Study design

This pilot study was a unblinded quasi-experimental design, with designated (non-randomized) intervention and control sites from which we recruited patients. This study was reviewed and approved by the UTHealth Houston Institutional Review Board and registered at clinicaltrials.gov (NCT06096506).

Intervention

The NCCM intervention has been described previously [21]. Briefly, five 90-minute, weekly classes were live-streamed via a video conferencing platform from the study site teaching kitchen, with participants cooking and engaging virtually from their home kitchens. Each week, the dietitian leading the course walked participants through a recipe designed to teach fundamental cooking skills (e.g., knife skills, sautéing, steaming). While foods were simmering or in the oven, the dietitian showed educational videos conveying nutrition information and facilitated discussions. The curriculum is based on Social Cognitive Theory [22,23], especially focusing on the constructs of outcome expectations, self-efficacy, behavioral capability (knowledge and skills), and social support. The recipes used during the sessions were developed with the intention of being culturally inclusive, with recipes pulling from an array of cultural traditions, paired with a discussion on how to personalize any future uses of the recipes for individual, family or cultural preferences. Participants were provided a $40 grocery card and shopping list prior to each class (a total of $200 across five classes) to purchase ingredients. We offered classes on weekday evenings, and the recipes cooked during class provided enough food for a family of four. Additionally, asynchronous virtual educational content (cooking skills videos, animated nutrition education videos, and additional recipes) was provided to engage participants beyond the synchronous sessions [24]. Prior to starting the study and intervention, we presented to and consulted with the Be Well^TM Acres Homes Steering Committee to seek input.

Study population

From January to August 2024, we recruited and screened patients from a clinic system serving the greater Houston area. The intervention clinics served a persistent poverty neighborhood (≥20% of an area’s population living in poverty for 30 or more years), while the control clinics served a demographically similar patient population in two different Houston neighborhoods. Patients were included if they were adults (18–70 years of age) receiving care at a participating clinic, had a diagnosis of type 2 diabetes, had an elevated BMI (≥25), had HbA1c labs and clinic-assessed weight completed within the last 3 months, spoke English or Spanish, and were able to obtain groceries before each class. We excluded patients without the technological equipment needed to participate (e.g., reliable internet and device – cell phone, tablet or laptop), patients who indicated they could not make the class times and patients with an uncontrolled impairment that interfered with their ability to participate.

A care coordinator working across the clinical sites assessed preliminary patient eligibility (age, type 2 diabetes diagnosis, BMI) to participate in the program and shared a flyer with a study interest form with these patients. Patients who were interested completed the form, including their contact information. Study staff then reached out to explain the study, and if participants consented to eligibility screening, they assessed all additional eligibility criteria. If eligible and still interested, study staff sent a link to the online informed consent, and participants completed written informed consent.

Data collection

Baseline data collection occurred from January to August 2024. Follow-up data collection occurred from February to October 2024. The primary outcomes of this study were feasibility and acceptability, and secondary outcomes were changes in psychosocial, behavioral, and biometric variables. The feasibility of recruitment and retention was assessed with project records, and acceptability was assessed with in-depth interviews.

We collected self-administered questionnaires electronically via REDCap at baseline and post-intervention follow-up. Participants received a $25 gift card for completing each questionnaire. HbA1c, BMI, and Blood Pressure were collected from usual care visits using Electronic Medical Record (EMR) data. Baseline EMR data included measures within 90 days of starting the program, and post-intervention EMR data included data measured within 90 days of program completion. We also extracted medication data, including anti-hypertensive and diabetes-related medications. Using the medication data available in the EMR over time, we assessed an increase in the type or dose of medications from baseline to post-intervention.

A subset of participants from the intervention group were invited to take part in interviews about their experience with various aspects of the program (e.g., cooking classes, recipes, recruitment, and engagement), aspects they found favorable or unfavorable, and any barriers to participation they encountered. Participants received an additional $25 gift card for interview participation. The interviews lasted approximately 20–45 minutes, were conducted by trained staff, and were recorded for transcription.

No harms were reported in this study.

Measures

In addition to the EMR data, we used pre- and post-test questionnaires. Perceived health was measured with one item with a 7-point response scale from “Excellent” to “Very Poor” [25]. Daily servings of vegetables and fruits were each measured with a single item adapted from a previously validated instrument, with a 6-point response scale from “None” to “4+ servings” [26]. Frequency of healthy food consumption behaviors was measured with a 7-item validated measure (e.g., “How often do you typically eat other non-fried vegetables like carrots, broccoli, green beans, or other vegetables?”), with response options on a 5-point scale from “Not at all” to “More than once a day” [27]. We created a score by averaging the items, with higher scores indicating greater frequency of eating healthful foods. We measured eating, cooking and shopping behaviors using 8 items adapted from a previously validated survey (e.g., “How often do you plan meals ahead of time?”) with a 5-point response scale from “Never” to “Always” [27]. We averaged the items, with higher scores indicating healthier behaviors (Cronbach’s alpha = 0.79). Perceived barriers to eating fruits and vegetables were measured with 9 items (e.g., “I don’t eat fruits and vegetables as much as I like because they cost too much) with responses on a 5-point Likert scale from “Strongly disagree” to “Strongly agree”. We averaged the items, with higher scores indicating more barriers (Cronbach’s alpha = 0.85). Lastly, we measured cooking self-efficacy with a 5-item scale adapted from prior work (e.g., “How sure are you that you can prepare fresh or frozen green vegetables (e.g., broccoli, spinach)?”) [28]. Response options were on a 5-point Likert scale from “Not at all sure” to “Extremely sure”. We averaged the items, with higher scores indicating greater cooking self-efficacy (Cronbach’s alpha = 0.82).

Demographic data were collected at baseline only for both groups. These included age, sex, race/ethnicity, language spoken at home, annual household income, highest level of education, employment status, health insurance, participation in food and health assistance programs, food insecurity, housing insecurity and housing problems.

Analyses

We assessed demographic characteristics of the full sample and compared the intervention group to the control group using t-tests for continuous and either Fisher’s exact test or chi-squared test for categorical variables, as appropriate. We used multilevel mixed-effects regression models to assess the association of changes in each psychosocial, behavioral, and biometric outcome between baseline and post-intervention in the intervention group compared to the control group. To account for repeated measures on each subject, multilevel mixed-effects regression models adjusted for ‘subject’ as a random effect were used to obtain estimations for both survey and biometric outcomes. Group and time were included as fixed effects, and group-by-time interaction terms were tested to assess between-group changes over time. We assessed demographic variables at baseline and adjusted for variables that were statistically significantly different between the intervention and control groups, while a priori planning to force in age, race/ethnicity, and sex into all models. We also extracted the medication data, created variables to identify the change in the use of anti-hypertensive and anti-diabetic medications, and tested for their potential confounding effects on biometric variables (i.e., if coefficients changed by 10% or more), respectively. We conducted complete case analyses for all variables and samples are provided in the table next to each variable. All analyses were performed using Stata (version 15.1, StataCorp), and P ≤ 0.05 was considered statistically significant.

We used the framework approach to analyze in-depth interviews [29]. Two team members familiarized themselves with the data by reading through the transcript (GM, NH) and identifying potential themes and subthemes. One coder (GM) then coded and charted the data into a matrix, with columns for themes/subthemes and rows for participants. Two team members (GM and NH) discussed, refined (as necessary), and collaboratively interpreted the findings.

Program enrollment

Qualitative interviews with program participants (n = 14) revealed several motivating factors for enrolling in the culinary classes. The primary motivators were a desire to change their diet and a health benefit expectation. Participants also noted that they wanted to learn different cooking methods, especially those geared toward diabetes patients, and were excited about the online format of the classes, which minimized driving and allowed family members to participate.

Facilitators and barriers to class participation

Participants reported that text message reminders helped them attend and prepare for classes, as they may have otherwise forgotten. Texting generally made it easy to communicate with program staff and reference class information at their convenience.

The virtual format of the classes reduced barriers related to transportation and travel time, which made the program more convenient, and the ability to cook in their kitchens ensured their comfort. One participant described the convenience of the virtual format, stating: “I think I would have had a hard time getting to the class on time. You never know how traffic can be. One day you can get there on time, and another day you can be stuck in traffic for about an hour or something.” Some participants experienced difficulties with the technology, including joining the virtual meeting, faulty internet connections, and issues with the sound. We provided technical assistance at every session, which participants reported was helpful and facilitated their participation. Additionally, the assistance with technology further encouraged their participation in the program more generally, as one participant expressed: “it was the interest that was shown to me, about me, really made me excited, because it feels good when someone wants to offer you help, especially when you need it.”

Shopping for ingredients

The gift cards were noted as essential to buy the food needed to participate, especially as most participants (84% of the sample) were food insecure. One participant said, “my budget is really tight, and so the amount that was given was enough and it helped out a lot.” A few participants reported that the gift cards gave them more options, as they otherwise would have been limited to buying the lowest-cost options. Many participants described enjoying the in-person grocery shopping experience and learning to navigate the store to locate unfamiliar ingredients. Other participants managed grocery shopping by ordering online and having their groceries delivered.

Learning facilitators

Participants emphasized that they found the hands-on and visual components of the classes most helpful, with watching and practicing nutritious meal preparation helping them build skills. One participant stated, “I like enjoying and having that involvement in the class… us all participating and doing something instead of just reading… The experience that I got from the cooking class, I learned a whole lot more, and I was able to incorporate that more into my daily life and how I cook and I feed me and my family now.” Participants reported that learning alongside others improved their experience, as they benefited from hearing peers’ experiences and the instructor’s responses to group questions. Sharing the results of their meal preparation and engaging in interactive discussions with their peers was commonly cited as a class component that participants enjoyed and found helpful. At least half of the participants expressed that the instructor significantly impacted the class experience. They described the instructor as patient, engaging with the participants in discussions, knowledgeable, and easy to understand.

Program recipes

Several participants expressed positive feedback about the recipes, noting that they enjoyed the combination of familiar and new foods, that their families found the meals appealing, and that they appreciated the quick and simple preparation methods. They also appreciated the flexibility and guidance in modifying recipes, which allowed them to tailor meals to individual tastes, ingredient availability, or cultural preferences, while still encouraging them to try new foods and cooking techniques. Some participants highlighted that a few recipes included foods they or their families were unfamiliar with or disliked.

Changes in perceptions

Participants reported previously believing or experiencing that healthier food options were more expensive than less healthy alternatives. Participation in the program helped some individuals realize that healthy eating was more affordable and healthy cooking took less time than they had anticipated. One participant described this as follows:

“Prior to I really had absolutely no confidence because I always assumed that eating healthier would equate to having to spend more money and it would be harder, it would be something harder to prepare and take a long time to prepare. Whereas with the class I learned that it doesn’t have to be very expensive. And it’s not as time consuming as I would have thought.”

Nonetheless, for others, budgetary realities remained a significant barrier, as illustrated by one participant: “I could take $40 and go get the ingredients and prepare that meal for that night for the class. But that’s just for one night. What am I going to do for the next 2 or 3 nights? So if I got $40, I’m gonna go in and try to make it stretch.“

Others reported a belief prior to participating in the program that cooking healthy food would be difficult and would not taste good, with one person saying: “changing the way you approach food and what you eat was the daunting part. Changing my whole attitude about food… But also not knowing if we would like the taste or not, I think is a thing that everyone had to look at, you know, thinking, oh, I don’t know if I’m going to like this, but the recipes were quite pleasing, so it turned out well.”