Applying a Smartwatch to Predict Work-related Fatigue for Emergency Healthcare Professionals: Machine Learning Method

• Published in: The western journal of emergency medicine 2023-07, Vol.24 (4), p.693-702
• Main Authors: Liu, Sot Shih-Hung, Ma, Cheng-Jiun, Chou, Fan-Ya, Cheng, Michelle Yuan-Chiao, Wang, Chih-Hung, Tsai, Chu-Lin, Duh, Wei-Jou, Huang, Chien-Hua, Lai, Feipei, Lu, Tsung-Chien
• Format: Article
• Language: English
• Subjects: ED Operations; Emergency medical care; Machine learning; Medical personnel; Nurses; Smartwatches; Wearable computers
• ISSN: 1936-900X; 1936-9018; 1936-9018
• DOI: 10.5811/westjem.58139

Healthcare professionals frequently experience work-related fatigue, which may jeopardize their health and put patient safety at risk. In this study, we applied a machine learning (ML) approach based on data collected from a smartwatch to construct prediction models of work-related fatigue for emergency clinicians. We conducted this prospective study at the emergency department (ED) of a tertiary teaching hospital from March 10-June 20, 2021, where we recruited physicians, nurses, and nurse practitioners. All participants wore a commercially available smartwatch capable of measuring various physiological data during the experiment. Participants completed the Multidimensional Fatigue Inventory (MFI) web form before and after each of their work shifts. We calculated and labeled the before-and-after-shift score differences between each pair of scores. Using several tree-based algorithms, we constructed the prediction models based on features collected from the smartwatch. Records were split into training/validation and testing sets at a 70:30 ratio, and we evaluated the performances using the area under the curve (AUC) measure of receiver operating characteristic on the test set. In total, 110 participants were included in this study, contributing to a set of 1,542 effective records. Of these records, 85 (5.5%) were labeled as having work-related fatigue when setting the MFI difference between two standard deviations as the threshold. The mean age of the participants was 29.6. Most of the records were collected from nurses (87.7%) and females (77.5%). We selected a union of 31 features to construct the models. For total participants, CatBoost classifier achieved the best performances of AUC (0.838, 95% confidence interval [CI] 0.742-0.918) to identify work-related fatigue. By focusing on a subgroup of nurses