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: 10664060
• 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/10664060
• 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; Coercion; Computer software; Conflict (Psychology); Couples; Data; Data Analytics; Development; Distal; Early Intervention; Economic Burden; Ecosystem; Educational Curriculum; Effectiveness; Emotions; Engineering; Engineering Psychology; Evaluation; Event; Exposure to; Family; Family Relationship; Fostering; General Population; 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; Populations at Risk; Process; Psychologist; Public Health; Randomized Clinical Trials; Reporting; Research; Resources; Risk; Role; Safety; Sampling; Schools; Service delivery model; Societies; Stress; Symptoms; System; Technology; Testing; Therapeutic; Time; Training and Education; Underserved Population; Work; adaptive intervention; analytical method; barrier to care; base; 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; machine learning algorithm; machine learning method; mobile computing; mobile sensing; novel; personalized predictions; physical conditioning; prevent; preventive intervention; programs; protective effect; psychologic; research and development; response; service delivery; social; substance use; symptomatic improvement; technology development; therapeutic effectiveness

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.

项目摘要/摘要 儿童早期心理健康问题是一个重大的公共卫生问题，影响广泛 在生活中同时发挥作用和在以后发挥作用。尽管儿童心理健康受到各种因素的影响 因素方面，与照顾者的关系质量起着至关重要的作用。批判性的，强迫性的，矛盾的父母- 儿童的互动一直与外化和内化症状的风险增加有关， 而支持性和抚养性关系已被证明具有保护作用。早期干预 因此，适应不良的家庭关系对于防止或抵消消极的发展轨迹至关重要 在高危儿童中。已经开发和使用了各种治疗方法来培养积极的父母- 儿童关系和改善儿童心理健康，包括父母培训/教育、亲身治疗、家庭 参观、学校课程和网络计划。然而，体制障碍干扰了可获得性， 这些传统的以任命和模块为基础的方法的普遍性和可接受性。此外， 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方面的限制 使其不足以满足高危人群的独特需求，这些高危人群更容易受益于 随时随地提供实时、实时、经济实惠的24小时支持 是最需要的。毫不奇怪，研究发现，大约有一半的家庭参加了传统的 以预约和模块为基础的精神卫生服务未能显示出足够的症状改善。只是在- 相比之下，时间适应性干预(JITAI)利用智能手机、可穿戴设备和人工智能(AI)来 在日常生活中识别和应对心理和行为过程以及背景事件 生活。尽管JITAI有可能改变人们接受心理健康支持的方式，但障碍在于 它们的成功、广泛的实施仍然存在。使用从智能手机和可穿戴设备收集的飞行员数据， 我们的心理学家和工程师组成的跨学科团队使用人工智能来构建机器学习算法来检测 夫妻之间的心理状态和背景事件，如持续的情绪和关系冲突。在 在当前的项目中，我们建议开发和测试一个JITAI，为动态中的家庭提供适当的支持 对背景事件的反应和心理状态的变化，以放大依恋纽带，调节情绪， 并干预不适应的亲子互动模式。在我们先前研究的基础上，我们将(1)建立 从商用移动设备上不引人注意地捕获实时数据的软件，(2)使用机器 学习开发自动监控与儿童相关的心理和行为过程的算法 心理健康，(3)推出按需提供干预的吉泰，(4)开展微随机临床 测试我们的吉泰减少儿童内化和外化的有效性、可接受性和安全性的试验 症状。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反应性。