Evaluation of the Accuracy, Usability, and User Perspectives of the Ecological Momentary Dietary Assessment App Traqq Among Dutch Adolescents: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
BACKGROUND: Self-reported dietary intake data are crucial in nutrition and health research; however, their accuracy is compromised by challenges such as portion size estimations, food identification, memory-related bias, social desirability bias, and reactivity bias. Dietary assessment in adolescents is particularly challenging due to irregular eating habits, meal skipping, and parent or peer influences, potentially resulting in misreporting. Leveraging adolescents' receptiveness to technology, we investigated the use of an innovative smartphone app (Traqq) that facilitates dietary assessment using repeated short recalls instead of traditional 24-hour recalls. Evaluation studies of the Traqq app in Dutch adults have shown successful results, but its suitability for other target populations, such as adolescents, requires further investigation. OBJECTIVE: We designed a comprehensive, 3-phase study to evaluate the Traqq app's accuracy using repeated short recalls, usability, and user perspectives among Dutch adolescents aged 12 to 18 years. This manuscript details the study setup, research methods, and basic characteristics in phases 1 and 2. METHODS: In phase 1, adolescents (aged 12-18 years) downloaded the Traqq app and completed a demographic questionnaire. It was used on 4 random school days over 4 weeks, using 2-hour recalls on 2 days and 4-hour recalls on 2 days. A food frequency questionnaire and 2 interviewer-administered 24-hour recalls served as dietary reference methods to assess the Traqq app's accuracy. In addition, usability was evaluated using the System Usability Scale and an experience questionnaire. In phase 2, user experiences were further explored through semistructured interviews within a subsample of 24 adolescents. These first 2 phases of this mixed methods study are now finalized for data collection. Phase 3 will focus on collecting user insights to inform app customization through cocreation sessions. RESULTS: . A total of 64 (63%) participants were girls, 81 (84%) attended high school, and 88 (92%) were born in the Netherlands. Interviews were held with 6 (25%) boys and 18 (75%) girls. Cocreation sessions will be planned after all data have been analyzed. CONCLUSIONS: In this holistic study, we combine quantitative and qualitative methods to evaluate the dietary assessment performance among adolescents of the Traqq app, which was initially designed for adults. Specifically, next to quantitative comparisons of the Traqq app's dietary assessment methods, we conducted semistructured interviews, and we will carry out cocreation sessions. With this user-centered, synergistic approach, we aim to establish a list of requirements for a dietary assessment app for adolescents, resulting in more efficient assessments, improved compliance, and enhanced overall accuracy in this population. TRIAL REGISTRATION: ISRCTN Registry ISRCTN46230386; https://www.isrctn.com/ISRCTN46230386. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/70194.
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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 | 0.065 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".