# Modeling pain variability in fibromyalgia

> **NIH NIH F31** · UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH · 2024 · $40,562

## Abstract

PROJECT SUMMARY
Fibromyalgia (FM) is a common and debilitating chronic pain condition. Given the unknown pathophysiology
and the lack of biomarkers in FM, accurate and clinically meaningful assessment of symptoms is critical. Pain
and other FM symptoms are typically studied as if they were static with any intra-individual variability either not
captured by measurement approaches, averaged out, or treated as noise/error. Yet, when asked, most FM
patients will report substantial fluctuations in their symptoms over time. Across chronic pain conditions, higher
pain variability is associated with a host of negative outcomes, and has been identified as a promising predictor
of treatment response. Yet, methodological tools that could characterize this variability have not been leveraged
to their full potential. The proposed project will use ecological momentary assessment (EMA) data (3X/daily
ratings of pain, mood, and stress for 8 days) from 72 FM patients collected as part of a NIAMS funded clinical
trial (R21AR082574) evaluating metformin as a treatment for FM alongside an added measure of short-term
variability in response to experimentally induced pain. Long-term variability in clinical pain has been highlighted
as a promising potential predictor of clinically relevant outcomes in chronic pain patients, but it has yet to be fully
evaluated in FM patients, and using short-term variability as a predictor is completely novel. While metrics of
pain variability likely have clinical utility on their own, decomposing factors (e.g., emotional state or endogenous
pain modulatory (EPM) functioning) that contribute to variable pain experiences could have even more utility.
The proposed research will apply and develop advanced models to characterize pain variability in fibromyalgia
patients. Aims 1 and 2 will determine the potential of both long-term variability of clinical pain (Aim 1) and short-
term variability in response to experimental pain (Aim 2) as potential behavioral phenotypes to predict clinically
relevant outcomes (symptom severity, functional ability, and treatment response). Aim 3 will craft a theoretically
informed model that will index the relative contributions of incoming pain signals, emotional state, and EPM
functioning for a given pain state in a given patient with the goal of outputting individualized pain profiles that can
be used in precision medicine contexts to target fibromyalgia treatments more effectively. This project is in line
with the "precision medicine for arthritis, musculoskeletal, and skin diseases" scientific theme in the 2020-2024
NIAMS strategic plan. This fellowship application describes detailed research and training plans that draw upon
the expertise of the Sponsor, Co-Sponsors, and Consultants to prepare the candidate to sucessfully launch a
career as an academic researcher at an R1 institution.

## Key facts

- **NIH application ID:** 10901131
- **Project number:** 1F31AR083815-01A1
- **Recipient organization:** UTAH STATE HIGHER EDUCATION SYSTEM--UNIVERSITY OF UTAH
- **Principal Investigator:** Mirinda Mar Whitaker
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $40,562
- **Award type:** 1
- **Project period:** 2024-05-23 → 2026-05-22

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10901131

## Citation

> US National Institutes of Health, RePORTER application 10901131, Modeling pain variability in fibromyalgia (1F31AR083815-01A1). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10901131. Licensed CC0.

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