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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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Esmaeilpour Z, Natarajan A, Su HW, Faranesh A, Friel C, Zanos TP, D'Angelo S, Heneghan C. Detection of Common Respiratory Infections, Including COVID-19, Using Consumer Wearable Devices in Health Care Workers: Prospective Model Validation Study. JMIR Form Res 2024; 8:e53716. [PMID: 39018555 PMCID: PMC11292157 DOI: 10.2196/53716] [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: 10/17/2023] [Revised: 02/12/2024] [Accepted: 06/17/2024] [Indexed: 07/19/2024] Open
Abstract
BACKGROUND The early detection of respiratory infections could improve responses against outbreaks. Wearable devices can provide insights into health and well-being using longitudinal physiological signals. OBJECTIVE The purpose of this study was to prospectively evaluate the performance of a consumer wearable physiology-based respiratory infection detection algorithm in health care workers. METHODS In this study, we evaluated the performance of a previously developed system to predict the presence of COVID-19 or other upper respiratory infections. The system generates real-time alerts using physiological signals recorded from a smartwatch. Resting heart rate, respiratory rate, and heart rate variability measured during the sleeping period were used for prediction. After baseline recordings, when participants received a notification from the system, they were required to undergo testing at a Northwell Health System site. Participants were asked to self-report any positive tests during the study. The accuracy of model prediction was evaluated using respiratory infection results (laboratory results or self-reports), and postnotification surveys were used to evaluate potential confounding factors. RESULTS A total of 577 participants from Northwell Health in New York were enrolled in the study between January 6, 2022, and July 20, 2022. Of these, 470 successfully completed the study, 89 did not provide sufficient physiological data to receive any prediction from the model, and 18 dropped out. Out of the 470 participants who completed the study and wore the smartwatch as required for the 16-week study duration, the algorithm generated 665 positive alerts, of which 153 (23.0%) were not acted upon to undergo testing for respiratory viruses. Across the 512 instances of positive alerts that involved a respiratory viral panel test, 63 had confirmed respiratory infection results (ie, COVID-19 or other respiratory infections detected using a polymerase chain reaction or home test) and the remaining 449 had negative upper respiratory infection test results. Across all cases, the estimated false-positive rate based on predictions per day was 2%, and the positive-predictive value ranged from 4% to 10% in this specific population, with an observed incidence rate of 198 cases per week per 100,000. Detailed examination of questionnaires filled out after receiving a positive alert revealed that physical or emotional stress events, such as intense exercise, poor sleep, stress, and excessive alcohol consumption, could cause a false-positive result. CONCLUSIONS The real-time alerting system provides advance warning on respiratory viral infections as well as other physical or emotional stress events that could lead to physiological signal changes. This study showed the potential of wearables with embedded alerting systems to provide information on wellness measures.
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Affiliation(s)
| | | | - Hao-Wei Su
- Google LLC, San Francisco, CA, United States
| | | | - Ciaran Friel
- Northwell Health, New Hyde Park, NY, United States
- Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, United States
| | - Theodoros P Zanos
- Northwell Health, New Hyde Park, NY, United States
- Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, New York, NY, United States
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Snir S, Chen Y, Yechezkel M, Patalon T, Shmueli E, Brandeau ML, Yamin D. Changes in behavior and biomarkers during the diagnostic decision period for COVID-19, influenza, and group A streptococcus (GAS): a two-year prospective cohort study in Israel. THE LANCET REGIONAL HEALTH. EUROPE 2024; 42:100934. [PMID: 38800112 PMCID: PMC11127217 DOI: 10.1016/j.lanepe.2024.100934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 04/26/2024] [Accepted: 04/29/2024] [Indexed: 05/29/2024]
Abstract
Background Limited knowledge exists regarding behavioral and biomarker shifts during the period from respiratory infection exposure to testing decisions (the diagnostic decision period), a key phase affecting transmission dynamics and public health strategy development. This study aims to examine the changes in behavior and biomarkers during the diagnostic decision period for COVID-19, influenza, and group A streptococcus (GAS). Methods We analyzed data from a two-year prospective cohort study involving 4795 participants in Israel, incorporating smartwatch data, self-reported symptoms, and medical records. Our analysis focused on three critical phases: the digital incubation period (from exposure to physiological anomalies detected by smartwatches), the symptomatic incubation period (from exposure to onset of symptoms), and the diagnostic decision period for influenza, COVID-19, and GAS. Findings The delay between initial symptom reporting and testing was 39 [95% confidence interval (CI): 34-45] hours for influenza, 53 [95% CI: 49-58] hours for COVID-19, and 38 [95% CI: 32-46] hours for GAS, with 73 [95% CI: 67-78] hours from anomalies in heart measures to symptom onset for influenza, 23 [95% CI: 18-27] hours for COVID-19, and 62 [95% CI: 54-68] hours for GAS. Analyzing the entire course of infection of each individual, the greatest changes in heart rates were detected 67.6 [95% CI: 62.8-72.5] hours prior to testing for influenza, 64.1 [95% CI: 61.4-66.7] hours prior for COVID-19, and 58.2 [95% CI: 52.1-64.2] hours prior for GAS. In contrast, the greatest reduction in physical activities and social contacts occurred after testing. Interpretation These findings highlight the delayed response of patients in seeking medical attention and reducing social contacts and demonstrate the transformative potential of smartwatches for identifying infection and enabling timely public health interventions. Funding This work was supported by the European Research Council, project #949850, the Israel Science Foundation (ISF), grant No. 3409/19, within the Israel Precision Medicine Partnership program, and a Koret Foundation gift for Smart Cities and Digital Living.
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Affiliation(s)
- Shachar Snir
- Industrial Engineering Department, Tel Aviv University, Tel Aviv, Israel
| | - Yupeng Chen
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
| | - Matan Yechezkel
- Industrial Engineering Department, Tel Aviv University, Tel Aviv, Israel
| | - Tal Patalon
- Kahn Sagol Maccabi Research and Innovation Center, Maccabi Healthcare Services, Tel Aviv, Israel
| | - Erez Shmueli
- Industrial Engineering Department, Tel Aviv University, Tel Aviv, Israel
| | - Margaret L. Brandeau
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
| | - Dan Yamin
- Industrial Engineering Department, Tel Aviv University, Tel Aviv, Israel
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
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Rentería LI, Greenwalt CE, Johnson S, Kviatkovsky SA, Dupuit M, Angeles E, Narayanan S, Zeleny T, Ormsbee MJ. Early Detection of COVID-19 in Female Athletes Using Wearable Technology. Sports Health 2024; 16:512-517. [PMID: 37401442 PMCID: PMC10333556 DOI: 10.1177/19417381231183709] [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: 07/05/2023] Open
Abstract
BACKGROUND Heart rate variability (HRV), respiratory rate (RR), and resting heart rate (RHR) are common variables measured by wrist-worn activity trackers to monitor health, fitness, and recovery in athletes. Variations in RR are observed in lower-respiratory infections, and preliminary data suggest changes in HRV and RR are linked to early detection of COVID-19 infection in nonathletes. HYPOTHESIS Wearable technology measuring HRV, RR, RHR, and recovery will be successful for early detection of COVID-19 in NCAA Division I female athletes. STUDY DESIGN Cohort study. LEVEL OF EVIDENCE Level 2. METHODS Female athletes wore WHOOP, Inc. bands through the 2020 to 2021 competitive season. Of the athletes who tested positive for COVID (n = 33), 14 had enough data to be assessed (N = 14; 20.0 ± 1.3 years; 69.8 ± 7.2 kg; 172.0 ± 8.3 cm). Roughly 2 weeks of noninfected days were used to set baseline levels of HRV, RR, recovery, and RHR to compare with -3, -2, and -1 days before a positive COVID-19 result. RESULTS Increases in RR (P = 0.02) were detected on day -3. RHR (P < 0.01) and RR increased (P < 0.01), while HRV decreased (P < 0.05) on day -1, compared with baseline. Differences were noted in all variables on the day of the positive COVID-19 result: decreased HRV (P < 0.05) and recovery scores (P < 0.01), and increased RHR (P < 0.01) and RR (P < 0.01). CONCLUSION In female athletes, wearable technology was successful in predicting COVID-19 infection through changes in RR 3 days before a positive test, and also HRV and RHR the day before a positive test. CLINICAL RELEVANCE Wearable technology may be used, as part of a multifaceted approach, for the early detection of COVID-19 in elite athletes through monitoring of HRV, RR, and RHR for overall team health.
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Affiliation(s)
- Liliana I. Rentería
- Institute of Sports Science and Medicine, Department of Nutrition and Integrative Physiology, Florida State University, Tallahassee, Florida
| | - Casey E. Greenwalt
- Institute of Sports Science and Medicine, Department of Nutrition and Integrative Physiology, Florida State University, Tallahassee, Florida
| | - Sarah Johnson
- Institute of Sports Science and Medicine, Department of Nutrition and Integrative Physiology, Florida State University, Tallahassee, Florida
| | - Shiloah A. Kviatkovsky
- Institute of Sports Science and Medicine, Department of Nutrition and Integrative Physiology, Florida State University, Tallahassee, Florida, and Center for Aging and Longevity, Geriatrics, University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - Marine Dupuit
- Institute of Sports Science and Medicine, Department of Nutrition and Integrative Physiology, Florida State University, Tallahassee, Florida, and Laboratory of the Metabolic Adaptations to Exercise under Physiological and Pathological Conditions (AME2P), Clermont Auvergne University, Clermont-Ferrand, France
| | - Elisa Angeles
- Institute of Sports Science and Medicine, Department of Nutrition and Integrative Physiology, Florida State University, Tallahassee, Florida
| | - Sachin Narayanan
- Institute of Sports Science and Medicine, Department of Nutrition and Integrative Physiology, Florida State University, Tallahassee, Florida
| | | | - Michael J. Ormsbee
- Institute of Sports Science and Medicine, Department of Nutrition and Integrative Physiology, Florida State University, Tallahassee, Florida, and School of Health Sciences, Discipline of Biokinetics, Exercise and Leisure Sciences, University of KwaZulu-Natal, Durban, South Africa
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Ryan JM, Navaneethan S, Damaso N, Dilchert S, Hartogensis W, Natale JL, Hecht FM, Mason AE, Smarr BL. Information theory reveals physiological manifestations of COVID-19 that correlate with symptom density of illness. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 4:1211413. [PMID: 38948084 PMCID: PMC11211556 DOI: 10.3389/fnetp.2024.1211413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 05/16/2024] [Indexed: 07/02/2024]
Abstract
Algorithms for the detection of COVID-19 illness from wearable sensor devices tend to implicitly treat the disease as causing a stereotyped (and therefore recognizable) deviation from healthy physiology. In contrast, a substantial diversity of bodily responses to SARS-CoV-2 infection have been reported in the clinical milieu. This raises the question of how to characterize the diversity of illness manifestations, and whether such characterization could reveal meaningful relationships across different illness manifestations. Here, we present a framework motivated by information theory to generate quantified maps of illness presentation, which we term "manifestations," as resolved by continuous physiological data from a wearable device (Oura Ring). We test this framework on five physiological data streams (heart rate, heart rate variability, respiratory rate, metabolic activity, and sleep temperature) assessed at the time of reported illness onset in a previously reported COVID-19-positive cohort (N = 73). We find that the number of distinct manifestations are few in this cohort, compared to the space of all possible manifestations. In addition, manifestation frequency correlates with the rough number of symptoms reported by a given individual, over a several-day period prior to their imputed onset of illness. These findings suggest that information-theoretic approaches can be used to sort COVID-19 illness manifestations into types with real-world value. This proof of concept supports the use of information-theoretic approaches to map illness manifestations from continuous physiological data. Such approaches could likely inform algorithm design and real-time treatment decisions if developed on large, diverse samples.
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Affiliation(s)
- Jacob M. Ryan
- Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, United States
| | - Shreenithi Navaneethan
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
| | - Natalie Damaso
- MIT Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, 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
| | - Joseph L. Natale
- Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, 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
- Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, United States
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
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Addleman JS, Lackey NS, DeBlauw JA, Hajduczok AG. Heart Rate Variability Applications in Strength and Conditioning: A Narrative Review. J Funct Morphol Kinesiol 2024; 9:93. [PMID: 38921629 PMCID: PMC11204851 DOI: 10.3390/jfmk9020093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 05/23/2024] [Accepted: 05/24/2024] [Indexed: 06/27/2024] Open
Abstract
Heart rate variability (HRV) is defined as the fluctuation of time intervals between adjacent heartbeats and is commonly used as a surrogate measure of autonomic function. HRV has become an increasingly measured variable by wearable technology for use in fitness and sport applications. However, with its increased use, a gap has arisen between the research and the application of this technology in strength and conditioning. The goal of this narrative literature review is to discuss current evidence and propose preliminary guidelines regarding the application of HRV in strength and conditioning. A literature review was conducted searching for HRV and strength and conditioning, aiming to focus on studies with time-domain measurements. Studies suggest that HRV is a helpful metric to assess training status, adaptability, and recovery after a training program. Although reduced HRV may be a sign of overreaching and/or overtraining syndrome, it may not be a sensitive marker in aerobic-trained athletes and therefore has different utilities for different athletic populations. There is likely utility to HRV-guided programming compared to predefined programming in several types of training. Evidence-based preliminary guidelines for the application of HRV in strength and conditioning are discussed. This is an evolving area of research, and more data are needed to evaluate the best practices for applying HRV in strength and conditioning.
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Affiliation(s)
- Jennifer S. Addleman
- College of Osteopathic Medicine, Touro University California, Vallejo, CA 94592, USA
| | - Nicholas S. Lackey
- Center for Applied Biobehavioral Sciences (CABS), Alliant International University, San Diego, CA 92131, USA;
| | - Justin A. DeBlauw
- Department of Health and Human Physiological Sciences, Skidmore College, Saratoga Springs, NY 12866, USA
| | - Alexander G. Hajduczok
- Department of Cardiology, Thomas Jefferson University Hospital, Philadelphia, PA 19107, USA;
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Liviero F, Scapellato ML, Volpin A, Battistella M, Fabris L, Brischigliaro L, Folino F, Moretto A, Mason P, Pavanello S. Long term follow-up of heart rate variability in healthcare workers with mild COVID-19. Front Neurol 2024; 15:1403551. [PMID: 38827576 PMCID: PMC11141692 DOI: 10.3389/fneur.2024.1403551] [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: 03/19/2024] [Accepted: 04/17/2024] [Indexed: 06/04/2024] Open
Abstract
Introduction Prior investigations into post-COVID dysautonomia often lacked control groups or compared affected individuals solely to healthy volunteers. In addition, no data on the follow-up of patients with SARS-CoV-2-related autonomic imbalance are available. Methods In this study, we conducted a comprehensive clinical and functional follow-up on healthcare workers (HCWs) with former mild COVID-19 (group 1, n = 67), to delineate the trajectory of post-acute autonomic imbalance, we previously detected in a case-control study. Additionally, we assessed HCWs for which a test before SARS-CoV-2 infection was available (group 2, n = 29), who later contracted SARS-CoV-2, aiming to validate findings from our prior case-control investigation. We evaluated autonomic nervous system heart modulation by means of time and frequency domain heart rate variability analysis (HRV) in HCWs during health surveillance visits. Short-term electrocardiogram (ECG) recordings, were obtained at about 6, 13 months and both at 6 and 13 months from the negative SARS-CoV-2 naso-pharyngeal swab (NPS) for group 1 and at about 1-month from the negative NPS for group 2. HCWs who used drugs, had comorbidities that affected HRV, or were hospitalized with severe COVID-19 were excluded. Results Group 1 was split into three subgroups clinically and functionally followed at, about 6 months (subgroup-A, n = 17), 13 months (subgroup-B, n = 37) and both at 6 and 13 months (subgroup-C, n = 13) from the negative SARS-CoV-2 NPS. In subgroup-A, at 6-month follow-up compared with baseline, the spectral components in the frequency domain HRV parameters, showed an increase in normalized high frequency power (nHF) (t = 2.99, p = 0.009), a decrease in the normalized low frequency power (nLF) (t = 2.98, p = 0.009) and in the LF/HF ratio (t = 3.13, p = 0.006). In subgroup B, the comparison of the spectral components in the frequency domain HRV parameters, at 13-month follow-up compared with baseline, showed an increase in nHF (t = 2.54, p = 0.02); a decrease in nLF (t = 2.62, p = 0.01) and in the LF/HF ratio (t = 4.00, p = 0.0003). In subgroup-C, at both 6 and 13-month follow-ups, the spectral components in the frequency domain HRV parameters were higher than baseline in nHF (t = 2.64, p = 0.02 and (t = 2.13, p = 0.05, respectively); lower in nLF (t = 2.64, p = 0.02 and (t = 2.13, p = 0.05, respectively), and in LF/HF (t = 1.92, p = 0.08 and (t = 2.43, p = 0.03, respectively). A significant proportion of HCWs reported persistent COVID-19 symptoms at both the 6 and 13-month follow-ups, seemingly unrelated to cardiac autonomic balance. In group 2 HCWs, at 1-month follow-up compared with baseline, the spectral components in the frequency domain HRV parameters, showed a decrease in nHF (t = 2.19, p = 0.04); an increase in nLF (t = 2.15, p = 0.04) and in LF/HF (t = 3.49, p = 0.002). Conclusion These results are consistent with epidemiological data suggesting a higher risk of acute cardiovascular complications during the first 30 days after COVID-19. The SARS-CoV-2 associated autonomic imbalance in the post-acute phase after recovery of mild COVID-19 resolved 6 months after the first negative SARS-CoV-2 NPS. However, a significant proportion of HCWs reported long-term COVID-19 symptoms, which dot not seems to be related to cardiac autonomic balance. Future research should certainly further test whether autonomic imbalance has a role in the mechanisms of long-COVID syndrome.
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Affiliation(s)
- Filippo Liviero
- Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy
- Occupational Medicine Unit, University Hospital of Padova, Padova, Italy
| | - Maria Luisa Scapellato
- Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy
- Occupational Medicine Unit, University Hospital of Padova, Padova, Italy
| | - Anna Volpin
- Occupational Medicine Unit, University Hospital of Padova, Padova, Italy
| | - Monica Battistella
- Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy
- Occupational Medicine Unit, University Hospital of Padova, Padova, Italy
| | - Laura Fabris
- Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy
- Occupational Medicine Unit, University Hospital of Padova, Padova, Italy
| | - Laura Brischigliaro
- Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy
| | - Franco Folino
- Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy
| | - Angelo Moretto
- Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy
- Occupational Medicine Unit, University Hospital of Padova, Padova, Italy
| | - Paola Mason
- Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy
- Occupational Medicine Unit, University Hospital of Padova, Padova, Italy
| | - Sofia Pavanello
- Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, Padova, Italy
- Occupational Medicine Unit, University Hospital of Padova, Padova, Italy
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Hirten RP, Danieletto M, Landell K, Zweig M, Golden E, Pyzik R, Kaur S, Chang H, Helmus D, Sands BE, Charney D, Nadkarni G, Bagiella E, Keefer L, Fayad ZA. Remote Short Sessions of Heart Rate Variability Biofeedback Monitored With Wearable Technology: Open-Label Prospective Feasibility Study. JMIR Ment Health 2024; 11:e55552. [PMID: 38663011 PMCID: PMC11082734 DOI: 10.2196/55552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 02/06/2024] [Accepted: 02/20/2024] [Indexed: 05/01/2024] Open
Abstract
BACKGROUND Heart rate variability (HRV) biofeedback is often performed with structured education, laboratory-based assessments, and practice sessions. It has been shown to improve psychological and physiological function across populations. However, a means to remotely use and monitor this approach would allow for wider use of this technique. Advancements in wearable and digital technology present an opportunity for the widespread application of this approach. OBJECTIVE The primary aim of the study was to determine the feasibility of fully remote, self-administered short sessions of HRV-directed biofeedback in a diverse population of health care workers (HCWs). The secondary aim was to determine whether a fully remote, HRV-directed biofeedback intervention significantly alters longitudinal HRV over the intervention period, as monitored by wearable devices. The tertiary aim was to estimate the impact of this intervention on metrics of psychological well-being. METHODS To determine whether remotely implemented short sessions of HRV biofeedback can improve autonomic metrics and psychological well-being, we enrolled HCWs across 7 hospitals in New York City in the United States. They downloaded our study app, watched brief educational videos about HRV biofeedback, and used a well-studied HRV biofeedback program remotely through their smartphone. HRV biofeedback sessions were used for 5 minutes per day for 5 weeks. HCWs were then followed for 12 weeks after the intervention period. Psychological measures were obtained over the study period, and they wore an Apple Watch for at least 7 weeks to monitor the circadian features of HRV. RESULTS In total, 127 HCWs were enrolled in the study. Overall, only 21 (16.5%) were at least 50% compliant with the HRV biofeedback intervention, representing a small portion of the total sample. This demonstrates that this study design does not feasibly result in adequate rates of compliance with the intervention. Numerical improvement in psychological metrics was observed over the 17-week study period, although it did not reach statistical significance (all P>.05). Using a mixed effect cosinor model, the mean midline-estimating statistic of rhythm (MESOR) of the circadian pattern of the SD of the interbeat interval of normal sinus beats (SDNN), an HRV metric, was observed to increase over the first 4 weeks of the biofeedback intervention in HCWs who were at least 50% compliant. CONCLUSIONS In conclusion, we found that using brief remote HRV biofeedback sessions and monitoring its physiological effect using wearable devices, in the manner that the study was conducted, was not feasible. This is considering the low compliance rates with the study intervention. We found that remote short sessions of HRV biofeedback demonstrate potential promise in improving autonomic nervous function and warrant further study. Wearable devices can monitor the physiological effects of psychological interventions.
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Affiliation(s)
- Robert P Hirten
- The Dr Henry D Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Matteo Danieletto
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Kyle Landell
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Micol Zweig
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Eddye Golden
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Renata Pyzik
- The BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Sparshdeep Kaur
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Helena Chang
- Center for Biostatistics, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Drew Helmus
- The Dr Henry D Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Bruce E Sands
- The Dr Henry D Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Dennis Charney
- Office of the Dean, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Girish Nadkarni
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Emilia Bagiella
- Center for Biostatistics, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Laurie Keefer
- The Dr Henry D Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Zahi A Fayad
- The BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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9
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Volpes G, Valenti S, Genova G, Barà C, Parisi A, Faes L, Busacca A, Pernice R. Wearable Ring-Shaped Biomedical Device for Physiological Monitoring through Finger-Based Acquisition of Electrocardiographic, Photoplethysmographic, and Galvanic Skin Response Signals: Design and Preliminary Measurements. BIOSENSORS 2024; 14:205. [PMID: 38667198 PMCID: PMC11048376 DOI: 10.3390/bios14040205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2024] [Revised: 04/12/2024] [Accepted: 04/18/2024] [Indexed: 04/28/2024]
Abstract
Wearable health devices (WHDs) are rapidly gaining ground in the biomedical field due to their ability to monitor the individual physiological state in everyday life scenarios, while providing a comfortable wear experience. This study introduces a novel wearable biomedical device capable of synchronously acquiring electrocardiographic (ECG), photoplethysmographic (PPG), galvanic skin response (GSR) and motion signals. The device has been specifically designed to be worn on a finger, enabling the acquisition of all biosignals directly on the fingertips, offering the significant advantage of being very comfortable and easy to be employed by the users. The simultaneous acquisition of different biosignals allows the extraction of important physiological indices, such as heart rate (HR) and its variability (HRV), pulse arrival time (PAT), GSR level, blood oxygenation level (SpO2), and respiratory rate, as well as motion detection, enabling the assessment of physiological states, together with the detection of potential physical and mental stress conditions. Preliminary measurements have been conducted on healthy subjects using a measurement protocol consisting of resting states (i.e., SUPINE and SIT) alternated with physiological stress conditions (i.e., STAND and WALK). Statistical analyses have been carried out among the distributions of the physiological indices extracted in time, frequency, and information domains, evaluated under different physiological conditions. The results of our analyses demonstrate the capability of the device to detect changes between rest and stress conditions, thereby encouraging its use for assessing individuals' physiological state. Furthermore, the possibility of performing synchronous acquisitions of PPG and ECG signals has allowed us to compare HRV and pulse rate variability (PRV) indices, so as to corroborate the reliability of PRV analysis under stationary physical conditions. Finally, the study confirms the already known limitations of wearable devices during physical activities, suggesting the use of algorithms for motion artifact correction.
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Affiliation(s)
| | | | | | | | | | | | | | - Riccardo Pernice
- Department of Engineering, University of Palermo, Viale delle Scienze, Building 9, 90128 Palermo, Italy; (G.V.); (S.V.); (G.G.); (C.B.); (A.P.); (L.F.); (A.B.)
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10
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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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11
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Braun BJ, Histing T, Menger MM, Herath SC, Mueller-Franzes GA, Grimm B, Marmor MT, Truhn D. Wearable activity data can predict functional recovery after musculoskeletal injury: Feasibility of a machine learning approach. Injury 2024; 55:111254. [PMID: 38070329 DOI: 10.1016/j.injury.2023.111254] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 10/23/2023] [Accepted: 11/26/2023] [Indexed: 01/29/2024]
Abstract
Delayed functional recovery after injury is associated with significant personal and socioeconomic burden. Identification of patients at risk for a prolonged recovery after a musculoskeletal injury is thus of high relevance. The aim of the current study was to show the feasibility of using a machine learning assisted model to predict functional recovery based on the pre- and immediate post injury patient activity as measured with wearable systems in trauma patients. Patients with a pre-existing wearable (smartphone and/or body-worn sensor), data availability of at least 7 days prior to their injury, and any musculoskeletal injury of the upper or lower extremity were included in this study. Patient age, sex, injured extremity, time off work and step count as activity data were recorded continuously both pre- and post-injury. Descriptive statistics were performed and a logistic regression machine learning model was used to predict the patient's functional recovery status after 6 weeks based on their pre- and post-injury activity characteristics. Overall 38 patients (7 upper extremity, 24 lower extremity, 5 pelvis, 2 combined) were included in this proof-of-concept study. The average follow-up with available wearable data was 85.4 days. Based on the activity data, a predictive model was constructed to determine the likelihood of having a recovery of at least 50 % of the pre-injury activity state by post injury week 6. Based on the individual activity by week 3 a predictive accuracy of over 80 % was achieved on an independent test set (F1=0,82; AUC=0,86; ACC=8,83). The employed model is feasible to assess the principal risk for a slower recovery based on readily available personal wearable activity data. The model has the potential to identify patients requiring additional aftercare attention early during the treatment course, thus optimizing return to the pre-injury status through focused interventions. Additional patient data is needed to adapt the model to more specifically focus on different fracture entities and patient groups.
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Affiliation(s)
- Benedikt J Braun
- University Hospital Tuebingen, Eberhard-Karls-University Tuebingen, BG Unfallklinik, Schnarrenbergstr. 95, Tuebingen 72076, Federal Republic of Germany.
| | - Tina Histing
- University Hospital Tuebingen, Eberhard-Karls-University Tuebingen, BG Unfallklinik, Schnarrenbergstr. 95, Tuebingen 72076, Federal Republic of Germany
| | - Maximilian M Menger
- University Hospital Tuebingen, Eberhard-Karls-University Tuebingen, BG Unfallklinik, Schnarrenbergstr. 95, Tuebingen 72076, Federal Republic of Germany
| | - Steven C Herath
- University Hospital Tuebingen, Eberhard-Karls-University Tuebingen, BG Unfallklinik, Schnarrenbergstr. 95, Tuebingen 72076, Federal Republic of Germany
| | - Gustav A Mueller-Franzes
- Departments of Diagnostic and Interventional Radiology, RWTH Aachen University Aachen, Aachen, Federal Republic of Germany
| | - Bernd Grimm
- Orthopaedic Trauma Institute (OTI), University of California, San Francisco General Hospital, San Franci-sco, CA, USA
| | - Meir T Marmor
- Department of Precision Health, Human Motion, Orthopaedics, Sports Medicine and Digital Methods Group, Lux-embourg Institute of Health, Strassen 1445, Luxembourg
| | - Daniel Truhn
- Departments of Diagnostic and Interventional Radiology, RWTH Aachen University Aachen, Aachen, Federal Republic of Germany
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12
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Hirten RP, Lin KC, Whang J, Shahub S, Churcher NK, Helmus D, Muthukumar S, Sands B, Prasad S. Longitudinal monitoring of IL-6 and CRP in inflammatory bowel disease using IBD-AWARE. BIOSENSORS & BIOELECTRONICS: X 2024; 16:100435. [PMID: 38317723 PMCID: PMC10843811 DOI: 10.1016/j.biosx.2023.100435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
There are limitations to monitoring modalities for chronic inflammatory conditions, including inflammatory bowel disease (IBD). Wearable devices are scalable mobile health technology that present an opportunity to monitor markers that have been linked to worsening, chronic inflammatory conditions and enable remote monitoring. In this research article, we evaluate and demonstrate a proof-of-concept wearable device to longitudinally monitor inflammatory and immune markers linked to IBD disease activity in sweat compared to expression in serum. Sixteen participants with an IBD-related hospital admission and a C-reactive protein (CRP) > 5 μg/mL were followed for up to 5 days. The sweat sensing device also known as IBD AWARE was worn to continuously measure CRP and interleukin-6 (IL-6) in the sweat of participants via electrochemical impedance spectroscopy. Serum samples were collected daily. A linear relationship between serum and sweat readings for CRP and IL-6 was demonstrated based on individual linear correlation coefficients. Pooled CRP and IL-6 serum-to-sweat ratios demonstrated improving correlation coefficients as serum cutoffs decreased. Between the first and last day of observation, significant and non-significant trends in serum CRP and IL-6 were observed in the sweat. Comparison of sweat measurements between the subjects with active IBD and 10 healthy subjects distinguished an inflamed and uninflamed state with an AUC of 0.85 (95% CI: 0.68-1.00) and a sensitivity and specificity of 82% and 70% at a CRP cutoff of 938.9 pg/mL. IBD AWARE wearable device holds promise in longitudinally monitoring individuals with IBD and other inflammatory diseases.
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Affiliation(s)
- Robert P. Hirten
- The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kai-Chun Lin
- Department of Bioengineering Engineering, The University of Texas at Dallas, Richardson, TX, USA
| | - Jessica Whang
- The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sarah Shahub
- Department of Bioengineering Engineering, The University of Texas at Dallas, Richardson, TX, USA
| | - Nathan K.M. Churcher
- Department of Bioengineering Engineering, The University of Texas at Dallas, Richardson, TX, USA
| | - Drew Helmus
- The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Bruce Sands
- The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Shalini Prasad
- Department of Bioengineering Engineering, The University of Texas at Dallas, Richardson, TX, USA
- EnLiSense LLC, Allen, TX, USA
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13
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Babu M, Lautman Z, Lin X, Sobota MHB, Snyder MP. Wearable Devices: Implications for Precision Medicine and the Future of Health Care. Annu Rev Med 2024; 75:401-415. [PMID: 37983384 DOI: 10.1146/annurev-med-052422-020437] [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: 11/22/2023]
Abstract
Wearable devices are integrated analytical units equipped with sensitive physical, chemical, and biological sensors capable of noninvasive and continuous monitoring of vital physiological parameters. Recent advances in disciplines including electronics, computation, and material science have resulted in affordable and highly sensitive wearable devices that are routinely used for tracking and managing health and well-being. Combined with longitudinal monitoring of physiological parameters, wearables are poised to transform the early detection, diagnosis, and treatment/management of a range of clinical conditions. Smartwatches are the most commonly used wearable devices and have already demonstrated valuable biomedical potential in detecting clinical conditions such as arrhythmias, Lyme disease, inflammation, and, more recently, COVID-19 infection. Despite significant clinical promise shown in research settings, there remain major hurdles in translating the medical uses of wearables to the clinic. There is a clear need for more effective collaboration among stakeholders, including users, data scientists, clinicians, payers, and governments, to improve device security, user privacy, data standardization, regulatory approval, and clinical validity. This review examines the potential of wearables to offer affordable and reliable measures of physiological status that are on par with FDA-approved specialized medical devices. We briefly examine studies where wearables proved critical for the early detection of acute and chronic clinical conditions with a particular focus on cardiovascular disease, viral infections, and mental health. Finally, we discuss current obstacles to the clinical implementation of wearables and provide perspectives on their potential to deliver increasingly personalized proactive health care across a wide variety of conditions.
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Affiliation(s)
- Mohan Babu
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA;
| | - Ziv Lautman
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA;
- Department of Bioengineering, Stanford University School of Medicine, Stanford, California, USA
| | - Xiangping Lin
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA;
| | - Milan H B Sobota
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA;
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA;
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14
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Zhang C, Sun A, Liao J, Zhang C, Yu K, Ma X, Wang G. COVID-19 surveillance based on consumer wearable devices. Digit Health 2024; 10:20552076241247374. [PMID: 38665889 PMCID: PMC11044784 DOI: 10.1177/20552076241247374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 03/15/2024] [Indexed: 04/28/2024] Open
Abstract
Background Consumer wearable devices such as wristbands and smartwatches have potential application value in communicable disease surveillance. Objective We investigated the ability of wearable devices to monitor COVID-19 patients of varying severity. Methods COVID-19 patients with mobile phones supporting wearable device applications were selected from Dalian Sixth People Hospital. Physiological parameters from the wearable devices and electronic questionnaires were collected from the device wearing until 14 days post-discharge. Clinical information during hospitalization was also recorded. Based on imaging data, the patients were categorized into the milder group without pneumonia and the more severe group with pneumonia. We plotted the curves of the physiological parameters of the two groups to compare the differences and changes. Results Ninety-eight patients were included in the analysis. The mean age was 39.6 ± 10.5 years, including 45 males (45.9%). There were 24 asymptomatic patients, 10 mild patients, 60 moderate patients, and 4 severe patients. Compared with the milder group, the more severe group had higher heart rate-related parameters, while the heart rate variability (HRV) was the opposite. In the more severe group, the heart rate-related parameters showed a downward trend from 0 to 7 days after the fever resolution. Among them, the resting heart rate and sleep heart rate decreased on the 25th day after the onset and were close to the milder group 1 week after discharge. Conclusions Consumer wearable devices have the potential to monitor respiratory infections. Heart rate-related parameters obtained from these devices can be sensitive indicators of COVID-19 severity and correlate with disease evolution. Trial registration ClinicalTrials.gov NCT04459637.
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Affiliation(s)
- Chunbo Zhang
- Department of Respiratory and Critical Care Medicine, Peking University First Hospital, Beijing, China
| | - Aijun Sun
- Dalian Sixth People Hospital, Dalian, Liaoning, China
| | - Jiping Liao
- Department of Respiratory and Critical Care Medicine, Peking University First Hospital, Beijing, China
| | - Chunbo Zhang
- Department of Respiratory and Critical Care Medicine, Peking University First Hospital, Beijing, China
| | - Kunyao Yu
- Department of Respiratory and Critical Care Medicine, Peking University First Hospital, Beijing, China
| | - Xiaoyu Ma
- Department of Respiratory and Critical Care Medicine, Peking University First Hospital, Beijing, China
| | - Guangfa Wang
- Department of Respiratory and Critical Care Medicine, Peking University First Hospital, Beijing, China
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15
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El-Toukhy S, Hegeman P, Zuckerman G, Anirban RD, Moses N, Troendle JF, Powell-Wiley TM. A prospective natural history study of post acute sequalae of COVID-19 using digital wearables: Study protocol. RESEARCH SQUARE 2023:rs.3.rs-3694818. [PMID: 38105936 PMCID: PMC10723530 DOI: 10.21203/rs.3.rs-3694818/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Background Post-acute sequelae of COVID-19 (PASC) is characterized by having 1 + persistent, recurrent, or emergent symptoms post the infection's acute phase. The duration and symptom manifestation of PASC remain understudied in nonhospitalized patients. Literature on PASC is primarily based on data from hospitalized patients where clinical indicators such as respiratory rate, heart rate, and oxygen saturation have been predictive of disease trajectories. Digital wearables allow for a continuous collection of such physiological parameters. This protocol outlines the design, aim, and procedures of a natural history study of PASC using digital wearables. Methods This is a single-arm, prospective, natural history study of a cohort of 550 patients, ages 18 to 65 years old, males or females who own a smartphone and/or a tablet that meets pre-determined Bluetooth version and operating system requirements, speak English, and provide documentation of a positive COVID-19 test issued by a healthcare professional or organization within 5 days before enrollment. The study aims to identify wearables collected physiological parameters that are associated with PASC in patients with a positive diagnosis. The primary endpoint is long COVID-19, defined as ≥ 1 symptom at 3 weeks beyond first symptom onset or positive diagnosis, whichever comes first. The secondary endpoint is chronic COVID-19, defined as ≥ 1 symptom at 12 weeks beyond first symptom onset or positive diagnosis. We hypothesize that physiological parameters collected via wearables are associated with self-reported PASC. Participants must be willing and able to consent to participate in the study and adhere to study procedures for six months. Discussion This is a fully decentralized study investigating PASC using wearable devices to collect physiological parameters and patient-reported outcomes. Given evidence on key demographics and risk profiles associated with PASC, the study will shed light on the duration and symptom manifestation of PASC in nonhospitalized patient subgroups and is an exemplar of use of wearables as population-level monitoring health tools for communicable diseases. Trial registration ClinicalTrials.gov NCT04927442.
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Affiliation(s)
| | - Phillip Hegeman
- National Institute on Minority Health and Health Disparities
| | | | | | - Nia Moses
- National Institute on Minority Health and Health Disparities
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16
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Li K, Cardoso C, Moctezuma-Ramirez A, Elgalad A, Perin E. Heart Rate Variability Measurement through a Smart Wearable Device: Another Breakthrough for Personal Health Monitoring? INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:7146. [PMID: 38131698 PMCID: PMC10742885 DOI: 10.3390/ijerph20247146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 11/06/2023] [Accepted: 11/27/2023] [Indexed: 12/23/2023]
Abstract
Heart rate variability (HRV) is a measurement of the fluctuation of time between each heartbeat and reflects the function of the autonomic nervous system. HRV is an important indicator for both physical and mental status and for broad-scope diseases. In this review, we discuss how wearable devices can be used to monitor HRV, and we compare the HRV monitoring function among different devices. In addition, we have reviewed the recent progress in HRV tracking with wearable devices and its value in health monitoring and disease diagnosis. Although many challenges remain, we believe HRV tracking with wearable devices is a promising tool that can be used to improve personal health.
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Affiliation(s)
- Ke Li
- Center for Preclinical Cardiovascular Research, The Texas Heart Institute, Houston, TX 77030, USA
| | - Cristiano Cardoso
- Center for Preclinical Cardiovascular Research, The Texas Heart Institute, Houston, TX 77030, USA
| | - Angel Moctezuma-Ramirez
- Center for Preclinical Cardiovascular Research, The Texas Heart Institute, Houston, TX 77030, USA
| | - Abdelmotagaly Elgalad
- Center for Preclinical Cardiovascular Research, The Texas Heart Institute, Houston, TX 77030, USA
| | - Emerson Perin
- Center for Clinical Research, The Texas Heart Institute, Houston, TX 77030, USA
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17
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Hirten RP, Danieletto M, Landell K, Zweig M, Golden E, Orlov G, Rodrigues J, Alleva E, Ensari I, Bottinger E, Nadkarni GN, Fuchs TJ, Fayad ZA. Development of the ehive Digital Health App: Protocol for a Centralized Research Platform. JMIR Res Protoc 2023; 12:e49204. [PMID: 37971801 PMCID: PMC10690532 DOI: 10.2196/49204] [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: 05/21/2023] [Revised: 09/14/2023] [Accepted: 09/22/2023] [Indexed: 11/19/2023] Open
Abstract
BACKGROUND The increasing use of smartphones, wearables, and connected devices has enabled the increasing application of digital technologies for research. Remote digital study platforms comprise a patient-interfacing digital application that enables multimodal data collection from a mobile app and connected sources. They offer an opportunity to recruit at scale, acquire data longitudinally at a high frequency, and engage study participants at any time of the day in any place. Few published descriptions of centralized digital research platforms provide a framework for their development. OBJECTIVE This study aims to serve as a road map for those seeking to develop a centralized digital research platform. We describe the technical and functional aspects of the ehive app, the centralized digital research platform of the Hasso Plattner Institute for Digital Health at Mount Sinai Hospital, New York, New York. We then provide information about ongoing studies hosted on ehive, including usership statistics and data infrastructure. Finally, we discuss our experience with ehive in the broader context of the current landscape of digital health research platforms. METHODS The ehive app is a multifaceted and patient-facing central digital research platform that permits the collection of e-consent for digital health studies. An overview of its development, its e-consent process, and the tools it uses for participant recruitment and retention are provided. Data integration with the platform and the infrastructure supporting its operations are discussed; furthermore, a description of its participant- and researcher-facing dashboard interfaces and the e-consent architecture is provided. RESULTS The ehive platform was launched in 2020 and has successfully hosted 8 studies, namely 6 observational studies and 2 clinical trials. Approximately 1484 participants downloaded the app across 36 states in the United States. The use of recruitment methods such as bulk messaging through the EPIC electronic health records and standard email portals enables broad recruitment. Light-touch engagement methods, used in an automated fashion through the platform, maintain high degrees of engagement and retention. The ehive platform demonstrates the successful deployment of a central digital research platform that can be modified across study designs. CONCLUSIONS Centralized digital research platforms such as ehive provide a novel tool that allows investigators to expand their research beyond their institution, engage in large-scale longitudinal studies, and combine multimodal data streams. The ehive platform serves as a model for groups seeking to develop similar digital health research programs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/49204.
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Affiliation(s)
- Robert P Hirten
- Henry D Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, United States
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Matteo Danieletto
- Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, United States
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Kyle Landell
- Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, United States
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Micol Zweig
- Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, United States
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Eddye Golden
- Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, United States
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Georgy Orlov
- Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, United States
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Jovita Rodrigues
- Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, United States
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Eugenia Alleva
- Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, United States
| | - Ipek Ensari
- Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, United States
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Erwin Bottinger
- Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, United States
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Girish N Nadkarni
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Thomas J Fuchs
- Hasso Plattner Institute for Digital Health at Mount Sinai, New York, NY, United States
- Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Zahi A Fayad
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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18
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Sanches CA, Silva GA, Librantz AFH, Sampaio LMM, Belan PA. Wearable Devices to Diagnose and Monitor the Progression of COVID-19 Through Heart Rate Variability Measurement: Systematic Review and Meta-Analysis. J Med Internet Res 2023; 25:e47112. [PMID: 37820372 PMCID: PMC10685286 DOI: 10.2196/47112] [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: 03/08/2023] [Revised: 06/28/2023] [Accepted: 10/10/2023] [Indexed: 10/13/2023] Open
Abstract
BACKGROUND Recent studies have linked low heart rate variability (HRV) with COVID-19, indicating that this parameter can be a marker of the onset of the disease and its severity and a predictor of mortality in infected people. Given the large number of wearable devices that capture physiological signals of the human body easily and noninvasively, several studies have used this equipment to measure the HRV of individuals and related these measures to COVID-19. OBJECTIVE The objective of this study was to assess the utility of HRV measurements obtained from wearable devices as predictive indicators of COVID-19, as well as the onset and worsening of symptoms in affected individuals. METHODS A systematic review was conducted searching the following databases up to the end of January 2023: Embase, PubMed, Web of Science, Scopus, and IEEE Xplore. Studies had to include (1) measures of HRV in patients with COVID-19 and (2) measurements involving the use of wearable devices. We also conducted a meta-analysis of these measures to reduce possible biases and increase the statistical power of the primary research. RESULTS The main finding was the association between low HRV and the onset and worsening of COVID-19 symptoms. In some cases, it was possible to predict the onset of COVID-19 before a positive clinical test. The meta-analysis of studies reported that a reduction in HRV parameters is associated with COVID-19. Individuals with COVID-19 presented a reduction in the SD of the normal-to-normal interbeat intervals and root mean square of the successive differences compared with healthy individuals. The decrease in the SD of the normal-to-normal interbeat intervals was 3.25 ms (95% CI -5.34 to -1.16 ms), and the decrease in the root mean square of the successive differences was 1.24 ms (95% CI -3.71 to 1.23 ms). CONCLUSIONS Wearable devices that measure changes in HRV, such as smartwatches, rings, and bracelets, provide information that allows for the identification of COVID-19 during the presymptomatic period as well as its worsening through an indirect and noninvasive self-diagnosis.
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Affiliation(s)
- Carlos Alberto Sanches
- Informatics and Knowledge Management Graduate Program, Universidade Nove de Julho, São Paulo, Brazil
| | - Graziella Alves Silva
- Informatics and Knowledge Management Graduate Program, Universidade Nove de Julho, São Paulo, Brazil
| | | | | | - Peterson Adriano Belan
- Informatics and Knowledge Management Graduate Program, Universidade Nove de Julho, São Paulo, Brazil
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19
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Hasasneh A, Hijazi H, Talib MA, Afadar Y, Nassif AB, Nasir Q. Wearable Devices and Explainable Unsupervised Learning for COVID-19 Detection and Monitoring. Diagnostics (Basel) 2023; 13:3071. [PMID: 37835814 PMCID: PMC10572947 DOI: 10.3390/diagnostics13193071] [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: 07/24/2023] [Revised: 09/20/2023] [Accepted: 09/22/2023] [Indexed: 10/15/2023] Open
Abstract
Despite the declining COVID-19 cases, global healthcare systems still face significant challenges due to ongoing infections, especially among fully vaccinated individuals, including adolescents and young adults (AYA). To tackle this issue, cost-effective alternatives utilizing technologies like Artificial Intelligence (AI) and wearable devices have emerged for disease screening, diagnosis, and monitoring. However, many AI solutions in this context heavily rely on supervised learning techniques, which pose challenges such as human labeling reliability and time-consuming data annotation. In this study, we propose an innovative unsupervised framework that leverages smartwatch data to detect and monitor COVID-19 infections. We utilize longitudinal data, including heart rate (HR), heart rate variability (HRV), and physical activity measured via step count, collected through the continuous monitoring of volunteers. Our goal is to offer effective and affordable solutions for COVID-19 detection and monitoring. Our unsupervised framework employs interpretable clusters of normal and abnormal measures, facilitating disease progression detection. Additionally, we enhance result interpretation by leveraging the language model Davinci GPT-3 to gain deeper insights into the underlying data patterns and relationships. Our results demonstrate the effectiveness of unsupervised learning, achieving a Silhouette score of 0.55. Furthermore, validation using supervised learning techniques yields high accuracy (0.884 ± 0.005), precision (0.80 ± 0.112), and recall (0.817 ± 0.037). These promising findings indicate the potential of unsupervised techniques for identifying inflammatory markers, contributing to the development of efficient and reliable COVID-19 detection and monitoring methods. Our study shows the capabilities of AI and wearables, reflecting the pursuit of low-cost, accessible solutions for addressing health challenges related to inflammatory diseases, thereby opening new avenues for scalable and widely applicable health monitoring solutions.
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Affiliation(s)
- Ahmad Hasasneh
- Department of Natural, Engineering, and Technology Sciences, Faculty of Graduate Studies, Arab American University, Ramallah P-600-699, Palestine;
| | - Haytham Hijazi
- Department of Informatics Engineering, CISUC-Centre for Informatics and Systems of the University of Coimbra, University of Coimbra, 3030-790 Coimbra, Portugal
- Intelligent Systems Department, Palestine Ahliya University, Bethlehem P-150-199, Palestine
| | - Manar Abu Talib
- College of Computing and Informatics, University of Sharjah, Sharjah 27272, United Arab Emirates; (M.A.T.); (Y.A.); (A.B.N.); (Q.N.)
| | - Yaman Afadar
- College of Computing and Informatics, University of Sharjah, Sharjah 27272, United Arab Emirates; (M.A.T.); (Y.A.); (A.B.N.); (Q.N.)
| | - Ali Bou Nassif
- College of Computing and Informatics, University of Sharjah, Sharjah 27272, United Arab Emirates; (M.A.T.); (Y.A.); (A.B.N.); (Q.N.)
| | - Qassim Nasir
- College of Computing and Informatics, University of Sharjah, Sharjah 27272, United Arab Emirates; (M.A.T.); (Y.A.); (A.B.N.); (Q.N.)
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20
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Hirten RP, Suprun M, Danieletto M, Zweig M, Golden E, Pyzik R, Kaur S, Helmus D, Biello A, Landell K, Rodrigues J, Bottinger EP, Keefer L, Charney D, Nadkarni GN, Suarez-Farinas M, Fayad ZA. A machine learning approach to determine resilience utilizing wearable device data: analysis of an observational cohort. JAMIA Open 2023; 6:ooad029. [PMID: 37143859 PMCID: PMC10152991 DOI: 10.1093/jamiaopen/ooad029] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 03/22/2023] [Accepted: 04/06/2023] [Indexed: 05/06/2023] Open
Abstract
Objective To assess whether an individual's degree of psychological resilience can be determined from physiological metrics passively collected from a wearable device. Materials and Methods Data were analyzed in this secondary analysis of the Warrior Watch Study dataset, a prospective cohort of healthcare workers enrolled across 7 hospitals in New York City. Subjects wore an Apple Watch for the duration of their participation. Surveys were collected measuring resilience, optimism, and emotional support at baseline. Results We evaluated data from 329 subjects (mean age 37.4 years, 37.1% male). Across all testing sets, gradient-boosting machines (GBM) and extreme gradient-boosting models performed best for high- versus low-resilience prediction, stratified on a median Connor-Davidson Resilience Scale-2 score of 6 (interquartile range = 5-7), with an AUC of 0.60. When predicting resilience as a continuous variable, multivariate linear models had a correlation of 0.24 (P = .029) and RMSE of 1.37 in the testing data. A positive psychological construct, comprised of resilience, optimism, and emotional support was also evaluated. The oblique random forest method performed best in estimating high- versus low-composite scores stratified on a median of 32.5, with an AUC of 0.65, a sensitivity of 0.60, and a specificity of 0.70. Discussion In a post hoc analysis, machine learning models applied to physiological metrics collected from wearable devices had some predictive ability in identifying resilience states and a positive psychological construct. Conclusions These findings support the further assessment of psychological characteristics from passively collected wearable data in dedicated studies.
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Affiliation(s)
- Robert P Hirten
- The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, New York, New York, USA
| | - Maria Suprun
- Department of Population Health Science and Policy, Center for Biostatistics, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Matteo Danieletto
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Micol Zweig
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Eddye Golden
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Renata Pyzik
- The BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Sparshdeep Kaur
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, New York, New York, USA
| | - Drew Helmus
- The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Anthony Biello
- The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Kyle Landell
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, New York, New York, USA
| | - Jovita Rodrigues
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, New York, New York, USA
| | - Erwin P Bottinger
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, New York, New York, USA
| | - Laurie Keefer
- The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Dennis Charney
- Office of the Dean, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Girish N Nadkarni
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, New York, New York, USA
- The Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Mayte Suarez-Farinas
- Department of Population Health Science and Policy, Center for Biostatistics, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Zahi A Fayad
- The BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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21
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Dolezalova N, Gkrania-Klotsas E, Morelli D, Moore A, Cunningham AC, Booth A, Plans D, Reed AB, Aral M, Rennie KL, Wareham NJ. Feasibility of using intermittent active monitoring of vital signs by smartphone users to predict SARS-CoV-2 PCR positivity. Sci Rep 2023; 13:10581. [PMID: 37386099 PMCID: PMC10310739 DOI: 10.1038/s41598-023-37301-y] [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: 03/06/2023] [Accepted: 06/20/2023] [Indexed: 07/01/2023] Open
Abstract
Early detection of highly infectious respiratory diseases, such as COVID-19, can help curb their transmission. Consequently, there is demand for easy-to-use population-based screening tools, such as mobile health applications. Here, we describe a proof-of-concept development of a machine learning classifier for the prediction of a symptomatic respiratory disease, such as COVID-19, using smartphone-collected vital sign measurements. The Fenland App study followed 2199 UK participants that provided measurements of blood oxygen saturation, body temperature, and resting heart rate. Total of 77 positive and 6339 negative SARS-CoV-2 PCR tests were recorded. An optimal classifier to identify these positive cases was selected using an automated hyperparameter optimisation. The optimised model achieved an ROC AUC of 0.695 ± 0.045. The data collection window for determining each participant's vital sign baseline was increased from 4 to 8 or 12 weeks with no significant difference in model performance (F(2) = 0.80, p = 0.472). We demonstrate that 4 weeks of intermittently collected vital sign measurements could be used to predict SARS-CoV-2 PCR positivity, with applicability to other diseases causing similar vital sign changes. This is the first example of an accessible, smartphone-based remote monitoring tool deployable in a public health setting to screen for potential infections.
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Affiliation(s)
| | - Effrossyni Gkrania-Klotsas
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Department of Infectious Diseases, Addenbrooke's Hospital, Box 25, Cambridge, UK
| | - Davide Morelli
- Huma Therapeutics Ltd., Millbank Tower, 21-24 Millbank, London, UK
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, UK
| | - Alex Moore
- Huma Therapeutics Ltd., Millbank Tower, 21-24 Millbank, London, UK.
| | | | - Adam Booth
- Huma Therapeutics Ltd., Millbank Tower, 21-24 Millbank, London, UK
| | - David Plans
- Huma Therapeutics Ltd., Millbank Tower, 21-24 Millbank, London, UK
- Department of Experimental Psychology, University of Oxford, Oxford, UK
- INDEX Group, Department of Science, Innovation, Technology, and Entrepreneurship, University of Exeter, Exeter, UK
| | - Angus B Reed
- Huma Therapeutics Ltd., Millbank Tower, 21-24 Millbank, London, UK
| | - Mert Aral
- Huma Therapeutics Ltd., Millbank Tower, 21-24 Millbank, London, UK
| | - Kirsten L Rennie
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
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22
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Mattison G, Canfell OJ, Forrester D, Dobbins C, Smith D, Reid D, Sullivan C. A step in the right direction: the potential role of smartwatches in supporting chronic disease prevention in health care. Med J Aust 2023; 218:384-388. [PMID: 37182214 DOI: 10.5694/mja2.51920] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 02/14/2023] [Accepted: 02/20/2023] [Indexed: 05/16/2023]
Affiliation(s)
- Graeme Mattison
- Queensland Digital Health Centre, University of Queensland, Brisbane, QLD
- Digital Health Cooperative Research Centre, Sydney, NSW
| | - Oliver J Canfell
- Queensland Digital Health Centre, University of Queensland, Brisbane, QLD
- Digital Health Cooperative Research Centre, Sydney, NSW
| | - Doug Forrester
- The Prince Charles Hospital, Brisbane, QLD
- Curtin University, Perth, WA
| | | | - Daniel Smith
- The Prince Charles Hospital, Brisbane, QLD
- University of Queensland, Brisbane, QLD
| | - David Reid
- The Prince Charles Hospital, Brisbane, QLD
- QIMR Berghofer Medical Research Institute, Brisbane, QLD
| | - Clair Sullivan
- Queensland Digital Health Centre, University of Queensland, Brisbane, QLD
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23
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Nestor B, Hunter J, Kainkaryam R, Drysdale E, Inglis JB, Shapiro A, Nagaraj S, Ghassemi M, Foschini L, Goldenberg A. Machine learning COVID-19 detection from wearables. Lancet Digit Health 2023; 5:e182-e184. [PMID: 36963907 PMCID: PMC10032660 DOI: 10.1016/s2589-7500(23)00045-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/24/2022] [Accepted: 02/21/2023] [Indexed: 03/24/2023]
Affiliation(s)
- Bret Nestor
- Department of Computer Science, University of Toronto, Toronto, ON M5S 3H5, Canada; Vector Institute, Toronto, ON, Canada; Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Jaryd Hunter
- Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
| | | | - Erik Drysdale
- Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Jeffrey B Inglis
- Interdepartmental Graduate Program in Dynamical Neuroscience, University of California, Santa Barbara, Santa Barbara, CA, USA
| | | | - Sujay Nagaraj
- Department of Computer Science, University of Toronto, Toronto, ON M5S 3H5, Canada; Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada
| | - Marzyeh Ghassemi
- Department of Computer Science, University of Toronto, Toronto, ON M5S 3H5, Canada; Vector Institute, Toronto, ON, Canada; Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Luca Foschini
- Evidation Health, San Mateo, CA, USA; Sage Bionetworks, Seattle, WA, USA
| | - Anna Goldenberg
- Department of Computer Science, University of Toronto, Toronto, ON M5S 3H5, Canada; Vector Institute, Toronto, ON, Canada; Genetics and Genome Biology, The Hospital for Sick Children, Toronto, ON, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON, Canada; CIFAR, Toronto, ON, Canada.
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24
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Jayaraman P, Crouse A, Nadkarni G, Might M. A Primer in Precision Nephrology: Optimizing Outcomes in Kidney Health and Disease through Data-Driven Medicine. KIDNEY360 2023; 4:e544-e554. [PMID: 36951457 PMCID: PMC10278804 DOI: 10.34067/kid.0000000000000089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 01/04/2023] [Indexed: 03/24/2023]
Abstract
This year marks the 63rd anniversary of the International Society of Nephrology, which signaled nephrology's emergence as a modern medical discipline. In this article, we briefly trace the course of nephrology's history to show a clear arc in its evolution-of increasing resolution in nephrological data-an arc that is converging with computational capabilities to enable precision nephrology. In general, precision medicine refers to tailoring treatment to the individual characteristics of patients. For an operational definition, this tailoring takes the form of an optimization, in which treatments are selected to maximize a patient's expected health with respect to all available data. Because modern health data are large and high resolution, this optimization process requires computational intervention, and it must be tuned to the contours of specific medical disciplines. An advantage of this operational definition for precision medicine is that it allows us to better understand what precision medicine means in the context of a specific medical discipline. The goal of this article was to demonstrate how to instantiate this definition of precision medicine for the field of nephrology. Correspondingly, the goal of precision nephrology was to answer two related questions: ( 1 ) How do we optimize kidney health with respect to all available data? and ( 2 ) How do we optimize general health with respect to kidney data?
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Affiliation(s)
- Pushkala Jayaraman
- The Charles Bronfman Institute for Personalized Medicine Icahn School of Medicine at Mount Sinai, New York, New York
| | - Andrew Crouse
- Hugh Kaul Precision Medicine Institute, University of Alabama at Birmingham, Birmingham, Alabama
| | - Girish Nadkarni
- The Charles Bronfman Institute for Personalized Medicine Icahn School of Medicine at Mount Sinai, New York, New York
- The Mount Sinai Clinical Intelligence Center (MSCIC), Icahn School of Medicine at Mount Sinai, New York, New York
- Division of Data Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- Barbara T Murphy Division of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Matthew Might
- Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
- Department of Computer Science, University of Alabama at Birmingham, Birmingham, Alabama
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25
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The WE SENSE study protocol: A controlled, longitudinal clinical trial on the use of wearable sensors for early detection and tracking of viral respiratory tract infections. Contemp Clin Trials 2023; 128:107103. [PMID: 37147083 PMCID: PMC10049920 DOI: 10.1016/j.cct.2023.107103] [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: 08/08/2022] [Revised: 12/06/2022] [Accepted: 01/20/2023] [Indexed: 03/31/2023]
Abstract
Background Viral respiratory tract infections (VRTI) are extremely common. Considering the profound social and economic impact of COVID-19, it is imperative to identify novel mechanisms for early detection and prevention of VRTIs, to prevent future pandemics. Wearable biosensor technology may facilitate this. Early asymptomatic detection of VRTIs could reduce stress on the healthcare system by reducing transmission and decreasing the overall number of cases. The aim of the current study is to define a sensitive set of physiological and immunological signature patterns of VRTI through machine learning (ML) to analyze physiological data collected continuously using wearable vital signs sensors. Methods A controlled, prospective longitudinal study with an induced low grade viral challenge, coupled with 12 days of continuous wearable biosensors monitoring surrounding viral induction. We aim to recruit and simulate a low grade VRTI in 60 healthy adults aged 18–59 years via administration of live attenuated influenza vaccine (LAIV). Continuous monitoring with wearable biosensors will include 7 days pre (baseline) and 5 days post LAIV administration, during which vital signs and activity-monitoring biosensors (embedded in a shirt, wristwatch and ring) will continuously monitor physiological and activity parameters. Novel infection detection techniques will be developed based on inflammatory biomarker mapping, PCR testing, and app-based VRTI symptom tracking. Subtle patterns of change will be assessed via ML algorithms developed to analyze large datasets and generate a predictive algorithm. Conclusion This study presents an infrastructure to test wearables for the detection of asymptomatic VRTI using multimodal biosensors, based on immune host response signature. CliniclTrials.govregistration:NCT05290792
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De Boer C, Ghomrawi H, Zeineddin S, Linton S, Kwon S, Abdullah F. A Call to Expand the Scope of Digital Phenotyping. J Med Internet Res 2023; 25:e39546. [PMID: 36917148 PMCID: PMC10132029 DOI: 10.2196/39546] [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: 05/13/2022] [Revised: 07/08/2022] [Accepted: 07/25/2022] [Indexed: 11/13/2022] Open
Abstract
Digital phenotyping refers to near-real-time data collection from personal digital devices, particularly smartphones, to better quantify the human phenotype. Methodology using smartphones is often considered the gold standard by many for passive data collection within the field of digital phenotyping, which limits its applications mainly to adults or adolescents who use smartphones. However, other technologies, such as wearable devices, have evolved considerably in recent years to provide similar or better quality passive physiologic data of clinical relevance, thus expanding the potential of digital phenotyping applications to other patient populations. In this perspective, we argue for the continued expansion of digital phenotyping to include other potential gold standards in addition to smartphones and provide examples of currently excluded technologies and populations who may uniquely benefit from this technology.
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Affiliation(s)
- Christopher De Boer
- Division of Pediatric Surgery, Department of Surgery, Ann & Robert H Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Hassan Ghomrawi
- Division of Pediatric Surgery, Department of Surgery, Ann & Robert H Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, United States.,Division of Rheumatology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, United States.,Department of Pediatrics, Ann & Robert H Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Suhail Zeineddin
- Division of Pediatric Surgery, Department of Surgery, Ann & Robert H Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Samuel Linton
- Division of Pediatric Surgery, Department of Surgery, Ann & Robert H Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
| | - Soyang Kwon
- The Smith Child Health Research Program, Ann & Robert H Lurie Children's Hospital of Chicago, Chicago, IL, United States
| | - Fizan Abdullah
- Division of Pediatric Surgery, Department of Surgery, Ann & Robert H Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, United States
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"Bring Your Own Device"-A New Approach to Wearable Outcome Assessment in Trauma. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:medicina59020403. [PMID: 36837604 PMCID: PMC9966638 DOI: 10.3390/medicina59020403] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/31/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023]
Abstract
Background and Objectives: Outcome data from wearable devices are increasingly used in both research and clinics. Traditionally, a dedicated device is chosen for a given study or clinical application to collect outcome data as soon as the patient is included in a study or undergoes a procedure. The current study introduces a new measurement strategy, whereby patients' own devices are utilized, allowing for both a pre-injury baseline measure and ability to show achievable results. Materials and Methods: Patients with a pre-existing musculoskeletal injury of the upper and lower extremity were included in this exploratory, proof-of-concept study. They were followed up for a minimum of 6 weeks after injury, and their wearable outcome data (from a smartphone and/or a body-worn sensor) were continuously acquired during this period. A descriptive analysis of the screening characteristics and the observed and achievable outcome patterns was performed. Results: A total of 432 patients was continuously screened for the study, and their screening was analyzed. The highest success rate for successful inclusion was in younger patients. Forty-eight patients were included in the analysis. The most prevalent outcome was step count. Three distinctive activity data patterns were observed: patients recovering, patients with slow or no recovery, and patients needing additional measures to determine treatment outcomes. Conclusions: Measuring outcomes in trauma patients with the Bring Your Own Device (BYOD) strategy is feasible. With this approach, patients were able to provide continuous activity data without any dedicated equipment given to them. The measurement technique is especially suited to particular patient groups. Our study's screening log and inclusion characteristics can help inform future studies wishing to employ the BYOD design.
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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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Respiratory psychophysiology and COVID-19: A research agenda. Biol Psychol 2023; 176:108473. [PMID: 36535514 PMCID: PMC9756651 DOI: 10.1016/j.biopsycho.2022.108473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 12/03/2022] [Accepted: 12/04/2022] [Indexed: 12/23/2022]
Abstract
After multiple waves of the COVID-19 pandemic, it has become clear that the impact of SARS-CoV-2 will carry on for years to come. Acutely infected patients show a broad range of disease severity, depending on virus variant, vaccination status, age and the presence of underlying medical and physical conditions, including obesity. Additionally, a large number of patients who have been infected with the virus present with post-COVID syndrome. In September 2020, the International Society for the Advancement of Respiratory Psychophysiology organized a virtual interest meeting on 'Respiratory research in the age of COVID-19', which aimed to discuss how research in respiratory psychophysiology could contribute to a better understanding of psychophysiological interactions in COVID-19. In the resulting current paper, we propose an interdisciplinary research agenda discussing selected research questions on acute and long-term neurobiological, physiological and psychological outcomes and mechanisms related to respiration and the airways in COVID-19, as well as research questions on comorbidity and potential treatment options, such as physical rehabilitation.
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Seshadri DR, Harlow ER, Thom ML, Emery MS, Phelan DM, Hsu JJ, Düking P, De Mey K, Sheehan J, Geletka B, Flannery R, Calcei JG, Karns M, Salata MJ, Gabbett TJ, Voos JE. Wearable technology in the sports medicine clinic to guide the return-to-play and performance protocols of athletes following a COVID-19 diagnosis. Digit Health 2023; 9:20552076231177498. [PMID: 37434736 PMCID: PMC10331194 DOI: 10.1177/20552076231177498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Accepted: 05/06/2023] [Indexed: 07/13/2023] Open
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has enabled the adoption of digital health platforms for self-monitoring and diagnosis. Notably, the pandemic has had profound effects on athletes and their ability to train and compete. Sporting organizations worldwide have reported a significant increase in injuries manifesting from changes in training regimens and match schedules resulting from extended quarantines. While current literature focuses on the use of wearable technology to monitor athlete workloads to guide training, there is a lack of literature suggesting how such technology can mediate the return to sport processes of athletes infected with COVID-19. This paper bridges this gap by providing recommendations to guide team physicians and athletic trainers on the utility of wearable technology for improving the well-being of athletes who may be asymptomatic, symptomatic, or tested negative but have had to quarantine due to a close exposure. We start by describing the physiologic changes that occur in athletes infected with COVID-19 with extended deconditioning from a musculoskeletal, psychological, cardiopulmonary, and thermoregulatory standpoint and review the evidence on how these athletes may safely return to play. We highlight opportunities for wearable technology to aid in the return-to-play process by offering a list of key parameters pertinent to the athlete affected by COVID-19. This paper provides the athletic community with a greater understanding of how wearable technology can be implemented in the rehabilitation process of these athletes and spurs opportunities for further innovations in wearables, digital health, and sports medicine to reduce injury burden in athletes of all ages.
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Affiliation(s)
- Dhruv R Seshadri
- University Hospitals Sports Medicine Institute, Cleveland, OH, USA
| | - Ethan R Harlow
- University Hospitals Sports Medicine Institute, Cleveland, OH, USA
- Department of Orthopaedic Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Mitchell L Thom
- University Hospitals Sports Medicine Institute, Cleveland, OH, USA
- Department of Orthopaedic Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Michael S Emery
- Sports Cardiology Center, Heart, Vascular, and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Dermot M Phelan
- Sanger Heart and Vascular Institute, Atrium Health, Charlotte, NC, USA
| | - Jeffrey J Hsu
- David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Peter Düking
- Integrative and Experimental Exercise Science, Department of Sport Science, University of Würzburg, Würzburg, Germany
| | | | | | - Benjamin Geletka
- University Hospitals Sports Medicine Institute, Cleveland, OH, USA
- Department of Orthopaedic Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
- University Hospitals Rehabilitation Services and Sports Medicine, Cleveland, OH, USA
| | - Robert Flannery
- University Hospitals Sports Medicine Institute, Cleveland, OH, USA
- Department of Orthopaedic Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Jacob G Calcei
- University Hospitals Sports Medicine Institute, Cleveland, OH, USA
- Department of Orthopaedic Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Michael Karns
- University Hospitals Sports Medicine Institute, Cleveland, OH, USA
- Department of Orthopaedic Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Michael J Salata
- University Hospitals Sports Medicine Institute, Cleveland, OH, USA
- Department of Orthopaedic Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Tim J Gabbett
- Gabbett Performance Solutions, Brisbane, Australia
- Centre for Health Research, University of Southern Queensland, Ipswich, Australia
- School of Science, Psychology and Sport, Federation University, Ballarat, Australia
| | - James E Voos
- University Hospitals Sports Medicine Institute, Cleveland, OH, USA
- Department of Orthopaedic Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
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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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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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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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Mohammadian M, Golchoobian R. Potential autonomic nervous system dysfunction in COVID-19 patients detected by heart rate variability is a sign of SARS-CoV-2 neurotropic features. Mol Biol Rep 2022; 49:8131-8137. [PMID: 35810238 DOI: 10.1007/s11033-022-07486-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 04/09/2022] [Accepted: 04/14/2022] [Indexed: 11/26/2022]
Abstract
Increasing evidence strongly support that the newly identified severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) leads to the development of COVID-19-associated central nervous system (CNS) manifestations. The presence of SARS-CoV-2 viral protein in the brainstem, which includes cardiovascular control centers, has been documented previously. Given the changes in autonomic nervous system function evaluated by heart rate variability (HRV) metrics, which are observed even prior to clinical signs, the potential effect of SARS-CoV-2 on the autonomic nervous system (ANS) center is likely. The integral parts of the brain renin-angiotensin system, as ACE2 enzyme, are highly expressed in the brainstem, which may also be involved in baroreflex sensitivity, playing an important role in HRV. SARS-CoV-2 may bind to ACE2 in order to enter the host brainstem cell and change baroreflex sensitivity due to the altered ratio of the concentration of angiotensin II (Ag II) to angiotensin (1-7). In this article, we discussed the information on the possibility that the SARS-CoV-2 viral particle by disrupting the homeostasis of the brain renin-angiotensin system even without brainstem neuropathological changes, may affect the function of the ANS center in the brainstem. SARS-CoV-2 could influence ANS function before affecting the immune system. It is possible that the altered HRV parameters imply the potential neurotropic characteristics of SARS-CoV-2. Therefore, this potential feature should be taken into account in diagnostic and therapeutic approaches for COVID-19 patients.
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Affiliation(s)
- Maryame Mohammadian
- Department of Physiology, School of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Ravieh Golchoobian
- Department of Physiology, School of Medicine, Babol University of Medical Sciences, Babol, Iran.
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Circadian patterns of heart rate, respiratory rate and skin temperature in hospitalized COVID-19 patients. PLoS One 2022; 17:e0268065. [PMID: 35797369 PMCID: PMC9262173 DOI: 10.1371/journal.pone.0268065] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 04/22/2022] [Indexed: 12/15/2022] Open
Abstract
Rationale
Vital signs follow circadian patterns in both healthy volunteers and critically ill patients, which seem to be influenced by disease severity in the latter. In this study we explored the existence of circadian patterns in heart rate, respiratory rate and skin temperature of hospitalized COVID-19 patients, and aimed to explore differences in circadian rhythm amplitude during patient deterioration.
Methods
We performed a retrospective study of COVID-19 patients admitted to the general ward of a tertiary hospital between April 2020 and March 2021. Patients were continuously monitored using a wireless sensor and fingertip pulse oximeter. Data was divided into three cohorts: patients who recovered, patients who developed respiratory insufficiency and patients who died. For each cohort, a population mean cosinor model was fitted to detect rhythmicity. To assess changes in amplitude, a mixed-effect cosinor model was fitted.
Results
A total of 429 patients were monitored. Rhythmicity was observed in heartrate for the recovery cohort (p<0.001), respiratory insufficiency cohort (p<0.001 and mortality cohort (p = 0.002). Respiratory rate showed rhythmicity in the recovery cohort (p<0.001), but not in the other cohorts (p = 0.18 and p = 0.51). Skin temperature also showed rhythmicity in the recovery cohort (p<0.001), but not in the other cohorts (p = 0.22 and p = 0.12). For respiratory insufficiency, only the amplitude of heart rate circadian pattern increased slightly the day before (1.2 (99%CI 0.16–2.2, p = 0.002)). In the mortality cohort, the amplitude of heart rate decreased (-1.5 (99%CI -2.6- -0.42, p<0.001)) and respiratory rate amplitude increased (0.72 (99%CI 0.27–1.3, p = 0.002) the days before death.
Conclusion
A circadian rhythm is present in heart rate of COVID-19 patients admitted to the general ward. For respiratory rate and skin temperature, rhythmicity was only found in patients who recover, but not in patients developing respiratory insufficiency or death. We found no consistent changes in circadian rhythm amplitude accompanying patient deterioration.
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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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Hirten RP, Tomalin L, Danieletto M, Golden E, Zweig M, Kaur S, Helmus D, Biello A, Pyzik R, Bottinger EP, Keefer L, Charney D, Nadkarni GN, Suarez-Farinas M, Fayad ZA. Evaluation of a machine learning approach utilizing wearable data for prediction of SARS-CoV-2 infection in healthcare workers. JAMIA Open 2022; 5:ooac041. [PMID: 35677186 PMCID: PMC9129173 DOI: 10.1093/jamiaopen/ooac041] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 04/28/2022] [Accepted: 05/15/2022] [Indexed: 11/16/2022] Open
Abstract
Objective To determine whether a machine learning model can detect SARS-CoV-2 infection from physiological metrics collected from wearable devices. Materials and Methods Health care workers from 7 hospitals were enrolled and prospectively followed in a multicenter observational study. Subjects downloaded a custom smart phone app and wore Apple Watches for the duration of the study period. Daily surveys related to symptoms and the diagnosis of Coronavirus Disease 2019 were answered in the app. Results We enrolled 407 participants with 49 (12%) having a positive nasal SARS-CoV-2 polymerase chain reaction test during follow-up. We examined 5 machine-learning approaches and found that gradient-boosting machines (GBM) had the most favorable validation performance. Across all testing sets, our GBM model predicted SARS-CoV-2 infection with an average area under the receiver operating characteristic (auROC) = 86.4% (confidence interval [CI] 84-89%). The model was calibrated to value sensitivity over specificity, achieving an average sensitivity of 82% (CI ±∼4%) and specificity of 77% (CI ±∼1%). The most important predictors included parameters describing the circadian heart rate variability mean (MESOR) and peak-timing (acrophase), and age. Discussion We show that a tree-based ML algorithm applied to physiological metrics passively collected from a wearable device can identify and predict SARS-CoV-2 infection. Conclusion Applying machine learning models to the passively collected physiological metrics from wearable devices may improve SARS-CoV-2 screening methods and infection tracking.
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Affiliation(s)
- Robert P Hirten
- Department of Medicine, The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, New York, New York, USA
| | - Lewis Tomalin
- Department of Population Health Science and Policy, Center for Biostatistics, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Matteo Danieletto
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Eddye Golden
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Micol Zweig
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Sparshdeep Kaur
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, New York, New York, USA
| | - Drew Helmus
- Department of Medicine, The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Anthony Biello
- Department of Medicine, The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Renata Pyzik
- The BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Erwin P Bottinger
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, New York, New York, USA
| | - Laurie Keefer
- Department of Medicine, The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Dennis Charney
- Office of the Dean, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Girish N Nadkarni
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, New York, New York, USA
- The Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Mayte Suarez-Farinas
- Department of Population Health Science and Policy, Center for Biostatistics, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Zahi A Fayad
- The BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Temple DS, Hegarty-Craver M, Furberg RD, Preble EA, Bergstrom E, Gardener Z, Dayananda P, Taylor L, Lemm NM, Papargyris L, McClain MT, Nicholson BP, Bowie A, Miggs M, Petzold E, Woods CW, Chiu C, Gilchrist KH. Wearable sensor-based detection of influenza in presymptomatic and asymptomatic individuals. J Infect Dis 2022; 227:864-872. [PMID: 35759279 PMCID: PMC9384446 DOI: 10.1093/infdis/jiac262] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 06/20/2022] [Accepted: 06/24/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The COVID-19 pandemic highlighted the need for early detection of viral infections in symptomatic and asymptomatic individuals to allow for timely clinical management and public health interventions. METHODS Twenty healthy adults were challenged with an influenza A (H3N2) virus and prospectively monitored from 7 days before through 10 days after inoculation, using wearable electrocardiogram and physical activity sensors (Clinical Trial: NCT04204493; https://clinicaltrials.gov/ct2/show/NCT04204993). This framework allowed for responses to be accurately referenced to the infection event. For each participant, we trained a semi-supervised multivariable anomaly detection model on data acquired before inoculation and used it to classify the post-inoculation dataset. RESULTS Inoculation with this challenge virus was well-tolerated with an infection rate of 85%. With the model classification threshold set so that no alarms were recorded in the 170 healthy days recorded, the algorithm correctly identified 16 of 17 (94%) positive presymptomatic and asymptomatic individuals, on average 58 hours post inoculation and 23 hrs before the symptom onset. CONCLUSION The data processing and modeling methodology show promise for the early detection of respiratory illness. The detection algorithm is compatible with data collected from smartwatches using optical techniques but needs to be validated in large heterogeneous cohorts in normal living conditions.
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Affiliation(s)
| | | | | | | | - Emma Bergstrom
- Department of Infectious Disease, Imperial College London, London, SWT 2AZ, UK
| | - Zoe Gardener
- Department of Infectious Disease, Imperial College London, London, SWT 2AZ, UK
| | - Peter Dayananda
- Department of Infectious Disease, Imperial College London, London, SWT 2AZ, UK
| | - Lydia Taylor
- Department of Infectious Disease, Imperial College London, London, SWT 2AZ, UK
| | - Nana Marie Lemm
- Department of Infectious Disease, Imperial College London, London, SWT 2AZ, UK
| | - Lukas Papargyris
- Department of Infectious Disease, Imperial College London, London, SWT 2AZ, UK
| | - Micah T McClain
- Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, 27710, USA
| | - Bradly P Nicholson
- Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, 27710, USA.,Institute for Medical Research, Durham, 27710, USA
| | - Aleah Bowie
- Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, 27710, USA
| | - Maria Miggs
- Institute for Medical Research, Durham, 27710, USA
| | - Elizabeth Petzold
- Center for Applied Genomics and Precision Medicine, Duke University School of Medicine, Durham, 27710, USA
| | - Christopher W Woods
- Institute for Medical Research, Durham, 27710, USA.,Hubert-Yeargan Center for Global Health, Duke University School of Medicine, Durham, 27710, USA
| | - Christopher Chiu
- Department of Infectious Disease, Imperial College London, London, SWT 2AZ, UK
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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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40
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Multiomics to elucidate inflammatory bowel disease risk factors and pathways. Nat Rev Gastroenterol Hepatol 2022; 19:399-409. [PMID: 35301463 PMCID: PMC9214275 DOI: 10.1038/s41575-022-00593-y] [Citation(s) in RCA: 57] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/21/2022] [Indexed: 02/07/2023]
Abstract
Inflammatory bowel disease (IBD) is an immune-mediated disease of the intestinal tract, with complex pathophysiology involving genetic, environmental, microbiome, immunological and potentially other factors. Epidemiological data have provided important insights into risk factors associated with IBD, but are limited by confounding, biases and data quality, especially when pertaining to risk factors in early life. Multiomics platforms provide granular high-throughput data on numerous variables simultaneously and can be leveraged to characterize molecular pathways and risk factors for chronic diseases, such as IBD. Herein, we describe omics platforms that can advance our understanding of IBD risk factors and pathways, and available omics data on IBD and other relevant diseases. We highlight knowledge gaps and emphasize the importance of birth, at-risk and pre-diagnostic cohorts, and neonatal blood spots in omics analyses in IBD. Finally, we discuss network analysis, a powerful bioinformatics tool to assemble high-throughput data and derive clinical relevance.
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Maschewski F, Nosthoff AV. Überwachungskapitalistische Biopolitik: Big Tech und die Regierung der Körper. ZEITSCHRIFT FÜR POLITIKWISSENSCHAFT 2022. [PMCID: PMC8805421 DOI: 10.1007/s41358-021-00309-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Der Artikel analysiert den Einzug Big Techs (der Fokus liegt auf Apple und Alphabet) in den Gesundheitsmarkt und beschreibt in Anlehnung an Michel Foucault und Shoshana Zuboff das Konzept einer „überwachungskapitalistischen Biopolitik“. Ziel ist, die Ausweitung des „Datenextraktivismus“ im Gesundheitswesen und der Gesundheitsforschung machtkritisch einzuordnen und damit einen Trend in der digitalen Gesundheitsfürsorge zu problematisieren, der sich in den letzten Jahren und, wie wir zeigen, besonders während der Coronakrise beschleunigt und ausgefächert hat. Anhand wissenschaftlicher und kommerzieller Projekte sowie Kooperationen im Bereich public health wird deutlich, dass zeitgenössische Formen der Biopolitik keineswegs auf staatliche Regime beschränkt sind. Stattdessen sind sie zunehmend über private Technologieunternehmen vermittelt, die dabei nicht nur intime Verhaltens- und Vitaldaten akkumulieren, sondern – qua proprietärer Algorithmen – auch den Zugang zu diesen kontrollieren und schließlich ihren Einfluss in exklusive Services und Produkte überführen. Ein besonderer Akzent des Artikels liegt zudem auf der voranschreitenden Verbreitung sogenannter Wearable-Technologien (Smartwatches etc.), über die sich nicht nur die herausgehobene Marktposition der Konzerne, sondern – in der Entwicklung von einem „quantifizierten Selbst“ zu einem „quantifizierten Kollektiv“ – auch ihre epistemische bzw. „infrastrukturelle Macht“ konkretisiert. Entgegen einer einseitig repressiven Perspektive auf biopolitische Praxen zeigen wir schließlich Ansätze einer Demokratisierung „überwachungskapitalistischer Biopolitik“ auf. Hierbei heben wir vier Topoi hervor, die von zentraler Bedeutung sind: Privatsphäre bzw. individuelle Souveränität, demokratische Deliberation, Pluralismus und epistemische Gleichheit.
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Affiliation(s)
- Felix Maschewski
- Institut für deutsche Literatur, Humboldt-Universität zu Berlin, Dorotheenstr. 24, 10117 Berlin, Deutschland
| | - Anna-Verena Nosthoff
- Institut für Politikwissenschaft, Universität Wien, Universitätsstraße 7, 1010 Wien, Österreich
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Föll S, Lison A, Maritsch M, Klingberg K, Lehmann V, Züger T, Srivastava D, Jegerlehner S, Feuerriegel S, Fleisch E, Exadaktylos A, Wortmann F. A Scalable Risk Scoring System for COVID-19 Inpatients Based on Consumer-grade Wearables: Statistical Analysis and Model Development. JMIR Form Res 2022; 6:e35717. [PMID: 35613417 PMCID: PMC9217156 DOI: 10.2196/35717] [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: 12/15/2021] [Revised: 04/06/2022] [Accepted: 05/09/2022] [Indexed: 11/13/2022] Open
Abstract
Background To provide effective care for inpatients with COVID-19, clinical practitioners need systems that monitor patient health and subsequently allow for risk scoring. Existing approaches for risk scoring in patients with COVID-19 focus primarily on intensive care units (ICUs) with specialized medical measurement devices but not on hospital general wards. Objective In this paper, we aim to develop a risk score for inpatients with COVID-19 in general wards based on consumer-grade wearables (smartwatches). Methods Patients wore consumer-grade wearables to record physiological measurements, such as the heart rate (HR), heart rate variability (HRV), and respiration frequency (RF). Based on Bayesian survival analysis, we validated the association between these measurements and patient outcomes (ie, discharge or ICU admission). To build our risk score, we generated a low-dimensional representation of the physiological features. Subsequently, a pooled ordinal regression with time-dependent covariates inferred the probability of either hospital discharge or ICU admission. We evaluated the predictive performance of our developed system for risk scoring in a single-center, prospective study based on 40 inpatients with COVID-19 in a general ward of a tertiary referral center in Switzerland. Results First, Bayesian survival analysis showed that physiological measurements from consumer-grade wearables are significantly associated with patient outcomes (ie, discharge or ICU admission). Second, our risk score achieved a time-dependent area under the receiver operating characteristic curve (AUROC) of 0.73-0.90 based on leave-one-subject-out cross-validation. Conclusions Our results demonstrate the effectiveness of consumer-grade wearables for risk scoring in inpatients with COVID-19. Due to their low cost and ease of use, consumer-grade wearables could enable a scalable monitoring system. Trial Registration Clinicaltrials.gov NCT04357834; https://www.clinicaltrials.gov/ct2/show/NCT04357834
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Affiliation(s)
- Simon Föll
- Department of Management, Technology, and Economics, ETH Zürich, Zürich, CH
| | - Adrian Lison
- Department of Management, Technology, and Economics, ETH Zürich, Zürich, CH
| | - Martin Maritsch
- Department of Management, Technology, and Economics, ETH Zürich, Zürich, CH
| | - Karsten Klingberg
- Department of Emergency Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, Bern, CH
| | - Vera Lehmann
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern, Bern, CH
| | - Thomas Züger
- Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern, Bern, CH.,Department of Management, Technology, and Economics, ETH Zürich, Zürich, CH
| | - David Srivastava
- Department of Emergency Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, Bern, CH
| | - Sabrina Jegerlehner
- Department of Emergency Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, Bern, CH
| | - Stefan Feuerriegel
- Department of Management, Technology, and Economics, ETH Zürich, Zürich, CH.,Institute of AI in Management, LMU Munich, Munich, DE
| | - Elgar Fleisch
- Department of Management, Technology, and Economics, ETH Zürich, Zürich, CH.,Institute of Technology Management, University of St. Gallen, St. Gallen, CH
| | - Aristomenis Exadaktylos
- Department of Emergency Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 16C, Bern, CH
| | - Felix Wortmann
- Institute of Technology Management, University of St. Gallen, St. Gallen, CH.,Department of Management, Technology, and Economics, ETH Zürich, Zürich, CH
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Mitratza M, Goodale BM, Shagadatova A, Kovacevic V, van de Wijgert J, Brakenhoff TB, Dobson R, Franks B, Veen D, Folarin AA, Stolk P, Grobbee DE, Cronin M, Downward GS. The performance of wearable sensors in the detection of SARS-CoV-2 infection: a systematic review. Lancet Digit Health 2022; 4:e370-e383. [PMID: 35461692 PMCID: PMC9020803 DOI: 10.1016/s2589-7500(22)00019-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Revised: 12/08/2021] [Accepted: 01/20/2022] [Indexed: 01/09/2023]
Abstract
Containing the COVID-19 pandemic requires rapidly identifying infected individuals. Subtle changes in physiological parameters (such as heart rate, respiratory rate, and skin temperature), discernible by wearable devices, could act as early digital biomarkers of infections. Our primary objective was to assess the performance of statistical and algorithmic models using data from wearable devices to detect deviations compatible with a SARS-CoV-2 infection. We searched MEDLINE, Embase, Web of Science, the Cochrane Central Register of Controlled Trials (known as CENTRAL), International Clinical Trials Registry Platform, and ClinicalTrials.gov on July 27, 2021 for publications, preprints, and study protocols describing the use of wearable devices to identify a SARS-CoV-2 infection. Of 3196 records identified and screened, 12 articles and 12 study protocols were analysed. Most included articles had a moderate risk of bias, as per the National Institute of Health Quality Assessment Tool for Observational and Cross-Sectional Studies. The accuracy of algorithmic models to detect SARS-CoV-2 infection varied greatly (area under the curve 0·52-0·92). An algorithm's ability to detect presymptomatic infection varied greatly (from 20% to 88% of cases), from 14 days to 1 day before symptom onset. Increased heart rate was most frequently associated with SARS-CoV-2 infection, along with increased skin temperature and respiratory rate. All 12 protocols described prospective studies that had yet to be completed or to publish their results, including two randomised controlled trials. The evidence surrounding wearable devices in the early detection of SARS-CoV-2 infection is still in an early stage, with a limited overall number of studies identified. However, these studies show promise for the early detection of SARS-CoV-2 infection. Large prospective, and preferably controlled, studies recruiting and retaining larger and more diverse populations are needed to provide further evidence.
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Affiliation(s)
- Marianna Mitratza
- Julius Global Health, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
| | | | - Aizhan Shagadatova
- Julius Global Health, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | | | - Janneke van de Wijgert
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | | | - Richard Dobson
- Institute of Health Informatics, University College London, London, UK
| | | | - Duco Veen
- Julius Global Health, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands; Julius Clinical Research BV, Zeist, Netherlands; Optentia Research Program, North-West University, Potchefstroom, South Africa
| | - Amos A Folarin
- Institute of Health Informatics, University College London, London, UK; National Institute for Health Research Maudsley Biomedical Research Centre, King's College London, London, UK; Department of Biostatistics and Health Informatics, South London and Maudsley NHS Foundation Trust, London, UK
| | - Pieter Stolk
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Diederick E Grobbee
- Julius Global Health, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands; Julius Clinical Research BV, Zeist, Netherlands
| | | | - George S Downward
- Julius Global Health, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
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Holko M, Litwin TR, Munoz F, Theisz KI, Salgin L, Jenks NP, Holmes BW, Watson-McGee P, Winford E, Sharma Y. Wearable fitness tracker use in federally qualified health center patients: strategies to improve the health of all of us using digital health devices. NPJ Digit Med 2022; 5:53. [PMID: 35469045 PMCID: PMC9038923 DOI: 10.1038/s41746-022-00593-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 03/29/2022] [Indexed: 11/09/2022] Open
Abstract
As the use of connected devices rises, an understanding of how digital health technologies can be used for equitable healthcare across diverse communities is needed. We surveyed 1007 adult patients at six Federally Qualified Health Centers regarding wearable fitness trackers. Findings indicate the majority interest in having fitness trackers. Barriers included cost and lack of information, revealing that broad digital health device adoption requires education, investment, and high-touch methods.
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Affiliation(s)
- Michelle Holko
- All of Us Research Program, National Institutes of Health, Bethesda, MD, USA.
| | - Tamara R Litwin
- All of Us Research Program, National Institutes of Health, Bethesda, MD, USA.
| | | | - Katrina I Theisz
- All of Us Research Program, National Institutes of Health, Bethesda, MD, USA
| | | | | | | | | | | | - Yashoda Sharma
- Weitzman Institute, Community Health Center, Inc., Middletown, CT, USA.
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Walter JR, Xu S, Stringer JS, Rogers JA. The Future of Remote Monitoring for Pregnancy. BRIDGE (WASHINGTON, D.C. : 1969) 2022; 52:16-24. [PMID: 38111590 PMCID: PMC10727511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Subscribe] [Scholar Register] [Indexed: 12/20/2023]
Affiliation(s)
- Jessica R Walter
- Department of Obstetrics and Gynecology, Northwestern University
| | - Shuai Xu
- Querrey Simpson Institute for Bioelectronics and Departments of Dermatology, Biomedical Engineering, and Pediatrics at Northwestern University
| | - Jeffrey S Stringer
- Department of Obstetrics and Gynecology at the University of North Carolina at Chapel Hill
- Global Women's Health Division
| | - John A Rogers
- Querrey Simpson Institute for Bioelectronics and the Departments of Biomedical Engineering, Materials Science, Computer Science, and Neurological Surgery at Northwestern University
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Gepner Y, Mofaz M, Oved S, Yechezkel M, Constantini K, Goldstein N, Eisenkraft A, Shmueli E, Yamin D. Utilizing wearable sensors for continuous and highly-sensitive monitoring of reactions to the BNT162b2 mRNA COVID-19 vaccine. COMMUNICATIONS MEDICINE 2022; 2:27. [PMID: 35603274 PMCID: PMC9053261 DOI: 10.1038/s43856-022-00090-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 02/15/2022] [Indexed: 12/23/2022] Open
Abstract
Background Clinical trial guidelines for assessing the safety of vaccines, are primarily based on self-reported questionnaires. Despite the tremendous technological advances in recent years, objective, continuous assessment of physiological measures post-vaccination is rarely performed. Methods We conducted a prospective observational study during the mass vaccination campaign in Israel. 160 participants >18 years who were not previously found to be COVID-19 positive and who received the BNT162b2 COVID-19 (Pfizer BioNTech) vaccine were equipped with an FDA-approved chest-patch sensor and a dedicated mobile application. The chest-patch sensor continuously monitored 13 different cardiovascular, and hemodynamic vitals: heart rate, blood oxygen saturation, respiratory rate, systolic and diastolic blood pressure, pulse pressure, mean arterial pressure, heart rate variability, stroke volume, cardiac output, cardiac index, systemic vascular resistance and skin temperature. The mobile application collected daily self-reported questionnaires on local and systemic reactions. Results We identify continuous and significant changes following vaccine administration in nearly all vitals. Markedly, these changes are observed even in presumably asymptomatic participants who did not report any local or systemic reaction. Changes in vitals are more apparent at night, in younger participants, and in participants following the second vaccine dose. Conclusion the considerably higher sensitivity of wearable sensors can revolutionize clinical trials by enabling earlier identification of abnormal reactions with fewer subjects. The safety of vaccines in clinical trials is primarily determined by participants completing self-reported questionnaires. We monitored various indicators of participant’s health using a chest-patch sensor in 160 participants before and after receiving the BNT162b2 COVID-19 (Pfizer BioNTech) vaccine. Participants were also asked to self-report their health via a mobile phone app. We observed significant changes in health indicators following vaccine administration. Changes were seen by chest patch sensor in both participants who did and did not report changes via the mobile phone app. Three days following vaccination, participant health indicators returned to the levels observed the day before vaccination in both groups. Using wearable sensors could potentially improve clinical trials by enabling earlier identification of abnormal reactions. Gepner et al. undertake a prospective observational study using a chest-patch sensor to monitor cardiovascular and hemodynamic vital signs following the BNT162b2 COVID-19 (Pfizer BioNTech) vaccine. Continuous and significant changes occurred in the vital signs, including in participants who did not report any reactions.
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Conroy B, Silva I, Mehraei G, Damiano R, Gross B, Salvati E, Feng T, Schneider J, Olson N, Rizzo AG, Curtin CM, Frassica J, McFarlane DC. Real-time infection prediction with wearable physiological monitoring and AI to aid military workforce readiness during COVID-19. Sci Rep 2022; 12:3797. [PMID: 35260671 PMCID: PMC8904796 DOI: 10.1038/s41598-022-07764-6] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 02/23/2022] [Indexed: 12/23/2022] Open
Abstract
Infectious threats, like the COVID-19 pandemic, hinder maintenance of a productive and healthy workforce. If subtle physiological changes precede overt illness, then proactive isolation and testing can reduce labor force impacts. This study hypothesized that an early infection warning service based on wearable physiological monitoring and predictive models created with machine learning could be developed and deployed. We developed a prototype tool, first deployed June 23, 2020, that delivered continuously updated scores of infection risk for SARS-CoV-2 through April 8, 2021. Data were acquired from 9381 United States Department of Defense (US DoD) personnel wearing Garmin and Oura devices, totaling 599,174 user-days of service and 201 million hours of data. There were 491 COVID-19 positive cases. A predictive algorithm identified infection before diagnostic testing with an AUC of 0.82. Barriers to implementation included adequate data capture (at least 48% data was needed) and delays in data transmission. We observe increased risk scores as early as 6 days prior to diagnostic testing (2.3 days average). This study showed feasibility of a real-time risk prediction score to minimize workforce impacts of infection.
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Affiliation(s)
- Bryan Conroy
- Philips Research North America, Cambridge, MA, USA
| | - Ikaro Silva
- Philips Research North America, Cambridge, MA, USA
| | | | | | - Brian Gross
- Philips Research North America, Cambridge, MA, USA
| | | | - Ting Feng
- Philips Research North America, Cambridge, MA, USA
| | | | - Niels Olson
- Defense Innovation Unit, Mountain View, CA, USA
| | | | - Catherine M Curtin
- Department of Surgery, Palo Alto Veteran Affairs Healthcare System, Palo Alto, CA, USA
| | - Joseph Frassica
- Philips Research North America, Cambridge, MA, USA.,Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
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48
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Liu S, Han J, Puyal EL, Kontaxis S, Sun S, Locatelli P, Dineley J, Pokorny FB, Costa GD, Leocani L, Guerrero AI, Nos C, Zabalza A, Sørensen PS, Buron M, Magyari M, Ranjan Y, Rashid Z, Conde P, Stewart C, Folarin AA, Dobson RJ, Bailón R, Vairavan S, Cummins N, Narayan VA, Hotopf M, Comi G, Schuller B, Consortium RC. Fitbeat: COVID-19 estimation based on wristband heart rate using a contrastive convolutional auto-encoder. PATTERN RECOGNITION 2022; 123:108403. [PMID: 34720200 PMCID: PMC8547790 DOI: 10.1016/j.patcog.2021.108403] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 08/30/2021] [Accepted: 10/24/2021] [Indexed: 05/19/2023]
Abstract
This study proposes a contrastive convolutional auto-encoder (contrastive CAE), a combined architecture of an auto-encoder and contrastive loss, to identify individuals with suspected COVID-19 infection using heart-rate data from participants with multiple sclerosis (MS) in the ongoing RADAR-CNS mHealth research project. Heart-rate data was remotely collected using a Fitbit wristband. COVID-19 infection was either confirmed through a positive swab test, or inferred through a self-reported set of recognised symptoms of the virus. The contrastive CAE outperforms a conventional convolutional neural network (CNN), a long short-term memory (LSTM) model, and a convolutional auto-encoder without contrastive loss (CAE). On a test set of 19 participants with MS with reported symptoms of COVID-19, each one paired with a participant with MS with no COVID-19 symptoms, the contrastive CAE achieves an unweighted average recall of 95.3 % , a sensitivity of 100 % and a specificity of 90.6 % , an area under the receiver operating characteristic curve (AUC-ROC) of 0.944, indicating a maximum successful detection of symptoms in the given heart rate measurement period, whilst at the same time keeping a low false alarm rate.
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Affiliation(s)
- Shuo Liu
- EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Jing Han
- EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
| | - Estela Laporta Puyal
- BSICoS Group, Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
- CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER-BNN), Madrid, Spain
| | - Spyridon Kontaxis
- BSICoS Group, Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
- CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER-BNN), Madrid, Spain
| | - Shaoxiong Sun
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Patrick Locatelli
- Department of Engineering and Applied Science, University of Bergamo, Bergamo, Italy
| | - Judith Dineley
- EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
| | - Florian B Pokorny
- EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
- Division of Phoniatrics, Medical University of Graz, Graz, Austria
| | - Gloria Dalla Costa
- Università Vita Salute San Raffaele and Experimental Neurophysiology Unit, Institute of Experimental Neurology, Scientific Institute Hospital San Raffaele, Milan, Italy
| | - Letizia Leocani
- Università Vita Salute San Raffaele and Experimental Neurophysiology Unit, Institute of Experimental Neurology, Scientific Institute Hospital San Raffaele, Milan, Italy
| | - Ana Isabel Guerrero
- Multiple Sclerosis Centre of Catalonia (Cemcat), Department of NeurologyNeuroimmunology, Hospital Universitari Vall dH́ebron, Universitat Autónoma de Barcelona, Barcelona, Spain
| | - Carlos Nos
- Multiple Sclerosis Centre of Catalonia (Cemcat), Department of NeurologyNeuroimmunology, Hospital Universitari Vall dH́ebron, Universitat Autónoma de Barcelona, Barcelona, Spain
| | - Ana Zabalza
- Multiple Sclerosis Centre of Catalonia (Cemcat), Department of NeurologyNeuroimmunology, Hospital Universitari Vall dH́ebron, Universitat Autónoma de Barcelona, Barcelona, Spain
| | - Per Soelberg Sørensen
- Danish Multiple Sclerosis Centre, Department of Neurology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Mathias Buron
- Danish Multiple Sclerosis Centre, Department of Neurology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Melinda Magyari
- Danish Multiple Sclerosis Centre, Department of Neurology, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Yatharth Ranjan
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Zulqarnain Rashid
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Pauline Conde
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Callum Stewart
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Amos A Folarin
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Institute of Health Informatics, University College London, London, United Kingdom
| | - Richard Jb Dobson
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Institute of Health Informatics, University College London, London, United Kingdom
| | - Raquel Bailón
- BSICoS Group, Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
- CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER-BNN), Madrid, Spain
| | | | - Nicholas Cummins
- EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
- The Department of Biostatistics and Health informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | | | - Matthew Hotopf
- The Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- South London and Maudsley National Health Service Foundation Trust, London, United Kingdom
| | - Giancarlo Comi
- Università Vita Salute San Raffaele, Casa di Cura Privata del Policlinico, Milan, Italy
| | - Björn Schuller
- EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany
- GLAM - Group on Language, Audio, & Music, Imperial College London, London, United Kingdom
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49
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Degraded Psychophysiological Status in Caregivers and Human Resources Staff during a COVID-19 Peak Unveiled by Psychological and HRV Testing at Workplace. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19031710. [PMID: 35162733 PMCID: PMC8835268 DOI: 10.3390/ijerph19031710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Revised: 01/25/2022] [Accepted: 01/28/2022] [Indexed: 12/04/2022]
Abstract
During COVID-19 pandemic peaks, healthcare professionals are a frontline workforce that deals with death on an almost daily basis and experiences a marked increase in workload. Returning home is also associated with fear of contaminating or be contaminated. An obvious consequence is stress accumulation and associated risks, especially in caregivers in mobility and possibly in human resource teams managing mobility. Here, during the second pandemic peak, we designed a 15-min testing procedure at the workplace, combining HADS and Brief COPE questionnaires with heart rate variability (HRV) recordings to evaluate psychophysiological status in four groups: caregivers in mobility (MOB); human resources teams managing mobility (ADM); caregivers without mobility (N-MOB); and university researchers teaching online (RES). Anxiety, depression, coping strategies, vagally-mediated heart rate regulation, and nonlinear dynamics (entropy) in cardiac autonomic control were quantified. Anxiety reached remarkably high levels in both MOB and ADM, which was reflected in vagal and nonlinear HRV markers. ADM maintained a better problem-solving capacity. MOB and N-MOB exhibited degraded problem-solving capacity. Multivariate approaches show how combining psychological and physiological markers helps draw highly group-specific psychophysiological profiles. Entropy in HRV and problem-solving capacity were highly relevant for that. Combining HADS and Brief COPE questionnaires with HRV testing at the workplace may provide highly relevant cues to manage mobility during crises as well as prevent health risks, absenteeism, and more generally malfunction incidents at hospitals.
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50
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Hijazi H, Abu Talib M, Hasasneh A, Bou Nassif A, Ahmed N, Nasir Q. Wearable Devices, Smartphones, and Interpretable Artificial Intelligence in Combating COVID-19. SENSORS (BASEL, SWITZERLAND) 2021; 21:8424. [PMID: 34960517 PMCID: PMC8709136 DOI: 10.3390/s21248424] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 12/14/2021] [Accepted: 12/15/2021] [Indexed: 12/23/2022]
Abstract
Physiological measures, such as heart rate variability (HRV) and beats per minute (BPM), can be powerful health indicators of respiratory infections. HRV and BPM can be acquired through widely available wrist-worn biometric wearables and smartphones. Successive abnormal changes in these indicators could potentially be an early sign of respiratory infections such as COVID-19. Thus, wearables and smartphones should play a significant role in combating COVID-19 through the early detection supported by other contextual data and artificial intelligence (AI) techniques. In this paper, we investigate the role of the heart measurements (i.e., HRV and BPM) collected from wearables and smartphones in demonstrating early onsets of the inflammatory response to the COVID-19. The AI framework consists of two blocks: an interpretable prediction model to classify the HRV measurements status (as normal or affected by inflammation) and a recurrent neural network (RNN) to analyze users' daily status (i.e., textual logs in a mobile application). Both classification decisions are integrated to generate the final decision as either "potentially COVID-19 infected" or "no evident signs of infection". We used a publicly available dataset, which comprises 186 patients with more than 3200 HRV readings and numerous user textual logs. The first evaluation of the approach showed an accuracy of 83.34 ± 1.68% with 0.91, 0.88, 0.89 precision, recall, and F1-Score, respectively, in predicting the infection two days before the onset of the symptoms supported by a model interpretation using the local interpretable model-agnostic explanations (LIME).
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Affiliation(s)
- Haytham Hijazi
- Department of Informatics Engineering, CISUC-Centre for Informatics and Systems of the University of Coimbra, University of Coimbra, P-3030-790 Coimbra, Portugal;
- Intelligent Systems Department, Palestine Ahliya University, Bethlehem P-150-199, Palestine
| | - Manar Abu Talib
- College of Computing and Informatics, University of Sharjah, Sharjah P-27272, United Arab Emirates; (A.B.N.); (N.A.); (Q.N.)
| | - Ahmad Hasasneh
- Department of Natural, Engineering, and Technology Sciences, Arab American University, Ramallah P-600-699, Palestine;
| | - Ali Bou Nassif
- College of Computing and Informatics, University of Sharjah, Sharjah P-27272, United Arab Emirates; (A.B.N.); (N.A.); (Q.N.)
| | - Nafisa Ahmed
- College of Computing and Informatics, University of Sharjah, Sharjah P-27272, United Arab Emirates; (A.B.N.); (N.A.); (Q.N.)
| | - Qassim Nasir
- College of Computing and Informatics, University of Sharjah, Sharjah P-27272, United Arab Emirates; (A.B.N.); (N.A.); (Q.N.)
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