Developing a Digital Tool to Calculate Protein Quality in Plant-Based Meals of Older Adults: User Engagement Design Approach With End Users
Notice bibliographique
Résumé
BACKGROUND: The global shift toward plant-based diets has been increasing, with more people making the transition for various reasons. In vulnerable subgroups such as older adults, the transition to plant-based diets deserves attention due to the potentially detrimental consequences of lower protein quantity and quality. OBJECTIVE: We aimed to develop a digital tool that ensures adequate protein quality in plant-based meals for older adults experiencing low protein intake through an interdisciplinary collaboration and user engagement with potential end users. METHODS: Three focus group interviews of Dutch and Danish dietitians and older adults as potential end users were conducted to identify their needs, preferences, and deal-breakers. Focus group interviews were based on a user-task-environment analysis, the Walt Disney method, the brainwriting method, and a cognitive walkthrough. The interview transcripts were analyzed with a thematic analysis. The front end and backend development of a potential tool took place in parallel and was well-synced to the focus group interviews. RESULTS: Both dietitians and older adults from Dutch and Danish sites expressed high interest in a tool that provides feedback and background information on protein quality, sustainability, and nutrients or micronutrients. The user-task-environment analysis delivered input among others that dietitians and older adults are good potential users, the tool should be functional as an app as well as a website and the tool should provide preprogrammed meals or recipes. The Walt Disney method delivered usable and realistic solutions to the 4 challenges presented. Thirty-two percent of the solutions on all themes presented with the brainwriting method appeared to be highly feasible and relevant, having the potential to be implemented in a tool. The cognitive walkthrough identified certain screens as unclear, necessitating revisions for improved understandability, for example, the need for explanation in selecting food item filters is shown in screenshot 2, with an overall usability score of 59%. CONCLUSIONS: Our user engagement design approach resulted in a prototype that ensured end users' wishes and needs, with a finetuned output tested in focus groups. We conclude that our user engagement design approach was a suitable and meaningful stepwise approach to ensure the relevance of the tool and identify potential barriers. The focus group results indicate that dietitians have a clear understanding and need for a tool to aid in meal planning for enhanced protein quality, highlighting its absence in their current resources despite increasing demands arising from the protein transition. Conversely, for older adults, the introduction of a digital tool appears less appropriate; instead, there is a necessity for foundational education on protein quality before such a tool can be effectively used. Future studies are needed to further implement the prototype into practice.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».