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.

项目概要 生命最初几年出现的情绪和行为问题通常会持续到以后的发展过程中 阶段和进入成年期，导致重大损害和社会成本然而，新出现的迹象。 精神病理学的行为很难与幼儿时期的规范不当行为区分开来，从而产生了“当 具体来说，早期的主要行为表现 精神病理学中，发脾气（例如尖叫、跺脚、打人）都是一种跨诊断症状 无数的疾病和对沮丧的幼儿的规范反应通常表现出来，但原因尚不清楚。 以及由于缺乏捕捉复杂性的研究，临床上显着的发脾气与正常发脾气有何不同， 在发脾气之前和发脾气期间，儿童和看护者体内发生的实时生物行为变化。 研究调查家庭环境中发生的发脾气特征，在多个层面上 分析，有可能区分临床与正常发脾气变异，并确定前兆阶段 发脾气可以转化为未来的干预措施。 根据发脾气的特征来区分有或没有精神病理学的儿童，以及 为了实现这些目标，研究团队利用实时数据准确预测未来的发脾气。 开发并成功试用了一款定制智能手表应用程序，旨在精确指示发病和 实时抵消发脾气，并与一系列可穿戴和非接触式设备同步测量 心率、呼吸、运动和声音特征的变化 60 个看护者-儿童二人组，其中 50%。 符合 DSM 5 障碍标准，将持续招募发脾气和生物行为信号。 记录了照顾者和儿童在家里过着正常生活的一个月。 建模和尖端机器学习将用于对是否存在进行分类 儿童的精神病理学，预测第二天发脾气的严重程度，并预测 该项目如果成功，将产生史无前例的数据，产生 对临床与规范背后的复杂时间和生物行为过程的新理解 发脾气和旨在在发脾气发生之前进行预测的算法这些产品的潜在风险很高。 意义重大，因为它们将使该领域转向开发下一代家庭自动化系统 协助在生命周期的早期诊断和治疗精神疾病。