Unobtrusive remote monitoring to identify and track meaningful change in daily function in community dwelling older adults at risk for Alzheimer's disease

重新提交1 R01 AG058687-01 PI： Adriana Seelye博士 项目摘要。阿尔茨海默病(AD)是一个日益严重的公共卫生问题，它具有深刻的负面影响 对患有这种疾病的个人及其家人、医疗保健系统和经济的影响。早些时候 识别和准确评估有高血压风险的老年人的轻度认知和功能退化 广告将是至关重要的，以便在疾病的早期阶段进行干预，并降低成本和 功能衰退的后果。项目目标。这项研究的总体目标是使用 家庭环境中不显眼的家庭传感器技术，以客观评估高水平的IADL 以一种新的方式运作，将更有效地识别功能缓慢的最早微妙下降 并最终威胁到独立。本研究的具体目标是：目标1将建立 最健壮的远程监控IADL变量和变量组合，以区分MCI 以及基于敏感性、特异性、准确性和总体ROC AUC的完整认知 分析。目标2将确定新的远程监测的IADL变量的区分能力 与现有的IADL问卷进行比较，以区分MCI患者和认知完整的患者 基于横断面分析的敏感度、特异度、准确度和总体ROC AUC。目标3将确定 并描述远程监控的IADL功能随时间变化的纵向轨迹(斜率 在使用广义线性混合效应模型的认知完整的人和MCI的人之间， 个人IADL分布及其作为结果的变化。项目方法。拟议中的项目 将把创新的计算和环境传感技术直接应用于参与者的家中和 客观评估老年人的IADL表现和变异性的社区环境 在多个临床相关功能领域无MCI长达4年。研究的参与也将 包括年度神经心理学和临床测试。机器学习的计算方法将是 用来检验通过这项研究产生的大量基于传感器的IADL候选变量 确定这些变量在区分MCI和完整认知方面的相对重要性 集团，横截面和纵向。项目影响力。这项研究中使用的方法将允许 研究人员、医生和护理人员主动识别和监控不断增加的恶化风险 认知功能(从正常衰老到MCI和从MCI到AD)以一种目前不同的方式进行 有可能改变AD预防试验并显著降低成本和后果 我们老龄化人口的减少。

Resubmission 1 R01 AG058687-01 PI: Adriana Seelye, Ph.D. Project Summary. Alzheimer's disease (AD) is a growing public health concern that has a profound negative impact on individuals living with the disease and their families, the healthcare system, and the economy. Early identification and accurate assessment of mild cognitive and functional deterioration in older adults at risk for AD will be critical in order to intervene at the earliest stages of the disease and to reduce the cost and consequences of functional declines. Project Objectives. The overall goal of this research is to use unobtrusive in-home sensor technologies in the home environment to objectively assess high-level IADL functioning in a new way that will more effectively identify the earliest subtle declines in functioning that slowly emerge and ultimately threaten independence. The specific aims for this research are: Aim 1 will establish the most robust remotely monitored IADL variables and combinations of variables to discriminate between MCI and intact cognition based on sensitivity, specificity, accuracy and overall ROC AUC using cross-sectional analyses. Aim 2 will determine the discriminatory ability of the new remotely monitored IADL variables compared to available IADL questionnaires to differentiate those with MCI relative to those with intact cognition based on sensitivity, specificity, accuracy and overall ROC AUC in cross-sectional analyses. Aim 3 will identify and characterize the longitudinal trajectories (slopes) of remotely monitored IADL functioning over time between those with intact cognition and those with MCI using generalized linear mixed effects models, with person-specific IADL distributions and their changes as outcomes. Project Methods. The proposed project will apply innovative computing and ambient sensing technologies directly in participants' home and community environments to objectively assess IADL performance and variability among older adults with and without MCI in multiple clinically relevant functional domains for up to 4 years. Study involvement will also include annual neuropsychological and clinical testing. Machine learning computational approaches will be used to examine a large number of sensor-based IADL candidate variables generated through this study to determine the relative importance of these variables for discriminating between MCI and intact cognition groups, cross-sectionally and longitudinally. Project Impact. The approaches used in this study will allow researchers, physicians, and caregivers to proactively identify and monitor increasing risks for deteriorating cognitive function (progressing from normal aging to MCI and from MCI to AD) in a way that is not currently possible, transforming AD prevention trials and significantly reducing the cost and consequences of functional decline in our aging population.