A New Tool for Nutrition App Quality Evaluation (AQEL): Development, Validation, and Reliability Testing
Notice bibliographique
Résumé
BACKGROUND: The extensive availability and increasing use of mobile apps for nutrition-based health interventions makes evaluation of the quality of these apps crucial for integration of apps into nutritional counseling. OBJECTIVE: The goal of this research was the development, validation, and reliability testing of the app quality evaluation (AQEL) tool, an instrument for evaluating apps' educational quality and technical functionality. METHODS: Items for evaluating app quality were adapted from website evaluations, with additional items added to evaluate the specific characteristics of apps, resulting in 79 initial items. Expert panels of nutrition and technology professionals and app users reviewed items for face and content validation. After recommended revisions, nutrition experts completed a second AQEL review to ensure clarity. On the basis of 150 sets of responses using the revised AQEL, principal component analysis was completed, reducing AQEL into 5 factors that underwent reliability testing, including internal consistency, split-half reliability, test-retest reliability, and interrater reliability (IRR). Two additional modifiable constructs for evaluating apps based on the age and needs of the target audience as selected by the evaluator were also tested for construct reliability. IRR testing using intraclass correlations (ICC) with all 7 constructs was conducted, with 15 dietitians evaluating one app. RESULTS: Development and validation resulted in the 51-item AQEL. These were reduced to 25 items in 5 factors after principal component analysis, plus 9 modifiable items in two constructs that were not included in principal component analysis. Internal consistency and split-half reliability of the following constructs derived from principal components analysis was good (Cronbach alpha >.80, Spearman-Brown coefficient >.80): behavior change potential, support of knowledge acquisition, app function, and skill development. App purpose split half-reliability was .65. Test-retest reliability showed no significant change over time (P>.05) for all but skill development (P=.001). Construct reliability was good for items assessing age appropriateness of apps for children, teens, and a general audience. In addition, construct reliability was acceptable for assessing app appropriateness for various target audiences (Cronbach alpha >.70). For the 5 main factors, ICC (1,k) was >.80, with a P value of <.05. When 15 nutrition professionals evaluated one app, ICC (2,15) was .98, with a P value of <.001 for all 7 constructs when the modifiable items were specified for adults seeking weight loss support. CONCLUSIONS: Our preliminary effort shows that AQEL is a valid, reliable instrument for evaluating nutrition apps' qualities for clinical interventions by nutrition clinicians, educators, and researchers. Further efforts in validating AQEL in various contexts are needed.
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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 | 0,009 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,009 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».