Abstract
Background Active virtual reality gaming (AVG) may be useful for children with developmental coordination disorder (DCD) to practice motor skills if their movement patterns are of good quality while engaged in AVG.
Objective This study aimed to examine: (1) the quality of motor patterns of children with DCD participating in AVG by comparing them with children with typical development (TD) and (2) whether differences existed in the motor patterns utilized with 2 AVG types: Sony PlayStation 3 Move and Microsoft Xbox 360 Kinect.
Design This was a quasi-experimental, biomechanical laboratory–based study.
Methods Twenty-one children with DCD, aged 10 to 12 years, and 19 age- and sex-matched children with TD played a match of table tennis on each AVG type. Hand path, wrist angle, and elbow angle were recorded using a motion analysis system. Linear mixed-model analyses were used to determine differences between DCD and TD groups and Move and Kinect AVG type for forehands and backhands.
Results Children with DCD utilized a slower hand path speed (backhand mean difference [MD]=1.20 m/s; 95% confidence interval [95% CI]=0.41, 1.98); greater wrist extension (forehand MD=34.3°; 95% CI=22.6, 47.0); and greater elbow flexion (forehand MD=22.3°; 95% CI=7.4, 37.1) compared with children with TD when engaged in AVG. There also were differences in movement patterns utilized between AVG types.
Limitations Only simple kinematic measures were compared, and no data regarding movement outcome were assessed.
Conclusions If a therapeutic treatment goal is to promote movement quality in children with DCD, clinical judgment is required to select the most appropriate AVG type and determine whether movement quality is adequate for unsupervised practice.
Developmental coordination disorder (DCD) is a condition in which children have a marked impairment in motor development affecting their daily life.1 It has been reported to affect between 5% and 10% of children worldwide.1,2 Presentations can vary substantially and may involve both gross and fine motor control,3 which may influence overall movement quality. Reported impairments include lack of body position awareness, delayed reaction times, and reduced movement speed.2,4–6 Joint movements lacking precision and fluency, coupled with increased variability in task performance, also have been reported.2,4 The increasingly accepted internal model deficit (IMD) theory suggests that these deficits result from suboptimal motor planning and sensoriperceptual integration.1,2,4,7,8 More specifically, this theory hypothesizes that children with DCD have difficulty correcting movements in real time due to an inability to use or generate predictive estimates of body position.2 Failure to manage such impairments may affect a child's physical and psychological health.9 The lack of age-appropriate motor skills and low confidence in motor skills may lead children with DCDs to participate less in physical activities.8 Lack of participation in sports and play can affect a developing child's ability to learn and practice motor skills.4 This limited time to practice and develop motor skills may result in a cycle in which psychosocial and physical factors feed back on each other to further affect the child's ability to learn and develop normally.1 Thus, there is an argument for health care professionals to manage these impairments of movement quality in early life to optimize short-term and long-term health.10
A poor understanding of DCD etiology2 has led to diverse intervention styles, with more than 30 different approaches being reported.10 No one style of intervention has gained universal acceptance.11 There is debate whether participation in physical activity alone is sufficient as an intervention for children with DCD,11 or whether promoting good-quality movement should be the focus.2 It has been suggested that interventions addressing movement quality may precipitate neuroplastic changes and adjust deficient internal modeling processes, which are critical to motor rehabilitation.2,12 To be most effective, it is thought that interventions should consider the heterogeneity in presentations of children with DCD and should be engaging and involve many opportunities for motor practice.1,10,13 Thus, it is important to consider interventions that are appealing to children to enhance adherence to motor practice opportunities from an early age. Electronic games may be a useful means for achieving this aim.
Electronic games have been defined as an interactive activity involving manipulation of figures on a screen.14 A 2007 study indicated that 94% of American school-aged children had played some form of electronic game in the previous 6 months.15 There is strong evidence that electronic games increase motivation and self-confidence in children.14,16,17 They also provide immediate feedback about performance, which has been shown to be important for motor skill development, and may be particularly important in children with DCD, who are reported as having deficits in internal feedback modeling.4 Additionally, electronic game play is often unsupervised, allowing for a considerable volume of use within a home setting.16,18
Traditionally, electronic gaming has involved the use of a gamepad, joystick, computer keyboard, or mouse game controller.19,20 These devices involve only fine hand movements with little gross movement.21 There has been an emerging trend toward active virtual reality gaming (AVG).20 Two gaming platforms that have recently adopted AVG play are the PlayStation 3 Move (Sony Computer Entertainment, Tokyo, Japan) and Xbox 360 Kinect (Microsoft Inc, Redmond, Washington). Both use a video camera placed near the television screen to capture the movements of the player to create a simulated character that mimics the movements of the player.
In recognition of the potential therapeutic benefits of AVG, a growing area of research is examining the training of motor skills through AVG.18,22–26 Recent studies have implied relationships between AVG intervention and improved endurance, coordination, and balance in children with neurodevelopmental conditions such as cerebral palsy and Down syndrome.25,27–29 Furthermore, AVG has been shown to effectively improve balance in children with DCD.24 This finding suggests that AVG might help children with DCD improve other aspects of gross motor skill development, an area future research aims to confirm.22
Active virtual reality gaming may be useful therapeutically to simply gain participation in some form of physical activity for psychological benefits.30 Alternatively, the therapeutic aim may be to promote good-quality movement, thereby facilitating neuroplastic changes and the normal development of other body systems.1,12,18 Although AVG seems promising in a motor rehabilitation setting, little is known regarding the movement patterns utilized during AVG. To be most effective as an intervention for enhancing movement quality, a greater understanding of the movement patterns of children with DCD during AVG is required. This knowledge will allow therapists to form targeted goals and prescription guidelines to address the movement quality issues, thereby optimizing the benefit gained from an AVG intervention.
The primary aim of this study was to determine whether differences existed in the motor patterns of children with DCD and children with typical development (TD) during AVG table tennis game play. We hypothesized that children with DCD would utilize different movement patterns compared with children with TD. In order to establish whether clinicians can plan standardized treatment across systems, the secondary aim of this study was to compare the movement patterns required between 2 popular AVG systems (Move and Kinect) during table tennis. We hypothesized that differences in the required movement patterns would exist between the 2 AVG types.
Method
Design
We conducted a quasi-experimental laboratory study comparing hand path measures and upper limb joint angles of 2 groups of children (DCD and TD) playing AVG table tennis on 2 consoles (Move and Kinect).
Participants
In this study, we collected data from 40 male and female participants between 10 and 12 years of age (refer to Tab. 1 for participant information). Twenty-one children with DCD were recruited into a randomized controlled trial (RCT)22 examining the impact of traditional electronic games and AVG on motor skill. Baseline data from the RCT (collected in early 2011) were utilized in the current study. Nineteen age- and sex-matched children who were developing typically (TD group) were recruited and tested in late 2012. Eleven of the 21 participants comprising the DCD group were classified as either obese or at risk of being obese based on sex and age-corrected body mass index percentile, compared with only 3 of the 17 participants in the TD group with complete data (Tab. 1).31 Recruitment to the study was through school and community notices and networks. Children were included in the DCD group if they scored in the 16th percentile or lower on the Movement Assessment Battery for Children, 2nd edition (MABC-2)32 and their motor impairments affected their activities of daily living based on the parent-reported Developmental Coordination Disorder Questionnaire (DCDQ) (a score of 15th percentile or lower). These scores are reflective of children considered at risk for DCD.33,34 The children had no other obvious disorder likely to affect their coordination and participation in the study in accordance with the diagnostic criteria listed in the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV).11,32,35 Children were included in the TD group if they scored higher than the 16th percentile on the MABC-2 and had no reports from parents or teachers of movement problems affecting activities of daily living or academic performance.
Characteristics of Children With Developmental Coordination Disorder (DCD) and Children With Typical Development (TD)a
Active Virtual Reality Games
Two AVG types were used: (1) Sony's PlayStation 3 Move (Move), which uses a single motion-sensing camera to track the movements of a handheld wand together with sensors contained within the wand for translation, acceleration, and rotation,18 and (2) Microsoft's Xbox Kinect (Kinect), which tracks whole-body movement in 3 dimensions using an infrared laser with dual camera sensors, eliminating the need for a handheld controller.36 The game used was table tennis, from PlayStation Move Sports Champions (Sony Computer Entertainment) and Xbox Kinect Sports (Microsoft Inc).
Procedure
Volunteers expressing interest in the study were provided with participant information sheets outlining the study details. Informed written consent and assent were obtained from the participating parents and children.
Children attended the Curtin University Motion Analysis Laboratory for 2 hours of testing. Each child performed the MABC-2 test. Parents or guardians of those children scoring in the 16th percentile or lower were asked to complete the DCDQ. Participants were fitted with a set of retroreflective body markers on their preferred hand, forearm, and upper limb. Body markers were fixed to specific anatomical locations in compliance with the protocol of the International Society of Biomechanics.7 A three-dimensional motion analysis system (Vicon, Oxford Metrics, Los Angeles, California) was utilized to track the position of the markers throughout data collection at a sampling rate of 250 Hz. This system has been reported to be one of the most accurate and reliable systems, with reconstruction errors <0.5 mm.37
Following a standardized set of instructions, participants played table tennis on Move for 5 practice points, followed by one full game to 11 points. This process was then repeated using Kinect. During practice, the investigator was able to provide feedback about technique (eg, full forehand and backhand stokes were encouraged); however, no feedback was given during the formal assessment. All tasks were performed in the same order against a computer-generated opponent using the beginner setting, and all children were allowed rest between tasks to minimize the effects of fatigue on subsequent tasks. A consistent order was used to minimize participant burden given that a number of tasks were performed on each AVG type as part of the RCT.
Data Processing
Vicon motion analysis software (Nexus, Oxford Metrics) was utilized to check marker trajectories for breaks that can result from occlusion of the markers. Gaps were filled using algorithmic interpolation between trajectory end points, with no break greater than 20 frames in duration. The data were then filtered with a quintic spline filter using a mean square error of 3, as determined by a residual analysis.38 A valid upper limb three-dimensional mathematical model39 that utilized previously published upper limb segment parameters40 and followed recommended biomechanical procedures was applied in order to calculate upper limb kinematics.7
Three forehand strokes and 3 backhand strokes were randomly identified from both Kinect and Move game play data. A stroke was defined as the end of the backswing until the end of the forward swing for both backhand and forehand. A stroke was considered a backhand when the dominant hand was on the nondominant side of the body, with the palm facing away from the screen at the start of the forward swing, and a forehand was defined by the dominant hand being on the dominant side of the body, with palm facing toward the screen at the start of the forward swing. Two participants in the TD group (1 female and 1 male) had motion analysis data that were missing or corrupted and, therefore, were not included in the analyses.
A customized LabVIEW program (National Instruments Corp, Austin, Texas) was used to output hand path distance and speed and wrist and elbow range of motion for each stroke. Statistical analyses were performed with SPSS (version 21, IBM Corp, Armonk, New York). Following assumption testing, linear mixed-model analyses were utilized to determine differences between DCD and TD groups (between-groups comparison) and Move and Kinect AVG type (repeated measures) and any interaction between group and AVG type. Alpha probability was set at .05.
Results
Hand Path Measures
Children with DCD utilized a significantly slower maximum hand speed than children with TD during the backhand strokes regardless of game type (mean difference [MD]=1.20 m/s; 95% confidence interval [95% CI]=0.41, 1.98; P=.04) (Tab. 2), although there were no significant differences in hand path distance. Post hoc power calculations for hand path distance demonstrated the observed power for detecting a difference in hand path distance between 2 independent groups (DCD group [n=21] and TD group [n=17]) using the observed between-groups difference (Move=0.49, Kinect=0.36, Tab. 2) and averaged within-group standard deviation (Move=0.51, Kinect=0.53, Tab. 2) was 0.841 for Move and 0.549 for Kinect (Using PS Power and Sample Size Calculations, version 3.1.2, Vanderbilt University, Nashville, Tennessee). Power estimates were for a main effect, not a group × game type interaction effect.
Hand Path Measurements Collected From Children With Developmental Coordination Disorder (DCD) and Children With Typical Development (TD) Playing Table Tennis Using Move and Kinect Active Virtual Reality Games (AVG)a
Differences between AVG types also were detected. Specifically, children's average hand path speed using Move was slower than when using Kinect (forehand MD=0.82 m/s; 95% CI=0.52, 1.12; P<.001; backhand MD=0.85 m/s; 95% CI=0.55, 1.14; P<.001; Tab. 2). This finding also was consistent for maximum speeds, showing slower maximum speeds on Move during forehand strokes (MD=1.79 m/s; 95% CI=1.22, 2.36; P<.001) and backhand strokes (MD=1.49 m/s; 95% CI=1.00, 1.98; P<.001). Using Move was associated with a significantly shorter hand path distance for forehand strokes (MD=0.45 m; 95% CI=0.26, 0.63; P<.001) and backhand strokes (MD=0.50 m; 95% CI=0.31, 0.69; P<.001), as illustrated in Figure 1. There were no group × AVG type interactions.
Hand path distance (mean ± standard deviation) during backhand strokes by children with developmental coordination disorder (DCD) and children with typical development (TD) using Move and Kinect active virtual reality games (AVG). Asterisk indicates significant differences between game types.
Wrist and Elbow Angle Variables
Overall, a significant difference between groups was found for maximum and minimum wrist angles during both forehand and backhand strokes (Tab. 3). The maximum wrist angle achieved was significantly different between groups, as the children with DCD played with significantly more wrist extension than the children with TD (forehand MD=34.3°; 95% CI=22.6, 47.0; P<.001; backhand MD=27.3°; 95% CI=13.8, 40.8; P<.001). Figure 2 presents the results for maximum wrist angle during forehands. These results were consistent for the minimum wrist angles (forehand MD=44.8°; 95% CI=29.4, 60.1; P<.001; backhand MD=34.6°; 95% CI=21.5, 47.7; P<.001), showing that children with DCD played table tennis on both consoles with significantly more wrist extension than children with TD (Tab. 3). Elbow angle results revealed that children with DCD utilized a significantly greater degree of maximum elbow flexion during forehand strokes (MD=2.3°; 95% CI=7.4, 37.1; P=.04) (Tab. 3).
Wrist and Elbow Angle (°) Measurements Collected From Children With Developmental Coordination Disorder (DCD) and Children With Typical Development (TD) Playing Table Tennis Using Move and Kinect Active Virtual Reality Games (AVG)a
Maximum wrist angle (°) (mean±standard deviation) achieved during forehand strokes by children with developmental coordination disorder (DCD) and children with typical development (TD) using Move and Kinect active virtual reality games (AVG). Asterisk indicates significant differences between groups.
No significant differences between AVG types were found for any wrist angle variable (Tab. 3). For elbow angle variables, a significantly greater minimum degree of elbow flexion was utilized on Move during forehand strokes (MD=12.1°; 95% CI=4.1, 20.1; P=.04), and a significantly smaller range was utilized on Move during backhand strokes (MD=13.7°; 95% CI=6.4, 21.1; P=.001). No group × AVG type interactions were found.
Discussion
This study observed how children with DCD play a table tennis AVG on Move and Kinect compared with children with TD. We found several key differences in support of our primary and secondary hypotheses. Possible reasons for these differences are discussed below.
Differences Between Children With DCD and Children With TD
Analysis of hand path variables revealed that children with DCD achieved a significantly slower maximum hand speed than TD children during backhand strokes (1.20 m/s less). Reduced movement speed has previously been reported as a motor impairment in children with DCD during real-life tasks such as reach-to-grasp41; our study showed this finding also is true for AVG play.
The IMD theory would explain these differences as a result of impaired feedforward and feedback mechanisms and sensory integration.2 The IMD theory suggests that children with DCD do not plan movements adequately prior to movement execution and do not use sensory feedback to adjust movement patterns at the completion of the movement.8 Deficient feedback processes in children with DCD may be facilitated through the various feedback mechanisms that AVG offers.42 Cognitive feedback (through game success) along with auditory, visual, and tactile feedback (provided by the handheld wand on Move) may assist higher cortical functioning to aid motor learning in children with DCD.42 Additionally, Kinect features a motion replay function that may enhance feedback and could be prescribed in therapeutic home use of AVG.
Furthermore, it is possible that children with DCD chose to utilize slower speeds of movement to enhance task accuracy during AVG play. Despite the fact that accuracy was not specifically recorded in this study, Fitts' law suggests that slower speeds are utilized to achieve higher accuracy.43,44 This speed-accuracy trade-off principle is applicable to the general population43,44; however, movement accuracy may be compromised at an even slower speed in children with DCD given the numerous reported impairments of motor coordination.45 As therapists, this principle may be strategically used during AVG intervention. For example, if achieving movement accuracy is a treatment goal, task speed may be reduced initially. As a progression, once task accuracy improves, movement speed may be increased under supervision.
Our findings revealed that children with DCD utilized only a wrist extension range to achieve forehand and backhand strokes. This finding was different from that of children with TD, who utilized ranges of wrist flexion and extension. Children with DCD also demonstrated a significantly larger degree of peak elbow flexion during forehand strokes. Although we are unable to state the exact cause of these differences, these results support previous evidence that children with DCD minimize the degrees of freedom associated with task execution in order to increase task outcome success.46 Additionally, as suggested by previous studies, a reduced ability to integrate sensory information may have resulted in the use of abnormal movement patterns to achieve the functional goal.2,12 Altered joint kinematics are associated with changes in muscle activation at the joint of interest and at joints proximally and distally.47 For example, Yu et al47 showed that elbow extension was predominantly associated with activation of the middle and anterior deltoid and supraspinatus muscles, whereas in a position of 90 degrees of elbow flexion, the anterior deltoid and subscapularis muscles were the dominant contributors to shoulder abduction. Therefore, if a treatment goal is to facilitate muscle activation, it is important to consider how joints distally and proximally could be implicated. These findings highlight the need for therapists to observe joint angles and movement strategies utilized by children with DCD, as this can affect muscle activation and overall movement quality.
Differences Between PlayStation Move and Xbox Kinect
The findings of our study indicated several key differences between Move and Kinect. Forehand and backhand strokes on Move were performed at a slower speed (up to 0.85 m/s less during backhand strokes) and utilized a smaller hand path distance (up to 0.50 m less during backhand strokes) compared with Kinect. These differences were consistent for children with DCD and children with TD and may stem from technical differences between AVG types. The hands-free play that Kinect offers de-weights the upper limb, potentially allowing for a faster speed and greater path of hand movement, compared with Move where the player grips a handheld wand, potentially constraining the movement.48 Additionally, the representation of the table tennis task and characters on screen were different between Move and Kinect. One example of this difference was the use of first-person perspective during table tennis using the PlayStation Move Sports Champions game and third-person perspective during table tennis on Xbox Kinect Sports. The differences in player perspective and projection of images on screen may have influenced the visual feedback received from each table tennis AVG.42
Our study revealed no significant differences for wrist angle variables between AVG types, which was surprising given the technical differences between Move and Kinect. We anticipated a difference given the weight of the handheld Move wand. It is possible that the wand weight was accounted for by compensations at the elbow joint rather than the wrist. The findings revealed 2 significant elbow angle differences between AVG types: greater minimum elbow flexion was utilized on Move during forehand strokes (12.1° greater), and a smaller elbow range of movement was utilized on Move during backhand strokes (13.7° less). Although our findings highlight that table tennis on Move and Kinect utilized different movement patterns, it is unclear at this stage whether this finding was due to software or display game differences (eg, images on screen) or input control hardware differences (eg, presence of a handheld wand on Move). Therefore, when choosing an AVG type to address a movement quality goal, clinicians should be aware that AVG types are not necessarily interchangeable.
Limitations
Although our study contributed several key findings to understanding the movement patterns of children with DCD during AVG play, it had a number of limitations. A methodological limitation of our study was the use of a consistent order of tasks on Move then Kinect to minimize participant burden, which may have introduced an order effect. In addition, the sample size for our current study was based on matching the sample size from the RCT study.22 The original RCT study had been powered to detect a 5-point difference on MABC-2 total impairment score for children with DCD (within-subjects design).21 Our current mixed-model study, therefore, had less power. Although the results presented in Tables 2 and 3 do not suggest any group × AVG type interaction, a small interaction may exist and may not have been detected in the analyses.
Differences also may have existed between children with more or less marked motor impairment; however, small subgroup numbers precluded such analysis in this study. Additionally, only the movement patterns of an upper limb game were analyzed. Further research on lower limb and full body AVG tasks is needed to be able to comment on the applicability of our findings to other body areas. Furthermore, as only simple joint kinematics were analyzed, future research should consider complex measures (such as acceleration and movement jerkiness) given the reported deficiencies in these areas for children with DCD.2 No data regarding forehand and backhand stroke success were assessed; associations between movement quality and functional outcome during AVG play is an area that could be explored in further studies. Participant feedback was utilized during the warm-up period to discourage “cheating” that is possible by utilizing small rapid “shakes” of the Wii input device.26 Therefore, these data might not be a true representation of how some players might adapt their technique to achieve game success. Finally, our study findings suggest several differences between the movement patterns utilized on both AVG types. However, it is unclear what design elements in the AVG types contributed to these differences. Further examination of spatial and temporal accuracy requirements of each system could be explored to identify if these components are key design elements that contribute to movement fidelity during play.
In conclusion, the results of this novel study show several key differences in the movement quality of children with DCD compared with children with TD during AVG play. Such differences have implications for the use of AVG for intervention purposes. One intervention goal may be to gain task engagement, thereby enhancing participation in physical activity for physical and mental health reasons. Alternatively, the goal of treatment may be to practice good-quality movements to facilitate neuroplastic changes and enhance motor learning. Under these circumstances, therapists need to assess each child individually and decide which aspects of the movement quality are deficient and whether AVG play can address these deficiencies. Consideration should be given to whether movement quality is adequate for unsupervised practice using AVG. Finally, as different AVG types elicit different movement qualities, clinical judgment is required to decide which AVG type and game are preferable for facilitating the desired motor learning.
Footnotes
Ms Gonsalves, Dr Campbell, and Dr Straker provided concept/idea/study design and data collection. All authors provided writing and data analysis. Dr Straker provided project management, fund procurement, facilities/equipment, and institutional liaisons. Ms Jensen provided consultation (including review of the manuscript before submission). The authors thank the participating children and families, Paul Davey, and Deborah Metcalf for their assistance.
Ethical approval for this study was obtained from the Curtin University Human Research Ethics Committee (Approval Numbers: HR11/2011 and PT215/2012).
The authors acknowledge the National Health and Medical Research Council (NHMRC) for their funding (project #533526; NHMRC Fellowship #425513 and #APP1019980).
- Received March 17, 2014.
- Accepted October 2, 2014.
- © 2015 American Physical Therapy Association