Use of Mobile Technology in the Care of Adolescents With Diabetes
Notice bibliographique
Résumé
Abstract BACKGROUND: The advent of personalised cell phones and mobile technology has created an increasing desire to integrate these resources into the care of pediatric patients with diabetes. Adolescents in particular often have poor diabetes self-management practises and fail to meet glyce-mic targets. Mobile technology use is prevalent among adolescents and the idea of using this technology to assist with diabetes self-care is appealing. However, integrating this technology into clinical practice is challenging and many practitioners do notknow where to start. OBJECTIVES: To describe the use, benefits, and limitations of mobile technology in diabetes care among adolescents and their parents, and to assess if mobile technology use is linked to lower HbA1c levels. DESIGN/METHODS: This cross sectional study involved adolescents age 11-18 years and their parents who were recruited during their regular diabetes patient care visits at two separate pediatric diabetes centres. Patients and parents completed a questionnaire designed by study authors. Patients' two most recent HbA1c levels were recorded following survey completion. RESULTS: 100 adolescents and 80 parents completed the questionnaire. Device ownership was high, with 89% of adolescents and 100% of parents owning at least 1 device. Only one third of the cohort reported using mobile technology for their diabetes care. The commonest reason for non-use was lack of awareness of apps for diabetes care (53% adolescents, 60% parents). Among mobile technology users, texting and calculation were the most frequently used apps. Apps for calorie and carb counting were also frequently used, among which CalorieKing™ was the highest reported (44% adolescents; 47% parents). Insulin pump specific programs including Diasend® and Medtronic CareLink® were reported by 14.8% adolescents and 14.3% parents. The average HbA1c for the entire cohort was 8.0%, with no statistically significant difference between adolescent mobile technology users and non-users (7.8% vs 8.3%; p=0.22). However, mean HbA1c was found to be lower among those adolescents whose parents used mobile technology for their management (7.6 % vs 8.2 %; p=0.04). CONCLUSION: Only a minority of adolescents use mobile technology for their diabetes care, and lack of awareness was the major barrier to mobile technology use. Basic smartphone functions including texting and calculation were the most cited apps used. Parental technology use was associated with improved glycemic control. Considering the widespread use of mobile technology among young people, there remains untapped potential for greater use of this technology towards improved self-care in adolescents with diabetes.
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».