Facilitating Health System Implementation of Physical Activity Screening and Referral to Community-Based Programs: Exercise is Medicine Greenville
Phase: NA
Conditions: Physical Inactivity, Dyslipidemia, Obesity and Overweight, Hypertension, Diabetes, Health Care Delivery, Patients, Chronic Disease, Exercise, Physical Activity, Implementation Science
Sponsor: University of South Carolina

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Protocol Title: Facilitating Health System Implementation of Physical Activity Screening 
and Referral to Community-Based Programs: Exercise is Medicine Greenville 
NCT Number: NCT07340580 
Document Version and Date: March 26, 2026, V1.1 
 
Principal Investigator 
• Jennifer Trilk, PhD 
 
Funding Sponsor: NHLBI (R01 mechanism) 
NIH Grant Number: 1R01HL173601-01A1  
IRB Number: [2222165-1] R01 – Implementation/Facilitation of EIMG Program 
 
Abbreviations 
ACSM 
American College of Sports Medicine 
ALC 
Administrative Leadership Council 
CSG 
Clinical Steering Group 
DRG 
Diagnosis Related Group 
DSMB 
Data Safety and Monitoring Board 
EHR 
Electronic Health Record 
EIMG 
Exercise is Medicine Greenville 
GLMM 
Generalized Linear Mixed Model 
HCPCS 
Healthcare Common Procedure Coding System 
i-PARIHS  
Integrated-Promoting Action on Research Implementation in Health 
Services 
IF 
Implementation Facilitation 
IV 
Instrumental Variable 
LISTS 
Longitudinal Implementation Strategy Tracking System 
NDC 
National Drug Codes 
ORCA 
Organizational Readiness to Change Assessment 
PA 
Physical Activity 
PAVS 
Physical Activity Vital Sign 
PHQ-9 
Patient Health Questionnaire-9 
PROMIS 
Patient Reported Outcomes Measurement Information System 
RE-AIM 
Reach, Effectiveness, Adoption, Implementation, Maintenance 
REDCap 
Research Electronic Data Capture 
YMCA 
Young Men's Christian Association 

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Table of Contents 
1 
Introduction ......................................................................................................... 5 
1.1 
Background and relevant literature.......................................................................... 5 
1.2 
The Exercise is Medicine Greenville (EIMG) Model .................................................... 7 
1.2.1 
EIMG in the Prisma Health-Upstate system ............................................................................... 7 
1.2.2 
Interface between Prisma Health-Upstate and the community PA facilities ............................... 8 
1.2.3 
Evidence-informed PA programming at community PA facilities ................................................ 8 
1.2.4 
Closing the loop: interfacing back to Prisma Health-Upstate ..................................................... 8 
1.2.5 
Program cost and financial assistance ...................................................................................... 9 
1.3 
Previous work ......................................................................................................... 9 
2 
Study design ...................................................................................................... 10 
3 
Theoretical framework ....................................................................................... 10 
4 
Study aims ......................................................................................................... 10 
5 
Investigational plan and design .......................................................................... 10 
5.1 
Implementation facilitation .................................................................................. 10 
5.1.1 
Baseline Phase ........................................................................................................................ 11 
5.1.2 
Pre-IF Phase ............................................................................................................................ 12 
5.1.3 
IF Phase .................................................................................................................................. 13 
5.1.4 
Post-IF Maintenance Phase ..................................................................................................... 15 
5.2 
Study outcome measures ..................................................................................... 15 
5.2.1 
Aim 1 ....................................................................................................................................... 15 
5.2.2 
Aim 2 ....................................................................................................................................... 18 
5.2.3 
Aim 3 ....................................................................................................................................... 20 
6 
Study population ................................................................................................ 23 
6.1 
Eligibility criteria ................................................................................................... 23 
6.1.1 
Primary care clinics ................................................................................................................. 23 
6.1.2 
Primary care clinic staff ........................................................................................................... 23 
6.1.3 
Patients ................................................................................................................................... 23 
6.2 
Subject recruitment .............................................................................................. 24 
6.2.1 
Primary care clinics ................................................................................................................. 25 
6.2.2 
Primary care clinic staff ........................................................................................................... 25 
6.2.3 
Patients ................................................................................................................................... 25 
7 
Study procedures............................................................................................... 26 
7.1 
Subject compensation .......................................................................................... 26 
7.2 
Subject withdrawal ............................................................................................... 26 
8 
Study administration, data handling and record keeping .................................... 27 
8.1 
Study oversight ..................................................................................................... 27 
8.2 
Confidentiality ...................................................................................................... 27 
8.3 
Data collection and management .......................................................................... 27 

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8.3.1 
Data collection ........................................................................................................................ 27 
8.3.2 
Data management................................................................................................................... 28 
8.4 
Risks .................................................................................................................... 28 
8.5 
Benefits ................................................................................................................ 28 
8.6 
Informed consent process/HIPAA authorization .................................................... 28 
8.6.1 
ORCA Survey ........................................................................................................................... 28 
8.6.2 
Individual interviews with clinic staff and patients ................................................................... 29 
8.6.3 
Accelerometry......................................................................................................................... 29 
9 
Study finances ................................................................................................... 29 
10 
Dissemination ................................................................................................ 29 
11 
Conclusions ................................................................................................... 30 
12 
References ..................................................................................................... 31 
 
 
 
 

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Synopsis 
A public health priority exists for the U.S. healthcare sector to integrate physical activity as 
part of the patient care model to prevent and treat chronic disease; however, 
implementation barriers exist, ranging from practice integration to information flow. In 
2016, a multi-organizational partnership between a medical school (University of South 
Carolina School of Medicine Greenville), health care system (Prisma Health-Upstate), and 
community organization (YMCA), launched Exercise is Medicine Greenville (EIMG), a 
comprehensive clinic-to-community approach that involves physical activity (PA) 
assessment, prescription, and referral of patients with chronic diseases to tailored, 
community-based physical activity programs. Since 2016, EIMG has grown to include 35 
Prisma Health-Upstate primary care clinics and 7 community physical activity centers 
covering >400 miles2. However, great variability exists in clinic-level implementation and 
provider-level adoption of EIMG and referral of patients to community-based PA programs. 
This pragmatic, stepped wedge, cluster randomized trial will examine the impact of 
implementation facilitation (IF) on the implementation and reach of EIMG with patients 
visiting EIMG-activated primary care clinics (i.e., provider referral rates). At six-month 
intervals, 35 randomly selected clinics (Wave 1: 6 clinics, Wave 2: 8 clinics, Wave 3: 10 
clinics, Wave 4: 11 clinics) will receive Pre-IF (3 months), active IF (6 months), and Post-IF 
maintenance (12 months). The specific aims of this study are to: 1) determine differences 
in the level of implementation (i.e., delivery fidelity) and reach (i.e., number, proportion, 
and representativeness of patients) at Prisma Health-Upstate clinics before and after IF, 2) 
assess the level of patient engagement in and the effectiveness of the 12-week, 
community-based PA programs, and 3) evaluate the costs of IF and the effects of 
increased EIMG referrals to the community-based PA program on patients costs and 
clinical outcomes. This mixed methods evaluation approach is guided by the RE-AIM 
framework to inform the assessment of implementation outcomes, and the i-PARIHS 
framework to describe contextual factors (i.e., determinants) influencing patient and clinic 
level outcomes. Through this work, successful IF strategies will be identified across 
heterogeneous health settings and greater understanding of factors related to patient 
participation in the community-based PA programs. Results of this study have the 
potential to provide important information on improving future implementation and 
scalability of PA integration in large health systems, optimizing clinic-community linkages, 
and cost savings related to the primary and secondary prevention of cardiovascular 
disease-related health outcomes in the general patient population.

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1 Introduction 
1.1 Background and relevant literature 
The evidence around the benefits of physical activity (PA) is irrefutable; PA is 
unquestionably a “best buy” for overall health and is effective in reducing a broad array of 
health conditions [1]. Exercise training is equally (or more) effective as drug therapy on 
mortality outcomes, secondary prevention of coronary heart disease, treatment of heart 
failure, and diabetes prevention [2]. Globally, physical inactivity causes 6-10% of all major 
non-communicable diseases and is responsible for 9% of premature deaths, rates similar 
to other established cardiovascular disease risk factors, such as smoking and obesity [3]. 
PA acutely improves blood pressure [4], glycemic control [5], and inflammation [6]. Regular 
PA reduces the risk of developing chronic conditions, such as heart disease, stroke, 
hypertension, type 2 diabetes, and several types of cancer [7, 8]. Given the multiple 
benefits of PA, it is remarkable that only 54.2% of U.S. adults meet aerobic activity 
recommendations, while only 24.2% meet both aerobic and strength training 
recommendations [9]. Engagement of the health sector is essential in increasing 
population PA levels with strategies well-described in the U.S. National PA Plan [10, 11]. 
Healthcare providers see a large portion of the general population, often several times a 
year. These ongoing, multiple contacts offer the ideal opportunity to provide brief, 
impactful PA counseling. The Toronto Charter [12], a global call to action for population-
based approaches, outlines several strategies for increasing PA in health settings including 
i) reorienting health services and funding systems, ii) screening of patient PA levels at 
primary care consultations, and iii) referral to community programs for insufficiently active 
patients. These overarching guidelines have been supported by calls to action by medical 
societies [13] and leading health professionals [14, 15]. Yet integration lags because health 
systems are not properly equipped/designed to promote PA. 
Similar to the SBIRT model [16] (Screening, Brief Intervention, and Referral to Treatment), 
an evidence-based, clinical strategy for addressing substance use disorders, Exercise is 
Medicine (EIM) [17] is a comprehensive model for integrating PA in clinic workflows. EIM is a 
framework to improve the identification and referral of physically inactive patients, 
connecting them to evidence-based PA programs. The individual components of the EIM 
model include: 1) assessing patient PA levels, 2) providing brief PA counseling or a PA 
prescription, and 3) referring patients to PA resources for further guidance. In some 
instances, a ‘navigator’ is used to increase referred patient engagement (Figure 1).

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Figure 1. Comprehensive model for physical activity integration into health systems.  
Assessing patient PA levels is a catalyst to subsequent prescription and referral [18]. The 
PA ‘Vital Sign’ (PAVS) is used by multiple U.S. health systems, demonstrating strong face 
and discriminant validity in identifying inactive individuals across sex, age groups, and 
disease conditions [19, 20]. Significant associations exist between PA levels and 
cardiometabolic risk factors, patient disease burden, and body mass index [21, 22]. PAVS 
integration into electronic health records (EHR) leads to greater PA progress note 
documentation, counseling, and referral compared to visits with no PAVS administration 
[23]. Despite PAVS integration in several U.S. health systems, few have adopted additional 
steps to address low patient PA levels.  
In terms of PA prescriptions, The New Zealand Green Prescription is the best-known 
program involving health professionals giving patients a written/electronic PA prescription, 
leading to increased PA levels compared to verbal advice alone [24]. Patients receiving a 
prescription, compared to usual care, were more likely to meet PA guidelines and achieve 
higher energy expenditure [25] with benefits extending out 2-3 years [26]. Outside of a few 
small trials, PA prescriptions have not been systematically integrated into U.S. health 
systems.  
The largest PA referral or exercise referral schemes exist in the United Kingdom, involving 
health professionals referring patients to community-based PA programs [27]. Exercise 
referral schemes increase patient PA levels, lower levels of depression and anxiety, and are 
moderately cost effective, particularly in older patients and those with greater 
cardiovascular disease risk factors [28, 29]. However, exercise referral schemes do not 
often involve a standardized PA assessment. In the U.S., no system-wide PA referral efforts 
have been established to date. Individual components of the “EIM model” have moderate-
to-strong evidence bases. Prisma Health-Upstate is the first major health system in the 
world to integrate all EIM components into their clinic workflow, connecting patients with 
chronic diseases to community-based PA programs.  

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1.2 The Exercise is Medicine Greenville (EIMG) Model 
1.2.1 EIMG in the Prisma Health-Upstate system 
The innovation under investigation in this study is the Exercise is Medicine Greenville 
(EIMG) Model, a clinic-to-community model which represents a joint effort across the 
University of South Carolina School of Medicine Greenville, Prisma Health-Upstate, and the 
YMCA of Greenville and Oconee counties [30]. EIMG implementation started in 2016 with 
programming of the PAVS and the electronic referral flow into the EHR, onboarding of two 
clinics with provider/staff training, and referring patients to community PA facilities. By 
December 2023, 35 Prisma Health-Upstate primary care clinics had adopted the EIMG 
model, covering >400 miles2 in Greenville and Oconee counties. Patients are eligible for an 
EIMG referral by providers if they are between 18-80 years of age and physically inactive 
(<150 min/week of moderate intensity aerobic activity), with or without a chronic health 
condition (i.e., overweight/obese, hypertension, type 2 diabetes, dyslipidemia). Three EIMG 
Clinician Decision Modules, i.e., 1) PA assessment, 2) PA prescription through patient order 
sets, and 3) patient referral to a community PA facility, are programmed and integrated into 
the EHR as part of clinical workflow (Table 1). An EIMG Referral Coordinator (employed by 
Prisma Health-Upstate) reviews patient eligibility, contacts them via telephone, confirms 
their interest, identifies their preferred location and ability to pay (or scholarship eligibility), 
and electronically sends HIPAA-compliant patient information to the YMCA Site 
Coordinator at the selected community PA facility for a warm “hand off”. 
Table 1: Exercise is Medicine Greenville clinical referral workflow integrated into electronic health 
record.  
Module 1:  
PA Assessment 
- Front office/nursing staff capture patient PA levels via the EHR-
integrated, 3-question PAVS:  
1) For an average week in the last 30 days, how many days per 
week did you engage in moderate to vigorous physical activity 
(like walking fast, running, jogging, dancing, swimming, biking, 
or other activities that cause a light or heavy sweat)? 
2) On those days that you engage in moderate to vigorous physical 
activity, how many minutes, on average, do you exercise? 
3) During the past month, how many times per week did you do 
physical activities or exercises to strengthen your muscles? 
- Best practice alert appears for patients not meeting national PA 
guidelines of 150 minutes of moderate intensity aerobic activity/week, 
stating the patient may qualify for EIMG. 
Module 2:  
PA Prescription 
- Healthcare providers inform eligible patients about EIMG, review 
risks, and provide basic PA education through the EIM Rx for Health 
[31] handouts for exercising with chronic diseases (e.g., diabetes, 
hypertension) developed by experts in the field.  
- Healthcare providers electronically start the EIMG Order Set for 
referral. 

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Module 3:  
EIMG Referral 
- Healthcare providers send referrals to EIMG Referral Coordinator 
through a series of EHR prompts. 
- Patient signs Release of Health Information to proceed with referral 
(either in clinic or through MyChart).  
 
Prisma Health-Upstate clinics are onboarded following standardized protocols that have 
been iteratively refined since 2016. Onboard training is provided to all clinic staff and 
includes a detailed explanation of the EIMG clinic workflow, responsibilities for each part of 
the EIMG process, and completing the risk severity assessment. After onboard training, 
access to the EIMG referral process (i.e., activation) in the EHR is ‘turned on’ allowing clinic 
staff to electronically identify and engage eligible patients.  
1.2.2 Interface between Prisma Health-Upstate and the community PA facilities 
The EIMG network includes seven community PA facilities (YMCA sites) across Greenville 
and Oconee counties (total reach >400 miles2). All staff undergo standardized EIMG 
operations training. YMCA Site Coordinators contact referred patients, review logistic and 
financial needs, and schedule onboarding with EIMG Exercise Professionals (EIMG Pros). 
The YMCA Site Coordinator is responsible for HIPAA protocols, implementing EIMG 
protocols, communicating timelines, and audit reporting.  
1.2.3 Evidence-informed PA programming at community PA facilities 
Referred patients are connected to qualified EIMG Pros, who lead the 12-week PA program 
over one-hour sessions two times per week. EIMG Pros have an accredited personal 
training certification, the EIM Credential [32], HIPAA and CITI training for the protection of 
human subjects, and REDCap database [33, 34] entry training. During onboarding, patients 
complete: 1) health history forms, 2) pre-participation surveys (i.e., PHQ-9, PROMIS Scale), 
and 3) measurement of height, body weight, blood pressure, and heart rate. Assessments 
are completed again at the end of the 12-week program. Rolling enrollment allows patients 
to onboard in <10 business days to optimize behavior change, with new patients learning 
from current program participants. 
The evidence-informed PA program is based on ACSM Position Stands and scientific 
evidence on meeting national guidelines of 150 min/week of moderate intensity aerobic 
activity. Sessions are guided by principles that training: 1) is progressive, 2) includes full 
body movements, and 3) incorporates active education to promote healthy lifestyle 
adoption. The sessions are structured to allow for flexible and adaptive delivery by EIMG 
Pros based on their unique participants and facility resources (i.e., equipment, space), 
similar to other adaptive and flexible health promotion interventions [35, 36]. EIMG Pros 
enter session data (i.e., patient attendance, assessments) into REDCap which is 
accessible to our research team.  
1.2.4 Closing the loop: interfacing back to Prisma Health-Upstate 
Patient activity (e.g., session completion, adverse events, patient discontinuation) is 
documented by EIMG Pros and sent by HIPAA-compliant email to the EIMG Referral 

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Coordinator, who then provides the patient’s summary to the healthcare provider through 
the EHR.  
1.2.5 Program cost and financial assistance 
Assuring that EIMG is affordable, YMCA of Greenville earmarks a percentage of their annual 
giving campaign to EIMG so that no patient is turned away due to their inability to pay. To 
date, >$350,000 is reserved for patient scholarships with 51% of patients receiving 
scholarships in the year 2024, a model replicable by community PA facilities across the 
country. 
1.3 Previous work 
At the end of 2025, 4204 patients had been properly referred to the EIMG Referral 
Coordinator (Figure 2) with 974 patients (23.2%) enrolling in the PA program. Analysis of 
patients (n=196) who participated in the PA program showed that patients experienced 
significant decreases in bodyweight (1.6±4.4 kg, p<0.001). Patients referred for 
hypertension lost an average of 1.4±4.4 kg (p=0.002) and decreased their systolic and 
diastolic BP by 7±15 (p<0.001) and 3±8 (p<0.0001) mmHg, respectively. In an exit survey of 
PA program graduates, all program components and personnel received high satisfaction 
scores (all provider and patient scores ≥4.75 on a 5-point Likert scale) [37]. 
Figure 2. Results for EIMG clinic-community referrals, 2016-2025.  
Referrals have been consistent over the program’s years, demonstrating sustainability of 
the EIMG model (Figure 2). However, large variations exist in referral patterns between 
EIMG clinics, and only a small fraction (<1%) of eligible patients receive EIMG referrals. This 
inefficiency in referral patterns makes it imperative to investigate strategies to optimize 
clinical implementation of EIMG. Strategies to overcome these contextual barriers could 
lead to significant advances in PA promotion by improving adoption, implementation, 
sustainability, and scale-up of efforts to identify, refer, and connect patients to evidence-
based PA interventions. Additionally, physical inactivity costs healthcare systems $53.8 
billion worldwide, contributing to $13.7 billion in productivity losses [38]. In the U.S., 
inadequate PA, independent of BMI levels, is associated with 11.1% of direct healthcare 
expenditures [39]. Conversely, PA interventions are cost-effective in improving population 
health outcomes [40, 41]. Cost savings have been noted when examining PA interventions 
introduced in healthcare settings. Linking primary care patients to community PA 
resources is likely to be cost-effective, but evidence is sparse [42]. To date, no studies have 

Page 10 of 34 
examined cost and outcome implications of integrating PA into a health system anywhere 
in the world. 
2 Study design 
This study will utilize a pragmatic, stepped wedge, cluster randomized design to examine 
the impact of implementation facilitation on increasing the reach (eligible patients that 
receive an EIMG referral) at 35 Prisma Health-Upstate primary care clinics where the EIMG 
model is currently activated.  
3 Theoretical framework 
The study design is guided by the Implementation Research Logic Model [43], a semi-
structured tool designed to integrate conceptual elements and improve the specification 
and rigor of implementation research. The integrated Promoting Action on Research 
Implementation in Health Services (i-PARIHS) [44] will be used to explore determinants of 
implementation while the RE-AIM framework [45], adapted for PA integration in health 
systems [46], will guide the evaluation of study outcomes.  
4 Study aims 
This study has three aims: 
• Aim 1 (primary aim): To determine clinic-level differences in implementation of 
EIMG (i.e., delivery fidelity) and reach (i.e., number, proportion, and 
representativeness) of patients receiving EIMG referrals pre- and post-
implementation facilitation. 
• Aim 2 (secondary aim): To assess the effectiveness of participating in the 
community-based, 12-week evidence-informed PA program on patient PA levels and 
health outcomes (i.e., body weight, blood pressure, hemoglobin A1c, lipid profiles). 
• Aim 3 (secondary aim): To evaluate, from the health system perspective, the costs of 
implementation facilitation and to estimate the effectiveness of increased EIMG 
referrals to the community-based PA on patient costs. 
5 Investigational plan and design 
5.1 Implementation facilitation 
The study will examine using implementation facilitation (IF) to enhance the 
implementation and reach of EIMG in real-world settings [47]. As more clinics adopt EIMG, 
it is essential to have implementation strategies, such as IF, that enhance reach, 
particularly in lower performing clinics, and improve health disparities. IF is widely used to 
address implementation challenges and increase fidelity to evidence-based practices. IF 
improves health care capacity, processes, and outcomes through trusting relationships 
between an external expert and clinic personnel, collaboratively changing care delivery 
processes [48, 49]. IF involves a set of implementation strategies that are flexibly selected 
in response to local circumstances at each clinic [50]. Primary care practices are nearly 

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three times more likely to adopt evidence-based practices when using IF, especially in 
under-resourced settings [51, 52]. The i-PARIHS framework posits that successful 
implementation results from the facilitation of an intervention as a function of site 
characteristics (e.g., clinics), intervention strategies/innovation (e.g., EIMG), and recipients 
(e.g., clinic staff/patients) [44] (Figure 3).  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 3. Integrated use of the i-PARIHS framework.  
 
Eligible primary care clinics (Section 6.1) will be randomly assigned to one of the four study 
waves. Each wave will go through four phases: 1) Baseline, 2) Pre-IF, 3) IF, and 4) Post-IF 
Maintenance (Figure 4). 
 
Figure 4. Cluster randomized, stepped wedge study design.  
 
5.1.1 Baseline Phase  
5.1.1.1 EHR data extraction 
The baseline phase will be used as a run-in period to gather initial data from the EHR, which 
will be used to establish baseline implementation and reach metrics prior to IF, and to 
establish profiles of each clinic for use by the internal team. Data to be collected includes 
clinical data (i.e., patient demographics, patient volume, visits where the PAVS was 
completed, payer mix, others as needed) and EIMG eligible visit and referral history (i.e., 
EIMG eligible visits, EIMG referral data, and visits where the PAVS was completed) at clinic 
level and individual provider level. EIMG referral history will be used to categorize clinics by 
referral rate. This data will not be shared with clinics but will be used in designing 
customized IF plans.  

Page 12 of 34 
5.1.1.2 Patient engagement 
Patient engagement in health implementation research can improve research quality, 
enhance implementation processes, and add the value of lived experience to a study [53]. 
As such, this study will include patient engagement and feedback to develop and inform 
tailored IF strategies. Patient engagement will be conducted through the University of 
South Carolina’s Patient Engagement Studio [54], and will be conducted over multiple 
sessions during the Baseline and Pre-IF Phases. A panel of patient experts has been 
created through the Patient Engagement Studio, composed of individuals who each 
manage different chronic conditions as well as some individuals who have participated in 
the EIMG program before, representing a variety of ages, careers, and racial/ethnic 
backgrounds that exist in the EIMG service area. The aims of patient engagement are to: 
1. Learn about patient experiences to inform IF strategies. 
2. Review patient-facing materials to ensure they integrate patient perspectives and 
improve the clarity, cultural relevance, and motivational appeal of the materials 
before implementation. 
3. Review the referral workflow through the patient lens to better understand how they 
experience the referral process and the contextual factors in clinic that support or 
hinder success. 
5.1.2 Pre-IF Phase 
Pre-IF will occur over three months preceding the IF phase. The phase objectives are split 
into three activities: (1) introduction and preparation, (2) learning, and (3) planning. 
Relationship building with clinic staff will be a primary focus throughout the entire phase. 
This work will be guided by appreciative inquiry [55], such as identifying organizational 
strengths, focusing on what is working, and reframing inquiries to stimulate positive 
discussion and reflection regarding system wide change. 
1) Introduction and preparation 
All clinics in the present study have received EIMG onboarding training; however, many 
were onboarded as early as 2016 and have since gone through numerous staff changes. 
The research team will identify the lead physician and the clinic manager for each clinic to 
(re-)establish contact with each clinic. The research team, led by the study physician, will 
request times to meet with clinic staff and stakeholders. The purpose of this initial meeting 
is to introduce EIMG, gather feedback, and outline future involvement in the study. 
2) Learning  
The research team will review and summarize data collected from the EHR to establish 
‘profiles’ for each clinic at the clinic and provider level, as described in the preceding 
section (Section 5.1.1). A needs assessment, guided by the i-PARIHS framework, will be 
conducted to understand the clinic workflow and barriers to EIMG implementation. As part 
of this needs assessment, the research team will conduct meetings, surveys, and workflow 
mapping with clinic staff and stakeholders (such as clinic champions (i.e., informal leaders 
in clinical organizations, highly influential individuals who hold the respect of the clinic 
[56]), physicians, advanced practice providers, registered nurses, medical assistants, front 

Page 13 of 34 
office assistants/intake personnel, and clinic leadership/practice managers; hereafter 
clinic staff). The Organizational Readiness to Change Assessment (ORCA) will be 
conducted [57] with clinic staff to assess their perceptions of the clinic environment, EIMG 
implementation barriers, organizational stability, and willingness to change [58]. This will 
provide a sense of the clinic’s culture, an understanding of competing demands 
(bandwidth), and opportunities for increasing buy-in (i.e., improving intrinsic motivation 
and trust, navigating organizational stress). A full description of these measures is given in 
Section 5.2.1. Workflow mapping will be conducted as per Taylor et al. 2025 [59] to develop 
a clinic-specific EIMG workflow with clinic staff. Facilitator notes will describe variations in 
EIMG implementation fidelity (e.g., implementation drift over time) and how the clinic 
workflow might be better adapted for EIMG implementation. Results will be combined with 
data from the EHR (Section 5.1.1) to inform IF Planning.  
3) Planning 
The research team will work collaboratively with clinic staff to co-create an ideal EIMG 
workflow model that maximizes efficiency by enhancing facilitators, overcoming barriers, 
and identifying peripheral effects of the workflow re-design. Outcomes from the ‘Learning ‘ 
phase will be mapped to a compilation of implementation strategies [60] and potential 
clinic-based activities (e.g., training sessions, brochures, working with clinic champions 
etc.). When selecting IF strategies, the research team will provide clinic staff with a 
‘toolbox’ of adaptive strategies and activities at both the provider- and clinic-level. This 
work will result in a co-created, tailored IF plan for each site, optimizing the workflow 
process for enhancing EIMG implementation and increasing overall reach [55]. We will map 
selected strategies backwards (i.e., examining IF strategies most used by clinics in each 
category) and forwards (i.e., selecting IF strategies that are most successful for each 
category) to iteratively refine our IF strategies for subsequent study waves. 
 
4) Relationship building 
Relationship building between clinic staff and the research team will be ongoing 
throughout the process of Pre-IF, IF, and Post-IF. The research team will determine 
leadership styles within the clinic, attempt to build strong relationships, and learn clinic 
staff’s preferred communication methods to maintain regular communication through 
check ins, emails, and progress reports. Through this relationship building, the research 
team will evaluate the vision and direction of the clinic, the commitment to quality 
improvement, and willingness to redirect resources towards improving EIMG 
implementation in their clinic.  
5.1.3 IF Phase 
The IF Phase will consist of a six-month, multilevel process to sustainably improve clinic 
infrastructure and workflow processes to reduce referral barriers and enhance clinic-
community coordination and patient engagement in EIMG. IF will be conducted by two 
members of the research team (facilitators trained in implementation science 
approaches), who have clinical backgrounds and experience ‘speaking the language’ of the 
practice, better equipping them to understand challenges and build trust among clinic 

Page 14 of 34 
staff. Relationship building will continue throughout the IF Phase, with the research team 
engaging with formal and informal leadership within the clinic to direct, motivate, and 
manage clinic staff engagement in IF activities. Facilitators will aim to build an atmosphere 
for clinic staff that is open, non-critical, and goal oriented, allowing them to voice feedback 
on IF activities [61].  
As part of a ‘co-creation’ approach to the IF Phase, clinic staff will work with the research 
team to implement feasible IF strategies and activities tailored to clinic needs. After 
collating and analyzing data from the Pre-IF ‘Learning ‘activities, the research team will 
present clinic staff with a toolbox of IF strategies matched to every identified barrier or gap 
in practice, including descriptions of what to expect if the strategy was implemented. Clinic 
staff will not only select feasible strategies but will also assist with building a timeline for 
introducing each IF activity, as well as timelines for IF ramp-up and peak IF. Deploying the 
tailored IF plan developed (Section 5.1.2), we will emphasize the importance of delivery re-
design and the potential for EIMG to improve patient health outcomes. IF strategies and 
activities may be modified during the IF Phase as needed based on clinic staff feedback 
(Section 5.1.3.2).  
5.1.3.1 Toolbox 
IF strategies that may be used include changing infrastructure, financial strategies, 
supporting clinicians, providing interactive assistance, adapting and tailoring to context, 
training and education for stakeholders, developing stakeholder relationships, using 
evaluative and iterative strategies, and engaging consumers  [60, 62]. IF strategies utilized 
with clinics will be mapped to later analysis and association with clinic outcomes [60]. One 
of our overarching aims is to avoid exacerbating health disparities (variations in outcomes 
that are avoidable). Instead of providing the same strategies to all clinics, IF is an inherent 
process of matching strategies and resources within settings, helping lower resource 
clinics overcome barriers, and facilitating access to innovations such as EIMG. We will 
focus on the impact of customized IF across clinics, being mindful that clinics in stronger 
financial health (e.g., external funding) may be more likely to engage in IF strategies [58]. 
We will gather information on the heterogeneity of resources across clinics (e.g., staff 
shortages, turnover, external funding) through the needs assessment conducted during 
Pre-IF (Section 5.1.2) and examine how this may reduce the impact of IF. We will also 
examine how power dynamics within a unit might impact communication, trust and 
respect necessary in IF [63] through mixed methods approaches at the end of the IF Phase.  
5.1.3.2 Monitoring and evaluation 
An IF fidelity assessment will capture the degree to which facilitation activities influence 
the implementation of EIMG over time. IF strategies used with clinics will be documented 
using the Longitudinal Implementation Strategy Tracking System (LISTS) [64]. LISTS allows 
facilitators to accurately characterize and monitor IF strategies over time, and record 
modifications or discontinuations. Additionally, facilitators will spend 10-15 minutes a 
week completing a Time Tracking Log, via Excel, describing the type, frequency, and 
duration of IF activities at each clinic. The PI will meet weekly with facilitators to debrief, 
identify site-specific needs, and review weekly activity logs. The research team will review 

Page 15 of 34 
study progress (e.g., IF strategies) over RE-AIM dimensions to allow for rapid, iterative 
changes to streamline and improve processes with subsequent study waves. We will 
operationalize a rapid learning system modifying the example of Glasgow et al. [65]:  
1) During regularly scheduled meetings, the research team will review the 
operationalization of the IF and discuss its impact across RE-AIM dimensions with 
clinic staff. 
2) Clinic staff will be asked to confidentially rate their current perception of the 
project’s importance at their clinic and their level of satisfaction with progress 
across each RE-AIM dimension using 5-point scales. 
3) The research team will review the ratings summarized from the individual rating 
sheets and engage in a reflective discussion to identify implementation barriers on 
which to focus and set actionable plans. 
4) Follow-up meetings, approximately 6 weeks after step 3, will occur between the 
facilitators and the clinic staff to discuss integrating the actionable plans as a part 
of the implementation facilitation process. 
The goal of IF is to create a sustainable EIMG model through shared decision-making in 
which practices choose goals and strategies that work best for them in creating a redefined 
vision of EIMG implementation. When transitioning to maintenance, this will be augmented 
by making sure that clinics and clinic staff have access to necessary resources and tools to 
support ongoing EIMG referrals, incorporating EIMG into the organizational memory, and 
checking in on clinic progress every 3-4 months. Additionally, the research team will ensure 
continued support to the clinic champion(s) using active and participatory techniques 
identified by Ritchie et al [66]. By the end of the IF phase, the research team and clinic staff 
will co-design a new set of goals, refine process maps, and develop strategies to continue 
feedback reports. Further, successful IF will lead to the establishment of long-term 
relationships and the provision of ongoing support as needed. 
5.1.4 Post-IF Maintenance Phase 
Each wave will have a minimum of 12-months of follow up time to allow for ongoing 
observation and evaluation of the long-term effects of IF on EIMG referral rates. Data will be 
extracted from EHRs at selected time points to allow for ongoing estimates of 
implementation and reach. Twelve months post-IF, we will examine the extent to which: 1) 
EIMG continues to be implemented at clinics and 2) the fidelity of delivery of the EIMG core 
components [46, 67]. Individual interviews, informed by the Integrated Sustainability 
Framework [68], will be conducted with clinic staff to address the sustainability of EIMG at 
the end of the post-IF Maintenance phase. This data will be integrated with estimates of 
implementation, reach, and effectiveness to identify factors influencing sustainability at 
participating clinics. 
5.2 Study outcome measures 
5.2.1 Aim 1 
Patient EHR data will be extracted from the EHR on an ongoing basis to calculate adoption, 
implementation, and reach before, during, and after IF. Adoption will be assessed at the 

Page 16 of 34 
provider level by determining the number, proportion, and representativeness (i.e., sex, 
age, specialty) of providers that utilize any EIMG component in their practice compared to 
peers that do not use EIMG. Implementation will be assessed by determining the extent to 
which all three steps of EIMG (i.e., assessment, prescription, patient referral) are 
conducted with each eligible patient at the provider level, aggregated at the clinic level. Our 
ultimate measure of implementation is the proportion of steps completed with eligible 
patients. Reach, our primary outcome measure, will be assessed by estimating the 
number and proportion of eligible patient visits engaged in EIMG by providers. This will be 
calculated by provider and aggregated at the clinic level. Operationally, a patient’s visit will 
be considered ‘engaged’ if they receive an EIMG referral. The proportion of patient visits 
reached is estimated by dividing patient visits with an EIMG referral by all eligible patient 
visits. An expanded reach indicator will examine the proportion of referred patients that: a) 
enroll, and b) complete the PA program. Representativeness will be determined by 
comparing characteristics of patients who participate in the PA programs compared to a) 
eligible patients who did not receive a referral, and b) eligible patients who received a 
referral but did not participate in the evidence-informed PA programs.  
 
We will conduct the Organizational Readiness to Change Assessment (ORCA) [57] with 
clinic staff at each participating clinic. The ORCA tool operationalizes constructs defined in 
the original PARIHS framework to measure organizational readiness to change in clinic 
settings. The ORCA consists of three major scales that measure the strength of evidence 
for the proposed innovation, organizational support for change, and organizational capacity 
to facilitate the change. We will ask clinic staff to complete the ORCA during the Pre-IF 
phase (to determine baseline levels), at the end of the IF phase (short-term impact of IF), 
and months 9-12 of the Post-IF Maintenance phase (long-term impact of IF). The ORCA 
should take no longer than 10 minutes to complete.  
 
In addition to the ORCA survey, we will also conduct semi-structured meetings and 
interviews with clinic staff to gain a more nuanced understanding of EIMG integration 
across clinics. While self-administered questionnaires or structured interviews cost less 
and reduce interviewer bias, semi-structured approaches foster rapport and are preferred 
when seeking information on respondents’ attitudes and perceptions on a topic [69]. Semi-
structured approaches also allow exploration of issues through extensive probing [70]. 
Meeting and interview guides will be grounded in the i-PARIHS framework [44] and 
structured to complement information gained through the ORCA. Semi-structured 
meetings will take place during the Pre-IF Phase during ‘Learning’ activities (Section 5.1.2). 
These meetings will be used to discuss barriers and facilitators in relation to EIMG, and will 
also explore knowledge and fit of EIMG, EIMG implementation, evaluation capacity, 
organizational culture, and external factors influencing EIMG adoption. Thus, the meeting 
guide will be complementary to the topics in the semi-structured interviews, which will be 
conducted with individual clinic staff at the end of the IF Phase. Semi-structured meetings 
will last 15-60 minutes, depending on availability of clinic staff, and the semi-structured 
interviews should take no longer than 40 minutes to complete.  
 

Page 17 of 34 
The impact of IF will be assessed through process and summative evaluations. For the 
process evaluation, facilitators will document activities with clinics as detailed summary 
notes and by completing a Time Tracking Log. Checklists will guide decision-making 
processes (e.g., implementation processes, staff member roles) and allow documentation 
of specific action items. After IF sessions, mixed methods data collection will be utilized to 
determine perceptions of IF, IF quality, length, and utility. After 6 months of IF, a summative 
evaluation will assess the effectiveness of the IF strategies. We will conduct final, semi-
structured interviews with clinic staff to gain a nuanced understanding of the impact of IF, 
triangulating findings with quantitative data (i.e., reach and implementation metrics) 
collected before, during and after IF. We will track adaptations over all phases of IF (the 
intervention) pragmatically through all clinics using mixed methods approaches.  
5.2.1.1 Sample size 
Sample size was established using the swCRTdesign package [71] (R version 4.2.3; R Core 
Team, 2023) and preliminary data collected from the established 35 primary care clinics. A 
constrained randomization was applied to eligible clinics, randomly generating 50,000 
unique schemes at each of the three randomization phases (a fourth was not needed as all 
remaining clinics will be randomized in the fourth phase in the stepped-wedge design). At 
each phase, the limit for usable schemes was set at the 10th percentile of statistical 
distance, and one scheme was randomly selected. The process was then repeated for 
each subsequent phase. The clinic characteristics used include:  
• Initial EIMG exposure: (1) Pre-Covid, (2) Post-Covid  
• Past study involvement1: (1) The Duke Endowment, (2) R56, (3) None  
• Nearest YMCA Site County: (1) Greenville, (2) Oconee  
• Distance to nearest YMCA Site (mean)  
• Patient volume per provider (mean)  
• 2024 EIMG referrals (mean)  
• 2025 EIMG referrals (mean)  
 
We chose to allocate clinics to our four waves as follows: Wave 1 (n=6 clinics), Wave 2 
(n=8), Wave 3 (n=10), and Wave 4 (n=11). This was to ensure all 35 Prisma Health-Upstate 
primary care clinics that have adopted the EIMG model were included in the study, that the 
anticipated learning curve of IF from wave to wave was considered, and that the 
anticipated increase in referral rates to the YMCA sites and staff was considered to allow 
them to adapt to the increase in patient load. We will request clinic staff working in clinics 
participating in the study to complete the online ORCA survey. We anticipate a response 
from 2-3 staff members per clinic at each of our three designated time points: during the 
Pre-IF phase, at the end of the IF phase, and months 9-12 of the post-IF Maintenance 
phase. We expect that surveys may be completed by the same individual across the 
 
1 Our team received an award from The Duke Endowment over a 2-year period (#7058-SP; July ’22 – 
June ‘24) to retrospectively examine the implementation and cost-effectiveness of EIMG in the 12 original 
Prisma Health-Upstate primary care clinics. Additionally, we received an NHLBI R56 award 
(1R56HL157218-01A1; Sept ’22 – Aug ‘23) that assists highly meritorious applications gather data and 
inform an R01 re-submission. 

Page 18 of 34 
different time points. We will also request clinic staff to complete semi-structured 
meetings (Pre-IF Phase) and interviews (IF Phase). We anticipate 2-3 staff members per 
clinic at each of our two designated time points: during the Pre-IF Phase, and at the end of 
the IF phase.  
5.2.1.2 Data analysis 
The effect of IF on overall reach and implementation will be analyzed using separate 
generalized linear mixed models (GLMM) [72-74]. The use of GLMM will take into account 
variability between clinics (random intercept), the random effect of time, and the differing 
effects of intervention vs. non-intervention within clinic. Our experimental units are the 
clinics themselves with the observational units being EIMG eligible patient visits nested 
within each clinic. The analytical models will have the following form: 
 
𝑙𝑜𝑔𝑖𝑡(𝑌𝑖𝑗) = 𝛽0 + 𝛽𝑗+ 𝜃𝑋𝑖𝑗+ 𝛽1𝑍𝑖+ 𝛼𝑖+ 𝛾𝑗+ 𝑢𝑇𝑖𝑋𝑖𝑗+ 𝑢𝐶𝑖(1 −𝑋𝑖𝑗) 
 
The subscripts i and j refer to clinic, and time, respectively. 𝑌𝑖𝑗 is the clinic level referral 
measure; 𝛽0 is the mean response across clinics; 𝛽𝑗 is the effect of time; 𝛽1 is the mean 
effect of clinic level covariates; 𝜃 is the effect of EIMG; 𝑋𝑖𝑗 is an indicator of intervention; 
and 𝑍𝑖 is clinic type. The remaining terms in the model represent random variability and are 
assumed to have an expected value of 0 and associated variance. Specifically, 𝛼𝑖,
𝛾𝑗, 𝑢𝑇𝑖,𝑎𝑛𝑑 𝑢𝐶𝑖  are the uncertainty terms associated with clinics, time, and the interaction 
between clinics and intervention status. Missing data will likely occur due to improper 
documentation of EIMG steps. Where practical, providers that have utilized, but failed to 
properly document the utilization of EIMG steps, will be asked to correct the entry. If a 
provider is unable or unwilling to correct the documentation, the utilization of EIMG step(s) 
will not be counted, thus generating the most conservative estimate of EIMG 
implementation and reach. Since all providers are employees of Prisma Health, 
representativeness variables are required elements of human resources documentation, 
and we do not anticipate any missingness in the data. 
 
5.2.2 Aim 2 
Effectiveness is the degree to which participating in the evidence-informed PA program 
improves patient PA levels and health outcomes (i.e., changes in cardiometabolic 
biometric values). Changes in PA levels will be evaluated through two strategies. First, 
patients will be screened for their PA levels during clinic visits using the PAVS. Patients that 
enroll in the PA program will complete the PAVS (to ensure consistency of assessment) at 
baseline (week 1), midpoint (week 6), and at program graduation (week 12) with the EIMG 
Pro, and at 12 weeks after the completion of the PA program via either the EIMG Referral 
Coordinator or via MyChart.  Second, to obtain an objective measure of changes in PA 
levels, all participants who enroll in the PA program will be asked to wear an accelerometer 
to: a) provide an objective measure of PA levels, b) determine agreement between 
subjective (patient-reported) and objective measures of PA, and c) measure objective 
changes in PA behavior as a result of participating in the 12-week PA program.  

Page 19 of 34 
 
Patients enrolled in the PA program will be asked wear an ActiGraph wGT3X-BT® (Ametris 
LLC, Pensacola, Florida, USA) accelerometer. Patients will wear the accelerometer for one 
week at three time points: 1) immediately after orientation (week 0), 2) week 13 
(corresponding to the end of the PA program), and 3) week 25 (corresponding to 12 weeks 
post-PA program). Wrist-worn triaxial accelerometers have proven to be valid and reliable 
in comparison to hip-worn accelerometers [75-77], to reduce participant burden, increase 
compliance, and capture upper-body movement. Patients will be asked to keep an activity 
log tracking non-wear time, time to bed, and time out of bed [78, 79]. We will use 
established cut points for wrist-worn accelerometers and follow previous analysis 
guidelines [80].  
 
Primary data for assessing changes in health outcomes (i.e., body weight, blood pressure, 
hemoglobin A1c, lipid profiles) will be extracted from the EHR to allow comparisons 
between eligible patients that receive an EIMG referral and participate in the PA program 
and eligible patients that do not receive an EIMG referral. Disease incidence, burden, and 
complications (i.e., the Charlson Comorbidity Index) will be calculated from extracted 
data. The Post-IF Maintenance phase will allow us to track patient health outcomes for a 
minimum of 12 months after PA program participation.  
5.2.2.1 Sample size 
To assess changes in PA levels, we plan to invite all eligible patients to participate in 
accelerometry data collection (Section 6.1.3). Based on estimates of EIMG clinic-
community referrals in previous years and anticipating an increase in EIMG referrals during 
Pre-IF and IF activities in primary care clinics, we aim to recruit at a minimum n=70 patients 
over one year. This sample size will allow us to estimate agreement between patient 
subjective (i.e., PAVS) and objective (i.e., accelerometry) PA levels. Participants will be 
asked to complete accelerometry data collection at three distinct time points as described 
in Section 5.2.2. To assess changes in patient health outcomes, data will be extracted for 
all patients who are eligible for EIMG (Section 6.1.3). 
5.2.2.2 Quantitative analysis 
Effectiveness of EIMG relative to improvement in patient-reported PA levels, body weight, 
blood pressure, hemoglobin A1c, and lipid profiles will be analyzed using separate GLMMs 
[72-74]. Improvement will be computed for each eligible subject in two ways: 1) [(final – 
starting) / starting clinical measure], and 2) achieving goal (e.g., 5% weight loss after 12 
weeks = 1, otherwise 0). EIMG eligible patients will fall into 1 of 3 categories: not referred; 
referred but did not participate; and referred and participated. Two measures of 
effectiveness will be considered: 1) improvement in participants only; and 2) referred 
patients (participated vs did not participate). Comparisons with non-referred patients may 
be biased by unmeasured patient- or clinic-level characteristics. For the dichotomized 
improvement measures, the model from Aim 1 will be utilized with the addition of 𝜃𝑝𝑋𝑖𝑗𝑘 
and 𝛽2𝑍𝑖𝑘 terms to account for the subject category effect and subject level covariates, 
respectively. The analytical model for the ratio improvement measure will be: 
𝑌𝑖𝑗𝑘= 𝛽0 + 𝛽𝑗+ 𝛽1𝑍𝑖+ 𝛽2𝑍𝑖𝑘+ 𝜃𝑋𝑖𝑗+ 𝜃𝑝𝑋𝑖𝑗𝑘+ 𝛼𝑖+ 𝛾𝑗+ 𝑢𝑇𝑖𝑋𝑖𝑗+ 𝑢𝐶𝑖(1 −𝑋𝑖𝑗) + 𝑒𝑖𝑗𝑘 

Page 20 of 34 
The subscripts i, j, and k refer to clinic, time, and individual, respectively. 𝑌𝑖𝑗𝑘 is the 
individual outcome; 𝛽0 is the mean response; 𝛽𝑗 is the effect of time; 𝛽1 is the effect of 
clinic level covariates; 𝛽2 is the effect of individual level covariates; 𝜃 is the effect of EIMG; 
𝜃𝑝 is the effect of participation; 𝑋𝑖𝑗 is an indicator of intervention; 𝑋𝑖𝑗𝑘 is an indicator of 
participation; 𝑍𝑖 is a clinic level covariate; and 𝑍𝑖𝑘 is a subject level covariate. The 
remaining terms in the model represent random variability and are assumed to have an 
expected value of 0 and associated variance. Specifically, 𝛼𝑖, 𝛾𝑗, 𝑢𝑇𝑖,𝑎𝑛𝑑 𝑢𝐶𝑖  are the 
uncertainty terms associated with clinics, time, and the interaction between clinics and 
intervention status. The 𝑒𝑖𝑗𝑘 is the un-explained deviance. 
5.2.2.3 Qualitative analysis 
As part of our mixed methods approach, we will conduct semi-structured individual 
interviews with a randomly selected sample of patients (blocked by sex and race) from 
each participating clinic (n=35) that receive an EIMG referral during the study period. We 
anticipate a 20% participation rate in the individual interviews (e.g., 300 patients referred * 
20% participation rate = approx. 60 completed interviews), across each of three different 
groups of patients. Patients will be selected from one of three categories: 1) patients who 
received an EIMG referral from their provider, but chose not to enroll in the PA program, will 
be queried about their awareness and reasons for not enrolling (i.e., perceived challenges 
to participation); 2) patients who received an EIMG referral and enrolled, but dropped out, 
will be asked about their perceptions and satisfaction with the PA program, motivations for 
enrolling, and reasons for discontinuation; and 3) patients who enrolled and completed the 
PA program will be queried about their motivations for enrolling, perceptions of and 
satisfaction with the program, and challenges encountered. Where possible, responses 
from the individual interviews will be linked back to our IF strategies, such as clarifying 
places for additional support and improvements to provider-patient interactions, to add to 
our comprehensive evaluation of the EIMG referral pathway. Open-ended responses will be 
uploaded to qualitative analysis software to apply codes developed deductively from the 
questions posed and inductively from responses [81], developing an initial coding scheme 
for each question. A coding manual with definitions for each code will ensure that high 
levels of intercoder reliability (>80%) are achieved throughout the coding process [82, 83]. 
Analyses will catalogue facilitators, barriers, and challenges receiving EIMG referrals and 
enrolling in the PA program.  
5.2.3 Aim 3 
The IRB-approved Prisma Health Data Repository will be used to capture the following 
information. Patient-level episodes of care for EIMG-eligible patients will be developed for 
patients across the study period (Figure 5).  
 

Page 21 of 34 
 
 
 
 
 
 
 
 
Figure 5. Defining the episode of care.  
 
The ambulatory visit date for each patient with an EIMG eligible diagnosis will be 
designated as the index visit date for a “candidate” episode of care. To ensure consistent 
episode definitions across time, candidate episodes will be excluded if the index date is 
within 90 days of the end of a previous care episode for that patient. Patients will also be 
excluded if they do not have ambulatory visits with vital statistics and lab results in the 
episode outcome period of Figure 5. For each patient, a 90-day Run-in-Period prior to the 
index date will be used to calculate patient baseline covariates at that visit. The Referral 
Decision Period is the 30 days after the index visit date. The Treatment Period is 120 days 
from the initial visit date, providing sufficient time for patient referral, entry into and 
completion of the 12-week PA program. The Outcome Period is the 90 days following the 
treatment period. Data sources include:  
1) Time Tracking Logs: All staff involved in delivering IF will log their time for these 
activities (Section 5.1.3.2).  
2) Program Data: All non-labor resources used to implement IF (e.g., printing of 
materials). 
3) Patient-Specific Healthcare Resources Used: For EIMG eligible patients, we will 
extract from the EHR during the episode of care: i) all healthcare utilization received 
by Healthcare Common Procedure Coding System (HCPCS), Diagnosis Related 
Group (DRG), and National Drug codes (NDC); and ii) the REDCap-based data file 
used by the EIMG program that captures resources used during the 12-week PA 
program.  
4) Resource Unit Price Data: Medicare standard pricing files for HCPCS, DRG and 
NDCs, and Prisma Health average wage rates by labor category (i.e., physician, 
EIMG Referral Coordinator, YMCA Site Coordinator).  
5) Patient-Specific Health Outcomes: Blood pressure, body weight, hemoglobin A1c, 
full lipid profiles, and PA levels will be collected from the EHR at the index visit and 
visits across the episode of care (Section 5.2.2). 
 
From the perspective of a healthcare system interested in assessing the costs of IF 
activities, we will calculate: 1) the Total Fixed Cost associated with developing the IF; 2) the 
Total Variable Cost of providing tailored IF across clinics; and 3) the number of EIMG 
eligible-patients (N) over the study period across clinics. Implementation costs will be 
calculating using two sources. First, Time Tracking Logs filled out by those devoted to 
conducting IF activities will assess resource use.  Then, labor costs will be calculated using 

Page 22 of 34 
average wage rates based on the relevant types of job titles conducting IF (e.g., program 
manager). Second, program data will be used for estimating non-labor related costs of IF 
(e.g. printing). We will calculate the total costs of providing tailored IF across both clinics 
and patients as follows: (1) total implementation cost per clinic calculated as 
 
𝐸𝐼𝑀𝐺 𝐼𝐹 𝐶𝑜𝑠𝑡𝑠 𝑝𝑒𝑟 𝐶𝑙𝑖𝑛𝑖𝑐= 𝑇𝑜𝑡𝑎𝑙 𝐼𝑚𝑝𝑙𝑒𝑚𝑒𝑛𝑡𝑎𝑡𝑖𝑜𝑛 𝐶𝑜𝑠𝑡𝑠 (𝑙𝑎𝑏𝑜𝑟+ 𝑛𝑜𝑛 𝑙𝑎𝑏𝑜𝑟 𝑐𝑜𝑠𝑡𝑠)
𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑙𝑖𝑛𝑖𝑐𝑠
 
 
and (2) cost per EIMG eligible patients (N) over the study period across clinics. Total 
implementation costs include labor and non-labor costs and will be allocated across the 
number of eligible patients (N) to calculate: 
  
𝐸𝐼𝑀𝐺 𝐼𝐹 𝐶𝑜𝑠𝑡𝑠 𝑃𝑒𝑟 𝐸𝑙𝑖𝑔𝑖𝑏𝑙𝑒 𝑃𝑎𝑡𝑖𝑒𝑛𝑡= 
𝑇𝑜𝑡𝑎𝑙 𝐼𝑚𝑝𝑙𝑒𝑚𝑒𝑛𝑡𝑎𝑡𝑖𝑜𝑛 𝐶𝑜𝑠𝑡𝑠 (𝑙𝑎𝑏𝑜𝑟+𝑛𝑜𝑛 𝑙𝑎𝑏𝑜𝑟 𝑐𝑜𝑠𝑡𝑠)
𝑁
  
 
To assess the estimate of effects of IF on EIMG referral rates and patient level healthcare 
costs and health outcomes, we will use data at the episode of care level. Outcomes will 
include Episode Healthcare Costs (the price-weighted sum of all healthcare resources 
used by patient “i” during the episode of care) and Episode-Specific Clinical Outcomes 
(change in clinical outcomes (blood pressure; body weight; hemoglobin A1c; full lipid 
profile and PA for patient “i”)) between the index visit and the first provider visit in the 
episode in the Outcome Period (Figure 5) in which this information is collected.  
5.2.3.1 Data analysis 
We will regress whether eligible patients were referred to EIMG during their index provider 
visit on variables representing: 1) whether the clinic providing the visit had received IF, 2) 
baseline patient clinical and demographic characteristics, 3) time trends, and 4) clinic-
specific fixed effects using a GLMM [84]. The parameter estimate of the effect of IF in this 
model will equal the effect of IF on the probability of a patient being referred to EIMG. The 
specification of clinic-specific binary variables (𝐿𝑗) and time trends (𝑀𝑘) will enable us to 
control for unmeasured clinic specific factors affecting referral and any global secular 
trends in EIMG referral patterns (e.g., stemming from new scientific findings reported 
during the study period) [85]. We will then estimate the effects of increases in EIMG 
referrals induced by IF by exploiting the stepped wedge rollout of IF across practices using 
instrumental variable (IV) estimation methods. The IV in this study will be a binary variable 
indicating whether the patient’s index visit within a care episode occurs after the clinic 
received EIMG IF. The IV estimation will yield unbiased estimates of the effect of EIMG 
referral on outcomes for the subset of patients whose referrals were a result of IF. This is 
known as the Complier Average Causal Effect [86-96]. Because our specified IV will be 
based on the randomized stepped wedge clinic rollout, the EIMG referral variation 
stemming from this instrument will be independent of unmeasured confounding variables. 
Combining estimates of IF costs per eligible patient with our IV estimates will enable us to 
estimate cost-effectiveness ratios summarizing the impacts of the IF. Estimated cost-
effectiveness ratios will equal the incremental cost per change patient clinical outcome for 
those patients whose EIMG referrals were induced by IF. 

Page 23 of 34 
6 Study population 
There are three groups of participants in this study i) primary care clinics (which will be the 
unit of randomization in the stepped wedge design), ii) primary care clinic staff, iii) primary 
care clinic patients.  
6.1 Eligibility criteria 
6.1.1 Primary care clinics 
Inclusion criteria: 
a) Currently EIMG-activated Prisma Health-Upstate Primary Care clinics (family or 
internal medicine) 
b) Adopted EIMG ≥6 months prior to the beginning of this study 
Exclusion criteria: 
a) Not EIMG-activated 
b) Adopted EIMG < 6 months prior to the beginning of this study 
c) Greater than 15 miles from the nearest community PA facility (YMCA site) 
6.1.2 Primary care clinic staff  
Inclusion criteria:  
a) ≥18 years of age 
b) Has worked at the Prisma Health-Upstate clinic since the start date of the 
intervention in the respective wave, i.e., Pre-IF start date for each wave 
c) Has clinical encounters with a minimum of 25 patients per month 
d) Study clinic is their primary clinic (>50% of their working time) 
e) Able to understand and communicate in English  
Exclusion criteria:  
a) <18 years of age 
b) Work at the Prisma Health-Upstate clinic started after the Pre-IF phase began 
c) Has clinical encounters with <25 patients per month 
d) Unable to speak or understand English 
e) Adults unable to provide consent  
6.1.3 Patients  
Inclusion criteria (must meet all the below):  
a) Age ≥18 and ≤80 years 
b) Clinically eligible (diagnosis of hypertension, dyslipidemia, obesity, 
diabetes, or physical inactivity) to receive an EIMG referral 
c) A healthcare visit with an eligible encounter type (evaluation, telemedicine, 
consult, office visit, e-visit, follow-up, appointment, education, 
multidisciplinary visit, nutrition, occupational medicine-office visit) 
d) A healthcare visit at a participating clinic 
Exclusion criteria: 
a) Age < 18 or > 80 years 
b) Current referral to physical therapy or occupational therapy 

Page 24 of 34 
c) Current referral to cardiac, pulmonary, or oncology rehab 
d) One of the following visit diagnoses: Alzheimer's disease, amyotrophic 
lateral sclerosis, angina or chest pain, congenital stenosis of aortic valve, 
congenital insufficiency of aortic valve, moderate or severe persistent 
asthma, typical or atypical atrial flutter, autonomic dysreflexia, acute 
bronchitis, cerebral infarction, encounter for chemotherapy, chronic kidney 
disease (stage 3-5), end stage renal disease, coma, acute chronic 
obstructive pulmonary disease, unspecified dementia, dependence on renal 
dialysis, trisomy 21, pulmonary embolism, venous embolism and 
thrombosis, history of falling, acute fracture, left ventricular failure, 
congestive heart failure, hypertensive urgency, emergency or crisis, diabetes 
with ketoacidosis, chronic respiratory failure, dependence on supplemental 
oxygen, myocardial infarction, osteoporosis with current fracture, encounter 
for palliative care, paraplegia or quadriplegia, Parkinson’s disease, 
pneumonia, encounter for supervision of pregnancy, encounter for radiation 
therapy, serious mental illness (schizophrenia, psychotic disorder), 
homicidal or suicidal ideations, sepsis, injury of spinal cord, presence of 
coronary stent, dependence on wheelchair or ambulatory aid 
For patients completing accelerometry data collection, the additional 
inclusion/exclusion criteria apply: 
Inclusion criteria:  
a) Received an EIMG referral and scheduled for orientation 
b) Attending orientation at two pre-determined community PA facilities (these sites 
were selected due to each being the largest urban and rural YMCA referral sites in 
the study)  
Exclusion criteria: 
a) Unable to provide verbal informed consent 
b) Planning on travel or vacation during data collection, as patient PA behavior 
is anticipated to differ from ‘everyday’ PA behavior at home  
c) Unable to wear a watch during work hours due to employment restrictions 
e.g., food service, healthcare, operating heavy machinery/construction, 
laboratory-based work etc.  
In addition, for patients completing interviews, the additional inclusion/exclusion 
criteria apply: 
Inclusion criteria:  
a) Received an EIMG referral 
Exclusion criteria: 
a) Unable to provide verbal informed consent 
b) Unable to speak or understand English 
c) <18 years of age 
6.2 Subject recruitment 

Page 25 of 34 
6.2.1 Primary care clinics  
Currently, 35 of 43 eligible Prisma Health-Upstate primary care clinics have received EIMG 
onboard training and activation and are thus eligible to participate in this study. The 
research team will begin contacting primary care clinics three to four weeks before the Pre-
IF Phase (Section 5.1.2) begins in their respective study wave. The purpose of this contact 
will be to briefly introduce the study activities from a clinical perspective and request time 
in a future meeting with clinic staff. 
6.2.2 Primary care clinic staff 
After (re-)establishing contact with each clinic in each wave, the research team will arrange 
time to meet with respective clinic staff during a large provider meeting. Clinic staff will be 
recruited at these and subsequent meetings, as well as through lead physicians and clinic 
managers, to complete the ORCA survey and semi-structured interviews. The research 
team will attempt to ensure a broad, representative sample of clinic staff. Completion of 
surveys or interviews does not obligate clinic staff to complete additional study activities. 
Once recruited for the ORCA survey, clinic staff will have the option to complete a paper 
copy of the survey, use a QR code to access an online version, or an email will be sent out 
to clinic staff that includes a link to the online survey. Additionally, our research team will 
follow up directly with providers (e.g., advanced practice providers, physicians). For clinic 
staff who elect to participate in interviews, these will be conducted during non-work hours. 
No more than four communication attempts will be made when recruiting participants to 
complete any of the above.  
6.2.3 Patients 
There are four distinct groups of patient participants in this study; 1) eligible patients 
(Section 6.1.3) who did not receive an EIMG referral during the study period, 2) eligible 
patients who received an EIMG referral during the study period but chose not to enroll in 
the PA program, 3) patients who received an EIMG referral during the study period, 
enrolled, but then dropped out, and 4) patients who enrolled during the study period and 
completed the PA program. Our research team will attempt to ensure a broad, 
representative sample of patients from diverse subgroups and social dimensions. 
Completion of interview or accelerometry data collection does not obligate patients to 
complete additional study activities. Group 1) will be obtained from de-identified EHR data 
collected as part of routine clinical practice and will be used to explore implementation 
and cost outcomes.  
 
To recruit patients for interviews (i.e., Groups 2-4), the research team will obtain a list of 
patients with an EIMG referral documented by the EIMG Referral Coordinator, who is both 
an employee of Prisma Health and a member of the study team. This list will be transferred 
to members of the research team who have permission to access identifiable Prisma 
Health-Upstate patient information. We will begin contacting EIMG-referred patients in 
months 1-6 of the second year of this study (Figure 4), and the process will be repeated 
with each subsequent wave every six months over years 2-4. Patients will be sent 
informational letters (on Prisma Health-Upstate letterhead) through the US postal service 

Page 26 of 34 
to alert them of the research study and that they may be contacted by a member of our 
research team. Approximately two to three weeks after the delivery of the letter, a member 
of the research team will attempt to contact patients via the phone to assess their interest 
in participating in an individual interview. Patients may also receive a text message in 
conjunction with the phone call, as we have found that many individuals do not respond to 
phone calls from unknown numbers. No more than three attempts (call + text is equivalent 
to one attempt, as persons may prefer one communication style over the other) will be 
made to contact. Patients that agree to participate in a phone interview will then select a 
future date and time that is convenient for them. 
 
The EIMG Referral Coordinator will recruit patients for accelerometry data collection (i.e., 
Groups 3 & 4). After receiving a complete referral, the Referral Coordinator contacts the 
patient via phone within three business days to discuss details of EIMG participation and 
arrange an orientation at the YMCA site of their choice. After assessing eligibility during this 
call, the Referral Coordinator will inform patients verbally about the opportunity to 
participate in accelerometry data collection. Patients will provide verbal informed consent 
to participate in accelerometry data collection to the Referral Coordinator and will be sent 
additional study information through electronic channels (i.e. email, text message link, 
etc.). The Referral Coordinator will transfer a list of patients who have provided verbal 
informed consent, as well as the date, time and location of their orientation (when 
available) to research team members who have permission to access identifiable Prisma 
Health patient information. The research team will then arrange distribution of 
accelerometers to the relevant community PA facilities, and patients will be fitted with the 
accelerometer by the EIMG Pros, who will also provide guidance on how to wear the 
device, at the orientation session (week 0) and at the end of the PA program (week 13). 
Patients will be contacted via phone by the EIMG Referral Coordinator at week 25 to 
arrange distribution of accelerometers, as the patient will have completed the PA program.   
7 Study procedures  
7.1 Subject compensation 
Clinic staff will receive a $50 gift card for completing the ORCA survey or individual 
interview to compensate them for their time and effort outside of their business hours, a 
value partially commensurate with their current salary. Patients will receive a $25 gift card 
for completing individual interviews, and $10 gift card per accelerometry data collection 
time point to compensate them for their time and effort.  
7.2 Subject withdrawal 
All potential participants will be informed that their employment standing/status (clinic 
staff), participation in the PA program, or health care (patients) will not be affected in any 
way based on their decision to participate in the study as noted on the informed consent 
forms. Participants may decide to end their study participation at any point throughout the 
study. 

Page 27 of 34 
8 Study administration, data handling and record keeping 
8.1 Study oversight 
Oversight to this study is provided by three separate committees: the Administrative 
Leadership Council (ALC), the Clinical Steering Group (CSG), and the Data Safety and 
Monitoring Board (DSMB). The ALC is designed to provide high level input to the study 
design to ensure the research fits with the mission, vision, and goals of both Prisma Health 
and University of South Carolina School of Medicine Greenville. The ALC will work closely 
with the PI to align the research with the function and operations of the Prisma Health 
system. The CSG is developed to pragmatically align the research with clinical practice. 
The CSG provides valuable input from front-line providers and leaders in the Prisma 
Health-Upstate primary care system to ensure smooth involvement of the primary care 
clinics in the implementation facilitation process. The DSMB will safeguard the interests of 
study participants and protect the integrity and credibility of the study. The DSMB will meet 
at minimum, every six months during the study period. The DSMB will convene urgently or 
hold ad hoc meetings to review any unexpected, serious, or high-volume adverse events to 
ensure participant safety. 
8.2 Confidentiality 
We will apply standard methods for protecting the confidentiality of participants by using a 
personal ID number on all study documents, storing data on password-protected 
computer data files, and storing physical forms (e.g., signed informed consent forms, 
notes from interviews) in locked file cabinets in locked offices belonging to the PI. Other 
than the ID number, names and any other personal identifying information will not be 
recorded on any study documentation other than one electronic linking document that will 
be stored on in a password protected electronic file. Only the PI and research team 
members conducting data collection will have access to the document linking participant 
identifying information and their study ID number. No protected health information will be 
collected or disclosed in this research study. No individual data will be shared with anyone 
outside of the research team. Participant responses will be aggregated and presented as 
summary findings so that no individuals can be personally identified. 
8.3 Data collection and management 
8.3.1 Data collection 
Research activities will be carried out by trained members of the research team. Patient-
level data will be extracted from the EHR for eligible patients in Prisma Health-Upstate 
primary care clinics participating in the research study via the Prisma Health Data 
Repository methods described in Section 5.2.3. Other research activities will be carried out 
in-person, virtually (i.e., the electronic survey), through an online platform (e.g., individual 
interviews with clinic staff using Teams), or over the telephone (e.g., individual interviews 
with patients). While in-person activities will be scheduled to take place in clinic or 
community PA facility settings, staff or patients may request to meet in other locations 
(e.g., outdoors, in research office) to complete study assessments. Regardless of the 

Page 28 of 34 
location or modality, all study activities will take place in quiet, private settings to ensure 
privacy and confidentiality of responses. 
8.3.2 Data management 
The PI will develop a Data Management Plan to describe how data will be handled during 
and after the study in accordance with institutional guidelines. A Data Sharing Plan will be 
developed by the PI to ensure that appropriate study data will be made accessible, no later 
than the time of publication or the end of performance period. The plan will outline policies 
for the storage and public accessibility of data beyond the life of the project. Data sharing 
agreements will be developed with Prisma Health-Upstate for any external investigators 
needing identifiable patient data (e.g., patient contact information to conduct individual 
interviews). In addition, the DMSB will provide independent oversight to data management 
and sharing. Data will be made available to outside investigators upon written request.  
8.4 Risks 
There may be minor risks to participating in this study associated with:  
• Information risks due to a loss of privacy and/or breach of confidentiality. To 
minimize this risk, a participant ID will be used on all study documents and securely 
stored in password-protected electronic files to ensure confidentiality. Further, 
responses will be aggregated across all individuals interviewed and/or who engage 
in accelerometry data collection, and no potentially identifiable information will be 
reported. The research team will ensure that all study participants are interviewed 
in a quiet, private setting where their responses are not heard by anyone else.  
• Psychological or emotional risks. There is a small chance that study participants 
may experience fear, stress, guilt, loss of self-esteem, or a triggering of past 
emotional experiences. Participants will be informed that they do not have to 
answer questions and can stop the interview at any time.  
• Discomfort with accelerometer. There is a small chance that patients may 
experience discomfort or skin irritation from wearing the accelerometer. Patients 
will be informed of this risk and asked to record periods of non-wear in the activity 
log. Patients will also be informed that they can stop participating at any time.  
8.5 Benefits 
Study participants will receive no direct benefit from participating in this research study. 
However, findings from our research will be used to increase the efficiency of incorporating 
EIMG into the patient workflow and improve the connection of patients to supportive PA 
resources.  
8.6 Informed consent process/HIPAA authorization 
Informed consent will be obtained prior to any participation in the study. 
8.6.1 ORCA Survey 
The first page of the online/paper survey will be a consent form providing a detailed 
explanation of the study and potential risks of participation. At the bottom of the page, 

Page 29 of 34 
participants will be asked to provide their consent to participate in the study before 
continuing to the survey. Individuals that do not agree to participate in the study will be 
thanked for their time and exited from the survey (online).  
8.6.2 Individual interviews with clinic staff and patients 
Participants will be notified prior to completing the informed consent that the interviews 
will be recorded to allow for accurate transcription in the future. All clinic staff who choose 
to participate in the individual interviews will receive an electronic version of the consent 
form prior to the individual interview. For patients, during the initial recruitment call, the 
research team will ask patients if they would like a copy of the informed consent form 
emailed or mailed to them ahead of their individual interview appointment. At the 
beginning of the individual interview appointment, clinic staff will take part in the informed 
consent process to obtain either their written (for in-person interviews) or verbal (for virtual 
interviews) consent. Patients will undergo the informed consent process virtually (over the 
phone) to obtain their verbal consent immediately prior to conducting the phone interview. 
Verbal consent to participate in the study will be documented by the research team.  
8.6.3 Accelerometry 
The EIMG Referral Coordinator will inform eligible patients about accelerometry data 
collection and send additional study information to patients who provide verbal informed 
consent to participate. Patients will have time to consider this information prior to their 
orientation. They will be offered the opportunity to participate in accelerometry data 
collection at their orientation session (week 0), at the end of the PA program (week 13), and 
12-weeks post-PA program (week 25). Verbal consent will be obtained via the EIMG 
Referral Coordinator, who is also a study team member, to participate at each time point 
and will be documented. If the patient attends orientation, consents to accelerometry data 
collection, but does not complete the PA program, they will not be contacted to complete 
subsequent accelerometry data collection time points.  
9 Study finances 
This study is financed through an R01 grant from the U.S. National Heart, Lung, and Blood 
Institute. No investigators have any conflicts of interest. 
10 Dissemination 
Based on initial deployment, an EIMG toolkit was developed and made available for health 
systems on a limited basis. In 2022, the EIMG toolkit was formally recognized at the White 
House Conference on Hunger, Nutrition and Health as a population health strategy and 
subsequently made freely available to >600 health systems, >750 YMCA associations, and 
CDC-funded state health departments focused on arthritis interventions. We will revise the 
toolkit, guided by the i-PARIHS framework, to incorporate information on clinic adoption 
(context), the intervention (EIMG), recipients (clinic staff and patients) and the overall 
impact of IF on increasing EIMG implementation. The updated toolkit will be made freely 
available to the CDC, DHHS, and all U.S. health systems for integrating PA referral 
pathways in primary care settings. The toolkit will also be disseminated through the EIM 

Page 30 of 34 
Global Network to leaders integrating PA into their regional/national health systems in >35 
countries around the world. 
11 Conclusions 
This study has the potential to significantly advance our understanding of optimally 
integrating a PA pathway that involves the prescription, referral, and engagement of 
patients in community-based PA programs. Integrating the EIM model in health systems to 
connect patients to available community resources has the potential to significantly alter 
clinical practice and improve population health outcomes. Information gained from this 
study will lead to the refinement of a generalizable approach that will inform future 
implementation strategies on optimizing and scaling up the integration of comprehensive 
PA models in Prisma Health systems in South Carolina, and U.S. health systems generally. 
Study findings will also provide cost estimates of potential savings to health systems 
comprehensively integrating PA as a population health management tool using clinical-
community linkages. Study findings and developed resources will be made available to 
health systems for broad scale-up with the goal of increasing patient engagement in 
community-based PA programs as an extension of health care. 

Page 31 of 34 
12 References 
 
1. 
Fiuza-Luces, C., et al., Exercise is the real polypill. Physiology (Bethesda), 2013. 28(5): p. 330–58. 
2. 
Naci, H. and J.P. Ioannidis, Comparative effectiveness of exercise and drug interventions on mortality outcomes: 
metaepidemiological study. Br J Sports Med, 2015. 49(21): p. 1414–22. 
3. 
Lee, I.-M., et al., Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of 
burden of disease and life expectancy. The Lancet, 2012. 380(9838): p. 219–229. 
4. 
Casonatto, J., et al., The blood pressure-lowering effect of a single bout of resistance exercise: A systematic 
review and meta-analysis of randomised controlled trials. Eur J Prev Cardiol, 2016. 23(16): p. 1700–1714. 
5. 
Colberg, S.R., et al., Physical Activity/Exercise and Diabetes: A Position Statement of the American Diabetes 
Association. Diabetes Care, 2016. 39(11): p. 2065–2079. 
6. 
Wolff-Hughes, D.L., et al., Total Activity Counts and Bouted Minutes of Moderate-to-Vigorous Physical Activity: 
Relationships With Cardiometabolic Biomarkers Using 2003-2006 NHANES. J Phys Act Health, 2015. 12(5): p. 
694–700. 
7. 
2018 Physical Activity Guidelines Advisory Committee, 2018 Physical Activity Guidelines Advisory Committee 
Scientific Report. 2018, US Department of Health and Human Services: Washington, DC. p. 779. 
8. 
Durstine, J.L., et al., Chronic disease and the link to physical activity. Journal of Sport and Health Science, 2013. 
2(1): p. 3–11. 
9. 
Elgaddal, N., E.A. Kramarow, and C. Reuben, Physical Activity Among Adults Aged 18 and Over: United States, 
2020. NCHS Data Brief, 2022(443): p. 1–8. 
10. 
Physical Activity Alliance. National Physical Activity Plan. 2026  [cited 2026 9 January]. 
11. 
Patrick, K., M. Pratt, and R.E. Sallis, The healthcare sector's role in the U.S. national physical activity plan. J Phys 
Act Health, 2009. 6 Suppl 2: p. S211–9. 
12. 
Bull, F.C., et al., The Toronto Charter for Physical Activity: a global call for action. J Phys Act Health, 2010. 7(4): p. 
421–2. 
13. 
Jacobson, D.M., et al., Physical activity counseling in the adult primary care setting: position statement of the 
American College of Preventive Medicine. Am J Prev Med, 2005. 29(2): p. 158–62. 
14. 
Vuori, I.M., C.J. Lavie, and S.N. Blair, Physical Activity Promotion in the Health Care System. Mayo Clinic 
Proceedings, 2013. 88(12): p. 1446–1461. 
15. 
Sallis, R.E., et al., Call to Action on Making Physical Activity Assessment and Prescription a Medical Standard of 
Care. Curr Sports Med Rep, 2016. 15(3): p. 207–14. 
16. 
Saitz, R., Screening and brief intervention enter their 5th decade. Subst Abus, 2007. 28(3): p. 3–6. 
17. 
Lobelo, F., M. Stoutenberg, and A. Hutber, The Exercise is Medicine Global Health Initiative: a 2014 update. Br J 
Sports Med, 2014. 48(22): p. 1627–33. 
18. 
Strath, S.J., et al., Guide to the assessment of physical activity: Clinical and research applications: a scientific 
statement from the American Heart Association. Circulation, 2013. 128(20): p. 2259–79. 
19. 
Coleman, K.J., et al., Initial validation of an exercise "vital sign" in electronic medical records. Med Sci Sports 
Exerc, 2012. 44(11): p. 2071–6. 
20. 
Greenwood, J.L., E.A. Joy, and J.B. Stanford, The Physical Activity Vital Sign: a primary care tool to guide 
counseling for obesity. J Phys Act Health, 2010. 7(5): p. 571–6. 
21. 
Ball, T.J., et al., Predictive Validity of an Adult Physical Activity "Vital Sign" Recorded in Electronic Health 
Records. J Phys Act Health, 2016. 13(4): p. 403–8. 
22. 
Young, D.R., et al., Associations between physical activity and cardiometabolic risk factors assessed in a 
Southern California health care system, 2010-2012. Prev Chronic Dis, 2014. 11: p. E219. 
23. 
Grant, R.W., et al., Exercise as a vital sign: a quasi-experimental analysis of a health system intervention to 
collect patient-reported exercise levels. J Gen Intern Med, 2014. 29(2): p. 341–8. 
24. 
Swinburn, B.A., et al., The green prescription study: a randomized controlled trial of written exercise advice 
provided by general practitioners. Am J Public Health, 1998. 88(2): p. 288–91. 
25. 
Elley, C.R., et al., Effectiveness of counselling patients on physical activity in general practice: cluster 
randomised controlled trial. Bmj, 2003. 326(7393): p. 793. 
26. 
Hamlin, M.J., et al., Long-term effectiveness of the New Zealand Green Prescription primary health care exercise 
initiative. Public Health, 2016. 140: p. 102–108. 
27. 
Pavey, T.G., et al., Effect of exercise referral schemes in primary care on physical activity and improving health 
outcomes: systematic review and meta-analysis. Bmj, 2011. 343: p. d6462. 

Page 32 of 34 
28. 
Murphy, S.M., et al., An evaluation of the effectiveness and cost effectiveness of the National Exercise Referral 
Scheme in Wales, UK: a randomised controlled trial of a public health policy initiative. J Epidemiol Community 
Health, 2012. 66(8): p. 745–53. 
29. 
Campbell, F., et al., A systematic review and economic evaluation of exercise referral schemes in primary care: a 
short report. Health Technology Assessment, 2015. 19(60): p. 1–110. 
30. 
Exercise is Medicine Greenville. Exercise is Medicine Greenville®. 2018  [cited 2026 09 January]; Available from: 
https://eimgreenville.org. 
31. 
American College of Sports Medicine. Rx for Health Series. 2021  [cited 2026 09 January]; Available from: 
https://www.exerciseismedicine.org/eim-in-action/health-care/resources/rx-for-health-series/. 
32. 
American College of Sports Medicine. Specialty Certificate Programs. 2026  [cited 2026 09 January]; Available 
from: https://www.acsm.org/certification/specialized. 
33. 
Harris, P.A., et al., The REDCap consortium: Building an international community of software platform partners. 
Journal of Biomedical Informatics, 2019. 95: p. 103208. 
34. 
Harris, P.A., et al., Research electronic data capture (REDCap)—A metadata-driven methodology and workflow 
process for providing translational research informatics support. Journal of Biomedical Informatics, 2009. 42(2): 
p. 377–381. 
35. 
Saunders, R.P., et al., Conceptualizing, implementing, and monitoring a structural health promotion intervention 
in an organizational setting. Health Promot Pract, 2013. 14(3): p. 343–53. 
36. 
Howie, E.K., et al., The 3-year evolution of a preschool physical activity intervention through a collaborative 
partnership between research interventionists and preschool teachers. Health Educ Res, 2014. 29(3): p. 491–
502. 
37. 
Porter, R.R., et al., Design and implementation of a clinic-to-community, physical activity health promotion 
model for healthcare providers. Preventive Medicine Reports, 2022. 26: p. 101697. 
38. 
Ding, D., et al., The economic burden of physical inactivity: a global analysis of major non-communicable 
diseases. The Lancet, 2016. 388(10051): p. 1311–1324. 
39. 
Carlson, S.A., et al., Inadequate physical activity and health care expenditures in the United States. Prog 
Cardiovasc Dis, 2015. 57(4): p. 315–23. 
40. 
Laine, J., et al., Cost-effectiveness of population-level physical activity interventions: a systematic review. Am J 
Health Promot, 2014. 29(2): p. 71–80. 
41. 
Wu, S., et al., Economic analysis of physical activity interventions. Am J Prev Med, 2011. 40(2): p. 149–58. 
42. 
GC, V., et al., Are brief interventions to increase physical activity cost-effective? A systematic review. British 
Journal of Sports Medicine, 2016. 50(7): p. 408–417. 
43. 
Smith, J.D., D.H. Li, and M.R. Rafferty, The Implementation Research Logic Model: a method for planning, 
executing, reporting, and synthesizing implementation projects. Implementation Science, 2020. 15(1): p. 84. 
44. 
Harvey, G. and A. Kitson, PARIHS revisited: from heuristic to integrated framework for the successful 
implementation of knowledge into practice. Implementation Science, 2016. 11(1): p. 33. 
45. 
Glasgow, R.E., et al., RE-AIM Planning and Evaluation Framework: Adapting to New Science and Practice With a 
20-Year Review. Front Public Health, 2019. 7: p. 64. 
46. 
Stoutenberg, M., et al., A Pragmatic Application of the RE-AIM Framework for Evaluating the Implementation of 
Physical Activity as a Standard of Care in Health Systems. Prev Chronic Dis, 2018. 15: p. E54. 
47. 
Curran, G.M., et al., Effectiveness-implementation hybrid designs: combining elements of clinical effectiveness 
and implementation research to enhance public health impact. Med Care, 2012. 50(3): p. 217–26. 
48. 
Parchman, M.L., et al., A Randomized Trial of External Practice Support to Improve Cardiovascular Risk Factors 
in Primary Care. Ann Fam Med, 2019. 17(Suppl 1): p. S40–s49. 
49. 
Taylor, E.F., et al., Enhancing the primary care team to provide redesigned care: the roles of practice facilitators 
and care managers. Ann Fam Med, 2013. 11(1): p. 80–3. 
50. 
Ritchie, M.J., et al., Evaluation of an implementation facilitation strategy for settings that experience significant 
implementation barriers. Implementation Science, 2015. 10(Suppl 1): p. A46. 
51. 
Ritchie, M.J., et al., Using implementation facilitation to foster clinical practice quality and adherence to 
evidence in challenged settings: a qualitative study. BMC Health Serv Res, 2017. 17(1): p. 294. 
52. 
Baskerville, N.B., C. Liddy, and W. Hogg, Systematic review and meta-analysis of practice facilitation within 
primary care settings. Ann Fam Med, 2012. 10(1): p. 63–74. 
53. 
Bisson, M., et al., Patient engagement in health implementation research: A logic model. Health Expectations, 
2023. 26(5): p. 1854–1862. 
54. 
University of South Carolina. Patient Engagement Studio.  [cited 2026 09 January]; Available from: 
https://sc.edu/about/centers_institutes/patient_engagement_studio/. 
55. 
Knox, L., et al., Primary Care Practice Facilitation Curriculum (Module 9). 2015, Agency for Healthcare Research 
and Quality: Rockville, MD. 

Page 33 of 34 
56. 
Miech, E.J., et al., Inside help: An integrative review of champions in healthcare-related implementation. SAGE 
Open Med, 2018. 6: p. 2050312118773261. 
57. 
Helfrich, C.D., et al., Organizational readiness to change assessment (ORCA): development of an instrument 
based on the Promoting Action on Research in Health Services (PARIHS) framework. Implement Sci, 2009. 4: p. 
38. 
58. 
Geonnotti, K., et al., Engaging Primary Care Practices in Quality Improvement: Strategies for Practice 
Facilitators. 2015, Agency for Healthcare Research and Quality: Rockville, MD. 
59. 
Taylor, N., et al., Advancing the Speed and Science of Implementation Using Mixed-Methods Process Mapping – 
Best Practice Recommendations. International Journal of Qualitative Methods, 2025. 24: p. 
16094069251340908. 
60. 
Powell, B.J., et al., A refined compilation of implementation strategies: results from the Expert 
Recommendations for Implementing Change (ERIC) project. Implement Sci, 2015. 10: p. 21. 
61. 
Ritchie, M.J., et al., Using Implementation Facilitation to Improve Healthcare Implementation Facilitation 
Training Manual (Version 3). 2020, Veterans Health Administration, Behavioral Health Quality Enhancement 
Research Initiative (QUERI). 
62. 
Yakovchenko, V., et al., Refining Expert Recommendations for Implementing Change (ERIC) strategy surveys 
using cognitive interviews with frontline providers. Implement Sci Commun, 2023. 4(1): p. 42. 
63. 
Morena, A.L., L.M. Gaias, and C. Larkin, Understanding the Role of Clinical Champions and Their Impact on 
Clinician Behavior Change: The Need for Causal Pathway Mechanisms. Front Health Serv, 2022. 2: p. 896885. 
64. 
Smith, J.D., et al., The Longitudinal Implementation Strategy Tracking System (LISTS): feasibility, usability, and 
pilot testing of a novel method. Implement Sci Commun, 2023. 4(1): p. 153. 
65. 
Glasgow, R.E., et al., Making Implementation Science More Rapid: Use of the RE-AIM Framework for Mid-Course 
Adaptations Across Five Health Services Research Projects in the Veterans Health Administration. Front Public 
Health, 2020. 8: p. 194. 
66. 
Ritchie, M.J., L.E. Parker, and J.E. Kirchner, From novice to expert: methods for transferring implementation 
facilitation skills to improve healthcare delivery. Implement Sci Commun, 2021. 2(1): p. 39. 
67. 
Shelton, R.C., D.A. Chambers, and R.E. Glasgow, An Extension of RE-AIM to Enhance Sustainability: Addressing 
Dynamic Context and Promoting Health Equity Over Time. Front Public Health, 2020. 8: p. 134. 
68. 
Shelton, R.C., B.R. Cooper, and S.W. Stirman, The Sustainability of Evidence-Based Interventions and Practices 
in Public Health and Health Care. Annu Rev Public Health, 2018. 39: p. 55–76. 
69. 
Strauss, A. and J.M. Corbin, Basics of qualitative research: Grounded theory procedures and techniques. 1990: 
Sage Publications, Inc. 
70. 
Berg, B.L., Qualitative Research Methods for the Social Sciences. 6th ed. 2001: Allyn and Bacon. 
71. 
Hughes, J., et al. Stepped Wedge Cluster Randomized Trial (SW CRT) Design. Published online October 14, 2022. 
2022  [cited 2026 12 January]; Available from: https://cran.r-
project.org/web/packages/swCRTdesign/swCRTdesign.pdf. 
72. 
Hussey, M.A. and J.P. Hughes, Design and analysis of stepped wedge cluster randomized trials. Contemp Clin 
Trials, 2007. 28(2): p. 182–91. 
73. 
Hemming, K., M. Taljaard, and A. Forbes, Analysis of cluster randomised stepped wedge trials with repeated 
cross-sectional samples. Trials, 2017. 18(1): p. 101. 
74. 
Nickless, A., et al., Mixed effects approach to the analysis of the stepped wedge cluster randomised trial—
Investigating the confounding effect of time through simulation. PLOS ONE, 2018. 13(12): p. e0208876. 
75. 
Clevenger, K.A., K.A. Pfeiffer, and A.H.K. Montoye, Cross-generational comparability of hip- and wrist-worn 
ActiGraph GT3X+, wGT3X-BT, and GT9X accelerometers during free-living in adults. J Sports Sci, 2020. 38(24): p. 
2794–2802. 
76. 
Liu, F., A.A. Wanigatunga, and J.A. Schrack, Assessment of Physical Activity in Adults Using Wrist 
Accelerometers. Epidemiol Rev, 2022. 43(1): p. 65–93. 
77. 
Ozemek, C., et al., Intermonitor reliability of the GT3X+ accelerometer at hip, wrist and ankle sites during 
activities of daily living. Physiol Meas, 2014. 35(2): p. 129–38. 
78. 
Evenson, K.R., et al., Historical development of accelerometry measures and methods for physical activity and 
sedentary behavior research worldwide: A scoping review of observational studies of adults. PLoS One, 2022. 
17(11): p. e0276890. 
79. 
Matthews, C.E., et al., Best practices for using physical activity monitors in population-based research. Med Sci 
Sports Exerc, 2012. 44(1 Suppl 1): p. S68–76. 
80. 
Gao, Z., et al., The Dilemma of Analyzing Physical Activity and Sedentary Behavior with Wrist Accelerometer 
Data: Challenges and Opportunities. J Clin Med, 2021. 10(24). 
81. 
Neuendorf, K.A., The Content Analysis Guidebook. 2017, SAGE Publications, Inc: Thousand Oaks, California. 

Page 34 of 34 
82. 
Syed, M. and S.C. Nelson, Guidelines for Establishing Reliability When Coding Narrative Data. Emerging 
Adulthood, 2015. 3(6): p. 375–387. 
83. 
Mays, N. and C. Pope, Quality in Qualitative Research, in Qualitative Research in Health Care. 2020. p. 211–233. 
84. 
Breslow, N.E. and D.G. Clayton, Approximate Inference in Generalized Linear Mixed Models. Journal of the 
American Statistical Association, 1993. 88(421): p. 9–25. 
85. 
Greene, W.H., Econometric Analysis. 7th ed. 2012: Pearson. 
86. 
Liang, Y., et al., Behavioral support intervention for uncontrolled hypertension: a complier average causal effect 
(CACE) analysis. Med Care, 2015. 53(2): p. e9–e15. 
87. 
Peugh, J.L., et al., Beyond intent to treat (ITT): A complier average causal effect (CACE) estimation primer. J Sch 
Psychol, 2017. 60: p. 7–24. 
88. 
Knox, C.R., et al., Treatment compliance and effectiveness of a cognitive behavioural intervention for low back 
pain: a complier average causal effect approach to the BeST data set. BMC Musculoskelet Disord, 2014. 15: p. 
17. 
89. 
Berg, J.K., et al., Using Complier Average Causal Effect Estimation to Determine the Impacts of the Good 
Behavior Game Preventive Intervention on Teacher Implementers. Adm Policy Ment Health, 2017. 44(4): p. 558–
571. 
90. 
Gruber, J.S., et al., Estimation of treatment efficacy with complier average causal effects (CACE) in a randomized 
stepped wedge trial. Am J Epidemiol, 2014. 179(9): p. 1134–42. 
91. 
Connell, A.M., Employing complier average causal effect analytic methods to examine effects of randomized 
encouragement trials. Am J Drug Alcohol Abuse, 2009. 35(4): p. 253–9. 
92. 
Ashworth, E., et al., Game On-Complier Average Causal Effect Estimation Reveals Sleeper Effects on Academic 
Attainment in a Randomized Trial of the Good Behavior Game. Prev Sci, 2020. 21(2): p. 222–233. 
93. 
Panayiotou, M., N. Humphrey, and A. Hennessey, Implementation matters: Using complier average causal effect 
estimation to determine the impact of the Promoting Alternative Thinking Strategies (PATHS) curriculum on 
children’s quality of life. Journal of Educational Psychology, 2020. 112(2): p. 236. 
94. 
Carmody, T., et al., A Complier Average Causal Effect Analysis of the Stimulant Reduction Intervention using 
Dosed Exercise Study. Contemp Clin Trials Commun, 2018. 10: p. 1–8. 
95. 
Huang, S., et al., An application of the Complier Average Causal Effect analysis to examine the effects of a family 
intervention in reducing illicit drug use among high-risk Hispanic adolescents. Fam Process, 2014. 53(2): p. 336–
47. 
96. 
Cowen, J.M., School Choice as a Latent Variable: Estimating the “Complier Average Causal Effect” of Vouchers 
in Charlotte. Policy Studies Journal, 2008. 36(2): p. 301–315.