I-Corps: A platform for collecting and analyzing biological and behavioral markers of preschool emotional and behavioral health

I-Corps项目的更广泛影响/商业潜力在于开发一种新的可扩展的客观评估方法，这种评估方法依赖于儿童的生理和行为反应，而不是仅仅依靠监护人对症状的观察，从而改变对青春期前儿童的识别和筛查。该工具具有可扩展性、简便性（约5分钟）、可访问性和自动化，无需/低提供商的努力，易于使用（对用户来说没有语言障碍，因为它是音调/蜂鸣声提示），其基础模型和生物标志物在已发表的同行评审研究中得到验证。提供年度终点满足了父母和提供者对儿童心理健康纵向跟踪的兴趣，随后减少了在这个脆弱年龄阶段（4-10岁）被忽视的儿童数量，而不会大大延长每年的健康检查预约。这项工作的结果在可获得性方面将是革命性的，因为目前市场上没有其他基于技术的幼儿常规心理健康评估工具。没有其他研究机构或商业机构在儿童早期心理健康行为评估和相关生物标志物方面收集了如此多的数据。这些细节赋予了我们在这一领域超越任何潜在竞争者的竞争优势。这个I-Corps项目的基础是发展对年轻人和儿童的常规筛查，为更好的临床医生联系奠定必要的基础。重要的技术创新包括开发新颖的、基于机器学习的算法，该算法仅使用来自手机的数据，就能强大地收集青春期前儿童在结构化情绪诱导任务中生理反应的定量信息。这种方法利用大量研究来证明这种数字筛选方法的可行性，并在此过程中收集了最大的青春期前心理健康生理反应数据集之一。这种新颖的测量方式和独特的、大型的、不断扩展的临床数据集的结合，为开发拟议的变革性数字识别工具提供了创新的基础。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is the development of a new scalable, objective assessment that relies on a child’s physiological and behavioral responses, rather than solely upon observation of symptoms by a guardian to transform the identification and screening of pre-adolescent children. This tool is scalable, brief (~5 minutes), accessible and automated for no/low provider effort, easily usable (no language barriers for users as it is tone/beep-prompted), with underlying models and biomarkers validated in published peer-reviewed research. Providing annual endpoints satisfies parental and provider interest in longitudinal tracking of childhood mental health, subsequently decreasing the number of children overlooked during this vulnerable age phase (4-10 years old) without greatly extending annual well-visit appointments. The outcomes of this work would be revolutionary in its availability, as there is no other technology-based routine mental health assessment tool for young children currently on the market. There is no other research or commercial institution that has aggregated as much data on early childhood mental health behavioral assessments and associated biomarkers. These specifics give a competitive edge over any potential forthcoming competitor in this space.This I-Corps project is based on the development of routine screening of young adults and children to lay the necessary foundation for better clinician connectivity. The significant technical innovations include the development of novel, machine learning based algorithms for robustly gathering quantitative information about pre-adolescent children’s physiological responses during structured mood induction tasks using only data from a mobile phone. This approach leverages numerous studies to demonstrate the feasibility of this digital screening method and in so doing collected one of the largest pre-adolescent mental health physiological response datasets. The combination of this novel measurement modality and a unique, large, and expanding clinical dataset provide an innovative foundation prepared to develop the proposed transformative digital identification tool.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.