Using passively collected person-generated health data to explore population-specific relationships between social determinants of health, sleep patterns, and cognitive outcomes

使用被动收集的个人生成的健康数据来探索健康、睡眠模式和认知结果的社会决定因素之间的特定人群关系

• 批准号: 10287279
• 负责人: Ritika Ratnam Chaturvedi
• 金额: $ 33.21万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10287279
• 关键词: African American; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Alzheimer’s disease biomarker; American; Amyloid; Behavior; Cellular Phone; Cerebrospinal Fluid; Clinical; Cognition; Cognitive; Cognitive Science; Crime; Data Collection; Detection; Development; Early Intervention; Economics; Environment; Funding; Future; Gait; Generations; Gold; Health; Health Status; Heterogeneity; Impaired cognition; Individual; Infrastructure; Institutional Racism; Intervention; Knowledge; Laboratories; Left; Life; Life Cycle Stages; Link; Low income; Measures; Methodology; Minority; Neighborhoods; Obstructive Sleep Apnea; Outcome; Parents; Pathology; Pathway interactions; Patient Self-Report; Periodicity; Persons; Physical activity; Population; Public Health; Research; Risk; Risk Factors; Sampling; Sensitivity and Specificity; Series; Sleep; Sleep Deprivation; Sleep Disorders; Social Interaction; Social Problems; Stress; Surveys; Symptoms; Technology; Time; TimeLine; Underserved Population; Validation; Work; behavior influence; biobehavior; brain tissue; clinical practice; cognitive change; cognitive function; cost effective; cost effectiveness; dementia risk; diet and exercise; digital; early onset; ethnic minority population; experience; fitbit; food insecurity; health care availability; health care disparity; health care quality; health data; high risk population; individual variation; innovation; interdisciplinary approach; large scale data; member; modifiable risk; multidisciplinary; poor sleep; predictive modeling; preventive intervention; prospective; racial and ethnic; response; screening; sleep health; sleep pattern; sleep quality; smartphone based assessment; social; social determinants; social health determinants; social structure; sociodemographic group; sociodemographics; tool; validation studies; wearable device; wearable sensor technology

Project Summary Alzheimer’s disease and related dementias (ADRDs) impose disproportionate burden in underserved populations (e.g., African Americans and other minorities, low-income individuals), which experience higher rates and earlier onset of cognitive decline. Social and structural determinants of health--including economic and educational disparities, healthcare access and quality, systemic racism, and lifetime stress--account for ~80% of modifiable risk factors and profoundly contribute to such disparities. However, social determinants are understudied in the context of ADRDs in the underserved, and there is limited understanding of how differential lifetime experience of social determinants across various populations may influence heterogeneity in cognitive outcomes. In addition, the ability to detect subtle cognitive changes in everyday life--often years prior to the onset of discernible ADRD symptoms--is a methodological challenge in gold-standard approaches. Such barriers impede development of effective screening, treatment, and preventive interventions that are appropriately tailored to underserved populations. Consumer digital technologies offer non-invasive tools for measuring cognitive change and experience of social determinants in everyday life. Today, individual lived-experiences and social determinants can be precisely characterized by person-generated health data (PGHD) (spanning environment, geolocation, diet, exercise, social interactions, and other activities) generated from nearly- ubiquitous smartphones and wearable devices. Applied to cognition, our team members at Evidation Health and others have demonstrated that several objective indicators passively collected from digital technologies may be associated with cognitive decline and may precede clinical ADRD manifestation by 10-15 years. In our NLM- funded parent R01LM013237, we propose a sustainable infrastructure to use continuous PGHD to explore the multi-level influence of social determinants on sleep health. Poor sleep health is an emerging risk factor for ADRDs that may be an important and understudied biobehavioral pathway linking individual social determinants with poor cognitive outcomes. Our objective here, in response to NOT-AG-20-034, is to (1) add clinically validated measures of cognition to our in-scope data collection and sleep study to (2) explore the dynamic and multilevel relationships between individual-level social determinants, physical activity, sleep health, cognitive function, and ADRD risk. We will collaborate with Dr. Sliwinski (U2CAG060408), whose group has developed a battery of clinically validated smartphone-based assessments of cognitive function. Collectively, our multidisciplinary approach will overlay (1) monthly self-reported health metrics; (2) periodic clinical measures of cognition; and, (3) continuous PGHD on a 12-month time series from each individual. This work may significantly advance cognitive science and transform public health, enabling future innovation towards reducing ADRD burden across all socio-demographic groups, including the historically underserved.

项目摘要 阿尔茨海默病和相关痴呆症（ADRD）对服务不足的人群造成不成比例的负担 群体（例如，非洲裔美国人和其他少数民族，低收入个人），其中经历较高的利率 和认知能力下降的早期发病。健康的社会和结构性决定因素-包括经济和 教育差距，医疗保健的获得和质量，系统性种族主义和终身压力-占约80% 可改变的风险因素，并深刻地促进了这种差异。然而，社会决定因素是 在服务不足的ADRD背景下研究不足，并且对差异如何理解有限 不同人群的社会决定因素的一生经历可能会影响认知能力的异质性。 结果。此外，在日常生活中发现细微认知变化的能力-通常在发病前几年 可辨别的ADRD症状-是金标准方法的方法学挑战。这些障碍 阻碍有效筛查、治疗和预防干预措施的发展， 专门针对服务不足的人群。消费者数字技术为测量提供了非侵入性工具 日常生活中的认知变化和社会决定因素的经验。今天，个人的生活经验和 社会决定因素可以通过个人生成的健康数据（PGHD）（跨越 环境、地理位置、饮食、锻炼、社交互动和其他活动）， 无处不在的智能手机和可穿戴设备。应用于认知，我们的团队成员在证据健康和 其他人已经证明，从数字技术中被动收集的一些客观指标可能 与认知能力下降相关，可能先于临床ADRD表现10-15年。在我们的NLM- 资助的母公司R 01 LM 013237，我们提出了一个可持续的基础设施，使用连续PGHD探索 社会因素对睡眠健康的多层次影响。睡眠不好是一个新的危险因素， ADRD可能是连接个体社会决定因素的重要且未充分研究的生物行为途径 认知能力差的人作为对NOT-AG-20-034的回应，我们的目的是（1）增加临床验证 认知的措施，我们的范围内的数据收集和睡眠研究（2）探索动态和多层次的 个人水平的社会决定因素，身体活动，睡眠健康，认知功能， ADRD风险。我们将与Sliwinski博士（U2 CAG 060408）合作，他的团队开发了一种电池， 临床验证的基于智能手机的认知功能评估。总体而言，我们的多学科 该方法将覆盖（1）每月自我报告的健康指标;（2）认知的定期临床测量;以及， (3)连续PGHD的12个月的时间序列从每个人。这项工作可能会大大推进 认知科学和改变公共卫生，使未来的创新，以减少ADRD的负担， 所有社会人口群体，包括历史上得不到充分服务的群体。