Fresh Food Farmacy: A Randomized Controlled Trial
Phase: NA
Conditions: Type 2 Diabetes
Sponsor: John B Bulger, DO

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CONFIDENTIAL AND PROPRIETARY
Version 1.4: 14FEB2020
1
COVER PAGE
Research Protocol - 2018-0297
Fresh Food Farmacy: A Randomized Controlled Trial
Version:
1.4
14FEB2020
Principal Investigator:
John Bulger, DO  
Chief Medical Officer
Geisinger Health Plan
100 North Academy Drive, Danville, PA 17822
570.271.8713
Co-Investigators
Michelle Passaretti, RN - Senior Director of Innovations, Steele 
Institute of Innovations
Allison Hess - -VP of Health, Steele Institute of Innovations
Marcella Alsan, MD, PhD
Stanford University
Center for Health Policy Center for Primary Care and Outcomes 
Research 
Stanford University 
117 Encina Commons, Room 186 
Stanford, CA 94305 
(650) 721-1352 
malsan@stanford.edu
John Cawley, PhD
2312 MVR Hall
Cornell University
Ithaca, NY 14853
(607) 255-0952
johncawley@cornell.edu
IRB NUMBER: 2018-0297
IRB Approved:  02/24/2020

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CONFIDENTIAL AND PROPRIETARY
Version 1.4: 14FEB2020
Joseph Doyle, PhD
MIT Sloan School of Management
77 Massachusetts Avenue, E62-516
Cambridge, MA 02139
(617) 452-3761
jjdoyle@mit.edu
Prepared with assistance from Geisinger Clinical Research Resources.
IRB NUMBER: 2018-0297
IRB Approved:  02/24/2020

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TABLE OF CONTENTS 
1
COVER PAGE.......................................................................................................................1
2
ABBREVIATIONS USED IN THE PROTOCOL ................................................................5
3
ABSTRACT...........................................................................................................................5
4
BACKGROUND AND SIGNIFICANCE....................................................................................6
Diet, Chronic Disease, and Food Insecurity........................................................................6
Rationale for the proposed project .....................................................................................8
Existing evidence..................................................................................................................9
5
HYPOTHESIS AND SPECIFIC AIMS...............................................................................10
5.1
Hypothesis .................................................................................................................10
5.2
Specific Aim 1:  Measure Effectiveness of FFF on clinical measures ....................10
5.3
Specific Aim 2:  Measure Effectiveness of FFF on healthcare utilization .............10
6
PRELIMINARY DATA..........................................................................................................11
7
STUDY DESIGN- RETROSPECTIVE STUDY .......................................................................12
7.1
Description..................................................................................................................12
7.2
Study Population.........................................................................................................12
7.2.1
Approximate Number of Subjects................................................................12
7.2.2
Inclusion Criteria..........................................................................................13
7.2.3
Exclusion Criteria.........................................................................................13
7.3
Study Date Range .......................................................................................................13
7.4
Study Timeline............................................................................................................13
8
STUDY DESIGN- PROSPECTIVE STUDY ............................................................................14
8.1
Description ................................................................................................................14
8.2
Study Population.......................................................................................................14
8.2.1
Approximate Number of Subjects .............................................................14
8.2.2
Inclusion Criteria ........................................................................................15
8.2.3
Exclusion Criteria........................................................................................15
8.2.4
Recruitment ..................................................................................................16
8.3
Study Duration ..........................................................................................................17
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IRB Approved:  02/24/2020

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8.3.1
Approximate Duration of Subject Participation.......................................17
8.3.2
Approximate Duration of Study.................................................................17
8.4
Procedures.................................................................................................................17
8.4.1
Study Time and Events Table ....................................................................19
8.4.2
Study Flow Diagram ...................................................................................20
8.5
Primary Endpoints....................................................................................................22
8.6
Secondary Endpoints................................................................................................23
9
STATISTICS .........................................................................................................................24
9.1.1
Statistical Analysis Plan..............................................................................24
9.1.2
Statistical Power and Sample Size Considerations ..................................26
9.2
Data Management .....................................................................................................26
9.2.1
Data Collection and Storage.......................................................................26
9.2.2
Records Retention ......................................................................................28
10
SAFETY MONITORING........................................................................................................28
10.1 Adverse Event Reporting..........................................................................................28
11
SAMPLE COLLECTION AND RETENTION.........................................................................28
11.1 Collection ...................................................................................................................28
11.1.1
Total Volume of Blood Collected ...............................................................28
11.2 Retention....................................................................................................................28
12
PROTECTION OF HUMAN SUBJECTS ................................................................................29
12.1 Informed Consent and HIPAA Authorization .........................................................29
12.2 Protection of Human Subjects Against Risks..........................................................31
12.3 Data Monitoring Plan................................................................................................32
13
PUBLICATION PLAN...........................................................................................................32
14
REFERENCES.......................................................................................................................33
IRB NUMBER: 2018-0297
IRB Approved:  02/24/2020

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2
ABBREVIATIONS USED IN THE PROTOCOL
Abbreviation
Term
AE
BMI
ED
EHR
FFF
GIRB
HbA1c
HEDIS
HIPAA
IRB
J-PAL
MTM
PHI
RCT
SAE
T2DM
Adverse Event
Body Mass Index
Emergency Department
Electronic Health Record
Fresh Food Farmacy
Geisinger IRB
Hemoglobin A1c 
Healthcare Effectiveness Data and Information Set
Health Insurance Portability and Accountability 
Act
Institutional Review Board
Jameel Poverty Action Lab at MIT
Medically-Tailored Meals
Protected Health Information
Randomized Controlled Trial
Serious adverse event
Type II Diabetes Mellitus
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ABSTRACT
We propose an Interventional Study of the effects of Geisinger’s Fresh Food Farmacy program 
on patient health.  In particular, we will conduct a pragmatic, prospective, randomized-controlled 
trial of Geisinger’s Fresh Food Farmacy (FFF) program.1
In July 2016, Geisinger began piloting the “Fresh Food Farmacy” (FFF) program for diabetic, 
food insecure patients in northeastern Pennsylvania.  Fresh Food Farmacy brings a “food-as-
medicine” approach to communities to combat high rates of obesity, pre-diabetes, and diabetes. 
The program evolves over time, but the key elements include the following.  Participants receive 
a referral to the Fresh Food Farmacy dietitian who provides a diet prescription for fresh food for 
themselves and their household, enough for 5 days a week, 2 meals a day. Participants also 
receive medication management services from a pharmacist, and receive case management 
services from a health manager or other health care professional. Their biometric measures 
(HbA1c, fasting glucose, weight, BMI, blood pressure, total cholesterol, LDL cholesterol, and 
triglycerides) and medication usage are tracked on a regular basis.  In addition, participants 
receive a comprehensive suite of services, including diabetes wellness classes; dietary 
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consultations; workshops to discuss challenges to healthy eating and diabetes management; self-
management training (including monthly group classes); cooking expos; and a welcome kit with 
measuring cups/spoons, recipes, and nutritional and program information.
FFF has received considerable attention, as it uses a holistic approach to address a key “social 
determinant of health” and providers, public health officials, and payers are increasingly willing 
to consider innovative approaches to improve the health of patients.  While the potential to 
benefit patients is high, there is a need for randomized-controlled trial evidence for such 
programs to establish their effectiveness.
Geisinger plans to expand the program in the coming months, first in Scranton and then in 
Lewistown.  The program faces capacity constraints such that the program cannot be offered to 
all eligible participants on the first day.  We propose a pragmatic, randomized controlled trial as 
a way to implement the expansion of the program by randomizing eligible participants to receive 
the program at the start of the expansion, while those randomized to control will receive the 
program six months later.  Such a structured roll out will allow a rigorous examination of the 
effects of the program for outcomes at 6 months, and for longer-term outcomes to the extent that 
effects take time to become apparent.  The advantage of this approach is that we can rigorously 
test the effects of the program to learn whether it should be further expanded at Geisinger, 
attempted at other healthcare systems, or whether new approaches should be considered instead.
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BACKGROUND AND SIGNIFICANCE 
Diet, Chronic Disease, and Food Insecurity
In the past century, changes in diet and lifestyle have led to a dramatic increase in chronic diet-
related disease; today, almost half of all American adults have one or more preventable chronic 
diseases that are related to poor diet, such as cardiovascular disease, high blood pressure, and 
Type 2 diabetes.2  These chronic diet-related diseases are collectively the #1 cause of death in the 
US, with over 800,000 deaths annually.3 
One diet-related chronic disease of particular concern is Type II diabetes, the prevalence of 
which has increased from less than 1% in 1958 to 7.4% in 2015.4  In 2015, 23.3 million 
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Americans were diagnosed with diabetes, and an estimated 7.2 million have diabetes but remain 
undiagnosed; diabetes is the #7 cause of death in the U.S. (24.7 per 100,000 persons), and 
treatment of diabetes was estimated to cost $245 billion in 2012.5 
Another consequence of the American diet is that obesity has risen at an unprecedentedly rapid 
rate.  Since 1980, the prevalence of adult obesity in the United States has more than doubled,6 
and as of 2015–2016, 39.8% of adults are obese,7 a level that the U.S. Surgeon General described 
as “epidemic”.8  Obesity represents a serious public health problem, raising the risk of Type II 
diabetes, cardiovascular disease, cancer, and asthma.9  Obesity raises health care costs by $2,741 
annually per obese adult; for the U.S. as a whole, this amounts to $190.2 billion per year, or 
20.6% of national health expenditures.10  Obesity is responsible for roughly 365,000 deaths per 
year in the U.S.11
The problem with the modern, Western diet is that it includes too much saturated fat, cholesterol, 
added sugars, sodium, and refined grains, and too little fresh fruits and vegetables and whole 
grains.12 It is estimated that following a healthy diet could more than halve mortality from diet-
related chronic diseases and could improve other measures of health in clinically meaningful 
ways.13  For example, Sacks et al. (2001) find that the “DASH” diet (which contains large 
amounts of fresh fruits and vegetables) and low-sodium intake reduced systolic blood pressure 
by 11.5 mm Hg in participants with hypertension.14  LDL cholesterol can be expected to fall by 
10% with a healthier diet.15 Sargrad et al. (2005) find that high-protein or high-carbohydrate 
diets that lead to weight loss among type 2 diabetic patients are associated with a 1.3% reduction 
in Hemoglobin A1c concentrations.16
There are many possible explanations for the low-quality Western diet, but for lower-income 
individuals a possible contributing factor is food insecurity, which is defined as the limited or 
uncertain availability of nutritionally adequate food.17  This could be due to low income or low 
access/availability of healthy food sources. Historically, food insecurity was associated with 
being underweight or stunted, but in the modern food environment, food insecurity can cause 
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and/or coexist with obesity and Type II diabetes.18  Approximately 1 in 4 Americans with cardio-
metabolic conditions that lead to cardiovascular-related death, such as hypertension, 
hyperlipidemia, and diabetes, experience “food insecurity” (i.e. difficulty accessing or affording 
nutritious food).19  
Despite the consensus that addressing diet and food insecurity is an important way to improve 
health, there remains considerable uncertainty over the effectiveness of various approaches. We 
propose to conduct an RCT to evaluate the “Fresh Food Farmacy,” an innovative approach that 
addresses food insecurity through the health care system.  This is in line with other programs that 
are being studied that follow a food-as-medicine approach.20 
Specific Details:
Disease/diagnosis:  Type II diabetes
Population to be studied:  Food-insecure patients of Geisinger 
Endpoints:  HbA1c; fasting glucose; weight, BMI; blood pressure; total cholesterol, LDL 
cholesterol, triglycerides (lipid panel); and healthcare utilization.
Rationale for the proposed project
In July 2016, Geisinger began piloting the “Fresh Food Farmacy” (FFF) program for diabetic, 
food insecure patients in central Pennsylvania.   The program brings a “food-as-medicine” 
approach to address a key social deterimant of health for these patients.  Patients are eligible for 
the Fresh Food Farmacy program if they meet the following criteria:
1. Type II Diabetes and an HbA1c ≥ 8.0% as determined in Geisinger EMR  
2. Food insecure based on a two-question survey instrument
3. Age > 18 and age < 85
4. Living within geographic reach of the program
A Fresh Food Farmacy dietitian prescribes fresh food for meals five days per week for each 
patient’s family.  Prescriptions are filled at no charge at a local clinic.  Complementary services 
include case management, wellness and cooking classes, recipes to use the fresh food, 
medication management, self-management training (including monthly group classes), and 
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biometric tracking.  Fresh Food Farmacy staff collaborate with the patient’s primary care 
provider to request and collect biometric measurements regularly (HbA1c and Lipid panel every 
6 months).  The full program is described here:  https://www.geisinger.org/freshfoodfarmacy
Through random assignment to the start-date of the intervention, we plan to study the impact of 
the FFF program on these biometrics and downstream utilization.  
FFF has received considerable attention, as it uses a holistic approach to address a key “social 
determinant of health” and providers, public health officials, and payers are increasingly willing 
to consider innovative approaches to improve the health of patients.  While the potential to 
benefit patients is high, there is a need for randomized-controlled trial evidence for such 
programs to establish their effectiveness.
Geisinger plans to expand the program in the coming months, first in Scranton and then in 
Lewistown.  The program faces capacity constraints such that the program cannot be offered to 
all eligible participants on the first day.  We propose a pragmatic randomized controlled trial as a 
way to implement the expansion of the program by randomizing eligible participants to receive 
the program at the start of the expansion (treatment group), while those randomized to the 
control group will receive the program approximately six months later.  Such a structured roll out 
will allow a rigorous examination of the effects of the program for outcomes at 6 months, and for 
longer-term outcomes to the extent that effects take time to become apparent.  The advantage of 
this approach is that we can rigorously test the effects of the program to learn whether it should 
be further expanded at Geisinger, attempted at other healthcare systems, or whether new 
approaches should be considered instead.
Existing evidence
Medically-tailored Meals
There is related research on “medically-tailored meals” (MTM) that are typically delivered to 
patients.  This dramatically lowers the cost of consuming a nutritious diet, both in terms of time 
cost for acquisition and preparation as well as the direct monetary cost of healthy ingredients.  
The provision of nutritious food by health care providers is becoming increasingly common.21  
These programs typically do not deliver a patient’s entire nutritional intake.  Rather, they are 
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designed to provide a significant supplement each week, roughly half of the patient’s intake. 
Meals are typically prepared under the supervision of a registered dietitian, so they are closely 
tailored to the nutritional recommendations for a patient’s medical condition.  
Little research has been conducted on the impact of such meal delivery to test whether the 
program improves health and is potentially cost effective.  Gurvey et al. 2013 report an 
observational study that found reductions in costs among Medicaid managed care beneficiaries 
with life-threatening illnesses on the order of 31%.22  Berkowitz et al. 2017 conducted an 
observational study of the Community Servings program in Massachusetts that provides 
medically-tailored meals to patients with life-threatening illnesses resulting in large reductions in 
ED usage and inpatients stays and a 16% reduction in healthcare spending overall.20
5
HYPOTHESIS AND SPECIFIC AIMS 
5.1
Hypothesis 
Our primary hypothesis is that the Fresh Food Farmacy program will improve patient health; 
specifically, we expect the program to result in significantly reduced levels of HbA1c, lower 
blood pressure, and fewer emergency department visits and inpatient admissions.  
5.2
Specific Aim 1:  Measure Effectiveness of FFF on clinical measures
Our first specific aim is to evaluate the effectiveness of FFF on clinical measures of health 
including HbA1c, fasting glucose, weight, BMI, blood pressure and lipid panel.  HbA1c is 
glycated hemoglobin, a long-term measure of glucose in the blood that is used to classify 
individuals’ diabetes status and to track management of diabetes. We will collect this 
information from the treatment and control groups in follow-up visits described below.
5.3
Specific Aim 2:  Measure Effectiveness of FFF on healthcare utilization
Our second specific aim is to evaluate the effectiveness of FFF on healthcare utilization 
including ED visits and a combination of ED visits and inpatient admissions, as well as costs to 
Geisinger Health Plan.  Direct utilization as part of the program will be considered as a 
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comparison to reductions in utilization after enrolling in the program.  We plan to study program 
participants as well as their family members who are expected to benefit from the healthy food.  
We will collect this information from administrative data from electronic health records and 
Geisinger Health Plan claims.  These are additional health outcome measures and provide a way 
to consider the cost effectiveness of the program.  Program data on the use of services by 
participants will be included for the full comparison of utilization.  Our cooperation with 
Geisinger, which not only conducts the FFF program but also maintains records of medical care 
utilization, is a strength of this proposal.  FFF also provides services for patients who are not 
Geisinger Health Plan members. Highmark (another local insurer) may share utilization data for 
their patients that are participating in the program in the future.  As with Geisinger data, any 
Highmark data would be shared with MIT researchers under a DUA that would be submitted to 
the IRB under an amendment.
6
PRELIMINARY DATA 
As proof of concept that was part of a quality improvement initiative at Geisinger led by PI 
Andrea Feinberg and analyzed by researchers at MIT, existing electronic health records of Fresh 
Food Farmacy participants were compared to diabetic patients living in Northumberland and 
Lackawanna counties.  Outcomes included changes in HbA1c, blood pressure and weight for 
patients as well as ED and inpatient admissions to Geisinger facilities.
Because participation in the Shamokin pilot program was not randomly assigned, there is no pre-
existing control group.  Instead, each FFF participant was matched to other Geisinger patients 
(who did not participate in FFF) based on age, sex, payer type, and baseline HbA1c, as well as 
the timing of HbA1c measurements.  Estimates of regression models based on these matched 
data imply that participation in FFF was associated with a reduction in A1C of roughly one point 
60-180 days after enrollment.   This non-randomized data suggests that the FFF program may be 
beneficial on these dimensions, but the non-randomized nature of the data and limited power 
underscore the importance of studying the question with a randomized controlled experiment.   
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This preliminary analysis has several limitations: a small sample of participants in Shamokin, the 
lack of random assignment, measurements of HbA1c and weight at different periods of time.  
However, despite the small sample and limited power there is a statistically significant reduction 
in A1c, which suggests that the FFF is a credible program and worth investigating further 
through a field RCT.
We intend to continue to work with this historical data to provide context for the RCT.  Details 
below.
7
STUDY DESIGN- RETROSPECTIVE STUDY
7.1
Description
A retrospective analysis component will be included in this study to allow historical data already 
collected in the EHR to provide context for the prospective, RCT.   
7.2
Study Population
7.2.1
Approximate Number of Subjects
For the historical data analysis for context, we will compare approximately 200 FFF participants 
in Shamokin to similar diabetic, non-FFF participant patients in Lackawanna, Mifflin, Juniata, 
and Northumberland counties.  We will also study household members: those who share the 
same residential address.  For this data-only portion of the research, approximately 20,000 
patients are considered to find the matches and test robustness to the ways the matching is 
conducted.
There are 2 groups of subjects identified in this retrospective protocol:

Group 1- Past and Current participants in the Shamokin FFF program.

Group 2- Diabetic, non-FFF participant patients in Lackawanna, Mifflin, Juniata, or 
Northumberland counties.  Patients will be matched based on observable characteristics 
including HbA1c, age, sex, and ZIP code with Group 1 participants (FFF Participants).
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7.2.2
Inclusion Criteria
Inclusion criteria for the retrospective study is consistent with the current FFF inclusion criteria:
1. Type II Diabetes and an HbA1c ≥ 8.0% as determined in Geisinger EMR
2. Food insecure based on a two-question survey instrument
3. Age > 18 & age < 85
4. Living within geographic reach of the program (Shamokin) or comparison areas: 
Lackawanna, Mifflin, Juniata, or Northumberland counties.
7.2.3
Exclusion Criteria
In general, patients who satisfy the following criteria are excluded from the FFF program; 
however, some may be allowed to enroll on a case-by-case basis: 
1. Not English speaking
2. On active chemotherapy
3. On hospice or palliative care
4. Active psychiatric disorder that would preclude participation in the program
5. Active medical disorder that would preclude participation in the classes, weekly 
clinic visits, and/ or result in a limited diet, including: 
o
Severe asthma
o
Severe COPD 
o
Colitis requiring steroid therapy
o
Moderate to severe kidney dysfunction with metabolic disorders
o
Celiac disease
6. Resides in a facility which provides meals.
Inclusion and exclusion criteria  applicable to program participants only; these criteria are 
not applicable to household members. 
7.3
Study Date Range
Data are analyzed from January 1, 2015 to December 31, 2019.  
7.4
Study Timeline
Planning and analysis will occur this year.  We aim to prepare a manuscript this year and seek 
publication through the following year.  Ongoing analysis in parallel with the prospective trial 
will augment the analysis of the RCT through 2021 as described below.
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8
STUDY DESIGN- PROSPECTIVE STUDY
8.1
Description
This is a pragmatic, randomized, prospective research study.  Qualifying subjects will be 
randomized 1:1 to receive either early participation in the FFF program (treatment group) or later 
participation (control group).  
8.2
Study Population
8.2.1 Approximate Number of Subjects
Approximately 500 Geisinger subjects will participate in this study with about half assigned to 
the treatment group and about half to the control group.  Approximately 2000 household 
members will be included in the data-only portion of the research.
There are 3 groups of subjects identified in this prospective protocol.
Group 1- Will be randomized to the treatment (“Begin Now”) group for the FFF program.  
Subjects will be consented to join the FFF study prior to learning their program start date.  They 
will join the FFF program right away when the program opens in their geographic area.  Data 
will be collected during the first 12 months of subject participation and EHR and claims data 
may be analyzed for an additional 12 month follow-up period (24 months in total).
Group 2- Will be randomized to the control (“Begin Later”) group for the FFF program.  These 
subjects will be consented to join the FFF study prior to learning their program start date.  They 
will join the FFF program approximately 6 months after the program opens in their geographic 
area.  Data will be collected during the first 6 months of subject participation and used as control 
data for the study.  EHR and claims data may be analyzed for an additional 12 month follow-up 
period (24 months in total from the start of the trial).
Group 3- Household Member data.  Consented subjects will be asked for the names and dates of 
birth (DOB) of individuals living in their household who will be consuming food and benefiting 
from the FFF program.  A waiver of consent is being requested in order to access health 
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information from the household members’ medical records.  EHR and claims data may be 
analyzed 24 months from the start of the trial by a household member.
8.2.2 Inclusion Criteria
The inclusion criteria for the RCT will mirror the FFF program’s eligibility criteria:
1. Type II Diabetes and an HbA1c ≥ 8.0 as determined in Geisinger EMR.  The most 
recent HbA1c value within the past 12 months will be used to determine 
eligibility.
2. Food insecure based on a two-question survey instrument
3. Age > 18 and age < 85
4. Living within geographic reach of the program
a.
Lewistown, PA
b. Scranton, PA
If the study is unable to reach its enrollment goal within 1 year, the HbA1c inclusion criteria for 
the study and program may be reduced to ≥7.5%. 
Patients should meet the above clinical criteria and be of able mind and body to participate with 
the educational, consultative and food provisional services. In addition, patients should 
understand and speak English.
8.2.3 Exclusion Criteria
Patients who satisfy the following criteria will be excluded from the proposed trial, although 
some may be allowed to enroll in the program itself on a case-by-case basis: 
1. Already enrolled in FFF in Shamokin
2. Not English speaking
3. On hospice or palliative care
4. Acute or chronic psychosis
5. Active medical disorder that would preclude participation in the classes, weekly 
clinic visits, or result in a limited diet, including: 
o
Cancer; active treatment
o
Steroid dependent asthma/ COPD/ emphysema
o
Steroid dependent Colitis
o
Chronic Kidney Disease with GFR< 30 mg/mmol
o
Celiac disease
o
Cirrhosis
o
Steroid dependent arthritis
6. Resides in a facility which provides meals
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Inclusion and exclusion criteria are applicable to program participants only; these criteria 
are not applicable to household members. 
8.2.4
Recruitment
Step 1.  Subject identification
In consultation with the investigators, study staff will coordinate the outreach, which is similar to 
outreach planned for the program.  With assistance from a Geisinger data broker, we intend to 
screen the EMR for eligible patients meeting criteria for age, diagnoses, HbA1c elevation > 
8.0%, and food insecurity, which is expected to be included in the EHR by the time of program 
enrollment.  In the event that this is not yet completed, we will screen patients for food insecurity 
digitally by MyGeisinger outreach, by phone or in person at a clinic visit. 
Step 2.  Outreach and Informed Consent
Once the patient is identified as eligible via EMR data pull, a study staff member will review the 
patient’s chart to ensure that the patient does not have any exclusionary criteria and contact the 
patient via phone to determine whether the patient is willing and capable of participating.  Study 
staff will describe the FFF program that is being introduced to their area and how participation 
will begin on a rolling basis over the coming months.  Study staff will then describe the study 
and determine if the patient is interested in hearing more about it.  For those interested in 
participating, the study will be explained in more detail and verbal informed consent will be 
requested. A recruitment script has been developed to ensure information regarding the program 
and study is conveyed accurately and consistently by research staff.  Study staff will be aware of 
diabetes-related resources and usual care for diabetics in case questions arise.  For patients who 
provide verbal consent, an information sheet describing the FFF study will be sent to the patient 
via mail.  
Step 3.  Randomization and program initiation
After the study enrollment procedures described in Step 2, study staff will consult a pre-
determined treatment group/control group assignment for the subject ID number.  Study staff 
will schedule patients’ initial program visit. 
Those who decline consent to be in the study will enter the program in the (non-study) manner 
used by FFF.  To distribute entry into the program in an equitable way, this continues to entail 
randomizing the start dates to begin at the time of initial intake or approximately six months 
later.   These patients will be excluded from the data analysis, except for a comparison of their 
eligibility variables to place the study population in context.
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8.3
Study Duration
8.3.1 Approximate Duration of Subject Participation
We expect subjects will participate in the study for up to 24 months.  Currently, participants do 
not graduate from the program, but this may change as the program evolves.
8.3.2 Approximate Duration of Study
This study will be completed in three years, defined as the last period when electronic medical 
records will be consulted for utilization outcomes.  
We expect the effects of the program will be detectable at 6 months, with some convergence at 
12 months as the control group enters the program and begins treatment; we expect the treatment 
group to still exhibit benefits relative to the control group at 12 months given that they will have 
participated in the program for twice as long and had more time to change their dietary habits 
and for the effects of such changes to be felt.  Utilization, such as ED usage, may be noticed in 
this later period.
8.4
Procedures
Baseline biometric measures (HbA1c, fasting glucose, weight, BMI, blood pressure and lipid 
panel) will be collected utilizing the most recent values available in the subjects’ medical records 
within the year prior to enrolling in the FFF study. These biometric measures will be used to 
determine eligibility for the study and during data analysis.
A set of blood tests (HbA1c, fasting glucose and lipid panel) will be collected for research 
immediately prior to the subject beginning the FFF program (month 0 for treatment group or 
approximately month 6 for the control group).  In addition, research participants will complete a 
baseline survey upon entry into the FFF program.  This survey will be completed on site at the 
FFF program clinic.
A set of research biometric measurements (HbA1c, fasting glucose, weight, BMI, blood pressure 
and lipid panel) will be collected at approximately the 6 and 12 month time points (plus or minus 
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1 month) for the treatment group.  A set of research biometric measurements (HbA1c, fasting 
glucose, weight, BMI, blood pressure and lipid panel will be collected at approximately the 12 
month time point (plus or minus 1 month) for the control group.  
In addition, all research participants will complete a survey after 6- and 12-months of 
participation (similar to the baseline survey) during FFF program visits.   For those who do not 
enroll in the study, we will not continue to investigate their outcomes at these intervals.  
Protocol required lab draws will be paid for by the study. Patients will be compensated with a 
$50 gift card for the time and effort to complete the lab work and the patient surveys.
Based on our experience with the FFF program, we expect nearly all the patients to continue 
with the study.  For the few who do drop out of the study but remain in the program, we will 
contact these subjects and collect outcome information, including clinical lab results and the 
ongoing survey.  For those who drop out of the FFF program, but remain in the study the lab 
work will be covered by the research study and the surveys will be completed by phone or e-
mail.  The patients will be offered a $50 gift card as compensation.  
8.4.1 Study Time and Events Table  
Study timeline (calendar time, H1=January-June, H2=July-December):
2018
H1
2018
H2
2019
H1
2019
H2
2020
H1
2020
H2
2021
H1
2021
H2
2022
H1
Planning
Enrollment
Outcome tracking
Analyze impact
Manuscript preparation
Study timeline (subject point of view):
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Treatment
Study Activity
Enrollment
Month 6
Month 12
Month 24
Informed Consent 1
X
Randomization
X
FFF Program Initiation 5
X
Survey 3
X
X
X
Research Labs 4
X
X
X
Health Care Utilization Review (EMR)
X
Control
Study Activity
Enrollment
Month 6
Month 12
Month 24
Informed Consent 1
X
Randomization
X
FFF Program Start 2
X
  Survey 3
X
X
Research Labs 4
X
X
Health Care Utilization Review (EMR)
X
1 Patients will be asked to provide verbal consent over the phone.
2 FFF program start will occur approximately 6 months after consent for Control patients.
3 Redcap surveys will be administered by FFF staff during program visits.
4 Research labs include: HbA1c, Fasting Glucose, lipid panel 
5 FFF program start will occur within 1 month after consent for Treatment patients.
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8.4.2 Study Flow Diagram
Data Pull for Screening 
Recruitment phone call & Informed Consent 
Control Group
Time 0: Continue Standard Care
Time 12 months:
Research labs draw
Time 12 months: 
Research labs drawn
Time 0: Research labs drawn
Initiate FFF Program
Treatment Group
Time 6 months: Research labs drawn
Initiate FFF Program
Time 6 months: 
Research labs drawn
Randomization 
Enrollment to Time 24 months: 
Healthcare utilization and FFF Program 
participation reviewed
Enrollment to Time 24 months: 
Healthcare utilization and FFF Program 
participation reviewed
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8.5
Primary Endpoints
The primary endpoint will be HbA1c at 6 months.  HbA1c is the primary measure for diabetes 
diagnosis and disease progression.  We explored the existing literature on the link between A1c 
and spending.  The literature is largely a set of correlations:  reductions in A1c are associated 
with lower costs in patients with diabetes.  In summary, improving A1c from poor to fair is 
associated with savings on the order of $1000 per year (sustained over time).  As expected, the 
effects are nonlinear so that larger cost savings are achievable for those with A1c > 9 compared 
to those between 7 and 8.  
Justification
HbA1c is meaningfully related to health and healthcare utilization.  Elevated levels are often 
noted to increase healthcare costs by $4000.  Menzen et al. (2010) controlled for a number of 
characteristics and estimated healthcare costs were $4000 higher over five years for those with 
A1c>10 compared to those with A1c <7.23   Juarez et al. (2013) used propensity score matching 
to control for differences across patients and found that having an A1c>=7 was associated with 
$2700 higher medical costs per year compared to those with A1c<7.  Importantly, the 
relationship is nonlinear with larger savings for patients with the higher levels of HbA1c 
considered here.24  
In Canada, McBrien et al. (2013) also find that, among diabetics, annual medical costs rise in a 
nonlinear fashion with A1c: C$27,064 for those with A1c <=7%, C$26,736 for those with A1c 
of 7.1 – 7.9%, C$28,687 for those with A1c of 8-9%, and C$32,629 for those with A1c>9%.25
Where many studies compare utilization across patients with different levels of A1c and control 
for patient characteristics in various ways, Wagner et al. (2001) took the approach of comparing 
changes in utilization with changes in A1c at the individual level.  This controls for fixed 
characteristics of the person that would otherwise be difficult to control, such as education level, 
stable attitudes toward health.26  They define an improvement as a 1 percentage point reduction 
that is sustained for 2 years.  Among those who achieved this improvement, healthcare costs 
were approximately $900 lower each of the following two years (or approximately $1440 in 
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2017 dollars).  This included a 2-percentage point reduction in hospitalization rates each year, 
compared to a 15% hospitalization rate overall.
Other studies look at disease management impacts on A1c and costs or drug adherence programs 
on costs.  One relatively large observational study is Oglesby et al. (2006), where direct medical 
costs attributable to type 2 diabetes were 16% lower for individuals with A1c<7 compared to 
those with A1c >7 and < 9, and 20% lower for those with A1c <7 compared to those with A1c > 
9.27
8.6
Secondary Endpoints
We will also examine HbA1c at 12 months, where we can compare 12 months of exposure to the 
program compared to 6 months of exposure to the program. This difference will be smaller if the 
gains in A1c improvement are greater during the initial 6 months, as expected.  The comparison 
will help us learn the rate of improvement over time.  In addition to 6- and 12- month results, we 
will also consider interim lab results in the patient’s EHR record to trace out the timing of 
effects.  
In addition, related measures that may be affected by the program:  Fasting glucose, weight, 
BMI, blood pressure, and lipid panel.  Reductions in BMI among patients with diabetes are 
associated with lower health care costs (Cawley et al., 2015). 28  
Utilization is also an important secondary input. This will be measured using EHR data and 
claims data from Geisinger Health Plan for those participants who are members.  Highmark 
(another local insurer) may share utilization data for their patients that are participating in the 
program as well.  In particular, ED visits, inpatient care, and outpatient visits will be considered. 
In addition, the EHR data will allow us to determine whether a subject completes wellness 
visits in terms of PCP and endocrinologist visits along with diabetes Healthcare 
Effectiveness Data and Information Set (HEDIS) measures, as another set of healthy 
behaviors that may be improved as a result of program participation.    We will measure 
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clinical measures and utilization measures for subjects’ household members as well to 
measure spillover effects. 
A third set of secondary outcome measures will come from the patient surveys conducted.  Each 
question will be analyzed and compared across treatment and control groups similar to the lab 
results.  The categories are food choices, healthy behaviors, self-assessed health,  and patient 
satisfaction.  
Additionally, we will include program participation outcomes to measure treatment intensity:  
visits to pick up the food, use of dietician services, and food choices.
9
STATISTICS
Statistical analysis will be conducted by our co-investigators: Marcella Alsan, MD, PhD; John 
Cawley, PhD, and Joseph Doyle, PhD.  This academic team is supported by the Jameel Poverty 
Action Lab (J-PAL) at MIT, which specializes in randomized-controlled trials in healthcare 
delivery.29
9.1.1
Statistical Analysis Plan
For both specific aims, we will estimate intent-to-treat models using Ordinary Least Squares 
regression. Consider an outcome, 
, such as HbA1c, for each subject i.  The estimating equation 
𝑌𝑖
is:
𝑌𝑖= 𝛽0 + 𝛽11(Treatment)𝑖+ 𝛽2𝑋𝑖+ 𝜀𝑖
For the historical data analysis,  
 is an indicator variable equal to one if the subject 
1(Treatment)𝑖
participated in the FFF program, and zero otherwise.  For the prospective trial analysis, 
 is an indicator equal to one if the subject was randomized to the treatment group 
1(Treatment)𝑖
and zero if the subject was randomized to the control group;  
 is a vector of control variables.  
𝑋𝑖
These control variables should be uncorrelated with the treatment indicator, but they can aid in 
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the precision of the estimate.  We propose standard demographic and prior-utilization controls:  
age (5-year age bins), sex (indicator that the patient is male), race/ethnicity (indicators for 
African American and Hispanic), as well as the most recent outcome recorded prior to 
enrollment (HbA1c at the baseline visit).  We will also include controls for the timing of the 
tests, including the number of days from baseline to the test and the month-year of enrollment in 
the study.  
The coefficient 
measures intent-to-treat: the causal effect of being randomized into the 
𝛽1
treatment group. This can differ from the effect of the program if there is non-compliance: some 
subjects in the treatment group may decide not to join the program or join for a very short time. 
At the initial pilot site, 17% of those who begin the program are no longer in the program at 6 
months. We plan to record outcomes for all subjects regardless of program participation and will 
estimate the equation using Two Stage Least Squares (2SLS), with the initial treatment 
assignment used as an instrumental variable for ultimate assignment. The 2SLS estimate 
measures the average causal effect of the program for those who choose to remain in it as a 
complementary parameter to the intent-to-treat analysis. In addition, if there is differential 
attrition from the study across treatment and control groups—despite our efforts to obtain 
outcomes from each subject including obtaining multiple forms of contact information for 
subjects and their family members and compensation for subjects’ time, we will explore 
bounding estimates under a range of assumptions about effect sizes for those not observed.
For survey responses, we will consider answers in the top and bottom categories as separate 
outcomes and will test robustness to using nonlinear estimators.  For patient satisfaction, we will 
calculate the net promoter score as an outcome as well.  To avoid concerns about multiple 
hypothesis testing, each category of questions will be compiled into an index and we will report 
p-values corrected for the multiple tests when presenting results for each question individually.  
In addition to outcome comparisons across all of the participants, we will consider heterogeneous 
treatment effects in three ways.  First, a priori we are interested in whether the program is 
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especially effective for different levels of baseline HbA1c to help target the program going 
forward.  For example, if effect sizes decrease with the baseline, we may want to expand the 
program into those with A1c > 7.5.  Next, we are interested in whether the program has different 
effects across the two locations where the expansions are planned to begin to investigate the 
external validity of the estimates.  Third, we plan to consider whether machine-learning 
techniques to allow the data to shed light on the types of patients where the effects are 
particularly large are feasible with the given sample size and statistical power.  This will likely 
entail normalizing the outcomes so that we measure effects relative to each participant’s baseline 
rather than absolute differences.
Interim results from the trial will be monitored on an ongoing basis by the MIT/J-PAL 
researchers (Drs. Alsan, Cawley and Doyle), while the clinician team will be blinded from these 
interim results so that such do not affect program management in a way that would not be 
feasible outside of the study setting.  Such blinding adds credibility to the results so that they are 
more likely to apply to other programs in the future that are not part of a study.  
9.1.2 Statistical Power and Sample Size Considerations
Using data for current (i.e. self-selected, non-randomized) FFF participants, we conducted 
parametric power calculations.  These take into account the increase in power from controlling 
for baseline characteristics, but qualitatively results were found when we did not include controls 
and when we used nonparametric (simulation) methods to estimate power.  Similar results were 
also found when we based the calculations on similar patients in the counties where the two new 
sites (Lewistown and Scranton) will be located. All power calculations were conducted using 
STATA Version 15.
The table below shows that with the expected sample size of 500 we will have substantial power 
to detect small minimal detectable effects (MDE), as well as the ability to conduct heterogeneity 
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analyses for the clinical outcomes.  For utilization outcomes, we will have power to detect the 
(albeit) large reductions in utilization found in the observational literature.  
Table 2:  Statistical Power to Detect Targeted Minimal Detectable Effects (MDE)
Mean
Std.Dev.
1 ‒ 𝑅2
Target MDE
Required N for 
MDE
Variable
HbA1c after 180 days
8.58
1.74
0.66
10%
55
HbA1c after 365 days
8.56
1.67
0.66
10%
56
Weight after 180 days
228
64
0.96
10%
228
Weight after 365 days
228
64
0.96
10%
228
Hospital Visit (Inpatient or 
ED) 0-180 days
0.39
0.49
0.85
30%
397
Hospital Visit (Inpatient or 
ED) 0-365 days
0.48
0.50
0.87
30%
278
Hospital Visit (Inpatient or 
ED) 90-365 days
0.40
0.49
0.92
30%
447
9.2
Data Management
9.2.1 Data Collection and Storage
Only approved study staff will have access to data collected for this research.  
Administrative data are collected by Geisinger’s data broker.  For the prospective RCT, under a 
Data Use Agreement reviewed by the Privacy Office/ISO, a limited dataset stripped of personal 
identifiers will be transferred securely to MIT.  
For the retrospective data exploration, PHI will be de-identified in accordance with a limited data 
set as allowed by the Research Collaboration Agreement between Geisinger and MIT.  The 
historical data includes diabetic subjects and their household members from Northumberland, 
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Mifflin, Juniata, and Lackawanna counties where our goal is to compare program participants to 
non-participants as described above.  
Once the data set is sent to MIT, the research team will review it for analysis. The resulting 
analytic file will be stored in a password-protected database on MIT’s secure network. Only 
research team members can access the data files. The research team members will review the 
charts and gather all the needed information. 
The limited dataset for the prospective portion of the study will also include dates of service and 
ZIP codes.
It will also include information relevant to all encounters:

Admissions/discharges, clinical procedures, medications administered, problem list 
entries, and lab values

Baseline demographic variables of patients (age, sex, ethnicity, payer, comorbidities)

FFF Program participation
Paid claims summed over various categories of care and timeframes around program entry are 
requested from Geisinger Health Plan as well.
The FFF program provides food for the entire household.  As a result, we are requesting data on 
household members as well.  For the prospective study, household members are identified by the 
subject to ensure an accurate listing of household members who are actively benefiting from the 
program.  Pulling data using the same address may lead to including household members who 
are not actively living in the residence (e.g. college student who is not living at home).  FFF 
plans to ask members about their family members in the future, and this will be used to check the 
household record extraction as well.  Data will be provided to MIT with an encrypted household 
identifier.  While we will not have follow-up visits with family members, we aim to observe 
clinical measures as they appear in the electronic health record.   Household IDs will facilitate 
this analysis.
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The retrospective data analysis will include the same data as the prospective study; however, 
household members will be identified by a shared home address due to no patient contact.
9.2.2 Records Retention
Records of data generated in the course of the study shall be retained for at least 6 years and 
could be used for future research studies submitted and approved by the IRB.
10 SAFETY MONITORING 
10.1 Adverse Event Reporting
This is a minimal risk study and there is no intervention apart from obtaining blood 
samples. The adverse event reporting and the reporting of unanticipated problems related 
to research will be limited to events directly related to the study procedures (venipuncture 
and breach of privacy/loss of confidentiality). All study-related unanticipated problems or 
adverse events will be recorded and reported.  
11 SAMPLE COLLECTION AND RETENTION 
11.1 Collection
As outlined in Section 8.4 of this protocol, subjects in the treatment group will provide a 
blood sample for research purposes prior to beginning the FFF program, at 6 months and 
12 months.  Subjects in the control group will provide a blood sample for research 
purposes prior to the beginning the FFF program (6 months into the study) and again after 
6 months in the program (12 months in the study). Processing will be completed in a 
Geisinger Laboratory and testing completed according to institutional policy. 
11.1.1 Total Volume of Blood Collected
The total volume of blood collected from each subject will be approximately 30 mL.
11.2 Retention
Records of data generated in the course of the study shall be retained for at least 6 years 
and could be used for future research studies submitted and approved by the IRB.
Identifiable samples can be destroyed at any time at the request of the subject.
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12 PROTECTION OF HUMAN SUBJECTS
12.1 Informed Consent and HIPAA Authorization
We are requesting a waiver from written informed consent and HIPAA authorization for 
subjects consented to FFF study.  Instead, we will request verbal consent from study 
participants.  The study will be described during the initial outreach prior to the patient’s 
initial visit.  A follow-up information sheet will be sent via mail reiterating the FFF study 
details, the fact that a study is occurring, and that the participant may choose to end study 
participation at any time.  
We are requesting this verbal consent with follow-up information instead of an in person 
written consent for three reasons.  First, this is a pragmatic trial in that the program is 
being rolled out to new participants in a randomized way to smooth the onboarding 
process.  This process is largely done by phone and our study staff will not be in the areas 
where the subjects are being recruited.  Second, written consent in this context could 
undermine the program.  For written consent we would need subjects to go to the FFF 
clinic, as home visits would not be feasible.  Such a visit would expose both the treatment 
and control group to the information campaigns hosted there and may dampen the 
enthusiasm for the program for anyone not beginning the program until a later date.  
Third, the study represents minimal risk to patients because the treatment is the offer of 
healthy, fresh food, and the main risks are data confidentiality.  These risks will be dealt 
with by sending only a limited data set to researchers at MIT under a data use agreement 
complete with review by Geisinger’s Information Security/Privacy Office.  Furthermore, the 
program is being rolled out regardless of whether a study occurs or not.   Last, the patient 
will not incur a cost to enroll in the program or have their outcomes measured.
For those who decline consent to be in the study, they will still enter the program based on 
their control or treatment status as determined by the pre-randomization, as this is the 
way the program is being rolled out in an equitable fashion: if we were to allow those who 
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do not consent to enter the program sooner, this would delay the entry of others.  In 
addition, allowing earlier entry to those who do not participate could lead to sample 
attrition from the control group, rendering the trial infeasible.  
Data for those who refuse consent will not be analyzed as part of the study, except for the 
eligibility variables that will be compared to those who did consent so that the study 
population can be placed in context.  
We also stress that given the lack of randomized controlled trial evidence for this type of 
program, there is scientific equipoise as to whether this program is effective.  
Household members of study participants
Household members may benefit from the program and we would like to measure effects 
for this group using administrative data.  Study subjects will be informed of this as part of 
the consent process.  
Household members are secondary subjects, however, and we are requesting a waiver of 
consent and HIPAA authorization.  Household members are identified by the subject to 
ensure an accurate listing of household members who are actively benefiting from the 
program.  Pulling data using the same address may lead to including household members 
who are not actively living in the residence (e.g. college student who is not living at home).  
Our request for the waivers are due to:
1. The study represents minimal risk to the household members because we will 
only analyze secondary data and will not contact them.  The main risks are data 
confidentiality.  These risks will be dealt with by sending only a limited data set to 
researchers at MIT under a data use agreement complete with review by Geisinger’s 
Information Security/Privacy Office.   
2.  It is not practical to carry out the research if each of these secondary research 
subjects needed to be contacted.
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Retrospective data analysis
The retrospective data analysis will use previously existing data to compare program 
participants to similar patients.  We are requesting a waiver of consent and HIPAA 
authorization for this analysis because consenting 20,000 patients is infeasible given the 
retrospective nature of the research.  We propose that this portion of the research carries 
minimal risks to patients, as the primary risk is data breach, the risk of which is minimized 
as described above and below.
12.2 Protection of Human Subjects Against Risks
The program provides nutrition advice and fresh food to recipients.  The main risk to 
subjects is data confidentiality.  As a result, we take a number of steps to protect subjects’ 
private data. 
Identifiable data will remain on Geisinger’s internal servers.  A deidentified study ID will be 
assigned to each participant and the table matching patient identifiers with the study IDs 
will be stored on a password-protected, encrypted file on Geisinger's internal server. Only 
key data study personnel will have access to this linked file. 
A limited data set will be constructed that will be stripped of name, address, medical record 
number, and social security number.  Dates of service and zip code of residence will be present. 
It will include the unique, scrambled identifiers for subject ID.
The limited dataset set will be transferred securely to MIT under a Data Use Agreement with 
Geisinger, complete with review by the Information Security/Privacy Office.  
Data at MIT will be stored on an institutional stationary server restricted to authorized hosts and 
users using IP-based host lists and Kerberos credentials. Kerberos credentials are assigned to 
specific individuals who are associated with MIT.  All computations and analytical work will be 
performed exclusively on these servers.  File based permissions will be set to restrict data access 
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to the research team.   Remote access to this server will be provided using the encrypted SSH 
protocol and require strong passwords.  The data security at MIT is managed by a dedicated staff 
of IT professionals.  This support includes account management, security patching, software 
installation and host monitoring.  Data is backed-up to a secondary NAS device which is 
accessible only by IT personnel.
No attempts by members of the MIT team will be made to identify or contact individual patients. 
Any publications of analysis or data will include only aggregated data not linked to any 
individual.
12.3 Data Monitoring Plan 
PI, Dr. John Bulger, will oversee overall data safety and monitoring for the project.  The 
Geisinger and MIT teams will continually assess the potential risks and participant safety 
throughout the study period and report to the PI.  Further, the GIRB will review progress of the 
study per GIRB policy.  Any protocol changes will be submitted to the IRB via a modification 
request and no changes will be implemented without IRB approval.
13 PUBLICATION PLAN  
We plan to apply for grant funding to supplement the enrollment and oversight at Geisinger and 
publish a manuscript in a peer-reviewed journal.
One year after the initial publication, we will seek to publish fully-deidentified data as is 
common for NIH-funded studies.30  The contents of such data will be governed by a Data Use 
Agreement between MIT and Geisinger.
14 REFERENCE
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1 Feinberg, Andrea, Hess, Allison, Passaretti, Michelle, Collbaugh, Stacy, and Thomas Lee. 
“Prescribing Food as a Specialty Drug.” NEJM Catalyst. Accessed April 27, 2018;  Feinberg, 
Andrea, Slotkin, Jonathan R., Hess Allison, and Alistair Erskin.  “How Geisinger Treats 
Diabetes by Giving Away Free, Healthy Food.”  Harvard Business Review. December 19, 
2017.
2 U.S. Department of Health and Human Services and U.S. Department of Agriculture. 2015–
2020 Dietary Guidelines for Americans. 8th Edition. December 2015. Available at 
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6 Flegal KM, MD Carroll, CL Ogden, and CL Johnson. Prevalence and trends in obesity among 
US adults: 1999-2000. JAMA. 2002; 288(14): 1723-1727
Flegal, Katherine M., Margaret D. Carroll, Brian K. Kit, and Cynthia L. Ogden. Prevalence of 
Obesity and Trends in the Distribution of Body Mass Index Among US Adults, 1999-
2010. JAMA. 2012; 307(5): E1-E7.
7 Hales, C.M., Carroll, M.D., Fryar, C.D., Ogden, C.L. Prevalence of Obesity Among Adults and 
Youth: United States, 2015–2016. NCHS Data Brief. Number 288. 2017.
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Healthy and Fit Nation. Rockville, MD: U.S. Department of Health and Human 
Services, Office of the Surgeon General. 2010.
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9 Trayhurn, P., Beattie, J.H.  Physiological role of adipose tissue: white adipose tissue as an 
endocrine and secretory organ. Proceedings of the Nutrition Society. 2001; 60:329–
339.
Dixon, John B.  The effect of obesity on health outcomes.  Molecular and Cellular 
Endocrinology. 2010; 316: 104–108.
10 Cawley, John and Chad Meyerhoefer.  The Medical Care Costs of Obesity: An Instrumental 
Variables Approach.  Journal of Health Economics. 2012; 31(1): 219-230.
11 Mokdad AH, Marks JS, Stroup DF, Gerberding JL. Correction: actual causes of death in the 
United States, 2000. JAMA. 2005; 293(3): 293-4.
12 U.S. Department of Health and Human Services and U.S. Department of Agriculture. 
2015–2020 Dietary Guidelines for Americans. 8th Edition. December 2015. Available at 
http://health.gov/dietaryguidelines/2015/guidelines/.  Accessed November 22, 2017.
13 Institute for Health Metrics and Evaluation. The State of US Health: Innovations, Insights, 
and Recommendations from the Global Burden of Disease Study. Institute for Health 
Metrics and Evaluation. 2013.
Sabaté, E. Adherence to long-term therapies: evidence for action. World Health 
Organization. 2003.
14 Sacks, F.M., Svetkey, L.P., Vollmer, W.M., et al. Effects on Blood Pressure of Reduced 
Dietary Sodium and the Dietary Approaches to Stop Hypertension (DASH) Diet. New 
England Journal of Medicine. 2001; 344: 3–10. doi:10.1056/NEJM200101043440101
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IRB NUMBER: 2018-0297
IRB Approved:  02/24/2020