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

项目摘要 传统上，对阿尔茨海默病和相关痴呆(ADRD)患者进行评估 不经常的，不精确的，在人为的环境中。这给监控造成了很大的障碍 ADRD的疾病进展和照顾者负担，并评估临床和 调查性干预。 数字评估措施--包括数字表型和人工智能(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)并且在ADRD中更频繁(例如，连续)测量 患者比在临床环境中使用的患者更多。这些评估也可能更多地监测安全 准确，确保服药依从性，并促进与患者的医疗人员沟通 一队。此外，由于这些措施的敏感性增强，他们可能会更好地预测 临床前个体转化为痴呆症。然而，在实施过程中存在障碍 这些数字技术。这些包括了解哪些技术最容易 在年长的人群中使用，但不一定是精通技术的人群，了解 在这些人群中采用这些技术，并确认这些技术 提供有关认知和行为疾病进展的易于处理和可预测的数据。至 为了解决这一问题，该提案将创建和培训一支“技术就绪型队列”，在此基础上实现数字化 可以测试与ADRD患者及其照顾者相关的评估。接下来，我们将展示 这些评估可以更好地预测ADRD中的关键认知和行为结果指标 而不是标准的临床或研究访问。同时，我们将反复评估和解决 在这些人群中采用这些技术的障碍。

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.