Enhancing Engagement with Digital Mental Health Care

加强数字心理保健的参与

基本信息

批准号：
10543165
负责人：
Tim ALTHOFF
金额：
$ 76.29万
依托单位：
UNIVERSITY OF WASHINGTON
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-12-24 至 2024-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10543165
关键词：
10 year old Address Advocacy Age Behavior Caring Categories Characteristics Classification Client Clinical Code Communication Data Detection Diagnosis Disease Dose Dropout Effectiveness Engineering Enrollment Ethnic Origin Focus Groups Frequencies Future Gender Goals Health Health behavior change Hour Income Informatics Intake Investments Language Learning Length Longevity Machine Learning Maintenance Marketing Measures Mediating Mental Health Mental Health Services Methods Modeling Motivation Movement Natural Language Processing Outcome Outcome Measure Participant Patient Recruitments Patient Self-Report Pattern Personal Satisfaction Population Process Provider Psychotherapy Randomized Recovery Research Resources Risk Sampling Self Efficacy Sequential Multiple Assignment Randomized Trial Series Services Severities Surveys Symptoms Techniques Technology Testing Text Time Universities Visit Volition Washington Well in self Work base behavior change behavior prediction comorbidity computer science coping demographics design digital digital mental health effectiveness study effectiveness testing expectation falls health goals improved machine learning method medical schools outcome prediction personalized strategies predictive marker prevent programs prospective psychoeducation psychoeducational randomized trial randomized, clinical trials response satisfaction screening self help skills success supervised learning theories tool trial design user centered design web app web page

项目摘要

Enhancing Engagement with Digital Mental Health Care Abstract Digital mental health (DMH) is the use of technology to improve population well-being through rapid disease detection, outcome measurement, and care 1. Although several randomized clinical trials have demonstrated that digital mental health tools are highly effective 2-6, most consumers do not sustain their use of these tools 7-9. The field currently lacks an understanding of DMH tool engagement, how engagement is associated with well-being, and what practices are effective at sustaining engagement. In this partnership between Mental Health America (MHA), Talkspace (TS) and the University of Washington (UW), we propose a naturalistic and experimental, theory-driven program 10,11 of research, with the aim of understanding 1) how consumer engagement in self-help and clinician assisted DMH varies and what engagement patterns exist, 2) the association between patterns of engagement and important consumer outcomes, and 3) the effectiveness of personalized strategies for optimal engagement with DMH treatment. This study will prospectively follow a large, naturalistic sample of MHA and TS consumers, and will apply machine learning, user-centered design strategies, and micro-randomized and sequential multiple assignment randomized (SMART) trials to address these aims. As is usual practice for both platforms, consumers will complete online mental health screening and assessment, and we will be able to classify participants by disease status and symptom severity. The sample we will be working with will not be limited by diagnosis or co-morbidities. Participants will be 10 years old and older and enter the MHA and TS platforms prospectively over 4 years. In order to test the first aim, we will identify a minimum of 100,000 consumers who have accessed MHA and TS platforms in the past. Participant data will be analyzed statistically to reveal differences in engagement and dropout across groups based on demographics, symptoms and platform activity. For aim 2, we will use supervised machine learning techniques to identify subtypes based on consumer demographics, engagement patterns with DMH, reasons for disengagement, success of existing MHA and TS engagement strategies, and satisfaction with the DMH tools, that are predictive of future engagement patterns. Finally, based on the outcomes from aim 2, in aim 3 we will conduct focus groups applying user-centered design strategies to identify and co-build potentially effective engagement strategies for particular client subtypes. We will then conduct a series of micro-randomized and SMART trials to determine which theory-driven engagement strategies, co-designed with users, have the greatest fit with subtypes developed under aim 2. We will test the effectiveness of these strategies to 1) prevent disengagement from those who are more likely to have poor outcomes after disengagement, 2) improve movement from motivation to volition and, 3) enhance optimal dose of DMH engagement and consequently improve mental health outcomes. These data will be analyzed using longitudinal mixed effects models with effect coding to estimate the effectiveness of each strategy on client engagement behavior and mental health outcomes.

加强数字心理保健的参与抽象的数字心理健康 (DMH) 是利用技术通过快速疾病改善人群福祉检测、结果测量和护理 1. 尽管多项随机临床试验表明数字心理健康工具非常有效 2-6，大多数消费者不会持续使用这些工具 7-9。这该领域目前缺乏对 DMH 工具参与度、参与度如何与幸福感相关的理解，以及哪些做法可以有效维持参与。在美国心理健康协会之间的合作中 (MHA)、Talkspace (TS) 和华盛顿大学 (UW)，我们提出了一种自然主义和实验性的、理论驱动的研究计划 10,11，目的是了解 1) 消费者如何参与自助临床医生协助的 DMH 各不相同，存在哪些参与模式，2) 模式之间的关联参与度和重要的消费者成果，以及 3) 个性化策略的有效性参与 DMH 治疗。这项研究将前瞻性地跟踪 MHA 的大量自然样本 TS 消费者，并将应用机器学习、以用户为中心的设计策略以及微随机和序贯多重分配随机 (SMART) 试验旨在实现这些目标。按照双方的惯例平台上，消费者将完成在线心理健康筛查和评估，我们将能够根据疾病状况和症状严重程度对参与者进行分类。我们将使用的样本不会受诊断或合并症的限制。参与者将年满 10 岁并进入 MHA 和 TS 预计在 4 年内推出平台。为了测试第一个目标，我们将确定至少 100,000 过去访问过 MHA 和 TS 平台的消费者。将对参与者数据进行统计分析根据人口统计数据、症状和平台揭示不同群体的参与度和退出率差异活动。对于目标 2，我们将使用监督机器学习技术来根据消费者识别子类型人口统计数据、DMH 的参与模式、脱离的原因、现有 MHA 和 TS 的成功参与策略以及对 DMH 工具的满意度，可预测未来的参与模式。最后，根据目标 2 的结果，在目标 3 中，我们将开展应用以用户为中心的设计的焦点小组为特定客户子类型识别和共同制定潜在有效的参与策略的策略。我们然后将进行一系列微观随机和 SMART 试验，以确定哪些理论驱动的参与与用户共同设计的策略与目标 2 下开发的子类型最适合。我们将测试这些策略的有效性在于：1）防止脱离那些更有可能陷入贫困的人脱离后的结果，2) 改善从动机到意志的运动，3) 提高最佳剂量 DMH 参与度，从而改善心理健康结果。这些数据将使用具有效果编码的纵向混合效果模型，用于估计每种策略对客户的有效性参与行为和心理健康结果。