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

项目摘要/摘要 社交孤立和孤独与认知功能减退和阿尔茨海默氏症风险增加相关 老年人的疾病(AD)。这是一个紧迫的公共卫生问题，因为世界范围内 断开连接。然而，关于社会脱节，特别是社会孤立对AD风险的影响的研究 依赖于对社会关系和行为的回顾自我报告措施，这一点受到阻碍。此外， 潜在的可修改的社会认知机制(例如，冷漠、失败主义的社会评价、有偏见的威胁 知觉)可能不同地导致孤立和孤独，但人们对此知之甚少。集成数字 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的日常智能手机调查，以及包括GPS在内的被动社交感知 使用智能手机传感器定位和量化社交互动，可以提供更精确和 可靠的探针，用于检测与CN老年人AD风险相关的社交断开，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 和干预性研究。我们的跨学科研究小组领导了EMA的开发和验证， 移动社交认知测试和可扩展的被动感知(GPS和语音感知)测量，以及社交 网络分析，以更准确地量化日常生活中的社会动态。我们还将我们的实时 将EMA数据转化为干预措施，减少影响日常社会脱节的社会认知偏差 (例如，社会威胁、失败主义态度)。第一次集成这些工具，我们建议调查 实时不适应社会认知偏差、社会隔离、孤独与AD风险之间的关系 128名认知正常(CN)老年人的生物标志物按危险程度分为高风险(N=)和低风险(N=) 脑脊液P-tau181、A-42和微妙认知功能减退标志物。我们建议实施实验室内标准 社会孤立、孤独的措施，以及EMA、GPS和社会互动数字检测措施 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)， 被动传感和实验室内测量，还将检查数字社会关系之间的关系 测量、实验室内测量和生物标记物。该项目的目标是表明EMA和被动社会 感知措施(1)可以区分高风险和低风险CN组；(2)与已知的A、β和P- Tau生物标志物；以及(3)与社会认知偏差有关，可以使用以下治疗方法来改变 面对面和数字认知行为治疗。这项研究的海量数据和数字产品将是 可用于未来探索老年人现实世界社交过程的研究。

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.