Systematic Predictive Analysis of Personalized Life Expectancy Using Smart Devices

Kang, James Jin; Adibi, Sasan

doi:10.3390/technologies6030074

Cited by 6 publications

(3 citation statements)

References 51 publications

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“…Innovative applications (apps) and smart devices such as smartphones and tablets have emerged as integral parts of people's lives [1]. They provide information to profile society [2], but also to make users aware of body signals important for predicting life expectancy [3], promoting patient engagement, self-management of diseases, and assist doctors to remotely follow up patients [4]. In particular, in the healthcare sector, we have seen the development of systems to assist users in different ways during the day [5] and night [6].…”

Section: Introductionmentioning

confidence: 99%

Accuracy of Mobile Applications versus Wearable Devices in Long-Term Step Measurements

Piccinini

Martinelli

Carbonaro

2020

Sensors

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Fitness sensors and health systems are paving the way toward improving the quality of medical care by exploiting the benefits of new technology. For example, the great amount of patient-generated health data available today gives new opportunities to measure life parameters in real time and create a revolution in communication for professionals and patients. In this work, we concentrated on the basic parameter typically measured by fitness applications and devices—the number of steps taken daily. In particular, the main goal of this study was to compare the accuracy and precision of smartphone applications versus those of wearable devices to give users an idea about what can be expected regarding the relative difference in measurements achieved using different system typologies. In particular, the data obtained showed a difference of approximately 30%, proving that smartphone applications provide inaccurate measurements in long-term analysis, while wearable devices are precise and accurate. Accordingly, we challenge the reliability of previous studies reporting data collected with phone-based applications, and besides discussing the current limitations, we support the use of wearable devices for mHealth.

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Section: Introductionmentioning

confidence: 99%

Accuracy of Mobile Applications versus Wearable Devices in Long-Term Step Measurements

Piccinini

Martinelli

Carbonaro

2020

Sensors

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“…Advancements in knowledge, science, and technology have been critical in curbing the global mortality rate and prolonging life expectancy [ 7 ]. Moreover, the progress in data science, such as big data analytics and cloud technologies, has enabled the development of personal life expectancy prediction models, which could be used to calculate health indexes from massive datasets collected over the years [ 8 ]. In recent times, the widespread application of Information and Communication Technologies (ICTs) in the health sector has resulted in significant improvements in the healthcare delivery system, such as promoting patient-centred healthcare, improving quality of care, and educating health professionals and patients [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Application of Smartphone Technologies in Disease Monitoring: A Systematic Review

et al. 2021

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Technologies play an essential role in monitoring, managing, and self-management of chronic diseases. Since chronic patients rely on life-long healthcare systems and the current COVID-19 pandemic has placed limits on hospital care, there is a need to explore disease monitoring and management technologies and examine their acceptance by chronic patients. We systematically examined the use of smartphone applications (apps) in chronic disease monitoring and management in databases, namely, Medline, Web of Science, Embase, and Proquest, published from 2010 to 2020. Results showed that app-based weight management programs had a significant effect on healthy eating and physical activity (p = 0.002), eating behaviours (p < 0.001) and dietary intake pattern (p < 0.001), decreased mean body weight (p = 0.008), mean Body Mass Index (BMI) (p = 0.002) and mean waist circumference (p < 0.001). App intervention assisted in decreasing the stress levels (paired t-test = 3.18; p < 0.05). Among cancer patients, we observed a high acceptance of technology (76%) and a moderately positive correlation between non-invasive electronic monitoring data and questionnaire (r = 0.6, p < 0.0001). We found a significant relationship between app use and standard clinical evaluation and high acceptance of the use of apps to monitor the disease. Our findings provide insights into critical issues, including technology acceptance along with regulatory guidelines to be considered when designing, developing, and deploying smartphone solutions targeted for chronic patients.

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“…As a consequence, paradigms such as Pervasive Computing [1] and the Internet of Things (IoT) [2] have taken advantage, and have rapidly gained ground in both professional and domestic fields, by means of the pervasive presence of these embedded devices that interoperate among them to share services and information [3,4]. However, due to this massive deployment of everyday objects, the security risks and threats could reach a global dimension.…”

Section: Introductionmentioning

confidence: 99%

Web of Things Platforms for Distance Learning Scenarios in Computer Science Disciplines: A Practical Approach

et al. 2019

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Problem-based learning is a widely used learning methodology in the field of technological disciplines, especially in distance education environments. In these environments, the most used tools, which provide learning scenarios, are remote and virtual laboratories. Internet of Things (IoT) devices can be used as remote or virtual laboratories. In addition to this, they can be organized/orchestrated to build remote maker spaces through the web. These types of spaces are called the Web of Things (WoT). This paper proposes the use of these types of spaces and their integration as practical activities into the curricula of technological subjects. This approach will allow us to achieve two fundamental objectives: (1) To improve the academic results (grades) of students; and (2) to increase engagement and interest of students in the studied technologies, including IoT devices. These platforms are modeled using archetypes based on different typologies and usage scenarios. In particular, these usage scenarios will implement a learning strategy for each problem to be solved. The current work shows the evolution of these archetypes and their application in the teaching of disciplines/subjects defined in computer science, such as distributed computing and cybersecurity.Technologies 2019, 7, 17 2 of 20 an application layer which allows things to be a part of the web, by means of using existing well-known standards. This way, the basics of WoT imply the use of web services programming APIs as REST (Representational State Transfer) [8], standard protocols as HTTP (Hypertext Transfer Protocol) [9], and communication technologies as WebSockets [10]. These elements are part of the application layer, which simplifies the building of applications involving the IoT [6].A first clear approach of this paradigms is the "Distributed Computing" context. This was the context of our first generation of remote laboratories, based on the IoT market. In order to achieve better learning results, the students had to program real low-cost IoT devices, managed by Raspberry Pi and Arduino Yun platforms, which were installed inside a scale mock-up of a smart house. They had to integrate them into the cloud using the WoT model. These innovative technologies are been applied inside the learning model of many courses [11,12], allowing students a smooth and natural approach to the previous mentioned technologies and their diverse applications [13][14][15][16][17].This first experience showed that there are more applications for this approach, aside from the "Distributed Computing" context. Our first generation presented some drawbacks. For instance, the creation of new experiments was ad-hoc. A computational load-balance system was also needed to avoid the fall of the services. It should take into account that the execution of processes takes place on low-cost IoT devices; these devices have reduced computational capabilities. Finally, lecturers cannot easily track the students' performance. They must search among logs to determine what actions were ca...

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Systematic Predictive Analysis of Personalized Life Expectancy Using Smart Devices

Cited by 6 publications

References 51 publications

Accuracy of Mobile Applications versus Wearable Devices in Long-Term Step Measurements

Accuracy of Mobile Applications versus Wearable Devices in Long-Term Step Measurements

Application of Smartphone Technologies in Disease Monitoring: A Systematic Review

Web of Things Platforms for Distance Learning Scenarios in Computer Science Disciplines: A Practical Approach

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