Going Mobile with a Multiaccess Service for the Management of Diabetic Patients

Lanzola, Giordano; Capozzi, Davide; d’Annunzio, Giuseppe; Ferrari, P; Bellazzi, Riccardo; Larizza, Cristiana

doi:10.1177/193229680700100518

Cited by 25 publications

(17 citation statements)

References 7 publications

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“…These manually adjusted interventions are consistent with telemedicine models, but they have limited scalability due to the provider burden required to review and deliver these interventions. Lanzola and colleagues have proposed a multi-tier intervention model in which lower-tier disease management interventions are provided via automated treatment algorithms and higher tier interventions involving the healthcare provider occur only if the lower-tier interventions are ineffective [68]. …”

Section: Review Of Mobile Health Behavior Interventionsmentioning

confidence: 99%

Health behavior models in the age of mobile interventions: are our theories up to the task?

Riley

Rivera

Atienza

et al. 2011

Behav. Med. Pract. Policy Res.

923

788

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Mobile technologies are being used to deliver health behavior interventions. The study aims to determine how health behavior theories are applied to mobile interventions. This is a review of the theoretical basis and interactivity of mobile health behavior interventions. Many of the mobile health behavior interventions reviewed were predominately one way (i.e., mostly data input or informational output), but some have leveraged mobile technologies to provide just-in-time, interactive, and adaptive interventions. Most smoking and weight loss studies reported a theoretical basis for the mobile intervention, but most of the adherence and disease management studies did not. Mobile health behavior intervention development could benefit from greater application of health behavior theories. Current theories, however, appear inadequate to inform mobile intervention development as these interventions become more interactive and adaptive. Dynamic feedback system theories of health behavior can be developed utilizing longitudinal data from mobile devices and control systems engineering models.

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Section: Review Of Mobile Health Behavior Interventionsmentioning

confidence: 99%

Health behavior models in the age of mobile interventions: are our theories up to the task?

Riley

Rivera

Atienza

et al. 2011

Behav. Med. Pract. Policy Res.

923

788

View full text Add to dashboard Cite

show abstract

“…The strong distinction between PU and MU faded during the first decade of the new century, as telemedicine services began to be deployed over stable network connections, eventually accessed through mobile devices such as smartphones [20] that were just appearing on the market. The previous partitioning into two opposite halves, each one targeting a specific set of functionalities did not provide the required flexibility anymore.…”

Section: The Evolution Of the Architectures For Remote Monitoringmentioning

confidence: 99%

Remote Blood Glucose Monitoring in mHealth Scenarios: A Review

Lanzola

Losiouk

Favero

et al. 2016

Sensors

Self Cite

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Glucose concentration in the blood stream is a critical vital parameter and an effective monitoring of this quantity is crucial for diabetes treatment and intensive care management. Effective bio-sensing technology and advanced signal processing are therefore of unquestioned importance for blood glucose monitoring. Nevertheless, collecting measurements only represents part of the process as another critical task involves delivering the collected measures to the treating specialists and caregivers. These include the clinical staff, the patient’s significant other, his/her family members, and many other actors helping with the patient treatment that may be located far away from him/her. In all of these cases, a remote monitoring system, in charge of delivering the relevant information to the right player, becomes an important part of the sensing architecture. In this paper, we review how the remote monitoring architectures have evolved over time, paralleling the progress in the Information and Communication Technologies, and describe our experiences with the design of telemedicine systems for blood glucose monitoring in three medical applications. The paper ends summarizing the lessons learned through the experiences of the authors and discussing the challenges arising from a large-scale integration of sensors and actuators.

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“…The AlarmNet architecture proposed in [9] [10] is a common architecture which integrates body sensor networks, environment sensors, back-end data management and analysis system, and local or remote user interface. Using mobiles with a multi-access service for the management of diabetic patients was proposed in [15], which was designed to collect data, either manually or automatically from the blood glucose meter; to monitor blood glucose levels; to suggest insulin dose adjustment when needed; to deliver monitoring data to a health care center.…”

Section: A Review Of Literaturementioning

confidence: 99%

Measuring the Blood Sugar Level and Time (BSLT) using Mobile Strategy to Remote Monitoring of DIABETES Chronic Diseases for Children in North African Countries

Sheta

Omer

Elkhtem

2015

JBEMi

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Chronic diseases for children such as DIABETES is a challenge for parents in all third world countries. Especially that percentage of giving birth to children with chronic diseases is increasing dramatically in North African countries. Thus the official task of parents to help their children in how to live and deal the condition with this chronic disease. In this paper we propose a child DIABETES monitoring system which is integrated sensors and smart phone to facilitate the management of chronic disease -DIABETES. The system automatically collects physical signs, such as Blood Glucose level. It allows users, especially children with diabetes to conveniently record daily test results and track long term health condition changes regardless of their locations. All the procedures are performed automatically without entering the user (Patient) any data on the system manually. Of the most important objectives of the application is help the parents to monitoring the level of sugar in the blood for their child at any time and alert them automatically to any risk.

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Going Mobile with a Multiaccess Service for the Management of Diabetic Patients

Cited by 25 publications

References 7 publications

Health behavior models in the age of mobile interventions: are our theories up to the task?

Health behavior models in the age of mobile interventions: are our theories up to the task?

Remote Blood Glucose Monitoring in mHealth Scenarios: A Review

Measuring the Blood Sugar Level and Time (BSLT) using Mobile Strategy to Remote Monitoring of DIABETES Chronic Diseases for Children in North African Countries

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