DIgital Assisted MONitoring for DiabeteS - I

Study Overview
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Brief Summary
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The study aims to validate the clinical efficacy of a telemedicine- and web-based system platform for Self-monitoring of blood glucose (SMBG) data transmission and analysis in terms of improved metabolic control, assessed by measuring changes in HbA1c, in insulin-treated diabetic patients. The system platform involves (i.) systematic (real-time and anywhere) transmission of SMBG data to a decision supported software (DSS)-assisted server, (ii.) web-based analysis of data, and (iii.) feedback on patients and medical staff to implement metabolic control. The expected outcome is that using this telemedicine-based system with transmission of SMBG data, web-based analysis of data and medical feedback to patients and medical team will improve glucose control in insulin-treated individuals with type 1 or type 2 diabetes mellitus.

Detailed Description
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Self-monitoring of blood glucose (SMBG) is currently recommended in all type 1 diabetic patients and type 2 diabetic treated with insulin (≥4 glucose testing per day), as a tool to favor achievement of glucose control. It is still debated whether SMBG is also useful to achieve improved glucose control in non-insulin treated type 2 diabetes due to discordant results from intervention trials [Farmer AJ, 2009]. In general, it is assumed that the usefulness of SMBG is fully met when SMBG is performed in a structured manner, and both the patient and medical team make use of the SMBG results to optimize lifestyle and drug therapy; this should result in more effective control of both hyperglycemia and hypoglycemia, resulting in less glucose variability [IDF, 2009]. SMBG is currently performed by a majority of diabetic patients, including non-insulin treated type 2 patients; however, the SMGB procedure is often far from being satisfactory due to inappropriate frequency of testing, data collection and data analysis, resulting in a suboptimal impact on disease management. Thus, multiple unmet needs currently exist in the SMBG procedure, which could be resolved by (i.) implementing appropriate SMBG testing frequencies (# blood glucose (BG) tests/day-week-month); (ii.) temporally localizing BG tests with respect to fasting periods vs. meals/snacks; (iii.) improving the quality control of the SMBG procedure (use of strips, calibration, testing, data collection and analysis); (iv.) making SMBG results available for statistical and medical analyses to monitor diabetes status and adjust lifestyle and drug therapy when needed; (v.) providing the patient with appropriate feed-back driven by SMBG data analysis; and (vi.) monitoring real-time SMBG data to detect emergency situations (e.g., severe hypoglycemia, persistent hyperglycemia) to assist the patient with appropriate and timely interventions. There is a growing body of evidence to suggest that telemedicine is an effective intervention for improving glucose control. Use of telephone contacts, video-conferencing, personal digital assistants and web-based systems offer new opportunities to bridge the gap in support for patients with diabetes between face to face visits with their health care providers. Several small-scale and non-randomized studies have found that patients exposed to telemonitoring interventions had lower HbA1c values than those without. Moreover, larger randomized controlled studies have also shown promising results. In the most comprehensive randomized comparison of telemonitoring interventions yet completed, the IDEATel study, 1,665 participants were randomized to a telemedicine unit or conventional care. At 1 year, patients in intervention group showed significantly lower HbA1c (-0.38 % p<0.01), systolic and diastolic blood pressure, (respectively, -3.4 mmHg, p= 0.001; -1.9 mmHg, p<0.001), and low density lipoprotein (LDL) cholesterol (-9.5 mg/dL, p< 0.001). In patients blindly assessed annually over a period of five years, it was found that the telemedicine group scored better than the standard care group on virtually all outcome measures at each annual evaluation. Mortality was not different between the groups, although power was limited [Shea S, 2009]. More recently, Charpentier et al., in a six-month multicenter study, enrolled 180 adult patients with type 1 diabetes on a basal-bolus insulin regimen, with baseline HbA1c ≥8%. Patients were randomized to either usual quarterly follow-up, or home use of a smartphone recommending insulin doses with quarterly visits, or use of the smartphone with short teleconsultations every two weeks but no visit until endpoint (Diabeo system). Use of the Diabeo system yielded a 0.91% decrease in HbA1c over controls and a 0.67% decrease in HbA1c 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 using the Diabeo system spent nearly 5 h less than patients in other groups in attending hospital visits [Charpentier G, 2011]. Similarly, Bujnowska-Fedak et al enrolled a total of 100 adult patients with type 2 diabetes, divided between insulin- and non-insulin-requiring, in a randomized, controlled trial aimed at investigating the effects of telehome monitoring. The experimental group (n = 50) received an in-home wireless glucose monitor and transmitter, whereas the control group (n = 50) was instructed to follow the conventional arrangement. There was an overall reduction in HbA1c values in both experimental and control groups after 6 months. A significant difference in HbA1c values between the groups was observed only among the noninsulin-requiring patients. The experimental group reported considerably less hyperglycemic and hypoglycemic events. The profile of the patient who benefited the most from telemonitoring consisted of older, more educated patients, who had acquired the disease relatively recently, and who spent most of the time at home. The experimental group had higher overall scores on quality of life measures and sense of control over diabetes [Bujnowska-Fedak MM, 2011]. A recent meta-analysis of 22 trials and a total of 1,657 participants showed that mobile phone interventions for diabetes self-management reduced HbA1c values by a mean of 0.5% over a median of 6-month follow-up duration. Interestingly, type 2 diabetes patients reported significantly greater reductions in HbA1c than type 1 diabetes patients (0.8 vs. 0.3%; P = 0.02) [Liang X, 2011]. In conclusion, telemedicine case management intervention and tele-home care may potentially assist in daily diabetes management via the establishment of an active interaction between diabetic patients and healthcare professionals. However, further evidence is warranted, particularly when considering new innovative systems with a potential for implementation of patient's assistance and of diabetes drug therapy remotely. We have recently developed a telemedicine system [Glucoonline®, 2011], which consists of a smartphone-connected glucometer, a software-implemented smartphone for real-time and anywhere BG data collection and transmission to a remote server, and a Decision Supported Software (DSS)-assisted server capable of performing data collection and analysis, and providing feed-back to the patient and the medical staff according to pre-defined specific algorithms. A pilot study showing the feasibility of using this system in 10 individuals with type 1 diabetes treated with a multiple daily injection (MDI) regimen over a 3-month period has been already carried out [Giorgino F, data on file]. We believe that this system could be now utilized in a prospective randomized controlled trial to investigate its suitability to enable improved glucose control in insulin-treated individuals with type 1 or type 2 diabetes mellitus.

Official Title
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A Telemedicine-based Intervention Study Involving Real-time and Anywhere Transmission of Blood Glucose Data to a Decision Supported Software-assisted Server With Web-based Analysis of Data and Medical Feedback on Metabolic Control.

Conditions
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Diabetes Mellitus

Intervention / Treatment
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* Procedure: Use of software-implemented glucometer for real-time collection and transmission of measured glucose values to remote server.
* Procedure: Use of regular glucometer


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: males and females age 18-70 yrs; insulin-treated diabetes (both type 1 and type 2 diabetes treated with at least 3 injections/day); diabetes diagnosis from at least 1 year; inadequate glycemic control (HbA1c ranging from 7.0% to 10.0%; local measurements within the last 6 weeks); ability and willingness to carry out SMBG; informed consent. Exclusion Criteria: diagnosis of diabetes within less than 1 year; impending complications of diabetes: proliferative retinopathy or maculopathy (with significant loss of visual function), severe renal failure (eGFR <30), severe neuropathy (autonomic dysfunction, peripheral neuropathy, gastroparesis); clinically significant, active (over the past 12 months) disease of the cardiovascular, gastrointestinal, neurological, genito-urinary, haematological systems or severe uncontrolled hypertension (SBP >180 mmHg; DBP >100 mmHg); diagnosis of active neoplasia within the last 5 years (history of chemotherapy or radiation treated malignancy within 5 years prior to study procedure, except for lymphoma); pregnancy or intention to become pregnant during the study; poor compliance to study procedures.

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 70 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Treatment
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: T-SMBG<br>This group will perform SMBG using a smartphone-connected glucometer implemented with a software for real-time collection and transmission of measured glucose values to the remote server. SMBG results will be immediately transmitted to the remote server, which will perform data collection and analysis, and provide feed-back to the patient and the medical staff according to pre-defined specific algorithms (Decision Supported Software, DSS). A specific algorithm incorporated into the DSS will allow the patients to self-calculate the dose of basal insulin to be administered according to the measured fasting blood glucose levels for consecutive periods of three days. Glucose data and analyses will be made accessible to the patients and medical staff anytime and anywhere via the web. Patients will be also assisted by the diabetes medical team located at or connected with a call center 24-hours/day, 7 days/week. | Procedure: Use of software-implemented glucometer for real-time collection and transmission of measured glucose values to remote server.<br>* Device is a smartphone-connected glucometer. The smartphone will be implemented with a software for real-time collection and transmission of measured glucose values to the remote server. Thus, the glucometer will be made hot for real-time and anywhere data transmission. In addition, at the time of blood glucose measuring, the patient will enter information on whether the measurement is being performed in the pre-prandial, post-prandial or absorptive periods, and will indicate which meals the measurement refers to (i.e., breakfast, lunch, dinner, snack). SMBG results will be immediately transmitted to the remote server, which will perform data collection and analysis, and provide feed-back to the patient and the medical staff according to pre-defined specific algorithms (Decision Supported Software, DSS).<br>|
| Active Comparator: SMBG<br>This group will perform SMBG using a regular glucometer and will report glucose data on paper charts (or download data from the glucometer onto the PC) at the planned study visits. Patients will not receive feed-back on their glucose levels nor instructions on how to potentially modify their drug therapy except when undergoing medical visits at the planned intervals. Patients, finally, will not be assisted by the diabetes team/call center. | Procedure: Use of regular glucometer<br> <br> |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Changes in HbA1c from baseline value |  | 3-month, 6-month | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Changes in HbA1c from baseline value in relation to HbA1c value at baseline |  | 6-month | 
| Percentages of patients with HbA1c >8.5% at 3 and 6 months |  | 3-month, 6-month | 
| Percentages of patients with HbA1c <7.0% at 3 and 6 months |  | 3-month, 6-month | 
| Percentages of patients with HbA1c <6.5% at 3 and 6 months |  | 3-month, 6-month | 
| Differences in fasting blood glucose levels |  | 3-month, 6-month | 
| Differences in blood glucose levels in the pre-prandial and post-prandial phases; meal-related glucose excursions (post/pre-meal delta) |  | 3-month, 6-month | 
| Frequency of hypoglycaemic episodes and relationship with changes in therapy |  | 3-month, 6-month | 
| Changes in therapy (drug type and doses; in relation to SMBG values) |  | 3-month, 6-month | 
| Appropriateness of glucose testing (% conformity based on recommended frequency of testing, also in relation to meals) |  | 3-month, 6-month | 
| Indices of glucose variability (Kovatchev's LBGI, HBGI, ADRR) |  | 3-month, 6-month | 
| Quality of life |  | 6-month | 
| Proportion of subjects with eGFR less than 60 and high LBGI, HBGI, or ADRR indices |  | 3-month, 6-month | 
| Subgroup analyses of changes in HbA1c from baseline according to patients' age (<45 yrs, >45yrs), type of diabetes (type 1, type 2), and diabetes duration (<5 yrs, >5 yrs) |  | 6-month | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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glycated hemoglobin, hyperglycemia, hypoglycemia, insulin, self-monitoring of blood glucose, telemedicine