Impact of a Farmers' Market Nutrition Coupon Program on Diet Quality and Psychosocial Well-being Among Low-income Adults: A Randomized Controlled Trial
Phase: NA
Conditions: Diet, Healthy, Health Risk Behaviors
Sponsor: University of Calgary

Impact of a farmers' market nutrition coupon program on diet quality and psychosocial well-
being among low-income adults 
 
 
NCT03952338 
 
October 27, 2024 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

BACKGROUND 
Income is amongst the strongest determinants of diet quality [1, 2] and overall health.[1] A clear 
socioeconomic gradient exists whereby individuals with lower incomes experience higher rates of 
nutrition-related chronic diseases [3-5] relative to those with higher incomes. Low household income is 
also a key determinant of household food insecurity,[6, 7] which is associated with lower diet quality [8-
11] and inadequate nutrient intake.[12, 13] Evidence suggests that low-income populations tend to 
consume diets lower in fruits and vegetables and higher in refined white grains, high-fat meats, fried 
foods and added fats.[14, 15] These inequities in diet quality may partly explain the greater vulnerability 
of low-income and food insecure populations to poor health outcomes [14, 16-18] and 
undernutrition.[12, 13]  
 
The many factors underpinning differential dietary patterns among low-income groups can be 
conceptualized through the socioecological model.[19, 20] The socioecological model depicts the 
complex and reciprocal interactions among multiple levels of influence, including individual, social, 
community, and policy level factors that shape dietary patterns and health outcomes.[19-22] At the 
individual level, factors such as psychological state [15, 23] and nutrition-related knowledge [23] have 
been shown to influence dietary patterns.[22, 24-27] For instance, high self-efficacy for consuming fruits 
and vegetables is associated with greater fruit and vegetable intake.[28, 29] The social level encompasses 
social and cultural contexts that influence dietary patterns.[23, 30] Low-income populations generally 
have low social support [29, 31] and social capital,[32-34], which are, in turn, associated with poorer 
dietary intake and health outcomes.[29, 35] The community level includes the physical environments in 
which people live and work.[20, 27] Studies from the United States (U.S.) [20, 36-39] and Canada [40] 
have shown that disadvantaged neighbourhoods generally have more fast food outlets, which are 
associated with greater purchasing and consumption of unhealthy foods.[37] The policy level 

encompasses policies that influence the distribution of dietary patterns and health outcomes across a 
population.[1, 41] Interventions at the policy level provide significant potential for sustainable, cost-
effective and equitable health impacts.[42, 43] Fiscal policies that influence food prices and affordability 
(e.g., taxation, subsidies) are particularly important for supporting healthy dietary patterns among low-
income groups,[44] because the economic resources of low-income populations are often insufficient to 
purchase healthy foods consistent with dietary recommendations.[14, 45, 46] Given the multi-level 
contexts in which dietary patterns are situated, interventions at any single level  (e.g., individual level 
interventions) are unlikely to substantially improve diet quality and health outcomes among low-income 
populations.[20, 47] Policies and interventions that address determinants of poor dietary patterns and ill 
health at all levels [48-50] are required to effectively reduce nutrition and health inequities.[51, 52] 
Notably, farmers’ market healthy food subsidy programs are growing in interest as multi-component 
interventions that aim to improve access to and intake of nutritious among low-income populations.[53-
56] As government funded food subsidy programs, farmers’ market subsidy programs clearly operate at 
the policy level.  
 
At the community level, farmers’ markets have the potential to alleviate barriers associated with 
accessing healthy foods,[39, 57, 58] as they offer fresh, local produce [39, 59, 60] and can be set up in 
communities that otherwise have limited access to healthy foods.[61] Although some studies suggest that 
the high perceived and objective cost of produce at farmers’ markets is a barrier for low-income 
populations,[62-64] others have found that low-income shoppers in the U.S. perceived farmers’ market 
prices to be reasonable/fair,[64] and that farmers’ market subsidy programs reduce food insecurity 
among program participants.[65, 66] Moreover, objective price comparisons in U.S. and Canadian 
markets showed that prices were lower or comparable to those at other food retailers.[39, 67, 68] 
Farmers’ market food subsidies also support local farmers and promote sustainable local food systems 

[69] by increasing awareness of farmers’ markets within communities [70] and increasing the customer 
base, thereby generating increased sales.[71]  
 
Farmers’ market food subsidy programs can also influence social level determinants of dietary intake.[28, 
72] Farmers’ markets act as social spaces, increasing social interactions between community members 
and farmers,[59, 73] thereby fostering a sense of community and increasing psychosocial well-being of 
program participants.[74] These social aspects of farmers’ markets are particularly important for low-
income groups, as social exclusion and isolation are associated with food insecurity and low-income 
status.[75, 76] Finally, at the individual level, farmers’ market healthy food subsidy programs have been 
shown to improve participant fruit and vegetable intake,[61, 66, 77-80] and farmers’ market programs 
that offer nutrition skill-building activities may enhance participant food- and nutrition-related knowledge 
and skills [28, 81] and attitudes towards the importance of fruit and vegetable consumption.[81] 
 
Farmers’ market food subsidy programs may, therefore, represent a promising multi-level approach to 
improving the diet quality and psychosocial well-being [82] of low-income populations; however, several 
knowledge gaps remain.[61] First, given the short-term nature of the intervention, it is unclear whether 
any positive program outcomes will be sustained over time. However, one study demonstrated that 
provision of a farmers’ market fruit and vegetable subsidy of $10/week for 6 months resulted in an 
increase in fruit and vegetable intake of 1.4 servings/1000kcal, which was sustained 6 months following 
program completion.[78] Second, although prior studies have examined the impact of farmers’ market 
subsidies on fruit and vegetable intake,[61, 66, 77-80] psychosocial well-being,[74] and food 
insecurity,[65, 66] potential positive impacts of farmers’ market subsidies on other relevant outcomes 
such as subjective social status, sense of community, mental well-being, and malnutrition risk have not 
been examined. Subjective social status and sense of community are closely associated with social 

participation and support.[83-85] In addition, poor mental well-being (e.g., depression, stress) in low-
income populations is often linked to financial strain [86, 87] and social isolation and exclusion.[76] 
Therefore, we hypothesize that the combined financial support from subsidies and the social aspects of 
shopping at farmers’ markets and participating in skill-building activities may improve participants’ mental 
well-being, subjective (i.e., perceived) social status, and sense of community. Moreover, farmers’ market 
subsidies may influence malnutrition risk by providing additional funds to purchase nutritious foods.  
 
In addition, most previous studies have been cross-sectional [57, 77, 88-92] or used a pre/post 
design,[53, 56, 93] and/or lacked a control group,[56, 57, 94] each of which does not allow for causal 
inference.[61] Most have also been conducted over short time frames. Randomized controlled trials (RCT) 
conducted over longer time frames can provide stronger evidence of the dietary and health impacts of 
farmers’ market food subsidy programs and their sustainability over time.[61] Furthermore, most studies 
have only assessed changes in fruit and vegetable consumption and have measured dietary intake using 
brief fruit and vegetable screeners [61] rather than more comprehensive and valid assessment tools such 
as food frequency questionnaires and 24-hour dietary recalls.[61, 70] Assessment of overall dietary intake 
is important, as when one aspect of diet changes, such as fruit and vegetable intake, concurrent changes 
occur in other aspects of dietary intake.[95] Moreover, studies suggest that use of farmers’ market food 
subsidies may differ according to age and sex;[80, 96-98] however, evidence is limited on how the 
impacts of such programs vary across these groups. Finally, the majority of studies have been conducted 
in the U.S., and evidence from other nations is sparse.[39] 
 
In British Columbia (BC), Canada, the average monthly cost to purchase a healthy diet for a family of four 
is $1,019,[99] nearly one-half the income from low-wage employment.[100] The BC Farmers’ Market 
Nutrition Coupon Program (FMNCP) is a healthy eating initiative that offers a healthy food subsidy, along 

with supportive nutrition skill-building activities, for low-income populations.[101] It is the only 
government-funded program of this type in Canada. Between 2007 and 2017, households received 
$15/week. This amount was determined based on a U.S. farmers’ market coupon program that provided 
on average $10-30 for participants [102] and based on the availability of funds. The amount increased to 
$21/week in 2017 to account for increased food costs. In 2018, the BC FMNCP served over 11,000 
individuals, including 532 pregnant women, 1,084 seniors, and 4,965 children.[103] The program 
facilitates access to nutritious foods for low-income families, pregnant women, and older adults by 
providing participants with coupons valued at $21/week to purchase fruits, vegetables, dairy, 
meat/poultry/fish, eggs, nuts, and cut herbs from participating BC farmers’ markets.[101] Farmers’ 
markets that participate in the BC FMNCP operate 1-2 days per week, with hours that vary by location. 
While coupons may only be redeemed from June to November, most communities offer indoor markets 
that are open year-round.[101, 104] The goal of the BC FMNCP is to provide financial support for low-
income households to purchase and consume healthier foods, thereby improving diet quality [47, 105] 
and overall health.[106, 107] The program also aims to minimize further marginalization of low-income 
individuals by encouraging their participation in farmers’ markets, which are important social spaces that 
may foster social and mental well-being.[54, 108, 109] Currently, the FMNCP operates in 57 BC 
communities and reaches over 3900 households;[101] however, the need remains substantial, with over 
15 communities on waiting lists to participate. It is unclear if the BC FMNCP is achieving its aims, as the 
program’s outcomes have not been rigourously investigated.  
 
This study was co-designed with the BC Association of Farmers’ Markets and the FMNCP in order to 
achieve the following objectives:  
1) Conduct a RCT to examine the impact of the BC FMNCP on the following outcomes immediately 
post-intervention and at 16 weeks post-intervention among low-income adults: 

a. mean overall diet quality (primary outcome);  
b. mean diet quality subscores, mental well-being scores, sense of community, odds of 
experiencing household food insecurity and odds of malnutrition risk (secondary 
outcomes); and  
c. mean subjective social status (exploratory outcome)  
METHODS 
Study design  
Using a parallel group RCT, we will collect data at three time points: baseline (Time 1; 0 weeks, June 
2019), immediately following the BC FMNCP (Time 2; 10-15 weeks, October 2019) and 16 weeks after the 
BC FMNCP ends (Time 3; 26-31 weeks, February 2020).  
 
Program overview 
The BC FMNCP functions through a collaborative partnership between the BC Association of Farmers’ 
Markets, the BC Ministry of Health, farmers’ markets and community partners (i.e., local non-profit 
organizations). The BC Association of Farmers’ Markets supports, develops and promotes farmers’ 
markets across BC [101] and oversees the operations of the FMNCP. The FMNCP is supported by the 
province of BC and the Provincial Health Services Authority. Community partners distribute coupons to 
program participants from their organization locations (e.g., pregnancy outreach and community services 
agencies) on a weekly or biweekly basis and offer nutrition skill-building activities such as cooking classes 
or community gardens to promote nutrition- and food-related knowledge and skills.[101] 
 
Recruitment 

The FMNCP Director will identify approximately 15 BC communities for the study (from the existing 
FMNCP and from program waiting lists) with the aim of achieving similar rural/urban coverage as the 
existing FMNCP. Within each community, the FMNCP director will recruit community partners by 
contacting those who are members of the BC Association of Farmers’ Markets and offer nutrition skill-
building activities for low-income groups. Community partners within study communities will be 
responsible for identifying and enrolling eligible low-income adults into the study from among their 
existing clients and will share study details via phone, email or in-person, using posters and other 
recruitment aids as needed. Community partners will assess eligibility using a screening questionnaire and 
will obtain voluntary, informed consent from eligible participants. 
 
Eligibility criteria 
Individuals will be eligible to participate if they meet the following criteria:  
• 
Adults (≥ 18 years) 
• 
Low-income as determined by community-specific thresholds (~$18,000/year annual household 
income before taxes)  
• 
No expected change in household income prior to study completion  
• 
£ 8 people living in the home (including the participant) 
• 
No expected change in household composition prior to study completion 
• 
Primary food shopper for the household 
• 
Does not self-report dementia or Alzheimer’s Disease 
• 
Able to speak, read and write in English (or have someone who can assist them) 
• 
No plans to move from principal residence prior to study completion 
• 
Has not previously participated in the BC FMNCP 
 

Randomization 
Following baseline data collection, eligible participants will be randomized to the FMNCP group (n=132) 
or a no-intervention control group (n=132), with a 1:1 allocation ratio. An independent researcher from 
the Clinical Research Unit at the University of Calgary will generate a blocked randomization sequence 
that stratifies participants into blocks according to sex (male, female), geographic location (rural, urban), 
pregnancy (yes, no) and breastfeeding (yes, no). Blocked randomization will help to ensure balanced 
representation of participants in study arms.[110] REDCap (Research Electronic Data Capture), a secure, 
web-based data collection and management application [111] hosted at the University of Calgary, will be 
used to randomize participants into the FMNCP and control groups on the basis of this randomization 
sequence. The study coordinator will subsequently communicate participant group assignments to 
community partners and participants. Allocation concealment will be ensured via secure storage of the 
randomization sequence separately from the participant database, which will only be accessible by the 
study coordinator and the Clinical Research Unit. Researchers will remain blinded to respondent 
condition throughout the study. Although participants cannot be blinded to group allocation, they will be 
blinded to the specific study objectives to reduce expectancy bias, whereby communication of expected 
study outcomes influences participants’ behaviour.[112-115] 
 
 Intervention 
In the existing BC FMNCP, community partners distribute one to two sheets of coupons per week (each 
sheet contains $21 in coupons) to program participants for a total of 16 sheets. Coupons can be used 
over 16-20 weeks to purchase fruits, vegetables, dairy, meat/poultry/fish, eggs, nuts, and cut herbs at 
participating BC farmers’ markets. However, to allow sufficient time to recruit participants for this study, 
community partners will distribute 16 coupon sheets to the FMNCP group over 10-15 weeks (households 
with 5-8 individuals will receive 32 coupon sheets). To ensure participants receive all 16 coupon sheets, 

community partners will provide two coupon sheets per household during weeks 1-6 of the intervention. 
Participants may redeem coupons at farmers’ markets at a frequency of their choice (e.g., redeem 
coupons weekly or redeem several weeks’ worth of coupons simultaneously). Participants in the FMNCP 
group will be invited to participate in nutrition skill-building activities (e.g., cooking classes) offered by 
community partners throughout the intervention period, although participation is not required (this is 
consistent with the existing FMNCP). The types and frequency of nutrition skill-building activities offered 
vary across community partners. For the duration of the study, the control group will not receive coupons 
nor be eligible to participate in nutrition skill-building activities but will be eligible to participate in the BC 
FMNCP the following farmers’ market season. As participants in the control group already receive other 
supports from community partners, they will continue to meet with their community partner as they 
normally would throughout the intervention period.  
 
Data collection 
Data will be collected from the FMNCP and control groups at three time points: Time 1: baseline (0 
weeks), Time 2: immediately post-intervention (10-15 weeks), and Time 3: 16 weeks post-intervention 
(26-31 weeks). At each time point, participants will complete a questionnaire assessing sociodemographic 
characteristics, health-related variables, and secondary and exploratory outcomes, followed by a 24-hour 
dietary recall to assess diet quality. The questionnaire and dietary recall will be integrated within a web-
based platform developed and pilot tested by the researchers [116]. A second dietary recall will be 
completed 2-5 days later to better estimate usual intake and account for within-individual variation in 
diet quality. All participants will receive cash incentives valued at $20 at baseline and $40 at each Time 2 
and 3. Participants will also receive small gifts prior to data collection at Time 2 and 3, which will serve as 
a reminder for the upcoming data collection.   
 

At baseline, researchers will provide participants with a username and password to access the web-based 
platform. Participants will be encouraged, but not required, to complete baseline data collection at a 
community partner location immediately after providing informed consent. Community partners will 
record whether data collection was completed at a community partner location or elsewhere (e.g., 
home). Immediately post-intervention and at 16 weeks post-intervention, participants will receive an 
email requesting that they complete data collection (i.e., questionnaire and dietary recall) at a location of 
their choice.  
 
Questionnaire 
The questionnaire will be administered via REDCap at all three time points and will collect data on 
sociodemographic characteristics, health-related variables, sense of community, mental well-being, 
household food insecurity, malnutrition risk, and subjective social status. Questions related to the FMNCP 
intervention (e.g., coupon receipt) will be included in the questionnaire at Time 2 only.  
 
Sociodemographic characteristics and health-related variables 
Sociodemographic characteristics and health-related variables that will be assessed include date of birth, 
sex, race/ethnicity, years lived in Canada, marital status, household size, number of children living in the 
home, perceived physical health, pregnancy/breastfeeding, smoking status, height, weight, educational 
level, employment status, annual household income, main source of income, and community of 
residence. 
 
Mental well-being  
Mental well-being will be assessed using the valid 14-item Warwick-Edinburgh Mental Well-Being 
Scale.[117] Scale items are positively phrased and assess various aspects of mental well-being such as 

positive affect (e.g., optimism), psychological functioning (e.g., self-confidence) and satisfaction with 
interpersonal relationships over the past two weeks.[118-120] The scale has been validated in a variety of 
age, sex, and socioeconomic status groups [120] and cultural contexts,[117] has captured change within 
short-term interventions,[121-124] and has demonstrated high test-retest reliability with an intra-class 
correlation of 0.83.[120] Responses are scored on a 5-point Likert scale from 1 (none of the time) to 5 (all 
of the time) and are summed to provide a single score ranging from 14 to 70.[117, 119] A higher score 
indicates higher perceived mental well-being.[117]  
 
Household food insecurity  
Household food insecurity will be assessed using Health Canada’s validated 18-item Household Food 
Security Survey Module (HFSSM), which includes a 10-item adult scale and an 8-item child scale for 
households with children under 18 years of age.[125] The HFSSM typically assesses experiences of 
household food insecurity over the past year;[126] however, similar to how the HFSSM has been modified 
in previous studies,[127-130] it will be modified to assess experiences of household food insecurity in the 
past month. The HFSSM assesses experiences of marginal (one affirmative response), moderate (adult 
subscale 2-5 affirmative responses/child subscale 2-4 affirmative responses) and severe (adult subscale ≥ 
6 affirmative responses/child subscale ≥ 5 affirmative responses) food insecurity.[125] The HFSSM has 
been validated in a variety of population groups and languages,[131, 132] has captured changes in food 
security status during short-term interventions,[129] and has good test-retest reliability with a Pearson 
correlation coefficient of r=0.75.[133]  
 
Sense of community  
Sense of community will be assessed using the validated 8-item Brief Sense of Community Scale.[134] 
Scale components are designed to assess each sense of community dimension according to the McMillan-

Chavis (1986) Model [135, 136] for sense of community, which includes four elements: membership, 
influence, integration and fulfillment of needs, and a shared emotional connection.[135, 136] Each item is 
scored using a Likert Scale of 1 (strongly disagree) to 5 (strongly agree).[134] Total sense of community 
scores can range from 8 to 40 with a higher score indicating greater needs fulfillment, group 
membership, influence and emotional connection within the community.  
 
Malnutrition risk  
Malnutrition risk will be calculated using the validated Malnutrition Universal Screening Tool 
(MUST).[137, 138] The MUST assesses malnutrition risk using body mass index (BMI) (scored as 0 = BMI > 
20, 1 = BMI 18.5-20, 2 = BMI < 18.5), unplanned weight loss in the past 3-6 months (scored as 0 = < 5% of 
body weight, 1 = 5-10% of body weight, 2 = > 10% of body weight) and acute disease effect score (acute 
illness with no or likely no nutritional intake for > 5 days).[138] Unplanned weight loss will be modified to 
the past 3 months to accommodate the study timeline. In addition, acute disease effect is unlikely to 
occur in community settings [138] and will, therefore, be excluded.[139, 140] Overall malnutrition risk will 
be obtained by adding together subscores for BMI and unplanned weight loss, with 0 indicating low risk, 1 
indicating medium risk, and ³2 indicating high risk of malnutrition.[139] The MUST is an appropriate tool 
to assess malnutrition in community-dwelling [140, 141] adults aged ³18 years,[141-143] as it was 
designed to screen for malnutrition in all patient groups and care settings.[141] The MUST has been used 
to assess change in short-term interventions [144] and has demonstrated high test-retest reliability with a 
Cohen’s kappa coefficient of k=0.94.[145] 
 
Subjective social status  
Subjective social status will be assessed using the validated MacArthur Scale of Subjective Social Status 
community ladder,[146, 147] which consists of a single-item visual analog scale whereby respondents 

place themselves on a ladder rung according to their perceived social standing relative to others in their 
community.[146, 148] Response values can range from 1 to 10, with a higher score indicating higher 
perceived social status.[146] The subjective social status community ladder has been used to capture 
changes from short-term interventions.[149]  
 
FMNCP intervention data  
At Time 2 only, participants in both the intervention and control groups will report whether they received 
FMNCP coupons and attended nutrition skill-building activities (to assess contamination of the control 
group), how often and how much of their own money was spent at farmers’ markets during the 
intervention period and the types of foods purchased.    
 
Dietary intake 
Participants will complete two unannounced dietary recalls at each time point. Twenty-four hour dietary 
recalls are a recommended dietary assessment method to evaluate the effect of an intervention on diet 
quality, as they have less systematic error than other self-reported dietary assessment tools.[150, 151] 
Administration of unannounced dietary recalls minimizes reactivity bias, where participants adjust their 
dietary intake in anticipation of having to report it.[152] 
 
Participants will record all foods and beverages consumed (excluding supplements) from midnight to 
midnight the previous day using Health Canada’s validated Automated Self-Administered 24-hour Dietary 
Recall (ASA24-Canada-2018),[151, 153-155] an automated online dietary assessment tool.[154, 155] The 
ASA24 collects information regarding dietary intake in a series of four steps: 1) foods consumed at each 
meal/snack, 2) queries regarding omitted meals/snacks, 3) details (e.g., cooking methods, portions), and 
4) review of commonly forgotten items.[151, 156] The ASA24 concludes with a question querying 

whether reported intake was less than usual, usual, or more than usual.[156] The ASA24 has been used 
with older, multi-ethnic, and disadvantaged adults [113-115, 151, 157] and was preferred by a majority of 
participants compared to interviewer-administered recalls;[113] however, in a recent study among BC 
FMNCP participants, we identified several usability issues with the ASA24.[116] For example, participants 
reported difficulties in searching for specific foods and making changes to entered meals.[116] We will 
aim to address these challenges by including a pictorial user guide in survey invitation emails, and by 
training community partners to assist participants in-person with the ASA24-Canada-2018. Further, 
participants and community partners will have access to a toll-free study helpline available 10 hours/day, 
6 days/week during data collection. Helpline operators will provide assistance via telephone or email, and 
include three registered dietitians and the study coordinator, all of whom completed a half-day training 
session. Interrater reliability in entering meals into the ASA24-Canada-2018 among the helpline operators 
was high, with an intraclass correlation of 0.98. 
 
The purpose of the helpline is two-fold: 1) to serve as a support platform for community partners to ask 
questions and update researchers, and 2) to assist participants in completing data collection. If needed, 
helpline operators will verbally read all questions to participants and enter their responses online on their 
behalf. To maintain blinding, operators will remind participants not to disclose their group assignment 
during the call. Supporting participants during data collection will help to minimize missing and inaccurate 
data and participant attrition. To further minimize attrition, if data collection is not completed within 48-
hours of the initial prompt, researchers will make up to four attempts to contact participants by email 
and/or phone. Community partners will also remind participants to complete data collection. 
 
Data collected by community partners and farmers’ market vendors 

Community partners will maintain records of the number of coupons distributed to each participant (by 
recording the unique bar code number on each coupon) and the frequency and types of nutrition skill-
building activities attended. Farmers’ market vendors will track coupon redemption and foods purchased 
with coupons (e.g., fruits, vegetables, dairy) by using checkboxes on the back of each coupon. Farmers’ 
market managers will collect redeemed coupons from vendors and submit them to the FMNCP. They will 
complete tracking sheets noting the number of coupons redeemed and foods purchased with coupons. 
 
Statistical analysis plan 
Healthy Eating Index-2015 
Diet quality scores and subscores will be calculated using the validated Healthy Eating Index-2015  (HEI-
2015),[158-162] a tool used to assess conformance with the 2015-2020 Dietary Guidelines for 
Americans.[161] HEI-2015 subscores will be examined to gain insight into the specific dietary components 
that change in response to the intervention.[163] HEI scores are associated with indicators of 
socioeconomic position [164] and chronic disease.[18, 160, 165-168] Although Canadian adaptations of 
the HEI have been developed, they have either not been validated, are not density-based, or reflect 
dietary recommendations that are no longer current.[9, 169] Given that dietary recommendations in 
Canada and the U.S. are similar,[170-172] the HEI-2015 remains an appropriate tool to assess diet quality 
of Canadians.[9] 
 
The HEI-2015 encompasses thirteen dietary components to assess overall diet quality,[161] including nine 
‘adequacy’ components (recommended foods/nutrients, including total fruits , whole fruits, total 
vegetables, greens and beans, whole grains, dairy, total protein foods, seafood and plant proteins, fatty 
acids) and four ‘moderation’ components (foods/nutrients recommended to limit, including refined 
grains, sodium, added sugars, saturated fats). Component scores are density-based and, therefore, 

independent of energy intake.[161, 163] Diet quality (total HEI-2015 scores and subscores) will be 
calculated using the simple HEI scoring algorithm.[161] This method provides scores at the individual level 
and can, therefore, accommodate the multi-level nature of our data and include covariates.[161] HEI-
2015 scores will be calculated using three nutrient databases linked to the ASA24-Canada-2018:[173] the 
Canadian Nutrient File and the U.S. Department of Agriculture’s (USDA) Food and Nutrient Database for 
Dietary Surveys to convert dietary intakes to energy and nutrient intakes, and the USDA Food Patterns 
Equivalents Database to convert dietary intakes to dietary constituents (e.g., fruits) and measurement 
units consistent with HEI-2015 scoring standards (e.g., cup-equivalents of fruit).[162, 163, 174, 175] 
Ratios for each of the dietary constituents (e.g., quantity of fruit per 1000 kcal) will be calculated for each 
participant and scored using HEI-2015 scoring standards. The total score for each participant will be 
derived by adding the scores for intake of ‘adequacy’ and ‘moderation’ components with possible scores 
ranging from 0-100. A higher score indicates a higher quality diet.[161] 
 
Statistical analyses 
Descriptive analyses will be conducted to examine participant characteristics by group at each time point. 
Characteristics of study completers (i.e., provided data at Time 3) and non-completers will also be 
compared.  
 
Analyses will be intention-to-treat, in which participants will be analysed within the groups to which they 
were randomized regardless of adherence (e.g., failure to redeem coupons) or dropout. The analyses will 
include all participants who provided data at baseline. Repeated measures mixed-effect regression will 
assess differences in changes in mean HEI-2015 scores, HEI-2015 subscores, mental well-being, sense of 
community, and subjective social status between the FMNCP and control groups immediately post-
intervention and 16 weeks post-intervention. Multinomial logistic regression will be used to assess 

differences in the odds of experiencing household food insecurity and malnutrition risk for the FMNCP 
group compared to the control group immediately post-intervention and 16 weeks post-intervention. 
Statistical models will include intervention group (FMNCP vs control), time from baseline, intervention-by-
time interaction, blocking variables (i.e., sex, rural/urban, pregnancy, breastfeeding), baseline values of 
the outcome, data collection mode (online, phone), household size, and place of data collection 
(community partner, other) as fixed effects covariates. Participant-specific (i.e., repeated measures) and 
rural/urban variations in outcomes will be modeled using random effects. Models will also include 
covariates specific to each outcome to increase the precision of estimates.[95] For the primary outcome 
of overall diet quality, models will include children living in the home (yes, no), sex, age, BMI, marital 
status, race/ethnicity, perceived health, smoking, day of dietary recall completion, and dietary recall 
number (i.e., dietary recall 1 or 2). Models that are and are not adjusted for an indicator of energy intake 
misreporting (the ratio of reported energy intake to total estimated energy expenditure) will be 
presented. Adjusted group differences (i.e., FMNCP vs control) in outcomes will be estimated using 95% 
confidence intervals and corresponding p-values.  
 
Subgroup analyses will examine whether the impact of the intervention on primary and secondary 
outcomes differs according to age group or sex. Dose-response analyses will examine whether the impact 
of the BC FMNCP on overall diet quality depends on the number of coupons redeemed and the number 
of nutrition skill-building activities attended. Interactions will be retained in statistical models if p<0.10. 
Analyses will be conducted in Stata (v15.1, Stata Corp, TX, USA), with p<0.05 indicating statistically 
significant differences between groups.  
 
Missing data 

Missing data will be handled using full information maximum likelihood under a missing at random 
assumption. We will also attempt to minimize missing data by reviewing all data within 24 hours of 
receipt and by contacting participants to fill-in missing or implausible responses within 48 hours. 
Participants who drop out of the study will be asked to provide reasons for drop out to assess the 
plausibility of a missing at random assumption.  
 
Sensitivity analyses 
Monte Carlo Markov Chain multiple imputation, inverse probability weighting and available case analysis 
will be used in a sensitivity analysis to investigate the impact of different assumptions about missing data 
on estimated program impacts.[176-178] Given the possibility of non-random attrition, pattern mixture 
methods models [179] will be used to explore the robustness of study findings to the assumption that 
data were missing not at random.[180]  
 
Sample size calculation  
The sample size was calculated from a RCT that investigated the impact of a fruit and vegetable rebate on 
HEI-2010 scores in low-income participants in the U.S.,[95] and a cross-sectional study that assessed 
average diet quality scores in disadvantaged Canadians.[36] In the RCT, diet quality in the intervention 
group was 4.7 points higher (95% CI 2.4, 7.1) at follow-up compared to controls.[95] This difference can 
be translated to, for example, an additional half serving of fruit per day, which is clinically meaningful and 
achievable.[107, 165] Assuming a type I error of 5%, an attrition rate of 30% by the 26- to 31-week follow 
up, and potential design effects based on sampling within different communities (estimated at 1.1, or an 
inflation of 10%), 264 participants are required for 80% power to detect a 4.7-point difference in diet 
quality. 
 
 

1. 
Solar O, Irwin A. A conceptual framework for action on the social determinants of 
health. Social Determinants of Health. Discussion Paper 2. Geneva, Switzerland: World Health 
Organization; 2010. 
2. 
Power EM. Determinants of healthy eating among low-income Canadians. Can J Public 
Health. 2005;96:S37-S42. 
3. 
Evans JM, Newton RW, Ruta DA, MacDonald TM, Morris AD. Socio-economic status, 
obesity and prevalence of Type 1 and Type 2 diabetes mellitus. Diabetic medicine : a journal of 
the British Diabetic Association. 2000;17(6):478-80. 
4. 
Tang M, Chen Y, Krewski D. Gender-related differences in the association between 
socioeconomic status and self-reported diabetes. Int J Epidemiol. 2003;32(3):381-5. 
5. 
Melchior M, Goldberg M, Krieger N, Kawachi I, Menvielle G, Zins M, et al. 
Occupational class, occupational mobility and cancer incidence among middle-aged men and 
women: a prospective study of the French GAZEL cohort*. Cancer causes & control : CCC. 
2005;16(5):515-24. 
6. 
Tarasuk V, Mitchell, A, Dachner, N. Household food insecurity in Canada 2014: 
Toronto: Research to identify policy options to reduce food insecurity (PROOF); 2016 [cited 
2019 January 7]. Available from: https://proof.utoronto.ca/. 
7. 
Jessiman-Perreault G, McIntyre L. The household food insecurity gradient and potential 
reductions in adverse population mental health outcomes in Canadian adults. SSM - Population 
Health. 2017;3:464-72. 
8. 
Kirkpatrick SI, Dodd KW, Reedy J, Krebs-Smith SM. Income and race/ethnicity are 
associated with adherence to food-based dietary guidance among US adults and children. J Acad 
Nutr Diet. 2012;112(5):624-35 e6. 
9. 
Guarriguet D. Diet quality in Canada. In: Canada S, editor. Ontario2009. p. 43-51. 
10. 
Wang DD LY, Chiuve SE, Hu FB, Willett WC. Improvements in US diet helped reduce 
disease burden and lower premature deaths, 1999–2012; Overall diet remains poor. Health 
affairs. 2015;34(11):1916–22. 
11. 
Kant AK SA, Graubard BI, Schairer C. A prospective study of diet quality and mortality 
in women. JAMA. 2000;283(16):2109-15. 
12. 
Bowman S. Low economic status is associated with suboptimal intakes of nutritious 
foods by adults in the National Health and Nutrition Examination Survey 1999-2002. Nutrition 
Research. 2007;27(9):515-23. 
13. 
Tarasuk V, Fitzpatrick S, Ward H. Nutrition inequities in Canada. Appl Physiol Nutr 
Metab. 2010;35(2):172-9. 
14. 
Darmon N DA. Does social class predict diet quality? Am J Clin Nutr. 2008;87:11. 
15. 
Moore CJ, Cunningham SA. Social position, psychological stress, and obesity: a 
systematic review. J Acad Nutr Diet. 2012;112(4):518-26. 
16. 
Tanumihardjo SA, Anderson C, Kaufer-Horwitz M, Bode L, Emenaker NJ, Haqq AM, et 
al. Poverty, obesity, and malnutrition: an international perspective recognizing the paradox. J Am 
Diet Assoc. 2007;107(11):1966-72. 
17. 
Hiza HA, Casavale KO, Guenther PM, Davis CA. Diet quality of Americans differs by 
age, sex, race/ethnicity, income, and education level. J Acad Nutr Diet. 2013;113(2):297-306. 
18. 
Schwingshackl L, Bogensberger B, Hoffmann G. Diet Quality as Assessed by the 
Healthy Eating Index, Alternate Healthy Eating Index, Dietary Approaches to Stop Hypertension 
Score, and Health Outcomes: An Updated Systematic Review and Meta-Analysis of Cohort 
Studies. J Acad Nutr Diet. 2018;118(1):74-100 e11. 

19. 
Townsend N, Foster C. Developing and applying a socio-ecological model to the 
promotion of healthy eating in the school. Public Health Nutr. 2013;16(6):1101-8. 
20. 
Story M, Kaphingst KM, Robinson-O'Brien R, Glanz K. Creating healthy food and eating 
environments: policy and environmental approaches. Annu Rev Public Health. 2008;29:253-72. 
21. 
Turrell G, Hewitt B, Patterson C, Oldenburg B. Measuring socio-economic position in 
dietary research: is choice of socio-economic indicator important? Public Health Nutr. 
2003;6(2):191-200. 
22. 
Robinson T. Applying the Socio-ecological Model to Improving Fruit and Vegetable 
Intake Among Low-Income African Americans. Journal of Community Health. 2008;33(6):395-
406. 
23. 
Raine KD. Determinants of Healthy Eating in Canada: An Overview and Synthesis. 
Canadian Journal of Public Health. 2005:S8-S14. 
24. 
Wang Y, Chen X. How much of racial/ethnic disparities in dietary intakes, exercise, and 
weight status can be explained by nutrition- and health-related psychosocial factors and 
socioeconomic status among US adults? J Am Diet Assoc. 2011;111(12):1904-11. 
25. 
Havas S AJ, Damron D, Langenberg P, Ballesteros M, Feldman R. Final Results of the 
Maryland WIC 5-A-Day Promotion Program. American Joumal of Public Health. 1998;88(8). 
26. 
Henry JL TA, Surkan PJ, Steeves EA, Hopkins LC, Gittelsohn J. Psychosocial 
determinants of food acquisition and preparation in low-income, urban African American 
households. Health Education & Behavior. 2018;45(6):10. 
27. 
Brown AG, Hudson LB, Chui K, Metayer N, Lebron-Torres N, Seguin RA, et al. 
Improving heart health among Black/African American women using civic engagement: a pilot 
study. BMC Public Health. 2017;17(1):112. 
28. 
Anderson JV BD, Brown RM, McLean DF, Garcia EM, Breer ML, Schillo BA. 5 a day 
fruit and vegetable intervention improves consumption in a low income population. J Am Diet 
Assoc. 2001;101(2):8. 
29. 
Steptoe  A P-PL, McKay C, Rink E, Hilton S, Cappuccio F. Psychological factors 
associated with fruit and vegetable intake and with biomarkers in adults from a low-income 
neighborhood. Health Psychology. 2003;22(2):8. 
30. 
Delormier T, Frohlich KL, Potvin L. Food and eating as social practice--understanding 
eating patterns as social phenomena and implications for public health. Sociol Health Illn. 
2009;31(2):215-28. 
31. 
Mackenbach JP. The persistence of health inequalities in modern welfare states: the 
explanation of a paradox. Soc Sci Med. 2012;75(4):761-9. 
32. 
Lancee B, Van de Werfhorst HG. Income inequality and participation: A comparison of 
24 European countries. Soc Sci Res. 2012;41(5):1166-78. 
33. 
Pichler F, Wallace C. Social capital and social class in Europe: the role of social 
networks in social stratification. European Sociological Review. 2009;25(3):319-32. 
34. 
Moore S, Stewart S, Teixeira A. Decomposing social capital inequalities in health. J 
Epidemiol Community Health. 2014;68(3):233-8. 
35. 
Carroll-Scott A, Gilstad-Hayden K, Rosenthal L, Peters SM, McCaslin C, Joyce R, et al. 
Disentangling neighborhood contextual associations with child body mass index, diet, and 
physical activity: the role of built, socioeconomic, and social environments. Soc Sci Med. 
2013;95:106-14. 

36. 
McInerney M, Csizmadi I, Friedenreich CM, Uribe FA, Nettel-Aguirre A, McLaren L, et 
al. Associations between the neighbourhood food environment, neighbourhood socioeconomic 
status, and diet quality: An observational study. BMC Public Health. 2016;16:984. 
37. 
Maguire ER BT, Monsivais P. Area deprivation and the food environment over time: A 
repeated cross-sectional study on take away outlet density and supermarket presence in Norfolk, 
UK, 1990–2008. Health & Place 2015;33:142–7. 
38. 
Smoyer-Tomic KE, Spence JC, Raine KD, Amrhein C, Cameron N, Yasenovskiy V, et 
al. The association between neighborhood socioeconomic status and exposure to supermarkets 
and fast food outlets. Health Place. 2008;14(4):740-54. 
39. 
Larsen K GJ. A farmers’ market in a food desert: Evaluating impacts on the price and 
availability of healthy food. Health &Place. 2009;15:1158–62. 
40. 
Minaker LM, Shuh A, Olstad DL, Engler-Stringer R, Black JL, Mah CL. Retail food 
environments research in Canada: A scoping review. Canadian Journal of Public Health. 
2016;107(S1):eS4-eS13. 
41. 
Gregson J, Foerster SB, Orr R, Jones L, Benedict J, Clarke B, et al. System, 
Environmental, and Policy Changes: Using the Social-Ecological Model as a Framework for 
Evaluating Nutrition Education and Social Marketing Programs with Low-Income Audiences. 
Journal of Nutrition Education. 2001;33:S4-S15. 
42. 
Mozaffarian D. Dietary and Policy Priorities for Cardiovascular Disease, Diabetes, and 
Obesity: A Comprehensive Review. Circulation. 2016;133:40. 
43. 
Graff SK, Kappagoda M, Wooten HM, McGowan AK, Ashe M. Policies for healthier 
communities: historical, legal, and practical elements of the obesity prevention movement. Annu 
Rev Public Health. 2012;33:307-24. 
44. 
Cassady D, Jetter KM, Culp J. Is price a barrier to eating more fruits and vegetables for 
low-income families? J Am Diet Assoc. 2007;107(11):1909-15. 
45. 
Turrell G. Socioeconomic differences in food preference and their influence on healthy 
food purchasing choices. Journal of Human Nutrition and Dietetics. 1998;11:15. 
46. 
Darmon N, Drewnowski A. Contribution of food prices and diet cost to socioeconomic 
disparities in diet quality and health: a systematic review and analysis. Nutr Rev. 
2015;73(10):643-60. 
47. 
Black AP BJ, Eyles H, Morris P, Vally H, O’Dea K. Food subsidy programs and the 
health and nutritional status of disadvantaged families in high income countries: a systematic 
review. BMC Public Health. 2012;12:24. 
48. 
McLeroy KR BD, Steckler A, Glanz K. An ecological perspective on health promotion 
programs. Health education quarterly. 1988;15(4):351-77. 
49. 
Lytvyak E, Olstad DL, Schopflocher DP, Plotnikoff RC, Storey KE, Nykiforuk CI, et al. 
Impact of a 3-year multi-centre community-based intervention on risk factors for chronic disease 
and obesity among free-living adults: the Healthy Alberta Communities study. BMC Public 
Health. 2016;16:344. 
50. 
World Health Organization PHAoC. Health equity through intersectoral action: an 
analysis of 18 country case studies. Public Health Agency of Canada; 2008. p. 40. 
51. 
Gans KM, Gorham G, Risica PM, Dulin-Keita A, Dionne L, Gao T, et al. A multi-level 
intervention in subsidized housing sites to increase fruit and vegetable access and intake: 
Rationale, design and methods of the ‘Live Well, Viva Bien’ cluster randomized trial. BMC 
Public Health. 2016;16(1). 

52. 
Gordon-Larsen P, Popkin B. Understanding socioeconomic and racial/ethnic status 
disparities in diet, exercise, and weight: underlying contextual factors and pathways. J Am Diet 
Assoc. 2011;111(12):1816-9. 
53. 
Durward CM, Savoie-Roskos M, Atoloye A, Isabella P, Jewkes MD, Ralls B, et al. 
Double Up Food Bucks Participation is Associated with Increased Fruit and Vegetable 
Consumption and Food Security Among Low-Income Adults. J Nutr Educ Behav. 2018. 
54. 
Freedman DA, Vaudrin N, Schneider C, Trapl E, Ohri-Vachaspati P, Taggart M, et al. 
Systematic Review of Factors Influencing Farmers' Market Use Overall and among Low-Income 
Populations. J Acad Nutr Diet. 2016;116(7):1136-55. 
55. 
Dimitri C, Oberholtzer L, Zive M, Sandolo C. Enhancing food security of low-income 
consumers: An investigation of financial incentives for use at farmers markets. Food Policy. 
2015;52:64-70. 
56. 
Savoie-Roskos M, Durward C, Jeweks M, LeBlanc H. Reducing Food Insecurity and 
Improving Fruit and Vegetable Intake Among Farmers' Market Incentive Program Participants. J 
Nutr Educ Behav. 2016;48(1):70-6 e1. 
57. 
Ruelas V, Iverson E, Kiekel P, Peters A. The role of farmers' markets in two low income, 
urban communities. J Community Health. 2012;37(3):554-62. 
58. 
Jilcott Pitts SB, Wu Q, McGuirt JT, Crawford TW, Keyserling TC, Ammerman AS. 
Associations between access to farmers' markets and supermarkets, shopping patterns, fruit and 
vegetable consumption and health indicators among women of reproductive age in eastern North 
Carolina, U.S.A. Public Health Nutr. 2013;16(11):1944-52. 
59. 
Lowery B, Sloane D, Payán D, Illum J, Lewis L. Do Farmers' Markets Increase Access to 
Healthy Foods for All Communities? Comparing Markets in 24 Neighborhoods in Los Angeles. 
Journal of the American Planning Association. 2016;82(3):252-66. 
60. 
Sadler RC. Strengthening the core, improving access: Bringing healthy food downtown 
via a farmers' market move. Applied Geography. 2016;67:119-28. 
61. 
McCormack LA, Laska MN, Larson NI, Story M. Review of the nutritional implications 
of farmers' markets and community gardens: a call for evaluation and research efforts. J Am Diet 
Assoc. 2010;110(3):399-408. 
62. 
Lucan SC, Maroko AR, Sanon O, Frias R, Schechter CB. Urban farmers' markets: 
Accessibility, offerings, and produce variety, quality, and price compared to nearby stores. 
Appetite. 2015;90:23-30. 
63. 
Wheeler AL, Chapman-Novakofski K. Farmers' Markets: Costs Compared With 
Supermarkets, Use Among WIC Clients, and Relationship to Fruit and Vegetable Intake and 
Related Psychosocial Variables. Journal of Nutrition Education and Behavior. 2014;46(3):S65-
S70. 
64. 
Freedman DA, Vaudrin N, Schneider C, Trapl E, Ohri-Vachaspati P, Taggart M, et al. 
Systematic Review of Factors Influencing Farmers’ Market Use Overall and among Low-Income 
Populations. Journal of the Academy of Nutrition and Dietetics. 2016;116(7):1136-55. 
65. 
Savoie-Roskos M, Durward C, Jeweks M, LeBlanc H. Reducing Food Insecurity and 
Improving Fruit and Vegetable Intake Among Farmers' Market Incentive Program Participants. J 
Nutr Educ Behav. 2016;48(1):70-6.e1. 
66. 
Durward CM, Savoie-Roskos M, Atoloye A, Isabella P, Jewkes MD, Ralls B, et al. 
Double Up Food Bucks Participation is Associated with Increased Fruit and Vegetable 
Consumption and Food Security Among Low-Income Adults. J Nutr Educ Behav. 
2018;51(3):342-7. 

67. 
Lee RE HK, Medina AV, Regan GR, Reese-Smith JY, Jokura Y, Maddock JE. A Picture 
of the Healthful Food Environment in Two Diverse Urban Cities. Environmental Health Insights. 
2010 4:49-60. 
68. 
McGuirt J, Jilcott S, Liu H, Ammerman A. Produce Price Savings for Consumers at 
Farmers' Markets Compared to Supermarkets in North Carolina. Journal of Hunger & 
Environmental Nutrition. 2011;6(1):86-98. 
69. 
Parks CA, Stern KL, Fricke HE, Clausen W, Fox TA, Yaroch AL. Food Insecurity 
Nutrition Incentive Grant Program: Implications for the 2018 Farm Bill and Future Directions. J 
Acad Nutr Diet. 2019. 
70. 
Anliker JA WM, Drake LT. An evaluation of the connecticut farmers’ market coupon 
program. Journal of Nutrition Education. 1992;24(4):7. 
71. 
Parks CA, Stern KL, Fricke HE, Clausen W, Fox TA, Yaroch AL. Food Insecurity 
Nutrition Incentive Grant Program: Implications for the 2018 Farm Bill and Future Directions. J 
Acad Nutr Diet. 2019;119(3):395-9. 
72. 
Dailey AB, Hess A, Horton C, Constantian E, Monani S, Wargo B, et al. Healthy options: 
a community-based program to address food insecurity. J Prev Interv Community. 
2015;43(2):83-94. 
73. 
Alonso AD, O'Neill MA. Investing in the social fabric of rural and urban communities: a 
comparative study of two Alabama farmers' markets. Community Development. 2011;42(3):392-
409. 
74. 
Smith LT, Johnson DB, Beaudoin S, Monsen ER, LoGerfo JP. Qualitative assessment of 
participant utilization and satisfaction with the Seattle Senior Farmers' Market Nutrition Pilot 
Program. Prev Chronic Dis. 2004;1(1):A06. 
75. 
Tarasuk VS. Household Food Insecurity with Hunger Is Associated with Women’s Food 
Intakes, Health and Household Circumstances. J Nutr 2001;131:2670–6. 
76. 
Goodman LA, Pugach M, Skolnik A, Smith L. Poverty and Mental Health Practice: 
Within and Beyond the 50-Minute Hour. J Clin Psychol. 2013;69(2):182-90. 
77. 
Olsho LE, Payne GH, Walker DK, Baronberg S, Jernigan J, Abrami A. Impacts of a 
farmers' market incentive programme on fruit and vegetable access, purchase and consumption. 
Public Health Nutr. 2015;18(15):2712-21. 
78. 
Herman DR, Harrison GG, Afifi AA, Jenks E. Effect of a targeted subsidy on intake of 
fruits and vegetables among low-income women in the Special Supplemental Nutrition Program 
for Women, Infants, and Children. Am J Public Health. 2008;98(1):98-105. 
79. 
Cohen AJ, Richardson CR, Heisler M, Sen A, Murphy EC, Hesterman OB, et al. 
Increasing Use of a Healthy Food Incentive: A Waiting Room Intervention Among Low-Income 
Patients. American Journal of Preventive Medicine. 2017;52(2):154-62. 
80. 
Young CR, Aquilante JL, Solomon S, Colby L, Kawinzi MA, Uy N, et al. Improving 
Fruit and Vegetable Consumption Among Low-Income Customers at Farmers Markets: Philly 
Food Bucks, Philadelphia, Pennsylvania, 2011. Preventing Chronic Disease. 2013;10. 
81. 
Dannefer R, Abrami A, Rapoport R, Sriphanlop P, Sacks R, Johns M. A Mixed-Methods 
Evaluation of a SNAP-Ed Farmers' Market-Based Nutrition Education Program. J Nutr Educ 
Behav. 2015;47(6):516-25 e1. 
82. 
Jilcott SB, Wade S, McGuirt JT, Wu Q, Lazorick S, Moore JB. The association between 
the food environment and weight status among eastern North Carolina youth. Public Health Nutr. 
2011;14(9):1610-7. 

83. 
Jorgensen BS, Jamieson RD, Martin JF. Income, sense of community and subjective 
well-being: Combining economic and psychological variables. Journal of Economic Psychology. 
2010;31(4):612-23. 
84. 
Brodsky AE OCP, Aronson RE. PSOC in community context: Multilevel correlates of a 
measure of psychological sense of community in low-income, urban neighborhoods. Journal of 
Community Psychology. 1999;27(6):659-79. 
85. 
Cundiff JM, Smith TW, Uchino BN, Berg CA. Subjective Social Status: Construct 
Validity and Associations with Psychosocial Vulnerability and Self-Rated Health. Int J Behav 
Med. 2013;20(1):148-58. 
86. 
Mistry RS LE, Benner AD, Chien N. Expanding the family economic stress model- 
Insights from a mixed methods approach. Journal of Marriage and Family. 2008(70):196-209. 
87. 
Conger RD, Conger KJ, Martin MJ. Socioeconomic Status, Family Processes,and 
Individual Development. Journal of Marriage and Family. 2010;72(3):685-704. 
88. 
Kunkel ME, Luccia B, Moore AC. Evaluation of the South Carolina seniors farmers' 
market nutrition education program. J Am Diet Assoc. 2003;103(7):880-3. 
89. 
Balsam A, Webber D, Oehlke B. The farmers' market coupon program for low-income 
elders. J Nutr Elder. 1994;13(4):35-42. 
90. 
Jilcott Pitts SB, Wu Q, Demarest CL, Dixon CE, Dortche CJ, Bullock SL, et al. Farmers' 
market shopping and dietary behaviours among Supplemental Nutrition Assistance Program 
participants. Public Health Nutr. 2015;18(13):2407-14. 
91. 
Jilcott Pitts SB GA, Wu Q, Leah Mayo M, Ward RK, McGuirt JT, Rafferty AP, 
Lancaster MF, Evenson KR, Keyserling TC, et al. Farmers’ market use is associated with fruit 
and vegetable consumption in diverse southern rural communities. Nutrition Journal. 2014;13. 
92. 
Young CR, Aquilante JL, Solomon S, Colby L, Kawinzi MA, Uy N, et al. Improving 
fruit and vegetable consumption among low-income customers at farmers markets: Philly Food 
Bucks, Philadelphia, Pennsylvania, 2011. Prev Chronic Dis. 2013;10:E166. 
93. 
Bowling AB, Moretti M, Ringelheim K, Tran A, Davison K. Healthy Foods, Healthy 
Families: combining incentives and exposure interventions at urban farmers’ markets to improve 
nutrition among recipients of US federal food assistance. Health Promotion Perspectives. 
2016;6(1):10-6. 
94. 
Freedman DA, Bell BA, Collins LV. The Veggie Project: a case study of a multi-
component farmers' market intervention. J Prim Prev. 2011;32(3-4):213-24. 
95. 
Olsho LE, Klerman JA, Wilde PE, Bartlett S. Financial incentives increase fruit and 
vegetable intake among Supplemental Nutrition Assistance Program participants: a randomized 
controlled trial of the USDA Healthy Incentives Pilot. Am J Clin Nutr. 2016;104(2):423-35. 
96. 
Hsiao B-s, Sibeko L, Wicks K, Troy LM. Mobile produce market influences access to 
fruits and vegetables in an urban environment. Public Health Nutrition. 2018;21(7):1332-44. 
97. 
Wetherill MS, Williams MB, Gray KA. SNAP-Based Incentive Programs at Farmers' 
Markets: Adaptation Considerations for Temporary Assistance for Needy Families (TANF) 
Recipients. Journal of Nutrition Education and Behavior. 2017;49(9):743-51.e1. 
98. 
Zepeda L. Which little piggy goes to market? Characteristics of US farmers' market 
shoppers. International Journal of Consumer Studies. 2009;33(3):250-7. 
99. 
Provincial Health Services Authority. Food costing in BC 2017: Assessing the 
affordability of healthy eating. 2017. 
100. 
BC Employment Standards Branch. Minimum wage factsheet 2016 [Available from: 
http://www2.gov.bc.ca/assets/gov/employment-business-and-economic-

development/employment-standards-workplace-safety/employment-standards/factsheets-
pdfs/pdfs/minimum_wage.pdf. 
101. 
BC Association of Farmers’ Markets. Farmers' Market Nutrition Coupon Program 2019 
[Available from: http://www.bcfarmersmarket.org/nutrition-coupon-program. 
102. 
Just RE WQ. Economic Evaluation of the Farmers' Market Nutrition Program. American 
Journal of Agricultural Economics. 1997;79:902-17. 
103. 
BC Association of Farmers’ Markets. BC Association of Farmers’ Markets Annual 
Report 2018. 2018  
104. 
The BC Farmers’ Market Trail. Find a BC Farmers' Market 2020 [Available from: 
https://bcfarmersmarkettrail.com/markets/. 
105. 
Harnack L, Oakes JM, Elbel B, Beatty T, Rydell S, French S. Effects of Subsidies and 
Prohibitions on Nutrition in a Food Benefit Program: A Randomized Clinical Trial. JAMA Intern 
Med. 2016;176(11):1610-8. 
106. 
Laaksonen M, Talala K, Martelin T, Rahkonen O, Roos E, Helakorpi S, et al. Health 
behaviours as explanations for educational level differences in cardiovascular and all-cause 
mortality: a follow-up of 60 000 men and women over 23 years. Eur J Public Health. 
2008;18(1):38-43. 
107. 
Yip CSC, Chan W, Fielding R. The Associations of Fruit and Vegetable Intakes with 
Burden of Diseases: A Systematic Review of Meta-Analyses. J Acad Nutr Diet. 2019. 
108. 
Holeva P. Growing Social Capital: Investigating the Relationship between Farmers' 
Markets and the Development of Community Support Networks in Ann Arbor, MI: Miami 
University; 2009. 
109. 
Smithers J, Lamarche J, AE J. Unpacking the terms of engagement with local food at the 
Farmers' Market: Insights from Ontario. Journal of Rural Studies. 2008;24:337-50. 
110. 
Efird J. Blocked randomization with randomly selected block sizes. Int J Environ Res 
Public Health. 2011;8(1):15-20. 
111. 
Harris PA TR, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data 
capture (REDCap) – A metadata-driven methodology and workflow process for providing 
translational research informatics support. J Biomed Inform. 2009;42(2):377-81. 
112. 
Molendijk ML, Fried EI, Van der Does W. The SMILES trial: do undisclosed recruitment 
practices explain the remarkably large effect? BMC Med. 2018;16(1):243. 
113. 
Thompson FE, Dixit-Joshi S, Potischman N, Dodd KW, Kirkpatrick SI, Kushi LH, et al. 
Comparison of Interviewer-Administered and Automated Self-Administered 24-Hour Dietary 
Recalls in 3 Diverse Integrated Health Systems. Am J Epidemiol. 2015;181(12):970-8. 
114. 
Kirkpatrick SI, Gilsing AM, Hobin E, Solbak NM, Wallace A, Haines J, et al. Lessons 
from Studies to Evaluate an Online 24-Hour Recall for Use with Children and Adults in Canada. 
Nutrients. 2017;9(2). 
115. 
Solbak NM, Siou G, Paek S, Rajabi AA, Vena JE, Kirkpatrick SI, et al., editors. 
Evaluating the feasibility of administering a combination of online dietary assessment tools in a 
cohort of adults in Alberta, Canada. International  Society of Behavioral Nutrition and Physical 
Activity Annual Meeting; 2017; Victoria, BC Canada. 
116. 
Kupis J, Johnson S, Hallihan G, Olstad DL. Assessing the Usability of the Automated 
Self-Administered Dietary Assessment Tool (ASA24) among Low-Income Adults. Nutrients. 
2019;11(1). 
117. 
Warwick Medical School. WEMWBS: Warwick-Edinburgh Mental Wellbeing Scale 
User guide - Version 2 Edinburgh: NHS Health Scotland; 2016 [Available from: 

http://www2.warwick.ac.uk/fac/med/research/platform/wemwbs/researchers/userguide/wemwbs
_user_guide_jp_02.02.16.pdf. 
118. 
Stewart-Brown S, Tennant A, Tennant R, Platt S, Parkinson J, Weich S. Internal 
construct validity of the Warwick-Edinburgh Mental Well-being Scale (WEMWBS): a Rasch 
analysis using data from the Scottish Health Education Population Survey. Health Qual Life 
Outcomes. 2009;7:15. 
119. 
Bartram DJ, Yadegarfar G, Sinclair JM, Baldwin DS. Validation of the Warwick-
Edinburgh Mental Well-being Scale (WEMWBS) as an overall indicator of population mental 
health and well-being in the UK veterinary profession. Vet J. 2011;187(3):397-8. 
120. 
Tennant R, Hiller L, Fishwick R, Platt S, Joseph S, Weich S, et al. The Warwick-
Edinburgh Mental Well-being Scale (WEMWBS): development and UK validation. Health Qual 
Life Outcomes. 2007;5(1). 
121. 
Manicavasagar V, Horswood D, Burckhardt R, Lum A, Hadzi-Pavlovic D, Parker G. 
Feasibility and Effectiveness of a Web-Based Positive Psychology Program for Youth Mental 
Health: Randomized Controlled Trial. Journal of Medical Internet Research. 2014;16(6). 
122. 
Powell J, Hamborg T, Stallard N, Burls A, McSorley J, Bennett K, et al. Effectiveness of 
a Web-Based Cognitive-Behavioral Tool to Improve Mental Well-Being in the General 
Population: Randomized Controlled Trial. Journal of Medical Internet Research. 2012;15(1). 
123. 
Schrank B, Brownell T, Jakaite Z, Larkin C, Pesola F, Riches S, et al. Evaluation of a 
positive psychotherapy group intervention for people with psychosis: pilot randomised controlled 
trial. Epidemiology and Psychiatric Sciences. 2015;25(3):235-46. 
124. 
Tew GA, Howsam J, Hardy M, Bissell L. Adapted yoga to improve physical function and 
health-related quality of life in physically-inactive older adults: a randomised controlled pilot 
trial. BMC Geriatrics. 2017;17(1). 
125. 
PROOF. Household Food Insecurity in Canada: A Guide to Measurement and 
Interpretation. 2018. 
126. 
Tarasuk V, Mitchell A, Dachner N. Household Food Insecurity in Canada. 2014. 
127. 
Tarasuk VS BG. Household food insecurity and hunger among families using food banks. 
Canadian Journal of Public Health. 1999 90(2):109-13. 
128. 
Tarasuk VS. Household Food Insecurity with Hunger Is Associated with Women's Food 
Intakes, Health and Household Circumstances. J Nutr. 2001 131:2670–6. 
129. 
Rivera RL, Maulding MK, Abbott AR, Craig BA, Eicher-Miller HA. SNAP-Ed 
(Supplemental Nutrition Assistance Program–Education) Increases Long-Term Food Security 
among Indiana Households with Children in a Randomized Controlled Study. The Journal of 
Nutrition. 2016;146(11):2375-82. 
130. 
Dachner N, Ricciuto L, Kirkpatrick SI, Tarasuk V. Food Purchasing and Food Insecurity: 
Among Low-income Families in Toronto. Canadian Journal of Dietetic Practice and Research. 
2010;71(3):e50-e6. 
131. 
Health Canada. Canadian Community Health Survey, Cycle 2.2, Nutrition (2004): 
Income-Related Household Food Security in Canada. In: Promotion TOoNPa, editor. 2007. 
132. 
Marques ES, Reichenheim ME, de Moraes CL, Antunes MM, Salles-Costa R. Household 
food insecurity: a systematic review of the measuring instruments used in epidemiological 
studies. Public Health Nutr. 2015;18(5):877-92. 
133. 
Derrickson JP FA, Anderson JEL. The Core Food Security Module Scale Measure Is 
Valid and Reliable When Used with Asians and Pacific Islanders. J Nutr. 2000 130:2666–74. 

134. 
Peterson NA, Speer PW, McMillan DW. Validation of A Brief Sense of Community 
Scale: Confirmation of the Principal Theory of Sense of Community. Journal of Community 
Psychology. 2008;36(1):61-73. 
135. 
McMillan DW, Chavis D. Sense of Comunity: A Definition and Theory. Journal of 
Community Psychology. 1986;14:6-23. 
136. 
Peterson NA, Speer PW, McMillan DW. Validation of A brief sense of community scale: 
Confirmation of the principal theory of sense of community. 2008;36(1):61-73. 
137. 
Power L, Mullally D, Gibney ER, Clarke M, Visser M, Volkert D, et al. A review of the 
validity of malnutrition screening tools used in older adults in community and healthcare settings 
- A MaNuEL study. Clin Nutr ESPEN. 2018;24:1-13. 
138. 
Todorovic V RC, Elia M. The ‘MUST’ explanatory booklet: A guide to the ‘Malnutrition 
Universal Screening Tool’ (‘MUST’) for adults Worcestershire, UK: The British Association for 
Parenteral and Enteral Nutrition (BAPEN); 2011 [Available from: 
https://www.bapen.org.uk/pdfs/must/must_explan.pdf. 
139. 
Kvamme JM, Olsen JA, Florholmen J, Jacobsen BK. Risk of malnutrition and health-
related quality of life in community-living elderly men and women: the Tromso study. Qual Life 
Res. 2011;20(4):575-82. 
140. 
Scott A. Screening for malnutrition in the community- the MUST tool.pdf>. British 
Journal of Community Nursing. 2008 13(9):406-12. 
141. 
The British Association for Parenteral and Enteral Nutrition. The ’MUST’ report. 
Nutritional screening for adults- A multidisciplinary responsibility. Development and use of the 
Malnutrition Universal Screening Tool (MUST) for adults. Redditch, Worcs: BAPEN Office; 
2003. 
142. 
Cawood AL, Elia M, Sharp SKE, Stratton RJ. Malnutrition self-screening by using 
MUST in hospital outpatients: validity, reliability, and ease of use. Am J Clin Nutr. 
2012;96(5):1000-7. 
143. 
Sandhu A, Mosli M, Yan B, Wu T, Gregor J, Chande N, et al. Self-Screening for 
Malnutrition Risk in Outpatient Inflammatory Bowel Disease Patients Using the Malnutrition 
Universal Screening Tool (MUST). Journal of Parenteral and Enteral Nutrition. 2015;40(4):507-
10. 
144. 
Chao P-C, Chuang H-J, Tsao L-Y, Chen P-Y, Hsu C-F, Lin H-C, et al. The Malnutrition 
Universal Screening Tool (MUST) and a nutrition education program for high risk cancer 
patients: strategies to improve dietary intake in cancer patients. BioMedicine. 2015;5(3). 
145. 
Cawood AL, Elia M, Sharp SK, Stratton RJ. Malnutrition self-screening by using MUST 
in hospital outpatients: validity, reliability, and ease of use. Am J Clin Nutr. 2012;96(5):1000-7. 
146. 
University of California, MacArthur Research Network on SES and Health,. MacArthur 
Subjective Social Status Scale 2019 [Available from: 
https://macses.ucsf.edu/research/psychosocial/commladder.php. 
147. 
Cundiff JM, Smith TW, Uchino BN, Berg CA. Subjective social status: construct validity 
and associations with psychosocial vulnerability and self-rated health. Int J Behav Med. 
2013;20(1):148-58. 
148. 
Zell E, Strickhouser JE, Krizan Z. Subjective social status and health: A meta-analysis of 
community and society ladders. Health Psychol. 2018;37(10):979-87. 
149. 
Landefeld JC, Burmaster KB, Rehkopf DH, Syme SL, Lahiff M, Adler-Milstein S, et al. 
The association between a living wage and subjective social status and self-rated health: A quasi-
experimental study in the Dominican Republic. Social Science & Medicine. 2014;121:91-7. 

150. 
National Institute of Health. Dietary Assessment Primer: Evaluating the Effect of an 
Intervention on Diet. In: Institute NC, editor. 
151. 
Kirkpatrick SI, Subar AF, Douglass D, Zimmerman TP, Thompson FE, Kahle LL, et al. 
Performance of the Automated Self-Administered 24-hour Recall relative to a measure of true 
intakes and to an interviewer-administered 24-h recall. Am J Clin Nutr. 2014;100(1):233-40. 
152. 
Thompson FE SA. Nutrition in the prevention and treatment of disease. third ed. 
Coultston AM BC, Ferruzzi MG, , editor: Elsevier; 2013. 
153. 
Subar AF, Kirkpatrick SI, Mittl B, Zimmerman TP, Thompson FE, Bingley C, et al. The 
Automated Self-Administered 24-hour dietary recall (ASA24): a resource for researchers, 
clinicians, and educators from the National Cancer Institute. J Acad Nutr Diet. 
2012;112(8):1134-7. 
154. 
ASA24- Canada. ASA24- Canada  [Available from: http://asa24.ca/index.html. 
155. 
National Cancer Institute. ASA24-Canada-2018 2019 [Available from: 
https://epi.grants.cancer.gov/asa24/respondent/asa24-canada-2018.html. 
156. 
National Cancer Institute. ASA24® Frequently Asked Questions (FAQs) 2018 [Available 
from: https://epi.grants.cancer.gov/asa24/resources/faq.html#output. 
157. 
Kirkpatrick SI, Guenther PM, Douglass D, Zimmerman T, Kahle LL, Atoloye A, et al. 
The Provision of Assistance Does Not Substantially Impact the Accuracy of 24-Hour Dietary 
Recalls Completed Using the Automated Self-Administered 24-H Dietary Assessment Tool 
among Women with Low Incomes. J Nutr. 2019;149(1):114-22. 
158. 
Guenther PM, Casavale KO, Reedy J, Kirkpatrick SI, Hiza HA, Kuczynski KJ, et al. 
Update of the Healthy Eating Index: HEI-2010. J Acad Nutr Diet. 2013;113(4):569-80. 
159. 
Guenther PM, Kirkpatrick SI, Reedy J, Krebs-Smith SM, Buckman DW, Dodd KW, et al. 
The Healthy Eating Index-2010 is a valid and reliable measure of diet quality according to the 
2010 Dietary Guidelines for Americans. J Nutr. 2014;144(3):399-407. 
160. 
Reedy J, Lerman JL, Krebs-Smith SM, Kirkpatrick SI, Pannucci TE, Wilson MM, et al. 
Evaluation of the Healthy Eating Index-2015. J Acad Nutr Diet. 2018;118(9):1622-33. 
161. 
National Cancer Institute. Overview and background of the Healthy Eating Index 2018 
[Available from: https://epi.grants.cancer.gov/hei/. 
162. 
Krebs-Smith SM, Pannucci TE, Subar AF, Kirkpatrick SI, Lerman JL, Tooze JA, et al. 
Update of the Healthy Eating Index: HEI-2015. J Acad Nutr Diet. 2018;118(9):1591-602. 
163. 
Kirkpatrick SI, Reedy J, Krebs-Smith SM, Pannucci TE, Subar AF, Wilson MM, et al. 
Applications of the Healthy Eating Index for Surveillance, Epidemiology, and Intervention 
Research: Considerations and Caveats. J Acad Nutr Diet. 2018;118(9):1603-21. 
164. 
Mullie P, Clarys P, Hulens M, Vansant G. Dietary patterns and socioeconomic position. 
European journal of clinical nutrition. 2010;64(3):231-8. 
165. 
Schwingshackl L, Hoffmann G. Diet quality as assessed by the Healthy Eating Index, the 
Alternate Healthy Eating Index, the Dietary Approaches to Stop Hypertension score, and health 
outcomes: a systematic review and meta-analysis of cohort studies. J Acad Nutr Diet. 
2015;115(5):780-800 e5. 
166. 
Potter J, Brown L, Williams RL, Byles J, Collins CE. Diet Quality and Cancer Outcomes 
in Adults: A Systematic Review of Epidemiological Studies. Int J Mol Sci. 2016;17(7). 
167. 
Schwingshackl L, Hoffmann G. Does a Mediterranean-Type Diet Reduce Cancer Risk? 
Curr Nutr Rep. 2016;5:9-17. 

168. 
Schwingshackl L, Schwedhelm C, Galbete C, Hoffmann G. Adherence to Mediterranean 
Diet and Risk of Cancer: An Updated Systematic Review and Meta-Analysis. Nutrients. 
2017;9(10). 
169. 
Woodruff SJ, Hanning RM. Development and implications of a revised Canadian 
Healthy Eating Index (HEIC-2009). Public Health Nutr. 2010;13(6):820-5. 
170. 
Government of Canada. Dietary Reference Intakes 2013 [Available from: 
https://www.canada.ca/en/health-canada/services/food-nutrition/healthy-eating/dietary-reference-
intakes.html. 
171. 
Government of Canada. Canada’s Food Guide 2019 [Available from: https://food-
guide.canada.ca/en/. 
172. 
U.S. Department of Health and Human Services and U.S. Department of Agriculture. 
2015–2020 Dietary Guidelines for Americans. 8th ed2015. 
173. 
National Cancer Institute. Comparison Among ASA24 Versions 2018 [Available from: 
https://epi.grants.cancer.gov/asa24/comparison.html#db. 
174. 
USDA Agricultural Research Service. Food Patterns Equivalent Database 2018 
[Available from: https://www.ars.usda.gov/northeast-area/beltsville-md-bhnrc/beltsville-human-
nutrition-research-center/food-surveys-research-group/docs/fped-overview/. 
175. 
National Cancer Institute. The Healthy Eating Index – Research uses of the HEI. 2018 
[Available from: https://epi.grants.cancer.gov/hei/uses.html. 
176. 
Dimitri C, Oberholtzer L, Zive M, Sandolo C. Enhancing food security of low-income 
consumers: an investigation of financial incentives for use at farmers' markets. Food Policy. 
2015;52:64-70. 
177. 
Fitzgerald J, Gottschalk P, Moffitt R. An analysis of sample attrition in panel data: The 
Michigan panel study of income dynamics 1997 [Available from: 
http://www.nber.org/papers/t0220.pdf. 
178. 
Baulch B, Quisumbing A. Testing and adjusting for attrition in Household Panel Data: 
Chronic Poverty Research Centre; 2011 [Available from: 
http://www.chronicpoverty.org/publications/details/testing-and-adjusting-for-attrition-in-
household-panel-data. 
179. 
Li P, Stuart EA. Best (but oft-forgotten) practices: missing data methods in randomized 
controlled nutrition trials. Am J Clin Nutr. 2019;109(3):504-8. 
180. 
Scharfstein D, McDermott A, Diaz I, Carone M, Lunardon N, Turkoz I. Global 
sensitivity analysis for repeated measures studies with informative drop-out: A semi-parametric 
approach. Biometrics. 2018;74(1):207-19.