PANDAS: Paediatric Attention-Deficit/Hyperactivity Disorder Application Software

Abstract

Attention-decit/hyperactivity disorder (ADHD) is a common neuropsychiatric disorder that impairs social, academic, and occupational functioning in children, adolescents and adults. It is estimated that approximately as high as 10% of South African children have ADHD. Some dilemmas are however present in terms of the treatment of the disorder: rstly, there are no risk-free methods for its treatment and secondly, no fully objective diagnostic assessments exist. To date, very few quantitative methods have been successfully implemented. It is therefore necessary to further investigate methods that objectively diagnose, treat, and manage the disorder. The aim of the study is thus to develop a novel method that can be used as an aid to provide screening of ADHD. The method proposed is the form of a tablet-based game with underlying algorithms. The objective of the method is to dierentiate between an ADHD individual versus a non-ADHD individual, based on the way they play the game. A beta-testing phase was done and comprised of 30 children (19 non-ADHD and 11 ADHD) between the ages of 4 and 18 years old. The machine learning model that was used was linear support vector machine (SVM). Two datasets were used: 1) game-play dataset which included data such as task completion time and number of mistakes made and 2) accelerometer data set from the tri-axial accelerometer. A feature set was extracted from these two datasets and the best features were selected using sequential forward selection. These best features were then used for developing the classier. A test set accuracy of 85.7% was achieved. Leave-one-out cross-validation (LOOCV) was performed and its accuracy was 83.5%. An overall classication accuracy of 86.5% was achieved. For the application of a screening tool, sensitivity was deemed an important metric and. The model achieved a sensitivity of 75% which was seen as acceptable. The results of the classier were indicative that a quantitative tool could indeed be developed to screen for ADHD. iv Stellenbosch University https://scholar.sun.ac.za I would like to extend the most sincere gratitude and thanks to the following individuals: Prof. Pieter Fourie, for his guidance, vision, enthusiasm and inspiration, without which this project would not be possible. Prof. Fourie spoke to me at the end of my nal year undergrad and convinced me to join him on this project. Ever since then, it's been a great journey towards creating a tangible solution that would change many people's lives for the better. Romano Swart, my colleague, whose support and teamwork in the conceptualisation phases of the game design was very valuable. Mark Atkinson and his team, for delivering a high quality game that met the requirements of the project within a feasible budget and time frame. My family, for being an ever-present support structure throughout the course of the study and throughout all of my endeavours. Lastly, I would like to extend great gratitude to Ms. Veronica Mwamfupe, whose moral support has been tremendous. She's encouraged me an...

The Use of Mobile Games in the Management of Patients With Attention Deficit Hyperactive Disorder: A Scoping Review

“…Among the 4 experimental studies, 1 study was an open-label, non-randomized trial (27), 2 were ethnographic studies (allowing participants to use the application while data was collected from the interaction) (23,25), and 1 study was a before-and-after study [measuring outcomes before and after intervention (32)]. There are 4 mixed methods studies utilized surveys to gather primary data (21,22,28,31), 2 studies utilized a before-andafter experimental design (14,17), and 1 study used a withdrawal study design (20). Notably, none of the studies included were randomized controlled trials (RCTs).…”

“…Note that there was one game with no game description and some games can fall under more than one category. There were 6 games (15, 16,22,23,25,27,30,34) in which the players responded to specific cues, execute specific actions according to instructions or to stimuli displayed on the application. For example, SnappyApp (16) required players to select only the bananas when they appear.…”

“…For example, SnappyApp (16) required players to select only the bananas when they appear. In other games, the players have to avoid the obstacles (22), find the objects (23) or follow the instructions (25).…”

“…The observations derived from the studies were summarized in Table 3. There were 12 studies that were targeted at treatment of patients with ADHD, while only one study collected information that aids in the screening of potential patients with ADHD (22).…”

The Making and Evaluation of Digital Games Used for the Assessment of Attention: Systematic Review

“…These studies compared the results of a game with the results of a cognitive task or clinical diagnosis and then used machine learning to build a classification model for the game results. For example, Jung et al [ 54 ] used machine learning to classify game scores by comparing them with MMSE scores, and Mwamba et al [ 33 ] classified game data based on children with and without a diagnosis of ADHD.…”