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The British Journal of Rheumatology, Vol 37, 71-81, Copyright © 1998 by British Society for Rheumatology


ORIGINAL PAPERS

Sleep in rheumatoid arthritis: a comparison with healthy subjects and studies of sleep/wake interactions

AM Drewes, L Svendsen, SJ Taagholt, K Bjerregard, KD Nielsen and B Hansen
Department of Internal Medicine M, Aalborg Hospital, Denmark.

Sleep complaints are frequent in patients with rheumatoid arthritis (RA) and sleep disturbances may contribute to pain and other daytime complaints. The aims of the current study were to compare ambulatory sleep recordings from consecutively selected patients with RA to those obtained in healthy controls, and to study the relationships between sleep structure and clinical symptoms. Sleep recordings were obtained from 41 out-patients with RA and 19 matched controls. All had clinical examinations and completed different questionnaires. Recordings were scored traditionally and, moreover, the electroencephalography (EEG) was subjected to frequency analysis. For the study of sleep-wake interactions in the patients, a graphical chain model was used. The patients had many sleep-related complaints. An increase in the number of periodic movements of the legs (PML) during sleep was seen in comparison with controls, but otherwise only minor differences were observed in classical sleep stages. Data from frequency analysis showed an increase in alpha (8-12 Hz)-EEG activity in sleep stages non-rapid eye movement (NREM) 2-4 in most sleep cycles. The statistical model demonstrated a complex but independent correlation between morning stiffness, pain and joint tenderness on the one hand, and awakenings, stage NREM2, slow-wave sleep and stage REM on the other, probably reflecting a relationship between sleep patterns and pain in RA. In conclusion, only the increase in PML and alpha-EEG activity distinguished the sleep in RA patients from that of healthy controls. However, the demonstrated interaction between daytime complaints and sleep patterns may increase the understanding and treatment of the disease. In future research, graphical chain models may improve our understanding of complex relationships between multiple variables.
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