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.

加强与数字心理保健的参与抽象的数字心理健康（DMH）是使用技术通过快速疾病来改善人口福祉检测，结果测量和护理1。数字心理健康工具是高效的2-6，大多数消费者都无法使用这些工具7-9。这目前，Field缺乏对DMH工具参与度的了解，与幸福感如何相关，哪些实践有效地维持参与。在美国心理健康之间的伙伴关系中（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，理论驱动的计划10,11研究，目的是了解1）消费者如何参与自助和临床医生协助DMH的不同以及存在哪些参与模式，2）参与和重要的消费者成果，以及3）个性化策略的最佳策略有效性参与DMH治疗。这项研究将前瞻性地遵循大量自然主义的MHA和 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型运输和顺序多个分配随机（智能）试验以解决这些目标。与两者的平常实践一样平台，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，进入MHA和TS 平台前景超过4年。为了测试第一个目标，我们将确定至少100,000 过去访问MHA和TS平台的消费者。参与者数据将在统计上进行分析根据人口统计学，症状和平台揭示各组的参与和辍学差异活动。对于AIM 2，我们将使用监督的机器学习技术来识别基于消费者的亚型人口统计学，与DMH的参与模式，脱离接触的原因，现有MHA和TS的成功参与策略以及对DMH工具的满意度，这些工具可预测未来的参与模式。最后，根据AIM 2的结果，在AIM 3中，我们将进行以用户为中心设计的焦点小组为特定客户子类型确定和共同确定和共同建立潜在有效参与策略的策略。我们然后将进行一系列微型和智能试验，以确定哪种理论驱动的参与与用户共同设计的策略与AIM 2开发的子类型最合适。我们将测试这些策略的有效性为1）防止与更可能贫穷的人脱离接触脱离接触后的结果，2）改善从动机到意志的运动，3）增强最佳剂量 DMH参与度并因此改善了心理健康结果。这些数据将使用纵向混合效应模型具有效果编码，以估计每种策略对客户的有效性参与行为和心理健康成果。