**4.3 DIABETe project**

The DIABETe project is scheduled to experiment a telemonitoring solution for at-home monitoring of type 1 and type 2 diabetic patients [29, 42]. The DIABETe telemonitoring project, conducted in Strasbourg (France), falls under the "telemedicine 2.0" category (as described above) [29, 44]. It has been developed and designed to optimize home monitoring of diabetic patients by detecting, via a telemonitoring 2.0 platform, situations with a risk of decompensation of diabetes and its complications (e.g., MI or CHF), the latter ultimately leading to hospitalization [29, 42]. The AI of the DIABETe platform (MyPredi™) automatically generates indicators of *health status* deterioration, i.e., *warning alerts* for any chronic disease worsening, particularly diabetes, its macrovascular complications, and cardiovascular comorbidities (e.g., arterial hypertension, chronic heart failure). For the patient, these situations may lead to hospitalization if not treated appropriately. To our knowledge, this is one of the first projects that use AI in addition to ICT. The platform comprises connected nonintrusive medical sensors (**Figure 5**), a touchscreen tablet connected by Wi-Fi, and a router or 3G/4G, rendering it possible to interact with the patient and provide education on treatment, diet, and lifestyle [29, 42].

**73**

**Figure 4.**

**Figure 5.**

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

The system (**Figure 6**) involves a server that hosts the patient's data and a secure Internet portal to which the patient and hospital- and nonhospital-based healthcare

DIABETe is based on a smart system comprising an inference engine and a medical ontology for personalized synchronous or asynchronous analysis of data specific to each patient and, if necessary, the sending of an AI-generated alert (MyPredi™) [29, 42]. DIABETe is run by a group bringing together the Strasbourg University Hospital

(*Hôpitaux Universitaires de Strasbourg*), East Regional Health Agency (*Agence* 

professionals can connect (**Figure 7**) [29, 42].

*DIABETe's connected nonintrusive medical sensors.*

*Efficacy of the software Diabeo™ (adapted from [40]).*

*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*

**Figure 4.** *Efficacy of the software Diabeo™ (adapted from [40]).*

**Figure 5.** *DIABETe's connected nonintrusive medical sensors.*

The system (**Figure 6**) involves a server that hosts the patient's data and a secure Internet portal to which the patient and hospital- and nonhospital-based healthcare professionals can connect (**Figure 7**) [29, 42].

DIABETe is based on a smart system comprising an inference engine and a medical ontology for personalized synchronous or asynchronous analysis of data specific to each patient and, if necessary, the sending of an AI-generated alert (MyPredi™) [29, 42].

DIABETe is run by a group bringing together the Strasbourg University Hospital (*Hôpitaux Universitaires de Strasbourg*), East Regional Health Agency (*Agence* 

*Geriatric Medicine and Gerontology*

**4.2 TELESAGE study**

**4.3 DIABETe project**

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

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.

The DIABETe project is scheduled to experiment a telemonitoring solution for at-home monitoring of type 1 and type 2 diabetic patients [29, 42]. The DIABETe telemonitoring project, conducted in Strasbourg (France), falls under the "telemedicine 2.0" category (as described above) [29, 44]. It has been developed and designed to optimize home monitoring of diabetic patients by detecting, via a telemonitoring 2.0 platform, situations with a risk of decompensation of diabetes and its complications (e.g., MI or CHF), the latter ultimately leading to hospitalization [29, 42]. The AI of the DIABETe platform (MyPredi™) automatically generates indicators of *health status* deterioration, i.e., *warning alerts* for any chronic disease worsening, particularly diabetes, its macrovascular complications, and cardiovascular comorbidities (e.g., arterial hypertension, chronic heart failure). For the patient, these situations may lead to hospitalization if not treated appropriately. To our knowledge, this is one of the first projects that use AI in addition to ICT. The platform comprises connected nonintrusive medical sensors (**Figure 5**), a touchscreen tablet connected by Wi-Fi, and a router or 3G/4G, rendering it possible to interact with the patient and provide education on treatment, diet, and lifestyle [29, 42].

role emotional subscales on the SF-36 (*p* = 0.03 and *p* = 0.01, respectively).

**72**

**Figure 6.** *DIABETe's platform.*


**Figure 7.** *DIABETe's Internet portal.*

*Régionale de Santé du Grand Est*), Bas-Rhin branch of France's National Health Insurance (*Caisse Primaire d'Assurance du Bas-Rhin*), and *Predimed Technology* start-up [29, 42]. This project is likely allowing an in-depth study to be carried out designed to improve diagnosis by machine learning and detect abnormalities in diabetic patients at an early time point.

The telemonitoring platform used in DIABETe was first validated in a monocentric study conducted in the Strasbourg University Hospital, carried out as part of the E-Care project, primarily focused on the problem of CHF [47, 48]. Between February 2014 and April 2015, 175 elderly patients (mean age of 72 years) were included into the E-care project; 30% of these patients suffered from type 2 diabetes. During this period, the telemonitoring platform was used on a daily basis by patients and healthcare professionals, according to a defined protocol of use specific to each patient. During the study, 1500 measurements were taken, generating 700 alerts in 68 patients. One hundred seven subjects (61.1%) had no alerts upon followup. Analysis of the warning alerts in the 68 other patients showed that MyPredi™

**75**

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

detected any worsening of the "patient's health," with a sensitivity, specificity, as well as positive and negative predictive values of 100, 30, 89, and 100%, respectively. In this experimentation, both the healthcare professionals and patients, even the frailest, used the E-care system without difficulty until the end of the study. The patients included in the DIABETe project were real-life type 1 and type 2 diabetic patients (n = 100) with (i) a "very high cardiovascular risk," when presenting a personal history of myocardial infarction or stroke, limb amputation, or cardiomyopathy and (ii) an "intensive" insulin therapy, with at least three injections per day or pump administration while offering them a personalized follow-up and education about their illness and its management [29, 42]. To date, several patients have been included. The results of this project are expected in late 2019–early 2020. The DIABETe project is based on an intelligent platform that likely assists healthcare professionals by automatically processing the information obtained from nonintrusive medical sensors (BG meter, BP monitor, actimeter, connected scale, etc.) as well as the subjective information provided by the patient himself (questionnaires) and his/her behavior (compliance), enabling it to detect and report, at an early time, these situations at risk of hospitalization [29, 42]. Patientand situation-adapted therapeutic education tools will be made available to the individual, and communication with the subject will likely occur via a touch pad. Alerts indicating a deterioration of the patient's condition will be generated by AI (new software version of MyPredi™ adapted for the management of diabetes) and transmitted to the health professionals in charge of the patient. The healthcare professional can thus anticipate the decompensation and initiate appropriate measures outside the emergency setting. An intermediate analysis is planned after the first 30 patients, possibly to set up a coordination cell with a nurse, as part of a delegation of tasks, as in TIM-HF2 [43]. Medical data can likewise be shared among health professionals, being part of a city-hospital network. Ultimately, an improvement in

DIABETe does not compete with Diabeo™ or other expert systems aimed at optimizing the glycemic balance, which is per se the main objective of diabetes management [41]. The DIABETe project focuses on the "global" management of diabetic patients through the detection of situations at risk of hospitalization: infection, cardiac decompensation, diabetic foot, as well as hypoglycemia and hyperglycemia episodes, potentially leading to hospitalizations [29, 42]. Regarding the remote monitoring platform used in DIABETe, an integration of or interfacing

In the future, telemedicine projects will have to address some of today's medical issues (challenge for "tomorrow telemedicine") [29, 30]. Thus, the new solutions of telemedicine have to take into account the coexistence in the same individual of numerous chronic pathologies (e.g., diabetes, CHF, chronic obstructive pulmonary disease, chronic renal failure, etc.) and comorbidities (high BP, dyslipidemia, etc.). They have to offer complete and "global" management, including both social and medical dimensions. They have to resolve the specificities of elderly patients: no appetite for new technologies and new uses and their main problems (e.g., falls,

In this setting, the new developments in telemedicine are also to resolve the multiplicity of health professionals working with the same patient and the multiplicity of medical organizations (e.g., with or without human resources, telemedical center, etc.) [29, 30]. Today, the logistical obstacles to the implementation of

with expert systems such as Diabeo™ [41, 42] appears possible.

**5. Perspectives regarding new developments in telemedicine**

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

the patients' quality of life is to be expected.

malnutrition, mild cognitive impairment, etc.).

#### *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*

detected any worsening of the "patient's health," with a sensitivity, specificity, as well as positive and negative predictive values of 100, 30, 89, and 100%, respectively. In this experimentation, both the healthcare professionals and patients, even the frailest, used the E-care system without difficulty until the end of the study.

The patients included in the DIABETe project were real-life type 1 and type 2 diabetic patients (n = 100) with (i) a "very high cardiovascular risk," when presenting a personal history of myocardial infarction or stroke, limb amputation, or cardiomyopathy and (ii) an "intensive" insulin therapy, with at least three injections per day or pump administration while offering them a personalized follow-up and education about their illness and its management [29, 42]. To date, several patients have been included. The results of this project are expected in late 2019–early 2020.

The DIABETe project is based on an intelligent platform that likely assists healthcare professionals by automatically processing the information obtained from nonintrusive medical sensors (BG meter, BP monitor, actimeter, connected scale, etc.) as well as the subjective information provided by the patient himself (questionnaires) and his/her behavior (compliance), enabling it to detect and report, at an early time, these situations at risk of hospitalization [29, 42]. Patientand situation-adapted therapeutic education tools will be made available to the individual, and communication with the subject will likely occur via a touch pad. Alerts indicating a deterioration of the patient's condition will be generated by AI (new software version of MyPredi™ adapted for the management of diabetes) and transmitted to the health professionals in charge of the patient. The healthcare professional can thus anticipate the decompensation and initiate appropriate measures outside the emergency setting. An intermediate analysis is planned after the first 30 patients, possibly to set up a coordination cell with a nurse, as part of a delegation of tasks, as in TIM-HF2 [43]. Medical data can likewise be shared among health professionals, being part of a city-hospital network. Ultimately, an improvement in the patients' quality of life is to be expected.

DIABETe does not compete with Diabeo™ or other expert systems aimed at optimizing the glycemic balance, which is per se the main objective of diabetes management [41]. The DIABETe project focuses on the "global" management of diabetic patients through the detection of situations at risk of hospitalization: infection, cardiac decompensation, diabetic foot, as well as hypoglycemia and hyperglycemia episodes, potentially leading to hospitalizations [29, 42]. Regarding the remote monitoring platform used in DIABETe, an integration of or interfacing with expert systems such as Diabeo™ [41, 42] appears possible.

#### **5. Perspectives regarding new developments in telemedicine**

In the future, telemedicine projects will have to address some of today's medical issues (challenge for "tomorrow telemedicine") [29, 30]. Thus, the new solutions of telemedicine have to take into account the coexistence in the same individual of numerous chronic pathologies (e.g., diabetes, CHF, chronic obstructive pulmonary disease, chronic renal failure, etc.) and comorbidities (high BP, dyslipidemia, etc.). They have to offer complete and "global" management, including both social and medical dimensions. They have to resolve the specificities of elderly patients: no appetite for new technologies and new uses and their main problems (e.g., falls, malnutrition, mild cognitive impairment, etc.).

In this setting, the new developments in telemedicine are also to resolve the multiplicity of health professionals working with the same patient and the multiplicity of medical organizations (e.g., with or without human resources, telemedical center, etc.) [29, 30]. Today, the logistical obstacles to the implementation of

*Geriatric Medicine and Gerontology*

*Régionale de Santé du Grand Est*), Bas-Rhin branch of France's National Health Insurance (*Caisse Primaire d'Assurance du Bas-Rhin*), and *Predimed Technology* start-up [29, 42]. This project is likely allowing an in-depth study to be carried out designed to improve diagnosis by machine learning and detect abnormalities in

The telemonitoring platform used in DIABETe was first validated in a monocentric study conducted in the Strasbourg University Hospital, carried out as part of the E-Care project, primarily focused on the problem of CHF [47, 48]. Between February 2014 and April 2015, 175 elderly patients (mean age of 72 years) were included into the E-care project; 30% of these patients suffered from type 2 diabetes. During this period, the telemonitoring platform was used on a daily basis by patients and healthcare professionals, according to a defined protocol of use specific to each patient. During the study, 1500 measurements were taken, generating 700 alerts in 68 patients. One hundred seven subjects (61.1%) had no alerts upon followup. Analysis of the warning alerts in the 68 other patients showed that MyPredi™

diabetic patients at an early time point.

**74**

**Figure 7.**

**Figure 6.**

*DIABETe's platform.*

*DIABETe's Internet portal.*


**Table 2.**

*Potential parameters to be evaluated in a telemedicine project for chronic disease management.*

telehealth are significant, as many health systems are not yet designed to integrate these technologies into existing information systems. It is therefore necessary to plan now for an interfacing of computer systems and the integration of future telemedicine solutions.

Considering the current problems of access to healthcare professionals, the new telemedicine solutions must be able to structure the patients' care pathways, a major medical topic that should interest our governments and authorities [28, 29]. Likewise, the E-care and DIABETe projects provide a means for healthcare professionals to exchange with each other, thereby facilitating patient access to medical resources. In this context, future research must also focus on the accessibility and practicality of telemedicine interventions.

Importantly, reimbursement remains a major concern and a barrier ("glass ceiling"). In fact, the healthcare delivered by telehealth is not covered by traditional fee-for-service payment models (e.g., in France, where all diabetic patients benefit from an integral treatment of their health expenses) [29]. The growth of value-based payment models may, however, provide incentives to implement telehealth as a strategy to provide high-quality, cost-effective, and coordinated care [29]. At country levels, variations in practice laws, restrictions on how telehealth can be delivered, and which patients should receive these services limit telemedicine's applicability as well [30].

Thus, to document the efficacy on the new telemedicine solutions, the future studies should integrate others objectives like potential targets to meet the needs and requirements of our societies, as listed in **Table 2**.

#### **6. Conclusions**

This review supports the efficacy of telemonitoring type 1 and type 2 diabetic patients. Several studies on diabetes telemonitoring, using diverse technologies, and transmitting different clinical, medical, and behavioral data were found. Significant impacts were observed, namely, at the behavioral, clinical, and structural levels. Minimal technical problems and cost-effectiveness analyses were reported. Four studies are dedicated specifically to elderly diabetic patients (all including <80-year-old patients).

Close management of diabetic patients, even elderly patients, through telemonitoring, showed the following: improvements in control of BG level and significant reduction in HbA1c, better appropriation of the disease by patients, greater adherence to therapeutic and hygiene-dietary measures, positive impact on comorbidities (arterial hypertension, weight, dyslipidemia), better patient's quality of life,

**77**

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

and, at least, good receptiveness by patients and patient empowerment. Moreover, a cost-effectiveness analysis found a potential in medical economy. To date, the magnitude of its effects remains debatable, especially with the variation in patients' characteristics (e.g., background, ability for self-management, medical condition),

To date, relatively few projects and trials in diabetic patients have been run within the "telemedicine 2.0" setting, using AI, ICT, and the Web 2.0. All these projects include real-life elderly diabetic patients. In this setting, it is the case of the project DIABETe. This project, as other projects listed in this review, is perfectly compatible with the care pathways being developed in chronic diseases by the authorities of industrialized countries, such as diabetes, chronic heart failure, and

Further investigation of telemonitoring efficacy and cost-effectiveness over longer periods of time and larger samples is needed. Assessment of the attitude of providers is also important considering their heavy workload and issues of

Grants from the Fondation de l'Avenir, the Agence Régionale de Santé du Grand-

M. Hajjam is the scientific director of *Predimed Technology* (www.predimedtechnology.fr). All other authors have declared that no competing interests exist.

EA, LM and MH designed the paper and conducted the literature searches. EA, LM, AAZ, and MH drafted the results and parts of the discussion. ST, JD, JH, NJ, and AEHH provided critical analysis, revised the whole manuscript, and approved the final version for publication. EA is responsible for all revisions and remains in

contact with the rest of the review team regarding status reports.

sample selection, and approach for treatment of control groups.

Est (ARS) and the Agence Nationale de la Recherche (ANR).

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

chronic obstructive pulmonary disease.

**Consent and Ethical approval**

Not applicable.

**Competing interest**

**Ethical approval**

**Contributorship**

**Guarantor**

EA.

Not applicable.

reimbursement.

**Funding**

*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*

and, at least, good receptiveness by patients and patient empowerment. Moreover, a cost-effectiveness analysis found a potential in medical economy. To date, the magnitude of its effects remains debatable, especially with the variation in patients' characteristics (e.g., background, ability for self-management, medical condition), sample selection, and approach for treatment of control groups.

To date, relatively few projects and trials in diabetic patients have been run within the "telemedicine 2.0" setting, using AI, ICT, and the Web 2.0. All these projects include real-life elderly diabetic patients. In this setting, it is the case of the project DIABETe. This project, as other projects listed in this review, is perfectly compatible with the care pathways being developed in chronic diseases by the authorities of industrialized countries, such as diabetes, chronic heart failure, and chronic obstructive pulmonary disease.

Further investigation of telemonitoring efficacy and cost-effectiveness over longer periods of time and larger samples is needed. Assessment of the attitude of providers is also important considering their heavy workload and issues of reimbursement.

### **Funding**

*Geriatric Medicine and Gerontology*

Number of hospitalization days

Number of days off work

Specific mortality of the considered chronic disease Number of hospitalization for the considered chronic

Number of re-hospitalization for the considered

Management costs for the considered chronic disease

Overall mortality

chronic disease

Health costs

Quality of life

**Table 2.**

disease

telemedicine solutions.

**6. Conclusions**

including <80-year-old patients).

practicality of telemedicine interventions.

and requirements of our societies, as listed in **Table 2**.

telehealth are significant, as many health systems are not yet designed to integrate these technologies into existing information systems. It is therefore necessary to plan now for an interfacing of computer systems and the integration of future

*Potential parameters to be evaluated in a telemedicine project for chronic disease management.*

Therapeutic education

Patient self-management

considered chronic disease

System use by health professionals

chronic disease City-hospital relations

Hygiene-dietary and therapeutic compliance Optimization of food and sports hygiene

Optimization of the care pathway for the

Structuring of the care pathway for the considered

Information sharing among health professionals

Considering the current problems of access to healthcare professionals, the new telemedicine solutions must be able to structure the patients' care pathways, a major medical topic that should interest our governments and authorities [28, 29]. Likewise, the E-care and DIABETe projects provide a means for healthcare professionals to exchange with each other, thereby facilitating patient access to medical resources. In this context, future research must also focus on the accessibility and

Importantly, reimbursement remains a major concern and a barrier ("glass ceiling"). In fact, the healthcare delivered by telehealth is not covered by traditional fee-for-service payment models (e.g., in France, where all diabetic patients benefit from an integral treatment of their health expenses) [29]. The growth of value-based payment models may, however, provide incentives to implement telehealth as a strategy to provide high-quality, cost-effective, and coordinated care [29]. At country levels, variations in practice laws, restrictions on how telehealth can be delivered, and which patients should receive these services limit telemedicine's applicability as well [30]. Thus, to document the efficacy on the new telemedicine solutions, the future studies should integrate others objectives like potential targets to meet the needs

This review supports the efficacy of telemonitoring type 1 and type 2 diabetic patients. Several studies on diabetes telemonitoring, using diverse technologies, and transmitting different clinical, medical, and behavioral data were found. Significant impacts were observed, namely, at the behavioral, clinical, and structural levels. Minimal technical problems and cost-effectiveness analyses were reported. Four studies are dedicated specifically to elderly diabetic patients (all

Close management of diabetic patients, even elderly patients, through telemonitoring, showed the following: improvements in control of BG level and significant reduction in HbA1c, better appropriation of the disease by patients, greater adherence to therapeutic and hygiene-dietary measures, positive impact on comorbidities (arterial hypertension, weight, dyslipidemia), better patient's quality of life,

**76**

Grants from the Fondation de l'Avenir, the Agence Régionale de Santé du Grand-Est (ARS) and the Agence Nationale de la Recherche (ANR).

#### **Consent and Ethical approval**

Not applicable.

#### **Competing interest**

M. Hajjam is the scientific director of *Predimed Technology* (www.predimedtechnology.fr). All other authors have declared that no competing interests exist.

#### **Ethical approval**

Not applicable.

#### **Guarantor**

EA.

#### **Contributorship**

EA, LM and MH designed the paper and conducted the literature searches. EA, LM, AAZ, and MH drafted the results and parts of the discussion. ST, JD, JH, NJ, and AEHH provided critical analysis, revised the whole manuscript, and approved the final version for publication. EA is responsible for all revisions and remains in contact with the rest of the review team regarding status reports.
