Evaluating the feasibility of administering a combination of online dietary assessment tools in a cohort of adults in Alberta, Canada
Bibliographic record
Abstract
Purpose: Evidence suggests that combining tools, such as 24-hour recalls and food frequency questionnaires, may allow more accurate assessment of diet in epidemiologic studies. Webbased technology should make this approach more feasible than in the past, but it is important to explore response rates and acceptability of such an approach in real-world settings. We sought to determine the feasibility of using a combination of online tools (Automated SelfAdministered 24-hour (ASA24) Dietary Assessment Tool and Diet History Questionnaire-II (DHQ-II)) in a sub-set of participants in Alberta’s Tomorrow Project (ATP); a prospective cohort of 55,000 adults >35y in Alberta, Canada. Methods: Invitations to the feasibility study were mailed to 550 ATP participants. Those who consented (n=331) were asked to complete a health questionnaire, four ASA24 recalls (approximately three weeks apart over a four month period, with staggered start dates between June and December 2016), followed by the DHQ-II, and an evaluation survey. Results: The majority of participants [mean (SD) age =57.1 (10.1)] were women (70.7%), urban residents (84.8%) and non-smokers (95.7%). Of the 229 participants who completed at least one ASA24, roughly equal proportions completed one (24.8%), two (24.5%), three (24.5%) and four recalls (26.2%). One third (n=102) of consenting participants did not respond to any ASA24 recall requests, with “lack of time” given as the primary reason. Only 41% of consenting participants (n=136) completed the DHQ-II; of these, 40% (n=55) completed all four recalls. Median (25th-75th percentile) completion times were 46 (26-64) minutes for the first ASA24 recall and 50 (40-90) minutes for the DHQ-II. Conclusions: Over half of participants completed at least two or more ASA24 recalls, and those who completed a greater number of recalls also completed the DHQ-II, demonstrating that the approach is feasible in the ATP cohort. However, response rates may be sensitive to the timing and frequency of recall administration. Future investigations will (i) evaluate the dietary data collected from each tool; (ii) explore methods of combining the data to optimize assessment of diet in the cohort, while accounting for the fact that not all participants will complete the entire dietary assessment protocol.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".