Background & Aims
Fibromyalgia (FM) symptoms are often studied statically with any intra-individual vari-
ability not measured, averaged out, or treated as noise/error [1]. Yet, most FM patients
will report notable fluctuations in thier symptoms [2]. Across chronic pain conditions,
higher pain variability is associated with negative outcomes [1] and is a potential predictor
for precision medicine [3]. Yet, what underlies variable pain experiences in FM is relatively
unknown, and pain variability is typically quantified using a narrow set of methodological
tools. One underused methodological tool is mixed-effects location scale (MELS) model-
ing [4]. MELS is a multi-level approach that explicitly models variance components and is
better powered and more reliable than other variability metrics (e.g., intra-individual stan-
dard deviation (iSD)) [5]. Here, we modeled how emotional distress and fatigue symptoms
impacted within- and between-person pain variability in FM patients using a MELS model.
Methods
310 FM patients were recruited and provided informed consent to participate. Participants
completed a battery of questions including assessments of pain, fatigue, and emotional distress 3X/day for 7 days. All questions were rated on a 0-6 scale. Mixed-effects location scale (MELS) modeling was used to model between-person and within-person variability alongside mean pain ratings over time as a function of emotional distress and fatigue. Emotional distress and fatigue were group mean centered. All variables were entered into the model simultaneously. MELS employs log-linear modeling, and thus exponentiated slopes were used for interpretation.
Results
Emotional distress (? = .15, p < .001) and fatigue (? = .32, p < .001) predicted higher pain ratings. Within-person pain variance differed significantly (z=14.82, p < .001), implying FM patients pain ratings vary in their consistency/erraticism. Emotional distress (z=-2.49, p = .01) and fatigue (z=2.14, p = .03) significantly predicted between-person pain variance, albeit in opposite directions. The fatigue exponentiated slope was 1.10, implying between-person pain variance increases by a factor of 10% for each one unit increase in fatigue. The emotional distress exponentiated slope was .91, implying between-person pain variance decreases by a factor of 9% for each one unit increase in emotional distress. Finally, fatigue (z=4.09, p < .001), but not emotional distress (z=.85, p = .39), significantly predicted within-person pain variance. The fatigue exponentiated slope was 1.09, implying within-person pain variance increases by a factor of 9% for each one unit increase in fatigue.
Conclusions
In our sample, we found fatigue and emotional distress significantly predicted between-person variability in pain ratings—and fatigue, but not emotional distress significantly predicted within-person variability in pain ratings. More specifically, our findings suggest when more fatigued than usual FM patients were more heterogeneous in their pain ratings. Yet, when more emotionally distressed than usual, FM patients were more homogeneous in their pain ratings. Finally, when looking at within-person variability our findings suggest when more fatigued than usual FM patients rate their pain more variably/erratically. Overall, the current study suggests MELS modeling can be a useful tool for investigating symptom variability in FM, and different symptoms of FM (fatigue vs. emotional distress) can have differential impacts on both within- and between- person variability in pain ratings.
References
References
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Roy Freeman, Troels S Jensen, Alban Latremoliere, John D Markman, Andrew SC Rice,
et al. Optimizing and accelerating the development of precision pain treatments for
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[5] Ryan W Walters, Lesa Hoffman, and Jonathan Templin. The power to detect and predict
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Presenting Author
Mirinda Whitaker
Poster Authors
Topics
- Specific Pain Conditions/Pain in Specific Populations: Fibromyalgia