SCH: INT: Collaborative Research: A Self-Adaptive Personalized Behavior Change System for Adolescent Preventive Healthcare

SCH：INT：合作研究：青少年预防保健的自适应个性化行为改变系统

• 批准号: 1344670
• 负责人: Elizabeth Ozer
• 金额: $ 104.32万
• 依托单位: University of California-San Francisco
• 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
• 起止时间: 2013-10-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1344670&HistoricalAwards=false
• 关键词: SCH; INT; Collaborative; Research; Self

The majority of morbidity and mortality during adolescence is preventable and related to behaviors such as substance use and vehicle-related injuries. Most adolescents visit a healthcare provider once a year, providing an ideal opportunity to integrate behavioral health screening into clinical care. Although the majority of adolescent health problems are amenable to behavioral intervention, few health information technology interventions have been integrated into adolescent care. With complementary theoretical advances (social-cognitive theories of behavior change) and technology advances (intelligent narrative-centered learning environments, user modeling, and machine learning), the field is now well positioned to design health behavior change systems that can realize significant impacts on behavior change for adolescent preventive health. Computationally-enabled models of behavior change hold significant promise for adolescent healthcare. The objective of the proposed research is to design, implement, and investigate INSPIRE, a self-adaptive personalized behavior change system for adolescent preventive health. INSPIRE will utilize a social-cognitive theory of behavior change built around a tight feedback loop in which a narrative-centered behavior change environment will produce improved behaviors in patients, and the resulting patient outcome data will be used by a reinforcement learning optimization system to learn refined computational behavior change models. With a focus on risky behaviors and an emphasis on substance use, adolescents will interact with INSPIRE to develop an experiential understanding of the dynamics and consequences of their substance use decisions. A unique feature of INSPIRE afforded by recent advances in machine learning will be its ability to optimize health behavior change at both the individual and population levels. At the individual level, INSPIRE will utilize a patient behavior model to personalize its behavior change narratives for individual adolescents. It will customize interactions based on an adolescent's goals and affective models. At the population level, INSPIRE will utilize reinforcement learning to adapt its narrative generation system to systematically increase its ability to improve two types of outcomes: behavior change and self-efficacy. The project will culminate with an experiment conducted with a fully implemented version of INSPIRE at outpatient clinics within the UC San Francisco Department of Pediatrics, Benioff Children's Hospital. It is anticipated that INSPIRE interventions will yield two types of outcomes: 1) improved health behavior through significant reductions in adolescent risky behavior, relative to standard of care; and 2) increased self-efficacy with respect to adolescents' ability to make good decisions about their health behaviors, relative to standard of care. Designed for natural integration into clinic workflow, interoperability with EHR and patient portal systems, and security and privacy requirements, INSPIRE will report patient behavior change summaries to healthcare providers. Through multi-platform deployments supporting laptop, desktop, tablet, and mobile computing devices, INSPIRE will serve as an empowering tool for adolescents, making them full participants in their own wellbeing. It will also enable researchers to run behavior analytics to investigate which properties of alternate interventions contribute most effectively to behavior change outcomes. Going forward, it is anticipated that INSPIRE will provide a testbed for a broad range of behavior change research and serve as the foundation for next-generation personalized preventive healthcare through computationally-enabled behavior change.

青春期的大多数发病率和死亡率是可以预防的，并且与诸如使用物质和与媒介物相关的伤害等行为有关。大多数青少年每年访问一次医疗保健提供者，为将行为健康筛查纳入临床护理提供了理想的机会。尽管大多数青少年健康问题都可以接受行为干预，但很少有健康信息技术干预措施已纳入青少年护理。随着互补的理论进步（行为改变的社会认知理论）和技术进步（智能叙事学习环境，用户建模和机器学习），该领域现在可以很好地设计健康行为变化系统，这些系统可以实现对青少年预防性健康行为改变的重大影响。具有计算能力的行为变化模型对青少年医疗保健具有巨大的希望。拟议的研究的目的是设计，实施和调查Inspire，这是一种自适应的个性化行为变化系统，可针对青少年预防健康。 Inspire将利用一种围绕紧密反馈循环构建的社会认知理论，以叙述性为中心的行为变化环境将在患者中产生改善的行为，并通过增强学习优化系统使用所得的患者结果数据来学习精致的计算行为变化模型。侧重于冒险行为并强调使用物质，青少年将与Inspire相互作用，以发展对其物质使用决策的动态和后果的体验式理解。机器学习最新进展所提供的INSPER的一个独特功能将是其在个人和人口水平上都优化健康行为变化的能力。在个人层面上，Inspire将利用患者行为模型来个性化其行为变化叙事，为个别青少年。它将根据青少年的目标和情感模型自定义互动。在人群层面，Inspire将利用强化学习调整其叙事生成系统，以系统地提高其改善两种结果的能力：行为改变和自我效能。该项目将通过一项实验实验，并在贝尼奥夫儿童医院（Benioff Children Hospital）的加州大学旧金山加州大学旧金山加州大学旧金山加州大学旧金山加州大学旧金山分校（UC San Francisco）分校内的门诊诊所进行了完全实施的Inspire。可以预计，激发干预措施将产生两种类型的结果：1）相对于护理标准，通过大量降低青少年风险行为改善了健康行为； 2）相对于护理标准，就青少年就其健康行为做出良好决定的能力提高了自我效能。 Inspire设计用于自然整合到临床工作流程，与EHR和患者门户网站系统以及安全性和隐私要求的互操作性以及安全性和隐私要求中，将向医疗保健提供者报告患者行为的更改摘要。通过支持笔记本电脑，台式机，平板电脑和移动计算设备的多平台部署，Inspire将成为青少年的授权工具，使他们成为自己的福祉。它还将使研究人员能够运行行为分析，以研究替代干预措施的哪些特性对行为改变结果最有效。展望未来，可以预料，Inspire将为广泛的行为改变研究提供测试台，并通过启用计算的行为改变来为下一代个性化的预防保健提供基础。