Design and Development of ‘Diet DQ Tracker’: A Smartphone Application for Augmenting Dietary Assessment
Bibliographic record
Abstract
The purpose of the current study was to describe the design, development, and validation of the 'Diet DQ Tracker'. The 'Diet DQ Tracker' is the first self-administered smartphone app designed to collect dietary data for diet diversity indicators. The main objective of the app was to replace the traditional methods of dietary data collection, such as in-person or telephone 24 h recall via pen and paper questionnaire or tablets. The real-time meal recording, extensive food database, and automatic score calculations and visualizations for MDD-W, IYCF-MDD, and HDDS have the potential to overcome the drawbacks of 24 h recalls. Recall depends on respondent memory, food expertise, and time consumption and demands skilled interviewers. Further, SAIN, LIM recommendations in the app prompt users to diversify diets with healthy foods. The pilot study determined the acceptability, feasibility, and relative validity of the 'Diet DQ Tracker' with a 24 h dietary recall. The results demonstrated minimal differences in dietary scores by both methodologies. The app, being convenient, easy to use, less time-consuming, and enjoyable, was preferred by the entire study sample over 24 h recall. The app will be continually updated with foods from different cultures for validating in large-scale studies. The future studies will help to improve the subsequent versions of the app.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".