Disease severity-based subgrouping of type 2 diabetes does not parallel differences in quality of life: the Maastricht Study.

AIMS/HYPOTHESIS: Type 2 diabetes is a highly heterogeneous disease for which new subgroups ('clusters') have been proposed based on disease severity: moderate age-related diabetes (MARD), moderate obesity-related diabetes (MOD), severe insulin-deficient diabetes (SIDD) and severe insulin-resistant diabetes (SIRD). It is unknown how disease severity is reflected in terms of quality of life in these clusters. Therefore, we aimed to investigate the cluster characteristics and cluster-wise evolution of quality of life in the previously defined clusters of type 2 diabetes. METHODS: We included individuals with type 2 diabetes from the Maastricht Study, who were allocated to clusters based on a nearest centroid approach. We used logistic regression to evaluate the cluster-wise association with diabetes-related complications. We plotted the evolution of HbA1c levels over time and used Kaplan-Meier curves and Cox regression to evaluate the cluster-wise time to reach adequate glycaemic control. Quality of life based on the Short Form 36 (SF-36) was also plotted over time and adjusted for age and sex using generalised estimating equations. The follow-up time was 7 years. Analyses were performed separately for people with newly diagnosed and already diagnosed type 2 diabetes. RESULTS: We included 127 newly diagnosed and 585 already diagnosed individuals. Already diagnosed people in the SIDD cluster were less likely to reach glycaemic control than people in the other clusters, with an HR compared with MARD of 0.31 (95% CI 0.22, 0.43). There were few differences in the mental component score of the SF-36 in both newly and already diagnosed individuals. In both groups, the MARD cluster had a higher physical component score of the SF-36 than the other clusters, and the MOD cluster scored similarly to the SIDD and SIRD clusters. CONCLUSIONS/INTERPRETATION: Disease severity suggested by the clusters of type 2 diabetes is not entirely reflected in quality of life. In particular, the MOD cluster does not appear to be moderate in terms of quality of life. Use of the suggested cluster names in practice should be carefully considered, as the non-neutral nomenclature may affect disease perception in individuals with type 2 diabetes and their healthcare providers.

Automated Insulin Delivery: The Artificial Pancreas Technical Challenges.

BACKGROUND: The automation of glucose control has been an important goal of diabetes treatment for many decades. The first artificial pancreas experiences were in-hospital, closely supervised, small-scale, and short-term studies that demonstrated their superiority over continuous subcutaneous insulin infusion therapy. At present, long-term outpatient studies are being conducted in free-living scenarios. AREAS OF UNCERTAINTY: The integration of multiple devices increases patients' burden and the probability of technical risks. Control algorithms must be robust to manage disturbance variables, such as physical exercise, meal composition, stress, illness, and circadian variations in insulin sensitivity. Extra layers of safety could be achieved through remote supervision. Dual-hormone systems reduce the incidence and duration of hypoglycemia, but the availability of stable pumpable glucagon needs to be solved. Faster insulin analogues are expected to improve all types of artificial pancreas. THERAPEUTIC ADVANCES: Artificial pancreas safety and feasibility are being demonstrated in outpatient studies. Artificial pancreas use increases the time of sensor-measured glucose in near-normoglycemia and reduces the risk of hyperglycemia and hypoglycemia. The benefits are observed both in single- and dual-hormone algorithms and in full- or semi-closed loop control. A recent meta-analysis including 41 randomized controlled trials showed that artificial pancreas use achieves a reduction of time in hyperglycemia (2 hours less than control treatment) and in hypoglycemia (20 minutes less); mean levels of continuous glucose sensor fell by 8.6 mg/dL over 24 hours and by 14.6 mg/dL overnight. The OpenAPS community uses Do It Yourself artificial pancreas in the real world since 2013, and a recent retrospective cross-over study (n = 20) compared continuous glucose sensor readings before and after initiation: mean levels of blood glucose fell by 7.4 mg/dL over 24 hours and time in range increased from 75.8% to 82.2% (92 minutes more). CONCLUSIONS: The outpatient use of artificial pancreas is safe and improves glucose control in outpatients with type 1 diabetes compared with the use of any type of insulin-based treatment. The availability of open-source solutions and data sharing is needed to foster the development of new artificial pancreas approaches and to promote the wide use of Big Data tools for knowledge discovery, decision support, and personalization.

Cluster analysis of adult individuals with type 1 diabetes: Treatment pathways and complications over a five-year follow-up period.

AIMS: To identify subgroups of adults with type 1 diabetes and analyse their treatment pathways and risk of diabetes-related complications over a 5-year follow-up. METHODS: We performed a k-means cluster analysis using the T1DExchange Registry (n = 6,302) to identify subgroups based on demographic and clinical characteristics. Annual reassessments linked treatment trajectories with these clusters, considering drug and technology use. Complication risks were analysed using Cox regression. RESULTS: Five clusters were identified: 1) A favourable combination of all variables (31.67 %); 2) Longer diabetes duration (22.63 %); 3) Higher HbA1c levels (13.28 %); 4) Higher BMI (15.25 %); 5) Older age at diagnosis (17.17 %). Two-thirds of patients remained in their initial cluster annually. Technology adoption showed improved glycaemic control over time. Cox proportional hazards showed different risk patterns: Cluster 1 had low complication risk; Cluster 2 had the highest risk for retinopathy, coronary artery disease and autonomic neuropathy; Cluster 3 had the highest risk for albuminuria, depression and diabetic ketoacidosis; Cluster 4 had increased risk for multiple complications; Cluster 5 had the highest risk for hypertension and severe hypoglycaemia, with elevated coronary artery disease risk. CONCLUSIONS: Clinical characteristics can identify subgroups of patients with T1DM showing differences in treatment and complications during follow-up.

A literature review of quality assessment and applicability to HTA of risk prediction models of coronary heart disease in patients with diabetes.

This literature review had two objectives: to identify models for predicting the risk of coronary heart diseases in patients with diabetes (DM); and to assess model quality in terms of risk of bias (RoB) and applicability for the purpose of health technology assessment (HTA). We undertook a targeted review of journal articles published in English, Dutch, Chinese, or Spanish in 5 databases from 1st January 2016 to 18th December 2022, and searched three systematic reviews for the models published after 2012. We used PROBAST (Prediction model Risk Of Bias Assessment Tool) to assess RoB, and used findings from Betts et al. 2019, which summarized recommendations and criticisms of HTA agencies on cardiovascular risk prediction models, to assess model applicability for the purpose of HTA. As a result, 71 % and 67 % models reporting C-index showed good discrimination abilities (C-index >= 0.7). Of the 26 model studies and 30 models identified, only one model study showed low RoB in all domains, and no model was fully applicable for HTA. Since the major cause of high RoB is inappropriate use of analysis method, we advise clinicians to carefully examine the model performance declared by model developers, and to trust a model if all PROBAST domains except analysis show low RoB and at least one validation study conducted in the same setting (e.g. country) is available. Moreover, since general model applicability is not informative for HTA, novel adapted tools may need to be developed.

A Systematic Review of Collective Evidences Investigating the Effect of Diabetes Monitoring Systems and Their Application in Health Care.

Introduction: Diabetes monitoring systems (DMS) are a possible approach for regular control of glucose levels in patients with Type 1 or 2 diabetes in order to improve therapeutic outcomes or to identify and modify inappropriate patient behaviors in a timely manner. Despite the significant number of studies observing the DMS, no collective evidence is available about the effect of all devices. Goal: To review and consolidate evidences from multiple systematic reviews on the diabetes monitoring systems and the outcomes achieved. Materials and methods: Internet-based search in PubMed, EMBASE, and Cochrane was performed to identify all studies relevant to the research question. The data regarding type of intervention, type of diabetes mellitus, type of study, change in clinical parameter(s), or another relevant outcome were extracted and summarized. Results: Thirty-three out of 1,495 initially identified studies, involving more than 44,100 patients with Type 1, Type 2, or gestational diabetes for real-time or retrospective Continuous Glucose Monitoring (CGMS), Sensor Augmented Pump Therapy (SAPT), Self-monitoring Blood Glucose (SMBG), Continuous subcutaneous insulin infusion (CSII), Flash Glucose Monitoring (FGM), Closed-loop systems and telemonitoring, were included. Most of the studies observed small nominal effectiveness of DMS. In total 11 systematic reviews and 15 meta-analyses, with most focusing on patients with Type 1 diabetes (10 and 6, respectively), reported a reduction in glycated hemoglobin (HbA1c) levels from 0.17 to 0.70% after use of DMS. Conclusion: Current systematic review of already published systematic reviews and meta-analyses suggests that no statistically significant difference exists between the values of HbA1c as a result of application of any type of DMS. The changes in HbA1c values, number and frequency of hypoglycemic episodes, and time in glucose range are the most valuable for assessing the appropriateness and effectiveness of DMS. Future more comprehensive studies assessing the effectiveness, cost-effectiveness, and comparative effectiveness of DMS are needed to stratify them for the most suitable diabetes patients' subgroups.

Managing gestational diabetes mellitus using a smartphone application with artificial intelligence (SineDie) during the COVID-19 pandemic: Much more than just telemedicine.

We describe our experience in the remote management of women with gestational diabetes mellitus during the COVID-19 pandemic. We used a mobile phone application with artificial intelligence that automatically classifies and analyses the data (ketonuria, diet transgressions, and blood glucose values), making adjustment recommendations regarding the diet or insulin treatment.

Method to generate a large cohort in-silico for type 1 diabetes.

BACKGROUND AND OBJECTIVE: In the last decade, several technological solutions have been proposed as artificial pancreas systems able to treat type 1 diabetes; most often they are built based on a control algorithm that needs to be validated before it is used with real patients. Control algorithms are usually tested with simulation tools that integrate mathematical models related mainly to the glucose-insulin dynamics, but other variables can be considered as well. In general, the simulators have a limited set of subjects. The main goal of this paper is to propose a new computational method to increase the number of virtual subjects, with physiological characteristics, included in the original mathematical models. METHODS: A subject is defined by a set of parameters given by a mathematical model. From the available reduced number of subjects in the model, the covariance of each parameter of every subject is obtained to establish a mathematical relationship. Then, new sets of parameters are calculated using linear regression methods; this generates larger cohorts, which allows for testing insulin therapies in open-loop or closed-loop scenarios. The new method proposed here increases the number of subjects in a virtual cohort using two versions of Hovorka's mathematical model. RESULTS: Two covariant cohorts are obtained with linear regression. Both cohorts are clustered to avoid overlapping in the glucose-insulin dynamics and are compared in terms of their qualitative and quantitative behaviours in the normoglycemic range. As a result, there have been generated two larger cohorts (256 subjects) than the original population, which contributes to improving the variability in in-silico tests. In addition, for analysing the characteristics of the covariant generation method, two random cohorts have been generated, where the parameters are obtained individually and independently from each other, exhibiting only distribution limitations so that these cohorts do not have physiological subjects. CONCLUSIONS: The proposed methodology has enabled the generation of a large cohort of 256 subjects, with different characteristics that are plausible in the T1DM population, significantly increasing the number of available subjects in existing mathematical models. The proposed methodology does not limit the number of subjects that can be generated and thus, it can be used to increase the number of cohorts provided by other mathematical models in diabetes, or even other scientific problems.

Real-time continuous glucose monitoring together with telemedical assistance improves glycemic control and glucose stability in pump-treated patients.

BACKGROUND: Real-time continuous glucose monitoring (CGM) has recently been incorporated into routine diabetes management because of the potential advantages it offers for glycemic control. The aim of our study was to evaluate the impact of the use of real-time CGM together with a telemedicine system in hemoglobin A1c and glucose variability in patients with type 1 diabetes treated with insulin pumps. METHODS: Ten patients (five women, 41.2 [range, 21-62] years old, duration of diabetes 14.9 [range, 3-52] years) were included in this randomized crossover study. Patients used the DIABTel telemedicine system throughout the study, and real-time CGM was used for 3 days every week during the intervention phase. At the end of the control phase, a blind 3-day CGM was performed. Glucose variability was evaluated using the Glucose Risk Index (GRI), a comparative analysis of continuous glucose values over two consecutive hours. RESULTS: Hemoglobin A1c decreased significantly (8.1 +/- 1.1% vs. 7.3 +/- 0.8%; P = 0.007) after the intervention phase, while no changes were observed during the control phase. The mean number of daily capillary glucose readings was higher during the intervention phase (4.7 +/- 1.1 vs. 3.8 +/- 1.0; P < 0.01), because of an increase in random analyses (1.22 +/- 0.3 vs. 0.58 +/- 0.1; P < 0.01), and there was also a significant increase in the mean number of bolus doses per day (5.23 +/- 1.1 vs. 4.4 +/- 0.8; P < 0.05). The GRI was higher during the control phase than during the experimental phase (9.6 vs. 6.25; P < 0.05). CONCLUSIONS: Real-time CGM in conjunction with the DIABTel system improves glycemic control and glucose stability in pump-treated patients with type 1 diabetes.

Artificial Intelligence Methodologies and Their Application to Diabetes.

In the past decade diabetes management has been transformed by the addition of continuous glucose monitoring and insulin pump data. More recently, a wide variety of functions and physiologic variables, such as heart rate, hours of sleep, number of steps walked and movement, have been available through wristbands or watches. New data, hydration, geolocation, and barometric pressure, among others, will be incorporated in the future. All these parameters, when analyzed, can be helpful for patients and doctors' decision support. Similar new scenarios have appeared in most medical fields, in such a way that in recent years, there has been an increased interest in the development and application of the methods of artificial intelligence (AI) to decision support and knowledge acquisition. Multidisciplinary research teams integrated by computer engineers and doctors are more and more frequent, mirroring the need of cooperation in this new topic. AI, as a science, can be defined as the ability to make computers do things that would require intelligence if done by humans. Increasingly, diabetes-related journals have been incorporating publications focused on AI tools applied to diabetes. In summary, diabetes management scenarios have suffered a deep transformation that forces diabetologists to incorporate skills from new areas. This recently needed knowledge includes AI tools, which have become part of the diabetes health care. The aim of this article is to explain in an easy and plane way the most used AI methodologies to promote the implication of health care providers-doctors and nurses-in this field.

Gestational Diabetes Management Using Smart Mobile Telemedicine.

Gestational diabetes (GDM) burden has been increasing progressively over the past years. Knowing that intrauterine exposure to maternal diabetes confers high risk for macrosomia as well as for future type 2 diabetes and obesity of the offspring, health care organizations try to provide effective control in spite of the limited resources. Artificial-intelligence-augmented telemedicine has been proposed as a helpful tool to facilitate an efficient widespread medical assistance to GDM. The aim of the study we present was to test the feasibility and acceptance of a mobile decision-support system for GDM, developed in the seventh framework program MobiGuide Project, which includes computer-interpretable clinical practice guidelines, access to data from the electronic health record as well as from glucose, blood pressure, and activity sensors. The results of this pilot study with 20 patients showed that the system is feasible. Compliance of patients with blood glucose monitoring was higher than that observed in a historical group of 247 patients, similar in clinical characteristics, who had been followed up for the 3 years prior to the pilot study. A questionnaire on the use of the telemedicine system showed a high degree of acceptance.

Decision Support in Diabetes Care: The Challenge of Supporting Patients in Their Daily Living Using a Mobile Glucose Predictor.

BACKGROUND: In type 1 diabetes mellitus (T1DM), patients play an active role in their own care and need to have the knowledge to adapt decisions to their daily living conditions. Artificial intelligence applications can help people with type 1 diabetes in decision making and allow them to react at time scales shorter than the scheduled face-to-face visits. This work presents a decision support system (DSS), based on glucose prediction, to assist patients in a mobile environment. METHODS: The system's impact on therapeutic corrective actions has been evaluated in a randomized crossover pilot study focused on interprandial periods. Twelve people with type 1 diabetes treated with insulin pump participated in two phases: In the experimental phase (EP) patients used the DSS to modify initial corrective decisions in presence of hypoglycemia or hyperglycemia events. In the control phase (CP) patients were asked to follow decisions without knowing the glucose prediction. A telemedicine platform allowed participants to register monitoring data and decisions and allowed endocrinologists to supervise data at the hospital. The study period was defined as a postprediction (PP) time window. RESULTS: After knowing the glucose prediction, participants modified the initial decision in 20% of the situations. No statistically significant differences were found in the PP Kovatchev's risk index change (-1.23 ± 11.85 in EP vs -0.56 ± 6.06 in CP). Participants had a positive opinion about the DSS with an average score higher than 7 in a usability questionnaire. CONCLUSION: The DSS had a relevant impact in the participants' decision making while dealing with T1DM and showed a high confidence of patients in the use of glucose prediction.

A web-based clinical decision support system for gestational diabetes: Automatic diet prescription and detection of insulin needs.

BACKGROUND: The growth of diabetes prevalence is causing an increasing demand in health care services which affects the clinicians' workload as medical resources do not grow at the same rate as the diabetic population. Decision support tools can help clinicians with the inspection of monitoring data, providing a preliminary analysis to ease their interpretation and reduce the evaluation time per patient. This paper presents Sinedie, a clinical decision support system designed to manage the treatment of patients with gestational diabetes. Sinedie aims to improve access to specialized healthcare assistance, to prevent patients from unnecessary displacements, to reduce the evaluation time per patient and to avoid gestational diabetes adverse outcomes. METHODS: A web-based telemedicine platform was designed to remotely evaluate patients allowing them to upload their glycaemia data at home directly from their glucose meter, as well as report other monitoring variables like ketonuria and compliance to dietary treatment. Glycaemia values, not tagged by patients, are automatically labelled with their associated meal by a classifier based on the Expectation Maximization clustering algorithm and a C4.5 decision tree learning algorithm. Two finite automata are combined to determine the patient's metabolic condition, which is analysed by a rule-based knowledge base to generate therapy adjustment recommendations. Diet recommendations are automatically prescribed and notified to the patients, whereas recommendations about insulin requirements are notified also to the physicians, who will decide if insulin needs to be prescribed. The system provides clinicians with a view where patients are prioritized according to their metabolic condition. A randomized controlled clinical trial was designed to evaluate the effectiveness and safety of Sinedie interventions versus standard care and its impact in the professionals' workload in terms of the clinician's time required per patient; number of face-to-face visits; frequency and duration of telematics reviews; patients' compliance to self-monitoring; and patients' satisfaction. RESULTS: Sinedie was clinically evaluated at "Parc Tauli University Hospital" in Spain during 17 months with the participation of 90 patients with gestational diabetes. Sinedie detected all situations that required a therapy adjustment and all the generated recommendations were safe. The time devoted by clinicians to patients' evaluation was reduced by 27.389% and face-to-face visits per patient were reduced by 88.556%. Patients reported to be highly satisfied with the system, considering it useful and trusting in being well controlled. There was no monitoring loss and, in average, patients measured their glycaemia 3.890 times per day and sent their monitoring data every 3.477days. CONCLUSIONS: Sinedie generates safe advice about therapy adjustments, reduces the clinicians' workload and helps physicians to identify which patients need a more urgent or more exhaustive examination and those who present good metabolic control. Additionally, Sinedie saves patients unnecessary displacements which contributes to medical centres' waiting list reduction.

Assessment of a personalized and distributed patient guidance system.

OBJECTIVES: The MobiGuide project aimed to establish a ubiquitous, user-friendly, patient-centered mobile decision-support system for patients and for their care providers, based on the continuous application of clinical guidelines and on semantically integrated electronic health records. Patients would be empowered by the system, which would enable them to lead their normal daily lives in their regular environment, while feeling safe, because their health state would be continuously monitored using mobile sensors and self-reporting of symptoms. When conditions occur that require medical attention, patients would be notified as to what they need to do, based on evidence-based guidelines, while their medical team would be informed appropriately, in parallel. We wanted to assess the system's feasibility and potential effects on patients and care providers in two different clinical domains. MATERIALS AND METHODS: We describe MobiGuide's architecture, which embodies these objectives. Our novel methodologies include a ubiquitous architecture, encompassing a knowledge elicitation process for parallel coordinated workflows for patients and care providers; the customization of computer-interpretable guidelines (CIGs) by secondary contexts affecting remote management and distributed decision-making; a mechanism for episodic, on demand projection of the relevant portions of CIGs from a centralized, backend decision-support system (DSS), to a local, mobile DSS, which continuously delivers the actual recommendations to the patient; shared decision-making that embodies patient preferences; semantic data integration; and patient and care provider notification services. MobiGuide has been implemented and assessed in a preliminary fashion in two domains: atrial fibrillation (AF), and gestational diabetes Mellitus (GDM). Ten AF patients used the AF MobiGuide system in Italy and 19 GDM patients used the GDM MobiGuide system in Spain. The evaluation of the MobiGuide system focused on patient and care providers' compliance to CIG recommendations and their satisfaction and quality of life. RESULTS: Our evaluation has demonstrated the system's capability for supporting distributed decision-making and its use by patients and clinicians. The results show that compliance of GDM patients to the most important monitoring targets - blood glucose levels (performance of four measurements a day: 0.87±0.11; measurement according to the recommended frequency of every day or twice a week: 0.99±0.03), ketonuria (0.98±0.03), and blood pressure (0.82±0.24) - was high in most GDM patients, while compliance of AF patients to the most important targets was quite high, considering the required ECG measurements (0.65±0.28) and blood-pressure measurements (0.75±1.33). This outcome was viewed by the clinicians as a major potential benefit of the system, and the patients have demonstrated that they are capable of self-monitoring - something that they had not experienced before. In addition, the system caused the clinicians managing the AF patients to change their diagnosis and subsequent treatment for two of the ten AF patients, and caused the clinicians managing the GDM patients to start insulin therapy earlier in two of the 19 patients, based on system's recommendations. Based on the end-of-study questionnaires, the sense of safety that the system has provided to the patients was its greatest asset. Analysis of the patients' quality of life (QoL) questionnaires for the AF patients was inconclusive, because while most patients reported an improvement in their quality of life in the EuroQoL questionnaire, most AF patients reported a deterioration in the AFEQT questionnaire. DISCUSSION: Feasibility and some of the potential benefits of an evidence-based distributed patient-guidance system were demonstrated in both clinical domains. The potential application of MobiGuide to other medical domains is supported by its standards-based patient health record with multiple electronic medical record linking capabilities, generic data insertion methods, generic medical knowledge representation and application methods, and the ability to communicate with a wide range of sensors. Future larger scale evaluations can assess the impact of such a system on clinical outcomes. CONCLUSION: MobiGuide's feasibility was demonstrated by a working prototype for the AF and GDM domains, which is usable by patients and clinicians, achieving high compliance to self-measurement recommendations, while enhancing the satisfaction of patients and care providers.

Patient-oriented Computerized Clinical Guidelines for Mobile Decision Support in Gestational Diabetes.

The risks associated with gestational diabetes (GD) can be reduced with an active treatment able to improve glycemic control. Advances in mobile health can provide new patient-centric models for GD to create personalized health care services, increase patient independence and improve patients' self-management capabilities, and potentially improve their treatment compliance. In these models, decision-support functions play an essential role. The telemedicine system MobiGuide provides personalized medical decision support for GD patients that is based on computerized clinical guidelines and adapted to a mobile environment. The patient's access to the system is supported by a smartphone-based application that enhances the efficiency and ease of use of the system. We formalized the GD guideline into a computer-interpretable guideline (CIG). We identified several workflows that provide decision-support functionalities to patients and 4 types of personalized advice to be delivered through a mobile application at home, which is a preliminary step to providing decision-support tools in a telemedicine system: (1) therapy, to help patients to comply with medical prescriptions; (2) monitoring, to help patients to comply with monitoring instructions; (3) clinical assessment, to inform patients about their health conditions; and (4) upcoming events, to deal with patients' personal context or special events. The whole process to specify patient-oriented decision support functionalities ensures that it is based on the knowledge contained in the GD clinical guideline and thus follows evidence-based recommendations but at the same time is patient-oriented, which could enhance clinical outcomes and patients' acceptance of the whole system.

Artificial pancreas using a personalized rule-based controller achieves overnight normoglycemia in patients with type 1 diabetes.

OBJECTIVE: This study assessed the efficacy of a closed-loop (CL) system consisting of a predictive rule-based algorithm (pRBA) on achieving nocturnal and postprandial normoglycemia in patients with type 1 diabetes mellitus (T1DM). The algorithm is personalized for each patient's data using two different strategies to control nocturnal and postprandial periods. RESEARCH DESIGN AND METHODS: We performed a randomized crossover clinical study in which 10 T1DM patients treated with continuous subcutaneous insulin infusion (CSII) spent two nonconsecutive nights in the research facility: one with their usual CSII pattern (open-loop [OL]) and one controlled by the pRBA (CL). The CL period lasted from 10 p.m. to 10 a.m., including overnight control, and control of breakfast. Venous samples for blood glucose (BG) measurement were collected every 20 min. RESULTS: Time spent in normoglycemia (BG, 3.9-8.0 mmol/L) during the nocturnal period (12 a.m.-8 a.m.), expressed as median (interquartile range), increased from 66.6% (8.3-75%) with OL to 95.8% (73-100%) using the CL algorithm (P<0.05). Median time in hypoglycemia (BG, <3.9 mmol/L) was reduced from 4.2% (0-21%) in the OL night to 0.0% (0.0-0.0%) in the CL night (P<0.05). Nine hypoglycemic events (<3.9 mmol/L) were recorded with OL compared with one using CL. The postprandial glycemic excursion was not lower when the CL system was used in comparison with conventional preprandial bolus: time in target (3.9-10.0 mmol/L) 58.3% (29.1-87.5%) versus 50.0% (50-100%). CONCLUSIONS: A highly precise personalized pRBA obtains nocturnal normoglycemia, without significant hypoglycemia, in T1DM patients. There appears to be no clear benefit of CL over prandial bolus on the postprandial glycemia.

PREDIRCAM eHealth platform for individualized telemedical assistance for lifestyle modification in the treatment of obesity, diabetes, and cardiometabolic risk prevention: a pilot study (PREDIRCAM 1).

BACKGROUND: Healthy diet and regular physical activity are powerful tools in reducing diabetes and cardiometabolic risk. Various international scientific and health organizations have advocated the use of new technologies to solve these problems. The PREDIRCAM project explores the contribution that a technological system could offer for the continuous monitoring of lifestyle habits and individualized treatment of obesity as well as cardiometabolic risk prevention. METHODS: PREDIRCAM is a technological platform for patients and professionals designed to improve the effectiveness of lifestyle behavior modifications through the intensive use of the latest information and communication technologies. The platform consists of a web-based application providing communication interface with monitoring devices of physiological variables, application for monitoring dietary intake, ad hoc electronic medical records, different communication channels, and an intelligent notification system. A 2-week feasibility study was conducted in 15 volunteers to assess the viability of the platform. RESULTS: The website received 244 visits (average time/session: 17 min 45 s). A total of 435 dietary intakes were recorded (average time for each intake registration, 4 min 42 s ± 2 min 30 s), 59 exercises were recorded in 20 heart rate monitor downloads, 43 topics were discussed through a forum, and 11 of the 15 volunteers expressed a favorable opinion toward the platform. Food intake recording was reported as the most laborious task. Ten of the volunteers considered long-term use of the platform to be feasible. CONCLUSIONS: The PREDIRCAM platform is technically ready for clinical evaluation. Training is required to use the platform and, in particular, for registration of dietary food intake.

How continuous monitoring changes the interaction of patients with a mobile telemedicine system.

BACKGROUND: The combination of telemedicine systems integrating mobile technologies with the use of continuous glucose monitors improves patients' glycemic control but demands a higher interaction with information technology tools that must be assessed. In this article, we analyze patients' behavior from the use-of-the-system point of view, identifying how continuous monitoring may change the interaction of patients with the mobile telemedicine system. METHODS: Patients' behavior were evaluated in a clinical experiment consisting of a 2-month crossover randomized study with 10 type 1 diabetes patients. During the entire experiment, patients used the DIABTel telemedicine system, and during the intervention phase, they wore a continuous glucose monitor. Throughout the experiment, all user actions were automatically registered. This article analyzes the occurrence of events and the behavior patterns in blood glucose (BG) self-monitoring and insulin adjustments. A subjective evaluation was also performed based on the answers of the patients to a questionnaire delivered at the end of the study. RESULTS: The number of sessions established with the mobile Smart Assistant was considerably higher during the intervention period than in the control period (29.0 versus 18.8, p < .05), and it was also higher than the number of Web sessions (29.0 versus 22.2, p < .01). The number of daily boluses was higher during the intervention period than in the control period (5.27 versus 4.40, p < .01). The number of daily BG measurements was also higher during the intervention period (4.68 versus 4.05, p < .05) and, in percentage, patients increased the BG measurements not associated to meals while decreasing the percentage of preprandial measurements. The subjective evaluation shows that patients would recommend the use of DIABTel in routine care. CONCLUSIONS: The use of a continuous glucose monitor changes the way patients manage their diabetes, as observed in the increased number of daily insulin bolus, the increased number of daily BG measurements, and the differences in the distribution of BG measurements throughout the day. Continuous monitoring also increases the interaction of patients with the information system and modifies their patterns of use. We can conclude that mobile technologies are especially useful in scenarios of tight monitoring in diabetes, and they are well accepted by patients.

Architecture of a wireless Personal Assistant for telemedical diabetes care.

PURPOSE: Advanced information technologies joined to the increasing use of continuous medical devices for monitoring and treatment, have made possible the definition of a new telemedical diabetes care scenario based on a hand-held Personal Assistant (PA). This paper describes the architecture, functionality and implementation of the PA, which communicates different medical devices in a personal wireless network. DESCRIPTION OF THE SYSTEM: The PA is a mobile system for patients with diabetes connected to a telemedical center. The software design follows a modular approach to make the integration of medical devices or new functionalities independent from the rest of its components. Physicians can remotely control medical devices from the telemedicine server through the integration of the Common Object Request Broker Architecture (CORBA) and mobile GPRS communications. Data about PA modules' usage and patients' behavior evaluation come from a pervasive tracing system implemented into the PA. RESULTS AND DISCUSSION: The PA architecture has been technically validated with commercially available medical devices during a clinical experiment for ambulatory monitoring and expert feedback through telemedicine. The clinical experiment has allowed defining patients' patterns of usage and preferred scenarios and it has proved the Personal Assistant's feasibility. The patients showed high acceptability and interest in the system as recorded in the usability and utility questionnaires. Future work will be devoted to the validation of the system with automatic control strategies from the telemedical center as well as with closed-loop control algorithms.

Automatic data processing to achieve a safe telemedical artificial pancreas.

BACKGROUND: The use of telemedicine for diabetes care has evolved over time, proving that it contributes to patient self-monitoring, improves glycemic control, and provides analysis tools for decision support. The timely development of a safe and robust ambulatory artificial pancreas should rely on a telemedicine architecture complemented with automatic data analysis tools able to manage all the possible high-risk situations and to guarantee the patient's safety. METHODS: The Intelligent Control Assistant system (INCA) telemedical artificial pancreas architecture is based on a mobile personal assistant integrated into a telemedicine system. The INCA supports four control strategies and implements an automatic data processing system for risk management (ADP-RM) providing short-term and medium-term risk analyses. The system validation comprises data from 10 type 1 pump-treated diabetic patients who participated in two randomized crossover studies, and it also includes in silico simulation and retrospective data analysis. RESULTS: The ADP-RM short-term risk analysis prevents hypoglycemic events by interrupting insulin infusion. The pump interruption has been implemented in silico and tested for a closed-loop simulation over 30 hours. For medium-term risk management, analysis of capillary blood glucose notified the physician with a total of 62 alarms during a clinical experiment (56% for hyperglycemic events). The ADP-RM system is able to filter anomalous continuous glucose records and to detect abnormal administration of insulin doses with the pump. CONCLUSIONS: Automatic data analysis procedures have been tested as an essential tool to achieve a safe ambulatory telemedical artificial pancreas, showing their ability to manage short-term and medium-term risk situations.

Definition of information technology architectures for continuous data management and medical device integration in diabetes.

The growing availability of continuous data from medical devices in diabetes management makes it crucial to define novel information technology architectures for efficient data storage, data transmission, and data visualization. The new paradigm of care demands the sharing of information in interoperable systems as the only way to support patient care in a continuum of care scenario. The technological platforms should support all the services required by the actors involved in the care process, located in different scenarios and managing diverse information for different purposes. This article presents basic criteria for defining flexible and adaptive architectures that are capable of interoperating with external systems, and integrating medical devices and decision support tools to extract all the relevant knowledge to support diabetes care.