Investigating intraindividual pain variability: methods, applications, issues, and directions. Issue 11 (November 2019)

• Record Type: Journal Article
• Authors: Mun, Chung Jung; Suk, Hye Won; Davis, Mary C.; Karoly, Paul; Finan, Patrick; Tennen, Howard; Jensen, Mark P.
• Is Part Of: Pain. Volume 160:Issue 11(2019)
• Publication Date: 2019-11
• Subjects: Pain variability -- Pain dynamics -- Intraindividual variability -- Instability -- Chronic pain -- Intensive longitudinal design -- Daily diary -- Autocorrelation -- Dynamic structural equation modeling; Pain -- Periodicals; Douleur -- Périodiques; Anesthésie -- Périodiques; Pain; Electronic journals; Periodicals; Electronic journals; 616.0472
• DOI: 10.1097/j.pain.0000000000001626
• Languages: English
• ISSNs: 0304-3959

Abstract

Abstract : Abstract: Pain is a dynamic experience subject to substantial individual differences. Intensive longitudinal designs best capture the dynamical ebb and flow of the pain experience across time and settings. Thanks to the development of innovative and efficient data collection technologies, conducting an intensive longitudinal pain study has become increasingly feasible. However, the majority of longitudinal studies have tended to examine average level of pain as a predictor or as an outcome, while conceptualizing intraindividual pain variation as noise, error, or a nuisance factor. Such an approach may miss the opportunity to understand how fluctuations in pain over time are associated with pain processing, coping, other indices of adjustment, and treatment response. The present review introduces the 4 most frequently used intraindividual variability indices: the intraindividual SD/variance, autocorrelation, the mean square of successive difference, and probability of acute change. In addition, we discuss recent development in dynamic structural equation modeling in a nontechnical manner. We also consider some notable methodological issues, present a real-world example of intraindividual variability analysis, and offer suggestions for future research. Finally, we provide statistical software syntax for calculating the aforementioned intraindividual pain variability indices so that researchers can easily apply them in their research. We believe that investigating intraindividual variability of pain will provide a new perspective for understanding the complex mechanisms underlying pain coping and adjustment, as well as for enhancing efforts in precision pain medicine. Audio accompanying this abstract is available online as supplemental digital content at http://links.lww.com/PAIN/A817 . Abstract : Supplemental Digital Content is Available in the Text.