Exploring Individuals’ Views and Feedback on a Nutritional Screening Mobile App: Qualitative Focus Group Study
Notice bibliographique
Résumé
BACKGROUND: Malnutrition is a major global health challenge. Worldwide, approximately 390 million adults are underweight, while 2.5 billion are overweight. The Malnutrition Universal Screening Tool (MUST) has been implemented successfully in the United Kingdom to assess the nutritional status of patients in health care settings. Currently, MUST is available as a web-based tool or as a paper-based version, However, the paper tool can lead to calculation errors, and web-based tools require internet access, limiting use in some communities. The MUST app uses clear and simple navigation and processes information precisely, so could potentially improve the accuracy and accessibility of malnutrition screening for health care professionals (HCP) in all settings. OBJECTIVE: This study aimed to explore the views of HCPs on the content, functionality, and usability of a newly developed mobile app for MUST. METHODS: We performed a qualitative study using deductive and inductive framework analysis. A series of online focus groups (~1 hour each) were conducted, exploring potential users' views on the app's content design, functionality, and usefulness, which was set in demonstration mode and not available for direct use with patients. Each focus group used a semistructured approach and predefined topic guide. Participants were recruited consecutively and United Kingdom-wide using advertisements through emails, newsletters, and on social media across appropriate local and national networks. Participants had the opportunity to look at the app on their phones before giving feedback and an on-screen demonstration of the app was provided during the focus group. Data were analyzed using deductive and inductive framework analysis. RESULTS: In total, 8 online focus groups were conducted between August 2022 and January 2023. Participants (n=32) were dietetic and nutrition HCPs or educators with experience in using MUST in clinical or community settings. Data analysis revealed three broad themes: (1) improving the app for better use in practice, (2) user experience of design, and (3) barriers and facilitators in different settings. Overall feedback for the app was positive with potential users considering it to be very useful for improving routine and accurate screening, particularly in the community, and mainly because of the automatic calculation feature, which may help with improving discrepancies. Participants generally considered the app to be for professional use only, stating that patients may find it too clinical or technical. Participants also made suggestions for app sustainability and improvements, such as incentives to complete the demographics section or the option to skip questions, and the addition of more subjective measures and instructions on measuring ulna length. CONCLUSIONS: The MUST app was positively evaluated by potential users, who reported it was user-friendly and an accessible way to screen for malnutrition risk, whilst improving the accuracy of screening and availability in community settings.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,023 | 0,035 |
| 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 | 0,006 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».