Affordable Robot-Based Assessment of Cognitive and Motor Impairment in People Living with HIV and HIV-Stroke

项目摘要 全世界约有3700万人感染人类免疫缺陷病毒（HIV），约71%的人 艾滋病毒感染者（PLWH）居住在低收入和中等收入国家（LMIC）。艾滋病毒进入中枢神经系统 (CNS)在感染后早期，增加了轻度至重度神经认知和心血管疾病的风险。 研究表明，约30%至40%的PLWH会有神经系统并发症，导致长期的运动和 认知障碍，包括HIV相关的神经认知障碍（HAND）。很少有研究探讨 如何最好地提供客观的评估，诊断和康复治疗工具，以帮助生活在 艾滋病毒和艾滋病毒中风导致的残疾。考虑到低收入和中等收入国家 极大地限制了基础设施和医疗资源，从而减少了获得一致的、高质量的 康复活动.我们的长期目标是开发创新且价格合理的机器人技术， LMIC的护理差距，并利用它们来帮助客观评估，诊断和治疗PLWH， 手和中风导致的运动和认知障碍。在这个R21项目中，我们利用我们负担得起的 机器人治疗系统，增强了客观指标以及运动和认知练习游戏，以评估 博茨瓦纳是世界上艾滋病毒感染率第四高的国家（20.3%），艾滋病毒感染者人数不断增加， 非传染性疾病患病率和有限的康复资源。目标1确定使用的同时效度和信度 机器人治疗系统，以客观地评估PLWH的认知和运动障碍。四十（40）名PLWH 将使用临床指标和新的基于机器人的指标对40名HIV阴性对照进行评估， 时间点，至少间隔1周。我们假设基于机器人的指标也具有重测信度 与通常需要康复的运动和认知功能的临床测量同时有效 专业知识和人力来完成。目标2确定开发基于机器人的分类的可行性 HIV相关的神经认知障碍（HAND）80例无卒中的PLWH将使用 增强的神经心理学（NP）电池需要满足最低的手弗拉斯卡蒂标准和机器人 评估任务。我们将使用Frascati标准和全球赤字评分（GDS）对PLWH进行分类 参与者分为无症状神经认知障碍（ANI），轻度神经认知障碍（MND）和HIV- 相关性痴呆（HAD）我们将使用派生的机器人指标开发回归模型并测试其能力 预测HAND和GDS评分。我们假设基于机器人的模型将对HAND进行特异性分类 和敏感性优于HAND筛查与国际艾滋病痴呆量表和蒙特利尔认知 考核接受者操作曲线（ROC）将用作经验验证。人口、艾滋病毒和 生活方式因素，如年龄，病毒载量，高血压，糖尿病，丙型肝炎和烟草将被用来确定 这增加了患手的风险。

PROJECT SUMMARY About 37 million people live with human immunodeficiency virus (HIV) worldwide and about 71% of people living with HIV (PLWH) reside in low and middle income countries (LMICs). HIV enters the central nervous system (CNS) early after infection and increases the risk for mild-to-severe neurocognitive and cardiovascular disorders. Studies show that about 30 to 40% of PLWH will have neurologic complications leading to long-term motor and cognitive disability, including HIV-Associated Neurocognitive Disorders (HAND). Few studies have examined how best to provide objective assessment, diagnostic, and rehabilitative treatment tools to aid people living with disabilities as a result of HIV and HIV-stroke in LMICs. The need for these tools is great in LMICs, given the greatly limited infrastructure and healthcare resources and thus decreased access to consistent, quality rehabilitation. Our long-term goal is to develop innovative and affordable robot technologies to bridge healthcare care gaps in LMICs and leverage them to help objectively assess, diagnose and treat PLWH presenting with motor and cognitive impairment due to HAND and Stroke. In this R21 project, we leverage our affordable robot therapy system augmented with objective metrics and motor and cognitive exergames to assess PLWH in Botswana—a country with the fourth highest HIV prevalence rate (20.3%) in the world, an increasing NCD prevalence rate, and limited rehab resources. Aim 1 determines concurrent validity and reliability of using the robot therapy system to objectively ASSESS cognitive, and motor impairments in PLWH. Forty (40) PLWH and 40 HIV-negative controls will be evaluated using both clinical metrics and new robot-based metrics at two time points, at least 1-week apart. We hypothesize that robot-based metrics will have test-retest reliability as well as concurrent validity with clinical measures of motor and cognitive function that usually require rehabilitation expertise and man-power to complete. Aim 2 determines the feasibility of developing a robot-based classification of HIV-associated neurocognitive disorders (HAND). 80 PLWH without stroke will be evaluated using an augmented neuropsychological (NP) battery required to meet minimal HAND Frascati criteria and the robot assessment tasks. We will use the Frascati criteria and global deficit scoring (GDS) to classify the PLWH participants into asymptomatic neurocognitive impairment (ANI), mild neurocognitive disorder (MND) and HIV- associated dementia (HAD). We will develop a regression model using derived robot metrics and test its ability to predict HAND and GDS score. We hypothesize that the robot-based model will classify HAND with specificity and sensitivity better than HAND screening with International HIV Dementia Scale and Montreal Cognitive Assessment. The Receiver Operator Curve (ROC) will be used as empirical validation. Demographic, HIV, and lifestyle factors such as age, viral load, hypertension, diabetes, hepatitis C, and tobacco will be used to determine which increased the risk for HAND.