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Telemedicine is defined by three characteristics: (1) using information and communication technologies, (2) covering a geographical distance, and (3) involving professionals who deliver care directly to a patient or a group of patients. It is said to improve chronic care management and self-management in patients with chronic diseases. However, currently available guidelines for the care of patients with diabetes, hypertension, or dyslipidemia do not include evidence-based guidance on which components of telemedicine are most effective for which patient populations.
The primary aim of this study was to identify, synthesize, and critically appraise evidence on the effectiveness of telemedicine solutions and their components on clinical outcomes in patients with diabetes, hypertension, or dyslipidemia.
We conducted an umbrella review of high-level evidence, including systematic reviews and meta-analyses of randomized controlled trials. On the basis of predefined eligibility criteria, extensive automated and manual searches of the databases PubMed, EMBASE, and Cochrane Library were conducted. Two authors independently screened the studies, extracted data, and carried out the quality assessments. Extracted data were presented according to intervention components and patient characteristics using defined thresholds of clinical relevance. Overall certainty of outcomes was assessed using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) tool.
Overall, 3564 references were identified, of which 46 records were included after applying eligibility criteria. The majority of included studies were published after 2015. Significant and clinically relevant reduction rates for glycated hemoglobin (HbA1c; ≤−0.5%) were found in patients with diabetes. Higher reduction rates were found for recently diagnosed patients and those with higher baseline HbA1c (>8%). Telemedicine was not found to have a significant and clinically meaningful impact on blood pressure. Only reviews or meta-analyses reporting lipid outcomes in patients with diabetes were found. GRADE assessment revealed that the overall quality of the evidence was low to very low.
The results of this umbrella review indicate that telemedicine has the potential to improve clinical outcomes in patients with diabetes. Although subgroup-specific effectiveness rates favoring certain intervention and population characteristics were found, the low GRADE ratings indicate that evidence can be considered as limited. Future updates of clinical care and practice guidelines should carefully assess the methodological quality of studies and the overall certainty of subgroup-specific outcomes before recommending telemedicine interventions for certain patient populations.
Diabetes is affecting 463 million people worldwide (aged between 20 and 79 years) [
The application of information and communication technologies (ICTs) in health care has been rapidly increasing worldwide. Telemedicine is defined by three characteristics: (1) using ICTs, (2) covering a geographical distance, and (3) involving professionals who deliver care directly to a patient or a group of patients [
However, detailed guidance is still lacking on how to choose and integrate tools for specific target groups in diabetes care [
Therefore, the primary objective of this umbrella review is to identify, synthesize, and critically appraise the evidence on the effectiveness of telemedicine solutions and their components on clinical outcomes—HbA1c, high-density lipoprotein (HDL), low-density lipoprotein (LDL), total cholesterol (TC), triglycerides (TGC), systolic BP (SBP), diastolic BP (DBP)—in patients with diabetes (type 1 diabetes [T1D] and T2D), hypertension, or dyslipidemia. Owing to the increasing number of available reviews and meta-analysis as well as the potential of addressing three prevalent chronic conditions with multiple digital interventions, the analysis was conducted as an umbrella review [
The research question is based on the Population, Intervention, Control, Outcome, and Time (PICOT) criteria:
We conducted an umbrella review using extensive automated and manual searches of the databases PubMed, EMBASE, and the Cochrane Library to identify relevant evidence on the effectiveness of telemedicine interventions on the three target diseases. Umbrella reviews summarize and contrast evidence from existing systematic reviews and meta-analyses by looking at specific outcomes across included records [
The search was carried out in October 2018. PICOT-criteria (
Population, Intervention, Control, Outcome, and Time and eligibility criteria.
Population, Intervention, Control, Outcome, and Time criteria | Eligibility | |
|
Inclusion | Exclusion |
Population | Humans; only studies addressing at least one of the predetermined target diseases within their initial search | Studies addressing chronic diseases in general, other than the three diseases defined, or not addressing any disease at all; specific populations (pregnant women and ethnical minorities); and animals |
Intervention | Primary studies applying telemedicine intervention specified as (1) using ICTsa, (2) covering distance, and (3) involving a health care provider for delivering care to the patient | Studies focusing solely on monitoring or data storage and exchange tools (such as electronic health records) |
Control | Usual care | No control group available or not specified |
Outcome | Effectiveness analyses allowing for quantitative comparisons between groups using clinical parameters (primary outcome HbA1cb, SBPc, DBPd, HDL-ce, LDL-cf, TCg, and TGCh) | Studies primarily investigating mortality, costs or cost-effectiveness, or feasibility; or efficacy |
Time | Follow-up time of at least three months | No or shorter follow-up periods described |
Study design | Study design being either a systematic review or meta-analysis of randomized controlled trials | Other, including a systematic review or meta-analysis of observational studies |
aICT: information and communication technology.
bHbA1c: glycated hemoglobin.
cSBP: systolic blood pressure.
dDBP: diastolic blood pressure.
eHDL-c: high-density lipoprotein cholesterol.
fLDL-c: low-density lipoprotein cholesterol.
gTC: total cholesterol.
hTGC: triglycerides.
Records that fulfilled the following eligibility criteria were included (
Relevant reviews or meta-analyses were excluded if their primary studies mainly assessed mortality, utilization of health services, the usability of the technology studied, or patients’ acceptance of or satisfaction with the telemedicine tools, or if no quantitative comparison based on clinical outcomes was reported. Studies evaluating interventions using automated feedback without involving a professional or those providing only monitoring of relevant parameters (without feedback) were excluded. In addition, studies evaluating telemedicine use of medical providers only or those in which the components of the intervention were not transparently described were excluded. Eligible records had to report a change in one of the specified clinical outcomes after a follow-up time of at least three months, as this period is in line with current treatment guidelines [
Conference abstracts or protocols were excluded as well. Research was excluded if it focused on specific countries or regions or targeted specified populations (eg, minorities and pregnant women with diabetes). We excluded those studies for which updates of the evidence—indicated by the same group of authors and/or application of identical search string—were available.
We further searched the reference lists of all relevant publications by hand, to identify any additional studies. After carrying out the title-abstract screening, we conducted a hand search in Google Scholar and the three most relevant journals in the field of digital health, as indicated by the highest number of potentially relevant publications (
Two authors (PT and LH) independently screened the records, extracted data, and carried out the quality assessments. The quality assessment of records was done using the Oxford Quality Assessment Questionnaire (OQAQ) to eliminate records of low quality before data extraction [
As the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) is the established tool for assessing the overall certainty of evidence by analyzing its risk of bias, imprecision, inconsistency, indirectness, and publication bias, it was used to assess the quality of included records [
The results of the included records were extracted using a piloted, standardized data extraction form. According to the methodological considerations for conducting umbrella or meta-reviews, the results were reported descriptively and in tabular form [
The presentation of data is descriptive; however, the results of meta-analyses and subgroup analyses were specifically analyzed to find effective components or modes of delivery (intensity and frequency) in subgroups or settings. In light of previous trials, a clinically relevant reduction of –0.5% in HbA1c is considered a suitable threshold (
Definition of clinically relevant differences in glycated hemoglobin.
Reduction rate in glycated hemoglobin (%) | Guidance | |
≤−0.5 | >.05 |
|
>−0.5, <0 | >.05 |
|
>0 | >.05 |
|
>−0.5, <0 | <.05 |
|
≤−0.5 | <.05 |
|
anon-significant but clinically relevant change.
bnon-significant and not clinically relevant change.
cnon-significant and not clinically relevant change.
dsignificant but not clinically relevant change.
esignificant and clinically relevant change.
In terms of BP control, a −10 mmHg reduction in SBP or a −5 mmHg reduction in DBP is considered as clinically relevant [
To compare overall treatment effects between baseline and follow-up, meta-analyses reporting treatment effects as mean differences (MD), standardized mean difference (SMD), Cohen
Overall, 3564 references were identified. After title-abstract screening, 119 records remained for further full-text analysis. Details of the extracted evidence are provided in the
Preferred Reporting Items for Systematic Reviews and Meta-analyses flowchart of the study selection process. OQAQ: Overview Quality Assessment Questionnaire.
Study designs included 16 systematic reviews [
An analysis of primary studies revealed significant overlaps among the 26 meta-analyses (
On a scale of 0 to 18, the median OQAQ score of the 46 included studies was 16 (IQR 1), indicating that they were good quality systematic reviews and meta-analyses.
Data from 16 systematic reviews were extracted (
Digital self-management in diabetes (T1D and T2D) was analyzed by 13 meta-analyses, of which 4 meta-analyses evaluated the effectiveness of mobile health (mHealth) [
Overall mean reductions in HbA1c of telemedicine interventions in patients with T1D ranged between −0.12% and −0.86% [
Although varying in range (−0.01% to −1.13%), telemedicine significantly reduced HbA1c in patients with T2D [
Effectiveness of telemedicine on glycated hemoglobin in patients with diabetes, according to intervention duration.
Application category and type of diabetes | Intervention duration | Trials, n | Patients, n | Outcome | MDa (95% CI) of percent change in HbA1cb | I2 (%) | Grading of Recommendations, Assessment, Development, and Evaluation | ||
|
|||||||||
|
T1Dc/T2Dd | 3 months | 3 | 203 |
|
−0.71 (−1.0 to −0.43) | .90 | 0 |
|
|
T1D/T2D | 6 months | 2 | 562 |
|
−0.52 (−0.75 to −0.29) | .65 | 0 |
|
|
T1D/T2D | 12 months | 6 | 1153 |
|
−0.55 (−0.7 to −0.39) | <.001 | 78 |
|
|
|||||||||
|
T1D | <6 months | 7 | NSg |
|
0.07 (−0.16 to 0.31) | NS | NS |
|
|
T1D | ≥6 months | 21 | NS |
|
−0.24 (−0.41 to −0.07) | NS | NS |
|
|
T2D | ≤3 months | 17 | 1377 |
|
−0.67 (−0.93 to −0.41) | NS | NS |
|
|
T2D | 4-6 months | 36 | 4538 |
|
−0.41 (−0.84 to 0.02) | NS | NS |
|
|
T2D | 7-11 months | 4 | 659 |
|
−0.66 (−1.18 to −0.15) | NS | NS |
|
|
T2D | ≥12 months | 36 | 10,237 |
|
−0.26 (−0.40 to −0.12) | NS | NS |
|
|
|||||||||
|
T2D | ≤3 months | 10 | NS |
|
−0.51 (−0.71 to −0.31) | <.001 | 41.8 |
|
|
T2D | >3 and ≤6 months | 10 | NS |
|
−0.48 (−0.68 to −0.28) | <.001 | 34.5 |
|
|
T2D | 3-4 months | 11 | 1613 |
|
−0.30 (−0.50 to −0.11) | <.001 | 89.1 |
|
|
T2D | >6 months | 15 | NS |
|
−0.35 (−0.53 to −0.18) | <.001 | 70.5 |
|
|
T2D | 6-8 months | 14 | 2389 |
|
−0.59 (−0.78 to −0.39) | <.001 | 84.8 |
|
|
T2D | 9-12 months | 7 | 1272 |
|
−0.21 (−0.35 to −0.075) | .131 | 39.1 |
|
|
T1D/T2D | ≤ 6 months | 30 | NS |
|
−0.56 (NS) | <.001 | 30 |
|
|
T1D/T2D | 6 months | 6 | 741 |
|
−0.57 (−0.85 to −0.30) | .099 | NS |
|
|
T1D/T2D | >6 months | 25 | NS |
|
−0.40 (NS) | <.001 | 25 |
|
|
T1D/T2D | 12 months | 7 | 3466 |
|
−0.30 (−0.48 to −0.11) | .099 | NS |
|
|
|||||||||
|
T2D | <6 months | 6 | NS |
|
−0.60 (−0.80 to −0.40) | <.001 | NS |
|
|
T2D | ≥6 months | 4 | NS |
|
−0.40 (−0.56 to −0.24) | <.001 | NS |
|
|
|||||||||
|
T1D/T2D | ≤3 months | 13 | 799 |
|
−0.54 (−0.80 to −0.28) | <.001 | 23 |
|
|
T1D/T2D | 3-12 months | 11 | 1465 |
|
−0.41 (−0.63 to −0.19) | <.001 | 25 |
|
|
T1D/T2D | >12 months | 10 | 2713 |
|
−0.36 (−0.59 to −0.14) | <.002 | 90 |
|
aMD: mean difference.
bHbA1c: glycated haemoglobin
cT1D: type 1 diabetes.
dT2D: type 2 diabetes.
eThe direction of the arrows indicates potential clinically relevant reduction rates (see
fGreen arrows show statistical significance.
gNS: not specified—cases in which no data were provided. Missing data on statistical significance were handled as nonsignificant.
Significant and clinically relevant reductions were found for short (≤3 months), middle (4-8 months), and long (>12 months) intervention durations. Digital health education, analyzed in the meta-analysis by Angeles et al [
Short-term intervention durations (≤6 months) of digital self-management showed greater mean reductions (−0.56%;
Although telemedicine interventions using feedback functions significantly reduced HbA1c in several studies [
In addition, feedback, provided either via human telephone calls (−1.13%, 95% CI −1.51 to −0.75;
Effectiveness of telemedicine on glycated hemoglobin in patients with diabetes, according to feedback mode, frequency, and intensity.
Application category and type of diabetes | Feedback characteristics | Trials, n | Patients, n | Outcome | MDa (95% CI) of percent change in HbA1c | I2 (%) | Grading of Recommendations, Assessment, Development, and Evaluation | ||
|
|||||||||
|
T1Db | App based | 5 | 336 |
|
−0.37 (−0.94 to 0.20) | .20 | 81.74 |
|
|
T1D | High intensityd | 13 | NS |
|
−0.24 (−0.49 to 0.01) | NSe | NS |
|
|
T1D | ≠ High intensity | 14 | NS |
|
−0.09 (−0.23 to 0.06) | NS | NS |
|
|
T1D | Audit + feedback | 24 | NS |
|
−0.22 (−0.38 to −0.06) | NS | NS |
|
|
T1D | No audit + feedback | 4 | NS |
|
0.01 (−0.27 to −0.30) | NS | NS |
|
|
|||||||||
|
T2Df | Human call/telephone | 5 | NS |
|
−1.13 (−1.51 to −0.75) | <.05 | 38 |
|
|
T2D | Human call/telephone | 12 | NS |
|
−0.53 (−0.81 to −0.26) | <.001 | 76.35 |
|
|
T2D | Manual | 6 | 1180 |
|
−0.44 (−0.74 to −0.15) | .04 | NS |
|
|
T2D | Manual | 22 | NS |
|
−0.50 (−0.65 to −0.34) | <.001 | 67.2 |
|
|
T2D | Automated | 5 | NS |
|
−0.50 (−0.69 to −0.32) | <.001 | 0 |
|
|
T2D | Automated calls | 2 | NS |
|
−0.01 (−0.32 to 0.29) | .94 | 0 |
|
|
T2D | Automated text | 9 | NS |
|
−0.36 (−0.47 to −0.24) | NS | 0 |
|
|
T2D | Text message | 3 | 380 |
|
−0.52 (−1.04 to 0.00) | <.05 | 73.5 |
|
|
T2D | Web-based | 13 | 2405 |
|
−0.41 (−0.55 to −0.27) | <.05 | 79.6 |
|
|
T2D | Web-based | 19 | NS |
|
−0.62 (−0.82 to −0.42) | <.001 | 77.57 |
|
|
|||||||||
|
T2D | Low frequency | 7 | 440 |
|
−0.33 (−0.59 to −0.07) | .01 | 47.35 |
|
|
T2D | High frequency | 5 | 326 |
|
−1.12 (−1.32 to −0.91) | <.001 | 0 |
|
|
T1D/T2D | Personalized feedback | 8 | NS |
|
−0.43 (−0.74 to −0.12) | <.001 | 75 |
|
|
T1D/T2D | ≠ Personalized feedback | 4 | NS |
|
−0.61 (−1.40 to 0.19) | .001 | 81 |
|
|
T1D/T2D | Frequency (daily) | 15 | NS |
|
−0.6 (−0.9 to −0.4) | .27 | NS |
|
|
T1D/T2D | Frequency (weekly) | 3 | NS |
|
−0.2 (−0.6 to 0.2) | .27 | NS |
|
|
T1D/T2D | Frequency (not specified) | 4 | NS |
|
−0.4 (−0.5 to −0.2) | .27 | NS |
|
aMD: mean difference.
bT1D: type 1 diabetes.
cThe direction of the arrows indicates potential clinically relevant reduction rates (see
dDirect contact at least once a week.
eNS: not specified—cases in which no data were provided. Missing data on statistical significance were handled as nonsignificant.
fT2D: type 2 diabetes.
gGreen arrows show statistical significance.
The meta-regression carried out by Huang et al [
Subgroup analyses on the effectiveness of telemedicine in certain patient populations (
Although differences were not always significant, those subgroups with higher baseline HbA1c (>7.5% or >8.0%) showed increased reductions rates [
Significant differences for age groups were sparse, as only three meta-analysis found significant reduction rates in patients with T2D [
For digital self-management, a shorter time since diagnosis (<8.5 years) was associated with significantly greater mean reduction in HbA1c (−0.83%, 95% CI −1.10 to −0.56;
Effectiveness of telemedicine on glycated hemoglobin in patients with diabetes, according to population characteristics.
Category of application and type of diabetes | Population characteristics | Trials, n | Patients, n | Outcome | MDa (95% CI) of percent change in HbA1cb | I2 (%) | Grading of Recommendations, Assessment, Development, and Evaluation | ||
|
|||||||||
|
T1Dc | Adults | 15 | 1256 |
|
−0.26 (−0.47 to −0.05) | <.01 | 79.7 |
|
|
T1D | Children and adolescents | 11 | 796 |
|
−0.12 (−0.30 to 0.05) | .70 | 0 |
|
|
T1D | Baseline HbA1c <9.0% | 16 | NS |
|
−0.06 (−0.02 to 0.09) | NSf | NS |
|
|
T1D | Baseline HbA1c ≥9.0% | 12 | NS |
|
−0.34 (−0.57 to −0.11) | NS | NS |
|
|
T2Dg | Baseline HbA1c <8.0% | 48 | 5720 |
|
−0.22 (−0.25 to −0.19) | NS | NS |
|
|
T2D | Baseline HbA1c ≥8.0% | 45 | 8100 |
|
−0.60 (−0.61 to −0.60) | NS | NS |
|
|
|||||||||
|
T2D | Age <55 years | 7 | 701 |
|
−0.67 (−1.15 to −0.20) | .52 | 75 |
|
|
T2D | Age ≥55 years | 8 | 541 |
|
−0.41 (−0.62 to −0.21) | .52 | 0 |
|
|
T2D | Age undetermined | 2 | 289 |
|
−0.72 (−1.60 to 0.16) | .52 | 47 |
|
|
T2D | Diagnosish <8.5 years ago | 7 | 549 |
|
−0.83 (−1.10 to 0.56) | .007 | 0 |
|
|
T2D | Diagnosish ≥8.5 years ago | 4 | 394 |
|
−0.22 (−0.44 to 0.01) | .007 | 0 |
|
|
T2D | Diagnosis timeh undetermined | 6 | 588 |
|
−0.43 (−0.71 to −0.30) | .007 | 55 |
|
|
T2D | Baseline HbA1c ≤8.0% | 6 | 590 |
|
−0.49 (−0.71 to −0.27) | .69 | 0 |
|
|
T2D | Baseline HbA1c ≤8.0% | 7 | NS |
|
−0.33 (−0.53 to −0.13) | <.05 | 46 |
|
|
T2D | Baseline HbA1c >7.0% | 11 | 1707 |
|
−0.33 (−0.48 to −0.18) | <.001 | 77.8 |
|
|
T2D | Baseline HbA1c >7.5% | 10 | 1921 |
|
−0.45 (−0.70 to −0.21) | <.001 | 80.4 |
|
|
T2D | Baseline HbA1c >8.0% | 11 | 941 |
|
−0.57 (−0.93 to −0.22) | .69 | 65 |
|
|
T2D | Baseline HbA1c >8.0% | 11 | NS |
|
−0.70 (−1.03 to −0.36) | <.05 | 81 |
|
|
T2D | Baseline BMI <30 kg/m2 | 5 | 359 |
|
−0.64 (−0.91 to −0.36) | .49 | 0 |
|
|
T2D | Baseline BMI ≥30 kg/m2 | 10 | 966 |
|
−0.43 (−0.68 to −0.17) | .49 | 35 |
|
|
T2D | Baseline BMI undetermined | 2 | 206 |
|
−0.96 (−2.76 to 0.85) | .49 | 91 |
|
|
T1D/T2D | Age <40 years | 14 | NS |
|
−0.32 | .02 | NS |
|
|
T1D/T2D | Age <40 years | 11 | NS |
|
−0.85 (−1.79 to 0.10) | .07 | 98 |
|
|
T1D/T2D | Age ≥40 years | 40 | NS |
|
−0.53 | <.001 | NS |
|
|
T1D/T2D | Age 41-50 years | 8 | NS |
|
−1.83 (−3.17 to −0.48) | <.001 | 96.2 |
|
|
T1D/T2D | Age >50 years | 17 | NS |
|
−1.05 (−1.50 to −0.60) | <.001 | 97 |
|
|
T1D/T2D | Baseline HbA1c <8.0% | 6 | NS |
|
−0.26 (−0.43 to −0.10) | .03 | NS |
|
|
T1D/T2D | Baseline HbA1c ≥ 8.0% | 8 | NS |
|
−0.64 (−0.93 to −0.35) | .03 | NS |
|
|
T1D/T2D | Baseline HbA1c <9.0% | NS | NS |
|
−0.35 | NS | NS |
|
|
T1D/T2D | Baseline HbA1c ≥9.0% | NS | NS |
|
−1.22 | NS | NS |
|
|
|||||||||
|
T2D | Baseline HbA1c <8.0% | 4 | 696 |
|
−0.33 (−0.59 to −0.06) | .02 | 70 |
|
|
T1D/T2D | Average age <25 years | 5 | NS |
|
−0.5 (−0.8 to −0.1) | .54 | NS |
|
|
T1D/T2D | Average age ≥25 years | 17 | NS |
|
−0.5 (−0.7 to −0.3) | .54 | NS |
|
|
T1D/T2D | BMI ≥25 kg/m2 | 7 | NS |
|
−0.8 (−1.1 to −0.5) | .93 | NS |
|
|
T1D/T2D | 24 kg/m2≤ BMI <25 kg/m2 | 3 | NS |
|
−0.8 (−1.7 to 0.1) | .93 | NS |
|
|
T1D/T2D | BMI unspecified | 12 | NS |
|
−0.3 (−0.5 to −0.1) | .93 | NS |
|
|
|||||||||
|
T2D | Age <55 years | 5 | NS |
|
−0.65 (−0.88 to −0.41) | <.001 | NS |
|
|
T2D | Age ≥55 years | 5 | NS |
|
−0.42 (−0.56 to −0.27) | .006 | NS |
|
|
T2D | Diagnosish <7 years ago | 4 | NS |
|
−0.61 (−0.79 to −0.42) | .001 | NS |
|
|
T2D | Diagnosish ≥7 years ago | 3 | NS |
|
−0.37 (−0.62 to −0.13) | .031 | NS |
|
|
T2D | Baseline HbA1c <8.0% | 5 | NS |
|
−0.71 (−0.93 to −0.48) | <.001 | NS |
|
|
T2D | Baseline HbA1c ≥8.0% | 5 | NS |
|
−0.38 (−0.53 to −0.24) | <.001 | NS |
|
aMD: mean difference.
bHbA1c: glycated hemoglobin.
cT1D: type 1 diabetes.
dThe direction of the arrows indicates potential clinically relevant reduction rates (see
eGreen arrows show statistical significance.
fNS: not specified—cases in which no data were provided. Missing data on statistical significance were handled as nonsignificant.
gT2D: type 2 diabetes.
hDiagnosis time: time since first diagnosis of diabetes.
Mean reductions of both SBP and DBP were also found in T2D patients. Toma et al [
Only 8 of the included studies reported on lipid profiles; 4 in T1D/T2D patients [
A total of 3 of the included meta-analyses focused on patients with hypertension [
The quality assessment of outcomes using the GRADE framework revealed the following levels of certainty (
Grading of Recommendations, Assessment, Development, and Evaluation assessment of certainty of glycated hemoglobin and systolic blood pressure/diastolic blood pressure outcomes.
GRADEa | HbA1cb, n (%) | SBPc/DBPd, n (%) |
|
—e | — |
|
2 (0.92) | — |
|
42 (19.8) | — |
|
170 (77.63) | 42 (100) |
aGRADE: Grading of Recommendations, Assessment, Development, and Evaluation.
bHbA1c: glycated hemoglobin.
cSBP: systolic blood pressure.
dDBP: diastolic blood pressure.
eNot applicable.
The main reasons for low-quality assessment results in both outcome categories were as follows:
Unclear or high-risk of bias: Missing allocation concealment, missing blinding of patients, study personnel and outcome assessors, high risk of selection bias and reporting bias (intention-to-treat analysis), and high or unclear losses to follow-up.
Inconsistency: High heterogeneity in subgroup analysis, inconsistent confidence intervals crossing the mark for no effect.
Indirectness: Differences in populations (type of diabetes, baseline HbA1c, age, duration of diabetes, and gender), differences in interventions (devices used, components combined, feedback intensity and frequency, and professional or professionals involved), and differences in settings (community, hospital, and primary care) in the pooled subgroups.
Imprecision: Large confidence intervals and small effect sizes mostly because of small sample sizes.
Publication bias: Visual and statistical or missing publication bias assessment; the reasons for the increased risk of publication bias mostly referred to the overrepresentation of smaller studies with higher effect sizes (favoring telemedicine). Furthermore, one reason is the paucity of data on mid- and long-term effects (6-12 months).
Underreporting of relevant information: Reporting of study duration, dropouts/missing data, and follow-up time. Guidance on this matter was further complicated as some authors did not make a clear distinction between study duration and follow-up [
Only for two outcomes (0.92%) measuring HbA1c, overall certainty was judged as moderate (
As the initial search did not identify records primarily targeting patients with dyslipidemia and subgroup analyses on HDL, LDL, TC, and TGC were sparse, no grading of lipid outcomes was performed.
High-level evidence from the 46 included meta-analyses and systematic reviews suggests that telemedicine interventions can be effective in improving clinical outcomes in patients with diabetes. Observed reduction rates are comparable with those of nonpharmacological eg, nutrition intervention [
In patients with diabetes, significant differences between telemedicine interventions and for certain population characteristics were identified. Telemedicine interventions embedded in frequent and intense patient-provider interactions and interventions with short durations (≤6 months) showed greater benefits. In addition, higher reduction rates were found for recently diagnosed patients and those with higher baseline HbA1c. However, quality assessment using GRADE revealed that overall and subgroup-specific certainty of evidence is low to very low. Therefore, the identified reduction rates have to be dealt with caution when translating them into evidence-based recommendations for treatment guidelines.
Telemedicine was not found to have a significant and clinically meaningful impact on BP. Assessing the certainty of SBP and DBP outcomes, GRADE only revealed very low ratings. No records primarily targeting patients with dyslipidemia were found.
According to the recent consensus report of the ADA and European Association for the Study of Diabetes, the application of telemedicine in diabetes is associated with a modest improvement in glycemic control [
Telemedicine has the potential to improve clinical outcomes in patients with diabetes. Mixed results were found for patients with hypertension, none for those with dyslipidemia.
Specific characteristics of the intervention (eg, high frequency and intensity of feedback/interaction and short treatment duration) and the patient (age <55 years, high baseline HbA1c, and recent diagnosis) seem to be associated with increased benefits in patients with diabetes.
An assessment of the overall certainty using GRADE resulted in low and very low ratings, indicating that effects have to be dealt with caution.
Looking at the characteristics of the telemedicine applications analyzed by the included meta-analyses, those encompassing frequent and intense patient-provider communication interactions showed greater benefit in HbA1c reduction. This was especially true for the combination of tele-case management with either teleconsultation (−1.20%, 95% CI −2.30 to −0.10;
With a longer duration of follow-ups, the quality of evidence steadily declines because of considerable risk of bias and heterogeneity of study populations and interventions included. As for digital self-management, the evidence base is larger yet more diverse, as SMS (1 meta-analysis), social networks (1 meta-analysis), and mHealth apps (4 meta-analysis) can be used. However, the quality of evidence for digital self-management is low to very low, irrespective of the basal technology or the type of diabetes.
In our analysis, some application types were found to reduce BP, for example, in SBP after tele-education (−4.05 mmHg, 95% CI −5.64 to −1.10), as well as strategies combining tele-education and telemonitoring (−3.91 mmHg) [
On the basis of the identified potential of telemedicine to provide individual self-management support, it is likely that embedded or additional components may have an additive and/or sustained impact on clinical outcomes. As such, recent evidence identified social media [
According to the included meta-analyses, telemedicine interventions are more effective for patients with T2D, higher baseline HbA1c, and a more recent diagnosis of diabetes. The increased potential for newly diagnosed patients was also identified by systematic reviews [
With the exception of a baseline BMI <30 kg/m2 (considered in one meta-analysis), all population-specific subgroup analyses were of low or very low evidence, the latter being more prevalent. This is also true for differences among age groups, for which no significant evidence was found. However, there was a tendency for higher reduction rates of HbA1c in younger patient cohorts with diabetes [
Overall, as the results concerning population characteristics are diverse and of low to very low quality, our analysis did not find enough high-level evidence to recommend telemedicine for the treatment of patients with both hypertension and diabetes.
Only reviews or meta-analyses reporting lipid outcomes in patients with diabetes were found. The extracted results on lipid outcomes are sparse and too heterogeneous to draw a conclusion on the effectiveness of telemedicine on these outcomes [
Robust systematic reviewing methods were used to generate an overview of high-quality evidence on the effects of telemedicine in three prevalent chronic conditions. The protocol of this umbrella review was presented to the research community [
In addition, some full-text articles were excluded because of their definition and application of the term “telemedicine,” which did not comply with standardized definitions, such as the one provided by Sood et al [
We also included different types of statistical analyses, including meta-analysis, network-meta-analysis, and meta-regression. Although the majority reported MD, there was a considerable methodological heterogeneity. This was because of the application of fixed- and random-effects models, as well as the reporting of SMD, Hedge
Owing to the multimodal and individualized nature of digital interventions, the low GRADE results, especially the increase I2, are not surprising. In addition, we found significant overlaps among the primary studies of the included records (
The results of this umbrella review indicate that telemedicine has the potential to improve clinical outcomes in patients with diabetes. Evidence extracted from systematic reviews and meta-analyses of RCTs showed subgroup-specific effectiveness rates favoring certain intervention and population characteristics. However, as indicated by the low GRADE ratings, evidence on the effectiveness of telemedicine in the three chronic conditions can be considered as limited.
Future updates of clinical care and practice guidelines should carefully assess the methodological quality of studies and assess the overall certainty of subgroup-specific outcomes before recommending telemedicine interventions for certain patient populations.
Population, Intervention, Control, Outcome, and Time criteria and principles of data extraction.
Number of manuscripts per journal after title/abstract screening.
Quality assessment for study inclusion.
List of excluded studies with reasons.
Characteristics of included records.
Results of included systematic reviews.
Results of included meta-analyses.
Grading of Recommendations Assessment, Development and Evaluation of glycated haemoglobin and diastolic blood pressure/systolic blood pressure outcomes.
References of multimedia appendices.
Electronic database search strategy.
American Diabetes Association
blood pressure
cardiovascular
diastolic blood pressure
European Society of Cardiology/European Society of Hypertension
Grading of Recommendations, Assessment, Development, and Evaluation
glycated hemoglobin
high-density lipoprotein
high-density lipoprotein cholesterol
information and communication technology
low-density lipoprotein
low-density lipoprotein cholesterol
mean difference
mobile health
Oxford Quality Assessment Questionnaire
Population, Intervention, Control, Outcome, and Time
randomized controlled trial
systolic blood pressure
standardized mean difference
social network services
type 1 diabetes
type 2 diabetes
total cholesterol
triglycerides
United Kingdom Prospective Diabetes Study
The authors wish to thank Jochen Schmitt, Hendrikje Lantzsch, and Kristin Kemple for their valuable input to the conduct of the umbrella review and the design of the manuscript. The work on this review was partly funded by the European Social Fund and the Free State of Saxony (Grant number: 100310385).
Parts of this manuscript were presented on a poster during a German Conference (Diabetes Kongress 2019) on May 30, 2019 and during a talk on October 10, 2019, (18 Deutscher Kongress für Versorgungsforschung) in Berlin.
PT and LH designed the study and also conducted the search. PT and LH were responsible for critical evaluation, analysis, and presentation of the results. PT, LH, and SO conducted the GRADE Assessment. PT and LH drafted the manuscript. PT, LH, SO, and PS critically evaluated the article and gave their final approval before submission.
None declared.