Sage Journals: Discover world-class research

Abstract

Background

Stroke is one of the most widespread reasons for acquired adult disability. Recent experimental studies have reported the beneficial influence of Wii Fit-based feedback on improving overall balance and gait for stroke survivors.

Methods

We conducted a systematic review of the literature using the following keywords to retrieve the data: feedback, biofeedback, stroke, visual, auditory, tactile, virtual reality, videogame rehabilitation, Nintendo Wii stroke, videogame stroke, exergame stroke, Nintendo Wii rehabilitation, balance, and gait. A review and meta-analysis of RCTs regarding Wii Fit-based rehabilitation accompanied by conventional therapy effects on Berg Balance Scale (BBS), Timed Up and Go (TUG), functional reach test, and gait (speed) in stroke survivors was conducted.

Objective

To determine the impacts of Wii Fit-based feedback combined with traditional therapy on balance and gait in stroke survivors.

Results

22 studies were included. The meta-analysis results revealed statistically significant improvements in functional ambulation measured using TUG (p < 0.0001), balance measured using BBS (p = 0.0001), and functional reach test (p = 0.01), but not in gait speed (p = 0.32) following Wii Fit-based feedback. Regarding the types of feedback, significant differences were found in BBS scores when mixed visual and auditory feedback was used.

Conclusion

Wii Fit-based feedback has desired effects on improving balance in stroke patients, making it a suitable adjunct to physical therapy.

Keywords

feedback wii fit stroke visual auditory stroke rehabilitation

Background

Stroke is one of the most widespread reasons for acquired adult disability and a common cause of morbidity and hospitalization worldwide (Brea et al., 2013; Mustafaoğlu et al., 2018). Stroke patients frequently suffer from motor and sensory deficits, which can result in difficulties with mobility, balance, and impaired motor control (Oliveira et al., 2011). It is estimated that 80% of survivors possess upper extremity motor problems, influencing their daily living activities execution, society’s performance, and quality of life (Bosomworth et al., 2021; Duncan Millar et al., 2019; Schnabel et al., 2021). Additionally, changes in body alignment take place, demanding the inclusion of therapeutic approaches aimed at enhancing postural control and weight-bearing symmetry (Cano-Mañas et al., 2020; Tessem et al., 2007; Tyson et al., 2006). The use of intensive, repetitive, and task-oriented physical therapy strategies can improve motor function in stroke patients by promoting neuroplasticity and motor learning (Pollock et al., 2014). Repetitive task training, which involves carrying out functionally appropriate tasks with a high level of intensity, improves transfer ability, balance, the function of the lower extremities, and gait speed in stroke survivors (French et al., 2016; Langhorne et al., 2009).

Virtual rehabilitation is a new therapeutic method according to simulation workouts that apply the technology of virtual reality. Through these kinds of interventions, therapists could be able to design therapeutic regimens that promote the neurological elements of brain plasticity: repetitive, intensive, and task-oriented practices in encouraging circumstances, resulting in enhanced adherence to rehabilitation (Burdea, 2003). Furthermore, these workout modalities are creative and fun ways to exercise (Karssemeijer et al., 2019).

Virtual treatment using the Nintendo Wii—one novel biofeedback resource that is being established for training—in addition to traditional physical therapy intervention has been executed in previous literature on patients with variability in stroke commencement and varying stages of recovery (Chen et al., 2015; You et al., 2005) and has been proven that it could be applied as a strategy to raise functionality and activities of daily living. There is currently insufficient information to draw conclusions regarding the effectiveness of Nintendo Wii-based virtual therapy for chronically physically disabled people caused by stroke (Dos Santos et al., 2015; Marques-Sule et al., 2021). Wii Fit from Nintendo has been used by some researchers alongside traditional therapy regimens in post-stroke rehabilitation. In this context, several investigations demonstrate that the Wii might be an effective supplementary therapy to conventional treatment for improving functionality (Cheok et al., 2015) and upper limb motor function (Aramaki et al., 2019; Carregosa et al., 2018) in stroke patients. Nevertheless, balance-related outcomes reveal various findings (Cheok et al., 2015; Karasu et al., 2018).

Virtual reality’s observational learning capabilities can activate mirror neurons in the cortex. Patients who experienced sensory feedback during the virtual reality intervention were more likely to achieve the desired motor activity (Levin & Demers, 2021). The feedback can boost use-dependent cortical plasticity development, resulting in improved motor control. Additionally, virtual reality training-induced functional improvement could dramatically promote the confidence and self-efficacy of participants in a new environment. Also, another benefit of virtual reality is that it can reduce patient costs and save on labor (Demers et al., 2021).

A 2015 systematic review by Cheok et al. (2015) investigated the effectiveness of Nintendo Wii compared with no intervention or other exercise interventions in the rehabilitation of stroke adults; the authors stated that the addition of Wii Fit to routine rehabilitation in chronic stroke patients meaningfully resulted in improved performance in some variables but not in all physical measures. Likewise, another 2015 systematic review by Dos Santos et al. (2015) indicated that the Nintendo Wii could enhance an individual’s motor function, but the data regarding balance and functional independence were ambiguous. These conclusions demonstrate that Wii Fit-based intervention could be a supplemental therapeutic strategy, although the influence remains unclear. Despite achievements that have been documented in the study of the beneficial effects of Wii Fit-based feedback intervention in addition to conventional therapy on stroke, the findings are still controversial in this regard. Moreover, the limited number of high-quality investigations directly examining Wii-based feedback is unable to make any judgments on the most efficient impact on balance performance and gait ability.

ed number of meta-analyses on the use of the Nintendo Wii in the rehabilitation of stroke victims have been conducted (Cheok et al., 2015; Dos Santos et al., 2015; Iruthayarajah et al., 2017); nevertheless, the purposes of each one varied within these reviews. There is evidence to support conducting an updated meta-analysis because more randomized controlled trials (RCTs) have since been published. This update comprises data pooling for balance-related outcomes and gait speed previously not fulfilled due to inadequate outcome data. Therefore, the current meta-analysis aimed to update previous pooled analyses, utilizing only level 1 (RCT) evidence investigating Wii Fit-based feedback intervention on balance and gait in post-stroke adult patients.

Methods

Study Design and Search Strategy

We used the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement (Page et al., 2021) as a guide for our study and this report. To collect relevant papers related to biofeedback therapy in stroke patients, online databases of CINAHL Medical Science, Medline, PubMed, and Scopus were searched with a mixture of keywords of “feedback”, “biofeedback”, “stroke”, “visual”, “auditory”, “tactile”, “virtual reality”, “videogame rehabilitation”, “Nintendo Wii stroke”, “videogame stroke”, “exergame stroke”, “Nintendo Wii rehabilitation”, “balance”, and “gait”. The search strategy comprising all the items from database inception was developed until June 15, 2022. After the initial screening, systematic reviews, meta-analyses, and all references were also searched to find additional research. The final selection of included investigations was agreed upon by two authors according to the following criteria: (1) study design: RCTs and controlled trials of Nintendo Wii-based feedback rehabilitation published in the English language, excluding review articles, conference, abstracts, and study protocols; (2) comparison intervention: studies that applied conventional therapy and/or other exercise programs; (3) population: men and women (age ≥18 years) with stroke; (4) Outcome measures: the outcome measures were functional mobility (measured by timed up and go (TUG), balance (measured by the berg balance scale (BBS), functional reach test, and gait speed.

Data Extraction

We extracted the following data: author, year of publication, country, demographic characteristics of patients, types of feedback (visual, auditory, tactile), protocol applied in terms of time, frequency, and number of sessions, mean and standard deviation (SD) of relevant outcome measures, and methodological quality. Two authors (SMGD and NMR) independently extracted the mentioned data from each study. The third author (RA) resolved any disagreements.

Study Quality

We utilized an adapted Physiotherapy Evidence Database (PEDro) scale (Maher et al., 2003) to determine the study’s methodological quality, which is an 11-item questionnaire considered to collect data. The studies were independently rated by two investigators (SMGD and RA), and any controversy was resolved by consensus with a third investigator (NMR). The PEDro scale has been reported to be valid (De Morton, 2009) and reliable (Maher et al., 2003). The interpretation of quality scores is as follows: 9 to 10, excellent; 6 to 8, good; 4 to 5, fair; and less than 4, poor (Pang et al., 2006). Only those RCTs scoring ≥5 on the PEDro scale—a value considered to be of moderate to high quality (Moseley et al., 2002) —were entered for analysis.

Publication Bias

To qualitatively measure publication bias, funnel plots of the effect size were generated for each study group by Comprehensive Meta-Analysis software (version two; Biostat Inc, Englewood, New Jersey, USA). Funnel plot asymmetry was assessed using Begg and Egger tests, and a significant publication bias was deemed if the p value was <0.10 (Borenstein et al., 2021). We used the trim and fill computation to measure the effect of publication biases on the interpretation of the results (Borenstein et al., 2021).

Data Synthesis and Statistical Analysis

We performed 2 separate analyses in the current review. Effect sizes were calculated to compare the effects of Wii Fit-based feedback rehabilitation versus conventional therapy on the improvement of balance-related outcome measures (i.e., time of TUG, score of BBS, and functional reach test) and gait performance. After considering the overall effect sizes, subgroup analyses were performed according to types of feedback (visual, mixed visual and auditory). The effect size of any outcomes was summarized by computing the mean difference (MD) between the experimental and control conditions at baseline and after intervention for all included RCTs. Due to the similar methods of reporting techniques for outcomes, MD with a 95% confidence interval (CI) was used. The baseline mean was subtracted from the after intervention mean, and the change in SD was calculated using study group subject numbers in conjunction with group p-values or 95% CI, where the change in mean and SD was not reported. If the RCTs had more than one post-intervention assessment period, only the data collected at the end of the intervention period were involved in the meta-analysis. We evaluated the heterogeneity among the RCTs using the I² statistic, with values >50% considered to show significant heterogeneity. (Higgins et al., 2003) A meta-analysis was conducted using forest plots and considered to have a 5% level of significance to demonstrate the significance of the results. We conducted this meta-analysis using Review Manager 5.4.1 (The Nordic Cochrane Centre, Copenhagen, Denmark).

Results

Study and Participant Characteristics

Initially, we found 752 papers from PubMed, Medline, CINAHL Medical Science, and Scopus databases and by hand searching. After duplicate titles and the exclusion of papers according to abstract and title were removed, 32 full-text papers remained for screening. 10 other papers were excluded due to the following reasons: (a) included healthy participants (Agmon et al., 2011; Bateni, 2012; Bieryla & Dold, 2013; Cone et al., 2015), (b) systematic review (Cheok et al., 2015; Felipe et al., 2020), (c) included no stroke patients (Padala et al., 2017), (d) lack of access to data (Fritz et al., 2013), (e) intervention not Wii (Zhang et al., 2020), and (f) the control group participated in a similar intervention (Bower et al., 2014). Twenty-two papers met our inclusion criteria and were included in the review (PRISMA flow diagram; Figure 1).

Figure 1.

Study selection represented by PRISMA flowchart.

Studies were conducted in the Republic of Korea (7), Spain (3), Taiwan (3), Italy (2), Brazil (1), Cyprus (1), Germany (1), Israel (1), Malaysia (1), Pakistan (1), and Turkey (1). The 22 included RCTs had 648 patients, 328 (∼51%) participants in the experimental group, and 320 (∼49%) in the control group. The included studies were published between 2011 and 2022.

Description of Included Articles

The 22 investigations included in this review were randomized studies (Table 1). Sample size ranged between 10 and 64 participants (adults). The mean age of participants in the experimental and control groups was 58.7 ± 10.5 and 59.0 ± 10.7 years, respectively. Table 1 provides a description of the included studies. Functional mobility was assessed by the Timed Up and Go test (TUGT) in 16 RCTs (Barcala et al., 2013; Park et al., 2017; Cho et al., 2012; Choi et al., 2018; Choi et al., 2017; Cikajlo et al., 2020; Gil-Gómez et al., 2011; Golla et al., 2018; Hung et al., 2014; Karasu et al., 2018; Lee et al., 2017; Lee et al., 2016; Marques-Sule et al., 2021; Park et al., 2017; Singh et al., 2013; Yatar & Yildirim, 2015). Functional balance was evaluated by the Berg Balance Scale in 14 RCTs (Anwar et al., 2022; Barcala et al., 2013; Cho et al., 2012; Choi et al., 2018; Gil-Gómez et al., 2011; Golla et al., 2018; Karasu et al., 2018; Lee et al., 2016, 2017; Marques-Sule et al., 2021; Morone et al., 2014; Park et al., 2017; Yang et al., 2015; Yatar & Yildirim, 2015). Seven studies (Cano-Mañas et al., 2020; Choi et al., 2017; Karasu et al., 2018; Lee et al., 2015, 2016, 2017; Yatar & Yildirim, 2015) investigated dynamic balance by functional reach test, and five intervention arms (Givon et al., 2016; Lee et al., 2015) investigated gait speed.

Table 1.

Characteristics of Included Studies.

Study	Country	Participants			Type of Feedback	Intervention	Relevant Outcome Measure(s)
Study	Country	n (EXP/CON) Gender	Age (yr) Mean (SD)	Time Since Stroke	Type of Feedback	Intervention	Relevant Outcome Measure(s)
Anwar et al. (2022)	Pakistan	34/34	EXP: 51.6 (7.2)CON: 51.4 (5.8)	NA	NA	VR training using Wii sports (tennis and boxing), Wii balance board, and Wii cooking mama games; 3 day/wk × 60 min × 6 wk	BBS
Barcala et al. (2013)	Brazil	10/109 M, 11 F	EXP: 65.2 (12.5)CON: 63.5 (14.5)	MonthsEXP: 12.3 (7.1)CON: 15.2 (6.6)	Visual	CT + VR using Wii fit; - EXP: CT 1 hour + VR, 30 min/day, 2 times/wk, 5 wk; - CON: CT 1 hour 30 min/day, 2 times/wk, 5 wk	BBS, TUG
Cano-Mañas et al. (2020)	Spain	23/2523 M/25 F	EXP: 60.4 (9.8)CON: 65.7 (10.4)	DaysEXP: 50.9 (18.4)CON: 54.5 (18.7)	NA	CT plus the experimental intervention, consisting of video-game based therapy with commercial video games using the Xbox 360° video games console and the Kinect; 3 sessions/wk × 90 min × 8 wk	FRT
Cho et al. (2012)	Korea	11/1114 M/8 F	EXP: 65.3 (8.4)CON: 63.1 (6.9)	MonthsEXP: 12.5 (2.6)CON: 12.6 (2.5)	NA	VR balance training with a video-game system; All participants: 5 time/wk × 30 min CT + 30 min occupational therapy × 6 wkEXP: Plus 3 times/wk × 30 min VR × 6 wk	BBS, TUG
Choi et al. (2017)	Korea	12/1215 M/9 F	EXP: 61.2 (5.6)CON: 61.9 (6.1)	MonthsEXP: 13.7 (3.9)CON: 14.2 (4.8)	NA	Constraint-induced movement therapy using used ski slalom and soccer heading from the Wii fit game system; All participants received 60 minutes a day 5 days a week for 4 weeks. EXP group also received 30 minutes a day 3 days a week for 4 weeks	TUG, FRT
Choi et al. (2018)	Korea	14/1417 M/11 F	EXP: 49.5 (23.0)CON: 51.0 (13.7)	NA	Auditory and visual	EXP group: Performed VR training using the Wii fit balance systemCON group: Performed general balance training for 30 minutes a day thrice a week for 6 weeksBoth groups: Received CT for 90 minutes a day, five times a week for 6 weeks	BBS, TUG
Cikajlo et al. (2020)	Italy	10/1015 M/5 F	EXP: 50.3 (7.9)CON: 51.8 (15.5)	YearsEXP: 4CON: 7.4	Auditory and visual	EXP group: Received 1 week of conventional balance training and an additional 15 min of daily of multiple-game exergamingCON group: Received 1 week of conventional balance training and an additional 15 min of daily intensive exercise	TUG
Gil-Gómez et al. (2011)	Spain	9/811 M/6 F	EXP: 45.8 (15.4)CON: 49.1 (21.2)	DaysEXP: 478.0 (324.8)CON: 675.5 (283.1)	Auditory and visual	EXP group: Received a total of 20 one-hour-sessions VR using a system based on the Nintendo® Wii balance boardCON group: Each patient participated in a total of 20 one-hour-sessions of standard rehabilitation	BBS, TUG
Givon et al. (2016)	Israel	24/2328 M/19 F	EXP: 56.7 (9.3)CON: 62.0 (9.3)	YearsEXP: 3.0 (1.8)CON: 2.6 (1.8)	NA	The intervention included 2 one-hour sessions a week for 3-months while gradually increasing the physical demandsEXP group: Each session started with a 5-minute group warm up of walking while playing a Wii fit walking/jogging game. Subsequently participants were divided up into the work stations; played games in pairs on one console then rotated to play another console with another partner. The occupational therapists combined their clinical knowledge with the characterization of the consoles to select the consoles and gamesCON group: The sessions started with a group warm up of upper and lower extremity movements, stretching, marching and arm swings, followed by walking around the rehabilitation building for 5 minutes. Participants were then divided into pairs or triads to perform functional activities such as picking up and transferring objects from one side of the room to the other	Gait speed
Golla et al. (2018)	Germany	6/57 M/4 F	EXP: 74.6 (10.1)CON: 73.5 (7.1)	WeeksEXP: 18.0 (3.2)CON: 19.0 (4.3)	NA	Participants received a 6-week (once per week) supervised balance training at the study center, followed by a 6-week (three times per week) unsupervised balance training at home. The Nintendo Wii™ balance board was used for one intervention arm and conventional balance exercises for the other intervention arm	BBS, TUG
Hung et al. (2014)	Taiwan	13/1518 M/10 F	EXP: 55.4 (10.0)CON: 53.4 (10.0)	MonthsEXP: 21.0 (11.3)CON: 15.9 (8.0)	Auditory and visual	EXP group: Performed 12-week Wii fit trainingCON group: The program provided weight-shifting exercises in anterior–posterior or medial–lateral directions and emphasized the weight-bearing of the affected leg for 12 weeks	TUG
Karasu et al. (2018)	Turkey	12/1110 M/13 F	EXP: 62.3 (11.8)CON: 64.1 (12.2)	DaysEXP: 29 (14–348)CON: 31 (13–324)	NA	EXP group: Received 20 min of balance exercise, 5 days a week, for 4 consecutive weeks, with Wii fit and Wii balance board, in addition to CT.CON group: Performed CT for 2–3 h a day, 5 days a week	BBS, TUG, FRT
Lee et al. (2015)	Korea	12/1216 M/8 F	EXP: 45.9 (12.3)CON: 49.2 (12.8)	NA	Auditory and visual	EXP group: Received VR-based training using the Nintendo Wii fit plus, for 30 min/day, 3 times/wk for 6 wkCON group: Received task-oriented training additional task-oriented programs for 30 min/day, 3 times/wk for 6 wkBoth groups: Underwent CT for 60 min/day, 5 times/wk for 6 wk	Velocity, FRT
Lee et al. (2016)	Korea	5/55 M/5 F	EXP: 65.2 (5.0)CON: 66.2 (3.4)	MonthsEXP: 3.1 (1.6)CON: 3.3 (1.1)	NA	EXP group: Additionally participated in the canoe game-based VR training program. The canoeing game in the Nintendo Wii sports Resort package was used as a VR training program. 30 min day, 3 days a week, for 4 weeksBoth groups: Participated in the same CT program that consisted of physical therapy (30 min × 2 times/day × 5days/wk × 4wk), occupational therapy (30 min × 2 times/day × 5days/wk × 4wk), and FES (15 min × 5 days/wk × 4wk)	BBS, TUG, FRT
Lee et al. (2017)	Taiwan	26/2134 M/13 F	EXP: 59.3 (8.9)CON: 55.8 (9.6)	DaysEXP: 839.8 (719.1)CON: 653.2 (589.7)	NA	EXP group: Received 45 minutes of ST and 45 minutes of interactive VR balance-related games twice a week for 6 weeksCON group: Received ST for 90 minutes, twice a week for 6 weeks	BBS, TUG, FRT
Marques-Sule et al. (2021)	Spain	15/1418 M/11 F	EXP: 61.5 (8.4)CON: 58.2 (7.4)	NA	NA	EXP group: Additionally received the Nintendo Wii rehabilitation program for 4 weeks, 2 sessions per weekBoth groups: Received CT for 4 weeks, 2 sessions per week	BBS, TUG
Morone et al. (2014)	Italy	25/25	EXP: 58.4 (9.6)CON: 62.0 (10.3)	DaysEXP: 61.0 (36.5)CON: 41.6 (36.9)	NA	EXP group: Performed balance training with Wii fit. 12 sessions of 20 minutes each of balance training, 3 times a week for 4 weeksCON group: Participated in usual balance therapy. They added to standard physiotherapy 20 minutes of balance therapy 3 times/week for 4 weeksBoth groups: Also treated with CT (40 min 2 times/day)	BBS
Park et al. (2017)	Korea	10/1010 M/10 F	EXP: 62.0 (17.1)CON: 65.3 (10.5)	MonthsEXP: 10.8 (7.1)CON: 14.1 (7.7)	Visual	EXP group: Participated in a 30-minute VR training session using Xbox Kinect, followed by a 30-minute session of CT.CON group: Participated in a 30-minute CT session onlyAll interventions were performed daily for 6 weeks	BBS, TUG
Singh et al. (2013)	Malaysia	15/15	EXP: 65.4 (9.8)CON: 67.0 (8.4)	MonthsEXP: 40.5 (41.8)CON: 34.9 (24.6)	NA	EXP group: Received 30 minutes of VR balance games in addition to 90 minutes of standard physiotherapyCON group: Continued with their 2 hours of routine standard physiotherapyBoth groups: Received 12 therapy sessions: Two-hour sessions twice per week for six continuous weeks	TUG
bing Song (2015)	Korea	20/2022 M/18 F	EXP: 51.4 (4.6)CON: 50.1 (7.8)	MonthsTotal: 14.3 (3.4)	Auditory and virtual	EXP group: Performed VR training using Xbox Kinect for 30 min per day, five times a week, for 8 weeksCON group: Performed ergometer training using an ergometer bicycle for 30 min per day, five times a week, for 8 weeks	TUG
Yang et al. (2015)	Taiwan	7/59 M/3 F	EXP: 62.4 (12.9)CON: 57.6 (17.3)	MonthsEXP: 6.0 (4.0)CON: 5.8 (3.3)	Visual	EXP group: Underwent 20 minutes of computer-generated interactive visual feedback training following 20 minutes of regular physical therapy three times a week for a total of 3 weeksCON group: Underwent 20 minutes of physical therapy after 20minutes of mirror visual feedback training	BBS
Yatar and Yildirim (2015)	Cyprus	15/1513 M/17 F	EXP: 62.8 (10.9)CON: 56.6 (16.4)	EXP: 3.7 (4.4)CON: 4.2 (4.9)	Auditory and virtual	All of the subjects received exercise training based on a neurodevelopmental approach in addition to either Wii fit or progressive balance training for total of 1 hour a day, 3 days per week for 4 weeks	BBS, TUG, FRT

Note. EXP, experimental; CON, control; M, male; F, female; SD, standard deviation; FRT, Functional Reach Test; FES, Functional Electrical Stimulation; BBS, Berg Balance Scale; CT, conventional therapy; TUG, Timed Up and Go test; VR, virtual reality; ST, strength training.

Outcome Measures

Functional Mobility

16 RCTs (Barcala et al., 2013; Park et al., 2017; Cho et al., 2012; Choi et al., 2018; Choi et al., 2017; Cikajlo et al., 2020; Gil-Gómez et al., 2011; Golla et al., 2018; Hung et al., 2014; Karasu et al., 2018; Lee et al., 2017; Lee et al., 2016; Marques-Sule et al., 2021; Park et al., 2017; Singh et al., 2013; Yatar & Yildirim, 2015) providing a total of 399 patients (205 participants in the experimental groups and 194 participants in the control groups), reported changes in functional mobility (measured by TUG) as an outcome measure. Random-effects model revealed statistically significant improvements in TUG test after Wii Fit-based feedback rehabilitation (MD = −0.83 s; 95% CI: −1.24 to −0.41; p < 0.0001, Figure 2). Subgroup analyses by types of feedback are provided in Table 2. These analyses revealed no significant improvements in studies that used visual (MD = −2.96 s, 95% CI: −6.05 to 0.12; p = 0.06, two studies) or mixed visual and auditory (MD = 0.57, 95% CI: −1.76 to 2.90; p = 0.63, six studies) modes of feedback.

Figure 2.

Effect size (95% CI) of Wii Fit-based feedback rehabilitation on TUG by pooling data from 16 studies comparing Wii Fit-based feedback versus standard care using random effects meta-analysis (n = 399).

Table 2.

Pooled Estimates of Balance Outcomes Within Subgroup.

Group	No of Arms	MD (95%CI)	p-value	p-heterogeneity	I², %
TUG (s)
Visual	2	−2.96 (−6.05, 0.12)	0.06	0.33	0
Visual + auditory	6	0.57 (−1.76, 2.90)	0.63	0.64	0
BBS score
Visual	3	3.81 (−2.69, 10.31)	0.25	0.01	78
Visual + auditory	3	2.47 (0.89, 4.06)	0.002	0.45	0

Note. Bold value depicts statistical significance (p < 0.05).

Balance

14 RCTs (Anwar et al., 2022; Barcala et al., 2013; Cho et al., 2012; Choi et al., 2018; Gil-Gómez et al., 2011; Golla et al., 2018; Karasu et al., 2018; Lee et al., 2016, 2017; Marques-Sule et al., 2021; Morone et al., 2014; Park et al., 2017; Yang et al., 2015; Yatar & Yildirim, 2015) providing a total of 394 patients (204 participants in the experimental groups and 190 participants in the control groups), reported changes in balance (measured by BBS) as an outcome measure. Statistically significant improvements were found for BBS after Wii Fit-based feedback rehabilitation (MD = 3.76; 95% CI: 1.84 to 5.68; p = 0.001, Figure 3). Subgroup analyses by the types of feedback revealed significant improvements in BBS score for studies used mixed of visual and auditory feedback (MD = 2.47, 95% CI: 0.89 to 4.06; p = 0.002, three studies) but not for studies used only visual feedback (MD = 3.81, 95% CI: −2.69 to 10.31; p = 0.25, three studies; Table 2).

Figure 3.

Effect size (95% CI) of Wii Fit-based feedback rehabilitation on BBS score by pooling data from 14 studies comparing Wii Fit-based feedback versus standard care using random effects meta-analysis (n = 394).

Functional Reach Test

Seven RCTs (Cano-Mañas et al., 2020; Choi et al., 2017; Karasu et al., 2018; Lee et al., 2015, 2016, 2017; Yatar & Yildirim, 2015) providing a total of 206 patients (105 participants in the experimental groups and 101 participants in the control groups) reported changes in functional reach test as an outcome measure. Statistically significant improvements were found for functional reach test after Wii Fit-based feedback rehabilitation (MD = 2.43 cm; 95% CI: 0.50 to 4.35; p = 0.01, Figure 4).

Figure 4.

Effect size (95% CI) of Wii Fit-based feedback rehabilitation on functional reach test by pooling data from seven studies comparing Wii Fit-based feedback versus standard care using random effects meta-analysis (n = 206).

Gait Speed

Two RCTs (5 arms; Givon et al., 2016; Lee et al., 2015) providing a total of 143 patients (72 participants in the experimental groups and 71 participants in the control groups) reported changes in gait speed as an outcome measure. The random-effects model revealed no statistically significant improvements in gait speed after Wii Fit-based feedback rehabilitation (MD = 0.08 m/s; 95% CI: −0.08 to 0.24; p = 0.32, Figure 5).

Figure 5.

Effect size (95% CI) of Wii Fit-based feedback rehabilitation on gait speed test by pooling data from two studies comparing Wii Fit-based feedback versus standard care using random effects meta-analysis (n = 143). ECNB, eyes-closed narrow base; ECWB, eyes-closed wide base; EONB, eyes-open narrow base; EOWB, eyes-open wide base.

Quality Assessment

The quality assessment of included RCTs is provided in Table 3. Overall, the median PEDro score was 7/11 points (range five–9). In general, studies met the criteria for explicitly declaring patients' eligibility (100%), statistical comparisons of treatment and control groups (100%), and providing means/SDs for important variables (100%), having comparable groups determined by baseline measurements (100%), achieving follow-up assessments for more than 85% of study participants (90.9%), random allocation to groups (86.4%). A moderate number of studies met criteria for blinding assessors (50%), and treatment allocation concealment (50%).

Table 3.

Studies that Meet the Criteria of the PEDro Scale.

Study	C1	C2	C3	C4	C5	C7	C8	C9	C10	C11	Overall PEDro
Anwar 2022	1	1	1	1	0	1	1	0	1	1	8
Barcala 2013	1	1	1	1	0	1	1	0	1	1	8
Park 2017	1	1	0	1	0	0	1	0	1	1	6
Cano-Mañas 2020	1	1	1	1	1	1	1	0	1	1	9
Cho 2012	1	1	0	1	0	0	1	0	1	1	6
Choi 2017	1	1	1	1	0	1	1	0	1	1	8
Choi 2018	1	1	0	1	0	0	0	1	1	1	6
Cikajlo 2020	1	0	0	1	0	0	1	0	1	1	5
Gil-Gómez 2011	1	1	1	1	0	0	0	0	1	1	6
Givon 2016	1	1	1	1	0	0	1	1	1	1	8
Golla 2018	1	1	0	1	0	0	1	0	1	1	6
Hung 2014	1	1	1	1	0	1	1	0	1	1	8
Karasu 2018	1	1	0	1	0	1	1	0	1	1	7
Lee 2015	1	0	0	1	0	1	1	0	1	1	6
Lee 2016	1	1	0	1	1	0	1	0	1	1	7
Lee 2017	1	1	0	1	0	1	1	0	1	1	7
Marques-Sule 2021	1	1	1	1	0	1	1	0	1	1	8
Morone 2014	1	1	1	1	1	0	1	0	1	1	8
Park 2017	1	1	1	1	0	1	1	0	1	1	8
Singh 2013	1	0	0	1	0	0	1	0	1	1	5
Yang 2015	1	1	1	1	0	1	1	0	1	1	8
Yatar 2015	1	1	0	1	0	0	1	0	1	1	6

Note. A “1” shows that research met that particular criterion, a “0” shows that research did not meet that criterion or that not enough information was provided to make an assessment. C1 = Eligibility criteria were specified; C2 = Participants were randomly allocated to groups; C3 = Treatment allocation was concealed; C4 = Groups were similar at baseline; C5 = Blinding of participants; C6 = Blinding of therapists administering treatment; C7 = Blinding of assessors for outcome measures; C8 = Measurement of key outcome from .85% of participants; C9 = Intention to treat analysis; C10 = Between-groups statistical comparison is reported for key outcome; C11 = Measures of central tendency and variability are provided.

Publication Bias

Funnel plots including Egger regression tests (p > 0.05 for all) for the different analyses did not suggest publication bias, nor did Duval and Tweedie’s trim and fill computation change the results (see Supplementary Figures S1-S4).

Discussion

The present systematic review and meta-analysis aimed to update previous pooled analyses, using only level 1 RCT evidence that evaluated the effect of Wii Fit-based feedback interventions on balance performance and gait ability in adults after stroke. Our primary analysis demonstrates that Wii Fit-based feedback interventions compared to standard rehabilitation care had a significant effect on TUG, BBS, and functional reach test. Through merged data analysis, it was discovered that mixed visual and auditory feedback offers meaningful improvements in balance (measured by BBS) compared to a single type of feedback (i.e., visual). In relation to gait ability, we found that gait speed did not significantly improve following Wii Fit-based feedback programs.

In general, Wii Fit-based feedback interventions appear to be more effective at promoting rehabilitation in chronic stroke patients with impaired balance than conventional rehabilitation approaches. A number of conceivable mechanisms that could explain these improvements in performance, in accordance with related theories presently available, are learning, reversal of learned nonuse, and skill training (Kleim et al., 2002; Remple et al., 2001; Taub et al., 1994).

The addition of Wii to conventional therapy appears to be secure and linked to sustained participation in ongoing rehabilitation when added to standard physiotherapy programs (Cheok et al., 2015). Our results reveal that Wii Fit-based feedback interventions significantly affected TUG (as a dynamic balance factor). These results are compatible with the results, who noted that the addition of Wii to standard care led to a significant improvement in TUG performance when compared with standard care alone (Cheok et al., 2015). The effect size obtained for TUG (−0.83 sec) in our meta-analysis was similar to the effect size obtained by Gary and colleagues (−0.8 sec). However, it was smaller than the minimal detectable change of 2.9 seconds for TUG performance in chronic patients with stroke (Flansbjer et al., 2005). Therefore, this finding may be explained by chance or measurement error.

Iruthayarajah et al. (2017) performed a systematic review and meta-analysis on the effectiveness of virtual reality interventions for enhancing balance in chronic stroke patients. In total, 20 investigations were selected that evaluated the Nintendo® Wii Fit balance board, treadmill training and virtual reality, and postural training using virtual reality. They observed that among all virtual reality intervention types, there was no significant improvement in either the BBS or TUG in favor of the Wii Fit intervention (Iruthayarajah et al., 2017), which is inconsistent with our findings. Another meta-analysis investigating whether balance or gait training utilizing virtual reality is more effective compared to conventional balance or gait training in post-stroke patients suggested that virtual reality training is more effective compared to balance or gait training alone for improving balance (time of TUG and BBS score) or gait ability (speed) in these patients. Nevertheless, these researchers assessed all types of virtual reality interventions (de Rooij et al., 2016).

Wii Fit intervention is a virtual reality technology that affects the learning of actual activity and allows a boost in training intensity, delivering 3-dimensional feedback through visual, sensory, and auditory stimulation. There is a wide range of explanations for the additional benefit that virtual reality has over most currently available standard therapies. A high degree of repetition and variety are used to develop patient-specific motor training in virtual reality. The physiological underpinnings of motor learning are repetitive training (French et al., 2016). In addition to repetitive training, diversity in practice is crucial for motor learning, considering that it will strengthen the ability to adjust to new situations (Imam & Jarus, 2014). A further benefit of using virtual reality is that it allows therapists to deliver personalized training where the patient’s demands and characteristics can be readily considered when determining the training exercises' level of difficulty and intensity (Merians et al., 2006). Exercises that are required for optimal learning can be given to stroke patients under controlled constraints (Fung et al., 2006; Moreira et al., 2013). Furthermore, in virtual reality training such as Wii Fit, individuals can receive more feedback on their performance than in real-world practice.

It is widely accepted that feedback enhances the rate of learning (Holden, 2005) and that stroke patients profit from therapies with augmented feedback (Winstein et al., 1999). It has been demonstrated that providing individuals with stroke with visual stimuli can assist them in improving their balance (Barclay-Goddard et al., 2004; Walker et al., 2016). Finally, it is believed that using virtual reality will promote activity adherence and enjoyment while reducing perceived exertion (Sveistrup, 2004). The individual and the intervention may impact how motivated and engaged participants are during a virtual reality intervention. However, none of the RCTs included in the current meta-analysis evaluated motivation.

Notably, people with stroke underperform balance when walking; hence, improvement in it would be of great assistance to them. Most patients deem playing games a fun activity in their leisure time. Regular conventional therapy was discovered to be dull for individuals and less adorable (Aramaki et al., 2019). The findings of our study will assist therapists in using virtual reality to rehabilitate post-stroke patients. When a patient attempts to use his muscles, his coordination with the brain improves. In an upright position, most of the postural muscles are engaged. On the other hand, conventional physical therapy is passive, which causes sluggish improvement. Postural muscles should be considered in treatment regimens. Stroke can occasionally directly impact the patient’s capacity to control the surrounding environment. Therefore, it is challenging to maintain balance when you are not sure about your position in the surroundings (Kim et al., 2015; Lee et al., 2019; Noveletto et al., 2018). This review demonstrated how virtual reality helps stroke patients regain functional independence.

Our meta-analysis identified several limitations that should be considered when interpreting the findings in patients who are post-stroke. First, we only evaluated 22 studies, and 14 of them have small sample sizes (n < 30), demonstrating that more studies with larger sample sizes are required to deliver more convincing results. Second, the wide inclusion and variety of the included RCTs bring some limitations with them. The demographics of stroke patients in the included RCTs varied, particularly in terms of the large range in time since stroke. The impact of virtual reality intervention was predicted to be higher in individuals early following stroke since brain plasticity and structural reorganization are higher early after lesions (Felling & Song, 2015), and endogenous recovery after stroke has been documented to achieve a plateau in a 6-month period (Kwakkel & Kollen, 2013). Third, the included investigations in the current study compared the addition of Wii Fit to standard therapy versus isolated standard therapy. Due to the experimental group receiving longer-term rehabilitation, this may have established a bias in favor of the experimental group. Finally, the results of this review are limited to gait and balance outcomes, specifically BBS scores, TUG scores, functional reach test scores, and gait speed; hence, its generalizability in terms of outcome measures is in doubt. We decided to include these uniform outcome measures to carry out a valuable meta-analysis.

Conclusion

Wii Fit-based feedback is a feasible regimen for post-stroke patients. The addition of Wii Fit interventions to conventional therapy sessions resulted in statistically significant improvements in balance but not in gait speed. Until additional large-scale trials are available to confirm the effectiveness of Wii Fit-based feedback in stroke rehabilitation, Wii Fit appears to be a supplemental, promising, and safe strategy for stroke rehabilitation with improved long-term observation.

Supplemental Material

Supplemental Material - Wii Fit-Based Biofeedback Rehabilitation Among Post-Stroke Patients: A Systematic Review and Meta-Analysis of Randomized Controlled Trial

Supplemental Material for Wii Fit-Based Biofeedback Rehabilitation Among Post-Stroke Patients: A Systematic Review and Meta-Analysis of Randomized Controlled Trial by Seyedeh Maryam Ghazavi Dozin, Nasser Mohammad Rahimi, and Reza Aminzadeh in Biological Research for Nursing

Footnotes

Author Contributions

Nasser Mohammad Rahimi contributed to acquisition, analysis, and interpretation drafted manuscript critically revised manuscript gave final approval agrees to be accountable for all aspects of work ensuring integrity and accuracy.

Reza Aminzadeh contributed to conception and design drafted manuscript critically revised manuscript gave final approval agrees to be accountable for all aspects of work ensuring integrity and accuracy.

Seyedeh Maryam Ghazavi Dozin contributed to conception and design drafted manuscript critically revised manuscript gave final approval agrees to be accountable for all aspects of work ensuring integrity and accuracy.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Nasser Mohammad Rahimi

Supplemental Material

Supplemental material for this article is available online.

References

Agmon

Perry

C. K.

Phelan

Demiris

Nguyen

H. Q.

(2011). A pilot study of Wii Fit exergames to improve balance in older adults. Journal of Geriatric Physical Therapy, 34(4), 161–167. https://doi.org/10.1519/JPT.0b013e3182191d98

Anwar

Karimi

Ahmad

Gilani

S. A.

Khalid

Aslam

A. S.

Hanif

(2022). Virtual reality training using Nintendo wii games for patients with stroke: Randomized controlled trial. JMIR Serious Games, 10(2), e29830. https://doi.org/10.2196/29830

Aramaki

A. L.

Sampaio

R. F.

Cavalcanti

Dutra

F. C. M. S. E.

(2019). Use of client-centered virtual reality in rehabilitation after stroke: A feasibility study. Arquivos de Neuro-Psiquiatria, 77(9), 622–631. https://doi.org/10.1590/0004-282X20190103

Barcala

Grecco

L. A. C.

Colella

Lucareli

P. R. G.

Salgado

A. S. I.

Oliveira

C. S.

(2013). Visual biofeedback balance training using wii fit after stroke: A randomized controlled trial. Journal of Physical Therapy Science, 25(8), 1027–1032. https://doi.org/10.1589/jpts.25.1027

Barclay‐Goddard

R. E.

Stevenson

T. J.

Poluha

Moffatt

Taback

S. P.

(2004). Force platform feedback for standing balance training after stroke. The Cochrane Database of Systematic Reviews, 2004(4), CD004129. https://doi.org/10.1002/14651858.CD004129.pub2

Bateni

(2012). Changes in balance in older adults based on use of physical therapy vs the wii fit gaming system: A preliminary study. Physiotherapy, 98(3), 211–216. https://doi.org/10.1016/j.physio.2011.02.004

Bieryla

K. A.

Dold

N. M.

(2013). Feasibility of Wii Fit training to improve clinical measures of balance in older adults. Clinical Interventions in aging, 8, 775.

bin Song

(2015). Effect of virtual reality games on stroke patients’ balance, gait, depression, and interpersonal relationships. Journal of Physical Therapy Science, 27(7), 2057–2781, https://doi.org/10.2147/CIA.S46164

Borenstein

Hedges

L. V.

Higgins

J. P.

Rothstein

H. R.

(2021). Introduction to meta-analysis. John Wiley & Sons.

10.

Bosomworth

Rodgers

Shaw

Smith

Aird

Howel

Wilson

Alvarado

Andole

Cohen

D. L.

Dawson

Fernandez-Garcia

Finch

Ford

G. A.

Francis

Hogg

Hughes

Price

C. I.

Ternent

van Wijck

(2021). Evaluation of the enhanced upper limb therapy programme within the Robot-Assisted Training for the Upper Limb after Stroke trial: Descriptive analysis of intervention fidelity, goal selection and goal achievement. Clinical Rehabilitation, 35(1), 119–134. https://doi.org/10.1177/0269215520953833

11.

Bower

K. J.

Clark

R. A.

McGinley

J. L.

Martin

C. L.

Miller

K. J.

(2014). Clinical feasibility of the Nintendo Wii™ for balance training post-stroke: A phase II randomized controlled trial in an inpatient setting. Clinical Rehabilitation, 28(9), 912–923. https://doi.org/10.1177/0269215514527597

12.

Brea

Laclaustra

Martorell

Pedragosa

À.

(2013). Epidemiología de la enfermedad vascular cerebral en España. Clínica e Investigación en Arteriosclerosis, 25(5), 211–217. https://doi.org/10.1016/j.arteri.2013.10.006

13.

Burdea

G. C.

(2003). Virtual rehabilitation–benefits and challenges. Yearbook of Medical Informatics, 12(01), 170–176. https://doi.org/10.1055/s-0038-1638156

14.

Cano-Mañas

M. J.

Collado-Vázquez

Rodríguez Hernández

Muñoz Villena

A. J.

Cano-de-la-Cuerda

(2020). Effects of video-game based therapy on balance, postural control, functionality, and quality of life of patients with subacute stroke: A randomized controlled trial. Journal of Healthcare Engineering, 2020, 5480315, https://doi.org/10.1155/2020/5480315

15.

Carregosa

A. A.

Aguiar Dos Santos

L. R.

Masruha

M. R.

Coêlho

M. L. d. S.

Machado

T. C.

Souza

D. C. B.

Passos

G. L. L.

Fonseca

E. P.

Ribeiro

N. M. d. S.

de Souza Melo

(2018). Virtual rehabilitation through Nintendo wii in poststroke patients: Follow-up. Journal of Stroke and Cerebrovascular Diseases: The Official Journal of National Stroke Association, 27(2), 494–498. https://doi.org/10.1016/j.jstrokecerebrovasdis.2017.09.029

16.

Chen

M.-H.

Huang

L.-L.

Lee

C.-F.

Hsieh

C.-L.

Lin

Y.-C.

Liu

Chen

M.-I.

W.-S.

(2015). A controlled pilot trial of two commercial video games for rehabilitation of arm function after stroke. Clinical Rehabilitation, 29(7), 674–682. https://doi.org/10.1177/0269215514554115

17.

Cheok

Tan

Low

Hewitt

(2015). Is Nintendo wii an effective intervention for individuals with stroke? A systematic review and meta-analysis. Journal of the American Medical Directors Association, 16(11), 923–932. https://doi.org/10.1016/j.jamda.2015.06.010

18.

Cho

K. H.

Lee

K. J.

Song

C. H.

(2012). Virtual-reality balance training with a video-game system improves dynamic balance in chronic stroke patients. The Tohoku Journal of Experimental Medicine, 228(1), 69–74. https://doi.org/10.1620/tjem.228.69

19.

Choi

Lee

(2018). Influence of Nintendo wii fit balance game on visual perception, postural balance, and walking in stroke survivors: A pilot randomized clinical trial. Games for Health Journal, 7(6), 377–384. https://doi.org/10.1089/g4h.2017.0126

20.

Choi

H.-S.

Shin

W.-S.

Bang

D.-H.

Choi

S.-J.

(2017). Effects of game-based constraint-induced movement therapy on balance in patients with stroke: A single-blind randomized controlled trial. American Journal of Physical Medicine & Rehabilitation, 96(3), 184–190. https://doi.org/10.1097/PHM.0000000000000567

21.

Cikajlo

Rudolf

Mainetti

Borghese

N. A.

(2020). Multi-exergames to set targets and supplement the intensified conventional balance training in patients with stroke: A randomized pilot trial. Frontiers in Psychology, 11, 572. https://doi.org/10.3389/fpsyg.2020.00572

22.

Cone

B. L.

Levy

S. S.

Goble

D. J.

(2015). Wii Fit exer-game training improves sensory weighting and dynamic balance in healthy young adults. Gait & Posture, 41(2), 711–715. https://doi.org/10.1016/j.gaitpost.2015.01.030

23.

Demers

Fung

Subramanian

S. K.

Lemay

Robert

M. T.

(2021). Integration of motor learning principles into virtual reality interventions for individuals with cerebral palsy: Systematic review. JMIR Serious Games, 9(2), e23822. https://doi.org/10.2196/23822

24.

De Morton

N. A.

(2009). The PEDro scale is a valid measure of the methodological quality of clinical trials: A demographic study. The Australian Journal of Physiotherapy, 55(2), 129–133. https://doi.org/10.1016/s0004-9514(09)70043-1

25.

de Rooij

I. J.

van de Port

I. G.

Meijer

J.-W. G.

(2016). Effect of virtual reality training on balance and gait ability in patients with stroke: Systematic review and meta-analysis. Physical Therapy, 96(12), 1905–1918. https://doi.org/10.2522/ptj.20160054

26.

Dos Santos

L. R. A.

Carregosa

A. A.

Masruha

M. R.

Dos Santos

P. A.

Da Silveira Coêlho

M. L.

Ferraz

D. D.

Da Silva Ribeiro

N. M.

(2015). The use of Nintendo wii in the rehabilitation of poststroke patients: A systematic review. Journal of Stroke and Cerebrovascular Diseases: The Official Journal of National Stroke Association, 24(10), 2298–2305. https://doi.org/10.1016/j.jstrokecerebrovasdis.2015.06.010

27.

Duncan Millar

van Wijck

Pollock

Ali

(2019). Outcome measures in post-stroke arm rehabilitation trials: Do existing measures capture outcomes that are important to stroke survivors, carers, and clinicians? Clinical Rehabilitation, 33(4), 737–749. https://doi.org/10.1177/0269215518823248

28.

Felipe

F. A.

de Carvalho

F. O.

Silva

É. R.

Santos

N. G. L.

Fontes

P. A.

de Almeida

A. S.

Garção

D. C.

Nunes

P. S.

de Souza Araújo

A. A.

(2020). Evaluation instruments for physical therapy using virtual reality in stroke patients: A systematic review. Physiotherapy, 106, 194–210. https://doi.org/10.1016/j.physio.2019.05.005

29.

Felling

R. J.

Song

(2015). Epigenetic mechanisms of neuroplasticity and the implications for stroke recovery. Experimental Neurology, 268, 37–45. https://doi.org/10.1016/j.expneurol.2014.09.017

30.

Flansbjer

U.-B.

Holmbäck

A. M.

Downham

Patten

Lexell

(2005). Reliability of gait performance tests in men and women with hemiparesis after stroke. Journal of Rehabilitation Medicine, 37(2), 75–82. https://doi.org/10.1080/16501970410017215

31.

French

Thomas

L. H.

Coupe

McMahon

N. E.

Connell

Harrison

Sutton

C. J.

Tishkovskaya

Watkins

C. L.

(2016). Repetitive task training for improving functional ability after stroke. The Cochrane Database of Systematic Reviews, 11(11), CD006073. https://doi.org/10.1002/14651858.CD006073.pub3

32.

Fritz

S. L.

Peters

D. M.

Merlo

A. M.

Donley

(2013). Active video-gaming effects on balance and mobility in individuals with chronic stroke: A randomized controlled trial. Topics in Stroke Rehabilitation, 20(3), 218–225. https://doi.org/10.1310/tsr2003-218

33.

Fung

Richards

C. L.

Malouin

McFadyen

B. J.

Lamontagne

(2006). A treadmill and motion coupled virtual reality system for gait training post-stroke. Cyberpsychology & Behavior: The Impact of the Internet, Multimedia and Virtual Reality on Behavior and Society, 9(2), 157–162. https://doi.org/10.1089/cpb.2006.9.157

34.

Gil-Gómez

J.-A.

Lloréns

Alcañiz

Colomer

(2011). Effectiveness of a wii balance board-based system (eBaViR) for balance rehabilitation: A pilot randomized clinical trial in patients with acquired brain injury. Journal of Neuroengineering and Rehabilitation, 8(1), 10–30. https://doi.org/10.1186/1743-0003-8-30

35.

Givon

Zeilig

Weingarden

Rand

(2016). 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. Clinical Rehabilitation, 30(4), 383–392. https://doi.org/10.1177/0269215515584382

36.

Golla

Müller

Wohlfarth

Jahn

Mattukat

Mau

(2018). Home-based balance training using wii Fit™: A pilot randomised controlled trial with mobile older stroke survivors. Pilot and Feasibility Studies, 4(1), 143–210. https://doi.org/10.1186/s40814-018-0334-0

37.

Higgins

J. P.

Thompson

S. G.

Deeks

J. J.

Altman

D. G.

(2003). Measuring inconsistency in meta-analyses. Bmj, 327(7414), 557–560. https://doi.org/10.1136/bmj.327.7414.557

38.

Holden

M. K.

(2005). Virtual environments for motor rehabilitation: Review. Cyberpsychology & Behavior: The Impact of the Internet, Multimedia and Virtual Reality on Behavior and Society, 8(3), 187–211. https://doi.org/10.1089/cpb.2005.8.187

39.

Hung

J.-W.

Chou

C.-X.

Hsieh

Y.-W.

W.-C.

M.-Y.

Chen

P.-C.

Chang

H.-F.

Ding

S.-E.

(2014). Randomized comparison trial of balance training by using exergaming and conventional weight-shift therapy in patients with chronic stroke. Archives of Physical Medicine and Rehabilitation, 95(9), 1629–1637. https://doi.org/10.1016/j.apmr.2014.04.029

40.

Imam

Jarus

(2014). Virtual reality rehabilitation from social cognitive and motor learning theoretical perspectives in stroke population. Rehabilitation research and practice, 2014.

41.

Iruthayarajah

McIntyre

Cotoi

Macaluso

Teasell

(2017). The use of virtual reality for balance among individuals with chronic stroke: A systematic review and meta-analysis. Topics in Stroke Rehabilitation, 24(1), 68–79. https://doi.org/10.1080/10749357.2016.1192361

42.

Karasu

A. U.

Batur

E. B.

Karataş

G. K.

(2018). Effectiveness of wii-based rehabilitation in stroke: A randomized controlled study. Journal of Rehabilitation Medicine, 50(5), 406–412. https://doi.org/10.2340/16501977-2331

43.

Karssemeijer

E. G.

Bossers

W. J.

Aaronson

J. A.

Sanders

L. M.

Kessels

R. P.

Olde Rikkert

M. G. M.

(2019). Exergaming as a physical exercise strategy reduces frailty in people with dementia: A randomized controlled trial. Journal of the American Medical Directors Association, 20(12), 1502–1508. https://doi.org/10.1016/j.jamda.2019.06.026

44.

Kim

Park

Lee

B.-H.

(2015). Effects of community-based virtual reality treadmill training on balance ability in patients with chronic stroke. Journal of Physical Therapy Science, 27(3), 655–658. https://doi.org/10.1589/jpts.27.655

45.

Kleim

J. A.

Barbay

Cooper

N. R.

Hogg

T. M.

Reidel

C. N.

Remple

M. S.

Nudo

R. J.

(2002). Motor learning-dependent synaptogenesis is localized to functionally reorganized motor cortex. Neurobiology of Learning and Memory, 77(1), 63–77. https://doi.org/10.1006/nlme.2000.4004

46.

Kwakkel

Kollen

(2013). Predicting activities after stroke: What is clinically relevant? International Journal of Stroke: Official Journal of the International Stroke Society, 8(1), 25–32. https://doi.org/10.1111/j.1747-4949.2012.00967.x

47.

Langhorne

Coupar

Pollock

(2009). Motor recovery after stroke: A systematic review. The Lancet. Neurology, 8(8), 741–754. https://doi.org/10.1016/S1474-4422(09)70150-4

48.

Lee

H.-C.

Huang

C.-L.

S.-H.

Sung

W.-H.

(2017). The effect of a virtual reality game intervention on balance for patients with stroke: A randomized controlled trial. Games for Health Journal, 6(5), 303–311. https://doi.org/10.1089/g4h.2016.0109

49.

Lee

H. S.

Park

Y. J.

Park

S. W.

(2019). The effects of virtual reality training on function in chronic stroke patients: A systematic review and meta-analysis. BioMed research international, 2019.

50.

Lee

H. Y.

Kim

Y. L.

Lee

S. M.

(2015). Effects of virtual reality-based training and task-oriented training on balance performance in stroke patients. Journal of Physical Therapy Science, 27(6), 1883–1888. https://doi.org/10.1589/jpts.27.1883

51.

Lee

M.-M.

Shin

D.-C.

Song

C.-H.

(2016). Canoe game-based virtual reality training to improve trunk postural stability, balance, and upper limb motor function in subacute stroke patients: A randomized controlled pilot study. Journal of Physical Therapy Science, 28(7), 2019–2024. https://doi.org/10.1589/jpts.28.2019

52.

Levin

M. F.

Demers

(2021). Motor learning in neurological rehabilitation. Disability and Rehabilitation, 43(24), 3445–3453. https://doi.org/10.1080/09638288.2020.1752317

53.

Maher

C. G.

Sherrington

Herbert

R. D.

Moseley

A. M.

Elkins

(2003). Reliability of the PEDro scale for rating quality of randomized controlled trials. Physical Therapy, 83(8), 713–721. https://doi.org/10.1093/ptj/83.8.713

54.

Marques-Sule

Arnal-Gómez

Buitrago-Jiménez

Suso-Martí

Cuenca-Martínez

Espí-López

G. V.

(2021). Effectiveness of Nintendo Wii and physical therapy in functionality, balance, and daily activities in chronic stroke patients. Journal of the American Medical Directors Association, 22(5), 1073–1080. https://doi.org/10.1016/j.jamda.2021.01.076

55.

Merians

A. S.

Poizner

Boian

Burdea

Adamovich

(2006). Sensorimotor training in a virtual reality environment: Does it improve functional recovery poststroke? Neurorehabilitation and Neural Repair, 20(2), 252–267. https://doi.org/10.1177/1545968306286914

56.

Moreira

M. C.

de Amorim Lima

A. M.

Ferraz

K. M.

Benedetti Rodrigues

M. A.

(2013). Use of virtual reality in gait recovery among post stroke patients–a systematic literature review. Disability and Rehabilitation. Assistive Technology, 8(5), 357–362. https://doi.org/10.3109/17483107.2012.749428

57.

Morone

Tramontano

Iosa

Shofany

Iemma

Musicco

Paolucci

Caltagirone

(2014). The efficacy of balance training with video game-based therapy in subacute stroke patients: A randomized controlled trial. BioMed Research International, 2014, 580861. https://doi.org/10.1155/2014/580861

58.

Moseley

A. M.

Herbert

R. D.

Sherrington

Maher

C. G.

(2002). Evidence for physiotherapy practice: A survey of the physiotherapy evidence database (PEDro). The Australian Journal of Physiotherapy, 48(1), 43–49. https://doi.org/10.1016/s0004-9514(14)60281-6

59.

Mustafaoğlu

Erhan

Yeldan

İ.

Ersöz Hüseyinsinoğlu

Gündüz

Razak Özdinçler

(2018). The effects of body weight-supported treadmill training on static and dynamic balance in stroke patients: A pilot, single-blind, randomized trial. Turkish Journal of Physical Medicine and Rehabilitation, 64(4), 344–352. https://doi.org/10.5606/tftrd.2018.2672

60.

Noveletto

Soares

Mello

Sevegnani

Eichinger

Hounsell

M. d. S.

Bertemes-Filho

(2018). Biomedical serious game system for balance rehabilitation of hemiparetic stroke patients. IEEE Transactions on Neural Systems and Rehabilitation Engineering: A Publication of the IEEE Engineering in Medicine and Biology Society, 26(11), 2179–2188. https://doi.org/10.1109/TNSRE.2018.2876670

61.

Oliveira

C. B.

Medeiros

Í. R.

Greters

M. G.

Frota

N. A.

Lucato

L. T.

Scaff

Conforto

A. B.

(2011). Abnormal sensory integration affects balance control in hemiparetic patients within the first year after stroke. Clinics, 66(12), 2043–2048. https://doi.org/10.1590/s1807-59322011001200008

62.

Padala

K. P.

Padala

P. R.

Lensing

S. Y.

Dennis

R. A.

Bopp

M. M.

Parkes

C. M.

Garrison

M. K.

Dubbert

P. M.

Roberson

P. K.

Sullivan

D. H.

(2017). Efficacy of wii-fit on static and dynamic balance in community dwelling older veterans: A randomized controlled pilot trial. Journal of Aging Research, 2017, 4653635. https://doi.org/10.1155/2017/4653635

63.

Page

M. J.

McKenzie

J. E.

Bossuyt

P. M.

Boutron

Hoffmann

T. C.

Mulrow

C. D.

Shamseer

Tetzlaff

J. M.

Moher

(2021). Updating guidance for reporting systematic reviews: Development of the PRISMA 2020 statement. Journal of Clinical Epidemiology, 134, 103–112. https://doi.org/10.1016/j.jclinepi.2021.02.003

64.

Pang

M. Y.

Eng

J. J.

Dawson

A. S.

Gylfadóttir

(2006). The use of aerobic exercise training in improving aerobic capacity in individuals with stroke: A meta-analysis. Clinical Rehabilitation, 20(2), 97–111. https://doi.org/10.1191/0269215506cr926oa

65.

Park

D.-S.

Lee

D.-G.

Lee

(2017). Effects of virtual reality training using xbox kinect on motor function in stroke survivors: A preliminary study. Journal of Stroke and Cerebrovascular Diseases: The Official Journal of National Stroke Association, 26(10), 2313–2319. https://doi.org/10.1016/j.jstrokecerebrovasdis.2017.05.019

66.

Pollock

Baer

Campbell

Choo

P. L.

Forster

Morris

Pomeroy

V. M.

Langhorne

(2014). Physical rehabilitation approaches for the recovery of function and mobility following stroke. The Cochrane Database of Systematic Reviews, 2014(4), CD001920. https://doi.org/10.1002/14651858.CD001920.pub3

67.

Remple

M. S.

Bruneau

R. M.

VandenBerg

P. M.

Goertzen

Kleim

J. A.

(2001). Sensitivity of cortical movement representations to motor experience: Evidence that skill learning but not strength training induces cortical reorganization. Behavioural Brain Research, 123(2), 133–141. https://doi.org/10.1016/s0166-4328(01)00199-1

68.

Schnabel

van Wijck

Bain

Barber

Dall

Fleming

Kerr

Langhorne

McConnachie

Molloy

Stanley

Young

H. J.

Kidd

(2021). Experiences of augmented arm rehabilitation including supported self-management after stroke: A qualitative investigation. Clinical Rehabilitation, 35(2), 288–301. https://doi.org/10.1177/0269215520956388

69.

Singh

D. K. A.

Mohd Nordin

N. A.

Aziz

N. A. A.

Lim

B. K.

Soh

L. C.

(2013). Effects of substituting a portion of standard physiotherapy time with virtual reality games among community-dwelling stroke survivors. BMC Neurology, 13(1), 199–207. https://doi.org/10.1186/1471-2377-13-199

70.

Sveistrup

(2004). Motor rehabilitation using virtual reality. Journal of Neuroengineering and Rehabilitation, 1(1), 1–8.

71.

Taub

Crago

J. E.

Burgio

L. D.

Groomes

T. E.

Cook

E. W.

III DeLuca

S. C.

Miller

N. E.

(1994). An operant approach to rehabilitation medicine: Overcoming learned nonuse by shaping. Journal of the Experimental Analysis of Behavior, 61(2), 281–293. https://doi.org/10.1901/jeab.1994.61-281

72.

Tessem

Hagstrøm

Fallang

(2007). Weight distribution in standing and sitting positions, and weight transfer during reaching tasks, in seated stroke subjects and healthy subjects. Physiotherapy Research International: The Journal for Researchers and Clinicians in Physical Therapy, 12(2), 82–94. https://doi.org/10.1002/pri.362

73.

Tyson

S. F.

Hanley

Chillala

Selley

Tallis

R. C.

(2006). Balance disability after stroke. Physical Therapy, 86(1), 30–38. https://doi.org/10.1093/ptj/86.1.30

74.

Walker

E. R.

Hyngstrom

A. S.

Schmit

B. D.

(2016). Influence of visual feedback on dynamic balance control in chronic stroke survivors. Journal of Biomechanics, 49(5), 698–703. https://doi.org/10.1016/j.jbiomech.2016.01.028

75.

Winstein

C. J.

Merians

A. S.

Sullivan

K. J.

(1999). Motor learning after unilateral brain damage. Neuropsychologia, 37(8), 975–987. https://doi.org/10.1016/s0028-3932(98)00145-6

76.

Yang

Y.-R.

Chen

Y.-H.

Chang

H.-C.

Chan

R.-C.

Wei

S.-H.

Wang

R.-Y.

(2015). Effects of interactive visual feedback training on post-stroke pusher syndrome: A pilot randomized controlled study. Clinical Rehabilitation, 29(10), 987–993. https://doi.org/10.1177/0269215514564898

77.

Yatar

G. I.

Yildirim

S. A.

(2015). Wii fit balance training or progressive balance training in patients with chronic stroke: A randomised controlled trial. Journal of Physical Therapy Science, 27(4), 1145–1151. https://doi.org/10.1589/jpts.27.1145

78.

You

S. H.

Jang

S. H.

Kim

Y.-H.

Hallett

Ahn

S. H.

Kwon

Y.-H.

Kim

J. H.

Lee

M. Y.

(2005). Virtual reality–induced cortical reorganization and associated locomotor recovery in chronic stroke: An experimenter-blind randomized study. Stroke, 36(6), 1166–1171. https://doi.org/10.1161/01.STR.0000162715.43417.91

79.

Zhang

You

Zhang

Zhao

Han

Liu

Jiang

Feng

(2020). Effects of visual feedback balance training with the Pro-kin system on walking and self-care abilities in stroke patients. Medicine, 99(39), e22425. https://doi.org/10.1097/MD.0000000000022425

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.40 MB