Validation of a self-administered web-based 24-h dietary recall for individuals with severe obesity undergoing bariatric surgery
Bibliographic record
Abstract
AIM: The study aimed to assess the validity of a web-based 24-h recall (R24W) for measuring dietary intakes in individuals living with severe obesity who are awaiting bariatric surgery, compared to a food record (FR), currently used in clinical settings. METHODS: A total of 51 individuals with severe obesity awaiting bariatric surgery were recruited (mean age: 45.3 ± 8.0 years). Before surgery, participants completed three R24W and a 3-day FR. Nutritional intakes were derived from the 2015 version of the Canadian Nutrient File. Mean differences in energy and nutrient intakes were assessed using percent differences and paired t-tests. Spearman correlations evaluated the associations between tools, while cross-classification analyses, weighted kappa scores, and Bland-Altman analyses were used to assess the degree of agreement. To determine validity, a seven-criterion validity analysis proposed by Lombard et al. was applied. The R24W was considered valid if it had three or fewer poor scores. RESULTS: Energy, carbohydrates, proteins, fats, saturated fat, dietary fiber, vitamin B6, vitamin B12, vitamin C, vitamin D, calcium, iron, magnesium, potassium, and sodium had three or fewer poor scores, suggesting that the R24W provides an estimation of these nutrient intakes similar to the FR. In contrast, vitamin A, riboflavin, thiamin, folate, niacin, and phosphorus scored below the threshold of poor scores. CONCLUSION: The R24W demonstrated good validity for assessing energy intake, macronutrients, and several key nutrients (e.g., iron and vitamin D) in this population. These findings support the use of the R24W as a clinical and valid tool for dietary assessment in the bariatric population.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".