A systematic review into the assessment of medical apps: motivations, challenges, recommendations and methodological aspect

Alamoodi, A. H.; Garfan, Salem; Zaidan, A. A.; Shuwandy, Moceheb Lazam; Alaa, Mussab; Alsalem, M. A.; Mohammed, Ali; Aleesa, Ahmed Marwan; Albahri, O. S.; Al-Hussein, Ward Ahmed; Alobaidi, O. R.

doi:10.1007/s12553-020-00451-4

Cited by 36 publications

(19 citation statements)

References 86 publications

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“…This study was conducted based on a systematic review protocol ( SLR ) that helps achieve a comprehensive understanding of the research interest while providing further information for future studies ( Alamoodi, et al, 2020 ). In addition, this research approach is considered a more well-structured method for research synthesis than conventional approaches because of its methodological process and identification metrics in identifying relevant studies ( Burgers et al, 2019 , Kushwah et al, 2019 , Loureiro et al, 2019 ).…”

Section: Systematic Review Protocolmentioning

confidence: 99%

Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review

Alamoodi

Zaidan

et al. 2021

Expert Systems with Applications

287

152

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Highlights Understanding Sentiment Analysis Role and Opinion Mining In Covid-19 and Other Infectious Diseases. Literature’s Categorization for Sentiment Analysis and Infectious Disease. Academic Challenges and Motivations of Sentiment Analysis with Infectious Diseases. Different Applications for Mitigating Infectious Diseases by Sentiment Analysis.

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Section: Systematic Review Protocolmentioning

confidence: 99%

Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review

Alamoodi

Zaidan

et al. 2021

Expert Systems with Applications

287

152

View full text Add to dashboard Cite

show abstract

“…to date, not been able to take up this burden, the authors, agreeing with other studies showing the impact of this lack of oversight, 43 argue that this oversight will have to fall to those who are using mHealth apps in their practices, medical researchers and practitioners, both singly and organizationally. Further, medical and nursing schools should consciously evaluate the apps they bundle for their students, ensuring safety and clinical efficacy, as well as teaching their students the importance of consistent evaluation of apps as they emerge on the market.…”

Section: Discussionmentioning

confidence: 77%

Medical Professionals and Medical Apps: Why Oversight Must Come from Within

Krieger¹,

Cotton²

2021

SHTT

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Phone or tablet-based healthcare applications, or "medical apps," play an important role in an evolving healthcare system. The effect of medical apps on consumers has been welldocumented; however, little attention has been paid to the impacts that apps have had on medical professionals, people whose best interests like in ensuring that medical apps positively impact patient outcomes. After a brief introduction, introducing the spectrum of problems surrounding medical apps, this paper will move its focus to issues of concern for medical practitioners who prescribe or use medical apps as a part of their approach to medical care. Given the current lack of regulatory oversight of medical apps and noting the potential for improper use of these mHealth technologies, the authors will argue that as qualified, well suited, and interested parties, medical professionals should help to shape this new regulatory and ethical landscape. Additionally, before concluding, the authors will provide concrete examples of ways that medical professionals have put these ideas into practice.

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“…Mobile applications have been used in assistive healthcare and other medical related cases [73][74][75]. The final stage of our solution displays the information to the end-user after processing data through an Android application installed on a smartphone.…”

Section: Mobile Applicationmentioning

confidence: 99%

CNN-Based Smart Sleep Posture Recognition System

Tang

Kumar

Nadeem

et al. 2021

IoT

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Sleep pattern and posture recognition have become of great interest for a diverse range of clinical applications. Autonomous and constant monitoring of sleep postures provides useful information for reducing the health risk. Prevailing systems are designed based on electrocardiograms, cameras, and pressure sensors, which are not only expensive but also intrusive in nature, and uncomfortable to use. We propose an unobtrusive and affordable smart system based on an electronic mat called Sleep Mat-e for monitoring the sleep activity and sleep posture of individuals living in residential care facilities. The system uses a pressure sensing mat constructed using piezo-resistive material to be placed on a mattress. The sensors detect the distribution of the body pressure on the mat during sleep and we use convolution neural network (CNN) to analyze collected data and recognize different sleeping postures. The system is capable of recognizing the four major postures—face-up, face-down, right lateral, and left lateral. A real-time feedback mechanism is also provided through an accompanying smartphone application for keeping a diary of the posture and send alert to the user in case there is a danger of falling from bed. It also produces synopses of postures and activities over a given duration of time. Finally, we conducted experiments to evaluate the accuracy of the prototype, and the proposed system achieved a classification accuracy of around 90%.

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A systematic review into the assessment of medical apps: motivations, challenges, recommendations and methodological aspect

Cited by 36 publications

References 86 publications

Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review

Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review

Medical Professionals and Medical Apps: Why Oversight Must Come from Within

CNN-Based Smart Sleep Posture Recognition System

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