CHS: Small: Collaborative Research: Tools for Mental Health Reflection: Integrating Social Media with Human-Centered Machine Learning

可穿戴设备和社交媒体的广泛采用正在生成关于人们日常生活中行为的人口规模数据。 先前的研究已经表明，机器学习技术可以如何使用这些数据来建模个人的健康和福祉属性;这些技术也是支持临床环境之外的自我监测实践和健康干预的工具的有前途的补充。 然而，大多数这类工具只能使简单的机制来审查一个人的数据，并要求个人积极自愿提供相关信息的高度遵守。这些局限性使这些工具无法有效地支持反思，即有意识地重新审视先前的经验以形成新的理解。反思是改善健康和维护健康的关键，最近的工作表明数据驱动的健康反思有希望。然而，有效地支持反思需要比简单地向患者展示他们的数据更复杂的技术。该项目将开发工具，通过将自愿共享和不引人注目地收集的社交媒体数据与机器学习分析的战略展示相结合，支持对饮食失调（艾德）的反思。界面设计将满足多个利益相关者的需求：患者，家庭成员和临床合作伙伴。 通过这样做，该研究将产生新的机制来支持艾德的治疗，超越现有的个人健康信息学工具，对艾德患者复杂的心理斗争敏感。拟议的研究将遵循多阶段过程，交错使用机器学习和以人为本的方法。第一阶段将寻求了解参与艾德反思的三个利益相关者（患者、临床医生和支持网络成员）的当前实践。在第二阶段，根据这些当前的实践，我们将开发理论驱动的，心理测量和临床验证的机器学习技术，以支持基于文本和视觉社交媒体数据分析的艾德推理和反思。第三阶段将使用参与式设计方法来开发包含这些机器学习技术的交互式工具，以支持三个利益相关者之间的艾德反思。最后阶段将包括通过实地部署来评估这些工具，以了解它们如何嵌入并影响当前的艾德反思实践。 这些活动将在机器学习和反思设计领域做出互补性贡献，为围绕心理健康的突出挑战提供新方法，并促进计算和临床研究人员之间的新合作。该研究的更广泛意义将包括在研究人员各自的校园开展心理健康外展活动，并促进下一代网络人类研究人员和专业人员的培训。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The widespread adoption of wearable devices and social media is generating population-scale data about people's behavior as situated in their everyday lives. Prior research has shown how machine learning techniques can use such data in modeling attributes of individuals' health and wellbeing; these techniques are also promising additions to tools that support self monitoring practices and health interventions outside of clinical contexts. However, most such tools enable only simple mechanisms to review one's data, and require high compliance from individuals actively volunteering relevant information. These limitations prevent such tools from effectively supporting reflection, that is, conscious re-examination of prior experiences to form new understanding. Reflection is a key to improved health and health maintenance, and recent work has shown the promise of data-driven health reflection. Effectively supporting reflection, however, requires more sophistication than simply showing a patient their data. This project will develop tools to support reflection for eating disorders (ED) by combining voluntarily shared and unobtrusively gathered social media data with strategic presentation of machine learning analyses. The interface designs will meet the needs of multiple stakeholders: patients, family members, and clinical partners. By doing so, the research will result in novel mechanisms to support the treatment of ED, going beyond existing personal health informatics tools by being sensitive to the complex psychological struggles of ED patients.The proposed research will follow a multi-phase process, interleaving the use of machine learning and human-centered approaches. The first phase will seek to understand the current practices of three stakeholders, patients, clinicians, and support network members, involved in ED reflection. In the second phase, informed by those current practices, we will develop theoretically-motivated, and psychometrically and clinically validated, machine learning techniques to support ED inference and reflection based on analysis of both textual and visual social media data. The third phase will use participatory design methods to develop interactive tools that encapsulate these machine learning techniques in order to support ED reflection among the three stakeholders. This final phase will include evaluating these tools through a field deployment to understand how they become embedded in and affect current practices of ED reflection. These activities will lead to complementary contributions in the areas of machine learning and of designing for reflection, offering novel approaches to outstanding challenges surrounding mental health and facilitating novel collaborations between computational and clinical researchers. Broader implications of the research will include conducting mental health outreach activities in the researchers' respective campuses and facilitating the training of the next generation of cyber-human researchers and professionals.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.