Categorization of mHealth Coaching Technologies for Children or Adolescents With Type 1 Diabetes: Systematic Review
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,064 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,010 | 0,012 |
| Bibliométrie | 0,022 | 0,019 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».