Clinical Improvements From Telemedicine Interventions for Managing Type 2 Diabetes Compared With Usual Care: Systematic Review, Meta-Analysis, and Meta-Regression
Bibliographic record
Abstract
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.059 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.060 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".