Usability and Feasibility Testing of Eating for Wellness (E4W) – An Image-Recognition Dietary Assessment Application for Adolescents with Obesity
Bibliographic record
Abstract
Background: Use of image-recognition dietary assessment apps in adolescents with obesity is not well studied.Purpose: To evaluate the usability, feasibility, and preliminary effectiveness of Eating for Wellness (E4W), an mHealth app, in improving dietary intake in adolescents with obesity attending an obesity clinic. Methods: Usability was tested through iterative cycles of patient and healthcare provider interviews with standardized scenario-based tasks. A pilot RCT of 32 adolescents (each enrolled for 1 month) was completed to assess feasibility of implementation and preliminary effectiveness. Results: Usability testing demonstrated that E4W was effective, efficient, and well-liked. Feasibility testing was hindered by technical challenges, patient engagement and adherence to food records, and there was no statistically significant improvement in dietary intake. However, patient acceptance and satisfaction scores were high. Conclusions: E4W was well-received by patients and HCPs. Further research is warranted to determine if E4W can improve dietary intake in adolescents with obesity.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".