An overview of mobile detection in the COVID-19 era

Een overzicht van mobiele detectie in het COVID-19-tijdperk

Mapping literature in a two-dimensional taxonomy. Credit: Health data science (2022). DOI: 10.34133/2022/9830476

According to a study published in Health data science.

Behind this work are the researchers at the Sensing System for Health Lab led by Dr. Laura Barnes of the University of Virginia. They have worked to promote health and wellness using mobile detection and data analysis techniques.

Mobile sensing, a digital monitoring tool, uses built-in sensors in mobile devices such as smartphones and wearables. As mobile sensing has become a promising way to track the trajectories of the pandemic by collecting data on an individual, community and global scale, this paper examined the study design, expected health outcomes, and existing limitations of such mobile human subjects. work to guide future practice. As such, this article stands out among an arsenal of articles on using mobile devices to respond to COVID-19.

“We reviewed the 1) objectives and designs of existing work, 2) the duration and population coverage, 3) the results and limitations, to better taxonomize and understand this topic,” said Zhiyuan Wang, Ph.D. . student with Sensing Systems for Health Lab.

“Existing work has demonstrated the ability of mobile sensing to not only 1) detect infection status remotely, but also 2) track disease progression longitudinally for personalized medicine, 3) passively track exposure, and 4) the impact of the pandemic on public health,” said Professor Laura Barnes, the laboratory director.

However, technical and societal limitations remain, including challenges related to data availability and systems adoption, clinical and application issues, and privacy and ethical concerns. These limitations have hindered further action by computer scientists, clinicians and epidemiologists in harnessing mobile sensing for human health.

Current or emerging technologies can address these limitations. For example, advances in data analytics and machine learning methods can help improve data quality due to their ability to process sparse, heterogeneous and multimodal mobile sensing data streams. Also, mobile sensing could be performed on an even larger scale, especially in clinical settings, by leveraging next-generation sensors and sensing platforms.

Other stakeholders can also have an impact on how mobile sensing can deliver clinical and social benefits. Such efforts may include reducing potential threats to privacy, equity and health inequalities; promoting technology and health literacy in all communities; and making trust-based and shared decisions that appropriately balance risks and benefits.

Barnes and her team want to see more works where computer scientists, clinicians and epidemiologists design and conduct the study in collaboration with experts in social sciences and public policy to enable more effective, scalable and socially equitable mobile health systems for infectious diseases.

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More information:
Zhiyuan Wang et al, Mobile Sensing in the COVID-19 Era: An Overview, Health data science (2022). DOI: 10.34133/2022/9830476

Provided by Health Data Science

Quote: A review of mobile detection in the COVID-19 era (September 2022, September 23) retrieved September 23, 2022 from

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