Dietary Assessment and Self-monitoring: With Nutrition Applications for Mobile Devices
Bibliographic record
Abstract
Nutrition applications for mobile devices (e.g., personal digital assistants, smartphones) are becoming increasingly accessible and can assist with the difficult task of intake recording for dietary assessment and self-monitoring. This review is a compilation and discussion of research on this tool for dietary intake documentation in healthy populations and those trying to lose weight. The purpose is to compare this tool with conventional methods (e.g., 24-hour recall interviews, paper-based food records). Research databases were searched from January 2000 to April 2011, with the following criteria: healthy or weight loss populations, use of a mobile device nutrition application, and inclusion of at least one of three measures, which were the ability to capture dietary intake in comparison with conventional methods, dietary self-monitoring adherence, and changes in anthropometrics and/or dietary intake. Eighteen studies are discussed. Two application categories were identified: those with which users select food and portion size from databases and those with which users photograph their food. Overall, positive feedback was reported with applications. Both application types had moderate to good correlations for assessing energy and nutrient intakes in comparison with conventional methods. For self-monitoring, applications versus conventional techniques (often paper records) frequently resulted in better self-monitoring adherence, and changes in dietary intake and/or anthropometrics. Nutrition applications for mobile devices have an exciting potential for use in dietetic practice.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".