Categorization of mHealth Coaching Technologies for Children or Adolescents With Type 1 Diabetes: Systematic Review
Bibliographic record
Abstract
Background: Managing type 1 diabetes in children and adolescents can be difficult for parents, health care professionals, and even patients. However, over the last decades, the quality of services provided to patients with diabetes has increased due to advances in IT. Objective: This study aims to comprehensively document the range of IT tools used in the management of diabetes among children and adolescents, with a focus on identifying the technologies most commonly used based on their frequency. In addition, the study aims to explore relevant methodologies for developing diabetes technology and provide valuable information to developers by delineating essential phases of the design process. Methods: The literature search was focused on MEDLINE (PubMed), Web of Science, and Google Scholar for relevant studies. Keywords such as "type 1 diabetes," "adolescents," "kids," "mHealth," "children," and "coaching" were combined using Boolean operators. The inclusion criteria were open access, English-language papers published between 2012 and 2023 focusing on patients younger than 18 years and aligned with our research goal. The exclusion criteria included irrelevant topics and papers older than 18 years. By applying the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) method, 2080 studies were recognized, and after selection, 33 papers were agreed upon between the researchers. Results: Four primary categories were defined: types of IT, methodology identification, purpose identification, and feature determination. Among these, mobile health (mHealth) apps emerged as the predominant type of information, garnering 27 mentions. In particular, user-centered design was identified as the most prevalent methodology, cited 22 times. The primary purpose of self-monitoring blood glucose values was mentioned 20 times, while patient education was the highest among common characteristics, with 23 mentions. Conclusions: Based on our research, we advocate for developers to focus on creating an mHealth app that integrates gamification techniques to develop innovative diabetes management solutions. This app should include vital functionalities such as blood glucose monitoring, strategies to improve hemoglobin A1c levels, carbohydrate tracking, and comprehensive educational materials for patients and caregivers. By prioritizing these features, developers can enhance the usability and effectiveness of the technology, thereby better supporting children or adolescents with diabetes in their daily management endeavors.
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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 | 0.015 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.022 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".