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

SCH：INT：协作研究：社会隔离的被动感知：数字表型方法

• 批准号: 10478269
• 负责人: Carlos Alberto Busso
• 金额: $ 28.05万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-23 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10478269
• 关键词: Accelerometer; Address; Algorithms; Area; Cellular Phone; Characteristics; Clinical; Clinical assessments; Collection; Computers; Data; Detection; Development; Digital Signal Processing; Ecological momentary assessment; Elderly; Emotions; Engineering; Evaluation; Feeling; Future; General Population; Goals; Gold; Health; Health Care Research; Human; In Situ; Individual; Instruction; Intervention; Knowledge; Left; Link; Location; Loneliness; Measurement; Medical; Mental Health; Meta-Analysis; Methodology; Methods; Modeling; Movement; Network-based; Obesity; Pathway Analysis; Patient Isolation; Patients; Persons; Phenotype; Population; Probability; Reporting; Research; Research Personnel; SMART health; Sampling; Scientist; Smoking; Social Behavior; Social Functioning; Social isolation; Statistical Methods; Structure; Summary Reports; Symptoms; System; Technology; Testing; Time; Work; base; clinical care; connected health; digital; digital health; experience; global health; improved; microphone; mortality; mortality risk; network models; recruit; research clinical testing; sensor; sensor technology; severe mental illness; smartphone Application; social; young adult

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.

社会隔离 - 包括“孤独”的客观现象和孤独的主题经验 （感知到隔离） - 全球是一个主要问题。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，不引人注目和实时测量的力量的方法 评估社会隔离。我们将一个具有社会行为专业知识的临床科学家团队汇集在一起 动态，工程师/计算机科学家在数字信号处理研究的最前沿以及生物统计学家 具有被动灵敏度技术方面的专家知识，以提供执行强大的方法论严格性 研究的目标。使用数字表型方法（即，逐矩数量 使用个人智能手机的数据原位人类表型），我们将开发和测试合并的算法 主动（生态瞬时评估）和被动（移动，位置，对话）的指标以改进 社会隔离的表征和预测。然后，我们将对A的承诺进行初步评估 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 样本以社会隔离为特征。 相关性（请参阅说明）： 该项目的发现将对全球健康有深远的应用，因为我们对社会隔离的理解为 早期死亡率和其他重大健康问题的关键因素凸显了对可扩展，全面和 个性化评估和干预方法。开发新方法来改善社会行为推论 暂时致密的智能手机数据将使数字健康研究的扩展领域受益。这项贡献超出了 该项目的应用目的，因为我们将开发的方法学进步可以应用于现有的大量语料库 数据和未来项目。最终，这项工作将为您提供针对社会隔离的可持续干预措施的交付 - 时间和日常情况。