A comparison of food portion size estimation by older adults, young adults and nutritionists
Bibliographic record
Abstract
OBJECTIVES: To investigate the ability of older adults, younger adults and nutritionists to assess portion size using traditional methods versus a computer-based method. This was to inform the development of a novel dietary assessment method for older adults "The NANA system". DESIGN: Older and younger adults assessed the portion size of self-served portions of foods from a buffet style set up using traditional and computerised portion size assessment aids. Nutritionists assessed the portion size of foods from digital photographs using computerised portion size aids. These estimates were compared to known weights of foods using univariate analyses of covariance (ANCOVA). SETTING: The University of Sheffield, United Kingdom. SUBJECTS: Forty older adults (aged 65 years and over), 41 younger adults (aged between 18 and 40 years) and 25 nutritionists. RESULTS: There was little difference in the abilities of older and younger adults to assess portion size using both assessment aids with the exception of small pieces morphology. Even though the methods were not directly comparable among the test groups, there was less variability in portion size estimates made by the nutritionists. CONCLUSION: Older adults and younger adults are similar in their ability to assess food portion size and demonstrate wide variability of estimation compared to the ability of nutritionists to estimate portion size from photographs. The results suggest that the use of photographs of meals consumed for portion size assessment by a nutritionist may improve the accuracy of dietary assessment. Improved portion size assessment aids are required for all age groups.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".