Department of Electrical and Biomedical Engineering, University of Vermont, Burlington, VT, United States of America.
Department of Psychiatry, University of Vermont, Burlington, VT, United States of America.
PLoS One. 2019 Jan 16;14(1):e0210267. doi: 10.1371/journal.pone.0210267. eCollection 2019.
There is a critical need for fast, inexpensive, objective, and accurate screening tools for childhood psychopathology. Perhaps most compelling is in the case of internalizing disorders, like anxiety and depression, where unobservable symptoms cause children to go unassessed-suffering in silence because they never exhibiting the disruptive behaviors that would lead to a referral for diagnostic assessment. If left untreated these disorders are associated with long-term negative outcomes including substance abuse and increased risk for suicide. This paper presents a new approach for identifying children with internalizing disorders using an instrumented 90-second mood induction task. Participant motion during the task is monitored using a commercially available wearable sensor. We show that machine learning can be used to differentiate children with an internalizing diagnosis from controls with 81% accuracy (67% sensitivity, 88% specificity). We provide a detailed description of the modeling methodology used to arrive at these results and explore further the predictive ability of each temporal phase of the mood induction task. Kinematical measures most discriminative of internalizing diagnosis are analyzed in detail, showing affected children exhibit significantly more avoidance of ambiguous threat. Performance of the proposed approach is compared to clinical thresholds on parent-reported child symptoms which differentiate children with an internalizing diagnosis from controls with slightly lower accuracy (.68-.75 vs. .81), slightly higher specificity (.88-1.00 vs. .88), and lower sensitivity (.00-.42 vs. .67) than the proposed, instrumented method. These results point toward the future use of this approach for screening children for internalizing disorders so that interventions can be deployed when they have the highest chance for long-term success.
目前非常需要快速、廉价、客观和准确的儿童精神病理学筛查工具。最有说服力的可能是在内在障碍（如焦虑和抑郁）的情况下，由于无法观察到症状，儿童没有得到评估——他们默默地受苦，因为他们从未表现出会导致诊断评估的破坏性行为。如果不加以治疗，这些疾病会导致长期的负面后果，包括药物滥用和自杀风险增加。本文提出了一种使用仪器化的 90 秒情绪诱发任务来识别患有内在障碍的儿童的新方法。参与者在任务期间的运动使用商用可穿戴传感器进行监测。我们表明，机器学习可以用于以 81%的准确率（67%的敏感性，88%的特异性）区分有内在诊断的儿童和对照组。我们提供了用于得出这些结果的建模方法的详细描述，并进一步探索了情绪诱发任务每个时间阶段的预测能力。对最能区分内在诊断的运动学指标进行了详细分析，结果表明，受影响的儿童明显更多地回避模糊的威胁。与区分有内在诊断的儿童和对照组的父母报告的儿童症状的临床阈值相比，提出的方法的性能略低（.68-.75 与.81），特异性略高（.88-1.00 与.88），敏感性略低（.00-.42 与.67）比所提出的仪器化方法。这些结果表明，未来可以使用这种方法对儿童进行内在障碍筛查，以便在干预措施最有可能取得长期成功的时候进行干预。
