Elsevier

Computers in Human Behavior

Volume 90, January 2019, Pages 204-214
Computers in Human Behavior

Full length article
A pilot study on evaluating children with autism spectrum disorder using computer games

https://doi.org/10.1016/j.chb.2018.08.057Get rights and content

Highlights

  • We designed computer games for the evaluation of ASD and developmental level.

  • Performance differences between children with ASD and TD children were noted.

  • Children with ASD vary in developmental level as indicated by performance.

  • Technology is an effective and potentially sensitive screening tool for ASD.

Abstract

Evaluation of children with Autism Spectrum Disorders (ASD) is crucial to clinical diagnosis and educational intervention. The traditional evaluation methods based on questionnaires and scales rely on the experience and expertise of the evaluator, are time-consuming and clinically demanding. Computer games can provide an objective, motiving and safe way for evaluating and reflecting children's development. Therefore, the study aimed to investigate a technology-based method using computer games to evaluate children with ASD. The performance of 40 children with ASD and 51 aged-matched typically developing (TD) children was compared. We found: 1) The completion ratio for children with ASD was lower than TD children for the tasks in most of the games. 2) Significant differences between the ASD and TD groups, but no significant differences within group. 3) The performance of the TD group was better than ASD and the efficiency of TD group was proportional to age. While more research is needed to confirm its reliability and validity, the findings indicate that computer games have great potential in the field of special education as an evaluation tool to clarify difficulties associated with autism.

Introduction

Autism Spectrum Disorder (ASD) is a lifelong neurodevelopmental disorder involving core deficits in interpersonal communication and social interactions, as well as restricted, repetitive mannerisms and interests (American Psychiatric Association, 2013). Children with ASD struggle with significant relationships and behavioral challenges that in most cases have serious implications for social inclusion in adulthood. They display a great variety of characteristics, ranging from speech disabilities, to severe limitations in social skills which impair their ability to develop peer relationships appropriate to age(Diagnostic and Statistical Manual of Mental Disorders, 2000). They also show some inability in terms of imagination, manifested in the difficulty to generalize between environments, a limited range of imaginative activities, and difficulty in predicting future events and abstract ideas (Bartoli, Garzotto, Gelsomini, Oliveto, & Valoriani, 2014). The percentage of children identified as having ASD has increased significantly in recent years, and became a public health concern. In terms of global prevalence, 1 in 160 children are estimated to have ASD (Elsabbagh, 2012). However, CDC's Autism and Developmental Disabilities Monitoring (ADDM) Network estimates 1 in 68 American children have ASD (Christensen et al., 2016). The cause or etiology is complex, involving both genetic and environmental factors (Hallmayer et al., 2011, Sandin et al., 2014). It is generally recognized that the most effective clinical route to treatment is early identification and intervention (Bradshaw et al., 2015, Howlin et al., 2009), with accurate evaluation or diagnosis as the precondition. Early detection and intervention is crucial for children with ASD to maximize gains in communication and social skills (Fakhoury, 2015). The endeavor affords the family and caregivers the opportunity to adjust, in some cases, trigger the resources required for professional care and treatment. Moreover, early detection and intervention can also produce significant health and economic benefits and provide the best chance for lifelong improvement and relative independence (Peters-Scheffer, Didden, Korzilius, & Matson, 2012; Chasson, Harris, & Neely, 2007). In terms of financial cost, ASD can be a heavy burden for the families of affected children (Lee, David, Rusyniak, Landa, & Newschaffer, 2007). The earlier children with ASD can be identified, the sooner they can get the services needed to reach full potential.

Children with ASD often have comorbid medical conditions, including intellectual disabilities, anxiety, and depression that implicate the evaluation and diagnosis (Tuchman & Rapin, 2002). ASD can be diagnosed as early as at age two, but most children are not diagnosed with ASD until after they are four. As of now, it remains difficult to obtain a diagnosis of ASD. However, there are some well-developed and tested methods for diagnosing autism (e.g., ADOS) and other screening tests that provide quantitative indicators, such as the Social Communication Questionnaire (SCQ). In the SCQ, the cut-off (usually 15 or above) identifies individuals who are likely to suffer from ASD and for whom more extensive evaluations should be undertaken (Rutter, Le Couteur, & Lord, 2003). The use of these tools is followed by more extensive diagnostic evaluation using validated assessment tools (e.g., the Autism Diagnostic Interview-Revised or ADI-R; SCQ; ADOS). Currently, evaluation relies heavily on the experience and expertise of specialists, and the traditional questionnaire/scale-based methods of evaluation depend on interpretive coding of child observations, parent interviews, and manual testing, which can be time-consuming and clinically demanding. Whilst it might be useful to have simple, accessible tests that provide behavioral correlates as indicators of the presence of autistic traits (e.g., for preliminary screening), significant amounts of research would be required to develop screening tools that are both valid and reliable.

A growing number of investigators are on the verge of discovering and developing new markers or techniques to improve the diagnosis or intervention of ASD. ASD can be measured directly using sensitive and reliable quantitative approaches (Gabriele et al., 2014, Ruggeri et al., 2014). Several studies have shown that the majority of people with ASD exhibit a natural affinity for technology and a positive attitude towards computer-based intervention and training (Ding and Marchionini, 1997; Goldsmith and LeBlanc, 2004; Bernardini et al., 2014, Dehkordi and Rias, 2014). A research group at MIT implemented emotion-recognition algorithms on a mobile device to help people with ASD who have difficulty recognizing emotions in face-to-face situations (Madsen, Kaliouby, Goodwin, & Picard, 2008). Zachary Warren's team developed a novel VR-based dynamic eye-tracking system for children with autism, which is capable of delivering individualized feedback based on a child's dynamic gaze patterns during VR-based interaction(Lahiri, Warren, Sarkar, 2011). Serious games are of special interest in the field of autism study, since their rule-based environments presents a safe, appealing vehicle for interventions to improve a person's level of socialization (Grossard et al., 2017). For intervention e.g. training on social skills, computer games (mainly refers to serious games) are very promising, they can support interactions in diverse contexts and situations, some of which are real life simulation games. However, the currently available computer games present some limitations in terms of the evidence of their clinical benefits.

Computers are popular and preferable among people with ASD because they are predictable, consistent, free from social demands, and specific in terms of focus of attention (Murray, 1997). Therefore, computer-based applications are considered useful tools for therapeutic and educational purposes (Kagohara, van der Meer, & Ramdoss, 2013; Chen, 2012, Chen et al., 2014). They are also good educational tools, as children with ASD often experience discomfort with unpredictable social environments and prefer a controlled learning environment (Wilkinson et al., 2008; Wainer & Ingersoll, 2011). Individuals with ASD enjoy learning and improving their skills with computer-based intervention. In recent years, multi-touch tabletop interfaces have become available. These consist of large touch displays placed horizontally, which allows multiple users’ input simultaneously. The system interprets the gestures of more than one collocated user as contributing to a single, combined command. Anna et al. (2016) employed smart tablet computers with touch-sensitive screens and embedded inertial movement sensors to record the movement kinematics and gesture forces while children played serious games; 37 children with ASD aged 3–6 and 45 age- and gender-matched TD children were included in the study. The experimental results support the notion that disruption to fine motor skill is a core feature of ASD and demonstrate that children with ASD can be computationally assessed using a smart device while having fun. Furthermore, several studies also indicate that computers have the potential to be used as evaluation tools for children with ASD (Bartolomé et al., 2013, Costa et al., 2012, Li and Elmaghraby, 2014).

In China, there are more than 10 million individuals with ASD (Lin, 2014) and because of high demand, professional and prompt evaluation and intervention are often difficult to obtain. Therefore, it is necessary to develop computer-based evaluation or intervention tools for ASD. The designed computer games might provide a motiving and safe alternative for evaluation, and objectively reflect children's developmental trajectories.

Section snippets

Participants

The present study was approved by our institutional review board. Participants consisted of 91 children aged 2–6 years, 40 (M = 4 yrs. 5 months, SD = 11 months) with a clinical diagnosis of ASD (ICD-10, 2010 Edition; World Health Organization, 2011; DSM-5, 2013), referred to as the ASD group, and 51 with typical development (M = 4 yrs.7 months, SD = 11 months), referred to as the TD group (see Table 1). All the participants have normal or corrected visual acuity and no other sensory or motor

Descriptive statistics

The completion rate used to evaluate the TD and ASD groups are shown in Table 2. Completion rate means the ratio of the number of people who complete the game to the total number. e.g., r = C/N. Participants who only partly completed the games are not included in C. Based on the descriptive statistics, we found that the completion rate for the TD group is higher than that for the children with ASD. For the TD group, the completion rate for children aged under 4 years old was 91.31% and that for

Discussion

This study describes whether it is possible to use computer games to distinguish children with and without ASD by analyzing their performance differences. The activities and learning measures in the evaluation game provide a quantitative description of children's capacities, which would be a more objective and feasible way of identifying ASD as compared to traditional qualitative methods. These games may also be used as a supportive tool in educational intervention for children with ASD.

Due to

Conclusion

This study presents a novel method of educational evaluation for children with ASD. A series of computer games were designed and implemented to explore the quantitative differences between children with ASD and TD children in the development fields of joint attention, ToM, visual search skills, fine motor skills, cognitive understanding, and concept classification. The results show that, in general, most of the children with ASD have lower development levels than the TD children, and there are

Funding

This work was supported by the National Social Science Foundation of China (Grant no. 16BSH107).

Disclosure statement

The authors have no potential conflict of interest to declare.

Acknowledgments

We thank our colleagues at the National Engineering Research Center for E-Learning of the Central China Normal University who participated in this study. We also appreciate the children and their teachers and families.

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