According to a study published in Mobile Sensing has demonstrated its power in ubiquitously and effectively monitoring COVID-19 in different population scales and time durations health data science.
Behind this work are the researchers at the Sensing System for Health Lab, led by Dr. Laura Barnes at the University of Virginia. They have worked to promote health and well-being using mobile sensing and data analysis techniques.
Mobile Sensing, a digital monitoring tool, uses embedded sensors in mobile devices such as smartphones and wearables. As mobile capture has become a promising way to monitor the trajectories of the pandemic by collecting data at the individual, community, and global levels, this paper examined the study designs, expected health outcomes, and existing limitations of such mobile-based human subjects working to monitor the to guide future practice. As such, this paper stands out in a series of articles on the use of mobile devices for COVID-19 response.
“We reviewed the 1) goals and designs of the existing work, 2) the record duration and population coverage, 3) the results and limitations to better taxonomize and understand this topic,” says Zhiyuan Wang, Ph.D. Student with Sensing Systems for Health Lab.
“Existing work has demonstrated the ability of mobile sensing to not only 1) remotely detect infection status, but also 2) longitudinally track disease progression for personalized medicine, 3) passively track exposures, and 4) track exposures Pandemic to be closely monitored for public health,” shared Professor Laura Barnes, the laboratory director.
However, technical and societal limitations remain, including challenges in data availability and system deployment, clinical and application issues, and privacy and ethical concerns. These limitations have hampered further action by computer scientists, clinicians, and epidemiologists in utilizing mobile sensors for human health.
Current or emerging technologies may provide a solution to these limitations. For example, advances in data analysis and machine learning methods can help improve data quality due to their ability to handle sparse, heterogeneous, and multimodal mobile ingestion data streams. In addition, mobile sensing could be performed at even larger scales, particularly in clinical settings, by leveraging next-generation sensors and sensor platforms.
Other stakeholders can also influence how mobile sensing can bring clinical and social benefits. Such efforts can include mitigating potential threats to privacy, equity and health inequalities; promoting technology and health literacy in all communities; and to make trust-based and shared decisions that appropriately balance risks and benefits.
Barnes and her team would like further work, in which computer scientists, clinicians and epidemiologists will design and implement the study alongside social science and public policy experts, to enable more effective, scalable and socially equitable mobile healthcare systems for infectious diseases.
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Zhiyuan Wang et al, Mobile Sensing in the COVID-19 Era: A Review, health data science (2022). DOI: 10.34133/2022/9830476
Provided by Health Data Science
Citation: A Review of Mobile Sensing in the COVID-19 era (2022, September 23), retrieved September 23, 2022 from https://medicalxpress.com/news/2022-09-mobile-covid-era.html
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