Lessons from Studies to Evaluate an Online 24-Hour Recall for Use with Children and Adults in Canada
Bibliographic record
Abstract
With technological innovation, comprehensive dietary intake data can be collected in a wide range of studies and settings. The Automated Self-Administered 24-hour (ASA24) Dietary Assessment Tool is a web-based system that guides respondents through 24-h recalls. The purpose of this paper is to describe lessons learned from five studies that assessed the feasibility and validity of ASA24 for capturing recall data among several population subgroups in Canada. These studies were conducted within a childcare setting (preschool children with reporting by parents), in public schools (children in grades 5-8; aged 10-13 years), and with community-based samples drawn from existing cohorts of adults and older adults. Themes emerged across studies regarding receptivity to completing ASA24, user experiences with the interface, and practical considerations for different populations. Overall, we found high acceptance of ASA24 among these diverse samples. However, the ASA24 interface was not intuitive for some participants, particularly young children and older adults. As well, technological challenges were encountered. These observations underscore the importance of piloting protocols using online tools, as well as consideration of the potential need for tailored resources to support study participants. Lessons gleaned can inform the effective use of technology-enabled dietary assessment tools in research.
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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 | 0.152 | 0.246 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".