Clinical Improvements From Telemedicine Interventions for Managing Type 2 Diabetes Compared With Usual Care: Systematic Review, Meta-Analysis, and Meta-Regression
Notice bibliographique
Résumé
Background Type 2 diabetes mellitus (T2DM) is a prevalent chronic metabolic disorder that poses substantial challenges to global health care systems and patient management. Telemedicine, defined as the use of information and communication technologies to enhance health care delivery, has emerged as a potential tool to improve access to care and facilitate the management of T2DM. Objective This systematic review and meta-analysis aimed to evaluate the clinical effectiveness of various telemedicine interventions compared with usual care in glycemic control, and cardiovascular health in adults with T2DM. Methods A comprehensive literature search was conducted across databases such as PubMed, Cochrane Library, and Web of Science for randomized controlled trials (RCTs) published up to August 23, 2024. Eligible RCTs compared telemedicine interventions with usual care in adults with T2DM. The primary outcome assessed was hemoglobin A1c (HbA1c) levels, while the secondary outcomes included mean glucose, fasting blood glucose, BMI, weight, systolic blood pressure, diastolic blood pressure, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol. The quality of the included studies was examined via the Cochrane risk-of-bias tool. Data were extracted and analyzed using a random-effects model, and meta-regression was performed to explore potential moderators. The quality of the evidence was assessed via the Grading of Recommendations, Assessment, Development, and Evaluation approach. Results A total of 58 RCTs, encompassing 13,942 participants, were included in the analysis. Our findings showed that telemedicine interventions significantly improved HbA1c levels compared with usual care (mean difference [MD] –0.38, 95% CI –0.49 to –0.27; Z=6.94; P<.001), despite high heterogeneity (I²=96%). Significant effects were also found for fasting blood glucose (MD –11.29, 95% CI –17.65 to –4.93; Z=3.48; P<.001), weight (MD –1.33, 95% CI –2.23 to –0.44; Z=2.91; P=.004), BMI (MD –0.43, 95% CI –0.72 to –0.13; Z=2.84; P=.004), systolic blood pressure (MD –2.14, 95% CI –3.02 to –1.26; Z=4.76; P<.001), and diastolic blood pressure (MD –1.24, 95% CI –2.02 to –0.46; Z=1.10; P=.002). No significant between-group differences were found in high-density lipoprotein cholesterol and low-density lipoprotein cholesterol improvement. Subgroup analyses revealed that telemedicine delivered by physicians, dietitians, and researchers achieved the most significant reductions in HbA1c levels. Short-term and long-term interventions showed significant HbA1c improvements, while medium-term interventions did not achieve statistical significance. Meta-regression analysis did not identify any statistically significant moderators. Conclusions This review highlights telemedicine’s superior effectiveness over usual care in improving HbA1c levels in patients with T2DM, regardless of the type of intervention. Telemedicine led by physicians, dietitians, and researchers showed the greatest efficacy in managing blood glucose levels. Furthermore, telemedicine interventions show promise for monitoring weight and cardiovascular health in patients with T2DM. Trial Registration PROSPERO CRD42024608130; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=608130
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 | 0,023 | 0,059 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,002 |
| Méta-épidémiologie (sens large) | 0,027 | 0,060 |
| Bibliométrie | 0,007 | 0,008 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,003 |
| 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 ».