User Preferences for an Image-Assisted Dietary Recall: Qualitative Study Comparing 3 Dietary Assessment Methods
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".