Efficacy and Safety of a Telemedicine System in Patients With Gestational Diabetes Mellitus (TELEGLAM): Single-Center, 2-Arm, Randomized, Open-Label, Parallel-Group Study
Notice bibliographique
Résumé
Abstract Background In the management of gestational diabetes mellitus (GDM), the usual medical treatment requires frequent visits for glucose monitoring and insulin dose adjustment, and this imposes significant physical, psychological, and economic burdens on pregnant women. As mobile health platforms become increasingly integrated into diabetes care, telemedicine may help alleviate these burdens; however, evidence evaluating its effectiveness as a replacement for routine in-person GDM care remains limited. Objective This study aims to evaluate the impact of telemedicine on the quality of life and costs for patients with GDM requiring insulin therapy. Methods This single-center, 2-arm, randomized, open-label, parallel-group study included patients with GDM who started insulin injection therapy. Participants were randomized to either the telemedicine or standard face-to-face care groups for 10 (SD 2) weeks. The telemedicine intervention used a smartphone-linked platform that enabled the automatic transfer of glucose data from connected glucose meters and facilitated real-time video consultations. Primary end points included costs and patient satisfaction. Costs were assessed using claims data, transportation calculations, and wage-based productivity losses, while patient satisfaction was evaluated through changes in the Problem Areas in Diabetes Survey and Diabetes Therapy-Related Quality of Life questionnaire scores. Secondary outcomes included glycemic control and perinatal outcomes. Results In total, 38 participants were included, with 18 assigned to the telemedicine group and 20 to the standard care group. Total costs (32,712, 95% CI 15,412‐50,013 vs 59,202, 95% CI 42,603‐75,800 Japanese yen; $284, 95% CI 134‐435 vs $515, 95% CI 370‐659, purchasing power parity [PPP]–adjusted; P =.01), direct non–health care costs (922, 95% CI −240 to 2084 vs 2561, 95% CI 1447‐3676 yen; $8, 95% CI −2 to 18 vs $22, 95% CI 13 to 32 PPP-adjusted; P =.02), and indirect costs (8981, 95% CI −7119 to 25,082 vs 32,832, 95% CI 17,384‐48,279 yen; $78, 95% CI −62 to 218 vs $285, 95% CI 151‐420 PPP-adjusted; P =.01) reduced significantly in the telemedicine group compared with the standard care group. The improvements in the Problem Areas in Diabetes Survey (−7.6, 95% CI −13.7 to −1.4; P =.02) and Diabetes Therapy–Related Quality of Life domain 1 (10.5, 95% CI 0.9-20.1; P =.03) scores from the baseline were significantly greater in the telemedicine group than that in the standard care group. Nonetheless, glycemic control and frequency of perinatal complications were comparable between the 2 groups. Consultation time was similar across groups, suggesting no added workload for clinicians. Conclusions In this randomized trial, mobile health–enabled telemedicine safely replaced routine in-person visits for patients with GDM requiring insulin therapy. Telemedicine significantly reduced psychological and economic burdens without compromising glycemic or perinatal outcomes, demonstrating its value as a patient-centered and cost-efficient model of care. These findings support the broader implementation of mobile-based telemedicine approaches in GDM management.
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 | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».