Remote Blood Glucose Monitoring in mHealth Scenarios: A Review <s> Artificial Pancreas in Adults <s> The design and implementation of telemedicine systems able to support the artificial pancreas need careful choices to cope with technological requirements while preserving performance and decision support capabilities. This article addresses the issue of designing a general architecture for the telemedicine components of an artificial pancreas and illustrates a viable solution that is able to deal with different use cases and is amenable to support mobile-health implementations. The goal is to enforce interoperability among the components of the architecture and guarantee maximum flexibility for the ensuing implementations. Thus, the design stresses modularity and separation of concerns along with adoption of clearly defined protocols for interconnecting the necessary components. This accounts for the implementation of integrated telemedicine systems suitable as short-term monitoring devices for supporting validation of closed-loop algorithms as well as devices meant to provide a lifelong tighter control on the patient state once the artificial pancreas has become the preferred treatment for patients with diabetes. <s> BIB001 </s> Remote Blood Glucose Monitoring in mHealth Scenarios: A Review <s> Artificial Pancreas in Adults <s> Telemedicine systems are seen as a possible solution for the remote monitoring of physiological parameters and can be particularly useful for chronic patients treated at home. Implementing those systems however has always required spending a great effort on the underlying infrastructure instead of focusing on the application cores as perceived by their users. This paper proposes an abstract unifying infrastructure for telemedicine services which is loosely based on the multi-agent paradigm. It provides the capability of transferring to the clinic any remotely acquired information, and possibly sending back updates to the patient. The infrastructure is a layered one, with the bottom layer acting at the data level and implemented in terms of a software library targeting a wide set of hardware devices. On top of this infrastructure several services can be written shaping the functionality of the telemedicine application while at the highest level, adhering to a simple agent model, it is possible to reuse those functional components porting the application to different platforms. The infrastructure has been successfully used for implementing a telemonitoring service for a randomized controlled study aimed at testing the effectiveness of the artificial pancreas as a treatment within the [email protected] project funded by the European Union. <s> BIB002 </s> Remote Blood Glucose Monitoring in mHealth Scenarios: A Review <s> Artificial Pancreas in Adults <s> OBJECTIVE ::: Inpatient studies suggest that model predictive control (MPC) is one of the most promising algorithms for artificial pancreas (AP). So far, outpatient trials have used hypo/hyperglycemia-mitigation or medical-expert systems. In this study, we report the first wearable AP outpatient study based on MPC and investigate specifically its ability to control postprandial glucose, one of the major challenges in glucose control. ::: ::: ::: RESEARCH DESIGN AND METHODS ::: A new modular MPC algorithm has been designed focusing on meal control. Six type 1 diabetes mellitus patients underwent 42-h experiments: sensor-augmented pump therapy in the first 14 h (open-loop) and closed-loop in the remaining 28 h. ::: ::: ::: RESULTS ::: MPC showed satisfactory dinner control versus open-loop: time-in-target (70-180 mg/dL) 94.83 vs. 68.2% and time-in-hypo 1.25 vs. 11.9%. Overnight control was also satisfactory: time-in-target 89.4 vs. 85.0% and time-in-hypo: 0.00 vs. 8.19%. ::: ::: ::: CONCLUSIONS ::: This outpatient study confirms inpatient evidence of suitability of MPC-based strategies for AP. These encouraging results pave the way to randomized crossover outpatient studies. <s> BIB003 </s> Remote Blood Glucose Monitoring in mHealth Scenarios: A Review <s> Artificial Pancreas in Adults <s> Type 1 Diabetes Mellitus (T1DM) is an autoimmune disease in which the insulin-producing beta cells of the pancreas are destroyed and insulin must be injected daily to enable the body to metabolize glucose. Standard therapy for T1DM involves self-monitoring of blood glucose (SMBG) several times daily with a blood glucose meter and injecting insulin via a syringe, pen or insulin pump. An “Artificial Pancreas” (AP) is a closed-loop control system that uses a continuous glucose monitor (CGM), an insulin pump and an internal algorithm to automatically manage insulin infusion to keep the subject’s blood glucose within a desired range. Although no fully closed-loop AP systems are currently commercially available there are intense academic and commercial efforts to produce safe and effective AP systems. In this paper we present the Diabetes Assistant (DiAs), an ultraportable AP research platform designed to enable home studies of Closed Loop Control (CLC) of blood glucose in subjects with Type 1 Diabetes Mellitus. DiAs consists of an Android (Google Inc., Mountain View, CA, USA) smartphone equipped with communication, control and user interface software wirelessly connected to a continuous glucose monitor and insulin pump. The software consists of a network of mobile applications with well-defined Application Programming Interfaces (APIs) running atop an enhanced version of Android with non-essential elements removed. CLC and safety applications receive real-time data from the CGM and pump, estimate the patient’s metabolic state and risk of hypo- and hyperglycemia, adjust the insulin infusion rate, raise alarms as needed and transmit de-identified data to a secure remote server. Some applications may be replaced by researchers wishing to conduct outpatient ambulatory studies of novel Closed Loop Control, Safety or User Interface modules. Over the past three years the DiAs platform has been used in a series of AP clinical trials sponsored by the National Institutes of Health, the Juvenile Diabetes Research Foundation, the Helmsley Charitable Trust and the European Union AP@Home project. Results of clinical trials using DiAs indicate that a smartphone with targeted operating system modifications and appropriate system software can be successfully used in outpatient clinical trials of FDA Class III medical devices such as Artificial Pancreas. <s> BIB004 </s> Remote Blood Glucose Monitoring in mHealth Scenarios: A Review <s> Artificial Pancreas in Adults <s> AIMS ::: To test in an outpatient setting the safety and efficacy of continuous subcutaneous insulin infusion (CSII) driven by a modular model predictive control (MMPC) algorithm informed by continuous glucose monitoring (CGM) measurement. ::: ::: ::: METHODS ::: 13 patients affected by type 1 diabetes participated to a non-randomized outpatient 42-h experiment that included two evening meals and overnight periods (in short, dinner & night periods). CSII was patient-driven during dinner & night period 1 and MMPC-driven during dinner&night period 2. The study was conducted in hotels, where patients could move around freely. A CGM system (G4 Platinum; Dexcom Inc., San Diego, CA, USA) and insulin pump (AccuChek Combo; Roche Diagnostics, Mannheim, Germany) were connected wirelessly to a smartphone-based platform (DiAs, Diabetes Assistant; University of Virginia, Charlottesville, VA, USA) during both periods. ::: ::: ::: RESULTS ::: A significantly lower percentage of time spent with glucose levels <3.9 mmol/l was achieved in period 2 compared with period 1: 1.96 ± 4.56% vs 12.76 ± 15.84% (mean ± standard deviation, p < 0.01), together with a greater percentage of time spent in the 3.9-10 mmol/l target range: 83.56 ± 14.02% vs 62.43 ± 29.03% (p = 0.04). In addition, restricting the analysis to the overnight phases, a lower percentage of time spent with glucose levels <3.9 mmol/l (1.92 ± 4.89% vs 12.7 ± 19.75%; p = 0.03) was combined with a greater percentage of time spent in 3.9-10 mmol/l target range in period 2 compared with period 1 (92.16 ± 8.03% vs 63.97 ± 2.73%; p = 0.01). Average glucose levels were similar during both periods. ::: ::: ::: CONCLUSIONS ::: The results suggest that MMPC managed by a wearable system is safe and effective during evening meal and overnight. Its sustained use during this period is currently being tested in an ongoing randomized 2-month study. <s> BIB005 </s> Remote Blood Glucose Monitoring in mHealth Scenarios: A Review <s> Artificial Pancreas in Adults <s> Summary Background An artificial pancreas (AP) that can be worn at home from dinner to waking up in the morning might be safe and efficient for first routine use in patients with type 1 diabetes. We assessed the effect on glucose control with use of an AP during the evening and night plus patient-managed sensor-augmented pump therapy (SAP) during the day, versus 24 h use of patient-managed SAP only, in free-living conditions. Methods In a crossover study done in medical centres in France, Italy, and the Netherlands, patients aged 18–69 years with type 1 diabetes who used insulin pumps for continuous subcutaneous insulin infusion were randomly assigned to 2 months of AP use from dinner to waking up plus SAP use during the day versus 2 months of SAP use only under free-living conditions. Randomisation was achieved with a computer-generated allocation sequence with random block sizes of two, four, or six, masked to the investigator. Patients and investigators were not masked to the type of intervention. The AP consisted of a continuous glucose monitor (CGM) and insulin pump connected to a modified smartphone with a model predictive control algorithm. The primary endpoint was the percentage of time spent in the target glucose concentration range (3·9–10·0 mmol/L) from 2000 to 0800 h. CGM data for weeks 3–8 of the interventions were analysed on a modified intention-to-treat basis including patients who completed at least 6 weeks of each intervention period. The 2 month study period also allowed us to asses HbA 1c as one of the secondary outcomes. This trial is registered with ClinicalTrials.gov, number NCT02153190. Findings During 2000–0800 h, the mean time spent in the target range was higher with AP than with SAP use: 66·7% versus 58·1% (paired difference 8·6% [95% CI 5·8 to 11·4], p 10·0 mmol/L; 31·6% vs 38·5%; −6·9% [–9·8% to −3·9], p vs 3·0%; −1·6% [–2·3 to −1·0], p 1c during the AP period was significantly greater than during the control period (−0·3% vs −0·2%; paired difference −0·2 [95% CI −0·4 to −0·0], p=0·047), taking a period effect into account (p=0·0034). No serious adverse events occurred during this study, and none of the mild-to-moderate adverse events was related to the study intervention. Interpretation Our results support the use of AP at home as a safe and beneficial option for patients with type 1 diabetes. The HbA 1c results are encouraging but preliminary. Funding European Commission. <s> BIB006 </s> Remote Blood Glucose Monitoring in mHealth Scenarios: A Review <s> Artificial Pancreas in Adults <s> OBJECTIVE After testing of a wearable artificial pancreas (AP) during evening and night (E/N-AP) under free-living conditions in patients with type 1 diabetes (T1D), we investigated AP during day and night (D/N-AP) for 1 month. ::: ::: RESEARCH DESIGN AND METHODS Twenty adult patients with T1D who completed a previous randomized crossover study comparing 2-month E/N-AP versus 2-month sensor augmented pump (SAP) volunteered for 1-month D/N-AP nonrandomized extension. AP was executed by a model predictive control algorithm run by a modified smartphone wirelessly connected to a continuous glucose monitor (CGM) and insulin pump. CGM data were analyzed by intention-to-treat with percentage time-in-target (3.9–10 mmol/L) over 24 h as the primary end point. ::: ::: RESULTS Time-in-target (mean ± SD, %) was similar over 24 h with D/N-AP versus E/N-AP: 64.7 ± 7.6 vs. 63.6 ± 9.9 ( P = 0.79), and both were higher than with SAP: 59.7 ± 9.6 ( P = 0.01 and P = 0.06, respectively). Time below 3.9 mmol/L was similarly and significantly reduced by D/N-AP and E/N-AP versus SAP (both P < 0.001). SD of blood glucose concentration (mmol/L) was lower with D/N-AP versus E/N-AP during whole daytime: 3.2 ± 0.6 vs. 3.4 ± 0.7 ( P = 0.003), morning: 2.7 ± 0.5 vs. 3.1 ± 0.5 ( P = 0.02), and afternoon: 3.3 ± 0.6 vs. 3.5 ± 0.8 ( P = 0.07), and was lower with D/N-AP versus SAP over 24 h: 3.1 ± 0.5 vs. 3.3 ± 0.6 ( P = 0.049). Insulin delivery (IU) over 24 h was higher with D/N-AP and SAP than with E/N-AP: 40.6 ± 15.5 and 42.3 ± 15.5 vs. 36.6 ± 11.6 ( P = 0.03 and P = 0.0004, respectively). ::: ::: CONCLUSIONS D/N-AP and E/N-AP both achieved better glucose control than SAP under free-living conditions. Although time in the different glycemic ranges was similar between D/N-AP and E/N-AP, D/N-AP further reduces glucose variability. <s> BIB007
Since 2007, our group has been active in the design and testing of a closed-loop strategy for the treatment of T1D in adult patients, the so-called Artificial Pancreas (AP). The AP  is a minimally-invasive device for the automatic glycemic control in T1D, modulating insulin infusion based on the measurements collected by a real-time CGM sensor. An AP encompasses at least three components tightly interconnected: a CGM sensor for continuously acquiring BGL readings; an insulin pump for delivering insulin boluses to the patient and a control algorithm driving the pump based on the readings acquired in real time from the CGM sensor. Additional components are usually represented by an interaction display, which is used by the patient to enter information used by the algorithm, such as meal intakes, manual BGL readings for calibrations or for commanding the extra boluses needed to compensate for the meals. Since 2010, we have been part of the AP@home project, an initiative funded by the European Union within the 7th Framework Programme aimed at designing and implementing the AP  . During the five-year project lifespan, several clinical trials were planned, enrolling patients over longer times and scheduling their experiments in environments getting each time closer to real-life conditions. The first experiments were conducted in 2011 on hospitalized patients under strict protocol prescriptions with the aim of enforcing the highest level of safety on them. In this highly controlled set-up, the duration of each experiment was limited to just one day including the overnight stay  . The study team (at least one clinician and one bioengineer) was constantly attending the experiment, limiting the need for a telemedicine system. Nevertheless, a first prototype version of the remote monitoring system was designed BIB001 , and eventually deployed and tested BIB002 . This first study proved that the AP was safe in a controlled environment, and thus we moved our research towards testing in less controlled environments, more closely resembling real-life BIB003 BIB005 . These studies, lasting two days, were held in a hotel where both the study team and the patients were lodged. Home use of the device was simulated with the study team minimizing the interaction with the patient and intervening only to solve technical or medical issues. The availability of a telemedicine system, making available to the study team the blood-glucose measures and any other medical or technical information  , was fundamental for the design of the study. This system guaranteed the highest level of safety during the simulation of independent AP use and helped in obtaining the study approval by the Ethics Committees. In fact, during the first hospital trial BIB003 , the brittleness of the connection between the AP and the sensor caused frequent disconnections that occurred particularly overnight when the patient was asleep  . However, the remote monitoring system signaled those problems to the staff that could promptly intervene fixing them and avoiding any risk for the patients. Eventually, in 2014-2015, a large trial was scheduled testing the use of the AP at home for two months in 30 adult patients BIB006 . Out of these patients, 20 subjects extended the AP use for one extra month BIB007 . Also in this case, the telemedicine system and the possibility to oversee the experiments were crucial to providing the required remote assistance during the trial. Throughout all of these trials, the main users of the telemedicine system were the members of the clinical team and the bioengineers overseeing the study. The effectiveness of the remote monitoring system was assessed after each trial, resulting in a close collaboration among clinicians and bioengineers, with the goal of improving it. Over time, summary panels were added to allow a prompt recognition of the situation by the staff; visual and audible alarms were implemented to immediately draw user attention whenever required; and the detailed patient chart was improved tracking additional events such as exercises, stress tests, notes, etc. Eventually, an additional component was developed sending summary messages or alarms to the staff phones, which were used as pagers. This was particularly useful during longer trials scheduled near the end of the project, when the supervision by the staff around the clock was no longer affordable. Although, for the AP@home project, the main users of the telemedicine system remained the members of the study team, we took the opportunity to investigate the patient perception of the usefulness of that tool. For this purpose, we administered ad hoc questionnaires about the assistance time required in case of complications, the quality of patient-physician communication, the reduction of control visits in terms of number and costs, the perceived serenity and safety, and the amount of money that patients would be willing to pay for the system. In nearly 80% of the answers, patients highlighted positive feelings towards the service. However, after comparing questionnaires administered during the hotel trials with those occurring at home, it emerged that, in the latter case, patients were more uncertain about the usefulness of the system. The increment in the percentage of answers expressing uncertainty, increasing from 3% at the hotel to 17% at home, could be due to the need of being more tightly monitored perceived by the patients when they are at home. The hardware used for remote monitoring was upgraded several times during the AP@home project development, paralleling the improvements on the AP, to reach the level of functionality and reliability required for the main trial at home. A detailed description of that evolution and the performance of each hardware configuration adopted is provided in  . The final configuration used in AP@home is reported in Figure 4 and sees the DiAs BIB004 as the core component playing both the roles of Body Gateway and Network Hub. The DiAs is a smartphone-based AP platform capable of running the control algorithm. To ensure the operation of the smartphone as a medical device, the Linux kernel of its operating system (i.e., Android TM ) was modified to remove any application not related to the study; prevent the installation of external applications from the store; forbid its use for issuing voice calls or surfing the web; and include self-checks concerning system integrity. The smartphone adopted for the study was a Nexus 5 by LG Electronics (Seoul, Korea) offering state-of-the-art Bluetooth wireless connectivity that was used for directly interacting with the peripherals: the CGM sensor and the insulin pump. Thus, according to this configuration, the smartphone acted as Body Gateway. While the availability of state-of-the-art technology is usually desirable, it is often a bottleneck when dealing with medical devices. In this study, the insulin pump of choice was the Roche Accu-Chek Spirit Combo by Roche Diagnostics GmbH (Mannheim, Germany), that natively offered the possibility of being wirelessly controlled using Bluetooth. However, the CGM of choice was the Dexcom G4 Platinum by Dexcom, Inc. (San Diego, CA, USA), which only provided a USB port and lacked Bluetooth connectivity. To overcome this problem, a USB to Bluetooth Low Energy converter (i.e., the Relay Box in Figure 4 ) was developed and employed in combination with the Dexcom G4 Platinum. The DiAs then acted as a Network Hub sending all the information acquired by the peripherals or generated internally by the AP controller to the remote monitoring clinic server using a 3G/UMTS connection in real time. Once on the clinic server, these data became eventually accessible through the web according to a standard SOA architecture for remote monitoring or analytic purposes BIB002 .