Leveraging Technology to Engage Supplemental Nutrition Assistance Program Consumers With Children at Farmers Markets: Qualitative Community-Engaged Approach to App Development
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,022 | 0,028 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,008 | 0,007 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,003 | 0,010 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».