Goalkeeper Game: A New Assessment Tool for Prediction of Gait Performance Under Complex Condition in People With Parkinson's Disease

Autor(es)

Stern, Rafael B. ^[1] ; D'Alencar, Matheus Silva ^[2] ; Uscapi, Yanina L. ^[3] ; Gubitoso, Marco D. ^[4] ; Roque, Antonio C. ^[5] ; Helene, Andre F. ^[3] ; Piemonte, Maria Elisa Pimentel ^[2]

Afiliação do(s) autor(es)

^[1] Univ Fed Sao Carlos, Dept Stat, Sao Carlos - Brazil

^[2] Univ Sao Paulo, Fac Med Sci, Dept Phys Therapy Speech Therapy & Occupat Therap, Sao Paulo - Brazil

^[3] Univ Sao Paulo, Inst Biosci, Dept Physiol, Sao Paulo - Brazil

^[4] Univ Sao Paulo, Inst Math & Stat, Dept Comp Sci, Sao Paulo - Brazil

^[5] Univ Sao Paulo, Sch Philosophy Sci & Letters Ribeirao Preto, Dept Phys, Sao Paulo - Brazil

• Tipo de documento: Artigo Científico
• Fonte: FRONTIERS IN AGING NEUROSCIENCE; v. 12, MAR 4 2020.
• Citações Web of Science: 0

Resumo

Background: People with Parkinson's disease (PD) display poorer gait performance when walking under complex conditions than under simple conditions. Screening tests that evaluate gait performance changes under complex walking conditions may be valuable tools for early intervention, especially if allowing for massive data collection. Objectives: To investigate the use of the Goalkeeper Game (GG) to predict impairment in gait performance under complex conditions in people with Parkinson's disease (PPD) and compare its predictive power with the one of the Montreal Cognitive Assessment (MoCA) test. Methods: 74 PPD (HY stages: 23 in stage 1; 31 in stage 2; 20 in stage 3), without dementia (MoCA cut-off 21), tested in ON period with dopaminergic medication were submitted to single individual cognitive/motor evaluation sessions. MoCA and GG were used to assess cognition, and the dynamic gait index (DGI) test was used to assess gait performance under complex condition. GG test resulted in 9 measures extracted via a statistical model. The predictive power of the GG measures and the MoCA score with respect to gait performance, as assessed by DGI, were compared. Results: The predictive models based on GG obtained a better score of prediction (65%) then MoCA (56%) for DGI scores (at a 50% specificity). Conclusion: GG is a novel tool for noninvasive screening that showed a superior predictive power in assessing gait performance under complex condition in people with PD than the well-established MoCa test. (AU)

• Processo FAPESP: 13/07699-0 - Centro de Pesquisa, Inovação e Difusão em Neuromatemática - NeuroMat
• Beneficiário: Oswaldo Baffa Filho
• Modalidade de apoio: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs