期刊
PAIN
卷 159, 期 7, 页码 1346-1358出版社
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/j.pain.0000000000001215
关键词
Pain intensity; Ecological momentary assessment; Markov switching; Regime switching; Time-series analysis; Intensive longitudinal data
资金
- National Institute of Arthritis and Musculoskeletal and Skin Diseases [R01 AR066200]
- National Cancer Institute [R01 CA085819]
- NATIONAL CANCER INSTITUTE [R01CA085819] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF ARTHRITIS AND MUSCULOSKELETAL AND SKIN DISEASES [R01AR066200] Funding Source: NIH RePORTER
Advances in pain measurement using ecological momentary assessments offer novel opportunities for understanding the temporal dynamics of pain. This study examined whether regime-switching models, which capture processes characterized by recurrent shifts between different states, provide clinically relevant information for characterizing individuals based on their temporal pain patterns. Patients with rheumatic diseases (N = 116) provided 7 to 8 momentary pain ratings per day for 2 weekly periods, separated by 3 months. Regime-switching models extracted measures of Average pain (mean level over time), Amplitude (magnitude of shifts in pain levels), Persistence (average duration of pain states), and Dominance (relative duration of higher vs lower pain states) for each patient and assessment period. After controlling for Average pain, the Persistence of pain states uniquely predicted emotional functioning measures, whereas the Dominance of higher pain uniquely predicted physical functioning and pain interference. Longitudinal analyses of changes over the 3 months largely replicated cross-sectional results. Furthermore, patients' retrospective judgments of their pain were uniquely predicted by Amplitude and Dominance of higher pain states, and global impressions of change over the 3 months were predicted by changes on Dominance, controlling for Average pain levels. The results suggest that regime-switching models can usefully capture temporal dynamics of pain and can contribute to an improved measurement of patients' pain intensity.
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