Adolescent Health in an Urban Environment

城市环境中的青少年健康

• 批准号: 9311866
• 负责人: Catherine A Calder
• 金额: $ 30.96万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-14 至 2022-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9311866
• 关键词: Adolescent; Adolescent Development; Affect; Algorithms; Area; Behavior; Budgets; Cellular Phone; Computer software; Consequentialism; County; Cues; Data; Data Collection; Data Sources; Dependence; Development; Disease; Ecological momentary assessment; Environment; Exposure to; Geographic Locations; Geography; Health; Health Sciences; Home environment; Homelessness; Human; Individual; Link; Location; Measures; Methodology; Methods; Modeling; Moods; Names; Nature; Ohio; Outcome; Participant; Pattern; Physiological; Population; Process; Prostitution; Research; Research Personnel; Risk Behaviors; Sampling; Schools; Series; Social Network; Specific qualifier value; Statistical Data Interpretation; Statistical Methods; Statistical Models; Stress; Structure; Survey Methodology; Surveys; Time; Tweens; Violence; Work; Youth; adolescent health; base; cost; data mining; disease transmission; drinking; experience; flexibility; health science research; interest; man; novel; residence; social; social media; urban setting

Project Summary Co-location networks – two-mode networks that capture connections between individuals and locations in geographic space – have broad relevance in the health sciences in areas ranging from the study of infectious disease transmission to understanding the influence of social processes on health outcomes and behaviors. Despite their broad relevance, however, statistical methods for understanding co-location networks are limited. This methodologically oriented proposal focuses on the development of a statistical framework for the study of co-location networks using a bilinear mixed-effects model with interacting latent activity pattern motifs and profiles. Through latent interacting random effects, our model captures the dependence between individuals based on their shared use of space and between locations based on the individuals who frequent them. Our flexible modeling framework uses a mixed-membership structure to relax the assumption that activity profiles are static and takes advantage of a data augmentation strategy to allow versions of the model with either direct or indirect specification of the dependence between actor-location ties. Our novel statistical models will be used in analyses of activity pattern data collected as part of the Adolescent Health and Development in Context (AHDC) Study, an ongoing data collection effort in Franklin County, Ohio. Through GPS-based smartphone tracking and space-time budget software, the AHDC Study provides rich detail on the co-location networks of adolescents in the study area. In addition, a wealth of survey data, smartphone-administered Ecological Momentary Assessments (capturing real-time measures of location, social network partner presence, activities, risk behaviors, and mood), and biomeasure data on the study participants are available. Recognizing that our proposed statistical model may not be able to capture the structure the co-location network structure of AHDC adolescents based entirely on their observed activity patterns, we also propose to embed relevant information derived from social media into our analyses through informative prior distributions on model parameters. To do so, we propose novel data mining algorithms to retrieve potential activity pattern motifs and coincident profiles from Twitter posts and network structure. In particular, we extend named entity identification methods to the spatial setting to automatically retrieve information relevant to activity patterns and develop novel methods for prioritizing activity pattern information based on its relevance to particular subpopulations (here, adolescents) using scalable sentiment analysis. Using our new statistical and data mining methodology, we will perform detailed statistical analyses to explore the relationship between spatial and socio-spatial exposures derived from an inferred co-location network and physiological stress in adolescents.

项目摘要 共同安置网络 - 两种模式网络，这些网络捕获个人和位置之间的连接 地理空间 - 在健康科学领域的健康科学方面具有广泛的相关性 疾病传播以了解社会过程对健康结果和行为的影响。 但是，尽管具有广泛的相关性，但了解共同定位网络的统计方法仍有限。 这项以方法论为导向的建议着重于开发研究的统计框架 使用双线性混合效应模型与具有相互作用的潜在活动模式图案和 概况。通过潜在的互动随机效应，我们的模型捕获了个体之间的依赖性 基于他们共同使用空间以及基于经常的个人之间的位置。我们的 灵活的建模框架使用混合成员结构来放松活动曲线的假设 是静态的，并利用数据增强策略，以允许使用任何一个直接的模型版本 或参与者位置领带之间依赖性的间接规范。我们的新颖统计模型将是 用于分析活动模式数据，作为青少年健康与发展的一部分收集 Context（AHDC）研究，俄亥俄州富兰克林县正在进行的数据收集工作。通过基于GPS的 智能手机跟踪和时空预算软件，AHDC研究提供了共同座位的丰富细节 研究区域的青少年网络。此外，大量的调查数据，智能手机管理 生态瞬间评估（捕获社交网络合作伙伴的实时测量， 提供研究参与者的存在，活动，风险行为和情绪）以及研究参与者的生物特征数据。 认识到我们提出的统计模型可能无法捕获结构 AHDC青少年的网络结构完全基于他们观察到的活动模式，我们还建议 通过信息丰富的先验分布，从社交媒体中衍生的相关信息纳入我们的分析 在模型参数上。为此，我们提出了新的数据挖掘算法来检索潜在的活动模式 来自Twitter帖子和网络结构的图案和一致的配置文件。特别是，我们扩展了命名实体 空间设置的识别方法，以自动检索与活动模式相关的信息和 开发新方法，以根据其特定的相关性来确定活动模式信息的优先级。 使用可扩展情感分析的亚群（此处，青少年）。使用我们的新统计和数据 采矿方法，我们将执行详细的统计分析以探索空间之间的关系 以及从推断的共处网络和身体压力中得出的社会空间暴露 青少年。