Improving HbA1c with Glucose Self-Monitoring in Diabetic Patients with EpxDiabetes, a Phone Call and Text Message-Based Telemedicine Platform: A Randomized Controlled Trial

The effect of telehealth on clinical outcomes in patients with hypertension and diabetes: A meta-analysis of 106,261 patients

INTRODUCTION

Telemedicine, propelled by recent technological advancements, has transformed healthcare delivery, notably benefiting patients with chronic non-communicable diseases (NCDs) such as systemic arterial hypertension and diabetes mellitus. This meta-analysis of randomized clinical trials aimed to assess the efficacy of telehealth-based interventions on disease control rates and clinical parameters among NCD patients, including systolic and diastolic blood pressure (SBP and DBP), fasting blood glucose (FBG), and glycated hemoglobin (HbA1c) levels.

METHODS

We conducted searches in PubMed, Scopus, Web of Science, and the Cochrane Database for interventional studies that compared tele-monitoring with usual care in patients with hypertension and type 2 diabetes mellitus. Odds ratios with 95% confidence intervals (CIs) were computed.

RESULTS

Our meta-analysis included 75 studies, encompassing a total of 106,261 patients, with 50,074 (47.12%) receiving usual care and 56,187 (52.88%) receiving tele-monitoring care. The telemedicine group was associated with a statistically significant reduction in SBP (mean difference (MD) -4.927 mmHg; 95% CI -6.193 to -3.660; p < 0.001; I² = 90%), DBP (MD -2.019 mmHg; 95% CI -2.679 to -1.359; p < 0.001; I² = 54%), FBG (MD -0.405 mmol/L; 95% CI -0.597 to -0.213; p < 0.001; I² = 32%), and HbA1c (MD -0.418%; 95% CI -0.525 to -0.312; p < 0.001; I² = 76%).

CONCLUSIONS

Our meta-analysis shows that telehealth technologies notably enhance blood pressure and blood glucose control. This supports integrating telemedicine into clinical protocols as a valuable complementary tool for managing hypertension and diabetes mellitus comprehensively.

Implementing mobile text messaging on glycemic control in patients with diabetes mellitus

BACKGROUND

Uncontrolled blood glucose may lead to serious complications in patients with type two diabetes mellitus (T2DM). Patients may not have the support, motivation, or encouragement to adhere to the lifestyle changes necessary to control their T2DM.

LOCAL PROBLEM

More than 75% of the primary care clinic's patients are diagnosed with T2DM, with most patients at the practice site having an average hemoglobin A1c (HbA1c) level of 8.5%. The primary care clinic did not use text messaging to disseminate diabetes self-management education and support (DSMES) as outlined in Standard 4 of the American Diabetic Association's (ADA) clinical practice guideline.

METHODS

This evidence-based quality improvement project was conducted in a rural outpatient primary care clinic to determine whether implementing the ADA's 2022 National Standards for DSMES using text messaging would affect HbA1C levels among adult patients with T2DM.

INTERVENTIONS

Patients were sent weekly text messages over a 12-week period. Text messages contained information promoting self-care, tips about healthy diet, exercise reminders, instructions about proper blood glucose monitoring, and reminders about medication adherence.

RESULTS

A total of 160 patients were included. A paired-sample t -test showed a reduction in HbA1c levels after the intervention from baseline (M = 7.53, SD = 1.72) to postimplementation (M = 6.91, SD = 0.89), t (159) = 11.88, p = .001.

CONCLUSION

Based on the results, implementing the ADA's National Standards for DSMES Standard 4 may affect HbA1c levels in this population.

User Engagement With mHealth Interventions to Promote Treatment Adherence and Self-Management in People With Chronic Health Conditions: Systematic Review

BACKGROUND

There are numerous mobile health (mHealth) interventions for treatment adherence and self-management; yet, little is known about user engagement or interaction with these technologies.

OBJECTIVE

This systematic review aimed to answer the following questions: (1) How is user engagement defined and measured in studies of mHealth interventions to promote adherence to prescribed medical or health regimens or self-management among people living with a health condition? (2) To what degree are patients engaging with these mHealth interventions? (3) What is the association between user engagement with mHealth interventions and adherence or self-management outcomes? (4) How often is user engagement a research end point?

METHODS

Scientific database (Ovid MEDLINE, Embase, Web of Science, PsycINFO, and CINAHL) search results (2016-2021) were screened for inclusion and exclusion criteria. Data were extracted in a standardized electronic form. No risk-of-bias assessment was conducted because this review aimed to characterize user engagement measurement rather than certainty in primary study results. The results were synthesized descriptively and thematically.

RESULTS

A total of 292 studies were included for data extraction. The median number of participants per study was 77 (IQR 34-164). Most of the mHealth interventions were evaluated in nonrandomized studies (157/292, 53.8%), involved people with diabetes (51/292, 17.5%), targeted medication adherence (98/292, 33.6%), and comprised apps (220/292, 75.3%). The principal findings were as follows: (1) >60 unique terms were used to define user engagement; "use" (102/292, 34.9%) and "engagement" (94/292, 32.2%) were the most common; (2) a total of 11 distinct user engagement measurement approaches were identified; the use of objective user log-in data from an app or web portal (160/292, 54.8%) was the most common; (3) although engagement was inconsistently evaluated, most of the studies (99/195, 50.8%) reported >1 level of engagement due to the use of multiple measurement methods or analyses, decreased engagement across time (76/99, 77%), and results and conclusions suggesting that higher engagement was associated with positive adherence or self-management (60/103, 58.3%); and (4) user engagement was a research end point in only 19.2% (56/292) of the studies.

CONCLUSIONS

The results revealed major limitations in the literature reviewed, including significant variability in how user engagement is defined, a tendency to rely on user log-in data over other measurements, and critical gaps in how user engagement is evaluated (infrequently evaluated over time or in relation to adherence or self-management outcomes and rarely considered a research end point). Recommendations are outlined in response to our findings with the goal of improving research rigor in this area.

TRIAL REGISTRATION

PROSPERO International Prospective Register of Systematic Reviews CRD42022289693; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022289693.

The effectiveness of telemedicine in the management of type 2 diabetes: A systematic review

Background

Type 2 diabetes, a lifestyle-related disease demanding daily self-management, is a significant health concern. In this context, the use of telemedicine as a management tool is a relatively new and promising approach. This study aims to contribute to the growing body of knowledge by identifying the effectiveness of telemedicine in managing type 2 diabetes through a systematic review approach.

Methods

Four databases were searched including PubMed, Virtual Health Library, Global Health Library, and Google Scholar on 27 July 2022. Additionally, a manual search was performed to identify any relevant articles that may have been missed. The quality of the included articles was rigorously assessed using the Study Quality Assessment Tools of the National Institute of Health.

Results

We analyzed data from 134 articles. All 134 studies were published between 2002 and 2022, including 103 controlled intervention trials, 13 cohort studies, 7 before-after (pre-post) studies with no control group, 1 initial trial, 1 case study, 1 pilot study, and 8 two-arm studies that did not report the study design. Accordingly, most studies show positive changes in glycemic index in every group using telemedicine. Overall, although the BMI and weight indices in the studies improved at the end of the course, the improvement values were considered insignificant.

Conclusion

Telemedicine may be a valuable solution for blood sugar management in patients with type 2 diabetes. However, the effectiveness of telemedicine in improving BMI and quality of life is unclear.

Variations in the Design and Use of Attention Control Groups in Type 2 Diabetes Randomized Controlled Trials: a Systematic Review

PURPOSE OF REVIEW

In type 2 diabetes (T2D) research, the phrase "attention control group" (ACGs) has been used with varying descriptions. We aimed to systematically review the variations in the design and use of ACGs for T2D studies.

RECENT FINDINGS

Twenty studies utilizing ACGs were included in the final evaluation. Control group activities had the potential to influence the primary outcome of the study in 13 of the 20 articles. Prevention of contamination across groups was not mentioned in 45% of the articles. Eighty-five percent of articles met or somewhat met the criteria for having comparable activities between the ACG and intervention arms. Wide variations in descriptions and the lack of standardization have led to an inaccurate use of the phrase "ACGs" when describing the control arm of trials, indicating a need for future research with focus on the adoption of uniform guidelines for use of ACGs in T2D RCTs.

Quality improvement strategies for diabetes care: Effects on outcomes for adults living with diabetes

BACKGROUND

There is a large body of evidence evaluating quality improvement (QI) programmes to improve care for adults living with diabetes. These programmes are often comprised of multiple QI strategies, which may be implemented in various combinations. Decision-makers planning to implement or evaluate a new QI programme, or both, need reliable evidence on the relative effectiveness of different QI strategies (individually and in combination) for different patient populations.

OBJECTIVES

To update existing systematic reviews of diabetes QI programmes and apply novel meta-analytical techniques to estimate the effectiveness of QI strategies (individually and in combination) on diabetes quality of care.

SEARCH METHODS

We searched databases (CENTRAL, MEDLINE, Embase and CINAHL) and trials registers (ClinicalTrials.gov and WHO ICTRP) to 4 June 2019. We conducted a top-up search to 23 September 2021; we screened these search results and 42 studies meeting our eligibility criteria are available in the awaiting classification section.

SELECTION CRITERIA

We included randomised trials that assessed a QI programme to improve care in outpatient settings for people living with diabetes. QI programmes needed to evaluate at least one system- or provider-targeted QI strategy alone or in combination with a patient-targeted strategy. - System-targeted: case management (CM); team changes (TC); electronic patient registry (EPR); facilitated relay of clinical information (FR); continuous quality improvement (CQI). - Provider-targeted: audit and feedback (AF); clinician education (CE); clinician reminders (CR); financial incentives (FI). - Patient-targeted: patient education (PE); promotion of self-management (PSM); patient reminders (PR). Patient-targeted QI strategies needed to occur with a minimum of one provider or system-targeted strategy.

DATA COLLECTION AND ANALYSIS

We dual-screened search results and abstracted data on study design, study population and QI strategies. We assessed the impact of the programmes on 13 measures of diabetes care, including: glycaemic control (e.g. mean glycated haemoglobin (HbA1c)); cardiovascular risk factor management (e.g. mean systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDL-C), proportion of people living with diabetes that quit smoking or receiving cardiovascular medications); and screening/prevention of microvascular complications (e.g. proportion of patients receiving retinopathy or foot screening); and harms (e.g. proportion of patients experiencing adverse hypoglycaemia or hyperglycaemia). We modelled the association of each QI strategy with outcomes using a series of hierarchical multivariable meta-regression models in a Bayesian framework. The previous version of this review identified that different strategies were more or less effective depending on baseline levels of outcomes. To explore this further, we extended the main additive model for continuous outcomes (HbA1c, SBP and LDL-C) to include an interaction term between each strategy and average baseline risk for each study (baseline thresholds were based on a data-driven approach; we used the median of all baseline values reported in the trials). Based on model diagnostics, the baseline interaction models for HbA1c, SBP and LDL-C performed better than the main model and are therefore presented as the primary analyses for these outcomes. Based on the model results, we qualitatively ordered each QI strategy within three tiers (Top, Middle, Bottom) based on its magnitude of effect relative to the other QI strategies, where 'Top' indicates that the QI strategy was likely one of the most effective strategies for that specific outcome. Secondary analyses explored the sensitivity of results to choices in model specification and priors.  Additional information about the methods and results of the review are available as Appendices in an online repository. This review will be maintained as a living systematic review; we will update our syntheses as more data become available.

MAIN RESULTS

We identified 553 trials (428 patient-randomised and 125 cluster-randomised trials), including a total of 412,161 participants. Of the included studies, 66% involved people living with type 2 diabetes only. Participants were 50% female and the median age of participants was 58.4 years. The mean duration of follow-up was 12.5 months. HbA1c was the commonest reported outcome; screening outcomes and outcomes related to cardiovascular medications, smoking and harms were reported infrequently. The most frequently evaluated QI strategies across all study arms were PE, PSM and CM, while the least frequently evaluated QI strategies included AF, FI and CQI. Our confidence in the evidence is limited due to a lack of information on how studies were conducted.  Four QI strategies (CM, TC, PE, PSM) were consistently identified as 'Top' across the majority of outcomes. All QI strategies were ranked as 'Top' for at least one key outcome. The majority of effects of individual QI strategies were modest, but when used in combination could result in meaningful population-level improvements across the majority of outcomes. The median number of QI strategies in multicomponent QI programmes was three.  Combinations of the three most effective QI strategies were estimated to lead to the below effects:  - PR + PSM + CE: decrease in HbA1c by 0.41% (credibility interval (CrI) -0.61 to -0.22) when baseline HbA1c < 8.3%; - CM + PE + EPR: decrease in HbA1c by 0.62% (CrI -0.84 to -0.39) when baseline HbA1c > 8.3%;  - PE + TC + PSM: reduction in SBP by 2.14 mmHg (CrI -3.80 to -0.52) when baseline SBP < 136 mmHg; - CM + TC + PSM: reduction in SBP by 4.39 mmHg (CrI -6.20 to -2.56) when baseline SBP > 136 mmHg;  - TC + PE + CM: LDL-C lowering of 5.73 mg/dL (CrI -7.93 to -3.61) when baseline LDL < 107 mg/dL; - TC + CM + CR: LDL-C lowering by 5.52 mg/dL (CrI -9.24 to -1.89) when baseline LDL > 107 mg/dL. Assuming a baseline screening rate of 50%, the three most effective QI strategies were estimated to lead to an absolute improvement of 33% in retinopathy screening (PE + PR + TC) and 38% absolute increase in foot screening (PE + TC + Other).

AUTHORS' CONCLUSIONS

There is a significant body of evidence about QI programmes to improve the management of diabetes. Multicomponent QI programmes for diabetes care (comprised of effective QI strategies) may achieve meaningful population-level improvements across the majority of outcomes. For health system decision-makers, the evidence summarised in this review can be used to identify strategies to include in QI programmes. For researchers, this synthesis identifies higher-priority QI strategies to examine in further research regarding how to optimise their evaluation and effects. We will maintain this as a living systematic review.

Effects of E-health-based interventions on glycemic control for patients with type 2 diabetes: a Bayesian network meta-analysis

The high disease burden of type 2 diabetes seriously affects the quality of life of patients, and with the deep integration of the Internet and healthcare, the application of electronic tools and information technology to has become a trend for disease management. The aim of this study was to evaluate the effectiveness of different forms and durations of E-health interventions in achieving glycemic control in type 2 diabetes patients. PubMed, Embase, Cochrane, and Clinical Trials.gov were searched for randomized controlled trials reporting different forms of E-health intervention for glycemic control in type 2 diabetes patients, including comprehensive measures (CM), smartphone applications (SA), phone calls (PC), short message service (SMS), websites (W), wearable devices (WD), and usual care. The inclusion criteria were as follows: (1) adults (age≥18) with type 2 diabetes mellitus; (2) intervention period ≥1 month; (3) outcome HbA1c (%); and (4) randomized control of E-health based approaches. Cochrane tools were used to assess the risk of bias. R 4.1.2 was used to conduct the Bayesian network meta-analysis. A total of 88 studies with 13,972 type 2 diabetes patients were included. Compared to the usual care group, the SMS-based intervention was superior in reducing HbA1c levels (mean difference (MD)-0.56, 95% confidence interval (CI): -0.82 to -0.31), followed by SA (MD-0.45, 95% CI: -0.61 to -0.30), CM (MD-0.41, 95% CI: -0.57 to -0.25), W (MD-0.39, 95% CI: -0.60 to -0.18) and PC (MD-0.32, 95% CI: -0.50 to -0.14) (p < 0.05). Subgroup analysis revealed that intervention durations of ≤6 months were most effective. All type of E-health based approaches can improve glycemic control in patients with type 2 diabetes. SMS is a high-frequency, low-barrier technology that achieves the best effect in lowering HbA1c, with ≤6 months being the optimal intervention duration.

Systematic review registration

https://www.crd.york.ac.uk/prospero, identifier CRD42022299896.

Frequent Monitoring of Blood Glucose Levels via a Remote Patient Monitoring System Helps Improve Glycemic Control

OBJECTIVE

This study aims to evaluate the effectiveness of the Vivovitals diabetes platform on improving glycemic control and reducing A1C in patients with uncontrolled type II diabetes mellitus by providing more accessible and direct patient care under the monitoring and oversight of their physician.

METHODS

This 12-week, prospective, pragmatic, single-center, double-arm study assessed the impact of the Vivovitals diabetes platform on glycemic control among 78 adults ≥ 18 years of age, with A1C ≥ 7.5% (58 mmol/mol) at baseline. The participants were randomized into two groups. The control group received usual clinic care, while the intervention group was provided with a smartphone linked telehealth application, a pre-configured glucometer, and access to the glycemic readings diary. Blood glucose levels of the intervention group were transmitted to the providers daily. Patients whose blood glucose levels were <70 mg/dL or >180mg/dL were contacted, and modifications were made to diet and medication. The two groups were compared at baseline and 12 weeks using nonparametric tests with p<0.05 considered statistically significant.

RESULTS

Over 12 weeks, the average A1C in the control group reduced by 0.474% (p=0.533; 95% CI: -0.425, -0.523) whereas the average A1C in the intervention group reduced 1.70% (p=0.002; 95% CI: -1.02, -2.39). The estimated treatment difference was expressed via Cohen's d which yielded 0.62. After 12 weeks, A1C values between the control and intervention groups were statistically significant (p = 0.001).

CONCLUSION

Use of the Vivovitals platform may help to improve glycemic control among individuals with type 2 diabetes mellitus.

Effectiveness, reach, uptake, and feasibility of digital health interventions for adults with type 2 diabetes: a systematic review and meta-analysis of randomised controlled trials

BACKGROUND

Digital health interventions have shown promising results for the management of type 2 diabetes, but a comparison of the effectiveness and implementation of the different modes is not currently available. Therefore, this study aimed to compare the effectiveness of SMS, smartphone application, and website-based interventions on improving glycaemia in adults with type 2 diabetes and report on their reach, uptake, and feasibility.

METHODS

In this systematic review and meta-analysis, we searched CINAHL, Cochrane Central, Embase, MEDLINE, and PsycInfo on May 25, 2022, for randomised controlled trials (RCTs) that examined the effectiveness of digital health interventions in reducing glycated haemoglobin A1c (HbA1c) in adults with type 2 diabetes, published in English from Jan 1, 2009. Screening was carried out using Covidence, and data were extracted following Cochrane's guidelines. The primary endpoint assessed was the change in the mean (and 95% CI) plasma concentration of HbA1c at 3 months or more. Cochrane risk of bias 2 was used to assess risk of bias. Data on reach, uptake, and feasibility were summarised narratively and data on HbA1c reduction were synthesised in a meta-analysis. Grading of Recommendations, Assessment, Development, and Evaluation criteria was used to evaluate the level of evidence. The study was registered with PROSPERO, CRD42021247845.

FINDINGS

Of the 3236 records identified, 56 RCTs from 24 regions (n=11 486 participants), were included in the narrative synthesis, and 26 studies (n=4546 participants) in the meta-analysis. 20 studies used SMS as the primary mode of delivery of the digital health intervention, 25 used smartphone applications, and 11 implemented interventions via websites. Smartphone application interventions reported higher reach compared with SMS and website-based interventions, but website-based interventions reported higher uptake compared with SMS and smartphone application interventions. Effective interventions, in general, included people with greater severity of their condition at baseline (ie, higher HbA1c) and administration of a higher dose intensity of the intervention, such as more frequent use of smartphone applications. Overall, digital health intervention group participants had a -0·30 (95% CI -0·42 to -0·19) percentage point greater reduction in HbA1c, compared with control group participants. The difference in HbA1c reduction between groups was statistically significant when interventions were delivered through smartphone applications (-0·42% [-0·63 to -0·20]) and via SMS (-0·37% [-0·57 to -0·17]), but not when delivered via websites (-0·09% [-0·64 to 0·46]). Due to the considerable heterogeneity between included studies, the level of evidence was moderate overall.

INTERPRETATION

Smartphone application and SMS interventions, but not website-based interventions, were associated with better glycaemic control. However, the studies' heterogeneity should be recognised. Considering that both smartphone application and SMS interventions are effective for diabetes management, clinicians should consider factors such as reach, uptake, patient preference, and context of the intervention when deciding on the mode of delivery of the intervention. Nine in ten people worldwide own a feature phone and can receive SMS and four in five people have access to a smartphone, with numerous smartphone applications being available for diabetes management. Clinicians should familiarise themselves with this modality of programme delivery and encourage people with type 2 diabetes to use evidence-based applications for improving their self-management of diabetes. Future research needs to describe in detail the mediators and moderators of the effectiveness and implementation of SMS and smartphone application interventions, such as the optimal dose, frequency, timing, user interface, and communication mode to both further improve their effectiveness and to increase their reach, uptake, and feasibility.

FUNDING

EU's Horizon 2020 Research and Innovation Programme.

Analyzing the Effectiveness of mHealth to Manage Diabetes Mellitus Among Adults Over 50: A Systematic Literature Review

Purpose

A total of 537 million suffered from diabetes mellitus in 2021, and the aging of the population will not abate this number in the future. Diabetes predisposes people to ailments and doubles the risk of COVID-19 mortality. mHealth has shown promise to help manage diabetes. The aim of this review is to objectively analyze research from the last 2.5 years to assess effectiveness where mHealth has been used as an intervention to help manage diabetes in older patients. We also analyzed patient satisfaction, quality, and barriers to adoption of mHealth to manage diabetes.

Patients and Methods

No human subjects were involved in this review. We queried four research databases for mHealth to manage diabetes in older adults. We conducted the review based on the Kruse Protocol for writing as systematic review and we reported our findings in accordance with PRISMA (2020).

Results

Thirty research articles from 11 countries were analyzed. Five interventions of mHealth were identified. Of these mHealth Short Message service (SMS) helped change behavior and encouraged self-care. mHealth SMS coupled with telemedicine for coaching showed positive effects on weight loss, BMI, diet, exercise, HbA1C, disease awareness, blood pressure, cholesterol, medication adherence, and foot care.

Conclusion

mHealth SMS coupled with telemedicine for coaching shows the greatest promise for educating, changing behavior, and realizing positive outcomes across a broad spectrum of health factors. The largest drawback is the cost of acquiring equipment and training users.

A Meta-Analysis of the Effectiveness of Telemedicine in Glycemic Management among Patients with Type 2 Diabetes in Primary Care

Introduction: Telemedicine interventions are gradually being used in primary health care to help patients with type 2 diabetes receive ongoing medical guidance. The purpose of this study was to analyze the effectiveness of using telemedicine in primary health care for the management of patients with type 2 diabetes. Methods: A systematic search was conducted from database inception to August 2021 in nine databases, including PubMed, Web of Science, Cochrane Library, EMBASE, EBSCO, CNKI, Wanfang Data, VIP, and CBM. Data extraction and quality assessment were performed for studies that met the inclusion criteria. The meta-analysis was performed using Review Manager 5.4 (Cochrane) and Stata v.16.0SE (College Station, TX, USA). Results: A total of 32 articles were included in this study. Analysis showed a reduction in glycated hemoglobin, fasting glucose, and postprandial glucose after the telemedicine intervention. Systolic blood pressure and self-efficacy improved significantly, but there was no significant improvement in weight, lipid metabolism, or diabetes awareness. Subgroup analysis based on the duration of intervention showed significant improvement in glycated hemoglobin at 6 months of intervention. Conclusions: Telemedicine interventions may help patients with type 2 diabetes to effectively control blood glucose and improve self-management in primary health care. There is only moderate benefit, and the benefit may not be sustained beyond 6 months. However, the evidence for the improvement in lipid metabolism is insufficient and further studies are needed.

Efficacy of telemedicine on glycaemic control in patients with type 2 diabetes: A meta-analysis

BACKGROUND

Telemedicine is defined as the delivery of health services via remote communication and technology. It is a convenient and cost-effective method of intervention, which has shown to be successful in improving glyceamic control for type 2 diabetes patients. The utility of a successful diabetes intervention is vital to reduce disease complications, hospital admissions and associated economic costs.

AIM

To evaluate the effects of telemedicine interventions on hemoglobin A1c (HbA1c), systolic blood pressure (SBP), diastolic blood pressure (DBP), body mass index (BMI), post-prandial glucose (PPG), fasting plasma glucose (FPG), weight, cholesterol, mental and physical quality of life (QoL) in patients with type 2 diabetes. The secondary aim of this study is to determine the effect of the following subgroups on HbA1c post-telemedicine intervention; telemedicine characteristics, patient characteristics and self-care outcomes.

METHODS

PubMed Central, Cochrane Library, Embase and Scopus databases were searched from inception until 18th of June 2020. The quality of the 43 included studies were assessed using the PEDro scale, and the random effects model was used to estimate outcomes and I 2 for heterogeneity testing. The mean difference and standard deviation data were extracted for analysis.

RESULTS

We found a significant reduction in HbA1c [-0.486%; 95% confidence interval (CI) -0.561 to -0.410, P < 0.001], DBP (-0.875 mmHg; 95%CI -1.429 to -0.321, P < 0.01), PPG (-1.458 mmol/L; 95%CI -2.648 to -0.268, P < 0.01), FPG (-0.577 mmol/L; 95%CI -0.710 to -0.443, P < 0.001), weight (-0.243 kg; 95%CI -0.442 to -0.045, P < 0.05), BMI (-0.304; 95%CI -0.563 to -0.045, P < 0.05), mental QoL (2.210; 95%CI 0.053 to 4.367, P < 0.05) and physical QoL (-1.312; 95%CI 0.545 to 2.080, P < 0.001) for patients following telemedicine interventions in comparison to control groups. The results of the meta-analysis did not show any significant reductions in SBP and cholesterol in the telemedicine interventions compared to the control groups. The telemedicine characteristic subgroup analysis revealed that clinical treatment models of intervention, as well as those involving telemonitoring, and those provided via modes of videoconference or interactive telephone had the greatest effect on HbA1c reduction. In addition, interventions delivered at a less than weekly frequency, as well as those given for a duration of 6 mo, and those lead by allied health resulted in better HbA1c outcomes. Furthermore, interventions with a focus on biomedical parameters, as well as those with an engagement level > 70% and those with a drop-out rate of 10%-19.9% showed greatest HbA1c reduction. The patient characteristics investigation reported that Hispanic patients with T2DM had a greater HbA1c reduction post telemedicine intervention. For self-care outcomes, telemedicine interventions that resulted in higher post-intervention glucose monitoring and self-efficacy were shown to have better HbA1c reduction.

CONCLUSION

The findings indicate that telemedicine is effective for improving HbA1c and thus, glycemic control in patients with type 2 diabetes. In addition, telemedicine interventions were also found to significantly improved other health outcomes as well as QoL scores. The results of the subgroup analysis emphasized that interventions in the form of telemonitoring, via a clinical treatment model and with a focus on biomedical parameters, delivered at a less than weekly frequency and 6 mo duration would have the largest effect on HbA1c reduction. This is in addition to being led by allied health, through modes such as video conference and interactive telephone, with an intervention engagement level > 70% and a drop-out rate between 10%-19.9%. Due to the high heterogeneity of included studies and limitations, further studies with a larger sample size is needed to confirm our findings.