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SCH: INT: Collaborative Research: FITTLE+: Theory and Models for Smartphone Ecological Momentary Intervention

SCH: INT: Collaborative Research: FITTLE+: Theory and Models for Smartphone Ecological Momentary Intervention
SCH:INT:合作研究:FITTLE:智能手机生态瞬时干预理论与模型
批准号:
1346066
负责人:
Peter Pirolli
金额:
$119.91万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2013
资助国家:
美国
项目状态:
已结题
起止时间:
2013-10-01 至 2017-10-31

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中文摘要
翻译
许多健康状况是由不健康的生活方式引起的,可以通过改变行为来改善。传统的行为改变方法(例如,减肥诊所;私人教练)在长期向大量人口提供专家个性化的日常支持方面存在瓶颈。迫切需要在饮食和健身等领域扩大现有成功的健康行为改变方法的覆盖范围和力度。智能手机平台提供了一个绝佳的机会,可以以巨大的规模经济将最有效的行为改变干预措施投射到日常生活中。智能手机还提供了一个极好的机会来收集丰富的、细粒度的数据,这些数据对于理解和预测人们日常生活中的行为变化动态是必要的。这些详细衡量和干预的机会带来的挑战是,当前的理论并不具有同样的细粒度和预测性。这个跨学科的项目研究支持通过智能手机集成到个人和群体日常生活中的细粒度行为改变建模和干预的理论和方法。菲特尔+开发了一种新的、变革性的智能手机交付的生态瞬时干预(EMI)形式,用于改善饮食和体力活动。这种方法将提供社交支持和自主计划的个性化指导,建立在移动感知、认知辅导和基于证据的社交设计方法的基础上。这一新方法的基础将需要新的健康行为变化的预测性计算理论。目前关于个体健康行为变化的粗粒度概念理论将被提炼成细粒度的预测计算模型。这些计算模型将能够基于移动感知和交互数据跟踪每一时刻的人类背景、活动和社交模式。利用这些监控功能,菲特尔+的S计算模型将支持基于潜在的动机、认知和社交机制对个人用户和群体进行评估和预测。这些预测模型还将用于计划和优化指导行动,包括详细的诊断、个性化目标以及根据背景和个人情况调整的干预措施。研究人员协作团队与减肥干预者在夏威夷的美国最大健康组织之一合作。该团队包括移动传感、人工智能、计算认知、社会心理学、人机交互、计算机辅导和测量理论方面的专业知识。
英文摘要
Many health conditions are caused by unhealthy lifestyles and can be improved by behavior change. Traditional behavior-change methods (e.g., weight-loss clinics; personal trainers) have bottlenecks in providing expert personalized day-to-day support to large populations for long periods. There is a pressing need to extend the reach and intensity of existing successful health behavior change approaches in areas such as diet and fitness. Smartphone platforms provide an excellent opportunity for projecting maximally effective interventions for behavior change into everyday life at great economies of scale. Smartphones also provide an excellent opportunity for collecting rich, fine-grained data necessary for understanding and predicting behavior-change dynamics in people going about their everyday lives. The challenge posed by these opportunities for detailed measurement and intervention is that current theory is not equally fine-grained and predictive. This interdisciplinary project investigates theory and methods to support fine-grained behavior-change modeling and intervention integrated via smartphone into the daily lives of individuals and groups. Fittle+ develops a new and transformative form of smartphone-delivered Ecological Momentary Intervention (EMI) for improving diet and physical activity. This approach will provide social support and autonomously planned and personalized coaching that builds on methods from mobile sensing, cognitive tutoring, and evidence-based social design. The foundation for this new approach will require new predictive computational theories of health behavior change. Current coarse-grained conceptual theories of individual health behavior change will be refined into fine-grained predictive computational models. These computational models will be capable of tracking moment-by-moment human context, activity, and social patterns based on mobile sensing and interaction data. Using these monitoring capabilities, Fittle+'s computational models will support assessment of, and predictions about, individual users and groups based on underlying motivational, cognitive, and social mechanisms. These predictive models will also be used to plan and optimize coaching actions including detailed diagnostics, individualized goals, and contextually and personally adapted interventions. The collaborative team of researchers works with weight-loss interventionists at one of nation's largest health organization's facility in Hawaii. The team includes expertise in mobile sensing, artificial intelligence, computational cognition, social psychology, human computer interaction, computer tutoring, and measurement theory.
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PIPP Phase I: Computational Theory of the Co-evolution of Pandemics, (Mis)information, and Human Mindsets and Behavior
RAPID: Improving Computational Epidemiology with Higher Fidelity Models of Human Behavior
SCH: INT: Collaborative Research: FITTLE+: Theory and Models for Smartphone Ecological Momentary Intervention
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