Toward optimizing digital mental health interventions: A clinical trial aimed at understanding what drives patient engagement.

优化数字心理健康干预措施：一项旨在了解推动患者参与的因素的临床试验。

• 批准号: 9977310
• 负责人: Jessica Morrow Lipschitz
• 金额: $ 18.13万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-09 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9977310
• 关键词: Adoption; Anxiety; Area; Attitude; Automobile Driving; Cellular Phone; Characteristics; Clinic Visits; Clinical; Clinical Data; Clinical Trials; Clinical Trials Design; Clinical effectiveness; Cognitive; Collaborations; Computers; Cost of Illness; Costs and Benefits; Cues; Data; Data Science; Disease; Distress; Dose; Effectiveness; Evaluation; Feedback; Foundations; Future; Goals; Grant; Habits; Human; Internet; Intervention; Intervention Studies; Interview; Investigational Therapies; Light; Literature; Logistics; Measures; Mental Depression; Mental Health; Methods; Modeling; Motivation; Notification; Outcome; Outcome Measure; Participant; Patient Care; Patient Self-Report; Patients; Population; Primary Health Care; Protocols documentation; Provider; Psychotherapy; Public Health; Randomized; Research; Research Personnel; Scientist; Self Efficacy; Series; System; Technology; Testing; Time; Touch sensation; Training; Transportation; arm; base; behavior change; behavioral construct; clinical implementation; comorbidity; cost; design; digital; digital models; effective therapy; evidence base; experience; field study; improved; innovation; meetings; mobile application; novel; patient engagement; programs; prototype; psychosocial; recruit; routine care; social; social stigma; theories; treatment adherence; trial design; user centered design

PROJECT SUMMARY Depression and anxiety are highly comorbid and costly diseases. Evidence-based psychotherapy is the first-line treatment but is underutilized and not scalable. Digital mental health interventions (DMHIs), delivered via the internet and/or mobile apps, have evolved as efficacious and potentially scalable treatments. To date, however, effectiveness in routine care is limited by insufficient patient engagement. In order to achieve the transformative potential of DMHIs, we must identify strategies to keep patients engaged without adding human support in a form that would limit scalability. Automated motivational push messaging (AMM) and light-touch human coach support (CS) offer two such strategies. The proposed research tests these strategies, while drawing preliminary conclusions about a hypothesized model of DMHI engagement based on the technology adoption and treatment adherence literature. The model posits that two systems-level constructs (social influence and facilitating conditions) and three patient-level constructs (attitude, self-efficacy, habit strength) drive DMHI engagement. In Study 1 (N=20), I will employ user-centered design to develop and refine a set AMMs targeting the three hypothesized patient-level engagement-driving constructs (Aim 1). In Study 2, I will recruit N=76 primary care patients with depression and/or anxiety via provider referral to an 8-week 2x2 factorial clinical trial whereby participants will all receive access to a DMHI with known efficacy and be randomized to an engagement strategy condition (i.e., a previously-validated CS protocol, newly-developed AMM, both or neither). To further understand how AMMs function, message delivery in the AMM arms will be micro-randomized: each day participants will be randomized to receive a message or not, such that they receive an average of 4.2 messages/week. Micro- randomization allows causal inference about the near-term impact of message delivery (i.e., are AMMs a cue to action) and the relationship between message impact and context (e.g., time of day the message is delivered). Measured outcome data will comprise level of engagement (operationalized as minutes of DMHI use), weekly self-reports on the five engagement-driving constructs, and weekly self-reports of clinical outcomes. I will test im pacts of each strategy on m easured outcom e data (Aim 2) and explore the hypothesized relationships between engagement-driving constructs and DMHI engagement (Aim 3). Clinical outcomes will be assessed, however, consistent with the experimental therapeutics model, this research leverages a DMHI with known efficacy, allowing the focus to be an upstream target (patient engagement) rather than the clinical outcomes themselves. The overarching goal is to influence the target so as to ultimately enhance clinical effectiveness. This project will build my expertise in clinical trial design and build my proficiency in user-centered design (i.e., rapid, prototype testing via field studies) and data science (i.e., analysis of intensive, correlated longitudinal data) methods commonly applied in DMHI optimization research. Findings will lay a foundation for R01s aimed at optimizing DMHIs for engagement, and ultimately effectiveness, when integrated into routine care.

项目概要 抑郁症和焦虑症是高度共存且代价高昂的疾病。循证心理治疗是一线治疗 治疗，但未得到充分利用且不可扩展。数字心理健康干预措施 (DMHI)，通过 互联网和/或移动应用程序已发展成为有效且具有潜在可扩展性的治疗方法。然而，迄今为止， 常规护理的有效性因患者参与不足而受到限制。为了实现变革 DMHI 的潜力，我们必须确定策略来保持患者的参与，而不需要增加人力支持 形式会限制可扩展性。自动激励推送消息 (AMM) 和轻触式人工教练 支持（CS）提供了两种这样的策略。拟议的研究测试了这些策略，同时得出了初步的结论 基于技术采用和治疗的 DMHI 参与假设模型的结论 坚持文学。该模型假设两个系统级构造（社会影响力和促进 条件）和三个患者层面的构建（态度、自我效能、习惯强度）推动 DMHI 参与。在 研究1（N=20），我将采用以用户为中心的设计来开发和完善一套针对这三个目标的AMM 假设的患者层面的参与驱动结构（目标 1）。在研究 2 中，我将招募 N=76 初级保健人员 通过提供者转介患有抑郁症和/或焦虑症的患者参加为期 8 周的 2x2 析因临床试验，其中 参与者都将获得具有已知功效的 DMHI 的访问权限，并被随机分配到参与策略 条件（即先前验证的 CS 协议、新开发的 AMM、两者或两者都不是）。为了进一步了解 AMM 如何运作，AMM 手臂中的消息传递将是微随机的：每天参与者都会 随机分配是否接收消息，以便他们平均每周收到 4.2 条消息。微- 随机化允许对消息传递的近期影响进行因果推断（即 AMM 是否是一个提示） 动作）以及消息影响和上下文之间的关系（例如消息传递的时间）。 测量的结果数据将包括每周的参与水平（以 DMHI 使用分钟数的形式进行操作） 关于五种参与驱动结构的自我报告，以及临床结果的每周自我报告。我会测试 每种策略对测量结果数据的影响（目标 2）并探索之间的假设关系 参与驱动结构和 DMHI 参与（目标 3）。然而，将评估临床结果， 与实验治疗模型一致，本研究利用具有已知功效的 DMHI， 允许重点关注上游目标（患者参与），而不是临床结果本身。 总体目标是影响目标，从而最终提高临床效果。该项目将 培养我在临床试验设计方面的专业知识，并培养我在以用户为中心的设计（即快速、原型设计）方面的熟练程度 通过实地研究进行测试）和数据科学（即密集、相关的纵向数据分析）方法 常用于 DMHI 优化研究。研究结果将为旨在优化的 R01 奠定基础 当 DMHI 融入日常护理时，可提高参与度并最终提高有效性。