Ubiquitous Risk Analysis of Physiological Data

Author(s):  
Daniele Apiletti ◽  
Elena Baralis ◽  
Giulia Bruno ◽  
Tania Cerquitelli

Current advances in sensing devices and wireless technologies are providing a high opportunity for improving care quality and reducing the medical costs. This chapter presents the architecture of a mobile healthcare system and provides an overview of mobile health applications. Furthermore, it proposes a framework for patient monitoring that performs real-time stream analysis of data collected by means of non-invasive body sensors. It evaluates a patient’s health conditions by analyzing different physiological signals to identify anomalies and activate alarms in risk situations.

Author(s):  
Daniele Apiletti

Current advances in sensing devices and wireless technologies are providing a high opportunity for improving care quality and reducing the medical costs. This chapter presents the architecture of a mobile healthcare system and provides an overview of mobile health applications. Furthermore, it proposes a framework for patient monitoring that performs real-time stream analysis of data collected by means of non-invasive body sensors. It evaluates a patient’s health conditions by analyzing different physiological signals to identify anomalies and activate alarms in risk situations. A risk function for identifying the instantaneous risk of each physiological parameter has been defined. The performance of the proposed system has been evaluated on public physiological data and promising experimental results are presented. By understanding the challenges and the current solutions of informatics appliances described in this chapter, new research areas can be further investigated to improve mobile healthcare services and design innovative medical applications.


2015 ◽  
Author(s):  
Roberto Moro Visconti ◽  
Alberto Larocca ◽  
Michele Marconi

2020 ◽  
Author(s):  
Claudia Eberle ◽  
Maxine Löhnert

BACKGROUND Gestational diabetes mellitus (GDM) emerges worldwide and is closely associated with short- and long-term health issues in women and their offspring, such as pregnancy and birth complications respectively comorbidities, Type 2 Diabetes (T2D), Metabolic Syndrome (MetS) as well as cardiovascular disease (CD). Against this background mobile health applications (mHealth-Apps) do open up new possibilities to improve the management of GDM clearly. OBJECTIVE Since there is – to our knowledge – no systematic literature review published, which focusses on the effectiveness of specific mHealth-Apps on clinical health-related short and long-term outcomes of mother and child, we conducted these much-needed analyses. METHODS Data sources: A systematic literature search in Medline (Pubmed), Cochrane Library, Embase, CINAHL and Web of Science was performed including full text publications since 2008 up to date. An additional manual search in references and Google Scholar was conducted subsequently. Study Eligibility Criteria: Women diagnosed with GDM using specific mHealth-Apps during pregnancy compared to control groups, which met main clinical parameters and outcomes in GDM management as well as maternity and offspring care. Study appraisal and synthesis methods: Study quality was assessed and rated “strong”, “moderate” or “weak” by using the Effective Public Health Practice Project (EPHPP) tool. Study results were strongly categorized by outcomes; an additional qualitative summary was assessed. Study selection: Overall, n= 114 studies were analyzed, n= 46 duplicates were removed, n=5 studies met the eligible criteria and n=1 study was assessed by manual search subsequently. In total, n=6 publications, analyzing n=408 GDM patients in the interventional and n=405 women diagnosed with GDM in the control groups, were included. These studies were divided into n=5 two-arm randomized controlled trials (RCT) and n=1 controlled clinical trial (CCT). RESULTS Distinct improvements in clinical parameters and outcomes, such as fasting blood glucoses (FBG), 2-hour postprandial blood glucoses (PBG), off target blood glucose measurements (OTBG), delivery modes and patient compliance were analyzed in GDM patients using specific mHealth-Apps compared to matched control groups. CONCLUSIONS mHealth-Apps clearly improve clinical outcomes in management of GDM effectively. More studies need to be done more in detail.


Author(s):  
Sahar Khenarinezhad ◽  
Ehsan Ghazanfari Savadkoohi ◽  
Leila Shahmoradi

Aim: During the epidemic and with an increase in coronavirus (COVID-19) disease prevalence, emergency care is essential to help people stay informed and undertake self-management measures to protect their health. One of these self-management procedures is the use of mobile apps in health. Mobile health (mHealth) applications include mobile devices in collecting clinical health data, sharing healthcare information for practitioners and patients, real-time monitoring of patient vital signs, and the direct provision of care (via mobile telemedicine). Mobile apps are increasing to improve health, but before healthcare providers can recommend these applications to patients, they need to be sure the apps will help change patients' lifestyles. Method: A search was conducted systematically using the keywords "Covid-19," "Coronavirus," "Covid-19, and Self-management" at the "Apple App Store". Then we evaluated the apps according to MARS criteria in May 2020. Results: A total of 145 apps for COVID-19 self-management were identified, but only 32 apps met our inclusion criteria after being assessed. The overall mean MARS score was 2.9 out of 5, and more than half of the apps had a minimum acceptability score (range 2.5-3.9). The "who academy" app received the highest functionality score. Who Academy, Corona-Care and First Responder COVID-19 Guide had the highest scores for behavior change. Conclusion: Our findings showed that few apps meet the quality, content, and functionality criteria for Covid-19 self-management. Therefore, developers should use evidence-based medical guidelines in creating mobile health applications so that, they can provide comprehensive and complete information to both patients and healthcare provider.


2017 ◽  
Vol 33 (9) ◽  
pp. 1-6
Author(s):  
Adam Rosenfeld ◽  
Sachin Pendse ◽  
Nicole R. Nugent

2018 ◽  
Vol 35 (4) ◽  
pp. 815-825 ◽  
Author(s):  
Hao-Yun Kao ◽  
Chun-Wang Wei ◽  
Min-Chun Yu ◽  
Tyng-Yeu Liang ◽  
Wen-Hsiung Wu ◽  
...  

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 9390-9403 ◽  
Author(s):  
Achilleas Papageorgiou ◽  
Michael Strigkos ◽  
Eugenia Politou ◽  
Efthimios Alepis ◽  
Agusti Solanas ◽  
...  

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