Potential of existing online 24-h dietary recall tools for national dietary surveys
Bibliographic record
Abstract
OBJECTIVE: To describe existing online, 24-h dietary recall (24-h DR) tools in terms of functionalities and ability to tackle challenges encountered during national dietary surveys, such as maximising response rates and collecting high-quality data from a representative sample of the population, while minimising the cost and response burden. DESIGN: A search (from 2000 to 2019) was conducted in peer-reviewed and grey literature. For each tool, information on functionalities, validation and user usability studies, and potential adaptability for integration into a new context was collected. SETTING: Not country-specific. PARTICIPANTS: General population. RESULTS: Eighteen online 24-h DR tools were identified. Most were developed in Europe, for children ≥10 years old and/or for adults. Eight followed the five multiple-pass steps but used various methodologies and features. Almost all tools (except three) validated their nutrient intake estimates, but with high heterogeneity in methodologies. User usability was not always assessed, and rarely by applying real-time methods. For researchers, eight tools developed a web platform to manage the survey and five appeared to be easily adaptable to a new context. CONCLUSIONS: Among the eighteen online 24-h DR tools identified, the best candidates to be used in national dietary surveys should be those that were validated for their intake estimates, had confirmed user and researcher usability, and seemed sufficiently flexible to be adapted to new contexts. Regardless of the tool, adaptation to another context will still require time and funding, and this is probably the most challenging step.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.061 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".