Adaptive Messaging to Support Depression Self-Management

支持抑郁症自我管理的自适应消息传递

• 批准号: 10301596
• 负责人: Rachel Kornfield
• 金额: $ 17.54万
• 依托单位: NORTHWESTERN UNIVERSITY AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10301596
• 关键词: Address; Adult; Affect; Algorithms; Anxiety; Behavior; Behavior Therapy; Behavioral; Cellular Phone; Clinical; Clinical Sciences; Cognitive; Communication; Control Groups; Data; Dimensions; Ecological momentary assessment; Educational workshop; Effectiveness; Effectiveness of Interventions; Environment; Feedback; Feeling suicidal; Funding; Goals; Growth; Health system; Individual; Intervention; Interview; Learning; Machine Learning; Mediation; Mental Depression; Mental Health; Mentors; Methodology; Modality; Modeling; Monitor; Morbidity - disease rate; Motivation; Outcome; Participant; Procedures; Program Development; Psychological reinforcement; Quality of life; Randomized; Randomized Controlled Trials; Recommendation; Regulation; Research; Science; Self Management; Severities; Social Interaction; Social support; Societies; System; Techniques; Technology; Testing; Text Messaging; Time; Training; Universities; Waiting Lists; adaptive intervention; base; behavior change; career; career development; cost; depressive symptoms; design; digital health; digital mental health; effectiveness trial; evidence base; improved; intervention refinement; learning algorithm; medical schools; meetings; mortality; preference; primary outcome; psychoeducation; randomized trial; recruit; response; secondary outcome; skills; success; tailored messaging; therapy design; tool; treatment effect; usability; user centered design

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.

总结 自动化的消息传递具有很大的潜力，可以为管理人员提供指导和支持 常见的心理健康状况，如抑郁症。然而，自动化消息传递的好处可能是 通过建设自动调整信息传递方法以满足用户需求的能力，使信息传递得到改善。PI的 职业目标是建立可扩展的数字心理健康干预措施，提供个性化的沟通 最大化用户参与有效管理心理健康问题。本提案概述了培训 和研究计划，以实现这一目标，最终在一个成功的R 01提案。培训计划全面展开 西北大学范伯格学院强大的机构支持和环境的优势 医学和行为干预技术中心，这是由主要导师领导的， 提议PI职业目标所需的培训目标包括：（1）以用户为中心的设计，（2） 适应性干预;（3）临床科学方法。该提案建立在PI的背景之上， 传播科学，侧重于有效的信息设计和社会支持的提供。在这 K 01，PI将致力于开发一种模型，为以下人员提供自动化、个性化的自我管理支持： 抑郁症，强调可以适应用户动机和能力波动的信息， 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， 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;（3）随机对照试验的试点程序和分析，并获得 干预措施有效性和作用机制的初步证据。对于目标3，我将测试 假设消息传递干预将导致比等待列表控制更大的抑郁症减少 自适应消息传递相对于非自适应消息传递将通过产生 加强自我管理。这项研究有望推动消息传递的设计 抑郁症的干预措施，最终扩大这些干预措施的范围和有效性。技能 通过开展本申请中提出的研究获得的信息将使PI能够成功竞争 R 01资金，以便进行更大的有效性试验。