Using Machine Learning to Optimize User Engagement and Clinical Response to Digital Mental Health Interventions

项目总结/摘要 数字干预提供了一种高度可扩展且相对具有成本和时间效益的方法， 获得心理健康服务。然而，有效性的证据来自规则组的平均值， 忽视了这样一个事实，即对一个病人有效的治疗可能对另一个病人不那么有效，甚至有害。 另此外，缺乏关于将个人与其最佳干预措施相匹配的指导。这些决定是 主要基于临床判断或“试错法”，这导致许多患者接受无效的 治疗或在达到缓解前需要多个疗程。机器学习（ML） 算法通过生成经验推导的 精确治疗规则（PTR），用于选择最佳治疗。到目前为止，关于发展的研究 PTR受到主要设计和统计问题的阻碍，包括样本量限制和缺乏 随机分配 拟议研究的主要目标是使用ML开发和测试PTR，以获得三个证据- 在现有的数字医疗保健系统SilverCloud Health（SC）中， 次要目的是更好地理解用户参与作为治疗反应机制。在 与Kaiser Permanente（KP）的初级保健医生合作，我们将进行大型（N = 1，800） 随机临床试验，参与者将被随机分配到三种数字干预措施之一， SC的套件：统一协议，抑郁空间和弹性空间。目标1将评估总体 三种数字干预措施的效果和参与模式。目标2将使用ML开发治疗方法- 匹配算法，并确定这些精确治疗规则导致临床改善的程度 成果和参与。目标3将确定用户参与度和其他常见和特定因素 (e.g.,工作联盟，消极思维）是治疗反应的机制。这项研究的结果将 提供关于三种主要数字干预措施的相对有效性的明确答案，确定 为具有不同据称作用机制的CBT干预措施制定PTR的价值，以及进一步 对常见和治疗特异性变化机制的理解。

PROJECT SUMMARY/ABSTRACT Digital interventions offer a highly scalable and relatively cost- and time-efficient approach to the delivery of accessible mental health services. However, evidence for efficacy comes from nomothetic group averages, overlooking the fact that a treatment that is effective for one patient may be less effective or even harmful for another. Further, guidance on matching individuals to their optimal intervention is lacking. These decisions are primarily based on clinical judgment or “trial and error,” which results in many patients receiving ineffective treatment or requiring multiple courses of treatment before achieving remission. Machine learning (ML) algorithms offer an alternative to conventional clinical decision-making by generating empirically derived precision treatment rules (PTRs) for selecting an optimal treatment. To date, research on the development of PTRs has been hindered by major design and statistical issues, including sample size limitations and lack of random assignment. The primary objective of the proposed study is to develop and test PTRs, using ML, for three evidence- based digital mental health interventions, within an existing digital healthcare system, SilverCloud Health (SC). A secondary objective is to better understand user-engagement as a mechanism of treatment response. In partnership with primary care physicians at Kaiser Permanente (KP), we will conduct a large (N = 1,800) randomized clinical trial where participants will be randomly assigned to one of three digital interventions in SC’s suite: Unified Protocol, Space from Depression, and Space for Resilience. Aim 1 will evaluate the overall effects and engagement patterns of the three digital interventions. Aim 2 will use ML to develop treatment- matching algorithms and determine the extent these precision treatment rules lead to improvements in clinical outcomes and engagement. Aim 3 will determine if user engagement and other common and specific factors (e.g., working alliance, negative thinking) are mechanisms of treatment response. The results of this study will provide a definitive answer regarding the relative effectiveness of three leading digital interventions, determine the value of developing PTRs for CBT interventions with different purported mechanisms of action, and further the understanding of common and treatment-specific mechanisms of change.