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Virtual Travel Training for Autism Spectrum Disorder: Proof-of-Concept Interventional Study

Virtual Travel Training for Autism Spectrum Disorder: Proof-of-Concept Interventional Study Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by impairments in social interaction and repetitive patterns of behavior, which can lead to deficits in adaptive behavior. In this study, a serious game was developed to train individuals with ASD for an important type of outdoor activity, which is the use of buses as a means of transportation. Objective: The aim of this study was to develop a serious game that defines a “safe environment” where the players became familiar with the process of taking a bus and to validate if it could be used effectively to teach bus-taking routines and adaptive procedures to individuals with ASD. Methods: In the game, players were placed in a three-dimensional city and were submitted to a set of tasks that involved taking buses to reach specific destinations. Participants with ASD (n=10) underwent between 1 to 3 training sessions. Participants with typical development (n=10) were also included in this study for comparison purposes and received 1 control session. Results: We found a statistically significant increase in the measures of knowledge of the process of riding a bus, a reduction in the electrodermal activity (a metric of anxiety) measured inside the bus environments, and a high success rate of their application within the game (93.8%). Conclusions: The developed game proved to be potentially useful in the context of emerging immersive virtual reality technologies, of which use in therapies and serious games is still in its infancy. Our findings suggest that serious games, using these technologies, can be used effectively in helping people with ASD become more independent in outdoor activities, specifically regarding the use of buses for transportation. (JMIR Serious Games 2018;6(1):e5) doi: 10.2196/games.8428 KEYWORDS Autism Spectrum Disorder; serious games; virtual reality; virtual reality therapy; travel train; bus. interests, or activities. Current ASD prevalence is estimated to Introduction be around 1% of the population, worldwide [1]. Given that a cure is yet to be found, individuals rely on interventional Autism Spectrum Disorder (ASD) is a neurodevelopmental approaches to improve and overcome their impairments. Further disorder responsible for impairments in social communication increasing the relevance of rehabilitation driven strategies is and interaction and restricted repetitive patterns of behavior, http://games.jmir.org/2018/1/e5/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e5 | p. 1 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Simões et al the fact that still only a minority of individuals with ASD are public transport can be particularly challenging for people with able to live independently in adulthood [1]. ASD due to deficits in adaptive behavior [14] and anxiety [15]. This project intended to ease this process by creating and Virtual reality (VR) consists of artificial, 3D, validating a game that prepares the players to use, in this case, computer-generated environments which the user can explore buses for transportation. This included not only teaching the and interact with [2]. VR usually takes advantage of different required skills, but also making them comfortable with the types of devices such as head-mounted displays and controllers involved procedures and environments. To our knowledge, this to deliver the stimulation and provide means of interaction that is the first study to use VR training for teaching the process of lead to immersive experiences [3]. The immersiveness of an bus-taking for people with ASD. experience is measured by the level of fidelity, concerning all sensory modalities, that a VR system can provide. Thus, Methods immersion is objective, measurable, and depends only on the technology used by the VR system. Presence, on the other hand, In this section, we describe the game, the experimental setup, is the human reaction to immersion, the feeling of actually being the participants, and the validation procedure. in the virtual environment and behaving as such [4,5,6]. In fact, Game Description the immersive VR technologies available nowadays are able to present users with experiences realistic enough to trick the mind The game was developed in-house with the aim of preparing and create a feeling of presence within the environment [7]. individuals with ASD to use buses for transportation. To achieve These technologies have already been used and proven effective that, it places the user in a three-dimensional city and sets a task in therapies for posttraumatic stress disorder [8], phobias [9], that is completed by riding buses to reach a specific destination. as well as ASD [10]. There are several reasons that justify the Figure 1 shows different screenshots of the city and the buses. use of VR in those approaches. Its capacity to provide safe, There are several different buses driving in 4 pre-defined routes realistic, and controlled environments [11] make therapy within the city. Figure 2 shows the map of the city with the 4 possible for people who, due to physical (eg paralysis) or bus lines available. The player can enter any of these buses, psychological (eg, anxiety) reasons, cannot undergo exposure validate his/her ticket, choose a place to sit, and press the STOP in real life situations. Moreover, this creates the possibility of button, requesting the bus to stop, and leave the bus. Before applying therapies at ease without the need to go to a specific starting the game, it is possible to choose from 7 different tasks, location where real exposure occurs. Finally, even when using 4 of them labelled by complexity as simple (the player only low-budget hardware and software, it has been proven that VR needs to take 1 bus to reach the destination) and 3 labelled as therapies can be as effective as exposure in real life [9]. complex (the player needs to take 2 buses to reach the Serious games have also been proven effective in ASD therapies, destination). Each task has 2 levels of difficulty: an easy and a not only for including game design techniques to keep the hard mode. The easy mode leads the player, step by step, to the players motivated, but also because individuals with ASD are destination, while in the hard mode, the player is only told the often interested in computer-based activities [12]. However, place he/she must go to. At the end of each task, a scoring according to Zakari et al [13], most of the serious games system evaluates the performance of the player on 2 different developed for ASD rehabilitation between 2004 and 2014 are components: “Actions” (the capacity of the player to memorize delivered through nonimmersive VR (eg, desktops, tablets) and and execute bus norms, eg, validating the ticket or sitting on focus mainly on communication and social skill development unreserved seats) and “Route” (the capacity of the player to [13]. This highlights both the importance of focusing on other plan a route to the destination, eg, if the player took the right relevant ASD impairments, including executive function in buses in the right bus stops). outdoor situations, as well as exploring the potential of these The game includes several objects/elements, such as people, new immersive technologies. traffic, and dogs, which might cause anxiety, depending on the The aim of this study was to develop and validate a serious player. For that reason, a biofeedback system was implemented game which uses a VR headset (Oculus Rift) as a proof of to ensure that, while the player becomes familiarized with the concept for rehabilitation of individuals with ASD. Teaching a environment, it never becomes hostile to him/her. This is person with ASD to use public transportation requires parents achieved by assessing the anxiety felt by the player through the or therapists to practice with them until they are ready and analysis of the electrodermal activity (EDA) and reducing the comfortable enough to perform this task alone (basically, the stimulus clutter the player is exposed to, in real time, in case same process used with people with typical development, but high anxiety levels are reached (eg, reducing the amount of with all the obstacles set by ASD particularities). In fact, being noise in the environment). Figure 3 schematizes the biofeedback able to engage in outdoor activities such as the efficient use of loop. http://games.jmir.org/2018/1/e5/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e5 | p. 2 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Simões et al Figure 1. Screenshot from the virtual environment, showing two views from the bus stop perspective on the top, and two views from inside the bus on the bottom. On the top left corner, we can see one bus stop, with other people waiting for the bus, and the map to be used by the participant on the wall. On the top right, we see a bus with its designated number signaled in red, and some traffic on the street. The bottom images show two perspectives from inside of the bus. Figure 2. City map showing the bus lines, stops, and important places like the hospital, church, restaurant, and others. http://games.jmir.org/2018/1/e5/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e5 | p. 3 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Simões et al Figure 3. Biofeedback loop diagram. The level of electrodermal activity is measured from the participant by the game. If it detects a peak of activity, it decreases the level of stimuli and noise in the scene. to provide a task baseline and to assess the capacity of the Recruitment serious game to identify differences between groups. For this project, 2 groups were selected: a clinical and a control The sessions took place in APPDA-Viseu for the ASD group. For the clinical group, 10 participants with ASD, whose participants and in the Institute of Biomedical Imaging and Life diagnosis followed the criteria established on the Diagnostic Sciences for the TD participants. In all of them, the same th and Statistical Manual of Mental Disorders, 5 Edition [1], were research staff was present. This study and all of the procedures recruited from Associação Portuguesa para as Perturbações do were reviewed and approved by the Ethics Commission of our Desenvolvimento e Autismo de Viseu (APPDA-Viseu). For the Faculty of Medicine from University of Coimbra and were clinical group, 9 males and 1 female were recruited, with a mean conducted in accordance with the declaration of Helsinki. age of 18.8 (SD 4.5) years. In the same group, 8 were without intellectual disability (IQ equal to 70 or higher) and 2 were with Session Procedure mild intellectual disability (IQ between 50 and 69 [16]). For In each session, the players received a tutorial (to learn or review the control group, 10 individuals with typical development (TD) the game controls) and a task. The task difficulty and complexity were also recruited; 4 males and 6 females, with a mean age of changed from session to session, as shown in Table 1. At the 21.9 (SD 3.56) years. The groups were matched by age end of every session, participants were asked to describe the (t(18)=−1.633, P=.12). Participants gave oral consent, and a process of riding a bus, from the moment they arrived at the written informed consent was obtained from their bus stop, until they reached their destination, but never received parents/guardians or themselves if they were adults with feedback on the answer given. Their responses were recorded sufficient autonomy. in a checklist containing all the steps existing in a bus trip (Textbox 1). Intervention Protocol The ASD group underwent an intervention of 3 sessions of Acquisition Setup increasing complexity and difficulty (see Table 1), with a In every session, players sat on a swivel chair and wore a duration between 20 and 40 minutes each. Of the 10 participants bracelet for wireless EDA recording (Biopac Bionomadix recruited with ASD, only 6 performed all the intervention BN-PPGED and MP150 amplifier). All tasks were run on a sessions of the study due to scheduling issues. During the laptop computer (Windows 8.1, 16.0 GB RAM and an IntelCore recruitment, participants completed a questionnaire to assess i7 2.50 GHz processor). The head-mounted display used was their experience in using buses for transportation. With an Oculus Rift Development Kit 2, firmware version 2.12, and a exception for 1 of the participants, all the participants were gamepad was used for input. Three participants with ASD and unable to take buses autonomously at the beginning of the study. 1 with TD received the tasks without the Oculus Rift, due to vision impairments. Figure 4 illustrates the setup used. The control group was submitted to a single session (corresponding to the first of the patients). This group was used Table 1. Task complexity and difficulty per session. Metric Session 1 Session 2 Session 3 Difficulty Easy Hard Hard Complexity Simple Simple Complex http://games.jmir.org/2018/1/e5/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e5 | p. 4 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Simões et al Textbox 1. Checklist with steps existing in a bus trip. Wait for the bus Enter in the bus Validate ticket Avoid reserved seats Sit Wait until getting close to the destination Press stop button Leave the bus Figure 4. Diagram of the setup used during the sessions, including the virtual reality headset, game controller, biosignal recorder, and the main computer. Anxiety Level Metrics and Outcome Measures The variation of EDA values during the session was used as a Because the main objective of the serious game was to teach measure of anxiety. To calculate it, we detrended the signal, how to take the bus, we defined two main outcome measures subtracting it to its best fit to a straight-line (for details, see to evaluate the knowledge of the process of riding the bus. One detrend implementation in MathWorks Matlab), then calculated is measured automatically by the game and the other is measured the standard deviations of the detrended EDA values. We then using the debriefing. Both consist of the percentage of the created heat maps of anxiety peaks in the “virtual city”, where checklist (Textbox 1) performed correctly. it is possible to highlight the game locations where EDA peaks occurred, corresponding to anxiety events felt by the players. Actions Accuracy If the locations of anxiety peaks are the same between subjects The game identified every action of the participant (entering and sessions, those locations will become red, but if they are the bus, ticket validation, etc) and calculated the actions sparse, their representations are green and blue. The heat maps accuracy based on the equation: number of correct were created using heatmap.js [17], an open source heat map actions/number of expected actions (Textbox 1). visualization library for JavaScript. Since the game has two Debriefing Accuracy major different situations (the city streets and the inside of the buses), two different conditions were defined: one representing After the game, we asked the participants to describe the the anxiety felt on the streets of the city and another with the step-by-step process of riding a bus and calculated an accuracy anxiety felt inside the buses. based on the same equation of the actions accuracy. Statistical Analysis Additional Metrics For every metric, normality was assessed using histograms, Task Duration Q-Q plots and the Kolmogorov-Smirnov test. Normality test Duration, in minutes, of the time taken to complete the task in results were used to choose between parametric and each session. Since the tasks changes in complexity and nonparametric test, accordingly. difficulty, this metric is not directly comparable between Clinical vs Control Group Analysis: sessions. Nevertheless, it is useful to analyze intersubject variability and to perform intergroup comparisons for the first The metrics actions accuracy, debriefing accuracy, and task session. duration were not normally distributed. Therefore, we used the http://games.jmir.org/2018/1/e5/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e5 | p. 5 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Simões et al Mann-Whitney U test for the between-group comparison of Clinical vs Control Group Results those metrics. Anxiety Level data was nevertheless normally Regarding the actions accuracy (percentage of correct actions distributed. Thus, we used two-sample t test for those performed during the task), we found a statistically significant between-groups comparisons. difference between groups. The Mann-Whitney U test indicated that the actions accuracy of the control group (median=100%) Intervention Analysis (Intragroup Analysis for the was greater than for the clinical group (median=88%), U=75.0, Clinical Group): P=.01. When analyzing the knowledge retained by the As expected from the intergroup analysis, Actions Accuracy, debriefing, the control group (median=100%) has greater Debriefing Accuracy, and Task Duration were not normally accuracy than the clinical group (median=62.5%), as shown by distributed. Paired data for last and first session were therefore the Mann-Whitney U test, U=80.5, P=.02 (Figure 5). compared using a Wilcoxon Sign-rank test. Regarding Anxiety In terms of Task Duration, Figure 6 shows the between-group Level, a one-sample t test was used for the paired data (last and differences. A Mann-Whitney U test showed that the task first session). Additionally, we generated heat maps of locations duration of the ASD group (median=3.76) was statistically where consistent peaks of anxiety where identified. significant higher than for the control group (median=3.19), U=18.0, P=.02. Results Regarding the anxiety level of each group, we see a trend for The results are organized in two sections, one for the intergroup higher values for the clinical group for all the scenarios (global analysis and another for the within-subject analysis of the EDA, inside the bus and outdoors, in the street). Figure 7 shows clinical group. In the figures presented, the error bars always the mean EDA fluctuations of each group in each condition. represent the standard error of the mean, and the horizontal bar However, two-sample t tests showed no statistically significant represents the median when considering nonparametric data differences (possibly because of the biofeedback (actions and debriefing accuracies, as well as task durations) implementation) for either of the conditions (global EDA: and the mean when considering parametric data (EDA t(18)=−0.60, P=.56; bus condition: t(18)=−0.99, P=.33; streets measures). condition: t(18)=−0.48, P=.63). Figure 5. Left: Actions accuracy for both groups in the first session. The control group (typical development) had a perfect performance, while participants in the clinical group missed some actions. Right: Debriefing accuracy for both groups. Higher, but not perfect, accuracy was found in the control group. http://games.jmir.org/2018/1/e5/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e5 | p. 6 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Simões et al Figure 6. Task duration for session 1 from each group. Figure 7. Anxiety levels for each group (mean and standard error) for the overall task and two subconditions: inside the bus and outside (streets). The clinical group presents higher values for all the settings, although without statistical significance. EDA: electrodermal activity. session 1 (median=68.8%) to session 3 (median=100.0%). Intervention Results Figure 9 illustrates the evolution of the debriefing checklist We then compared the main outcome measures pre- and accuracy throughout the sessions. postintervention for the 6 participants who completed the 3 Regarding the task duration, since the tasks increased in sessions. The in-game measure (actions accuracy) evolved complexity and difficulty along the intervention, the task positively throughout the sessions (preintervention median duration is not directly comparable. However, it is important accuracy=75.0%, postintervention median accuracy=93.8%; to verify that the time to successfully complete the task did not see Figure 8), with a Wilcoxon signed-rank test showing a small statistically increase, even with exposure to harder levels (Figure trend for significant differences (Z=1.63, P=.10). Even though 10). In session 1, the task was performed in the “easy” mode, the differences were not statistically significant when using in which players are guided step by step, from the starting “in-game” measures, the same test applied to the Debriefing position until the final location. Tasks in “easy” mode do not Accuracy show a significant increase (Z=2.22, P=.03) from http://games.jmir.org/2018/1/e5/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e5 | p. 7 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Simões et al require as much planning as the ones in “hard” mode, but slowly Figure 11 shows the mean differences and their error. However, introduce the player to this concept and demonstrate how buses only the bus condition shows a strong tendency to significance can be used to travel between bus stations. The “hard” mode (t(5)=−2.36, P=.07). The general EDA values showed a weak tasks, on the other hand, only tell the player where they must tendency towards an effect (t(5)=−1.93, P=.11) and the EDA go to complete the task (eg, “go to the hospital”). In these tasks, values on the streets had the smaller decrease with the players have to analyze the map to discover which bus or buses intervention (t(5)=−1.48, P=.20). they can take to reach the final destination. Furthermore, the We created heatmaps using the peaks of anxiety of each tasks received in sessions 1 and 2 required the player to take 1 participant and separated the conditions inside the bus and bus, while the task from session 3 required the player to take 2 outside. Figure 12 shows the streets scenario for each of the buses. A Wilcoxon signed-rank test show no difference between sessions, and Figure 13 shows the same metrics but for the the easiest and simpler (first session) task and the most complex inside the bus condition. and difficult task (last session), Z=0.105, P=.92. According to Figure 12, players felt most anxious in 3 different The anxiety levels decreased from the first to the last session, situations (Textbox 2). for both overall EDA and for the bus and streets conditions. Figure 8. Actions accuracy for the clinical group, measured “inside the game” throughout the intervention sessions. Figure 9. Debriefing accuracy of the intervention group for each session. http://games.jmir.org/2018/1/e5/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e5 | p. 8 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Simões et al Figure 10. Time taken by each participant in the clinical group to complete the task in each intervention session. Figure 11. Decrease observed in anxiety levels, measured by electrodermal activity variability, between the last session and the first. EDA: electrodermal activity. http://games.jmir.org/2018/1/e5/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e5 | p. 9 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Simões et al Figure 12. Anxiety peaks heat map from session 1 (left) to session 3 (right) for the times the participant was not inside of the bus environment. Most of the locations represent bus stops, where participants need to make the decision of what bus to take and wait for it to arrive. Figure 13. Anxiety peaks heat map from session 1 (left) to session 3 (right) for the times the participant was inside of the bus environment. The locations are much more dispersed through the route than in the outside the bus scenario. There is a visible decrease in frequency of anxiety peaks from the first to the last session. http://games.jmir.org/2018/1/e5/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e5 | p. 10 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Simões et al Textbox 2. Situations in which players felt anxious. When waiting for the bus at the bus stops (red areas near the bus stop signs) When planning the trip or looking for the correct bus stop in the starting areas (ie, green areas near the restaurant in session 1, the fire station in stage 2, one of the bus stops in session 3) When reaching or looking for the destination in the finishing areas (ie, green areas near the police station in session 1, the hospital in session 2, and the fire station in session 3). proving the validity of the rehabilitation target and confirming Discussion the capacity of the game to, by itself, identify the deficits of target participants in the process of taking the bus. Additionally, Principal Findings the time needed to complete the task was increased for the This project aimed to assess the potential of the developed clinical group. Despite having mean anxiety levels above the serious game and here presents it as a tool for rehabilitation. control group, this difference was not significant, perhaps due The game is, to our knowledge, the first application specifically to the small statistical power resulting from a small group of developed to teach ASD individuals to use public transportation participants, as well as the implementation of biofeedback, systems. In addition, we were able to measure which decreases differences between groups. psychophysiological markers of anxiety, paving the way for The intervention was successful in increasing the accuracy of future biofeedback applications. With only three sessions, it the process description during the debriefing, showing a was possible to improve the knowledge of the participants statistically significant improvement in the theoretical regarding the norms of the bus-taking process and to reduce the knowledge of the process, which was the main outcome anxiety levels felt by the participants during that process. measure. When evaluated inside the game by the user actions, The impacts of a learning tool with this purpose are broad since the increase was not statistically significant, but showed a it trains executive functions and might increase the autonomy tendency that we believe additional sessions or a larger of the users, providing them with a new way of moving through intervention group would further confirm. It was also successful a city. It is also a way to make cities more inclusive, providing in decreasing the anxiety felt by participants, especially inside people with special needs ways to successfully use this type of the bus. By using heat maps to represent the anxiety peaks public service. recorded, it was possible to understand that participants with ASD felt more anxious in bus stops and near the starting and Some studies have been conducted using VR training for ASD, finishing areas. This led to the conclusion that, when outside usually focusing on training other skills. Most interventional the buses, players felt most anxious when planning the trip, approaches target social performance training [18,19] or job when looking for the bus stop, when waiting for the bus, and interviewing [20]. Gaming platforms [21] and brain-computer when looking for the final destination. Inside the bus, we interfaces [22] were also suggested in the literature for autism observed a desensitization to stress throughout the sessions, training, but without validation with patients. Although these with a final session showing fewer peaks of EDA activity. are important targets of intervention, our work focuses on a more specific task of executive function that is relevant for the Despite the increase of task complexity and difficulty across needs of daily life. Our pilot validation study aimed to assess sessions, the time duration to complete the task did not increase, not only the efficacy of the application, but also the acceptance suggesting a learning effect and adaptation to the serious game. of the solution with this specific clinical population. Few studies Conclusions perform fully immersive interventions, and the difficulties of combining them with biofeedback create a technology apparatus By using the game as a therapeutic intervention tool, in just that could potentially be disruptive to the participants. The 4 three sessions it was possible to improve the general efficiency drop-outs during the intervention occurred exclusively due to of participants and expose them to peculiar scenarios in which scheduling issues. No patient dropped the study due to they could train their planning skills. More importantly, it was discomfort or raised any difficulty in using the setup. There possible to nearly extinguish the anxiety felt in bus environments were specific cases where the Oculus device was not used, but and teach the bus-taking norms necessary for the autonomous all of those were related to vision impairments, not to lack of use of buses for transportation, both in theoretical and practical tolerance from the user. contexts. Future studies should conduct randomized controlled trials, with larger intervention groups, to replicate the findings The baseline comparison with the control group was clear in and extend them to other clinical populations with executive identifying the impairments in the clinical group. Both the function deficits and lack of autonomy. debriefing of the procedure for taking the bus and the in-game actions showed statistically different results between groups, Acknowledgments This study was supported by the AAC SI/2011/HomeTech/QREN Compete, cofinanced by FEDER, the Portuguese Foundation for Science and Technology, the European Projects BRAINTRAIN (FP7-HEALTH-2013-Innovation-1-602186BrainTrain), H2020-STIPED Project number: 731827, FCT (Fundação para a Ciência e Tecnologia) UID/NEU/04539/2013, http://games.jmir.org/2018/1/e5/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e5 | p. 11 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Simões et al POCI-01-0145-FEDER-007440, and PhD grant SFRH/BD/77044/2011. The authors would like to thank APPDA-Viseu and all the participants and parents who collaborated in this study. Conflicts of Interest None declared. References 1. American PA. Diagnostic and Statistical Manual of Mental Disorders. 5th edition. 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Abbreviations ASD: Autism Spectrum Disorder EDA: electrodermal activity http://games.jmir.org/2018/1/e5/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e5 | p. 12 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Simões et al HMD: head-mounted display IQ: Intelligence Quotient TD: typical development VR: virtual reality Edited by G Eysenbach; submitted 13.07.17; peer-reviewed by B Bie; comments to author 17.11.17; revised version received 20.11.17; accepted 22.11.17; published 20.03.18 Please cite as: Simões M, Bernardes M, Barros F, Castelo-Branco M JMIR Serious Games 2018;6(1):e5 URL: http://games.jmir.org/2018/1/e5/ doi: 10.2196/games.8428 PMID: 29559425 ©Marco Simões, Miguel Bernardes, Fernando Barros, Miguel Castelo-Branco. Originally published in JMIR Serious Games (http://games.jmir.org), 20.03.2018. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Serious Games, is properly cited. The complete bibliographic information, a link to the original publication on http://games.jmir.org, as well as this copyright and license information must be included. http://games.jmir.org/2018/1/e5/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e5 | p. 13 (page number not for citation purposes) XSL FO RenderX http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png JMIR Serious Games JMIR Publications

Virtual Travel Training for Autism Spectrum Disorder: Proof-of-Concept Interventional Study

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Abstract

Background: Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by impairments in social interaction and repetitive patterns of behavior, which can lead to deficits in adaptive behavior. In this study, a serious game was developed to train individuals with ASD for an important type of outdoor activity, which is the use of buses as a means of transportation. Objective: The aim of this study was to develop a serious game that defines a “safe environment” where the players became familiar with the process of taking a bus and to validate if it could be used effectively to teach bus-taking routines and adaptive procedures to individuals with ASD. Methods: In the game, players were placed in a three-dimensional city and were submitted to a set of tasks that involved taking buses to reach specific destinations. Participants with ASD (n=10) underwent between 1 to 3 training sessions. Participants with typical development (n=10) were also included in this study for comparison purposes and received 1 control session. Results: We found a statistically significant increase in the measures of knowledge of the process of riding a bus, a reduction in the electrodermal activity (a metric of anxiety) measured inside the bus environments, and a high success rate of their application within the game (93.8%). Conclusions: The developed game proved to be potentially useful in the context of emerging immersive virtual reality technologies, of which use in therapies and serious games is still in its infancy. Our findings suggest that serious games, using these technologies, can be used effectively in helping people with ASD become more independent in outdoor activities, specifically regarding the use of buses for transportation. (JMIR Serious Games 2018;6(1):e5) doi: 10.2196/games.8428 KEYWORDS Autism Spectrum Disorder; serious games; virtual reality; virtual reality therapy; travel train; bus. interests, or activities. Current ASD prevalence is estimated to Introduction be around 1% of the population, worldwide [1]. Given that a cure is yet to be found, individuals rely on interventional Autism Spectrum Disorder (ASD) is a neurodevelopmental approaches to improve and overcome their impairments. Further disorder responsible for impairments in social communication increasing the relevance of rehabilitation driven strategies is and interaction and restricted repetitive patterns of behavior, http://games.jmir.org/2018/1/e5/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e5 | p. 1 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Simões et al the fact that still only a minority of individuals with ASD are public transport can be particularly challenging for people with able to live independently in adulthood [1]. ASD due to deficits in adaptive behavior [14] and anxiety [15]. This project intended to ease this process by creating and Virtual reality (VR) consists of artificial, 3D, validating a game that prepares the players to use, in this case, computer-generated environments which the user can explore buses for transportation. This included not only teaching the and interact with [2]. VR usually takes advantage of different required skills, but also making them comfortable with the types of devices such as head-mounted displays and controllers involved procedures and environments. To our knowledge, this to deliver the stimulation and provide means of interaction that is the first study to use VR training for teaching the process of lead to immersive experiences [3]. The immersiveness of an bus-taking for people with ASD. experience is measured by the level of fidelity, concerning all sensory modalities, that a VR system can provide. Thus, Methods immersion is objective, measurable, and depends only on the technology used by the VR system. Presence, on the other hand, In this section, we describe the game, the experimental setup, is the human reaction to immersion, the feeling of actually being the participants, and the validation procedure. in the virtual environment and behaving as such [4,5,6]. In fact, Game Description the immersive VR technologies available nowadays are able to present users with experiences realistic enough to trick the mind The game was developed in-house with the aim of preparing and create a feeling of presence within the environment [7]. individuals with ASD to use buses for transportation. To achieve These technologies have already been used and proven effective that, it places the user in a three-dimensional city and sets a task in therapies for posttraumatic stress disorder [8], phobias [9], that is completed by riding buses to reach a specific destination. as well as ASD [10]. There are several reasons that justify the Figure 1 shows different screenshots of the city and the buses. use of VR in those approaches. Its capacity to provide safe, There are several different buses driving in 4 pre-defined routes realistic, and controlled environments [11] make therapy within the city. Figure 2 shows the map of the city with the 4 possible for people who, due to physical (eg paralysis) or bus lines available. The player can enter any of these buses, psychological (eg, anxiety) reasons, cannot undergo exposure validate his/her ticket, choose a place to sit, and press the STOP in real life situations. Moreover, this creates the possibility of button, requesting the bus to stop, and leave the bus. Before applying therapies at ease without the need to go to a specific starting the game, it is possible to choose from 7 different tasks, location where real exposure occurs. Finally, even when using 4 of them labelled by complexity as simple (the player only low-budget hardware and software, it has been proven that VR needs to take 1 bus to reach the destination) and 3 labelled as therapies can be as effective as exposure in real life [9]. complex (the player needs to take 2 buses to reach the Serious games have also been proven effective in ASD therapies, destination). Each task has 2 levels of difficulty: an easy and a not only for including game design techniques to keep the hard mode. The easy mode leads the player, step by step, to the players motivated, but also because individuals with ASD are destination, while in the hard mode, the player is only told the often interested in computer-based activities [12]. However, place he/she must go to. At the end of each task, a scoring according to Zakari et al [13], most of the serious games system evaluates the performance of the player on 2 different developed for ASD rehabilitation between 2004 and 2014 are components: “Actions” (the capacity of the player to memorize delivered through nonimmersive VR (eg, desktops, tablets) and and execute bus norms, eg, validating the ticket or sitting on focus mainly on communication and social skill development unreserved seats) and “Route” (the capacity of the player to [13]. This highlights both the importance of focusing on other plan a route to the destination, eg, if the player took the right relevant ASD impairments, including executive function in buses in the right bus stops). outdoor situations, as well as exploring the potential of these The game includes several objects/elements, such as people, new immersive technologies. traffic, and dogs, which might cause anxiety, depending on the The aim of this study was to develop and validate a serious player. For that reason, a biofeedback system was implemented game which uses a VR headset (Oculus Rift) as a proof of to ensure that, while the player becomes familiarized with the concept for rehabilitation of individuals with ASD. Teaching a environment, it never becomes hostile to him/her. This is person with ASD to use public transportation requires parents achieved by assessing the anxiety felt by the player through the or therapists to practice with them until they are ready and analysis of the electrodermal activity (EDA) and reducing the comfortable enough to perform this task alone (basically, the stimulus clutter the player is exposed to, in real time, in case same process used with people with typical development, but high anxiety levels are reached (eg, reducing the amount of with all the obstacles set by ASD particularities). In fact, being noise in the environment). Figure 3 schematizes the biofeedback able to engage in outdoor activities such as the efficient use of loop. http://games.jmir.org/2018/1/e5/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e5 | p. 2 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Simões et al Figure 1. Screenshot from the virtual environment, showing two views from the bus stop perspective on the top, and two views from inside the bus on the bottom. On the top left corner, we can see one bus stop, with other people waiting for the bus, and the map to be used by the participant on the wall. On the top right, we see a bus with its designated number signaled in red, and some traffic on the street. The bottom images show two perspectives from inside of the bus. Figure 2. City map showing the bus lines, stops, and important places like the hospital, church, restaurant, and others. http://games.jmir.org/2018/1/e5/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e5 | p. 3 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Simões et al Figure 3. Biofeedback loop diagram. The level of electrodermal activity is measured from the participant by the game. If it detects a peak of activity, it decreases the level of stimuli and noise in the scene. to provide a task baseline and to assess the capacity of the Recruitment serious game to identify differences between groups. For this project, 2 groups were selected: a clinical and a control The sessions took place in APPDA-Viseu for the ASD group. For the clinical group, 10 participants with ASD, whose participants and in the Institute of Biomedical Imaging and Life diagnosis followed the criteria established on the Diagnostic Sciences for the TD participants. In all of them, the same th and Statistical Manual of Mental Disorders, 5 Edition [1], were research staff was present. This study and all of the procedures recruited from Associação Portuguesa para as Perturbações do were reviewed and approved by the Ethics Commission of our Desenvolvimento e Autismo de Viseu (APPDA-Viseu). For the Faculty of Medicine from University of Coimbra and were clinical group, 9 males and 1 female were recruited, with a mean conducted in accordance with the declaration of Helsinki. age of 18.8 (SD 4.5) years. In the same group, 8 were without intellectual disability (IQ equal to 70 or higher) and 2 were with Session Procedure mild intellectual disability (IQ between 50 and 69 [16]). For In each session, the players received a tutorial (to learn or review the control group, 10 individuals with typical development (TD) the game controls) and a task. The task difficulty and complexity were also recruited; 4 males and 6 females, with a mean age of changed from session to session, as shown in Table 1. At the 21.9 (SD 3.56) years. The groups were matched by age end of every session, participants were asked to describe the (t(18)=−1.633, P=.12). Participants gave oral consent, and a process of riding a bus, from the moment they arrived at the written informed consent was obtained from their bus stop, until they reached their destination, but never received parents/guardians or themselves if they were adults with feedback on the answer given. Their responses were recorded sufficient autonomy. in a checklist containing all the steps existing in a bus trip (Textbox 1). Intervention Protocol The ASD group underwent an intervention of 3 sessions of Acquisition Setup increasing complexity and difficulty (see Table 1), with a In every session, players sat on a swivel chair and wore a duration between 20 and 40 minutes each. Of the 10 participants bracelet for wireless EDA recording (Biopac Bionomadix recruited with ASD, only 6 performed all the intervention BN-PPGED and MP150 amplifier). All tasks were run on a sessions of the study due to scheduling issues. During the laptop computer (Windows 8.1, 16.0 GB RAM and an IntelCore recruitment, participants completed a questionnaire to assess i7 2.50 GHz processor). The head-mounted display used was their experience in using buses for transportation. With an Oculus Rift Development Kit 2, firmware version 2.12, and a exception for 1 of the participants, all the participants were gamepad was used for input. Three participants with ASD and unable to take buses autonomously at the beginning of the study. 1 with TD received the tasks without the Oculus Rift, due to vision impairments. Figure 4 illustrates the setup used. The control group was submitted to a single session (corresponding to the first of the patients). This group was used Table 1. Task complexity and difficulty per session. Metric Session 1 Session 2 Session 3 Difficulty Easy Hard Hard Complexity Simple Simple Complex http://games.jmir.org/2018/1/e5/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e5 | p. 4 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Simões et al Textbox 1. Checklist with steps existing in a bus trip. Wait for the bus Enter in the bus Validate ticket Avoid reserved seats Sit Wait until getting close to the destination Press stop button Leave the bus Figure 4. Diagram of the setup used during the sessions, including the virtual reality headset, game controller, biosignal recorder, and the main computer. Anxiety Level Metrics and Outcome Measures The variation of EDA values during the session was used as a Because the main objective of the serious game was to teach measure of anxiety. To calculate it, we detrended the signal, how to take the bus, we defined two main outcome measures subtracting it to its best fit to a straight-line (for details, see to evaluate the knowledge of the process of riding the bus. One detrend implementation in MathWorks Matlab), then calculated is measured automatically by the game and the other is measured the standard deviations of the detrended EDA values. We then using the debriefing. Both consist of the percentage of the created heat maps of anxiety peaks in the “virtual city”, where checklist (Textbox 1) performed correctly. it is possible to highlight the game locations where EDA peaks occurred, corresponding to anxiety events felt by the players. Actions Accuracy If the locations of anxiety peaks are the same between subjects The game identified every action of the participant (entering and sessions, those locations will become red, but if they are the bus, ticket validation, etc) and calculated the actions sparse, their representations are green and blue. The heat maps accuracy based on the equation: number of correct were created using heatmap.js [17], an open source heat map actions/number of expected actions (Textbox 1). visualization library for JavaScript. Since the game has two Debriefing Accuracy major different situations (the city streets and the inside of the buses), two different conditions were defined: one representing After the game, we asked the participants to describe the the anxiety felt on the streets of the city and another with the step-by-step process of riding a bus and calculated an accuracy anxiety felt inside the buses. based on the same equation of the actions accuracy. Statistical Analysis Additional Metrics For every metric, normality was assessed using histograms, Task Duration Q-Q plots and the Kolmogorov-Smirnov test. Normality test Duration, in minutes, of the time taken to complete the task in results were used to choose between parametric and each session. Since the tasks changes in complexity and nonparametric test, accordingly. difficulty, this metric is not directly comparable between Clinical vs Control Group Analysis: sessions. Nevertheless, it is useful to analyze intersubject variability and to perform intergroup comparisons for the first The metrics actions accuracy, debriefing accuracy, and task session. duration were not normally distributed. Therefore, we used the http://games.jmir.org/2018/1/e5/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e5 | p. 5 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Simões et al Mann-Whitney U test for the between-group comparison of Clinical vs Control Group Results those metrics. Anxiety Level data was nevertheless normally Regarding the actions accuracy (percentage of correct actions distributed. Thus, we used two-sample t test for those performed during the task), we found a statistically significant between-groups comparisons. difference between groups. The Mann-Whitney U test indicated that the actions accuracy of the control group (median=100%) Intervention Analysis (Intragroup Analysis for the was greater than for the clinical group (median=88%), U=75.0, Clinical Group): P=.01. When analyzing the knowledge retained by the As expected from the intergroup analysis, Actions Accuracy, debriefing, the control group (median=100%) has greater Debriefing Accuracy, and Task Duration were not normally accuracy than the clinical group (median=62.5%), as shown by distributed. Paired data for last and first session were therefore the Mann-Whitney U test, U=80.5, P=.02 (Figure 5). compared using a Wilcoxon Sign-rank test. Regarding Anxiety In terms of Task Duration, Figure 6 shows the between-group Level, a one-sample t test was used for the paired data (last and differences. A Mann-Whitney U test showed that the task first session). Additionally, we generated heat maps of locations duration of the ASD group (median=3.76) was statistically where consistent peaks of anxiety where identified. significant higher than for the control group (median=3.19), U=18.0, P=.02. Results Regarding the anxiety level of each group, we see a trend for The results are organized in two sections, one for the intergroup higher values for the clinical group for all the scenarios (global analysis and another for the within-subject analysis of the EDA, inside the bus and outdoors, in the street). Figure 7 shows clinical group. In the figures presented, the error bars always the mean EDA fluctuations of each group in each condition. represent the standard error of the mean, and the horizontal bar However, two-sample t tests showed no statistically significant represents the median when considering nonparametric data differences (possibly because of the biofeedback (actions and debriefing accuracies, as well as task durations) implementation) for either of the conditions (global EDA: and the mean when considering parametric data (EDA t(18)=−0.60, P=.56; bus condition: t(18)=−0.99, P=.33; streets measures). condition: t(18)=−0.48, P=.63). Figure 5. Left: Actions accuracy for both groups in the first session. The control group (typical development) had a perfect performance, while participants in the clinical group missed some actions. Right: Debriefing accuracy for both groups. Higher, but not perfect, accuracy was found in the control group. http://games.jmir.org/2018/1/e5/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e5 | p. 6 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Simões et al Figure 6. Task duration for session 1 from each group. Figure 7. Anxiety levels for each group (mean and standard error) for the overall task and two subconditions: inside the bus and outside (streets). The clinical group presents higher values for all the settings, although without statistical significance. EDA: electrodermal activity. session 1 (median=68.8%) to session 3 (median=100.0%). Intervention Results Figure 9 illustrates the evolution of the debriefing checklist We then compared the main outcome measures pre- and accuracy throughout the sessions. postintervention for the 6 participants who completed the 3 Regarding the task duration, since the tasks increased in sessions. The in-game measure (actions accuracy) evolved complexity and difficulty along the intervention, the task positively throughout the sessions (preintervention median duration is not directly comparable. However, it is important accuracy=75.0%, postintervention median accuracy=93.8%; to verify that the time to successfully complete the task did not see Figure 8), with a Wilcoxon signed-rank test showing a small statistically increase, even with exposure to harder levels (Figure trend for significant differences (Z=1.63, P=.10). Even though 10). In session 1, the task was performed in the “easy” mode, the differences were not statistically significant when using in which players are guided step by step, from the starting “in-game” measures, the same test applied to the Debriefing position until the final location. Tasks in “easy” mode do not Accuracy show a significant increase (Z=2.22, P=.03) from http://games.jmir.org/2018/1/e5/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e5 | p. 7 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Simões et al require as much planning as the ones in “hard” mode, but slowly Figure 11 shows the mean differences and their error. However, introduce the player to this concept and demonstrate how buses only the bus condition shows a strong tendency to significance can be used to travel between bus stations. The “hard” mode (t(5)=−2.36, P=.07). The general EDA values showed a weak tasks, on the other hand, only tell the player where they must tendency towards an effect (t(5)=−1.93, P=.11) and the EDA go to complete the task (eg, “go to the hospital”). In these tasks, values on the streets had the smaller decrease with the players have to analyze the map to discover which bus or buses intervention (t(5)=−1.48, P=.20). they can take to reach the final destination. Furthermore, the We created heatmaps using the peaks of anxiety of each tasks received in sessions 1 and 2 required the player to take 1 participant and separated the conditions inside the bus and bus, while the task from session 3 required the player to take 2 outside. Figure 12 shows the streets scenario for each of the buses. A Wilcoxon signed-rank test show no difference between sessions, and Figure 13 shows the same metrics but for the the easiest and simpler (first session) task and the most complex inside the bus condition. and difficult task (last session), Z=0.105, P=.92. According to Figure 12, players felt most anxious in 3 different The anxiety levels decreased from the first to the last session, situations (Textbox 2). for both overall EDA and for the bus and streets conditions. Figure 8. Actions accuracy for the clinical group, measured “inside the game” throughout the intervention sessions. Figure 9. Debriefing accuracy of the intervention group for each session. http://games.jmir.org/2018/1/e5/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e5 | p. 8 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Simões et al Figure 10. Time taken by each participant in the clinical group to complete the task in each intervention session. Figure 11. Decrease observed in anxiety levels, measured by electrodermal activity variability, between the last session and the first. EDA: electrodermal activity. http://games.jmir.org/2018/1/e5/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e5 | p. 9 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Simões et al Figure 12. Anxiety peaks heat map from session 1 (left) to session 3 (right) for the times the participant was not inside of the bus environment. Most of the locations represent bus stops, where participants need to make the decision of what bus to take and wait for it to arrive. Figure 13. Anxiety peaks heat map from session 1 (left) to session 3 (right) for the times the participant was inside of the bus environment. The locations are much more dispersed through the route than in the outside the bus scenario. There is a visible decrease in frequency of anxiety peaks from the first to the last session. http://games.jmir.org/2018/1/e5/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e5 | p. 10 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Simões et al Textbox 2. Situations in which players felt anxious. When waiting for the bus at the bus stops (red areas near the bus stop signs) When planning the trip or looking for the correct bus stop in the starting areas (ie, green areas near the restaurant in session 1, the fire station in stage 2, one of the bus stops in session 3) When reaching or looking for the destination in the finishing areas (ie, green areas near the police station in session 1, the hospital in session 2, and the fire station in session 3). proving the validity of the rehabilitation target and confirming Discussion the capacity of the game to, by itself, identify the deficits of target participants in the process of taking the bus. Additionally, Principal Findings the time needed to complete the task was increased for the This project aimed to assess the potential of the developed clinical group. Despite having mean anxiety levels above the serious game and here presents it as a tool for rehabilitation. control group, this difference was not significant, perhaps due The game is, to our knowledge, the first application specifically to the small statistical power resulting from a small group of developed to teach ASD individuals to use public transportation participants, as well as the implementation of biofeedback, systems. In addition, we were able to measure which decreases differences between groups. psychophysiological markers of anxiety, paving the way for The intervention was successful in increasing the accuracy of future biofeedback applications. With only three sessions, it the process description during the debriefing, showing a was possible to improve the knowledge of the participants statistically significant improvement in the theoretical regarding the norms of the bus-taking process and to reduce the knowledge of the process, which was the main outcome anxiety levels felt by the participants during that process. measure. When evaluated inside the game by the user actions, The impacts of a learning tool with this purpose are broad since the increase was not statistically significant, but showed a it trains executive functions and might increase the autonomy tendency that we believe additional sessions or a larger of the users, providing them with a new way of moving through intervention group would further confirm. It was also successful a city. It is also a way to make cities more inclusive, providing in decreasing the anxiety felt by participants, especially inside people with special needs ways to successfully use this type of the bus. By using heat maps to represent the anxiety peaks public service. recorded, it was possible to understand that participants with ASD felt more anxious in bus stops and near the starting and Some studies have been conducted using VR training for ASD, finishing areas. This led to the conclusion that, when outside usually focusing on training other skills. Most interventional the buses, players felt most anxious when planning the trip, approaches target social performance training [18,19] or job when looking for the bus stop, when waiting for the bus, and interviewing [20]. Gaming platforms [21] and brain-computer when looking for the final destination. Inside the bus, we interfaces [22] were also suggested in the literature for autism observed a desensitization to stress throughout the sessions, training, but without validation with patients. Although these with a final session showing fewer peaks of EDA activity. are important targets of intervention, our work focuses on a more specific task of executive function that is relevant for the Despite the increase of task complexity and difficulty across needs of daily life. Our pilot validation study aimed to assess sessions, the time duration to complete the task did not increase, not only the efficacy of the application, but also the acceptance suggesting a learning effect and adaptation to the serious game. of the solution with this specific clinical population. Few studies Conclusions perform fully immersive interventions, and the difficulties of combining them with biofeedback create a technology apparatus By using the game as a therapeutic intervention tool, in just that could potentially be disruptive to the participants. The 4 three sessions it was possible to improve the general efficiency drop-outs during the intervention occurred exclusively due to of participants and expose them to peculiar scenarios in which scheduling issues. No patient dropped the study due to they could train their planning skills. More importantly, it was discomfort or raised any difficulty in using the setup. There possible to nearly extinguish the anxiety felt in bus environments were specific cases where the Oculus device was not used, but and teach the bus-taking norms necessary for the autonomous all of those were related to vision impairments, not to lack of use of buses for transportation, both in theoretical and practical tolerance from the user. contexts. Future studies should conduct randomized controlled trials, with larger intervention groups, to replicate the findings The baseline comparison with the control group was clear in and extend them to other clinical populations with executive identifying the impairments in the clinical group. Both the function deficits and lack of autonomy. debriefing of the procedure for taking the bus and the in-game actions showed statistically different results between groups, Acknowledgments This study was supported by the AAC SI/2011/HomeTech/QREN Compete, cofinanced by FEDER, the Portuguese Foundation for Science and Technology, the European Projects BRAINTRAIN (FP7-HEALTH-2013-Innovation-1-602186BrainTrain), H2020-STIPED Project number: 731827, FCT (Fundação para a Ciência e Tecnologia) UID/NEU/04539/2013, http://games.jmir.org/2018/1/e5/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e5 | p. 11 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Simões et al POCI-01-0145-FEDER-007440, and PhD grant SFRH/BD/77044/2011. The authors would like to thank APPDA-Viseu and all the participants and parents who collaborated in this study. Conflicts of Interest None declared. References 1. American PA. Diagnostic and Statistical Manual of Mental Disorders. 5th edition. 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Procedia Technology 2014;16:1417-1423. [doi: 10.1016/j.protcy.2014.10.161] 22. Simões M, Carvalho P, Castelo-Branco M. Virtual reality and brain-computer interface for joint-attention training in autism. 2012 Presented at: Proceedings of the 9th Intl Conf. on Disability, Virtual Reality and Assoc. Technologies; Sep; Laval, France p. 10-12. Abbreviations ASD: Autism Spectrum Disorder EDA: electrodermal activity http://games.jmir.org/2018/1/e5/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e5 | p. 12 (page number not for citation purposes) XSL FO RenderX JMIR SERIOUS GAMES Simões et al HMD: head-mounted display IQ: Intelligence Quotient TD: typical development VR: virtual reality Edited by G Eysenbach; submitted 13.07.17; peer-reviewed by B Bie; comments to author 17.11.17; revised version received 20.11.17; accepted 22.11.17; published 20.03.18 Please cite as: Simões M, Bernardes M, Barros F, Castelo-Branco M JMIR Serious Games 2018;6(1):e5 URL: http://games.jmir.org/2018/1/e5/ doi: 10.2196/games.8428 PMID: 29559425 ©Marco Simões, Miguel Bernardes, Fernando Barros, Miguel Castelo-Branco. Originally published in JMIR Serious Games (http://games.jmir.org), 20.03.2018. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Serious Games, is properly cited. The complete bibliographic information, a link to the original publication on http://games.jmir.org, as well as this copyright and license information must be included. http://games.jmir.org/2018/1/e5/ JMIR Serious Games 2018 | vol. 6 | iss. 1 | e5 | p. 13 (page number not for citation purposes) XSL FO RenderX

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JMIR Serious Games – JMIR Publications

Published: Mar 20, 2018

Keywords: Autism Spectrum Disorder; serious games; virtual reality; virtual reality therapy; travel train; bus.

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