Evaluation of a Web-Based Dietary Assessment Tool (myfood24) in Norwegian Women and Men Aged 60-74 Years: Usability Study
Bibliographic record
Abstract
BACKGROUND: A healthy diet throughout the life course improves health and reduces the risk of disease. There is a need for new knowledge of the relation between diet and health, but existing methods to collect information on food and nutrient intake have their limitations. Evaluations of new tools to assess dietary intake are needed, especially in old people, where the introduction of new technology might impose challenges. OBJECTIVE: We aimed to examine the usability of a new web-based dietary assessment tool in older adult women and men. METHODS: A total of 60 women and men (participation 83%, 57% women) aged 60-74 years recruited by convenience and snowball sampling completed a 24-hour web-based dietary recall using the newly developed Norwegian version of Measure Your Food On One Day (myfood24). Total energy and nutrient intakes were calculated in myfood24, primarily on the basis of the Norwegian Food Composition Table. No guidance or support was provided to complete the recall. Usability was assessed using the system usability scale (SUS), where an SUS score of ≥68 was considered satisfactory. We examined the responses to single SUS items and the mean (SD) SUS score in groups stratified by sex, age, educational level, and device used to complete the recall (smartphone, tablet device, or computer). RESULTS: The mean total energy intake was 5815 (SD 3093) kJ. A total of 14% of participants had an energy intake of <2100 kJ (ie, 500 kilocalories) and none had an intake of >16,800 kJ (ie, 4000 kilocalories). Mean energy proportions from carbohydrates, fat, protein, alcohol, and fiber was within the national recommendations. The mean SUS score was 55.5 (SD 18.6), and 27% of participants had SUS scores above the satisfactory product cut-off. Higher SUS scores were associated with younger age and lower education, but not with the type of device used. CONCLUSIONS: We found the overall usability of a new web-based dietary assessment tool to be less than satisfactory in accordance with standard usability criteria in a sample of 60-74-year-old Norwegians. The observed total energy intakes suggest that several of the participants underreported their intake during the completion of the dietary recall. Implementing web-based dietary assessment tools in older adults is feasible, but guidance and support might be needed to ensure valid completion.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".