MoodRing: A multi-stakeholder platform to monitor and manage adolescents' depression in primary care with passive mobile sensing.

MoodRing：一个多利益相关者平台，通过被动移动传感来监测和管理初级保健中的青少年抑郁症。

• 批准号: 9908603
• 负责人: Afsaneh Doryab
• 金额: $ 22.34万
• 依托单位: NURELM E-BUSINESS SOFTWARE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-24 至 2021-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9908603
• 关键词: Accelerometer; Acute; Address; Adolescent; Adult; Affect; Algorithms; Awareness; Behavior; Behavioral; Bile fluid; Bipolar Disorder; Businesses; Car Phone; Caring; Case Manager; Cellular Phone; Childhood; Clinic Visits; Clinical; Cluster randomized trial; Code; Communication; Cost Savings; Data; Data Collection; Data Reporting; Depression and Suicide; Development; Devices; Diagnostic; Ethnic Origin; Family; Feedback; Focus Groups; Foundations; Frequencies; Goals; Guidelines; Health; Health Care Costs; Health Personnel; Health Services Accessibility; Healthcare; Healthcare Systems; Home environment; Hospitalization; Human; Human Resources; Income; Individual; Information Resources Management; Internet; Intervention; Interview; Knowledge; Lead; Machine Learning; Major Depressive Disorder; Manic; Medical center; Mental Depression; Mental Health; Mental Health Services; Methods; Modeling; Monitor; Moods; Movement; National Institute of Mental Health; Outcome; Parents; Patient Self-Report; Patients; Pattern; Pharmaceutical Preparations; Phase; Population; Primary Health Care; Provider; Psyche structure; Questionnaires; Race; Randomized Controlled Trials; Recommendation; Reporting; Research Personnel; Resolution; Secure; Self Efficacy; Self Management; Services; Severities; Severity of illness; Sleep; Small Business Technology Transfer Research; Social support; Solid; Suicide; Symptoms; System; Technology; Telephone; Testing; Time; Translating; Travel; Triage; Update; Visit; Youth; adolescent health; aged; care coordination; care providers; child depression; clinical decision-making; college; computer science; cost; depressive symptoms; design; efficacy trial; experience; follow-up; frontier; health belief; health care service utilization; health service use; heart rate variability; improved; improved outcome; innovation; medication compliance; mobile application; mobile computing; pediatric patients; persistent symptom; phase 1 study; phase 2 study; physical conditioning; population based; primary care setting; routine screening; satisfaction; screening; sensor; skills; sleep quality; social media; suicide rate; system architecture; therapy adherence; treatment as usual; treatment response; trend; university student; usability

ABSTRACT As rates of adolescent depression and suicidality continue to trend upwards, the healthcare system struggles to address the need for and lack of mental health service use. The pediatric patient-centered medical home model may improve adolescent depression outcomes by enhancing access to and coordinating care, as well as providing ongoing monitoring. Unfortunately, despite guideline recommendations, over 2/3 of adolescents identified with depression symptoms in primary care do not receive symptom monitoring and 19% do not re- ceive symptom reassessment. This lack of symptom monitoring and reassessment can result in untoward health outcomes including a decrease in functioning, increased use of acute and crisis services, and hospitali- zations due to suicidality. Current technologies which incorporate data passively collected from smartphones offer an opportunity for intercurrent monitoring between patient visits which limits burden on the patient to self- report and limits burden on the healthcare system, allowing primary care teams to triage contacting and as- sessing patients a system identifies with an increase in disease severity. This formative study will demonstrate the usability and potential clinical utility of MoodRing, a technology intervention which will collect passive mo- bile phone sensor data on aspects of adolescent phone use related to depressive symptom severity (e.g. com- munication patterns, social media use, travel) and integrate this data into a multi-user (adolescent, parent, pri- mary care provider/care manager) platform from which symptoms can be viewed and secure communication can occur. MoodRing, as supported by Health Belief Model, may lead to improved quality of depression man- agement (increased symptom reassessment, therapy/medication adherence) through increasing self-efficacy, social support from parent and care team, as well as encouraging application of self-management skills through increased self-management knowledge, skills, and symptom feedback. MoodRing builds on a solid foundation of investigators experienced in design of technology interventions to increase adolescent initiation of depression treatment, who have already developed machine algorithms for passive sensing and a small business partner with vast experience in working with health researchers to develop multi-user web/mobile platforms. This STTR Phase I study seeks to accomplish two aims. The first is to apply a machine learning pipeline developed for college-aged youth to adolescents with depression and determine whether self-reported depressive symptoms can be reliably predicted from passive data with at least 85% accuracy. The second is the user design and system architecture of MoodRing. If milestones are achieved that models are successful at predicting depressive symptoms and the proposed MoodRing intervention is acceptable to adolescents, par- ents, and primary care providers/care managers, then we will pursue the STTR Phase II study. The aims of Phase II include the development and subsequent efficacy trial of MoodRing. Specifically, we will conduct a cluster randomized controlled trial in a primary care setting of MoodRing as compared to usual care.

摘要 随着青少年抑郁症和自杀率继续上升，医疗保健系统正在努力 解决精神卫生服务使用的需求和缺乏。以病人为中心的儿科医疗之家 该模型还可以通过增加获得和协调护理来改善青少年抑郁症的结果， 提供持续监控。不幸的是，尽管有指南的建议，超过2/3的青少年 在初级保健中确定有抑郁症状的人没有接受症状监测，19%的人没有重新接受监测。 接受症状再评估。这种缺乏症状监测和重新评估可能会导致不良的 健康结果，包括功能下降，急性和危机服务的使用增加，以及住院治疗， 因自杀而引起的。目前的技术，包括从智能手机被动收集的数据 为患者就诊之间的并发监测提供了机会，这限制了患者的自我负担， 报告并限制医疗保健系统的负担，允许初级保健团队对联系人进行分类，并作为- 系统对患者进行评估，识别疾病严重程度的增加。这项研究将证明 MoodRing的可用性和潜在临床效用，这是一种技术干预，将收集被动运动， 胆汁手机传感器数据，关于青少年手机使用与抑郁症状严重程度相关的方面（例如，com， 通信模式，社交媒体使用，旅行），并将这些数据集成到多用户（青少年，父母，小学生， 玛丽护理提供者/护理管理者）平台，从该平台可以查看症状并保护通信 可能发生。健康信念模型支持的MoodRing可能会改善抑郁症患者的质量- 通过提高自我效能感来改善（增加症状重新评估，治疗/药物依从性）， 家长和照顾小组社会支持，以及鼓励运用自我管理技能 通过增加自我管理知识、技能和症状反馈。MoodRing建立在一个坚实的 在设计技术干预措施以提高青少年启蒙方面经验丰富的调查人员的基础 他们已经开发出了被动感知的机器算法和一个小的 在与健康研究人员合作开发多用户Web/移动的方面拥有丰富经验的业务合作伙伴 平台本STTR第一阶段研究旨在实现两个目标。首先是应用机器学习 管道开发的大学适龄青年到青少年抑郁症，并确定是否自我报告 抑郁症状可以从被动数据中可靠地预测，准确率至少为85%。二是 MoodRing的用户设计和系统架构。如果达到了里程碑，则模型在 预测抑郁症状和建议的MoodRing干预是可以接受的青少年，部分- 如果我们有足够的时间和时间，以及初级保健提供者/护理管理人员，那么我们将继续进行STTR II期研究。的目的 第二阶段包括MoodRing的开发和随后的疗效试验。具体而言，我们将进行一项 在初级保健环境中进行的MoodRing与常规护理相比的随机对照试验。