Passive Monitoring of Walking Cadence as a Novel Tool for Aging and Cognitive Health Assessment

步行节奏的被动监测作为衰老和认知健康评估的新工具

• 批准号: 10755452
• 负责人: Niteesh K Choudhry
• 金额: $ 33.32万
• 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/10755452
• 关键词: Aging; Alzheimer' s Disease; Alzheimer' s disease risk; Cognition; Cognitive; Collection; Data; Development; Disease; Early Diagnosis; General Population; Health; Impaired cognition; Intervention; Knowledge; Life; Measures; Monitor; Motor; Nerve Degeneration; Persons; Phenotype; Physical activity; Prevention; Therapeutic Clinical Trial; Walking; cognitive function; cognitive testing; digital; early detection biomarkers; health assessment; high risk; machine learning model; novel; novel therapeutic intervention; potential biomarker; screening; tool; wearable device

Project Summary Despite enormous efforts, therapeutic clinical trials for Alzheimer’s disease (AD) have largely failed, possibly because interventions are initiated too late into the disease course when neurodegeneration has begun and is irreversible. Early detection of people at high risk of AD is thus particularly important to help in the effort to develop prevention and treatment options. We hypothesize that subtle changes in motor function may serve as a potential biomarker of early signs of cognitive impairment. Tracking changes in cadence will enable the assessment of potential parallel trajectories of motor/cognitive decline and may prove to be an indicator of declining cognition. This study will leverage a large collection of physical activity and cognitive assessment data, and derive novel digital phenotypes from commercially available wearable devices (Aim 1). We will then assess the correlation of these novel measures with declining cognitive function, and develop advanced machine learning models to identify people at high risk to develop cognitive impairment (Aim 2). Given the increasing popularity of wearable devices, the knowledge and tools developed from the current project could be readily applicable to the general population for the large-scale screening of cognitive health.

项目摘要 尽管付出了巨大的努力，但阿尔茨海默病（AD）的治疗性临床试验仍在继续。 很大程度上失败了，可能是因为干预措施在疾病发生后太晚 当神经退行性变已经开始并且是不可逆的。早期发现人 因此，对AD高风险人群的预防尤为重要， 和治疗选择。我们假设运动功能的细微变化 作为认知障碍早期迹象的潜在生物标志物。跟踪变化 节奏将能够评估运动/认知的潜在平行轨迹 这可能是认知能力下降的一个指标。这项研究将利用 大量的身体活动和认知评估数据，并得出新的 来自市售可穿戴设备的数字表型（Aim 1）。然后我们将 评估这些新措施与认知功能下降的相关性， 开发先进的机器学习模型，以识别高风险人群， 认知障碍（目标2）。鉴于可穿戴设备的日益普及， 从当前项目中开发的知识和工具可以随时适用于 对普通人群进行大规模的认知健康筛查。