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{
    "pk": 17135,
    "title": "Applying a Smartwatch to Predict Work-related Fatigue for Emergency Healthcare Professionals: Machine Learning Method",
    "subtitle": null,
    "abstract": "Introduction: \nHealthcare 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.\nMethods:\n 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. Wecalculated 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.\nResults:\n 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 <35 years in age, XGBoost classifier obtained excellent performance of AUC (0.928, 95% CI 0.839–0.991) on the test set.\nConclusion:\n By using features derived from a smartwatch, we successfully built ML models capable of classifying the risk of work-related fatigue in the ED. By collecting more data to optimize the models, it should be possible to use smartwatch-based ML models in the future to predict work-related fatigue and adopt preventive measures for emergency clinicians.",
    "language": "en",
    "license": {
        "name": "Creative Commons Attribution 4.0",
        "short_name": "CC BY 4.0",
        "text": "Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.\r\n\r\nNo additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.",
        "url": "https://creativecommons.org/licenses/by/4.0"
    },
    "keywords": [
        {
            "word": "work-related fatigue"
        },
        {
            "word": "smartwatch"
        },
        {
            "word": "machine learning"
        },
        {
            "word": "Multidimensional Fatigue Inventory"
        },
        {
            "word": "emergency department"
        }
    ],
    "section": "Emergency Department Operations",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/15b4c3t2",
    "frozenauthors": [
        {
            "first_name": "Sot Shih-Hung",
            "middle_name": "",
            "last_name": "Liu",
            "name_suffix": "",
            "institution": "National Taiwan University Hospital, Department of Emergency Medicine, Taipei, Taiwan; National Taiwan University, Graduate Institute of Biomedical Electronics and Bioinformatics, Taipei, Taiwan",
            "department": "None"
        },
        {
            "first_name": "Cheng-Jiun",
            "middle_name": "",
            "last_name": "Ma",
            "name_suffix": "",
            "institution": "MOST Joint Research Center for AI Technology and All VISTA Healthcare (AINTU), Taipei, Taiwan",
            "department": "None"
        },
        {
            "first_name": "Fan-Ya",
            "middle_name": "",
            "last_name": "Chou",
            "name_suffix": "",
            "institution": "National Taiwan University Hospital, Department of Emergency Medicine, Taipei, Taiwan; National Taiwan University, Department of Emergency Medicine, College of Medicine, Taipei, Taiwan",
            "department": "None"
        },
        {
            "first_name": "Michelle Yuan-Chiao",
            "middle_name": "",
            "last_name": "Cheng",
            "name_suffix": "",
            "institution": "MOST Joint Research Center for AI Technology and All VISTA Healthcare (AINTU), Taipei, Taiwan",
            "department": "None"
        },
        {
            "first_name": "Chih-Hung",
            "middle_name": "",
            "last_name": "Wang",
            "name_suffix": "",
            "institution": "National Taiwan University Hospital, Department of Emergency Medicine, Taipei, Taiwan; National Taiwan University, Department of Emergency Medicine, College of Medicine, Taipei, Taiwan",
            "department": "None"
        },
        {
            "first_name": "Chu-Lin",
            "middle_name": "",
            "last_name": "Tsai",
            "name_suffix": "",
            "institution": "National Taiwan University Hospital, Department of Emergency Medicine, Taipei, Taiwan; National Taiwan University, Department of Emergency Medicine, College of Medicine, Taipei, Taiwan",
            "department": "None"
        },
        {
            "first_name": "Wei-Jou",
            "middle_name": "",
            "last_name": "Duh",
            "name_suffix": "",
            "institution": "MOST Joint Research Center for AI Technology and All VISTA Healthcare (AINTU), Taipei, Taiwan",
            "department": "None"
        },
        {
            "first_name": "Chien-Hua",
            "middle_name": "",
            "last_name": "Huang",
            "name_suffix": "",
            "institution": "National Taiwan University Hospital, Department of Emergency Medicine, Taipei, Taiwan; National Taiwan University, Department of Emergency Medicine, College of Medicine, Taipei, Taiwan",
            "department": "None"
        },
        {
            "first_name": "Feipei",
            "middle_name": "",
            "last_name": "Lai",
            "name_suffix": "",
            "institution": "National Taiwan University, Graduate Institute of Biomedical Electronics and Bioinformatics, Taipei, Taiwan; National Taiwan University, Department of Computer Science and Information Engineering, Taipei, Taiwan",
            "department": "None"
        },
        {
            "first_name": "Tsung-Chien",
            "middle_name": "",
            "last_name": "Lu",
            "name_suffix": "",
            "institution": "National Taiwan University Hospital, Department of Emergency Medicine, Taipei, Taiwan; National Taiwan University, Department of Emergency Medicine, College of Medicine, Taipei, Taiwan",
            "department": "None"
        }
    ],
    "date_submitted": "2022-07-23T15:37:40Z",
    "date_accepted": "2022-07-23T15:37:40Z",
    "date_published": "2023-07-07T18:01:10Z",
    "render_galley": null,
    "galleys": [
        {
            "label": "",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/westjem/article/17135/galley/8656/download/"
        }
    ]
}