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. 2018 Nov;63(4):612-620.
doi: 10.1037/rep0000242. Epub 2018 Oct 8.

Positive factors, pain, and function in adults with multiple sclerosis

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Positive factors, pain, and function in adults with multiple sclerosis

Anne Arewasikporn et al. Rehabil Psychol. 2018 Nov.

Abstract

Objective: Much is known about the associations between negative factors and adjustment to chronic pain. However, less is known about how positive factors (e.g., positive affect [PA], resilience) function in relation to disability and mood in individuals with multiple sclerosis (MS). To better understand how positive factors contribute to function we sought to determine if (a) PA and/or trait resilience moderate the associations between pain intensity and function (pain interference, depressive symptoms), and (b) trait resilience concurrently mediates the association between PA and function in a sample of individuals with MS.

Method: There were 455 adults with MS who provided data via an ongoing survey.

Results: Controlling for negative affect, demographic, and disease-related variables, cross-sectional path analysis revealed the following: PA and trait resilience did not moderate the associations between pain intensity and pain interference (βPA = 0.01, p = .86; βRESILIENCE = 0.04, p = .33), and pain intensity and depression (βPA = 0.01, p = .79; βRESILIENCE = -0.02, p = .60). However, trait resilience significantly mediated the associations between PA and both criterion variables (abINTERFERENCE = -0.03, p = .03; abDEPRESSION = -0.13, p < .001).

Conclusions: The findings provide preliminary support for the conclusion that PA is indirectly related to pain interference and depression via resilience, rather than serves as a protective function. The findings are consistent with theoretical models suggesting that increases in PA build personal resources. Research examining the potential benefits of increasing PA and resilience to improve pain outcomes in individuals with MS is warranted. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

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Figures

Figure 1.
Figure 1.
Path analysis model showing standardized parameter estimates of hypothesized pathways. Black arrows denote significant paths, black dashed arrows denote marginal paths, gray paths denote nonsignificant paths. p<.10, *p<.05, **p<.01, ***p<. 001

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