Review
Abstract
Background: Type 1 diabetes mellitus (T1DM) significantly affects patients’ quality of life and can be life-threatening, necessitating improved monitoring strategies. Telemedicine, which leverages telecommunications technologies to deliver health care services and expertise, has the potential to enhance T1DM management. However, its effectiveness remains to be fully established.
Objective: This study aims to evaluate the effectiveness of various telemedicine-based carbohydrate-counting (CC) interventions in patients with T1DM.
Methods: This systematic review and meta-analysis searched 5 databases—PubMed, Web of Science, CINAHL, Embase, and Cochrane—as well as reference lists of retrieved articles on September 26, 2024, for randomized controlled trials (RCTs) assessing the effectiveness of telemedicine-based CC interventions in reducing glycated hemoglobin A1c (HbA1c) levels in patients with T1DM.
Results: From 3612 citations, we identified 18 eligible RCTs (n=1627) from 14 regions for inclusion in the meta-analysis. Participants in the telemedicine intervention group experienced a 0.35% reduction in HbA1c levels (95% CI –0.54 to –0.16) compared with the control group. A total of 13 studies used smartphone apps, 4 used connected and wearable glucometers, and 3 delivered the intervention through web-based systems. Significant reductions in HbA1c were observed across smartphone apps (–0.36%, 95% CI –0.63% to –0.09%), connected and wearable glucometers (–0.35%, 95% CI –0.56% to –0.14%), and web-based systems (–0.36%, 95% CI –0.71% to –0.02%). Considerable heterogeneity was noted (I2=81%, P<.001). Telemedicine-based CC interventions also increased time in range by 9.59% (95% CI 6.50%-12.67%). However, evidence regarding treatment satisfaction, total daily insulin dose, and hypoglycemia remains inconclusive. Subgroup analysis showed that telemedicine platform variety did not significantly contribute to heterogeneity, while meta-regression indicated that the impact on HbA1c was most pronounced in trials conducted in Asia.
Conclusions: Compared with usual care, telemedicine-delivered CC interventions improved HbA1c and time in range but did not significantly impact other clinically relevant outcomes in patients with T1DM. High-quality, large-scale RCTs are needed to draw definitive conclusions. These findings provide health care professionals with updated evidence on the role of telemedicine in glycemic control for patients with T1DM.
Trial Registration: PROSPERO CRD42024523025; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024523025
doi:10.2196/59579
Keywords
Introduction
Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disease characterized by absolute insulin deficiency due to the immune-mediated destruction of pancreatic β-cells, resulting in hyperglycemia [,]. T1DM accounts for 5%-10% of all diabetes cases [], and its global incidence is rising dramatically []. Patients with T1DM require lifelong insulin therapy to maintain blood glucose (BG) levels within recommended ranges and to reduce the risk of both acute and long-term complications [].
The Academy of Nutrition and Dietetic Sciences has shown that for patients with T1DM, a pivotal element of management is carbohydrate counting (CC) to determine the appropriate preprandial insulin dosage []. Ideally, patients should learn to calculate their carbohydrate intake and adjust mealtime insulin doses accordingly []. However, CC is considered one of the most onerous tasks in T1DM care []. As the effectiveness of CC may be limited by patient adherence and the inability to estimate carbohydrate content accurately, its assessment is often imprecise [].
The issue of suboptimal glycemic control among patients with T1DM is partly attributed to the complexities of accurately calculating mealtime insulin doses []. Furthermore, regular consultations with a specialist are necessary. The intensive and ongoing need for treatment imposes a significant burden, negatively affecting the quality of life of both patients and their families []. Telemedicine (TM) may help address these challenges [].
TM refers to the remote delivery of clinical services through electronic information and telecommunication technologies. It is utilized across a wide range of health care services, including health assessment, diagnosis, intervention, consultation, supervision, and access to information []. The American Telemedicine Association defines TM as the use of medical information exchanged from one site to another via electronic communication to improve patients’ clinical health. This includes a growing number of applications and services that utilize 2-way video, smartphones, wireless tools, and other telecommunication technologies []. TM can be classified based on the communication method (text, video, or audio), communication time (synchronous or asynchronous), the purpose of the consultation (initial consultation or follow-up consultation), and participants in the remote consultation (patient-to-doctor, caregiver-to-doctor, doctor-to-doctor, or health care worker-to-doctor) [].
In chronic disease management, particularly diabetes, the integration of TM technology with medical professionals has yielded remarkable results []. The American Diabetes Association (ADA 2022) states that TM is a growing field that may improve access to care for people with diabetes []. With advancements in technology, TM has evolved beyond simple phone calls and video consultations to include applications such as augmented reality, virtual reality, and artificial intelligence []. The incorporation of these emerging technologies not only broadens the definition of TM but also presents new opportunities and challenges for managing chronic diseases such as diabetes. A wide range of new technologies is expected to help alleviate the burden of T1DM. While the potential and feasibility of technology-based approaches have been well established, their effectiveness remains unclear.
Upon reviewing the relevant literature, we found no systematic reviews or meta-analyses evaluating the efficacy of various TM approaches in CC interventions for glycemic control among patients with T1DM. Current evidence suggests that CC is an effective method for lowering HbA1c, and patients with T1DM are encouraged to use CC rather than alternative approaches []. However, no conclusive evidence is available regarding the effectiveness of TM-delivered CC interventions for T1DM management. This study aimed to conduct a systematic review and quantitative synthesis of randomized controlled trials (RCTs) to assess the effectiveness of a TM-delivered CC intervention in patients with T1DM. The findings may contribute to the development of new approaches to improve the quality of life of patients with T1DM.
Methods
Overview
The evaluation protocol was prospectively registered in PROSPERO (International Prospective Register of Systematic Reviews; CRD42024523025). We conducted a systematic review and meta-analysis following the guidelines outlined in the Cochrane Handbook [] and adhered to the 2020 PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for systematic review reporting [].
Literature Search
We conducted a comprehensive systematic search across 5 databases: PubMed, Web of Science, CINAHL, Embase, and Cochrane. Our search included all forms of electronic communication, such as virtual reality, augmented reality, artificial intelligence, smartphone apps, TM, and SMS text messages. The search covered all records from the inception of each database to September 26, 2024. The complete search strategy for each database is detailed in -. Additionally, we manually searched the reference lists of retrieved articles.
Inclusion and Exclusion Criteria
The inclusion criteria, defined using the PICOS (Patients, Implementation, Comparison, Outcomes, Study) format, were as follows: (1) patients diagnosed with type 1 diabetes; (2) implementation of the CC method via TM; (3) studies categorizing participants into experimental and control groups, including both usual and standard care (comparison); (4) HbA1c levels (outcomes); and (5) RCTs (study).
The exclusion criteria were as follows: (1) studies that were incomplete, including research protocols and ongoing studies; (2) reports lacking sufficient details on patient outcome measures; and (3) studies with insufficient statistical data for quantitative outcomes, such as mean, SD, and median with range.
Study Selection
First, the literature search results from all retrieved databases were exported into EndNote X9 (Clarivate Analytics) for management and duplicate removal. Two researchers (YL and YY) independently screened titles and abstracts, followed by a full-text review to confirm eligibility. A third researcher (FL) resolved any disagreements or discrepancies through deliberation until a consensus was reached.
Data Extraction
Two researchers (YL and YY) independently extracted data from the 10 articles, and another researcher (FL) verified the extracted information. Discrepancies were resolved through discussion. If data were unavailable, the authors were contacted.
We extracted trial characteristics (study name, author, year, country, number of centers, design, duration, and sample size) and patient characteristics (age, sex, and duration of diabetes) from the selected studies. Additionally, we extracted details of the TM intervention (type, duration, and other specifics) and results (mean, SD, SE, 95% CIs, statistical significance at follow-up time points, primary and secondary outcomes, and validated measurement instruments).
Outcomes
The primary outcome was the HbA1c level. Secondary outcomes included treatment satisfaction (measured using a validated tool), total daily insulin dose (TDD), and time in range (TIR).
Risk of Bias Assessment
Two reviewers independently assessed the risk of bias in RCTs using the revised Cochrane Risk of Bias (RoB) 2 tool and calculated the weighted Cohen κ coefficient to measure agreement []. In case of disagreement, a third reviewer (FL) facilitated discussions to reach a consensus. The RoB 2 tool evaluates 5 key areas of potential bias in RCTs: the randomization process, deviations from intended interventions, missing outcome data, outcome measurement, and selective reporting of outcomes. Each area includes a series of questions with 3 response options—“low,” “some concern,” and “high”—which classify the level of bias risk. Based on these classifications, studies are categorized as having “low,” “some concern,” or “high” overall risk of bias. The assessments in each area contribute to the overall judgment of bias risk in the study results. If no bias is identified in any area, the overall risk is deemed low. If at least one area raises some concern, the overall risk is categorized as “some concern.” If a high risk of bias is found in any area, the study is considered to have a high overall risk of bias [].
We assessed publication bias using the Egger test [] and examined contour-enhanced funnel plots []. This is particularly important in meta-analyses, as studies with positive and significant results are more likely to be published in high-impact journals compared with those with negative findings [].
Data Synthesis and Analysis
Statistical analysis was performed using the metafor package in R 4.4.1 software (R Foundation) []. For HbA1c and TIR, the mean difference (MD) and 95% CI were calculated. Given that treatment satisfaction was measured using different validation scales, and TDD used other measurement methods and units, we calculated the standardized mean difference (SMD) and 95% CIs. When studies reported baseline and follow-up values but not change-from-baseline SDs, the missing SDs were calculated based on the baseline and follow-up SDs, and the average correlation coefficient (r) was estimated from other identified studies using the following formula:
If the SE was reported instead of the SD, it was converted to SD using the following formula:
where n is the sample size. By contrast, if a 95% CI was reported instead of SD or SE, the SD can be calculated according to the Cochrane Manual []. Studies that did not report SDs, SEs, or 95% CIs were excluded from meta-analysis [].
Given the expected heterogeneity in study populations and procedures, a random-effects model was used to combine the effect sizes and SDs of the studies. Heterogeneity was assessed by examining the forest plot and calculating the degree of inconsistency (I2) among studies. I2 represents the proportion of variability in study results attributable to heterogeneity rather than chance []. An I2 value of 0%-40% indicated low heterogeneity, 30%-60% indicated moderate heterogeneity, 50%-90% indicated substantial heterogeneity, and 75%-100% indicated considerable heterogeneity. To assess the influence of individual studies, we conducted a leave-one-out analysis, systematically excluding each study and reanalyzing the data set to determine whether any single study disproportionately affected the results. Sensitivity analyses were performed to evaluate the robustness of the findings. Additionally, subgroup analyses based on the type of intervention were conducted to explore potential sources of heterogeneity. Finally, meta-regression was used to examine the impact of demographic and intervention characteristics on variations in HbA1c levels, aiming to identify underlying sources of heterogeneity.
Results
Study Selection
The initial database search yielded 3612 records, with an additional 10 identified through reference list screening. After removing 185 duplicates, 3437 abstracts were screened, of which 3317 were excluded for not meeting the selection criteria, and 2 lacked full-text access. Subsequently, 118 full-text articles were assessed, with exclusions detailed in 
Figure 1. Ultimately, 19 articles met the inclusion criteria and were included in the final analysis. One of these articles reported 2 distinct intervention groups within a 3-arm trial, comparing both intervention groups against a control, resulting in the inclusion of 20 trials in the meta-analysis [-]. The interrater agreement between evaluators (YL and YY) was strong, with a κ value of 0.91.
Characteristics of the Included Studies
Table 1 summarizes the characteristics of the 20 included studies, encompassing 1627 participants from 14 regions. The publication years ranged from 2004 to 2023, with over 50% (12/20, 60%) published after 2018. Geographically, the studies were conducted in Europe (n=12), America (n=6), and Asia (n=2). All studies were RCTs, with 17 applying a parallel-group design and 3 utilizing a crossover design. Regarding the study population, 7 focused on children and adolescents with T1DM, 11 on adults with T1DM, and 2 included participants of all ages with T1DM.
| Study | Country | Centers, n | RCTa design | Population | Participants, n | Age (years), mean (SD) | Male, n (%) | HbA1cb, mean (SD) | Duration of diabetes (years), mean (SD) | ||||
| Intervention group | Control group | Intervention group | Control group | Intervention group | Control group | Intervention group | Control group | ||||||
| Kowalska et al [] | Poland | 1 | Parallel | Age <18 years and T1DMc diagnosed at least 1 year prior | 53 | 53 | 9.8 (4.3) | 12.1 (3.7) | 44 (41.5) | 7.6 (1) | 7.4 (1) | 4.52 (2.7) | 5.4 (3.5) |
| Alfonsi et al [] | Canada | 1 | Crossover | Age 10-17 years and T1DM diagnosed at least 6 months prior | 22 | 22 | 13.98 (1.57) | 13.98 (1.76) | 27 (61.4) | 8.41 (1.8) | 8.35 (1.32) | 6.08 (4.14) | 6.44 (4.45) |
| Charpentier et al [] | France | 17 | Parallel | Age >18 years and T1DM diagnosed at least 1 year prior | 59 | 61 | 32.26 (12.07) | 36.8 (14.1) | 43 (35.8) | 9.11 (1.14) | 8.91 (0.90) | 14.7 (9.1) | 16.9 (10.5) |
| Rossi et al [] | England, Italy, and Spain | 7 | Parallel | Age ≥8 years with T1DM | 67 | 63 | 35.4 (9.5) | 36.1 (9.4) | 56 (43.1) | 8.2 (0.8) | 8.4 (0.7) | 17.1 (10) | 15.8 (10.7) |
| de Oliveira et al [] | Canada | 1 | Parallel | T1DM or LADAd diagnosed at least 1 year prior | 22 | 23 | 22.6e | 38.7e | 20 (44.4) | 7.28 | 7.76 | N/Af | N/A |
| Rossi et al [] | Italy | 12 | Parallel | Age ≥18 years with T1DM | 63 | 64 | 38.4 (10.3) | 34.3 (10.0) | 60 (47.2) | 8.4 (0.1) | 8.5 (0.1) | N/A | N/A |
| Gunawardena et al [] | Sri Lanka | 1 | Parallel | Age 18-80 years and DM diagnosed at least 6 months prior | 35 | 32 | 52 (12) | 53 (11) | 50 (74.6) | 9.5 (1.6) | 9.4 (1.3) | 11 (6) | 11 (7) |
| Lee et al [] | Korea | 1 | Parallel | Age 19-79 years and T1DM diagnosed at least 1 year prior | 18 | 18 | 45.4 (12.3) | 43.1 (14.6) | 17 (47.2) | 9.2 (2.0) | 8.8 (1.1) | 16.0 (10.4) | 18.2 (10.5) |
| Castensøe-Seidenfaden et al [] | Denmark | 6 | Parallel | Age 14-22 years and T1DM diagnosed at least 1 year prior | 76 | 75 | 17.6 (2.6) | 17.6 (2.7) | 70 (46.4) | 8.3 (4.3) | 7.7 (4.7) | 81.1 (18.0) | 76.2 (14.9) |
| Schmidt et al [] | Denmark | 2 | Parallel | Age 18-65 years and T1DM diagnosed at least 1 year prior | 22 | 8 | 42 (10) | 46 (9) | 14 (32.6) | 8.8 (0.7) | 9.1 (0.7) | 21 (9) | 14 (12) |
| Klee et al [] | Switzerland | 1 | Crossover | Age 10-18 years and T1DM diagnosed at least 6 months prior | 16 | 16 | 13.3 (2.3) | 13.3 (2.3) | N/A | 8.8 (0.7) | 8.8 (0.7) | N/A | N/A |
| Hommel et al [] | Denmark | 1 | Parallel | Age ≥18 years and T1DM diagnosed at least 1 year prior | 84 | 84 | 46.9 (14.4) | 47.1 (12.7) | 96 (57.1) | 8.9 (0.7) | 9.0 (0.8) | 23.4 (13.9) | 22.0 (13.9) |
| Boukhors et al [] | Canada | 1 | Crossover | T1DM diagnosed at least 1 year prior | 10 | 10 | 39.3 (10.1) | 39.3 (10.1) | 14 (70.0) | 7.7 (0.9) | 7.7 (0.9) | N/A | N/A |
| Montanari et al [] | Brazil | 1 | Parallel | Age 18-45 years with T1DM | 33 | 35 | 26.0 (7.04) | 27.82 (5.98) | 40 (58.8) | 9.6 (1.5) | 9.0 (0.5) | 16.86 (6.07) | 18.41 (6.54) |
| Montanari et al [] | Brazil | 1 | Parallel | Age 18-45 years with T1DM | 43 | 35 | 26.81 (7.06) | 27.82 (5.98) | 50 (64.1) | 9.0 (0.5) | 9.0 (0.5) | 16.47 (7.55) | 18.41 (6.54) |
| Secher et al [] | Denmark | 5 | Parallel | Age ≥18 years and T1DM diagnosed at least 1 year prior | 41 | 42 | 47.2 (15.1) | 44.6 (13.5) | 55 (26.4) | 8.0 (0.74) | 8.2 (0.52) | 16 (12.59) | 16.5 (13.3) |
| Chatzakis et al [] | Greece | 1 | Parallel | Age 7-17 years with T1DM | 40 | 40 | 13.8 (3) | 13.2 (2.7) | 39 (48.8) | 8.25 (0.8) | 7.9 (0.62) | 6.7 (4.4) | 6.1 (3.8) |
| Wadwa et al [] | America | 3 | Parallel | Age 2-6 years and T1DM diagnosed at least 1 month prior | 68 | 34 | 3.84 (1.23) | 4.06 (1.25) | 52 (51.0) | 7.5 (1.2) | 7.7 (0.9) | N/A | N/A |
| Ballesta et al [] | Spain | 1 | Parallel | Age >18 years and T1DM diagnosed at least 6 months prior | 26 | 29 | 52.5 (12.4) | 50.1 (12.5) | 27 (49.1) | 7.52 (0.72) | 7.61 (0.69) | 24.5 (12.2) | 20.0 (10.5) |
| Enander et al [] | Sweden | 3 | Parallel | Age ≤18 years with T1DM | 14 | 14 | 14.1 (3.2) | 13.2 (4.0) | N/A | 7.2 (0.6) | 7.7 (1.0) | N/A | N/A |
aRCT: randomized controlled trial.
bHbA1c: hemoglobin A1c.
cT1DM: type 1 diabetes mellitus.
dLADA: latent autoimmune diabetes in adults.
eSD was not reported in the study.
fN/A: not applicable.
Intervention Characteristics
CC interventions using different TM models exhibited considerable variability in glycemic control outcomes among patients with T1DM (Table 2). The interventions were categorized into 3 groups based on their content: smartphone apps (n=13), web-based systems (n=3), and connected or wearable glucometers (n=4).
Currently, smartphone apps are the most widely used, encompassing various types. These include the iSpy mobile app, OneTouch Reveal, Young with Diabetes mHealth app, Webdia mHealth app, GLIC APP, MySugr, Euglia, Social Diabetes, and the “artificial pancreas.” The web-based category comprises integrated network systems such as the ELKa system, cloud-based platforms, and computer programs accessible via the internet. Internet-connected glucometers, such as the Accu-Chek Connect by Roche, integrate self-monitoring of BG results with additional features, including an insulin calculator and a food diary. These tools assist users in calculating carbohydrate intake and managing BG levels more effectively while enabling clinicians to review accurate BG patterns for treatment adjustments [].
| Study | Technology | Control | Length | Primary outcome | Secondary outcomes | ||
| Satisfaction treatment | Time in range | Total daily insulin dose | |||||
| Kowalska et al [] | ELKa system | Usual care | 26 weeks | HbA1ca | N/Ab | N/A | ✓ |
| Alfonsi et al [] | iSpy mobile app | Usual care | 3 months | HbA1c | N/A | N/A | N/A |
| Charpentier et al [] | Diabeo software | Paper diaries followed up at the hospital outpatient clinic | 6 months | HbA1c | N/A | N/A | N/A |
| Rossi et al [] | Diabetes Interactive Diary TMc system | Usual care | 6 months | HbA1c | ✓ | N/A | N/A |
| de Oliveira et al [] | OneTouch Reveal mobile phone app | Usual care | 12 months | HbA1c | N/A | N/A | ✓ |
| Rossi et al [] | Diabetes Interactive Diary TM system | Usual care | 6 months | HbA1c | ✓ | N/A | N/A |
| Gunawardena et al [] | A smart glucose manager–based mobile app | Usual care | 6 months | HbA1c | N/A | N/A | N/A |
| Lee et al [] | Cloud system | Usual care | 12 weeks | HbA1c | ✓ | ✓ | ✓ |
| Castensøe-Seidenfaden et al [] | Young with Diabetes mHealth app | Usual care | 12 months | HbA1c | N/A | N/A | N/A |
| Schmidt et al [] | Accu-Chek Aviva Expert bolus calculator device | Hours of structured small-group instructions | 16 weeks | HbA1c | ✓ | N/A | ✓ |
| Klee et al [] | Webdia mHealth app | Usual care | 3 months | HbA1c | N/A | N/A | N/A |
| Hommel et al [] | StenoABC automated bolus calculator | Attending a 3.5-hour training session | 12 months | HbA1c | N/A | N/A | N/A |
| Boukhors et al [] | The computer program was accessible via the internet | Patients will record their blood glucose in their journals and adjust it according to the algorithm | 4 months | HbA1c | N/A | N/A | ✓ |
| Montanari et al [] | Intelligent glucometer (COMBO) | Usual care | 6 months | HbA1c | N/A | ✓ | N/A |
| Montanari et al [] | GLIC APP | Usual care | 6 months | HbA1c | N/A | N/A | N/A |
| Secher et al [] | MySugr app | Routine care | 26 weeks | HbA1c | N/A | ✓ | ✓ |
| Chatzakis et al [] | Euglia app | Usual care | 12 months | HbA1c | ✓ | N/A | N/A |
| Wadwa et al [] | AP app | Usual care | 16 weeks | HbA1c | N/A | ✓ | N/A |
| Ballesta et al [] | Social Diabetes app | Usual care | 6 months | HbA1c | N/A | N/A | N/A |
| Enander et al [] | Cozmo pump | Usual care | 12 months | HbA1c | N/A | N/A | ✓ |
aHbA1c: hemoglobin A1c.
bN/A: not applicable.
cTM: telemedicine.
Risk of Bias
Figure 2 presents the risk of biased outcomes for the 19 included articles, highlighting variability in study quality. Of these, 7 articles (37%) exhibited a low risk of bias, 11 (58%) demonstrated some risk of bias, and 1 (5%) had a high risk of bias. The high risk of bias was primarily attributed to inadequate descriptions of randomization methods, the absence of participant and researcher blinding during intervention allocation, and deviations from the planned intervention. Given the impracticality of blinding participants in TM interventions, all trials were conducted using an open-label design, resulting in at least one identified risk of bias per study. Our assessment revealed that 15 out of 19 articles (79%) adequately reported and described appropriate randomization methods, while 13 (68%) effectively communicated assignment concealment procedures. Additionally, 11 studies (58%) adhered to the intention-to-treat principle. As shown in Table 3, interrater agreement for risk of bias assessments was high, with Cohen κ values ranging from 0.642 to 1.00 across domains. A risk of bias summary is presented in 
Figure 3.
| Study | D1: Randomization process | D2: Deviations from intended interventions | D3: Missing outcome data | D4: Measurement of the outcome | D5: Selection of the reported result | D6: Overall bias |
| Kowalska et al [] | Low | High | Low | Low | Low | Some concerns |
| Alfonsi et al [] | Low | High | Low | Low | Low | Some concerns |
| Charpentier et al [] | Low | Low | Low | Low | Low | Low |
| Rossi et al [] | Low | Low | Low | Low | Some concerns | Some concerns |
| de Oliveira et al [] | High | High | Low | Low | Low | High |
| Rossi et al [] | Low | Low | Low | Low | Some concerns | Some concerns |
| Gunawardena et al [] | Some concerns | Low | Low | Low | Low | Some concerns |
| Lee et al [] | Some concerns | Low | Low | Low | Low | Some concerns |
| Castensøe-Seidenfaden et al [] | Low | Low | Low | Low | Low | Low |
| Schmidt et al [] | Low | Some concerns | Low | Low | Low | Some concerns |
| Klee et al [] | Low | Some concerns | Low | Low | Low | Some concerns |
| Hommel et al [] | Some concerns | Some concerns | Low | Low | Some concerns | Some concerns |
| Boukhors et al [] | Low | Low | Low | Low | Low | Low |
| Montanari et al [] | Some concerns | Low | Low | Low | Low | Some concerns |
| Secher et al [] | Low | Low | Low | Low | Low | Low |
| Chatzakis et al [] | Low | Low | Low | Low | Low | Low |
| Wadwa et al [] | Low | Low | Low | Low | Low | Low |
| Ballesta et al [] | Low | Low | Some concerns | Low | Low | Some concerns |
| Enander et al [] | Low | Low | Low | Low | Low | Low |
| κ | 0.747 | 1.00 | 0.642 | 1.00 | 0.826 | 0.912 |
Egger test revealed significant asymmetry in study distribution (P=.003). This asymmetry was also evident in the contour-enhanced funnel plot of HbA1c (
Figure 4; see also [-]), suggesting a degree of publication bias consistent with the Egger test results. In the profile-enhanced funnel plot after applying the trim-and-fill method (
Figure 5; see also [-]), 9 additional studies (represented by modest circles) were required to correct the observed asymmetry. While some studies fell within the nonsignificant region (white areas), indicating that no significant studies were left unpublished, others appeared in the statistically significant region (gray area), suggesting that certain significant studies may not have been published. This implies that factors beyond publication bias may have contributed to the observed funnel plot asymmetry for HbA1c.
Primary Outcome: HbA1c
Overview
A meta-analysis of 20 trials assessing HbA1c levels found that CC intervention through TM led to an overall reduction of –0.35% (95% CI –0.54% to –0.16%) compared with the control group (
Figure 6; see also [-]). However, the heterogeneity test indicated substantial variability among studies (I2=81%, P<.01).
Subgroup Analyses
Considerable heterogeneity was observed among the studies (I2=81%, P<.001). Among intervention types, smartphone apps showed the most substantial reduction in HbA1c (–0.36%, 95%CI –0.63% to –0.09%), with a significant effect; however, heterogeneity remained high (I2=86%, P<.001). Interventions using connected and wearable glucose meters also significantly reduced HbA1c (–0.34%, 95% CI –0.52% to –0.16%), but with low heterogeneity (I2=20%, P=.29). The smallest reduction in HbA1c was observed with network-based systems (–0.36%, 95% CI –0.71% to –0.02%), which also had a significant effect, with very low heterogeneity (I2=0%, P=.68). Despite these differences, the subgroup analysis of intervention approaches did not indicate significant heterogeneity (P>.99; Figure 6).
Sensitivity Analyses
Figure 7 (see also [-]) presents the results of the sensitivity analyses, performed by sequentially removing 1 study (trial) at a time. The random-effects model demonstrated that the MD in HbA1c levels remained stable throughout, indicating that the findings were robust, reliable, and not disproportionately influenced by any single study.
Meta-Analysis
We conducted a univariate meta-regression analysis to explore the influence of population and intervention characteristics on heterogeneity (Table 4). The results indicated that trial region was a significant factor affecting heterogeneity (P<.05). However, follow-up duration (P=.40), sample size (P=.34), percentage of male participants, baseline HbA1c level, intervention type, age, and risk of bias did not significantly contribute to heterogeneity. While trials conducted in Europe (n=12) and America (n=6) showed comparable MDs (–0.31% and –0.26%, respectively), trials from Asia (n=2) demonstrated a significantly greater reduction in HbA1c with TM intervention (MD –1.26%, 95% CI –1.88% to –0.64%).
| Variable | Sample, n | Mean difference (95% CI), % | P value | I2 statistic, % | |||
| Duration of follow-up (months) | 20 | 0.03 (–0.03 to 0.09) | .40 | 82.51 | |||
| Sample size | 20 | 0.00 (0.00 to 0.00) | .34 | 82.69 | |||
| Risk of bias | 20 | 82.98 | |||||
| Low | 7 | –0.32 (–0.66 to 0.02) | .07 | ||||
| Some concerns | 12 | –0.41 (–0.67 to –0.15) | <.001 | ||||
| High | 1 | –0.05 (–0.84 to 0.74) | .90 | ||||
| Baseline HbA1c(%) | 20 | 81.79 | |||||
| >8.0 | 14 | –0.44 (–0.67 to –0.22) | <.001 | ||||
| <8.0 | 6 | –0.14 (–0.48 to 0.20) | .42 | ||||
| Age (years) | 20 | 82.60 | |||||
| >18 | 11 | –0.39 (–0.65 to –0.13) | <.001 | ||||
| <18 | 7 | –0.39 (–0.73 to –0.05) | .03 | ||||
| All age stages | 2 | –0.07 (–0.69, 0.55) | .83 | ||||
| Location | 20 | 74.90 | |||||
| America | 6 | –0.31 (–0.62 to 0.00) | .04 | ||||
| Europe | 12 | –0.26 (–0.46 to –0.06) | .01 | ||||
| Asia | 2 | –1.26 (–1.88 to –0.64) | <.001 | ||||
| TMb type | 20 | 84.22 | |||||
| Internet system | 3 | –0.36 (–0.94 to 0.23) | .23 | ||||
| Smartphone-based mobile medical apps | 13 | –0.35 (–0.59 to –0.11) | <.001 | ||||
| Connected and wearable blood glucose meters | 4 | –0.37 (–0.83 to 0.10) | .12 | ||||
aHbA1c: hemoglobin A1c.
bTM: telemedicine.
Secondary Outcomes
Satisfaction With Diabetes Treatment
Five trials [,,,,] examined the effect of TM-based CC interventions on satisfaction with diabetes treatment. The Diabetes Treatment Satisfaction Questionnaire (DTSQ), a widely used 8-item scale, was the primary assessment tool. Of these items, 6 contributed to a total score ranging from 0 (indicating high dissatisfaction) to 36 (indicating high satisfaction), while the remaining 2 were analyzed separately to assess the perceived frequency of hyperglycemia and hypoglycemia episodes []. Both the DTSQs (state version) and DTSQc (change version) were evaluated. The findings revealed a modest increase in patient satisfaction with TM-based CC interventions, with a change in satisfaction score of 0.14 (95% CI –0.65 to –0.93). However, this difference was not statistically significant compared with standard care. While all 5 studies reported improved treatment satisfaction, only 3 [,,] demonstrated a significant difference (
Figure 8).
Time in Range
Five trials [,,,] assessed the effect of TM-based CC interventions on glucose TIR events. TIR represents the percentage of time that BG remains within the target range (70-180 mg/dL in all 5 trials) over 24 hours, providing a visual depiction of daily glucose fluctuations in individuals with T1DM. The 2019 International Congress on Advanced Diabetes Technology and Treatment (ATTD) and the 2020 China Guidelines for the Prevention and Treatment of Type 2 Diabetes Mellitus (T2DM) [] strongly recommend integrating TIR with self-monitoring of blood glucose, continuous glucose monitoring, and HbA1c to comprehensively assess glycemic control. This combination has become a key reference index for clinical diabetes management. According to the International Consensus on Continuous Glucose Monitoring, a 5% increase in TIR is associated with a reduced risk of chronic diabetic complications []. The findings demonstrated a significant increase in TIR, with a mean improvement of 9.59% (95% CI 6.50% to 12.67%). Moreover, TM-based CC interventions significantly enhanced TIR and reduced the incidence of diabetic complications compared with conventional care. Four trials (3 studies [,,]) reported statistically significant improvements in TIR, whereas 1 trial [] did not observe significant changes (
Figure 9).
Total Daily Insulin Dose
Seven trials [,,,,,,] were included in the meta-analysis of TDD. All studies reported insulin doses either as units per day or as units per kilogram per day. The results indicated a slight increase in TDD (0.09, 95% CI −0.12 to 0.31) with the TM-delivered CC intervention. However, no significant difference was observed between the TM-based CC intervention and usual care. While 4 studies [,,,] reported no significant changes in TDD, 3 studies [,,] showed an increase, though none reached statistical significance (
Figure 10).
Adverse Effects
Three studies [,,] did not mention hypoglycemia, and inconsistencies in its definition and reporting prevented a meta-analysis. Eight studies [,,,-] defined hypoglycemia based on objective BG values, with cut-off thresholds ranging from <3.9 mmol/L to <2.5 mmol/L. Meanwhile, 7 studies [-,-] defined hypoglycemic events as those requiring assistance from another person.
Discussion
Summary and Interpretation of Findings
This meta-analysis of RCTs evaluating TM-based CC interventions for BG control in patients with T1DM included data from 20 studies completed on September 26, 2024. TM-based CC interventions significantly reduced HbA1c levels by –0.35% (95% CI –0.54% to –0.16%) and improved glycemic control compared with controls. Considerable heterogeneity was observed among trials (I2=81%, P<.01), which may have led to an underestimation of the intervention effect and contributed to the asymmetry in the funnel plot, potentially due to factors other than publication bias. TM-based CC interventions significantly improved TIR (9.59%, 95% CI 6.50%-12.67%). However, no significant differences were found in hypoglycemia, treatment satisfaction, or TDD, possibly due to the short duration of the included trials.
The included studies were categorized based on the type of TM intervention: smartphone apps, connected and wearable glucometers, and web-based systems. Subgroup analysis showed that all intervention types had statistically significant effects on HbA1c levels. Meta-regression analysis indicated that the study region was significantly associated with changes in HbA1c levels. In terms of average HbA1c reduction, Asian participants appeared to benefit more than North American and European populations. However, this conclusion should be interpreted with caution, as only 2 studies from Asia were included, compared with 12 from Europe and 6 from North America.
Comparison With Prior Work
HbA1c
The findings of this study suggest that remote medical interventions for CC can significantly reduce HbA1c levels, aligning with previous research [,]. However, the magnitude of reduction varies across studies. For instance, Eberle and Stichling [] reported a significant HbA1c reduction in patients with T1DM and T2DM (MD –0.64%, 95% CI –1.01% to –0.26%), whereas Lee et al [] observed a smaller effect in patients with T1DM (MD –0.18%, 95% CI –0.33% to –0.04%). These discrepancies may be attributed to differences in patient characteristics, intervention implementation, and study sample sizes. Additionally, subgroup analysis in this study indicated that smartphone app–based remote interventions significantly reduced HbA1c levels in patients with T1DM, a finding that contrasts with Hou et al [], who reported minimal differences between intervention and control groups. This inconsistency may stem from the inclusion of fewer and lower-quality studies, contributing to high heterogeneity.
Satisfaction With Diabetes Treatment
The results indicated that CC interventions delivered via TM did not significantly improve treatment satisfaction in patients with T1DM, consistent with findings from other studies. Similarly, a recent study by Zhang et al [] on the effects of TM in children and adolescents with T1DM also reported no significant improvement in treatment satisfaction.
Time in Range
Our findings indicate that remote medical intervention for CC significantly improved TIR, aligning with previous research. A recent study on the impact of a closed-loop insulin system in patients with T1DM reported similar effects (MD 10.32%, 95% CI 8.70%-11.95%) []. This suggests that remote medical intervention can help patients better manage BG fluctuations and increase the time their blood sugar remains within target ranges.
Total Daily Insulin Dose
This study found that CC interventions using TM had no statistically significant effect on TDD in patients with T1DM. However, limited research has examined TDD as an outcome measure.
Strengths and Limitations
This study has several strengths. First, our research strategy involved an extensive search across multiple databases, enhancing the comprehensiveness of the review. Second, we adhered to rigorous systematic review and meta-analysis standards following PRISMA () and Cochrane guidelines. Third, we conducted comprehensive sensitivity analyses to ensure the robustness of our findings. Finally, subgroup and meta-regression analyses were performed to validate our results.
This study has several limitations. The primary limitation is the considerable heterogeneity among the included studies. Despite conducting subgroup analyses and meta-regression to explore potential sources of heterogeneity, the results should be interpreted with caution due to the influence of uncontrolled or unmeasured factors. Additionally, our review excluded RCT registries, ongoing studies, and gray literature. While the inclusion of gray literature in systematic reviews remains debated [], limiting the analysis to published studies may introduce publication bias. Furthermore, we restricted our search to English-language publications, which may affect the generalizability of our findings. Another limitation is the potential subjectivity in bias assessments using the RoB2 tool. Finally, many trials had small sample sizes, short durations, and lacked blinding. Most studies were conducted in developed countries, reflecting the limited availability and feasibility of technology-based interventions in low-income settings.
Implications for Practice and Future Research
Our findings have several practical implications. First, remote medical interventions for CC demonstrate beneficial effects on glycemic control in patients with T1DM. Thus, we recommend incorporating TM-based CC interventions into long-term glucose management strategies. However, successful implementation requires consideration of factors such as patient learning ability, adaptability, acceptance of technology, and the level of medical team support. As the time needed to achieve independent glucose management varies among individuals, long-term studies are warranted to further assess these interventions [].
Recent cross-sectional research on T1DM indicates that the likelihood of diabetic retinopathy increases with rising HbA1c levels. The United Kingdom Prospective Diabetes Study (UKPDS) [] found that a 1% reduction in average HbA1c was associated with a 21% reduction in diabetes-related deaths, a 14% reduction in the risk of myocardial infarction, and a 37% reduction in microvascular complications in patients with T2DM. If remote medical care–based CC interventions were implemented universally for patients with T1DM, they could potentially reduce diabetes-related deaths by 7.4%, myocardial infarction risk by 4.9%, and microvascular complications by 13%. Such interventions may also improve glycemic control, lower the risk of macrovascular and microvascular complications, and enhance quality of life. However, other important outcomes, including hypoglycemia incidence, TIR, quality of life, and self-management behaviors, remain understudied and require further evaluation. Additional research should also focus on the needs and characteristics of special populations, such as children and older adults, to enhance acceptance and effectiveness [].
Given these findings, our results suggest that remote medical care–based CC interventions hold significant potential for clinical application. Future efforts should focus on advancing technological innovation to develop more intelligent and user-friendly remote medical devices and apps, such as integrating artificial intelligence to provide personalized treatment recommendations. Additionally, strengthening international collaboration is essential to facilitate global implementation, particularly in low-income countries and remote areas. Encouraging patients to integrate these interventions into their daily lives and work could further enhance glycemic management in T1DM. Future research should prioritize evaluating the long-term effects and cost-effectiveness of remote medical interventions in diabetes management. Moreover, exploring their combined use with other diabetes management strategies could provide further insights. Large-scale, multicenter, and long-term follow-up clinical trials are necessary to assess their efficacy and safety in glycemic control for patients with T1DM. These findings would offer critical evidence to support policy makers in promoting and expanding the use of remote medical care in diabetes management.
Conclusions
Our systematic review and meta-analysis suggest that TM-based CC interventions can be effective for glycemic control in patients with T1DM in RCTs. However, a robust, large-scale trial is needed to draw definitive conclusions. These findings may inform the development of new strategies to enhance T1DM management.
Acknowledgments
This research was supported by the Science and Technology Development Plan Project Fund of Jilin Province (grant 20230203060SF) and the Medical and Health Talents Special Fund of Jilin Province (grant JLSWSRCZX2023-29).
Authors' Contributions
The study’s conception, design, and data interpretation were collectively undertaken by all authors. YL and YY were responsible for screening and extracting the data, and YL performed statistical analysis. YL wrote the first draft of the manuscript, which was later revised by all the authors for important intellectual content. All authors have read and approved the final manuscript.
Conflicts of Interest
None declared.
Multimedia Appendix 2
Web of Science Core Collection search trail (search updated 26/09/2024).
DOCX File , 17 KBMultimedia Appendix 6
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist.
PDF File (Adobe PDF File), 145 KBReferences
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Abbreviations
| ADA: American Diabetes Association |
| ATTD: Advanced Diabetes Technology and Treatment |
| BG: blood glucose |
| CC: carbohydrate counting |
| DTSQ: Diabetes Treatment Satisfaction Questionnaire |
| HbA1c: hemoglobin A1c |
| MD: mean difference |
| PICOS: Patients, Implementation, Comparison, Outcomes, Study |
| PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses |
| PROSPERO: International Prospective Register of Systematic Reviews |
| RCT: randomized controlled trial |
| SMD: standardized mean difference |
| T1DM: type 1 diabetes mellitus |
| T2DM: type 2 diabetes mellitus |
| TDD: total daily insulin dose |
| TIR: time in range |
| TM: telemedicine |
| UKPDS: United Kingdom Prospective Diabetes Study |
Edited by KJ Craig; submitted 16.04.24; peer-reviewed by P Sun, AN Ali; comments to author 17.09.24; revised version received 10.11.24; accepted 11.02.25; published 10.04.25.
Copyright©Yang Li, Yue Yang, Xiaoqin Liu, Xinting Zhang, Fei Li. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 10.04.2025.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.