ADHD360: DESIGNING AN INFORMATION SYSTEM FOR SYMPTOM DIAGNOSIS AND IMPROVEMENT OF ATTENTION DEFICIT HYPERACTIVITY DISORDER

Introduction

Attention Deficit and Hyperactivity Disorder (ADHD) is one of the most common childhood disorders [1]. ADHD is characterized by persistent symptoms of inattention and/or impulsivity and hyperactivity for at least six months in two or more settings that negatively impact on the person’s development and functioning [2]. The diagnosis is a multiparametric but not standardized procedure while different treatment approaches are commonly evaluated through systematic monitoring [3]. Systematic monitoring of child behaviors provides invaluable information, but is often
biased due to subjectivity.
The main objective of the project ADHD360, is the development of a novel, integrated platform based on a serious game, which initially aims at the diagnosis of the disorder. After identifying the core symptoms that differentiate an ADHD from a non-ADHD child, using state-of-the-art machine learning (ML) methods, the platform can be used for planning an intervention. At this stage, ADHD360 will provide also tools for measuring the effectiveness of the intervention. The project, is co‐financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE [Τ1ΕΔΚ-01680].

Methods

The first step is the design and implementation of a serious game for ADHD diagnosis. Pilot tests will be executed in order to test the diagnostic accuracy. Based on results, we’ll parameterize it accordingly, as well as extend it as an alternative intervention method. At least twenty (20) children will be recruited, with ages ranging from 7 to 16 years. Half of the recruited children/adolescents (10) will be ADHD diagnosed/certified by the Ministry of Health and Social Solidarity while the control group will be free of ADHD diagnosis.
Initially, children and adolescents recruited they will interact with the platform in a living lab. In this stage, we’ll collect the required data for processing using modern ML methods. During the second stage of pilots, the participants will be divided by the intensity of platform use (2 times/week and 4 times/week). The mean duration playtime will be between 30 and 45 minutes for ten (10) weeks in total.

Project Milestones

During the first stage of the project, specific diagnostic behaviors of people with ADHD were defined, based on the DSM-V manual, along with neuropsychological tools that could be transferred to serious games training a specific ADHD behavior. Furthermore, ADHD behaviors and neuropsychological tools were matched with mini-games incorporated within the serious game. Subsequently, serious game design will be based on specific game mechanics. Game mechanics will be used to collect data regarding the user’s gameplay, actions and behavior of the user. The core gameplay could be easily customized, as well as the front elements of the
game. Thus, the need for redesign is greatly reduced while, at the same time, we keep a dynamic game difficulty balancing across the levels.

Future Perspectives

Specifying the protocol procedures and submitting the results to the Bioethics Committee of the Medical School (AUTH) is a priority goal. Subsequently, it is deemed necessary to run the initial part of the pilots using the first stable version of the end product, in order to estimate platform’s capacity to discriminate ADHD from non-ADHD users.

References

[1] T. E. Wilens and T. J. Spencer, “Understanding AttentionDeficit/Hyperactivity Disorder from Childhood to Adulthood,” Postgrad. Med., vol. 122, no. 5, pp. 97–109, Sep. 2010.
[2] American Psychiatric Association, The Diagnostic and Statistical Manual of Mental Disorders (5th ed.), 5th ed. Arlington, VA: American Psychiatric Publishing, 2013.
[3] C. T. Gualtieri and L. G. Johnson, “ADHD: Is Objective Diagnosis Possible?,” Psychiatry (Edgmont)., vol. 2, no. 11, pp. 44–53, Nov. 2005.

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