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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。

基本信息

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
项目类别：
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

项目摘要

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.

项目总结