Skip to main content
NCBI home page
Wiley Open Access Collection logoLink to Wiley Open Access Collection
. 2026 May 24;70(8):858–870. doi: 10.1111/jir.70124

Balancing in Virtual Reality: Motor Learning and Functional Improvement in Down Syndrome

Evelyn Ardai 1,2,3, Tibor Vámos 3,4,5,6, Gabriella Papp 1, Andrea Berencsi 3,4,7,✉
PMCID: PMC13327201  PMID: 42178816

ABSTRACT

Background

Motor learning in individuals with Down syndrome (DS) may be influenced by syndrome‐specific motor and cognitive characteristics. Virtual reality (VR) systems are becoming increasingly popular and offer a valuable opportunity to study motor learning. To date, only a few studies have examined motor learning to improve balance in DS. This study aimed to investigate motor learning and functional motor performance in young adults with DS during VR‐based training.

Methods

Participants with DS (n = 31) and typical development (TD = 28) between 18 and 30 years of age took part in the study. Participants were randomly allocated into training (DS Wii and TD Wii) and control (DS C and TD C) groups. Training groups participated in a 4‐week‐long balance training with two Nintendo Wii balance games, whereas their performance was monitored. All participants underwent pre‐ and posttraining assessment of functional motor performance.

Results

During the VR training, the DS Wii and TD Wii groups' game scores show significant performance improvements in both games. Learning curves showed great individual differences, especially in DS. Although TD groups outperformed DS groups in the functional motor tests, DS Wii group's pre‐ and post‐training results indicated a significant improvement in functional motor performance.

Conclusion

These findings demonstrate that individuals with DS are able to learn in a VR environment and successfully transfer the acquired skills to functional abilities. TD individuals also improved their functional performance or reached a plateau. We conclude that VR is an effective system for engaging DS individuals in motor training for improving balance skills.

Keywords: balance, Down syndrome, motor learning, virtual reality

1. Introduction

Down syndrome (DS) is the most common chromosomal disorder associated with intellectual disability (ID) and can occur across any ethnicities. The global prevalence of DS is approximately 1/800. It is primarily caused by trisomy of chromosome 21 (Bull 2020). The motor profile of individuals with DS is characterized by generalized muscle hypotonia, ligamentous laxity and hypermobility, reduced muscle strength (Galli et al. 2008; Wang et al. 2012), lower limb orthopaedic conditions such as pes planus (Galli et al. 2008) and individuals with DS also present difficulties in agonist and antagonist muscle cocontraction (Alesi and Battaglia 2019; Bieć et al. 2014; Giustino et al. 2021). These biomechanical characteristics can affect postural control (Bieć et al. 2014; Giustino et al. 2021; Massó‐Ortigosa et al. 2024; Silva et al. 2017) that is essential for performing everyday activities (Leite et al. 2018; Villarroya et al. 2012).

Postural control requires two distinct processes, the integration of multisensory inputs and the organization and execution of an adequate motor response (Villarroya et al. 2012; Vuillerme et al. 2001). These processes may show deficits from early childhood and continue to affect postural control in later life (Rigoldi et al. 2011; Vuillerme et al. 2001), resulting in balance dysfunction in individuals with DS (Galli et al. 2008; Rigoldi et al. 2011). Moreover, difficulties in postural control can negatively affect the independence and activities of daily living of individuals with DS (Horvat et al. 2013; Rigoldi et al. 2011). Therefore, studying the characteristics of postural control and its improvement is essential for the development of rehabilitation strategies in DS (Villarroya et al. 2012).

Virtual reality (VR) technology has gained attention in the field of assessment in motor learning (Cavalcante Neto et al. 2021; de Mello Monteiro et al. 2017; Smits‐Engelsman et al. 2015) and real‐word skills (Franze et al. 2024; Michalski et al. 2023). VR‐based active video game systems, such as the Nintendo Wii, are widely available on the commercial market. These systems simulate real‐world games and sports activities and promote interaction and active engagement (Mouawad et al. 2011; Silva et al. 2017; Wuang et al. 2011). They also allow for controlled manipulation of practice conditions and offer features for monitoring performance over time (Smits‐Engelsman et al. 2015). Additionally, the continuous feedback provided to users makes VR technology a potentially applicable tool in the field of motor learning (Cavalcante Neto et al. 2021; Menezes et al. 2015; Smits‐Engelsman et al. 2015). Furthermore, active video game systems offer a multifaceted learning environment for individuals with disabilities, where acquired skills can be transferred to real‐world contexts (Franze et al. 2024; Michalski et al. 2023).

Motor learning is a complex process, ideally involving improved performance on the task and retention and transfer of skills over time (Smits‐Engelsman et al. 2015). The characteristics of motor learning in individuals with ID may differ from those observed in TD individuals (Berencsi et al. 2016, 2017). However, limited research has specifically examined motor learning in individuals with DS in a VR environment (de Mello Monteiro et al. 2017). Although there have been studies focusing on functional skill improvement following VR‐based interventions (Berg et al. 2012; Lin and Wuang 2012; Silva et al. 2017; Wuang et al. 2011), a focus on the characteristics of motor learning in terms of performance improvements throughout the training process (Schmidt et al. 2019) is still lacking in the literature. A further important question is whether performance improvement in a VR environment is associated with improvement of functional abilities, which reflects the transfer of acquired skills (Franze et al. 2024; Michalski et al. 2023; Vámos et al. 2018).

Therefore, the primary aim of the study is to determine the learning characteristics during balance training using VR technology in DS and TD. The main focus is on examining group‐level performance changes during practice without assessing game performance retention. A secondary objective is to explore whether improvement in VR environment transfers to improvement in functional balance skills after VR training, which refers to the short‐term changes in functional motor abilities following a 4‐week training intervention. To address these questions, we investigate learning performance and pre‐ and posttraining functional motor performance in young adults with DS and age‐matched TD individuals in a course of a VR balance training programme. We hypothesize that the pretraining performance of TD participants would be superior to that of participants with DS in balance, mobility and muscle strength (Wang et al. 2012). Moreover, we hypothesized that both DS and TD participants' game performance would improve over the sessions of VR‐based balance training (Cavalcante Neto et al. 2021; Smits‐Engelsman et al. 2015). In addition to game performance, we also expected improvements in functional balance (Smits‐Engelsman et al. 2015) and a potential indirect improvement in muscle strength (Lin and Wuang 2012; Silva et al. 2017).

2. Methods

2.1. Participants

Thirty‐one young adult participants with DS (mean age: 21.2 ± 2.2SD, male n = 17) and 31 TD (mean age: 25.73 ± 8.2 SD, male n = 10) participants were included in the study (Table 1, Table S16). Demographic and anthropometric variables were tested for homogeneity of variance using chi‐square and Levene's test. After the analysis of homogeneity of variances, the parametric and nonparametric tests indicated significant difference only with regard to age between the groups (p = 0.006).

TABLE 1.

Means and standard deviations (M ± SD) of participants' anthropometric characteristics.

Variables DS Wii (n = 16) DS C (n = 15) TD Wii (n = 12) TD C (n = 16)
Age (year) 21.0 ± 2.34 21.5 ± 2.17 25.6 ± 8.84 25.9 ± 7.15
Sex (M/F) 9/7 8/7 4/8 6/10
Height (cm) 159.2 ± 9.49 162.1 ± 8.65 171.7 ± 9.92 169.3 ± 11.41
Weight (kg) 67.6 ± 13.80 66.1 ± 12.97 75.9 ± 14.99 67.3 ± 16.37
BMI (kg/m2) 26.47 ± 3.69 25.14 ± 4.75 25.68 ± 3.93 23.24 ± 3.98
Open in a new tab

Abbreviations: C, control group; DS, Down syndrome; TD, typical developmental; Wii, Wii training group.

DS participants were recruited from a convenience sample from three special educational centres for children with intellectual disability. TD participants were recruited from Eötvös Loránd University Bárczi Gusztáv Faculty of Special Needs Education. Inclusion criteria were (a) diagnosis of DS based on the analysis of the karyotype for DS participants; (b) age ranging between 18 and 30 years; (c) being able to stand and walk independently; (d) being able to understand and follow simple instructions; and (e) parent(s)/legal tutors and/or participants willing to provide written consent (information adapted for DS participants). Exclusion criteria were as follows: (a) severe musculoskeletal problems (e.g., hip, knee, foot or spinal deformities that limit exercise); (b) severe or not corrected visual or hearing problems (not able to follow visual image or instructions of the game); (c) previous history of neurological disorders such as traumatic brain injury, dizziness and epilepsy; and (d) uncontrolled cardiovascular disease. Excluded were participants who carried coexisting autism, ADHD or cerebral palsy. The study protocol was approved by the local ethics committee (KEB/2021/022), and all the participants or their parents/legal tutors provided written informed consent.

DS and TD participants were randomly allocated to the training groups: Wii‐based exercise groups [DS Wii group (n = 16) and TD Wii group (n = 15)] or control groups [DS control (DS C) group (n = 15) and TD control (TD C) group (n = 16)]. Three participants from the TD Wii group did not complete the Wii training because they were unable to attend all training sessions. Thus, 59 participants completed all the training sessions and assessment procedures (Figure 1).

FIGURE 1.

FIGURE 1

Open in a new tab

The CONSORT diagram of study participants. C group, control group; DS, Down syndrome; TD, typical developmental; Wii group, Wii training group.

A post hoc power analysis was conducted using G*Power 3.1. (Faul et al. 2009; Kang 2021) Based on the observed effect sizes measured in analysis of variables on alpha level of 0.05, and the total sample size, the achieved statistical power exceeded > 0.80, indicating sufficient power to detect the observed effects. Post hoc power analyses indicated adequate power for all main outcomes.

2.2. Procedure and Materials

2.2.1. Procedure

All data were collected at the beginning and after the training programme by a certified special needs education therapist. The investigator was not blinded. The procedure was carried out in an isolated room, for the DS participants in their school and for the TD participants at the university. Participants were tested individually. Participants familiarized themselves with the experimental setup and DS participants were supported by total communication. First, height, weight, foot physical examination and randomly ordered functional motor assessment were performed. The training groups then participated in a 4‐week Wii Balance training programme. During the training period, control groups continued with their usual daily activities without modifications (Figure 2).

FIGURE 2.

FIGURE 2

Open in a new tab

The evaluation and the procedure.

2.2.2. Motor Learning Paradigm and Performance Measurement With Nintendo Wii

Nintendo Wii Balance Board (WBB) is an accessory designed for the Nintendo Wii gaming console and Wii Fit balance activities. The balance board contains four pressure sensors, which allow the user to control an avatar while playing balance games.

In Penguin slide game, the player tilts his/her body left and right to guide the penguin catching as many fish as possible. The result is determined by the number of the collected fish (game score). In Soccer heading game, the player guides the goalkeeper to head soccer balls by tilting into the path of them. The result is determined by the number of the headed balls (game score). The movement patterns of these games allow to facilitate a complex development of balance.

The Nintendo Wii allows for individualized rates of progression. The games operate on a staircase principle whereby task difficulty increases in response to individual performance. One of the selection criteria was that participants were able to understand verbal and visual instructions. All participants familiarized themselves with the games prior to the start of the training. Verbal and physical assistance was provided to all participants to support them in controlling their Mii characters. Task comprehension and execution were further supported by continuous visual feedback from the Wii and the immediate on‐screen display of scores. Throughout the practice sessions, feedback was provided to all participants, and assistance was offered to help them interpret their game scores.

DS Wii and TD Wii participants followed the same Nintendo Wii Balance training using a projected screen and a WBB placed 1.5 m from the wall. Each game was practiced five times per session with 1‐min rests. Participants stood on the WBB in sneakers, guided by on‐screen instructions and an investigator who was responsible for operating the console and recording data. Participants completed two individual sessions per week, individually. The training programme consisted of eight sessions under 4 weeks, integrated into school PE lessons for the DS Wii group and university activities for the TD Wii group. The environmental conditions were identical for both groups. Control groups (DS C and TD C) continued regular PE and their daily activities. Participants were asked to report their usual physical activity level by a questionnaire during the study. Group differences in physical activity levels were examined using chi‐square tests of independence based on the distribution of participants across usual activity level. The distribution of physical activity levels did not differ significantly between groups (Table S17).

2.2.3. Functional Motor Assessment

Functional motor performance was determined using the following tests: (1) Berg Balance Scale (BBS) assesses balance and risk of falls through 14 functional tasks, each scored on a scale from 0 to 4, with 56 as the maximum total (Berg 1989). (2) Five times sit‐to‐stand test (FTSST) measures lower limb strength along with balance and postural control. The time taken to complete the test is recorded and expressed in seconds (Guerrero et al. 2023; Whitney et al. 2005). (3) Timed up and go test (TUG) maps a person's mobility, functional balance and gait speed. The total time taken is recorded and expressed in seconds (Guerrero et al. 2023; Podsiadlo and Richardson 1991). (4) Plank Test assesses core muscle endurance in a functional manner. The time, participants hold in plank position, is recorded and expressed in second (maximum 30 s) (Boyer et al. 2013). (5) 30‐s sit up test measures core strength and endurance, particularly in the abdominal muscles. The total number of sit ups are recorded for 30 s (Skowroński et al. 2009).

2.3. Statistical Analysis

The learning effect was investigated by analysing Nintendo Wii training outcomes (game scores in each training session) of the DS and TD training groups. A two‐way mixed model of analysis of variance (ANOVA) was performed on each dependent variable with repeated measures on training sessions (training sessions × two intervention groups). Multiple comparisons with LSD were performed when a significant main effect or interaction was present.

Functional motor assessment outcomes (BBS, FTSST, TUG, Plank Test and 30‐s sit up test) were analysed and compared between the four study groups in the means of pretraining and posttraining performance. A two‐way mixed model ANOVA (pre‐ and posttraining × four study groups) was performed on each dependent variable. Multiple comparisons with LSD were performed when a significant main effect or interaction was present. The significance level was set at p < 0.05, and SPSS Version 20.0 (IBM Corporation, New York, NY, USA) was used for the analysis.

3. Results

3.1. Motor Learning With Nintendo Wii

3.1.1. Pinguin Slide Game Results

Mixed model ANOVA (DS/TD training groups × training session) on the Penguin slide game scores indicated a significant main effect for session (F = 36.112, df = 7, p < 0.001, η p 2 = 0.581) and a significant main effect for group (F = 39.110, df = 1, p < 0.001) with a large effect size (η p 2 = 0.601) indicating that the TD group consistently achieved higher game scores than the DS group across training sessions. The results demonstrate no significant interaction between session and group (F = 1.985, df = 7, η p 2 = 0.071).

The DS Wii group's game scores show significant performance improvements in Penguin slide game (Figure 3, Table S1) when comparing the results of the first and eighth training sessions (p < 0.001) with no significant improvement through the first three training sessions (p > 0.05) (Table S5).

FIGURE 3.

FIGURE 3

Open in a new tab

DS Wii group Penguin slide game scores during the eight practice sessions (P). Individual game scores are indicated with dotted lines and group mean is indicated with a black continuous line. DS, Down syndrome; P 1–8, practice sessions. *p < 0.001. The diagram illustrates progressive game performance improvement across the eight training sessions for all participants in the DS Wii group, with increasing individual game scores and significant improvement in group mean performance over time.

Based on the results of outlier detection using the outlier labelling rule (Tukey 1977), only one unidimensional outlier was observed in the participants' individual game scores (participant's code: TDWII001). The analysis does not include the outlier participant's game scores. The TD Wii group's game scores show significant gaming performance improvements in the Penguin slide game (Figure 4, Table S3). When comparing the results of the first and eighth training sessions, the gaming performance within the TD Wii group shows a significant improvement (p < 0.001), but there was no significant improvement through the last three training sessions (p > 0.05) (Table S6).

FIGURE 4.

FIGURE 4

Open in a new tab

TD Wii group Penguin slide game scores during the eight practice sessions (P). Individual game scores are indicated with dotted lines and group mean is indicated with a black continuous line. TD, typical developmental; P 1–8, practice sessions. *p < 0.001. The diagram shows individual differences within the TD Wii group from the first training session and significant individual game performance improvement by the final training session, with increasing group mean performance over time.

Analysing the main effect of the group based on the training session results, there are significant differences in gaming performance between the DS Wii and TD Wii groups across all training sessions (p < 0.01). The TD Wii group achieved significantly higher game score in the Penguin slide game than the DS Wii group during the following training sessions: P1 (p = 0.003); P2 (p = 0.003); P3 (p < 0.001); P4 (p < 0.001); P5 (p < 0.001); P6 (p < 0.001); P7 (p < 0.001); P8 (p = 0.002) (Figure 5, Table S9).

FIGURE 5.

FIGURE 5

Open in a new tab

DS Wii and TD Wii groups Penguin slide game scores during the eight practice sessions (P). The DS Wii group mean scores are indicated with red line and the TD Wii group mean scores are indicated with a black continuous line. DS, Down syndrome; TD, typical developmental; P 1–8, practice sessions. The figure shows significant differences between the DS Wii and TD Wii groups and consistent improvement of both groups in the means of game performance during the training sessions.

3.1.2. Soccer Heading Game Results

Mixed model ANOVA (DS/TD training groups × training session) on the Soccer heading results indicated a significant main effect for training session (F = 82.187, df = 7, p < 0.001, η p 2 = 0.760) and a significant main effect for group (F = 95.685, df = 1, p < 0.001) with a large effect size (η p 2 = 0.786), indicating that the TD group consistently achieved higher game scores than the DS group across training sessions. The results also demonstrate a significant interaction between game and group (F = 56.853, df = 7, p < 0.001, η p 2 = 0.686).

Based on the results of the DS Wii group in the Soccer heading game (Figure 6, Table S2), there is a trend level improvement in gaming performance from first to eighth training session (p = 0.065) but significant improvement between the third and eighth (p < 0.05); fourth and eighth (p < 0.05); fifth and eighth (p < 0.05) training sessions (Table S7). The learning curves show high variability in individual performances.

FIGURE 6.

FIGURE 6

Open in a new tab

DS Wii group Soccer heading game scores during the eight practice sessions (P). Individual game scores are indicated with dotted lines and group mean is indicated with a black continuous line. DS, Down syndrome; P 1–8, practice sessions. The diagram shows individual and group mean game performance improvement across the eight training sessions, with all participants performing better in the final session than at baseline. The diagram also shows high variability of the individual learning patterns of the participants in DS Wii group.

The TD Wii group's game scores (Figure 7, Table S4) show significant gaming performance improvements in Soccer heading game between the first and eighth training sessions (p < 0.001). There was a significant improvement (p < 0.05) through the training sessions (Table S8).

FIGURE 7.

FIGURE 7

Open in a new tab

TD Wii group Soccer heading game scores during practice sessions (P). Individual game scores are indicated with dotted lines and group mean is indicated with a black continuous line. TD, typical developmental; P 1–8, practice sessions. *p < 0.001. The diagram illustrates the variable individual learning patterns and the overall improvement in mean game performance across the eight training sessions in the TD Wii group.

There was a significant difference in gaming performance between the DS Wii and TD Wii groups across all training sessions (p < 0.001) (Table S10). Based on the results of the Soccer heading game scores, significant differences can be observed in the game performance of the DS Wii and TD Wii groups during all training sessions. According to the analysis, the TD Wii group achieved significantly higher game results in the Soccer heading game during the following training sessions than the DS Wii group: T1 (p = 0.002); T2 (p < 0.001); T3 (p < 0.001); T4 (p < 0.001); T5 (p < 0.001); T6 (p < 0.001); T7 (p < 0.001); T8 (p < 0.001) (Figure 8, Table S10).

FIGURE 8.

FIGURE 8

Open in a new tab

DS Wii and TD Wii groups Soccer heading game scores during the eight practice sessions (P). The DS Wii group mean scores are indicated with red line and the TD Wii group mean scores are indicated with a black line. DS, Down syndrome; TD, typical developmental; P 1–8, practice sessions. The figure shows significant group mean game performance differences between the DS Wii and TD Wii groups during the training sessions but consistent improvement in both groups. Note that individual variability increased, especially in TD group, as practice progressed.

3.2. Functional Motor Assessment Results

3.2.1. TUG Test (Figure 9A)

FIGURE 9.

FIGURE 9

Open in a new tab

The participants pre‐ and posttraining results in functional motor assessment. The vertical axis of the figures shows the motor test scores. Bars of the same colour or pattern indicate the pre‐ and posttraining results for each group. Note that DS training group improved performance in all functional tests and TD training group in (A–C) while likely reaching a ceiling effect in (D) and (E). C, control group; Down syndrome; TD, typical developmental; Wii, Wii training group. *p < 0.001; ** p < 0.05; DS.

In TUG test, lower scores indicate better performance. ANOVA showed significant main effect for group (F = 10.123, df = 3, p < 0.001) with a large effect size (η p 2 = 0.356) and for pre‐ and posttraining (F = 36.768, df = 1, p < 0.001, η p 2 = 0.401) with significant interaction (F = 13.750, df = 3, p < 0.001, η p 2 = 0.429) and reflect clear performance differences between the DS and TD participants. TD participants (TD Wii and TD C) outperformed DS participants (DS Wii and DS C) in pre‐ and posttests (p < 0.001) indicating faster task execution by the TD participants. There was no significant difference between the DS Wii and DS C groups or between the TD Wii and TD C groups (p > 0.05) performance in pre‐ and posttraining (Table S11). Significant improvement was found between pre‐ and posttraining results in the DS Wii (p < 0.001) and TD Wii (p < 0.005) groups but not in DS C (p = 0.836) and TD C (p = 0.281) groups (Figure 9A). These findings indicated that both the DS Wii and TD Wii groups' participants improved in mobility, functional balance and gait speed, as evidenced by the TUG test results.

3.2.2. FTSST Test (Figure 9B)

Similar to the TUG test, in FTSST test the lower scores indicate better performance. ANOVA showed significant main effect for group (F = 17.916, df = 3, p < 0.001), with a large effect size (η p 2 = 0.494) and pre‐ and posttraining main effects (F = 11.169, df = 1, p < 0.005, η p 2 = 0.169) with significant interaction (F = 4.426, df = 3, p < 0.05, η p 2 = 0.194), reflecting clear performance differences between the DS and TD participants. TD participants' (TD Wii and TD C) performance was higher than DS participants' (DS Wii and DS C) both in pre‐ and posttests (p < 0.001), indicating faster task execution by the TD participants. FTSST test showed no significant differences between DS Wii and DS C groups (p > 0.05) or between TD Wii and TD C groups (p > 0.05) in pre‐ and posttests, respectively. Significant improvement was found between pre‐ and posttraining results in DS Wii (p < 0.001) and TD Wii (p < 0.05) groups but not in DS C (p = 0.590) and TD C (p = 0.710) groups (Figure 9B, Table S12). Although the results indicating that the TD participants achieved higher test results than the DS participants in overall, Wii training resulted in clear functional improvement in lower limb strength along with balance and postural control. That is both DS Wii and TD Wii groups' participants benefit from training.

3.2.3. 30‐s Sit Up Test (Figure 9C)

In 30‐s sit up test, higher scores indicate better performance. ANOVA showed significant main effect for group (F = 22.789, df = 3, p < 0.001) with a large effect size (η p 2 = 0.554) and for pre‐ and posttraining (F = 41.935, df = 1, p < 0.001, η p 2 = 0.433). Group × pre‐ and posttraining interaction was also significant (F = 3.887, df = 3, p < 0.05, η p 2 = 0.175). The results of the 30‐s sit up test also reflect clear performance differences between the DS and TD participants. TD groups showed higher performance than DS groups (p < 0.001) both pre‐ and posttest, meaning that TD individuals were able to complete a greater number of repetitions during the task than DS individuals. Performance improved by posttraining test in DS Wii (p < 0.001) and TD Wii (p < 0.001) but not in the DS C group (p = 0.397). At the same time, despite not participating in the training, members of the TD C group showed higher posttest results compared to their initial performance (p < 0.05). There were no significant differences (p > 0.05) between the DS Wii and DS C groups or between TD Wii and TD C groups performance in pre‐ and posttraining, respectively (Table S13). Although the TD participants outperformed the DS participants in 30‐s sit up test in overall, Wii training resulted in clear functional improvement in core strength and endurance, particularly in the abdominal muscles in both DS Wii and TD Wii training group.

3.2.4. Plank Test (Figure 9D)

In Plank test, higher scores also indicate better performance. ANOVA showed significant main effect for group (F = 33.840, df = 3, p < 0.001) with a large effect size (η p 2 = 0.649) and pre‐ and posttraining (F = 6.304, df = 1, p < 0.05, η p 2 = 0.103) with significant interaction (F = 8.930, df = 3, p < 0.001, η p 2 = 0.328), indicating significant performance differences between TD and DS groups both pre‐ and posttraining (p < 0.001) meaning that during the task, TD individuals were able to maintain the body position required by the test for a longer duration than DS individuals. Neither pre‐training nor posttraining results showed significant difference between DS Wii and DS C groups or between TD Wii and TD C groups (p > 0.05) (Table S14). DS C and TD C groups showed no significant changes by posttest (p > 0.05). DS Wii group improved significantly after training (p < 0.001) but the TD Wii group reached maximum performance in pretest, therefore did not improve further (Figure 9D). Although the TD participants outperformed the DS participants in Plank test in overall, Wii training resulted in clear functional improvement in core muscle endurance in DS Wii group, means that the DS Wii groups' participants benefit from training.

3.2.5. Berg Test (Figure 9E)

In Berg test, higher scores also indicate better performance similar to the Plank or the 30‐s sit up test. ANOVA showed significant main effect for group (F = 16.724, df = 3, p < 0.001) with a large effect size (η p 2 = 0.477) and pre‐ and posttraining (F = 25.315, df = 1, p < 0.001, η p 2 = 0.315) with significant interaction (F = 11.461, df = 3, p < 0.001, η p 2 = 0.385), reflecting clear performance differences between the DS and TD participants. TD groups outperformed DS groups both pre‐ and posttest (p < 0.001), meaning that TD individuals were able to complete a greater number of balance exercises independently during the task than DS individuals. Regarding pre‐ and posttraining results DS Wii (p < 0.001) group improved significantly but not the DS C (p = 0.503), TD Wii (p = 0.084) and TD C (p = 0.829) groups. The posttraining performance showed significant differences between the DS Wii and DS C groups (p = 0.003) Berg test results with no further difference between DS Wii and DS C, and TD Wii and TD C groups, respectively (p > 0.05) (Table S15). Although the results indicating that the TD participants achieved higher test results than the DS participants in overall, Wii training resulted in clear functional improvement in balance and postural control in DS Wii group. It means that the DS Wii groups' participants benefit from training and based on the posttraining results.

4. Discussion

The aim of the present study was to investigate the characteristics of motor learning and map the functional transfer effect of a balance training in a VR environment in young adult individuals with DS and TD. Our findings indicated a clear learning effect during training for both groups. Furthermore, the training was followed by a positive transfer effect to functional balance, mobility and an indirect transfer effect to core and lower limb muscle strength in young individuals with DS and TD.

Our findings demonstrate for the first time, that VR balance training enhances gaming performance in individuals with DS with a differential learning curve from TD. In the Penguin slide game, the DS group mean performance exhibited a consistent and significant improvement across training sessions, with no evidence of a ceiling effect up to the eighth session (Figure 3, Tables S1 and S5). TD participants displayed higher baseline performance, showed a significant improvement during training sessions, but their performance plateaued in the final three sessions, indicating a possible ceiling effect (Figure 4, Tables S3 and S6). In the Soccer heading game, the DS group did not exhibit significant changes in mean performance during the first three sessions, although the individual performance variability showed a decreasing trend. This lack of improvement at group level during the first three sessions may indicate a prolonged fast phase of motor learning (Dayan and Cohen 2011), potentially caused by the cognitive load of the task or the difficulty of identifying effective postural strategies. Despite the lacking significant difference between the first and last sessions, significant improvement from the third to the eighth session confirmed learning effect in the DS Wii group (Figure 5, Tables S2 and S7). TD participants started from a higher level and showed a continuous improvement without a ceiling effect (Figure 6, Tables S4 and S8). Children with developmental coordination disorder (DCD) and TD demonstrated similar learning pattern with Nintendo Wii (Smits‐Engelsman et al. 2015). After participants completed 100 repetitions of Ski Slalom game, no ceiling effect was observed that indicates potential for further improvement. Similar to our balance training, young adult individuals with DS gradually reduced the number and the variability of errors and improved performance during anticipatory timing task (de Mello Monteiro et al. 2017) in VR environment and finger force production task (Latash et al. 2002). We found high level individual differences in baseline performance and improvement not only in ID but also in TD in the present study. In ID, fine motor learning was characterized by high variability in both baseline performance and learning trajectories with Williams syndrome (WS). Nonetheless, task‐specific practice led to measurable learning effects, even though WS participants typically did not reach the performance levels of TD participants by the end of the training (Berencsi et al. 2016, 2017) similar to our present findings. Our results support that Nintendo Wii VR games can effectively facilitate motor learning (Cavalcante Neto et al. 2021; Smits‐Engelsman et al. 2015) both in DS and TD. Furthermore, DS and TD group‐level differences in learning curves (i.e., prolonged fast phase and ceiling effect) and individual variability in both groups suggests that stages of motor learning and individual speed of learning need to be considered when designing motor training programmes in VR environment.

The present findings indicate that a 4‐week VR‐based balance training using commercially available gaming systems may represent a feasible and effective intervention for improving motor learning. This is consistent with evidence from special needs education, where interactive and feedback‐rich virtual environments have been shown to support skill development and provide continuous feedback that enhances learning and motivation across diverse user groups, including individuals with intellectual disabilities (Ardai et al. 2022; Standen and Brown 2005). Moreover, there is an indication that skills acquired in virtual environments can transfer to real‐world tasks, reinforcing the practical relevance of such interventions for everyday functional outcomes (Franze et al. 2024; Michalski et al. 2023).

In our study, functional skill improvement was also present after VR training that resulted in enhanced lower limb and core muscle strength, as well as improved balance and mobility function in the DS training group. These results are consistent with transfer effect of VR training with Nintendo Wii to core muscle strength (Silva et al. 2017), to lower limb muscle strength in adult individuals with DS (Lin and Wuang 2012), to improvement in balance in children (Wuang et al. 2011) with DS. We also demonstrated that transfer to various functional skills was also present in TD that has been partly previously showed by Smits‐Engelsman et al. (2015) following the VR training.

The observed improvement in functional motor skills in the training groups supports the use of VR‐based balance exercises. These findings are especially relevant for clinical practice because improvement in functional outcomes such as mobility and balance is more directly reflected in participation and independence of the participants in everyday tasks. Similar findings have been reported in clinical rehabilitation settings, where VR‐based training was shown to induce indirect improvements in functional skills such as balance and ambulatory skills, after practicing VR‐based balance games. Improvements in postural control and mobility are particularly relevant because these domains directly affect participation and independence in everyday activities such as self‐care and mobility (Hung et al. 2014; Karasu et al. 2018).

The interactive nature of commercially available VR applications requires active and immersive participation from the user and provides real movement experiences and clear performance goals. The pronounced differences observed in individual performance and learning patterns highlight the need for individual progression strategies that account for varying baseline/initial skill levels and learning rates. In populations characterized by developmental heterogeneity, such as individuals with DS or other intellectual disabilities, flexible modulation of task difficulty and progression may be essential to ensure optimal skill acquisition when implementing VR‐based training into clinical or educational settings. Fixed training protocols may be less effective than those dynamically customizable to individual performance profiles, particularly in individuals with ID.

In addition, our findings indicate that monitoring individual response patterns across sessions may be essential for identifying performance decline, stagnation or rapid improvement, allowing timely adjustments in task difficulty. Systematic reassessment of functional motor skills alongside the game performance measures may further support decision‐making of the therapists or investigators regarding progression and task variability. Furthermore, monitoring performance improvement can align with functional goals relevant to everyday activities and participation.

4.1. Limitations and Future Directions

Several limitations of the present study should be acknowledged when interpreting the findings. First, although clear group‐level improvements were observed in game performance in the training groups, the absence of follow‐up retention measurement limits conclusions regarding long‐term changes due to motor learning. Although the primary aim of the study was to examine short‐term performance changes and functional transfer during a structured training period rather than to assess long‐term retention.

Second, modest improvement in 30‐s sit up test results of TD control group may reflect alternative explanations, like the test–retest effects, increased familiarity with the assessment procedure, natural performance variability or heightened motivation. These factors cannot be fully excluded due to the nature of the experimental design.

Third, the study consists of a commercially available VR‐based tool that simultaneously engages multiple balance‐related games such as weight shifting, postural control and dynamic stability. Although this supports generalized balance improvements, it limits the ability to identify which specific balance subcomponents were most strongly affected by the training.

Additionally, baseline differences between the DS and TD groups may have influenced the results, e.g., in terms of the potential for improvement, but at the same time, the results clearly answered our research question in the sense that learning occurs in DS, and this has a transfer effect on functional abilities. The relatively small sample size and characteristics, as well as the short follow‐up period, may also limit the generalizability of the results. Finally, although the low cost and the accessibility of the tool support its potential clinical implications, the results could not be widely generalized, because other VR‐based interventions may use different task structure, different type of feedback or level of the immersion. Future research should include larger and more diverse samples, as well as a structured follow‐up period to assess the retention and the long‐term clinical relevance of practice in various VR‐based environments.

5. Conclusion

During balance training in the VR environment, participants with DS and TD exhibited learning that transferred to improvements in functional abilities of muscle strength, mobility and balance. Our study clearly demonstrated that the VR environment is effective in engaging individuals with DS in motor learning tasks, whereas differential learning curves suggest the need for individual adjustment of task difficulty.

Funding

The equipment used in the research was funded by the ‘Doctoral Projects’ consortium research grant scheme of Eötvös Loránd University Faculty of Education and Psychology—Doctoral School of Education. The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript.

Ethics Statement

Written informed consent was obtained from all participants and/or their parent(s)/legal tutors. This study received ethics approval from the Research Ethics Committee of Eötvös Loránd University Bárczi Gusztáv Faculty of Special Needs Education (KEB/2021/022).

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Table S1: DS Wii group Penguin slide game scores.

Table S2: DS Wii group Soccer heading game scores.

Table S3: TD Wii group Penguin slide game scores.

Table S4: TD Wii group Soccer heading game scores.

Table S5: Session‐by‐session comparison of Penguin Slide game results in the DS Wii group.

Table S6: Session‐by‐session comparison of Penguin Slide game results in the TD Wii group.

Table S7: Session‐by‐session comparison of Soccer heading game results in the DS Wii group.

Table S8: Session‐by‐session comparison of Soccer heading game results in the TD Wii group.

Table S9: Session‐by‐session comparison of Penguin slide game results between the DS Wii and the TD Wii groups.

Table S10: Session‐by‐session comparison of Soccer heading game results between the DS Wii and the TD Wii groups.

Table S11: Timed up and go (TUG) test results.

Table S12: Five times sit‐to‐stand (FTSST) results.

Table S13: 30‐s sit up test results.

Table S14: Plank test results.

Table S15: Berg functional balance (BERG) test results.

Table S16: Group homogeneity analyses results.

Table S17: Analyses of physical activity level of the groups.

JIR-70-858-s001.docx (148.6KB, docx)

Acknowledgements

We would like to express our gratitude to all DS and TD participants of the study, the colleagues from the special education institutions and Eötvös Loránd University Bárczi Gusztáv Faculty of Special Needs Education for their valuable contributions that made this study possible.

Data Availability Statement

The data are not publicly available due to privacy or ethical restrictions.

References

  1. Alesi, M. , and Battaglia G.. 2019. “Motor Development and Down Syndrome.” In International Review of Research in Developmental Disabilities, vol. 56, 169–211. Elsevier. 10.1016/bs.irrdd.2019.06.007. [DOI] [Google Scholar]
  2. Ardai, E. , Vámos T., Papp G., and Berencsi A.. 2022. “Virtual Reality Therapy in Special Needs Education: From Therapy to Inclusion.” Gyermeknevelés Tudományos Folyóirat 10, no. 2–3: article 2–3. 10.31074/gyntf.2022.3.259.271. [DOI] [Google Scholar]
  3. Berencsi, A. , Bódizs R., Gombos F., László S., and Kovács I.. 2017. “Sigma Frequency Dependent Motor Learning in Williams Syndrome.” Scientific Reports 7, no. 1: 16759. 10.1038/s41598-017-12489-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Berencsi, A. , Gombos F., and Kovács I.. 2016. “Capacity to Improve Fine Motor Skills in Williams Syndrome.” Journal of Intellectual Disability Research 60, no. 10: 956–968. 10.1111/jir.12317. [DOI] [PubMed] [Google Scholar]
  5. Berg, K. 1989. “Measuring Balance in the Elderly: Preliminary Development of an Instrument.” Physiotherapy Canada 41, no. 6: 304–311. 10.3138/ptc.41.6.304. [DOI] [Google Scholar]
  6. Berg, P. , Becker T., Martian A., Danielle P. K., and Wingen J.. 2012. “Motor Control Outcomes Following Nintendo Wii Use by a Child With Down Syndrome.” Pediatric Physical Therapy 24, no. 1: 78–84. 10.1097/PEP.0b013e31823e05e6. [DOI] [PubMed] [Google Scholar]
  7. Bieć, E. , Zima J., Wójtowicz D., Wojciechowska‐Maszkowska B., Kręcisz K., and Kuczyński M.. 2014. “Postural Stability in Young Adults With Down Syndrome in Challenging Conditions.” PLoS ONE 9, no. 4: e94247. 10.1371/journal.pone.0094247. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Boyer, C. , Tremblay M., Saunders T., et al. 2013. “Feasibility, Validity, and Reliability of the Plank Isometric Hold as a Field‐Based Assessment of Torso Muscular Endurance for Children 8–12 Years of Age.” Pediatric Exercise Science 25, no. 3: 407–422. 10.1123/pes.25.3.407. [DOI] [PubMed] [Google Scholar]
  9. Bull, M. J. 2020. “Down Syndrome.” New England Journal of Medicine 382, no. 24: 2344–2352. 10.1056/NEJMra1706537. [DOI] [PubMed] [Google Scholar]
  10. Cavalcante Neto, J. L. , Steenbergen B., Zamunér A. R., and Tudella E.. 2021. “Wii Training Versus Non‐Wii Task‐Specific Training on Motor Learning in Children With Developmental Coordination Disorder: A Randomized Controlled Trial.” Annals of Physical and Rehabilitation Medicine 64, no. 2: 101390. 10.1016/j.rehab.2020.03.013. [DOI] [PubMed] [Google Scholar]
  11. Dayan, E. , and Cohen L. G.. 2011. “Neuroplasticity Subserving Motor Skill Learning.” Neuron 72, no. 3: 443–454. 10.1016/j.neuron.2011.10.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. de Mello Monteiro, C. B. , da Silva T. D., de Abreu L. C., et al. 2017. “Short‐Term Motor Learning Through Non‐Immersive Virtual Reality Task in Individuals With Down Syndrome.” BMC Neurology 17, no. 1: 71. 10.1186/s12883-017-0852-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Faul, F. , Erdfelder E., Buchner A., and Lang A.‐G.. 2009. “Statistical Power Analyses Using G*Power 3.1: Tests for Correlation and Regression Analyses.” Behavior Research Methods 41, no. 4: 1149–1160. 10.3758/BRM.41.4.1149. [DOI] [PubMed] [Google Scholar]
  14. Franze, A. , Loetscher T., Gallomarino N. C., Szpak A., Lee G., and Michalski S. C.. 2024. “Immersive Virtual Reality Is More Effective Than Non‐Immersive Devices for Developing Real‐World Skills in People With Intellectual Disability.” Journal of Intellectual Disability Research 68, no. 12: 1358–1373. 10.1111/jir.13177. [DOI] [PubMed] [Google Scholar]
  15. Galli, M. , Rigoldi C., Mainardi L., Tenore N., Onorati P., and Albertini G.. 2008. “Postural Control in Patients With Down Syndrome.” Disability and Rehabilitation 30, no. 17: 1274–1278. 10.1080/09638280701610353. [DOI] [PubMed] [Google Scholar]
  16. Giustino, V. , Messina G., Alesi M., La Mantia L., Palma A., and Battaglia G.. 2021. “Study of Postural Control and Body Balance in Subjects With Down Syndrome.” Human Movement 22, no. 1: 66–71. 10.5114/hm.2021.98466. [DOI] [Google Scholar]
  17. Guerrero, K. , Umagat A., Barton M., et al. 2023. “The Effect of a Telehealth Exercise Intervention on Balance in Adults With Down Syndrome.” Journal of Applied Research in Intellectual Disabilities : JARID 36, no. 2: 385–393. 10.1111/jar.13068. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Horvat, M. , Croce R., Tomporowski P., and Barna M. C.. 2013. “The Influence of Dual‐Task Conditions on Movement in Young Adults With and Without Down Syndrome.” Research in Developmental Disabilities 34, no. 10: 3517–3525. 10.1016/j.ridd.2013.06.038. [DOI] [PubMed] [Google Scholar]
  19. Hung, J.‐W. , Chou C.‐X., Hsieh Y.‐W., et al. 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, no. 9: 1629–1637. 10.1016/j.apmr.2014.04.029. [DOI] [PubMed] [Google Scholar]
  20. Kang, H. 2021. “Sample Size Determination and Power Analysis Using the G*Power Software.” Journal of Educational Evaluation for Health Professions 18: 17. 10.3352/jeehp.2021.18.17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Karasu, A. , Batur E., and Karataş G.. 2018. “Effectiveness of Wii‐Based Rehabilitation in Stroke: A Randomized Controlled Study.” Journal of Rehabilitation Medicine 50, no. 5: 406–412. 10.2340/16501977-2331. [DOI] [PubMed] [Google Scholar]
  22. Latash, M. L. , Kang N., and Patterson D.. 2002. “Finger Coordination in Persons With Down Syndrome: Atypical Patterns of Coordination and the Effects of Practice.” Experimental Brain Research 146, no. 3: 345–355. 10.1007/s00221-002-1189-3. [DOI] [PubMed] [Google Scholar]
  23. Leite, J. C. , Neves J. C. d. J., Vitor L. G. V., and Fujisawa D. S.. 2018. “Evaluation of Postural Control in Children and Adolescents With Down Syndrome Aged Eight to Twelve Years Old.” Journal of Human Growth and Development 28, no. 1: 50. 10.7322/jhgd.127335. [DOI] [Google Scholar]
  24. Lin, H.‐C. , and Wuang Y.‐P.. 2012. “Strength and Agility Training in Adolescents With Down Syndrome: A Randomized Controlled Trial.” Research in Developmental Disabilities 33, no. 6: 2236–2244. 10.1016/j.ridd.2012.06.017. [DOI] [PubMed] [Google Scholar]
  25. Massó‐Ortigosa, N. , Rey‐Abella F., Gutiérrez‐Vilahú L., Milà R., Guerra‐Balic M., and Oviedo G. R.. 2024. “Analysis of the Centre of Pressure in Bipedal Stance Among Individuals With and Without Intellectual Disabilities, Individuals With Down Syndrome and Dancers With Down Syndrome.” Journal of Intellectual Disability Research 68, no. 5: 524–536. 10.1111/jir.13127. [DOI] [PubMed] [Google Scholar]
  26. Menezes, L. , Massetti T., Oliveira F., et al. 2015. “Motor Learning and Virtual Reality in Down Syndrome; a Literature Review.” International Archives of Medicine 8: 119. 10.3823/1718. [DOI] [Google Scholar]
  27. Michalski, S. C. , Gallomarino N. C., Szpak A., et al. 2023. “Improving Real‐World Skills in People With Intellectual Disabilities: An Immersive Virtual Reality Intervention.” Virtual Reality 27, no. 4: 3521–3532. 10.1007/s10055-023-00759-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Mouawad, M. R. , Doust C. G., Max M. D., and McNulty P. A.. 2011. “Wii‐Based Movement Therapy to Promote Improved Upper Extremity Function Post‐Stroke: A Pilot Study.” Journal of Rehabilitation Medicine 43, no. 6: 527–533. 10.2340/16501977-0816. [DOI] [PubMed] [Google Scholar]
  29. Podsiadlo, D. , and Richardson S.. 1991. “The Timed ‘Up & Go’: A Test of Basic Functional Mobility for Frail Elderly Persons.” Journal of the American Geriatrics Society 39, no. 2: 142–148. 10.1111/j.1532-5415.1991.tb01616.x. [DOI] [PubMed] [Google Scholar]
  30. Rigoldi, C. , Galli M., Mainardi L., Crivellini M., and Albertini G.. 2011. “Postural Control in Children, Teenagers and Adults With Down Syndrome.” Research in Developmental Disabilities 32, no. 1: 170–175. 10.1016/j.ridd.2010.09.007. [DOI] [PubMed] [Google Scholar]
  31. Schmidt, R. A. , Lee T. D., Winstein C. J., Wulf G., and Zelaznik H. N.. 2019. Motor Control and Learning: A Behavioral Emphasis. Sixth ed. Human Kinetics. [Google Scholar]
  32. Silva, V. , Campos C., Sá A., et al. 2017. “Wii‐Based Exercise Program to Improve Physical Fitness, Motor Proficiency and Functional Mobility in Adults With Down Syndrome.” Journal of Intellectual Disability Research 61, no. 8: 755–765. 10.1111/jir.12384. [DOI] [PubMed] [Google Scholar]
  33. Skowroński, W. , Horvat M., Nocera J., Roswal G., and Croce R.. 2009. “Eurofit Special: European Fitness Battery Score Variation Among Individuals With Intellectual Disabilities.” Adapted Physical Activity Quarterly 26, no. 1: 54–67. 10.1123/apaq.26.1.54. [DOI] [PubMed] [Google Scholar]
  34. Smits‐Engelsman, B. C. M. , Jelsma L. D., Ferguson G. D., and Geuze R. H.. 2015. “Motor Learning: An Analysis of 100 Trials of a Ski Slalom Game in Children With and Without Developmental Coordination Disorder.” PLoS ONE 10, no. 10: e0140470. 10.1371/journal.pone.0140470. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Standen, P. J. , and Brown D. J.. 2005. “Virtual Reality in the Rehabilitation of People With Intellectual Disabilities: Review.” Cyberpsychology & Behavior 8, no. 3: 272–282. 10.1089/cpb.2005.8.272. [DOI] [PubMed] [Google Scholar]
  36. Tukey, J. W. 1977. Exploratory Data Analysis. Addison‐Wesley. [Google Scholar]
  37. Vámos, T. , Berencsi A., Fazekas G., and Kullmann L.. 2018. “Precise Isometric Hand Grip Learning of Hemiparetic Stroke Patients.” International Journal of Rehabilitation Research 41, no. 2: 180–182. 10.1097/MRR.0000000000000273. [DOI] [PubMed] [Google Scholar]
  38. Villarroya, M. A. , González‐Agüero A., Moros‐García T., de la Flor Marín M., Moreno L. A., and Casajús J. A.. 2012. “Static Standing Balance in Adolescents With Down Syndrome.” Research in Developmental Disabilities 33, no. 4: 1294–1300. 10.1016/j.ridd.2012.02.017. [DOI] [PubMed] [Google Scholar]
  39. Vuillerme, N. , Marin L., and Debû B.. 2001. “Assessment of Static Postural Control in Teenagers With Down Syndrome.” Adapted Physical Activity Quarterly 18: 417–433. 10.1123/apaq.18.4.417. [DOI] [Google Scholar]
  40. Wang, H.‐Y. , Long I.‐M., and Liu M.‐F.. 2012. “Relationships Between Task‐Oriented Postural Control and Motor Ability in Children and Adolescents With Down Syndrome.” Research in Developmental Disabilities 33, no. 6: 1792–1798. 10.1016/j.ridd.2012.05.002. [DOI] [PubMed] [Google Scholar]
  41. Whitney, S. L. , Wrisley D. M., Marchetti G. F., Gee M. A., Redfern M. S., and Furman J. M.. 2005. “Clinical Measurement of Sit‐to‐Stand Performance in People With Balance Disorders: Validity of Data for the Five‐Times‐Sit‐to‐Stand Test.” Physical Therapy 85, no. 10: 1034–1045. 10.1093/ptj/85.10.1034. [DOI] [PubMed] [Google Scholar]
  42. Wuang, Y.‐P. , Chiang C.‐S., Su C.‐Y., and Wang C.‐C.. 2011. “Effectiveness of Virtual Reality Using Wii Gaming Technology in Children With Down Syndrome.” Research in Developmental Disabilities 32, no. 1: 312–321. 10.1016/j.ridd.2010.10.002. [DOI] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

Table S1: DS Wii group Penguin slide game scores.

Table S2: DS Wii group Soccer heading game scores.

Table S3: TD Wii group Penguin slide game scores.

Table S4: TD Wii group Soccer heading game scores.

Table S5: Session‐by‐session comparison of Penguin Slide game results in the DS Wii group.

Table S6: Session‐by‐session comparison of Penguin Slide game results in the TD Wii group.

Table S7: Session‐by‐session comparison of Soccer heading game results in the DS Wii group.

Table S8: Session‐by‐session comparison of Soccer heading game results in the TD Wii group.

Table S9: Session‐by‐session comparison of Penguin slide game results between the DS Wii and the TD Wii groups.

Table S10: Session‐by‐session comparison of Soccer heading game results between the DS Wii and the TD Wii groups.

Table S11: Timed up and go (TUG) test results.

Table S12: Five times sit‐to‐stand (FTSST) results.

Table S13: 30‐s sit up test results.

Table S14: Plank test results.

Table S15: Berg functional balance (BERG) test results.

Table S16: Group homogeneity analyses results.

Table S17: Analyses of physical activity level of the groups.

JIR-70-858-s001.docx (148.6KB, docx)

Data Availability Statement

The data are not publicly available due to privacy or ethical restrictions.

Articles from Journal of Intellectual Disability Research are provided here courtesy of Wiley

RESOURCES