ArticlePloS one2018
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.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Smart agent system for insulin infusion protocol management: a simulation-based human factors evaluation study.BMJ quality & safety · 2021Trial
- RutiSafeNet: a behavioral risk and nursing workload monitoring tool for open-door acute inpatient mental health units.Frontiers in psychiatry · 2026Article
- Article
- Eye tracking and audio sensors to evaluate surgeon's non-technical skills: An empirical study.Applied ergonomics · 2024Article
- Wearable Systems for Unveiling Collective Intelligence in Clinical Settings.Sensors (Basel, Switzerland) · 2023Review
- Correlation Between the Social Network Structure and Well-Being of Health Care Workers in Intensive Care Units: Prospective Observational Study.Interactive journal of medical research · 2023Article
- The Implication of Physically Demanding and Hazardous Work on Retirement Timing.International journal of environmental research and public health · 2022Article
- The Future of Critical Care: Optimizing Technologies and a Learning Healthcare System to Potentiate a More Humanistic Approach to Critical Care.Critical care explorations · 2022Article
- Using team science in vascularized composite allotransplantation to improve team and patient outcomes.Frontiers in psychology · 2022Article
- Social Network Analysis of Intensive Care Unit Health Care Professionals Measured by Wearable Sociometric Badges: Longitudinal Observational Study.Journal of medical Internet research · 2020Observational
- How ICU Patient Severity Affects Communicative Interactions Between Healthcare Professionals: A Study Utilizing Wearable Sociometric Badges.Frontiers in medicine · 2020Article
- Use of a Real-Time Location System to Understand Resident Location in an Academic Medical Center.Journal of graduate medical education · 2019Article
- Laborious but Elaborate: The Benefits of Really Studying Team Dynamics.Frontiers in psychology · 2019Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
objectiveTo establish the validity of sensor-based measures of work processes for predicting perceived mental and physical exertion of critical care nurses. MATERIALS AND
methodsRepeated 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.
resultsThere 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.
conclusionsSensor-based measures are predictive of perceived exertion, and are viable complements to traditional task demand measures of workload.
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