A systematic review on the application of machine learning models in psychometric questionnaires for the diagnosis of attention deficit hyperactivity disorder.

Attention deficit hyperactivity disorder is one of the most prevalent neurodevelopmental disorders worldwide. Recent studies show that machine learning has great potential for the diagnosis of attention deficit hyperactivity disorder. The aim of the present article is to systematically review the scientific literature on machine learning studies for the diagnosis of attention deficit hyperactivity disorder, focusing on psychometric questionnaire tools. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines were adopted. The review protocol was registered in the PROSPERO database. A search was conducted in three databases-Web of Science Core Collection, Scopus and Pubmed-with the aim of identifying studies that apply ML techniques to support the diagnosis of attention deficit hyperactivity disorder. A total of 17 empirical studies were found that met the established inclusion criteria. The results showed that machine learning can be used to increase the accuracy of attention deficit hyperactivity disorder diagnosis. Machine learning techniques are useful and effective strategies that can complement traditional diagnostics in patients with attention deficit hyperactivity disorder.

Adherence, frequency, and long-term follow-up of video game-based treatments in patients with attention-deficit/hyperactivity disorder: A systematic review.

BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder in children and adolescents. Recent studies show that video games have great potential for the treatment and rehabilitation of ADHD patients. The aim of the present review is to systematically review the scientific literature on the relationship between video games and ADHD, focusing on adherence to treatment, frequency of the intervention, and the long-term follow-up of video games in children and adolescents with ADHD. METHODS: The preferred reporting items for systematic reviews and meta-analyses guidelines were adopted. The review protocol was registered in PROSPERO database. We searched in three databases, PubMed, Medline, and Web of Science to identify studies examining the association between video game interventions in ADHD patients. RESULTS: A total of 18 empirical studies met the established inclusion criteria. The results showed that video games-based interventions can be used to improve ADHD symptoms and display high adherence to treatment. In addition, in the studies reviewed, the most common intervention frequency is 30 min three to five times per week. However, there is little evidence from studies with video games showing long-term effects in patients with ADHD. CONCLUSION: Video games are useful and effective interventions that can complement traditional treatments in patients with ADHD.