# Personalized Models of Depression and Chronic Pain

> **NIH NIH F31** · WASHINGTON UNIVERSITY · 2021 · $46,036

## Abstract

Project Summary
Depression is a common and disabling mental health condition that can also have serious implications for
physical health. As many as 41% of individuals with depression report disabling chronic pain1. Individuals with
depression are likely to be prescribed higher doses of opioids, contributing to greater risk of abuse, overdose,
and suicide4–6. Psychological interventions may alleviate both physical and emotional suffering; however, it is
currently difficult to identify patients who are most likely to benefit from these approaches2,3. Personalized
(i.e., idiographic or N = 1) models of depression and pain could lead to more precise treatment targets
for patients with co-occurring symptoms. For example, if increased depressive symptoms predict increased
pain in the daily life of Patient A, treatment for depression may be effective in reducing both psychological and
physical symptoms. In contrast, if depressive symptoms are predicted by increased pain in the daily life of
Patient B, pain-focused interventions may be indicated. In the current study, I will collect ecological
momentary assessment data from patients with co-occurring depressive symptoms and chronic pain
(N = 75). Each individual will provide approximately 105 data points, allowing for the development of
personalized symptom models using dynamic structural equation modeling14. Personalized models will be used
to assess the degree to which depression predicts pain and pain predicts depression for different individuals
(Aim 1). I hypothesize that prospective relationships between depressed mood and pain will vary on a
continuum between individuals, as opposed to the alternative hypothesis that these relationships are present
for all or no individuals. If increased depression predicts increased pain severity in daily life for some
individuals, this would suggest that personalized models might be useful in tailoring treatment. I will also collect
ambulatory blood pressure and heart rate data to assess the degree to which depressed mood increases
physiological arousal, which in turn predicts perceived pain (Aim 2). I hypothesize that these relationships will
also vary between individuals, with some individuals exhibiting a relationship indicative of physiological arousal
as a mechanism whereby depression perpetuates pain. Finally, in order to foster understanding and
development of personalized models, I will assess moderators of individual-level relationships (Aim 3). This
proposal will provide training in the application of personalized medicine approaches to the
assessment of co-occurring depression and pain. My long-term goal is to lead clinical trials assessing the
feasibility and utility of tailoring treatment for depression and co-occurring pain based on personalized models.
In this proposal, I will take a first step towards developing these models. I will also receive interdisciplinary
training in mood disorders, chronic pain, and physiological functioning from my men...

## Key facts

- **NIH application ID:** 10151349
- **Project number:** 1F31MH124291-01A1
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Madelyn Frumkin
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $46,036
- **Award type:** 1
- **Project period:** 2021-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10151349

## Citation

> US National Institutes of Health, RePORTER application 10151349, Personalized Models of Depression and Chronic Pain (1F31MH124291-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10151349. Licensed CC0.

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