Adaptive Messaging to Support Depression Self-Management

摘要 自动化消息传递具有极大的潜力，可以扩展对管理人员的指导和支持的访问 常见的心理健康问题，如抑郁症。然而，自动消息传递的好处可能是 通过建设自动调整报文传送方法以满足用户需求的能力而得到改进。少年派的 职业目标是建立可扩展的数字心理健康干预，提供个性化交流 最大限度地提高用户在有效管理心理健康问题方面的参与度。这份提案概述了培训 研究计划实现这一目标，最终成功地提出了R01提案。培训计划已经满了。 西北大学范伯格商学院强大的制度支持和环境优势 医学和行为干预技术中心，由这项研究的主要导师领导 求婚。PI的职业目标所必需的培训目标包括(1)以用户为中心的设计，(2) 适应性干预；(3)临床科学方法论。这项建议建立在私人投资公司在#年的背景 传播学，其重点是有效的信息设计和提供社会支持。在这 K01，PI将致力于开发一种模式，以提供自动化、个性化的自我管理支持 抑郁，强调消息传递可以适应用户动机和能力的波动 实施认知和行为自我管理策略。这将通过以下方式实现 具体目标：(1)开展以用户为中心的设计活动和可用性测试，以设计和完善 消息传递系统；(2)将强化学习算法集成到系统中，使内容适应 用户及其背景；以及(3)随机对照试验的试点程序和分析，并获得 干预的有效性和行动机制的初步证据。对于目标3，我将测试 消息传递干预将比等待名单控制更大程度地减少抑郁的假设 组，并且自适应消息传递将通过产生 更多地参与自我管理。这项研究有望推动消息传递的设计 对抑郁症的干预，最终扩大了这些干预措施的覆盖范围和有效性。技能 通过执行本申请中建议的研究而获得的信息将使PI能够成功竞争 为R01提供资金，以便进行更大规模的有效性试验。

Summary Automated messaging has significant potential to extend access to guidance and support for those managing common mental health conditions like depression. However, benefits of automated messaging could be improved through building capacity to automatically adapt messaging approaches to users’ needs. The PI’s career goal is to establish scalable digital mental health interventions that deliver individualized communication to maximize users’ engagement in effectively managing mental health concerns. This proposal outlines training and research plans to achieve this goal, culminating in a successful R01 proposal. The training plan takes full advantage of the strong institutional support and environment at Northwestern University's Feinberg School of Medicine and the Center for Behavioral Intervention Technologies, which is led by the primary mentor for this proposal. Training goals necessary to the PI’s career goal include growth in (1) user-centered design, (2) adaptive interventions, and (3) clinical science methodologies. This proposal builds on the PI’s background in communication science, which has focused on effective message design and social support provision. In this K01, the PI will aim to develop a model for delivering automated, individualized self-management support for depression, emphasizing messaging that can accommodate fluctuations in users’ motivations and abilities to carry out cognitive and behavioral self-management strategies. This will be achieved through the following specific aims: (1) conduct user-centered design activities and usability testing to design and refine a messaging system; (2) integrate a reinforcement learning algorithm into the system that adapts content to users and their contexts; and (3) pilot procedures and analyses for a randomized controlled trial and obtain preliminary evidence of the intervention’s effectiveness and mechanisms of action. For Aim 3, I will test hypotheses that messaging interventions will result in greater reduction in depression than a waitlist control group, and that adaptive messaging will reduce depression relative to non-adaptive messaging by producing greater engagement in self-management. This research is expected to advance the design of messaging interventions for depression, ultimately extending the reach and effectiveness of these interventions. Skills acquired by carrying out the research proposed in this application will allow the PI to successfully compete for R01 funding in order to conduct a larger effectiveness trial.