M. Elena Hernando

Architecture of a wireless Personal Assistant for telemedical diabetes care

PURPOSEnAdvanced 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.nnnDESCRIPTION OF THE SYSTEMnThe 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.nnnRESULTS AND DISCUSSIONnThe 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 Assistants 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.

A telemedicine system that includes a personal assistant improves glycemic control in pump-treated patients with type 1 diabetes.

Background: The DIABTel system, a Web-based telemedicine application, integrates a whole communication system (glucometer, insulin pump, wireless hand-held assistant) for medical remote advice. We sought to evaluate, in terms of glycemic control, the DIABTel system in a randomized crossover clinical study. Methods: Ten patients with type 1 diabetes [5 women, age 40.6 (21–62) years, diabetes duration 14.7 (3–52) years] were included. During the 4-week active phase, data sent by patients were analyzed by the physician and modifications of the basal rate and bolus were advised in the following 24 hours. During the control phase, patients sent glucose data without any feedback from the medical center. Results: The mean numbers of daily glucose values and bolus sent by patients during the active period were 4.46 ± 0.91 and 4.58 ± 0.89, respectively. The personal digital assistant functionalities used more frequently by patients were (times per week) data visualization (8.1 ± 6.8), data download from the insulin pump (6.8 ± 3.3), and synchronization with the telemedicine server (8.5 ± 4.9). After the experimental phase, serum fructosamine decreased significantly (393 ± 32 vs 366 ± 25 μmol/liter; p < 0.05) and hemoglobin A1c (HbA1c) tended to decrease (8.0 ± 0.6 vs 7.78 ± 0.6; p = 0.073), whereas no changes were observed during the control phase. The number of treatment modifications proposed and performed by the patients correlated with the change observed in HbA1c during the active phase (r = −0.729, p = 0.017). Conclusions: The DIABTel system, a telemedicine system that includes a wireless personal assistant for remote treatment advising, allows better glycemic control in pump-treated patients with type 1 diabetes. To our knowledge, this is the first study that demonstrates improved glycemic control with the use of a telemedicine system that incorporates insulin delivery data.

Quantifying the Acute Changes in Glucose with Exercise in Type 1 Diabetes: A Systematic Review and Meta-Analysis

BackgroundThe acute impact of different types of physical activity on glycemic control in type 1 diabetes has not been well quantified.ObjectivesOur objective was to estimate the rate of change (RoC) in glucose concentration induced acutely during the performance of structured exercise and at recovery in subjects with type 1 diabetes.MethodsWe searched for original articles in the PubMed, MEDLINE, Scopus, and Cochrane databases. Search terms included type 1 diabetes, blood glucose, physical activity, and exercise. Eligible studies (randomized controlled trials and non-randomized experiments) encompassed controlled physical activity sessions (continuous moderate [CONT], intermittent high intensity [IHE], resistance [RESIST], and/or a resting reference [REST]) and reported excursions in glucose concentration during exercise and after its cessation. Data were extracted by graph digitization to compute two RoC measures from population profiles: RoCE during exercise and RoCR in recovery.ResultsTen eligible studies were found from 540 publications. Meta-analyses of exercise modalities versus rest yielded the following: RoCE −4.43xa0mmol/Lxa0h−1 (pxa0<xa00.00001, 95xa0% confidence interval [CI] −6.06 to −2.79) and RoCR +0.70xa0mmol/Lxa0h−1 (pxa0=xa00.46, 95xa0% CI −1.14 to +2.54) for CONT vs. REST; RoCE −5.25xa0mmol/L·h−1 (pxa0<xa00.00001, 95xa0% CI −7.02 to −3.48) and RoCR +0.72xa0mmol/Lxa0h−1 (pxa0=xa00.71, 95xa0% CI −3.10 to +4.54) for IHE vs. REST; RoCE −2.61xa0mmol/Lxa0h−1 (pxa0=xa00.30, 95xa0% CI −7.55 to +2.34) and RoCR −0.02xa0mmol/Lxa0h−1 (pxa0=xa01.00, 95xa0% CI −7.58 to +7.53) for RESIST vs. REST.ConclusionsNovel RoC magnitudes RoCE, RoCR reflected rapid decays of glycemia during CONT exercise and gradual recoveries immediately afterwards. RESIST showed more constrained decays, whereas discrepancies were found for IHE.

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

Background: 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. Methods: 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). Results: 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. Conclusions: 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.

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

BACKGROUNDnThe 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.nnnMETHODSnA 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 patients 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 clinicians time required per patient; number of face-to-face visits; frequency and duration of telematics reviews; patients compliance to self-monitoring; and patients satisfaction.nnnRESULTSnSinedie 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.nnnCONCLUSIONSnSinedie 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.

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 patients 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.

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.

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.

Enhancing Guideline-Based Decision Support with Distributed Computation Through Local Mobile Application

We introduce the need for a distributed guideline-based decision support (DSS) process, describe its characteristics, and explain how we implemented this process within the European Union’s MobiGuide project. In particular, we have developed a mechanism of sequential, piecemeal projection, i.e., downloading small portions of the guideline from the central DSS server, to the local DSS in the patients mobile device, which then applies that portion, using the mobile devices local resources. The mobile device sends a callback to the central DSS when it encounters a triggering pattern predefined in the projected module, which leads to an appropriate predefined action by the central DSS, including sending a new projected module, or directly controlling the rest of the workflow. We suggest that such a distributed architecture that explicitly defines a dialog between a central DSS server and a local DSS module, better balances the computational load and exploits the relative advantages of the central server and of the local mobile device.

Automatic classification of glycaemia measurements to enhance data interpretation in an expert system for gestational diabetes

Highly accurate classifier to associate glycemia with main meals.Design based on the evaluation of machine learning and feature selection strategies.Application in a telemedicine and decision support system in a clinical environment.Minimal patient intervention. (1.22% of glycemia measurements reclassified).Enhance quality of decision support and recommendations provided in telemedicine. Expert systems for diabetes care need to automatically evaluate glycaemia measurements in relationship to meals to correctly determine patients metabolic condition and generate recommendations about therapy adjustments. Most glucose meters allow patients to manually label each measurement with a meal tag, but as this utility is not always used, a completion procedure is needed. Classification methods are usually based on predefined mealtimes and present insufficient accuracy that might affect the automatic data analysis. Expert systems in diabetes require a reliable method to manage incomplete glycaemia data so that they can determine if patients metabolic condition is altered due to a specific meal or due to an extended fasting period.This paper presents the design and application of a classification module to automatically assign the appropriate meal and moment of measurement to incomplete glycaemia data. Different machine learning techniques were studied in order to design the best classification algorithm in terms of accuracy. The selected classifier was implemented with a C4.5 decision tree with 7 input features selected with a wrapper evaluator and the genetic search algorithm, which achieved 95.45% of accuracy with the training set on cross-validation. The classification module was integrated in the Sinedie expert system for gestational diabetes care and was evaluated in a clinical environment for 8 months with 42 patients. A total of 7,113 glycaemia measurements were uploaded by patients into the Sinedie system and were completed by the classification module. The 98.79% of the measurements were correctly classified, while patients modified the automatic classification of 1.21% of them. Classification results were improved by 21.04% compared to a classification based on predefined mealtimes. The automatic classification of glycaemia measurements minimizes the patients intervention, allows structuring measurements in relationship to meals and makes automatic data interpretation by expert systems more reliable.