Examining the Advantages of Using Multiple Web-Based Dietary Assessment Instruments to Measure Population Dietary Intake: The PREDISE Study
Bibliographic record
Abstract
BACKGROUND: Combining traditional dietary assessment instruments has been suggested to improve precision of dietary intake estimates. However, this has not been investigated using web-based 24-h recall (R24W) or a web-based food-frequency questionnaire (wFFQ). OBJECTIVE: The aim of this study was to compare different combinations of web-based instruments to assess population-level dietary intake estimates (means and percentiles) and their precision, either with or without statistical modeling of within-person day-to-day variations. METHODS: As part of the cross-sectional PREDISE study, 1025 French-speaking adults completed 3 randomly allocated R24W and 1 wFFQ within 21 d. Crude estimates of intake were generated from either 1 or 3 repeated R24W. The National Cancer Institute (NCI) method was used to account for within-person variation. Usual intakes were modeled from 1 R24W repeated in a subsample (40%) and from 3 R24W, with or without consideration of data from the wFFQ. RESULTS: Using crude data from 3 R24W increased precision of estimates and modified distribution of intakes compared with using data from only 1 R24W. Using NCI-modeled data from 3 repeated R24W had no impact on the precision around mean intakes but increased precision of low and high percentiles intake estimates compared with NCI-modeled data from a partially repeated R24W. Considering data from a wFFQ in combination with data derived from 3 R24W did not influence the precision of intake estimates of most foods and nutrients. CONCLUSIONS: The data suggest that relying on repeated measures of food and nutrient intake through R24W is preferable to single assessment when within-person variation is not considered. Data also suggest that when NCI modeling is applied, using 3 R24W only improves the precision of low and high percentiles intake estimates compared with using a partially repeated web-based recall.
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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 | 0.063 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".