Evaluation of the Diet Tracking Smartphone Application Keenoa<sup>™</sup>: A Qualitative Analysis
Bibliographic record
Abstract
Keenoa™ is a novel Canadian diet application (app) currently used by Canadian dietitians to collect diet-related data from clients. The goal of this study was to evaluate Keenoa™ based on user feedback and compare it to a conventional pen and paper method. One hundred and two participants were recruited and randomly assigned to record their diets using this application for 3 nonconsecutive days. Following this, participants were invited to complete an online "exit" survey. Seventy-two subjects responded, with 50 completing an open-ended question asking for general feedback about the app. Data were reviewed and 3 main themes emerged: strengths, challenges, and future recommendations. Strengths associated with the app consisted of picture recognition software, the additional commentary feature, and the overall pleasant data collection process. Challenges that were identified included inconsistencies with the barcode scanning features, the limited food database, time to enter food details, and software issues. Future recommendations included using a larger food database, pairing dietary intake with physical activity monitoring, and having accessible nutritional data. Despite these limitations, participants preferred using mobile apps to record diet compared with traditional written food diaries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.039 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".