Loftness Bryn C, Halvorson-Phelan Julia, O'Leary Aisling, Bradshaw Carter, Prytherch Shania, Berman Isabel, Torous John, Copeland William L, Cheney Nick, McGinnis Ryan S, McGinnis Ellen W
University of Vermont's Complex Systems Center and M-Sense Research Group.
University of Vermont Medical Center Department of Psychiatry.
medRxiv. 2023 Nov 29:2023.01.19.23284753. doi: 10.1101/2023.01.19.23284753.
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.
儿童心理健康问题很常见，具有损害性，若不治疗可能会发展成慢性病。儿童并非其情绪和行为健康的可靠报告者，而照料者往往会无意中少报或多报儿童症状，这使得评估具有挑战性。客观的情绪和行为健康的生理及行为测量方法正在出现。然而，这些方法通常需要专门设备以及数据和传感器工程方面的专业知识来进行管理和分析。为应对这一挑战，我们开发了ChAMP（儿童数字表型评估与管理）系统，该系统包括一个移动应用程序，用于在一系列情绪诱导任务中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。作为原理验证，我们展示了来自101名4至8岁儿童的ChAMP系统数据，这些儿童有或没有被诊断出心理健康障碍。基于这些数据训练的机器学习模型以70 - 73%的平衡准确率检测特定障碍的存在，与既定的家长报告测量方法的临床阈值结果相似（平衡准确率为63 - 82%）。使用夏普利加法解释（SHAP）描述了模型架构中受青睐的特征。典型相关分析揭示了每种障碍的预测因子与相关症状严重程度之间存在中度至强关联（r = 0.51 - 0.83）。开源的ChAMP系统提供了与临床相关的数字生物标志物，这些生物标志物日后可能会补充家长对儿童情绪和行为健康的报告测量方法，用于检测有潜在心理健康问题的儿童，并降低了对探索儿童心理健康数字表型感兴趣的研究人员的准入门槛。
