Αn Information System for Symptom Diagnosis and Improvement of Attention Deficit Hyperactivity Disorder (ADHD360)
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 43
- 试验地点
- 1
- 主要终点
- Explore whether the game analytics could discriminate the ADHD from non-ADHD users of the ADHD360 platform.
研究概览
简要总结
ADHD360 will be an innovative integrated platform for early ADHD diagnosis and intervention against its symptoms. In the core of the platform design there will be a serious game along with a mobile application to monitor behavior and to evaluate the intervention.
详细描述
The ADHD360 project develop an integrated platform having as core elements a serious game along with a mobile application for monitoring of ADHD behaviors in a SMART (Specific, Measurable, Attainable, Realistic and Timely) way. The design of the serious game is based on both Diagnostic and Statistical Manual of Mental Disorders (version V(American Psychiatric Association, 2013)) along with neuropsychological tools, easily transferred to game, on a specific ADHD behavior. The primary objective of the project is to explore whether the game analytics along with the monitoring data could discriminate the ADHD from non-ADHD users. The secondary objective is to use the platform as an intervention. To this scope, a two-phase pilot study will be performed recruiting at least twenty (20) participants (10 ADHD; 10 non-ADHD) with ages ranging from 7 to 16 years. In the first stage, participants will undergo a neuropsychological evaluation as well as interact with the serious game two times (30-45 minutes/each time). After all participants have completed the first part of the pilot tests, a preliminary analysis of the data will be carried out using modern Machine Learning Methods in order to explore the discriminating capacity of the game. In the second stage, participants will interact with the platform for ten (10) weeks in total (2-3 times/30-45 minutes each). At the end of the second stage, the participants will undergo a neuropsychological evaluation following the procedures of the first one. The partners involved in the implementation of the project are the Intelligent Systems Lab (School of Computer Science, AUTH), the MEDPHYS Laboratory (School of Medicine, AUTH) and the Second Method (TSM) company. The partners cover the expertise required in data analysis, machine learning, medical record keeping, software development and game design (gamification). ADHD360 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].
研究设计
- 研究类型
- Interventional
- 分配方式
- Non Randomized
- 干预模型
- Parallel
- 主要目的
- Supportive Care
- 盲法
- None
入排标准
- 年龄范围
- 7 Years 至 16 Years（Child）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •(1) Participants should be between 7 and 16 years old
- •(2) Diagnosed ADHD by an approved body of Ministry of Health
- •(3) Participants willing to follow the study protocol and procedures
- •(4) Participants with ADHD symptoms that they are not induced by an organic disease
- •(5) Participants' parents voluntarily provided written consent for their children's participation in the study.
排除标准
- •(1) Participants in ADHD group having other disorders apart from ADHD
- •(2) Parents who refuse to give written consent for their children's participation in the study.
结局指标
主要结局
Explore whether the game analytics could discriminate the ADHD from non-ADHD users of the ADHD360 platform.
时间窗: 8 months
After all participants completed the first part of the clinical trials, an analysis of the data collected from the platform will be carried out. This includes processing recorded gameplay scores for the extraction of useful features that, in turn, shall be used for training and evaluating modern Machine Learning methods, such as Neural Networks, Support Vector Machines (SVMs), Random Forests, Decision Trees, and/or k-Nearest Neighbors (kNN), in order to learn the differentiating properties of ADHD cases against non-ADHD cases within the game.
Explore whether the monitoring data could discriminate the ADHD from non-ADHD users of the ADHD360 platform.
时间窗: 8 months
After all participants completed the first part of the clinical trials, an analysis of the data collected from the platform will be carried out. This includes processing recorded data of attention for the extraction of useful features that, in turn, shall be used for training and evaluating modern Machine Learning methods, such as Neural Networks, Support Vector Machines (SVMs), Random Forests, Decision Trees, and/or k-Nearest Neighbors (kNN), in order to learn the differentiating properties of ADHD cases against non-ADHD cases within the game.
Explore whether the game analytics along with the monitoring data could discriminate the ADHD from non-ADHD users of the ADHD360 platform.
时间窗: 8 months
After all participants completed the first part of the clinical trials, an analysis of the data collected from the platform will be carried out. This includes processing recorded gameplay time for the extraction of useful features that, in turn, shall be used for training and evaluating modern Machine Learning methods, such as Neural Networks, Support Vector Machines (SVMs), Random Forests, Decision Trees, and/or k-Nearest Neighbors (kNN), in order to learn the differentiating properties of ADHD cases against non-ADHD cases within the game.
Investigate the impact of ADHD360 platfrom as intervention on general intelligence index
时间窗: 12 months
Change in WISC-III
次要结局
- Change in attention(10 weeks)
- Change in the frequency of ADHD symptoms(10 weeks)
研究者
Panos Bamidis
Professor
Aristotle University Of Thessaloniki