# Implementing and Evaluating the Comprehensive Integration of Physical Activity into a Major Health System and Connecting Patients to Community-Based Physical Activity Programs

> **NIH NIH R56** · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · 2022 · $640,668

## Abstract

The U.S. healthcare sector has great potential for promoting physical activity (PA) for chronic disease
prevention and treatment; however, implementation barriers exist, ranging from practice integration to
information flow. In 2016, the first multi-organizational partnership between a large academic healthcare
system and a national PA organization launched Exercise is Medicine Greenville (EIMG®), a comprehensive
clinic-to-community approach that involves PA assessment, prescription, and referral of patients with chronic
diseases to tailored, community-based PA programs. Since 2016, EIMG® has grown to include 18 provider
clinics covering 342 urban/rural miles2 and 6 community PA centers covering 213 urban/rural miles2. However,
great variability across participating clinics exists in correctly identifying eligible patients, providing referrals,
and engaging patients in the community-based PA programs. A pragmatic, stepped wedge, cluster randomized
trial using a mixed methods approach will examine the implementation and reach of EIMG® across newly
onboarded primary care clinics (i.e., clinic workflow, referral process), ultimately leading to patient engagement
in community-based, evidence-informed PA programs. The RE-AIM framework will inform the assessment of
implementation outcomes, while the i-PARIHS framework will be used to more fully understand contextual
factors (i.e., determinants) influencing patient and clinic level outcomes. Our specific aims are to: (1) to
determine differences in provider-level adoption (i.e., proportion of providers that initiate use), implementation
(i.e., delivery fidelity), and reach (i.e., number, proportion, and representativeness of patients) of EIMG® at
newly onboarded primary health clinics; (2) assess the effectiveness of patient engagement in the evidence-
informed, 12-week PA programs at the community PA centers on patient self-reported PA levels and health
outcomes (i.e., body weight, blood pressure, hemoglobin A1c, lipid profiles) captured in their electronic health
records, and (3) evaluate the cost of implementing EIMG® and the cost-effectiveness (i.e., changes in PA
levels and improvements in health outcomes versus health care utilization costs) of patients participating in
EIMG® vs. standard of care (i.e., no engagement in EIMG®). As an exploratory aim, we will evaluate long-term
adaptations to the implementation and sustainability of EIMG® in the primary care clinics. Finally, we will pay
careful attention to issues of health equity across all RE-AIM dimensions. This study has the potential to
significantly change clinical practice and improve population health outcomes by informing future strategies on
optimizing and scaling up the integration of comprehensive PA models into U.S. health systems. This study
also will provide information on cost estimates of potential savings to health systems that may implement PA
as a population health management tool through the use of clinical-community linkages.

## Key facts

- **NIH application ID:** 10705363
- **Project number:** 1R56HL157218-01A1
- **Recipient organization:** UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
- **Principal Investigator:** Mark Stoutenberg
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $640,668
- **Award type:** 1
- **Project period:** 2022-09-23 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10705363

## Citation

> US National Institutes of Health, RePORTER application 10705363, Implementing and Evaluating the Comprehensive Integration of Physical Activity into a Major Health System and Connecting Patients to Community-Based Physical Activity Programs (1R56HL157218-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10705363. Licensed CC0.

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