Browsing School of Nursing by Subject "Fatigue"
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Nurse Fatigue Increases the Risk of Sickness AbsenceIntroduction: Sickness absence (SA) is problematic in occupations requiring 24/7 coverage where one person's SA cascades into more work days, longer shift durations and elevated fatigued states for remaining workers. As part of this dissertation, a systematic literature review found strong evidence that fatigue increased the risk of SA in the workforce. Few studies examined this relationship in nurses, despite reported high fatigue and differences in shiftwork characteristics. Fatigue-risk scores generated from bio-mathematical fatigue models are popular in safety-sensitive industries and may be useful for assessing and monitoring fatigue on nursing units and predicting SA. Purpose: The purpose of this study was to explore prospective associations between work-related fatigue, bio-mathematically modeled fatigue-risk and SA in 12-hour shift hospital nurses. Methods: Two studies were conducted that used retrospective cohort design of hospital nurses representing four nursing units from a major pediatric hospital. Baseline data on work-related fatigue were from Fatigue Risk, Alertness Management Effectiveness (FRAME) study (n=40) using the self-reported Occupational Fatigue Exhaustion Recovery Scale. Data on fatigue-risk scores were generated from work-rest schedules of 197 nurses working 41,538 shifts using Fatigue Audit InterDyne (FAID) and Fatigue Risk Index (FRI) software programs. Work-related fatigue and fatigue-risk scores were then linked to SA data that were extracted from the hospital's attendance system. The statistical approach was generalized linear mixed models that account for non-independency of repeated measures. Results: The SA rate in both studies was ~5%. Among FRAME participants, for every 1SD increase in acute fatigue scores, nurses were 1.29 times more likely to be absent from work (OR=1.29, 95%CI=1.02-1.63). In the bio-mathematical model study, when FAID-scores were moderate (scores=41-79, OR=1.38, 95%CI=1.21-1.58) or high (scores=81-150, OR=1.67, 95%CI=1.42-1.95), nurses were more likely to take SA than nurses with lower (<41) scores. Similarly, when FRI-scores were >60, nurses were 1.58 times (95%CI=1.05-2.37) more likely to take SA compared to nurses with lower scores. Conclusion: Work-related acute fatigue and fatigue-risk modeled bio-mathematically significantly predicted nurses' SA. While surveys are instrumental in identifying the nature and severity of fatigue, bio-mathematical fatigue models may be more practical to monitor for day-to-day fatigue changes in the workplace.
Steps Towards an Intervention: Exploring Correlates and Measurement of Fatigue in OsteoarthritisBackground: Fatigue affects up to 90% of adults with osteoarthritis and contributes to disability and reduced quality of life. Treatment options are non-specific and limited to self-management. These limitations are due to least two gaps in current research: the lack of a standardized, reliable, and valid fatigue measure, and the lack of mechanistic insight. Purpose: To begin to address these limitations, the purposes of this three-manuscript dissertation were: 1) to examine standardized, valid, and reliable measures of osteoarthritis fatigue and 2) to explore correlates of fatigue to provide mechanistic insight. Methods: The first manuscript is a narrative literature review of osteoarthritis fatigue correlates. The second and third manuscripts analyze data from cross-sectional, retrospective studies. Analyzing pilot study data in SPSS and WINSTEPS, the second manuscript examines psychometrics of the standardized PROMIS Fatigue Short Forms 8a and 7a in osteoarthritis. The third manuscript uses data from the 2007-2010 National Health and Nutrition Examination Survey (NHANES) to examine fatigue correlates. Using SPSS complex samples analysis, adjusted logistic regression models were generated to predict odds of osteoarthritis fatigue as a function of a biological correlate (i.e., systemic inflammation: c-reactive protein [CRP] and white blood cell count [WBCC]). Results: Correlates of osteoarthritis fatigue include age, gender, medications, comorbidities, anxiety, depression, joint pain, physical activity, physical exercise, physical function, sleep quality, and systemic inflammation. The 8a and 7a were reliable (α =.86-.93) in adults with osteoarthritis. Differences existed in 8a, but not 7a, total scores, between adults with (N=20) and without osteoarthritis (t29=-2.8, p<.001; N=11). From the NHANES data, with every 1 mg/dL increase in CRP, adults with osteoarthritis had 3.19 times higher odds of fatigue (95% CI 1.11-9.19, p=.03) when controlling for age, pain, depression, sleep quantity, sleep disturbances, and body mass index. Conclusion: These findings have begun to fill the gaps that hindered development and testing of targeted interventions. Future research is necessary to gain more understanding of the use of the 7a and 8a in osteoarthritis and to delineate the relationship between other correlates, including additional systemic inflammatory markers, and fatigue in osteoarthritis. This will propel development and testing of targeted interventions.