Perceptions of self-monitoring dietary intake according to a plate-based approach: A qualitative study
Bibliographic record
Abstract
Dietary self-monitoring is a behaviour change technique used to help elicit and sustain dietary changes over time. Current dietary self-monitoring tools focus primarily on itemizing foods and counting calories, which can be complex, time-intensive, and dependent on health literacy. Further, there are no dietary self-monitoring tools that conform to the plate-based approach of the 2019 Canada Food Guide (CFG), wherein the recommended proportions of three food groups are visually represented on a plate without specifying daily servings or portion sizes. This paper explored the perceptions of end-users (i.e., general public) and Registered Dietitians of iCANPlateTM-a dietary self-monitoring mobile application resembling the CFG. Qualitative data were collected through virtual focus groups. Focus group questions were based on the Capability, Opportunity, Motivation-Behaviour (COM-B) theoretical framework to explore perceptions of using the CFG and currently available dietary self-monitoring tools. The prototype iCANPlateTM (version 0.1) was presented to gain feedback on perceived barriers and facilitators of its use. Focus group discussions were audio recorded and verbatim transcribed. Trained researchers used thematic analysis to code and analyze the transcripts independently. Seven focus groups were conducted with Registered Dietitians (n = 44) and nine focus groups with members from the general public (n = 52). During the focus groups, participants mainly discussed the capabilities and opportunities required to use the current iteration of iCANPlateTM. Participants liked the simplicity of the application and its capacity to foster self-awareness of dietary behaviours rather than weight control or calorie counting. However, concerns were raised regarding iCANPlateTM's potential to improve adherence to dietary self-monitoring due to specific characteristics (i.e., insufficient classifications, difficulty in conceptualizing proportions, and lack of inclusivity). Overall, participants liked the simplicity of iCANPlateTM and its ability to promote self-awareness of dietary intakes, primarily through visual representation of foods on a plate as opposed to reliance on numerical values or serving sizes, were benefits of using the app. Findings from this study will be used to further develop the app with the goal of increasing adherence to plate-based dietary approaches.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".