Mobile personal health care system for patients with diabetes

In this thesis, we propose a personal diabetes monitoring system which integrates wearable sensors, 3G mobile phone, smart home technologies and Google sheet to facilitate the management of chronic disease - diabetes. The system utilizes wearable sensors and 3G cellular phone to automatically collect physical signs, such as blood glucose level, blood pressure and exercise data like heart rate, breathing rate and skin temperature. It allows users, especially seniors with diabetes, to conveniently record daily test results and track long term health condition changes regardless of their locations. It does so without having to ask users to manually input them into the system. The system also utilizes Google sheet to manage Personal Health Records (PHRs), which not only bridges the gaps between patients and different health care providers but enabling accesses to patients\u27 PHRs anywhere and anytime by taking advantage of the universal accessibility of Google sheet. The system further integrates with GPS, Google Search and Google Map functionalities to facilitate the user to find all hospitals near to his/her current location including address, phone number, directions to the selected hospital and street view of the selected hospital

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

Performance assessment of a closed-loop system for diabetes management

Telemedicine systems can play an important role in the management of diabetes, a chronic condition that is increasing worldwide. Evaluations on the consistency of information across these systems and on their performance in a real situation are still missing. This paper presents a remote monitoring system for diabetes management based on physiological sensors, mobile technologies and patient/ doctor applications over a service-oriented architecture that has been evaluated in an international trial (83,905 operation records). The proposed system integrates three types of running environments and data engines in a single serviceoriented architecture. This feature is used to assess key performance indicators comparing them with other type of architectures. Data sustainability across the applications has been evaluated showing better outcomes for full integrated sensors. At the same time, runtime performance of clients has been assessed spotting no differences regarding the operative environmentThe authors wish to acknowledge the consortium of the METABO project (funded by the European Commission, Grant nr. 216270) for their commitment during concept development and trial execution.Martínez Millana, A.; Fico, G.; Fernández Llatas, C.; Traver Salcedo, V. (2015). Performance assessment of a closed-loop system for diabetes management. Medical and Biological Engineering and Computing. 53(12):1295-1303. doi:10.1007/s11517-015-1245-3S129513035312Bellazzi R, Larizza C, Montani A et al (2002) A telemedicine support dor diabetes management: the T-IDDM project. Comput Methods Programs Biomed 69:147–161Boloor K, Chirkova R, Salo T, Viniotis Y (2011) Analysis of response time percentile service level agreements in soa-based applications. IEEE global telecommunications conference (GLOBECOM 2011), dec. 2011, pp 1–6Cartwright M et al (2013) Effect of telehealth on quality of life and psychological outcomes over 12 months: nested study of patient reported outcomes in a pragmatic, cluster randomised controlled trial. BMJ 346:f653Chen I-Y et al (2008) Pervasive digital monitoring and transmission of pre-care patient biostatics with an OSGi, MOM and SOA based remote health care system. In: Proceedings of the 6th annual IEEE international conference on PerCom. Hong KongFico G, Fioravanti A, Arredondo MT, Leuteritz JP, Guillén A, Fernandez D (2011) A user centered design approach for patient interfaces to a diabetes IT platform. Conf Proc IEEE Eng Med Biol Soc 2011:1169–1172Fioravanti A, Fico G, Arredondo MT, Salvi D, Villalar JL (2010) Integration of heterogeneous biomedical sensors into an ISO/IEEE 11073 compliant application. In: Engineering in medicine and biology society (EMBC), 2010 Annual international conference of the IEEE, pp 1049–1052García Saez G et al (2009) Architecture of a wireless personal assistant for telemedical diabetes care. Int J Med Inform 9(78):391–403Gómez EJ, Hernando ME et al (2008) The INCA system: a further step towards a telemedical artificial pancreas. IEEE Trans Inf Technol Biomed 12(4):470–479Harrison’s Principles of Internal Medicine (2011) McGraw-Hill. ISBN:978-0071748896. Ed. July 2011Ke X, Li W et al (2010) WCDMA KPI framework definition methods and applications. ICCET proceedings V4-471–V4-475Klonof D (2013) Twelve modern digital technologies that are transforming decision making for diabetes and all areas of health care. J Diabetes Sci Technol 7(2):291–295Lanzola G et al (2007) Going mobile with a multiaccess service for the management of diabetic patients. J Diabetes Sci Technol 1(5):730–737Ma C et al (2006) Empowering patients with essential information and communication support in the context of diabetes. Int J Med Inform 75(8):577–596Müller AJ, Knuth M, Nikolaus KS, Krivánek R, Küster F, Hasslacher C (2013) First clinical evaluation of a new percutaneous optical fiber glucose sensor for continuous glucose monitoring in diabetes. J Diabetes Sci Technol 7:13Nundy S et al (2012) Using mobile health to support chronic care model: developing an institutional model. Int J Telemed Appl 2012, Art Id 871925. doi: 10.1155/2012/871925Obstfelder A, Engeseth KH, Wynn R (2007) Characteristic of succesfully implemented telemedical applications. Implement Sci 2:25Pravin P et al (2012) A framework for the comparison of mobile patient monitoring systems. J Biomed Inf 45:544–556Reichel A, Rietzsch H, Ludwig B, Röthig K, Moritz A, Bornstein S (2013) Self-adjustment of insulin dose using graphically depicted self-monitoring of blood glucose measurements in patients with type 1 diabetes mellitus. J Diabetes Sci Technol 7(1):156–162Ryan D et al (2012) Clinical and cost effectiveness of mobile phone supported self-monitoring of asthma: multicenter randomized controlled trial. BMJ 344:e1756Schade DS et al (2005) To pump or not to pump. Diabetes Technol Therapeutics 7:845–848Stravroula G, Bartsocas CS et al (2010) SMARTDIAB: a communication and information technology approach for the intelligent monitoring, management and follow-up of type 1 diabetes patients. IEEE Trans Inf Technol Biomed 14(3):622–633The Diabetes Control and Complications Trial Research Group (1993) The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med 329(14):977–986Trief PM, Morin PC, Izquierdo R, Teresi JA, Eimicke JP, Goland R, Starren J, Shea S, Winstock RS (2006) Depression and glycaemic control in elderly etchnically diverse patients with diabetes: the IDEATel project. Diabetes Care 29(4):830–835van der Weegentres S et al (2013) The development of a mobile monitoring and feedback tool to stimulate physical activity of people with a chronic disease in primary care: a user-centered design. JMIR 1(2):e8Wakefield BJ et al (2014) Effect of home telemonitoring on glycemic and blood pressure control in primary care clinic patients with diabetes. Telemed e-Health 20(3):199–205. doi: 10.1089/tmj.2013.0151Winkler S et al (2011) A new telemonitoring system intended for chronic heart failure patients using mobile technology—Feasibility Study. Int J Cardiol 153:55–58Zhou YY, Kanter MH, Wang JJ, Garrido T (2010) Improved quality at kaiser permanente through e-mail between physicians and patients. Health Aff 29(7):1370–137

Design and Evaluation of a Pervasive Coaching and Gamification Platform for Young Diabetes Patients

Self monitoring, personal goal-setting and coaching, education and social support are strategies to help patients with chronic conditions in their daily care. Various tools have been developed, e.g., mobile digital coaching systems connected with wearable sensors, serious games and patient web portals to personal health records, that aim to support patients with chronic conditions and their caregivers in realizing the ideal of self-management. We describe a platform that integrates these tools to support young patients in diabetes self-management through educational game playing, monitoring and motivational feedback. We describe the design of the platform referring to principles from healthcare, persuasive system design and serious game design. The virtual coach is a game guide that can also provide personalized feedback about the user’s daily care related activities which have value for making progress in the game world. User evaluations with patients under pediatric supervision revealed that the use of mobile technology in combination with web-based elements is feasible but some assumptions made about how users would connect to the platform were not satisfied in reality, resulting in less than optimal user experiences. We discuss challenges with suggestions for further development of integrated pervasive coaching and gamification platforms in medical practice

How Continuous Monitoring Changes the Interaction of Patients with a Mobile Telemedicine System

The use of 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

Use of m-Health Technology for Preventive Interventions to Tackle Cardiometabolic Conditions and Other Non-Communicable Diseases in Latin America- Challenges and Opportunities

In Latin America, cardiovascular disease (CVD) mortality rates will increase by an estimated 145% from 1990 to 2020. Several challenges related to social strains, inadequate public health infrastructure, and underfinanced healthcare systems make cardiometabolic conditions and non-communicable diseases (NCDs) difficult to prevent and control. On the other hand, the region has high mobile phone coverage, making mobile health (mHealth) particularly attractive to complement and improve strategies toward prevention and control of these conditions in low- and middle-income countries. In this article, we describe the experiences of three Centers of Excellence for prevention and control of NCDs sponsored by the National Heart, Lung, and Blood Institute with mHealth interventions to address cardiometabolic conditions and other NCDs in Argentina, Guatemala, and Peru. The nine studies described involved the design and implementation of complex interventions targeting providers, patients and the public. The rationale, design of the interventions, and evaluation of processes and outcomes of each of these studies are described, together with barriers and enabling factors associated with their implementation.Fil: Beratarrechea, Andrea Gabriela. Instituto de Efectividad Clínica y Sanitaria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Diez Canseco, Francisco. Universidad Peruana Cayetano Heredia; PerúFil: Irazola, Vilma. Instituto de Efectividad Clínica y Sanitaria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Miranda, Jaime. Universidad Peruana Cayetano Heredia; PerúFil: Ramirez Zea, Manuel. Institute of Nutrition of Central America and Panama; GuatemalaFil: Rubinstein, Adolfo Luis. Instituto de Efectividad Clínica y Sanitaria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

A Framework for the Design of a Mobile-Based Alert System for Outpatient Adherence in Nigeria

Nigeria ranks among the countries with the highest child and maternal mortality rate. Chronic diseases are the most common contributors to the diseases burden in Nigeria most especially Malaria, Tuberculosis (TB) and HIV/AIDS. Adherence to long-term therapy in out-patient condition is required to reduce and curb the prevalence of these diseases. Poor adherence to long-term therapies severely compromises the effectiveness of treatment; making this a critical issue in population health both from the perspective of quality of life and of health economics. This work introduces a m-technology based system that will provide an easy way of complying with drug regimen. It will make use of the Short Messaging Service (SMS) of mobile phones to provide reminders at dosing times. It will design architecture for mobile health interventions and develop a prototype SMS-based system to improve out-patient adherence. This system will be deployed over a period of time at selected hospitals and chronic disease management centers in selected states in Nigeria, and the adherence rates measured via health outcomes and evaluated. This would provide a significant positive return on investment through primary prevention (of risk factors) and secondary prevention of adverse health outcomes. It will also inform predictions of future population health outcomes predicted by treatment efficacy data. Keywords: out-patient, m-technology, adherence, chronic diseases, Nigeria, SM

Systematic review of communication technologies to promote access and engagement of young people with diabetes into healthcare

Background: Research has investigated whether communication technologies (e.g. mobile telephony, forums, email) can be used to transfer digital information between healthcare professionals and young people who live with diabetes. The systematic review evaluates the effectiveness and impact of these technologies on communication. Methods: Nine electronic databases were searched. Technologies were described and a narrative synthesis of all studies was undertaken. Results: Of 20,925 publications identified, 19 met the inclusion criteria, with 18 technologies assessed. Five categories of communication technologies were identified: video-and tele-conferencing (n = 2); mobile telephony (n = 3); telephone support (n = 3); novel electronic communication devices for transferring clinical information (n = 10); and web-based discussion boards (n = 1). Ten studies showed a positive improvement in HbA1c following the intervention with four studies reporting detrimental increases in HbA1c levels. In fifteen studies communication technologies increased the frequency of contact between patient and healthcare professional. Findings were inconsistent of an association between improvements in HbA1c and increased contact. Limited evidence was available concerning behavioural and care coordination outcomes, although improvement in quality of life, patientcaregiver interaction, self-care and metabolic transmission were reported for some communication technologies. Conclusions: The breadth of study design and types of technologies reported make the magnitude of benefit and their effects on health difficult to determine. While communication technologies may increase the frequency of contact between patient and health care professional, it remains unclear whether this results in improved outcomes and is often the basis of the intervention itself. Further research is needed to explore the effectiveness and cost effectiveness of increasing the use of communication technologies between young people and healthcare professionals