Pilot Study of the Nutrition-Supported Diabetes Education Program (NU-DSMP) Among Low-Income Adults With Type 2 Diabetes
Phase: NA
Conditions: Diabetes Mellitus, Type 2
Sponsor: University of California, San Francisco

NU-DSMP Protocol and Analysis Plan  
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Title: Pilot Study of the Nutrition-Supported Diabetes Education Program 
(NU-DSMP) Among Low-Income Adults with Type 2 Diabetes 
 
Official NIH Title: Pilot Study of the Nutrition-Enhanced Wellness for Diabetes Self-
Management Program (NEW-DSMP) among Low-Income Adults with Type II Diabetes 
 
 
 
 
 
 
 
 
 
Date: April 17, 2025 
                                        NCT Number: NCT05067452 

NU-DSMP Protocol and Analysis Plan  
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NUTRITION-SUPPORTED DIABETES EDUCATION PROGRAM (NU-DSMP) PILOT STUDY  
ANALYSIS PLAN   
Table of Contents  
Section 1: Administrative Information ......................................................................................... 4  
1) 
Title and trial registration ................................................................................................. 4  
2) 
SAP version number with dates ....................................................................................... 4  
3) 
Protocol version ............................................................................................................... 4  
4) 
Roles and responsibilities – names, affiliations, and roles of SAP contributors ............... 4  
5) 
Signatures ....................................................................................................................... 4 
Section 2: Introduction ............................................................................................................... 5 
1) 
Background and rationale ................................................................................................ 5  
2) 
Objectives   ...................................................................................................................... 6  
Section 3: Study Methods .......................................................................................................... 7  
1) 
Trial design ...................................................................................................................... 7  
2) 
Intervention description.................................................................................................... 7 
3) 
Data collection................................................................................................................. 8 
4) 
Randomization ................................................................................................................ 10 
5) 
Sample size ..................................................................................................................... 10  
6) 
Framework ...................................................................................................................... 10  
7) 
Statistical interim analyses and stopping guidance......................................................... 10  
8) 
Timing of final analyses................................................................................................... 10  
9) 
Timing of outcome assessments ..................................................................................... 11  
Section 4: Statistical Principles .................................................................................................. 11  
1) 
Confidence intervals ........................................................................................................ 11 
2) 
Adherence and protocol deviations .................................................................................. 11 
3) 
Analysis populations ........................................................................................................ 12 
a. Intention-to-treat. ...........................................................................................................12 
               b. Per protocol. ................................................................................................................ 12 
Section 5: Trial Population ......................................................................................................... 12 
1) 
Screening Data ................................................................................................................ 12 
2) 
Eligibility .......................................................................................................................... 13 
3) 
Recruitment ....................................................................................................................14 

NU-DSMP Protocol and Analysis Plan  
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4) 
Withdrawal/follow-up ......................................................................................................14 
5) 
Baseline patient characteristics ......................................................................................14 
Section 6: Analysis ....................................................................................................................15 
1) 
Outcome definitions.........................................................................................................15  
2) 
Analysis methods ...........................................................................................................17 
3) 
Sensitivity analysis...... ....................................................................................................18 
4) 
Subgroup analysis...........................................................................................................18 
5) 
Missing data ...................................................................................................................18 
6) 
Additional analyses ........................................................................................................19  
7) 
Harms ............................................................................................................................19 
8) 
Statistical software .........................................................................................................20 
9) 
References .....................................................................................................................20  
Signature Page ......................................................................................................................22 
  
  
  
  
 

NU-DSMP Protocol and Analysis Plan  
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Section 1: Administrative Information  
1) Title and trial registration  
a. Title: Pilot Study of the Nutrition-Supported Diabetes Education Program (NU-DSMP) 
Among Low-Income Adults with Type 2 Diabetes 
b. Official NIH title/previous title: Pilot Study of the Nutrition-Enhanced Wellness for 
Diabetes Self-Management Program (NEW-DSMP) among Low-Income Adults with 
Type II Diabetes 
c. Trial registration number: TBD 
2) SAP version number with dates  
a. Version 1 –09/20/2021  
b. Version 2 – 04/17/2025 
a. Section 6 – Analysis Methods updated with actual analysis approach for HbA1c 
medical record data 
3) Protocol version   
This SAP references NU-DSMP Protocol Version 1.1, dated 09/20/2021  
4) Roles and responsibilities – names, affiliations, and roles of SAP contributors  
a. Kartika Palar, PhD — Principal Investigator (PI). University of California, San Francisco.   
b. Edward A. Frongillo, Jr., PhD—Senior statistician. University of South Carolina, 
Columbia.   
c. Andrea Pedroza-Tobias, MS— Analyst. University of California, San Francisco.  
d. Marika Dy, MPH – Analyst. University of California, San Francisco 
5) Signatures  
a. PI  
b. Senior statistician   
 
 

NU-DSMP Protocol and Analysis Plan  
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Section 2: Introduction  
1) Background and rationale   
Improving diabetes health is a public-health imperative. Diabetes mellitus affects 30.3 million people 
in the US and is a leading cause of disability and cardiovascular disease.1 In 2017, total costs for type 2 
diabetes mellitus (T2DM) in the US were $327 billion.2 Low-income individuals in the US have twice the 
odds of T2DM3 compared to the general population. More than 30% of low-income households are also 
food insecure,4 defined as having limited access to nutritious food for a healthy life. Food insecurity is 
associated with poor diabetes control,9,10 morbidity, and mortality7,8 via worsened nutrition (diet quality, 
obesity), mental health (stress, depression), and health behaviors (non-adherence, missed clinic 
visits).5,6 Behavioral coping strategies triggered by food insecurity, such as purchasing and consuming 
cheap, energy-dense foods,7,8 can persist into periods of food security, altering dietary habits and 
continuing to undermine diabetes health.9,10  
Diabetes-healthy food support may improve diabetes health for low-income individuals, yet its 
optimal design, positioning (i.e. within healthcare vs. nutrition safety-net), and effectiveness is still 
unresolved. Our preliminary work shows that diabetes-healthy food support is feasible and may 
improve T2DM health for low-income individuals.11,12 Yet where and how such food support is located 
may affect its impact on diabetes health. For instance, food pantries may not be the optimal setting for 
direct provision of diabetes self-management education with food support to improve T2DM 
outcomes.13 Alternatively, locating medically tailored food support within healthcare-related settings, 
integrated into diabetes self-management programs, may be a more effective strategy to support the 
nutritional and self-management needs of low-income people with T2DM. 
The Diabetes Self-Management Program (DSMP) is a national, evidence-based,14-16 ancillary 
healthcare service providing group-based diabetes self-management education to people with diabetes 
over 6 weeks. The DSMP covers topics such as healthy eating, exercise, medication use, and symptom 
management, but does not address structural barriers to diabetes self-management, such as reduced 
access to diabetes-healthy food, which may limit its effectiveness in vulnerable populations.16,17 While 
consistent access to diabetes-healthy food is essential for ongoing diabetes self-management, insights 
from psychology and economics suggest that  access to healthy food may also promote behavior 
change and support habit formation for diabetes-healthy diets17-19. Providing diabetes-healthy food 
support together with the DSMP may be a promising strategy to improve T2DM outcomes for low-
income individuals who face resource and behavioral constraints to healthy eating.  
Together with our partners Project Open Hand, a non-profit organization providing healthy 
food support to chronically ill individuals, and the Contra Costa Health System, a county safety-

NU-DSMP Protocol and Analysis Plan  
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net health system, we are conducting the Nutrition Supported Diabetes Education Program 
(NU-DSMP) Pilot Study. Our goal for this pilot randomized trial is to investigate the feasibility, 
acceptability and preliminary impact of a novel, multicomponent diabetes health intervention. NU-
DSMP will provide 12 weeks of diabetes-healthy food support and nutrition case-management to 
low-income individuals with T2DM, in conjunction with participation in a DSMP group.  
2) Objectives  
Aim 1: Conduct planning to refine the design of the NU-DSMP intervention and study 
procedures. Conduct expert consultation and stakeholder meetings across diabetes clinical 
providers, diabetes self-management educators, nutrition organizations and health care payers 
and organizations to refine the NU-DSMP intervention and identify and set up procedures with 
new study partners responsive to conducting the study in the context of COVID-19. 
 
Aim 2: Pilot test the NU-DSMP intervention among low-income individuals with T2DM, 
compared to the DSMP alone, to assess feasibility, acceptability and preliminary impact 
on diabetes health. We will randomize a total of 72 adults living with T2DM 1:1 to the 
intervention (diabetes-healthy food support plus the DSMP) or control (DSMP only) arms and 
conduct surveys and medical record review at baseline (DSMP start), 6 weeks (DSMP ends - 
survey only), 12 weeks (end of food intervention), and 24 weeks (to assess durability). Control 
participants will receive diabetes-healthy food support at the end of the 24-week follow-up 
period. The primary outcome is glycemic control (HbA1c); secondary outcomes are health-
related quality of life, acute care utilization and hypoglycemic episodes. We will also assess 
intermediate outcomes including preliminary impact on nutrition, mental health, and behavioral 
parameters. 
 
Aim 3: Conduct process evaluation to understand strengths and weaknesses of the 
intervention and trial design. We will collect and analyze qualitative in-depth interview data 
from at least 25 intervention participants to explore perceived benefits and challenges of the 
intervention, program adherence and satisfaction, and suggestions for implementation. We will 
also review nutrition case-management notes from intervention participants and collect 
administrative data on intervention exposure (number of food deliveries received and case-
management sessions received). Quantitative data from intervention and control participants will 
assess food utilization, feasibility and the acceptability of study procedures, and acceptability of 
the control condition. 

NU-DSMP Protocol and Analysis Plan  
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Section 3: Study Methods  
1) Trial design  
A pragmatic RCT among low-income individuals with T2DM will be conducted to test the 
feasibility, acceptability, and preliminary impact of NU-DSMP, a twelve-week diabetes-
healthy food support and nutrition case-management intervention provided in conjunction 
with participation in a DSMP group, compared to participation in a DSMP group alone. 
Participants will be randomized 1:1 to the intervention (n=36) versus control (n=36), using 
a parallel design.  
 
2) Intervention description 
NU-DSMP Intervention: The NU-DSMP intervention builds a diabetes-tailored nutrition 
intervention into the delivery of routine evidence-based group-based diabetes self-management 
education. 
 
Base condition provided to both study arms: Diabetes Self-Management Program (DSMP): 
The Diabetes Self-Management Program (DSMP) is an evidence-based diabetes self-
management education program consisting of weekly group sessions covering healthy eating, 
exercise, medication use, and symptom management. Real-world DSMP programs in English 
and Spanish will be used as the base condition in both intervention and control arms for testing 
the additional benefit of the nutritional intervention. We are not testing the impact of the DSMP 
nor altering the program in any way. We are testing the impact of diabetes-tailored food support 
in a population receiving the DSMP. 
 
NU-DSMP Intervention: 
The intervention will provide: 
1) Diabetes-healthy food support: Twelve weeks of diabetes-healthy food support consisting 
of both meals and groceries (weekly meal delivery; monthly grocery box delivery) meeting 
American Diabetes Association guidelines and approximately one-third of daily energy 
requirements. Food support will include medically tailored meals, produced and home-delivered 
by Project Open Hand, and grocery boxes focused on diabetes-healthy, shelf-stable items from 
Project Open Hand's grocery center, plus a hand-out with nutritional information and recipes. 
 
2) Nutrition case-management: Experienced Project Open Hand client services staff will 
provide three individualized encounters with intervention participants to set up and support their 

NU-DSMP Protocol and Analysis Plan  
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receipt of medically tailored meals and groceries, including assessing preferences and any 
barriers, troubleshooting any issues with the intervention, and checking on participant well-
being. 
 
Additional services provided to the control condition: 
Control condition: After 24-week follow-up (or about 6 months), control participants will receive 
monthly diabetes-healthy, home-delivered grocery boxes for three months focused on diabetes-
healthy, shelf-stable items, plus educational materials with nutritional information and recipes. 
 
5) 
Data collection   
Study measurements using surveys and medical record review will be assessed at 
baseline, 6 weeks (end of DSMP), 12 weeks (end of food intervention), and at 24 weeks 
(to assess durability of any improvements). Qualitative interviews will be collected between 
weeks 12 and 24 to explore participant experiences in the study as part of our process 
evaluation. 
 
At baseline, 6 weeks, 12 weeks, and 24 weeks the following data will be collected in both arms: 
a) Surveys. Trained UCSF research assistants (RAs), including Spanish/English bilingual RAs, 
will administer the structured interview either over the phone or in person, depending on 
public health protocols and acceptability of in-person visits to participants due to COVID-19. 
When visits are conducted in-person, interviews will take place in private offices at POH, in a 
private room at the partner clinic (if available), in a private location at a community space 
(e.g., library, community center), or at a private location of the participants choosing (e.g., 
home visit).  
The content of the interviews will cover: 
• 
Socio-demographic information.  
• 
Food insecurity and diet quality. 
• 
Physical activity. 
• 
Mental health (e.g depressive symptoms, diabetes distress, self-efficacy for following 
T2DM recommendations). 
• 
Diabetes medications  
• 
Adherence to T2DM medications. 
• 
Health-related quality of life. 
• 
Diabetes co-morbidities (e.g., nephropathy, retinopathy). 

NU-DSMP Protocol and Analysis Plan  
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• 
In addition, at 6-week, 12-week, and 24-week follow-up, the structured interview will also 
include questions to assess participant experiences in the intervention and control 
conditions (i.e., food utilization such as sharing or waste), barriers to intervention use, 
and program satisfaction in DSMP and food program).  
 
b) Medical record review (baseline, 12 weeks and 24 weeks). Medical records from CCHS will 
be used during recruitment to confirm the following eligibility criteria: T2DM diagnosis, 
HbA1c greater than or equal to 7%, and other clinical exclusion criteria (i.e., stage 5 chronic 
kidney disease or on/recommended to be on dialysis, type 1 diabetes). In addition, they will 
confirm whether a patient has uncontrolled T2DM (<9% vs. ≥9%) for frequency monitoring 
during randomization. CCHS will provide the following data abstracted from their medical 
records from 6 months prior to enrollment through 9 months after enrollment: 1) HbA1c 
values (point of care test and lab serum results); 2) utilization of acute care, including 
hospitalizations and emergency department use; and 3) weight and height. 
 
c) Qualitative interviews.  
In-depth, semi-structured qualitative interviews of up to 90 minutes will be 
conducted with at least 25 (or until saturation) between weeks 12 and 24 of the study. We 
will interview completers and non-completers to understand barriers to study participation. 
The interviews will be grounded in a phenomenological perspective that centers 
understanding participant experience, combined with content analysis to allow focus on 
specific domains of interest. We will include questions about perceived health benefits (or 
lack thereof) due to the intervention, reasons for intervention adherence or non-
adherence, preferences for meal and grocery box design, barriers and facilitators to 
program participation, positive and negative intervention experiences, and suggestions for 
implementation. Interviews will be audio-recorded and transcribed. 
 
d) Administrative records from POH. POH will provide the following information: 
o Records of receipt of delivered food. POH will provide administrative records of weekly 
delivered food for each participant.  
 
4) Randomization   
72 participants will be randomized 1:1 to the intervention and control arms. Given the 
small sample size, we will not stratify randomization; however, our lead statistician will 
monitor frequencies of sex at birth and poor diabetes control (A1c < 9%, vs. ≥ 9%) in 

NU-DSMP Protocol and Analysis Plan  
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each randomization arm. Significant imbalances may be corrected by temporarily shifting 
the randomization scheme to preferentially recruit participants into the arm with the 
underrepresented characteristic. Details of the randomization method is stored securely 
in REDCap. Randomization based on a computer-generated assignment will occur after 
the participant has provided informed consent and completed baseline assessments.   
5) Sample size    
We will enroll 72 adults with T2DM 1:1 to intervention or control, such that each study arm will 
have 36 people. This sample size balances organizational capacity to implement the 
intervention within R21 cost and time constraints, with our goal to assess feasibility and 
preliminary impact in this population. As a feasibility trial, this study is not powered to detect 
impact on our primary outcome, HbA1c; however, effect sizes from this study will be used to 
power a possible future full-scale trial of the NU-DSMP intervention, if the study deems it is 
feasible, acceptable, and preliminarily effective.   
6) Framework  
The superiority hypothesis testing framework will be used, testing whether exposure to the 
intervention results in better outcomes than exposure to the control arm. Comparisons will be 
presented as differences between arms in changes in outcomes from baseline to follow-up.  
7) Statistical interim analyses and stopping guidance  
a. Information in interim analyses specifying what interim analyses will be carried out and 
listing time points  
i. None planned   
b. Any planned adjustment of the significance level due to interim analysis  
i. No  
c. Details of guidelines for stopping the trial early  
i. None  
  
8) Timing of final analyses  
Analysis is planned to begin in June 2022 upon completion of all field data collection in May 
2022, when the 12-week follow-up of all participants is done. Analysis to evaluate the durability 
of the intervention will commence after the end of 24 weeks of follow-up (August 2022).   

NU-DSMP Protocol and Analysis Plan  
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7) Timing of outcome assessments   
Research staff will administer surveys at baseline, 6 weeks, 12 weeks, and 24 weeks in both 
study arms. Data will be collected within a window period around each data collection time point 
of up to 1 month.    
Section 4: Statistical Principles   
1) Confidence intervals and P Values  
a. Level of statistical significance. No significance testing will be conducted unless 
required by a journal. We will report 95% confidence intervals and exact p-values (or 
p<0.001).  
b. Description and rationale for any adjustment for multiplicity and, if so, detailing how 
the type I error is to be controlled. The primary outcomes were established in the 
protocol, and thus no adjustments will be made for multiplicity.  
c. Confidence intervals to be reported. 95% confidence intervals will be reported alongside 
exact p-values.  
  
2) Adherence and protocol deviations  
a. Definition of exposure to the intervention and how this is assessed including 
extent of exposure:  
Adherence to the intervention will be assessed through the following mechanisms:  
i. 
Case management: Attends at least the first case management session (out of 
3)  
ii. Meals: Receives meals at least 50% of intervention weeks (>= 6 weeks of 
meals).   
iii. Groceries: Receives at least 1 grocery box 
b. Description of how adherence to the intervention will be presented – Adherence 
to the intervention will be presented through a brief description in the narrative 
summarizing the percent of weekly engagement with meal deliveries (out of 12 weeks), 
% of grocery boxes received (out of 3 boxes), and % of food reported eaten, as well as 
% of case management sessions attended (out of 3).  
c. Protocol deviation. The following are pre-defined minor protocol violations:  
i. 
Participants that for any reason are not seen within the visit window (up to 1 
month later).   

NU-DSMP Protocol and Analysis Plan  
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ii. Investigators miss giving a questionnaire or a section of the questionnaire to 
the participant.  
The following are pre-defined major protocol violations:  
i. 
Participants that are mistakenly enrolled in the study without meeting the 
inclusion criteria.   
ii. Participants in the control arm receiving meals or groceries from Project  
Open Hand during the active intervention period (baseline to 3 months).   
iii. Participants for whom informed consent was not obtained prior to any study-
specific procedures.  
iv. Lapse in study approval   
The number (and percentage) of participants with major and minor protocol deviations will be 
summarized by arm with details of type of deviation provided. The participants that are included 
in the ITT analysis data set will be used as the denominator to calculate the percentages. No 
formal statistical testing will be undertaken.  
  
3) Analysis populations.  
a. Intention-to-treat.  The primary analysis will be intent to treat (ITT). The ITT analysis 
will include all participants in both arms who were enrolled and completed all 
baseline assessments, regardless of if they received the intervention or not.   
b. Per protocol. A secondary analysis will be per-protocol, including control participants 
with baseline and follow-up evaluations who did not have any major protocol 
violations, and intervention participants with baseline and follow-up evaluations who 
adhered to the intervention (i.e., attended at least the first case management 
session, received 1 out of 3 grocery boxes, and received home-delivered meals at 
least 50% of weeks in intervention period), and no major protocol violations.   
Section 5: Trial Population  
1) Screening Data   
a. Participants will be recruited from Contra Costa Health Services (CCHS), a safety-net 
county health system serving low-income individuals in Contra Costa County in the San 
Francisco Bay Area.   

NU-DSMP Protocol and Analysis Plan  
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Participants will be recruited via provider referral or via recruitment flyers and/or tabling 
in CCHS clinics. UCSF will screen patients either over the phone or in-person for 
eligibility based on study inclusion and exclusion criteria using the study recruitment 
script. The research team will obtain confirmation of diabetes diagnosis, HbA1c test date 
and result and lack of co-morbidity exclusions from CCHS.  
  
A summary will be provided indicating the number of participants screened, number of 
participants eligible, number of participants not eligible and reason for non-eligibility, 
number of participants enrolled, number of participants not enrolled and the reason for 
non-enrollment, and the number of participants randomized to each arm.  
  
2) Eligibility – summary of eligibility criteria  
  
Inclusion Criteria:  
1. Confirmed diagnosis of type 2 diabetes mellitus (T2DM) in the medical record  
2. Most recent HbA1c (within 1 year) ≥ 7% 
3. Receives primary care for diabetes from Contra Costa Health Services 
4. Is a current member of Contra Costa Health Plan 
5. Age ≥18 years 
6. Has a point-of-care HbA1c test in the medical record in the last month; OR their 
doctor has an active order for an HbA1c test for the potential participant; OR the 
participant is eligible to have a test ordered as part of usual care 
7. Speaks English or Spanish 
8. Adequate cognitive and hearing capacity to complete study measures 
9. Willing to participate in the online or telephone DSMP education, and if randomized 
to the intervention, to receive home-delivered meals and groceries 
10. Has the ability to engage with simple reading materials (e.g. directions to join the 
education session by phone or Zoom) on their own or with the support of a family 
member or friend 
11. Has access to a device (telephone, tablet, and/or computer) that can be used to 
receive remote DSMP education (possible via phone, computer or tablet) and 
complete study assessments (phone only; or tablet or computer with phone 
capabilities) (does not need to be participant's own device) 
 
Exclusion Criteria:  

NU-DSMP Protocol and Analysis Plan  
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1. Currently pregnant, currently breastfeeding, up to 6 months postpartum, or plans 
to become pregnant during the course of the study.  
2. Has confirmed Type 1 DM 
3. Has confirmed stage 5 chronic kidney disease, end stage renal disease or is on 
dialysis or expected to start dialysis in the next 6 months 
4. Does not have the means to receive delivery of, store (e.g. refrigerator or freezer), 
and heat or prepare (e.g. microwave or stove) intervention food. 
5. Has a food allergy, intolerance or preferred diet that POH cannot accommodate 
with meal delivery (e.g. vegan diet). POH can accommodate many but not all diet 
restrictions.  
6. Does not live in Contra Costa County or plans to move out of the county in the 
next 6 months  
7. Being a current POH client, past POH client who stopped services less than 6 
months prior, or past or present participants in other POH medically tailored meals 
studies 
8. Another household member is already enrolled in the NU-DSMP study  
 
3) Recruitment – A CONSORT flow diagram will be used to summarize the number of participants 
who were:  
a. Assessed for eligibility at screening  
i. Eligible at screening  
ii. Non eligible at screening, and reasons why were not eligible  
b. Eligible and randomized  
i. Eligible and randomized  
ii. Eligible but non-randomized, and reason  
c. Lost to follow up at 12 weeks, and reason  
d. Lost to follow up at 24 weeks, and reason  
e. Discontinued the intervention, and reason  
f. 
Randomized and included in primary analysis  
g. Randomized and excluded from the primary analysis, (if any), and reason.   
  
4) Withdrawal/follow-up   
Reasons and details of withdrawal at twelve and twenty-four weeks for both arms will be 
reported. This information will be summarized in the CONSORT flow diagram. In addition, the 
numbers of losses to follow up will be summarized by treatment arm.   
  
5)  Baseline patient characteristics.  
a. List of baseline characteristics to be summarized. Sociodemographic and clinical 
characteristics at baseline overall and by study arm will be described. These 

NU-DSMP Protocol and Analysis Plan  
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characteristics includes age, sex at birth, gender identity, household size, educational 
attainment, income, housing situation, relationship status, language(s) spoken, 
employment status, nutritional (household food insecurity, diet quality) mental health 
(depressive symptoms score, diabetes distress), behavioral (physical activity, diabetes 
medication adherence, diabetes self-efficacy, smoking and alcohol use), clinical 
outcomes at baseline (HbA1c, BMI), health-related quality of life and acute care 
utilization. 
  
b. Details of how baseline characteristics will be descriptively summarized. 
Categorical variables will be presented as numbers and percentages. Continuous 
variables will be summarized by mean and SD for variables with normal distribution, and 
by median and IQR if data are skewed. Minimum and maximum values will also be 
presented for continuous data. Tests of statistical significance will not be performed for 
baseline characteristics.   
Section 6: Analysis  
1)  Outcome definitions   
Primary Outcome Measures:  
1. Hemoglobin A1c. We will collect HbA1c from the medical record at twelve weeks and 
twenty-four weeks, The change in HbA1c levels (%) from baseline to twelve weeks by study 
arm will be reported.  
2. Food security. Food insecurity will be measured with the 10-item adult module USDA 
Household Food Security Survey Module (HFSSM), validated in many populations. The 
score ranges from 0 to 10. Higher scores indicate higher severity of food insecurity. The 
change in the food insecurity scores from baseline to twelve weeks by study arms will be 
reported. 
Secondary Outcome Measures:  
1. Glucose control. The change in the proportion of participants with glucose control, defined as 
HbA1c <9%, from baseline to twelve weeks by study arms will be reported. 
2. Low food security. Food security as a binary response will be assessed, identifying a 
participant having low or very low food security or food security (vs. food security or marginal 

NU-DSMP Protocol and Analysis Plan  
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food secure) by study arm. The change in the proportion of participants having food security 
from baseline to twelve weeks by study arm will be reported. 
3. Health-related quality of life (HRQL). Health-related quality of life will be measured using the 
Summary Index of Unhealthy Days collected via the CDC Healthy Days scale. This scale asks 
the number of days in the past 30 days the person felt physically or mentally unwell. The 
summary index then estimates the number of recent days when a person's physical and 
mental health was good (or better) and is calculated by subtracting the number of unhealthy 
days from 30 days. The change in healthy days from baseline to twelve weeks by study arm 
will be reported.  
4. Fruit and vegetable consumption. Dietary information using the Dietary Screener 
Questionnaire (DSQ) will be collected. The DSQ obtains information on the frequency of 
consumption of fruits and vegetables. These responses are then converted to cup equivalents 
per day using a scoring algorithm based on the NHANES 24-hour recall. The change in fruit 
and vegetable consumption (servings/day) from baseline to twelve weeks by study arm will be 
reported. 
5. Added sugar consumption. Dietary information using the Dietary Screener Questionnaire 
(DSQ) will be collected. The DSQ obtains information on the frequency of consumption of food 
items with added sugars. These responses are then converted to teaspoon equivalents per 
day using a scoring algorithm based on the NHANES 24-hour recall. Added sugars 
consumption (teaspoon equivalents per day) from baseline to twelve weeks by study arm will 
be reported. 
6. Depressive symptoms.  The 8-item Patient Health Questionnaire (PHQ-8) will be used to 
evaluate depressive symptoms. The PHQ-8 score ranges from 0 to 24, with higher scores 
indicating higher levels of depression. The change in PHQ-8 scores from baseline to twelve 
weeks by study arm will be reported. 
7. Diabetes self-efficacy. The 8-item Diabetes Self-Efficacy scale will be used to assess 
confidence in one's ability to manage numerous self-care behaviors. The scores ranges from 
8 to 40, with higher scores indicating more confidence in self-managing their diabetes. The 
changes in the scores from baseline to twelve weeks by study arm will be reported. 
 
Other Pre-specified Outcome Measures: 
 
1. Durability of HbA1c after intervention end. To evaluate durability of changes, if any, in 
HbA1c after the intervention ended, the change in HbA1c levels (%) from 12 weeks to 24 
weeks by study arm will be reported. 

NU-DSMP Protocol and Analysis Plan  
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2. Durability of glucose control after intervention end. To evaluate durability of changes, if 
any, in glycemic control after the intervention ended, the change in the percent of 
participants with HbA1c levels less than 9% from 12 weeks to 24 weeks by study arm will 
be reported. 
3. Durability of health-related quality of life after intervention end. To evaluate the durability 
of changes, if any, in health-related quality of life, the change in healthy days from 12 
weeks to 24 weeks by study arm will be reported. 
 
2) Analysis methods  
a. Preliminary/Descriptive analyses. Frequency tables for all variables and measures of 
central tendency and variability for continuous variables will characterize the sample and 
be stratified by randomization arm to check for non-equivalence. If the two arms differ at 
baseline on any covariates, the Rubin causal model (e.g., propensity scores, double-
robust estimation) will be used to obtain the desired marginal effect estimates under the 
counterfactual assumption of balanced arm. Although the computerized data collection 
protocol and the presence of an interviewer should minimize missing data, the analyst 
will examine patterns of non-response, and inspect distributions of mediating and 
outcome variables to identify outlying or unusual values and assess distributional 
characteristics. Validity and reliability of scale constructs via confirmatory factor analysis 
and internal consistency will be verified.  
  
b. Analyses for Aim 2 (Diabetes and other health and behavioral outcomes): The 
intervention is intended to improve clinical and other health and behavioral outcomes 
suggested by our intervention model. Although we are not powered to detect changes in 
HbA1c, we will test the preliminary hypothesis that the NU-DSMP intervention will lead 
to improved (i.e., lower) mean HbA1c (primary outcome). For the primary analysis, an 
intent-to-treat analysis will be performed using analysis of covariance (ANCOVA) 
implemented as a linear regression of the follow-up HbA1c closest to the 12-week 
assessment date on a categorical variable for randomization arm, baseline HbA1c and 
the number of days between the baseline and follow-up HbA1c value. This model was 
selected to maximize use of available medical record HbA1c data.  
 
Secondary outcomes (collected via survey at 12 and 24 weeks) will be analyzed using 
the Stata mixed procedure assessing whether the intervention resulted in differentially 
improved changes from baseline to twelve weeks in the primary outcome. The model will 

NU-DSMP Protocol and Analysis Plan  
18  
specify mixed (i.e., fixed and random) effects, be estimated by maximum likelihood, use 
all the longitudinal data, and account for variability among and within and individuals 
using an exchangeable covariance structure. The fixed effects specified will be arm 
(intervention or control), visit (baseline or six months), and their interaction as a product 
term. The random effect will be individuals. The model is equivalent to a repeated-
measures or differences-indifferences model. The interaction term estimates the 
difference-in-differences. A fully fixed-effects model (with individuals as a fixed rather 
than random effect) and an analysis of covariance with the outcome at 12 weeks 
regressed on the outcome at baseline and arm will be examined as robustness checks.   
  
Additional analyses will be done to a) check robustness if the two arms differ at baseline 
by controlling for the propensity score (or the covariates that were important in the 
propensity score); and b) adjust for medication use.  
  
For primary and secondary outcomes, Box-Cox transformations will be used to correct 
for skewness if needed.   
  
c. Analyses for aim 3 (process evaluation). 
Quantitative process evaluation data (structured interview and administrative data) and 
qualitative data (interviews and weekly team meetings) will be analyzed separately and 
then compared to cross-validate findings and generate lessons learned. Qualitative data 
analysis will be performed using content coding procedures to identify key themes using 
Dedoose software. Emergent themes will be identified and coded inductively. Coding 
consensus will be considered when transcripts achieve ≥90 percent coder agreement.  
 
3) Sensitivity analysis  
a. No sensitivity analyses will be done beyond those described above.  
  
4) Subgroup analysis  
a. Additional models and statistical interactions (i.e., product terms) will be performed to 
identify characteristics of individuals who most benefitted from the intervention, such as 
biological sex, food insecurity, poor T2DM control (A1c ≥ 9), and insulin use.  

NU-DSMP Protocol and Analysis Plan  
19  
5) Missing data  
The study will use several strategies to account for and address missing data. Missing data will 
be categorized into missed individual questions and missed visits.  
a. Missed individual questions: Multiple imputation will be used to address incomplete data 
under the weak assumption that incomplete data arise from a conditionally missing-at 
random mechanism (MAR) rather than the missing-completely-at-random process 
assumed by ad hoc methods such as listwise deletion. Auxiliary variables will be 
included to help meet the MAR assumption and sensitivity analyses will be conducted 
with weighted multiple imputation to assess the MAR assumption. Information on percent 
of questions missing and imputed will be reported for each variable as appropriate in 
study manuscripts.   
b. Missed visits: When a participant misses a visit, this will be noted in the study register, 
and no data will be imputed for this participant for that visit.   
  
6) Additional analyses  
a) Durability of health benefits: To assess whether any improvements in primary or secondary 
outcomes were sustained in the intervention arm, researchers will estimate effects of the 
intervention on outcomes measured at 24 weeks using the same statistical modeling 
techniques as the main outcomes measured at 12 weeks. Mixed models specifying two visit 
fixed effects will be used, one for differences between baseline and twelve weeks and one 
specifying differences between twelve and twenty-four weeks. The random effect will be 
individuals.  
 
7) Harms Research will be conducted according to Good Clinical Practice guidelines, the U.S. 
Code of Federal Regulations (CFR) Title 21 CFR (Part 50 – Protection of Human Subjects and 
Part 56 – Institutional Review Boards), and the Declaration of Helsinki.  
  
a) Data safety: Participant confidentiality will be prioritized following strict guidelines on 
privacy. For the proposed study, all data will be collected in private and be handled as 
confidentially as possible within the law. For interviews and medical record data collected as 
part of the study, participants will be assigned a unique study identification number, and the 
participant’s name or other public identifiers (e.g., phone number, address) will not be 
included with any data. All consent and other material with personal identifiers (e.g., 
recruitment and follow-up tracking documents) will be kept separately from all data sources. 

NU-DSMP Protocol and Analysis Plan  
20  
The project manager will keep consent materials in a locked cabinet in a locked office and/or 
in a password protected file on an encrypted computer. Research assistants and support 
staff will be trained on procedures for maintaining privacy and will sign a pledge of 
confidentiality. All electronic records (computer, online, audio-recorder) will be password 
protected and encrypted to prohibit illicit access. When these procedures are followed, it is 
highly unlikely that any information revealed by participants during the study will be 
disclosed to the analyst or anyone outside the research team.  
  
b) Adverse events. The PI will report all serious events and problems having to do with 
participant safety to UCSF’s Human Research Protection Program within ten working days. 
All serious adverse events and events associated with the study participation will be 
reported in writing. Dr. Palar will annually provide a discussion of any problems noticed 
during the previous year of the study to the UCSF Human Research Protection Program 
during the annual review. Participants will be provided with information on how to contact 
the study staff to report serious adverse events and adverse events associated with study 
participation.  Field staff will be trained to collect information about serious adverse events 
that will be sent to the study principal investigators. All serious adverse events related to the 
study, will be reported by arm. No formal statistical analysis will be done.  
8) Statistical software  
The following software systems will be used in the analysis: 1) Stata SE version 14 [College 
Station, TX: StataCorp LP]; Stat Transfer 14.  
9) References  
1. 
Centers for Disease Control and Prevention. National Diabetes Statistics Report, 2017.  Estimates 
of Diabetes and Its Burden in the United States. 
https://www.cdc.gov/diabetes/pdfs/data/statistics/national-diabetes-statistics-report.pdf, 2018. 
2. 
Association AD. Economic Costs of Diabetes in the US in 2017. Diabetes care 2018; 41(5): 917. 
3. 
Gaskin DJ, Thorpe Jr RJ, McGinty EE, et al. Disparities in diabetes: the nexus of race, poverty, 
and place. American journal of public health 2014; 104(11): 2147-55. 
4. 
Coleman-Jensen A, Nord M, Singh A. Household Food Security in the United States in 2012, 
2013. 
5. 
Weiser S, Palar K, Hatcher A, S. Y, Frongillo EA, Laraia BA. Food insecurity and health: A 
Conceptual Framework. In: Ivers L, ed. Food Insecurity and Public Health. Boston, MA: CRC 
Press; 2014. 
6. 
Seligman HK, Schillinger D. Hunger and socioeconomic disparities in chronic disease. New 
England Journal of Medicine 2010; 363(1): 6-9. 
7. 
Drewnowski A, Specter S. Poverty and obesity: the role of energy density and energy costs. The 
American journal of clinical nutrition 2004; 79(1): 6-16. 

NU-DSMP Protocol and Analysis Plan  
21  
8. 
Drewnowski A. Obesity, diets, and social inequalities. Nutrition Reviews 2009; 67: S36-S9. 
9. 
Hartline-Grafton HL, Rose D, Johnson CC, Rice JC, Webber LS. Energy density of foods, but not 
beverages, is positively associated with body mass index in adult women. European journal of 
clinical nutrition 2009; 63(12): 1411-8. 
10. Kant AK, Graubard BI. Secular trends in patterns of self-reported food consumption of adult 
Americans: NHANES 1971-1975 to NHANES 1999–2002. The American journal of clinical nutrition 
2006; 84(5): 1215-23. 
11. Palar K, Napoles T, Hufstedler LL, et al. Comprehensive and Medically Appropriate Food Support 
Is Associated with Improved HIV and Diabetes Health. Journal of Urban Health 2017; 94(1): 87-99. 
12. Seligman HK, Lyles C, Marshall MB, et al. A Pilot Food Bank Intervention Featuring Diabetes-
Appropriate Food Improved Glycemic Control Among Clients In Three States. Health Aff (Millwood) 
2015; 34(11): 1956-63. 
13. Seligman HK, Smith M, Rosenmoss S, Marshall MB, Waxman E. Comprehensive Diabetes Self-
Management Support From Food Banks: A Randomized Controlled Trial. Am J Public Health 
2018; 108(9): 1227-34. 
14. Lorig KR, Sobel DS, Stewart AL, et al. Evidence suggesting that a chronic disease self-
management program can improve health status while reducing hospitalization: a randomized trial. 
Medical care 1999; 37(1): 5-14. 
15. Lorig K, Ritter PL, Villa FJ, Armas J. Community-based peer-led diabetes self-management: a 
randomized trial. Diabetes Educ 2009; 35(4): 641-51. 
16. Lorig K, Ritter PL, Ory MG, Whitelaw N. Effectiveness of a generic chronic disease self-
management program for people with type 2 diabetes: a translation study. Diabetes Educ 2013; 
39(5): 655-63. 
17. Lally P, Gardner B. Promoting habit formation. Health Psychology Review 2013; 7(sup1): S137-
S58. 
18. List JA, Samek AS. The behavioralist as nutritionist: leveraging behavioral economics to improve 
child food choice and consumption. Journal of health economics 2015; 39: 135-46. 
19. Lin PY, Wood W, Monterosso J. Healthy eating habits protect against temptations. Appetite 2016; 
103: 432-40. 
 
 

NU-DSMP Protocol and Analysis Plan  
22  
 
Signature Page  
  
Declaration   
  
I have reviewed and agree to the Statistical Analysis Pan as presented in this document.  
   
  
  
_______________________________  
  
__4/17/2025__ _____  
Kartika Palar, Principal Investigator   
  
  
Date   
 
  
 
  
__4/17/2025__ ______  
Edward A. Frongillo, Senior Statistician   
  
  
  
Date    
  
_______________________________________