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Quer G, Kolbeinsson A, Radin JM, Foschini L, Pandit J. Optimizing COVID-19 testing resources use with wearable sensors. PLOS DIGITAL HEALTH 2024; 3:e0000584. [PMID: 39236011 DOI: 10.1371/journal.pdig.0000584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 07/16/2024] [Indexed: 09/07/2024]
Abstract
The timely identification of infectious pre-symptomatic and asymptomatic cases is key towards preventing the spread of a viral illness like COVID-19. Early identification has been done through routine testing programs, which are indeed costly and potentially burdensome for individuals who should be tested with high frequency. A supplemental tool is represented by wearable technology, that can passively monitor and identify individuals at high risk, alerting them to take a test. We designed a Markov chain model and simulated a routine testing and a wearable testing strategy to estimate the number of tests required and the average number of days in which an individual is infectious and undetected. According to our model, with 2 test per month available, we have that the number of infectious and undetected days is 4.1 in the case of routine testing, while it decreases by 46% and 27% with a wearable testing strategy in the presence or absence of self-reported symptoms. The proposed parametric model can be used for different viral illnesses by tuning its parameters. It shows that wearable technology informing a testing strategy can significantly reduce the number of infectious days in which an individuals can spread the virus. With the same number of infectious days, by using wearables we can potentially reduce the number of required tests and the cost of the testing strategy.
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Affiliation(s)
- Giorgio Quer
- Scripps Research Translational Institute, La Jolla, California, United States of America
| | | | - Jennifer M Radin
- Scripps Research Translational Institute, La Jolla, California, United States of America
| | - Luca Foschini
- Sage Bionetworks, Seattle, Washington, United States of America
| | - Jay Pandit
- Scripps Research Translational Institute, La Jolla, California, United States of America
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Nkengue MJ, Zeng X, Koehl L, Tao X, Dassonville F, Dumont N, Ye-Lehmann S, Akwa Y, Ye H. An intelligent garment for long COVID-19 real-time monitoring. Comput Biol Med 2024; 181:109067. [PMID: 39182371 DOI: 10.1016/j.compbiomed.2024.109067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2024] [Revised: 08/13/2024] [Accepted: 08/21/2024] [Indexed: 08/27/2024]
Abstract
As monitoring and diagnostic tools for long COVID-19 cases, wearable systems and supervised learning-based medical image analysis have proven to be useful. Current research on these two technical roadmaps has various drawbacks, despite their respective benefits. Wearable systems allow only the real-time monitoring of physiological parameters (heart rate, temperature, blood oxygen saturation, or SpO2). Therefore, they are unable to conduct in-depth investigations or differentiate COVID-19 from other illnesses that share similar symptoms. Medical image analysis using supervised learning-based models can be used to conduct in-depth analyses and provide precise diagnostic decision support. However, these methods are rarely used for real-time monitoring. In this regard, we present an intelligent garment combining the precision of supervised learning-based models with real-time monitoring capabilities of wearable systems. Given the relevance of electrocardiogram (ECG) signals to long COVID-19 symptom severity, an explainable data fusion strategy based on multiple machine learning models uses heart rate, temperature, SpO2, and ECG signal analysis to accurately assess the patient's health status. Experiments show that the proposed intelligent garment achieves an accuracy of 97.5 %, outperforming most of the existing wearable systems. Furthermore, it was confirmed that the two physiological indicators most significantly affected by the presence of long COVID-19 were SpO2 and the ST intervals of ECG signals.
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Affiliation(s)
- Marc Junior Nkengue
- Univ. Lille, ENSAIT, Laboratoire Génie et Matériaux Textile (GEMTEX), F-59000, Lille, France; Univ. Lille, Ecole Centrale Lille, F-59000, Lille, France.
| | - Xianyi Zeng
- Univ. Lille, ENSAIT, Laboratoire Génie et Matériaux Textile (GEMTEX), F-59000, Lille, France
| | - Ludovic Koehl
- Univ. Lille, ENSAIT, Laboratoire Génie et Matériaux Textile (GEMTEX), F-59000, Lille, France
| | - Xuyuan Tao
- Univ. Lille, ENSAIT, Laboratoire Génie et Matériaux Textile (GEMTEX), F-59000, Lille, France
| | - François Dassonville
- Univ. Lille, ENSAIT, Laboratoire Génie et Matériaux Textile (GEMTEX), F-59000, Lille, France
| | - Nicolas Dumont
- Univ. Lille, ENSAIT, Laboratoire Génie et Matériaux Textile (GEMTEX), F-59000, Lille, France
| | - Shixin Ye-Lehmann
- Univ. Paris-Saclay, Diseases and Hormones of the Nervous System, F-94000, Paris, France
| | - Yvette Akwa
- Univ. Paris-Saclay, Diseases and Hormones of the Nervous System, F-94000, Paris, France
| | - Hanwen Ye
- Univ. Paris-Saclay, Diseases and Hormones of the Nervous System, F-94000, Paris, France
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Quer G, Coughlin E, Villacian J, Delgado F, Harris K, Verrant J, Gadaleta M, Hung TY, Ter Meer J, Radin JM, Ramos E, Adams M, Kim L, Chien JW, Baca-Motes K, Pandit JA, Talantov D, Steinhubl SR. Feasibility of wearable sensor signals and self-reported symptoms to prompt at-home testing for acute respiratory viruses in the USA (DETECT-AHEAD): a decentralised, randomised controlled trial. Lancet Digit Health 2024; 6:e546-e554. [PMID: 39059887 PMCID: PMC11296689 DOI: 10.1016/s2589-7500(24)00096-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 03/20/2024] [Accepted: 05/02/2024] [Indexed: 07/28/2024]
Abstract
BACKGROUND Early identification of an acute respiratory infection is important for reducing transmission and enabling earlier therapeutic intervention. We aimed to prospectively evaluate the feasibility of home-based diagnostic self-testing of viral pathogens in individuals prompted to do so on the basis of self-reported symptoms or individual changes in physiological parameters detected via a wearable sensor. METHODS DETECT-AHEAD was a prospective, decentralised, randomised controlled trial carried out in a subpopulation of an existing cohort (DETECT) of individuals enrolled in a digital-only observational study in the USA. Participants aged 18 years or older were randomly assigned (1:1:1) with a block randomisation scheme stratified by under-represented in biomedical research status. All participants were offered a wearable sensor (Fitbit Sense smartwatch). Participants in groups 1 and 2 received an at-home self-test kit (Alveo be.well) for two acute respiratory viral pathogens: SARS-CoV-2 and respiratory syncytial virus. Participants in group 1 could be alerted through the DETECT study app to take the at-home test on the basis of changes in their physiological data (as detected by our algorithm) or due to self-reported symptoms; those in group 2 were prompted via the app to self-test only due to symptoms. Group 3 served as the control group, without alerts or home testing capability. The primary endpoints, assessed on an intention-to-treat basis, were the number of acute respiratory infections presented (self-reported) and diagnosed (electronic health record), and the number of participants using at-home testing in groups 1 and 2. This trial is registered with ClinicalTrials.gov, NCT04336020. FINDINGS Between Sept 28 and Dec 30, 2021, 450 participants were recruited and randomly assigned to group 1 (n=149), group 2 (n=151), or group 3 (n=150). 179 (40%) participants were male, 264 (59%) were female, and seven (2%) identified as other. 232 (52%) were from populations historically under-represented in biomedical research. 118 (39%) of the 300 participants in groups 1 and 2 were prompted to self-test, with 61 (52%) successfully completing self-testing. Participants were prompted to home-test more frequently due to symptoms (41 [28%] in group 1 and 51 [34%] in group 2) than due to detected physiological changes (26 [17%] in group 1). Significantly more participants in group 1 received alerts to test than did those in group 2 (67 [45%] vs 51 [34%]; p=0·047). Of the 61 individuals who were prompted to test and successfully did so, 19 (31%) tested positive for a viral pathogen-all for SARS-CoV-2. The individuals diagnosed as positive for SARS-CoV-2 in the electronic health record were eight (5%) in group 1, four (3%) in group 2, and two (1%) in group 3, but it was difficult to confirm if they were tied to symptomatic episodes documented in the trial. There were no adverse events. INTERPRETATION In this direct-to-participant trial, we showed early feasibility of a decentralised programme to prompt individuals to use a viral pathogen diagnostic test based on symptoms tracked in the study app or physiological changes detected using a wearable sensor. Barriers to adequate participation and performance were also identified, which would need to be addressed before large-scale implementation. FUNDING Janssen Pharmaceuticals.
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Affiliation(s)
- Giorgio Quer
- Scripps Research Translational Institute, La Jolla, CA, USA.
| | - Erin Coughlin
- Scripps Research Translational Institute, La Jolla, CA, USA
| | - Jorge Villacian
- Janssen Pharmaceutical Research and Development, San Diego, CA, USA
| | - Felipe Delgado
- Scripps Research Translational Institute, La Jolla, CA, USA
| | - Katherine Harris
- Janssen Pharmaceutical Research and Development, San Diego, CA, USA
| | - John Verrant
- Janssen Pharmaceutical Research and Development, San Diego, CA, USA
| | | | - Ting-Yang Hung
- Scripps Research Translational Institute, La Jolla, CA, USA
| | - Janna Ter Meer
- Scripps Research Translational Institute, La Jolla, CA, USA
| | | | - Edward Ramos
- Scripps Research Translational Institute, La Jolla, CA, USA
| | - Monique Adams
- Janssen Pharmaceutical Research and Development, San Diego, CA, USA
| | - Lomi Kim
- Janssen Pharmaceutical Research and Development, San Diego, CA, USA
| | - Jason W Chien
- Janssen Pharmaceutical Research and Development, San Diego, CA, USA
| | | | - Jay A Pandit
- Scripps Research Translational Institute, La Jolla, CA, USA
| | - Dmitri Talantov
- Janssen Pharmaceutical Research and Development, San Diego, CA, USA
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Gou F, Liu J, Xiao C, Wu J. Research on Artificial-Intelligence-Assisted Medicine: A Survey on Medical Artificial Intelligence. Diagnostics (Basel) 2024; 14:1472. [PMID: 39061610 PMCID: PMC11275417 DOI: 10.3390/diagnostics14141472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2024] [Revised: 07/04/2024] [Accepted: 07/05/2024] [Indexed: 07/28/2024] Open
Abstract
With the improvement of economic conditions and the increase in living standards, people's attention in regard to health is also continuously increasing. They are beginning to place their hopes on machines, expecting artificial intelligence (AI) to provide a more humanized medical environment and personalized services, thus greatly expanding the supply and bridging the gap between resource supply and demand. With the development of IoT technology, the arrival of the 5G and 6G communication era, and the enhancement of computing capabilities in particular, the development and application of AI-assisted healthcare have been further promoted. Currently, research on and the application of artificial intelligence in the field of medical assistance are continuously deepening and expanding. AI holds immense economic value and has many potential applications in regard to medical institutions, patients, and healthcare professionals. It has the ability to enhance medical efficiency, reduce healthcare costs, improve the quality of healthcare services, and provide a more intelligent and humanized service experience for healthcare professionals and patients. This study elaborates on AI development history and development timelines in the medical field, types of AI technologies in healthcare informatics, the application of AI in the medical field, and opportunities and challenges of AI in the field of medicine. The combination of healthcare and artificial intelligence has a profound impact on human life, improving human health levels and quality of life and changing human lifestyles.
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Affiliation(s)
- Fangfang Gou
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
| | - Jun Liu
- The Second People's Hospital of Huaihua, Huaihua 418000, China
| | - Chunwen Xiao
- The Second People's Hospital of Huaihua, Huaihua 418000, China
| | - Jia Wu
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang 550025, China
- Research Center for Artificial Intelligence, Monash University, Melbourne, Clayton, VIC 3800, Australia
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Moebus M, Holz C. Personalized interpretable prediction of perceived sleep quality: Models with meaningful cardiovascular and behavioral features. PLoS One 2024; 19:e0305258. [PMID: 38976698 PMCID: PMC11230538 DOI: 10.1371/journal.pone.0305258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Accepted: 05/28/2024] [Indexed: 07/10/2024] Open
Abstract
Understanding a person's perceived quality of sleep is an important problem, but hard due to its poor definition and high intra- as well as inter-individual variation. In the short term, sleep quality has an established impact on cognitive function during the following day as well as on fatigue. In the long term, good quality sleep is essential for mental and physical health and contributes to quality of life. Despite the need to better understand sleep quality as an early indicator for sleep disorders, perceived sleep quality has been rarely modeled for multiple consecutive days using biosignals. In this paper, we present novel insights on the association of cardiac activity and perceived sleep quality using an interpretable modeling approach utilizing the publicly available intensive-longitudinal study M2Sleep. Our method takes as input signals from commodity wearable devices, including motion and blood volume pulses. Despite processing only simple and clearly interpretable features, we achieve an accuracy of up to 70% with an AUC of 0.76 and reduce the error by up to 36% compared to related work. We further argue that collected biosignals and sleep quality labels should be normalized per-participant to enable a medically insightful analysis. Coupled with explainable models, this allows for the interpretations of effects on perceived sleep quality. Analysis revealed that besides higher skin temperature and sufficient sleep duration, especially higher average heart rate while awake and lower minimal activity of the parasympathetic and sympathetic nervous system while asleep increased the chances of higher sleep quality.
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Affiliation(s)
- Max Moebus
- Department of Computer Science, ETH Zurich, Zürich, Switzerland
| | - Christian Holz
- Department of Computer Science, ETH Zurich, Zürich, Switzerland
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Talib MA, Afadar Y, Nasir Q, Nassif AB, Hijazi H, Hasasneh A. A tree-based explainable AI model for early detection of Covid-19 using physiological data. BMC Med Inform Decis Mak 2024; 24:179. [PMID: 38915001 PMCID: PMC11194929 DOI: 10.1186/s12911-024-02576-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 06/13/2024] [Indexed: 06/26/2024] Open
Abstract
With the outbreak of COVID-19 in 2020, countries worldwide faced significant concerns and challenges. Various studies have emerged utilizing Artificial Intelligence (AI) and Data Science techniques for disease detection. Although COVID-19 cases have declined, there are still cases and deaths around the world. Therefore, early detection of COVID-19 before the onset of symptoms has become crucial in reducing its extensive impact. Fortunately, wearable devices such as smartwatches have proven to be valuable sources of physiological data, including Heart Rate (HR) and sleep quality, enabling the detection of inflammatory diseases. In this study, we utilize an already-existing dataset that includes individual step counts and heart rate data to predict the probability of COVID-19 infection before the onset of symptoms. We train three main model architectures: the Gradient Boosting classifier (GB), CatBoost trees, and TabNet classifier to analyze the physiological data and compare their respective performances. We also add an interpretability layer to our best-performing model, which clarifies prediction results and allows a detailed assessment of effectiveness. Moreover, we created a private dataset by gathering physiological data from Fitbit devices to guarantee reliability and avoid bias.The identical set of models was then applied to this private dataset using the same pre-trained models, and the results were documented. Using the CatBoost tree-based method, our best-performing model outperformed previous studies with an accuracy rate of 85% on the publicly available dataset. Furthermore, this identical pre-trained CatBoost model produced an accuracy of 81% when applied to the private dataset. You will find the source code in the link: https://github.com/OpenUAE-LAB/Covid-19-detection-using-Wearable-data.git .
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Affiliation(s)
- Manar Abu Talib
- Department of Computer Science, College of Computing and Informatics, University of Sharjah, P.O. Box 27272, Sharjah, UAE.
| | - Yaman Afadar
- Department of Computer Engineering, College of Computing and Informatics, University of Sharjah, Sharjah, UAE
| | - Qassim Nasir
- Department of Computer Engineering, College of Computing and Informatics, University of Sharjah, Sharjah, UAE
| | - Ali Bou Nassif
- Department of Computer Engineering, College of Computing and Informatics, University of Sharjah, Sharjah, UAE
| | - Haytham Hijazi
- Centre for Informatics and Systems of the University of Coimbra (CISUC), University of Coimbra, Coimbra, P-3030-290, Portugal
- Intelligent Systems Department, Ahliya University, Bethlehem, P-150-199, Palestine
| | - Ahmad Hasasneh
- Department of Natural, Engineering and Technology Sciences, Faculty of Graduate Studies, Arab American University, P.O. Box 240, Ramallah, Palestine
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Lang AL, Hohmuth N, Višković V, Konigorski S, Scholz S, Balzer F, Remschmidt C, Leistner R. COVID-19 Vaccine Effectiveness and Digital Pandemic Surveillance in Germany (eCOV Study): Web Application-Based Prospective Observational Cohort Study. J Med Internet Res 2024; 26:e47070. [PMID: 38833299 PMCID: PMC11185909 DOI: 10.2196/47070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 08/18/2023] [Accepted: 03/22/2024] [Indexed: 06/06/2024] Open
Abstract
BACKGROUND The COVID-19 pandemic posed significant challenges to global health systems. Efficient public health responses required a rapid and secure collection of health data to improve the understanding of SARS-CoV-2 and examine the vaccine effectiveness (VE) and drug safety of the novel COVID-19 vaccines. OBJECTIVE This study (COVID-19 study on vaccinated and unvaccinated subjects over 16 years; eCOV study) aims to (1) evaluate the real-world effectiveness of COVID-19 vaccines through a digital participatory surveillance tool and (2) assess the potential of self-reported data for monitoring key parameters of the COVID-19 pandemic in Germany. METHODS Using a digital study web application, we collected self-reported data between May 1, 2021, and August 1, 2022, to assess VE, test positivity rates, COVID-19 incidence rates, and adverse events after COVID-19 vaccination. Our primary outcome measure was the VE of SARS-CoV-2 vaccines against laboratory-confirmed SARS-CoV-2 infection. The secondary outcome measures included VE against hospitalization and across different SARS-CoV-2 variants, adverse events after vaccination, and symptoms during infection. Logistic regression models adjusted for confounders were used to estimate VE 4 to 48 weeks after the primary vaccination series and after third-dose vaccination. Unvaccinated participants were compared with age- and gender-matched participants who had received 2 doses of BNT162b2 (Pfizer-BioNTech) and those who had received 3 doses of BNT162b2 and were not infected before the last vaccination. To assess the potential of self-reported digital data, the data were compared with official data from public health authorities. RESULTS We enrolled 10,077 participants (aged ≥16 y) who contributed 44,786 tests and 5530 symptoms. In this young, primarily female, and digital-literate cohort, VE against infections of any severity waned from 91.2% (95% CI 70.4%-97.4%) at week 4 to 37.2% (95% CI 23.5%-48.5%) at week 48 after the second dose of BNT162b2. A third dose of BNT162b2 increased VE to 67.6% (95% CI 50.3%-78.8%) after 4 weeks. The low number of reported hospitalizations limited our ability to calculate VE against hospitalization. Adverse events after vaccination were consistent with previously published research. Seven-day incidences and test positivity rates reflected the course of the pandemic in Germany when compared with official numbers from the national infectious disease surveillance system. CONCLUSIONS Our data indicate that COVID-19 vaccinations are safe and effective, and third-dose vaccinations partially restore protection against SARS-CoV-2 infection. The study showcased the successful use of a digital study web application for COVID-19 surveillance and continuous monitoring of VE in Germany, highlighting its potential to accelerate public health decision-making. Addressing biases in digital data collection is vital to ensure the accuracy and reliability of digital solutions as public health tools.
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Affiliation(s)
| | - Nils Hohmuth
- d4l Data4Life gGmbH, Potsdam, Germany
- Institute of Medical Informatics, Charité University Medicine Berlin, Berlin, Germany
| | | | - Stefan Konigorski
- Digital Health Center, Hasso Plattner Institute for Digital Engineering, Potsdam, Germany
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Statistics, Harvard University, Cambridge, MA, United States
| | - Stefan Scholz
- Health Services Research and Health Economics, Martin Luther University Halle-Wittenberg, Halle Saale, Germany
| | - Felix Balzer
- Institute of Medical Informatics, Charité University Medicine Berlin, Berlin, Germany
| | | | - Rasmus Leistner
- Department of Gastroenterology, Infectiology and Rheumatology, Charité University Medicine Berlin, Berlin, Germany
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Dunn J, Coravos A, Fanarjian M, Ginsburg GS, Steinhubl SR. Remote digital health technologies for improving the care of people with respiratory disorders. Lancet Digit Health 2024; 6:e291-e298. [PMID: 38402128 PMCID: PMC10960683 DOI: 10.1016/s2589-7500(23)00248-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 10/01/2023] [Accepted: 11/30/2023] [Indexed: 02/26/2024]
Abstract
Respiratory diseases are a leading cause of morbidity and mortality globally. However, existing systems of care, built around scheduled appointments, are not well designed to support the needs of people with chronic and acute respiratory conditions that can change rapidly and unexpectedly. Home-based and personal digital health technologies (DHTs) allow implementation of new models of care catering to the unique needs of individuals. The high number of respiratory triggers and unique responses to them require a personalised solution for each patient. The real-world, repetitive monitoring capabilities of DHTs enable identification of the normal operating characteristics for each individual and, therefore, recognition of the earliest deviations from that state. However, despite this potential, the number of clinical efficacy studies of DHTs is quite small. Evaluation of clinical effectiveness of DHTs in improving health quality in real-world settings is urgently needed.
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Affiliation(s)
- Jessilyn Dunn
- Biomedical Engineering Department, Duke University, Durham, NC, USA
| | | | | | - Geoffrey S Ginsburg
- Department of Medicine, Duke University, Durham, NC, USA; All of Us Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Steven R Steinhubl
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA.
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Kasl P, Keeler Bruce L, Hartogensis W, Dasgupta S, Pandya LS, Dilchert S, Hecht FM, Gupta A, Altintas I, Mason AE, Smarr BL. Utilizing Wearable Device Data for Syndromic Surveillance: A Fever Detection Approach. SENSORS (BASEL, SWITZERLAND) 2024; 24:1818. [PMID: 38544080 PMCID: PMC10975930 DOI: 10.3390/s24061818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 02/29/2024] [Accepted: 03/06/2024] [Indexed: 04/01/2024]
Abstract
Commercially available wearable devices (wearables) show promise for continuous physiological monitoring. Previous works have demonstrated that wearables can be used to detect the onset of acute infectious diseases, particularly those characterized by fever. We aimed to evaluate whether these devices could be used for the more general task of syndromic surveillance. We obtained wearable device data (Oura Ring) from 63,153 participants. We constructed a dataset using participants' wearable device data and participants' responses to daily online questionnaires. We included days from the participants if they (1) completed the questionnaire, (2) reported not experiencing fever and reported a self-collected body temperature below 38 °C (negative class), or reported experiencing fever and reported a self-collected body temperature at or above 38 °C (positive class), and (3) wore the wearable device the nights before and after that day. We used wearable device data (i.e., skin temperature, heart rate, and sleep) from the nights before and after participants' fever day to train a tree-based classifier to detect self-reported fevers. We evaluated the performance of our model using a five-fold cross-validation scheme. Sixteen thousand, seven hundred, and ninety-four participants provided at least one valid ground truth day; there were a total of 724 fever days (positive class examples) from 463 participants and 342,430 non-fever days (negative class examples) from 16,687 participants. Our model exhibited an area under the receiver operating characteristic curve (AUROC) of 0.85 and an average precision (AP) of 0.25. At a sensitivity of 0.50, our calibrated model had a false positive rate of 0.8%. Our results suggest that it might be possible to leverage data from these devices at a public health level for live fever surveillance. Implementing these models could increase our ability to detect disease prevalence and spread in real-time during infectious disease outbreaks.
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Affiliation(s)
- Patrick Kasl
- Shu Chien-Gene Lay Department of Bioengineering, University of California San Diego, San Diego, CA 92093-0021, USA;
| | - Lauryn Keeler Bruce
- UC San Diego Health Department of Biomedical Informatics, University of California San Diego, San Diego, CA 92093-0021, USA;
| | - Wendy Hartogensis
- UCSF Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA 92093-0021, USA; (W.H.); (L.S.P.); (F.M.H.); (A.E.M.)
| | - Subhasis Dasgupta
- San Diego Supercomputer Center, University of California San Diego, San Diego, CA 92093-0021, USA; (S.D.); (A.G.); (I.A.)
| | - Leena S. Pandya
- UCSF Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA 92093-0021, USA; (W.H.); (L.S.P.); (F.M.H.); (A.E.M.)
| | - Stephan Dilchert
- Department of Management, Zicklin School of Business, Baruch College, The City University of New York, New York, NY 10010, USA;
| | - Frederick M. Hecht
- UCSF Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA 92093-0021, USA; (W.H.); (L.S.P.); (F.M.H.); (A.E.M.)
| | - Amarnath Gupta
- San Diego Supercomputer Center, University of California San Diego, San Diego, CA 92093-0021, USA; (S.D.); (A.G.); (I.A.)
- Halıcıoğlu Data Science Institute, University of California San Diego, San Diego, CA 92093-0021, USA
| | - Ilkay Altintas
- San Diego Supercomputer Center, University of California San Diego, San Diego, CA 92093-0021, USA; (S.D.); (A.G.); (I.A.)
- Halıcıoğlu Data Science Institute, University of California San Diego, San Diego, CA 92093-0021, USA
| | - Ashley E. Mason
- UCSF Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA 92093-0021, USA; (W.H.); (L.S.P.); (F.M.H.); (A.E.M.)
| | - Benjamin L. Smarr
- Shu Chien-Gene Lay Department of Bioengineering, University of California San Diego, San Diego, CA 92093-0021, USA;
- Halıcıoğlu Data Science Institute, University of California San Diego, San Diego, CA 92093-0021, USA
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Godin R, Hejazi S, Reuel NF. Advancements in Airborne Viral Nucleic Acid Detection with Wearable Devices. ADVANCED SENSOR RESEARCH 2024; 3:2300061. [PMID: 38764891 PMCID: PMC11101210 DOI: 10.1002/adsr.202300061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Indexed: 05/21/2024]
Abstract
Wearable health sensors for an expanding range of physiological parameters have experienced rapid development in recent years and are poised to disrupt the way healthcare is tracked and administered. The monitoring of environmental contaminants with wearable technologies is an additional layer of personal and public healthcare and is also receiving increased focus. Wearable sensors that detect exposure to airborne viruses could alert wearers of viral exposure and prompt proactive testing and minimization of viral spread, benefitting their own health and decreasing community risk. With the high levels of asymptomatic spread of COVID-19 observed during the pandemic, such devices could dramatically enhance our pandemic response capabilities in the future. To facilitate advancements in this area, this review summarizes recent research on airborne viral detection using wearable sensing devices as well as technologies suitable for wearables. Since the low concentration of viral particles in the air poses significant challenges to detection, methods for airborne viral particle collection and viral sensing are discussed in detail. A special focus is placed on nucleic acid-based viral sensing mechanisms due to their enhanced ability to discriminate between viral subtypes. Important considerations for integrating airborne viral collection and sensing on a single wearable device are also discussed.
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Affiliation(s)
- Ryan Godin
- Department of Chemical and Biological Engineering, Iowa State University
| | - Sepehr Hejazi
- Department of Chemical and Biological Engineering, Iowa State University
| | - Nigel F. Reuel
- Department of Chemical and Biological Engineering, Iowa State University
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11
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Pandit JA, Pawelek JB, Leff B, Topol EJ. The hospital at home in the USA: current status and future prospects. NPJ Digit Med 2024; 7:48. [PMID: 38413704 PMCID: PMC10899639 DOI: 10.1038/s41746-024-01040-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 02/14/2024] [Indexed: 02/29/2024] Open
Abstract
The annual cost of hospital care services in the US has risen to over $1 trillion despite relatively worse health outcomes compared to similar nations. These trends accentuate a growing need for innovative care delivery models that reduce costs and improve outcomes. HaH-a program that provides patients acute-level hospital care at home-has made significant progress over the past two decades. Technological advancements in remote patient monitoring, wearable sensors, health information technology infrastructure, and multimodal health data processing have contributed to its rise across hospitals. More recently, the COVID-19 pandemic brought HaH into the mainstream, especially in the US, with reimbursement waivers that made the model financially acceptable for hospitals and payors. However, HaH continues to face serious challenges to gain widespread adoption. In this review, we evaluate the peer-reviewed evidence and discuss the promises, challenges, and what it would take to tap into the future potential of HaH.
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Affiliation(s)
- Jay A Pandit
- Scripps Translational Research Institute, Scripps Research, La Jolla, CA, USA.
| | - Jeff B Pawelek
- Scripps Translational Research Institute, Scripps Research, La Jolla, CA, USA
| | - Bruce Leff
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Eric J Topol
- Scripps Translational Research Institute, Scripps Research, La Jolla, CA, USA
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12
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Jaiswal SJ, Gadaleta M, Quer G, Radin JM, Waalen J, Ramos E, Pandit J, Owens RL. Objectively measured peri-vaccination sleep does not predict COVID-19 breakthrough infection. Sci Rep 2024; 14:4655. [PMID: 38409137 PMCID: PMC10897487 DOI: 10.1038/s41598-024-53743-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 02/04/2024] [Indexed: 02/28/2024] Open
Abstract
Prior studies have shown that sleep duration peri-vaccination influences an individual's antibody response. However, whether peri-vaccination sleep affects real-world vaccine effectiveness is unknown. Here, we tested whether objectively measured sleep around COVID-19 vaccination affected breakthrough infection rates. DETECT is a study of digitally recruited participants who report COVID-19-related information, including vaccination and illness data. Objective sleep data are also recorded through activity trackers. We compared the impact of sleep duration, sleep efficiency, and frequency of awakenings on reported breakthrough infection after the 2nd vaccination and 1st COVID-19 booster. Logistic regression models were created to examine if sleep metrics predicted COVID-19 breakthrough infection independent of age and gender. Self-reported breakthrough COVID-19 infection following 2nd COVID-19 vaccination and 1st booster. 256 out of 5265 individuals reported a breakthrough infection after the 2nd vaccine, and 581 out of 2583 individuals reported a breakthrough after the 1st booster. There was no difference in sleep duration between those with and without breakthrough infection. Increased awakening frequency was associated with breakthrough infection after the 1st booster with 3.01 ± 0.65 awakenings/hour in the breakthrough group compared to 2.82 ± 0.65 awakenings/hour in those without breakthrough (P < 0.001). Cox proportional hazards modeling showed that age < 60 years (hazard ratio 2.15, P < 0.001) and frequency of awakenings (hazard ratio 1.17, P = 0.019) were associated with breakthrough infection after the 1st booster. Sleep duration was not associated with breakthrough infection after COVID vaccination. While increased awakening frequency during sleep was associated with breakthrough infection beyond traditional risk factors, the clinical implications of this finding are unclear.
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Affiliation(s)
| | | | - Giorgio Quer
- The Scripps Research Institute, La Jolla, CA, USA
| | | | - Jill Waalen
- The Scripps Research Institute, La Jolla, CA, USA
| | - Edward Ramos
- The Scripps Research Institute, La Jolla, CA, USA
| | - Jay Pandit
- The Scripps Research Institute, La Jolla, CA, USA
| | - Robert L Owens
- University of California San Diego School of Medicine, La Jolla, CA, USA
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13
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Gilbert S, Baca-Motes K, Quer G, Wiedermann M, Brockmann D. Citizen data sovereignty is key to wearables and wellness data reuse for the common good. NPJ Digit Med 2024; 7:27. [PMID: 38347159 PMCID: PMC10861551 DOI: 10.1038/s41746-024-01004-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 01/03/2024] [Indexed: 02/15/2024] Open
Affiliation(s)
- Stephen Gilbert
- Else Kröner Fresenius Center for Digital Health, TUD Dresden University of Technology, Dresden, Germany.
| | - Katie Baca-Motes
- The Scripps Research Institute, La Jolla, CA, USA
- CareEvolution LLC, Ann Arbor, MI, USA
| | - Giorgio Quer
- The Scripps Research Institute, La Jolla, CA, USA
| | | | - Dirk Brockmann
- Center Synergy of Systems, TUD Dresden University of Technology, Dresden, Germany
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14
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Valdez ZI, Díaz LA, Vizcardo Cornejo M, Ravelo-García AG. A Permutation Entropy analysis to determine significant daily intervals to improve risk stratification tasks from COVID patients. Biomed Phys Eng Express 2024; 10:025010. [PMID: 38198732 DOI: 10.1088/2057-1976/ad1d0a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 01/10/2024] [Indexed: 01/12/2024]
Abstract
SARS-CoV-2 infection has a wide range of clinical manifestations making its diagnosis difficult, which is an important problem to solve. We evaluated heart rate data extracted from the Stanford University database. The data set considers heart rate and step records of 118 patients, where 90 correspond to healthy individuals and 28 patients with COVID. Each daily record was divided into 5-minute segments, providing 288 data per patient. The date of symptom onset was considered as a reference point to extract subsets of data whose variability was considerable, such as 30 days before the date and 30 days after it. Each of the 60 segments of 288 data per patient was treated using Permutation Entropy, Approximate Entropy, Spectral Entropy and Singular Value Decomposition Entropy. The average of the data from each group was used to construct the circadian profiles which were analyzed using the Mann-Whitney-Wilcoxon test, determining the most relevant 5-minute segments, whose p-value was less than 0.05. In this way, the Spectral Entropy was discarded as it did not show any significantly different segment. The efficiency of the method was reflected in the performance of a logistic model for binary classification proposed in this work, which reflected an accuracy of 94.12% in the PE case, 88% in the ApEn case and 94% in the SVDE case. The proposed analysis turns out to be highly efficient when detecting significant segments that allow improving the classification tasks carried out by Machine Learning models, which provides a basis for the study of statistics such as entropy to delimit databases and improve the performance of classifier models.
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Affiliation(s)
- Zayd Isaac Valdez
- Escuela de Física, Universidad Nacional de San Agustin de Arequipa, Av. Independencia s/n, Arequipa, 04002, Arequipa, Peru
| | - Luz Alexandra Díaz
- Escuela de Física, Universidad Nacional de San Agustin de Arequipa, Av. Independencia s/n, Arequipa, 04002, Arequipa, Peru
| | - Miguel Vizcardo Cornejo
- Escuela de Física, Universidad Nacional de San Agustin de Arequipa, Av. Independencia s/n, Arequipa, 04002, Arequipa, Peru
| | - Antonio G Ravelo-García
- Departmento de Señales y Comunicaciones, Universidad Las Palmas de Gran Canaria, Campus Tafira, Las Palmas de Gran Canaria, 35017, Las Palmas, Spain
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15
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Gupta V, Kariotis S, Rajab MD, Errington N, Alhathli E, Jammeh E, Brook M, Meardon N, Collini P, Cole J, Wild JM, Hershman S, Javed A, Thompson AAR, de Silva T, Ashley EA, Wang D, Lawrie A. Unsupervised machine learning to investigate trajectory patterns of COVID-19 symptoms and physical activity measured via the MyHeart Counts App and smart devices. NPJ Digit Med 2023; 6:239. [PMID: 38135699 PMCID: PMC10746711 DOI: 10.1038/s41746-023-00974-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 11/30/2023] [Indexed: 12/24/2023] Open
Abstract
Previous studies have associated COVID-19 symptoms severity with levels of physical activity. We therefore investigated longitudinal trajectories of COVID-19 symptoms in a cohort of healthcare workers (HCWs) with non-hospitalised COVID-19 and their real-world physical activity. 121 HCWs with a history of COVID-19 infection who had symptoms monitored through at least two research clinic visits, and via smartphone were examined. HCWs with a compatible smartphone were provided with an Apple Watch Series 4 and were asked to install the MyHeart Counts Study App to collect COVID-19 symptom data and multiple physical activity parameters. Unsupervised classification analysis of symptoms identified two trajectory patterns of long and short symptom duration. The prevalence for longitudinal persistence of any COVID-19 symptom was 36% with fatigue and loss of smell being the two most prevalent individual symptom trajectories (24.8% and 21.5%, respectively). 8 physical activity features obtained via the MyHeart Counts App identified two groups of trajectories for high and low activity. Of these 8 parameters only 'distance moved walking or running' was associated with COVID-19 symptom trajectories. We report a high prevalence of long-term symptoms of COVID-19 in a non-hospitalised cohort of HCWs, a method to identify physical activity trends, and investigate their association. These data highlight the importance of tracking symptoms from onset to recovery even in non-hospitalised COVID-19 individuals. The increasing ease in collecting real-world physical activity data non-invasively from wearable devices provides opportunity to investigate the association of physical activity to symptoms of COVID-19 and other cardio-respiratory diseases.
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Affiliation(s)
- Varsha Gupta
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR), Singapore, Republic of Singapore
- Bioinformatics Institute, Agency for Science Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Sokratis Kariotis
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR), Singapore, Republic of Singapore
- Department of Neuroscience, University of Sheffield, Sheffield, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Mohammed D Rajab
- Department of Computer Science, University of Sheffield, Sheffield, UK
| | - Niamh Errington
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Elham Alhathli
- Department of Neuroscience, University of Sheffield, Sheffield, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- Department of Nursing, Faculty of Applied Medical Sciences, Taif University, Taif, Saudi Arabia
| | - Emmanuel Jammeh
- Department of Neuroscience, University of Sheffield, Sheffield, UK
| | - Martin Brook
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- Insigneo Institute for in-silico Medicine, University of Sheffield, Sheffield, UK
| | - Naomi Meardon
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Paul Collini
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Joby Cole
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Jim M Wild
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- Insigneo Institute for in-silico Medicine, University of Sheffield, Sheffield, UK
| | - Steven Hershman
- Department of Cardiovascular Medicine, Stanford University, Stanford, CA, USA
| | - Ali Javed
- Department of Cardiovascular Medicine, Stanford University, Stanford, CA, USA
| | - A A Roger Thompson
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Thushan de Silva
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Euan A Ashley
- Department of Cardiovascular Medicine, Stanford University, Stanford, CA, USA
| | - Dennis Wang
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR), Singapore, Republic of Singapore
- Bioinformatics Institute, Agency for Science Technology and Research (A*STAR), Singapore, Republic of Singapore
- Department of Computer Science, University of Sheffield, Sheffield, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Allan Lawrie
- National Heart and Lung Institute, Imperial College London, London, UK.
- Insigneo Institute for in-silico Medicine, University of Sheffield, Sheffield, UK.
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16
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Kang JY, Bae YS, Chie EK, Lee SB. Predicting Deterioration from Wearable Sensor Data in People with Mild COVID-19. SENSORS (BASEL, SWITZERLAND) 2023; 23:9597. [PMID: 38067970 PMCID: PMC10708735 DOI: 10.3390/s23239597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 11/29/2023] [Accepted: 11/30/2023] [Indexed: 12/18/2023]
Abstract
Coronavirus has caused many casualties and is still spreading. Some people experience rapid deterioration that is mild at first. The aim of this study is to develop a deterioration prediction model for mild COVID-19 patients during the isolation period. We collected vital signs from wearable devices and clinical questionnaires. The derivation cohort consisted of people diagnosed with COVID-19 between September and December 2021, and the external validation cohort collected between March and June 2022. To develop the model, a total of 50 participants wore the device for an average of 77 h. To evaluate the model, a total of 181 infected participants wore the device for an average of 65 h. We designed machine learning-based models that predict deterioration in patients with mild COVID-19. The prediction model, 10 min in advance, showed an area under the receiver characteristic curve (AUC) of 0.99, and the prediction model, 8 h in advance, showed an AUC of 0.84. We found that certain variables that are important to model vary depending on the point in time to predict. Efficient deterioration monitoring in many patients is possible by utilizing data collected from wearable sensors and symptom self-reports.
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Affiliation(s)
- Jin-Yeong Kang
- Department of Medical Informatics, Keimyung University, Daegu 42601, Republic of Korea;
- Department of Statistics and Data Science, Yonsei University, Seoul 03722, Republic of Korea
| | - Ye Seul Bae
- Department of Family Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 03181, Republic of Korea;
- Department of Future Healthcare Planning, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul 03181, Republic of Korea
| | - Eui Kyu Chie
- Department of Radiation Oncology, Seoul National University College of Medicine, Seoul 03080, Republic of Korea;
| | - Seung-Bo Lee
- Department of Medical Informatics, Keimyung University, Daegu 42601, Republic of Korea;
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17
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Waalen J. Mobile Health and Preventive Medicine. Med Clin North Am 2023; 107:1097-1108. [PMID: 37806725 DOI: 10.1016/j.mcna.2023.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Wearable devices providing health-related data (mobile health [mHealth]) have grown in numbers and types of data available over the past 2 decades. Applications in prevention with some of the longest track records are activity trackers to promote fitness (primary prevention), mobile electrocardiogram devices to detect arrhythmias (secondary prevention), and continuous glucose monitoring to improve glycemic control in type 2 diabetes (tertiary prevention). Continued integration of multiple diverse data streams and improved interfaces with individuals (such as artificial intelligence-driven health coaches), and health care teams (as in the hospital-at-home concept), promise to optimize use of mHealth to improve clinical and public health outcomes.
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Affiliation(s)
- Jill Waalen
- University of California, San Diego/San Diego State University General Preventive Medicine Residency Program & Scripps Research Translational Institute, 3344 North Torrey Pines Court, La Jolla, CA 92037, USA.
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18
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Ghomrawi HMK, O'Brien MK, Carter M, Macaluso R, Khazanchi R, Fanton M, DeBoer C, Linton SC, Zeineddin S, Pitt JB, Bouchard M, Figueroa A, Kwon S, Holl JL, Jayaraman A, Abdullah F. Applying machine learning to consumer wearable data for the early detection of complications after pediatric appendectomy. NPJ Digit Med 2023; 6:148. [PMID: 37587211 PMCID: PMC10432429 DOI: 10.1038/s41746-023-00890-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 08/01/2023] [Indexed: 08/18/2023] Open
Abstract
When children are discharged from the hospital after surgery, their caregivers often rely on subjective assessments (e.g., appetite, fatigue) to monitor postoperative recovery as objective assessment tools are scarce at home. Such imprecise and one-dimensional evaluations can result in unwarranted emergency department visits or delayed care. To address this gap in postoperative monitoring, we evaluated the ability of a consumer-grade wearable device, Fitbit, which records multimodal data about daily physical activity, heart rate, and sleep, in detecting abnormal recovery early in children recovering after appendectomy. One hundred and sixty-two children, ages 3-17 years old, who underwent an appendectomy (86 complicated and 76 simple cases of appendicitis) wore a Fitbit device on their wrist for 21 days postoperatively. Abnormal recovery events (i.e., abnormal symptoms or confirmed postoperative complications) that arose during this period were gathered from medical records and patient reports. Fitbit-derived measures, as well as demographic and clinical characteristics, were used to train machine learning models to retrospectively detect abnormal recovery in the two days leading up to the event for patients with complicated and simple appendicitis. A balanced random forest classifier accurately detected 83% of these abnormal recovery days in complicated appendicitis and 70% of abnormal recovery days in simple appendicitis prior to the true report of a symptom/complication. These results support the development of machine learning algorithms to predict onset of abnormal symptoms and complications in children undergoing surgery, and the use of consumer wearables as monitoring tools for early detection of postoperative events.
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Affiliation(s)
- Hassan M K Ghomrawi
- Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Center for Health Services and Outcomes Research, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Center for Global Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Medicine (Rheumatology), Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - Michela Carter
- Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Division of Pediatric Surgery, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | | | - Rushmin Khazanchi
- Shirley Ryan AbilityLab, Chicago, IL, USA
- Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - Christopher DeBoer
- Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Division of Pediatric Surgery, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Samuel C Linton
- Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Division of Pediatric Surgery, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Suhail Zeineddin
- Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Division of Pediatric Surgery, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - J Benjamin Pitt
- Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Division of Pediatric Surgery, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Megan Bouchard
- Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Division of Pediatric Surgery, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Angie Figueroa
- Division of Pediatric Surgery, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Soyang Kwon
- Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Pediatrics, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
| | - Jane L Holl
- Department of Neurology and Center for Healthcare Delivery Science and Innovation, Biological Sciences Division, University of Chicago, Chicago, IL, USA
| | - Arun Jayaraman
- Shirley Ryan AbilityLab, Chicago, IL, USA
- Department of Physical Medicine and Rehabilitation, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Physical Therapy and Human Movement Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Fizan Abdullah
- Department of Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
- Center for Global Surgery, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
- Division of Pediatric Surgery, Ann and Robert H. Lurie Children's Hospital of Chicago, 225 East Chicago Avenue, Box 63, Chicago, IL, 60611, USA.
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19
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Mishra S, Aamna B, Parida S, Dan AK. Carbon-based biosensors: Next-generation diagnostic tool for target-specific detection of SARS-CoV-2 (COVID-19). TALANTA OPEN 2023; 7:100218. [PMID: 37131405 PMCID: PMC10125215 DOI: 10.1016/j.talo.2023.100218] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 04/01/2023] [Accepted: 04/24/2023] [Indexed: 05/04/2023] Open
Abstract
Severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) was declared a global pandemic in 2020. Having rapidly spread around the globe, with the emergence of new variants, there is a crucial need to develop diagnostic kits for its rapid detection. Since it validated accuracy and reliability, the reverse transcription polymerase chain reaction (RT-PCR) test has been declared the gold standard for disease detection. However, despite its reliability, the requirement of specialized facilities, reagents, and duration of a PCR run limits its usage for rapid detection. There is thus a continuous increase in the design and development of rapid, point-of-care (PoC), and cost-effective diagnostic kits. In this review, we discuss the potential of carbon-based biosensors for target-specific detection of coronavirus disease 19 (COVID-19) and present an overview of investigation within the timeframe of the last four years (2019-2022), which have developed novel platforms using carbon nanomaterial-based approaches for viral detection. The approaches discussed offer rapid, accurate, and cost-effective strategies for COVID-19 detection for healthcare personnel and research workers.
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Affiliation(s)
- Shivam Mishra
- School of Biotechnology, Kalinga Institute of Industrial Technology (Deemed to be University), Bhubaneswar, Odisha, 751024, India
| | - Bari Aamna
- School of Biotechnology, Kalinga Institute of Industrial Technology (Deemed to be University), Bhubaneswar, Odisha, 751024, India
| | - Sagarika Parida
- Department of Botany, School of Applied Sciences, Centurion University of Technology and Management, Bhubaneswar, Odisha, 752050, India
| | - Aritra Kumar Dan
- School of Biotechnology, Kalinga Institute of Industrial Technology (Deemed to be University), Bhubaneswar, Odisha, 751024, India
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20
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Wiedermann M, Rose AH, Maier BF, Kolb JJ, Hinrichs D, Brockmann D. Evidence for positive long- and short-term effects of vaccinations against COVID-19 in wearable sensor metrics. PNAS NEXUS 2023; 2:pgad223. [PMID: 37497048 PMCID: PMC10368316 DOI: 10.1093/pnasnexus/pgad223] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 05/26/2023] [Accepted: 06/26/2023] [Indexed: 07/28/2023]
Abstract
Vaccines are among the most powerful tools to combat the COVID-19 pandemic. They are highly effective against infection and substantially reduce the risk of severe disease, hospitalization, ICU admission, and death. However, their potential for attenuating long-term changes in personal health and health-related wellbeing after a SARS-CoV-2 infection remains a subject of debate. Such effects can be effectively monitored at the individual level by analyzing physiological data collected by consumer-grade wearable sensors. Here, we investigate changes in resting heart rate, daily physical activity, and sleep duration around a SARS-CoV-2 infection stratified by vaccination status. Data were collected over a period of 2 years in the context of the German Corona Data Donation Project with around 190,000 monthly active participants. Compared to their unvaccinated counterparts, we find that vaccinated individuals, on average, experience smaller changes in their vital data that also return to normal levels more quickly. Likewise, extreme changes in vitals during the acute phase of the disease occur less frequently in vaccinated individuals. Our results solidify evidence that vaccines can mitigate long-term detrimental effects of SARS-CoV-2 infections both in terms of duration and magnitude. Furthermore, they demonstrate the value of large-scale, high-resolution wearable sensor data in public health research.
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Affiliation(s)
| | - Annika H Rose
- Computational Epidemiology Group, Robert Koch Institute, 13353 Berlin, Germany
- Institute for Theoretical Biology and Integrated Research Institute for the Life-Sciences, Humboldt University of Berlin, 10115 Berlin, Germany
| | - Benjamin F Maier
- Computational Epidemiology Group, Robert Koch Institute, 13353 Berlin, Germany
- DTU Compute, Technical University of Denmark, Kongens Lyngby 2800, Denmark
- Copenhagen Center for Social Data Science, University of Copenhagen, Copenhagen 1353, Denmark
| | - Jakob J Kolb
- Computational Epidemiology Group, Robert Koch Institute, 13353 Berlin, Germany
- Institute for Theoretical Biology and Integrated Research Institute for the Life-Sciences, Humboldt University of Berlin, 10115 Berlin, Germany
| | - David Hinrichs
- Computational Epidemiology Group, Robert Koch Institute, 13353 Berlin, Germany
- Institute for Theoretical Biology and Integrated Research Institute for the Life-Sciences, Humboldt University of Berlin, 10115 Berlin, Germany
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21
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Hicks JL, Boswell MA, Althoff T, Crum AJ, Ku JP, Landay JA, Moya PML, Murnane EL, Snyder MP, King AC, Delp SL. Leveraging Mobile Technology for Public Health Promotion: A Multidisciplinary Perspective. Annu Rev Public Health 2023; 44:131-150. [PMID: 36542772 PMCID: PMC10523351 DOI: 10.1146/annurev-publhealth-060220-041643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Health behaviors are inextricably linked to health and well-being, yet issues such as physical inactivity and insufficient sleep remain significant global public health problems. Mobile technology-and the unprecedented scope and quantity of data it generates-has a promising but largely untapped potential to promote health behaviors at the individual and population levels. This perspective article provides multidisciplinary recommendations on the design and use of mobile technology, and the concomitant wealth of data, to promote behaviors that support overall health. Using physical activity as anexemplar health behavior, we review emerging strategies for health behavior change interventions. We describe progress on personalizing interventions to an individual and their social, cultural, and built environments, as well as on evaluating relationships between mobile technology data and health to establish evidence-based guidelines. In reviewing these strategies and highlighting directions for future research, we advance the use of theory-based, personalized, and human-centered approaches in promoting health behaviors.
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Affiliation(s)
- Jennifer L Hicks
- Department of Bioengineering, Stanford University, Stanford, California, USA;
| | - Melissa A Boswell
- Department of Bioengineering, Stanford University, Stanford, California, USA;
| | - Tim Althoff
- Allen School of Computer Science & Engineering, University of Washington, Seattle, Washington, USA
| | - Alia J Crum
- Department of Psychology, Stanford University, Stanford, California, USA
| | - Joy P Ku
- Department of Bioengineering, Stanford University, Stanford, California, USA;
| | - James A Landay
- Department of Computer Science, Stanford University, Stanford, California, USA
| | - Paula M L Moya
- Department of English and the Center for Comparative Studies in Race and Ethnicity, Stanford University, Stanford, California, USA
| | | | - Michael P Snyder
- Department of Genetics, Stanford School of Medicine, Stanford University, Stanford, California, USA
| | - Abby C King
- Department of Epidemiology and Population Health, and Department of Medicine (Stanford Prevention Research Center), Stanford School of Medicine, Stanford University, Stanford, California, USA
| | - Scott L Delp
- Department of Bioengineering and Department of Mechanical Engineering, Stanford University, Stanford, California, USA
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22
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Sobel JA, Levy J, Almog R, Reiner-Benaim A, Miller A, Eytan D, Behar JA. Descriptive characteristics of continuous oximetry measurement in moderate to severe covid-19 patients. Sci Rep 2023; 13:442. [PMID: 36624254 PMCID: PMC9828367 DOI: 10.1038/s41598-022-27342-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 12/30/2022] [Indexed: 01/11/2023] Open
Abstract
Non-invasive oxygen saturation (SpO2) is a central vital sign used to shape the management of COVID-19 patients. Yet, there have been no report quantitatively describing SpO2 dynamics and patterns in COVID-19 patients using continuous SpO2 recordings. We performed a retrospective observational analysis of the clinical information and 27 K hours of continuous SpO2 high-resolution (1 Hz) recordings of 367 critical and non-critical COVID-19 patients hospitalised at the Rambam Health Care Campus, Haifa, Israel. An absolute SpO2 threshold of 93% most efficiently discriminated between critical and non-critical patients, regardless of oxygen support. Oximetry-derived digital biomarker (OBMs) computed per 1 h monitoring window showed significant differences between groups, notably the cumulative time below 93% SpO2 (CT93). Patients with CT93 above 60% during the first hour of monitoring, were more likely to require oxygen support. Mechanical ventilation exhibited a strong effect on SpO2 dynamics by significantly reducing the frequency and depth of desaturations. OBMs related to periodicity and hypoxic burden were markedly affected, up to several hours before the initiation of the mechanical ventilation. In summary, OBMs, traditionally used in the field of sleep medicine research, are informative for continuous assessment of disease severity and response to respiratory support of hospitalised COVID-19 patients. In conclusion, OBMs may improve risk stratification and therapy management of critical care patients with respiratory impairment.
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Affiliation(s)
- Jonathan A. Sobel
- grid.6451.60000000121102151Faculty of Biomedical Engineering, Technion, Israel Institute of Technology, Haifa, Israel
| | - Jeremy Levy
- grid.6451.60000000121102151Faculty of Biomedical Engineering, Technion, Israel Institute of Technology, Haifa, Israel ,grid.6451.60000000121102151Faculty of Electrical Engineering, Technion, Israel Institute of Technology, Haifa, Israel
| | - Ronit Almog
- grid.413731.30000 0000 9950 8111Rambam Health Care Campus, Haifa, Israel
| | - Anat Reiner-Benaim
- grid.7489.20000 0004 1937 0511Department of Epidemiology, Biostatistics and Community Health Sciences, Faculty of Health Sciences, Ben Gurion University of the Negev Beer-Sheva, Beer-Sheva, Israel
| | - Asaf Miller
- grid.413731.30000 0000 9950 8111Rambam Health Care Campus, Haifa, Israel
| | - Danny Eytan
- grid.413731.30000 0000 9950 8111Rambam Health Care Campus, Haifa, Israel
| | - Joachim A. Behar
- grid.6451.60000000121102151Faculty of Biomedical Engineering, Technion, Israel Institute of Technology, Haifa, Israel
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23
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Kwon CY. The Impact of SARS-CoV-2 Infection on Heart Rate Variability: A Systematic Review of Observational Studies with Control Groups. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:909. [PMID: 36673664 PMCID: PMC9859268 DOI: 10.3390/ijerph20020909] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 12/26/2022] [Accepted: 12/31/2022] [Indexed: 05/13/2023]
Abstract
Autonomic nervous system (ANS) dysfunction can arise after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and heart rate variability (HRV) tests can assess its integrity. This review investigated the relationship between the impact of SARS-CoV-2 infection on HRV parameters. Comprehensive searches were conducted in four electronic databases. Observational studies with a control group reporting the direct impact of SARS-CoV-2 infection on the HRV parameters in July 2022 were included. A total of 17 observational studies were included in this review. The square root of the mean squared differences of successive NN intervals (RMSSD) was the most frequently investigated. Some studies found that decreases in RMSSD and high frequency (HF) power were associated with SARS-CoV-2 infection or the poor prognosis of COVID-19. Also, decreases in RMSSD and increases in the normalized unit of HF power were related to death in critically ill COVID-19 patients. The findings showed that SARS-CoV-2 infection, and the severity and prognosis of COVID-19, are likely to be reflected in some HRV-related parameters. However, the considerable heterogeneity of the included studies was highlighted. The methodological quality of the included observational studies was not optimal. The findings suggest rigorous and accurate measurements of HRV parameters are highly needed on this topic.
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Affiliation(s)
- Chan-Young Kwon
- Department of Oriental Neuropsychiatry, College of Korean Medicine, Dongeui University, 52-57, Yangjeong-ro, Busanjin-gu, Busan 47227, Republic of Korea
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24
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Pavliuk O, Kolesnyk H. Machine-learning method for analyzing and predicting the number of hospitalizations of children during the fourth wave of the COVID-19 pandemic in the Lviv region. JOURNAL OF RELIABLE INTELLIGENT ENVIRONMENTS 2023; 9:17-26. [PMID: 36065343 PMCID: PMC9434091 DOI: 10.1007/s40860-022-00188-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 08/04/2022] [Indexed: 11/13/2022]
Abstract
The purpose of this paper is to develop a machine-learning model for analyzing and predicting the number of hospitalizations of children in the Lviv region during the fourth wave of the COVID-19 pandemic. This wave is characterized by dominance of a new strain of the virus-Omicron-that spreads faster than previous ones and often affects children. Their high sociability and a low level of vaccination in Ukraine resulted in a sharp increase in the number of hospitalizations. The complexity of the research is also related to the geolocation of the Lviv region. This article analyzes and predicts the number of hospitalizations of children during the fourth wave of the COVID-19 pandemic for the first time for the Lviv region. Data were obtained from publicly available resources. Public Domain Software-the Python programming language and the Pandas library-was used for software implementation of the machine-learning method: the developed model consists of two components-analysis and prediction. The analysis of the number of hospitalized children was performed using the Pearson correlation coefficient. Short- and medium-term predictions were made with the use of non-iterative SGTM neural-like structures that were taught in supervised mode and tested in online mode. The RMS and maximum ones that were reduced to the range of error values of short-term (up to a week) and medium-term (up to 2 weeks) predictions did not exceed 0.48% and 0.61% and 1.81% and 2.83%, respectively. The developed model can also be used for predicting other COVID-19 parameters.
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Affiliation(s)
- Olena Pavliuk
- grid.10067.300000 0001 1280 1647Department of Automated Control Systems, Lviv Polytechnic National University, Stepan Bandera Str., 12, Lviv, 79013 Ukraine
| | - Halyna Kolesnyk
- grid.10067.300000 0001 1280 1647Foreign Languages Department, Lviv Polytechnic National University, Stepan Bandera Str., 12, Lviv, 79013 Ukraine
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25
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Sezgin E. Artificial intelligence in healthcare: Complementing, not replacing, doctors and healthcare providers. Digit Health 2023; 9:20552076231186520. [PMID: 37426593 PMCID: PMC10328041 DOI: 10.1177/20552076231186520] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 06/20/2023] [Indexed: 07/11/2023] Open
Abstract
The utilization of artificial intelligence (AI) in clinical practice has increased and is evidently contributing to improved diagnostic accuracy, optimized treatment planning, and improved patient outcomes. The rapid evolution of AI, especially generative AI and large language models (LLMs), have reignited the discussions about their potential impact on the healthcare industry, particularly regarding the role of healthcare providers. Concerning questions, "can AI replace doctors?" and "will doctors who are using AI replace those who are not using it?" have been echoed. To shed light on this debate, this article focuses on emphasizing the augmentative role of AI in healthcare, underlining that AI is aimed to complement, rather than replace, doctors and healthcare providers. The fundamental solution emerges with the human-AI collaboration, which combines the cognitive strengths of healthcare providers with the analytical capabilities of AI. A human-in-the-loop (HITL) approach ensures that the AI systems are guided, communicated, and supervised by human expertise, thereby maintaining safety and quality in healthcare services. Finally, the adoption can be forged further by the organizational process informed by the HITL approach to improve multidisciplinary teams in the loop. AI can create a paradigm shift in healthcare by complementing and enhancing the skills of healthcare providers, ultimately leading to improved service quality, patient outcomes, and a more efficient healthcare system.
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Affiliation(s)
- Emre Sezgin
- Center for Biobehavioral Health, Abigail
Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH, USA
- Department of Pediatrics, The Ohio State University College of
Medicine, Columbus, OH, USA
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26
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Pho GN, Thigpen N, Patel S, Tily H. Feasibility of Measuring Physiological Responses to Breakthrough Infections and COVID-19 Vaccine Using a Wearable Ring Sensor. Digit Biomark 2023; 7:1-6. [PMID: 37008738 PMCID: PMC10062187 DOI: 10.1159/000528874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 12/16/2022] [Indexed: 03/31/2023] Open
Abstract
Continuous monitoring using commercial-grade wearable technology was used to quantify the physiological response to reported COVID-19 infections and vaccinations in five biometric measurements. Larger responses were observed following confirmed COVID-19 infection reported by unvaccinated versus vaccinated individuals. Responses following reported vaccination were smaller in both magnitude and duration compared to infection and mediated by both dose number and age. Our results suggest commercial-grade wearable technology as a potential platform on which to build screening tools for early detection of illness, including COVID-19 breakthrough cases.
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Affiliation(s)
| | | | | | - Hal Tily
- Ōura Health Ltd, San Francisco, CA, USA
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27
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Khondakar KR, Kaushik A. Role of Wearable Sensing Technology to Manage Long COVID. BIOSENSORS 2022; 13:62. [PMID: 36671900 PMCID: PMC9855989 DOI: 10.3390/bios13010062] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 12/19/2022] [Accepted: 12/26/2022] [Indexed: 06/17/2023]
Abstract
Long COVID consequences have changed the perception towards disease management, and it is moving towards personal healthcare monitoring. In this regard, wearable devices have revolutionized the personal healthcare sector to track and monitor physiological parameters of the human body continuously. This would be largely beneficial for early detection (asymptomatic and pre-symptomatic cases of COVID-19), live patient conditions, and long COVID monitoring (COVID recovered patients and healthy individuals) for better COVID-19 management. There are multitude of wearable devices that can observe various human body parameters for remotely monitoring patients and self-monitoring mode for individuals. Smart watches, smart tattoos, rings, smart facemasks, nano-patches, etc., have emerged as the monitoring devices for key physiological parameters, such as body temperature, respiration rate, heart rate, oxygen level, etc. This review includes long COVID challenges for frequent monitoring of biometrics and its possible solution with wearable device technologies for diagnosis and post-therapy of diseases.
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Affiliation(s)
- Kamil Reza Khondakar
- School of Health Sciences and Technology, University of Petroleum and Energy Studies, Dehradun 248007, Uttarakhand, India
| | - Ajeet Kaushik
- NanoBioTech Laboratory, Department of Environmental Engineering, Florida Polytechnic University, Lakeland, FL 33805-8531, USA
- Department of Chemical Engineering, University of Johannesburg, Johannesburg 2094, South Africa
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28
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Cleary JL, Fang Y, Sen S, Wu Z. A caveat to using wearable sensor data for COVID-19 detection: The role of behavioral change after receipt of test results. PLoS One 2022; 17:e0277350. [PMID: 36584148 PMCID: PMC9803125 DOI: 10.1371/journal.pone.0277350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 10/25/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Recent studies indicate that wearable sensors can capture subtle within-person changes caused by SARS-CoV-2 infection and play a role in detecting COVID-19 infections. However, in addition to direct effects of infection, wearable sensor data may capture changes in behavior after the receipt of COVID test results. At present, it remains unclear to what extent the observed discriminative performance of the wearable sensor data is affected by behavioral changes upon receipt of the test results. METHODS We conducted a retrospective study of wearable sensor data in a sample of medical interns who had symptoms and received COVID-19 test results from March to December 2020, and calculated wearable sensor metrics incorporating changes in step, sleep, and resting heart rate for interns who tested positive (cases, n = 22) and negative (controls, n = 83) after symptom onset. All these interns had wearable sensor data available for > 50% of the days in pre- and post-symptom onset periods. We assessed discriminative accuracy of the metrics via area under the curve (AUC) and tested the impact of behavior changes after receiving test results by comparing AUCs of three models: all data, pre-test-result-only data, and post-test-result-only data. RESULTS Wearable sensor metrics differentiated between symptomatic COVID-19 positive and negative individuals with good accuracy (AUC = 0.75). However, the discriminative capacity of the model with pre-test-result-only data substantially decreased (AUC from 0.75 to 0.63; change = -0.12, p = 0.013). The model with post-test-result-only data did not produce similar reductions in discriminative capacity. CONCLUSIONS Changes in wearable sensor data, especially physical activity and sleep, are robust indicators of COVID-19 infection, though they may be reflective of a person's behavior change after receiving a positive test result as opposed to a physiological signature of the virus. Thus, wearable sensor data could facilitate the monitoring of COVID-19 prevalence, but not yet replace SARS-CoV-2 testing.
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Affiliation(s)
- Jennifer L. Cleary
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI, United States of America
- Department of Psychology, University of Michigan, Ann Arbor, MI, United States of America
| | - Yu Fang
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI, United States of America
| | - Srijan Sen
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI, United States of America
- Department of Psychiatry, University of Michigan Medical School, Ann Arbor, MI, United States of America
| | - Zhenke Wu
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States of America
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29
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Klein A, Puldon K, Dilchert S, Hartogensis W, Chowdhary A, Anglo C, Pandya LS, Hecht FM, Mason AE, Smarr BL. Methods for detecting probable COVID-19 cases from large-scale survey data also reveal probable sex differences in symptom profiles. Front Big Data 2022; 5:1043704. [PMID: 36438983 PMCID: PMC9685297 DOI: 10.3389/fdata.2022.1043704] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 10/26/2022] [Indexed: 11/03/2023] Open
Abstract
Background Daily symptom reporting collected via web-based symptom survey tools holds the potential to improve disease monitoring. Such screening tools might be able to not only discriminate between states of acute illness and non-illness, but also make use of additional demographic information so as to identify how illnesses may differ across groups, such as biological sex. These capabilities may play an important role in the context of future disease outbreaks. Objective Use data collected via a daily web-based symptom survey tool to develop a Bayesian model that could differentiate between COVID-19 and other illnesses and refine this model to identify illness profiles that differ by biological sex. Methods We used daily symptom profiles to plot symptom progressions for COVID-19, influenza (flu), and the common cold. We then built a Bayesian network to discriminate between these three illnesses based on daily symptom reports. We further separated out the COVID-19 cohort into self-reported female and male subgroups to observe any differences in symptoms relating to sex. We identified key symptoms that contributed to a COVID-19 prediction in both males and females using a logistic regression model. Results Although the Bayesian model performed only moderately well in identifying a COVID-19 diagnosis (71.6% true positive rate), the model showed promise in being able to differentiate between COVID-19, flu, and the common cold, as well as periods of acute illness vs. non-illness. Additionally, COVID-19 symptoms differed between the biological sexes; specifically, fever was a more important symptom in identifying subsequent COVID-19 infection among males than among females. Conclusion Web-based symptom survey tools hold promise as tools to identify illness and may help with coordinated disease outbreak responses. Incorporating demographic factors such as biological sex into predictive models may elucidate important differences in symptom profiles that hold implications for disease detection.
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Affiliation(s)
- Amit Klein
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
| | - Karena Puldon
- Osher Center for Integrative Health, University of California, San Francisco, San Francisco, CA, United States
| | - Stephan Dilchert
- Department of Management, Zicklin School of Business, Baruch College, The City University of New York, New York, NY, United States
| | - Wendy Hartogensis
- Osher Center for Integrative Health, University of California, San Francisco, San Francisco, CA, United States
| | - Anoushka Chowdhary
- Osher Center for Integrative Health, University of California, San Francisco, San Francisco, CA, United States
| | - Claudine Anglo
- Osher Center for Integrative Health, University of California, San Francisco, San Francisco, CA, United States
| | - Leena S. Pandya
- Osher Center for Integrative Health, University of California, San Francisco, San Francisco, CA, United States
| | - Frederick M. Hecht
- Osher Center for Integrative Health, University of California, San Francisco, San Francisco, CA, United States
| | - Ashley E. Mason
- Osher Center for Integrative Health, University of California, San Francisco, San Francisco, CA, United States
| | - Benjamin L. Smarr
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
- Halicioglu Data Science Institute, University of California, San Diego, La Jolla, CA, United States
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30
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Kaklauskas A, Abraham A, Ubarte I, Kliukas R, Luksaite V, Binkyte-Veliene A, Vetloviene I, Kaklauskiene L. A Review of AI Cloud and Edge Sensors, Methods, and Applications for the Recognition of Emotional, Affective and Physiological States. SENSORS (BASEL, SWITZERLAND) 2022; 22:7824. [PMID: 36298176 PMCID: PMC9611164 DOI: 10.3390/s22207824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 09/28/2022] [Accepted: 10/12/2022] [Indexed: 06/16/2023]
Abstract
Affective, emotional, and physiological states (AFFECT) detection and recognition by capturing human signals is a fast-growing area, which has been applied across numerous domains. The research aim is to review publications on how techniques that use brain and biometric sensors can be used for AFFECT recognition, consolidate the findings, provide a rationale for the current methods, compare the effectiveness of existing methods, and quantify how likely they are to address the issues/challenges in the field. In efforts to achieve the key goals of Society 5.0, Industry 5.0, and human-centered design better, the recognition of emotional, affective, and physiological states is progressively becoming an important matter and offers tremendous growth of knowledge and progress in these and other related fields. In this research, a review of AFFECT recognition brain and biometric sensors, methods, and applications was performed, based on Plutchik's wheel of emotions. Due to the immense variety of existing sensors and sensing systems, this study aimed to provide an analysis of the available sensors that can be used to define human AFFECT, and to classify them based on the type of sensing area and their efficiency in real implementations. Based on statistical and multiple criteria analysis across 169 nations, our outcomes introduce a connection between a nation's success, its number of Web of Science articles published, and its frequency of citation on AFFECT recognition. The principal conclusions present how this research contributes to the big picture in the field under analysis and explore forthcoming study trends.
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Affiliation(s)
- Arturas Kaklauskas
- Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Sauletekio Ave. 11, LT-10223 Vilnius, Lithuania
| | - Ajith Abraham
- Machine Intelligence Research Labs, Scientific Network for Innovation and Research Excellence, Auburn, WA 98071, USA
| | - Ieva Ubarte
- Institute of Sustainable Construction, Vilnius Gediminas Technical University, Sauletekio Ave. 11, LT-10223 Vilnius, Lithuania
| | - Romualdas Kliukas
- Department of Applied Mechanics, Vilnius Gediminas Technical University, Sauletekio Ave. 11, LT-10223 Vilnius, Lithuania
| | - Vaida Luksaite
- Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Sauletekio Ave. 11, LT-10223 Vilnius, Lithuania
| | - Arune Binkyte-Veliene
- Institute of Sustainable Construction, Vilnius Gediminas Technical University, Sauletekio Ave. 11, LT-10223 Vilnius, Lithuania
| | - Ingrida Vetloviene
- Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Sauletekio Ave. 11, LT-10223 Vilnius, Lithuania
| | - Loreta Kaklauskiene
- Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Sauletekio Ave. 11, LT-10223 Vilnius, Lithuania
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Bae YS, Sung S, Lee J, Lee H, Chie EK. Building and implementing a contactless clinical trial protocol for patients with COVID-19: A Korean perspective. Front Med (Lausanne) 2022; 9:975243. [PMID: 36186782 PMCID: PMC9520334 DOI: 10.3389/fmed.2022.975243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 08/31/2022] [Indexed: 11/23/2022] Open
Abstract
Introduction To effectively manage patients with coronavirus disease 2019 (COVID-19) while minimizing contact between medical staff, clinical trial protocol that facilitates contactless patient management was designed to predict deterioration of disease condition and monitor mental health status. Methods Through consultation with infectious disease specialists and psychiatrists, this study identified main clinical indicators related to respiratory and non-respiratory outcomes, and mental health. Telehealth devices that could collect relevant data indicators were explored. The following three modes were identified: wearable devices, video calls, and online questionnaires. Clinical trial protocol was implemented to patients confirmed with COVID-19 infection and admitted to Seongnam residential treatment centers between September 1, 2021 and December 30, 2021. Data were collected from wearable devices, video calls, online questionnaires, and from electronic health records. Participant satisfaction was assessed through an online survey at the time of discharge. Results In total, 120 asymptomatic and mildly symptomatic COVID-19 patients participated in this trial. Seven types of physiological and life log data were collected from 87 patients using wearable devices, video and audio recordings, and online mental health-related questionnaire. Most participants were satisfied with the overall trial process, but perceived difficulties in using telehealth devices. Conclusion This trial collected simultaneously generated multimodal patient data using various telehealth devices in a contactless setting for COVID-19 patients. Data collected in this study will be used to build a remote patient management system based on the prediction algorithms.
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Affiliation(s)
- Ye Seul Bae
- Office of Hospital Information, Seoul National University Hospital, Seoul, South Korea
- Department of Family Medicine, Seoul National University Hospital, Seoul, South Korea
| | - Sumi Sung
- Office of Hospital Information, Seoul National University Hospital, Seoul, South Korea
| | - Jungeun Lee
- Office of Hospital Information, Seoul National University Hospital, Seoul, South Korea
| | - Hyeonji Lee
- Office of Hospital Information, Seoul National University Hospital, Seoul, South Korea
| | - Eui Kyu Chie
- Office of Hospital Information, Seoul National University Hospital, Seoul, South Korea
- Department of Radiation Oncology, College of Medicine, Seoul National University, Seoul, South Korea
- Medical Research Center, Institute of Radiation Medicine, Seoul National University, Seoul, South Korea
- *Correspondence: Eui Kyu Chie
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Duarte N, Arora RK, Bennett G, Wang M, Snyder MP, Cooperstock JR, Wagner CE. Deploying wearable sensors for pandemic mitigation: A counterfactual modelling study of Canada's second COVID-19 wave. PLOS DIGITAL HEALTH 2022; 1:e0000100. [PMID: 36812624 PMCID: PMC9931244 DOI: 10.1371/journal.pdig.0000100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 08/08/2022] [Indexed: 11/18/2022]
Abstract
Wearable sensors can continuously and passively detect potential respiratory infections before or absent symptoms. However, the population-level impact of deploying these devices during pandemics is unclear. We built a compartmental model of Canada's second COVID-19 wave and simulated wearable sensor deployment scenarios, systematically varying detection algorithm accuracy, uptake, and adherence. With current detection algorithms and 4% uptake, we observed a 16% reduction in the second wave burden of infection; however, 22% of this reduction was attributed to incorrectly quarantining uninfected device users. Improving detection specificity and offering confirmatory rapid tests each minimized unnecessary quarantines and lab-based tests. With a sufficiently low false positive rate, increasing uptake and adherence became effective strategies for scaling averted infections. We concluded that wearable sensors capable of detecting presymptomatic or asymptomatic infections have potential to help reduce the burden of infection during a pandemic; in the case of COVID-19, technology improvements or supporting measures are required to keep social and resource costs sustainable.
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Affiliation(s)
- Nathan Duarte
- Department of Electrical and Computer Engineering, Faculty of Engineering, McGill University, Montreal, Canada
| | - Rahul K. Arora
- Department of Community Health Sciences, University of Calgary, Calgary, Canada
- Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom
| | - Graham Bennett
- Department of Economics, Faculty of Arts, McGill University, Montreal, Canada
| | - Meng Wang
- Department of Genetics, Stanford University School of Medicine, Stanford University, California, United States of America
| | - Michael P. Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford University, California, United States of America
| | - Jeremy R. Cooperstock
- Department of Electrical and Computer Engineering, Faculty of Engineering, McGill University, Montreal, Canada
| | - Caroline E. Wagner
- Department of Bioengineering, Faculty of Engineering, McGill University, Montreal, Canada
- * E-mail:
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Shandhi MMH, Cho PJ, Roghanizad AR, Singh K, Wang W, Enache OM, Stern A, Sbahi R, Tatar B, Fiscus S, Khoo QX, Kuo Y, Lu X, Hsieh J, Kalodzitsa A, Bahmani A, Alavi A, Ray U, Snyder MP, Ginsburg GS, Pasquale DK, Woods CW, Shaw RJ, Dunn JP. A method for intelligent allocation of diagnostic testing by leveraging data from commercial wearable devices: a case study on COVID-19. NPJ Digit Med 2022; 5:130. [PMID: 36050372 PMCID: PMC9434073 DOI: 10.1038/s41746-022-00672-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 08/03/2022] [Indexed: 12/16/2022] Open
Abstract
Mass surveillance testing can help control outbreaks of infectious diseases such as COVID-19. However, diagnostic test shortages are prevalent globally and continue to occur in the US with the onset of new COVID-19 variants and emerging diseases like monkeypox, demonstrating an unprecedented need for improving our current methods for mass surveillance testing. By targeting surveillance testing toward individuals who are most likely to be infected and, thus, increasing the testing positivity rate (i.e., percent positive in the surveillance group), fewer tests are needed to capture the same number of positive cases. Here, we developed an Intelligent Testing Allocation (ITA) method by leveraging data from the CovIdentify study (6765 participants) and the MyPHD study (8580 participants), including smartwatch data from 1265 individuals of whom 126 tested positive for COVID-19. Our rigorous model and parameter search uncovered the optimal time periods and aggregate metrics for monitoring continuous digital biomarkers to increase the positivity rate of COVID-19 diagnostic testing. We found that resting heart rate (RHR) features distinguished between COVID-19-positive and -negative cases earlier in the course of the infection than steps features, as early as 10 and 5 days prior to the diagnostic test, respectively. We also found that including steps features increased the area under the receiver operating characteristic curve (AUC-ROC) by 7-11% when compared with RHR features alone, while including RHR features improved the AUC of the ITA model's precision-recall curve (AUC-PR) by 38-50% when compared with steps features alone. The best AUC-ROC (0.73 ± 0.14 and 0.77 on the cross-validated training set and independent test set, respectively) and AUC-PR (0.55 ± 0.21 and 0.24) were achieved by using data from a single device type (Fitbit) with high-resolution (minute-level) data. Finally, we show that ITA generates up to a 6.5-fold increase in the positivity rate in the cross-validated training set and up to a 4.5-fold increase in the positivity rate in the independent test set, including both symptomatic and asymptomatic (up to 27%) individuals. Our findings suggest that, if deployed on a large scale and without needing self-reported symptoms, the ITA method could improve the allocation of diagnostic testing resources and reduce the burden of test shortages.
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Affiliation(s)
| | - Peter J Cho
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Ali R Roghanizad
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Karnika Singh
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Will Wang
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Oana M Enache
- Department of Biostatistics & Bioinformatics, Duke University, Durham, NC, USA
| | - Amanda Stern
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Rami Sbahi
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Bilge Tatar
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Sean Fiscus
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Qi Xuan Khoo
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Yvonne Kuo
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Xiao Lu
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Joseph Hsieh
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Alena Kalodzitsa
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Amir Bahmani
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Arash Alavi
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Utsab Ray
- Department of Genetics, Stanford University, Stanford, CA, USA
| | | | - Geoffrey S Ginsburg
- All of Us Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Dana K Pasquale
- Department of Sociology, Duke University, Durham, NC, USA.,Department of Population Health Sciences, School of Medicine, Duke University, Durham, NC, USA
| | - Christopher W Woods
- Division of Infectious Diseases, Duke University Medical Center, Durham, NC, USA.,Durham VA Medical Center, Durham, NC, USA
| | - Ryan J Shaw
- School of Nursing, Duke University, Durham, NC, USA.,Duke Mobile App Gateway, Clinical and Translational Science Institute, Duke University, Durham, NC, USA
| | - Jessilyn P Dunn
- Department of Biomedical Engineering, Duke University, Durham, NC, USA. .,Department of Biostatistics & Bioinformatics, Duke University, Durham, NC, USA.
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Sensor-based surveillance for digitising real-time COVID-19 tracking in the USA (DETECT): a multivariable, population-based, modelling study. Lancet Digit Health 2022; 4:e777-e786. [PMID: 36154810 PMCID: PMC9499390 DOI: 10.1016/s2589-7500(22)00156-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 07/19/2022] [Accepted: 08/02/2022] [Indexed: 11/22/2022]
Abstract
Background Traditional viral illness surveillance relies on in-person clinical or laboratory data, paper-based data collection, and outdated technology for data transfer and aggregation. We aimed to assess whether continuous sensor data can provide an early warning signal for COVID-19 activity as individual physiological and behavioural changes might precede symptom onset, care seeking, and diagnostic testing. Methods This multivariable, population-based, modelling study recruited adult (aged ≥18 years) participants living in the USA who had a smartwatch or fitness tracker on any device that connected to Apple HealthKit or Google Fit and had joined the DETECT study by downloading the MyDataHelps app. In the model development cohort, we included people who had participated in DETECT between April 1, 2020, and Jan 14, 2022. In the validation cohort, we included individuals who had participated between Jan 15 and Feb 15, 2022. When a participant joins DETECT, they fill out an intake survey of demographic information, including their ZIP code (postal code), and surveys on symptoms, symptom onset, and viral illness test dates and results, if they become unwell. When a participant connects their device, historical sensor data are collected, if available. Sensor data continue to be collected unless a participant withdraws from the study. Using sensor data, we collected each participant's daily resting heart rate and step count during the entire study period and identified anomalous sensor days, in which resting heart rate was higher than, and step count was lower than, a specified threshold calculated for each individual by use of their baseline data. The proportion of users with anomalous data each day was used to create a 7-day moving average. For the main cohort, a negative binomial model predicting 7-day moving averages for COVID-19 case counts, as reported by the Centers for Disease Control and Prevention (CDC), in real time, 6 days in the future, and 12 days in the future in the USA and California was fitted with CDC-reported data from 3 days before alone (H0) or in combination with anomalous sensor data (H1). We compared the predictions with Pearson correlation. We then validated the model in the validation cohort. Findings Between April 1, 2020, and Jan 14, 2022, 35 842 participants enrolled in DETECT, of whom 4006 in California and 28 527 in the USA were included in our main cohort. The H1 model significantly outperformed the H0 model in predicting the 7-day moving average COVID-19 case counts in California and the USA. For example, Pearson correlation coefficients for predictions 12 days in the future increased by 32·9% in California (from 0·70 [95% CI 0·65–0·73] to 0·93 [0·92–0·94]) and by 12·2% (from 0·82 [0·79–0·84] to 0·92 [0·91–0·93]) in the USA from the H0 model to the H1 model. Our validation model also showed significant correlations for predictions in real time, 6 days in the future, and 12 days in the future. Interpretation Our study showed that passively collected sensor data from consenting participants can provide real-time disease tracking and forecasting. With a growing population of wearable technology users, these sensor data could be integrated into viral surveillance programmes. Funding The National Center for Advancing Translational Sciences of the US National Institutes of Health, The Rockefeller Foundation, and Amazon Web Services.
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Naikoo GA, Arshad F, Hassan IU, Awan T, Salim H, Pedram MZ, Ahmed W, Patel V, Karakoti AS, Vinu A. Nanomaterials-based sensors for the detection of COVID-19: A review. Bioeng Transl Med 2022; 7:e10305. [PMID: 35599642 PMCID: PMC9110902 DOI: 10.1002/btm2.10305] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 02/07/2022] [Accepted: 02/08/2022] [Indexed: 12/13/2022] Open
Abstract
With the threat of increasing SARS-CoV-2 cases looming in front of us and no effective and safest vaccine available to curb this pandemic disease due to its sprouting variants, many countries have undergone a lockdown 2.0 or planning a lockdown 3.0. This has upstretched an unprecedented demand to develop rapid, sensitive, and highly selective diagnostic devices that can quickly detect coronavirus (COVID-19). Traditional techniques like polymerase chain reaction have proven to be time-inefficient, expensive, labor intensive, and impracticable in remote settings. This shifts the attention to alternative biosensing devices that can be successfully used to sense the COVID-19 infection and curb the spread of coronavirus cases. Among these, nanomaterial-based biosensors hold immense potential for rapid coronavirus detection because of their noninvasive and susceptible, as well as selective properties that have the potential to give real-time results at an economical cost. These diagnostic devices can be used for mass COVID-19 detection to understand the rapid progression of the infection and give better-suited therapies. This review provides an overview of existing and potential nanomaterial-based biosensors that can be used for rapid SARS-CoV-2 diagnostics. Novel biosensors employing different detection mechanisms are also highlighted in different sections of this review. Practical tools and techniques required to develop such biosensors to make them reliable and portable have also been discussed in the article. Finally, the review is concluded by presenting the current challenges and future perspectives of nanomaterial-based biosensors in SARS-CoV-2 diagnostics.
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Affiliation(s)
- Gowhar A. Naikoo
- Department of Mathematics and SciencesCollege of Arts and Applied Sciences, Dhofar UniversitySalalahSultanate of Oman
| | - Fareeha Arshad
- Department of Mathematics and SciencesCollege of Arts and Applied Sciences, Dhofar UniversitySalalahSultanate of Oman
| | - Israr U. Hassan
- College of Engineering, Dhofar UniversitySalalahSultanate of Oman
| | - Tasbiha Awan
- Department of Mathematics and SciencesCollege of Arts and Applied Sciences, Dhofar UniversitySalalahSultanate of Oman
| | - Hiba Salim
- Department of Mathematics and SciencesCollege of Arts and Applied Sciences, Dhofar UniversitySalalahSultanate of Oman
| | - Mona Z. Pedram
- Faculty of Mechanical Engineering‐Energy DivisionK.N. Toosi University of TechnologyTehranIran
| | - Waqar Ahmed
- School of Mathematics and Physics, College of ScienceUniversity of LincolnLincolnUK
| | - Vaishwik Patel
- Global Innovative Center for Advanced NanomaterialsCollege of Engineering, Science and Environment, The University of NewcastleCallaghanAustralia
| | - Ajay S. Karakoti
- Global Innovative Center for Advanced NanomaterialsCollege of Engineering, Science and Environment, The University of NewcastleCallaghanAustralia
| | - Ajayan Vinu
- Global Innovative Center for Advanced NanomaterialsCollege of Engineering, Science and Environment, The University of NewcastleCallaghanAustralia
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Cheong SHR, Ng YJX, Lau Y, Lau ST. Wearable technology for early detection of COVID-19: A systematic scoping review. Prev Med 2022; 162:107170. [PMID: 35878707 PMCID: PMC9304072 DOI: 10.1016/j.ypmed.2022.107170] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 06/29/2022] [Accepted: 07/17/2022] [Indexed: 11/23/2022]
Abstract
Wearable technology is an emerging method for the early detection of coronavirus disease 2019 (COVID-19) infection. This scoping review explored the types, mechanisms, and accuracy of wearable technology for the early detection of COVID-19. This review was conducted according to the five-step framework of Arksey and O'Malley. Studies published between December 31, 2019 and December 15, 2021 were obtained from 10 electronic databases, namely, PubMed, Embase, Cochrane, CINAHL, PsycINFO, ProQuest, Scopus, Web of Science, IEEE Xplore, and Taylor & Francis Online. Grey literature, reference lists, and key journals were also searched. All types of articles describing wearable technology for the detection of COVID-19 infection were included. Two reviewers independently screened the articles against the eligibility criteria and extracted the data using a data charting form. A total of 40 articles were included in this review. There are 22 different types of wearable technology used to detect COVID-19 infections early in the existing literature and are categorized as smartwatches or fitness trackers (67%), medical devices (27%), or others (6%). Based on deviations in physiological characteristics, anomaly detection models that can detect COVID-19 infection early were built using artificial intelligence or statistical analysis techniques. Reported area-under-the-curve values ranged from 75% to 94.4%, and sensitivity and specificity values ranged from 36.5% to 100% and 73% to 95.3%, respectively. Further research is necessary to validate the effectiveness and clinical dependability of wearable technology before healthcare policymakers can mandate its use for remote surveillance.
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Affiliation(s)
- Shing Hui Reina Cheong
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Yu Jie Xavia Ng
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Ying Lau
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Siew Tiang Lau
- Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
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Smart Consumer Wearables as Digital Diagnostic Tools: A Review. Diagnostics (Basel) 2022; 12:diagnostics12092110. [PMID: 36140511 PMCID: PMC9498278 DOI: 10.3390/diagnostics12092110] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/26/2022] [Accepted: 08/29/2022] [Indexed: 11/17/2022] Open
Abstract
The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains.
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Wang Z, Xiong H, Tang M, Boukhechba M, Flickinger TE, Barnes LE. Mobile Sensing in the COVID-19 Era: A Review. HEALTH DATA SCIENCE 2022; 2022:9830476. [PMID: 36408201 PMCID: PMC9629686 DOI: 10.34133/2022/9830476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Accepted: 07/18/2022] [Indexed: 12/03/2022]
Abstract
Background During the COVID-19 pandemic, mobile sensing and data analytics techniques have demonstrated their capabilities in monitoring the trajectories of the pandemic, by collecting behavioral, physiological, and mobility data on individual, neighborhood, city, and national scales. Notably, mobile sensing has become a promising way to detect individuals' infectious status, track the change in long-term health, trace the epidemics in communities, and monitor the evolution of viruses and subspecies. Methods We followed the PRISMA practice and reviewed 60 eligible papers on mobile sensing for monitoring COVID-19. We proposed a taxonomy system to summarize literature by the time duration and population scale under mobile sensing studies. Results We found that existing literature can be naturally grouped in four clusters, including remote detection, long-term tracking, contact tracing, and epidemiological study. We summarized each group and analyzed representative works with regard to the system design, health outcomes, and limitations on techniques and societal factors. We further discussed the implications and future directions of mobile sensing in communicable diseases from the perspectives of technology and applications. Conclusion Mobile sensing techniques are effective, efficient, and flexible to surveil COVID-19 in scales of time and populations. In the post-COVID era, technical and societal issues in mobile sensing are expected to be addressed to improve healthcare and social outcomes.
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Affiliation(s)
- Zhiyuan Wang
- School of Engineering and Applied Science, University of Virginia, Charlottesville, USA
| | - Haoyi Xiong
- Big Data Lab, Baidu Research, Baidu Inc., BeijingChina
| | - Mingyue Tang
- School of Engineering and Applied Science, University of Virginia, Charlottesville, USA
| | - Mehdi Boukhechba
- School of Engineering and Applied Science, University of Virginia, Charlottesville, USA
| | - Tabor E. Flickinger
- Department of Medicine, University of Virginia, Charlottesville, Virginia, USA
| | - Laura E. Barnes
- School of Engineering and Applied Science, University of Virginia, Charlottesville, USA
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Scala I, Rizzo PA, Bellavia S, Brunetti V, Colò F, Broccolini A, Della Marca G, Calabresi P, Luigetti M, Frisullo G. Autonomic Dysfunction during Acute SARS-CoV-2 Infection: A Systematic Review. J Clin Med 2022; 11:jcm11133883. [PMID: 35807167 PMCID: PMC9267913 DOI: 10.3390/jcm11133883] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 06/29/2022] [Accepted: 06/29/2022] [Indexed: 02/04/2023] Open
Abstract
Although autonomic dysfunction (AD) after the recovery from Coronavirus disease 2019 (COVID-19) has been thoroughly described, few data are available regarding the involvement of the autonomic nervous system (ANS) during the acute phase of SARS-CoV-2 infection. The primary aim of this review was to summarize current knowledge regarding the AD occurring during acute COVID-19. Secondarily, we aimed to clarify the prognostic value of ANS involvement and the role of autonomic parameters in predicting SARS-CoV-2 infection. According to the PRISMA guidelines, we performed a systematic review across Scopus and PubMed databases, resulting in 1585 records. The records check and the analysis of included reports’ references allowed us to include 22 articles. The studies were widely heterogeneous for study population, dysautonomia assessment, and COVID-19 severity. Heart rate variability was the tool most frequently chosen to analyze autonomic parameters, followed by automated pupillometry. Most studies found ANS involvement during acute COVID-19, and AD was often related to a worse outcome. Further studies are needed to clarify the role of autonomic parameters in predicting SARS-CoV-2 infection. The evidence emerging from this review suggests that a complex autonomic nervous system imbalance is a prominent feature of acute COVID-19, often leading to a poor prognosis.
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Affiliation(s)
- Irene Scala
- School of Medicine and Surgery, Catholic University of Sacred Heart, Largo Francesco Vito, 1, 00168 Rome, Italy; (I.S.); (P.A.R.); (S.B.); (F.C.); (A.B.); (G.D.M.); (P.C.)
| | - Pier Andrea Rizzo
- School of Medicine and Surgery, Catholic University of Sacred Heart, Largo Francesco Vito, 1, 00168 Rome, Italy; (I.S.); (P.A.R.); (S.B.); (F.C.); (A.B.); (G.D.M.); (P.C.)
| | - Simone Bellavia
- School of Medicine and Surgery, Catholic University of Sacred Heart, Largo Francesco Vito, 1, 00168 Rome, Italy; (I.S.); (P.A.R.); (S.B.); (F.C.); (A.B.); (G.D.M.); (P.C.)
| | - Valerio Brunetti
- Dipartimento di Scienze dell’Invecchiamento, Neurologiche, Ortopediche e Della Testa-Collo, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy; (V.B.); (G.F.)
| | - Francesca Colò
- School of Medicine and Surgery, Catholic University of Sacred Heart, Largo Francesco Vito, 1, 00168 Rome, Italy; (I.S.); (P.A.R.); (S.B.); (F.C.); (A.B.); (G.D.M.); (P.C.)
| | - Aldobrando Broccolini
- School of Medicine and Surgery, Catholic University of Sacred Heart, Largo Francesco Vito, 1, 00168 Rome, Italy; (I.S.); (P.A.R.); (S.B.); (F.C.); (A.B.); (G.D.M.); (P.C.)
- Dipartimento di Scienze dell’Invecchiamento, Neurologiche, Ortopediche e Della Testa-Collo, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy; (V.B.); (G.F.)
| | - Giacomo Della Marca
- School of Medicine and Surgery, Catholic University of Sacred Heart, Largo Francesco Vito, 1, 00168 Rome, Italy; (I.S.); (P.A.R.); (S.B.); (F.C.); (A.B.); (G.D.M.); (P.C.)
- Dipartimento di Scienze dell’Invecchiamento, Neurologiche, Ortopediche e Della Testa-Collo, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy; (V.B.); (G.F.)
| | - Paolo Calabresi
- School of Medicine and Surgery, Catholic University of Sacred Heart, Largo Francesco Vito, 1, 00168 Rome, Italy; (I.S.); (P.A.R.); (S.B.); (F.C.); (A.B.); (G.D.M.); (P.C.)
- Dipartimento di Scienze dell’Invecchiamento, Neurologiche, Ortopediche e Della Testa-Collo, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy; (V.B.); (G.F.)
| | - Marco Luigetti
- School of Medicine and Surgery, Catholic University of Sacred Heart, Largo Francesco Vito, 1, 00168 Rome, Italy; (I.S.); (P.A.R.); (S.B.); (F.C.); (A.B.); (G.D.M.); (P.C.)
- Dipartimento di Scienze dell’Invecchiamento, Neurologiche, Ortopediche e Della Testa-Collo, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy; (V.B.); (G.F.)
- Correspondence: ; Tel.: +39-06-30154435
| | - Giovanni Frisullo
- Dipartimento di Scienze dell’Invecchiamento, Neurologiche, Ortopediche e Della Testa-Collo, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, 00168 Rome, Italy; (V.B.); (G.F.)
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Smartphone apps in the COVID-19 pandemic. Nat Biotechnol 2022; 40:1013-1022. [PMID: 35726090 DOI: 10.1038/s41587-022-01350-x] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 05/04/2022] [Indexed: 01/08/2023]
Abstract
At the beginning of the COVID-19 pandemic, analog tools such as nasopharyngeal swabs for PCR tests were center stage and the major prevention tactics of masking and physical distancing were a throwback to the 1918 influenza pandemic. Overall, there has been scant regard for digital tools, particularly those based on smartphone apps, which is surprising given the ubiquity of smartphones across the globe. Smartphone apps, given accessibility in the time of physical distancing, were widely used for tracking, tracing and educating the public about COVID-19. Despite limitations, such as concerns around data privacy, data security, digital health illiteracy and structural inequities, there is ample evidence that apps are beneficial for understanding outbreak epidemiology, individual screening and contact tracing. While there were successes and failures in each category, outbreak epidemiology and individual screening were substantially enhanced by the reach of smartphone apps and accessory wearables. Continued use of apps within the digital infrastructure promises to provide an important tool for rigorous investigation of outcomes both in the ongoing outbreak and in future epidemics.
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Goergen CJ, Tweardy MJ, Steinhubl SR, Wegerich SW, Singh K, Mieloszyk RJ, Dunn J. Detection and Monitoring of Viral Infections via Wearable Devices and Biometric Data. Annu Rev Biomed Eng 2022; 24:1-27. [PMID: 34932906 PMCID: PMC9218991 DOI: 10.1146/annurev-bioeng-103020-040136] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Mounting clinical evidence suggests that viral infections can lead to detectable changes in an individual's normal physiologic and behavioral metrics, including heart and respiration rates, heart rate variability, temperature, activity, and sleep prior to symptom onset, potentially even in asymptomatic individuals. While the ability of wearable devices to detect viral infections in a real-world setting has yet to be proven, multiple recent studies have established that individual, continuous data from a range of biometric monitoring technologies can be easily acquired and that through the use of machine learning techniques, physiological signals and warning signs can be identified. In this review, we highlight the existing knowledge base supporting the potential for widespread implementation of biometric data to address existing gaps in the diagnosis and treatment of viral illnesses, with a particular focus on the many important lessons learned from the coronavirus disease 2019 pandemic.
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Affiliation(s)
- Craig J Goergen
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA;
| | | | - Steven R Steinhubl
- physIQ Inc., Chicago, Illinois, USA
- Scripps Research Translational Institute, La Jolla, California, USA
| | | | - Karnika Singh
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
| | | | - Jessilyn Dunn
- Department of Biomedical Engineering, Duke University, Durham, North Carolina, USA
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Mertz L. Better Tech for Detecting COVID-19. IEEE Pulse 2022; 13:2-8. [PMID: 35727753 DOI: 10.1109/mpuls.2022.3175345] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Earlier, Better Detection of SARS-CoV-2 infection and exposure is a necessary part of battling COVID-19, and many research groups are working on new methods to do it.
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Quer G, Gadaleta M, Radin JM, Andersen KG, Baca-Motes K, Ramos E, Topol EJ, Steinhubl SR. Inter-individual variation in objective measure of reactogenicity following COVID-19 vaccination via smartwatches and fitness bands. NPJ Digit Med 2022; 5:49. [PMID: 35440684 PMCID: PMC9019018 DOI: 10.1038/s41746-022-00591-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Accepted: 03/11/2022] [Indexed: 01/07/2023] Open
Abstract
The ability to identify who does or does not experience the intended immune response following vaccination could be of great value in not only managing the global trajectory of COVID-19 but also helping guide future vaccine development. Vaccine reactogenicity can potentially lead to detectable physiologic changes, thus we postulated that we could detect an individual's initial physiologic response to a vaccine by tracking changes relative to their pre-vaccine baseline using consumer wearable devices. We explored this possibility using a smartphone app-based research platform that enabled volunteers (39,701 individuals) to share their smartwatch data, as well as self-report, when appropriate, any symptoms, COVID-19 test results, and vaccination information. Of 7728 individuals who reported at least one vaccination dose, 7298 received an mRNA vaccine, and 5674 provided adequate data from the peri-vaccine period for analysis. We found that in most individuals, resting heart rate (RHR) increased with respect to their individual baseline after vaccination, peaked on day 2, and returned to normal by day 6. This increase in RHR was greater than one standard deviation above individuals' normal daily pattern in 47% of participants after their second vaccine dose. Consistent with other reports of subjective reactogenicity following vaccination, we measured a significantly stronger effect after the second dose relative to the first, except those who previously tested positive to COVID-19, and a more pronounced increase for individuals who received the Moderna vaccine. Females, after the first dose only, and those aged <40 years, also experienced a greater objective response after adjusting for possible confounding factors. These early findings show that it is possible to detect subtle, but important changes from an individual's normal as objective evidence of reactogenicity, which, with further work, could prove useful as a surrogate for vaccine-induced immune response.
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Affiliation(s)
- Giorgio Quer
- Scripps Research Translational Institute, 3344N Torrey Pines Ct Plaza Level, La Jolla, CA, 92037, USA.
| | - Matteo Gadaleta
- Scripps Research Translational Institute, 3344N Torrey Pines Ct Plaza Level, La Jolla, CA, 92037, USA
| | - Jennifer M Radin
- Scripps Research Translational Institute, 3344N Torrey Pines Ct Plaza Level, La Jolla, CA, 92037, USA
| | - Kristian G Andersen
- Scripps Research Translational Institute, 3344N Torrey Pines Ct Plaza Level, La Jolla, CA, 92037, USA
| | - Katie Baca-Motes
- Scripps Research Translational Institute, 3344N Torrey Pines Ct Plaza Level, La Jolla, CA, 92037, USA
| | - Edward Ramos
- Scripps Research Translational Institute, 3344N Torrey Pines Ct Plaza Level, La Jolla, CA, 92037, USA
- CareEvolution, 625N Main Street, Ann Arbor, MI, 48104, USA
| | - Eric J Topol
- Scripps Research Translational Institute, 3344N Torrey Pines Ct Plaza Level, La Jolla, CA, 92037, USA
| | - Steven R Steinhubl
- Scripps Research Translational Institute, 3344N Torrey Pines Ct Plaza Level, La Jolla, CA, 92037, USA
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Building predictive model for COVID-19 using artificial neural network (ANN) algorithm. Int J Health Sci (Qassim) 2022. [DOI: 10.53730/ijhs.v6ns2.6077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Machine learning plays an important role in addressing the pandemic crisis to analyse, identify and to forecast the infection and the spread of any contagious virus. Nowadays, most of the organizations and researchers are moving towards machine learning algorithms to develop predictive models, trying to reduce the death rate and to identify the patients who are at the increased risk of mortality. The major challenge of Covid-19 is, its identification and classification, due to the fact that the symptoms of Covid -19 are similar to other infectious diseases such as viral fever, typhoid, dengue, pneumonia and other lung infectious diseases. The objective of this paper is to build a predictive model for covid-19 using the Artificial Neural Network (ANN), a supervised machine learning Algorithm. In this study, the data set from Kaggle Sírio-Libanês data for AI and Analytics by the Data Intelligence Team has been used to build the predictive model. It is observed that there is 73% of accuracy in predicting the number of corona infected cases.
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Nnaji C, Jin Z, Karakhan A. Safety and health management response to COVID-19 in the construction industry: A perspective of fieldworkers. PROCESS SAFETY AND ENVIRONMENTAL PROTECTION : TRANSACTIONS OF THE INSTITUTION OF CHEMICAL ENGINEERS, PART B 2022; 159:477-488. [PMID: 35068710 PMCID: PMC8766253 DOI: 10.1016/j.psep.2022.01.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 12/28/2021] [Accepted: 01/02/2022] [Indexed: 05/21/2023]
Abstract
The COVID-19 outbreak has significantly impacted the construction industry. The pandemic can exacerbate an already dire safety and health situation in the industry and negatively impact construction employees and employers. The present study investigates the safety and health measures implemented by construction firms in the United States (US), their effectiveness and usefulness, and workers' satisfaction with these COVID-19 measures. A questionnaire survey was developed and distributed to construction fieldworkers in the US to collect their perspectives on the implemented COVID-19 measures in the construction industry. A total of 187 valid responses were received and analyzed to achieve the aim of the study. Results revealed that strategies implemented to increase social distance and minimize group gathering to 10 persons in certain workstations were perceived to be substantially more effective than job-site screening strategies. Furthermore, smaller contractors implemented fewer safety measures and perceived them to be significantly less effective than those used by medium- and large-sized contractors. Fieldworkers were favorably disposed toward using technologies, such as video-conferencing apps and wearable sensing devices, to slow the spread of COVID-19 on construction job sites. The present study contributes to the body of knowledge by identifying safety and health measures to mitigate the spread of COVID-19 in construction. Practically, the study findings provide valuable insights to inform the successful implementation of safety strategies in the construction industry during a pandemic. The results are crucial for industry practitioners responsible for developing and revising pre- and post-pandemic safety and health plans.
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Affiliation(s)
- Chukwuma Nnaji
- Department of Civil, Construction, and Environmental Engineering, The University of Alabama, 3043 HM Comer, Tuscaloosa, AL 35487, USA
| | - Ziyu Jin
- Department of Civil, Construction, and Environmental Engineering, University of New Mexico, Centennial Engineering Center Room 3052, Albuquerque, NM 87131, USA
| | - Ali Karakhan
- Department of Reconstruction & Projects, University of Baghdad, Al-Jadriya, Baghdad, Iraq
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Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments. Symmetry (Basel) 2021. [DOI: 10.3390/sym14010016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
This paper presents the role of artificial intelligence (AI) and other latest technologies that were employed to fight the recent pandemic (i.e., novel coronavirus disease-2019 (COVID-19)). These technologies assisted the early detection/diagnosis, trends analysis, intervention planning, healthcare burden forecasting, comorbidity analysis, and mitigation and control, to name a few. The key-enablers of these technologies was data that was obtained from heterogeneous sources (i.e., social networks (SN), internet of (medical) things (IoT/IoMT), cellular networks, transport usage, epidemiological investigations, and other digital/sensing platforms). To this end, we provide an insightful overview of the role of data-driven analytics leveraging AI in the era of COVID-19. Specifically, we discuss major services that AI can provide in the context of COVID-19 pandemic based on six grounds, (i) AI role in seven different epidemic containment strategies (a.k.a non-pharmaceutical interventions (NPIs)), (ii) AI role in data life cycle phases employed to control pandemic via digital solutions, (iii) AI role in performing analytics on heterogeneous types of data stemming from the COVID-19 pandemic, (iv) AI role in the healthcare sector in the context of COVID-19 pandemic, (v) general-purpose applications of AI in COVID-19 era, and (vi) AI role in drug design and repurposing (e.g., iteratively aligning protein spikes and applying three/four-fold symmetry to yield a low-resolution candidate template) against COVID-19. Further, we discuss the challenges involved in applying AI to the available data and privacy issues that can arise from personal data transitioning into cyberspace. We also provide a concise overview of other latest technologies that were increasingly applied to limit the spread of the ongoing pandemic. Finally, we discuss the avenues of future research in the respective area. This insightful review aims to highlight existing AI-based technological developments and future research dynamics in this area.
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