The ChAMP App: A Scalable mHealth Technology for Detecting Digital Phenotypes of Early Childhood Mental Health
The ChAMP App: A Scalable mHealth Technology for Detecting Digital Phenotypes of Early Childhood Mental Health
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DOI：
10.1109/jbhi.2023.3337649
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发表时间：
2024-04-01
影响因子：
7.7
通讯作者：
McGinnis，Ellen W.
中科院分区：
文献类型：
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作者：
Loftness，Bryn C.;Halvorson-Phelan，Julia;McGinnis，Ellen W.
儿童心理健康问题是常见的，损害性的，如果不治疗可能成为慢性的。儿童不是他们情绪和行为健康的可靠报告者，照顾者经常无意中低估或高估儿童症状，使评估具有挑战性。情绪和行为健康的客观生理和行为措施正在出现。然而，这些方法通常需要数据和传感器工程方面的专业设备和专业知识来管理和分析。为了应对这一挑战，我们开发了ChAMP（儿童数字表型评估和管理）系统，其中包括一个移动的应用程序，用于在一系列情绪诱导任务期间收集运动和音频数据，以及一个用于提取数字生物标志物的开源平台。作为原理的证明，我们提供了来自101名4-8岁儿童的ChAMP系统数据，这些儿童有和没有诊断出精神健康障碍。在这些数据上训练的机器学习模型以70-73%的平衡准确度检测特定疾病的存在，其结果与已建立的父母报告措施的临床阈值（63-82%的平衡准确度）相似。在模型架构中青睐的功能描述使用Shapley加法简化（SHAP）。典型相关分析揭示了每种疾病的预测因子与相关症状严重程度之间的中度至强相关性（r = 0.51 - 0.83）。开源的ChAMP系统提供了临床相关的数字生物标志物，这些生物标志物可能会在以后补充父母报告的情绪和行为健康指标，用于检测具有潜在心理健康状况的儿童，并降低了有兴趣探索儿童心理健康数字表型的研究人员的进入门槛。
Childhood mental health problems are common, impairing, and can become chronic if left untreated. Children are not reliable reporters of their emotional and behavioral health, and caregivers often unintentionally under- or over-report child symptoms, making assessment challenging. Objective physiological and behavioral measures of emotional and behavioral health are emerging. However, these methods typically require specialized equipment and expertise in data and sensor engineering to administer and analyze. To address this challenge, we have developed the ChAMP (Childhood Assessment and Management of digital Phenotypes) System, which includes a mobile application for collecting movement and audio data during a battery of mood induction tasks and an open-source platform for extracting digital biomarkers. As proof of principle, we present ChAMP System data from 101 children 4–8 years old, with and without diagnosed mental health disorders. Machine learning models trained on these data detect the presence of specific disorders with 70–73% balanced accuracy, with similar results to clinical thresholds on established parent-report measures (63–82% balanced accuracy). Features favored in model architectures are described using Shapley Additive Explanations (SHAP). Canonical Correlation Analysis reveals moderate to strong associations between predictors of each disorder and associated symptom severity (r = .51–.83). The open-source ChAMP System provides clinically-relevant digital biomarkers that may later complement parent-report measures of emotional and behavioral health for detecting kids with underlying mental health conditions and lowers the barrier to entry for researchers interested in exploring digital phenotyping of childhood mental health.