The Burnout PRedictiOn Using Wearable aNd ArtIficial IntelligEnce (BROWNIE) study: a decentralized digital health protocol to predict burnout in registered nurses
Abstract Background When job demand exceeds job resources, burnout occurs. Burnout in healthcare workers extends beyond negatively affecting their functioning and physical and mental health; it also has been associated with poor medical outcomes for patients. Data-driven technology holds promise for...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2024-02-01
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| Series: | BMC Nursing |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12912-024-01711-8 |
| _version_ | 1797275284256325632 |
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| author | Angelina R. Wilton Katharine Sheffield Quantia Wilkes Sherry Chesak Joel Pacyna Richard Sharp Paul E. Croarkin Mohit Chauhan Liselotte N. Dyrbye William V. Bobo Arjun P. Athreya |
| author_facet | Angelina R. Wilton Katharine Sheffield Quantia Wilkes Sherry Chesak Joel Pacyna Richard Sharp Paul E. Croarkin Mohit Chauhan Liselotte N. Dyrbye William V. Bobo Arjun P. Athreya |
| author_sort | Angelina R. Wilton |
| collection | DOAJ |
| description | Abstract Background When job demand exceeds job resources, burnout occurs. Burnout in healthcare workers extends beyond negatively affecting their functioning and physical and mental health; it also has been associated with poor medical outcomes for patients. Data-driven technology holds promise for the prediction of occupational burnout before it occurs. Early warning signs of burnout would facilitate preemptive institutional responses for preventing individual, organizational, and public health consequences of occupational burnout. This protocol describes the design and methodology for the decentralized Burnout PRedictiOn Using Wearable aNd ArtIficial IntelligEnce (BROWNIE) Study. This study aims to develop predictive models of occupational burnout and estimate burnout-associated costs using consumer-grade wearable smartwatches and systems-level data. Methods A total of 360 registered nurses (RNs) will be recruited in 3 cohorts. These cohorts will serve as training, testing, and validation datasets for developing predictive models. Subjects will consent to one year of participation, including the daily use of a commodity smartwatch that collects heart rate, step count, and sleep data. Subjects will also complete online baseline and quarterly surveys assessing psychological, workplace, and sociodemographic factors. Routine administrative systems-level data on nursing care outcomes will be abstracted weekly. Discussion The BROWNIE study was designed to be decentralized and asynchronous to minimize any additional burden on RNs and to ensure that night shift RNs would have equal accessibility to study resources and procedures. The protocol employs novel engagement strategies with participants to maintain compliance and reduce attrition to address the historical challenges of research using wearable devices. Trial Registration NCT05481138. |
| first_indexed | 2024-03-07T15:12:03Z |
| format | Article |
| id | doaj.art-7296f8a09da0475d871c2a2c03c70307 |
| institution | Directory Open Access Journal |
| issn | 1472-6955 |
| language | English |
| last_indexed | 2024-03-07T15:12:03Z |
| publishDate | 2024-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Nursing |
| spelling | doaj.art-7296f8a09da0475d871c2a2c03c703072024-03-05T18:36:56ZengBMCBMC Nursing1472-69552024-02-0123111410.1186/s12912-024-01711-8The Burnout PRedictiOn Using Wearable aNd ArtIficial IntelligEnce (BROWNIE) study: a decentralized digital health protocol to predict burnout in registered nursesAngelina R. Wilton0Katharine Sheffield1Quantia Wilkes2Sherry Chesak3Joel Pacyna4Richard Sharp5Paul E. Croarkin6Mohit Chauhan7Liselotte N. Dyrbye8William V. Bobo9Arjun P. Athreya10Dept. of Molecular Pharmacology and Experimental Therapeutics, Mayo ClinicDivision of Nursing Research, Mayo ClinicDivision of Nursing Research, Mayo ClinicDivision of Nursing Research, Mayo ClinicDept. of Quantitative Health Sciences, Mayo ClinicDept. of Quantitative Health Sciences, Mayo ClinicDept. of Molecular Pharmacology and Experimental Therapeutics, Mayo ClinicDept. of Psychiatry and Psychology, Mayo ClinicDept. of Medicine, University of Colorado Anschutz School of MedicineDept. of Psychiatry and Psychology, Mayo ClinicDept. of Molecular Pharmacology and Experimental Therapeutics, Mayo ClinicAbstract Background When job demand exceeds job resources, burnout occurs. Burnout in healthcare workers extends beyond negatively affecting their functioning and physical and mental health; it also has been associated with poor medical outcomes for patients. Data-driven technology holds promise for the prediction of occupational burnout before it occurs. Early warning signs of burnout would facilitate preemptive institutional responses for preventing individual, organizational, and public health consequences of occupational burnout. This protocol describes the design and methodology for the decentralized Burnout PRedictiOn Using Wearable aNd ArtIficial IntelligEnce (BROWNIE) Study. This study aims to develop predictive models of occupational burnout and estimate burnout-associated costs using consumer-grade wearable smartwatches and systems-level data. Methods A total of 360 registered nurses (RNs) will be recruited in 3 cohorts. These cohorts will serve as training, testing, and validation datasets for developing predictive models. Subjects will consent to one year of participation, including the daily use of a commodity smartwatch that collects heart rate, step count, and sleep data. Subjects will also complete online baseline and quarterly surveys assessing psychological, workplace, and sociodemographic factors. Routine administrative systems-level data on nursing care outcomes will be abstracted weekly. Discussion The BROWNIE study was designed to be decentralized and asynchronous to minimize any additional burden on RNs and to ensure that night shift RNs would have equal accessibility to study resources and procedures. The protocol employs novel engagement strategies with participants to maintain compliance and reduce attrition to address the historical challenges of research using wearable devices. Trial Registration NCT05481138.https://doi.org/10.1186/s12912-024-01711-8BurnoutWearablesArtificial intelligenceMachine learning |
| spellingShingle | Angelina R. Wilton Katharine Sheffield Quantia Wilkes Sherry Chesak Joel Pacyna Richard Sharp Paul E. Croarkin Mohit Chauhan Liselotte N. Dyrbye William V. Bobo Arjun P. Athreya The Burnout PRedictiOn Using Wearable aNd ArtIficial IntelligEnce (BROWNIE) study: a decentralized digital health protocol to predict burnout in registered nurses BMC Nursing Burnout Wearables Artificial intelligence Machine learning |
| title | The Burnout PRedictiOn Using Wearable aNd ArtIficial IntelligEnce (BROWNIE) study: a decentralized digital health protocol to predict burnout in registered nurses |
| title_full | The Burnout PRedictiOn Using Wearable aNd ArtIficial IntelligEnce (BROWNIE) study: a decentralized digital health protocol to predict burnout in registered nurses |
| title_fullStr | The Burnout PRedictiOn Using Wearable aNd ArtIficial IntelligEnce (BROWNIE) study: a decentralized digital health protocol to predict burnout in registered nurses |
| title_full_unstemmed | The Burnout PRedictiOn Using Wearable aNd ArtIficial IntelligEnce (BROWNIE) study: a decentralized digital health protocol to predict burnout in registered nurses |
| title_short | The Burnout PRedictiOn Using Wearable aNd ArtIficial IntelligEnce (BROWNIE) study: a decentralized digital health protocol to predict burnout in registered nurses |
| title_sort | burnout prediction using wearable and artificial intelligence brownie study a decentralized digital health protocol to predict burnout in registered nurses |
| topic | Burnout Wearables Artificial intelligence Machine learning |
| url | https://doi.org/10.1186/s12912-024-01711-8 |
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