# Center for Accelerating Precision Pain Self-Management

> **NIH NIH P20** · UNIVERSITY OF CONNECTICUT STORRS · 2020 · $218,111

## Abstract

Abstract (Administrative Core)
 The Administrative Core is central to achieving the overall specific aims of the Center for Accelerating
Precision Pain Self-Management (CAPPS-M). The management of resources is the focus of this core in the
context of self-management of pain using the paradigm of self-management as explicated by the Individual and
Family Self Management Theory (IFSMT), common data elements (CDEs) and centralized processes. This
focus forms the crux of how the research generated by the pilot projects and other products of the CAPPS-M
advance the science of pain self-management. We will build on our experiences and on the science generated
by members of our administrative core. According to the IFSMT, self-management is a process by which
individuals and families use knowledge and beliefs, self-regulation skills and abilities and social facilitation to
engage in self-management behaviors with the goal of achieving optimal symptom management and quality of
life1. Self-management is uniquely applicable to managing the symptom of pain across the lifespan and
particularly suited to interdisciplinary research. The CAPPS-M will provide an infrastructure to facilitate the
collaboration of scientists who will advance the science of pain self-management.

## Key facts

- **NIH application ID:** 9931288
- **Project number:** 5P20NR016605-05
- **Recipient organization:** UNIVERSITY OF CONNECTICUT STORRS
- **Principal Investigator:** Angela Renee Starkweather
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $218,111
- **Award type:** 5
- **Project period:** — → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9931288

## Citation

> US National Institutes of Health, RePORTER application 9931288, Center for Accelerating Precision Pain Self-Management (5P20NR016605-05). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/9931288. Licensed CC0.

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