# Applying big data to improve mental health outcomes with restless legs syndrome treatment

> **NIH NIH R36** · UNIVERSITY OF FLORIDA · 2021 · $36,797

## Abstract

PROJECT SUMMARY/ABSTRACT
Restless legs syndrome (RLS) is a complex neurological and sleep disorder that has a high comorbid
prevalence of mental health conditions. The cardinal feature of RLS is symptom manifestation at nighttime that
disrupts restful sleep and subsequent daytime functioning. Risks for new onset and exacerbations of mental
health conditions are further increased because of psychiatric adverse effects (AEs) associated with RLS
pharmacotherapy. Dopamine agonists (DAs) and gabapentinoids are first-line treatments for moderate-to-
severe primary RLS. DAs are associated with serious AEs including hallucinations, psychosis, symptoms of
mania and most notably, impulse control disorders (ICDs). The most common ICD is gambling disorder, but
there are other related impulsive behaviors that are less described. These effects have been well described in
Parkinson’s disease treated with DAs, but the magnitude of risk in RLS is not well known. Given these risks
and other safety concerns, preference for RLS treatment has shifted to gabapentinoids; however,
gabapentinoids carry risks of suicidality. Although this effect is rare, it is not clear if the safety profile of
gabapentinoids is ideal for long-term use in RLS patients who are at risk for anxiety, depression, insomnia, and
suicidality independent of RLS treatment use. Therefore, the objective of this R36 Dissertation Award is to
conduct a population-based assessment of mental health outcomes among treated RLS patients using
pharmacoepidemiological methods. This proposal will employ big data, or real-world data (RWD), consisting of
nationally representative health care encounters and self-reported health data. In Aim 1, drug utilization
research methods will be used to characterize RLS pharmacotherapy utilization and prescribing among the
primary early-onset RLS population including pre-existing mental health comorbidities using RWD from 2012-
2019. In Aim 2, DAs and gabapentinoids will be compared to assess the relationship between each treatment
class on the risk of onset or worsening of mental health conditions. Emergency department and inpatient data
will be used to detect exacerbations of mental health comorbidities. In Aim 3, DAs and gabapentinoids will be
compared on the risk of onset of ICDs and suicidality. An innovative exploratory outcome of impulse control
behaviors will be developed. Overall, this research aims to improve the mental health burden of the RLS
population, to add to the literature on ICDs, to contribute to suicide prevention research, and to demonstrate
application of pharmacoepidemiology within mental health research. This project serves as the trainee’s
doctoral dissertation and provides an invaluable opportunity to facilitate transition to an independent research
scientist. Through the proposed project and supervision under a robust mentorship team, the trainee will
achieve her long-term goal of pursuing a research career applying her passion for mental health, ...

## Key facts

- **NIH application ID:** 10312873
- **Project number:** 1R36MH127826-01
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Brianna Costales
- **Activity code:** R36 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $36,797
- **Award type:** 1
- **Project period:** 2021-08-04 → 2022-08-03

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10312873

## Citation

> US National Institutes of Health, RePORTER application 10312873, Applying big data to improve mental health outcomes with restless legs syndrome treatment (1R36MH127826-01). Retrieved via AI Analytics 2026-06-07 from https://api.ai-analytics.org/grant/nih/10312873. Licensed CC0.

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