Gaming Technology in Pediatric Cerebral Palsy and Related Neuromuscular Conditions: A Scoping Review

Abstract

Background

Children with cerebral palsy (CP) and related neuromuscular conditions often experience barriers to physical activity due to motor and cognitive impairments. Active video games (AVG), with or without adaptive technologies (AT), have demonstrated potential to improve outcomes in these populations.

Objective

This scoping review mapped existing research on AVG and AT in pediatric CP and related neuromuscular conditions, summarizing intervention characteristics and effects on physical, cognitive, and quality of life (QoL) outcomes.

Methods

The review followed PRISMA-ScR guidelines. Four databases were searched up to November 2025 (MEDLINE, Embase, Web of Science, and IEEE Xplore), supplemented by a manual citation search using Google Scholar. Eligible studies included participants under 18 years with CP and related neuromuscular conditions, reported physical, cognitive, or QoL outcomes, employed AVG, virtual/augmented reality, or gamified interventions, and were peer-reviewed and published in English.

Results

A total of 192 studies were included, of which 178 examined the impact of AVG and immersive technology on physical outcomes in children with either CP (n = 173), muscular dystrophy (n = 7), or CP and a mix of related conditions (n = 12). Fewer studies included cognitive (n = 17) and QoL (n = 46) outcome measures. Findings highlighted substantial heterogeneity in study design, outcome measures, gaming devices, and AT implemented, limiting comparability and generalization. Despite this variability, AVG interventions seemed to improve patient outcomes, especially in physical function.

Conclusion

We underscore the need for standardized measures to guide the development of gaming interventions that benefit patient outcomes.

Keywords

cerebral palsy duchenne muscular dystrophy rehabilitation child adolescent exercise quality of life cognition

Introduction

Children with chronic neurodevelopmental conditions, such as cerebral palsy (CP), and related neuromuscular conditions experience gross motor limitations affecting participation in physical activity and exercise (Buckon et al., 2022; Clutterbuck et al., 2019; Iskandar & Finnell, 2022; Vandervelde et al., 2009; Vitrikas et al., 2020). These neuromuscular conditions include, but are not limited to, muscular dystrophy, spinal muscular atrophy, and spina bifida (Buckon et al., 2022; Clutterbuck et al., 2019; Iskandar & Finnell, 2022; Vandervelde et al., 2009). While differing in etiology and presenting pathophysiological characteristics, the included conditions have overlapping impairments that limit movement, posture, and participation in daily activities (Buckon et al., 2022; Clutterbuck et al., 2019; Heyn et al., 2019; Iskandar & Finnell, 2022; Vandervelde et al., 2009; Vitrikas et al., 2020). Additionally, the progressive and non-progressive gross motor constraints are often associated with a sedentary lifestyle, leading to an increased risk of metabolic syndrome and cardiac, bone, and soft tissue pathologies (Alves, 2019; Brambilla et al., 2011; Deguise et al., 2021; Heyn et al., 2019; Hollung et al., 2020; Iolascon et al., 2019; Rosenbaum et al., 2007). While previous work has highlighted the beneficial effect of traditional exercise in these clinical populations, there remain significant challenges in exercise implementation due to the heterogeneity in physical and cognitive ability (Alves, 2019; Clutterbuck et al., 2019; Ng et al., 2018; Soares et al., 2023).

One approach to improving the healthcare management of this population has been the incorporation of active video games (AVG) and adaptive technology (AT), such as integrating virtual or augmented reality (VR/AR) into robotic mobility aid interventions (Bulea et al., 2017; Clutterbuck et al., 2019; Heutinck et al., 2018; Hickman et al., 2017; Iosa et al., 2022; Massetti et al., 2018). AVG is an umbrella term that refers to video games that require physical movement beyond standard controller inputs and can range in complexity from simple movement-based tasks to challenging physical routines. In clinical settings, these games have been paired with AT, including robotic mobility aids and biofeedback, to further support the needs of children across both movement and cognitive limitations (Bulea et al., 2017; Iosa et al., 2022; Kiper et al., 2024; Lins et al., 2019; Piccinini et al., 2022). AVG with and without these adaptive devices have been demonstrated to improve gross motor function, cognition, and quality of life (QoL) measures (Aran et al., 2020; Fu et al., 2022; Jha et al., 2021; Tarakci et al., 2020).

In an intervention combining virtual reality and robotic-assisted gait training (RAGT), Fu et al. demonstrated improvements in measures of passive standing, walking endurance, as well as lower body muscle tension, activation, and coordination (Fu et al., 2022). Similar improvements have been reported when assessing upper limb function (El-Shamy, 2018). In an AVG intervention examining the efficacy of upper limb robotic therapy, children with hemiplegic CP saw functional increases in the quality of upper limb movement and measures of spasticity (El-Shamy, 2018). While both interventions are promising and demonstrate the potential of these protocols, they report similar limitations, including issues regarding their sample and inability to generalize their findings beyond the scope of their study (El-Shamy, 2018; Fu et al., 2022). This trend is seen in traditional exercise protocols, where there is a demonstrated improvement in the targeted physical outcomes without the ability to generalize their findings due to the characteristics of the sample or the specificity of the intervention (Nsenga et al., 2013). Previous traditional physical activity guidelines and recommendations have highlighted a need for future work to address the limitations regarding the lack of standardized evaluative outcomes and the ambulatory bias in the clinical populations studied (Toldi et al., 2021; Verschuren et al., 2016). AVG interventions have tremendous potential to provide tailored activities for heterogeneous clinical populations independent of motor limitation (Figure 1). However, the generalizability of research findings on the efficacy of these active interventions is limited by factors including the heterogeneity of the patient population, the range of gaming and adaptive devices used, the clinical assessments chosen, and a lack of post-intervention follow-up. This highlights the need to map the evolving technological landscape to guide the development of future technologies and immersive interventions to better serve the pediatric neurodevelopmental and neuromuscular clinical population across the spectrum of motor and cognitive limitations. To date, no scoping review has comprehensively mapped the range of gaming and immersive technologies used across the full spectrum of motor limitations in pediatric CP and related neuromuscular conditions. This gap is particularly limiting given that gross motor constraints, ranging from partial ambulatory limitation to full dependence on mobility aids, shape which AVG modalities are feasible and therapeutically appropriate.

Figure 1.

Examples of active video game interventions (Arnoni et al., 2022; Ricklin et al., 2018; Saussez et al., 2023; Velasco et al., 2017; see Supplementary References for full citations).

This scoping review aims to map the literature regarding the use and influence of AVG and AT interventions to improve patient outcomes in children with CP and related neuromuscular conditions. The research aims were to identify: (1) what gaming and immersive technology have been used, (2) how AT, including robotic mobility aids and biofeedback devices, were used to complement the technology-enhanced experience, (3) trends in the characteristics of the active interventions and outcome measurements reported, and (4) the impact of active interventions on patient outcomes.

Methods

Search Strategy

A search strategy was developed with a Research Librarian to retrieve a sample of published peer-reviewed articles and reviews. A three-stage literature search methodology was employed, including an initial limited and final comprehensive database search, as well as a manual post-screening forward and backward citation search. The initial limited search was developed for MEDLINE (Ovid) to identify keywords, index terms, and articles that met the inclusion criteria. The keywords and index terms were then used to create a comprehensive search that was translated for each database included. The following comprehensive search included all identified keywords, index terms, and a pediatric search filter developed by the University of Alberta Libraries (Campbell et al., 2014). The databases included in the final search were MEDLINE (OVID; 1946 to November 2025), Embase (Ovid; 1974-2025), Web of Science Core Collection (1900-2025), and IEEE Xplore (2000-2025). All databases were searched from inception to November 25, 2025. The search strategy, including all identified keywords and index terms, was adapted for each included information source as needed. The research team then performed a citation search to identify any additional studies that referenced the final selected articles using Google Scholar.

Selection Criteria and Procedure

To be included in the current scoping review, studies were required to meet the following inclusion criteria: (1) patients under the age of 18 years, (2) a diagnosis of either CP, muscular dystrophy, spina bifida, or spinal muscular atrophy, (3) reported outcomes included physical measurements related to musculoskeletal health, cardiorespiratory fitness, balance, or movement measures associated with improved QoL (i.e., motor skill refinement), or cognitive outcomes, or patient experience, (4) complete with published findings, (5) employed an AVG, VR/AR, or gamified intervention aimed at improving one of the previously mentioned study outcomes of interest, (6) peer-reviewed and written in English.

Following the comprehensive search, all identified records were uploaded to Covidence, collated, and de-duplicated. Three research team members independently screened titles and abstracts to assess eligibility based on the inclusion criteria. The same reviewers then evaluated the full text of selected citations in detail. Reasons for excluding full-text articles are documented within this scoping review. Any disagreements regarding article selection were resolved by a fourth team member.

Data Charting

Full-text articles were reviewed independently and in duplicate by two team members. Disagreements regarding study relevance during data extraction were resolved through discussion until consensus was achieved. Data extraction was also conducted independently and in duplicate using a standardized spreadsheet. Extracted data included publication year, country, study design, sample size, participant age and sex, treatment duration, presence of post-intervention follow-up, neurodevelopmental or neuromuscular condition, Gross Motor Function Classification System (GMFCS) level, gaming or immersive technologies used, inclusion of robot-assisted technology or biofeedback, presence of a control group, and outcome measures related to physical, cognitive, or QoL outcomes.

Synthesis of Results

Extracted data were summarized descriptively to highlight study characteristics, outcome measures, and reported effects. Table 1 presents participant and study characteristics, including health condition, GMFCS level (if reported), study population (children or children and adults), sex, age, study design, intervention duration, presence of a control, and post-intervention follow-up. Table 2 summarizes the frequency of physical outcome measures across all studies, while Table 3 outlines the studies that included cognitive measures and their reported outcomes (significant improvements, non-significant improvements, mixed results, or no effect). The details of gaming, robotic, and biofeedback devices used are also shared (Table 4), and Table 5 summarizes the reported intervention effects categorized by intervention characteristics such as whether a custom or commercially available device was used, whether the study location was at-home or in a research or healthcare facility, and the gaming platform of the intervention. In line with scoping review methodology, the overall objective of our analysis was to characterize the landscape of existing research in the area of AVG and AT in pediatric CP and related neuromuscular conditions, and a formal critical appraisal of study quality was not in the scope of the present work (Levac et al., 2010).

Table 1.

Participant and Intervention Characteristics.

Characteristics	Total records (n = 192)	Percentage of total studies (%)
Health conditions
Cerebral palsy	173	90.1%
Muscular dystrophy	7	3.6%
Spinal muscle atrophy	-	-
Spina bifida	-	-
Mixed CP conditions	12	6.3%
Gross Motor Function Classification System
Level 1	10	5.2%
Level 2	6	3.1%
Level 3	4	2.1%
Level 4	2	1.0%
Level 5	-	-
Multi-level, maximum level 2	38	19.8%
Multi-level, maximum level 3	43	22.4%
Multi-level, maximum level 4	24	12.5%
Multi-level, maximum level 5	5	2.6%
Other classification	36	18.8%
Not reported	24	12.5%
Population
Children	183	95.3%
Children and adults	9	4.7%
Sex
Both	165	85.9%
Female	2	1.0%
Male	17	8.9%
Not reported	8	4.2%
Age
Mean	10	-
Range	3-27	-
Types of evidence
Randomized controlled trial (RCT)	74	38.5%
Non-RCT evaluation studies	97	50.5%
Case studies	21	10.9%
Immersive integration
Virtual or augmented reality (VR/AR)	11	5.7%
Rehabilitation game (no VR/AR)	181	94.3%
Robot device
Yes	43	22.4%
No	149	77.6%
Location/setting
Healthcare or academic settings	153	79.7%
At-home	24	12.5%
Multiple settings	11	5.7%
Not reported	4	2.1%
Duration
≤ 4 weeks	73	38.0%
5-8 weeks	64	33.3%
> 8 weeks	54	28.1%
Not reported	1	0.5%
Sample size
≤ 10	52	27.1%
11-20	55	28.6%
21-30	33	17.2%
31-40	20	10.4%
> 40	32	16.7%
Control present?
Yes	117	60.9%
No	75	39.1%
Post-intervention follow-up?
Yes	41	21.4%
No	151	78.6%

Table 2.

Frequency of Physical Outcomes Across all Included Interventions. BOTMP-2 Balance & RSA Subsets, Bruininks-Oserestsky Test of Motor Proficiency-2 Balance and Running Speed & Agility Subtests; BOTMP-2 Balance & RSA Subsets, Bruininks-Oserestsky Test of Motor Proficiency-2 Balance Subtest; BOTMP-Short Form, Bruininks-Oserestsky Test of Motor Proficiency Short Form; GMFCS III-specific Shuttle Run Test, Gross Motor Function Classification Scale III-specific Shuttle Run Test; GMFM 88 Dimension B, Gross Motor Function Measure 88 Dimension B; Kids-Mini-BESTest, Kids-Mini-Balance Evaluation System Test; K-TMCS, Korean-Trunk Control Measurement Scale; MAUULF F/P/S/G/R, Melbourne Assessment of Unilateral Upper Limb Function (Reaching Forward, Pronation/Supination, Grasp, Release); mCTSIB, Modified Clinical Test of Sensory Integration and Balance; SACND, Sitting Assessment for Children with Neuromotor Dysfunction.

10 or more interventions

Physical outcome	Frequency		Physical outcome	Frequency
Pediatric Balance Scale	28		Box and Block Test	16
Timed Up and Go	23		Gait Analysis Scores	15
6-Minute Walking Test	22		10-Meter Walking Test	14
Postural Biomechanical Kinematics	21		ABILHAND-Kids	14
Modified Ashworth Scale	18		Physical Task Performance	12
Gross Motor Function Measure 88	17		Jebsen-Taylor Hand Function Test	11
Grip Strength	17		Lower Body Range of Motion	11
Quality of Upper Extremity Skills Test	16		Upper Body Biomechanical Kinematics	10

5 – 9 interventions

Physical outcome	Frequency		Physical outcome	Frequency
Melbourne Assessment of Unilateral Upper Limb Function	9		Electromyography	7
Ankle Strength	9		Gross Motor Function Measure 88 - Dimension E	7
Lower Body Active Range of Motion	9		Lower Body Biomechanical Kinematics	7
Nine-Hole Peg Test	9		Bruininks-Oseretsky Test of Motor Proficiency	6
Upper Body Range of Motion	9		Lower Limb Strength (Not Ankle)	6
Selective Control Assessment of the Lower Extremity	8		Assisting Hand Assessment	6
Trunk Control Measurement Scale	8		Peabody Developmental Motor Scale-2	6
Gross Motor Function Measure 88 - Dimension D	8		Sit-to-Stand Test	5

2 – 4 interventions

Physical outcome	Frequency		Physical outcome	Frequency
Gross Motor Function Measure Unspecified - Dimension E	4		Upper Limb Physician's Rating Scale	3
Pinch Strength	4		Fugl-Meyer Assessment	3
Upper Limb Strength (Not hand)	4		Functional Reach Test	3
Bruininks-Oseretsky Test of Motor Proficiency-2	4		Melbourne Assessment of Unilateral UL Function-2	3
Berg Balance Scale	4		Movement Assessment Battery for Children-2	3
Upper Body Active Range of Motion	4		BOTMP-2 Balance & RSA Subsets	2
Timed Up and Down Stairs	4		Cervical Range of Motion	2
Gross Motor Function Measure 66	4		Duruoz Hand Index	2
Gross Motor Function Measure 66 - Dimension D	3		Fully body Biomechanical Kinematics	2
Gross Motor Function Measure 66 - Dimension E	3		Gross Motor Function Measure Unspecified - Dimension D	2
Gross Motor Function Measure Unspecified	3		Gross Motor Function Classification Scale	2
Gait Profile Score	3		Gross Motor Function Measure Challenge Module	2
1-Minute Walking Test	3		Lateral Step Up	2
2-Minute Walking Test	3		Modified Star Excursion Balance Test	2
Functional Mobility Scale	3		Modified Tardieu Scale	2
Grasp	3		Sit and Reach	2

1 intervention

Physical outcome		Physical outcome		Physical outcome
10 Steps Climbing Test		GMFCS III-specific Shuttle Run Test		Performance of Upper Limb Module for DMD
ABILOCO-Kids		Grip Power		Postural Stability Test Eyes Closed
Activities Scale for Kids		GMFM 88 - Dimension B		Postural Stability Test Eyes Open
ACTIVLIM-CP		Gross Motor Function Measure Item Set		Reactive Balance Test
Ankle Power		Gross Motor Performance Measure		Romberg Test
Ashworth Scale (not modified)		Half-Kneel to Standing Test		Selective Control of Upper Extremity Scale
Assessment of Motor and Process Skills		Kids-Mini-BESTest		Selective Motor Control
Assisted 6-Minute Cycle Test		K-TCMS		SACND
Becure Balance Assessment		Limit of Stability Test		Spoon and Knife Task
Bimanual Fine Motor Function		Manual Ability Classification System		Standing from Supine Position Test
BOTMP-2 Balance Subtest		Manual Form Perception Test		Surface Electromyography
BOTMP Short Form		Maximal Achieved Ventilation		Tandem Stance Test
Center-of-Pressure Sway		Maximal Static Expiratory Pressure		Test of Arm Selective Control
Chailey Levels of Ability (Box Sitting)		Maximal Static Inspiratory Pressure		Trunk Impairment Scale
Community Balance and Mobility Scale		Maximum Voluntary Ventilation		T-shirt Donning & Doffing Test
Disability of Arm, Shoulder, and Hands		MAUULF F/P/S/G/R		Unimanual Aiming Test
Dynamic Balance Test		Minnesota Manual Dexterity Test		Unimanual Reach-to-Grasp
Dynamic Gait Index		mCTSIB		Upper Body Profile Score
Energy Expenditure		Modified Functional Reach Test		Upper Body Variable Score
Eye Movement Accuracy		Modified Modified Ashworth Scale		Ventilatory Muscle Endurance
Five Times Sit to Stand		Modified Sensory Organization Test		Wingate Arm Cranking Test
Fullerton Advanced Balance Scale		Modified Thomas Test		Wolf Motor Function Test
Functional Forward React Test		Motor Function Measurement-32
Functional Sideways React Test		Pediatric Reach Test

Table 3.

Impact of Cognitive Outcomes Measures. The Darker Weightings Highlight the Number of Outcome Effects in a Given Study.

Table 4.

Gaming, Robotic, and Biofeedback Characteristics.

Characteristics	Total records (n = 192)	Percentage (%)
Console (n = 62)
Wii	31	50.0%
Xbox 360	20	32.3%
PlayStation 2	6	09.7%
Xbox One	2	03.2%
PlayStation 3	2	03.2%
PlayStation 4	1	01.6%
Computer (n = 126)
Computer	123	97.6%
Computer + console	3	02.4%
Tablet or custom touchscreens (n = 4)
Tablet	3	75.0%
Custom touchscreen	1	25.0%
Robot devices (n = 42)
Lokomat	17	40.5%
Armeo Spring	7	16.7%
Unspecified Ankle Robot	3	07.1%
PedBotHome	3	07.1%
PediAnkleBot	2	04.8%
IntelliStretch	2	04.8%
Haptic Master	2	04.8%
RoboGait	1	02.4%
ReaHaptic Handle	1	02.4%
REAPlan	1	02.4%
EskoGT	1	02.4%
Amadeo	1	02.4%
Unspecific Robotic Skeleton	1	02.4%
Biofeedback devices (n = 32)
Visual Gait Feedback	14	43.8%
Electromyography	8	25.0%
Visual Upper Body Feedback	3	09.4%
Surface Electromyography	3	09.4%
Heart rate monitor	2	06.3%
Galvanic skin response	1	03.1%
Electroencephalography	1	03.1%

Table 5.

Impact of Physical and Quality of Life Outcomes. Percentages are Calculated by Dividing the Outcomes Measured Effect by the Total Outcome Measure Effects in a Given Category. When Reporting the Percentage of Significant Effects for Physical Outcomes That Used a Commercial Device, we Calculated 260 Divided by the Total Number of Outcomes Reported in This Category, 449, Resulting in 57.9%.

	Outcome measure effect	Percentage of total outcome measures (%)
Custom devices/software vs. commercial
Physical outcomes
Commercial (console or computer) (n = 153)
Significant effect	260	57.9%
Non-significant improvement	59	13.1%
Mixed	61	13.6%
No effect	69	15.4%
Custom devices/software (n = 23)
Significant effect	58	73.4%
Non-significant improvement	4	5.1%
Mixed	6	7.6%
No effect	11	13.9%
Quality of life outcomes
Commercial (console or computer (n = 41)
Significant effect	23	44.2%
Non-significant improvement	13	25.0%
Mixed	7	13.5%
No effect	9	17.3%
Custom devices/software (n = 4)
Significant effect	2	40.0%
Non-significant improvement	2	40.0%
Mixed	1	20.0%
No effect	-	-
At-home vs. not at-home interventions
Physical outcomes
At-home (console or computer) (n = 23)
Significant effect	28	45.9%
Non-significant improvement	10	16.4%
Mixed	7	11.5%
No effect	16	26.2%
Not at-home (console or computer) (n = 152)
Significant effect	289	62.3%
Non-significant improvement	52	11.2%
Mixed	60	12.9%
No effect	63	13.6%
Quality of life outcomes
At-home (console or computer) (n = 11)
Significant effect	4	28.6%
Non-significant improvement	6	42.9%
Mixed	1	7.1%
No effect	3	21.4%
Not at-home (console or computer) (n = 34)
Significant effect	21	48.8%
Non-significant improvement	9	20.9%
Mixed	7	16.3%
No effect	6	14.0%
Console vs. computer
Physical outcomes
Console (n = 58)
Significant effect	127	71.3%
Non-significant improvement	23	12.9%
Mixed	11	6.2%
No effect	17	9.6%
Computer (n = 125)
Significant effect	191	54.6%
Non-significant improvement	40	11.4%
Mixed	56	16.0%
No effect	63	18.0%
Quality of life outcomes
Console (n = 13)
Significant effect	7	46.7%
Non-significant improvement	2	13.3%
Mixed	3	20.0%
No effect	3	20.0%
Computer (n = 32)
Significant effect	18	42.9%
Non-significant improvement	13	31.0%
Mixed	5	11.9%
No effect	6	14.3%

Results:

Study Selection

Figure 2 illustrates the PRISMA flowchart detailing the study selection process. A total of 7,171 records were identified through database searches, with an additional 43 located via manual searching. After the removal of duplicates, 4,741 unique records remained. Title and abstract screening excluded 4,194 records that did not meet the inclusion criteria, leaving 547 articles for full-text review. Of these, 355 were excluded for the following reasons: absence of primary data or summary statistics (n = 92), lack of relevant outcomes (n = 49), no integration of gaming technologies (n = 147), inclusion of an incorrect clinical population (n = 26), non-English language (n = 13), and identification of full-text duplicates (n = 28). This selection process left a total of 192 records for analysis (See Supplementary material for full list of included studies).

Figure 2.

PRISMA flowchart.

Study Design and Characteristics

The included studies were published between 1989 and 2025, with 143 (75%) published from 2016 onwards (Figure 3). The interventions took place across 36 countries, with the United States of America (n = 25), Turkey (n = 23), and Brazil (n = 15) being the most frequently represented. The full details of study design, sample size, neurodevelopmental or neuromuscular condition, gross motor classification (GMFCS), duration, setting, and participant characteristics are reported in Table 1.

Figure 3.

Publication year. Articles published in the active gaming literature. The numbers above each bar correspond to the number of articles published in a given year.

Physical Outcomes Measures

Among the studies in our sample, 178 (92.7%) examined the impact of AVG and immersive technology on physical outcomes in children with either CP (n = 173), muscular dystrophy (n = 7), or CP and a mix of related conditions (n = 12). The primary physical outcome of interest for each study was categorized as upper body (n = 67), lower body (n = 30), full body (n = 80), and above the neck (n = 1). Studies were further categorized by the primary outcomes physical modality focusing on stability (n = 22), gait (n = 20), physical dexterity (excluding hand function) (n = 12), manual dexterity (hand function only) (n = 5), cardiovascular outcomes (n = 2), oculomotor outcomes (n = 1), and 116 studies looked at a mix of these modalities, often involving a combination of either gait and stability or physical and manual dexterity. There were 547 physical outcomes across the 178 studies measuring gross motor function (Table 2). When reviewing the impact of AVG and immersive technology on physical outcomes, we found that 173 studies reported positive effects (significant or non-significant improvements) for their primary outcome, and five reported no change. The studies showing no change commonly cited limitations related to small sample size and sample heterogeneity (Ammann-Reiffer et al., 2020; Heutinck et al., 2018; Ramstrand & Lygnegård, 2012).

Cognitive and Quality of Life Outcomes Measures

There were fewer studies including cognitive (n = 17) and QoL (n = 46) outcome measures. A total of 19 distinct cognitive and 28 QoL outcome measures were identified, with motor learning being the most assessed cognitive measure and the Canadian Occupational Performance Measure (COPM) being the most frequently used QoL measure. Most studies that included cognitive outcome measures reported positive effects (significant or non-significant improvements) (Table 3). In contrast, five studies reported no effect on QoL outcomes, with similar limitations to the physical outcome measures, including a lack of a post-intervention follow-up and the generalizability of their findings (Gagliardi et al., 2018; Gilliaux et al., 2015; Güç et al., 2023; Mitchell et al., 2016).

Gaming and Immersive Technology Devices

A total of 181 studies included interventions with AVG without VR/AR, while 11 included VR/AR integration. Most studies (123, 64.1%) used a computer as the primary technology platform. In contrast, 62 used a gaming console, four used a tablet or custom touchscreen, and three used a combination of a computer and a gaming console (Table 4). The consoles included the Xbox 360, Xbox One, Wii, PlayStation 2, 3, and 4.

Computer Outcomes

Computers were the primary technology platform in 126 interventions, and these studies assessed 344 physical, 18 cognitive, and 42 QoL outcomes. The most common physical outcome measures in computer-based interventions were the Quality of Upper Extremity Skills Test (QUEST) (n = 10), 6-Minute Walking Test (6MWT) (n = 17), and a version of the Gross Motor Function Measure (GMFM) including the GMFM-88 (GMFM-88) (n = 19), for upper, lower, and full-body measures of function. These three outcomes demonstrated significant or non-significant increases in physical function. Gagliardi et al. found significant increases in 6MWT and GMFM-88 in children with CP following a four-week immersive virtual reality gait training protocol (Gagliardi et al., 2018). Similarly, Cimolin et al. demonstrated significant improvements in the QUEST following a four-week upper limb intervention (Cimolin et al., 2019). However, Daliri et al. reported mixed results with QUEST following a 20-week upper limb intervention, noting that only the grasp subset showed significant improvement (Daliri et al., 2023). In contrast, Ammann-Reiffer et al. found no effect of their combined AVG and RAGT intervention for either the 6MWT or GMFM-88 and no carry-over effects were observed during the 5-week follow-up session (Ammann-Reiffer et al., 2020). Of the 18 cognitive outcomes, motor learning was the most common outcome measure (n = 3). However, of the three studies measuring motor learning, two reported mixed results, and one reported non-significant improvements (Malheiros et al., 2015; Massetti et al., 2018; Quadrado et al., 2019). Importantly, all three studies focused on children with muscular dystrophy (Malheiros et al., 2015; Massetti et al., 2018; Quadrado et al., 2019). The most common QoL outcome for computer-based interventions was the COPM (n = 8). Of the eight studies measuring COPM, five found significant improvements, one reported mixed results within subscales, and two reported non-significant increases (Bono et al., 2022; Hernandez et al., 2021; Karlsson et al., 2019; Preston et al., 2016; Reid, 2002; Roberts et al., 2020; Weightman et al., 2011).

Console Outcomes

Consoles were used in 62 interventions, and these studies assessed 177 physical, five cognitive, and 15 QoL outcomes. The most commonly used consoles were the Wii (n = 31), Xbox 360 (n = 20), and the PlayStation 2 (n = 6). The most common physical outcome measures in this group were the 10-Meter Walking Test (10WT) (n = 7), ABILHAND-Kids (n = 6), and Pediatric Balance Scale (PBS) (n = 13) for upper, lower, and full-body measures of function. Cho et al. demonstrated that following an eight-week treadmill training protocol combined with the Wii, children significantly improved in the 10WT and the PBS (Cho et al., 2016). In contrast, the PBS demonstrated non-significant improvements following a four-week intervention using the Xbox 360 (Jung et al., 2018). ABILHAND-Kids also improved significantly following a six-week upper limb intervention combining neurodevelopmental treatment and the Wii (Acar et al., 2016). In another six-week upper limb intervention using the Wii, Winkel et al. reported mixed results, with several children with more severe motor limitations having lower scores following training (Winkels et al., 2013). Within the four studies that measured cognitive outcomes, there were five total outcomes, which included the Hacettepe Psychological Adaptive Scale, the Korean-Developmental Test of Visual Perception, the Test for Visual-Perceptual Skills-Third Edition, and both simple and discriminative reaction time (Okmen et al., 2013; Pourazar et al., 2018; Sajan et al., 2017; Shin et al., 2015). All outcomes showed significant improvements following their AVG intervention, except the Test for Visual-Perceptual Skills (Okmen et al., 2013; Pourazar et al., 2018; Sajan et al., 2017; Shin et al., 2015). The most common QoL outcome in console-focused studies was the Wee-Functional Independence Measure (WeeFIM) (n = 4). The WeeFIM was demonstrated to significantly improve following all four interventions (Acar et al., 2016; Goyal et al., 2022; Jha et al., 2021; Tarakci et al., 2016).

Robotic and Biofeedback Devices

Forty-two interventions included a robotic device. The Lokomat (n = 17) and Armeo Spring (n = 7) were the primary devices used in the lower and upper body interventions, respectively. Thirty-two studies utilized biofeedback devices, which included visual gait and upper body feedback systems, electromyography, heart rate monitors, surface-EMG, galvanic skin response, and electroencephalography (Table 4). Of the 42 gaming interventions that included a robotic device, 38 (90.5%) demonstrated improvements in physical function, while cognitive and QoL outcomes were less frequently assessed in this sample.

Discussion

Overall, AVG with and without AT appear to improve physical, cognitive, and QoL outcomes in children with CP and muscular dystrophy. However, this review highlights several key issues in the literature limiting our ability to draw causal inferences across interventions, including the lack of gold-standard measures to evaluate physical outcomes or post-intervention follow-ups, underrepresentation of children with more severe motor limitations, and heterogeneity in study design. These issues are pervasive across the active gaming and health literature for pediatric neurodevelopmental and neuromuscular care, which may slow the development of effective rehabilitative gaming technology (Crebbin et al., 2023; Hickman et al., 2017; Iosa et al., 2022).

The observed improvements associated with AVG interventions may be partially explained by established principles of motor learning and neuroplasticity. Gaming-based interventions often incorporate high repetition, task-specific practice, and real-time feedback, which are key drivers of motor skill acquisition and neuroplastic adaptation (Kachmar et al., 2025). Additionally, the interactive and engaging nature of AVG may enhance motivation and attention, supporting increased practice intensity and adherence compared to traditional rehabilitation approaches. These features may be particularly relevant in pediatric populations, where sustained engagement and task salience are critical for promoting motor learning and functional gains.

Comparing Study Design

Despite these proposed motor learning and neuroplasticity mechanisms, the variability in patient motor and cognitive ability is unavoidable in this clinical population. This inherent heterogeneity in the patient population, combined with limited generalizability due to study design, has constrained the conclusions we can draw in terms of discussing which AVG interventions and outcomes appear optimal across motor and cognitive limitation spectrums, as well as whether AVG with or without AT may be more effective than traditional exercise protocols for these patient populations. However, the descriptive analysis conducted here reveals trends in physical and QoL outcomes and allows for high-level comparisons on the effectiveness of interventions using a console or computer as the primary gaming platform, interventions at home or not at home, and finally, custom or study-specific devices and interventions with commercially available tools (Table 5).

From a clinical implementation perspective, this heterogeneity also limits the translation of AVG interventions into routine rehabilitation practice. Wide variation in intervention duration, session frequency, and intensity makes it difficult to identify optimal therapy dosage or determine which protocols are most appropriate for children across the spectrum of motor and cognitive limitations. Patient selection likely plays a central role in intervention success, as factors such as gross motor function, targeted movement domains (e.g., upper versus lower limb function), the alignment between cognitive ability and gaming task demands, and the need for adaptive support may all influence feasibility and therapeutic benefit. Collectively, these findings suggest that future clinical protocols should more clearly define intervention dosage, participant selection criteria, and progression parameters to support reproducibility and implementation in pediatric rehabilitation settings.

Console vs. computers. Most physical and QoL outcome measures in console and computer interventions reported improvements, significant or not. The distribution of categorized outcomes was similar between console and computer interventions for physical and QoL outcome measures. Considering the similarities, it is worth noting that the most frequently used console was the Nintendo Wii, which was discontinued in 2013. Although the Wii demonstrated improvements in motor function and QoL in recent work, the continued use and reliance on hardware no longer commercially available limits future innovation (El-Shamy & El-Banna, 2020; Jung et al., 2018; Park et al., 2021; Radwan et al., 2023; Shakiba et al., 2021; Valenzuela et al., 2021; Wang et al., 2021b). It may be prudent for future study design to prioritize the integration of effective hardware from previously available console devices into computer-driven interventions. Previous work has already demonstrated the potential improvements in patient outcomes when combining a computer with complementary console devices such as the Microsoft Kinect and Wii Balance Board (Bonnechère et al., 2017; Chen et al., 2018; Malick et al., 2022; Şahin et al., 2020).

At home vs. research/healthcare settings. A higher percentage of significant improvements in physical and QoL outcomes were reported in research or healthcare settings than in at-home interventions. This may be due to limitations associated with at-home interventions, including difficulties assessing outcomes, concerns regarding family influence, and the lack of supervision or real-time feedback (Chiu et al., 2018; Choi et al., 2023; Silva et al., 2021). As study design standards evolve, focusing on structured clinical and research settings may help establish best practices before adapting interventions for the convenience of at-home use. In future work, to ensure their effectiveness, at-home interventions should be intentionally co-designed with patient communities, incorporating their input throughout development and implementation (Canadian Institutes of Health Research, 2020; Wang et al., 2021a).

Custom vs. commercial. Commercial gaming systems and rehabilitation-specific (custom-developed) systems serve distinct roles within pediatric neurorehabilitation. Commercial systems are widely accessible, cost-effective, and may enhance engagement and adherence. However, they are not designed to target specific impairments or adapt to individual motor limitations. In contrast, rehabilitation-specific devices and software allow for greater control over task demands, progression, and feedback, enabling more targeted and individualized therapeutic interventions. Studies using custom rehabilitation-specific devices and software reported a higher percentage of significantly improved physical outcome measures than those using commercially available devices. However, the observed differences may be influenced by the number of physical outcomes measured within each sample, with 449 outcomes assessed in the commercial sample and 79 in the custom sample. Similar findings were observed for QoL measures, though only one study included a custom device and QoL outcomes (Weightman et al., 2011). These distinctions suggest that commercial systems may support scalable and accessible interventions, whereas custom rehabilitation-specific platforms may be better suited for targeted clinical applications requiring individualized progression and precision.

Taken together, these findings highlight the potential for AVG interventions to serve as a flexible and engaging addition to pediatric neurorehabilitation. Interventions appear most effective when they are tailored to the child's motor and cognitive abilities, incorporate task-specific and repetitive practice, and are delivered within structured environments that support feedback and progression. The integration of AT may further enhance accessibility and allow interventions to be adapted across a wider range of motor limitations. However, the substantial heterogeneity in study design and outcome measures underscores the need for standardized protocols and clinically meaningful endpoints to support translation into routine care. Establishing these foundations will be critical to guiding the development and implementation of effective, evidence-informed, and patient-centered gaming-based rehabilitation strategies.

Limited Evidence for Neuromuscular Conditions in Active Gaming Rehabilitation

Following the completion of the study selection process, we were left with 192 studies with only seven interventions, including patients with muscular dystrophy, and zero for spina bifida and spinal muscle atrophy (Table 1). This paucity of available literature may be due to the additional challenges associated with the progressive characteristics of conditions such as muscular dystrophy and spinal muscle atrophy (Alves, 2019; Buckon et al., 2022; Deguise et al., 2021; Iolascon et al., 2019; Vandervelde et al., 2009). The remaining 185 studies included exclusively CP (n = 173) or mixed CP conditions (n = 12), which included CP and other conditions that are outside the scope of this review. The vast majority of included studies targeting CP may reflect the trends in chosen interventions and outcomes in this review, given the focus on potential neuroplasticity and motor-learning improvements in gaming-based rehabilitation for children with CP (Demers et al., 2021; Toovey et al., 2017). In contrast, the limited research on neuromuscular conditions is likely due to their progressive nature and differing therapeutic priorities such as function preservation, fatigue management, and maintained autonomy and QoL (Dowling et al., 2018; Iskandar & Finnell, 2022).

Limitations and Future Directions

Findings should be considered with attention to the limitations of the review. Our findings represent a snapshot of the literature, and due to the fast pace of developments in gaming technology, ongoing updates will be necessary. Additionally, although this review includes work from 36 countries, we only collected data from studies written in English. Substantial heterogeneity in study samples, outcome measures, technologies implemented, and intervention intensity and duration, limited comparability and precluded causal inferences. Furthermore, we did not conduct a formal quality appraisal of included studies, consistent with scoping review methodology, and the possibility of publication bias cannot be excluded, as studies reporting null or negative findings may be underrepresented in the published record.

We acknowledge that there are other methods for comparing interventions and outcomes across differing populations, such as systematic reviews with meta-analysis. Future reviews should include evaluations of intervention quality and measures of study bias to assess intervention efficacy. Additionally, future work implementing AVG interventions in this population would benefit from established gold-standard measures of physical outcomes, which should be stratified by motor limitation scales such as the GMFCS. While participant heterogeneity is unavoidable in CP and related neuromuscular care research, assigning condition-specific benchmarks for upper, lower, full-body, and above-the-neck measures across the movement limitation spectrum would improve our ability to assess intervention efficacy. Future study designs should incorporate post-intervention follow-ups to determine whether improvements in measured outcomes are sustained beyond the immediate intervention period. Additionally, future work focusing on neuromuscular conditions should aim to repurpose and adapt promising CP interventions with attention to the differing therapeutic priorities of each specific condition.

Conclusion

The findings of this scoping review point to immense heterogeneity in active gaming study design and outcomes measured. The review also suggests that AVG with and without AT may improve physical, cognitive, and QoL outcomes in pediatric CP and muscular dystrophy populations. Results suggest a need to consolidate outcomes measured and identify gold standards to improve the generalizability of research in this field. By reducing the heterogeneity in study design, the literature will better position itself to aid the development of gaming technology aimed at improving patient outcomes.

Supplemental Material

sj-docx-1-nre-10.1177_10538135261445647 - Supplemental material for Gaming Technology in Pediatric Cerebral Palsy and Related Neuromuscular Conditions: A Scoping Review

Supplemental material, sj-docx-1-nre-10.1177_10538135261445647 for Gaming Technology in Pediatric Cerebral Palsy and Related Neuromuscular Conditions: A Scoping Review by Jordan Stevenson, Susanna E Martin, Mahala G English, Colleen Pawliuk and Julie M Robillard in NeuroRehabilitation

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Supplemental material, sj-docx-2-nre-10.1177_10538135261445647 for Gaming Technology in Pediatric Cerebral Palsy and Related Neuromuscular Conditions: A Scoping Review by Jordan Stevenson, Susanna E Martin, Mahala G English, Colleen Pawliuk and Julie M Robillard in NeuroRehabilitation

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Supplemental material, sj-docx-3-nre-10.1177_10538135261445647 for Gaming Technology in Pediatric Cerebral Palsy and Related Neuromuscular Conditions: A Scoping Review by Jordan Stevenson, Susanna E Martin, Mahala G English, Colleen Pawliuk and Julie M Robillard in NeuroRehabilitation

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Supplemental material, sj-xlsx-4-nre-10.1177_10538135261445647 for Gaming Technology in Pediatric Cerebral Palsy and Related Neuromuscular Conditions: A Scoping Review by Jordan Stevenson, Susanna E Martin, Mahala G English, Colleen Pawliuk and Julie M Robillard in NeuroRehabilitation

Footnotes

Acknowledgements

We are grateful to Dr. Kishore Mulpuri and the healthcare providers at the BC Children's Hospital Orthopaedic Clinic for their continued guidance and support.

Ethical Approval

This article does not contain any studies with human or animal participants.

Consent to Participate

Not applicable.

Authors’ Contribution

J.S.: Conceptualization (equal), data curation (lead), investigation (lead), methodology (lead), formal analysis (lead), validation (equal), visualization (lead), and writing–review and editing (lead). S.M., M.E.; Investigation (equal), and validation (equal). C.P.: Investigation (supporting). J.M.R.: Conceptualization (equal), investigation (supporting), methodology (supporting), visualization (supporting), writing–review and editing (supporting), and supervision (lead).

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by the BC Children's Hospital Foundation, the BC Children's Hospital Research Institute, and the Department of Graduate and Postdoctoral Studies at the University of British Columbia.

Declaration of Conflicting Interests

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

Supplementary Material

Supplemental material for this article is available online.

ORCID iDs

Jordan Stevenson

Mahala G English

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