SMARTCLOTH-Odyssey for Nutrition Education in Type 1 Diabetes: Development and Acceptability Study of a Gamified Web-Based Platform for Diabetes Nurse Educators Using a Human-Centered Design Approach (Preprint)
Bibliographic record
Abstract
Background: The onset of type 1 diabetes mellitus (T1DM) in adolescence represents a challenge for patients, families, and health care professionals. Diabetes nurse educators identify a lack of adolescent-friendly educational resources, especially in nutrition education. Digital technology offers an opportunity to develop innovative tools to address these needs from a user-centered approach. Objective: We aimed to design and evaluate the acceptability of the SMARTCLOTH-Odyssey platform, an interactive web tool designed to support diabetes nurse educators in delivering nutrition education based on carbohydrate counting to adolescents with recent T1DM, evaluated from the perspective of nurse educators. Methods: A mixed study was carried out in three phases: (1) qualitative analysis through two focus groups with nurse educators in the Andalusian Community to identify barriers and needs; (2) development of the digital tool following a human-centered design methodology, incorporating narrative and gamified elements; and (3) quantitative evaluation of acceptability through an ad hoc questionnaire applied to 40 professionals who tested the platform. Results: Participants highlighted the lack of homogeneous and adapted debut materials, and the need for emotionally sensitive and visually appealing tools. SMARTCLOTH-Odyssey was rated positively in terms of usefulness, clarity, and clinical applicability. A total of 97.5% (39/40) felt it could facilitate nutrition education, and 90% (36/40) said they would use it in their practice. The platform was perceived as intuitive, innovative, and adapted to the characteristics of the adolescent patient. Conclusions: SMARTCLOTH-Odyssey constitutes a viable and well-valued proposal by nurse educators. It has the potential to improve nutrition education from the diagnosis of T1DM. Its participatory development reinforces its applicability and sustainability in real clinical contexts.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".