SCH: INT: Collaborative Research: Passive sensing of social isolation: A digital phenotying approach

社会孤立--包括“孤独”的客观现象和孤独的主观体验 （感知孤立）-是全球性的一个主要问题。我们在拟议项目中的目标是利用统计 利用基于智能手机的连续、不显眼和实时测量能力的方法 社会隔离的评估。我们汇集了一批在社会行为方面有专长的临床科学家 动力学、数字信号处理研究前沿的工程师/计算机科学家和生物统计学家 具有被动传感技术方面的专业知识，以提供执行 研究的目的。使用数字表型分析方法（即，每时每刻量化的个人层面 人类表型原位使用数据从个人智能手机），我们将开发和测试算法， 主动（生态瞬时评估）和被动（移动、位置、对话）指标，以改善 社会隔离的表征和预测。然后，我们将进行初步评估的承诺， 社会隔离过渡状态的动态网络分析，然后将这种方法应用于临床 以社会孤立为特征的样本。 相关性（参见说明）： 该项目的发现将对全球健康产生深远的影响，因为我们对社会孤立的新理解是一种 早期死亡率和其他重大健康问题的关键因素强调了对可扩展的，全面的， 个性化的评估和干预方法。开发新的方法来改善社会行为的推断， 时间密集的智能手机数据将有利于数字健康研究领域的扩大。这种贡献超越了 该项目的应用目标，因为我们将开发的方法论进步可以应用于现有的大量语料库， 数据和未来的项目。最终，这项工作将为针对IEAL中社会隔离的可持续干预措施的实施提供信息， 时间和日常生活中。

Social isolation-including both the objective phenomenon of 'aloneness' and the subjective experience of loneliness (perceived isolation)-is a major problem globally. Our goal in the proposed project is to capitalize on statistical methods for harnessing the power of smartphone-based measurement of continuous, unobtrusive, and real-time assessment of social isolation. We bring together a team of clinical scientists with expertise in social behavior dynamics, engineers/computer scientists at the forefront of research on digital signal processing, and biostatisticians with expert knowledge in passive sensing technology to provide robust methodological rigor needed to execute the study's aims. Using a digital phenotyping approach (i.e., the moment-by-moment quantification of the individual-level human phenotype in situ using data from personal smartphones), we will develop and test algorithms that incorporate both active (ecological momentary assessment) and passive (movement, location, conversation) metrics to improve characterization and prediction of social isolation. We will then conduct a preliminary evaluation of the promise of a dynamic network analysis of social isolation transition states, followed by application of this approach to a clinical sample characterized by social isolation. RELEVANCE (See instructions): Findings from this project will have far-reaching application to global health, as our·emerging understanding of social isolation as a key contributor to early mortality and other significant health problems highlights the need for a scalable, comprehensive, and personalized assessment and intervention approach. Developing new methods for improving inference of social behavior from temporally-dense smartphone data will benefit an expanding area of research in digital health. This contribution extends beyond the applied aims of the project, as the methodological advancements we will develop can be applied to a large corpus of existing data and future projects. Ultimately, this work will inform the delivery of sustainable interventions targeting social isolation in ieal- time and in daily contexts.