Enhancing Engagement with Digital Mental Health Care

加强数字心理保健的参与

• 批准号: 10321669
• 负责人: Tim ALTHOFF
• 金额: $ 78.05万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 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/10321669
• 关键词: 10 year old; Address; Advocacy; Age; Americas; Behavior; Caring; Categories; Characteristics; Client; Clinical; Code; 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; Measurement; Measures; Mediating; Mental Health; Mental Health Services; Methods; Modeling; Motivation; Movement; Natural Language Processing; Outcome; Participant; Patient Recruitments; Patient Self-Report; Pattern; Periodicity; Personal Satisfaction; Population; Process; Provider; Psychotherapy; Randomized; Randomized Clinical Trials; Recovery; Research; Resources; Risk; Sampling; Self Efficacy; Sequential Multiple Assignment Randomized Trial; Series; Services; Severities; Statistical Data Interpretation; Surveys; Symptoms; Techniques; Technology; Testing; Text; Time; Universities; Visit; Volition; Washington; Well in self; Work; base; behavior change; 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; randomized trial; response; satisfaction; screening; self help; skills; success; supervised learning; theories; tool; trial design; user centered design; web page

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.

加强与数字精神卫生保健的接触 摘要 数字心理健康(DMH)是使用技术通过快速疾病来改善人们的幸福感 检测、结果测量和护理1.尽管一些随机临床试验已经证明 数字心理健康工具是非常有效的2-6，大多数消费者不支持他们使用这些工具7-9。这个 外地目前缺乏对DMH工具参与度、参与度如何与幸福感相关联的了解， 以及哪些做法在维持参与度方面是有效的。在精神健康美国之间的这一伙伴关系中 (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 以理论为导向的研究计划10，11，目的是了解1)消费者如何参与自助 和临床医生辅助的DMH不同，以及存在什么参与模式，2)模式之间的关联 参与度和重要的消费者结果，以及3)个性化策略的有效性，以实现最佳 参与DMH治疗。这项研究将前瞻性地跟踪MHA和MHA的大量自然主义样本 TS消费者，并将应用机器学习、以用户为中心的设计策略和微随机化和 针对这些目标的顺序多重分配随机(SMART)试验。这是双方的惯常做法 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 根据疾病状态和症状严重程度对参与者进行分类。我们将处理的样本不会是 受诊断或并存疾病的限制。参赛者年龄在10岁及以上，并进入MHA和TS 平台预期超过4年。为了测试第一个目标，我们将确定至少100,000个 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、症状和平台显示不同群体在参与度和退学方面的差异 活动。对于目标2，我们将使用有监督的机器学习技术来识别基于消费者的子类型 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 参与度战略和对DMH工具的满意度，可预测未来的参与度模式。 最后，基于目标2的结果，在目标3中，我们将应用以用户为中心的设计来进行焦点小组 确定并共同构建针对特定客户子类型的潜在有效参与策略的策略。我们 然后将进行一系列微随机和智能试验，以确定哪种理论驱动的参与 与用户共同设计的策略与在目标2下开发的子类型最匹配。我们将测试 这些策略的有效性：1)防止脱离那些更有可能患有贫困的人 脱离接触后的结果，2)改善从动机到意志的运动，3)提高最佳剂量 提高DMH的参与度，从而改善心理健康状况。将使用以下工具分析这些数据 使用效果编码的纵向混合效果模型来评估每种策略对客户的有效性 参与行为和心理健康结果。