User Preferences for an Image-Assisted Dietary Recall: Qualitative Study Comparing 3 Dietary Assessment Methods
Notice bibliographique
Résumé
BACKGROUND: Technology-assisted 24-hour dietary recall (24HR) methods offer the potential for scalable population dietary assessment, but current challenges include balancing accuracy and cost against participant burden and acceptability of these methods. Qualitative methods present a novel approach to understanding potential barriers and enablers to the acceptability of 24HR methods, but remain relatively unexplored. OBJECTIVE: This study aimed to explore users' experience, acceptability, and preferences for 3 technology-assisted 24HR methods. METHODS: Participants in a crossover controlled feeding study were invited to undertake a poststudy interview. Initially, the feeding study participants were randomized into one of three separate feeding days where they consumed breakfast, lunch, and dinner on a single day. On the following day, they undertook a 24HR via the Automated Self-Administered 24-hour Dietary Assessment Tool (ASA24), Intake24, or an Image-Assisted Interviewer-Administered 24-hour dietary recall (IA-24HR). When assigned to IA-24HR, participants viewed the images they captured with a mobile food record (mFR) app on the feeding day during the interview. On completing all 3 methods, 26 participants (ages 21 to 56 years) undertook semistructured interviews. The interview audio recordings were transcribed, and inductive content analysis was undertaken. RESULTS: Overall, participants wanted the 24HR methods to be easy, with the technology features of all methods considered helpful. A total of 5 content categories described users' experiences of the three 24HR methods: (1) "Put my food in the list," (2) "It's really hard to know portions," (3) ASA24 "was a painful process," (4) access to "images helped jog my memory," (5) Intake24 is "fairly quick," and (6) IA-24HR method preference. Participants expressed a preference for taking images with the mFR app. IA-24HR helped participants recall food and beverages consumed and increased perceptions of recall accuracy. CONCLUSIONS: This novel qualitative research found that 24HR methods need to be as easy as possible for users. The participant burden of food and beverage identification and portion size estimation was evident across methods. Findings highlight the importance of using qualitative methods to explore user preferences for dietary assessment methods and confirm the need to reduce the user burden associated with 24HR methods. People want embedded technologies to enhance digitized versions of the traditional 24HR methods. The use of their own food images within the mFR app is an example of digital advancements within scalable 24-hour dietary assessments. TRIAL REGISTRATION: Australia New Zealand Clinical Trials Registry ACTRN12621000209897; www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=381165. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/32891.
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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 | 0,040 | 0,052 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».