**3.3 Randomized trial on home telemonitoring for the management of metabolic and cardiovascular risk in patients with type 2 diabetes**

This study evaluated whether a home telehealth (HT) system can improve metabolic control and overall cardiovascular risk in individuals with type 2 diabetes, compared with usual practice [35]. This study was a randomized, parallel-group, open-label, multicenter study conducted in general practice (29 general practitioners) including 302 patients, with a follow-up of 12 months. The HT system (for the telemedicine group of diabetic patients, n = 153) offers to the patient the possibility to monitor body weight, BG values, and BP values, associated with remote educational support and feedback to the general practitioner [35]. The use of the HT system was associated with a statistically significant reduction in HbA1c levels (principal criterion) compared with the control group: estimated mean difference of 0.33 ± 0.1 (*p* = 0.001) [35]. No difference was documented for body weight, BP, and lipid profile (all principal criteria). The proportion of patients reaching the target of HbA1c (HbA1c < 7.0%) was higher in the HT group than in the control group after 6 months, 33.0 vs. 18.7% (*p* = 0.009), and 12 months, 28.1 vs. 18.5% (*p* = 0.07). As for quality of life (evaluated with the 36-item short-form health survey), significant differences in favor of the HT group were detected as for physical functioning (*p* = 0.01) and mental health (*p* = 0.005). On an economic level, a lower number of specialist visits was reported in the telemedicine group: incidence rate ratio of 0.72 (95% confidence interval, 0.51–1.01; *p* = 0.06).

### **3.4 Study assessed the utility and cost-effectiveness of an automated Diabetes Remote Monitoring and Management System (DRMS)**

This study assessed the utility and cost-effectiveness of an automated Diabetes Remote Monitoring and Management System (DRMS) in glycemic control versus usual care [36]. In this randomized, controlled study, patients with uncontrolled diabetes on insulin were randomized to use the DRMS or usual care. Participants in both groups were followed up for 6 months and had three clinic visits during the study period (at 0, 3, and 6 months [35]). The DRMS used text messages or phone calls to remind patients to test their BG and to report results via an automated system, with no human interaction unless a patient had severely high or low BG. The DRMS made adjustments to insulin dose(s) based on validated algorithms. Participants reported medication adherence through the Morisky Medication Adherence Scale-8, and diabetes-specific quality of life through the diabetes daily quality-of-life questionnaire. A cost-effectiveness analysis was conducted based on the estimated overall costs of DRMS and usual care. A total of 98 diabetic patients (60% of female) treated with insulin therapy were enrolled [36]. The mean age of the patients was 59 years. At the end, 87 patients (89%) have completed the follow-up. HbA1c was similar between the DRMS and control groups at 3 months, 7.60 vs. 8.10%, and at 6 months, 8.10 vs. 7.90% (*p* = ns) (principal criterion) [42]. Changes from baseline to 6 months were not statistically significant for self-reported medication adherence and diabetes-specific quality of life, except for the Daily Quality of Life-Social/Vocational Concerns subscale score (*p* = 0.04).

**69**

*State of Art of Telemonitoring in Patients with Diabetes Mellitus, with a Focus on Elderly Patients*

**3.5 The Telescot Diabetes Pragmatic Multicenter Randomized Controlled Trial**

The Telescot Diabetes is a randomized, parallel, investigator-blind controlled trial with centralized randomization in family practices in four regions of the United Kingdom [37]. This study included 321 patients with relatively wellcontrolled type 2 diabetes, with an HbA1c > 7.46%. In Telescot Diabetes, 160 people were randomized to the intervention group and 161 to the usual care group [37]. The supported telemonitoring intervention involved self-measurement and transmission to a secure Website of twice-weekly morning and evening glucose for review by family practice clinicians who were not blinded to allocation group. The control group received usual care, with at least annual review and more frequent reviews for people with poor glycemic or BP control. HbA1c assessed at ninth month was the primary outcome. The mean (SD) HbA1c at follow-up was 7.92% in the intervention group vs. 8.36% in the usual care group [37]. For primary analysis, adjusted mean HbA1c was 0.51% lower (95% CI 0.22% to 0.81%, (principal criterion) (*p* = 0.0007)). For secondary analyses, adjusted mean ambulatory systolic BP was 3.06 mmHg lower (95% CI 0.56–5.56 mmHg, *p* = 0.017) and mean ambulatory diastolic BP was 2.17 mmHg lower (95% CI 0.62–3.72, *p* = 0.006) among people in the intervention group when compared with usual care after adjustment. No significant differences were identified between groups in weight, treatment pattern, adherence to medication, or quality of life in secondary analyses. During the study, the number of telephone calls was greater between nurses and patients in the intervention compared with control group: rate ratio of 7.50 (95% CI 4.45–12.65, *p* < 0.0001), but no other significant differences between groups in the use of

Educ@dom is a multicenter, randomized, controlled, prospective study [38]. The primary objective of this study is to compare the efficacy of telemonitoring to standard monitoring in terms of changes in HbA1c after a 1-year follow-up period. The secondary objectives are clinical (changes in knowledge, physical activity, weight, etc.) and medical-economic. The Educ@dom study included 282 patients, 141 patients in each arm [38]. For patients in the intervention group, the device will be given to them for 1 year and then withdrawn during the second year of followup. The anticipated benefits of this research are an improvement in BG management in patients with type 2 diabetes by improving their lifestyle while rationalizing recourse to consultations in order to reduce the incidence of complications and cost

Over the last 5 years, new-generation telemedicine projects and studies have emerged in the setting of chronic diseases setting, especially in the setting of chronic heart failure, chronic obstructive pulmonary diseases, and type 1 and type 2 diabetes [29, 39–42]. They support transmission and remote interpretation of patients' data for follow-up and preventive interventions. These projects and studies have for main objectives to evaluate the use of technology to implement medical and cost-effective healthcare management on a large scale for diabetes management. Using *PubMed* database and *Google Scholar*, we have identified three of such projects and studies in the field of diabetes management: Telemonitoring and Health Counseling for Self-Management Support from Lindberg et al.,

in the long term. The results of this study are expected in 2019–2020.

**4. New-generation projects and studies in diabetes**

*DOI: http://dx.doi.org/10.5772/intechopen.83384*

health services were identified between groups.

**3.6 Educ@dom**

*State of Art of Telemonitoring in Patients with Diabetes Mellitus, with a Focus on Elderly Patients DOI: http://dx.doi.org/10.5772/intechopen.83384*

#### **3.5 The Telescot Diabetes Pragmatic Multicenter Randomized Controlled Trial**

The Telescot Diabetes is a randomized, parallel, investigator-blind controlled trial with centralized randomization in family practices in four regions of the United Kingdom [37]. This study included 321 patients with relatively wellcontrolled type 2 diabetes, with an HbA1c > 7.46%. In Telescot Diabetes, 160 people were randomized to the intervention group and 161 to the usual care group [37]. The supported telemonitoring intervention involved self-measurement and transmission to a secure Website of twice-weekly morning and evening glucose for review by family practice clinicians who were not blinded to allocation group. The control group received usual care, with at least annual review and more frequent reviews for people with poor glycemic or BP control. HbA1c assessed at ninth month was the primary outcome. The mean (SD) HbA1c at follow-up was 7.92% in the intervention group vs. 8.36% in the usual care group [37]. For primary analysis, adjusted mean HbA1c was 0.51% lower (95% CI 0.22% to 0.81%, (principal criterion) (*p* = 0.0007)). For secondary analyses, adjusted mean ambulatory systolic BP was 3.06 mmHg lower (95% CI 0.56–5.56 mmHg, *p* = 0.017) and mean ambulatory diastolic BP was 2.17 mmHg lower (95% CI 0.62–3.72, *p* = 0.006) among people in the intervention group when compared with usual care after adjustment. No significant differences were identified between groups in weight, treatment pattern, adherence to medication, or quality of life in secondary analyses. During the study, the number of telephone calls was greater between nurses and patients in the intervention compared with control group: rate ratio of 7.50 (95% CI 4.45–12.65, *p* < 0.0001), but no other significant differences between groups in the use of health services were identified between groups.

#### **3.6 Educ@dom**

*Geriatric Medicine and Gerontology*

7.81% at the end of the program (*p* < 0.0001) [34]. Systolic BP (principal criterion) also declined significantly: 130.7 mmHg at baseline vs. 122.9 mmHg at the end (*p* = 0.0001). Low-density lipoprotein content decreased significantly: 103.9 mg/dL at baseline vs. 93.7 mg/dL at the end (*p* = 0.0263). Knowledge of diabetes and arterial hypertension increased significantly (*p* < 0.001 for both). Patient engagement and medication adherence also improved, but not significantly. Per questionnaires

**3.3 Randomized trial on home telemonitoring for the management of metabolic** 

This study evaluated whether a home telehealth (HT) system can improve metabolic control and overall cardiovascular risk in individuals with type 2 diabetes, compared with usual practice [35]. This study was a randomized, parallel-group, open-label, multicenter study conducted in general practice (29 general practitioners) including 302 patients, with a follow-up of 12 months. The HT system (for the telemedicine group of diabetic patients, n = 153) offers to the patient the possibility to monitor body weight, BG values, and BP values, associated with remote educational support and feedback to the general practitioner [35]. The use of the HT system was associated with a statistically significant reduction in HbA1c levels (principal criterion) compared with the control group: estimated mean difference of 0.33 ± 0.1 (*p* = 0.001) [35]. No difference was documented for body weight, BP, and lipid profile (all principal criteria). The proportion of patients reaching the target of HbA1c (HbA1c < 7.0%) was higher in the HT group than in the control group after 6 months, 33.0 vs. 18.7% (*p* = 0.009), and 12 months, 28.1 vs. 18.5% (*p* = 0.07). As for quality of life (evaluated with the 36-item short-form health survey), significant differences in favor of the HT group were detected as for physical functioning (*p* = 0.01) and mental health (*p* = 0.005). On an economic level, a lower number of specialist visits was reported in the telemedicine group: incidence

at study end, patients felt the telemonitoring program was useful.

**and cardiovascular risk in patients with type 2 diabetes**

rate ratio of 0.72 (95% confidence interval, 0.51–1.01; *p* = 0.06).

**Remote Monitoring and Management System (DRMS)**

Daily Quality of Life-Social/Vocational Concerns subscale score (*p* = 0.04).

**3.4 Study assessed the utility and cost-effectiveness of an automated Diabetes** 

This study assessed the utility and cost-effectiveness of an automated Diabetes Remote Monitoring and Management System (DRMS) in glycemic control versus usual care [36]. In this randomized, controlled study, patients with uncontrolled diabetes on insulin were randomized to use the DRMS or usual care. Participants in both groups were followed up for 6 months and had three clinic visits during the study period (at 0, 3, and 6 months [35]). The DRMS used text messages or phone calls to remind patients to test their BG and to report results via an automated system, with no human interaction unless a patient had severely high or low BG. The DRMS made adjustments to insulin dose(s) based on validated algorithms. Participants reported medication adherence through the Morisky Medication Adherence Scale-8, and diabetes-specific quality of life through the diabetes daily quality-of-life questionnaire. A cost-effectiveness analysis was conducted based on the estimated overall costs of DRMS and usual care. A total of 98 diabetic patients (60% of female) treated with insulin therapy were enrolled [36]. The mean age of the patients was 59 years. At the end, 87 patients (89%) have completed the follow-up. HbA1c was similar between the DRMS and control groups at 3 months, 7.60 vs. 8.10%, and at 6 months, 8.10 vs. 7.90% (*p* = ns) (principal criterion) [42]. Changes from baseline to 6 months were not statistically significant for self-reported medication adherence and diabetes-specific quality of life, except for the

**68**

Educ@dom is a multicenter, randomized, controlled, prospective study [38]. The primary objective of this study is to compare the efficacy of telemonitoring to standard monitoring in terms of changes in HbA1c after a 1-year follow-up period. The secondary objectives are clinical (changes in knowledge, physical activity, weight, etc.) and medical-economic. The Educ@dom study included 282 patients, 141 patients in each arm [38]. For patients in the intervention group, the device will be given to them for 1 year and then withdrawn during the second year of followup. The anticipated benefits of this research are an improvement in BG management in patients with type 2 diabetes by improving their lifestyle while rationalizing recourse to consultations in order to reduce the incidence of complications and cost in the long term. The results of this study are expected in 2019–2020.

## **4. New-generation projects and studies in diabetes**

Over the last 5 years, new-generation telemedicine projects and studies have emerged in the setting of chronic diseases setting, especially in the setting of chronic heart failure, chronic obstructive pulmonary diseases, and type 1 and type 2 diabetes [29, 39–42]. They support transmission and remote interpretation of patients' data for follow-up and preventive interventions. These projects and studies have for main objectives to evaluate the use of technology to implement medical and cost-effective healthcare management on a large scale for diabetes management. Using *PubMed* database and *Google Scholar*, we have identified three of such projects and studies in the field of diabetes management: Telemonitoring and Health Counseling for Self-Management Support from Lindberg et al.,

TELESAGE, and DIABETe [39–42]. All these projects include elderly diabetic patients. Of note for the first time, one the telemedicine projects developed for chronic diseases management, the TIM-HF2 study [43], has recently demonstrated the usefulness of telemedicine in chronic heart failure, with statistical significance, in a prospective randomized study (the *gold standard* of evidence-based medicine [EBM]).

Between August 13, 2013, and May 12, 2017, 1571 patients (mean age of 70 years) were included in the TIM-HF2 study and randomly assigned to remote patient management (n = 796) or standard care (n = 775) [43]. At baseline, all patients exhibited a left ventricular ejection fraction of <45% and NYHA II or III while receiving treatment with diuretics. In TIM-HF2 study, the percentage of days lost due to unplanned cardiovascular hospital admissions and all-cause death was 4.88% (95% CI 4.55–5.23) in the remote patient management group vs. 6.64% (6.19–7.13) in the standard care group (ratio 0.80, 95% CI: 0.65–1) (*p* = 0.0460). The all-cause death rate was 7.86 (95% CI: 6.14–10.10) per 100 person-years of follow-up in the remote patient management group vs. 11.34 (95% CI: 9.21–13.95) per 100 personyears of follow-up in the standard care group (hazard ratio [HR] 0.70, 95% CI: 0.5–0.96) (*p* = 0.0280) (**Figure 3**). Cardiovascular mortality did not significantly differ between both groups (HR 0.671, 95% CI: 0.45–1.01; *p* = 0.056).

The TIM-HF2 study utilized a noninvasive, multiparameter telemonitoring system installed in the patient's home, comprising a three-channel ECG, BP-monitoring device, and weighing scales, by means of which the information was transferred remotely [43]. Patients received a mobile phone in order to contact the telemedical center in case of emergency. Patients were likewise followed via monthly phone interviews. For this TIM-HF2 care strategy, the key component was a well-structured telemedical center with physicians and HF nurses (*center of coordination*), available 24 hours a day and every day a week, able to act promptly according to the individual patient risk profile. The actions taken by the telemedical center staff included changes in medication and admission to hospital, as needed, in addition to educational activities.

In this setting, we believe that, thanks to technological innovations in connected health-monitoring devices, the telemonitoring of type 2 diabetic patients using

**Figure 3.**

*TIM-HF2 trial. Rate of cumulative events in patients randomly assigned to remote patient management (n = 796) or usual care (n = 775) (adapted from [43]).*

**71**

*State of Art of Telemonitoring in Patients with Diabetes Mellitus, with a Focus on Elderly Patients*

These new-generation telemedicine projects in diabetes (Telemonitoring and Health Counseling for Self-Management Support from Lindberg et al., TELESAGE, DIABETe) [39–42] are often known as *telemedicine 2.0* projects, given that they all utilize new information and communication technologies (ICT) and the Web (tools

Most projects and studies rely on the standard connected tools for monitoring

• Adjust the BG level to the patient's activity (software Diabeo™ [see below])

• Predict patient risks of diabetes decompensation [42, 45]. In this later situation, the cloud-based software aggregates, cleans, and analyzes patient data to allow for identifying patterns that may indicate potential risks and provide predictive insights on healthcare outcomes, as the software MyPredi™ (see below) [29, 42].

In the setting of chronic diseases, as in chronic heart disease or in diabetes, several informatics solutions or tools have been developed and used, such as artificial neural network (ANN) algorithms, data mining software, and ontology [45, 46]. In this context of AI, three clinical datasets are of particular interest: (1) patients' phenotype; (2) patients' electronic medical records containing physicians' notes, laboratory test results, as well as other information on diseases, treatments, and epidemiology that may be of interest for association studies and predictive modeling on prognosis and drug responses; and (3) literature knowledge including

Besides these tools, it must be emphasized that diabetes telemonitoring may use, as for CHF telemonitoring, implantable invasive devices that send either sporadically or continuously data to the receiving physician (automatic telemonitoring) (*outside the scope of this paper*) [30]. In management of diabetes, implantable telemonitoring devices for multiparameters including mainly BG-insulin levels

**4.1 Telemonitoring and Health Counseling for Self-Management Support of** 

The objective of this study (Telemonitoring and Health Counseling for Self-Management Support) was to investigate whether the introduction of a health technology-supported self-management program involving telemonitoring and health counseling had beneficial effects on HbA1c, other clinical variables (weight, body mass index, BP, blood lipid profile), and health-related quality of life (HRQoL), as measured using the short-form health survey (SF-36) version 2 in patients with type 2 diabetes [39]. This was a pragmatic randomized controlled trial of patients with type 2 diabetes. Both the control (n = 79) and intervention groups (n = 87) received usual care [39]. The intervention group also participated in additional health promotion activities with the use of the Prescribed Healthcare Web application for self-monitoring of BG and BP. About every second month or when needed, the

type 1 and type 2 diabetes, such as glucose meters, BP, heart rate monitors, weighing scales, and pulse oximeters, which relay the collected information via Bluetooth, 3G, or 4G [29, 39–42]. Several projects also include continuous glycemic monitoring solution and often a video-call [29, 30]. Several of these telemedicine projects use machine learning, also called artificial intelligence (AI), in order

therapeutic educational tools is likely to help them adapt to their treatment and

lifestyle habits and therefore improve BG management [29].

for the *e-Health 2.0*) (as defined in **Table 1**) [44].

to be able to:

[40, 41].

rules on diabetes management [46].

**Patients with Type 2 Diabetes**

monitoring have recently proven to be an effective approach.

*DOI: http://dx.doi.org/10.5772/intechopen.83384*

*State of Art of Telemonitoring in Patients with Diabetes Mellitus, with a Focus on Elderly Patients DOI: http://dx.doi.org/10.5772/intechopen.83384*

therapeutic educational tools is likely to help them adapt to their treatment and lifestyle habits and therefore improve BG management [29].

These new-generation telemedicine projects in diabetes (Telemonitoring and Health Counseling for Self-Management Support from Lindberg et al., TELESAGE, DIABETe) [39–42] are often known as *telemedicine 2.0* projects, given that they all utilize new information and communication technologies (ICT) and the Web (tools for the *e-Health 2.0*) (as defined in **Table 1**) [44].

Most projects and studies rely on the standard connected tools for monitoring type 1 and type 2 diabetes, such as glucose meters, BP, heart rate monitors, weighing scales, and pulse oximeters, which relay the collected information via Bluetooth, 3G, or 4G [29, 39–42]. Several projects also include continuous glycemic monitoring solution and often a video-call [29, 30]. Several of these telemedicine projects use machine learning, also called artificial intelligence (AI), in order to be able to:


In the setting of chronic diseases, as in chronic heart disease or in diabetes, several informatics solutions or tools have been developed and used, such as artificial neural network (ANN) algorithms, data mining software, and ontology [45, 46]. In this context of AI, three clinical datasets are of particular interest: (1) patients' phenotype; (2) patients' electronic medical records containing physicians' notes, laboratory test results, as well as other information on diseases, treatments, and epidemiology that may be of interest for association studies and predictive modeling on prognosis and drug responses; and (3) literature knowledge including rules on diabetes management [46].

Besides these tools, it must be emphasized that diabetes telemonitoring may use, as for CHF telemonitoring, implantable invasive devices that send either sporadically or continuously data to the receiving physician (automatic telemonitoring) (*outside the scope of this paper*) [30]. In management of diabetes, implantable telemonitoring devices for multiparameters including mainly BG-insulin levels monitoring have recently proven to be an effective approach.

#### **4.1 Telemonitoring and Health Counseling for Self-Management Support of Patients with Type 2 Diabetes**

The objective of this study (Telemonitoring and Health Counseling for Self-Management Support) was to investigate whether the introduction of a health technology-supported self-management program involving telemonitoring and health counseling had beneficial effects on HbA1c, other clinical variables (weight, body mass index, BP, blood lipid profile), and health-related quality of life (HRQoL), as measured using the short-form health survey (SF-36) version 2 in patients with type 2 diabetes [39]. This was a pragmatic randomized controlled trial of patients with type 2 diabetes. Both the control (n = 79) and intervention groups (n = 87) received usual care [39]. The intervention group also participated in additional health promotion activities with the use of the Prescribed Healthcare Web application for self-monitoring of BG and BP. About every second month or when needed, the

*Geriatric Medicine and Gerontology*

addition to educational activities.

*(n = 796) or usual care (n = 775) (adapted from [43]).*

[EBM]).

TELESAGE, and DIABETe [39–42]. All these projects include elderly diabetic patients. Of note for the first time, one the telemedicine projects developed for chronic diseases management, the TIM-HF2 study [43], has recently demonstrated the usefulness of telemedicine in chronic heart failure, with statistical significance, in a prospective randomized study (the *gold standard* of evidence-based medicine

were included in the TIM-HF2 study and randomly assigned to remote patient management (n = 796) or standard care (n = 775) [43]. At baseline, all patients exhibited a left ventricular ejection fraction of <45% and NYHA II or III while receiving treatment with diuretics. In TIM-HF2 study, the percentage of days lost due to unplanned cardiovascular hospital admissions and all-cause death was 4.88% (95% CI 4.55–5.23) in the remote patient management group vs. 6.64% (6.19–7.13) in the standard care group (ratio 0.80, 95% CI: 0.65–1) (*p* = 0.0460). The all-cause death rate was 7.86 (95% CI: 6.14–10.10) per 100 person-years of follow-up in the remote patient management group vs. 11.34 (95% CI: 9.21–13.95) per 100 personyears of follow-up in the standard care group (hazard ratio [HR] 0.70, 95% CI: 0.5–0.96) (*p* = 0.0280) (**Figure 3**). Cardiovascular mortality did not significantly

differ between both groups (HR 0.671, 95% CI: 0.45–1.01; *p* = 0.056).

The TIM-HF2 study utilized a noninvasive, multiparameter telemonitoring system installed in the patient's home, comprising a three-channel ECG, BP-monitoring device, and weighing scales, by means of which the information was transferred remotely [43]. Patients received a mobile phone in order to contact the telemedical center in case of emergency. Patients were likewise followed via monthly phone interviews. For this TIM-HF2 care strategy, the key component was a well-structured telemedical center with physicians and HF nurses (*center of coordination*), available 24 hours a day and every day a week, able to act promptly according to the individual patient risk profile. The actions taken by the telemedical center staff included changes in medication and admission to hospital, as needed, in

In this setting, we believe that, thanks to technological innovations in connected

health-monitoring devices, the telemonitoring of type 2 diabetic patients using

*TIM-HF2 trial. Rate of cumulative events in patients randomly assigned to remote patient management* 

Between August 13, 2013, and May 12, 2017, 1571 patients (mean age of 70 years)

**70**

**Figure 3.**

general practitioner or the DM nurse reviewed the results and the healthcare activity plan. Analyses of the data showed that there were no significant differences between the groups in the primary outcome HbA1c level (*p* = 0.33) and in the secondary outcome HRQoL as measured using SF-36 [39]. A total of 80% of the patients in the intervention group at the baseline and 98% of the responders after 19-month intervention were familiar with using a personal computer (*p* = 0.001). After 19 months, no responders reported significantly poorer mental health in social functioning and role emotional subscales on the SF-36 (*p* = 0.03 and *p* = 0.01, respectively).

## **4.2 TELESAGE study**

TELESAGE (*Suivi A Grande Echelle d'une population de diabétiques de type 1 et de type 2 sous schéma insulinique basal bolus par la TELEmédecine* [large-scale follow-up of a population of type 1 and type 2 diabetics under basal insulin regimen bolus by telemedicine]) is a 6-month open-label parallel-group, multicenter study, including adult patients (n = 180) with type 1 diabetes (>1 year), on a basal-bolus insulin regimen (> 6 months), with HbA1c ≥ 8%, conducted in approximately 100 centers in France [40, 41]. These type 1 diabetic patients were randomized to usual quarterly follow-up (G1), home use of a smartphone recommending insulin doses (Diabeo™ software) with quarterly visits (G2), or the use of the smartphone with short teleconsultations every 2 weeks but no visit until point end (G3) [40, 41]. The primary objective of TELESAGE will be to investigate the effect of the Diabeo™ telemedicine system versus usual follow-up, with respect to improvements in the HbA1c levels (principal criterion) of diabetic patients with poorly controlled basal-bolus insulin levels (n = 696). The study will compare a control group (group 1 [G1], usual follow-up) with two Diabeo™ telemedicine systems: (1) physician-assisted telemedicine (group 2 [G2]) and (2) nurse-assisted telemonitoring and teleconsultations by a diabetologist's task delegation (group 3 [G3]). At 6 months, the mean HbA1c level is as follows: 8.41 ± 1.04% in G3 vs. 8.63 ± 1.07% in G2 vs. 9.10 ± 1.16% in G1 (*p* = 0.0019 for G1–G3 comparison) (**Figure 4**) [40, 41]. The Diabeo™ system gave a 0.91% (0.60–1.21) improvement in HbA1c over controls and a 0.67% (0.35–0.99) reduction when used without teleconsultation. There was no difference in the frequency of hypoglycemic episodes or in medical time spent for hospital or telephone consultations. However, patients in G1 and G2 spent nearly 5 h more than G3 patients attending hospital visits.
