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: 10867550
• 负责人: MATTHEW WILLIAM AHLE
• 金额: $ 24.1万
• 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/10867550
• 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.

项目摘要/摘要 幼儿期心理健康问题是一个重大的公共卫生问题，影响广泛 在生活中的同时和以后的生活中发挥作用。虽然儿童心理健康受到各种因素的影响， 在这些因素中，与照顾者的关系质量起着关键作用。挑剔，强迫，和冲突的父母- 儿童互动一直与外化和内化症状的风险增加有关， 而支持和养育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。各种各样的治疗方法已经开发出来，并用于培养积极的父母- 儿童关系和改善儿童心理健康，包括家长培训/教育，面对面治疗，家庭 访问、学校研讨会和网络节目。然而，系统性障碍妨碍了无障碍， 这些传统的基于任命和模块的方法的可推广性和可接受性。此外，委员会认为， 以家庭为中心的灵活性、个人响应能力和这些服务的广泛可用性方面的局限性 使其不足以满足高危人群的独特需求， 在真实的时间和真实的生活中提供方便、廉价的24小时支持-何时何地支持 是最需要的。毫不奇怪，研究发现，大约一半的家庭谁参加传统的 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。就在- 相比之下，时间适应性干预（JITAIs）利用智能手机、可穿戴设备和人工智能（AI）， 识别和应对心理和行为过程以及日常发生的背景事件 生活尽管JITAIs有可能改变人们接受心理健康支持的方式，但 它们仍然得到成功和广泛的执行。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跨学科团队使用人工智能构建机器学习算法， 心理状态和背景事件，如持续的情绪和关系冲突，在夫妇。在 目前的项目，我们建议开发和测试一个JITAI，以提供适当的支持，家庭在动态 对环境事件的反应和心理状态的转变，以放大依恋关系，调节情绪， 并干预适应不良的亲子互动模式。基于我们之前的研究，我们将（1）建立 从商用移动的设备不引人注目地捕获实时数据的软件，（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，（4）开展微随机临床 一项测试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响应性。