Evaluation of the Accuracy, Usability, and User Perspectives of the Ecological Momentary Dietary Assessment App Traqq Among Dutch Adolescents: Protocol for a Mixed Methods Study
Notice bibliographique
Résumé
BACKGROUND: Self-reported dietary intake data are crucial in nutrition and health research; however, their accuracy is compromised by challenges such as portion size estimations, food identification, memory-related bias, social desirability bias, and reactivity bias. Dietary assessment in adolescents is particularly challenging due to irregular eating habits, meal skipping, and parent or peer influences, potentially resulting in misreporting. Leveraging adolescents' receptiveness to technology, we investigated the use of an innovative smartphone app (Traqq) that facilitates dietary assessment using repeated short recalls instead of traditional 24-hour recalls. Evaluation studies of the Traqq app in Dutch adults have shown successful results, but its suitability for other target populations, such as adolescents, requires further investigation. OBJECTIVE: We designed a comprehensive, 3-phase study to evaluate the Traqq app's accuracy using repeated short recalls, usability, and user perspectives among Dutch adolescents aged 12 to 18 years. This manuscript details the study setup, research methods, and basic characteristics in phases 1 and 2. METHODS: In phase 1, adolescents (aged 12-18 years) downloaded the Traqq app and completed a demographic questionnaire. It was used on 4 random school days over 4 weeks, using 2-hour recalls on 2 days and 4-hour recalls on 2 days. A food frequency questionnaire and 2 interviewer-administered 24-hour recalls served as dietary reference methods to assess the Traqq app's accuracy. In addition, usability was evaluated using the System Usability Scale and an experience questionnaire. In phase 2, user experiences were further explored through semistructured interviews within a subsample of 24 adolescents. These first 2 phases of this mixed methods study are now finalized for data collection. Phase 3 will focus on collecting user insights to inform app customization through cocreation sessions. RESULTS: . A total of 64 (63%) participants were girls, 81 (84%) attended high school, and 88 (92%) were born in the Netherlands. Interviews were held with 6 (25%) boys and 18 (75%) girls. Cocreation sessions will be planned after all data have been analyzed. CONCLUSIONS: In this holistic study, we combine quantitative and qualitative methods to evaluate the dietary assessment performance among adolescents of the Traqq app, which was initially designed for adults. Specifically, next to quantitative comparisons of the Traqq app's dietary assessment methods, we conducted semistructured interviews, and we will carry out cocreation sessions. With this user-centered, synergistic approach, we aim to establish a list of requirements for a dietary assessment app for adolescents, resulting in more efficient assessments, improved compliance, and enhanced overall accuracy in this population. TRIAL REGISTRATION: ISRCTN Registry ISRCTN46230386; https://www.isrctn.com/ISRCTN46230386. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/70194.
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 | 0,065 | 0,042 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,004 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,020 | 0,004 |
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 ».