1
|
Zhang Y, Zheng XT, Zhang X, Pan J, Thean AVY. Hybrid Integration of Wearable Devices for Physiological Monitoring. Chem Rev 2024; 124:10386-10434. [PMID: 39189683 DOI: 10.1021/acs.chemrev.3c00471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/28/2024]
Abstract
Wearable devices can provide timely, user-friendly, non- or minimally invasive, and continuous monitoring of human health. Recently, multidisciplinary scientific communities have made significant progress regarding fully integrated wearable devices such as sweat wearable sensors, saliva sensors, and wound sensors. However, the translation of these wearables into markets has been slow due to several reasons associated with the poor system-level performance of integrated wearables. The wearability consideration for wearable devices compromises many properties of the wearables. Besides, the limited power capacity of wearables hinders continuous monitoring for extended duration. Furthermore, peak-power operations for intensive computations can quickly create thermal issues in the compact form factor that interfere with wearability and sensor operations. Moreover, wearable devices are constantly subjected to environmental, mechanical, chemical, and electrical interferences and variables that can invalidate the collected data. This generates the need for sophisticated data analytics to contextually identify, include, and exclude data points per multisensor fusion to enable accurate data interpretation. This review synthesizes the challenges surrounding the wearable device integration from three aspects in terms of hardware, energy, and data, focuses on a discussion about hybrid integration of wearable devices, and seeks to provide comprehensive guidance for designing fully functional and stable wearable devices.
Collapse
Affiliation(s)
- Yu Zhang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore
| | - Xin Ting Zheng
- Institute of Materials Research and Engineering (IMRE), Agency for Science Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore 138634, Singapore
| | - Xiangyu Zhang
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore
| | - Jieming Pan
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore
| | - Aaron Voon-Yew Thean
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore
| |
Collapse
|
2
|
El-Toukhy S, Hegeman P, Zuckerman G, Das AR, Moses N, Troendle J, Powell-Wiley TM. Study of Postacute Sequelae of COVID-19 Using Digital Wearables: Protocol for a Prospective Longitudinal Observational Study. JMIR Res Protoc 2024; 13:e57382. [PMID: 39150750 PMCID: PMC11364950 DOI: 10.2196/57382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 05/03/2024] [Accepted: 06/14/2024] [Indexed: 08/17/2024] Open
Abstract
BACKGROUND Postacute sequelae of COVID-19 (PASC) remain understudied in nonhospitalized patients. Digital wearables allow for a continuous collection of physiological parameters such as respiratory rate and oxygen saturation that have been predictive of disease trajectories in hospitalized patients. OBJECTIVE This protocol outlines the design and procedures of a prospective, longitudinal, observational study of PASC that aims to identify wearables-collected physiological parameters that are associated with PASC in patients with a positive diagnosis. METHODS This is a single-arm, prospective, observational study of a cohort of 550 patients, aged 18 to 65 years, male or female, who own a smartphone or a tablet that meets predetermined Bluetooth version and operating system requirements, speak English, and provide documentation of a positive COVID-19 test issued by a health care professional within 5 days before enrollment. The primary end point is long COVID-19, defined as ≥1 symptom at 3 weeks beyond the first symptom onset or positive diagnosis, whichever comes first. The secondary end point is chronic COVID-19, defined as ≥1 symptom at 12 weeks beyond the first symptom onset or positive diagnosis. Participants must be willing and able to consent to participate in the study and adhere to study procedures for 6 months. RESULTS The first patient was enrolled in October 2021. The estimated year for publishing the study results is 2025. CONCLUSIONS This is a fully decentralized study investigating PASC using wearable devices to collect physiological parameters and patient-reported outcomes. The study will shed light on the duration and symptom manifestation of PASC in nonhospitalized patient subgroups and is an exemplar of the use of wearables as population-level monitoring health tools for communicable diseases. TRIAL REGISTRATION ClinicalTrials.gov NCT04927442; https://clinicaltrials.gov/study/NCT04927442. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/57382.
Collapse
Affiliation(s)
- Sherine El-Toukhy
- Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Rockville, MD, United States
| | - Phillip Hegeman
- Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Rockville, MD, United States
| | - Gabrielle Zuckerman
- Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Rockville, MD, United States
| | | | - Nia Moses
- Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Rockville, MD, United States
| | - James Troendle
- Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States
| | - Tiffany M Powell-Wiley
- Division of Intramural Research, National Institute on Minority Health and Health Disparities, National Institutes of Health, Rockville, MD, United States
- Division of Intramural Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, United States
| |
Collapse
|
3
|
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.
Collapse
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
| | | |
Collapse
|
4
|
Markovic A, Kovacevic V, Brakenhoff TB, Veen D, Klaver P, Mitratza M, Downward GS, Grobbee DE, Cronin M, Goodale BM. Physiological Response to the COVID-19 Vaccine: Insights From a Prospective, Randomized, Single-Blinded, Crossover Trial. J Med Internet Res 2024; 26:e51120. [PMID: 39083770 PMCID: PMC11325110 DOI: 10.2196/51120] [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: 07/21/2023] [Revised: 04/05/2024] [Accepted: 04/30/2024] [Indexed: 08/02/2024] Open
Abstract
BACKGROUND Rapid development and implementation of vaccines constituted a crucial step in containing the COVID-19 pandemic. A comprehensive understanding of physiological responses to these vaccines is important to build trust in medicine. OBJECTIVE This study aims to investigate temporal dynamics before and after COVID-19 vaccination in 4 physiological parameters as well as the duration of menstrual cycle phases. METHODS In a prospective trial, 17,825 adults in the Netherlands wore a medical device on their wrist for up to 9 months. The device recorded their physiological signals and synchronized with a complementary smartphone app. By means of multilevel quadratic regression, we examined changes in wearable-recorded breathing rate, wrist skin temperature, heart rate, heart rate variability, and objectively assessed the duration of menstrual cycle phases in menstruating participants to assess the effects of COVID-19 vaccination. RESULTS The recorded physiological signals demonstrated short-term increases in breathing rate and heart rate after COVID-19 vaccination followed by a prompt rebound to baseline levels likely reflecting biological mechanisms accompanying the immune response to vaccination. No sex differences were evident in the measured physiological responses. In menstruating participants, we found a 0.8% decrease in the duration of the menstrual phase following vaccination. CONCLUSIONS The observed short-term changes suggest that COVID-19 vaccines are not associated with long-term biophysical issues. Taken together, our work provides valuable insights into continuous fluctuations of physiological responses to vaccination and highlights the importance of digital solutions in health care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.1186/s13063-021-05241-5.
Collapse
Affiliation(s)
- Andjela Markovic
- Department of Psychology, University of Fribourg, Fribourg, Switzerland
- Department of Social Neuroscience and Social Psychology, Institute of Psychology, University of Bern, Bern, Switzerland
- Department of Pulmonology, University Hospital Zurich, Zurich, Switzerland
- Ava Aktiengesellschaft (AG), Zurich, Switzerland
| | - Vladimir Kovacevic
- Ava Aktiengesellschaft (AG), Zurich, Switzerland
- The Institute for Artificial Intelligence Research and Development of Serbia, Belgrade, Serbia
| | | | - Duco Veen
- Department of Methodology & Statistics, Utrecht University, Utrecht, Netherlands
- Optentia Research Programme, North-West University, Potchefstroom, South Africa
| | | | - Marianna Mitratza
- Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, Netherlands
| | - George S Downward
- Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, Netherlands
| | - Diederick E Grobbee
- Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, Netherlands
| | | | - Brianna M Goodale
- Ava Aktiengesellschaft (AG), Zurich, Switzerland
- Julius Clinical, Zeist, Netherlands
| |
Collapse
|
5
|
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.
Collapse
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
| |
Collapse
|
6
|
Armoundas AA, Ahmad FS, Bennett DA, Chung MK, Davis LL, Dunn J, Narayan SM, Slotwiner DJ, Wiley KK, Khera R. Data Interoperability for Ambulatory Monitoring of Cardiovascular Disease: A Scientific Statement From the American Heart Association. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2024; 17:e000095. [PMID: 38779844 DOI: 10.1161/hcg.0000000000000095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
Wearable devices are increasingly used by a growing portion of the population to track health and illnesses. The data emerging from these devices can potentially transform health care. This requires an interoperability framework that enables the deployment of platforms, sensors, devices, and software applications within diverse health systems, aiming to facilitate innovation in preventing and treating cardiovascular disease. However, the current data ecosystem includes several noninteroperable systems that inhibit such objectives. The design of clinically meaningful systems for accessing and incorporating these data into clinical workflows requires strategies to ensure the quality of data and clinical content and patient and caregiver accessibility. This scientific statement aims to address the best practices, gaps, and challenges pertaining to data interoperability in this area, with considerations for (1) data integration and the scope of measures, (2) application of these data into clinical approaches/strategies, and (3) regulatory/ethical/legal issues.
Collapse
|
7
|
Bao Z, Lu S, Zhang D, Wang G, Cui X, Liu G. Wearable Microneedle Patch for Colorimetric Detection of Multiple Signature Biomarkers in vivo Toward Diabetic Diagnosis. Adv Healthc Mater 2024; 13:e2303511. [PMID: 38353398 DOI: 10.1002/adhm.202303511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 02/05/2024] [Indexed: 02/19/2024]
Abstract
Type 2 diabetes is rapidly emerging as a global public health problem. While blood glucose monitoring has been the primary method of managing diabetes for decades, the increasing global prevalence of the disease suggests that there might be a need to identify additional biomarkers for a more precise early diagnosis. Herein, a microneedle patch based wearable sensor is developed for the purpose of diabetic diagnosis. Utilizing methacrylic acid modified gelatin and polyvinyl alcohol in the fabrication of microneedles has improved their mechanical properties for skin penetration and increased swelling capacity for interstitial fluid extraction, thanks to the double crosslinking mechanism. The fabricated microneedles are further integrated with test paper functionalized with enzyme and dye molecules to detect multiple signature biomarkers of diabetes in vivo through a colorimetric reaction. Such a wearable microneedle patch holds significant promise for the real-time monitoring of various biomarkers related to chronic diseases and aging.
Collapse
Affiliation(s)
- Ziting Bao
- CUHK(SZ)-Boyalife Joint Laboratory for Regenerative Medicine Engineering, School of Medicine, The Chinese University of Hong Kong, Shenzhen, 518172, China
- Ciechanover Institute of Precision and Regenerative Medicine, School of Medicine, The Chinese University of Hong Kong, Shenzhen, 518172, China
| | - Sheng Lu
- CUHK(SZ)-Boyalife Joint Laboratory for Regenerative Medicine Engineering, School of Medicine, The Chinese University of Hong Kong, Shenzhen, 518172, China
- Ciechanover Institute of Precision and Regenerative Medicine, School of Medicine, The Chinese University of Hong Kong, Shenzhen, 518172, China
| | - Duo Zhang
- CUHK(SZ)-Boyalife Joint Laboratory for Regenerative Medicine Engineering, School of Medicine, The Chinese University of Hong Kong, Shenzhen, 518172, China
| | - Guanyu Wang
- Ciechanover Institute of Precision and Regenerative Medicine, School of Medicine, The Chinese University of Hong Kong, Shenzhen, 518172, China
| | - Xiaolin Cui
- CUHK(SZ)-Boyalife Joint Laboratory for Regenerative Medicine Engineering, School of Medicine, The Chinese University of Hong Kong, Shenzhen, 518172, China
- Ciechanover Institute of Precision and Regenerative Medicine, School of Medicine, The Chinese University of Hong Kong, Shenzhen, 518172, China
| | - Guozhen Liu
- CUHK(SZ)-Boyalife Joint Laboratory for Regenerative Medicine Engineering, School of Medicine, The Chinese University of Hong Kong, Shenzhen, 518172, China
- Ciechanover Institute of Precision and Regenerative Medicine, School of Medicine, The Chinese University of Hong Kong, Shenzhen, 518172, China
| |
Collapse
|
8
|
Naik K, Goyal RK, Foschini L, Chak CW, Thielscher C, Zhu H, Lu J, Lehár J, Pacanoswki MA, Terranova N, Mehta N, Korsbo N, Fakhouri T, Liu Q, Gobburu J. Current Status and Future Directions: The Application of Artificial Intelligence/Machine Learning for Precision Medicine. Clin Pharmacol Ther 2024; 115:673-686. [PMID: 38103204 DOI: 10.1002/cpt.3152] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 11/28/2023] [Indexed: 12/18/2023]
Abstract
Technological innovations, such as artificial intelligence (AI) and machine learning (ML), have the potential to expedite the goal of precision medicine, especially when combined with increased capacity for voluminous data from multiple sources and expanded therapeutic modalities; however, they also present several challenges. In this communication, we first discuss the goals of precision medicine, and contextualize the use of AI in precision medicine by showcasing innovative applications (e.g., prediction of tumor growth and overall survival, biomarker identification using biomedical images, and identification of patient population for clinical practice) which were presented during the February 2023 virtual public workshop entitled "Application of Artificial Intelligence and Machine Learning for Precision Medicine," hosted by the US Food and Drug Administration (FDA) and University of Maryland Center of Excellence in Regulatory Science and Innovation (M-CERSI). Next, we put forward challenges brought about by the multidisciplinary nature of AI, particularly highlighting the need for AI to be trustworthy. To address such challenges, we subsequently note practical approaches, viz., differential privacy, synthetic data generation, and federated learning. The proposed strategies - some of which are highlighted presentations from the workshop - are for the protection of personal information and intellectual property. In addition, methods such as the risk-based management approach and the need for an agile regulatory ecosystem are discussed. Finally, we lay out a call for action that includes sharing of data and algorithms, development of regulatory guidance documents, and pooling of expertise from a broad-spectrum of stakeholders to enhance the application of AI in precision medicine.
Collapse
Affiliation(s)
- Kunal Naik
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Rahul K Goyal
- Center for Translational Medicine, University of Maryland School of Pharmacy, Baltimore, Maryland, USA
| | | | | | | | - Hao Zhu
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - James Lu
- Modeling & Simulation/Clinical Pharmacology, Genentech Inc., South San Francisco, California, USA
| | | | - Michael A Pacanoswki
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Nadia Terranova
- Quantitative Pharmacology, Ares Trading S.A. (an affiliate of Merck KGaA, Darmstadt, Germany), Lausanne, Switzerland
| | - Neha Mehta
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | | | - Tala Fakhouri
- Office of Medical Policy, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Qi Liu
- Office of Clinical Pharmacology, Office of Translational Sciences, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland, USA
| | - Jogarao Gobburu
- Center for Translational Medicine, University of Maryland School of Pharmacy, Baltimore, Maryland, USA
| |
Collapse
|
9
|
Dunn J, Coravos A, Fanarjian M, Ginsburg GS, Steinhubl SR. Remote digital health technologies for improving the care of people with respiratory disorders. Lancet Digit Health 2024; 6:e291-e298. [PMID: 38402128 PMCID: PMC10960683 DOI: 10.1016/s2589-7500(23)00248-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 10/01/2023] [Accepted: 11/30/2023] [Indexed: 02/26/2024]
Abstract
Respiratory diseases are a leading cause of morbidity and mortality globally. However, existing systems of care, built around scheduled appointments, are not well designed to support the needs of people with chronic and acute respiratory conditions that can change rapidly and unexpectedly. Home-based and personal digital health technologies (DHTs) allow implementation of new models of care catering to the unique needs of individuals. The high number of respiratory triggers and unique responses to them require a personalised solution for each patient. The real-world, repetitive monitoring capabilities of DHTs enable identification of the normal operating characteristics for each individual and, therefore, recognition of the earliest deviations from that state. However, despite this potential, the number of clinical efficacy studies of DHTs is quite small. Evaluation of clinical effectiveness of DHTs in improving health quality in real-world settings is urgently needed.
Collapse
Affiliation(s)
- Jessilyn Dunn
- Biomedical Engineering Department, Duke University, Durham, NC, USA
| | | | | | - Geoffrey S Ginsburg
- Department of Medicine, Duke University, Durham, NC, USA; All of Us Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Steven R Steinhubl
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA.
| |
Collapse
|
10
|
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.
Collapse
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
| |
Collapse
|
11
|
Hong W. Advances and Opportunities of Mobile Health in the Postpandemic Era: Smartphonization of Wearable Devices and Wearable Deviceization of Smartphones. JMIR Mhealth Uhealth 2024; 12:e48803. [PMID: 38252596 PMCID: PMC10823426 DOI: 10.2196/48803] [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: 05/07/2023] [Revised: 11/08/2023] [Accepted: 12/20/2023] [Indexed: 01/24/2024] Open
Abstract
Mobile health (mHealth) with continuous real-time monitoring is leading the era of digital medical convergence. Wearable devices and smartphones optimized as personalized health management platforms enable disease prediction, prevention, diagnosis, and even treatment. Ubiquitous and accessible medical services offered through mHealth strengthen universal health coverage to facilitate service use without discrimination. This viewpoint investigates the latest trends in mHealth technology, which are comprehensive in terms of form factors and detection targets according to body attachment location and type. Insights and breakthroughs from the perspective of mHealth sensing through a new form factor and sensor-integrated display overcome the problems of existing mHealth by proposing a solution of smartphonization of wearable devices and the wearable deviceization of smartphones. This approach maximizes the infinite potential of stagnant mHealth technology and will present a new milestone leading to the popularization of mHealth. In the postpandemic era, innovative mHealth solutions through the smartphonization of wearable devices and the wearable deviceization of smartphones could become the standard for a new paradigm in the field of digital medicine.
Collapse
Affiliation(s)
- Wonki Hong
- Department of Digital Healthcare, Daejeon University, Daejeon, Republic of Korea
| |
Collapse
|
12
|
Wang K, Cao S, Kaur J, Ghafurian M, Butt ZA, Morita P. Heart rate prediction with contactless active assisted living technology: a smart home approach for older adults. Front Artif Intell 2024; 6:1342427. [PMID: 38282903 PMCID: PMC10811001 DOI: 10.3389/frai.2023.1342427] [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: 11/21/2023] [Accepted: 12/29/2023] [Indexed: 01/30/2024] Open
Abstract
Background As global demographics shift toward an aging population, monitoring their heart rate becomes essential, a key physiological metric for cardiovascular health. Traditional methods of heart rate monitoring are often invasive, while recent advancements in Active Assisted Living provide non-invasive alternatives. This study aims to evaluate a novel heart rate prediction method that utilizes contactless smart home technology coupled with machine learning techniques for older adults. Methods The study was conducted in a residential environment equipped with various contactless smart home sensors. We recruited 40 participants, each of whom was instructed to perform 23 types of predefined daily living activities across five phases. Concurrently, heart rate data were collected through Empatica E4 wristband as the benchmark. Analysis of data involved five prominent machine learning models: Support Vector Regression, K-nearest neighbor, Random Forest, Decision Tree, and Multilayer Perceptron. Results All machine learning models achieved commendable prediction performance, with an average Mean Absolute Error of 7.329. Particularly, Random Forest model outperformed the other models, achieving a Mean Absolute Error of 6.023 and a Scatter Index value of 9.72%. The Random Forest model also showed robust capabilities in capturing the relationship between individuals' daily living activities and their corresponding heart rate responses, with the highest R2 value of 0.782 observed during morning exercise activities. Environmental factors contribute the most to model prediction performance. Conclusions The utilization of the proposed non-intrusive approach enabled an innovative method to observe heart rate fluctuations during different activities. The findings of this research have significant implications for public health. By predicting heart rate based on contactless smart home technologies for individuals' daily living activities, healthcare providers and public health agencies can gain a comprehensive understanding of an individual's cardiovascular health profile. This valuable information can inform the implementation of personalized interventions, preventive measures, and lifestyle modifications to mitigate the risk of cardiovascular diseases and improve overall health outcomes.
Collapse
Affiliation(s)
- Kang Wang
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Shi Cao
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Jasleen Kaur
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Moojan Ghafurian
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
| | - Zahid Ahmad Butt
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Plinio Morita
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
- Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada
- Centre for Digital Therapeutics, Techna Institute, University Health Network, Toronto, ON, Canada
- Institute of Health Policy, Management, and Evaluation, University of Toronto, Toronto, ON, Canada
| |
Collapse
|
13
|
Gupta V, Kariotis S, Rajab MD, Errington N, Alhathli E, Jammeh E, Brook M, Meardon N, Collini P, Cole J, Wild JM, Hershman S, Javed A, Thompson AAR, de Silva T, Ashley EA, Wang D, Lawrie A. Unsupervised machine learning to investigate trajectory patterns of COVID-19 symptoms and physical activity measured via the MyHeart Counts App and smart devices. NPJ Digit Med 2023; 6:239. [PMID: 38135699 PMCID: PMC10746711 DOI: 10.1038/s41746-023-00974-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 11/30/2023] [Indexed: 12/24/2023] Open
Abstract
Previous studies have associated COVID-19 symptoms severity with levels of physical activity. We therefore investigated longitudinal trajectories of COVID-19 symptoms in a cohort of healthcare workers (HCWs) with non-hospitalised COVID-19 and their real-world physical activity. 121 HCWs with a history of COVID-19 infection who had symptoms monitored through at least two research clinic visits, and via smartphone were examined. HCWs with a compatible smartphone were provided with an Apple Watch Series 4 and were asked to install the MyHeart Counts Study App to collect COVID-19 symptom data and multiple physical activity parameters. Unsupervised classification analysis of symptoms identified two trajectory patterns of long and short symptom duration. The prevalence for longitudinal persistence of any COVID-19 symptom was 36% with fatigue and loss of smell being the two most prevalent individual symptom trajectories (24.8% and 21.5%, respectively). 8 physical activity features obtained via the MyHeart Counts App identified two groups of trajectories for high and low activity. Of these 8 parameters only 'distance moved walking or running' was associated with COVID-19 symptom trajectories. We report a high prevalence of long-term symptoms of COVID-19 in a non-hospitalised cohort of HCWs, a method to identify physical activity trends, and investigate their association. These data highlight the importance of tracking symptoms from onset to recovery even in non-hospitalised COVID-19 individuals. The increasing ease in collecting real-world physical activity data non-invasively from wearable devices provides opportunity to investigate the association of physical activity to symptoms of COVID-19 and other cardio-respiratory diseases.
Collapse
Affiliation(s)
- Varsha Gupta
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR), Singapore, Republic of Singapore
- Bioinformatics Institute, Agency for Science Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Sokratis Kariotis
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR), Singapore, Republic of Singapore
- Department of Neuroscience, University of Sheffield, Sheffield, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Mohammed D Rajab
- Department of Computer Science, University of Sheffield, Sheffield, UK
| | - Niamh Errington
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Elham Alhathli
- Department of Neuroscience, University of Sheffield, Sheffield, UK
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- Department of Nursing, Faculty of Applied Medical Sciences, Taif University, Taif, Saudi Arabia
| | - Emmanuel Jammeh
- Department of Neuroscience, University of Sheffield, Sheffield, UK
| | - Martin Brook
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- Insigneo Institute for in-silico Medicine, University of Sheffield, Sheffield, UK
| | - Naomi Meardon
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Paul Collini
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Joby Cole
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Jim M Wild
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
- Insigneo Institute for in-silico Medicine, University of Sheffield, Sheffield, UK
| | - Steven Hershman
- Department of Cardiovascular Medicine, Stanford University, Stanford, CA, USA
| | - Ali Javed
- Department of Cardiovascular Medicine, Stanford University, Stanford, CA, USA
| | - A A Roger Thompson
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Thushan de Silva
- Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, UK
| | - Euan A Ashley
- Department of Cardiovascular Medicine, Stanford University, Stanford, CA, USA
| | - Dennis Wang
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research (A*STAR), Singapore, Republic of Singapore
- Bioinformatics Institute, Agency for Science Technology and Research (A*STAR), Singapore, Republic of Singapore
- Department of Computer Science, University of Sheffield, Sheffield, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Allan Lawrie
- National Heart and Lung Institute, Imperial College London, London, UK.
- Insigneo Institute for in-silico Medicine, University of Sheffield, Sheffield, UK.
| |
Collapse
|
14
|
Lee S, Bi L, Chen H, Lin D, Mei R, Wu Y, Chen L, Joo SW, Choo J. Recent advances in point-of-care testing of COVID-19. Chem Soc Rev 2023; 52:8500-8530. [PMID: 37999922 DOI: 10.1039/d3cs00709j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2023]
Abstract
Advances in microfluidic device miniaturization and system integration contribute to the development of portable, handheld, and smartphone-compatible devices. These advancements in diagnostics have the potential to revolutionize the approach to detect and respond to future pandemics. Accordingly, herein, recent advances in point-of-care testing (POCT) of coronavirus disease 2019 (COVID-19) using various microdevices, including lateral flow assay strips, vertical flow assay strips, microfluidic channels, and paper-based microfluidic devices, are reviewed. However, visual determination of the diagnostic results using only microdevices leads to many false-negative results due to the limited detection sensitivities of these devices. Several POCT systems comprising microdevices integrated with portable optical readers have been developed to address this issue. Since the outbreak of COVID-19, effective POCT strategies for COVID-19 based on optical detection methods have been established. They can be categorized into fluorescence, surface-enhanced Raman scattering, surface plasmon resonance spectroscopy, and wearable sensing. We introduced next-generation pandemic sensing methods incorporating artificial intelligence that can be used to meet global health needs in the future. Additionally, we have discussed appropriate responses of various testing devices to emerging infectious diseases and prospective preventive measures for the post-pandemic era. We believe that this review will be helpful for preparing for future infectious disease outbreaks.
Collapse
Affiliation(s)
- Sungwoon Lee
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| | - Liyan Bi
- School of Special Education and Rehabilitation, Binzhou Medical University, Yantai, 264003, China
| | - Hao Chen
- School of Environmental and Material Engineering, Yantai University, Yantai 264005, China
| | - Dong Lin
- School of Pharmacy, Bianzhou Medical University, Yantai, 264003, China
| | - Rongchao Mei
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Yantai 264003, China
| | - Yixuan Wu
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Yantai 264003, China
| | - Lingxin Chen
- CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Yantai 264003, China
- School of Pharmacy, Bianzhou Medical University, Yantai, 264003, China
| | - Sang-Woo Joo
- Department of Information Communication, Materials, and Chemistry Convergence Technology, Soongsil University, Seoul 06978, South Korea
| | - Jaebum Choo
- Department of Chemistry, Chung-Ang University, Seoul 06974, South Korea.
| |
Collapse
|
15
|
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.
Collapse
Affiliation(s)
| | - Phillip Hegeman
- National Institute on Minority Health and Health Disparities
| | | | | | - Nia Moses
- National Institute on Minority Health and Health Disparities
| | | | | |
Collapse
|
16
|
Nazaret A, Tonekaboni S, Darnell G, Ren SY, Sapiro G, Miller AC. Modeling personalized heart rate response to exercise and environmental factors with wearables data. NPJ Digit Med 2023; 6:207. [PMID: 37968567 PMCID: PMC10651837 DOI: 10.1038/s41746-023-00926-4] [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: 03/31/2023] [Accepted: 09/20/2023] [Indexed: 11/17/2023] Open
Abstract
Heart rate (HR) response to workout intensity reflects fitness and cardiorespiratory health. Physiological models have been developed to describe such heart rate dynamics and characterize cardiorespiratory fitness. However, these models have been limited to small studies in controlled lab environments and are challenging to apply to noisy-but ubiquitous-data from wearables. We propose a hybrid approach that combines a physiological model with flexible neural network components to learn a personalized, multidimensional representation of fitness. The physiological model describes the evolution of heart rate during exercise using ordinary differential equations (ODEs). ODE parameters are dynamically derived via a neural network connecting personalized representations to external environmental factors, from area topography to weather and instantaneous workout intensity. Our approach efficiently fits the hybrid model to a large set of 270,707 workouts collected from wearables of 7465 users from the Apple Heart and Movement Study. The resulting model produces fitness representations that accurately predict full HR response to exercise intensity in future workouts, with a per-workout median error of 6.1 BPM [4.4-8.8 IQR]. We further demonstrate that the learned representations correlate with traditional metrics of cardiorespiratory fitness, such as VO2 max (explained variance 0.81 ± 0.003). Lastly, we illustrate how our model is naturally interpretable and explicitly describes the effects of environmental factors such as temperature and humidity on heart rate, e.g., high temperatures can increase heart rate by 10%. Combining physiological ODEs with flexible neural networks can yield interpretable, robust, and expressive models for health applications.
Collapse
|
17
|
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.
Collapse
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
| |
Collapse
|
18
|
Palanisamy S, Lee LY, Kao CF, Chen WL, Wang HC, Shen ST, Jian JW, Yuan SSF, Kung YA, Wang YM. One-step-one-pot hydrothermally derived metal-organic-framework-nanohybrids for integrated point-of-care diagnostics of SARS-CoV-2 viral antigen/pseudovirus utilizing electrochemical biosensor chip. SENSORS AND ACTUATORS. B, CHEMICAL 2023; 390:133960. [PMID: 37193120 PMCID: PMC10170875 DOI: 10.1016/j.snb.2023.133960] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 04/24/2023] [Accepted: 05/09/2023] [Indexed: 05/18/2023]
Abstract
The COVID-19 pandemic has become a global catastrophe, affecting the health and economy of the human community. It is required to mitigate the impact of pandemics by developing rapid molecular diagnostics for SARS-CoV-2 virus detection. In this context, developing a rapid point-of-care (POC) diagnostic test is a holistic approach to the prevention of COVID-19. In this context, this study aims at presenting a real-time, biosensor chip for improved molecular diagnostics including recombinant SARS-CoV-2 spike glycoprotein and SARS-CoV-2 pseudovirus detection based on one-step-one-pot hydrothermally derived CoFeBDCNH2-CoFe2O4 MOF-nanohybrids. This study was tested on a PalmSens-EmStat Go POC device, showing a limit of detection (LOD) for recombinant SARS-CoV-2 spike glycoprotein of 6.68 fg/mL and 6.20 fg/mL in buffer and 10% serum-containing media, respectively. To validate virus detection in the POC platform, an electrochemical instrument (CHI6116E) was used to perform dose dependent studies under similar experimental conditions to the handheld device. The results obtained from these studies were comparable indicating the capability and high detection electrochemical performance of MOF nanocomposite derived from one-step-one-pot hydrothermal synthesis for SARS-CoV-2 detection for the first time. Further, the performance of the sensor was tested in the presence of Omicron BA.2 and wild-type D614G pseudoviruses.
Collapse
Affiliation(s)
- Sathyadevi Palanisamy
- Institute of Biomedical Engineering, National Tsing Hua University, Hsinchu 300, Taiwan
| | - Li-Yun Lee
- Department of Biological Science and Technology, Institute of Molecular Medicine and Bioengineering, Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Yang Ming Chiao Tung University, 75 Bo-Ai Street, Hsinchu 300, Taiwan
| | - Chih-Fei Kao
- Department of Biological Science and Technology, Institute of Molecular Medicine and Bioengineering, Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Yang Ming Chiao Tung University, 75 Bo-Ai Street, Hsinchu 300, Taiwan
| | - Wen-Liang Chen
- Department of Biological Science and Technology, Institute of Molecular Medicine and Bioengineering, Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Yang Ming Chiao Tung University, 75 Bo-Ai Street, Hsinchu 300, Taiwan
| | - Hsiang-Ching Wang
- Biomedical Technology and Device Research Lab, Industrial Technology Research Institute, Hsinchu 300, Taiwan
| | - San-Tai Shen
- AnTaimmu BioMed Co., Ltd, Unit 304, No. 1, Lixing 1st Road, East District, Hsinchu 300, Taiwan
| | - Jhih-Wei Jian
- AnTaimmu BioMed Co., Ltd, Unit 304, No. 1, Lixing 1st Road, East District, Hsinchu 300, Taiwan
| | - Shyng-Shiou F Yuan
- Translational Research Center, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Department of Obstetrics and Gynecology, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan
- Faculty and College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan
| | - Yu-An Kung
- Research Center for Emerging Viral Infections, College of Medicine, Chang Gung University, Taoyuan City, Taiwan
| | - Yun-Ming Wang
- Department of Biological Science and Technology, Institute of Molecular Medicine and Bioengineering, Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Yang Ming Chiao Tung University, 75 Bo-Ai Street, Hsinchu 300, Taiwan
| |
Collapse
|
19
|
Liu JH, Shih CY, Huang HL, Peng JK, Cheng SY, Tsai JS, Lai F. Evaluating the Potential of Machine Learning and Wearable Devices in End-of-Life Care in Predicting 7-Day Death Events Among Patients With Terminal Cancer: Cohort Study. J Med Internet Res 2023; 25:e47366. [PMID: 37594793 PMCID: PMC10474512 DOI: 10.2196/47366] [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: 03/17/2023] [Revised: 07/02/2023] [Accepted: 07/14/2023] [Indexed: 08/19/2023] Open
Abstract
BACKGROUND An accurate prediction of mortality in end-of-life care is crucial but presents challenges. Existing prognostic tools demonstrate moderate performance in predicting survival across various time frames, primarily in in-hospital settings and single-time evaluations. However, these tools may fail to capture the individualized and diverse trajectories of patients. Limited evidence exists regarding the use of artificial intelligence (AI) and wearable devices, specifically among patients with cancer at the end of life. OBJECTIVE This study aimed to investigate the potential of using wearable devices and AI to predict death events among patients with cancer at the end of life. Our hypothesis was that continuous monitoring through smartwatches can offer valuable insights into the progression of patients at the end of life and enable the prediction of changes in their condition, which could ultimately enhance personalized care, particularly in outpatient or home care settings. METHODS This prospective study was conducted at the National Taiwan University Hospital. Patients diagnosed with cancer and receiving end-of-life care were invited to enroll in wards, outpatient clinics, and home-based care settings. Each participant was given a smartwatch to collect physiological data, including steps taken, heart rate, sleep time, and blood oxygen saturation. Clinical assessments were conducted weekly. The participants were followed until the end of life or up to 52 weeks. With these input features, we evaluated the prediction performance of several machine learning-based classifiers and a deep neural network in 7-day death events. We used area under the receiver operating characteristic curve (AUROC), F1-score, accuracy, and specificity as evaluation metrics. A Shapley additive explanations value analysis was performed to further explore the models with good performance. RESULTS From September 2021 to August 2022, overall, 1657 data points were collected from 40 patients with a median survival time of 34 days, with the detection of 28 death events. Among the proposed models, extreme gradient boost (XGBoost) yielded the best result, with an AUROC of 96%, F1-score of 78.5%, accuracy of 93%, and specificity of 97% on the testing set. The Shapley additive explanations value analysis identified the average heart rate as the most important feature. Other important features included steps taken, appetite, urination status, and clinical care phase. CONCLUSIONS We demonstrated the successful prediction of patient deaths within the next 7 days using a combination of wearable devices and AI. Our findings highlight the potential of integrating AI and wearable technology into clinical end-of-life care, offering valuable insights and supporting clinical decision-making for personalized patient care. It is important to acknowledge that our study was conducted in a relatively small cohort; thus, further research is needed to validate our approach and assess its impact on clinical care. TRIAL REGISTRATION ClinicalTrials.gov NCT05054907; https://classic.clinicaltrials.gov/ct2/show/NCT05054907.
Collapse
Affiliation(s)
- Jen-Hsuan Liu
- Department of Family Medicine, National Taiwan University Hospital Hsin-Chu Branch, Hsin-Chu, Taiwan
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
- Department of Family Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
| | - Chih-Yuan Shih
- Department of Family Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
- Department of Family Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Hsien-Liang Huang
- Department of Family Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
- Department of Family Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Jen-Kuei Peng
- Department of Family Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
- Department of Family Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Shao-Yi Cheng
- Department of Family Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
- Department of Family Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Jaw-Shiun Tsai
- Department of Family Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
- Department of Family Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Feipei Lai
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| |
Collapse
|
20
|
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.
Collapse
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
| |
Collapse
|
21
|
Abir FF, Chowdhury MEH, Tapotee MI, Mushtak A, Khandakar A, Mahmud S, Hasan MA. PCovNet+: A CNN-VAE anomaly detection framework with LSTM embeddings for smartwatch-based COVID-19 detection. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023; 122:106130. [PMID: 37006447 PMCID: PMC10047244 DOI: 10.1016/j.engappai.2023.106130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 02/20/2023] [Accepted: 03/09/2023] [Indexed: 06/19/2023]
Abstract
The world is slowly recovering from the Coronavirus disease 2019 (COVID-19) pandemic; however, humanity has experienced one of its According to work by Mishra et al. (2020), the study's first phase included a cohort of 5,262 subjects, with 3,325 Fitbit users constituting the majority. However, among this large cohort of 5,262 subjects, most significant trials in modern times only to learn about its lack of preparedness in the face of a highly contagious pathogen. To better prepare the world for any new mutation of the same pathogen or the newer ones, technological development in the healthcare system is a must. Hence, in this work, PCovNet+, a deep learning framework, was proposed for smartwatches and fitness trackers to monitor the user's Resting Heart Rate (RHR) for the infection-induced anomaly. A convolutional neural network (CNN)-based variational autoencoder (VAE) architecture was used as the primary model along with a long short-term memory (LSTM) network to create latent space embeddings for the VAE. Moreover, the framework employed pre-training using normal data from healthy subjects to circumvent the data shortage problem in the personalized models. This framework was validated on a dataset of 68 COVID-19-infected subjects, resulting in anomalous RHR detection with precision, recall, F-beta, and F-1 score of 0.993, 0.534, 0.9849, and 0.6932, respectively, which is a significant improvement compared to the literature. Furthermore, the PCovNet+ framework successfully detected COVID-19 infection for 74% of the subjects (47% presymptomatic and 27% post-symptomatic detection). The results prove the usability of such a system as a secondary diagnostic tool enabling continuous health monitoring and contact tracing.
Collapse
Affiliation(s)
- Farhan Fuad Abir
- Department of Electrical and Computer Engineering, University of Central Florida, Orlando, FL, United States
| | | | - Malisha Islam Tapotee
- Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka 1000, Bangladesh
| | - Adam Mushtak
- Clinical Imaging Department, Hamad Medical Corporation, Doha, Qatar
| | - Amith Khandakar
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Sakib Mahmud
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Md Anwarul Hasan
- Department of Mechanical and Industrial Engineering, Qatar University, Doha 2713, Qatar
| |
Collapse
|
22
|
Yang DM, Chang TJ, Hung KF, Wang ML, Cheng YF, Chiang SH, Chen MF, Liao YT, Lai WQ, Liang KH. Smart healthcare: A prospective future medical approach for COVID-19. J Chin Med Assoc 2023; 86:138-146. [PMID: 36227021 PMCID: PMC9847685 DOI: 10.1097/jcma.0000000000000824] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
COVID-19 has greatly affected human life for over 3 years. In this review, we focus on smart healthcare solutions that address major requirements for coping with the COVID-19 pandemic, including (1) the continuous monitoring of severe acute respiratory syndrome coronavirus 2, (2) patient stratification with distinct short-term outcomes (eg, mild or severe diseases) and long-term outcomes (eg, long COVID), and (3) adherence to medication and treatments for patients with COVID-19. Smart healthcare often utilizes medical artificial intelligence (AI) and cloud computing and integrates cutting-edge biological and optoelectronic techniques. These are valuable technologies for addressing the unmet needs in the management of COVID. By leveraging deep learning/machine learning capabilities and big data, medical AI can perform precise prognosis predictions and provide reliable suggestions for physicians' decision-making. Through the assistance of the Internet of Medical Things, which encompasses wearable devices, smartphone apps, internet-based drug delivery systems, and telemedicine technologies, the status of mild cases can be continuously monitored and medications provided at home without the need for hospital care. In cases that develop into severe cases, emergency feedback can be provided through the hospital for rapid treatment. Smart healthcare can possibly prevent the development of severe COVID-19 cases and therefore lower the burden on intensive care units.
Collapse
Affiliation(s)
- De-Ming Yang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Microscopy Service Laboratory, Basic Research Division, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Institute of Biophotonics, School of Biomedical Science and Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
- Address correspondence. Dr. De-Ming Yang, Microscopy Service Laboratory, Basic Research Division, Department of Medical Research, Taipei Veterans General Hospital, 201, Section 2, Shi-Pai Road, Taipei 112, Taiwan, ROC. E-mail address: (D.-M. Yang). and Dr. Kung-Hao Liang, Laboratory of Systems Biomedical Science, Department of Medical Research, Taipei Veterans General Hospital, 201, Section 2, Shi-Pai Road, Taipei 112, Taiwan, ROC. E-mail: (K.-H. Liang)
| | - Tai-Jay Chang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Laboratory of Genome Research, Basic Research Division, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- School of Biomedical science and Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
| | - Kai-Feng Hung
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Mong-Lien Wang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Yen-Fu Cheng
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Su-Hua Chiang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Mei-Fang Chen
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Yi-Ting Liao
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Laboratory of Systems Biomedical Science, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Institute of Food Safety and Health Risk Assessment, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
| | - Wei-Qun Lai
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Microscopy Service Laboratory, Basic Research Division, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Institute of Biophotonics, School of Biomedical Science and Engineering, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
| | - Kung-Hao Liang
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Laboratory of Systems Biomedical Science, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
- Institute of Food Safety and Health Risk Assessment, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC
- Address correspondence. Dr. De-Ming Yang, Microscopy Service Laboratory, Basic Research Division, Department of Medical Research, Taipei Veterans General Hospital, 201, Section 2, Shi-Pai Road, Taipei 112, Taiwan, ROC. E-mail address: (D.-M. Yang). and Dr. Kung-Hao Liang, Laboratory of Systems Biomedical Science, Department of Medical Research, Taipei Veterans General Hospital, 201, Section 2, Shi-Pai Road, Taipei 112, Taiwan, ROC. E-mail: (K.-H. Liang)
| |
Collapse
|
23
|
Li D, Sun C, Mei X, Yang L. Achieving broad availability of SARS-CoV-2 detections via smartphone-based analysis. Trends Analyt Chem 2023; 158:116878. [PMID: 36506266 PMCID: PMC9728015 DOI: 10.1016/j.trac.2022.116878] [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: 08/15/2022] [Revised: 12/01/2022] [Accepted: 12/06/2022] [Indexed: 12/13/2022]
Abstract
With the development of COVID-19, widely available tests are in great demand. Naked-eye SARS-CoV-2 test kits have recently been developed as home tests, but their sensitivity and accuracy are sometimes limited. Smartphones can convert various signals into digital information, potentially improving the sensitivity and accuracy of these home tests. Herein, we summarize smartphone-based detections for SARS-CoV-2. Optical detections of non-nucleic acids using various sensors and portable imaging systems, as well as nucleic acid analyses based on LAMP, CRISP, CATCH, and biosensors are discussed. Furthermore, different electrochemical detections were compared. We show results obtained using relatively complex equipment, complicated programming procedures, or custom smartphone apps, and describe methods for obtaining information with only simple setups and free software on smartphones. Then, the combined costs of typical smartphone-based detections are evaluated. Finally, the prospect of improving smartphone-based strategies to achieve broad availability of SARS-CoV-2 detection is proposed.
Collapse
Affiliation(s)
- Dan Li
- Jinzhou Medical University, Jinzhou, China
| | - Cai Sun
- AECC Shenyang Liming Aero-Engine Co, Ltd., Shenyang, China
| | - Xifan Mei
- Jinzhou Medical University, Jinzhou, China,Corresponding author
| | - Liqun Yang
- NHC Key Laboratory of Reproductive Health and Medical Genetics (China Medical University), Liaoning Research Institute of Family Planning (The Affiliated Reproductive Hospital of China Medical University), Shenyang, China,Corresponding author
| |
Collapse
|
24
|
Cleary JL, Fang Y, Sen S, Wu Z. A caveat to using wearable sensor data for COVID-19 detection: The role of behavioral change after receipt of test results. PLoS One 2022; 17:e0277350. [PMID: 36584148 PMCID: PMC9803125 DOI: 10.1371/journal.pone.0277350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 10/25/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Recent studies indicate that wearable sensors can capture subtle within-person changes caused by SARS-CoV-2 infection and play a role in detecting COVID-19 infections. However, in addition to direct effects of infection, wearable sensor data may capture changes in behavior after the receipt of COVID test results. At present, it remains unclear to what extent the observed discriminative performance of the wearable sensor data is affected by behavioral changes upon receipt of the test results. METHODS We conducted a retrospective study of wearable sensor data in a sample of medical interns who had symptoms and received COVID-19 test results from March to December 2020, and calculated wearable sensor metrics incorporating changes in step, sleep, and resting heart rate for interns who tested positive (cases, n = 22) and negative (controls, n = 83) after symptom onset. All these interns had wearable sensor data available for > 50% of the days in pre- and post-symptom onset periods. We assessed discriminative accuracy of the metrics via area under the curve (AUC) and tested the impact of behavior changes after receiving test results by comparing AUCs of three models: all data, pre-test-result-only data, and post-test-result-only data. RESULTS Wearable sensor metrics differentiated between symptomatic COVID-19 positive and negative individuals with good accuracy (AUC = 0.75). However, the discriminative capacity of the model with pre-test-result-only data substantially decreased (AUC from 0.75 to 0.63; change = -0.12, p = 0.013). The model with post-test-result-only data did not produce similar reductions in discriminative capacity. CONCLUSIONS Changes in wearable sensor data, especially physical activity and sleep, are robust indicators of COVID-19 infection, though they may be reflective of a person's behavior change after receiving a positive test result as opposed to a physiological signature of the virus. Thus, wearable sensor data could facilitate the monitoring of COVID-19 prevalence, but not yet replace SARS-CoV-2 testing.
Collapse
Affiliation(s)
- Jennifer L. Cleary
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI, United States of America
- Department of Psychology, University of Michigan, Ann Arbor, MI, United States of America
| | - Yu Fang
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI, United States of America
| | - Srijan Sen
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI, United States of America
- Department of Psychiatry, University of Michigan Medical School, Ann Arbor, MI, United States of America
| | - Zhenke Wu
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, United States of America
| |
Collapse
|
25
|
Semiz S. COVID19 biomarkers: What did we learn from systematic reviews? Front Cell Infect Microbiol 2022; 12:1038908. [PMID: 36583110 PMCID: PMC9792992 DOI: 10.3389/fcimb.2022.1038908] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 11/24/2022] [Indexed: 12/15/2022] Open
Abstract
The coronavirus disease 2019 (COVID19) pandemic continues to represent a substantial public health concern. It can rapidly progress to severe disease, with poor prognosis and a high mortality risk. An early diagnosis and specific prognostic tools can help healthcare providers to start interventions promptly, understand the likely prognosis and to identify and treat timely individuals likely to develop severe disease with enhanced mortality risk. Here we focused on an impressive set of systematic reviews and meta-analyses that were performed since the start of the COVID19 pandemic and summarized their results related to the levels of hematologic, inflammatory, immunologic biomarkers as well as markers of cardiac, respiratory, hepatic, gastrointestinal and renal systems and their association with the disease progression, severity and mortality. The evidence outlines the significance of specific biomarkers, including inflammatory and immunological parameters (C-reactive protein, procalcitonin, interleukin-6), hematological (lymphocytes count, neutrophil-to-lymphocyte ratio, D-dimer, ferritin, red blood cell distribution width), cardiac (troponin, CK-MB, myoglobin), liver (AST, ALT, total bilirubin, albumin) and lung injury (Krebs von den Lungen-6) that can be used as prognostic biomarkers to aid the identification of high-risk patients and the prediction of serious outcomes, including mortality, in COVID19. Thus, these parameters should be used as essential tools for an early risk stratification and adequate intervention in improving disease outcomes in COVID19 patients.
Collapse
|
26
|
Javaid A, Zghyer F, Kim C, Spaulding EM, Isakadze N, Ding J, Kargillis D, Gao Y, Rahman F, Brown DE, Saria S, Martin SS, Kramer CM, Blumenthal RS, Marvel FA. Medicine 2032: The future of cardiovascular disease prevention with machine learning and digital health technology. Am J Prev Cardiol 2022; 12:100379. [PMID: 36090536 PMCID: PMC9460561 DOI: 10.1016/j.ajpc.2022.100379] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/21/2022] [Accepted: 08/28/2022] [Indexed: 11/30/2022] Open
Abstract
Machine learning (ML) refers to computational algorithms that iteratively improve their ability to recognize patterns in data. The digitization of our healthcare infrastructure is generating an abundance of data from electronic health records, imaging, wearables, and sensors that can be analyzed by ML algorithms to generate personalized risk assessments and promote guideline-directed medical management. ML's strength in generating insights from complex medical data to guide clinical decisions must be balanced with the potential to adversely affect patient privacy, safety, health equity, and clinical interpretability. This review provides a primer on key advances in ML for cardiovascular disease prevention and how they may impact clinical practice.
Collapse
Affiliation(s)
- Aamir Javaid
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
| | - Fawzi Zghyer
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
| | - Chang Kim
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
| | - Erin M. Spaulding
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
| | - Nino Isakadze
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
| | - Jie Ding
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
| | - Daniel Kargillis
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
| | - Yumin Gao
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
| | - Faisal Rahman
- Division of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Donald E. Brown
- School of Data Science, University of Virginia, Charlottesville, VA, USA
| | - Suchi Saria
- Machine Learning and Healthcare Laboratory, Departments of Computer Science, Statistics, and Health Policy, Malone Center for Engineering in Healthcare, and Armstrong Institute for Patient Safety and Quality, Johns Hopkins University, Baltimore, MD, USA
| | - Seth S. Martin
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
| | - Christopher M. Kramer
- Cardiovascular Division, Department of Medicine, University of Virginia Health, Charlottesville, VA, USA
| | - Roger S. Blumenthal
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
| | - Francoise A. Marvel
- Johns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA
| |
Collapse
|
27
|
Smart Consumer Wearables as Digital Diagnostic Tools: A Review. Diagnostics (Basel) 2022; 12:diagnostics12092110. [PMID: 36140511 PMCID: PMC9498278 DOI: 10.3390/diagnostics12092110] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/26/2022] [Accepted: 08/29/2022] [Indexed: 11/17/2022] Open
Abstract
The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains.
Collapse
|
28
|
Risch M, Grossmann K, Aeschbacher S, Weideli OC, Kovac M, Pereira F, Wohlwend N, Risch C, Hillmann D, Lung T, Renz H, Twerenbold R, Rothenbühler M, Leibovitz D, Kovacevic V, Markovic A, Klaver P, Brakenhoff TB, Franks B, Mitratza M, Downward GS, Dowling A, Montes S, Grobbee DE, Cronin M, Conen D, Goodale BM, Risch L. Investigation of the use of a sensor bracelet for the presymptomatic detection of changes in physiological parameters related to COVID-19: an interim analysis of a prospective cohort study (COVI-GAPP). BMJ Open 2022; 12:e058274. [PMID: 35728900 PMCID: PMC9240454 DOI: 10.1136/bmjopen-2021-058274] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 04/04/2022] [Indexed: 11/29/2022] Open
Abstract
OBJECTIVES We investigated machinelearningbased identification of presymptomatic COVID-19 and detection of infection-related changes in physiology using a wearable device. DESIGN Interim analysis of a prospective cohort study. SETTING, PARTICIPANTS AND INTERVENTIONS Participants from a national cohort study in Liechtenstein were included. Nightly they wore the Ava-bracelet that measured respiratory rate (RR), heart rate (HR), HR variability (HRV), wrist-skin temperature (WST) and skin perfusion. SARS-CoV-2 infection was diagnosed by molecular and/or serological assays. RESULTS A total of 1.5 million hours of physiological data were recorded from 1163 participants (mean age 44±5.5 years). COVID-19 was confirmed in 127 participants of which, 66 (52%) had worn their device from baseline to symptom onset (SO) and were included in this analysis. Multi-level modelling revealed significant changes in five (RR, HR, HRV, HRV ratio and WST) device-measured physiological parameters during the incubation, presymptomatic, symptomatic and recovery periods of COVID-19 compared with baseline. The training set represented an 8-day long instance extracted from day 10 to day 2 before SO. The training set consisted of 40 days measurements from 66 participants. Based on a random split, the test set included 30% of participants and 70% were selected for the training set. The developed long short-term memory (LSTM) based recurrent neural network (RNN) algorithm had a recall (sensitivity) of 0.73 in the training set and 0.68 in the testing set when detecting COVID-19 up to 2 days prior to SO. CONCLUSION Wearable sensor technology can enable COVID-19 detection during the presymptomatic period. Our proposed RNN algorithm identified 68% of COVID-19 positive participants 2 days prior to SO and will be further trained and validated in a randomised, single-blinded, two-period, two-sequence crossover trial. Trial registration number ISRCTN51255782; Pre-results.
Collapse
Affiliation(s)
- Martin Risch
- Dr Risch Medical Laboratory, Vaduz, Liechtenstein
- Central Laboratory, Canton Hospital Graubünden, Chur, Switzerland
- Dr Risch Medical Laboratory, Buchs, Switzerland
| | - Kirsten Grossmann
- Dr Risch Medical Laboratory, Vaduz, Liechtenstein
- Faculty of Medical Sciences, Private University in the Principality of Liechtenstein, Triesen, Liechtenstein
| | - Stefanie Aeschbacher
- Cardiovascular Research Institute Basel (CRIB), University Hospital Basel, University of Basel, Basel, Switzerland
| | | | - Marc Kovac
- Dr Risch Medical Laboratory, Buchs, Switzerland
| | - Fiona Pereira
- Department of Metabolism, Digestive Diseases and Reproduction, Imperial College London, London, UK
| | | | | | | | - Thomas Lung
- Dr Risch Medical Laboratory, Buchs, Switzerland
| | - Harald Renz
- Institute of Laboratory Medicine and Pathobiochemistry, Molecular Diagnostics, Philipps University Marburg, Marburg, Germany
| | - Raphael Twerenbold
- Cardiovascular Research Institute Basel (CRIB), University Hospital Basel, University of Basel, Basel, Switzerland
- Department of Cardiology and University Center of Cardiovascular Science, University Heart and Vascular Center Hamburg, Hamburg, Germany
| | | | | | | | - Andjela Markovic
- Ava AG, Zurich, Switzerland
- Department of Psychology, University of Fribourg, Fribourg, Switzerland
- Department of Pulmonology, University Hospital Zurich, Zurich, Switzerland
| | | | | | | | - Marianna Mitratza
- UMC Utrecht, Utrecht, The Netherlands
- Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, The Netherlands
| | - George S Downward
- UMC Utrecht, Utrecht, The Netherlands
- Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, The Netherlands
| | - Ariel Dowling
- Takeda Pharmaceuticals, Digital Clinical Devices, Cambridge, UK
| | | | - Diederick E Grobbee
- UMC Utrecht, Utrecht, The Netherlands
- Julius Global Health, Julius Center for Health Sciences and Primary Care, University Medical Center, Utrecht, The Netherlands
| | | | - David Conen
- Population Health Research Institute, McMaster University, Hamilton, Ontario, Canada
| | | | - Lorenz Risch
- Dr Risch Medical Laboratory, Vaduz, Liechtenstein
- Dr Risch Medical Laboratory, Buchs, Switzerland
- Center of Laboratory Medicine, University Institute of Clinical Chemistry, University of Bern, Bern, Switzerland
| |
Collapse
|