Perceptions of a dietary self-monitoring mobile app resembling the Canada’s food guide : a qualitative study
Bibliographic record
Abstract
Background and Purpose: Dietary self-monitoring is a behavioural technique that helps to elicit and sustain dietary changes over time. Current dietary self-monitoring tools focus on itemizing foods and serving sizes (portions), making them complex, time-consuming, and hard to use for people with limited or low health literacy. Furthermore, there are no plate-based dietary self-monitoring tools that conform to the 2019 Canada Food Guide (CFG). This thesis explored the perceptions of potential end-users (i.e., members from the general public) and Registered Dietitians (RDs) on a dietary self-monitoring mobile application (app) resembling the CFG, called iCANPlateᵀᴹ. Methods: Qualitative data were collected through virtual focus groups. Questions in the focus group guide were based on the Capability, Opportunity, Motivation- Behaviour (COM-B) model to explore perceptions of using the CFG and available dietary self-monitoring tools. A prototype of iCANPlateᵀᴹ (version 0.1) was presented to gain feedback on perceived barriers and facilitators to using the app, and suggestions on future versions. Trained researchers used transcripts of audio-recorded focus groups to conduct thematic analysis. Results: Seven focus groups with RDs (n=44) and nine focus groups with members from the general public (n=52) were conducted. During the focus groups, participants discussed potential facilitators and barriers to using the current iteration of iCANPlate. They were interested in the simplicity of iCANPlate and its capacity to foster self-awareness of dietary behaviours rather than weight or calorie counting. However, concerns were raised regarding iCANPlate’s potential to improve adherence to dietary self-monitoring, primarily caused by a lack of food classifications, conceptualizing proportions, and lack of inclusivity. In addition, participants suggested necessary and optional components for iCANPlate’s future versions. Conclusions: Overall, participants liked the simplicity of iCANPlate 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. 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 | 0.013 | 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 | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".