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Liao Y, Luo N. Does internet use benefit health?-PSM-DID evidence from China's CHARLS. PLoS One 2024; 19:e0306393. [PMID: 38980834 PMCID: PMC11232967 DOI: 10.1371/journal.pone.0306393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Accepted: 06/13/2024] [Indexed: 07/11/2024] Open
Abstract
Amid the increasing global internet penetration, understanding the impact of internet use on residents' health is crucial. This aids in formulating more effective health policies and provides empirical evidence for promoting health equity and improving overall public health. Drawing on the China Health and Retirement Longitudinal Study (CHARLS), this paper employs the Propensity Score Matching-Difference in Differences (PSM-DID) method to examine the impact of the internet on individual health and further explores the pathways through which the internet affects health. We introduce the research background and significance in the introduction. Then, in the theoretical analysis, it incorporates internet variables into the Becker health demand model to analyze changes in health demand and impact pathways. The empirical analysis tests the theoretical findings, leading to empirical results. Finally, the study discusses the results and provides relevant recommendations. The findings indicate significant positive effects of the internet on both physical and psychological health. These effects are realized through reducing health information asymmetry, lowering health costs, and increasing exposure to health-promoting environments. In the heterogeneity analysis, economic-related internet content shows a significant positive impact on resident health. Intensive internet use adversely affects psychological health. The beneficial effects of the internet on health are more pronounced among older individuals, those covered by medical insurance, and regions with higher levels of digital economy. Based on these findings, the study offers policy recommendations concerning individuals' internet use patterns, the digital evolution of the healthcare industry, and government infrastructure development.
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Affiliation(s)
- Yinkai Liao
- School of Economics and Trade, Hunan University, Changsha, Hunan, China
| | - Nengsheng Luo
- School of Economics and Trade, Hunan University, Changsha, Hunan, China
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2
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Ryan JM, Navaneethan S, Damaso N, Dilchert S, Hartogensis W, Natale JL, Hecht FM, Mason AE, Smarr BL. Information theory reveals physiological manifestations of COVID-19 that correlate with symptom density of illness. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 4:1211413. [PMID: 38948084 PMCID: PMC11211556 DOI: 10.3389/fnetp.2024.1211413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 05/16/2024] [Indexed: 07/02/2024]
Abstract
Algorithms for the detection of COVID-19 illness from wearable sensor devices tend to implicitly treat the disease as causing a stereotyped (and therefore recognizable) deviation from healthy physiology. In contrast, a substantial diversity of bodily responses to SARS-CoV-2 infection have been reported in the clinical milieu. This raises the question of how to characterize the diversity of illness manifestations, and whether such characterization could reveal meaningful relationships across different illness manifestations. Here, we present a framework motivated by information theory to generate quantified maps of illness presentation, which we term "manifestations," as resolved by continuous physiological data from a wearable device (Oura Ring). We test this framework on five physiological data streams (heart rate, heart rate variability, respiratory rate, metabolic activity, and sleep temperature) assessed at the time of reported illness onset in a previously reported COVID-19-positive cohort (N = 73). We find that the number of distinct manifestations are few in this cohort, compared to the space of all possible manifestations. In addition, manifestation frequency correlates with the rough number of symptoms reported by a given individual, over a several-day period prior to their imputed onset of illness. These findings suggest that information-theoretic approaches can be used to sort COVID-19 illness manifestations into types with real-world value. This proof of concept supports the use of information-theoretic approaches to map illness manifestations from continuous physiological data. Such approaches could likely inform algorithm design and real-time treatment decisions if developed on large, diverse samples.
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Affiliation(s)
- Jacob M. Ryan
- Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, United States
| | - Shreenithi Navaneethan
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
| | - Natalie Damaso
- MIT Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA, United States
| | - Stephan Dilchert
- Department of Management, Zicklin School of Business, Baruch College, The City University of New York, New York, NY, United States
| | - Wendy Hartogensis
- Osher Center for Integrative Health, University of California, San Francisco, San Francisco, CA, United States
| | - Joseph L. Natale
- Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, United States
| | - Frederick M. Hecht
- Osher Center for Integrative Health, University of California, San Francisco, San Francisco, CA, United States
| | - Ashley E. Mason
- Osher Center for Integrative Health, University of California, San Francisco, San Francisco, CA, United States
| | - Benjamin L. Smarr
- Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA, United States
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
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Rutter LA, Cope H, MacKay MJ, Herranz R, Das S, Ponomarev SA, Costes SV, Paul AM, Barker R, Taylor DM, Bezdan D, Szewczyk NJ, Muratani M, Mason CE, Giacomello S. Astronaut omics and the impact of space on the human body at scale. Nat Commun 2024; 15:4952. [PMID: 38862505 PMCID: PMC11166943 DOI: 10.1038/s41467-024-47237-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 03/22/2024] [Indexed: 06/13/2024] Open
Abstract
Future multi-year crewed planetary missions will motivate advances in aerospace nutrition and telehealth. On Earth, the Human Cell Atlas project aims to spatially map all cell types in the human body. Here, we propose that a parallel Human Cell Space Atlas could serve as an openly available, global resource for space life science research. As humanity becomes increasingly spacefaring, high-resolution omics on orbit could permit an advent of precision spaceflight healthcare. Alongside the scientific potential, we consider the complex ethical, cultural, and legal challenges intrinsic to the human space omics discipline, and how philosophical frameworks may benefit from international perspectives.
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Affiliation(s)
- Lindsay A Rutter
- Transborder Medical Research Center, University of Tsukuba, 305-8575, Tsukuba, Japan
- Department of Genome Biology, Institute of Medicine, University of Tsukuba, 305-8575, Tsukuba, Japan
- School of Chemistry, University of Glasgow, Glasgow, G12 8QQ, UK
| | - Henry Cope
- School of Medicine, University of Nottingham, Derby, DE22 3DT, UK
| | - Matthew J MacKay
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10065, USA
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, 10021, USA
- The WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Raúl Herranz
- Centro de Investigaciones Biológicas "Margarita Salas" (CSIC), Ramiro de Maeztu 9, Madrid, 28040, Spain
| | - Saswati Das
- Department of Biochemistry, Atal Bihari Vajpayee Institute of Medical Sciences & Dr. Ram Manohar Lohia Hospital, New Delhi, 110001, India
| | - Sergey A Ponomarev
- Department of Immunology and Microbiology, Institute for the Biomedical Problems, Russian Academy of Sciences, 123007, Moscow, Russia
| | - Sylvain V Costes
- Space Biosciences Division, NASA Ames Research Center, Moffett Field, CA, 94035, USA
| | - Amber M Paul
- Embry-Riddle Aeronautical University, Department of Human Factors and Behavioral Neurobiology, Daytona Beach, FL, 32114, USA
| | - Richard Barker
- Department of Botany, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Deanne M Taylor
- Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Daniela Bezdan
- Institute of Medical Genetics and Applied Genomics, University of Tübingen, Tübingen, 72076, Germany
- NGS Competence Center Tübingen (NCCT), University of Tübingen, Tübingen, 72076, Germany
- yuri GmbH, Meckenbeuren, 88074, Germany
| | - Nathaniel J Szewczyk
- School of Medicine, University of Nottingham, Derby, DE22 3DT, UK
- Ohio Musculoskeletal and Neurological Institute (OMNI), Heritage College of Osteopathic Medicine, Ohio University, Athens, OH, 45701, USA
| | - Masafumi Muratani
- Transborder Medical Research Center, University of Tsukuba, 305-8575, Tsukuba, Japan
- Department of Genome Biology, Institute of Medicine, University of Tsukuba, 305-8575, Tsukuba, Japan
| | - Christopher E Mason
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, 10065, USA.
- The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Weill Cornell Medicine, New York, NY, 10021, USA.
- The WorldQuant Initiative for Quantitative Prediction, Weill Cornell Medicine, New York, NY, 10065, USA.
- The Feil Family Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, 10065, USA.
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Guardado S, Karampela M, Isomursu M, Grundstrom C. Use of Patient-Generated Health Data From Consumer-Grade Devices by Health Care Professionals in the Clinic: Systematic Review. J Med Internet Res 2024; 26:e49320. [PMID: 38820580 PMCID: PMC11179023 DOI: 10.2196/49320] [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: 05/26/2023] [Revised: 04/05/2024] [Accepted: 04/11/2024] [Indexed: 06/02/2024] Open
Abstract
BACKGROUND Mobile health (mHealth) uses mobile technologies to promote wellness and help disease management. Although mHealth solutions used in the clinical setting have typically been medical-grade devices, passive and active sensing capabilities of consumer-grade devices like smartphones and activity trackers have the potential to bridge information gaps regarding patients' behaviors, environment, lifestyle, and other ubiquitous data. Individuals are increasingly adopting mHealth solutions, which facilitate the collection of patient-generated health data (PGHD). Health care professionals (HCPs) could potentially use these data to support care of chronic conditions. However, there is limited research on real-life experiences of HPCs using PGHD from consumer-grade mHealth solutions in the clinical context. OBJECTIVE This systematic review aims to analyze existing literature to identify how HCPs have used PGHD from consumer-grade mobile devices in the clinical setting. The objectives are to determine the types of PGHD used by HCPs, in which health conditions they use them, and to understand the motivations behind their willingness to use them. METHODS A systematic literature review was the main research method to synthesize prior research. Eligible studies were identified through comprehensive searches in health, biomedicine, and computer science databases, and a complementary hand search was performed. The search strategy was constructed iteratively based on key topics related to PGHD, HCPs, and mobile technologies. The screening process involved 2 stages. Data extraction was performed using a predefined form. The extracted data were summarized using a combination of descriptive and narrative syntheses. RESULTS The review included 16 studies. The studies spanned from 2015 to 2021, with a majority published in 2019 or later. Studies showed that HCPs have been reviewing PGHD through various channels, including solutions portals and patients' devices. PGHD about patients' behavior seem particularly useful for HCPs. Our findings suggest that PGHD are more commonly used by HCPs to treat conditions related to lifestyle, such as diabetes and obesity. Physicians were the most frequently reported users of PGHD, participating in more than 80% of the studies. CONCLUSIONS PGHD collection through mHealth solutions has proven beneficial for patients and can also support HCPs. PGHD have been particularly useful to treat conditions related to lifestyle, such as diabetes, cardiovascular diseases, and obesity, or in domains with high levels of uncertainty, such as infertility. Integrating PGHD into clinical care poses challenges related to privacy and accessibility. Some HCPs have identified that though PGHD from consumer devices might not be perfect or completely accurate, their perceived clinical value outweighs the alternative of having no data. Despite their perceived value, our findings reveal their use in clinical practice is still scarce. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) RR2-10.2196/39389.
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Affiliation(s)
- Sharon Guardado
- Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
| | - Maria Karampela
- Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
| | - Minna Isomursu
- Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland
| | - Casandra Grundstrom
- Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway
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Vogel C, Grimm B, Marmor MT, Sivananthan S, Richter PH, Yarboro S, Hanflik AM, Histing T, Braun BJ. Wearable Sensors in Other Medical Domains with Application Potential for Orthopedic Trauma Surgery-A Narrative Review. J Clin Med 2024; 13:3134. [PMID: 38892844 PMCID: PMC11172495 DOI: 10.3390/jcm13113134] [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: 02/12/2024] [Revised: 05/01/2024] [Accepted: 05/09/2024] [Indexed: 06/21/2024] Open
Abstract
The use of wearable technology is steadily increasing. In orthopedic trauma surgery, where the musculoskeletal system is directly affected, focus has been directed towards assessing aspects of physical functioning, activity behavior, and mobility/disability. This includes sensors and algorithms to monitor real-world walking speed, daily step counts, ground reaction forces, or range of motion. Several specific reviews have focused on this domain. In other medical fields, wearable sensors and algorithms to monitor digital biometrics have been used with a focus on domain-specific health aspects such as heart rate, sleep, blood oxygen saturation, or fall risk. This review explores the most common clinical and research use cases of wearable sensors in other medical domains and, from it, derives suggestions for the meaningful transfer and application in an orthopedic trauma context.
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Affiliation(s)
- Carolina Vogel
- University Hospital Tuebingen on Behalf of the Eberhard-Karls-University Tuebingen, BG Unfallklinik, Schnarrenbergstr. 95, 72076 Tuebingen, Germany; (C.V.); (T.H.)
| | - Bernd Grimm
- Luxembourg Institute of Health, Department of Precision Health, Human Motion, Orthopaedics, Sports Medicine and Digital Methods Group, 1445 Strassen, Luxembourg;
| | - Meir T. Marmor
- Orthopaedic Trauma Institute (OTI), San Francisco General Hospital, University of California, San Francisco, CA 94158, USA;
| | | | - Peter H. Richter
- Department of Trauma and Orthopaedic Surgery, Esslingen Hospotal, 73730 Esslingen, Germany;
| | - Seth Yarboro
- Deptartment Orthopaedic Surgery, University of Virginia, Charlottesville, VA 22908, USA;
| | - Andrew M. Hanflik
- Department of Orthopaedic Surgery, Southern California Permanente Medical Group, Downey Medical Center, Kaiser Permanente, Downey, CA 90027, USA;
| | - Tina Histing
- University Hospital Tuebingen on Behalf of the Eberhard-Karls-University Tuebingen, BG Unfallklinik, Schnarrenbergstr. 95, 72076 Tuebingen, Germany; (C.V.); (T.H.)
| | - Benedikt J. Braun
- University Hospital Tuebingen on Behalf of the Eberhard-Karls-University Tuebingen, BG Unfallklinik, Schnarrenbergstr. 95, 72076 Tuebingen, Germany; (C.V.); (T.H.)
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6
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Liu J. Promoting a healthy lifestyle: exploring the role of social media and fitness applications in the context of social media addiction risk. HEALTH EDUCATION RESEARCH 2024; 39:272-283. [PMID: 38244589 DOI: 10.1093/her/cyad047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 09/13/2023] [Accepted: 12/22/2023] [Indexed: 01/22/2024]
Abstract
The popularity of social networks turns them into a legal method for promoting a healthy lifestyle, which benefits not only people but also different countries' governments. This research paper aimed to examine the Keep fitness app integrated into WeChat, Weibo and QQ as regards long-term improvements in health-related behaviors (physical activity, nutrition, health responsibility, spiritual growth, interpersonal relationships and stress management) and assess the associated risk of increased social media addiction. Students from Lishui University in China (N = 300) participated in this study, and they were formed into control and experimental groups. The Healthy Lifestyle Behavior Scale and Social Media Disorder Scale were used as psychometric instruments. The Keep app was found to improve respondents' scores on the parameters of physical activity, nutrition and health responsibility (P = 0.00). However, the level of dependence on social media did not change in either the control or the experimental group during the year of research (P ≥ 0.05). It is concluded that fitness apps can be an effective tool to promote healthy lifestyles among young people in China and other countries. The feasibility of government investment in fitness apps to promote healthy lifestyles is substantiated.
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Affiliation(s)
- Junfeng Liu
- Department of Physical Education, Lishui University, 17-104 Liangyue Lake Yayuan, Yanquan Street, Liandu District, Lishui, Zhejiang 323000, China
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Walter JR, Lee JY, Yu L, Kim B, Martell K, Opdycke A, Scheffel J, Felsl I, Patel S, Rangel S, Serao A, Edel C, Bharat A, Xu S. Use of artificial intelligence to develop predictive algorithms of cough and PCR-confirmed COVID-19 infections based on inputs from clinical-grade wearable sensors. Sci Rep 2024; 14:8072. [PMID: 38580712 PMCID: PMC10997665 DOI: 10.1038/s41598-024-57830-4] [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: 08/15/2023] [Accepted: 03/21/2024] [Indexed: 04/07/2024] Open
Abstract
There have been over 769 million cases of COVID-19, and up to 50% of infected individuals are asymptomatic. The purpose of this study aimed to assess the use of a clinical-grade physiological wearable monitoring system, ANNE One, to develop an artificial intelligence algorithm for (1) cough detection and (2) early detection of COVID-19, through the retrospective analysis of prospectively collected physiological data from longitudinal wear of ANNE sensors in a multicenter single arm study of subjects at high risk for COVID-19 due to occupational or home exposures. The study employed a two-fold approach: cough detection algorithm development and COVID-19 detection algorithm development. For cough detection, healthy individuals wore an ANNE One chest sensor during scripted activity. The final performance of the algorithm achieved an F-1 score of 83.3% in twenty-seven healthy subjects during biomarker validation. In the COVID-19 detection algorithm, individuals at high-risk for developing COVID-19 because of recent exposures received ANNE One sensors and completed daily symptom surveys. An algorithm analyzing vital parameters (heart rate, respiratory rate, cough count, etc.) for early COVID-19 detection was developed. The COVID-19 detection algorithm exhibited a sensitivity of 0.47 and specificity of 0.72 for detecting COVID-19 in 325 individuals with recent exposures. Participants demonstrated high adherence (≥ 4 days of wear per week). ANNE One shows promise for detection of COVID-19. Inclusion of respiratory biomarkers (e.g., cough count) enhanced the algorithm's predictive ability. These findings highlight the potential value of wearable devices in early disease detection and monitoring.
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Affiliation(s)
- Jessica R Walter
- Department of Obstetrics and Gynecology, Northwestern University, Chicago, IL, USA
| | - Jong Yoon Lee
- Sibel Health, Chicago, USA
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, USA
| | - Lian Yu
- Sibel Health, Chicago, USA
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, USA
| | - Brandon Kim
- Sibel Health, Chicago, USA
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, USA
| | - Knute Martell
- Department of Dermatology, Northwestern University Feinberg School of Medicine, Chicago, USA
| | | | | | | | - Soham Patel
- Department of Dermatology, Northwestern University Feinberg School of Medicine, Chicago, USA
| | - Stephanie Rangel
- Department of Dermatology, Northwestern University Feinberg School of Medicine, Chicago, USA
| | - Alexa Serao
- Department of Dermatology, Northwestern University Feinberg School of Medicine, Chicago, USA
| | - Claire Edel
- Department of Dermatology, Northwestern University Feinberg School of Medicine, Chicago, USA
| | - Ankit Bharat
- Department of Surgery, Northwestern University, Chicago, IL, USA
| | - Shuai Xu
- Sibel Health, Chicago, USA.
- Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, USA.
- Department of Dermatology, Northwestern University Feinberg School of Medicine, Chicago, USA.
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Alsulami S, Konstantinidis ST, Wharrad H. Use of wearables among Multiple Sclerosis patients and healthcare Professionals: A scoping review. Int J Med Inform 2024; 184:105376. [PMID: 38359683 DOI: 10.1016/j.ijmedinf.2024.105376] [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: 08/01/2023] [Revised: 01/28/2024] [Accepted: 02/10/2024] [Indexed: 02/17/2024]
Abstract
INTRODUCTION Multiple sclerosis (MS) is an increasingly prevalent chronic, autoimmune, and inflammatory central nervous system illness, whose common symptoms undermine the quality of life of patients and their families. Recent technical breakthroughs potentially offer continuous, reliable, sensitive, and objective remote monitoring solutions for healthcare. Wearables can be useful for evaluating falls, fatigue, sedentary behavior, exercise, and sleep quality in people with MS (PwMS). OBJECTIVE This scoping review of relevant literature explores studies investigating the perceptions of patients and healthcare professionals (HCPs) about the use of wearable technologies in the management of MS. METHODS The Joanna Briggs Institute methodology for scoping reviews was used. The search strategy was applied to the databases, MEDLINE via Ovid, Embase, APA PsycInfo, and CINAHL. Further searches were performed in IEEE, Scopus, and Web of Science. The review considered studies reporting quantitative or qualitative data on perceptions and experiences of PwMS and HCPs concerning wearables' usability, satisfaction, barriers, and facilitators. RESULTS 10 studies were included in this review. Wearables' usefulness and accessibility, ease of use, awareness, and motivational tool potential were patient-perceived facilitators of use. Barriers related to anxiety and frustration, complexity, and the design of wearables. Perceived usefulness and system requirements are identified as facilitators of using wearables by HCPs, while data security concerns and fears of increased workload and limited effectiveness in the care plan are identified as barriers to use wearables. CONCLUSIONS This review contributes to our understanding of the benefits of wearable technologies in MS by exploring perceptions of both PwMS and HCPs. The scoping review provided a broad overview of facilitators and barriers to wearable use in MS. There is a need for further studies underlined with sound theoretical frameworks to provide a robust evidence-base for the optimal use of wearables to empower healthcare users and providers.
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Affiliation(s)
- Shemah Alsulami
- Faculty of Medicine & Health Sciences, University of Nottingham, School of Health Sciences, Queen's Medical Centre, B floor, Nottingham, NG7 2UH, UK; College of Business Administration, King Saud University, Department of Health Administration, Building 3, Riyadh, 12371, KSA, Saudi Arabia.
| | - Stathis Th Konstantinidis
- Faculty of Medicine & Health Sciences, University of Nottingham, School of Health Sciences, Queen's Medical Centre, B floor, Nottingham, NG7 2UH, UK.
| | - Heather Wharrad
- Faculty of Medicine & Health Sciences, University of Nottingham, School of Health Sciences, Queen's Medical Centre, B floor, Nottingham, NG7 2UH, UK.
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9
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Gardner CL, Raps SJ, Kasuske L. Cross-sectional Analysis of Health Behavior Tracking, Perceived Health, Fitness, and Health Literacy Among Active-Duty Air Force Personnel. Comput Inform Nurs 2024; 42:176-183. [PMID: 37580053 DOI: 10.1097/cin.0000000000001060] [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: 08/16/2023]
Abstract
There is a paucity of evidence connecting health literacy, perceived wellness, self-reported fitness activity, or military readiness to wearable devices. Moreover, we do not currently know the prevalence and impact of health tracker device use in the active-duty Air Force population. This prospective cross-sectional survey assessed self-reported fitness activity, health-related quality of life, health literacy, and health behavior tracking practices and preferences among active-duty Air Force service members. Four hundred twenty-eight respondents completed an online survey, with 247 selecting tracking a health behavior and 181 selecting that they did not track a health behavior. Demographic characteristics of the sample showed no significant differences in age, sex distribution, or mode of service. We found that there were no significant differences in self-reported aerobic and strength training frequency, health literacy, or health-related quality of life. More than half of nontracking respondents either had not considered or had no interest in tracking health behaviors. Nearly three-quarters of tracking respondents tracked more than one health behavior. Further research could explore the extent to which these technologies improve fitness, health outcomes, and overall readiness in the military, involving longitudinal studies tracking fitness improvements and health outcomes among service members using wearable devices.
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Affiliation(s)
- Cubby L Gardner
- Author Affiliations: Daniel K. Inouye Graduate School of Nursing, Uniformed Services University of the Health Sciences, Bethesda, MD (Drs Gardner and Kasuske); and Nurse Scientist, Joint Base San Antonio, TX (Dr Raps)
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10
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Shandhi MMH, Singh K, Janson N, Ashar P, Singh G, Lu B, Hillygus DS, Maddocks JM, Dunn JP. Assessment of ownership of smart devices and the acceptability of digital health data sharing. NPJ Digit Med 2024; 7:44. [PMID: 38388660 PMCID: PMC10883993 DOI: 10.1038/s41746-024-01030-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 02/05/2024] [Indexed: 02/24/2024] Open
Abstract
Smart portable devices- smartphones and smartwatches- are rapidly being adopted by the general population, which has brought forward an opportunity to use the large volumes of physiological, behavioral, and activity data continuously being collected by these devices in naturalistic settings to perform research, monitor health, and track disease. While these data can serve to revolutionize health monitoring in research and clinical care, minimal research has been conducted to understand what motivates people to use these devices and their interest and comfort in sharing the data. In this study, we aimed to characterize the ownership and usage of smart devices among patients from an expansive academic health system in the southeastern US and understand their willingness to share data collected by the smart devices. We conducted an electronic survey of participants from an online patient advisory group around smart device ownership, usage, and data sharing. Out of the 3021 members of the online patient advisory group, 1368 (45%) responded to the survey, with 871 female (64%), 826 and 390 White (60%) and Black (29%) participants, respectively, and a slight majority (52%) age 58 and older. Most of the respondents (98%) owned a smartphone and the majority (59%) owned a wearable. In this population, people who identify as female, Hispanic, and Generation Z (age 18-25), and those completing higher education and having full-time employment, were most likely to own a wearable device compared to their demographic counterparts. 50% of smart device owners were willing to share and 32% would consider sharing their smart device data for research purposes. The type of activity data they are willing to share varies by gender, age, education, and employment. Findings from this study can be used to design both equitable and cost-effective digital health studies, leveraging personally-owned smartphones and wearables in representative populations, ultimately enabling the development of equitable digital health technologies.
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Affiliation(s)
| | - Karnika Singh
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | | | - Perisa Ashar
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Geetika Singh
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Baiying Lu
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - D Sunshine Hillygus
- Department of Political Science, Trinity College of Arts & Sciences, Duke University, Durham, NC, USA
- Sanford School of Public Policy, Duke University, Durham, NC, USA
| | | | - Jessilyn P Dunn
- Department of Biomedical Engineering, Duke University, Durham, NC, USA.
- Duke University, Department of Biostatistics & Bioinformatics, Durham, NC, USA.
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11
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Babu M, Lautman Z, Lin X, Sobota MHB, Snyder MP. Wearable Devices: Implications for Precision Medicine and the Future of Health Care. Annu Rev Med 2024; 75:401-415. [PMID: 37983384 DOI: 10.1146/annurev-med-052422-020437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Wearable devices are integrated analytical units equipped with sensitive physical, chemical, and biological sensors capable of noninvasive and continuous monitoring of vital physiological parameters. Recent advances in disciplines including electronics, computation, and material science have resulted in affordable and highly sensitive wearable devices that are routinely used for tracking and managing health and well-being. Combined with longitudinal monitoring of physiological parameters, wearables are poised to transform the early detection, diagnosis, and treatment/management of a range of clinical conditions. Smartwatches are the most commonly used wearable devices and have already demonstrated valuable biomedical potential in detecting clinical conditions such as arrhythmias, Lyme disease, inflammation, and, more recently, COVID-19 infection. Despite significant clinical promise shown in research settings, there remain major hurdles in translating the medical uses of wearables to the clinic. There is a clear need for more effective collaboration among stakeholders, including users, data scientists, clinicians, payers, and governments, to improve device security, user privacy, data standardization, regulatory approval, and clinical validity. This review examines the potential of wearables to offer affordable and reliable measures of physiological status that are on par with FDA-approved specialized medical devices. We briefly examine studies where wearables proved critical for the early detection of acute and chronic clinical conditions with a particular focus on cardiovascular disease, viral infections, and mental health. Finally, we discuss current obstacles to the clinical implementation of wearables and provide perspectives on their potential to deliver increasingly personalized proactive health care across a wide variety of conditions.
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Affiliation(s)
- Mohan Babu
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA;
| | - Ziv Lautman
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA;
- Department of Bioengineering, Stanford University School of Medicine, Stanford, California, USA
| | - Xiangping Lin
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA;
| | - Milan H B Sobota
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA;
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA;
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12
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Shen X, Kellogg R, Panyard DJ, Bararpour N, Castillo KE, Lee-McMullen B, Delfarah A, Ubellacker J, Ahadi S, Rosenberg-Hasson Y, Ganz A, Contrepois K, Michael B, Simms I, Wang C, Hornburg D, Snyder MP. Multi-omics microsampling for the profiling of lifestyle-associated changes in health. Nat Biomed Eng 2024; 8:11-29. [PMID: 36658343 PMCID: PMC10805653 DOI: 10.1038/s41551-022-00999-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Accepted: 12/14/2022] [Indexed: 01/21/2023]
Abstract
Current healthcare practices are reactive and use limited physiological and clinical information, often collected months or years apart. Moreover, the discovery and profiling of blood biomarkers in clinical and research settings are constrained by geographical barriers, the cost and inconvenience of in-clinic venepuncture, low sampling frequency and the low depth of molecular measurements. Here we describe a strategy for the frequent capture and analysis of thousands of metabolites, lipids, cytokines and proteins in 10 μl of blood alongside physiological information from wearable sensors. We show the advantages of such frequent and dense multi-omics microsampling in two applications: the assessment of the reactions to a complex mixture of dietary interventions, to discover individualized inflammatory and metabolic responses; and deep individualized profiling, to reveal large-scale molecular fluctuations as well as thousands of molecular relationships associated with intra-day physiological variations (in heart rate, for example) and with the levels of clinical biomarkers (specifically, glucose and cortisol) and of physical activity. Combining wearables and multi-omics microsampling for frequent and scalable omics may facilitate dynamic health profiling and biomarker discovery.
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Affiliation(s)
- Xiaotao Shen
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Ryan Kellogg
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Daniel J Panyard
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Nasim Bararpour
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Kevin Erazo Castillo
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Brittany Lee-McMullen
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Alireza Delfarah
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Jessalyn Ubellacker
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Sara Ahadi
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Yael Rosenberg-Hasson
- Human Immune Monitoring Center, Microbiology and Immunology, Stanford University Medical Center, Stanford, CA, USA
| | - Ariel Ganz
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Kévin Contrepois
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Basil Michael
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Ian Simms
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Chuchu Wang
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
| | - Daniel Hornburg
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
- Stanford Center for Genomics and Personalized Medicine, Stanford, CA, USA.
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13
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Dynamic monitoring of thousands of biochemical analytes using microsampling. Nat Biomed Eng 2024; 8:5-6. [PMID: 36697922 DOI: 10.1038/s41551-023-01005-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
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14
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Litvinova O, Hammerle FP, Stoyanov J, Ksepka N, Matin M, Ławiński M, Atanasov AG, Willschke H. Patent and Bibliometric Analysis of the Scientific Landscape of the Use of Pulse Oximeters and Their Prospects in the Field of Digital Medicine. Healthcare (Basel) 2023; 11:3003. [PMID: 37998496 PMCID: PMC10671755 DOI: 10.3390/healthcare11223003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 11/02/2023] [Accepted: 11/11/2023] [Indexed: 11/25/2023] Open
Abstract
This study conducted a comprehensive patent and bibliometric analysis to elucidate the evolving scientific landscape surrounding the development and application of pulse oximeters, including in the field of digital medicine. Utilizing data from the Lens database for the period of 2000-2023, we identified the United States, China, the Republic of Korea, Japan, Canada, Australia, Taiwan, and the United Kingdom as the predominant countries in patent issuance for pulse oximeter technology. Our bibliometric analysis revealed a consistent temporal trend in both the volume of publications and citations, underscoring the growing importance of pulse oximeters in digitally-enabled medical practice. Using the VOSviewer software(version 1.6.18), we discerned six primary research clusters: (1) measurement accuracy; (2) integration with the Internet of Things; (3) applicability across diverse pathologies; (4) telemedicine and mobile applications; (5) artificial intelligence and deep learning; and (6) utilization in anesthesiology, resuscitation, and intensive care departments. The findings of this study indicate the prospects for leveraging digital technologies in the use of pulse oximetry in various fields of medicine, with implications for advancing the understanding, diagnosis, prevention, and treatment of cardio-respiratory pathologies. The conducted patent and bibliometric analysis allowed the identification of technical solutions to reduce the risks associated with pulse oximetry: improving precision and validity, technically improved clinical diagnostic use, and the use of machine learning.
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Affiliation(s)
- Olena Litvinova
- Department of Management and Quality Assurance in Pharmacy, National University of Pharmacy, Ministry of Health of Ukraine, 61002 Kharkiv, Ukraine
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, 1090 Vienna, Austria;
| | - Fabian Peter Hammerle
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, 1090 Vienna, Austria;
- Department of Anesthesia, General Intensiv Care and Pain Management, Medical University of Vienna, 1090 Vienna, Austria
| | | | - Natalia Ksepka
- Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, 05-552 Magdalenka, Poland; (N.K.); (M.M.); (M.Ł.)
| | - Maima Matin
- Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, 05-552 Magdalenka, Poland; (N.K.); (M.M.); (M.Ł.)
| | - Michał Ławiński
- Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, 05-552 Magdalenka, Poland; (N.K.); (M.M.); (M.Ł.)
- Department of General, Gastroenterologic and Oncologic Surgery, Medical University of Warsaw, 02-097 Warsaw, Poland
| | - Atanas G. Atanasov
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, 1090 Vienna, Austria;
- Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, 05-552 Magdalenka, Poland; (N.K.); (M.M.); (M.Ł.)
| | - Harald Willschke
- Ludwig Boltzmann Institute Digital Health and Patient Safety, Medical University of Vienna, 1090 Vienna, Austria;
- Department of Anesthesia, General Intensiv Care and Pain Management, Medical University of Vienna, 1090 Vienna, Austria
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15
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Hirai K, Fujimoto Y, Bamba Y, Kageyama Y, Ima H, Ichise A, Sasaki H, Nakagawa R. Continuous Monitoring of Changes in Heart Rate during the Periprocedural Course of Carotid Artery Stenting Using a Wearable Device: A Prospective Observational Study. Neurol Med Chir (Tokyo) 2023; 63:526-534. [PMID: 37648537 PMCID: PMC10725827 DOI: 10.2176/jns-nmc.2023-0093] [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: 04/26/2023] [Accepted: 07/03/2023] [Indexed: 09/01/2023] Open
Abstract
This prospective observational study will evaluate the change in heart rate (HR) during the periprocedural course of carotid artery stenting (CAS) via continuous monitoring using a wearable device. The participants were recruited from our outpatient clinic between April 2020 and March 2023. They were instructed to continuously wear the device from the last outpatient visit before admission to the first outpatient visit after discharge. The changes in HR of interest throughout the periprocedural course of CAS were assessed. In addition, the Bland-Altman analysis was adopted to compare the HR measurement made by the wearable device during CAS with that made by the electrocardiogram (ECG). A total of 12 patients who underwent CAS were included in the final analysis. The time-series analysis revealed that a percentage change in HR decrease occurred on day 1 following CAS and that the most significant HR decrease rate was 12.1% on day 4 following CAS. In comparing the measurements made by the wearable device and ECG, the Bland-Altman analysis revealed the accuracy of the wearable device with a bias of -1.12 beats per minute (bpm) and a precision of 3.16 bpm. Continuous HR monitoring using the wearable device indicated that the decrease in HR following CAS could persist much longer than previously reported, providing us with unique insights into the physiology of carotid sinus baroreceptors.
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Affiliation(s)
| | | | - Yohei Bamba
- Department of Neurosurgery, Osaka Rosai Hospital
| | - Yu Kageyama
- Department of Neurosurgery, Osaka Rosai Hospital
| | - Hiroyuki Ima
- Department of Neurosurgery, Osaka Rosai Hospital
| | - Ayaka Ichise
- Department of Neurosurgery, Osaka Rosai Hospital
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16
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Waalen J. Mobile Health and Preventive Medicine. Med Clin North Am 2023; 107:1097-1108. [PMID: 37806725 DOI: 10.1016/j.mcna.2023.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Wearable devices providing health-related data (mobile health [mHealth]) have grown in numbers and types of data available over the past 2 decades. Applications in prevention with some of the longest track records are activity trackers to promote fitness (primary prevention), mobile electrocardiogram devices to detect arrhythmias (secondary prevention), and continuous glucose monitoring to improve glycemic control in type 2 diabetes (tertiary prevention). Continued integration of multiple diverse data streams and improved interfaces with individuals (such as artificial intelligence-driven health coaches), and health care teams (as in the hospital-at-home concept), promise to optimize use of mHealth to improve clinical and public health outcomes.
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Affiliation(s)
- Jill Waalen
- University of California, San Diego/San Diego State University General Preventive Medicine Residency Program & Scripps Research Translational Institute, 3344 North Torrey Pines Court, La Jolla, CA 92037, USA.
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17
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Hasasneh A, Hijazi H, Talib MA, Afadar Y, Nassif AB, Nasir Q. Wearable Devices and Explainable Unsupervised Learning for COVID-19 Detection and Monitoring. Diagnostics (Basel) 2023; 13:3071. [PMID: 37835814 PMCID: PMC10572947 DOI: 10.3390/diagnostics13193071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 09/20/2023] [Accepted: 09/22/2023] [Indexed: 10/15/2023] Open
Abstract
Despite the declining COVID-19 cases, global healthcare systems still face significant challenges due to ongoing infections, especially among fully vaccinated individuals, including adolescents and young adults (AYA). To tackle this issue, cost-effective alternatives utilizing technologies like Artificial Intelligence (AI) and wearable devices have emerged for disease screening, diagnosis, and monitoring. However, many AI solutions in this context heavily rely on supervised learning techniques, which pose challenges such as human labeling reliability and time-consuming data annotation. In this study, we propose an innovative unsupervised framework that leverages smartwatch data to detect and monitor COVID-19 infections. We utilize longitudinal data, including heart rate (HR), heart rate variability (HRV), and physical activity measured via step count, collected through the continuous monitoring of volunteers. Our goal is to offer effective and affordable solutions for COVID-19 detection and monitoring. Our unsupervised framework employs interpretable clusters of normal and abnormal measures, facilitating disease progression detection. Additionally, we enhance result interpretation by leveraging the language model Davinci GPT-3 to gain deeper insights into the underlying data patterns and relationships. Our results demonstrate the effectiveness of unsupervised learning, achieving a Silhouette score of 0.55. Furthermore, validation using supervised learning techniques yields high accuracy (0.884 ± 0.005), precision (0.80 ± 0.112), and recall (0.817 ± 0.037). These promising findings indicate the potential of unsupervised techniques for identifying inflammatory markers, contributing to the development of efficient and reliable COVID-19 detection and monitoring methods. Our study shows the capabilities of AI and wearables, reflecting the pursuit of low-cost, accessible solutions for addressing health challenges related to inflammatory diseases, thereby opening new avenues for scalable and widely applicable health monitoring solutions.
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Affiliation(s)
- Ahmad Hasasneh
- Department of Natural, Engineering, and Technology Sciences, Faculty of Graduate Studies, Arab American University, Ramallah P-600-699, Palestine;
| | - Haytham Hijazi
- Department of Informatics Engineering, CISUC-Centre for Informatics and Systems of the University of Coimbra, University of Coimbra, 3030-790 Coimbra, Portugal
- Intelligent Systems Department, Palestine Ahliya University, Bethlehem P-150-199, Palestine
| | - Manar Abu Talib
- College of Computing and Informatics, University of Sharjah, Sharjah 27272, United Arab Emirates; (M.A.T.); (Y.A.); (A.B.N.); (Q.N.)
| | - Yaman Afadar
- College of Computing and Informatics, University of Sharjah, Sharjah 27272, United Arab Emirates; (M.A.T.); (Y.A.); (A.B.N.); (Q.N.)
| | - Ali Bou Nassif
- College of Computing and Informatics, University of Sharjah, Sharjah 27272, United Arab Emirates; (M.A.T.); (Y.A.); (A.B.N.); (Q.N.)
| | - Qassim Nasir
- College of Computing and Informatics, University of Sharjah, Sharjah 27272, United Arab Emirates; (M.A.T.); (Y.A.); (A.B.N.); (Q.N.)
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18
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Sadri A. Is Target-Based Drug Discovery Efficient? Discovery and "Off-Target" Mechanisms of All Drugs. J Med Chem 2023; 66:12651-12677. [PMID: 37672650 DOI: 10.1021/acs.jmedchem.2c01737] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
Target-based drug discovery is the dominant paradigm of drug discovery; however, a comprehensive evaluation of its real-world efficiency is lacking. Here, a manual systematic review of about 32000 articles and patents dating back to 150 years ago demonstrates its apparent inefficiency. Analyzing the origins of all approved drugs reveals that, despite several decades of dominance, only 9.4% of small-molecule drugs have been discovered through "target-based" assays. Moreover, the therapeutic effects of even this minimal share cannot be solely attributed and reduced to their purported targets, as they depend on numerous off-target mechanisms unconsciously incorporated by phenotypic observations. The data suggest that reductionist target-based drug discovery may be a cause of the productivity crisis in drug discovery. An evidence-based approach to enhance efficiency seems to be prioritizing, in selecting and optimizing molecules, higher-level phenotypic observations that are closer to the sought-after therapeutic effects using tools like artificial intelligence and machine learning.
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Affiliation(s)
- Arash Sadri
- Lyceum Scientific Charity, Tehran, Iran, 1415893697
- Interdisciplinary Neuroscience Research Program (INRP), Students' Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran, 1417755331
- Faculty of Pharmacy, Tehran University of Medical Sciences, Tehran, Iran, 1417614411
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19
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Ju F, Wang Y, Yin B, Zhao M, Zhang Y, Gong Y, Jiao C. Microfluidic Wearable Devices for Sports Applications. MICROMACHINES 2023; 14:1792. [PMID: 37763955 PMCID: PMC10535163 DOI: 10.3390/mi14091792] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Revised: 09/14/2023] [Accepted: 09/18/2023] [Indexed: 09/29/2023]
Abstract
This study aimed to systematically review the application and research progress of flexible microfluidic wearable devices in the field of sports. The research team thoroughly investigated the use of life signal-monitoring technology for flexible wearable devices in the domain of sports. In addition, the classification of applications, the current status, and the developmental trends of similar products and equipment were evaluated. Scholars expect the provision of valuable references and guidance for related research and the development of the sports industry. The use of microfluidic detection for collecting biomarkers can mitigate the impact of sweat on movements that are common in sports and can also address the issue of discomfort after prolonged use. Flexible wearable gadgets are normally utilized to monitor athletic performance, rehabilitation, and training. Nevertheless, the research and development of such devices is limited, mostly catering to professional athletes. Devices for those who are inexperienced in sports and disabled populations are lacking. Conclusions: Upgrading microfluidic chip technology can lead to accurate and safe sports monitoring. Moreover, the development of multi-functional and multi-site devices can provide technical support to athletes during their training and competitions while also fostering technological innovation in the field of sports science.
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Affiliation(s)
- Fangyuan Ju
- College of Physical Education, Yangzhou University, Yangzhou 225127, China; (F.J.); (Y.W.); (M.Z.); (Y.Z.)
| | - Yujie Wang
- College of Physical Education, Yangzhou University, Yangzhou 225127, China; (F.J.); (Y.W.); (M.Z.); (Y.Z.)
| | - Binfeng Yin
- School of Mechanical Engineering, Yangzhou University, Yangzhou 225127, China;
| | - Mengyun Zhao
- College of Physical Education, Yangzhou University, Yangzhou 225127, China; (F.J.); (Y.W.); (M.Z.); (Y.Z.)
| | - Yupeng Zhang
- College of Physical Education, Yangzhou University, Yangzhou 225127, China; (F.J.); (Y.W.); (M.Z.); (Y.Z.)
| | - Yuanyuan Gong
- Institute of Physical Education, Shanghai Normal University, Shanghai 200234, China;
| | - Changgeng Jiao
- Institute of Physical Education, Shanghai Normal University, Shanghai 200234, China;
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20
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Zheng M, Charvat J, Zwart SR, Mehta SK, Crucian BE, Smith SM, He J, Piermarocchi C, Mias GI. Time-resolved molecular measurements reveal changes in astronauts during spaceflight. Front Physiol 2023; 14:1219221. [PMID: 37520819 PMCID: PMC10376710 DOI: 10.3389/fphys.2023.1219221] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 06/28/2023] [Indexed: 08/01/2023] Open
Abstract
From the early days of spaceflight to current missions, astronauts continue to be exposed to multiple hazards that affect human health, including low gravity, high radiation, isolation during long-duration missions, a closed environment and distance from Earth. Their effects can lead to adverse physiological changes and necessitate countermeasure development and/or longitudinal monitoring. A time-resolved analysis of biological signals can detect and better characterize potential adverse events during spaceflight, ideally preventing them and maintaining astronauts' wellness. Here we provide a time-resolved assessment of the impact of spaceflight on multiple astronauts (n = 27) by studying multiple biochemical and immune measurements before, during, and after long-duration orbital spaceflight. We reveal space-associated changes of astronauts' physiology on both the individual level and across astronauts, including associations with bone resorption and kidney function, as well as immune-system dysregulation.
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Affiliation(s)
- Minzhang Zheng
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, United States
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, United States
| | | | - Sara R. Zwart
- University of Texas Medical Branch, Galveston, TX, United States
| | | | | | | | - Jin He
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, United States
| | - Carlo Piermarocchi
- Department of Physics and Astronomy, Michigan State University, East Lansing, MI, United States
| | - George I. Mias
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, United States
- Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, MI, United States
- Department of Physics and Astronomy, Michigan State University, East Lansing, MI, United States
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21
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Dolezalova N, Gkrania-Klotsas E, Morelli D, Moore A, Cunningham AC, Booth A, Plans D, Reed AB, Aral M, Rennie KL, Wareham NJ. Feasibility of using intermittent active monitoring of vital signs by smartphone users to predict SARS-CoV-2 PCR positivity. Sci Rep 2023; 13:10581. [PMID: 37386099 PMCID: PMC10310739 DOI: 10.1038/s41598-023-37301-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 06/20/2023] [Indexed: 07/01/2023] Open
Abstract
Early detection of highly infectious respiratory diseases, such as COVID-19, can help curb their transmission. Consequently, there is demand for easy-to-use population-based screening tools, such as mobile health applications. Here, we describe a proof-of-concept development of a machine learning classifier for the prediction of a symptomatic respiratory disease, such as COVID-19, using smartphone-collected vital sign measurements. The Fenland App study followed 2199 UK participants that provided measurements of blood oxygen saturation, body temperature, and resting heart rate. Total of 77 positive and 6339 negative SARS-CoV-2 PCR tests were recorded. An optimal classifier to identify these positive cases was selected using an automated hyperparameter optimisation. The optimised model achieved an ROC AUC of 0.695 ± 0.045. The data collection window for determining each participant's vital sign baseline was increased from 4 to 8 or 12 weeks with no significant difference in model performance (F(2) = 0.80, p = 0.472). We demonstrate that 4 weeks of intermittently collected vital sign measurements could be used to predict SARS-CoV-2 PCR positivity, with applicability to other diseases causing similar vital sign changes. This is the first example of an accessible, smartphone-based remote monitoring tool deployable in a public health setting to screen for potential infections.
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Affiliation(s)
| | - Effrossyni Gkrania-Klotsas
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Department of Infectious Diseases, Addenbrooke's Hospital, Box 25, Cambridge, UK
| | - Davide Morelli
- Huma Therapeutics Ltd., Millbank Tower, 21-24 Millbank, London, UK
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, UK
| | - Alex Moore
- Huma Therapeutics Ltd., Millbank Tower, 21-24 Millbank, London, UK.
| | | | - Adam Booth
- Huma Therapeutics Ltd., Millbank Tower, 21-24 Millbank, London, UK
| | - David Plans
- Huma Therapeutics Ltd., Millbank Tower, 21-24 Millbank, London, UK
- Department of Experimental Psychology, University of Oxford, Oxford, UK
- INDEX Group, Department of Science, Innovation, Technology, and Entrepreneurship, University of Exeter, Exeter, UK
| | - Angus B Reed
- Huma Therapeutics Ltd., Millbank Tower, 21-24 Millbank, London, UK
| | - Mert Aral
- Huma Therapeutics Ltd., Millbank Tower, 21-24 Millbank, London, UK
| | - Kirsten L Rennie
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Nicholas J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
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22
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Shiwani MA, Chico TJA, Ciravegna F, Mihaylova L. Continuous Monitoring of Health and Mobility Indicators in Patients with Cardiovascular Disease: A Review of Recent Technologies. SENSORS (BASEL, SWITZERLAND) 2023; 23:5752. [PMID: 37420916 DOI: 10.3390/s23125752] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 06/01/2023] [Accepted: 06/12/2023] [Indexed: 07/09/2023]
Abstract
Cardiovascular diseases kill 18 million people each year. Currently, a patient's health is assessed only during clinical visits, which are often infrequent and provide little information on the person's health during daily life. Advances in mobile health technologies have allowed for the continuous monitoring of indicators of health and mobility during daily life by wearable and other devices. The ability to obtain such longitudinal, clinically relevant measurements could enhance the prevention, detection and treatment of cardiovascular diseases. This review discusses the advantages and disadvantages of various methods for monitoring patients with cardiovascular disease during daily life using wearable devices. We specifically discuss three distinct monitoring domains: physical activity monitoring, indoor home monitoring and physiological parameter monitoring.
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Affiliation(s)
- Muhammad Ali Shiwani
- Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield S1 3JD, UK
| | - Timothy J A Chico
- Department of Infection, Immunity and Cardiovascular Disease, The Medical School, The University of Sheffield, Sheffield S10 2RX, UK
| | - Fabio Ciravegna
- Dipartimento di Informatica, Università di Torino, 10124 Turin, Italy
| | - Lyudmila Mihaylova
- Department of Automatic Control and Systems Engineering, The University of Sheffield, Sheffield S1 3JD, UK
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23
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Shim J, Fleisch E, Barata F. Wearable-based accelerometer activity profile as digital biomarker of inflammation, biological age, and mortality using hierarchical clustering analysis in NHANES 2011-2014. Sci Rep 2023; 13:9326. [PMID: 37291134 PMCID: PMC10250365 DOI: 10.1038/s41598-023-36062-y] [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: 02/09/2023] [Accepted: 05/29/2023] [Indexed: 06/10/2023] Open
Abstract
Repeated disruptions in circadian rhythms are associated with implications for health outcomes and longevity. The utilization of wearable devices in quantifying circadian rhythm to elucidate its connection to longevity, through continuously collected data remains largely unstudied. In this work, we investigate a data-driven segmentation of the 24-h accelerometer activity profiles from wearables as a novel digital biomarker for longevity in 7,297 U.S. adults from the 2011-2014 National Health and Nutrition Examination Survey. Using hierarchical clustering, we identified five clusters and described them as follows: "High activity", "Low activity", "Mild circadian rhythm (CR) disruption", "Severe CR disruption", and "Very low activity". Young adults with extreme CR disturbance are seemingly healthy with few comorbid conditions, but in fact associated with higher white blood cell, neutrophils, and lymphocyte counts (0.05-0.07 log-unit, all p < 0.05) and accelerated biological aging (1.42 years, p < 0.001). Older adults with CR disruption are significantly associated with increased systemic inflammation indexes (0.09-0.12 log-unit, all p < 0.05), biological aging advance (1.28 years, p = 0.021), and all-cause mortality risk (HR = 1.58, p = 0.042). Our findings highlight the importance of circadian alignment on longevity across all ages and suggest that data from wearable accelerometers can help in identifying at-risk populations and personalize treatments for healthier aging.
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Affiliation(s)
- Jinjoo Shim
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland.
| | - Elgar Fleisch
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
- Centre for Digital Health Interventions, Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland
| | - Filipe Barata
- Centre for Digital Health Interventions, Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
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Babu M, Snyder M. Multi-Omics Profiling for Health. Mol Cell Proteomics 2023; 22:100561. [PMID: 37119971 PMCID: PMC10220275 DOI: 10.1016/j.mcpro.2023.100561] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 04/20/2023] [Accepted: 04/23/2023] [Indexed: 05/01/2023] Open
Abstract
The world has witnessed a steady rise in both non-infectious and infectious chronic diseases, prompting a cross-disciplinary approach to understand and treating disease. Current medical care focuses on treating people after they become patients rather than preventing illness, leading to high costs in treating chronic and late-stage diseases. Additionally, a "one-size-fits all" approach to health care does not take into account individual differences in genetics, environment, or lifestyle factors, decreasing the number of people benefiting from interventions. Rapid advances in omics technologies and progress in computational capabilities have led to the development of multi-omics deep phenotyping, which profiles the interaction of multiple levels of biology over time and empowers precision health approaches. This review highlights current and emerging multi-omics modalities for precision health and discusses applications in the following areas: genetic variation, cardio-metabolic diseases, cancer, infectious diseases, organ transplantation, pregnancy, and longevity/aging. We will briefly discuss the potential of multi-omics approaches in disentangling host-microbe and host-environmental interactions. We will touch on emerging areas of electronic health record and clinical imaging integration with muti-omics for precision health. Finally, we will briefly discuss the challenges in the clinical implementation of multi-omics and its future prospects.
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Affiliation(s)
- Mohan Babu
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA
| | - Michael Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, California, USA.
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25
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Matsumoto H, Tomoto K, Kawase G, Iitani K, Toma K, Arakawa T, Mitsubayashi K, Moriyama K. Real-Time Continuous Monitoring of Oral Soft Tissue Pressure with a Wireless Mouthguard Device for Assessing Tongue Thrusting Habits. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115027. [PMID: 37299753 DOI: 10.3390/s23115027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 05/11/2023] [Accepted: 05/18/2023] [Indexed: 06/12/2023]
Abstract
In orthodontics, understanding the pressure of oral soft tissues on teeth is important to elucidate the cause and establish treatment methods. We developed a small wireless mouthguard (MG)-type device that continuously and unrestrainedly measures pressure, which had previously been unachieved, and evaluated its feasibility in human subjects. First, the optimal device components were considered. Next, the devices were compared with wired-type systems. Subsequently, the devices were fabricated for human testing to measure tongue pressure during swallowing. The highest sensitivity (51-510 g/cm2) with minimum error (CV < 5%) was obtained using an MG device with polyethylene terephthalate glycol and ethylene vinyl acetate for the lower and upper layers, respectively, and with a 4 mm PMMA plate. A high correlation coefficient (0.969) was observed between the wired and wireless devices. In the measurements of tongue pressure on teeth during swallowing, 132.14 ± 21.37 g/cm2 for normal and 201.17 ± 38.12 g/cm2 for simulated tongue thrust were found to be significantly different using a t-test (n = 50, p = 6.2 × 10-19), which is consistent with the results of a previous study. This device can contribute to assessing tongue thrusting habits. In the future, this device is expected to measure changes in the pressure exerted on teeth during daily life.
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Affiliation(s)
- Hidekazu Matsumoto
- Department of Maxillofacial Orthognathics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo 113-8549, Japan
| | - Keisuke Tomoto
- Department of Biomedical Devices and Instrumentation, Institute of Biomaterials and Bioengineering, Tokyo Medical and Dental University, Tokyo 101-0062, Japan
| | - Gentaro Kawase
- Department of Biomedical Devices and Instrumentation, Institute of Biomaterials and Bioengineering, Tokyo Medical and Dental University, Tokyo 101-0062, Japan
| | - Kenta Iitani
- Department of Biomedical Devices and Instrumentation, Institute of Biomaterials and Bioengineering, Tokyo Medical and Dental University, Tokyo 101-0062, Japan
| | - Koji Toma
- Department of Biomedical Devices and Instrumentation, Institute of Biomaterials and Bioengineering, Tokyo Medical and Dental University, Tokyo 101-0062, Japan
- Department of Electronic Engineering, Shibaura Institute of Technology, College of Engineering, Tokyo 135-8548, Japan
| | - Takahiro Arakawa
- Department of Biomedical Devices and Instrumentation, Institute of Biomaterials and Bioengineering, Tokyo Medical and Dental University, Tokyo 101-0062, Japan
- Department of Electric and Electronic Engineering, Tokyo University of Technology, Tokyo 192-0982, Japan
| | - Kohji Mitsubayashi
- Department of Biomedical Devices and Instrumentation, Institute of Biomaterials and Bioengineering, Tokyo Medical and Dental University, Tokyo 101-0062, Japan
| | - Keiji Moriyama
- Department of Maxillofacial Orthognathics, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo 113-8549, Japan
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26
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Keshet A, Reicher L, Bar N, Segal E. Wearable and digital devices to monitor and treat metabolic diseases. Nat Metab 2023; 5:563-571. [PMID: 37100995 DOI: 10.1038/s42255-023-00778-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 03/07/2023] [Indexed: 04/28/2023]
Abstract
Cardiometabolic diseases are a major public-health concern owing to their increasing prevalence worldwide. These diseases are characterized by a high degree of interindividual variability with regards to symptoms, severity, complications and treatment responsiveness. Recent technological advances, and the growing availability of wearable and digital devices, are now making it feasible to profile individuals in ever-increasing depth. Such technologies are able to profile multiple health-related outcomes, including molecular, clinical and lifestyle changes. Nowadays, wearable devices allowing for continuous and longitudinal health screening outside the clinic can be used to monitor health and metabolic status from healthy individuals to patients at different stages of disease. Here we present an overview of the wearable and digital devices that are most relevant for cardiometabolic-disease-related readouts, and how the information collected from such devices could help deepen our understanding of metabolic diseases, improve their diagnosis, identify early disease markers and contribute to individualization of treatment and prevention plans.
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Affiliation(s)
- Ayya Keshet
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Lee Reicher
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
- Lis Maternity and Women's Hospital, Tel Aviv Sourasky Medical Center, Tel Aviv University (affiliated with Sackler Faculty of Medicine), Tel Aviv, Israel
| | - Noam Bar
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Eran Segal
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
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Xiao T, Dong X, Lu Y, Zhou W. High-Resolution and Multidimensional Phenotypes Can Complement Genomics Data to Diagnose Diseases in the Neonatal Population. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:204-215. [PMID: 37197647 PMCID: PMC10110825 DOI: 10.1007/s43657-022-00071-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 08/03/2022] [Accepted: 08/08/2022] [Indexed: 05/19/2023]
Abstract
Advances in genomic medicine have greatly improved our understanding of human diseases. However, phenome is not well understood. High-resolution and multidimensional phenotypes have shed light on the mechanisms underlying neonatal diseases in greater details and have the potential to optimize clinical strategies. In this review, we first highlight the value of analyzing traditional phenotypes using a data science approach in the neonatal population. We then discuss recent research on high-resolution, multidimensional, and structured phenotypes in neonatal critical diseases. Finally, we briefly introduce current technologies available for the analysis of multidimensional data and the value that can be provided by integrating these data into clinical practice. In summary, a time series of multidimensional phenome can improve our understanding of disease mechanisms and diagnostic decision-making, stratify patients, and provide clinicians with optimized strategies for therapeutic intervention; however, the available technologies for collecting multidimensional data and the best platform for connecting multiple modalities should be considered.
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Affiliation(s)
- Tiantian Xiao
- Division of Neonatology, Children’s Hospital of Fudan University, National Children’s Medical Center, 399 Wanyuan Road, Shanghai, 201102 China
- Department of Neonatology, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610000 China
| | - Xinran Dong
- Center for Molecular Medicine, Pediatric Research Institute, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, 201102 China
| | - Yulan Lu
- Center for Molecular Medicine, Pediatric Research Institute, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, 201102 China
| | - Wenhao Zhou
- Division of Neonatology, Children’s Hospital of Fudan University, National Children’s Medical Center, 399 Wanyuan Road, Shanghai, 201102 China
- Center for Molecular Medicine, Pediatric Research Institute, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, 201102 China
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28
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The WE SENSE study protocol: A controlled, longitudinal clinical trial on the use of wearable sensors for early detection and tracking of viral respiratory tract infections. Contemp Clin Trials 2023; 128:107103. [PMID: 37147083 PMCID: PMC10049920 DOI: 10.1016/j.cct.2023.107103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 12/06/2022] [Accepted: 01/20/2023] [Indexed: 03/31/2023]
Abstract
Background Viral respiratory tract infections (VRTI) are extremely common. Considering the profound social and economic impact of COVID-19, it is imperative to identify novel mechanisms for early detection and prevention of VRTIs, to prevent future pandemics. Wearable biosensor technology may facilitate this. Early asymptomatic detection of VRTIs could reduce stress on the healthcare system by reducing transmission and decreasing the overall number of cases. The aim of the current study is to define a sensitive set of physiological and immunological signature patterns of VRTI through machine learning (ML) to analyze physiological data collected continuously using wearable vital signs sensors. Methods A controlled, prospective longitudinal study with an induced low grade viral challenge, coupled with 12 days of continuous wearable biosensors monitoring surrounding viral induction. We aim to recruit and simulate a low grade VRTI in 60 healthy adults aged 18–59 years via administration of live attenuated influenza vaccine (LAIV). Continuous monitoring with wearable biosensors will include 7 days pre (baseline) and 5 days post LAIV administration, during which vital signs and activity-monitoring biosensors (embedded in a shirt, wristwatch and ring) will continuously monitor physiological and activity parameters. Novel infection detection techniques will be developed based on inflammatory biomarker mapping, PCR testing, and app-based VRTI symptom tracking. Subtle patterns of change will be assessed via ML algorithms developed to analyze large datasets and generate a predictive algorithm. Conclusion This study presents an infrastructure to test wearables for the detection of asymptomatic VRTI using multimodal biosensors, based on immune host response signature. CliniclTrials.govregistration:NCT05290792
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Scott RT, Sanders LM, Antonsen EL, Hastings JJA, Park SM, Mackintosh G, Reynolds RJ, Hoarfrost AL, Sawyer A, Greene CS, Glicksberg BS, Theriot CA, Berrios DC, Miller J, Babdor J, Barker R, Baranzini SE, Beheshti A, Chalk S, Delgado-Aparicio GM, Haendel M, Hamid AA, Heller P, Jamieson D, Jarvis KJ, Kalantari J, Khezeli K, Komarova SV, Komorowski M, Kothiyal P, Mahabal A, Manor U, Garcia Martin H, Mason CE, Matar M, Mias GI, Myers JG, Nelson C, Oribello J, Parsons-Wingerter P, Prabhu RK, Qutub AA, Rask J, Saravia-Butler A, Saria S, Singh NK, Snyder M, Soboczenski F, Soman K, Van Valen D, Venkateswaran K, Warren L, Worthey L, Yang JH, Zitnik M, Costes SV. Biomonitoring and precision health in deep space supported by artificial intelligence. NAT MACH INTELL 2023. [DOI: 10.1038/s42256-023-00617-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
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30
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Zheng M, Charvat J, Zwart SR, Mehta S, Crucian BE, Smith SM, He J, Piermarocchi C, Mias GI. Time-resolved molecular measurements reveal changes in astronauts during spaceflight. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.17.530234. [PMID: 36993537 PMCID: PMC10055136 DOI: 10.1101/2023.03.17.530234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
From the early days of spaceflight to current missions, astronauts continue to be exposed to multiple hazards that affect human health, including low gravity, high radiation, isolation during long-duration missions, a closed environment and distance from Earth. Their effects can lead to adverse physiological changes and necessitate countermeasure development and/or longitudinal monitoring. A time-resolved analysis of biological signals can detect and better characterize potential adverse events during spaceflight, ideally preventing them and maintaining astronauts' wellness. Here we provide a time-resolved assessment of the impact of spaceflight on multiple astronauts (n=27) by studying multiple biochemical and immune measurements before, during, and after long-duration orbital spaceflight. We reveal space-associated changes of astronauts' physiology on both the individual level and across astronauts, including associations with bone resorption and kidney function, as well as immune-system dysregulation.
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31
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Menon SP, Shukla PK, Sethi P, Alasiry A, Marzougui M, Alouane MTH, Khan AA. An Intelligent Diabetic Patient Tracking System Based on Machine Learning for E-Health Applications. SENSORS (BASEL, SWITZERLAND) 2023; 23:3004. [PMID: 36991714 PMCID: PMC10052330 DOI: 10.3390/s23063004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 02/19/2023] [Accepted: 03/07/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND Continuous surveillance helps people with diabetes live better lives. A wide range of technologies, including the Internet of Things (IoT), modern communications, and artificial intelligence (AI), can assist in lowering the expense of health services. Due to numerous communication systems, it is now possible to provide customized and distant healthcare. MAIN PROBLEM Healthcare data grows daily, making storage and processing challenging. We provide intelligent healthcare structures for smart e-health apps to solve the aforesaid problem. The 5G network must offer advanced healthcare services to meet important requirements like large bandwidth and excellent energy efficacy. METHODOLOGY This research suggested an intelligent system for diabetic patient tracking based on machine learning (ML). The architectural components comprised smartphones, sensors, and smart devices, to gather body dimensions. Then, the preprocessed data is normalized using the normalization procedure. To extract features, we use linear discriminant analysis (LDA). To establish a diagnosis, the intelligent system conducted data classification utilizing the suggested advanced-spatial-vector-based Random Forest (ASV-RF) in conjunction with particle swarm optimization (PSO). RESULTS Compared to other techniques, the simulation's outcomes demonstrate that the suggested approach offers greater accuracy.
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Affiliation(s)
- Sindhu P. Menon
- School of Computing and Information Technology, Reva University, Bangalore 560064, Karnataka, India
| | - Prashant Kumar Shukla
- Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur 522302, Andhra Pradesh, India
| | - Priyanka Sethi
- Department of Physiotherapy, Faculty of Allied Health Sciences, Manav Rachna International Institute of Research & Studies, Faridabad 121004, Haryana, India
| | - Areej Alasiry
- College of Computer Science, King Khalid University, Abha 61413, Saudi Arabia
| | - Mehrez Marzougui
- College of Computer Science, King Khalid University, Abha 61413, Saudi Arabia
| | | | - Arfat Ahmad Khan
- Department of Computer Science, College of Computing, Khon Kaen University, Khon Kaen 40002, Thailand
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Moeller T, Moehler F, Krell-Roesch J, Dežman M, Marquardt C, Asfour T, Stein T, Woll A. Use of Lower Limb Exoskeletons as an Assessment Tool for Human Motor Performance: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:3032. [PMID: 36991743 PMCID: PMC10057915 DOI: 10.3390/s23063032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 03/06/2023] [Accepted: 03/07/2023] [Indexed: 06/19/2023]
Abstract
Exoskeletons are a promising tool to support individuals with a decreased level of motor performance. Due to their built-in sensors, exoskeletons offer the possibility of continuously recording and assessing user data, for example, related to motor performance. The aim of this article is to provide an overview of studies that rely on using exoskeletons to measure motor performance. Therefore, we conducted a systematic literature review, following the PRISMA Statement guidelines. A total of 49 studies using lower limb exoskeletons for the assessment of human motor performance were included. Of these, 19 studies were validity studies, and six were reliability studies. We found 33 different exoskeletons; seven can be considered stationary, and 26 were mobile exoskeletons. The majority of the studies measured parameters such as range of motion, muscle strength, gait parameters, spasticity, and proprioception. We conclude that exoskeletons can be used to measure a wide range of motor performance parameters through built-in sensors, and seem to be more objective and specific than manual test procedures. However, since these parameters are usually estimated from built-in sensor data, the quality and specificity of an exoskeleton to assess certain motor performance parameters must be examined before an exoskeleton can be used, for example, in a research or clinical setting.
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Affiliation(s)
- Tobias Moeller
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
| | - Felix Moehler
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
| | - Janina Krell-Roesch
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
| | - Miha Dežman
- Institute for Anthropomatics and Robotics, High Performance Humanoid Technologies (H2T), Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
| | - Charlotte Marquardt
- Institute for Anthropomatics and Robotics, High Performance Humanoid Technologies (H2T), Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
| | - Tamim Asfour
- Institute for Anthropomatics and Robotics, High Performance Humanoid Technologies (H2T), Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
| | - Thorsten Stein
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
| | - Alexander Woll
- Institute of Sports and Sports Science, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
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Wu Z, Li X, Feng Z, Wan C, Li Y, Li T, Yang Q, Liu X, Ren M, Li J, Shang X, Zhang X, Huang X. Stable and Dynamic Multiparameter Monitoring on Chests Using Flexible Skin Patches with Self-Adhesive Electrodes and a Synchronous Correlation Peak Extraction Algorithm. Adv Healthc Mater 2023; 12:e2202629. [PMID: 36604167 DOI: 10.1002/adhm.202202629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 12/26/2022] [Indexed: 01/07/2023]
Abstract
Advances in wearable bioelectronics interfacing directly with skin offer important tools for non-invasive measurements of physiological parameters. However, wearable monitoring devices majorly conduct static sensing to avoid signal disturbance and unreliable contact with the skin. Dynamic multiparameter sensing is challenging even with the advanced flexible skin patches. This epidermal electronics system with self-adhesive conductive electrodes to supply stable skin contact and a unique synchronous correlation peak extraction (SCPE) algorithm to minimize motion artifacts in the photoplethysmogram (PPG) signals. The skin patch system can simultaneously and precisely monitor electrocardiogram (ECG), PPG, body temperature, and acceleration on chests undergoing daily activities. The low latency between the ECG and the PPG signals enables the SCPE algorithm that leads to reduced errors in deduced heart rates and improved performance in oxygen level determination than conventional adaptive filtering and wavelet transformation approaches. Dynamic multiparameter recording over 24 h by the system can reflect the circadian patterns of the wearers with low disturbance from motion artifacts. This demonstrated system may be applied for health monitoring in large populations to alleviate pressure on medical systems and assist management of public health crisis.
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Affiliation(s)
- Ziyue Wu
- Department of Biomedical Engineering, Tianjin University, 92 Weijin Road, Tianjin, 300072, China
| | - Xueting Li
- Institute of Wearable Technology and Bioelectronics, Qiantang Science and Technology Innovation Center, 1002 23rd Street, Hangzhou, Zhejiang, 310018, China
| | - Zhijie Feng
- Department of Biomedical Engineering, Tianjin University, 92 Weijin Road, Tianjin, 300072, China
| | - Chunxue Wan
- Department of Biomedical Engineering, Tianjin University, 92 Weijin Road, Tianjin, 300072, China
| | - Ya Li
- Department of Biomedical Engineering, Tianjin University, 92 Weijin Road, Tianjin, 300072, China
| | - Tianyu Li
- Department of Biomedical Engineering, Tianjin University, 92 Weijin Road, Tianjin, 300072, China
| | - Qing Yang
- Center of Flexible Wearable Technology, Institute of Flexible Electronic Technology of Tsinghua, 906 Asia-Pacific Road, Jiaxing, Zhejiang, 314006, China
| | - Xinyu Liu
- Department of Biomedical Engineering, Tianjin University, 92 Weijin Road, Tianjin, 300072, China
| | - Miaoning Ren
- Department of Biomedical Engineering, Tianjin University, 92 Weijin Road, Tianjin, 300072, China
| | - Jiameng Li
- Department of Biomedical Engineering, Tianjin University, 92 Weijin Road, Tianjin, 300072, China
| | - Xue Shang
- Department of Biomedical Engineering, Tianjin University, 92 Weijin Road, Tianjin, 300072, China
| | - Xiangyu Zhang
- Department of Biomedical Engineering, Tianjin University, 92 Weijin Road, Tianjin, 300072, China
| | - Xian Huang
- Department of Biomedical Engineering, Tianjin University, 92 Weijin Road, Tianjin, 300072, China.,Center of Flexible Wearable Technology, Institute of Flexible Electronic Technology of Tsinghua, 906 Asia-Pacific Road, Jiaxing, Zhejiang, 314006, China
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Mun E, Cho J. Review of Internet of Things-Based Artificial Intelligence Analysis Method through Real-Time Indoor Air Quality and Health Effect Monitoring: Focusing on Indoor Air Pollution That Are Harmful to the Respiratory Organ. Tuberc Respir Dis (Seoul) 2023; 86:23-32. [PMID: 36288738 PMCID: PMC9816487 DOI: 10.4046/trd.2022.0087] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 10/26/2022] [Indexed: 12/23/2022] Open
Abstract
Everyone is aware that air and environmental pollutants are harmful to health. Among them, indoor air quality directly affects physical health, such as respiratory rather than outdoor air. However, studies that have examined the correlation between environmental and health information have been conducted with public data targeting large cohorts, and studies with real-time data analysis are insufficient. Therefore, this research explores the research with an indoor air quality monitoring (AQM) system based on developing environmental detection sensors and the internet of things to collect, monitor, and analyze environmental and health data from various data sources in real-time. It explores the usage of wearable devices for health monitoring systems. In addition, the availability of big data and artificial intelligence analysis and prediction has increased, investigating algorithmic studies for accurate prediction of hazardous environments and health impacts. Regarding health effects, techniques to prevent respiratory and related diseases were reviewed.
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Affiliation(s)
- EunMi Mun
- Department of Software Engineering, Jeonbuk National University, Jeonju, Republic of Korea
| | - Jaehyuk Cho
- Department of Software Engineering, Jeonbuk National University, Jeonju, Republic of Korea,Address for correspondence Jaehyuk Cho, Ph.D. Department of Software Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju 54896, Republic of Korea Phone 82-63-270-4771 Fax 82-63-270-4767 E-mail
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Jiang P, Gao F, Liu S, Zhang S, Zhang X, Xia Z, Zhang W, Jiang T, Zhu JL, Zhang Z, Shu Q, Snyder M, Li J. Longitudinally tracking personal physiomes for precision management of childhood epilepsy. PLOS DIGITAL HEALTH 2022; 1:e0000161. [PMID: 36812648 PMCID: PMC9931296 DOI: 10.1371/journal.pdig.0000161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 11/13/2022] [Indexed: 12/24/2022]
Abstract
Our current understanding of human physiology and activities is largely derived from sparse and discrete individual clinical measurements. To achieve precise, proactive, and effective health management of an individual, longitudinal, and dense tracking of personal physiomes and activities is required, which is only feasible by utilizing wearable biosensors. As a pilot study, we implemented a cloud computing infrastructure to integrate wearable sensors, mobile computing, digital signal processing, and machine learning to improve early detection of seizure onsets in children. We recruited 99 children diagnosed with epilepsy and longitudinally tracked them at single-second resolution using a wearable wristband, and prospectively acquired more than one billion data points. This unique dataset offered us an opportunity to quantify physiological dynamics (e.g., heart rate, stress response) across age groups and to identify physiological irregularities upon epilepsy onset. The high-dimensional personal physiome and activity profiles displayed a clustering pattern anchored by patient age groups. These signatory patterns included strong age and sex-specific effects on varying circadian rhythms and stress responses across major childhood developmental stages. For each patient, we further compared the physiological and activity profiles associated with seizure onsets with the personal baseline and developed a machine learning framework to accurately capture these onset moments. The performance of this framework was further replicated in another independent patient cohort. We next referenced our predictions with the electroencephalogram (EEG) signals on selected patients and demonstrated that our approach could detect subtle seizures not recognized by humans and could detect seizures prior to clinical onset. Our work demonstrated the feasibility of a real-time mobile infrastructure in a clinical setting, which has the potential to be valuable in caring for epileptic patients. Extension of such a system has the potential to be leveraged as a health management device or longitudinal phenotyping tool in clinical cohort studies.
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Affiliation(s)
- Peifang Jiang
- National Clinical Research Center for Child Health, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Feng Gao
- National Clinical Research Center for Child Health, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Sixing Liu
- SensOmics, Inc. Burlingame, California, United States of America
| | - Sai Zhang
- SensOmics, Inc. Burlingame, California, United States of America
| | - Xicheng Zhang
- National Clinical Research Center for Child Health, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, California, United States of America
| | - Zhezhi Xia
- National Clinical Research Center for Child Health, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Weiqin Zhang
- National Clinical Research Center for Child Health, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tiejia Jiang
- National Clinical Research Center for Child Health, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jason L. Zhu
- SensOmics, Inc. Burlingame, California, United States of America
| | - Zhaolei Zhang
- SensOmics, Inc. Burlingame, California, United States of America
- Donnelly Centre, Department of Computer Science and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- * E-mail: (ZZ); (QS); (MS); (JL)
| | - Qiang Shu
- National Clinical Research Center for Child Health, Children’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
- * E-mail: (ZZ); (QS); (MS); (JL)
| | - Michael Snyder
- SensOmics, Inc. Burlingame, California, United States of America
- * E-mail: (ZZ); (QS); (MS); (JL)
| | - Jingjing Li
- SensOmics, Inc. Burlingame, California, United States of America
- * E-mail: (ZZ); (QS); (MS); (JL)
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36
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Zhang W, Wan Z, Li X, Li R, Luo L, Song Z, Miao Y, Li Z, Wang S, Shan Y, Li Y, Chen B, Zhen H, Sun Y, Fang M, Ding J, Yan Y, Zong Y, Wang Z, Zhang W, Yang H, Yang S, Wang J, Jin X, Wang R, Chen P, Min J, Zeng Y, Li T, Xu X, Nie C. A population-based study of precision health assessments using multi-omics network-derived biological functional modules. Cell Rep Med 2022; 3:100847. [PMID: 36493776 PMCID: PMC9798030 DOI: 10.1016/j.xcrm.2022.100847] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 10/05/2022] [Accepted: 11/11/2022] [Indexed: 12/13/2022]
Abstract
Recent technological advances in multi-omics and bioinformatics provide an opportunity to develop precision health assessments, which require big data and relevant bioinformatic methods. Here we collect multi-omics data from 4,277 individuals. We calculate the correlations between pairwise features from cross-sectional data and then generate 11 biological functional modules (BFMs) in males and 12 BFMs in females using a community detection algorithm. Using the features in the BFM associated with cardiometabolic health, carotid plaques can be predicted accurately in an independent dataset. We developed a model by comparing individual data with the health baseline in BFMs to assess health status (BFM-ash). Then we apply the model to chronic patients and modify the BFM-ash model to assess the effects of consuming grape seed extract as a dietary supplement. Finally, anomalous BFMs are identified for each subject. Our BFMs and BFM-ash model have huge prospects for application in precision health assessment.
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Affiliation(s)
- Wei Zhang
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Ziyun Wan
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Xiaoyu Li
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China,BGI Education Center, University of the Chinese Academy of Sciences, Shenzhen 518083, China
| | - Rui Li
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Lihua Luo
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China,BGI Education Center, University of the Chinese Academy of Sciences, Shenzhen 518083, China
| | - Zijun Song
- The First Affiliated Hospital, Institute of Translational Medicine, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yu Miao
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China,BGI Education Center, University of the Chinese Academy of Sciences, Shenzhen 518083, China
| | - Zhiming Li
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Shiyu Wang
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China,BGI Education Center, University of the Chinese Academy of Sciences, Shenzhen 518083, China
| | - Ying Shan
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Yan Li
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Bangwei Chen
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China,School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Hefu Zhen
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Yuzhe Sun
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Mingyan Fang
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Jiahong Ding
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Yizhen Yan
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Yang Zong
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Zhen Wang
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Wenwei Zhang
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Huanming Yang
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China,James D. Watson Institute of Genome Sciences, Hangzhou 310058, China
| | - Shuang Yang
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Jian Wang
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China,James D. Watson Institute of Genome Sciences, Hangzhou 310058, China
| | - Xin Jin
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Ru Wang
- School of Exercise and Health, Shanghai Frontiers Science Research Base of Exercise and Metabolic Health, Shanghai University of Sport, Shanghai, China
| | - Peijie Chen
- School of Exercise and Health, Shanghai Frontiers Science Research Base of Exercise and Metabolic Health, Shanghai University of Sport, Shanghai, China
| | - Junxia Min
- The First Affiliated Hospital, Institute of Translational Medicine, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yi Zeng
- Center for Healthy Aging and Development Studies, National School of Development, Peking University, Beijing, China
| | - Tao Li
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Xun Xu
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China
| | - Chao Nie
- BGI-Shenzhen, Shenzhen 518083, China,China National GeneBank, Shenzhen 518120, China,Corresponding author
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Patil V, Singhal DK, Naik N, Hameed BMZ, Shah MJ, Ibrahim S, Smriti K, Chatterjee G, Kale A, Sharma A, Paul R, Chłosta P, Somani BK. Factors Affecting the Usage of Wearable Device Technology for Healthcare among Indian Adults: A Cross-Sectional Study. J Clin Med 2022; 11:jcm11237019. [PMID: 36498594 PMCID: PMC9740494 DOI: 10.3390/jcm11237019] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Revised: 11/18/2022] [Accepted: 11/24/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Wearable device technology has recently been involved in the healthcare industry substantially. India is the world's third largest market for wearable devices and is projected to expand at a compound annual growth rate of ~26.33%. However, there is a paucity of literature analyzing the factors determining the acceptance of wearable healthcare device technology among low-middle-income countries. METHODS This cross-sectional, web-based survey aims to analyze the perceptions affecting the adoption and usage of wearable devices among the Indian population aged 16 years and above. RESULTS A total of 495 responses were obtained. In all, 50.3% were aged between 25-50 years and 51.3% belonged to the lower-income group. While 62.2% of the participants reported using wearable devices for managing their health, 29.3% were using them daily. technology and task fitness (TTF) showed a significant positive correlation with connectivity (r = 0.716), health care (r = 0.780), communication (r = 0.637), infotainment (r = 0.598), perceived usefulness (PU) (r = 0.792), and perceived ease of use (PEOU) (r = 0.800). Behavioral intention (BI) to use wearable devices positively correlated with PEOU (r = 0.644) and PU (r = 0.711). All factors affecting the use of wearable devices studied had higher mean scores among participants who were already using wearable devices. Male respondents had significantly higher mean scores for BI (p = 0.034) and PEOU (p = 0.009). Respondents older than 25 years of age had higher mean scores for BI (p = 0.027) and Infotainment (p = 0.032). CONCLUSIONS This study found a significant correlation with the adoption and acceptance of wearable devices for healthcare management in the Indian context.
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Affiliation(s)
- Vathsala Patil
- Department of Oral Medicine and Radiology, Manipal College of Dental Sciences, Manipal, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
| | - Deepak Kumar Singhal
- Department of Public Health Dentistry, Manipal College of Dental Sciences, Manipal, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
- Correspondence: (D.K.S.); (N.N.); Tel.: +91-8310874339 (N.N.)
| | - Nithesh Naik
- Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
- iTRUE (International Training and Research in Uro-Oncology and Endourology) Group, Manipal 576104, Karnataka, India
- Curiouz TechLab Private Limited, BIRAC-BioNEST, Government of Karnataka Bioincubator, Manipal 576104, Karnataka, India
- Correspondence: (D.K.S.); (N.N.); Tel.: +91-8310874339 (N.N.)
| | - B. M. Zeeshan Hameed
- iTRUE (International Training and Research in Uro-Oncology and Endourology) Group, Manipal 576104, Karnataka, India
- Curiouz TechLab Private Limited, BIRAC-BioNEST, Government of Karnataka Bioincubator, Manipal 576104, Karnataka, India
- Department of Urology, Father Muller Medical College, Mangalore 575001, Karnataka, India
| | - Milap J. Shah
- iTRUE (International Training and Research in Uro-Oncology and Endourology) Group, Manipal 576104, Karnataka, India
- Robotics and Urooncology, Max Hospital and Max Institute of Cancer Care, New Delhi 110024, India
| | - Sufyan Ibrahim
- iTRUE (International Training and Research in Uro-Oncology and Endourology) Group, Manipal 576104, Karnataka, India
- Department of Neurosurgery, Mayo Clinic, Rochester, MN 55902, USA
| | - Komal Smriti
- Department of Oral Medicine and Radiology, Manipal College of Dental Sciences, Manipal, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
| | - Gaurav Chatterjee
- Department of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
| | - Ameya Kale
- Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
| | - Anshika Sharma
- Department of Psychology, Amity University, Noida 201313, Uttar Pradesh, India
| | - Rahul Paul
- iTRUE (International Training and Research in Uro-Oncology and Endourology) Group, Manipal 576104, Karnataka, India
- Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA
- Center for Biologics Evaluation and Research (CBER), U.S. Food and Drug Administration, Silver Spring, MD 20993, USA
| | - Piotr Chłosta
- Department of Urology, Jagiellonian University in Krakow, 31-007 Kraków, Poland
| | - Bhaskar K. Somani
- Department of Urology, University Hospital Southampton NHS Trust, Southampton SO16 6YD, UK
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Machine learning and ontology in eCoaching for personalized activity level monitoring and recommendation generation. Sci Rep 2022; 12:19825. [PMID: 36400793 PMCID: PMC9674665 DOI: 10.1038/s41598-022-24118-4] [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: 08/09/2022] [Accepted: 11/10/2022] [Indexed: 11/19/2022] Open
Abstract
Leading a sedentary lifestyle may cause numerous health problems. Therefore, passive lifestyle changes should be given priority to avoid severe long-term damage. Automatic health coaching system may help people manage a healthy lifestyle with continuous health state monitoring and personalized recommendation generation with machine learning (ML). This study proposes a semantic ontology model to annotate the ML-prediction outcomes and personal preferences to conceptualize personalized recommendation generation with a hybrid approach. We use a transfer learning approach to improve ML model training and its performance, and an incremental learning approach to handle daily growing data and fit them into the ML models. Furthermore, we propose a personalized activity recommendation algorithm for a healthy lifestyle by combining transfer learning, incremental learning, the proposed semantic ontology model, and personal preference data. For the overall experiment, we use public and private activity datasets collected from healthy adults (n = 33 for public datasets; n = 16 for private datasets). The standard ML algorithms have been used to investigate the possibility of classifying daily physical activity levels into the following activity classes: sedentary (0), low active (1), active (2), highly active (3), and rigorous active (4). The daily step count, low physical activity, medium physical activity, and vigorous physical activity serve as input for the classification models. We first use publicly available Fitbit datasets to build the initial classification models. Subsequently, we re-use the pre-trained ML classifiers on the real-time MOX2-5 dataset using transfer learning. We test several standard algorithms and select the best-performing model with optimized configuration for our use case by empirical testing. We find that DecisionTreeClassifier with a criterion "entropy" outperforms other ML classifiers with a mean accuracy score of 97.50% (F1 = 97.00, precision = 97.00, recall = 98.00, MCC = 96.78) and 96.10% (F1 = 96.00, precision = 96.00, recall = 96.00, MCC = 96.10) in Fitbit and MOX2-5 datasets, respectively. Using transfer learning, the DecisionTreeClassifier with a criterion "entropy" outperforms other classifiers with a mean accuracy score of 97.99% (F1 = 98.00, precision = 98.00, recall = 98.00, MCC = 96.79). Therefore, the transfer learning approach improves the machine learning model performance by ≈ 1.98% for defined datasets and settings on MOX2-5 datasets. The Hermit reasoner outperforms other reasoners with an average reasoning time of 1.1-2.1 s, under defined settings in our proposed ontology model. Our proposed algorithm for personalized recommendations conceptualizes a direction to combine the classification results and personal preferences in an ontology for activity eCoaching. The proposed method of combining machine learning technology with semantic rules is an invaluable asset in personalized recommendation generation. Moreover, the semantic rules in the knowledge base and SPARQL (SPARQL Protocol and RDF Query Language) query processing in the query engine helps to understand the logic behind the personalized recommendation generation.
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39
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Rafl J, Bachman TE, Rafl-Huttova V, Walzel S, Rozanek M. Commercial smartwatch with pulse oximeter detects short-time hypoxemia as well as standard medical-grade device: Validation study. Digit Health 2022; 8:20552076221132127. [PMID: 36249475 PMCID: PMC9554125 DOI: 10.1177/20552076221132127] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 09/22/2022] [Indexed: 11/29/2022] Open
Abstract
OBJECTIVE We investigated how a commercially available smartwatch that measures peripheral blood oxygen saturation (SpO2) can detect hypoxemia compared to a medical-grade pulse oximeter. METHODS We recruited 24 healthy participants. Each participant wore a smartwatch (Apple Watch Series 6) on the left wrist and a pulse oximeter sensor (Masimo Radical-7) on the left middle finger. The participants breathed via a breathing circuit with a three-way non-rebreathing valve in three phases. First, in the 2-minute initial stabilization phase, the participants inhaled the ambient air. Then in the 5-minute desaturation phase, the participants breathed the oxygen-reduced gas mixture (12% O2), which temporarily reduced their blood oxygen saturation. In the final stabilization phase, the participants inhaled the ambient air again until SpO2 returned to normal values. Measurements of SpO2 were taken from the smartwatch and the pulse oximeter simultaneously in 30-s intervals. RESULTS There were 642 individual pairs of SpO2 measurements. The bias in SpO2 between the smartwatch and the oximeter was 0.0% for all the data points. The bias for SpO2 less than 90% was 1.2%. The differences in individual measurements between the smartwatch and oximeter within 6% SpO2 can be expected for SpO2 readings 90%-100% and up to 8% for SpO2 readings less than 90%. CONCLUSIONS Apple Watch Series 6 can reliably detect states of reduced blood oxygen saturation with SpO2 below 90% when compared to a medical-grade pulse oximeter. The technology used in this smartwatch is sufficiently advanced for the indicative measurement of SpO2 outside the clinic. TRIAL REGISTRATION ClinicalTrials.gov NCT04780724.
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Affiliation(s)
- Jakub Rafl
- Department of Biomedical Technology, Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czech Republic,Jakub Rafl, Department of Biomedical Technology, Faculty of Biomedical Engineering, Czech Technical University in Prague, nam. Sitna 3105, CZ-272 01 Kladno, Czech Republic.
| | - Thomas E Bachman
- Department of Biomedical Technology, Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czech Republic
| | - Veronika Rafl-Huttova
- Department of Biomedical Technology, Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czech Republic
| | - Simon Walzel
- Department of Biomedical Technology, Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czech Republic
| | - Martin Rozanek
- Department of Biomedical Technology, Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czech Republic
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40
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Brannon GE, Ray M, Cho P, Baum M, Beg MS, Bevers T, Schembre SM, Basen-Engquist K, Liao Y. A qualitative study to explore the acceptability and usefulness of personalized biofeedback to motivate physical activity in cancer survivors. Digit Health 2022; 8:20552076221129096. [PMID: 36238756 PMCID: PMC9551329 DOI: 10.1177/20552076221129096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 09/11/2022] [Indexed: 11/07/2022] Open
Abstract
Objective Many cancer survivors do not meet recommended levels of exercise, despite the
benefits physical activity offers. This study aimed to understand
experiences of insufficiently active overweight/obese breast or colorectal
cancer survivors, in efforts to (1) examine regular physical activity
barriers, and (2) determine perceptions and acceptability of a remotely
delivered physical activity intervention utilizing wearable sensors and
personalized feedback messages. Methods In-person and virtual small group interviews were conducted engaging
overweight/obese cancer survivors (n = 16, 94% female, 94%
breast cancer survivors) in discussions resulting in 314 pages of
transcribed data analyzed by multiple coders. Results All participants expressed needing to increase physical activity, identifying
lack of motivation centering on survivorship experiences and symptom
management as the most salient barrier. They indicated familiarity with
activity trackers (i.e., Fitbit) and expressed interest in biosensors (i.e.,
continuous glucose monitors [CGMs]) as CGMs show biological metrics in
real-time. Participants reported (1) personalized feedback messages can
improve motivation and accountability; (2) CGM acceptability is high given
survivors’ medical history; and (3) glucose data is a relevant health
indicator and they appreciated integrated messages (between Fitbit and CGM)
in demonstrating how behaviors immediately affect one's body. Conclusions This study supports the use of wearable biosensors and m-health interventions
to promote physical activity in cancer survivors. Glucose-based biofeedback
provides relevant and motivating information for cancer survivors regarding
their daily activity levels by demonstrating the immediate effects of
physical activity. Integrating biofeedback into physical activity
interventions could be an effective behavioral change strategy to promote a
healthy lifestyle in cancer survivors.
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Affiliation(s)
- Grace E. Brannon
- Department of Communication, University of Texas at
Arlington, Arlington, TX, USA
| | - Madison Ray
- Department of Communication, University of Texas at
Arlington, Arlington, TX, USA
| | - Patrick Cho
- Department of Behavioral Science, The University of Texas MD Anderson Cancer
Center, Houston, TX, USA
| | - Miranda Baum
- Department of Behavioral Science, The University of Texas MD Anderson Cancer
Center, Houston, TX, USA
| | - Muhammad Shaalan Beg
- Division of Hematology/Medical Oncology,
University of
Texas Southwestern Medical Center, Dallas,
TX, USA
| | - Therese Bevers
- Department of Clinical Cancer Prevention,
The University
of Texas MD Anderson Cancer Center,
Houston, TX, USA
| | - Susan M. Schembre
- Department of Family and Community Medicine, College of Medicine,
University of Arizona, Tucson, Arizona, USA
| | - Karen Basen-Engquist
- Department of Behavioral Science, The University of Texas MD Anderson Cancer
Center, Houston, TX, USA
| | - Yue Liao
- Department of Behavioral Science, The University of Texas MD Anderson Cancer
Center, Houston, TX, USA,Department of Kinesiology, University of Texas at
Arlington, Arlington, TX, USA,Yue Liao, Department of Kinesiology,
University of Texas at Arlington, 500 West Nedderman Drive, MAC 147, Arlington,
TX 76019, USA. E-mail:
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Mosnaim GS, Greiwe J, Jariwala SP, Pleasants R, Merchant R. Digital Inhalers and Remote Patient Monitoring for Asthma. THE JOURNAL OF ALLERGY AND CLINICAL IMMUNOLOGY. IN PRACTICE 2022; 10:2525-2533. [PMID: 35779779 DOI: 10.1016/j.jaip.2022.06.026] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 05/27/2022] [Accepted: 06/20/2022] [Indexed: 06/15/2023]
Abstract
Digital inhaler systems, remote patient monitoring, and remote therapeutic monitoring offer great promise as diagnostic tools and therapeutic interventions to improve adherence and inhaler technique for patients with difficult-to-control asthma. In turn, improvements in adherence and inhaler technique may translate into decreasing the need for high side effect treatments such as oral corticosteroids and costly therapies including biologics. Although more clinical trials are needed, studies that use digital inhaler systems to collect objective real-time data on medication-taking behavior via electronic medication monitors and feed this data back to patients on their mobile asthma app, and to health care professionals on the clinician dashboard to counsel patients, show positive outcomes. This article addresses the use of these diagnostic and therapeutic tools in asthma care, how to choose a digital inhaler system, how to teach patients to use the system, strategies for the adoption of these technologies in large health care systems as well as smaller practices, coding and reimbursement, liability concerns, and research gaps.
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Affiliation(s)
- Giselle S Mosnaim
- Division of Allergy, Asthma and Immunology, Department of Medicine, NorthShore University HealthSystem, Evanston, Ill.
| | - Justin Greiwe
- Bernstein Allergy Group, Inc, Cincinnati, Ohio; Division of Immunology/Allergy Section, Department of Internal Medicine, The University of Cincinnati College of Medicine, Cincinnati, Ohio
| | - Sunit P Jariwala
- Division of Allergy/Immunology, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY
| | - Roy Pleasants
- Department of Medicine, Duke School of Medicine, Raleigh, NC
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Activity monitoring and patient-reported outcome measures in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome patients. PLoS One 2022; 17:e0274472. [PMID: 36121803 PMCID: PMC9484698 DOI: 10.1371/journal.pone.0274472] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 08/26/2022] [Indexed: 11/19/2022] Open
Abstract
Introduction Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a disease with no validated specific and sensitive biomarker, and no standard approved treatment. In this observational study with no intervention, participants used a Fitbit activity tracker. The aims were to explore natural symptom variation, feasibility of continuous activity monitoring, and to compare activity data with patient reported outcome measures (PROMs). Materials and methods In this pilot study, 27 patients with mild to severe ME/CFS, of mean age 42.3 years, used the Fitbit Charge 3 continuously for six months. Patients wore a SenseWear activity bracelet for 7 days at baseline, at 3 and 6 months. At baseline and follow-up they completed the Short Form 36 Health Survey (SF-36) and the DePaul Symptom Questionnaire–Short Form (DSQ-SF). Results The mean number of steps per day decreased with increasing ME/CFS severity; mild 5566, moderate 4991 and severe 1998. The day-by-day variation was mean 47% (range 25%–79%). Mean steps per day increased from the first to the second three-month period, 4341 vs 4781 steps, p = 0.022. The maximum differences in outcome measures between 4-week periods (highest vs lowest), were more evident in a group of eight patients with milder disease (baseline SF-36 PF > 50 or DSQ-SF < 55) as compared to 19 patients with higher symptom burden (SF-36 PF < 50 and DSQ-SF > 55), for SF-36 PF raw scores: 16.9 vs 3.4 points, and for steps per day: 958 versus 479 steps. The correlations between steps per day and self-reported SF-36 Physical function, SF-36 Social function, and DSQ-SF were significant. Fitbit recorded significantly higher number of steps than SenseWear. Resting heart rates were stable during six months. Conclusion Continuous activity registration with Fitbit Charge 3 trackers is feasible and useful in studies with ME/CFS patients to monitor steps and resting heart rate, in addition to self-reported outcome measures. Clinical trial registration Clinicaltrials.gov: NCT04195815.
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Duan N, Norman D, Schmid C, Sim I, Kravitz RL. Personalized Data Science and Personalized (N-of-1) Trials: Promising Paradigms for Individualized Health Care. HARVARD DATA SCIENCE REVIEW 2022; 4:10.1162/99608f92.8439a336. [PMID: 38009133 PMCID: PMC10673628 DOI: 10.1162/99608f92.8439a336] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2023] Open
Abstract
The term 'data science' usually refers to the process of extracting value from big data obtained from a large group of individuals. An alternative rendition, which we call personalized data science (Per-DS), aims to collect, analyze, and interpret personal data to inform personal decisions. This article describes the main features of Per-DS, and reviews its current state and future outlook. A Per-DS investigation is of, by, and for an individual, the Per-DS investigator, acting simultaneously as her own investigator, study participant, and beneficiary, and making personalized decisions for study design and implementation. The scope of Per-DS studies may include systematic monitoring of physiological or behavioral patterns, case-crossover studies for symptom triggers, pre-post trials for exposure-outcome relationships, and personalized (N-of-1) trials for effectiveness. Per-DS studies produce personal knowledge generalizable to the individual's future self (thus benefiting herself) rather than knowledge generalizable to an external population (thus benefiting others). This endeavor requires a pivot from data mining or extraction to data gardening, analogous to home gardeners producing food for home consumption-the Per-DS investigator needs to 'cultivate the field' by setting goals, specifying study design, identifying necessary data elements, and assembling instruments and tools for data collection. Then, she can implement the study protocol, harvest her personal data, and mine the data to extract personal knowledge. To facilitate Per-DS studies, Per-DS investigators need support from community-based, scientific, philanthropic, business, and government entities, to develop and deploy resources such as peer forums, mobile apps, 'virtual field guides,' and scientific and regulatory guidance.
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Affiliation(s)
- Naihua Duan
- Department of Psychiatry, Columbia University, New York, NY)
| | - Daniel Norman
- Santa Monica Sleep Disorders Center, Los Angeles, CA
| | | | - Ida Sim
- Department of Medicine, University of California San Francisco, San Francisco, CA
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Duarte N, Arora RK, Bennett G, Wang M, Snyder MP, Cooperstock JR, Wagner CE. Deploying wearable sensors for pandemic mitigation: A counterfactual modelling study of Canada's second COVID-19 wave. PLOS DIGITAL HEALTH 2022; 1:e0000100. [PMID: 36812624 PMCID: PMC9931244 DOI: 10.1371/journal.pdig.0000100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 08/08/2022] [Indexed: 11/18/2022]
Abstract
Wearable sensors can continuously and passively detect potential respiratory infections before or absent symptoms. However, the population-level impact of deploying these devices during pandemics is unclear. We built a compartmental model of Canada's second COVID-19 wave and simulated wearable sensor deployment scenarios, systematically varying detection algorithm accuracy, uptake, and adherence. With current detection algorithms and 4% uptake, we observed a 16% reduction in the second wave burden of infection; however, 22% of this reduction was attributed to incorrectly quarantining uninfected device users. Improving detection specificity and offering confirmatory rapid tests each minimized unnecessary quarantines and lab-based tests. With a sufficiently low false positive rate, increasing uptake and adherence became effective strategies for scaling averted infections. We concluded that wearable sensors capable of detecting presymptomatic or asymptomatic infections have potential to help reduce the burden of infection during a pandemic; in the case of COVID-19, technology improvements or supporting measures are required to keep social and resource costs sustainable.
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Affiliation(s)
- Nathan Duarte
- Department of Electrical and Computer Engineering, Faculty of Engineering, McGill University, Montreal, Canada
| | - Rahul K. Arora
- Department of Community Health Sciences, University of Calgary, Calgary, Canada
- Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom
| | - Graham Bennett
- Department of Economics, Faculty of Arts, McGill University, Montreal, Canada
| | - Meng Wang
- Department of Genetics, Stanford University School of Medicine, Stanford University, California, United States of America
| | - Michael P. Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford University, California, United States of America
| | - Jeremy R. Cooperstock
- Department of Electrical and Computer Engineering, Faculty of Engineering, McGill University, Montreal, Canada
| | - Caroline E. Wagner
- Department of Bioengineering, Faculty of Engineering, McGill University, Montreal, Canada
- * E-mail:
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Shandhi MMH, Cho PJ, Roghanizad AR, Singh K, Wang W, Enache OM, Stern A, Sbahi R, Tatar B, Fiscus S, Khoo QX, Kuo Y, Lu X, Hsieh J, Kalodzitsa A, Bahmani A, Alavi A, Ray U, Snyder MP, Ginsburg GS, Pasquale DK, Woods CW, Shaw RJ, Dunn JP. A method for intelligent allocation of diagnostic testing by leveraging data from commercial wearable devices: a case study on COVID-19. NPJ Digit Med 2022; 5:130. [PMID: 36050372 PMCID: PMC9434073 DOI: 10.1038/s41746-022-00672-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 08/03/2022] [Indexed: 12/16/2022] Open
Abstract
Mass surveillance testing can help control outbreaks of infectious diseases such as COVID-19. However, diagnostic test shortages are prevalent globally and continue to occur in the US with the onset of new COVID-19 variants and emerging diseases like monkeypox, demonstrating an unprecedented need for improving our current methods for mass surveillance testing. By targeting surveillance testing toward individuals who are most likely to be infected and, thus, increasing the testing positivity rate (i.e., percent positive in the surveillance group), fewer tests are needed to capture the same number of positive cases. Here, we developed an Intelligent Testing Allocation (ITA) method by leveraging data from the CovIdentify study (6765 participants) and the MyPHD study (8580 participants), including smartwatch data from 1265 individuals of whom 126 tested positive for COVID-19. Our rigorous model and parameter search uncovered the optimal time periods and aggregate metrics for monitoring continuous digital biomarkers to increase the positivity rate of COVID-19 diagnostic testing. We found that resting heart rate (RHR) features distinguished between COVID-19-positive and -negative cases earlier in the course of the infection than steps features, as early as 10 and 5 days prior to the diagnostic test, respectively. We also found that including steps features increased the area under the receiver operating characteristic curve (AUC-ROC) by 7-11% when compared with RHR features alone, while including RHR features improved the AUC of the ITA model's precision-recall curve (AUC-PR) by 38-50% when compared with steps features alone. The best AUC-ROC (0.73 ± 0.14 and 0.77 on the cross-validated training set and independent test set, respectively) and AUC-PR (0.55 ± 0.21 and 0.24) were achieved by using data from a single device type (Fitbit) with high-resolution (minute-level) data. Finally, we show that ITA generates up to a 6.5-fold increase in the positivity rate in the cross-validated training set and up to a 4.5-fold increase in the positivity rate in the independent test set, including both symptomatic and asymptomatic (up to 27%) individuals. Our findings suggest that, if deployed on a large scale and without needing self-reported symptoms, the ITA method could improve the allocation of diagnostic testing resources and reduce the burden of test shortages.
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Affiliation(s)
| | - Peter J Cho
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Ali R Roghanizad
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Karnika Singh
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Will Wang
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Oana M Enache
- Department of Biostatistics & Bioinformatics, Duke University, Durham, NC, USA
| | - Amanda Stern
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Rami Sbahi
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Bilge Tatar
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Sean Fiscus
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Qi Xuan Khoo
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Yvonne Kuo
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Xiao Lu
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Joseph Hsieh
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Alena Kalodzitsa
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Amir Bahmani
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Arash Alavi
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Utsab Ray
- Department of Genetics, Stanford University, Stanford, CA, USA
| | | | - Geoffrey S Ginsburg
- All of Us Research Program, National Institutes of Health, Bethesda, MD, USA
| | - Dana K Pasquale
- Department of Sociology, Duke University, Durham, NC, USA.,Department of Population Health Sciences, School of Medicine, Duke University, Durham, NC, USA
| | - Christopher W Woods
- Division of Infectious Diseases, Duke University Medical Center, Durham, NC, USA.,Durham VA Medical Center, Durham, NC, USA
| | - Ryan J Shaw
- School of Nursing, Duke University, Durham, NC, USA.,Duke Mobile App Gateway, Clinical and Translational Science Institute, Duke University, Durham, NC, USA
| | - Jessilyn P Dunn
- Department of Biomedical Engineering, Duke University, Durham, NC, USA. .,Department of Biostatistics & Bioinformatics, Duke University, Durham, NC, USA.
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Smart Consumer Wearables as Digital Diagnostic Tools: A Review. Diagnostics (Basel) 2022; 12:diagnostics12092110. [PMID: 36140511 PMCID: PMC9498278 DOI: 10.3390/diagnostics12092110] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/26/2022] [Accepted: 08/29/2022] [Indexed: 11/17/2022] Open
Abstract
The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains.
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Zhou W, Chan YE, Foo CS, Zhang J, Teo JX, Davila S, Huang W, Yap J, Cook S, Tan P, Chin CWL, Yeo KK, Lim WK, Krishnaswamy P. High-Resolution Digital Phenotypes From Consumer Wearables and Their Applications in Machine Learning of Cardiometabolic Risk Markers: Cohort Study. J Med Internet Res 2022; 24:e34669. [PMID: 35904853 PMCID: PMC9377462 DOI: 10.2196/34669] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 04/12/2022] [Accepted: 05/29/2022] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Consumer-grade wearable devices enable detailed recordings of heart rate and step counts in free-living conditions. Recent studies have shown that summary statistics from these wearable recordings have potential uses for longitudinal monitoring of health and disease states. However, the relationship between higher resolution physiological dynamics from wearables and known markers of health and disease remains largely uncharacterized. OBJECTIVE We aimed to derive high-resolution digital phenotypes from observational wearable recordings and to examine their associations with modifiable and inherent markers of cardiometabolic disease risk. METHODS We introduced a principled framework to extract interpretable high-resolution phenotypes from wearable data recorded in free-living conditions. The proposed framework standardizes the handling of data irregularities; encodes contextual information regarding the underlying physiological state at any given time; and generates a set of 66 minimally redundant features across active, sedentary, and sleep states. We applied our approach to a multimodal data set, from the SingHEART study (NCT02791152), which comprises heart rate and step count time series from wearables, clinical screening profiles, and whole genome sequences from 692 healthy volunteers. We used machine learning to model nonlinear relationships between the high-resolution phenotypes on the one hand and clinical or genomic risk markers for blood pressure, lipid, weight and sugar abnormalities on the other. For each risk type, we performed model comparisons based on Brier scores to assess the predictive value of high-resolution features over and beyond typical baselines. We also qualitatively characterized the wearable phenotypes for participants who had actualized clinical events. RESULTS We found that the high-resolution features have higher predictive value than typical baselines for clinical markers of cardiometabolic disease risk: the best models based on high-resolution features had 17.9% and 7.36% improvement in Brier score over baselines based on age and gender and resting heart rate, respectively (P<.001 in each case). Furthermore, heart rate dynamics from different activity states contain distinct information (maximum absolute correlation coefficient of 0.15). Heart rate dynamics in sedentary states are most predictive of lipid abnormalities and obesity, whereas patterns in active states are most predictive of blood pressure abnormalities (P<.001). Moreover, in comparison with standard measures, higher resolution patterns in wearable heart rate recordings are better able to represent subtle physiological dynamics related to genomic risk for cardiometabolic disease (improvement of 11.9%-22.0% in Brier scores; P<.001). Finally, illustrative case studies reveal connections between these high-resolution phenotypes and actualized clinical events, even for borderline profiles lacking apparent cardiometabolic risk markers. CONCLUSIONS High-resolution digital phenotypes recorded by consumer wearables in free-living states have the potential to enhance the prediction of cardiometabolic disease risk and could enable more proactive and personalized health management.
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Affiliation(s)
- Weizhuang Zhou
- Institute for Infocomm Research, Agency for Science Technology and Research (A*STAR), Singapore, Singapore
| | - Yu En Chan
- Institute for Infocomm Research, Agency for Science Technology and Research (A*STAR), Singapore, Singapore
| | - Chuan Sheng Foo
- Institute for Infocomm Research, Agency for Science Technology and Research (A*STAR), Singapore, Singapore
| | - Jingxian Zhang
- Institute for Infocomm Research, Agency for Science Technology and Research (A*STAR), Singapore, Singapore
| | - Jing Xian Teo
- SingHealth Duke-NUS Institute of Precision Medicine, Singapore, Singapore
| | - Sonia Davila
- SingHealth Duke-NUS Institute of Precision Medicine, Singapore, Singapore.,SingHealth Duke-NUS Genomic Medicine Centre, Singapore, Singapore.,Cardiovascular and Metabolic Disorders Program, Duke-NUS Medical School, Singapore, Singapore
| | - Weiting Huang
- Department of Cardiology, National Heart Centre Singapore, Singapore, Singapore
| | - Jonathan Yap
- Department of Cardiology, National Heart Centre Singapore, Singapore, Singapore.,Duke-NUS Medical School, Singapore, Singapore
| | - Stuart Cook
- Cardiovascular and Metabolic Disorders Program, Duke-NUS Medical School, Singapore, Singapore
| | - Patrick Tan
- SingHealth Duke-NUS Institute of Precision Medicine, Singapore, Singapore.,Cancer and Stem Biology Program, Duke-NUS Medical School, Singapore, Singapore.,Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore.,Genome Institute of Singapore, Agency for Science Technology and Research (A*STAR), Singapore, Singapore
| | - Calvin Woon-Loong Chin
- Department of Cardiology, National Heart Centre Singapore, Singapore, Singapore.,Duke-NUS Medical School, Singapore, Singapore
| | - Khung Keong Yeo
- SingHealth Duke-NUS Institute of Precision Medicine, Singapore, Singapore.,Department of Cardiology, National Heart Centre Singapore, Singapore, Singapore.,Duke-NUS Medical School, Singapore, Singapore
| | - Weng Khong Lim
- SingHealth Duke-NUS Institute of Precision Medicine, Singapore, Singapore.,SingHealth Duke-NUS Genomic Medicine Centre, Singapore, Singapore.,Cancer and Stem Biology Program, Duke-NUS Medical School, Singapore, Singapore
| | - Pavitra Krishnaswamy
- Institute for Infocomm Research, Agency for Science Technology and Research (A*STAR), Singapore, Singapore
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Temporal response characterization across individual multiomics profiles of prediabetic and diabetic subjects. Sci Rep 2022; 12:12098. [PMID: 35840765 PMCID: PMC9284494 DOI: 10.1038/s41598-022-16326-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 07/08/2022] [Indexed: 11/08/2022] Open
Abstract
Longitudinal deep multiomics profiling, which combines biomolecular, physiological, environmental and clinical measures data, shows great promise for precision health. However, integrating and understanding the complexity of such data remains a big challenge. Here we utilize an individual-focused bottom-up approach aimed at first assessing single individuals’ multiomics time series, and using the individual-level responses to assess multi-individual grouping based directly on similarity of their longitudinal deep multiomics profiles. We used this individual-focused approach to analyze profiles from a study profiling longitudinal responses in type 2 diabetes mellitus. After generating periodograms for individual subject omics signals, we constructed within-person omics networks and analyzed personal-level immune changes. The results identified both individual-level responses to immune perturbation, and the clusters of individuals that have similar behaviors in immune response and which were associated to measures of their diabetic status.
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49
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Krizea M, Gialelis J, Protopsaltis G, Mountzouris C, Theodorou G. Empowering People with a User-Friendly Wearable Platform for Unobtrusive Monitoring of Vital Physiological Parameters. SENSORS (BASEL, SWITZERLAND) 2022; 22:5226. [PMID: 35890907 PMCID: PMC9317673 DOI: 10.3390/s22145226] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 07/02/2022] [Accepted: 07/08/2022] [Indexed: 06/15/2023]
Abstract
Elderly people feel vulnerable especially after they are dismissed from health care facilities and return home. The purpose of this work was to alleviate this sense of vulnerability and empower these people by giving them the opportunity to unobtrusively record their vital physiological parameters. Bearing in mind all the parameters involved, we developed a user-friendly wrist-wearable device combined with a web-based application, to adequately address this need. The proposed compilation obtains the photoplethysmogram (PPG) from the subject's wrist and simultaneously extracts, in real time, the physiological parameters of heart rate (HR), blood oxygen saturation (SpO2) and respiratory rate (RR), based on algorithms embedded on the wearable device. The described process is conducted solely within the device, favoring the optimal use of the available resources. The aggregated data are transmitted via Wi-Fi to a cloud environment and stored in a database. A corresponding web-based application serves as a visualization and analytics tool, allowing the individuals to catch a glimpse of their physiological parameters on a screen and share their digital information with health professionals who can perform further processing and obtain valuable health information.
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Affiliation(s)
- Maria Krizea
- Applied Electronics Laboratory, Department of Electrical and Computer Engineering, University of Patras, 26504 Patras, Greece; (M.K.); (G.P.); (C.M.); (G.T.)
- Industrial Systems Institute, ATHENA RC, 26504 Patras, Greece
| | - John Gialelis
- Applied Electronics Laboratory, Department of Electrical and Computer Engineering, University of Patras, 26504 Patras, Greece; (M.K.); (G.P.); (C.M.); (G.T.)
- Industrial Systems Institute, ATHENA RC, 26504 Patras, Greece
| | - Grigoris Protopsaltis
- Applied Electronics Laboratory, Department of Electrical and Computer Engineering, University of Patras, 26504 Patras, Greece; (M.K.); (G.P.); (C.M.); (G.T.)
| | - Christos Mountzouris
- Applied Electronics Laboratory, Department of Electrical and Computer Engineering, University of Patras, 26504 Patras, Greece; (M.K.); (G.P.); (C.M.); (G.T.)
| | - Gerasimos Theodorou
- Applied Electronics Laboratory, Department of Electrical and Computer Engineering, University of Patras, 26504 Patras, Greece; (M.K.); (G.P.); (C.M.); (G.T.)
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50
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Hirten RP, Tomalin L, Danieletto M, Golden E, Zweig M, Kaur S, Helmus D, Biello A, Pyzik R, Bottinger EP, Keefer L, Charney D, Nadkarni GN, Suarez-Farinas M, Fayad ZA. Evaluation of a machine learning approach utilizing wearable data for prediction of SARS-CoV-2 infection in healthcare workers. JAMIA Open 2022; 5:ooac041. [PMID: 35677186 PMCID: PMC9129173 DOI: 10.1093/jamiaopen/ooac041] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 04/28/2022] [Accepted: 05/15/2022] [Indexed: 11/16/2022] Open
Abstract
Objective To determine whether a machine learning model can detect SARS-CoV-2 infection from physiological metrics collected from wearable devices. Materials and Methods Health care workers from 7 hospitals were enrolled and prospectively followed in a multicenter observational study. Subjects downloaded a custom smart phone app and wore Apple Watches for the duration of the study period. Daily surveys related to symptoms and the diagnosis of Coronavirus Disease 2019 were answered in the app. Results We enrolled 407 participants with 49 (12%) having a positive nasal SARS-CoV-2 polymerase chain reaction test during follow-up. We examined 5 machine-learning approaches and found that gradient-boosting machines (GBM) had the most favorable validation performance. Across all testing sets, our GBM model predicted SARS-CoV-2 infection with an average area under the receiver operating characteristic (auROC) = 86.4% (confidence interval [CI] 84-89%). The model was calibrated to value sensitivity over specificity, achieving an average sensitivity of 82% (CI ±∼4%) and specificity of 77% (CI ±∼1%). The most important predictors included parameters describing the circadian heart rate variability mean (MESOR) and peak-timing (acrophase), and age. Discussion We show that a tree-based ML algorithm applied to physiological metrics passively collected from a wearable device can identify and predict SARS-CoV-2 infection. Conclusion Applying machine learning models to the passively collected physiological metrics from wearable devices may improve SARS-CoV-2 screening methods and infection tracking.
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Affiliation(s)
- Robert P Hirten
- Department of Medicine, The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, New York, New York, USA
| | - Lewis Tomalin
- Department of Population Health Science and Policy, Center for Biostatistics, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Matteo Danieletto
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Eddye Golden
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Micol Zweig
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Sparshdeep Kaur
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, New York, New York, USA
| | - Drew Helmus
- Department of Medicine, The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Anthony Biello
- Department of Medicine, The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Renata Pyzik
- The BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Erwin P Bottinger
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, New York, New York, USA
| | - Laurie Keefer
- Department of Medicine, The Dr. Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Dennis Charney
- Office of the Dean, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Nash Family Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Girish N Nadkarni
- The Hasso Plattner Institute for Digital Health at the Mount Sinai, New York, New York, USA
- The Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Mayte Suarez-Farinas
- Department of Population Health Science and Policy, Center for Biostatistics, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Zahi A Fayad
- The BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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