A Qualitative Evaluation of the eaTracker® Mobile App
Bibliographic record
Abstract
Background: eaTracker® is Dietitians of Canada’s online nutrition/activity self-monitoring tool accessible via website and mobile app. The purpose of this research was to evaluate the eaTracker® mobile app based on user perspectives. Methods: One-on-one semi-structured interviews were conducted with adult eaTracker® mobile app users who had used the app for ≥ 1 week within the past 90 days. Participants (n = 26; 89% female, 73% 18–50 years) were recruited via email. Interview transcripts were coded using first level coding and pattern coding, where first level codes were grouped according to common themes. Results: Participants mentioned several positive aspects of the mobile app which included: (a) Dashboard displays; (b) backed by dietitians; (c) convenience and ease of use; (d) portion size entry; (e) inclusion of food and physical activity recording; and (f) ability to access more comprehensive information via the eaTracker® website. Challenges with the mobile app included: (a) Search feature; (b) limited food database; (c) differences in mobile app versus website; and (d) inability to customize dashboard displayed information. Suggestions were provided to enhance the app. Conclusion: This evaluation provides useful information to improve the eaTracker® mobile app and also for those looking to develop apps to facilitate positive nutrition/physical activity behavior change.
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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 | 0.034 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".