| Autor: |
Runge, N., de Baets, L., Labie, C., Nijs, J., Verschueren, S., Malfliet, A., de Vlam, K., Van Assche, D., Mairesse, O. |
| Předmět: |
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| Zdroj: |
Pain Practice; 2022 Supplement, Vol. 22, p48-49, 2p |
| Abstrakt: |
Introduction: Up to >50% of individuals with symptomatic knee osteoarthritis (KOA) also report insomnia. Research suggests that the insomnia-pain link is bi-directional and complex with other factors like anxiety and mood being relevant. Network analysis allows to estimate these dynamic system interactions and to illustrate them graphically. This is an advantage over traditional statistical models (e.g. regression models) which investigate aspects of systems in isolation1. However, network analysis has not yet been utilized in the field of KOA and insomnia. Methods: We will use baseline data from a randomized controlled trial to estimate a cross-sectional network model connecting insomnia and pain in patients with KOA. The network structure will be estimated using Gaussian Graphical models with LASSO-regularization2. Established measures for sleep, pain, depression, anxiety, physical activity, pain catastrophizing and fatigue will be included. We will further analyse global network density and node centrality1. Results: The results will provide us a better understanding of how the network connecting pain and insomnia is built and which factors are most influential. Discussion: Our study will be the first that uses network analysis to understand the connection between KOA pain and insomnia. This will provide relevant clinical insights and interest other researchers in this exciting area of research. Process evaluation: Network analyses are new to me and there are not many examples in the field of chronic pain. It took me some time to understand the basics but the more I read the more excited I get about the opportunities these methods offer. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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