The Development and Systematic Evaluation of an AI-Assisted Just-in-Time-Adaptive-Intervention for Improving Child Mental Health

人工智能辅助改善儿童心理健康的及时适应性干预的开发和系统评估

• 批准号: 10861394
• 负责人: MATTHEW WILLIAM AHLE
• 金额: $ 5.86万
• 依托单位: COLLIGA APPS CORP.
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-08 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10861394
• 关键词: Address; Adult; Age; Aggressive behavior; Algorithms; Anxiety; Appointment; Area; Artificial Intelligence; Behavior; Behavioral; Businesses; Caregivers; Cellular Phone; Cessation of life; Child; Child Mental Health; Child Rearing; Childhood; Computer software; Conflict (Psychology); Couples; Data; Data Analytics; Development; Distal; Early Intervention; Economic Burden; Ecosystem; Educational Curriculum; Effectiveness; Emotions; Engineering; Evaluation; Event; Exposure to; Family; Family Relationship; Fostering; Goals; Health Services Accessibility; Heart Diseases; Home visitation; Hour; Impairment; Individual; Internet; Intervention; Learning; Life; Link; Longevity; Machine Learning; Malignant Neoplasms; Measurement; Measures; Mediating; Mental Depression; Mental Health; Mental Health Services; Methods; Modality; Modeling; Monitor; Moods; National Institute of Mental Health; Outcome; Parent-Child Relations; Parents; Pattern; Performance; Persons; Phase; Play; Population; Populations at Risk; Process; Psychologist; Psychology; Public Health; Reporting; Research; Resources; Risk; Role; Safety; Sampling; Schools; Scientific Advances and Accomplishments; Service delivery model; Societies; Stress; Symptoms; System; Technology; Testing; Therapeutic; Time; Training and Education; Underserved Population; Work; adaptive intervention; analytical method; barrier to care; comparison control; contagion; cost effective; data sharing; design; digital; early childhood; efficacy evaluation; efficacy testing; family support; flexibility; foster child; handheld mobile device; health care availability; improved; in vivo monitoring; innovation; innovative technologies; interdisciplinary collaboration; invention; machine learning algorithm; machine learning method; machine learning prediction algorithm; mobile computing; mobile sensing; new technology; physical conditioning; prevent; preventive intervention; programs; protective effect; psychologic; randomized, clinical trials; research and development; response; service delivery; social; substance use; symptomatic improvement; technology development; technology platform; therapeutic effectiveness; wearable device

PROJECT SUMMARY/ABSTRACT Early childhood mental health problems constitute a significant public health concern with wide-ranging impacts on functioning both concurrently and later in life. Although childhood mental health is influenced by a variety of factors, the quality of relationships with caregivers plays a critical role. Critical, coercive, and conflictual parent- child interactions have been consistently linked with increased risk of externalizing and internalizing symptoms, whereas supportive and nurturing relationships have been shown to confer protective effects. Early intervention of maladaptive family relationships is thus crucial for preventing or offsetting negative developmental trajectories in at-risk children. A variety of therapeutic methods have been developed and employed to foster positive parent- child relationships and improve child mental health, including parent training/education, in-person therapy, home visiting, school curriculums, and web programs. However, systematic obstacles interfere with the accessibility, generalizability, and acceptability of these traditional appointment- and module-based approaches. Furthermore, limitations in the family-centered flexibility, individual responsiveness, and broad availability of these services render them inadequate to address the unique needs of at-risk populations who would benefit from more readily accessible and inexpensive 24-hour support that is provided in real time and real life—when and where support is needed most. Not surprisingly, research finds that roughly half of the families who do participate in traditional appointment- and module-based mental health services fail to show sufficient symptom improvement. Just-in- time adaptive interventions (JITAIs), in contrast, utilize smartphones, wearables, and artificial intelligence (AI) to identify and respond to psychological and behavioral processes and contextual events as they unfold in everyday life. Although JITAIs have the potential to transform the way people receive mental health support, barriers to their successful, wide-scale implementation remain. Using pilot data collected from smartphones and wearables, our interdisciplinary team of psychologists and engineers used AI to build machine learning algorithms to detect psychological states and contextual events, such as ongoing moods and relationship conflict, in couples. In the current project, we propose developing and testing a JITAI to provide opportune supports to families in dynamic response to contextual events and shifting psychological states to amplify attachment bonds, regulate emotion, and intervene in maladaptive parent-child interactional patterns. Building on our prior research, we will (1) build software to unobtrusively capture real-time data from commercially-available mobile devices, (2) use machine learning to develop algorithms to automatically monitor psychological and behavioral processes relevant to child mental health, (3) launch a JITAI to provide as-needed intervention, and (4) carry out a micro-randomized clinical trial to test the efficacy, acceptability, and safety of our JITAI for decreasing child internalizing and externalizing symptoms. Our project will contribute to the development of technology ecosystems and service delivery models with the power to meaningfully transform the accessibility and dynamic responsiveness of mental health care.

项目概要/摘要 幼儿心理健康问题是一个具有广泛影响的重大公共卫生问题 尽管儿童心理健康受到多种因素的影响。 因素中，与照顾者的关系质量起着至关重要的作用。 儿童互动一直与外化和内化症状的风险增加有关， 而支持性和培育性关系已被证明具有保护作用。 因此，适应不良的家庭关系对于预防或抵消消极的发展轨迹至关重要 已经开发并采用了多种治疗方法来培养高危儿童。 儿童关系并改善儿童心理健康，包括家长培训/教育、面对面治疗、家庭治疗 然而，系统性障碍干扰了可访问性、 这些传统的基于预约和模块的方法的普遍性和可接受性。 这些服务以家庭为中心的灵活性、个人响应能力和广泛可用性的局限性 使它们不足以满足高危人群的独特需求，而这些人群更容易从中受益 在现实生活中随时随地提供方便且廉价的 24 小时支持 毫不奇怪，研究发现大约有一半的家庭参与了传统活动。 基于预约和模块的心理健康服务未能显示出足够的症状改善。 相比之下，时间自适应干预 (JITAI) 利用智能手机、可穿戴设备和人工智能 (AI) 识别并应对日常生活中发生的心理和行为过程以及情境事件 尽管 JITAI 有潜力改变人们接受心理健康支持的方式，但仍存在障碍。 使用从智能手机和可穿戴设备收集的试点数据，他们的成功、大规模实施仍然存在。 我们的跨学科心理学家和工程师团队使用人工智能构建机器学习算法来检测 夫妻的心理状态和情境事件，例如持续的情绪和关系冲突。 在当前的项目中，我们建议开发和测试 JITAI，为动态的家庭提供适当的支持 对情境事件和心理状态变化的反应，以增强纽带依恋，调节情绪， 基于我们之前的研究，我们将（1）建立适应不良的亲子互动模式。 软件以不显眼的方式从市售移动设备捕获实时数据，(2) 使用机器 学习开发算法来自动监控与儿童相关的心理和行为过程 心理健康，(3) 启动 JITAI 提供按需干预，以及 (4) 开展微观随机临床 测试我们的 JITAI 减少儿童内化和外化的有效性、可接受性和安全性的试验 我们的项目将有助于技术生态系统和服务交付模式的发展。 有能力有意义地改变精神卫生保健的可及性和动态响应能力。