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.

社会孤立——包括“孤独”的客观现象和孤独的主观体验