The Science of Diabetes Self-Management and Care 2025, Vol. 51(6) 674–698 © The Author(s) 2025 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/26350106251378717 journals.sagepub.com/home/tde
Abstract
Purpose: The purpose of this systematic review and meta-analysis was to synthesize the best available evidence regarding the effectiveness of telehealth on glycemic stability, blood sugar, and quality of life (QoL) in patients with type 1 and type 2 diabetes.
Methods: Nine electronic databases were used for searching for published and unpublished randomized controlled trials (RCTs) conducted between 2010 and 2022, supplemented by hand search on references of relevant reviews. Two independent reviewers screened, appraised, and extracted data using standardized tools. Meta-analyses were performed using review manager software. Subgroup and sensitivity analysis were conducted.
Results: Twenty-three RCTs were included. Most studies supported the positive effects of telehealth interventions on glycemic stability and blood sugar, varying from small to large effect sizes. However, the pooled effect of QoL was very small. The use of mobile applications and telephone-delivered interventions appeared to be more effective than the internet platform. Studies conducted on young adults with type 1 diabetes and in European countries showed stronger effects.
Conclusions: This review suggested that telehealth interventions had the potential to improve glycemic stability and blood sugar. Health care professionals may adopt telehealth interventions for people with type 1 diabetes. Future research may explore participants’ experiences of the telehealth interventions in Asian countries.
Diabetes is a serious noncommunicable disease, recognized as an important cause of premature death and disability worldwide.1 There are 4 types of diabetes: type 1, type 2, gestational diabetes, and diabetes due to other causes.2 Type 1 diabetes and type 2 diabetes are mostly found among children and adolescents. Type 1 diabetes results from autoimmune-induced insulin insufficiency for controlling blood sugar, whereas type 2 diabetes is linked with genetics and inappropriate eating behaviors. The prevalence of diabetes around the world has consistently increased. In 2019, there were 463 million people with diabetes, and it is expected to increase to 700 million in 2045.3 There is a need for effective management of diabetes and for prevention interventions of diabetes complications.
The aims of diabetes management are to maintain normal blood sugar, improve symptoms, and minimize the risk of long-term complications. Poor management of diabetes results in blindness, kidney failure, heart attacks, stroke, and amputations.4 A survey reported that most children and adolescents with diabetes were unable to achieve normal blood sugar and that 10% to 45% of diabetes complications involved blood vessels, resulting in blindness and kidney failure.5 Thus, it is crucial to monitor blood sugar and to establish effective working relationships between patients and their health care professionals.
Glycemic stability is an important goal in the management of patients with diabetes. This term is unbiased and free from judgment.6 Glycemic stability remains a main therapeutic target for preventing organ damage and other complications. Poor glycemic stability is a major public health issue among patients with type 2 diabetes and a significant risk factor for the progression of diabetic complications.7 Glycemic stability can be measured by glycated hemoglobin (A1C) and blood sugar levels. A1C refers to average plasma glucose concentration. It develops when hemoglobin, a protein in red blood cells, joins with glucose in the blood, becoming “glycated.”8 By measuring A1C, clinicians can get an overall picture of average blood sugar in a period of weeks/months.2 Furthermore, blood sugar or blood glucose is measured from venous blood after at least 8 hours of overnight fasting.2
Quality of life (QoL) is an important health outcome, representing a goal of health interventions. Diabetes affects people’s QoL, and it becomes worse when complications develop or comorbidities coexist.9 The progression of diabetes, especially with poor glycemic stability, leads to life-threatening complications. QoL can be assessed by the diabetes quality of life (DQoL) measure.10 This scale has 4 dimensions of diabetes impact: satisfaction, treatment impact, anxiety for complications, and social issues. The DQoL has been widely used despite its limitations. Lower scores in this scale are associated with diabetic complications and glycemic stability.11
There is a growing body of evidence that supports the use of advanced and innovative technologies, such as telehealth, to monitor and manage people with diabetes at a distance.12-14 Nowadays, telehealth has been used to care for patients in remote areas by health care professionals by using information and communication technology. Telehealth encompasses exchanging information for diagnosis, delivering treatments and prevention interventions, consulting evaluation, and continuing follow-ups for people living in community.12 Telehealth refers to the exchange of medical information from one location to another using electronic communications or digital technologies, such as desktop, laptop, mobile phones, and other wireless equipment.15 There are several forms of telehealth for patients with diabetes to monitor their blood sugar, such as automated messages, telephone call, human message response system, human message with telephone call, teleconferencing,13,14 and electronic logbook with web-based application.12
Telehealth appears to effectively decrease blood sugar or A1C.14 The diabetes care and education specialist plays a pivotal role in championing the integration of technology in diabetes management. These specialists serve as key advocates for leveraging digital health tools, ensuring that patients and health care providers maximize the potential of telehealth interventions.16 However, the magnitude of the effect of telehealth remains unclear due to variations in diabetes types and populations. There are existing systematic reviews (SRs) on telehealth among people with type 1 diabetes and type 2 diabetes.19 Nevertheless, the pooled intervention effects of telehealth on glycemic stability and QoL remain inconclusive. Consequently, the researchers conducted this SR to synthesize the best available evidence concerning the effectiveness of telehealth on glycemic stability, blood sugar, and QoL among patients with type 1 diabetes or type 2 diabetes.
Research Question 1: In comparison to controls, what is the effectiveness of telehealth on glycemic stability, blood sugar, and QoL in patients with type 1 diabetes or type 2 diabetes?
This review was guided by the Cochrane handbook for systematic reviews of interventions18 and preferred reporting items for systematic reviews and meta-analysis (PRISMA).19 It has been registered on the PROPERO with reference number CRD42023434630.
The PICO (populations, interventions, comparator interventions, and outcomes) framework20 guided the selection of eligibility criteria. This review considered studies (a) examining patients with type 1 diabetes or type 2 diabetes regardless of sociocultural backgrounds, severity of diabetes, and settings; (b) delivering all telehealth interventions, such as internet, mobile application, telephone, and text messenger; and (c) comparing telehealth with standard care (usual care), active interventions, or no-intervention control. A primary outcome was glycemic stability as measured by A1C, and secondary outcomes were blood glucose, and QoL. Only randomized controlled trials (RCTs) reported in English between 2010 and 2022 were considered.
A comprehensive search was conducted using 12 databases. Published studies were identified from 9 electronic databases, including PubMed, EMBASE, Cochrane, MEDLINE, SCOPUS, Web of Science, ScienceDirect, ProQuest, and CINALH. Subsequently, gray literature (eg, unpublished thesis and conference proceedings) was searched using 3 databases, encompassing Google Scholar, Scopus, and ProQuest. Next, a hand search was conducted on reference lists of relevant RCTs and SRs.
Search terms including “type 1 diabetes,” “type 2 diabetes,” “telehealth,” “glycemic stability,” “A1C,” “blood sugar,” and “quality of life” were used in the search. Furthermore, Medical subject headings (MeSH terms) and control vocabulary were identified for each database. The MeSH terms and keywords were combined for a comprehensive search. Boolean operators were used: “AND” to combine terms, “OR” to broaden terms, and “NOT” to narrow terms. Based on the eligibility criteria, 2 reviewers independently screened the list of titles/abstracts identified through searches for systematic reviews. Disagreements were resolved by discussion and consensus. A full search strategy for all the databases is included in Table 1.
A selection process was guided by the PRISMA flow diagram.20 EndNote software21 was used to manage identified studies and remove duplicated recorded from various databases. First, 2 reviewers independently screened titles/abstracts according to the eligibility criteria. Next, we retrieved and screened full-text files of the eligible records. Disagreements were resolved by discussion and consensus. The third reviewer would be involved if there were disagreements between the 2 reviewers. The 2 sets of extracted data were then compared for quality and validity purposes. Finally, the included studies were assessed on their suitability for meta-analysis. Studies unsuitable for meta-analyses were retained for narrative synthesis.
Two independent reviewers assessed risk of bias of the included studies using the Cochrane Collaboration risk of bias tool.18 This checklist consists of 6 domains: random sequence generation (selection bias), allocation concealment (selection bias), blinding of participants and personnel (performance bias), blinding of outcome assessment (detection bias), incomplete outcome data (attrition bias), and selective outcome reporting (reporting bias).18 Each domain was rated as high, low, or unclear, with reasons to support each judgment and evidence. All studies were included regardless of the rating scores. If 2 reviewers were unable to achieve agreement, consensus, adjudication by a third independent reviewer, would be deemed necessary.
Two independent reviewers extracted information using the Cochrane Data Extraction form.22 The extracted data included study characteristics (authors, year of publication, study design, settings), publication details, participants (sample size), intervention characteristics, comparator, outcome measurements, and findings. Disagreement between the 2 reviewers would be resolved through discussion or by the third reviewer. If there was missing information, the researchers would contact study authors.
GRADEpro GDT software23 was used to summarize the quality of evidence of the studies’ analyses and strength of recommendations (GRADE). This checklist comprises 5 domains: study design, risk of bias, inconsistency, indirectness, and imprecision. The GRADE was rated as very low, low, moderate, or high, with reasons to support overall confidence of the findings.
Meta-analysis was performed using review manager software (RevMan).22 Pooled intervention effects are represented by standardized mean difference (SMD) for continuous variables. SMD is a statistical parameter that represents a standardized intervention effect on study outcomes with different measurement scales.24 In meta-analysis, SMD is calculated by dividing the mean difference (MD) between the treatment and control groups by the pooled standard deviation of the samples.24 Then, SMD would be interpreted as very small (<0.2), small (0.2-0.5), moderate (0.5-0.7), or large ( ≥0.8).25 Heterogeneity across the studies was assessed using chi-square and I2 values. I2 considers effect size, magnitude, and heterogeneity, and it is expressed as a percentage.18 The presence of substantial heterogeneity (I2 > 60%) would warrant sensitivity and/or subgroup analysis and the use of a random-effects model. A funnel plot generated in RevMan 5 was used to assess publication bias. In subgroup analysis, studies with varying platforms and frequency of telehealth, diabetes types, patient’s age, setting, and QoL were examined. Heterogeneity could be improved by extracting these studies into different groups and conducting meta-analysis separately.26
A total of 3379 records were identified from 12 databases and hand searching. After removing duplicates, 1844 records remained. After the abstract/title screening, 1535 records were removed. Next, 116 full texts were screened according to the eligibility criteria. Twenty-three articles met the eligibility criteria, 16 studies were included in the meta-analysis, and 7 studies were used for narrative review. Reasons for exclusion are listed in full in the PRISMA diagram (Figure 1).
The studies were conducted across 12 different countries: Greece (n = 2),27,28 Italy (n = 2),13,29 Israel (n = 2),30,31 United States (n = 7),32-38 Switzerland (n = 1),39 Portland (n = 1),40 Florida (n = 1),41 Spain (n = 2),42,43 United Kingdom (n = 2),44,45 China (n = 1),46 Australia (n = 1),47 and France (n = 1).48 Most studies used only 1 platform (n = 18), and others utilized more than 1 (n = 5). The delivery platforms included mobile application (n = 15), telephone (n = 9), text message (n = 5), and internet (n = 4). One RCT can use more than 1 platform. Characteristics of included RCTs are presented in Table 2. In addition, characteristics of interventions in each of the 20 studies are presented in Table 3.
Most studies had low risk of bias on random sequence generation, selective reporting, and other bias (Figures 2 and 3). Interestingly, most studies had unclear risk of bias on allocation concealment, performance bias, and detection bias. Regarding the GRADE assessment, it is suggested the overall confidence of findings was moderate for A1C and high for blood sugar and QoL (Table 4).
Another subgroup analysis was conducted according to the number of telehealth platforms: single-platform (n = 8) and multiplatform (n = 5). Subgroup differences were not significant (χ2 = 0.07, P = .79). Both the singleplatform (SMD = –0.21, 95% CI, –0.50, –0.07) and multiplatform subgroups (SMD = –0.16, 95%CI, –0.38, –0.05) had small effect sizes. High heterogeneity was observed in the single-platform (I2 = 62%, χ2 = 18.37, P = .01) but not in the multiplatform subgroup (I2 = 43%, χ2 = 7.05, P = .13; Figure 4).
Frequency of telehealth was categorized into 3 groups: single times (n = 6), multiple times (n = 4), and unidentified frequency (n = 3). The subgroup differences were not statistically significant (χ2 = 2.25, P = .32). Nevertheless, studies in the multiple-times subgroup had higher effect sizes with high heterogeneity (Figure 5). Studies in the single-times group had a small effect size with low heterogeneity.
There were 2 groups of diabetes types: (a) type 1 diabetes only and (b) mixed of type 1 diabetes and type 2 diabetes. Subgroup difference was not significant (χ2 = 0.49, P = .49). Nevertheless, type 1 diabetes subgroup had a larger effect size (SMD = –0.21, 95% CI, –0.41, –0.02) with moderate heterogeneity (I2 = 57%, χ2 = 25.42, P = .008; Figure 6).
Patients’ age range were divided into 2 subgroups: young adult (n = 11) and mix-age subgroups (n = 2). The young adults had the age range of 9 to 24, and the mix-age group had age ranging from 18 to 55. There was no significant difference (χ2 = 0.03, P = 0.87) between the young adult (Risk Ratio [RR] = –0.19) and mix-age subgroup (RR = –0.25). The young-adult subgroup demonstrated moderate heterogeneity (I2 = 50%, χ2 = 20.07, P = .03), and 1 RCT (3 comparisons) showed the strongest effect sizes.27 The mix-age subgroup showed high heterogeneity (I2 = 83%, χ2 = 5.88, P = .02; Figure 7).
A subgroup analysis was conducted according to geographical locations across 3 different continents: Asia (n = 2), Europe (n = 8), and North America (n = 3). The subgroup differences were not statistically significant (I2 = 62.8%, χ2 = 5.37, P = .07). The Europe subgroup had the highest effect size (SMD = –0.33, 95% CI, –0.55, –0.11) with moderate heterogeneity (Figure 8).
Six RCTs examined the effect of telehealth on glycemic. However, their findings reported a narrative summary due to insufficient statistical parameters (eg, mean and standard deviation). Three out of 6 RCTs supported the effectiveness of telehealth. One RCT32 reported that participants who used the MyDay mobile application showed significantly improved glycemic stability in comparison with control group (χ2 = 7.12, P = .028). The MyDay app allowed flexible creation of data collection content, format, and timing. Participants were able to take photos of meals and store them in the app gallery.
One RCT32 examined an interactive computer-based tool (iDecide), consisting of 4 main sections. The first section presents information and illustrative animations on diabetes, medications, diet, and physical activity affecting blood sugar. In the second section, participants viewed their own risk of diabetes complications and pictographs of changed complication risks. In the third section, participants reviewed their current diabetes medications and barriers to taking medications. In the fourth section, participants are prompted to set goals, develop a specific action plan, and generate specific questions/concerns to discuss with their doctor. Results suggested that participants in the intervention group improved A1C to 8.38% (P < .001) at 6 months and at 12 months, improved A1C to 8.52% (P = .002). Another RCT33 delivered IDEAL CGM (continuous glucose monitor), web-based education modules, to the patients with type 1 diabetes over 6 weeks. Results indicated that the telehealth group shown reduction in glycemic stability at 12 months. Nevertheless, 3 RCTs32,45,46 did not observe the significant effects of telehealth.
Effectiveness of Telehealth on Secondary Outcomes Blood Sugar
Change scores of blood sugar. Two RCTs examined the effect of telehealth on changed scores of blood sugar. Results revealed high heterogeneity (χ2 = 19.51, P < .0001, I2 = 95%), and therefore, the pooled effect was not used. One study43 had a strong effect size (SMD = –1.06, 95% CI, –1.72, 1.05). In this study, the intervention group used smartphone applications to monitor blood sugar in real time, and the control group used self-monitoring of blood glucose (Figure 9).
One RCT examined the effect of telehealth on an average blood sugar (mean/standard deviation).29 The intervention group received a control diabetes management system and remote monitoring and control diabetes management system (MDRS), and the control group received sensor-augmented pump therapy. The MDRS was a reliable and safe remote monitoring system at home, comprising 2 modules: a remote monitoring module and a safety module. The remote monitoring module provided real-time tracking, monitoring, and control of the patient’s glycemic status. This technique provided real-time learning abilities and safety modules designed to prevent hypoglycemia episodes and prolonged hyperglycemia episodes. Findings showed that the MDRS group (mean = 159.6 mg/dL, SD = 26.6) had lower blood sugar in comparison to the control group (mean = 166.3 mg/dL, SD = 46.8). Another RCT41 tested the combination of telemedicine system and mobile technologies with the use of CGMs. Findings suggested that patients changed diabetes management by increasing the number of daily boluses of insulin injection (5.27 vs 4.40, P < .01) and increased number of daily blood glucose measurements (4.68 vs 4.05, P < .05).
Three RCTs examined the effect of telehealth on QoL. The results suggested a very small effect size (SMD = –0.10, 95% CI, –0.32, 0.12) with no heterogeneity (χ2 = 0.55, P = .76, I2 = 0%; Figure 10).
This review investigated the effectiveness of telehealth interventions on glycemic stability, blood sugar, and QoL in patients with type 1 diabetes and type 2 diabetes. Twenty-three RCTs fulfilled the inclusion criteria. Most included studies revealed that telehealth interventions improved glycemic stability (A1C) and blood sugar in comparison with controls. Subgroup analyses on glycemic stability further suggested greater effects of telehealth on type 1 diabetes, mobile application, telephone, multiple-time intervention, and European subgroups. Nevertheless, telehealth interventions had a very small effect on QoL (SMD = –0.10), indicating that the difference in QoL between the intervention group and the control was minimal. This suggests that the impact of the intervention might not be noticeable or meaningful in a real-world setting.24
Telehealth interventions had a slightly larger pooled effect size (intervention effect) on people with type 1 diabetes than that of the mixed group. A possible explanation might be linked to patients’ adherence with the use of telehealth. Most patients with type 1 diabetes were adolescents, who were accustomed to technology such as mobile applications.29 A previous SR added that telemedicine significantly reduced glycemic stability, blood pressure and cholesterol in patients with type 2 diabetes.17
Positive findings on A1C were observed among studies utilizing mobile applications or telephone in delivering telehealth interventions. Two RCTs produced larger effect sizes, one of which29 used the mobile application Euglyca in people with type 1 diabetes. Euglyca calculated an appropriate insulin bolus dose for each meal based on carbohydrates, lipids, glucose levels, and personalized parameters. All these parameters in the Euglyca application were adjusted any time of the day for any type of meal that tailored to the patient’s specific needs. Euglyca might motivate self-management actions with less burden. Another RCT30 utilized telenursing in the management of type 1 diabetes. All participants recorded their blood glucose 3 times a day, 7 days a week, for 3 months. Subsequently, they reported the recorded values to a diabetes specialist nurse via telephone. Later, they received telephone calls every Thursday from the coordinator-nurse for diabetes management to offer support. Furthermore, results from narrative summary further supported the effectiveness of telehealth intervention on glycemic stability, especially via mobile applications.34,36,37 It is perceived that telehealth might motivate self-monitoring and self-management of diabetes, contributing to improved glycemic stability.47 These findings are consistent with a previous SR regarding the positive effects of telehealth interventions for diabetes control among patients with diabetes.49
Interestingly, studies conducted in European countries had slightly greater effect size than that of other regions. A possible reason is that most people in European countries were more independent and more accustomed to selfcare than those in Asian cultures. They used mobile applications and could manage diabetes by themselves in a safe and effective way, contributing to improved glycemic stability.29 Nevertheless, they were receptive to nurses’ telephone calls for blood glucose measurements, advice, and support.32
Telehealth interventions improved blood sugar among people with diabetes. One RCT45 generated a large effect, and the intervention group used a mobile application CGM to monitor blood sugar in real time. All participants installed the CGM app on their own personal smartphone. They were also trained to use the CGM to check their blood sugar many times per day. Furthermore, another RCT43 also supported the effectiveness of CGM on improving patients’ glycemic stability. Possible explanations for the positive results could be that the reduced daily burden of checking blood sugar enhance diabetes management.50 These findings are consistent with systematic reviews in those positive effects of transmission of blood glucose to computer network via glucometer or Bluetooth glucometer for achieving glycemic targets in people with type 1 diabetes.17
Telehealth interventions had a small effect on QoL, which contradicts with a previous SR that reported telehealth interventions were effective for improving QOL.17 However, only 1 RCT38 in this SR showed the potential to improve QOL in people with type 1 diabetes. In this RCT,38 the intervention group received text messages to enhance their personal awareness on diabetes management. Interestingly, a survey found that most of American teens owned mobile phones and that at least 63% used text messages daily.51 Furthermore, text message was found to be a favorite mode of communication to enhance diabetes management by extending health interventions beyond traditional care.
Telehealth interventions had the potential to lower glycemic stability (A1C), especially for those delivered with mobile applications and telephone-delivered systems. Therefore, when preparing interventions for patients with type 1 diabetes and type 2 diabetes, health care providers may deliver them via those platforms. Using the mobile applications, the following telehealth may be offered: CGM, Euglyca, MyDay app, and iDecide program. With the apps, people with diabetes will be able to share relevant data at any time, even outside clinical settings, strengthening self-monitoring and self-management. Furthermore, interventions may include telephone calls (provided by diabetes specialist nurses or general health care providers) or text messages. These communication and education for patients with diabetes will enhance QoL and improve glycemic stability among patients. Additionally, the unique remote monitoring and control diabetes management system (MDRS) may be used for the remote monitoring of blood sugar.
The strengths of this SR lie in a comprehensive search of published and gray literature to minimize publication bias. Furthermore, the use of RCT, meta-analysis, and subgroup analyses helped generate strong evidence to support the telehealth interventions for people with type 1 diabetes and type 2 diabetes. The GRADE assessment ranging from moderate to high suggested that the evidence can be translated to clinical practice. Nonetheless, this review is not without limitations. Specifically, all RCTs were reported in English, and therefore, RCTs reported in other languages may be overlooked. Moreover, the included RCTs for glycemic stability had unclear risk of bias on allocation concealment, performance assessors, and detection assessors.
This review examined the best available evidence concerning the effectiveness of telehealth interventions on glycemic stability, blood sugar, and QoL in patients with type 1 diabetes and type 2 diabetes. The findings suggested that telehealth interventions had the potential to improve glycemic stability (A1C) and blood sugar. Greater effects of telehealth were observed on type 1 diabetes, mobile application, telephone, multiple-time intervention, and European subgroups. Nevertheless, telehealth interventions had a very small effect on QoL. Certain telehealth interventions could be offered to people with diabetes in clinical settings. More RCTs are required for other outcomes such as self-efficacy.
SS was involved in protocol development, data screening, data extraction, data appraisal, and data analysis. PKY contributed to protocol development, data extraction, and data appraisal. SS wrote the first draft of the manuscript. Both SS and PKY reviewed and edited the manuscript and approved the final version of the manuscript.
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
The authors received no financial support for the research, authorship, and/or publication of this article.
This is systematic review, and therefore, ethical approval is not applicable.
This systematic review follows the PRISMA guideline.
PKY.
Sathima Suratham https://orcid.org/0000-0002-4460-205X
Piyanee Klainin-Yobas https://orcid.org/0000-0003-2581-4572
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From Department of Pediatric Nursing, Faculty of Nursing, Mahidol University, Bangkoknoi, Bangkok, Thailand (Dr Suratham); and Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore (Dr Klainin-Yobas).
Corresponding Author: Piyanee Klainin-Yobas, Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Level 5, Centre of Translation Medicine, Block MD6M 14 Medical Drive, 117599, Singapore. Email: nurpk@nus.edu.sg
