Testing the Reliability of a New Diet Tracking App That Mirrors Canada’s Food Guide, iCANPlate
Bibliographic record
Abstract
Objectives: To assess the accuracy of the iCANPlate app in capturing dietary intake by comparing entries from general users to those of a registered dietitian (RD, the expert)Methods: An observational study was conducted in April 2024 at the University of British Columbia (Vancouver) with n59 adults (79.66% women) who tested iCANPlate by recording a preselected lunch provided to them in a laboratory setting.A researcher photographed each meal, and a registered dietitian (RD) used the photos to quantify the food group proportions of each participant's meal.Since the data consisted of continuous values, inter-rater reliability between participant and RD recordings for each food group category was analyzed using Pearson's correlation coefficients.Results: The app exhibited moderate inter-rater reliability for Fruits and Vegetables (r 0.62, p < 0.0001), Proteins (r 0.53, p < 0.0001) and Other foods (r 0.42, p < 0.01).Fair agreement was observed for Grains (r 0.39, p < 0.01).Beverages demonstrated poor agreement, with no statistical significance (r 0.22, p > 0.05 ).Variability in inter-rater reliability may stem from participants' education levels and socioeconomic backgrounds, influencing their food exposure.Conclusions: iCANPlate demonstrated moderate interrater reliability for documenting most food categories.However, users did not accurately track beverages, highlighting an area for improvement.These findings support iCANPlate's potential for dietary self-monitoring while emphasizing the need for more research to refine and optimize its reliability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".