A smart mobile, self-configuring, context-aware architecture for personal health monitoring

Esposito, Massimo; Minutolo, Aniello; Megna, Rosario; Forastiere, Manolo; Magliulo, Mario; Pietro, Giuseppe De

doi:10.1016/j.engappai.2017.09.019

Cited by 52 publications

(34 citation statements)

References 30 publications

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“…The digitalization of healthcare can increase efficiency and allows for the provision of better-quality healthcare services, offering with many benefits to stakeholder (Laurenza, Quintano, Schiavone, & Vrontis, 2018). Recently, many innovative technological solutions have been appeared to solve people's health-related needs (Ashraf, Hasan, Lewis, Hasan, & Ray, 2017;Esposito et al, 2018). As an evolving healthcare sector, mobile health (mHealth) services have been receiving increasing attention in latest years as an alternative solution (Zhao, Ni, & Zhou, 2018).…”

Section: Introductionmentioning

confidence: 99%

Understanding the insight of factors affecting mHealth adoption

Kaium

Bao²,

Alam³

et al. 2019

IJRBS

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Numerous studies have addressed the different context of mHealth services among diverse user groups. But due to a lack of understanding the insight of factors affecting the mHealth adoption, it’s crucial need to conduct a systematic review on this issue. The objective of this study was to synthesize the present understanding of the influential factors of mHealth adoption. We performed a systematic literature search on eight electronically reputed scientific databases from 2010 to March 2019, such as Science Direct, Springer, IEEE Xplore, JMIR, Taylor & Francis, Emerald, Mary Ann Liebert and Google Scholar. This was accomplished by gathering data including authors, countries, years, target population, sample size, models/theories, and key influential factors. Primarily, a total of 2969 potentially relatable papers were found, of which 50 met the inclusion criteria. It was found that cross-sectional approach, survey methods and structural equation modeling (SEM) were the most explored research methodologies whereas PLS-SEM was found to be the largest used analysis tools. From the analysis, a total of ninety-four influential factors were clearly recognized and the findings represent that the following 15 factors appeared most recurrently and significantly; perceived usefulness, perceived ease of use, social-influence, subjective norms, self-efficacy, trust, facilitating conditions, technology anxiety, performance expectancy, effort expectancy, cost, attitude, resistance to change, perceived privacy and security, and perceived behavioral control. The research results have significant theoretical and practical implications for mHealth services providers, researchers and policy makers with regards to the Sustainable Development Goals (SDGs) allied to healthcare.

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Section: Introductionmentioning

confidence: 99%

Understanding the insight of factors affecting mHealth adoption

Kaium

Bao²,

Alam³

et al. 2019

IJRBS

View full text Add to dashboard Cite

show abstract

“…Although Bluetooth and Zigbee do not meet the requirements in terms of data-rate, reliability, and SAR of certain medical applications (e.g., in hospital environments) [20], most of the reported WBAN implementations rely on these standards [25]- [30]. However, to the best of the authors' knowledge, no realization of WBANs has been reported based on IEEE 802.15.6, which can be explained by the availability of Bluetooth and Zigbee transceivers and their ease of use compared to the former.…”

Section: Existing Wireless Technologies For Wbans and Related Workmentioning

confidence: 99%

Enabling Communication Technologies for Medical Wireless Body-Area Networks

Haddad

Khalighi

2019

2019 Global LIFI Congress (GLC)

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The increasing percentage of aging population and chronic diseases on one hand, and the advances in the development of integrated short range wireless technologies on the other hand, have created a growing interest in the development of medical telemonitoring and telecare systems through the use of medical on-body sensor networks, also known as medical wireless body area networks (WBANs). This paper provides an overview of the main wireless technologies that can be used for WBANs in the medical domain and discusses the major requirements in such applications. While radio frequency technologies are well established solutions for interconnecting WBANs, the high risk of interference in such networks motivates the use of alternative or complementary technologies including optical wireless communications.Index Terms-Wireless sensor networks; body area networks; medical on-body networks; optical wireless communications.

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“…Contextual information can also be used to send feedback to the patient, to a caregiver, or to the medical staff when an anomaly is detected. The detection of anomalies can be performed directly on the device worn by the patient via embedded data processing [7], [8], [9], or data can be transferred to the cloud first, where important computing resources can be mobilized for data analysis [10].…”

Section: Related Workmentioning

confidence: 99%

Adaptive Strategies for Patient Monitoring in Mobile Health Applications

Bagot

Launay

Guidec

2019

2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS)

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Remotely monitoring the health status of patients in all their activities of daily living is an interesting prospect. Yet an m-Health application devoted to patient monitoring should be able to deal with constrained and unstable operating conditions, such as limited power budget and disruption-prone network connectivity. This paper presents REGAS, a middleware system that makes it possible for a developer to define and enforce adaptive strategies, so their application can continuously adjust its behavior to changing operating conditions. Experimental results confirm that carefully designed strategies can have a significant impact on transmission delays, as well as on the power consumption of an m-Health monitoring application.

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A smart mobile, self-configuring, context-aware architecture for personal health monitoring

Cited by 52 publications

References 30 publications

Understanding the insight of factors affecting mHealth adoption

Understanding the insight of factors affecting mHealth adoption

Enabling Communication Technologies for Medical Wireless Body-Area Networks

Adaptive Strategies for Patient Monitoring in Mobile Health Applications

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