背景:工作特点,例如远程工作速率,已经研究了与压力的关系。然而,使用与工作相关的数据来改进适合个人生活方式的高性能压力预测模型尚未得到评估。
目的:本研究旨在开发一种新颖的,高性能算法,用于预测具有相似工作特征的一组员工中员工的压力。
方法:这项前瞻性观察研究评估了参与者对基于网络的问卷的回答,包括使用可穿戴设备收集的考勤记录和数据。为期12周(2022年1月17日至2022年4月10日)的数据收集自194名Shionogi集团员工。参与者穿着FitbitCharge4可穿戴设备,收集每日睡眠数据,活动,和心率。每日工作班次数据包括工作时间的详细信息。每周问卷答复包括抑郁/焦虑的K6问卷,行为问卷,错过了午餐的天数。所提出的预测模型使用具有与预测目标人相似的工作风格特征的邻域聚类(N=20)。前一周的数据预测了下周的压力水平。通过选择合适的训练数据,比较了三种模型:(1)单模型,(2)提出办法1,和(3)提出办法2。从极端梯度提升(XGBoost)模型中计算了前10个提取特征的Shapley加法解释(SHAP),以评估按远程办公率(平均值)分类的数量和贡献方向:低:<0.2(超过4天/周办公室),中间:0.2至<0.6(办公室2至4天/周),和高:≥0.6(少于2天/周办公室)。
结果:使用来自190名参与者的数据,远程工作率从0%到79%不等。所提出的方法2的曲线下面积(AUC)为0.84(真阳性vs假阳性:0.77vs0.26)。在远程办公率较低的参与者中,提取的大部分特征都与睡眠有关,其次是活动和工作。在远程办公率高的参与者中,大多数特征都与活动有关,其次是睡眠和工作。SHAP分析显示,对于远程办公率高的参与者,不吃午饭,工作多于/少于计划,心率波动较大,和较低的平均睡眠时间有助于压力。在远程办公率较低的参与者中,上班太早或迟到(上午9点之前/之后),高于/低于平均心率,降低心率波动,和燃烧更多/更少的卡路里比正常有助于压力。
结论:根据远程工作速率形成具有相似工作方式的邻域聚类,并将其用作训练数据,从而提高了预测性能。邻域聚类方法的有效性由远程工作级别之间的贡献特征及其贡献方向的差异来表示。
背景:UMINUMIN000046394;https://www.乌明。AC.jp/ctr/index。htm.
BACKGROUND: Work characteristics, such as teleworking rate, have been studied in relation to stress. However, the use of work-related data to improve a high-performance stress prediction model that suits an individual\'s lifestyle has not been evaluated.
OBJECTIVE: This study aims to develop a novel, high-performance algorithm to predict an employee\'s stress among a group of employees with similar working characteristics.
METHODS: This prospective observational study evaluated participants\' responses to web‑based questionnaires, including attendance records and data collected using a wearable device. Data spanning 12 weeks (between January 17, 2022, and April 10, 2022) were collected from 194 Shionogi Group employees. Participants wore the Fitbit Charge 4 wearable device, which collected data on daily sleep, activity, and heart rate. Daily work shift data included details of working hours. Weekly questionnaire responses included the K6 questionnaire for depression/anxiety, a behavioral questionnaire, and the number of days lunch was missed. The proposed prediction model used a neighborhood cluster (N=20) with working-style characteristics similar to those of the prediction target person. Data from the previous week predicted stress levels the following week. Three models were compared by selecting appropriate training data: (1) single model, (2) proposed method 1, and (3) proposed method 2. Shapley Additive Explanations (SHAP) were calculated for the top 10 extracted features from the Extreme Gradient Boosting (XGBoost) model to evaluate the amount and contribution direction categorized by teleworking rates (mean): low: <0.2 (more than 4 days/week in office), middle: 0.2 to <0.6 (2 to 4 days/week in office), and high: ≥0.6 (less than 2 days/week in office).
RESULTS: Data from 190 participants were used, with a teleworking rate ranging from 0% to 79%. The area under the curve (AUC) of the proposed method 2 was 0.84 (true positive vs false positive: 0.77 vs 0.26). Among participants with low teleworking rates, most features extracted were related to sleep, followed by activity and work. Among participants with high teleworking rates, most features were related to activity, followed by sleep and work. SHAP analysis showed that for participants with high teleworking rates, skipping lunch, working more/less than scheduled, higher fluctuations in heart rate, and lower mean sleep duration contributed to stress. In participants with low teleworking rates, coming too early or late to work (before/after 9 AM), a higher/lower than mean heart rate, lower fluctuations in heart rate, and burning more/fewer calories than normal contributed to stress.
CONCLUSIONS: Forming a neighborhood cluster with similar working styles based on teleworking rates and using it as training data improved the prediction performance. The validity of the neighborhood cluster approach is indicated by differences in the contributing features and their contribution directions among teleworking levels.
BACKGROUND: UMIN UMIN000046394; https://www.umin.ac.jp/ctr/index.htm.