Dimensions of Hospital Nurse Fatigue: Improving Clinical Outcomes with Translational Research

Purpose: The purpose of this study was to evaluate a strategy to profile fatigue dimensions for hospital nurses. The American Nurses Association (2014) updated a position statement to address nurse fatigue to promote safety and health in September 2014. The statement emphasizes the joint or shared roles and responsibilities of registered nurse and their employers to reduce risks from nurse fatigue. A presidential task force from the American College of Occupational and Environmental Medicine (2012) on fatigue risk management recognized the significant impact of fatigue for employees in the 24/7 work environments. Many fatigue instruments have been used to test nurse fatigue, each measuring unique concepts and dimensions of fatigue. This research proposes to advance the investigation of hospital nurse fatigue using the framework of a hospital nurse fatigue theory (Drake, Luna, Georges & Barker-Steege, 2012) and evaluate fatigue dimensions sensitive to hospital nurses. The health, safety, and productivity of hospital nurses are at significant risk and can benefit from methods that clarify fatigue risk profiles and design targeted interventions.

Methods: A secondary analysis of a hospital nurse fatigue survey was conducted using a cohort of patient-care (not manager or director) nurses. The initial study received Institutional Review Board approval and was a 100-item online survey. The hospital nurse fatigue survey was emailed to approximately 1000 hospital nurses. Of the 420 responders, 340 nurses completed 90% the survey items and 245 identified as patient-care nurse cohort. Latent profile analysis (LPA) was used to identify fatigue profiles for the patient-care nurse cohort based on five instruments that measure different concepts of fatigue: the Chalder Physical Fatigue Scale, Chalder Mental Fatigue Scale, Occupational Fatigue Exhaustion Recovery (OFER) Chronic Fatigue scale, OFER Acute Fatigue scale, and OFER Intershift Recovery scale.  The investigators used the Mplus version 7.1 to conduct LPA and a range of information criteria such as AIC (Akaike’s information criterion), BIC (Bayesian information criterion), and CAIC (consistent AIC) to determine the best fit for the number of model profiles.  Competing models (k profiles vs. k-1 profiles) were also evaluated using the Lo-Mendell-Rubin likelihood ratio test and the Vuong-Lo-Mendell-Rubin likelihood ratio test. ANOVA was performed comparing fatigue with professional, adaptive and bio-political variables to characterize differences between the profile groups.

Results: A model with three latent profiles emerged as the best fit. The three profiles were categorized as: high fatigue/low recovery (23% of sample), moderate fatigue and recovery (30%), and low fatigue/high recovery (47%). Nurses in the high/fatigue low recovery group were significantly less likely to participate in meditation or exercise, have lower levels of job satisfaction and rate their hospital safety practice scores lower. Low fatigue/high recovery nurses were more likely to have less sleepiness, be older, worked as a nurse more years and rated their professional competency higher.

Conclusion: The model with three latent profiles was a significant improvement upon a two-profile model. It is possible that more experienced hospital nurses may underrate their levels of fatigue, however it is likely they have developed strategies to improve recovery and have lower rates of fatigue. Strategies to improve work recovery and lower fatigue can be re-evaluated with informed awareness by nurses and employers.  Hospital nurse fatigue is multidimensional and can be grouped into risk profiles to inform nurse fatigue policy, provide and test relevant interventions and promote improvements in related clinical outcomes.