Continuous ADL monitoring using computer vision to maintain independence and improve HRQoL in older adults at risk for AD/ADRD

使用计算机视觉进行连续 ADL 监测，以保持独立性并改善有 AD/ADRD 风险的老年人的 HRQoL

• 批准号: 10432682
• 负责人: Jiunn Benjamin Heng
• 金额: $ 19.25万
• 依托单位: CHERRY LABS, INC.
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10432682
• 关键词: Activities of Daily Living; Address; Adverse event; Age; Aging; Algorithms; Alzheimer' s Disease; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Artificial Intelligence; Behavior; Behavioral; Blinded; Caregivers; Caring; Clinical; Clinical Management; Cognitive; Communication; Communities; Computer Vision Systems; Consumption; Coughing; Cross-Sectional Studies; Data; Data Collection; Data Set; Dementia; Detection; Elderly; Enrollment; Ensure; Evolution; Expert Systems; Family; Focus Groups; Foundations; Four-dimensional; Future; Goals; Healthcare; Home; Home environment; Hour; Impaired cognition; Incidence; Individual; Intervention; Learning; Link; Living Wills; Location; Machine Learning; Measurement; Measures; Medical Records; Methods; Modeling; Modernization; Monitor; Nature; Nursing Homes; Organism; Outcome; Participant; Patient Self-Report; Patients; Persons; Phase; Phenotype; Privacy; Prognosis; Provider; Randomized; Randomized Controlled Trials; Recording of previous events; Risk; Secure; Single-Blind Study; Skilled Nursing Facilities; Support System; Symptoms; System; Testing; Time; Update; Work; artificial intelligence algorithm; base; clinical encounter; clinically actionable; clinically relevant; design; efficacy trial; falls; functional status; health related quality of life; improved; indexing; information processing; mild cognitive impairment; novel; patient home care; pilot test; prevent; primary outcome; privacy preservation; programs; sensor; treatment as usual

To help older adults age independently at home, effectively monitoring and detecting changes in ADLs are critical for preventing adverse events and maintaining health-related quality of life (HRQoL). However, ADLs are time consuming to capture, highly subjective, and rarely documented in most clinical encounters. Artificial intelligence (AI) computer vision is capable of automatically capturing a continuous timestream of activities and may address these limitations, yet has been criticized for the “blackbox” nature of algorithms. Our preliminary data identified that a unique AI approach using computer vision can capture ADLs without large tagged datasets to learn a behavior while preserving privacy. Our hypothesis is that ADL-related data captured by an explainable AI monitoring system can be a key contributor to preventing ADL-deficit associated adverse events and maintaining HRQoL among individuals with Alzheimer's Disease and Alzheimer's Disease Related Dementias (AD/ADRD) residing in home settings. In the proposed work, we will develop (R21) and assess (R33) a highly personalized and clinically interpretable AI system, known as Cherry AI, to monitor ADLs, detect changes early, predict relevant adverse events, and support healthcare planning for Program for All- inclusive Care for the Elderly (PACE) providers, with an ultimate goal of maintaining HRQoL among PACE enrollees with or without dementia. In the R21 phase (Stage 0), we will refine Cherry AI algorithms and conduct focus groups of PACE clinicians to identify and summarize factors involved in clinical management plans for ADLs. We will enroll PACE enrollees with a history of ADL deficits and varied cognitive profiles [total n=20, 10 w/ mild cognitive impairment; 10 w/ subjective cognitive decline] and monitor ADLs in homes using Cherry AI. PACE clinicians will evaluate participants’ ADLs using the Modified Barthel Index. Correlations between Cherry AI-measured and clinician-rated ADLs will be evaluated. Qualitative focus groups of 10-15 home care clinicians will be used to improve the Cherry AI interface. Specific aims include (1) refining Cherry AI algorithms and (2) enhancing interpretability of the Cherry AI system to help clinicians make ADL related management plans. In the R33 phase (pilot test, Stage I), we will assess the ability of Cherry AI to help maintain or improve HRQoL in PACE enrollees with AD/ADRD by predicting future changes in ADLs and associated adverse events, and assisting with ADL-related management. PACE enrollees (n=80) with a history of ADL deficits will be stratified on cognitive phenotype and randomly assigned to one of two groups: Cherry AI (intervention) vs. usual care (control) in a pilot single-blind randomized controlled trial. We will use linear mixed- effect models to examine Cherry AI’s effect on maintaining HRQoL compared to PACE’s usual care. Specific aims include comparing changes of HRQoL, incidence of adverse events, and changes in PACE management plans between groups. This study will lead to an efficacy trial of Cherry AI monitoring to improve HRQoL for community-dwelling seniors with AD/ADRD.

为了帮助老年人在家中独立年龄，有效监视和检测ADL的变化至关重要 用于防止不良事件并维持与健康相关的生活质量（HRQOL）。但是，ADL是时间 在大多数临床相遇中消耗捕获，高度主观且很少记录。人工智能 （ai）计算机视觉能够自动捕获连续的及时活动，并可能解决 这些局限性，但对于算法的“黑匣子”性质至关重要。我们的初步数据 确定使用计算机视觉的唯一AI方法可以捕获ADL，而无需大型标记数据集 在保护隐私的同时学习行为。我们的假设是通过可解释的ADL相关数据 AI监视系统可能是防止ADL障碍相关的广告事件的关键因素和 维持阿尔茨海默氏病和阿尔茨海默氏病与痴呆症患者之间的HRQOL （AD/ADRD）居住在家庭设置中。在拟议的工作中，我们将开发（R21）和评估（R33） 个性化和临床上可解释的AI系统，称为樱桃AI，用于监视ADL，检测 尽早更改，预测相关的广告事件，并为所有计划提供医疗保健计划 对老年人（PACE）提供者的包容性护理，其最终目标是维持HRQOL 有或没有痴呆症的步伐参加。在R21阶段（阶段0），我们将完善樱桃AI算法 并进行焦点临床医生的焦点小组，以识别和总结涉及临床的因素 ADL的管理计划。我们将注册ADL的历史的速度定义和不同的认知能力 剖面[总n = 20，10 W/轻度认知障碍； 10 W/主观认知下降]，并监视ADL 使用樱桃AI的房屋。 PACE临床医生将使用改良的Barthel指数评估参与者的ADL。 将评估樱桃AI测量和临床评级ADL之间的相关性。定性焦点小组 10-15位家庭护理临床医生将用于改善樱桃AI界面。具体目的包括（1）精炼 樱桃AI​​算法和（2）增强樱桃AI系统的解释性，以帮助临床医生制作ADL 相关管理计划。在R33阶段（试点测试，第一阶段）中，我们将评估樱桃AI的能力 通过预测ADL的未来变化，帮助维护或改善AD/ADRD招募的HRQOL 以及相关的不良事件，并协助与ADL相关的管理。 pace注册（n = 80） ADL定义的史将在认知表型上进行分层，并随机分配给两个组之一： 在单盲随机对照试验中，Cherry AI（干预）与常规护理（对照）。我们将使用 与PACE的平常相比 关心。具体目的包括比较HRQOL的变化，不良事件的发生以及节奏变化 组之间的管理计划。这项研究将导致对樱桃AI监测的有效试验以改进 HRQOL为社区居住的老年人提供广告/ADRD。