Using Wearable Devices and Machine Learning to Forecast Preschool Tantrums and Identify Clinically Significant Variants.

使用可穿戴设备和机器学习来预测学龄前发脾气并识别具有临床意义的变异。

• 批准号: 10373392
• 负责人: Adam Grabell
• 金额: $ 19.4万
• 依托单位: UNIVERSITY OF MASSACHUSETTS AMHERST
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10373392
• 关键词: Address; Adult; Aggressive behavior; Algorithm Design; Algorithms; Artificial Intelligence; Behavior; Behavioral; Caregivers; Characteristics; Child; Clinical; Complex; Custom; DSM-V; Data; Detection; Development; Devices; Diagnosis; Discrimination; Disease; Early Diagnosis; Early identification; Emotions; Exhibits; Frustration; Future; Goals; Healthcare Systems; Heart Rate; Home; Home environment; Impairment; Individual; Intervention; Lead; Life; Link; Longevity; Machine Learning; Measures; Mental Health; Mental Health Services; Mental disorders; Methodology; Moods; Motor Activity; Movement; Nursery Schools; Pattern; Phase; Preschool Child; Problem behavior; Process; Provider; Psychopathology; Questionnaires; Research; Research Personnel; Respiration; Rewards; Risk; Severities; Signal Transduction; Sleep; Statistical Models; Symptoms; System; Techniques; Testing; Time; Translating; Variant; Weather; Work; actigraphy; base; biobehavior; clinically significant; design; early childhood; in vivo; innovation; model design; multimodality; next generation; novel; peer; poor sleep; programs; recruit; response; smart watch; societal costs; tool; wearable device

Project Summary Mood and behavior problems emerging in the first few years of life often persist across later developmental stages and into adulthood, resulting in significant impairment and societal costs. However, the emerging signs of psychopathology are difficult to differentiate from normative misbehavior in early childhood, creating a “when to worry” problem for caregivers and providers. Specifically, the cardinal behavioral manifestation of early psychopathology, the temper tantrum (e.g., screaming, stamping, hitting), is both a transdiagnostic symptom of myriad disorders and a normative response to frustration young children commonly exhibit. It is unknown why and how clinically significant vs. normative tantrums differ due to a paucity of research capturing the complex, real-time, bio-behavioral changes occurring within both the child and caregiver, prior to and during tantrums. Research investigating the characteristics of tantrums occurring in the home environment, at multiple levels of analysis, has the potential to differentiate clinical vs. normative tantrum variants, and identify a precursor phase to tantrums that could be translated into future interventions. The Specific Aims of the proposed study are to discriminate children with and without psychopathology based on the characteristics of their tantrums, and accurately forecast future tantrums using real-time data. To accomplish these aims, the study team has developed and successfully piloted a custom smart-watch app designed to precisely denote the onset and offset of tantrums in real time and synchronize with an array of wearable and contactless devices measuring heart rate, respiration, movement, and changes in vocal features. Sixty caregiver-child dyads, 50% of whom meet criteria for a DSM 5 disorder, will be recruited. Tantrums and bio-behavioral signals will be continuously recorded in the home for one month as caregivers and children live their normal lives. Conventional statistical modeling and cutting-edge machine learning will be used to classify the presence or absence of psychopathology in children, predict the severity level of the following day’s tantrums, and anticipate an individual tantrum before it occurs. This project, if successful, would produce first-of-its-kind data yielding a new understanding of the complex temporal and bio-behavioral processes underlying clinical vs. normative tantrums and algorithms designed to predict tantrums before they occur. These products are potentially highly significant as they will allow the field to pivot to developing next-generation, home-based, automated systems to assist in diagnosing and treating mental illness earlier in the lifespan.

项目摘要 情绪和行为问题在生命的最初几年中出现 阶段到成年，导致巨大的损害和社会成本。但是，新兴的标志 心理病理学的童年很难与童年时期的正常不当行为区分开来，创造了一个“当 担心护理人员和提供者的问题。特别是，早期的基本行为表现 心理病理学，温度发脾气（例如，尖叫，冲压，击打）都是经诊断的症状 少数疾病和对挫败感的正常反应通常暴露在幼儿中。不知道为什么 以及由于研究捕获复合物的缺乏，临床上有显着的发脾气与正常发脾气如何 发脾气之前和期间，在儿童和照顾者内发生了实时的生物行为变化。 研究调查在家庭环境中发脾气的特征，多个层次 分析具有区分临床与正常发脾变量的潜力，并确定前体相 发脾气，可以转化为将来的干预措施。拟议研究的具体目的是 根据发脾气的特征来区分有或没有心理病理学的儿童， 使用实时数据准确预测未来发脾气。为了实现这些目标，研究团队有 开发并成功试用了一个定制的智能观察应用程序，旨在精确表示发作和 实时抵消发脾气，并与一系列可穿戴和无接触式设备进行同步 心率，呼吸，运动和人声特征的变化。六十个照顾者 - 孩子二元组，其中50％ 将招募DSM 5疾病的符合标准。发脾气和生物行为信号将连续 当护理人员和孩子过着正常的生活，在家中记录了一个月。常规统计 建模和尖端的机器学习将用于对存在或不存在 儿童心理病理学，预测第二天发脾气的严重程度，并预测 单个发脾气发生之前。如果成功的话，该项目将产生首个数据，产生 对复杂临时过程的复杂临时和生物行为过程的新理解与正常 发脾气和算法旨在预测发脾气。这些产品可能高度 意义重大，因为它们将使该领域能够枢纽开发下一代，自动化系统 帮助诊断和治疗精神疾病。