Does a Virtual Coach Offer a Better Solution for Weight Reduction in Ventral Hernia Patients With Obesity?
Phase: NA
Conditions: Obesity, Ventral Hernia
Sponsor: University of Florida

IRB202300391 
PI-Sacco; 16FEB23 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Does a Virtual Coach Offer a Better Solution for Weight Reduction in Ventral Hernia 
Patients with Obesity? 
 
PI: Dr. Jana Sacco 
 
NCT05797974 
 
 
 
 
 
PROTOCOL 
 
February 16, 2023 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

IRB202300391 
PI-Sacco; 16FEB23 
 
University of Florida IRB-01 
Protocol Template 
 
  
1. Title: Does a Virtual Coach Offer a Better Solution for Weight Reduction in 
Ventral Hernia Patients with Obesity? 
 
2. Investigators: 
 
• Principal Investigator: Jana Sacco MD 
• Sub-Investigators: Brian Celso, PhD; Mariam Louis, MD; Ruchir Puri, MD; 
Jennifer Mull; Leigh Neumayer, MD; Fern Webb, PhD 
• Student: Zach Littlefield, MD; Supervisory Chair: Cassandra White, MD; 
Committee member: Brian Celso, PhD 

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3. Abstract: Many ventral hernia patients are obese and have trouble losing weight. 
Obesity is a known risk factor for hernias and perioperative complications. Wound 
and systemic complications increase the likelihood of both hernia recurrences and 
hospital readmissions. Additionally, postponement of surgery for failure to achieve 
an optimal weight prior to elective operations may delay care, and often increases 
the complexity of the surgery and postoperative complications. While preoperative 
weight optimization is generally recommended prior to operations, ventral hernia 
patients with obesity often struggle with weight loss. The medical/scientific 
question to address is: can the use of a more technologically advanced tool to 
overcome the physical, psychological, and social barriers that interfere with weight 
reduction prior to surgery minimize the adverse effects of surgical delay? The 
purpose for this research is to create a MyChart-enabled virtual coach that assists 
obese patients lose weight prior to ventral hernia surgery. We intend to show how 
the use of a virtual coach is more effective in preoperative weight reduction prior to 
ventral hernia repair over usual care. Correspondingly, this may lower unplanned 
hospital readmissions. For this Pragmatic Clinical Trial, where randomization is not 
possible, we will implement the use of propensity score matching that sorts 
individuals into different study arms as if randomly assigned. The primary outcome 
is the average net amount of time-dependent weight change per group over six 
months. Secondary outcomes are for the intervention group, patient satisfaction 
with the virtual coach and for both groups, quality of life. We will also identify areas 
of social and economic disadvantage that may contribute to higher obesity rates. 
Machine learning (ML) modeling will be used to determine the important features 
for weight lost over the course of the study. The impact of this work will be to 
demonstrate efficacy and realized workflow efficiencies within a hospital-based 
surgery clinic. 
 

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4. Background: Over two million abdominal surgeries are performed in the United 
States annually and approximately 20-25% of these patients will go on to develop 
an incisional ventral hernia. Individuals with obesity frequently find themselves the 
targets of prejudicial attitudes from others and the social stigma attached to 
obesity. The obesity epidemic in the U.S. has only exacerbated hernia repair 
complications following surgical procedures. Additionally, the postponement of 
surgery for weight reduction further increases the likelihood of post-operative 
complications. Any delay in achieving optimal weight prior to elective surgery may 
result in more complex operations or the possible need for emergency surgery 
while waiting for elective repair. Perioperative complications such as wound 
infection can likely result in hospital readmissions.  
 
Preoperative weight optimization is generally recommended prior to any ventral 
hernia surgery and is even more important for open operations that are associated 
with a higher incidence of surgical site infection. Despite the availability of a variety 
of programs for weight loss, there appear to be inconsistencies in engagement, 
high attrition rates, and poor sustained weight loss over time. Furthermore, many 
weight loss programs fail to consider ideal strategies to reach and engage diverse 
audiences that address social determinants related to obesity or to scale programs 
to maximize effectiveness. Thus, there remain significant social and economic 
disparities based on race and ethnicity, which also lead to surgery delays. Hence, 
pre-operative weight optimization reduces both complications from hernia repair 
and hernia recurrence reducing the possibility of hospital readmission. 
 
5. Specific Aims: 
Aim 1: Develop a system that utilizes a MyChart-enabled virtual coach designed to 
facilitate weight loss for patients awaiting hernia repair surgery that couples with 
their EPIC Electronic Health Record (EHR).  
Aim 2: Conduct a two-arm, pragmatic clinical trial that compares the use of the 
integrated virtual coach to standard of care on preoperative weight loss for hernia 
repair patients.  
Aim 3: ML modeling with patient demographics and the Area Deprivation Index 
(ADI) which provides a socio-economic score for each hernia repair patient to 
explore weight-related social determinants of health (SDOH).   
 
6. Research Plan: 
For Aim 1, we will engage in formative work to build and adapt the MyChart-
enabled virtual coach with existing evidence-based persuasive weight-loss 
message content. We will then beta test the virtual coach to establish usability and 

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functionality in a minimum viable product while in development with basic features, 
but enough to attract end-users. Additionally, we will construct a library of weight-
loss message content based on the numerous inquiries from hernia patients with 
obesity who utilized the virtual coach service provided through their EHR platform. 
The inquiries will be collated and indexed by frequency and importance to build an 
AI-enabled Chatbot for an Agency for Health Research and Quality grant. 
For Aim 2, we will engage hernia surgery patients and offer the use of our 
MyChart-enabled virtual coach to aid patients with obesity who elected to undergo 
non-surgical weight reduction prior to ventral hernia surgery. A comparator group 
will consist of those patients who chose to receive usual care and follow a patient-
directed weight loss strategy, however, consent to participate. The comparator 
group will be recommended to diet and exercise prior to surgery by his or her 
surgeon. Propensity score matching will be utilized as a statistical technique to 
form a control group as if individuals were randomly assigned. 
For Aim 3, we will develop a ML algorithm to detect features for obese hernia 
surgery patients based on type of weight loss strategy and SDOH for a more 
precise risk assessment of surgery outcome based on the role of those social 
determinants associated with obesity. To complete this aim, we will utilize 
information readily available in the EHR to evaluate a ML classification model and 
risk stratify patients based on demographics and SDOH with a high degree of 
accuracy. A supervised approach wherein a ML algorithm will be built with a 
training set, then tested on a separate dataset to refine and validate the model. 
We will make the most of our patient data through the use of technology that 
incorporates SDOH influences on the diverse ethnic and social population of the 
Jacksonville area. 
Research setting: The research setting is the University of Florida College of 
Medicine-Jacksonville (UFCOM–J) located at UF Health Jacksonville, FL in the 
city’s urban core and serves as the region’s safety-net hospital. The 2021 
estimated population of Duval County, (Jacksonville) Florida is 1,021,827. In Duval 
County, 66.2% of adults, approximately 676,500 are overweight or have obesity. 
Many University of Florida College of Medicine-Jacksonville (UFCOM-J) outpatient 
clinics within the City of Jacksonville and surrounding areas treat these individuals 
with obesity. 
Methodology  
Participants: Patients diagnosed with obesity who are required to lose weight prior 
to ventral hernia repair. 
Inclusion criteria: Patients 18 years of age and older with a Body Mass Index (BMI) 
of 30 and above and diagnosed with Obesity by the ICD 10 Code: E66.9 who have 
been evaluated by a surgeon and offered elective ventral hernia repair Also, have 
a complete medical record that allows for statistical calculations to be performed.  

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Exclusion criteria: Pregnant females, patients with severe mental disorders, 
prescribed psychiatric medications associated with weight gain, a history of a 
Substance Use Disorder, patients on long-term steroid therapy, and patients with 
insufficient medical records. 
Instruments 
The Body Mass Index (BMI) is a person's weight in kilograms (or pounds) divided 
by the square of height in meters (or feet). The BMI is categorized as 
underweight(under 18.5 kg/m2), normal weight(18.5 to 24.9), overweight (25 to 
29.9), or obese (30 or more) based on tissue mass and height. A high BMI can 
indicate high body fatness.  
Satisfaction with the virtual coach will be measured by a single question, “How 
satisfied are you with the virtual coach.” and rated on a 7 point Likert scale, from 1, 
Not satisfied at all, 4 Neutral to 7, Very satisfied. 
The 12-question HerQLes is a self-reported outcome measure assessing the 
impact of health on an individual's everyday life and has been validated as a 
hernia specific quality of life measure. 
The Area Deprivation Index (ADI) measures neighborhood disadvantage using a 
combination of 17 variables such as income, education, employment, and housing 
quality, and is often used to inform health delivery and policy, especially for the 
most disadvantaged neighborhood groups. This measure has been validated and 
is actively used by a number of organizations including the Centers for Medicare 
and Medicaid Services. The ADI using neighborhood disadvantage is often used 
to inform health delivery and policy, especially for the most disadvantaged 
neighborhood groups. 
Procedures 
This is a Pragmatic Clinical Trial research design. The University of Florida 
Institutional Review Board will act as the approval body for this study. We will first 
obtain approval for the study from the UF Institutional Review Board. Our plan for 
recruiting trial participants will occur during routine clinical care in the outpatient 
surgery settings to maximize generalizability of the trial’s results. There are also 
comprehensive disease registries embedded in the EPIC EHR for recruitment 
purposes. We will consent participants while explaining that choosing to opt out 
does not have any bearing on their treatment. As our patient population is 
approximately 50% African American, we do not anticipate any specific barriers to 
recruitment diversity. We also have the presence of an electronic health record 
(EPIC EHR) system that has a long-standing record for its maturity within our 
healthcare system that is fully integrated with other data systems.  

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To access the virtual coaching application, we provide a digital health technology 
(MyChart-enabled virtual coach) that couples with their EHR to encourage 
adherence to recommended weight loss practices. The virtual coach monitor, a 
Registered Nurse, will respond to inquiries by participants in a timely fashion from 
a library of evidence-based information on weight and weight management 
strategies. The virtual coach will base solutions from a weight neutral approach 
that serves as an alternative to usual care. The virtual coach will provide 
instruction on practices for weight loss that focus on such topics as mindful eating, 
healthy food choices, and increased physical activities for reaching a more optimal 
weight. The virtual coach will have monthly weight check-ins with each patient to 
assess progress and will intermittently send words of encouragement and 
conversation starters to increase engagement. Additionally, the EPIC-based 
platform will allow people to engage more directly with a system prior to surgery 
that helps them stay on track while providing support for patients who struggle with 
changing habits or behaviors closely linked to their diet and physical activity. 
Multiple data points will be collected including the magnitude of weight change 
preoperatively, number of interactions and degree of satisfaction with the virtual 
coach. Those who choose usual care although consent to participate will be in the 
comparator group and receive direction to focus on weight, dieting and exercise. 
Additionally, both the intervention and comparator groups will complete a baseline 
HerQLes and again at six months or during their preoperative testing, whichever 
comes first.  
We will develop a ML model using the UFCOM-J EPIC database, the most 
comprehensive source of locally derived information on hospital admissions and 
treatment outcomes in our area. Provided in electronic format, this encounter-level 
data will include patient baseline characteristics (e.g. demographics such as age, 
sex, race, ethnicity, BMI, and ADI), dates and results of diagnostic tests, disease 
characteristics, proposed treatments (e.g. surgery), and payer (e.g. type of 
insurance or uninsured). The University of Florida has the UF Integrated Data 
Repository database (IDR) for extraction of digitized EHR data. We will validate 
and characterize our ML model based on the UF IDR. The purpose of using a ML 
approach is to produce a model that classifies patients into risk groups that will be 
enhanced with the data from the ADI for a more precise analysis associated with 
SDOH. Our investigators are familiar with typical strategies used to recruit 
participants routinely served in the healthcare setting. Additionally, the research 
infrastructure to support using the EPIC EHR for the study will be through the 
Center for Data Solution (CDS) that is comprised of three hubs: Advanced 
Analytics: houses analysts with expertise in data science, machine learning, 
database architecture, and bioinformatics. Epidemiology: to assist with translation 
of results to ensure that our work has lasting impact on the diverse patient 
population in our Florida communities. Biostatistics: offer traditional analysis for 
both retrospective and prospective studies.  
Statistical Analysis Plan  

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Descriptive summaries will be frequencies and percentages for categorical 
variables and, medians, and quartiles for numeric variables. Unadjusted 
comparisons between groups (virtual coach vs usual care) will be done using the 
Pearson’s Chi-square test (or Fisher’s exact test) for categorical data, and using 
Independent t-tests (or the nonparametric Wilcoxon rank sum tests) for continuous 
data to measure the degree of difference between the pre-surgery and post-
surgery weight loss. The primary outcome is the average net amount of time-
dependent weight change per group over six months. Secondary outcomes are for 
the intervention group, patient satisfaction with the virtual coach and for both 
groups, quality of life. Multiple regression modeling, adjusting for all covariates, will 
be used to determine the contribution of the predictor variables with the outcome 
weight lost over the course of the study. A time-dependent weight variable will be 
calculated to control for the repeated measures.  
For this Pragmatic Clinical Trial, where randomization is not possible, we will 
implement the “real-world” use of propensity score matching. The model assumes 
that individuals are sorted into different study arms as if randomly assigned. 
Regression essentially projects the behavior of individuals in one group outside 
the observed range to form a comparison for the other at common values of the 
covariate. This method will be utilized to specify a function measuring the proximity 
of one case to another based on the time-dependent weight variable. Cases will 
be grouped to minimize the distance between matched cases and unlike 
regression, the various forms of matching do not presume linearity.  
For the ML modeling, traditional analyses such as univariate and multivariable 
logistic regressions will be employed to compare model predictive power with the 
ML algorithm. As the ML model is more exploratory, we will compare various 
classification techniques such as logistic regression, decision trees, random forest 
trees, neural networks, gradient boosted trees, naïve Bayes and support vector 
machines. Those models will be evaluated using receiver operating characteristic 
curves (ROC) and their area under the curve (AUC). The level of significance will 
be set at 0.05.  
Sample Size, Power, Data Safety and Management Plan 
The total sample size estimated for the main research analysis include both virtual 
coach participants and usual care participants. The sample size calculation was 
performed using an alpha of 0.05, beta 0.20, a 10% dropout rate, and based on 
the assumption in the primary outcome of a 7% weight change between the two 
groups. This also assumes a two fold increase in quality of life for the virtual coach 
participants compared to the control group. Thus, a total of 60 patients are 
estimated to be needed for randomization. IBM Corp. All analyses will be done in 
SPSS® Version 29.0 Released 2022. IBM SPSS Statistics for Windows, Version 
29.0. Armonk, NY: IBM Corp. 

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There a no Data Safety Monitoring Board (DSMB) or an oversight committee for 
this research project. 
Data Management Plan and Data Confidentiality: Models and statistical analysis 
will be performed using mainstream software and libraries. Data collected will be 
entered and stored exclusively on the REDCap research site (a UF secure site 
maintained solely for protecting research data) and de-identified. 
7. Possible Discomforts and Risks: Physical risk related to the behavioral intervention 
is minimal. While this is a minimal risk study, there is always a potential for loss of 
patient confidentiality to the subject. Measures to prevent these risks are that 
participation in the study will be voluntary and not impact standard clinical medical 
care. We take significant measures to avoid loss of confidentiality, including only 
allowing authorized, IRB approved, study personnel to have access to subject 
records. We also strive to prevent the loss of confidentiality by keeping all paper 
documents locked in secure files and keeping electronic data stored in password-
protected databases. As an extra precaution, all study data and university 
computers are secured in a badge-access only building, further preventing the 
possibility of data being compromised. Furthermore, the data collected will be 
entered and stored exclusively on the REDCap research site (a UF secure site 
maintained solely for protecting research data) and de-identified. 
 
8. Possible Benefits: There are no potential benefits associated with participation 
for research subjects. However, the proposed research will be used to develop 
interventions to improve safety and outcomes for future surgical patients. 
 
9. Conflict of Interest: The study investigators have no conflicts of interest to 
declare.