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Sensor-based measurement of critical care nursing workload: Unobtrusive measures of nursing activity complement traditional task and patient level indicators of workload to predict perceived exertion
OBJECTIVE: To establish the validity of sensor-based measures of work processes for predicting perceived mental and physical exertion of critical care nurses. MATERIALS AND METHODS: Repeated measures mixed-methods study in a surgical intensive care unit. Wearable and environmental sensors captured w...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6193584/ https://www.ncbi.nlm.nih.gov/pubmed/30312326 http://dx.doi.org/10.1371/journal.pone.0204819 |
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author | Rosen, Michael A. Dietz, Aaron S. Lee, Nam Wang, I-Jeng Markowitz, Jared Wyskiel, Rhonda M. Yang, Ting Priebe, Carey E. Sapirstein, Adam Gurses, Ayse P. Pronovost, Peter J. |
author_facet | Rosen, Michael A. Dietz, Aaron S. Lee, Nam Wang, I-Jeng Markowitz, Jared Wyskiel, Rhonda M. Yang, Ting Priebe, Carey E. Sapirstein, Adam Gurses, Ayse P. Pronovost, Peter J. |
author_sort | Rosen, Michael A. |
collection | PubMed |
description | OBJECTIVE: To establish the validity of sensor-based measures of work processes for predicting perceived mental and physical exertion of critical care nurses. MATERIALS AND METHODS: Repeated measures mixed-methods study in a surgical intensive care unit. Wearable and environmental sensors captured work process data. Nurses rated their mental (ME) and physical exertion (PE) for each four-hour block, and recorded patient and staffing-level workload factors. Shift was the grouping variable in multilevel modeling where sensor-based measures were used to predict nursing perceptions of exertion. RESULTS: There were 356 work hours from 89 four-hour shift segments across 35 bedside nursing shifts. In final models, sensor-based data accounted for 73% of between-shift, and 5% of within-shift variance in ME; and 55% of between-shift, and 55% of within-shift variance in PE. Significant predictors of ME were patient room noise (ß = 0.30, p < .01), the interaction between time spent and activity levels outside main work areas (ß = 2.24, p < .01), and the interaction between the number of patients on an insulin drip and the burstiness of speaking (ß = 0.19, p < .05). Significant predictors of PE were environmental service area noise (ß = 0.18, p < .05), and interactions between: entropy and burstiness of physical transitions (ß = 0.22, p < .01), time speaking outside main work areas and time at nursing stations (ß = 0.37, p < .001), service area noise and time walking in patient rooms (ß = -0.19, p < .05), and average patient load and nursing station speaking volume (ß = 0.30, p < .05). DISCUSSION: Analysis yielded highly predictive models of critical care nursing workload that generated insights into workflow and work design. Future work should focus on tighter connections to psychometric test development methods and expansion to a broader variety of settings and professional roles. CONCLUSIONS: Sensor-based measures are predictive of perceived exertion, and are viable complements to traditional task demand measures of workload. |
format | Online Article Text |
id | pubmed-6193584 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-61935842018-11-05 Sensor-based measurement of critical care nursing workload: Unobtrusive measures of nursing activity complement traditional task and patient level indicators of workload to predict perceived exertion Rosen, Michael A. Dietz, Aaron S. Lee, Nam Wang, I-Jeng Markowitz, Jared Wyskiel, Rhonda M. Yang, Ting Priebe, Carey E. Sapirstein, Adam Gurses, Ayse P. Pronovost, Peter J. PLoS One Research Article OBJECTIVE: To establish the validity of sensor-based measures of work processes for predicting perceived mental and physical exertion of critical care nurses. MATERIALS AND METHODS: Repeated measures mixed-methods study in a surgical intensive care unit. Wearable and environmental sensors captured work process data. Nurses rated their mental (ME) and physical exertion (PE) for each four-hour block, and recorded patient and staffing-level workload factors. Shift was the grouping variable in multilevel modeling where sensor-based measures were used to predict nursing perceptions of exertion. RESULTS: There were 356 work hours from 89 four-hour shift segments across 35 bedside nursing shifts. In final models, sensor-based data accounted for 73% of between-shift, and 5% of within-shift variance in ME; and 55% of between-shift, and 55% of within-shift variance in PE. Significant predictors of ME were patient room noise (ß = 0.30, p < .01), the interaction between time spent and activity levels outside main work areas (ß = 2.24, p < .01), and the interaction between the number of patients on an insulin drip and the burstiness of speaking (ß = 0.19, p < .05). Significant predictors of PE were environmental service area noise (ß = 0.18, p < .05), and interactions between: entropy and burstiness of physical transitions (ß = 0.22, p < .01), time speaking outside main work areas and time at nursing stations (ß = 0.37, p < .001), service area noise and time walking in patient rooms (ß = -0.19, p < .05), and average patient load and nursing station speaking volume (ß = 0.30, p < .05). DISCUSSION: Analysis yielded highly predictive models of critical care nursing workload that generated insights into workflow and work design. Future work should focus on tighter connections to psychometric test development methods and expansion to a broader variety of settings and professional roles. CONCLUSIONS: Sensor-based measures are predictive of perceived exertion, and are viable complements to traditional task demand measures of workload. Public Library of Science 2018-10-12 /pmc/articles/PMC6193584/ /pubmed/30312326 http://dx.doi.org/10.1371/journal.pone.0204819 Text en © 2018 Rosen et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Rosen, Michael A. Dietz, Aaron S. Lee, Nam Wang, I-Jeng Markowitz, Jared Wyskiel, Rhonda M. Yang, Ting Priebe, Carey E. Sapirstein, Adam Gurses, Ayse P. Pronovost, Peter J. Sensor-based measurement of critical care nursing workload: Unobtrusive measures of nursing activity complement traditional task and patient level indicators of workload to predict perceived exertion |
title | Sensor-based measurement of critical care nursing workload: Unobtrusive measures of nursing activity complement traditional task and patient level indicators of workload to predict perceived exertion |
title_full | Sensor-based measurement of critical care nursing workload: Unobtrusive measures of nursing activity complement traditional task and patient level indicators of workload to predict perceived exertion |
title_fullStr | Sensor-based measurement of critical care nursing workload: Unobtrusive measures of nursing activity complement traditional task and patient level indicators of workload to predict perceived exertion |
title_full_unstemmed | Sensor-based measurement of critical care nursing workload: Unobtrusive measures of nursing activity complement traditional task and patient level indicators of workload to predict perceived exertion |
title_short | Sensor-based measurement of critical care nursing workload: Unobtrusive measures of nursing activity complement traditional task and patient level indicators of workload to predict perceived exertion |
title_sort | sensor-based measurement of critical care nursing workload: unobtrusive measures of nursing activity complement traditional task and patient level indicators of workload to predict perceived exertion |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6193584/ https://www.ncbi.nlm.nih.gov/pubmed/30312326 http://dx.doi.org/10.1371/journal.pone.0204819 |
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