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.

项目摘要 抑郁和焦虑是高度合并和昂贵的​​疾病。循证心理治疗是一线 治疗但不足，不可扩展。数字心理健康干预措施（DMHIS），通过 互联网和/或移动应用程序已发展为有效且可能可扩展的处理。但是，迄今为止 常规护理中的有效性受患者参与不足的限制。为了实现变革 DMHI的潜力，我们必须确定策略，以保持患者参与，而不会在 形式将限制可伸缩性。自动动机推动消息（AMM）和轻触式人工教练 支持（CS）提供两种此类策略。提出的研究测试这些策略，同时绘制初步 关于基于技术采用和治疗的假设DMHI参与模型的结论 依从性文学。该模型认为两个系统级结构（社会影响力和促进） 条件）和三个患者级别的结构（态度，自我效能，习惯力量）推动了DMHI的参与度。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针对这三个的AMM 假设的患者级参与驾驶结构（AIM 1）。在研究2中，我将招募n = 76初级保健 抑郁症和/或焦虑的患者通过提供者转介到为期8周的2x2阶乘临床试验中 参与者都将获得具有已知功效的DMHI访问权限，并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，无论是还是不）。进一步理解 AMM的功能，AMM武器中的消息传递将被微随机化：每天参与者都会 随机接收是否接收消息，以使他们平均每周接收4.2消息。微- 随机化允许有关消息传递的近期影响的因果推断（即，是AMMS的提示 动作）与消息影响与上下文之间的关系（例如，消息传递的时间）。 测得的结果数据将包括参与水平（以DMHI使用的分钟为单位），每周 关于五个参与式驾驶结构以及临床结果的每周自我报告的自我报告。我会测试 IM在MEAS的结果数据（AIM 2）上的每种策略协议，并探索了假设的关系 参与式驾驶结构和DMHI参与度（AIM 3）。但是，将评估临床结果 与实验治疗模型一致，该研究利用了具有已知功效的DMHI， 允许重点是上游目标（患者参与），而不是临床结果本身。 总体目标是影响目标，以最终提高临床有效性。这个项目将 建立我在临床试验设计方面的专业知识，并建立我以用户为中心设计的熟练程度（即快速，原型 通过现场研究进行测试）和数据科学（即对密集，相关纵向数据的分析）方法 通常应用于DMHI优化研究。调查结果将为旨在优化的R01奠定基础 DMHI纳入常规护理时，订婚，最终有效。