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Trajectories of Fatigue and Their Influence on Stress, Anxiety, and Mood During the Covid-19 Lockdowns: A Cohort Study with Ecological Momentary Assessment
27 Pages Posted: 24 Jul 2024
More...Abstract
Background: Fatigue is a debilitating symptom characterised by tiredness, weakness, and lack of energy, affecting approximately 20% of the global adult population. The COVID-19 pandemic resulted in high levels of fatigue, while affecting individuals differently. This study aimed to identify resilient individuals and those at risk of experiencing fatigue during the first COVID-19 lockdown to determine risk and protective factors and to predict fatigue, stress, anxiety, and mood dynamics during the second lockdown.
Methods: In this ecological momentary assessment (EMA) study, 292 participants (231 women; Mage = 35·3 years, SDage = 13·1) provided data on fatigue, stress, anxiety, and mood for two seven-day measurement periods (20,343 observations) during the first two national lockdowns in Austria and Germany. Additionally, 85 participants provided hair strands for the analysis of hair cortisol as an endocrine measure of long-term stress. We used growth mixture modelling to identify latent trajectory classes for average daily fatigue during the first lockdown. Based on these classes, we used linear mixed models to predict longitudinal responses and hair cortisol during the second lockdown.
Outcomes: We identified a high (n = 82), moderate (n = 152), and low (n = 58) fatigue class. Highly fatigued individuals were characterised by younger age, lower educational level, lower socioeconomic status, higher chronic stress, and higher loneliness. Moreover, individuals who were fatigued during the first lockdown were more likely to be fatigued, stressed, and anxious and report low mood during the second lockdown. Fatigue classes did not predict differences in hair cortisol levels.
Interpretation: Our findings reveal distinct fatigue classes during the first lockdown, with age, education, socioeconomic status, chronic stress, and loneliness as risk factors of fatigue. Identifying fatigued individuals early during crises could help inform appropriate intervention strategies for those most at risk.
Funding: University of Vienna; Austrian Science Fund (FWF).
Declaration of Interest: We declare no competing interests.
Ethical Approval: The local ethics review board of University of Vienna approved the study. All participants provided electronic consent at the beginning of the study.
Keywords: Fatigue, hair cortisol, ecological momentary assessment, growth mixture modelling, latent class growth analysis, COVID-19
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