Home Telemonitoring of Patients With Type 2 Diabetes: A Meta-Analysis and Systematic Review

Abstract

Telehealth has emerged as an evolving care management strategy that is playing an increasingly vital role, particularly with the onset of the coronavirus disease 2019 pandemic. A meta-analysis of 20 randomized controlled trials was conducted to test the effectiveness of home telemonitoring (HTM) in patients with type 2 diabetes in reducing A1C, blood pressure, and BMI over a median 180-day study duration. HTM was associated with a significant reduction in A1C by 0.42% (P = 0.0084). Although we found statistically significant changes in both systolic and diastolic blood pressure (−0.10 mmHg [P = 0.0041] and −0.07 mmHg [P = 0.044], respectively), we regard this as clinically nonsignificant in the context of HTM. Comparisons across different methods of transmitting vital signs suggest that patients logging into systems with moderate interaction with the technology platform had significantly higher reductions in A1C than those using fully automatic transmission methods or fully manual uploading methods. A1C did not vary significantly by study duration (from 84 days to 5 years). HTM has the potential to provide patients and their providers with timely, up-to-date information while simultaneously improving A1C.

Diabetes is a chronic disease that has been, and continues to be, a major public health concern worldwide, with ∼463 million adults (aged 20–79 years) living with diabetes and a projected incidence of 629 million cases by 2045 (1). It is among the top 10 causes of death in adults, and type 2 diabetes accounts for 90–95% of all diabetes cases (1,2). Globally, 10% of all health care expenditures are spent on diabetes ($760 billion) (1,2). In 2020, it was estimated that 34.2 million Americans—∼1 in 10—have diabetes. In addition, the prevalence of diabetes in adults increases with age, with 26.8% of adults diagnosed with the disease by 65 years of age (2). Type 2 diabetes can cause substantial morbidity and mortality, including increased risk of heart attack or stroke (3). Because of the long-term care management requirements of type 2 diabetes, patients may also struggle with care maintenance over the life span.

Home telemonitoring (HTM) has emerged as an evolving care management strategy that has taken on an increasingly vital role in the past decade (4), which has intensified further during the coronavirus disease 2019 (COVID-19) pandemic. According to the World Health Organization, telehealth is “a collection of means or methods for enhancing health care, public health, and support using telecommunications and virtual technologies” (4). Technically, telemedicine is a subset of telehealth. Telemedicine typically describes direct clinical services, whereas telehealth refers to a broad range of health-related services such as patient care, education, and remote monitoring (5). HTM is defined as an automated process used for the transmission of data on a patient’s health care status from the patient’s home to a health care setting via the internet with a computer, digital tablet, smartphone, or other connected device. HTM is a method for health care providers to track patients’ vital signs and quickly intervene if necessary (6,7). It is a patient-oriented strategy that relies on telecommunication and information technologies to provide timelier, more convenient, and cost-effective virtual support for patients. Although HTM technology initially targeted patients with limited access to health care—especially those living in rural areas—the COVID-19 pandemic and the subsequent Executive Order 13940 issued on 3 August 2020 (8) have expanded the use and utilization of telehealth across a broad spectrum of populations, specialties, and types of clinical encounters.

There is mounting evidence that HTM can improve care management of patients with type 2 diabetes (9–12). A recent meta-analysis by Nangrani et al. (13) demonstrated an association between telehealth and a clinical decrease in A1C compared with usual care (mean difference −0.17%, 95% CI −0.25 to −0.09%, P <0.0001) but no significant reductions in BMI or blood pressure. A review by Wu et al. (14) found that telehealth was more effective in controlling both A1C and blood pressure in patients with type 1 or type 2 diabetes. This review revealed an A1C reduction of 0.22% (95% CI 0.28–0.15%, P <0.001) and significant decreases in systolic (weighted mean difference −1.92, 95% CI −2.49 to −1.34, P <0.001) and diastolic blood pressure (weighted mean difference −1.31, 95% CI −2.39 to −0.23, P <0.001). Moreover, this review found that patients with an A1C >9% who require at least six interactions with physicians throughout a year may experience greater improvement by telehealth intervention (14). No BMI change was observed in this study (14). A recent study by Lee et al. (10) also demonstrated how various feedback methods including phone calls, text messages, or Web platforms can result in A1C reduction and found that telephone interaction was the most effective, followed by a connected blood glucose monitoring system.

Research Design and Methods

Overview

We performed a systematic review and meta-analysis of randomized controlled trials (RCTs) published from 2015 to 2019 that assessed the impact of HTM (using digital tablet, smartphone, or a Web-based platform) compared with usual care (outpatient) on the management of adult patients with type 2 diabetes. Targeted outcomes included A1C, blood glucose, blood pressure, and BMI. To better compare different levels of technology and study characteristics such as study duration, we adopted a systematic comparative approach to examine the effectiveness of technologies using subgroup analyses. This review was performed according to the Cochrane Collaboration’s methodological guidelines and registered with the PROSPERO (the International Prospective Register of Systematic Reviews) (15). The review was reported according to accepted guidelines (Figure 1). All statistical analyses were performed using the dmetar, metafor, meta packages for R software, v. 3.6 (16–19).

FIGURE 1.

PRISMA flowchart.

Selection Criteria

To be included in this review, articles had to be written in English, published in a peer-reviewed journal, and targeting a population of adults (≥18 years of age) with a primary diagnosis of type 2 diabetes. Studies included RCTs with the aforementioned definition of HTM as an intervention that used comparators of standard outpatient care (usual care) or other telehealth interventions and reported A1C outcomes. Systematic reviews were excluded. In addition, if the same authors produced several publications of the same data source, the latest one was included and other versions were excluded.

Data Sources and Search

To ensure that the results would be applicable to current practice, the search strategy was limited to RCTs published between 1 January 2015 and 31 January 2019. Electronic bibliographic databases included the Cochrane Library, PubMed, and EMBASE.

Data Inclusion

All data were extracted independently and in duplicate by two researchers using a standardized protocol. The abstracts of all articles identified were screened to determine whether they met the inclusion criteria. In the preliminary screening stage, all titles and abstracts were reviewed by two authors according to the inclusion criteria based on a priori selection criteria for eligibility. References that did not clearly meet all criteria were deferred for full text review. Any disagreements were resolved through discussion until consensus was reached.

One of five teams of reviewers, each consisting of two abstractors (one health services researcher and one clinician researcher), independently assessed full-text articles for study inclusion. Search terms included “diabetes mellitus,” “mobile applications,” “type 2 diabetes,” “telecommunications,” and “teleconference,” among others. A total of 2,835 studies in the databases were identified with our search terms, of which 319 were duplicates. Overall, 58 studies met the initial eligibility criteria. Studies such as observational studies, pilot studies, and protocol descriptions were not included. Figure 1 shows the systematic review study flowchart that demonstrates the inclusion and exclusion process.

In total, 20 RCTs (20–39) met the criteria of the systematic review of HTM interventions and study design. Discrepancies were discussed by the two reviewers until consensus was reached. Studies were reviewed again to confirm that they met inclusion criteria; those that did not were excluded. Eligible HTM interventions involved the transmission of self-monitored physiological vital measures (e.g., blood glucose and blood pressure) either via fully automatic transmission, fully manual uploading, or transmission with moderate patient interaction (such as patients pushing a button).

Data Extraction and Analysis

Relevant data from the included studies were extracted using the researchers’ designed forms. A standardized spreadsheet was used to extract all of the relevant data on study characteristics, intervention details, and outcomes. Specifically, the data included the first author, year of publication, country of origin, HTM intervention, study duration, control group information, outcomes, and results. We applied a Hartung, Knapp, Sidik and Jonkman random-effects meta-analysis, which is known to perform better than other forms of analysis when trials of similar sizes are combined (40,41). Effect size was represented by the Hedges’ g statistic (42), which directly corresponds to the standardized mean difference in A1C between the control and intervention arms of all included studies.

Outcomes and Covariates

The primary outcome was change in A1C. To assess this measure with consistent units, a researcher translated all A1C values into percentages. Changes in blood pressure (systolic and diastolic) and BMI, which are also important clinical outcomes representing goals of treatment, were secondary outcomes. Blood pressure measurements were all converted to mmHg for a consistent statistical comparison. To further compare different patients’ baseline A1C (inclusion can have an impact on glycemia control), patients were considered to have controlled glycemia if they had an A1C <7%, whereas those having an A1C ≥7% were considered to have uncontrolled glycemia, in accordance with American Diabetes Association recommendations (43).

The Cochran Q statistic was used to estimate statistical heterogeneity among the included studies, and P <0.05 was considered statistically significant. In the presence of significant heterogeneity, the I² statistic was used to determine the level of heterogeneity based on the Higgins and Thompson’s standard (44).

Risk of Bias

Data from the selected studies were independently extracted by two reviewers using a data extraction form. A risk-of-bias assessment tool, summarized in the Cochrane Handbook for Systematic Reviews of Interventions, v. 6.0 (45), was applied to assess the quality of each study. The studies were evaluated separately based on seven domains: random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, selective reporting, and other bias. Two reviewers (X.Z. and R.P.) subjectively reviewed all selected studies and assigned a value of “low risk,” “unclear risk,” or “high risk” to these domains.

Data and Resource Availability

The datasets generated and/or analyzed during this study are available from the corresponding author upon reasonable request.

Results

Selected Studies and Baseline Characteristics

Table 1 summarizes the 20 selected studies, including their country, duration, number of participants, baseline characteristics of participants, mean A1C change, and type of data transmission used. Seven were conducted in the United States, three in Europe, nine in Asia, and one in Australia. Of a total of 4,739 participants (median age 58.3 years), 2,358 were randomized to the telehealth group, and the remaining 2,381 received usual care. Most studies had been implemented using wireless technologies such as Bluetooth connectivity for monitoring. We focused on the methods of data transmission by separating the studies into three subgroups: 1) “transmission with moderate patient interaction (such as patients pushing a button” 2) “automatic transmission (patients did not actively upload data),” and 3) “patients manually transmit data.” Nine studies used automated transmission of patients’ vital signs (i.e., blood pressure), three used moderate patient interaction, and five required patients to manually upload to either a digital app or Web platform to transmit their vital signs. Three studies did not report their method of transmitting data. The median length of the intervention was 6 months, with one study having a 5-year follow-up period. All 20 studies provided participants with ongoing support after HTM data were collected within their follow-up period, either as consultation from a physician or as two-way communication via the app or platform. Feedback mechanisms varied based on the design of the technology used, and two of the relatively newer studies (those by Kim et al. [26] in 2019 and Lim et al. [27] in 2016) used algorithm-based reports and messages generating automatically for patients to view on their end once data were received.

Table 1.

Summary of Included Studies and the A1C Outcomes in Each

Study	Country	Study Duration, days	Subjects, n	Male, %	Mean Age, years	A1C Criterion for Participant Recruitment, %	Mean A1C Change, %	Type of Data Transmission
Bujnowska-Fedak et al., 2011 (20)	Poland	180	100	54	55.5	NA	−0.06	Patients manually uploaded
Cho et al., 2006 (21)	Korea	900	80	61	52.9	NA	−0.5	Patients manually uploaded
Cho et al., 2009 (22)	Korea	180	80	78.2	48.1	NA	−0.3	Transmitted automatically
Cho et al., 2011 (23)	Korea	84	71	39.4	64.2	>7	−0.3	Transmitted automatically
Hsu et al., 2016 (24)	U.S.	84	40	66	53.5	>9	−1.2	Transmitted automatically
Kim, 2007 (25)	Korea	84	51	75	46.2	>7	−0.72	Patients pressed buttons
Kim et al., 2019 (26)	Korea	168	184	48	58.3	>7	−0.8	NR
Lim et al., 2016 (27)	U.S.	180	100	75	65.1	>7	−0.6	Transmitted automatically
Liou et al., 2014 (28)	Taiwan	180	95	50.4	56.8	>7	−0.5	NR
Nicolucci et al., 2015 (29)	Italy	360	302	61	58.4	>7.5	−0.34	Patients manually uploaded
Quinn et al., 2011 (30)	U.S.	360	52	NA	66.5	>7.5	−0.2	Transmitted automatically
Shea et al., 2009 (31)	U.S.	1,460	1,665	37.2	70.3	NA	−0.12	Transmitted automatically
Tang et al., 2013 (32)	U.S.	360	415	41.2	53.7	>7.5	−0.23	NR
Tildesley et al., 2010 (33)	Canada	180	415	61.7	59.5	>7	−0.9	Transmitted automatically
Wakefield et al., 2014 (34)	U.S.	180	108	44.4	60.1	>8	−0.1	Patients pressed a button
Wang et al., 2017 (35)	China	180	212	54.7	53.3	>7	−0.6	Transmitted automatically
Warren et al., 2018 (36)	Australia	180	211	37	61.3	>7.5	−0.6	Patients pressed a button
Wild et al., 2016 (37)	U.K.	180	320	66.7	60.5	>7	−0.5	Transmitted automatically
Yoo et al., 2009 (38)	Korea	84	124	58.6	58.2	NA	−0.5	Patients manually uploaded
Zhou et al., 2014 (39)	China	180	114	NA	NA	NA	−0.22	Patients manually uploaded

NA, not applicable; NR, not reported.

Glycemic Control

All included studies used change in A1C as an outcome measure to evaluate the effectiveness of HTM for type 2 diabetes management. As previously mentioned, we chose the random-effects model because it pays more attention to small studies when pooling overall effects in a meta-analysis. With the exception of one large-sample study by Shea et al. (31), most of the selected studies had relatively small samples, with a median size of 56 participants in both groups.

The overall effect using the random effect model was significant (standardized mean difference Hedges’ g −0.42, 95% CI −0.59 to −0.26), with an overall decrease in A1C as shown in Figure 2. However, there was heterogeneity detected across studies (I² = 61.3%, P <0.01), indicating a moderate to high degree of heterogeneity.

FIGURE 2.

Forest plot of A1C change before and after HTM.

A funnel plot with Egger’s test (46) was conducted and confirmed that there was no publication bias (bias = −0.73, P = 0.47). Because of the degree of heterogeneity, to further detect the true effect of HTM, we conducted a P_curve analysis (47) as an alternative way to assess publication bias and estimate the true effect reflected in our collected data. This approach rules out some cases in which researchers might have selectively removed outliers and chosen outcomes to reach a significant result, a practice sometimes referred to as “P hacking.” Our results indicated that there was a true effect in terms of the differences we found, with an overall observed power of 93% (95% CI 81–98%).

Blood Pressure Control

Blood pressure was reported in nine studies (Figures 3 and 4). We pooled all systolic and diastolic blood pressure results to look at the effectiveness of HTM intervention. One study (27) did not report systolic blood pressure. Both standardized mean differences appeared to indicate that the interventions reduced both systolic (Hedges’ g = −0.07, 95% CI −0.14 to −0.002], P = 0.044) and diastolic blood pressure (Hedges’ g = −0.10, 95% CI −0.16 to −0.03, P <0.005). Statistical heterogeneity was not detected for either diastolic (Q = 7.38, P = 0.49) or systolic (Q = 5.66, P = 0.77) blood pressure. Although statistically significant, clinically, blood pressure change within 1 mmHg is considered insignificant, especially within the context of HTM.

FIGURE 3.

Forest plot of systolic blood pressure change before and after HTM.

FIGURE 4.

Forest plot of diastolic blood pressure change before and after HTM.

BMI Change

BMI was also examined using the random-effects model. Standardized mean differences in BMI between groups appeared to be insignificant (Hedges’ g = 0.09, P = 0.54).

Subgroup Analyses

Data Transmission Methods

Given the various telehealth technologies, as well as the different data transmission methods, used in the interventions, we performed subgroup meta-analyses to assess whether their impact on glycemic control differed.

Overall differences between transmission methods were found (Hedges’ g = −0.40, 95% CI −0.50 to −0.30), with moderate heterogeneity between studies (Q = 50.69, P = 0.0001, I² = 62%). Between-group mean differences showed that patients with uncomplicated interactions to transfer data (Hedges’ g = −0.65, 95% CI − 1.06 to −0.24) performed better in A1C than those with automatic data transmission (Hedges’ g = −0.44, 95% CI − 0.70 to −0.18) or manual transmission (g = −0.20, 95% CI − 0.43 to −0.03).

Length of Studies

The median length of the selected studies was 6 months (range 12 weeks to 5 years). The test for subgroup differences using the random-effects model was not significant (Hedges’ g = −0.35, P = 0.76) for different study duration periods.

Baseline A1C

Of the 20 studies included in our analysis, 4 did not indicate an A1C inclusion criterion, 2 recruited patients with both controlled and uncontrolled glycemia, and 13 recruited only patients with uncontrolled glycemia. We further grouped the studies of uncontrolled glycemia into A1C levels at recruitment; three studies recruited patients with an A1C >7.5%, and two studies recruited those with an A1C >8% and >9%, respectively (Table 1).

Evidence using the random-effects model demonstrated that differences with regard to baseline A1C were significant (Hedges’ g = −0.33, 95% CI − 0.45 to −0.21, P <0.0001). The baseline group with A1C >9% performed best among the groups (Hedge’s g = −0.62, 95% CI −1.23 to −0.013), followed by the group with baseline A1C >8% (Hedges’ g = −0.53, 95% CI −1.04 to −0.01).

Discussion

This systematic review of 20 studies demonstrated some promising results regarding HTM in terms of glycemic and blood pressure control for patients with type 2 diabetes.

In light of the proliferation of telehealth, the relevance of these results is especially timely. Before the COVID-19 global health pandemic, widespread implementation of HTM was traditionally hampered by regulations, reimbursement limitations, and other policy issues (2). Today, in the COVID-19 era, HTM has become a mainstream method of chronic care management. It is therefore of utmost importance that we fully understand the impact of HTM on clinical outcomes.

Our findings are in accordance with several recent systematic reviews, such as an analysis by Wu et al. (14) of 19 RCTs. In addition to looking at A1C and blood pressure, our study specifically explored data transmission methods (i.e., whether data were transmitted fully automatically, fully manually, or with moderate interaction by patients). Previous studies (e.g., Lee et al. [10]), specifically focused on technology type (e.g., telephone vs. smartphone vs. Web platform). Our novel approach explored the human side of home monitoring, emphasizing the patient’s role. Our results suggest that patients with a definite but uncomplicated role in data uploading procedures had greater A1C reductions over time than those whose data were transmitted completely manually or fully automatically. Further, patients seeing and attending to (uploading) their vital sign data without having to manually input the data achieve better outcomes. They also do better than those whose vital signs are automatically transmitted, requiring no patient action.

This finding makes sense from a behavioral framework perspective. According to the Health Belief Model (48), an individual course of action depends on subject’s perception of benefits of and barriers to this action; the decision-making process often involves the subject’s use of a cue or trigger to action. Such prompts can be the result of either external factors such as health care providers or internal factors (such as physiological cues). Our findings also align with Situated Learning Theory, which proposes the concept of patient learning through active participation rather than through regular educational lectures/notes (49). Asking patients to actively engage while not requiring them to do too much manual work appears to build self-efficacy in managing type 2 diabetes.

We also explored the length of HTM interventions. There were no significant differences between interventions with a duration <6 months and those of longer duration. These findings support previous results from Shea et al. (31), who found the HTM treatment effect to increase the most from baseline to 12 months and then remain steady in the second, third, and fourth years. Perhaps for patients requiring chronic care management assistance, what is learned after 1 year is maintained thereafter. This finding suggests that interventions within the first 6 months to 1 year are consequential, as health learning peaks at this point. This strategy should be explored further in future studies of type 2 diabetes.

There are several limitations to our review. First, we report moderate to high heterogeneity across studies. Although our P_curve analysis was added as additional evidence, it is still possible that the heterogeneity of studies may be an issue, as results were not fully conclusive with regard to the existence of substantial heterogeneity. Thus, although our findings are strong, we cannot definitively conclude that HTM is effective in general or that certain methods of data transmission are better than others. Second, although our findings are similar to those of Lee et al. (10) that HTM performed better among patients with a mean baseline A1C >8% regardless of insulin, we found that those with higher baseline A1C levels (>8%) experienced further reductions using HTM. Although this finding is somewhat expected (i.e., regression toward the mean), it was based on only two studies, and future research should include RCTs recruiting patients with type 2 diabetes who have a wide range of baseline A1C levels (i.e., both controlled and uncontrolled glycemia) and with larger sample sizes.

Conclusion

Compared with usual care, the addition of HTM appears to significantly improve A1C in patients with type 2 diabetes and especially in those with severely uncontrolled glycemia at baseline. Although there was substantial heterogeneity, the pooled analyses showed that HTM lowered A1C by 0.42% over 6 months and by 0.28% beyond 6 months. Meanwhile, pairwise subgroup comparison showed that longer intervention duration does not lead to significantly greater glycemic control.