilli S, Gubitz G, Casaubon LK, Dowlatshahi D, Lindsay MP. Canadian stroke best practice recommendations: rehabilitation, recovery, and community participation following stroke. Part one: rehabilitation and recovery following stroke; update 2019. Int J Stroke. 2020;15:763–788. doi: 10.1177/1747493019897843. [DOI] [PubMed] [Google Scholar]
  • 18.Management of Stroke Rehabilitation Working Group. VA/DOD clinical practice guideline for the management of stroke rehabilitation. J Rehabil Res Dev. 2010;47:1–43. [PubMed] [Google Scholar]
  • 19.de Rooij IJM, van de Port IGL, Punt M, Abbink-van Moorsel PJM, Kortsmit M, van Eijk RPA, Visser-Meily JMA, Meijer JG. Effect of virtual reality gait training on participation in survivors of subacute stroke: a randomized controlled trial. Phys Ther. 2021;101:pzab051. doi: 10.1093/ptj/pzab051. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Wu J, Zeng A, Chen Z, Wei Y, Huang K, Chen J, Ren Z. Effects of virtual reality training on upper limb function and balance in stroke patients: systematic review and meta-meta-analysis. J Med Internet Res. 2021;23:e31051. doi: 10.2196/31051. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Mekbib DB, Han J, Zhang L, Fang S, Jiang H, Zhu J, Roe AW, Xu D. Virtual reality therapy for upper limb rehabilitation in patients with stroke: a meta-analysis of randomized clinical trials. Brain Inj. 2020;34:456–465. doi: 10.1080/02699052.2020.1725126. [DOI] [PubMed] [Google Scholar]
  • 22.Gibbons EM, Thomson AN, de Noronha M, Joseph S. Are virtual reality technologies effective in improving lower limb outcomes for patients following stroke - a systematic review with meta-analysis. Top Stroke Rehabil. 2016;23:440–457. doi: 10.1080/10749357.2016.1183349. [DOI] [PubMed] [Google Scholar]
  • 23.Kim DY, Ryu B, Oh BM, Kim DY, Kim DS, Kim DY, Kim DK, Kim EJ, Lee HY, Choi H, Kim HS, Lee HH, Kim HJ, Oh HM, Seok H, Park J, Park J, Park JG, Kim JM, Lee J, Shin JH, Lee JK, Oh JS, Park KD, Kim KT, Chang MC, Chun MH, Kim MW, Kang MG, Song MK, Choi M, Ko MH, Kim NY, Paik NJ, Jung SH, Yoon SY, Lim SH, Lee SJ, Yoo SD, Lee SH, Yang SN, Park SW, Lee SY, Han SJ, Lee SJ, Bok SK, Ohn SH, Im S, Pyun SB, Hyun SE, Kim SH, Ko SH, Jee S, Kwon S, Kim TW, Chang WH, Chang WK, Yoo WK, Kim YH, Yoo YJ, Kim YW, Shin YI, Park YG, Choi YH, Kim Y KSNR Stroke CPG Writing Group. Clinical practice guideline for stroke rehabilitation in Korea-part 1: rehabilitation for motor function (2022) Brain Neurorehabil. 2023;16:e18. doi: 10.12786/bn.2023.16.e18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA. Cochrane handbook for systematic reviews of interventions. Hoboken: Wiley; 2019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Guyatt G, Oxman AD, Akl EA, Kunz R, Vist G, Brozek J, Norris S, Falck-Ytter Y, Glasziou P, DeBeer H, Jaeschke R, Rind D, Meerpohl J, Dahm P, Schünemann HJ. GRADE guidelines: 1. Introduction-GRADE evidence profiles and summary of findings tables. J Clin Epidemiol. 2011;64:383–394. doi: 10.1016/j.jclinepi.2010.04.026. [DOI] [PubMed] [Google Scholar]
  • 26.Ahmad MA, Singh DKA, Mohd Nordin NA, Hooi Nee K, Ibrahim N. Virtual reality games as an adjunct in improving upper limb function and general health among stroke survivors. Int J Environ Res Public Health. 2019;16:5144. doi: 10.3390/ijerph16245144. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Ain QU, Khan S, Ilyas S, Yaseen A, Tariq I, Liu T, Wang J. Additional effects of Xbox Kinect training on upper limb function in chronic stroke patients: a randomized control trial. Healthcare (Basel) 2021;9:242. doi: 10.3390/healthcare9030242. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Alves SS, Ocamoto GN, de Camargo PS, Santos ATS, Terra AMSV. Effects of virtual reality and motor imagery techniques using Fugl Meyer Assessment scale in post-stroke patients. Int J Ther Rehabil. 2018;25:587–596. [Google Scholar]
  • 29.Anwar N, Karimi H, Ahmad A, Mumtaz N, Saqulain G, Gilani SA. A novel virtual reality training strategy for poststroke patients: a randomized clinical trial. J Healthc Eng. 2021;2021:6598726. doi: 10.1155/2021/6598726. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Aslam M, Ain QU, Fayyaz P, Malik AN. Exer-gaming reduces fall risk and improves mobility after stroke. J Pak Med Assoc. 2021;71:1673–1675. doi: 10.47391/JPMA.875. [DOI] [PubMed] [Google Scholar]
  • 31.Brunner I, Skouen JS, Hofstad H, Aßmus J, Becker F, Sanders AM, Pallesen H, Qvist Kristensen L, Michielsen M, Thijs L, Verheyden G. Virtual Reality Training for Upper Extremity in Subacute Stroke (VIRTUES): a multicenter RCT. Neurology. 2017;89:2413–2421. doi: 10.1212/WNL.0000000000004744. [DOI] [PubMed] [Google Scholar]
  • 32.Cannell J, Jovic E, Rathjen A, Lane K, Tyson AM, Callisaya ML, Smith ST, Ahuja KD, Bird ML. The efficacy of interactive, motion capture-based rehabilitation on functional outcomes in an inpatient stroke population: a randomized controlled trial. Clin Rehabil. 2018;32:191–200. doi: 10.1177/0269215517720790. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Cano-Mañas MJ, Collado-Vázquez S, Rodríguez Hernández J, Muñoz Villena AJ, Cano-de-la-Cuerda R. Effects of video-game based therapy on balance, postural control, functionality, and quality of life of patients with subacute stroke: a randomized controlled trial. J Healthc Eng. 2020;2020:5480315. doi: 10.1155/2020/5480315. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.da Silva Ribeiro NM, Ferraz DD, Pedreira É, Pinheiro Í, da Silva Pinto AC, Neto MG, Dos Santos LR, Pozzato MG, Pinho RS, Masruha MR. Virtual rehabilitation via Nintendo Wii® and conventional physical therapy effectively treat post-stroke hemiparetic patients. Top Stroke Rehabil. 2015;22:299–305. doi: 10.1179/1074935714Z.0000000017. [DOI] [PubMed] [Google Scholar]
  • 35.Fritz SL, Peters DM, Merlo AM, Donley J. Active video-gaming effects on balance and mobility in individuals with chronic stroke: a randomized controlled trial. Top Stroke Rehabil. 2013;20:218–225. doi: 10.1310/tsr2003-218. [DOI] [PubMed] [Google Scholar]
  • 36.Givon N, Zeilig G, Weingarden H, Rand D. Video-games used in a group setting is feasible and effective to improve indicators of physical activity in individuals with chronic stroke: a randomized controlled trial. Clin Rehabil. 2016;30:383–392. doi: 10.1177/0269215515584382. [DOI] [PubMed] [Google Scholar]
  • 37.Henrique PPB, Colussi EL, De Marchi ACB. Effects of exergame on patients’ balance and upper limb motor function after stroke: a randomized controlled trial. J Stroke Cerebrovasc Dis. 2019;28:2351–2357. doi: 10.1016/j.jstrokecerebrovasdis.2019.05.031. [DOI] [PubMed] [Google Scholar]
  • 38.Huang CY, Chiang WC, Yeh YC, Fan SC, Yang WH, Kuo HC, Li PC. Effects of virtual reality-based motor control training on inflammation, oxidative stress, neuroplasticity and upper limb motor function in patients with chronic stroke: a randomized controlled trial. BMC Neurol. 2022;22:21. doi: 10.1186/s12883-021-02547-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Hung JW, Chou CX, Chang YJ, Wu CY, Chang KC, Wu WC, Howell S. Comparison of Kinect2Scratch game-based training and therapist-based training for the improvement of upper extremity functions of patients with chronic stroke: a randomized controlled single-blinded trial. Eur J Phys Rehabil Med. 2019;55:542–550. doi: 10.23736/S1973-9087.19.05598-9. [DOI] [PubMed] [Google Scholar]
  • 40.Hung JW, Chou CX, Hsieh YW, Wu WC, Yu MY, Chen PC, Chang HF, Ding SE. Randomized comparison trial of balance training by using exergaming and conventional weight-shift therapy in patients with chronic stroke. Arch Phys Med Rehabil. 2014;95:1629–1637. doi: 10.1016/j.apmr.2014.04.029. [DOI] [PubMed] [Google Scholar]
  • 41.Junior VADS, Santos MS, Ribeiro NMDS, Maldonado IL. Combining proprioceptive neuromuscular facilitation and virtual reality for improving sensorimotor function in stroke survivors: a randomized clinical trial. J Cent Nerv Syst Dis. 2019;11:1179573519863826. doi: 10.1177/1179573519863826. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Kang MG, Yun SJ, Lee SY, Oh BM, Lee HH, Lee SU, Seo HG. Effects of upper-extremity rehabilitation using smart glove in patients with subacute stroke: results of a prematurely terminated multicenter randomized controlled trial. Front Neurol. 2020;11:580393. doi: 10.3389/fneur.2020.580393. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Kiper P, Agostini M, Luque-Moreno C, Tonin P, Turolla A. Reinforced feedback in virtual environment for rehabilitation of upper extremity dysfunction after stroke: preliminary data from a randomized controlled trial. BioMed Res Int. 2014;2014:752128. doi: 10.1155/2014/752128. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Kiper P, Szczudlik A, Agostini M, Opara J, Nowobilski R, Ventura L, Tonin P, Turolla A. Virtual reality for upper limb rehabilitation in subacute and chronic stroke: a randomized controlled trial. Arch Phys Med Rehabil. 2018;99:834–842.e4. doi: 10.1016/j.apmr.2018.01.023. [DOI] [PubMed] [Google Scholar]
  • 45.Laffont I, Froger J, Jourdan C, Bakhti K, van Dokkum LEH, Gouaich A, Bonnin HY, Armingaud P, Jaussent A, Picot MC, Le Bars E, Dupeyron A, Arquizan C, Gelis A, Mottet D. Rehabilitation of the upper arm early after stroke: video games versus conventional rehabilitation. A randomized controlled trial. Ann Phys Rehabil Med. 2020;63:173–180. doi: 10.1016/j.rehab.2019.10.009. [DOI] [PubMed] [Google Scholar]
  • 46.Lee IW, Kim YN, Lee DK. Effect of a virtual reality exercise program accompanied by cognitive tasks on the balance and gait of stroke patients. J Phys Ther Sci. 2015;27:2175–2177. doi: 10.1589/jpts.27.2175. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.Lloréns R, Gil-Gómez JA, Alcañiz M, Colomer C, Noé E. Improvement in balance using a virtual reality-based stepping exercise: a randomized controlled trial involving individuals with chronic stroke. Clin Rehabil. 2015;29:261–268. doi: 10.1177/0269215514543333. [DOI] [PubMed] [Google Scholar]
  • 48.Mekbib DB, Debeli DK, Zhang L, Fang S, Shao Y, Yang W, Han J, Jiang H, Zhu J, Zhao Z, Cheng R, Ye X, Zhang J, Xu D. A novel fully immersive virtual reality environment for upper extremity rehabilitation in patients with stroke. Ann N Y Acad Sci. 2021;1493:75–89. doi: 10.1111/nyas.14554. [DOI] [PubMed] [Google Scholar]
  • 49.Miclaus R, Roman N, Caloian S, Mitoiu B, Suciu O, Onofrei RR, Pavel E, Neculau A. Non-immersive virtual reality for post-stroke upper extremity rehabilitation: a small cohort randomized trial. Brain Sci. 2020;10:655. doi: 10.3390/brainsci10090655. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Ögün MN, Kurul R, Yaşar MF, Turkoglu SA, Avci Ş, Yildiz N. Effect of leap motion-based 3D immersive virtual reality usage on upper extremity function in ischemic stroke patients. Arq Neuropsiquiatr. 2019;77:681–688. doi: 10.1590/0004-282X20190129. [DOI] [PubMed] [Google Scholar]
  • 51.Oh YB, Kim GW, Han KS, Won YH, Park SH, Seo JH, Ko MH. Efficacy of virtual reality combined with real instrument training for patients with stroke: a randomized controlled trial. Arch Phys Med Rehabil. 2019;100:1400–1408. doi: 10.1016/j.apmr.2019.03.013. [DOI] [PubMed] [Google Scholar]
  • 52.Rodríguez-Hernández M, Polonio-López B, Corregidor-Sánchez AI, Martín-Conty JL, Mohedano-Moriano A, Criado-Álvarez JJ. Effects of specific virtual reality-based therapy for the rehabilitation of the upper limb motor function post-ictus: randomized controlled trial. Brain Sci. 2021;11:555. doi: 10.3390/brainsci11050555. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Song GB, Park EC. Effect of virtual reality games on stroke patients’ balance, gait, depression, and interpersonal relationships. J Phys Ther Sci. 2015;27:2057–2060. doi: 10.1589/jpts.27.2057. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Thielbar KO, Lord TJ, Fischer HC, Lazzaro EC, Barth KC, Stoykov ME, Triandafilou KM, Kamper DG. Training finger individuation with a mechatronic-virtual reality system leads to improved fine motor control post-stroke. J Neuroeng Rehabil. 2014;11:171. doi: 10.1186/1743-0003-11-171. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Fisher RS, Acharya JN, Baumer FM, French JA, Parisi P, Solodar JH, Szaflarski JP, Thio LL, Tolchin B, Wilkins AJ, Kasteleijn-Nolst Trenité D. Visually sensitive seizures: an updated review by the Epilepsy Foundation. Epilepsia. 2022;63:739–768. doi: 10.1111/epi.17175. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Table 1

Search terms and strategies

bn-18-e2-s001.xls (59KB, xls)
Supplementary Table 2

Characteristics of included studies

bn-18-e2-s002.xls (64.5KB, xls)

Articles from Brain & NeuroRehabilitation are provided here courtesy of Korean Society for NeuroRehabilitation

RESOURCES