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Effects of HIIT exergame in VR on attention and executive function in young people with adhd: randomized clinical trial with Move Sapiens®

Grant number: 24/08827-6
Support Opportunities:Regular Research Grants
Duration: November 01, 2024 - October 31, 2026
Field of knowledge:Health Sciences - Medicine - Psychiatry
Principal Investigator:Eurípedes Constantino Miguel Filho
Grantee:Eurípedes Constantino Miguel Filho
Host Institution: Faculdade de Medicina (FM). Universidade de São Paulo (USP). São Paulo , SP, Brazil
Associated researchers:Arthur Caye ; Pietro Krauspenhar Merola

Abstract

Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder characterized by inattention, hyperactivity, and impulsivity, affecting 3-5% of the global school-age population. This study explores the 'Move Sapiens®' exergame in Virtual Reality (VR) as a therapeutic tool for adolescents aged 12 to 17 with ADHD. Incorporating a short model of High-Intensity Interval Training (HIIT) with 12 cycles of 16 seconds of maximum effort followed by 20 seconds of rest, the study primarily aims to assess improvements in executive function and attention through 20 sessions of interaction with 'Move Sapiens®' in VR over four weeks. Secondary objectives include assessing the reduction of symptoms related to sleep problems and anxiety. Outcomes will be measured using validated scales for executive function, attention, sleep quality, and anxiety. Participant recruitment will be conducted in Porto Alegre, through graduates of the Attention Deficit/Hyperactivity Disorder Program (PRODAH), and in Indaiatuba, via specialized public health services. The randomized clinical trial will take place at the Hospital de Clínicas de Porto Alegre-RS and At the Centro Escola de Especialidade Médicas (CEEM), UniEduk in Indaiatuba-SP. In the control condition, participants will experience an adapted version of the 'Move Sapiens®' exergame without intense physical activity. The efficacy of the intervention will be compared with the placebo control condition, using a mixed-effects model for analysis, which is robust against missing data, providing a more accurate estimate of the treatment effects. (AU)

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