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: 10655284
• 负责人: Adam Grabell
• 金额: $ 23.35万
• 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 2025-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10655284
• 关键词: Address; Adult; Aggressive behavior; Algorithm Design; Algorithms; Artificial Intelligence; Behavior; Behavioral; Caregivers; Characteristics; Child; Classification; Clinical; Complex; Custom; DSM-V; Data; Detection; Development; Devices; Diagnosis; Diagnostic; Discrimination; Disease; Early Diagnosis; Early identification; Emotions; Exhibits; Frustration; Future; Goals; Healthcare Systems; Heart Rate; Home; Home environment; Impairment; Individual; Intervention; 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; care providers; 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 在生命的最初几年出现的情绪和行为问题往往会持续到后来的发育阶段 阶段和成年期，导致严重的损伤和社会代价。然而，新出现的迹象表明 很难与儿童早期的正常不良行为区分开来，创造了一种“当 担心“照顾者和提供者的问题。具体地说，早期的主要行为表现 精神病理学，即脾气暴躁(如尖叫、踩踏、殴打)，既是一种跨诊断症状 幼儿通常会表现出无数的障碍和对挫折的标准反应。原因尚不清楚。 以及由于缺乏捕捉复杂性的研究，临床意义上的发脾气与标准的发脾气有多么不同， 在发脾气前和发脾气期间，在孩子和照顾者内部发生的实时生物行为变化。 调查家庭环境中发脾气的特点的研究，在多个层面上 分析，有可能区分临床和标准的Tantrum变种，并确定前驱阶段 发脾气，这可能会转化为未来的干预。拟议研究的具体目的是 根据儿童发脾气的特点区分患有和不患有精神病的儿童，以及 使用实时数据准确预测未来的发脾气情况。为了实现这些目标，研究小组已经 开发并成功试用了一款定制的智能手表应用程序，旨在准确地指示 实时补偿发脾气，并与一系列可穿戴和非接触式设备同步测量 心率、呼吸、运动和发声特征的变化。60个照顾孩子的双胞胎，其中50% 符合DSM 5障碍标准的，将被招募。发脾气和生物行为信号会持续不断 在家中记录一个月，作为照顾者和儿童过着他们正常的生活。常规统计学 将使用建模和尖端机器学习来对存在或不存在 儿童的精神病理学，预测第二天发脾气的严重程度，并预计 在发脾气之前先发一次脾气。该项目如果成功，将产生首个此类数据，产生 对临床与规范背后复杂的时间和生物行为过程的新理解 发脾气和设计用于在发脾气发生之前预测发脾气的算法。这些产品具有很高的潜在价值 意义重大，因为它们将使该领域转向开发下一代、基于家庭的自动化系统 协助诊断和治疗生命早期的精神疾病。