Machine Learning Identifies Smartwatch-Based Physiological Biomarker for Predicting Disruptive Behavior in Children: A Feasibility Study.
Machine Learning Identifies Smartwatch-Based Physiological Biomarker for Predicting Disruptive Behavior in Children: A Feasibility Study.
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DOI：
10.1089/cap.2023.0038
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发表时间：
2023-11
影响因子：
1.9
通讯作者：
中科院分区：
文献类型：
--
作者：
父母经常购买和询问智能手表设备来监控孩子的行为和功能。这项试点研究检验了使用智能手表监控来预测破坏性行为的可行性和准确性。该研究招募了因破坏性行为而住院治疗的 7-10 岁儿童（N = 10）。研究小组在研究参与期间完成了连续的行为表型分析。机器学习协议检查了严重的行为爆发（操作为身体约束之前的事件）以准备训练数据。监督机器学习方法通过交叉验证进行训练，以预测三种行为状态——平静、有趣和破坏性。参与者对每个协议智能手表使用的遵守率为 90%。决策树派生出心率、睡眠和运动活动的条件依赖性来预测行为。交叉验证表明，使用这些条件依赖性预测孩子的行为状态的准确度为 80.89%。这项研究证明了对有严重破坏行为的儿童进行 7 天连续智能手表监测的可行性。机器学习方法描述了即将发生的破坏性行为的预测生物标志物。未来的验证研究将检查智能手表的生理生物标志物，以增强行为干预、增加父母对治疗的参与度，并证明幼儿药物临床试验中的目标参与度。
Parents frequently purchase and inquire about smartwatch devices to monitor child behaviors and functioning. This pilot study examined the feasibility and accuracy of using smartwatch monitoring for the prediction of disruptive behaviors. The study enrolled children (N = 10) aged 7–10 years hospitalized for the treatment of disruptive behaviors. The study team completed continuous behavioral phenotyping during study participation. The machine learning protocol examined severe behavioral outbursts (operationalized as episodes that preceded physical restraint) for preparing the training data. Supervised machine learning methods were trained with cross-validation to predict three behavior states—calm, playful, and disruptive. The participants had a 90% adherence rate for per protocol smartwatch use. Decision trees derived conditional dependencies of heart rate, sleep, and motor activity to predict behavior. A cross-validation demonstrated 80.89% accuracy of predicting the child's behavior state using these conditional dependencies. This study demonstrated the feasibility of 7-day continuous smartwatch monitoring for children with severe disruptive behaviors. A machine learning approach characterized predictive biomarkers of impending disruptive behaviors. Future validation studies will examine smartwatch physiological biomarkers to enhance behavioral interventions, increase parental engagement in treatment, and demonstrate target engagement in clinical trials of pharmacological agents for young children.
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影响因子：
5.6
作者：
Chinoy ED;Cuellar JA;Huwa KE;Jameson JT;Watson CH;Bessman SC;Hirsch DA;Cooper AD;Drummond SPA;Markwald RR
通讯作者：
Markwald RR
影响因子：
26.1
作者：
Voss, Catalin;Schwartz, Jessey;Wall, Dennis P.
通讯作者：
Wall, Dennis P.
影响因子：
5
作者：
Mackintosh, Kelly A.;Chappel, Stephanie E.;Ridgers, Nicola D.
通讯作者：
Ridgers, Nicola D.
影响因子：
5.9
作者：
Chaffin, Mark;Funderburk, Beverly;Gurwitch, Robin
通讯作者：
Gurwitch, Robin
DOI：
10.1056/nejmoa1901183
发表时间：
2019-11-14
期刊：
The New England journal of medicine
影响因子：
--
作者：
Perez MV;Mahaffey KW;Hedlin H;Rumsfeld JS;Garcia A;Ferris T;Balasubramanian V;Russo AM;Rajmane A;Cheung L;Hung G;Lee J;Kowey P;Talati N;Nag D;Gummidipundi SE;Beatty A;Hills MT;Desai S;Granger CB;Desai M;Turakhia MP;Apple Heart Study Investigators
通讯作者：
Apple Heart Study Investigators