An Assessment of the Validity of an Audio-Video Method of Food Journaling for Dietary Quantity and Quality
Bibliographic record
Abstract
OBJECTIVE: To validate an audio-video (AV) method of food journaling, in a free-living scenario, compared to direct, weighed food assessment. DESIGN AND SETTING: =30), were documented using the AV method: participants video-recorded their tray while audio-recording a description of their selected meal, after which the research team digitally weighed each food item and created an itemized diary record of the food. VARIABLES MEASURED: Data from the AV method and from the weighed food diaries were transcribed and entered into a nutrition software analysis program (Nutribase Pro 10.0). Nutrient outputs were compared between the two methods including kilocalories, macronutrients, and selected micronutrients. ANALYSES: Using mean scores for each variable, Wilcoxon signed-rank test and Spearman's correlation coefficients were conducted. Interclass correlation coefficient (ICC) was calculated for absolute agreement between the two methods to assess interrater reliability. RESULTS: With the exception of Vitamin E and total weight, nutrient values were highly correlated between methods and were statistically significant given alpha = 0.05, power = 0.95, and effect size of 0.70. CONCLUSIONS: The AV method may be a meaningful alternative to diary recording in a free-living setting.
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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 | 0.034 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".