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

项目摘要/摘要 数字干预提供了一种高度可扩展且相对经济高效的方法来交付 无障碍心理健康服务。然而，有效性的证据来自于正常群体的平均水平， 忽视了这样一个事实，即对一个患者有效的治疗可能不那么有效，甚至对 又一个。此外，缺乏将个体与其最佳干预相匹配的指导。这些决定是 主要基于临床判断或“反复试错”，导致许多患者接受无效治疗 治疗或需要多个疗程才能缓解。机器学习(ML) 算法提供了一种替代传统临床决策的方法，通过生成经验式派生的 用于选择最佳治疗的精确治疗规则(PTR)。到目前为止，关于发展的研究 PTRS受到重大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开发和测试使用ML的PTRS，以获得以下三种证据： 基于数字心理健康干预，在现有的数字医疗系统SilverCloud Health(SC)内。 第二个目标是更好地理解用户参与是治疗反应的一种机制。在……里面 与Kaiser Permanente(KP)的初级保健医生合作，我们将进行一项大型(N=1800) 随机临床试验，参与者将被随机分配到三种数字干预中的一种 SC的套装：统一协议、不受压抑的空间和弹性空间。目标1将评估整体 三种数字干预的效果和参与模式。AIM 2将使用ML开发治疗- 匹配算法，并确定这些精确治疗规则在多大程度上导致临床改善 成果和参与度。目标3将确定用户参与度和其他共同和特定的因素 (例如，工作联盟、消极思维)是治疗反应的机制。这项研究的结果将 就三种领先的数字干预的相对有效性提供明确的答案，确定 为具有不同声称作用机制的CBT干预措施制定PTRS的价值，并进一步 对常见的和特定于治疗的改变机制的理解。

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.