Validation of a Tablet Application for Assessing Dietary Intakes Compared with the Measured Food Intake/Food Waste Method in Military Personnel Consuming Field Rations
Bibliographic record
Abstract
The collection of accurate dietary intakes using traditional dietary assessment methods (e.g., food records) from military personnel is challenging due to the demanding physiological and psychological conditions of training or operations. In addition, these methods are burdensome, time consuming, and prone to measurement errors. Adopting smart-phone/tablet technology could overcome some of these barriers. The objective was to assess the validity of a tablet app, modified to contain detailed nutritional composition data, in comparison to a measured food intake/waste method. A sample of Canadian Armed Forces personnel, randomized to either a tablet app (n = 9) or a weighed food record (wFR) (n = 9), recorded the consumption of standard military rations for a total of 8 days. Compared to the gold standard measured food intake/waste method, the difference in mean energy intake was small (−73 kcal/day for tablet app and −108 kcal/day for wFR) (p > 0.05). Repeated Measures Bland-Altman plots indicated good agreement for both methods (tablet app and wFR) with the measured food intake/waste method. These findings demonstrate that the tablet app, with added nutritional composition data, is comparable to the traditional dietary assessment method (wFR) and performs satisfactorily in relation to the measured food intake/waste method to assess energy, macronutrient, and selected micronutrient intakes in a sample of military personnel.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".