The telehealth intervention in most of the selected trials involved self-monitoring of blood glucose and data transmission, either manually or electronically, with feedback (n = 14)[8,22–26,28,30–36]; in the remaining studies, the procedures were not specifically mentioned. Mobile phone, telephone, Internet, modem, or Bluetooth communication was employed to transmit monitoring data in these 14 studies. Most studies required the participants to monitor and transmit the data weekly or less than a week and provided feedback through text messages, standardized messages, phone calls, Internet-based communications, a website or email. Generally, the feedback included advice on medication adjustments, a healthy diet and physical activity. The approaches used to deliver education included telephone calls,[19,20,22,27,29,32] web-based educational modules,[21,23,24,26,31,35] Internet-based communication,[25,35] videoconferencing,[8,36] and short message service. Education was mostly administered by a multidisciplinary team that consisted of nurses, physicians, clinical health psychologists, diabetologists, or exercise experts (n = 7),[8,20,24,26,28,29,35] while others were simply by nurses,[27,34,36] clinicians,[8,31] or endocrinologists. All of these educational strategies aimed to enhance patient motivation, self-efficacy, and self-management ability.
3.2 Quality assessment
Figure 2A summarizes the quality of entire included trials in this study, while Fig. 2B presents the quality of the individual trials included. As shown in Fig. 2A, the allocation sequence was randomly generated in all trials. Every study had reported the concealment of the allocation and addressed incomplete outcome data adequately. Due to the nature of telehealth, it was impossible for the patients to be blinded to their allocation. However, some trails were designed such that the patient allocation remained unknown to the outcome assessors.
3.3 Diabetic control outcomes
The data from 16 RCTs were pooled to find the effects of diverse telehealth approaches on HbA1c. As shown in Fig. 3A, the HbA1c levels in the telehealth group were significantly lower than those in the usual care group (weighted mean difference = −0.22%; 95% CI, −0.28 to −0.15; P < .001). The statistical heterogeneity was low (I2 = 46%), and a fixed-effects model was used in this analysis. As in the studies by Lorig et al and Wakefield et al, data for the mean or standard deviation at the final visit were not given, these 2 studies were not pooled with the other studies. Both studies found that HbA1c was improved in the telehealth group compared with the usual care group, while no significant between-group differences were observed in the study by Leichter et al, which was not pooled with other studies because of heterogeneity. In addition, Table 1 shows that the average change in HbA1c in the telehealth group was approximately −1.22% when the baseline level of the participants was 9.0% or above, and the average change in HbA1c was approximately −0.35% when the baseline level was lower than 9.0%.
3.3.2 Blood pressure
Blood pressure was reported in 9 studies, and the results are presented in Table 2. Eight of these studies were pooled and examined both systolic blood pressure (Fig. 3B) and diastolic blood pressure (Fig. 3C). Figure 3B and C shows a statistically significant decrease in systolic blood pressure (weighted mean difference = −1.92; 95% CI, −2.49 to −1.34; P < .001) and diastolic blood pressure (weighted mean difference = −1.31; 95% CI, −2.39 to −0.23; P < .001) in the telehealth group compared to the usual care group. As the statistical heterogeneity was higher than 50%, a random-effects model was applied in the meta-analysis of diastolic blood pressure.
3.3.3 BMI, total cholesterol, and quality of life
BMI was reported in 10 studies (Table 3) and Fig. 3D shows that there was no significant difference between the telehealth group and the usual care group in controlling BMI (weighted mean difference = −0.14; 95% CI, −1.13 to 0.68; P = .79). Six included trials reported the total cholesterol outcomes, as shown in Table 3. Only 2 trials[8,30] reported that the total cholesterol was significantly lower in the telehealth group than that in the usual care group; 4 studies[28,32,35,36] showed a nonsignificant difference between this 2 groups over the duration of follow-up. The outcomes of quality of life were not pooled because the measurement instruments used in these trials varied significantly. Two studies[21,28] stated that quality of life improved in the telehealth group while no statistically significant difference was found; 1 study showed that the impairment of quality of life decreased (P < .001) in the telehealth group versus the usual care group.
The results of this meta-analysis indicated that compared with usual care, telehealth had a positive effect on glycemic and blood pressure control, while no significant difference was found in the control of BMI. For total cholesterol and quality of life, telehealth was similar or superior to usual care.
The results of this review related to HbA1c control are generally consistent with some previous reviews. Liang et al demonstrated that telehealth intervention reduced HbA1c by a mean of 0.5% (P < .001); Zhai et al found a lower HbA1c level in the telehealth group (P < .001) than that in the usual care group; and Lee et al showed that telehealth improved HbA1c by −0.18% (P = .01). DelliFraine and Dansky failed to support a link between telehealth and diabetes outcomes (the effect size was 0.13; Z = 1.3). In this study, the sample size ranged from 31 to 141, and the evidence base for HbA1c was limited (only 5 included studies studied HbA1c). Moreover, participants with other serious diseases were not excluded in the original studies, which could potentially affect the accuracy of the results. Verhoeven et al also found no significant difference between the teleconsultation and usual care groups after conducting a meta-analysis of 6 RCTs (P = .82). In this meta-analysis, the duration of follow-up was 3 to 4 months in the original trails, which was not long enough to capture valid data. As Lee et al illustrated, a longer duration (>6 months) could result in larger effects. In our meta-analysis, the sample size ranged from 100 to 1665, and the duration of follow-up was at least 6 months, which may provide more effective evidence.
Participants with higher baseline HbA1c levels (≥9%) may be associated with greater effects when receiving a telehealth intervention. The results of this study showed that the average HbA1c change in the recruited patients with higher baseline HbA1c levels (≥9%) was larger than that in patients with lower baseline HbA1c levels (<9.0%) (−1.22% vs −0.35%). This difference may have occurred because participants with higher baseline levels had poorer self-care management, relating to healthy eating, exercise adherence as well as medication administration. Telehealth offered a mechanism to improve medication administration, such as regular reminders and adjustments of the medication dose for patients when needed. Benefiting from information and communication technologies, telehealth introduced high-quality diabetes self-management education to individuals who lived in remote areas, which can facilitate patients’ healthy eating, exercise adherence and self-monitoring of blood glucose. Thus, telehealth could potentially enhance self-care management for participants and result in lower HbA1c levels. Lee et al also illustrated, higher baseline HbA1c (≥9%) levels were related to larger effects after the telehealth intervention. Therefore, it can be speculated that targeting patients with higher HbA1c (≥9%) levels with telehealth interventions could achieve greater effects. Different intervention frequencies may influence the effect of telehealth. In the study conducted by Leichter et al, the endocrinologist analyzed the transmitted data and provided feedback regarding treatment changes via the Internet and telephone only during the 3rd and 9th months of the study period. The remaining intervention was the same as usual care. Eventually, no superior effect was found in the telehealth group over the usual care group in this study. However, in most of rest studies, the intervention frequency ranged from weekly to monthly. For instance, in the study of Wild et al participants received suggestions on lifestyle modifications and treatment adjustments weekly from the primary care nurses based on the participants’ results. This study found that telehealth intervention reduced HbA1c by a mean of 0.51% (P < .001) compared with that in the usual care group. Additionally, Walker et al demonstrated that 6 times interventions in 12 months were the minimum frequency associated with a significant decrease in HbA1c. Therefore, we speculated that frequent intervention (at least 6 times 1 year) may result in better outcomes, or the usefulness of telehealth could be weakened. Further prospective and randomized studies are needed to identify the telehealth strategies and protocols that would be most beneficial for patients.
The results of this study demonstrated that blood pressure significantly decreased in the telehealth group (P < .001), while no significant difference in BMI (P = .79) was found. Among the pooled studies, most of the mean baseline systolic blood pressures were above 130 mm Hg but under 140 mm Hg, which cannot be diagnosed with hypertension. Because all the included studies mainly targeted the participants with diabetes, whose HbA1c levels were abnormal, their blood pressure, BMI, and blood lipid levels were not sure to be under control. Thus, the difference in effects on systolic blood pressure between the telehealth and usual care groups was probably larger when targeting the participants with hypertension. The results obtained for diastolic blood pressure and BMI should be interpreted with caution due to the high level of heterogeneity (diastolic blood pressure, I2 = 74%; BMI, I2 = 97%). Other outcomes, including total cholesterol and quality of life, were limited and reported in only 6 and 3 studies, respectively. More studies should measure these important outcomes to draw an explicit conclusion regarding the utilization of telehealth interventions.
The findings of this study held promise in supporting telehealth practice and policy. Stratton et al demonstrated that for type 2 diabetes patients, reducing the 1% mean HbA1c level would be related to a 21% reduction in diabetes-related death and a 37% reduction in microvascular complications, such as neuropathy, retinopathy, and blindness. As mentioned above, compared with usual care, telehealth could achieve a 0.22% mean HbA1c reduction, and it could be speculated that approximately 170,940 diabetes-related deaths could have been avoided, if this intervention was implemented in 2012; because a total of 3.7 million deaths were associated with blood glucose levels. Therefore, it is worth promoting the adoption and sustainability of this innovation by policy makers.
There are several limitations to this systematic review. First, only 25% of the included studies showed successful blinding of the outcome assessment, which may lead to performance and detection bias. Second, only limited guidance about the outcomes of telehealth in managing diabetes could be provided by this meta-analysis. Several areas need further clarification. For instance, it would be helpful to identify whether the effects of telehealth are influenced by the frequency and pattern of data delivery, the strength and mode of intervention, the baseline level of the indicator and the target participants. Further research should be conducted to provide more valid evidence for the effects and sustainable implementation of telehealth.
The findings showed evidence that telehealth holds promise in improving the clinical effectiveness of diabetes management. Targeting patients with higher HbA1c (≥9%) levels and delivering more frequent intervention (at least 6 times 1 year) may achieve greater improvement.
Conceptualization: Cong Wu, Zixiang Wu, Lingfei Yang, Meng Zhang, Xiaoying Chen, Yongmiao Pan.
Data curation: Cong Wu, Zixiang Wu, Lingfei Yang, Xiaoying Chen, Yongmiao Pan.
Formal analysis: Cong Wu, Zixiang Wu, Qian Zhu.
Investigation: Wenjun Zhu.
Methodology: Cong Wu, Zixiang Wu, Yongmiao Pan.
Project administration: Yongmiao Pan.
Resources: Cong Wu.
Supervision: Cong Wu, Meng Zhang, Yongmiao Pan.
Writing – original draft: Cong Wu, Zixiang Wu, Lingfei Yang, Wenjun Zhu, Meng Zhang, Qian Zhu, Xiaoying Chen, Yongmiao Pan.
Writing – review & editing: Cong Wu, Zixiang Wu, Lingfei Yang, Wenjun Zhu, Meng Zhang, Qian Zhu, Xiaoying Chen, Yongmiao Pan.
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Keywords:Copyright © 2018 The Authors. Published by Wolters Kluwer Health, Inc. All rights reserved.
clinical outcomes; diabetes management; meta-analysis; systematic review; telehealth