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

使用机器学习优化用户参与度和对数字心理健康干预措施的临床反应

• 批准号: 10442069
• 负责人: Todd J. Farchione
• 金额: $ 64.71万
• 依托单位: BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-10 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10442069
• 关键词: Address; Algorithms; Anxiety; Area; Behavioral; Characteristics; Clinical; Cognitive; Cognitive Therapy; Development; Disease remission; Effectiveness; Emotional disorder; Evidence based intervention; Health; Healthcare Systems; Individual; Individual Differences; Internet; Intervention; Judgment; Lead; Machine Learning; Mental Depression; Mental Health Services; Mental disorders; National Institute of Mental Health; Neurotic Disorders; Outcome; Patients; Pattern; Personal Satisfaction; Precision therapeutics; Primary Care Physician; Protocols documentation; Psychological Impact; Randomized; Randomized Clinical Trials; Recommendation; Research; Sample Size; Supervision; Symptoms; System; Testing; Therapeutic; Thinking; Time; Work; algorithm development; algorithmic methodologies; base; clinical decision-making; clinical trial participant; common treatment; cost; cost effective; design; digital; digital delivery; digital healthcare; digital intervention; digital mental health; evidence base; health care delivery; improved; ineffective therapies; machine learning algorithm; optimal treatments; personalized medicine; programs; relative effectiveness; resilience; response; theories; treatment response

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.

项目概要/摘要 数字干预提供了一种高度可扩展且相对具有成本效益和时间效益的方法来交付 然而，有效性的证据来自于群体平均值， 忽视了这样一个事实，即对一名患者有效的治疗可能对其他患者效果较差，甚至有害。 此外，缺乏对个人与其干预措施进行最佳匹配的指导。 主要基于临床判断或“反复试验”，这导致许多患者接受了无效的治疗 治疗或需要多个疗程才能获得缓解 机器学习 (ML)。 算法通过生成经验得出的结果，提供了传统临床决策的替代方案 用于选择最佳治疗的精确治疗规则（PTR） 迄今为止，有关开发的研究。 PTR 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使用 ML 开发和测试 PTR，以获得三个证据： 基于现有数字医疗保健系统 SilverCloud Health (SC) 的数字心理健康干预措施。 第二个目标是更好地理解用户参与作为治疗反应的机制。 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、抑郁空间和恢复空间目标 1 将评估总体情况。 目标 2 将使用 ML 来开发治疗方法的效果和参与模式。 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 目标 3 将确定用户参与度以及其他常见和特定因素。 （例如，工作联盟、消极思维）是治疗反应的机制。本研究的结果将是。 提供关于三种主要数字干预措施相对有效性的明确答案，确定 开发具有不同作用机制的 CBT 干预 PTR 的价值，以及进一步 对常见和特定治疗变化机制的理解。