Leveraging Technology to Engage Supplemental Nutrition Assistance Program Consumers With Children at Farmers Markets: Qualitative Community-Engaged Approach to App Development
Bibliographic record
Abstract
Background: Fruit and vegetable consumption is lower than national trends among people receiving Supplemental Nutrition Assistance Program (SNAP) benefits due to economic and physical access barriers. Monetary nutrition incentive programs at farmers markets aim to reduce these barriers to improve diet quality among SNAP consumers. We leveraged community-engaged methods to collaboratively design a mobile app to increase the use of both nutrition incentive programs and farmers markets among SNAP households with children. This population represents about 35% of all SNAP households providing the dual benefit of improving diet for both adults and children. Objective: In this paper, we share the iterative, community-engaged development process used to design a technology intervention that encourages the integration of farmers markets into the food shopping routines of SNAP consumers with children. Methods: Our qualitative community-engaged approach was informed by human-centered design, following the inspiration and ideation phases of this framework. In the "inspiration" phase, we worked with community nutrition experts to define both the goal of and target audience for the app (ie, SNAP households with children). In the subsequent "ideation" phase, we completed 3 stages of data collection. We developed 2 interface prototypes and received feedback from end users on design and usability preferences before selecting a baseline model. Additional feedback gathered from qualitative interviews with 20 SNAP consumers with children was incorporated into the app's version 1 (V1) development. We then shared V1 with SNAP consumers, children, and farmers market managers to test the app's functionality, design, and utility. Results: In the "inspiration" phase, the community nutrition partners identified SNAP consumers with children younger than 18 years as the target population for the app. In the "ideation" phase, we successfully created V1 through 3 stages of a qualitative, community-engaged process. First, about 75% (n=3) of SNAP consumers and all farmers market managers selected a grocery shopping design option for the layout of the app. Second, we integrated features identified by SNAP consumers with children into the app design, such as market information (ie, location with GPS address links, hours, website), likely available market inventory, market events, and grocery shopping checklists. Finally, we obtained recommendations for future versions of the app, including real-time changes in market hours, additional notification options, and grocery list personalization during a demonstration of V1. Both SNAP consumers and farmers market managers expressed interest in the app's launch and utility. Conclusions: It is feasible for community nutrition researchers to successfully design a community-engaged mobile app with the assistance of software developers. The community-engaged approach was key to us integrating potential end users' preferences in the design of V1. Future work will assess the app's impact on low-income families' use of local farmers markets and nutrition incentive programs, as well as fruit and vegetable consumption.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".