# Predicting Pediatric Sickle Cell Disease Acute Pain Using Mathematical Models Based on mHealth Data

> **NIH NIH R21** · VIRGINIA COMMONWEALTH UNIVERSITY · 2022 · $420,357

## Abstract

PROJECT SUMMARY
Sickle cell disease (SCD) affects over 20 million people living worldwide and approximately 100,000 individuals
living in the United States. Individuals with SCD are at increased risk of mortality, poor health-related quality of
life, and high health care utilization. Pain is the primary factor linked to poor health outcomes and increased
medical costs for individuals with SCD. The majority of SCD pain episodes are unanticipated; leading to a lack
of prophylactic pain management, increased use of opioids and other health care, and poor quality of life.
Accurate mathematical models to predict SCD pain in pediatric patients would facilitate the development,
testing, and maximizing of the timing of implementation of interventions to improve their effectiveness, and
reduce the use of opioids; thus, minimizing the risk of opioid dependence. Our central hypothesis is that
dynamic mathematical models that incorporate time-varying mobile health (mHealth) variables will increase the
accuracy of prediction of individual daily pediatric SCD pain features – pain severity, onset, and exacerbations.
We also hypothesize that changes in mHealth data are important precursors to changes in pediatric SCD pain.
Our short-term goal is to develop and test a dynamic mathematical modeling framework that includes
combinations of mHealth variables to identify the best model formulations for predicting individual daily
pediatric SCD pain features. The proposed study will leverage existing data from a previous project focused on
the relationship between sleep and SCD pain – to date, the largest study that incorporates ecological
momentary assessments (EMAs) and actigraphy measures for youth with SCD. The previous analyses of
these data did not consider the dynamic nature of the relationships or examine the range of mHealth data
available. To accomplish this goal, we aim to (1) construct a framework consisting of a dynamic mathematical
model that focuses on predicting pediatric SCD pain severity that can utilize various combinations of mHealth
variables, and (2) determine which modeling framework instances – mHealth data combinations coupled with
the model – are effective for predicting individual SCD pain severity patterns. Then (3) we will use the
framework instances selected to predict risk of pediatric SCD pain onset and pain exacerbations and
determine which mHealth variable combinations are most successful at predicting these pain features.
Specifically, we will apply machine learning algorithms and assess the ability of each instance of the modeling
framework to predict pain onset or exacerbation the next day.

## Key facts

- **NIH application ID:** 10599401
- **Project number:** 1R21DE032583-01
- **Recipient organization:** VIRGINIA COMMONWEALTH UNIVERSITY
- **Principal Investigator:** Reginald McGee
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $420,357
- **Award type:** 1
- **Project period:** 2022-09-20 → 2025-09-19

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10599401

## Citation

> US National Institutes of Health, RePORTER application 10599401, Predicting Pediatric Sickle Cell Disease Acute Pain Using Mathematical Models Based on mHealth Data (1R21DE032583-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10599401. Licensed CC0.

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