Nutrition Management Miniprograms in WeChat: Evaluation of Functionality and Quality
Notice bibliographique
Résumé
Background: With the rise in people's living standards and aging populations, a heightened emphasis has been placed in the field of medical and health care. In recent years, there has been a drastic increase in nutrition management in domestic research circles. The mobile nutritional health management platform based on WeChat miniprograms has been widely used to promote health and self-management and to monitor individual nutritional health status in China. Nevertheless, there has been a lack of comprehensive scientific evaluation regarding the functionality and quality of the diverse range of nutritional miniprograms that have surfaced in the market. Objective: This study aimed to evaluate the functionality and quality of China's WeChat nutrition management miniprogram by using the User Version of the Mobile Application Rating Scale (uMARS). Methods: This observational study involves quantitative methods. A keyword search for "nutrition," "diet," "food," and "meal" in Chinese or English was conducted on WeChat, and all miniprograms pertaining to these keywords were thoroughly analyzed. Then, basic information including name, registration date, update date, service type, user scores, and functional scores was extracted from January 2017 to November 2023. Rating scores were provided by users based on their experience and satisfaction with the use of the WeChat miniprogram, and functional scores were integrated and summarized for the primary functions of each miniprogram. Moreover, the quality of nutrition management applets was evaluated by 3 researchers independently using the uMARS. Results: Initially, 27 of 891 miniprograms identified were relevant to nutrition management. Among them, 85.2% (23/27) of them offered features for diet management, facilitating recording of daily dietary intake to evaluate nutritional status; 70.4% (19/27) provided resources for nutrition education and classroom instruction; 59.3% (16/27) included functionalities for exercise management, allowing users to record daily physical activity; and only 44.4% (12/27) featured components for weight management. The total quality score on the uMARS ranged 2.85-3.88 (median 3.38, IQR 3.14-3.57). Engagement scores on the uMARS varied from 2.00 to 4.33 (median 3.00, IQR 2.67-3.67). Functional dimension scores ranged from 3.00 to 4.00 (median 3.33, IQR 3.33-3.67), with a lower score of 2.67 and a higher score of 4.33 outside the reference range. Aesthetic dimension scores ranged from 2.33 to 4.67 (median 3.67, IQR 3.33-4.00). Informational dimension scores ranged from 2.33 to 4.67 (median 3.33, IQR 2.67-3.67). Conclusions: Our findings from the uMARS highlight a predominant emphasis on health aspects over nutritional specifications in the app supporting WeChat miniprograms related to nutrition management. The quality of these miniprograms is currently at an average level, with considerable room for functional improvements in the future.
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 | 0,006 | 0,017 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,005 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 ».