Digital detection of social isolation and loneliness markers of risk for Alzheimer's disease

对阿尔茨海默病风险的社会隔离和孤独标记进行数字检测

• 批准号: 10521991
• 负责人: Colin A. Depp
• 金额: $ 219.3万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10521991
• 关键词: Affective; Alzheimer disease detection; Alzheimer' s Disease; Alzheimer' s disease pathology; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Amyloid beta-Protein; Attitude; Biological Markers; COVID-19 pandemic; Cellular Phone; Clinical assessments; Cognitive; Cognitive Therapy; Data; Detection; Development; Early Diagnosis; Ecological momentary assessment; Elderly; Feedback; Frequencies; Goals; Home; Impaired cognition; Individual; Interdisciplinary Study; Intervention; Intervention Studies; Life; Location; Loneliness; Measurement; Measures; Modification; Monitor; Neuropsychological Tests; Observational Study; Pathology; Pathway Analysis; Patient Self-Report; Perception; Persons; Public Health; Research; Resources; Risk; Risk Marker; Social Behavior; Social Interaction; Social Network; Social Processes; Social isolation; Structure; Surveys; Technology; Test Result; Testing; Thinking; Time; Translating; Validation; Variant; Voice; base; cognitive testing; digital; experience; improved; new technology; novel; phenotypic data; primary outcome; psychologic; sensor; sensor technology; social; social cognition; social relationships; standard measure; tau Proteins; tau-1; tool

Project Summary/Abstract Social isolation and loneliness are associated with increased risk for cognitive decline and Alzheimer’s disease (AD) in older adults. This is a pressing public health concern given worldwide increases in social disconnectedness. Yet, research on the effect of social disconnection, especially social isolation, on risk for AD is hindered by reliance on retrospective self-report measures of social relationships and behaviors. Moreover, potentially modifiable social cognition mechanisms (e.g., apathy, defeatist social appraisals, biased threat perception) that may differentially contribute to isolation and loneliness are poorly understood. Integrated digital technology measurement approaches using ecological momentary assessment (EMA), which involves multiple daily smartphone surveys about social behavior and experiences, and passive social sensing, including GPS location and quantification of social interactions using smartphone sensors, could provide more precise and reliable probes for detection of social disconnection related to risk for AD in CN older adults, and could also reveal novel modifiable social cognition treatment targets to mitigate risk. Measurement problems, such as incomplete and inconsistent coverage of daily social behavior and experiences, have hampered observational and interventional research. Our inter-disciplinary research group has led development and validation of EMA, mobile social cognitive testing, and scalable passive sensing (GPS and voice sensing) measures, and social network analyses, to more precisely quantify social dynamics in daily life. We have also translated our real-time EMA data into interventions that reduce social cognitive biases that influence day-to-day social disconnection (e.g., social threats, defeatist attitudes). For the first time integrating these tools, we propose to investigate associations between real-time maladaptive social cognitive biases, social isolation, loneliness and AD risk biomarkers in 128 cognitively normal (CN) older adults divided into high (N=64) and low (N=64) risk based on CSF P-tau181, A42 and subtle cognitive decline (SCD) markers. We propose to administer in-lab standard measures, as well as EMA, GPS and social interaction digital detection measures, of social isolation, loneliness and social cognitive biases. We propose to compare high- and low-risk CN groups on EMA (primary outcome), passive sensing and in-lab measures, and will also examine relationships between digital social relationship measures, in-lab measures, and biomarkers. The goals of the project are to show that EMA and passive social sensing measures (1) can differentiate high- and low-risk CN groups; (2) are associated with known Aβ and P- tau biomarkers; and (3) are associated with social cognition biases that can be modified using treatments like in-person and digital cognitive-behavioral therapy. The immense data and digital products of this study would be available for future research probing real-world social processes in older adults.

项目概要/摘要 社会孤立和孤独与认知能力下降和阿尔茨海默病的风险增加有关 老年人疾病（AD）。鉴于全球社会人口的增加，这是一个紧迫的公共卫生问题 脱节性。然而，关于社会脱节（尤其是社会隔离）对 AD 风险影响的研究 由于依赖对社会关系和行为的回顾性自我报告测量而受到阻碍。而且， 潜在可改变的社会认知机制（例如冷漠、失败主义社会评价、有偏见的威胁 人们对可能导致孤立和孤独感不同的因素知之甚少。综合数字 使用生态瞬时评估（EMA）的技术测量方法，涉及多个 关于社交行为和体验以及被动社交感知（包括 GPS）的日常智能手机调查 使用智能手机传感器对社交互动进行定位和量化，可以提供更精确和更准确的信息 用于检测与 CN 老年人 AD 风险相关的社会脱节的可靠探针，并且还可以 揭示新的可修改的社会认知治疗目标以降低风险。测量问题，例如 对日常社会行为和经历的不完整和不一致的报道阻碍了观察 和介入研究。我们的跨学科研究小组领导了 EMA 的开发和验证， 移动社交认知测试、可扩展的被动传感（GPS 和语音传感）测量以及社交 网络分析，以更精确地量化日常生活中的社会动态。我们还翻译了实时 EMA 数据用于减少影响日常社交脱节的社会认知偏差的干预措施 （例如，社会威胁、失败主义态度）。首次集成这些工具，我们建议进行调查 实时适应不良的社会认知偏差、社会孤立、孤独感和 AD 风险之间的关联 128 名认知正常 (CN) 老年人的生物标志物根据不同风险分为高风险 (N=64) 和低风险 (N=64) CSF P-tau181、A42 和微妙认知衰退 (SCD) 标记物。我们建议管理实验室标准 社交隔离、孤独感的措施以及 EMA、GPS 和社交互动数字检测措施 和社会认知偏差。我们建议比较高风险和低风险 CN 组的 EMA（主要结果）， 被动传感和实验室测量，还将研究数字社会关系之间的关系 测量、实验室测量和生物标志物。该项目的目标是表明 EMA 和被动社交 感知措施 (1) 可以区分高风险和低风险 CN 群体； (2) 与已知的 Aβ 和 P-相关 tau 生物标志物； (3) 与社会认知偏差有关，可以使用诸如 面对面和数字认知行为治疗。这项研究的大量数据和数字产品将 可用于未来探索老年人现实世界社会过程的研究。