A Mobile Application to Improve Procurement and Distribution of Healthful Foods & Beverages in Low Income Urban Communities
Phase: NA
Conditions: Obesity
Sponsor: Johns Hopkins Bloomberg School of Public Health

A Mobile Application to Improve Procurement and Distribution of Healthful Foods & Beverages in 
Baltimore City (BUD) 
 
 
 
NCT05010018 
 
 
 
 
Date of Protocol:  07/01/2020 
 
 

 
A. 
SIGNIFICANCE 
A.1. Changing the food environment to prevent obesity in low-income African Americans 
The prevalence of obesity and chronic disease in the US has increased dramatically in recent decades, and is 
higher among African Americans (AA). 1–5 Obesity greatly increases the risk for chronic disease. 6–8 The diets of 
low-income urban AAs tend to be high in sugary drinks and high fat foods both of which increase risks for 
obesity and chronic disease, 9–12 and lack recommended amounts of fruits and vegetables (FV), which are 
protective for chronic disease. 13–15 While the evidence is mixed, recent reviews conclude that the food access 
(including availability and price), can influence diet and risk of developing obesity and associated health 
outcomes, 16–28 and that actions to improve the food environment are justifiable.29 Low-income minority 
neighborhoods tend to have greater access to higher-priced nutrient poor foods, obtained at small food stores 
and carryout restaurants, 30–36 and reduced access to lower-priced nutrient dense foods in supermarkets, which 
have been shown to reduce health risks. 37–40 Recent studies analyzing price elasticity of fruits and vegetables 
found that a decrease in price of healthier food has a long-term impact on consumption of these items among 
low-income individuals. 41,42 In addition, small food store trials have improved access, stocking, and 
consumption of healthier foods and beverages in these settings.43–46 
A.2. Access to healthy, affordable foods is a problem for small stores in low-income urban 
communities 
Small independently owned food stores (referred to as corner stores) in low-income settings have low access 
to affordable healthy foods and beverages to stock in their stores.47–49 Stocking healthy, affordable foods in 
small urban stores is impeded by the lack of an adequate distribution network.50–52 While high-sugar and -fat 
foods and beverages are commonly delivered directly to these stores by manufacturers/distributors, healthier 
foods such as fresh produce, milk, and 100% whole wheat bread typically must be obtained by the small store 
owners themselves at local wholesalers or at grocery stores where availability, selection and quality are often 
poor, and pricing may be high.47,49 Those wholesalers who do offer delivery services commonly require a large 
minimum order cost (e.g., $1000), well beyond what is feasible for corner stores. Consequently, there is a 
great need for strategies to increase access to healthier foods in small food stores, in a manner that is 
cost-effective for the stores and ultimately to consumers. 
A.3. Digital strategies are effective in changing health behaviors and improving health outcomes 
Evidence showing impact of digital health interventions used at the patient, provider, and health systems levels 
has grown dramatically.53 Digital interventions have been shown to successfully improve health behaviors 
ranging from diet to smoking cessation54,55, and health outcomes, including weight loss. 56–59 A strength of 
digital strategies lies in the near ubiquity of social media/digital communications platforms, which allows for the 
engagement of peers for driving individual and group-level behavior change. 60 Digital ‘networking’ to reinforce 
or drive behavior has been tested on a wide range of strategies from diabetes management in adolescents,61 
to weight reduction 62and diabetes prevention 63, to increasing consumer spending through group discounts 64. 
The BUD application will develop a digital social networking solution to address a critical gap in low-income 
urban settings in the United States: the lack of affordable supply channels to deliver healthier products to small 
corner stores. This digital tool will stimulate new supply-side behaviors (e.g., networking among small store 
owners), while also facilitating the application of proven economic strategies to reduce food costs. BUD 
represents an innovative interdisciplinary digital strategy that draws upon proven approaches, including 
collective purchasing65 and delivery sharing to provide a cost-effective and sustainable solution. 
A.4. Need for technologies to improve distribution of healthier foods to small food stores 
Despite the proliferation of applications which allow individual ready-to-eat food ordering, digital innovations to 
improve access to healthy foods are limited in low-income settings. Current online services such as PeaPod 
and Instacart for supermarket chains and UberEats, OrderUp, and GrubHub for prepared foods are generally 
unavailable in food deserts.66–70 Part of this issue relates to delivery challenges. While there are some services 
that include free delivery to centralized pick-up locations [Virtual supermarket, Books and Bread, etc.], these 
services are characterized by limited geographic availability and low reach.71 More recent entries to online food 
delivery services such as AmazonFresh are restricted to high-priced items, such as organic fruits and 
vegetables. Thus, there is a need to enhance the capabilities of small retail food stores, ubiquitous in low- 

income urban settings, that uses technology to enable them to improve their stock and quality of affordable 
healthful foods. Specifically, we propose to develop and pilot a mobile web application for an online 
delivery service of healthy foods from local producers, farmers’ markets and existing wholesalers to 
small urban corner stores in Baltimore. Why haven’t private businesses already developed delivery apps 
such as we propose for small food stores located in low-income urban communities? Based on our formative 
work, we would argue that this gap relates to a kind of systemic bias– expressed in the perception that there is 
little or no consumer demand for these foods, but our research has shown otherwise.72 The development and 
successful testing of the BUD app could help resolve this market failure. Once the benefits are shown (e.g., 
sales, sustainability), we anticipate that the private sector will adopt and improve this solution. 
A.5. Evidence for pass-through of lower prices in urban corner stores 
Pricing greatly affects food-purchasing decisions at all levels of the food system, from producers to consumers. 
The highest price point for delivery is “the last mile”, a supply chain term used to describe the last leg of 
delivery of a product which is often the least efficient and most expensive.47 Through collective purchasing and 
delivery we aim to lower the cost of “the last mile.” Our previous work supports the notion that cost savings 
accrued in the last mile will be passed on to consumers.73,74 Additionally, small urban food stores charge 10- 
57% more for staple food items than large supermarkets.75 This proportion may create an economically 
favorable margin for a pass through of reduced prices to consumers if small stores receive discounts that allow 
them to practice more competitive pricing. It is common for companies to engage in off-invoice trade 
promotions; in which manufacturers sell products to accounts below the suggested retail price to create a large 
enough margin to allow the product to be discounted to consumers.76 However, documentation of this practice 
is limited due to a lack of transparency in the industry and varying business practices among companies and 
accounts. While data are limited, it appears that small stores will stock healthier foods and beverages if 
incentivized by reasonable cost, and that there is the potential to expect at least partial pass-through 
of reduced prices to consumers. Even if cost pass-through does not occur immediately, a 
successfully distribution app will reduce uncertainty and allow owners to plan and ultimately pass 
lower costs on to consumers. 
A.6. Evidence for the effectiveness of collective purchasing (and delivery sharing) in food retail 
Collective or cooperative purchasing refers to the practice by which individuals or smaller institutions band 
together to jointly purchase greater quantities at lower prices. Cooperative purchasing has been used 
extensively by school meal programs, and has been found to reduce costs.77 A London study examined a 
growing online grocery purchasing market and the potential for economic, environmental and social cost 
savings involved in coordinated food delivery throughout the city.78 The researchers found that shared delivery 
helped to spread the burden of the “last mile problem”. Furthermore, collective purchasing provides information 
on aggregate demand over a large number of grocers, which would be less variable than the information at the 
individual grocer level. Such information can reduce uncertainty about demand and allow corner store owners 
to plan for restocking in a more cost efficient manner. Although limited, available evidence suggests that 
collective purchasing of food has been successful at improving the retail food environment in some 
settings. However, this has not been demonstrated in small urban corner stores, nor has the delivery 
problem been addressed. 
A.7. Summary 
There is great need for innovative strategies to improve the distribution of affordable healthful foods and 
beverages to small food stores in low-income urban communities in cost-effective ways. Digital strategies offer 
a promising approach, but substantial work is needed to develop a workable app that will be acceptable to 
wholesalers, producers, and small store owners, yielding an affordable solution for the delivery of healthy foods 
and beverages – that will ultimately lead to improved access (availability, price) to these products for 
consumers. We propose to develop and pilot a working version of the Baltimore Urban food Distribution 
(BUD) app, which will address these challenges, offering a cost-effective way for small urban food 
stores to stock a broader range of healthy, affordable products, and will assess the app for feasibility. 
B. 
INNOVATION 
Our study team includes a medical anthropologist community nutritionist (Gittelsohn), a digital health/mHealth 
expert (Labrique), a health economist (Trujillo), a systems engineer (Igusa), and a biostatistician (Ogburn). Dr. 

Gittelsohn has worked on chronic disease prevention in low-income, ethnic minority communities for more than 
25 years.43,73,79–82 His findings, including behavioral, psychosocial, cultural, sociodemographic, and 
environmental obesity predictors,83–90 have been used to design culturally- and setting-relevant instruments and 
interventions at the individual, family and institutional (particularly food retail) levels.45,79,80,91–93 He has 
conducted three separate trials aimed at increasing access to healthier foods and beverages in Baltimore 
corner stores43,80,94 . These trials successfully increased stocking and sales of healthier foods43,44,95, increasing 
purchasing and consumption of these foods among low-income individuals82, and have been shown to 
decrease child body mass index (BMI).94 Dr. Igusa has experience developing web apps for a variety of public 
health applications including contraceptive decision support96, systems dynamics modeling of community 
resilience after natural disasters97, a wiki-survey for policy makers considering deployment of autonomous 
vehicles98 and a self-evaluation tool for promoting healthy relationships for Native American youth99. In each 
case, he worked with a team of stakeholders96,97,99 to identify and address design issues, assess and improve 
usability, and, for the Native American web application, incorporate culturally relevant content.99 The app that is 
most closely related to the grant was created by Drs. Igusa and Gittelsohn in collaboration with the Baltimore 
City Mayor's Office for the purpose of supporting a staple foods ordinance.100 Dr. Labrique is Director of the 
JHU Global mHealth Initiative, and has developed methods for the development, refinement, testing and 
evaluation of Digital Health technologies, in close collaboration with international agencies such as WHO and 
UNICEF. His group has developed interoperable software for community health workers, with complementary 
monitoring and evaluation procedures to ensure high quality, fidelity and impact of these technologies, within 
the complex ecosystems where they are deployed.81,101–103 
 
B.1. First randomized control trial to assess the effectiveness of an app to improve access to healthy 
foods in low-income communities 
 
While apps and websites have been developed for promoting healthy foods in low-income settings66,104–106, the 
impact of these technologies on heathy food access, purchases and intake has not been rigorously examined 
using randomized controlled trials. We will assess feasibility of the BUD app, and conduct an RCT that tests 
its impact at the store level. A future clinical trial will assess its impact on consumer behavior and health 
outcomes. To ensure acceptability, we will use gold-standard systems principles of user-centered design and 
testing to maximize the fit and user experience of the BUD app. As part of our preliminary formative research, 
we developed a wireframe model of BUD, working closely with 11 corner store owners, and 7 food 
distributors/producers. Overall, acceptability and perceived operability was high, but challenges were also 
identified, such as small stores that operate on a cash-based system, small stores’ ability to display and/or 
provide adequate storage for perishable items such as fresh produce, lack of affordable delivery methods to 
corner stores for smaller amounts of products, limited variety of products wholesalers and producers carry, and 
a perceived lack of healthy food demand from consumer. We recognize the challenges faced in implementing 
a successful BUD app in Baltimore’s low-income food environment and will test innovative solutions to 
overcome these challenges. We will develop and pilot a working web-based version of the BUD app, and 
will assess its effectiveness in improving the small store food environment. 
B.2. BUD app will link small urban food stores with local producers and suppliers 
To our knowledge, this is the first trial to develop, pilot and carefully evaluate an app to connect small urban 
food stores located in low-income communities with multiple local producers and suppliers. This will, in turn, 
improve the healthy food supplier network for small stores. Our team has substantial experience working to 
improve the urban food environment, and in conducting social marketing, social media and health 
communications interventions to improve dietary behaviors in low-income ethnic minority populations.43,73,79,80,82 
We have decades of experience operating in the Baltimore business and marketing environment (Gittelsohn), 
and have worked with local producers previously.107 Furthermore, we will conduct substantial formative 
research as part of the app development process, guided by co-investigators with experience in integrating 
technology into public health research and implementation (Labrique, Igusa), and determining pricing 
strategies (Trujillo). The BUD app will allow Baltimore corner store owners to access a wider range of 
healthy foods and beverages cost-effectively, and will allow efficient planning of food stocks. 
B.3. Use of innovative group purchasing feature to reduce costs 
The BUD app will test an innovative group purchasing feature (BuddyUp!), similar to Groupon, to reduce the 
prices of the foods ordered, and keep delivery costs low by harnessing collective buying power. Wholesalers 

and producers will set prices at two levels: 1) individual purchase of smaller quantities commonly ordered by 
small corner stores; and 2) group purchase of larger quantities at a reduced price. BUD will include a chat 
feature, which will enable small store owners to communicate with each other to coordinate group purchases. 
The chat feature will initially support the languages spoken by most Baltimore corner store owners: English, 
Korean, Spanish, and Mandarin.108 Both producers and wholesalers have shown excitement about the ability 
to add BuddyUp! deals to products; in particular, to help promote seasonal produce and storage crops. The 
BuddyUp! collective purchasing feature will allow small store owners to purchase relatively small 
quantities of healthier foods, at an affordable delivery and total cost. 
B.4. Will identify novel, culturally appropriate delivery methods from suppliers to small corner stores 
Distributors make the delivery of sugar sweetened beverages and high-fat snacks easy for small store owners. 
Yet, no such mechanism exists to cover delivery of healthier foods/beverages, such as fresh produce, to 
corner stores.49 Baltimore has 13 food distribution sources, including wholesalers, distributors and warehouse 
stores. Of these, 4 sources (31%) do not deliver to small stores (Costco, Sam’s Club, BJs Wholesale Club, 
Jetro Cash & Carry). Of the remaining 9 sources, 5 (38%) require substantial minimum purchasing levels 
beyond what small corner stores can afford (Prima Foods, George J Falter Co, Hearn Kirkwood, Belair 
Produce Inc., B Greens Co). The remaining 4 sources carry only specialty products (e.g., seafood only, meat 
only) or do not carry fresh produce (Nino’s Food Distribution, Pastore’s Wholesale Grocers). Instacart, a 
grocery delivery service was also considered, but considered impractical in this context due to: 1) Poor 
consumer reviews (e.g. rotten produce, incorrect foods delivered); 2) Delivery fees which can add up quickly 
for multiple small orders in a week; 3) Services only offered in English. 
We will explore incorporating at least three different mechanisms for addressing this issue into the BUD app: 1) 
partner with an existing online delivery service (e.g., Hungry Harvest, UberEats); 2) utilize a suppliers’ existing 
delivery service (if they have one); and 3) small store owners share delivery services between each other 
through a feature called BuddyLift!.68,109 The BuddyLift! feature will permit store owners engaged in collective 
buying to take on the delivery role in exchange for further reduction in price. This feature will be voluntary and 
any owner placing an order may participate and provide the delivery service. Initial formative research 
indicates mixed support for the BuddyLift feature, with small store owners enthusiastic about the feature in 
principle, but expressing concerns about how well the honor system it implies would work. We will work to 
resolve these challenges during the formative phase of the proposed research, but anticipate that one possible 
solution will be coordination between store owners through the same language chat feature. Importantly, 
collective action already exists between the members of some groups of small store owners, such as Korean 
Americans, making such an approach plausible. 
B.5. We will examine the costs/benefits of using the BUD app in relation to sales in small food stores 
Led by our economist (Trujillo), we will conduct a financial cost–benefit analysis of the use of the BUD app 
from the wholesaler, producer and retailer perspectives. We will include in our analysis monthly, and annual 
totals, including costs at baseline, personnel costs (salaries and benefits), materials and supplies, equipment, 
and transportation costs, among other items. We will also monetize the time of delivering the intervention and 
the time devoted to training. We plan to differentiate between initial costs and deployment costs. Initial costs 
will account for all costs during the pre-implementation phase of the intervention. Post-intervention costs will 
capture the variable and fixed costs of running the program. We will integrate these templates to estimate the 
total costs of the intervention and compare these figures to the benefits of the program.110 Among the potential 
savings from using BUD, we will include: prices of foods; total net revenues (sales minus discounts); 
transportation cost of food, transportation costs to buy products; inventory and rotation; financial costs of 
working capital among other items. Other benefits associated with the BUD are linked with improvement in 
health and human capital. We would consider these savings broadly using our sample of 190 low-income 
urban consumers, assessed pre- and post-intervention variation and will monetize outcomes. We will assess 
the economic viability of the BUD app, an essential component of feasibility. 
C. APPROACH 
 
C.1. Overview and study design 
The overall goal of this R34 application is to develop a stable mobile application to improve access to healthier 
foods and beverages in small corner corners in low-income urban settings, and to generate preliminary 

findings in support of a full-scale clinical trial. The first part of the study will focus on developing a working, 
mobile accessible version of the BUD app. The second part will employ a randomized controlled trial study 
design where we will test the app, introduce different features to address identified challenges, and assess 
feasibility and impact primarily at the store level. Thirty-eight small corner stores located in East Baltimore will 
be randomized into treatment (n=19 receiving the BUD app) or comparison (n=19). The study will be 
conducted in seven overlapping phases, over a 33-month period: 
Phase 1: Formative research (months 1-5) 
Phase 2: BUD app development (months 3-8) 
Phase 3: BUD function and usability testing (months 7-12) 
 
Phase 4: Recruitment and baseline data collection (months 10-12) 
Phase 5: Randomization, BUD pilot app implementation, process evaluation (months 13-20) 
Phase 6: Post intervention data collection (months 21-23) 
Phase 7: Data analysis and reporting (months 24-33) 
C.2. Study setting and collaborations 
 
The BUD study will take place in low-income areas of East Baltimore. Baltimore City has 633 corner stores, 
185 convenience stores, 47 supermarkets, 18 farmers markets, 24 urban farm sites, and 6 public markets.111 
East Baltimore is a largely low income part of the city, representing about a third of the city’s area. In East 
Baltimore there are 12 supermarkets, approximately 168 corner stores, 88 convenience stores, 2 farmers 
markets, 9 urban farms, and 1 public market.112 Of corner stores, about two thirds are owned and operated by 
Korean Americans, roughly 15% are operated by Chinese Americans, and 5% by Hispanic Americans. Most 
corner store owners locate their stores in areas not being served by supermarkets. Low income urban food 
systems throughout the United States have many similarities 72,113–116, including low access to 
supermarkets/grocery stores115,117 and high access to small convenience/corner stores, 45,72,118 fast food, 119 
and carryout restaurants.113 These food sources tend to primarily carry foods high in calories, fat, salt and 
sugar,120 and lack affordable healthier options. Small independently owned food sources (e.g., corner stores, 
carryouts) are most common, but these stores often find healthy foods difficult to source, and unhealthy foods 
easy to obtain.72,75,80,121–124 Poor healthy food distribution patterns in Baltimore and similar urban food systems 
are largely due to a distribution gap between small independent food sources and larger 
wholesalers/distributers.125 The BUD research team will partner with various city stakeholders including the 
Food Policy Director, the Baltimore Food Policy Initiative, and the Baltimore City Health Department (see LOS). 
In addition, we have obtained strong and detailed letters of support and commitment from 2 wholesalers 
(including B. Greens, a local wholesaler commonly used by Baltimore corner store owners126), 1 produce 
distributor, 16 East Baltimore corner stores, the Farm Alliance of Baltimore (representing 11 urban farms). 
C.3. Additional formative research (Phase 1) 
As part of our preliminary studies, we have conducted substantial formative research (i.e., in-depth interviews 
with 5 distributors, 9 wholesalers, 7 local farmers, 20 corner store owners, and 7 local food system experts). 
Several key challenges for the BUD app were identified.124 Delivery challenges include produce 
spoilage, perceived neighborhood safety, delivery minimums, cost, and scheduling. For example, one local 
wholesaler does not have a delivery minimum, however, they refuse delivery to areas perceived to be 
unsafe because of the risk of theft. Other wholesalers offer free delivery with a high minimum, which is difficult 
for small stores with limited budgets and storage. An additional challenge to app usage is to allow various 
payment options as many stores and wholesalers/producers prefer to operate on a cash-based system while 
web-based formats, including BUD, use credit or debit payment systems. Among wholesalers and producers, 
challenges include varying prices, the effort it takes to input or update products, and integration with current 
order management processes. Therefore, application design should emphasize convenience and well- 
constructed design permitting relatively effortless product information updates for wholesalers/producers. 

Additional considerations will need to be made to ease use of the application for store owners, including 
streamlined product pages, working in multiple user languages (English, Korean, Mandarin, Spanish), 
simple return protocol, and product descriptions to inform produce quality. 
We will conduct substantial additional formative research to refine the Baltimore Urban food Distribution (BUD) 
app into an effective mobile app that addresses the challenges described above. In-depth interviews with 
wholesalers/distributors, local producers and corner store owners (n=16) will be conducted to further explore 
reactions to key app features, specifically to the group purchasing (BuddyUp!) and delivery (BuddyLift!) 
features, to refine these features, add features as needed, and to incorporate recommendations for 
implementation and usage of the app. This will include assessment of levels and types of subsidies (price 
reduction, free delivery, etc.) that will be needed to encourage small store owner adoption and maintained use 
of the app. We will purposively select small store owners of different ethnicities for these interviews, including 
Korean, Mandarin and Spanish-speaking. We will explore wholesalers’ and producers’ willingness to provide 
discounts based on collective purchasing. We will develop detailed case studies of the ordering and delivery 
practices of 10 small corner stores by conducting repeated in-depth interviews. We will also conduct 20 
interviews with low income consumers to assess how best to promote targeted foods and beverages. 
One key outcome of the formative work will be to determine the most feasible means of delivering the healthier 
products to corner stores. Several options will be considered and refined, including the proposed BuddyLift! 
feature, partnerships with local food delivery services that have expressed interest in operating in low-income 
neighborhoods such as HungryHarvest, and use of online delivery services such as UberEats.68,109 Once 
developed, an improved BUD app graphical user interface (GUI), messages, and survey instruments will be 
tested in a second round of in-depth interviews with corner store owners and producers/wholesalers (n=16). All 
interviews will be audio-recorded, transcribed, and translated into English (if needed) for analysis using Atlas-ti 
8.4. Research protocol and intervention materials will be reviewed and approved by the Johns Hopkins 
Bloomberg School of Public Health Institutional Review Board. 
C.4. BUD App Development (Phase 2) 
Results from the formative research phase will be incorporated into the development of the BUD mobile app 
(Aim 1). We will develop the app using Bootstrap 4.0, a multi-platform framework for building web-based 
apps.127 While web-based apps are slightly less user-friendly than “native” iPhone and Android apps, they are 
more versatile in that they will run on all smartphones and laptops. Moreover, adjustments to the system can 
be more easily accommodated. We will subscribe to an app-based delivery management service (Onfleet) to 
manage real-time driver pickup and drop off scheduling.128 With this development and deployment strategy, we 
will be able to substantially reduce the cost of app development, and by using an app subscription service to 
handle delivery logistics. The laptop version of the app will be configured as a dashboard so that it can be used 
by the research team as they monitor the distribution of healthful foods in the Baltimore City low-income 
neighborhoods. In addition, we will identify and recruit the 38 corner stores in this phase. Technical 
components of the app will be built using an array of software libraries and methods in the public domain (C.6). 
C.5. Multilevel food environment intervention and behavior change framework 
Figure 1. BUD app conceptual framework 
 

Figure 1 presents the conceptual framework guiding the study, adapted from Social Cognitive Theory and the 
Social Ecological Model.129,130 The BUD application addresses physical-environmental factors (e.g. food 
availability, price) and mediates between the food source (producers, wholesalers) and corner store levels to 
influence corner store owner stocking of healthier products, and ultimately consumer purchasing. Change in 
small store owner psychosocial factors (e.g. intentions, self-efficacy) as well as the expected improvements in 
their access to a broader range of healthier foods and beverages, will result in increased stocking and sales of 
healthier foods, and lower prices for these foods (if already stocked by a store). Ultimately, we anticipate that 
consumers who shop at intervention stores will improve their psychosocial factors, and increase purchasing 
and consumption of healthier foods and beverages, compared to consumers from comparison stores. A long- 
term outcome to be tested in the subsequent clinical trial is reduction in obesity. 
C.6. Description of the Baltimore food Distribution (BUD) application 
The BUD app will provide storeowners with a convenient and cost-effective tool for purchasing healthy foods 
and beverages from local producers and wholesalers. BUD will be linked to Onfleet, an app-based delivery 
management service that will use product source, destination information, and driver availability to schedule 
deliveries. BUD will feature an attractive user interface, and have twelve main modules: 
Module 1 (Opening the App): Opening the BUD application will direct the user to a main screen that allows app 
users to choose whether they are producers/wholesalers or corner store owners. The main screen also 
provides a link to general information about how to use the application.131 
Module 2 (Producer/Wholesaler Sign Up): Selecting the producer/wholesaler button on the main screen directs 
the user to either sign in or create an account by completing a series of online forms.132 On completion, the 
user is returned to the producer/distributor sign in page. 
Module 3 (Uploading Products): A producer/wholesaler can upload products for sale by first selecting the 
category and completing the Add Product online form (Figure 2).133,134 Producers/wholesalers can add the 
product with the price as stated and/or apply a BuddyUp! deal (see Module 7). The wholesaler/producer sets 
the terms of the BuddyUp! deal, including the amount of the price reduction, minimal amount to be sold, 
expiration date of deal (optional), distance allowed between small stores participating in the deal, delivery 
charges and possible discounts (e.g., BuddyLift!). 
Module 4 (Updating Products): A producer/wholesaler can make changes to a previously uploaded product 
(e.g., its price, minimal amount to be sold, etc.) by selecting the product, which opens that products information 
page, and updating the form.135 
Module 5 (Add a BuddyUp! Deal): A producer/wholesaler can add BuddyUp! deals to current products, 
skipping the product information page. 
Module 6 (Small Store owner Sign Up): Selecting the store owner button on the main screen directs the corner 
store owner to either sign in or create an account by completing a series of online forms. After completion, the 
user is automatically directed back to the store owner sign-in page.136 
Figure 2: Uploading Products on the BUD App 
 

Module 7 (BuddyUp!): The BuddyUp! feature allows store owners to buy healthy foods at a lower price by 
facilitating shared purchasing of items in bulk among application users. Selecting the BuddyUp! icon, located 
on the storeowner home page, directs the user to a page of products currently available for BuddyUp! deals 
(Note: cost reduction, amount to purchase, delivery distance parameters are set by the 
wholesaler/producer).137 After product selection, store owners are prompted though a series of questions to 
select their quantity and determine the timeframe, or how long they will wait for the deal. 
Module 8 (Chat Feature): Once completed, the chat feature can be utilized to prompt and invite other corner 
storeowners within a pre-specified radius to participate in the deal, share price information and BuddyUp! The 
chat feature will support communications in English, Korean, Mandarin and Spanish (Figure 3).138–140 
Figure 3. BuddyUp! Chat Feature 
Module 9 (Current 
BuddyUp! Deals): The 
BuddyUp! feature also 
allows store owners to 
join BuddyUp! deals 
already in progress. 
Selecting the “join a 
BuddyUp! deal” icon, 
permits storeowners to 
view the products other 
storeowners within a 
specified radius have 
selected, but need 
additional participation 
to complete the deal 
(Figure 4). 
Module 10 (Shopping and Delivery): Store owners can shop for items by category or select the search icon to 
search for an item by name.141,142 Once products have been selected (including price), multiple delivery options 
are offered, including possibly the existing delivery service offered by the supplier, another online delivery 
partner (e.g., Hungry Harvest), or BuddyLift! 
Figure 4. BuddyUp! Deal Feature 
Module 11 (BuddyLift!): The 
BuddyLift! feature offers an 
additional price reduction for one 
corner store owner participating in 
a BuddyUp! deal who agrees to 
collect and distribute the items in 
that deal, or on a regular basis 
(Figure 5) It is possible that some 
corner stores may form a standing 
group (similar to informal Korean 
loan collectives) and alternate 
pick-up and delivery duties. 
Module 12 (User Satisfaction): 
The BUD app will provide multiple 
opportunities for users 
(wholesalers, producers, small store owners) to give feedback on their level of satisfaction with the app, and to 
provide suggestions for improvement. After their first, third and fifth usage of the app, users will be prompted 
with a screen asking them to comment on their level of satisfaction with such issues as timeliness, cost, and 
ease of use. In addition, users will have the ability to provide unprompted comments and suggestions at any 
time. 

Figure 5. BuddyLift! Feature 
C.7. BUD 
Functionality and 
Usability Testing 
(Phase 3) 
 
Once the BUD 
mobile app has 
been developed, it 
will be tested for 
system 
functionality, 
stability and 
usability. 
Functionality and 
Stability: 
Components of 
the application will be tested independently and together as a combined application to ensure that expected 
outputs are produced against specified, prescribed inputs. Scenarios will be developed to ‘test’ the system on 
different devices, to ensure consistent software behavior occurs. Functional test scripts and checklists will be 
developed and applied to ensure all intended system components work as desired and expected. Once basic 
system tests are complete, we will subject the BUD app to stress testing to ensure, under unfavorable 
conditions, that the app does not crash – for example, when incorrect sequences are followed, “back” buttons 
pressed, or “home” buttons inadvertently pressed. Different user-error scenarios will be documented and 
simulated. Finally, speed and responsiveness of the system will be tested under different connectivity 
conditions (i.e., wifi, cellular, hotspot). 
Usability: We will use client-centered design methods to evaluate and grade the user-friendliness of the 
interface, evaluate the ‘learning curve’ of new users, followed by user proficiency testing on days 0, 1, and 3 
after training (see C.8). We will also plan repeat user-satisfaction assessments at various time points after 
deployment to assess whether the system meets the expectations of the end-users. Usability testing will 
include store owners of different ethnicities, including Korean, Mandarin and Spanish speaking. BUD will be 
configured to produce multiple outputs, including the capability to collect and store all purchase and delivery 
service data, including costs and delivery times, in a central server. We will also build a simple survey within 
the app with rating scales and a text box so that users can provide feedback on any aspect of the BUD 
program. A SQL (Structured Query Language) database will be used so that the data can be readily accessed 
using standard statistical packages such as Stata 14.1. We will also allow BUD users to view charts of 
aggregated quantities, such as trends in produce prices and delivery times. We have identified existing delivery 
management software (Onfleet), which will allow for stores to coordinate deliveries more efficiently and will 
alleviate some mentioned challenges. This program-wide information will be continuously updated to keep the 
users and the research coordinators informed about the program operational status. Security issues are 
considered in the Data Safety and Monitoring Plan. 
 
C.8. BUD Pilot Evaluation (Phases 4) 
Detailed sampling rationale, power calculations, eligibility, recruitment and retention information are provided in 
the PHS Human Subjects and Clinical Trials Information form. We will assess feasibility of the BUD app from 
the perspective of wholesalers, producers, small store owners and consumers (Aim 2). We will evaluate the 
impact of use of BUD on corner store (n=38) stocking and sales of healthier foods and beverages (Aim 3), and 
on a sample of 5 adult customers per corner store (n=190) to assess changes in purchasing and dietary 
behaviors as part of the pilot (Aim 4). All measures will be conducted at baseline and immediately post- 
intervention, with additional measures of retail impacts. Several of the instruments described below will be 
newly developed or adapted as part of the R34 work. Additional description of these instruments is provided in 
the Outcome Measures table. 

C.8.1. Wholesaler instrument: Wholesaler impact data will be collected with a Wholesaler Impact 
Questionnaire (WIQ) that will assess wholesaler characteristics, BUD app usage (# foods uploaded, pricing, 
use of BuddyUp! option, etc.), and stocking and sales of promoted foods and beverages. This will be 
completed at baseline, and following each intervention stage, to assess impact (5 times). We have previously 
collected and reported stocking and sales data from wholesalers successfully with a previously developed 
instrument.143 In addition, we will develop new semi-structured questions to assess satisfaction with the BUD 
app (e.g., ease of use, responsiveness, problem solving, timeliness), as well perceived sustainability. 
C.8.2. Producer instrument: A Producer Impact Questionnaire (PIQ) will be conducted to assess producer 
characteristics (i.e. farm, size, months producing), BUD app usage (FV uploaded, deals offered, sales on the 
app), producer psychosocial characteristics (self-efficacy, intentions, expectations), and stocking and sales of 
promoted foods and beverages. This will be completed at baseline, and following each intervention stage, to 
assess impact (5 times). We will modify an existing instrument used for a pilot trial in Baltimore’s urban 
farms.107 We will develop additional questions to address user satisfaction and perceived sustainability as 
described in C.9.1. 
C.8.3. Small food store level instrument(s): Impact data for corner stores will be collected with a Store Impact 
Questionnaire (SIQ) that will assess store characteristics, self-reported BUD app usage, corner store owner 
psychosocial characteristics (self-efficacy, intentions, expectations), and prices of promoted foods and 
beverages. This instrument will be completed at baseline, and following each intervention stage, to assess 
impact (5 times). We will modify a previously developed instrument to conduct these measures.43,74 Stocking 
(availability) of promoted foods will be collected via direct observation at the five time points. Sales of 
promoted foods will be collected using a tablet PC programmed for the store owner/manager to record unit 
sales of promoted foods and beverages at the time and point of sale. Detailed description of this software and 
its feasibility is presented in the Narrative Study Description and Outcome Measures table. 
C.8.4. Consumer-level instruments: The Adult Impact Questionnaire (AIQ) will be a modified version of a 
survey that we have used in previous studies. This instrument will collect sociodemographic information and 
food purchasing behavior such as how many times a product was bought and from which type of store (i.e., 
corner store, grocery store, urban farm, etc.). In particular, we will assess purchasing of promoted products 
from participating corner stores. Body weight to the nearest 0.1 kg and body composition (% body fat and lean 
mass) will be measured with Bioimpedance Analysis (BIA) using the Tanita-BIA (Model BF679W). Height to 
the nearest 1 mm will be measured with a stadiometer (Seca 213). Waist and hip circumference will be 
measured to the nearest 0.1 cm with a measuring tape. Three measures will be made, and the closest two 
measures averaged. The AIQ will be performed at baseline and post-intervention (2 times). The Adult Block 
Food Frequency Questionnaire (FFQ) will estimate adult consumer food intake and nutrient consumption (e.g., 
total energy intake, total fat, added sugar, sugar sweetened beverage and fruit and vegetable intake). Several 
studies have validated this instrument.144,145 Completed FFQs will be analyzed by NutritionQuest (Berkeley, 
California) and estimates of food patterns and nutrient intakes will be generated. The FFQ will be performed at 
baseline and post-intervention (2 times). All consumer measures will take place at the Johns Hopkins 
Bloomberg School of Public Health in a private office. 
C.8.5. Process evaluation instruments: Process evaluation instruments will be developed to measure the 
reach, dose delivered, and fidelity of the BUD intervention implementation. The team will develop specific 
standards for each process evaluation component that will be measured through multiple interventionist data 
collection forms, as well as through the BUD app itself. We will incorporate both quantitative and qualitative 
open-ended questions which will permit us to assess user experiences with the BUD app in terms of 
satisfaction, including challenges and potential solutions. All instruments will be electronic forms developed 
using REDCap, as we have used for our ongoing multicomponent intervention trial.146 See Outcome Measures 
and Statistical Design Plan for more details. 
C.8.6. Data generated by the BUD application: We will have metrics of all foods and beverages uploaded to 
the BUD app by suppliers, as well as orders and deliveries to each small store produced by the BUD app. This 
will include information on level/frequency of user engagement, how many of and which products were 
delivered, and when. During the appropriate intervention stage, the application will provide the number of times 
a store participated in the BuddyUp! module, who participated in a BuddyLift! activity, and other associated 
data, such as prices paid. This information will be stored in a database and will also be updated daily in a web- 
based dashboard with graphical displays sized for a laptop. We have carefully defined user engagement 

variables and system-generated data to be captured and stored by the system for later analysis, including 
time(s) and duration of sessions, frequency of use and other technical data (battery charge, connectedness). In 
addition, should formative research indicate this is requested, we will allow select data to be accessible to 
users (small store owners, producers, wholesalers) in a graphical form, including their own usage patterns of 
the BUD app (e.g., foods purchased, dates, etc.), and cost savings accrued. 
C.9. BUD Pilot App Implementation (Phase 5) 
We will pilot the BUD app in 19 intervention corner stores over an 8-month period in East Baltimore. We will 
select stores only located in East Baltimore for logistical ease, and to keep delivery costs lower. The pilot will 
be conducted in four stages (2 months each), differentiated by the types of foods and beverages promoted, 
BUD features activated, and limited subsidy (BUD Credit) provided. During the first weeks of each stage, 
training of participating corner store owners and producers/wholesalers will take place – focusing on use of the 
BUD app and any new features, specific foods/beverages and guidance for promotion. Initial training will be 
followed up by proficiency testing where intervention store owners will be requested to complete certain 
functions (e.g., enroll in a BuddyUp! deal, fill out a user satisfaction form, etc.). With each subsequent stage, 
more app features will be made available to users (Table 1). During stage one, the basic BUD app interface 
will be introduced to corner store owners and low-sugar beverages will be promoted. In stage two, in addition 
to the basic BUD app interface, the BuddyUp! feature will be introduced to store owners, and fresh fruits and 
vegetables will be promoted. In stage 3, the BuddyLift! feature will be activated, and low-fat whole grains. 
Stage four will focus on reinforcement. On the BUD app, promoted items will be highlighted on the homepage 
(e.g., Great Deal! Only 1 more buyer needed to get strawberries delivered for just $3/pound! Click here for 
more information). 
Bundled subsidy: To generate initial engagement and use of the app, we will bundle the app with initial limited 
subsidies (BUD Credit), which reflects common promotional practices for the introduction of new apps. A 
subsidy ($100/stage) will be offered for stages 1 and 2 only. The subsidy will only be usable for the products 
being promoted during that stage. Small store owners will only be able to spend $50/week maximum in order 
to encourage multiple orders, building familiarity with BUD’s functions. BUD Credit will be usable to pay all or 
part of any transaction (for example, a corner store owner could use $5 to cover part of the delivery costs of 
$25 worth of strawberries, or $30 to cover the entire cost). 
Promoting consumer demand: Concurrent with delivery of the BUD app, point of purchase promotions, such as 
posters and shelf labels promoting BUD healthful foods and beverages will be displayed by study team 
members at wholesalers and in stores to build consumer demand for these products. We have developed 
these materials already through multiple previous trials in corner stores43, but will refine them based on 
formative research. These promotional materials will be provided at both intervention and comparison stores 
so that we can ascribe impact to the BUD app alone. 
Stage 1: Low-sugar beverages (water, unsweetened flavored water). Storeowner training will include how to 
download the application in the preferred language, sign up, sign in, shop for products, make a purchase, view 
purchase history, and re-purchase products. Producer and wholesaler training will include: how to download 
the app, language, sign up, sign in, upload products to sell, and update the status of current products. 
 
Stage 2: Fresh fruits and vegetables. Storeowner training will focus on how to participate in a BuddyUp! deal, 
and use the chat feature. Producer and wholesaler training will focus on how to add a BuddyUp! deal. 
Stage 3: Low-Fat whole grains (e.g., brown rice, 100% whole grain bread, low-sugar granola bars). Storeowner 
training will focus on how to participate in BuddyLift! using Onfleet management. Producer and wholesaler 
training will be a booster of stage 1 and 2, if requested. No subsidy will be provided. 
Stage 4: All previously promoted foods and beverages. Trainings will focus on maintaining engagement with 
the app, ensuring profitability for small stores and wholesalers/producers. No subsidy will be provided 
 
During the training period at the start of each phase, we will collect interview-based process measures. 
Rigorous process evaluation will also be conducted at the food delivery, app metrics, and store-levels to 
measure quality of implementation of the trial according to initial research protocol in terms of reach, dose 
delivered, and fidelity. Electronic forms will be completed using REDCap, as appropriate.147 We will conduct 

biweekly monitoring and feedback sessions where we consider process indicators in terms of how well we are 
meeting implementation standards, including consideration of usage statistics generated by the BUD app. We 
will refine intervention delivery and develop new strategies to improve usage and user satisfaction as needed. 
Table 1. Four stages of BUD pilot app implementation 
 
BUD Strategies 
Stage 1 
Stage 2 
Stage 3 
Stage 4 
Technology 
BUD App 
BUD App + 
BuddyUp! 
BUD App + BuddyUp! 
+ BuddyLift! 
BUD App + BuddyUp! 
+ BuddyLift! 
Promoted Foods 
Low-sugar 
Beverages 
Fresh Fruits and 
Vegetables 
Low-fat Whole Grains, 
Snacks 
Low-sugar Beverages 
+FV +Whole Grains, 
Snacks 
BUD Credit 
$100 
$100 
$0 
$0 
 
C.10. Post-intervention Data Collection/Feasibility Assessment (Phase 6) 
All phase 4 data collection instruments (wholesaler, producer, food store, consumer) will be repeated in Phase 
6 to assess change associated with the BUD intervention. We will also conduct in-depth interviews with our 19 
intervention corner store owners and participating wholesalers/producers to understand feasibility of the BUD 
app. This will include questions on acceptability (economic, cultural), operability (challenges, ease in using the 
app and its functions), and whether they would continue to use the app (perceived sustainability). In addition, 
we will conduct a follow-up study in 20 small stores in West Baltimore, Philadelphia and Detroit, where we 
demonstrate the BUD app, and assess feasibility through in-depth interviews. Additional description of 
feasibility constructs is provided in the Statistical Design Plan. 
C.11. Data Analysis (Phase 7) 
A detailed description of data analysis procedures, and hypotheses to be tested in relation to each study aim is 
provided in the Statistical Design Plan section of the PHS Human Subjects and Clinical Trials Information form. 
C.12. Study Challenges and Strengths 
Challenges: Most small store owners in Baltimore currently use SmartPhones for web access but are relatively 
inexperienced in making online purchases for their stores. Cultural and language barriers need to be 
considered throughout the development of the app, which may need to include language options within its user 
interface and considerations for how to make novel phrases such as “BuddyUp!” appropriate among store 
owners of different cultural and ethnic identities. Differences in payment preferences will be a challenge. Store 
owners prefer cash on delivery payment, but some wholesalers are not willing to offer this out of concern for 
product theft and the safety of their delivery drivers. In the pilot, we will initially subsidize the cost of delivery, as 
a means of reducing potential barriers of adoption as we measure the efficacy of the intervention. A key 
challenge is determining which distribution system (e.g., OrderUp, UberEats, Instacart, BuddyLift!, 
HungryHarvest, among others) will be the most efficient, cost-effective, culturally appropriate and sustainable, 
either alone or in combination.67,109 Figuring out this solution is one of the key objectives of the formative 
research (Aim 1). Finally, as the app will be piloted in East Baltimore, concerns with potential generalizability to 
other low income urban settings exists. We have included in phase 6 a qualitative assessment of feasibility in 
three different urban settings. 
Strengths: While there are apps that connect food sources and consumers, there are no apps that we know of 
that connect local producers and wholesalers to small urban corner stores. BUD will allow small store owners 
to connect to a variety of producers and suppliers at once and will allow access healthier products for their 
stores in an affordable manner. BUD has the potential to offer a sustainable and easy to disseminate solution 
to the crisis of supply and delivery in low-income urban settings. The R34 proposal is based on substantial 
previous formative research, including the development and display of a wire frame of the BUD app to multiple 
types of users. This has allowed us to anticipate many of the challenges that will be faced during the pilot, and 
to develop strategies to address them. 
 
 
 

Statistical Design Plan 
The data analysis plan is a series of steps including data entry, data cleaning, exploratory data analysis, and 
multivariate analysis, led by our biostatistician (Ogburn) and by our economist (Trujillo) for the economic 
analysis. Data will be managed at JHU and the data manager will review data for quality. Qualitative data will 
be de-identified, transcribed and translated (if needed) by research assistants, kept on password protected 
computers, and analyzed using Atlas-ti 8.4. Quantitative data will be de-identified, entered into Microsoft 
Access databases and analyzed using Stata 14. These data will be collected through direct observation (e.g., 
availability), electronic point of sale data collection (e.g., sales), surveys (e.g., consumer purchasing), and 
anthropometry (e.g., BMI). We will look at the change in availability, sales, and purchasing of healthier foods 
scores from baseline to post-intervention comparing intervention and comparison groups, at the store and 
consumer levels. Models will control for covariates that predict the outcomes (e.g., store size or consumer age 
and sex), and standard errors will account for the clustering of repeated measures within stores.  
Feasibility analysis (Aims 1 and 2) 
 In-depth interviews will be analyzed in ATLAS.ti for themes, and used to inform changes to the BUD app and 
to assess feasibility from the perspective of users. Feasibility analysis will focus on assessing economic and 
cultural acceptability, operability and perceived/planned sustained use of the BUD app, including barriers and 
enhancing factors. From the corner store owner/wholesaler/producer perspectives, acceptability will be 
assessed according to whether the new products are perceived to be in demand, profitable, easy to obtain 
and store. Operability will be assessed primarily in terms of the BUD app, in terms of whether store owners 
indicate (and demonstrate) that they can and do continue to use the different features of the app (e.g., 
BuddyUp, BuddyLift, Buddy Chat, etc.). Perceived sustainability will be assessed in terms of whether store 
owners (and other users, such as wholesalers, producers) indicate that they plan to continue to use the BUD 
app, how much, and for what purposes (e,g, which specific foods. Textual data will be coded by trained staff 
using a codebook. The codebook will emphasize these feasibility constructs. Qualitative analysis will center 
on providing contextually rich descriptions of each aspect of feasibility, and describing some of the key 
sources of variation (e.g., by store size, owner ethnicity, etc.). Analysis of the 20 qualitative consumer 
interviews will focus on identifying those healthier products would be most acceptable to them in terms of 
demand, cost, taste and other similar characteristics. The analysis will also identify best in-store strategies to 
promote these foods. 
We will conduct a detailed quantitative process evaluation (in terms of reach, dose delivered, and fidelity), and 
results will serve as additional measures of feasibility, particularly in terms of establishing operability and 
potential sustainability. Three primary process dimensions will be assessed: reach, dose delivered and fidelity 
– each will likely have multiple measures – and standards will be set for each measure to monitor quality of 
intervention implementation. For example, reach will be assessed by the percent of intervention corner stores 
ever using the BUD App for ordering, and the number of wholesalers/producers who ever put products on the 
app. The standard could be set at 100%, meaning that each corner stores/wholesaler/producer used the app 
at least one. Dose delivered will be evaluated as the percent of BUD app trainings successfully completed by 
corner store owners (out of 18 possible intervention stores) – currently planned at one/phase of the 
intervention (n=4), # communications with store owners (out of X possible communications, determined by a 
standard to be set), and delivery of promotional materials to encourage consumer demand (out of X possible 
deliveries, determined by a standard to be set). Fidelity will be assessed as frequency of usage (e.g., once per 
week, twice per week) of the BUD App to offer/order foods and beverages by wholesalers/producers/corner 
store owners, as well as in terms of their use of different features of the app (e.g., BuddyUp, BuddyLift, 
BuddyChat, etc). We will set standards to monitor quality of intervention implementation for the reach, dose 
and fidelity measures, and revise these standards based on the experience of the R34 trial for the planned 
future full-scale trial. 
Feasibility will also be assessed via analysis of user satisfaction responses collected during operation of the 
BUD app, as well as in terms of responses to the open-ended questions that will be part of the process 
evaluation measures. 
Analysis of pilot trial store impact data (Aim 3) 
We hypothesize that there will be significantly increased availability (stocking) and sales of healthy promoted 
foods (whole grain, fruit, vegetable, and low-fat, low-sugar foods) and beverages in intervention stores using 
the BUD app, as compared to comparison stores. Using direct observation-based stocking data, a healthy 
food availability index (HFAI)152 will be calculated, and we will initially examine the difference from pre- to 

postintervention comparing intervention and control stores. We will also look at change in availability and 
sales of specific promoted product categories from pre- to post-intervention at each specific phase of 
intervention. Mixed-effect models, treating the stores as a random effect, will be used to evaluate the effect of 
the intervention on availability/sales of healthful food and beverage scores comparing corner store 
intervention and comparison groups, adjusting for covariates, such as store size and food stocking at 
baseline. We hypothesize that prices of some promoted foods and beverages will decrease given promoted 
foods/beverages will be subsidized during the first two phases of the intervention, as compared to baseline (if 
stocked at baseline). Furthermore, we will conduct additional tests to see if a pass-through price effect 
occurred related to the initial subsidy. Since the subsidy is short term (just phases 1 and 2 of the intervention), 
relatively small, and intended to generate initial usage of the app, we do not anticipate a significant price 
effect. 
Analysis of pilot trial consumer impact data (Aim 4) 
Secondary outcome analyses will include assessment of change in consumer-level variables. From the adult 
consumer impact questionnaire (AIQ) on purchasing frequency of promoted foods and beverages (i.e. fruit 
and vegetables, low-fat snacks, low-sugar beverages, whole grain foods), and using the Block FFQ data on 
the intake of the same products, we will compare the change in purchasing and consumption among 
consumers of intervention stores to the comparison store consumers. These analyses will be adjusted for 
potential confounders, such as age, sex, income, participation in food assistance programs, and other 
household covariates when appropriate. We will use linear mixed-effect models with individual and store 
random effects to identify potential predictors of participation. We hypothesize there will be a trend toward 
increased purchasing and consumption of healthy promoted foods and beverages among intervention 
consumers compared to consumers that are sampled from comparison stores. 
Economic analysis (Aim 5) 
We will conduct a cost–benefit analysis of the use of the BUD App, from the wholesaler, producer, retailer and 
consumer perspectives. We hypothesize that the savings promoted by the BUD App will outweigh the costs of 
developing and implementing the intervention. In other words, we hypothesis that the use of the BUD App will 
have a high rate of return for the wholesaler as well as retailers and consumers.  
Among the potential savings from using BUD, we will include: reduction in prices of foods; increase in total net 
revenues (sales minus discounts); reduction in transportation cost of food, reduction in transportation costs to 
buy products; inventory and rotation; financial costs of working capital among other items. Other benefits 
associated with the BUD are linked with improvement in health and human capital of consumers. We would 
consider these savings broadly using our sample of 170 low-income urban consumers, assessed pre- and 
post-intervention variation and will monetize benefits associated to these outcomes. We will separate between 
short-term and long-term benefits and discount future benefits.  
For cost, we will include in our analyses monthly and annual totals, including costs at baseline, personnel 
costs (salaries and benefits), material and supplies, equipment, and transportation costs, among other items. 
We will also monetize the time of delivering the intervention and the time devoted to training. We plan to 
differentiate between initial costs and deployment costs. Initial costs will account all costs during the pre-
implementation phase of the intervention. Post-intervention costs will capture the variable and fixed costs of 
running the program. These costs will include recurrent and capital costs associated to the program. We plan 
to integrate these templates to estimate the total costs of the intervention and compare these figures to the 
benefits of the program.110 We will conduct sensitivity analyses to test how robust our results are, in relation 
to the assumptions of our financial cost model. 
Use of Data for Power Calculation for Planned Main Trial 
The proposed R34 is expected to provide information needed to power the planned, future RCT. The R34 is 
designed to provide adequate precision of estimates of the primary outcome(s) of the full trial. We will look at 
baseline measures of multiple outcomes, and how these change over time. We will examine correlations of 
outcomes within store over time, between participants (customers) within stores, and within participants over 
time. We will use these data to assess clustering, permitting us to estimate the ICC, and precision around it, 
and to modify our main trial sample size estimates as needed. Ultimately, this will address gaps in the 
research for others who want to perform interventions with similar outcomes. 
Power Calculation and Study Design 

This is a randomized group-treatment trial, where individual stores will be randomized to two study conditions: 
1) usage of a web-based app (BUD) to order healthier food and beverages + in-store point-of-purchase 
promotional materials + limited subsidies (treatment); or 2) only in-store point-of-purchase promotional 
materials (comparison). In order to ensure a balanced study design, prior to randomization, we will stratify 
corner stores by two factors to balance potential confounders: 1) length of time in business at this location (>2 
years vs <2years); and 2) store size (0-2 aisles vs 2-4 aisles), and then randomize matched pairs to treatment 
versus comparison. 
We recognize that clustering may occur, given that storeowners will share a change agent (BUD app) with 
other storeowners assigned to intervention group, and will potentially interact with each other as part of the 
functioning of the BUD app.153 We will control for baseline covariates of all participating stores that may 
predict study outcomes. 
The feasibility pilot study is powered based on seeing impact at the corner store level. Store eligibility criteria 
for participation in the trial are as follows: 1) store owner/manager willing to order food through a smartphone 
or other internet-enabled device; 2) store located in a low-income neighborhood classified as a healthy food 
priority area in East Baltimore; 3) classified as small food store (< 4 aisles, < 2 cashier); 4) English, Korean, 
Spanish or Mandarin-speaking; and 5) located >0.25 miles from a supermarket. 
Sample size calculations for corner stores were based on the analysis of a simple difference in change in the 
mean of Healthy Food Availability Index (HFAI) scores.152 We used preliminary impact data from our 
previous trial (BHCK)80, with a reported mean change in HFAI µ(σ) = 1.67(5.35) and µ(σ) = 5.65(4.95) in the 
control and intervention groups, respectively. Due to the price incentive features (subsidy, group purchasing 
discounts) that will be available to BUD stores owners to stock healthier foods and beverages in phases 1 and 
2, we estimate that the effect of the BUD intervention will be higher than our previous studies with corner 
stores. Therefore, we estimated a total sample size of 34 stores (17 per arm) for two-sample comparison of 
mean assuming a difference in healthy food availability (HFAI) of 6 points, variance of 24, intraclass 
correlation of 0.0001, type I error of 5% and power level set at 80%. We will increase our store sample size by 
2 per treatment arm (n=38) to account for possible loss to follow-up. As mentioned, we will also control for 
baseline covariance, which will help to increase the power of the study. Some of the potential factors to 
control for will include store size and baseline HFAI. 
The consumer sample size of this feasibility trial is intended to obtain an estimate of effect size and the 
variance in food consumption, purchasing, and body mass index, in order to plan a larger clinical trial, and to 
provide evidence of recruitment and retention ability. Therefore, the R34 pilot study is not powered to provide 
impact at the consumer level (which will be the primary outcomes for the full-scale clinical trial). Consumer 
eligibility criteria are as follows: 1) frequent consumer of the food store (shop at least once a week); 2) 
live/work within ¼ mile radius from the store; 3) >21 years old and < 75 years old; 4) live in a household of at 
least 2 persons. We will use systematic sampling to recruit the consumer study evaluation sample in and 
around each of the 38 small food stores (5 adult consumers per food store).