Passive Mobile Self-Tracking of Mental Health by Veterans with Serious Mental Illness

患有严重精神疾病的退伍军人的心理健康被动移动自我跟踪

• 批准号: 10249984
• 负责人: Alexander S. Young
• 金额: --
• 依托单位: VA GREATER LOS ANGELES HEALTHCARE SYSTEM
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10249984
• 关键词: Adherence; Age; Area; Awareness; Behavior; Behavior monitoring; Businesses; Categories; Cellular Phone; Clinical; Communication; Data; Data Analyses; Data Reporting; Data Security; Development; Diagnosis; Early Intervention; Educational process of instructing; Feasibility Studies; Feedback; Focus Groups; Goals; Habits; Health Status; Healthcare; Homelessness; Hospitalization; Imprisonment; Individual; Informatics; Institutional Review Boards; Intervention; Interview; Job loss; Light; Location; Measures; Mental Health; Mental Health Services; Methodology; Methods; Modeling; Monitor; Movement; Ownership; Patients; Persons; Pharmaceutical Preparations; Pilot Projects; Population; Predictive Value; Privacy; Relapse; Reporting; Research; Research Methodology; Research Personnel; Risk; Risk Behaviors; Safety; Security; Self Management; Signs and Symptoms; Sleep; Social Interaction; Stress; Substance abuse problem; Suicide; Symptoms; Telephone; Testing; Time; Uncertainty; Veterans; Visit; Work; acceptability and feasibility; analytical method; behavior change; behavior prediction; data exchange; data privacy; effectiveness study; effectiveness/implementation study; handheld mobile device; high dimensionality; improved; innovation; interest; medication compliance; member; military veteran; mobile application; mobile computing; mobile phone based intervention; mobile sensing; mobile sensor; novel strategies; perceived stress; pilot test; psychiatric symptom; psychotic-like experiences; rapid detection; relapse risk; sensor; service utilization; severe mental illness; smartphone application; sound; therapy development; tool; transmission process; treatment and outcome; treatment as usual; usability; user centered design

Background: Serious mental illnesses are common, disabling, challenging to treat, and require years of monitoring with adjustments in treatments. Stress or reduced medication adherence can lead to rapid worsening in symptoms and functioning with consequences that include relapse, job loss, homelessness, incarceration, hospitalization and suicide. In usual care, clinician visits are infrequent, with intervals ranging from monthly to yearly. Communication between patients and clinicians between visits is challenging and often nonexistent. Patient illness exacerbations and relapses generally occur with little or no clinician awareness in real time, leaving little opportunity to adjust treatments. Significance/Impact: For the large population of Veterans with serious mental illness, tools are needed that passively monitor their mental health status, allowing them to self-track their behaviors, quickly detect worsening of mental health, and support prompt assessment and intervention. At least 60% of Veterans with serious mental illness use a smart phone. These generate data that characterize sociability, activity, and sleep. Changes in these behaviors are warning signs of relapse. Passive self-tracking could be used to identify and predict worsening of illness in real time. Innovation: Passive mobile sensing is a novel approach to illness self-tracking and monitoring. There has been relatively little research on passive self-tracking in serious mental illness, with limited analytics development in this area, and none in VA. Specific Aims: This project studies passive mobile sensing with Veterans in treatment for serious mental illness. Data are used for self-tracking of behaviors and symptoms. While passive mobile sensing has been feasible, acceptable and safe in patients with serious mental illness, these are studied for the first time in VA. Analytics are developed that use passive data to predict behaviors and symptoms. This project responds to the HSR&D priority areas of Mental Health and Healthcare Informatics. The project has these objectives: 1. Conduct user-centered design of passive mobile self-tracking to support Veterans’ management of their mental health. 2. Study the feasibility, acceptability and safety of passive self-tracking of mental health that includes feedback of mental health status to the Veteran. 3. Use mobile sensor and phone utilization data to develop individualized estimates of sociability, activities, and sleep as measured by weekly interviews. 4. Study the predictive value of using data on sociability, activities, and sleep to identify exacerbations of psychiatric symptoms. Methodology: Activities can be assessed with data on movement, location, and habits. Sociability can be assessed with data on communication and public interactions. Sleep can be assessed using data on light, sound, movement, and phone use. Investigators on this project developed “mWellness,” a functional mobile app that monitors and transmits mobile sensor and utilization data. Focus groups and in-lab usability testing will inform further app and intervention development. Mixed methods research will study deployment in Veterans who passively self-track their behaviors and psychiatric symptoms. If this project meets intended goals, the VA will have a mobile analytics platform that continuously monitors the behaviors and symptoms of patients with serious mental illness. Next Steps/Implementation: Results inform development of a study of the effectiveness and implementation of these methods to improve Veteran assessment, treatment and outcomes. Results are also applicable to other populations with mental health risk and to the use of similar methods in usual care.

背景：严重的精神疾病很常见、致残、治疗具有挑战性，并且需要数年时间 监测并调整治疗。压力或药物依从性降低会导致快速 症状和功能恶化，后果包括复发、失业、无家可归、 监禁、住院和自杀。在日常护理中，临床医生就诊的频率不高，间隔时间不等 从每月到每年。就诊期间患者和临床医生之间的沟通通常具有挑战性 不存在的。患者病情恶化和复发通常在临床医生很少或没有意识到的情况下发生 实时，几乎没有机会调整治疗。 意义/影响：对于大量患有严重精神疾病的退伍军人来说，工具是 需要被动地监测他们的心理健康状况，使他们能够快速自我跟踪自己的行为 发现心理健康状况恶化，并支持及时评估和干预。至少 60% 的退伍军人 患有严重精神疾病的人使用智能手机。这些生成的数据表征社交性、活动性和 睡觉。这些行为的改变是旧病复发的警告信号。被动自我跟踪可用于识别 并实时预测病情恶化。 创新：无源移动传感是一种疾病自我跟踪和监测的新颖方法。那里 关于严重精神疾病的被动自我追踪的研究相对较少，分析有限 这一领域的发展，而弗吉尼亚州则没有。 具体目标：该项目研究了退伍军人的被动移动传感技术，用于治疗严重的 精神疾病。数据用于自我跟踪行为和症状。虽然无源移动传感已经 这些对于患有严重精神疾病的患者来说是可行的、可接受的和安全的，这是首次在 弗吉尼亚州。分析技术使用被动数据来预测行为和症状。该项目响应 心理健康和医疗保健信息学的 HSR&D 优先领域。该项目有以下目标： 1.以用户为中心设计被动移动自我追踪，支持退伍军人管理 他们的心理健康。 2. 研究心理健康被动自我追踪的可行性、可接受性和安全性，包括 向退伍军人反馈心理健康状况。 3. 使用移动传感器和电话使用数据来开发个性化的社交能力估计， 活动和睡眠（通过每周访谈进行测量）。 4. 研究使用社交、活动和睡眠数据来识别病情加重的预测价值 的精神症状。 方法：可以使用有关运动、位置和习惯的数据来评估活动。社交能力可以 通过沟通和公共互动的数据进行评估。可以使用光数据来评估睡眠， 声音、动作和电话的使用。该项目的研究人员开发了“mWellness”，一种功能性移动设备 监控和传输移动传感器和利用率数据的应用程序。焦点小组和实验室可用性测试 将为进一步的应用程序和干预开发提供信息。混合方法研究将研究部署 退伍军人被动地自我追踪自己的行为和精神症状。如果这个项目符合预期 为了实现目标，退伍军人管理局将拥有一个移动分析平台，持续监控患者的行为和症状 患有严重精神疾病的患者。 后续步骤/实施：结果为有效性和有效性研究的开展提供信息 实施这些方法以改善退伍军人评估、治疗和结果。结果也是 适用于其他有心理健康风险的人群以及在日常护理中使用类似的方法。