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

不显眼的远程监控可识别和跟踪有阿尔茨海默病风险的社区老年人日常功能的有意义的变化

• 批准号: 9762804
• 负责人: Adriana Hughes
• 金额: $ 54.08万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9762804
• 关键词: Activities of Daily Living; Alzheimer disease prevention; Alzheimer' s Disease; Alzheimer' s disease risk; Assessment tool; Behavior Therapy; Caregivers; Clinical; Clinical Trials; Cognition; Cognitive; Communities; Consumption; Cross-Sectional Studies; Data; Data Analytics; Data Collection; Dementia; Detection; Deterioration; Development; Disease; Doctor of Philosophy; Early identification; Elderly; Environment; Family; Frequencies; Future; Goals; Healthcare Systems; Home environment; Impairment; Individual; Machine Learning; Measures; Methods; Modeling; Monitor; Neurobiology; Neurodegenerative Disorders; Neuropsychological Tests; Outcome; Outcome Measure; Participant; Patient Self-Report; Patient-Focused Outcomes; Patients; Pattern; Performance; Persons; Pharmacotherapy; Physicians; Prevention; Prevention trial; Problem Solving; Process; Provider; Public Health; Questionnaires; Research; Research Personnel; Risk; Schedule; Sensitivity and Specificity; Signal Transduction; Testing; Time; Travel; aging population; base; clinically relevant; cognitive change; cognitive function; computerized data processing; cost; daily functioning; disease diagnosis; functional decline; innovation; instrumental activity of daily living; normal aging; pre-clinical; research clinical testing; sensor; sensor technology; time use

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.

重新提交1 R01 AG058687-01 PI： Adriana Seelye博士 项目摘要。阿尔茨海默氏病（AD）是一个日益严重的公共卫生问题 对患有疾病及其家人的个人，医疗保健系统和经济的影响。早期的 鉴定和准确评估有风险的老年人的轻度认知和功能恶化 广告对于在疾病的最早阶段进行干预并降低成本和 功能下降的后果。项目目标。这项研究的总体目标是使用 家庭环境中的不受欢迎的内部传感器技术客观地评估高级IADL 以一种新的方式运行，该方式将更有效地确定最早的微妙下降，从而缓慢地发挥作用 出现并最终威胁到独立。这项研究的具体目的是：AIM 1将确定 最强大的远程监测的IADL变量和变量的组合以区分MCI 基于灵敏度，特异性，准确性和整体ROC AUC的完整认知，使用横截面 分析。 AIM 2将确定新的远程监控IADL变量的歧视能力 与可用的IADL问卷相比，与具有完整认知的人相比，具有MCI的人相比 基于横截面分析中的灵敏度，特异性，准确性和总体ROC AUC。 AIM 3将确定 并表征远程监测的IADL的纵向轨迹（斜率）随着时间的推移 在具有完整认知的人与使用通用线性混合效应模型的人之间， 特定于人的IADL分布及其作为结果的变化。项目方法。拟议的项目 将直接在参与者的家中应用创新的计算和环境传感技术 社区环境以客观评估具有和 在多个临床相关的功能领域中没有MCI长达4年。研究参与也将 包括年度神经心理学和临床测试。机器学习计算方法将是 用于检查通过本研究生成的大量基于传感器的IADL候选变量 确定这些变量对于区分MCI和完整认知的相对重要性 组，横截面和纵向。项目影响。本研究中使用的方法将允许 研究人员，医师和护理人员会主动识别和监控增加恶化的风险 认知功能（从正常老化到MCI，从MCI发展到AD），目前不是 可能，改变AD预防试验并大大降低功能的成本和后果 人口老龄化的下降。