Medizinische Universität Graz - Research portal
Selected Publication:
Jordan, S.
VAS-Assessment and Prediction of Fatigue in the General Population and in People with Multiple Sclerosis.
[ Diplomarbeit/Master Thesis (UNI) ] Universität Graz; 2024. pp.81.
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- Authors Med Uni Graz:
- Advisor:
- Pinter Daniela Theresia
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- Abstract:
- Introduction: In the general population about 10-20% report to suffer from fatigue (i.e., a subjectively perceived exhaustion). Fatigue is also a common and debilitating symptom affecting the majority of people with multiple sclerosis (pwMS). However, contribution factors of fatigue are poorly understood in both populations. Given limited time resources for assessment of fatigue and diverse contributing factors (sleep quality, physical activity, resilience) in clinical practice, this study aimed to explore whether visual analog scales (VAS) could serve as a potential alternative to more comprehensive, standardized questionnaires. Additionally, the study sought to identify the most relevant predictors of fatigue in the general population and in pwMS. Methods: In this cross-sectional study, demographic data, clinical variables (disease duration, severity of disability), and questionnaires assessing fatigue (FSMC), resilience (RS-13), anxiety and depression (HADS), sleep quality (PSQI), physical activity (GPAQ), as well as VAS corresponding to the questionnaires were collected. We obtained data from participants in the general population (N = 138) via an online survey and from pwMS (N = 53) during routine clinical check-ups. Results: Fatigue was highly prevalent, affecting 50% of the general population and 68% of pwMS. Fatigue assessed via the FSMC correlated strongly with the VAS of trait fatigue in the general population and pwMS (r = .61/r = .60). Strong to moderate correlations between VAS and questionnaires were observed for sleep quality and resilience in both populations, whereas for physical activity only a weak correlation was found in the general population and no significant correlation in pwMS. In the general population, resilience was the strongest predictor of total fatigue (β = -.39, p < .001), followed by depressive symptoms (β = .22, p = .01) and education (β = -.15, p = .04), explaining 30% of variance. Among pwMS, anxiety (β = .42, p = .001) was the most important predictor of total fatigue, followed by resilience (β = -.31, p = .01) and age (β = .25, p = .04), explaining 33% of variance. Conclusion: We found that fatigue is highly prevalent in both populations. While VAS are not a suitable replacement for more comprehensive questionnaires to assess physical activity or the main outcome of a study, they may serve as a potential alternative for fatigue, sleep or resilience assessment if defined as covariates. The prediction models showed that resilience and emotional factors are important contributors to fatigue. These findings emphasize the potential of psychoeducational interventions targeting anxiety, depression and resilience.