A systematic review of exergame usability as home-based balance training tool for older adults usability of exergames as home-based balance training.

BACKGROUND: Exergames are a fun, viable, attractive, and safe way to engage in physical exercise for most patient populations, including older adults. Their use in the home environment enables an expanded understanding about its applicability and its impact on clinical outcomes that can contribute to improved functionality and quality of life in this population. This systematic review aimed to synthesize the evidence on the usability of exergames as a tool for home-based balance training in older adults. METHODS: The search was realized in 6 electronic databases and were included 1) randomized controlled trials with exergames home-based training as intervention, 2) studies involving older adults (aged 60 years or older) described as having impaired static or dynamic balance, 3) that compared the effects of exergames to usual care, health education or no intervention, and 4) reported usability and balance outcomes. The Cochrane Risk of Bias tool for randomized trials version 2 and the Grading of Recommendations Assessment, Development, and Evaluation were used to evaluate the methodological quality of studies and levels of evidence for outcomes. RESULTS: After screening 1107 records, we identified 4 trials were included. The usability score of exergames was classified as an acceptable, good, and feasible tool. The pooled effect indicated improvements in favor of the exergame group for functional balance by TUG test (MD = -5.90; 95%CI = -10.29 to -1.51) with low-certainty evidence and Tinetti scale (MD = 4.80; 95%CI = 3.36 to 6.24) with very low-certainty evidence. Analyzing the different immersion level, it was observed a significant difference in the experimental group for the immersive exergames (MD = -9.14; 95%CI = -15.51 to -2.77) with very low-certainty evidence. CONCLUSION: Exergames applied at home showed good usability and had significant effects on functional balance compared to usual care or no intervention, especially in the immersive modality. TRIAL REGISTRATION: PROSPERO registration number: CRD42022343290.

Um anjo para ELA: arquitetura baseada em visão computacional aplicada ao monitoramento on-line de parâmetros respiratórios de pacientes com Esclerose Lateral Amiotrófica (ELA) em ambiente hospitalar / An angel for ALS: architecture based on computer vision applied for on-line monitoring respiratory parameters of patients with Amyotrophic Lateral Sclerosis (ALS) in hospital environment

INTRODUÇÃO: A Esclerose Lateral Amiotrófica (ELA) é uma doença neurodegenerativa, caracterizada por uma progressiva e fatal perda de neurônios motores do córtex cerebral, tronco encefálico e medula espinhal, mas que mantém preservada a atividade intelectual e cognitiva do paciente. Pacientes acometidos por essa doença irão invariavelmente necessitar do auxílio de ventiladores mecânicos. MÉTODOS: Foi utilizado um conjunto de hardware e software para realizar o monitoramento dos parâmetros respiratórios dos pacientes em leitos hospitalares como forma de auxiliar à equipe de saúde. O monitoramento desses parâmetros deu-se por meio de uma webcam, que capturava os valores exibidos na tela do ventilador mecânico, e do emprego de técnicas de visão computacional e Optical Character Recognition (OCR). Neste sentido, o sistema foi testado sob três condições de luminosidade diferentes para verificar a eficácia do mesmo. RESULTADOS: O sistema apresentou uma média geral de acertos de 94.90%. Além disso, quando a interferência luminosa foi mínima, o sistema obteve uma média geral de acertos de 97,76%. CONCLUSÃO: A adoção de um sistema computacional baseado em visão computacional para auxílio da equipe de saúde no monitoramento hospitalar de pacientes com ELA mostrou-se satisfatória. No entanto, a pesquisa mostrou que a adoção de um sistema com maior imunidade à interferências luminosas externas tende a apresentar melhores resultados.

INTRODUCTION: Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by a progressive and fatal loss of motor neurons in the cerebral cortex, brainstem and spinal cord. In spite of that, the patient's intellectual and cognitive activity remains preserved. Patients affected by this disease will invariably need the help of mechanical ventilators. METHODS: A set of hardware and software was used to perform the monitoring of respiratory parameters of patients in hospital beds as a means of assisting the healthcare team. The monitoring of these parameters was performed by a webcam that captured the values displayed on the screen of the ventilator, and the employment of computer vision techniques and Optical Character Recognition (OCR). In this sense, the system was tested under three different lighting conditions to verify its effectiveness. RESULTS:The system presented an overall average of 94.90% of correct answers. Furthermore, when the luminous interference was minimum, it achieved an overall average of success of 97.76%. CONCLUSION: The adoption of a computational system based on computer vision to aid the healthcare team in hospital monitoring of patients with ALS was satisfactory. However, the research has shown that the adoption of a system with greater immunity to external light interference tends to achieve better results.