DIgital Assisted MONitoring for DiabeteS - I (DIAMONDS)

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.