Saito Tomoki, Suzuki Hikaru, Kishi Akifumi
JMDC Inc., Tokyo, Japan.
Graduate School of Education, The University of Tokyo, Tokyo, Japan.
Front Digit Health. 2022 Apr 14;4:861808. doi: 10.3389/fdgth.2022.861808. eCollection 2022.
The prevention and treatment of mental illness is a serious social issue. Prediction and intervention, however, have been difficult because of lack of objective biomarkers for mental illness. The objective of this study was to use biometric data acquired from wearable devices as well as medical examination data to build a predictive model that can contribute to the prevention of the onset of mental illness. This was an observational study of 4,612 subjects from the health database of society-managed health insurance in Japan provided by JMDC Inc. The inputs to the predictive model were 3-months of continuous wearable data and medical examinations within and near that period; the output was the presence or absence of mental illness over the following month, as defined by insurance claims data. The features relating to the wearable data were sleep, activity, and resting heart rate, measured by a consumer-grade wearable device (specifically, Fitbit). The predictive model was built using the XGBoost algorithm and presented an area-under-the-receiver-operating-characteristic curve of 0.712 ( = 0.02, a repeated stratified group 10-fold cross validation). The top-ranking feature importance measure was wearable data, and its importance was higher than the blood-test values from medical examinations. Detailed verification of the model showed that predictions were made based on disrupted sleep rhythms, mild physical activity duration, alcohol use, and medical examination data on disrupted eating habits as risk factors. In summary, the predictive model showed useful accuracy for grouping the risk of mental illness onset, suggesting the potential of predictive detection, and preventive intervention using wearable devices. Sleep abnormalities in particular were detected as wearable data 3 months prior to mental illness onset, and the possibility of early intervention targeting the stabilization of sleep as an effective measure for mental illness onset was shown.
精神疾病的预防和治疗是一个严重的社会问题。然而，由于缺乏精神疾病的客观生物标志物，预测和干预一直很困难。本研究的目的是利用从可穿戴设备获取的生物特征数据以及医学检查数据来构建一个预测模型，该模型有助于预防精神疾病的发作。这是一项对由JMDC公司提供的日本社会管理健康保险健康数据库中的4612名受试者进行的观察性研究。预测模型的输入是连续3个月的可穿戴数据以及该时间段内及临近时间段的医学检查；输出是根据保险理赔数据定义的接下来一个月内是否患有精神疾病。与可穿戴数据相关的特征包括睡眠、活动和静息心率，由消费级可穿戴设备（具体为Fitbit）测量。预测模型使用XGBoost算法构建，受试者工作特征曲线下面积为0.712（ = 0.02，重复分层组10折交叉验证）。排名最高的特征重要性度量是可穿戴数据，其重要性高于医学检查中的血液检测值。对该模型的详细验证表明，预测是基于睡眠节律紊乱、轻度身体活动持续时间、饮酒以及饮食习惯紊乱的医学检查数据等风险因素做出的。总之，该预测模型在对精神疾病发作风险进行分组方面显示出有用的准确性，表明了使用可穿戴设备进行预测检测和预防性干预的潜力。特别是睡眠异常在精神疾病发作前3个月被检测为可穿戴数据，这表明了以稳定睡眠为目标进行早期干预作为预防精神疾病发作有效措施的可能性。
