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: 10380604
• 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/10380604
• 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; Social Facilitation; System; Technology; Testing; Time; Touch sensation; Training; Transportation; arm; base; behavior change; behavioral construct; clinical implementation; comorbidity; cost; design; digital mental health; digital models; effective therapy; evidence base; experience; field study; implementation intervention; improved; innovation; meetings; mobile application; novel; patient engagement; programs; prototype; psychosocial; recruit; routine care; social influence; 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.

项目总结 抑郁症和焦虑症是高度并存且代价高昂的疾病。循证心理治疗是一线 治疗，但未得到充分利用，且不可扩展。数字心理健康干预(DMHIS)，通过 互联网和/或移动应用程序已经演变为有效的和潜在的可扩展的治疗方法。然而，到目前为止， 常规护理的有效性受到患者参与度不足的限制。为了实现变革性 对于DMHIS的潜力，我们必须确定在不增加人力支持的情况下保持患者参与的策略 将限制可伸缩性的表单。自动激励推送消息(AMM)和轻松的人力培训 支持(CS)提供两种这样的策略。拟议的研究对这些策略进行了测试，同时得出了初步的结论 关于基于技术采用和治疗的DMHI参与假设模型的结论 遵守文献。该模型假设了两个系统层面的结构(社会影响力和促进 条件)和三个患者层面的结构(态度、自我效能、习惯强度)驱动DMHI参与。在……里面 研究一(N=20)，我将采用以用户为中心的设计来开发和提炼一套针对这三个目标的AMMS 假想的患者层面的参与度驱动结构(目标1)。在研究2中，我将招募N=76名初级保健人员 抑郁和/或焦虑的患者通过提供者转介到为期8周的2x2因素临床试验， 所有参与者都将获得已知有效性的DMHI，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，两者都有或都没有)。为了进一步了解 AMM如何运作，AMM臂中的消息传递将是微随机的：每天的参与者将 随机地接收或不接收消息，使得他们平均每周接收4.2条消息。微型机 随机化允许对消息传递的近期影响进行因果推断(即，AMMS是否暗示 动作)以及消息影响和上下文之间的关系(例如，消息被递送的时间)。 衡量的结果数据将包括每周的参与度(以DMHI使用分钟数计算) 关于五个参与度驱动结构的自我报告，以及每周临床结果的自我报告。我要测试一下 每种策略对测量的结果数据(目标2)的影响，并探索假设的关系 参与度驱动结构和DMHI参与度(目标3)。然而，临床结果将得到评估， 与实验治疗学模型一致，这项研究利用了已知疗效的DMHI， 使重点成为上游目标(患者参与)，而不是临床结果本身。 首要目标是影响目标，从而最终提高临床疗效。这个项目将 积累我在临床试验设计方面的专业知识，并熟练掌握以用户为中心的设计(即快速原型设计 通过实地研究进行测试)和数据科学(即对密集、相关的纵向数据进行分析)方法 广泛应用于DMHI优化研究。这些发现将为旨在优化R01的基础 DMHIS用于参与，并最终在整合到常规护理中时有效。