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: 10010441
• 负责人: Jonathan S Comer
• 金额: $ 41.68万
• 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 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10010441
• 关键词: Address; Adult; Age; Aggressive behavior; Alcohol or Other Drugs use; 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; Healthcare; 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; Preventive Intervention; 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 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; novel; personalized predictions; physical conditioning; prevent; programs; protective effect; psychologic; research and development; response; service delivery; social; 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 幼儿心理健康问题构成了重要的公共卫生问题，并具有广泛影响 同时和以后的生活。尽管儿童心理健康受到各种各样的影响 因素，与看护者的关系质量起着至关重要的作用。批判，强制和冲突父母 - 儿童互动一直与外在化和内在化症状的风险增加有关， 支持和培育关系已显示出来赋予受保护的影响。早期干预 因此，适应不良的家庭关系对于预防或抵消负面发展轨迹至关重要 在高危儿童中。已经开发并采用了多种治疗方法来促进父母阳性 儿童关系并改善儿童心理健康，包括父母培训/教育，亲自疗法，家庭 访问，学校课程和网络课程。但是，系统的障碍会干扰可访问性， 这些传统任命和基于模块的方法的概括性以及可接受性。此外， 以家庭为中心的灵活性，个人响应能力以及这些服务的广泛可用性的限制 使他们不足以满足高危人群的独特需求，这些人口将从更轻松地受益 实时和现实生活提供的可访问且便宜的24小时支持 - 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家庭 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。刚刚 相反 识别并应对心理和行为过程和上下文事件每天展开 尽管Jitais有可能改变人们获得心理健康支持的方式，但障碍 他们成功的大规模实施仍然存在。使用从智能手机和可穿戴设备收集的试验数据， 我们的心理学家和工程师的跨学科团队使用人工智能来构建机器学习算法来检测 夫妻中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，我们建议开发和测试Jitai，以为动态的家庭提供机会支持 对上下文事件的反应和转移心理状态以扩大依恋纽带，调节情感， 并干预适应不良的亲子互动模式。在我们先前的研究的基础上，我们将（1）构建 从商业上可用的移动设备中毫不客气地捕获实时数据的软件，（2）使用机器 学习开发算法以自动监测与儿童相关的心理和行为过程 心理健康，（3）发起Jitai进行急需的干预，（4）进行微型临床 试验以测试Jitai降低儿童内在化和外部化的效率，可接受性和安全性 症状。我们的项目将有助于技术生态系统和服务交付模型的发展 有权改变心理保健的可及性和动态反应能力。