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养老，有效监测和检测 ADL 的变化至关重要 然而，ADL 是时间。 花费大量时间来捕捉，高度主观，并且在大多数临床遭遇中很少被记录。 （AI）计算机视觉能够自动捕捉连续时间流的活动，并可以解决 这些限制，但由于我们的初步数据的“黑箱”性质而受到批评。 发现使用计算机视觉的独特人工智能方法可以捕获 ADL，而无需大型标记数据集 我们的假设是，ADL 相关数据由可解释的对象捕获。 人工智能监控系统可以成为预防 ADL 缺陷相关不良事件的关键贡献者 维持阿尔茨海默病和阿尔茨海默病相关痴呆患者的 HRQoL （AD/ADRD）居住在家庭环境中。在拟议的工作中，我们将开发（R21）并高度评估（R33）。 个性化和临床可解释的人工智能系统，称为 Cherry AI，用于监控 ADL、检测 尽早改变，预测相关不良事件，并支持全民计划的医疗保健规划- 包容性老年人护理 (PACE) 提供者的最终目标是维持老年人的 HRQoL 在 R21 阶段（第 0 阶段），我们将完善 Cherry AI 算法。 并开展 PACE 焦点小组来识别和总结临床中涉及的因素 我们将招募有 ADL 缺陷历史和不同认知能力的 PACE 参与者。 概况 [总数 = 20，10 例有轻度认知障碍；10 例有主观认知下降] 并监测 ADL 使用 Cherry AI 的家庭将使用修正巴塞尔指数评估参与者的 ADL。 将评估 Cherry AI 测量的 ADL 与临床医生评定的 ADL 之间的相关性。 10-15 名家庭护理教区居民将用于改进 Cherry AI 界面，具体目标包括 (1) 完善。 Cherry AI 算法以及 (2) 增强 Cherry AI 系统的可解释性，以帮助顾客进行 ADL 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，帮助维持或改善患有 AD/ADRD 的 PACE 参与者的 HRQoL 以及相关的不良事件，并协助 PACE 参与者 (n=80) 进行 ADL 相关管理。 ADL 缺陷史将根据认知表型进行分层，并随机分配到以下两组之一： 我们将在试点单盲随机对照试验中使用 Cherry AI（干预）与常规护理（对照）。 线性混合效应模型，用于检查 Cherry AI 与通常的 PACE 相比对维持 HRQoL 的影响 具体目标包括比较 HRQoL 的变化、不良事件的发生率以及 PACE 的变化。 这项研究将导致 Cherry AI 监控的有效性试验，以改善群体之间的管理计划。 患有 AD/ADRD 的社区老年人的 HRQoL。