Design, implementation, and evaluation of complex telenutrition interventions used for diabetes self-management and monitoring
Bibliographic record
Abstract
Background: The global increase in diabetes prevalence has heightened the need for interventions focused on health promotion, prevention, management, and clinical monitoring. The challenges for affected individuals and health professionals are numerous, including monitoring diet, physical activity, medication adherence, and blood glucose levels. Objective: This article aims to describe the current state of knowledge on the processes of design, implementation, and evaluation of complex telenutrition interventions used for diabetes self-management and monitoring with a health professional, with the goal of improving adherence, usability, and sustained use. Methods: Research was conducted using the MEDLINE, CINAHL, and Embase databases, limited to articles in English and French on the development or evaluation of complex telehealth interventions for type 2 diabetes or pre-diabetes. Three reviewers independently selected each of the articles, and the principal researcher analyzed them. Results: The findings reveal strong end-user involvement, including patients and various health professionals, in the design and evaluation of digital tools, promoting multidisciplinary diabetes management. Telehealth interventions were designed to be used across various platforms and devices, enhancing accessibility. The analysis highlights the importance of ease of use of monitoring technologies, with a trend toward automation and integration with wearable devices for simplified monitoring and rapid adjustments. The analysis also emphasizes the need for rigorous usability evaluations to ensure that technologies meet user needs. Interventions that incorporated theoretical models of behavior change tended to show high levels of user satisfaction and adoption and encouraged patient engagement in managing their condition. Conclusion: This review reveals that complex telenutrition interventions represent a significant advancement for diabetes management. They enable close collaboration between patients and health professionals, enhancing effective diabetes self-management through more accessible and user-friendly digital platforms. The results highlight the importance of user-centered design of telehealth solutions for the success of these initiatives.
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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 | 0.070 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".