Creation of a technology-ready cohort for patients with Alzheimer's disease and related dementias and their caregivers

为阿尔茨海默病和相关痴呆症患者及其护理人员创建技术就绪队列

• 批准号: 10782660
• 负责人: Niteesh K Choudhry
• 金额: $ 30.23万
• 依托单位: UNIVERSITY OF MASSACHUSETTS AMHERST
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-30 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10782660
• 关键词: Address; Adoption; Alzheimer' s disease patient; Alzheimer' s disease related dementia; Artificial Intelligence; Behavioral; Caregiver Burden; Caregivers; Clinical; Cognitive; Communication; Data; Dementia; Disease Progression; Frequencies; Home environment; Individual; Intervention; Measurement; Measures; Medical; Monitor; Outcome Measure; Patient Monitoring; Patients; Phenotype; Population; Research; Safety; Technology; Testing; Training; Visit; behavioral outcome; cohort; digital; digital assessment; digital technology; interest; machine learning algorithm; medication compliance; pre-clinical

Project Summary Traditionally, Alzheimer’s Disease and related dementias (ADRD) patients are evaluated infrequently, imprecisely, and in artificial settings. This creates significant barriers to monitoring disease progression and caregiver burden in ADRD, and to assessing the impact of clinical and investigational interventions. Digital assessment measures— including digital phenotyping and artificial intelligence (AI) assessments and machine learning algorithms— can provide more reliable, more naturalistic (i.e., in a patient’s home environment) and more frequent (e.g., continuous) measurements in ADRD patients than those used in clinical settings. These assessments may also monitor safety more accurately, assure medication adherence, and facilitate communication with a patient’s medical team. Moreover, because of the enhanced sensitivity of these measures, they may better predict conversion to dementia in preclinical individuals. However, there are barriers to implementing these digital technologies. These include understanding which technologies can be most readily used in older— and not necessarily tech-savvy— populations, understanding the barriers to the adoption of these technologies in these populations, and confirming that these technologies provide tractable and predictive data regarding cognitive and behavioral disease progression. To address this, this proposal will create and train a “technology-ready cohort” upon which digital assessments relevant to ADRD patients and their caregivers can be tested. Next, we will show that these assessments better predict key cognitive and behavioral outcome measures in ADRD than standard clinical or research visits. In parallel, we will iteratively evaluate and address barriers to the adoption of these technologies in these populations.

项目摘要 传统上，评估阿尔茨海默病和相关痴呆（ADRD）患者 不经常，不精确，在人为的设置。这给监测工作造成了重大障碍 ADRD的疾病进展和护理人员负担，并评估临床和 调查干预。 数字评估措施-包括数字表型分析和人工智能（AI） 评估和机器学习算法-可以提供更可靠，更自然的（即， 在患者的家庭环境中）和更频繁（例如，ADRD中的连续）测量 患者比那些在临床环境中使用。这些评估还可以更多地监测安全性 准确，确保药物依从性，并促进与患者的医疗沟通 团队此外，由于这些措施的敏感性增强，它们可以更好地预测 在临床前个体中转化为痴呆。然而，在实施方面存在障碍， 这些数字技术。其中包括了解哪些技术最容易 在老年人中使用-不一定精通技术-人群，了解 在这些人群中采用这些技术，并确认这些技术 提供关于认知和行为疾病进展的易处理和预测数据。到 为了解决这一问题，该提案将创建和培训一个“技术就绪的群体”， 可以测试与ADRD患者及其护理者相关的评估。接下来，我们将展示 这些评估能够更好地预测ADRD中的关键认知和行为结果指标， 比标准的临床或研究访问。与此同时，我们将迭代地评估和解决 在这些人群中采用这些技术的障碍。