A Smartphone-Based Cloud Computing Tool for Managing Type 1 Diabetes in Ontarians

Scoping Review of Healthcare Literature on Mobile, Wearable, and Textile Sensing Technology for Continuous Monitoring

Remote monitoring of health can reduce frequent hospitalisations, diminishing the burden on the healthcare system and cost to the community. Patient monitoring helps identify symptoms associated with diseases or disease-driven disorders, which makes it an essential element of medical diagnoses, clinical interventions, and rehabilitation treatments for severe medical conditions. This monitoring can be expensive and time-consuming and provide an incomplete picture of the state of the patient. In the last decade, there has been a significant increase in the adoption of mobile and wearable devices, along with the introduction of smart textile solutions that offer the possibility of continuous monitoring. These alternatives fuel a technology shift in healthcare, one that involves the continuous tracking and monitoring of individuals. This scoping review examines how mobile, wearable, and textile sensing technology have been permeating healthcare by offering alternate solutions to challenging issues, such as personalised prescriptions or home-based secondary prevention. To do so, we have selected 222 healthcare literature articles published from 2007 to 2019 and reviewed them following the PRISMA process under the schema of a scoping review framework. Overall, our findings show a recent increase in research on mobile sensing technology to address patient monitoring, reflected by 128 articles published in journals and 19 articles in conference proceedings between 2014 and 2019, which represents 57.65% and 8.55% respectively of all included articles.

Aplicativos para adolescentes com diabetes mellitus tipo 1: revisão integrativa da literatura

Resumo Objetivo: Examinar os recursos de aplicativos para dispositivos móveis destinados ao autocuidado de adolescentes com diabetes mellitus tipo 1. Métodos: Revisão integrativa por meio da busca de artigos nos periódicos indexados nas bases de dados: Cumulative Index to Nursing and Allied Health Literature, Cochrane Library, Literatura Latino-Americana em Ciências da Saúde, PubMed (National Library of Medicine), Scopus, e Web of Science no período de 2012 a 2017, utilizando os seguintes descritores: aplicativos móveis, autocuidado e diabetes mellitus tipo 1, bem como respectivos descritores em inglês e espanhol. Resultados: A busca gerou um total de 248 artigos, dos quais 12 artigos atenderam os critérios de seleção. Os recursos dos aplicativos foram examinados a partir das funções de controle glicêmico, insulinoterapia, alimentação, atividade física, abordagem dos sentimentos e relações sociais. Observou-se que nenhum artigo descreveu um aplicativo que integrasse todos os recursos examinados. Conclusão: Os recursos de aplicativos para dispositivos móveis foram apontados como necessários para auxiliar no controle glicêmico de adolescentes com diabetes mellitus tipo 1.

Physiological Informatics: Collection and Analyses of Data from Wearable Sensors and Smartphone for Healthcare

Physiological data from wearable sensors and smartphone are accumulating rapidly, and this provides us the chance to collect dynamic and personalized information as phenotype to be integrated to genotype for the holistic understanding of complex diseases. This integration can be applied to early prediction and prevention of disease, therefore promoting the shifting of disease care tradition to the healthcare paradigm. In this chapter, we summarize the physiological signals which can be detected by wearable sensors, the sharing of the physiological big data, and the mining methods for the discovery of disease-associated patterns for personalized diagnosis and treatment. We discuss the challenges of physiological informatics about the storage, the standardization, the analyses, and the applications of the physiological data from the wearable sensors and smartphone. At last, we present our perspectives on the models for disentangling the complex relationship between early disease prediction and the mining of physiological phenotype data.