Predicting Workers’ Stress: Application of a High-Performance Algorithm Using Working-Style Characteristics (Preprint)
Authors: Iwamoto Hiroki , Nakano Saki , Tajima Ryotaro , Kiguchi Ryo , Yoshida Yuki , Kitanishi Yoshitake , Aoki Yasunori 
Published: August 2024

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: &lt;0.2 (more than 4 days/week in office), middle: 0.2 to &lt;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.

CLINICALTRIAL

UMIN UMIN000046394; https://www.umin.ac.jp/ctr/index.htm