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 R 01 AG 058687 -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，具有深刻的负面影响， 对疾病患者及其家庭、医疗保健系统和经济的影响。早期 识别和准确评估老年人轻度认知和功能衰退的风险 AD将是至关重要的，以便在疾病的最早阶段进行干预，并降低成本， 功能衰退的后果。项目目标。本研究的总体目标是利用 在家庭环境中使用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，将更有效地识别最早的微妙功能下降， 并最终威胁到独立。本研究的具体目标是：目标1将建立 最可靠的远程监测IADL变量和变量组合，以区分MCI 基于敏感性、特异性、准确性和总体ROC AUC，使用横断面 分析。目标2将确定新的远程监测IADL变量的辨别能力 与现有的IADL问卷进行比较，以区分MCI患者与认知完整者 基于横断面分析中的灵敏度、特异性、准确性和总体ROC AUC。目标3将确定 并描述远程监测的IADL功能随时间的纵向轨迹（斜率） 使用广义线性混合效应模型，在认知完整者和MCI者之间进行比较， 个人特定的IADL分布及其变化作为结果。项目方法。拟建项目 将在参与者的家中直接应用创新的计算和环境传感技术， 社区环境，以客观地评估老年人的IADL表现和变异性， 在多个临床相关功能领域中没有MCI，长达4年。研究参与也将 包括每年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，医生和护理人员主动识别和监测不断增加的恶化风险， 认知功能（从正常衰老到MCI，从MCI到AD）， 可能，改变AD预防试验，并显着降低功能性治疗的成本和后果， 减少我们的老龄化人口。