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: 10650307
• 负责人: Jiunn Benjamin Heng
• 金额: $ 0.2万
• 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 2023-11-20
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10650307
• 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; Clinical; Clinical Management; Cognitive; Collaborations; Communication; Communities; Computer Vision Systems; Consumption; Coughing; Cross-Sectional Studies; Data; Data Collection; Data Set; Dementia; Detection; Dimensions; Early Diagnosis; Elderly; Eligibility Determination; Enrollment; Ensure; Evolution; Expert Systems; Family; Focus Groups; Foundations; Friends; Future; Goals; Healthcare; Home; Home environment; Hour; Impaired cognition; Incidence; Individual; Intervention; Learning; Link; Living Wills; Location; Long-Term Care for Elderly; Machine Learning; Measurement; Measures; Medical Records; 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; artificial intelligence method; 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衰老，有效地监测和检测ADL的变化至关重要 用于预防不良事件和维持健康相关的生活质量(HRQOL)。然而，ADL就是时间 耗费精力捕捉，主观性很强，在大多数临床接触中很少有记录。人工智能 (AI)计算机视觉能够自动捕获连续的活动时间流，并可以处理 然而，这些局限性却因算法的“黑箱”性质而受到批评。我们的初步数据 确定了一种使用计算机视觉的独特人工智能方法可以在没有大型标记数据集的情况下捕获ADL 在保护隐私的同时学习一种行为。我们的假设是，与ADL相关的数据由可解释的 人工智能监测系统可以是预防ADL缺陷相关不良事件的关键因素 阿尔茨海默病和阿尔茨海默病相关痴呆患者的HRQOL维持 (AD/ADRD)居住在家庭环境中。在拟议的工作中，我们将开发(R21)和评估(R33)一个高度 个性化和临床可解释的人工智能系统，称为Cherry AI，用于监控ADLS、检测 及早改变，预测相关不良事件，并支持全民计划的医疗规划- 普惠性老年人护理(PACE)提供者，最终目标是在 PACE参与者患有或不患有痴呆症。在R21阶段(阶段0)，我们将完善Cherry AI算法 并对PACE临床医生进行焦点小组，以确定和总结临床涉及的因素 ADL的管理计划。我们将招收有ADL缺陷史和不同认知水平的PACE参与者 资料[总n=20，10 w/轻度认知障碍；10 w/主观认知下降]和监测ADL 使用樱桃人工智能的家庭。PACE临床医生将使用修改后的Barthel指数评估参与者的ADL。 将评估Cherry AI测量的ADL和临床医生评级的ADL之间的相关性。定性焦点小组 10-15名家庭护理临床医生将用于改善Cherry AI界面。具体目标包括(1)提炼 Cherry AI算法和(2)增强Cherry AI系统的可解释性，帮助临床医生进行ADL 相关管理计划。在R33阶段(试点测试，第一阶段)，我们将评估Cherry AI的能力 通过预测ADL的未来变化，帮助维持或改善有AD/ADRD的PACE参与者的HRQOL 和相关的不良事件，并协助ADL相关的管理。登记人数(n=80)与 ADL缺陷史将根据认知表型分层，并随机分配到两组中的一组： 在一项先导性单盲随机对照试验中，樱桃人工智能(干预)与常规护理(对照)进行了比较。我们将使用 线性混合效应模型检验Cherry AI维持HRQL的效果与PACE的通常效果比较 关心。具体目标包括比较HRQL的变化、不良事件的发生率和PACE的变化 小组之间的管理计划。这项研究将导致樱桃人工智能监测的有效性试验，以提高 患有AD/ADRD的社区老年人的HRQOL