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Soltani S, Viswanath VK, Kasl P, Hartogensis W, Dilchert S, Hecht FM, Mason AE, Smarr BL. Testing the Impact of Intensive, Longitudinal Sampling on Assessments of Statistical Power and Effect Size Within a Heterogeneous Human Population: Natural Experiment Using Change in Heart Rate on Weekends as a Surrogate Intervention. J Med Internet Res 2025; 27:e60284. [PMID: 40397926 DOI: 10.2196/60284] [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: 05/29/2024] [Revised: 03/14/2025] [Accepted: 04/15/2025] [Indexed: 05/23/2025] Open
Abstract
BACKGROUND The recent emergence of wearable devices has made feasible the passive gathering of intensive, longitudinal data from large groups of individuals. This form of data is effective at capturing physiological changes between participants (interindividual variability) and changes within participants over time (intraindividual variability). The emergence of longitudinal datasets provides an opportunity to quantify the contribution of such longitudinal data to the control of these sources of variability for applications such as responder analysis, where traditional, sparser sampling methods may hinder the categorization of individuals into these phenotypes. OBJECTIVE This study aimed to quantify the gains made in statistical power and effect size among statistical comparisons when controlling for interindividual variability and intraindividual variability compared with controlling for neither. METHODS Here, we test the gains in statistical power from controlling for interindividual and intraindividual variability of resting heart rate, collected in 2020 for over 40,000 individuals as part of the TemPredict study on COVID-19 detection. We compared heart rate on weekends with that on weekdays because weekends predictably change the behavior of most individuals, though not all, and in different ways. Weekends also repeat consistently, making their effects on heart rate feasible to assess with confidence over large populations. We therefore used weekends as a model system to test the impact of different statistical controls on detecting a recurring event with a clear ground truth. We randomly and iteratively sampled heart rate from weekday and weekend nights, controlling for interindividual variability, intraindividual variability, both, or neither. RESULTS Between-participant variability appeared to be a greater source of structured variability than within-participant fluctuations. Accounting for interindividual variability through within-individual sampling required 40× fewer pairs of samples to achieve statistical significance with 4× to 5× greater effect size at significance. Within-individual sampling revealed differential effects of weekends on heart rate, which were obscured by aggregated sampling methods. CONCLUSIONS This work highlights the leverage provided by longitudinal, within-individual sampling to increase statistical power among populations with heterogeneous effects.
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Affiliation(s)
- Severine Soltani
- Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA, United States
- Shu Chien-Gene Lay Department of Bioengineering, University of California San Diego, La Jolla, CA, United States
| | - Varun K Viswanath
- Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA, United States
| | - Patrick Kasl
- Shu Chien-Gene Lay Department of Bioengineering, University of California San Diego, La Jolla, CA, United States
| | - Wendy Hartogensis
- Osher Center for Integrative Health, University of California San Francisco, San Francisco, CA, United States
| | - Stephan Dilchert
- Zicklin School of Business, Baruch College, New York, NY, 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
- Shu Chien-Gene Lay Department of Bioengineering, University of California San Diego, La Jolla, CA, United States
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, United States
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Song Y, Bae J, Shin J, Yang J, Kim S, Bahn S, Yun MH. Real-time motion sickness measurement: Feasibility and application of the real-time motion sickness scale (RMS). APPLIED ERGONOMICS 2025; 128:104507. [PMID: 40373506 DOI: 10.1016/j.apergo.2025.104507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2024] [Revised: 02/28/2025] [Accepted: 03/10/2025] [Indexed: 05/17/2025]
Abstract
Motion sickness can occur in environments where new technologies, such as electric vehicles, are applied. Since motion sickness impedes user experience and the adoption of these technologies, accurate prediction is essential. This necessitates precise measurement of motion sickness variations across different environments to improve predictive model accuracy. However, current questionnaires lack detailed temporal and symptom-specific information, failing to provide accurate real-time data. To address this, we introduce the Real-Time Motion Sickness Scale (RMS), a practical tool designed for continuous monitoring of significant motion sickness symptoms. The RMS was developed by enhancing existing questionnaires through a systematic literature review and pilot tests. We validated its feasibility through a real-driving experiment with 24 passengers in an electric vehicle. The results demonstrated that the RMS accurately describes motion sickness severity and captures real-time changes in detail. Based on these findings, we discuss the applications of the RMS in motion sickness-provocative environments.
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Affiliation(s)
- Yein Song
- Department of Industrial Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea.
| | - Jaehoo Bae
- Department of Industrial Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea.
| | - Jiyeon Shin
- Department of Industrial Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea.
| | - Jaesik Yang
- R&D Center (Namyang), Hyundai Motors, 103 Hyundaikia-ro, Namyang-eup, Hwaseong-si, Gyeonggi-do, 18280, Republic of Korea.
| | - Seonghyeon Kim
- R&D Center (Namyang), Hyundai Motors, 103 Hyundaikia-ro, Namyang-eup, Hwaseong-si, Gyeonggi-do, 18280, Republic of Korea.
| | - Sangwoo Bahn
- Department of Industrial and Management Systems Engineering, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Gyeonggi-do, Yongin-si, 17104, South Korea.
| | - Myung Hwan Yun
- Department of Industrial Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea; Institute for Industrial Systems Innovation, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, South Korea.
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3
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Tan J, Zhou Z, Zheng H, Li Y, Wang H, Yang Q, Tian H, Chen H, Xie J, Li Z, Chen Y. Emerging themes and future directions in space radiation health research: a bibliometric exploration from 2013 to 2022. RADIATION AND ENVIRONMENTAL BIOPHYSICS 2025; 64:211-227. [PMID: 40156613 PMCID: PMC12049392 DOI: 10.1007/s00411-025-01115-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/24/2024] [Accepted: 02/18/2025] [Indexed: 04/01/2025]
Abstract
The impact of space radiation on health (SRHE) is extensive and significantly influences public health and space operations, making it essential to analyze global collaboration networks and track developmental trends over the last decade. However, bibliometric analysis in this area remains limited. This study aims to outline publication trends, citation patterns, major journals, key authors, institutional and national collaborations, and to explore emerging themes and future directions. A bibliometric analysis was conducted using CiteSpace, Bibliometrix in R, and VOSviewer on SRHE research from the Web of Science Core Collection up to November 12, 2023. The analysis included 390 records from 4,857 journals, involving 1,918 authors across 701 institutions in 53 countries. The predominant publications were Articles and Review Articles in Life Sciences and Biomedicine, with a notable publication surge in 2020. The most cited work was by Li et al. (2017), with Cucinotta F.A. as the most prolific author. The USA led in publications, citations, and collaboration strength, followed by Germany and China. Key journals include Radiation Research, Plos One, Life Sciences in Space Research, and Health Physics. Research has focused on radiation exposure effects, DNA damage repair, astronaut health risks, and radiation protection, with emerging trends in microgravity, astrobiology, and lifespan research, which examines the biological, psychological, and social aspects of aging and the entire life course, aiming to understand and extend the health span-the period of life free from chronic diseases and age-related disabilities-rather than just the total lifespan. Future research may benefit from focusing on personalized radiation protection, exploring biological mechanisms, and embracing technological innovations, based on the trends observed in this study.
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Affiliation(s)
- Jianhui Tan
- Department of Thyroid, Breast and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, No.69 North Dongxia Road, Shantou, Guangdong, 515041, PR China
| | - Zhongming Zhou
- Department of Thyroid, Breast and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, No.69 North Dongxia Road, Shantou, Guangdong, 515041, PR China
| | - Huihui Zheng
- Department of Thyroid, Breast and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, No.69 North Dongxia Road, Shantou, Guangdong, 515041, PR China
| | - Yanpo Li
- Department of Thyroid, Breast and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, No.69 North Dongxia Road, Shantou, Guangdong, 515041, PR China
| | - Haiting Wang
- Department of Thyroid, Breast and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, No.69 North Dongxia Road, Shantou, Guangdong, 515041, PR China
| | - Qiuping Yang
- Department of Thyroid, Breast and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, No.69 North Dongxia Road, Shantou, Guangdong, 515041, PR China
| | - Huiting Tian
- Department of Thyroid, Breast and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, No.69 North Dongxia Road, Shantou, Guangdong, 515041, PR China
| | - Haolin Chen
- Department of Thyroid, Breast and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, No.69 North Dongxia Road, Shantou, Guangdong, 515041, PR China
| | - Jiayi Xie
- Department of Thyroid, Breast and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, No.69 North Dongxia Road, Shantou, Guangdong, 515041, PR China
| | - Zhiyang Li
- Department of Thyroid, Breast and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, No.69 North Dongxia Road, Shantou, Guangdong, 515041, PR China.
| | - Yexi Chen
- Department of Thyroid, Breast and Hernia Surgery, General Surgery, The Second Affiliated Hospital of Shantou University Medical College, No.69 North Dongxia Road, Shantou, Guangdong, 515041, PR China.
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Menassa M, Wilmont I, Beigrezaei S, Knobbe A, Arita VA, Valderrama JF, Bridge L, Verschuren WMM, Rennie KL, Franco OH, van der Ouderaa F. The future of healthy ageing: Wearables in public health, disease prevention and healthcare. Maturitas 2025; 196:108254. [PMID: 40157094 DOI: 10.1016/j.maturitas.2025.108254] [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: 08/12/2024] [Revised: 03/10/2025] [Accepted: 03/21/2025] [Indexed: 04/01/2025]
Abstract
Wearables have evolved into accessible tools for sports, research, and interventions. Their use has expanded to real-time monitoring of behavioural parameters related to ageing and health. This paper provides an overview of the literature on wearables in disease prevention and healthcare over the life course (not only in the older population), based on insights from the Future of Diagnostics Workshop (Leiden, January 2024). Wearable-generated parameters include blood glucose, heart rate, step count, energy expenditure, and oxygen saturation. Integrating wearables in healthcare is protracted and far from mainstream implementation, but promises better diagnosis, biomonitoring, and assessment of medical interventions. The main lifestyle factors monitored directly with wearables or through smartphone applications for disease prevention include physical activity, energy expenditure, gait, sleep, and sedentary behaviour. Insights on dietary consumption and nutrition have resulted from continuous glucose monitors. These factors are important for healthy ageing due to their effect on underlying disease pathways. Inclusivity and engagement, data quality and ease of interpretation, privacy and ethics, user autonomy in decision making, and efficacy present challenges to but also opportunities for their use, especially by older people. These need to be addressed before wearables can be integrated into mainstream medical and public health strategies. Furthermore, six key considerations need to be tackled: 1) engagement, health literacy, and compliance with personalised feedback, 2) technical and standardisation requirements for scalability, 3) accountability, data safety/security, and ethical concerns, 4) technological considerations, access, and capacity building, 5) clinical relevance and risk of overdiagnosis/overmedicalisation, and 6) the clinician's perspective in implementation.
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Affiliation(s)
- Marilyne Menassa
- Department of Global Public Health & Bioethics, Julius Center for Health Science and Primary Care, UMC Utrecht, Utrecht University, the Netherlands.
| | - Ilona Wilmont
- Institute for Computing and Information Sciences, Data Science, Radboud University Nijmegen, Nijmegen, the Netherlands; Stichting Je Leefstijl Als Medicijn, the Netherlands
| | - Sara Beigrezaei
- Department of Global Public Health & Bioethics, Julius Center for Health Science and Primary Care, UMC Utrecht, Utrecht University, the Netherlands
| | - Arno Knobbe
- Leiden Institute of Advanced Computer Science, Universiteit Leiden, Leiden, the Netherlands
| | - Vicente Artola Arita
- Department of Global Public Health & Bioethics, Julius Center for Health Science and Primary Care, UMC Utrecht, Utrecht University, the Netherlands
| | - Jose F Valderrama
- Department of Global Public Health & Bioethics, Julius Center for Health Science and Primary Care, UMC Utrecht, Utrecht University, the Netherlands
| | - Lara Bridge
- Department of Global Public Health & Bioethics, Julius Center for Health Science and Primary Care, UMC Utrecht, Utrecht University, the Netherlands
| | - W M Monique Verschuren
- Department of Global Public Health & Bioethics, Julius Center for Health Science and Primary Care, UMC Utrecht, Utrecht University, the Netherlands; National Institute for Public Health and the Environment, Bilthoven, the Netherlands
| | - Kirsten L Rennie
- MRC Epidemiology Unit, University of Cambridge, Cambridge, United Kingdom
| | - Oscar H Franco
- Department of Global Public Health & Bioethics, Julius Center for Health Science and Primary Care, UMC Utrecht, Utrecht University, the Netherlands
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Beqari J, Powell J, Hurd J, Potter AL, McCarthy M, Srinivasan D, Wang D, Cranor J, Zhang L, Webster K, Kim J, Rosenstein A, Zheng Z, Lin TH, Li J, Fang Z, Zhang Y, Anderson A, Madsen J, Anderson J, Clark A, Yang ME, Nurko A, El-Jawahri AR, Sundt TM, Melnitchouk S, Jassar AS, D’Alessandro D, Panda N, Schumacher-Beal LY, Wright CD, Auchincloss HG, Sachdeva UM, Lanuti M, Colson YL, Langer N, Osho A, Yang CFJ, Li X. A Pilot Study Using Machine-learning Algorithms and Wearable Technology for the Early Detection of Postoperative Complications After Cardiothoracic Surgery. Ann Surg 2025; 281:514-521. [PMID: 38482684 PMCID: PMC11399322 DOI: 10.1097/sla.0000000000006263] [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] [Indexed: 04/04/2024]
Abstract
OBJECTIVE To evaluate whether a machine-learning algorithm (ie, the "NightSignal" algorithm) can be used for the detection of postoperative complications before symptom onset after cardiothoracic surgery. BACKGROUND Methods that enable the early detection of postoperative complications after cardiothoracic surgery are needed. METHODS This was a prospective observational cohort study conducted from July 2021 to February 2023 at a single academic tertiary care hospital. Patients aged 18 years or older scheduled to undergo cardiothoracic surgery were recruited. Study participants wore a Fitbit watch continuously for at least 1 week preoperatively and up to 90 days postoperatively. The ability of the NightSignal algorithm-which was previously developed for the early detection of Covid-19-to detect postoperative complications was evaluated. The primary outcomes were algorithm sensitivity and specificity for postoperative event detection. RESULTS A total of 56 patients undergoing cardiothoracic surgery met the inclusion criteria, of which 24 (42.9%) underwent thoracic operations and 32 (57.1%) underwent cardiac operations. The median age was 62 (Interquartile range: 51-68) years and 30 (53.6%) patients were female. The NightSignal algorithm detected 17 of the 21 postoperative events at a median of 2 (Interquartile range: 1-3) days before symptom onset, representing a sensitivity of 81%. The specificity, negative predictive value, and positive predictive value of the algorithm for the detection of postoperative events were 75%, 97%, and 28%, respectively. CONCLUSIONS Machine-learning analysis of biometric data collected from wearable devices has the potential to detect postoperative complications-before symptom onset-after cardiothoracic surgery.
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Affiliation(s)
- Jorind Beqari
- Department of Surgery, Massachusetts General Hospital, Boston, MA
| | - Joseph Powell
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH
| | - Jacob Hurd
- Department of Surgery, Massachusetts General Hospital, Boston, MA
| | | | - Meghan McCarthy
- Department of Surgery, Massachusetts General Hospital, Boston, MA
| | | | - Danny Wang
- Department of Surgery, Massachusetts General Hospital, Boston, MA
| | - James Cranor
- Department of Surgery, Massachusetts General Hospital, Boston, MA
| | - Lizi Zhang
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH
| | - Kyle Webster
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH
| | - Joshua Kim
- Department of Surgery, Massachusetts General Hospital, Boston, MA
| | | | - Zeyuan Zheng
- Department of Surgery, Massachusetts General Hospital, Boston, MA
| | - Tung Ho Lin
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH
| | - Jing Li
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH
| | - Zhengyu Fang
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH
| | - Yuhang Zhang
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH
| | - Alex Anderson
- Department of Surgery, Massachusetts General Hospital, Boston, MA
| | - James Madsen
- Department of Surgery, Massachusetts General Hospital, Boston, MA
| | - Jacob Anderson
- Department of Surgery, Massachusetts General Hospital, Boston, MA
| | - Anne Clark
- Department of Surgery, Massachusetts General Hospital, Boston, MA
| | - Margaret E. Yang
- Department of Surgery, Massachusetts General Hospital, Boston, MA
| | - Andrea Nurko
- Department of Surgery, Massachusetts General Hospital, Boston, MA
| | | | - Thoralf M. Sundt
- Department of Surgery, Massachusetts General Hospital, Boston, MA
| | | | | | | | - Nikhil Panda
- Department of Surgery, Massachusetts General Hospital, Boston, MA
| | | | | | | | - Uma M. Sachdeva
- Department of Surgery, Massachusetts General Hospital, Boston, MA
| | - Michael Lanuti
- Department of Surgery, Massachusetts General Hospital, Boston, MA
| | | | - Nathaniel Langer
- Department of Surgery, Massachusetts General Hospital, Boston, MA
| | - Asishana Osho
- Department of Surgery, Massachusetts General Hospital, Boston, MA
| | | | - Xiao Li
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH
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Lee CL, Chuang CK, Chiu HC, Chang YH, Tu YR, Lo YT, Lin HY, Lin SP. Understanding Genetic Screening: Harnessing Health Information to Prevent Disease Risks. Int J Med Sci 2025; 22:903-919. [PMID: 39991772 PMCID: PMC11843151 DOI: 10.7150/ijms.101219] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2024] [Accepted: 12/17/2024] [Indexed: 02/25/2025] Open
Abstract
Genetic screening analyzes an individual's genetic information to assess disease risk and provide personalized health recommendations. This article introduces the public to genetic screening, explaining its definition, principles, history, and common types, including prenatal, newborn, adult disease risk, cancer, and pharmacogenetic screening. It elaborates on the benefits of genetic screening, such as early risk detection, personalized prevention, family risk assessment, and reproductive decision-making. The article also notes limitations, including result interpretation uncertainty, psychological and ethical issues, and privacy and discrimination risks. It provides advice on selecting suitable screening, consulting professionals, choosing reliable institutions, and understanding screening purposes and limitations. Finally, it discusses applying screening results through lifestyle adjustments, regular check-ups, and preventive treatments. By comprehensively introducing genetic screening, the article aims to raise public awareness and encourage utilizing this technology to prevent disease and maintain health.
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Affiliation(s)
- Chung-Lin Lee
- Department of Pediatrics, MacKay Memorial Hospital, Taipei, Taiwan
- Institute of Clinical Medicine, National Yang-Ming Chiao-Tung University, Taipei, Taiwan
- International Rare Disease Center, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, Mackay Medical College, New Taipei City, Taiwan
- Mackay Junior College of Medicine, Nursing and Management, Taipei, Taiwan
| | - Chih-Kuang Chuang
- Division of Genetics and Metabolism, Department of Medical Research, MacKay Memorial Hospital, Taipei, Taiwan
- College of Medicine, Fu-Jen Catholic University, Taipei, Taiwan
| | - Huei-Ching Chiu
- Department of Pediatrics, MacKay Memorial Hospital, Taipei, Taiwan
| | - Ya-Hui Chang
- Department of Pediatrics, MacKay Memorial Hospital, Taipei, Taiwan
- International Rare Disease Center, MacKay Memorial Hospital, Taipei, Taiwan
| | - Yuan-Rong Tu
- Division of Genetics and Metabolism, Department of Medical Research, MacKay Memorial Hospital, Taipei, Taiwan
| | - Yun-Ting Lo
- International Rare Disease Center, MacKay Memorial Hospital, Taipei, Taiwan
| | - Hsiang-Yu Lin
- Department of Pediatrics, MacKay Memorial Hospital, Taipei, Taiwan
- International Rare Disease Center, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, Mackay Medical College, New Taipei City, Taiwan
- Mackay Junior College of Medicine, Nursing and Management, Taipei, Taiwan
- Division of Genetics and Metabolism, Department of Medical Research, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan
| | - Shuan-Pei Lin
- Department of Pediatrics, MacKay Memorial Hospital, Taipei, Taiwan
- International Rare Disease Center, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, Mackay Medical College, New Taipei City, Taiwan
- Division of Genetics and Metabolism, Department of Medical Research, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Infant and Child Care, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan
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Cho PJ, Olaye IM, Shandhi MMH, Daza EJ, Foschini L, Dunn JP. Identification of key factors related to digital health observational study adherence and retention by data-driven approaches: an exploratory secondary analysis of two prospective longitudinal studies. Lancet Digit Health 2025; 7:e23-e34. [PMID: 39722250 PMCID: PMC11725373 DOI: 10.1016/s2589-7500(24)00219-x] [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: 06/07/2023] [Revised: 07/03/2024] [Accepted: 09/27/2024] [Indexed: 12/28/2024]
Abstract
BACKGROUND Longitudinal digital health studies combine passively collected information from digital devices, such as commercial wearable devices, and actively contributed data, such as surveys, from participants. Although the use of smartphones and access to the internet supports the development of these studies, challenges exist in collecting representative data due to low adherence and retention. We aimed to identify key factors related to adherence and retention in digital health studies and develop a methodology to identify factors that are associated with and might affect study participant engagement. METHODS In this exploratory secondary analysis, we used data from two separate prospective longitudinal digital health studies, conducted among adult participants (age ≥18 years) during the COVID-19 pandemic by the BIG IDEAs Laboratory (BIL) at Duke University (Durham, NC, USA; April 2, 2020 to May 25, 2021) and Evidation Health (San Mateo, CA, USA; April 4 to Aug 31, 2020). Prospective daily or weekly surveys were administered for up to 15 months in the BIL study and daily surveys were administered for 5 months in the Evidation Health study. We defined metrics related to adherence to assess how participants engage with longitudinal digital health studies and developed models to infer how demographic factors and the day of survey delivery might be associated with these metrics. We defined retention as the time until a participant drops out of the study. For the purpose of clustering analysis, we defined three metrics of survey adherence: (1) total number of surveys completed, (2) participation regularity (ie, frequency of filling out surveys consecutively), and (3) time of activity (ie, engagement pattern relative to enrolment time). We assessed these metrics and explored differences by age, sex, race, and day of survey delivery. We analysed the data by unsupervised clustering, survival analysis, and recurrent event analysis with multistate modelling, with analyses restricted to individuals who provided data on age, sex, and race. FINDINGS In the BIL study, 5784 unique participants with the required demographic data completed 388 600 unique daily surveys (mean 67 [SD 90] surveys per participant). In the Evidation Health study, 89 479 unique participants with the required demographic data completed 2 080 992 unique daily surveys (23 [32] surveys per participant). Participants were grouped into adherence clusters based on the three metrics of adherence, and we identified statistically discernible differences in age, race, and sex between clusters. Most of the individuals aged 18-29 years were observed in the clusters with low or medium adherence, whereas the oldest age group (≥60 years) was generally more represented in clusters with high adherence than younger age groups. For retention, survival analysis indicated that 18-29 years was the age group with the highest risk of exiting the study at any given point in time (BIL study, hazard ratio [HR] for 18-29 years vs ≥60 years, 1·69 [95% CI 1·53-1·86; p<0·0001]; Evidation Health study, HR 1·50 [1·47-1·53; p<0·0001]). Sex and race were not discernible predictors of retention in the BIL study. In the Evidation Health study, male participants (vs female participants; HR 0·96 [0·94-0·98]; p<0·0001) and White participants (vs Asian participants; HR 0·96 [0·93-0·98; p=0·0004) had a lower risk of study exit, and Other race participants (vs Asian participants) had a higher risk of study exit (HR 1·10 [1·06-1·14; p<0·0001]). Recurrent event analysis confirmed age as the factor most associated with adherence; for the 18-29 years age group (vs ≥60 years group), the transition intensity from an active to inactive state per day in the BIL study was 1·661 (95% CI 1·606-1·718) and in the Evidation Health study was 1·108 (1·094-1·121). Participation patterns were variable by race and sex between the studies. INTERPRETATION Our analyses revealed that age was consistently associated with adherence and retention, with younger participants having lower adherence and higher dropout rates than older participants. Unsupervised clustering and survival analyses are established methods in this field, whereas the use of recurrent event analysis, was, to our knowledge, the first instance of the application of this method to remote digital health data. These methods can help to understand participant engagement in digital health studies, supporting targeted measures to improve adherence and retention. FUNDING US National Science Foundation, US National Institutes of Health, Microsoft AI for Health, Duke Clinical and Translational Science Institute, North Carolina Biotechnology Center, Duke MEDx, Duke Bass Connections, Duke Margolis Center for Health Policy, and Duke Office of Information Technology.
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Affiliation(s)
- Peter J Cho
- Biomedical Engineering Department, Duke University, Durham, NC, USA
| | | | | | | | | | - Jessilyn P Dunn
- Biomedical Engineering Department, Duke University, Durham, NC, USA; Biostatistics and Bioinformatics Department, Duke University, Durham, NC, USA.
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Matabuena M, Ghosal A, Meiring W, Petersen A. Predicting distributions of physical activity profiles in the National Health and Nutrition Examination Survey database using a partially linear Fréchet single index model. Biostatistics 2024; 26:kxaf013. [PMID: 40408136 DOI: 10.1093/biostatistics/kxaf013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 04/15/2025] [Accepted: 04/16/2025] [Indexed: 05/25/2025] Open
Abstract
Object-oriented data analysis is a fascinating and evolving field in modern statistical science, with the potential to make significant contributions to biomedical applications. This statistical framework facilitates the development of new methods to analyze complex data objects that capture more information than traditional clinical biomarkers. This paper applies the object-oriented framework to analyze physical activity levels, measured by accelerometers, as response objects in a regression model. Unlike traditional summary metrics, we utilize a recently proposed representation of physical activity data as a distributional object, providing a more nuanced and complete profile of individual energy expenditure across all ranges of monitoring intensity. A novel hybrid Fréchet regression model is proposed and applied to US population accelerometer data from National Health and Nutrition Examination Survey (NHANES) 2011 to 2014. The semi-parametric nature of the model allows for the inclusion of nonlinear effects for critical variables, such as age, which are biologically known to have subtle impacts on physical activity. Simultaneously, the inclusion of linear effects preserves interpretability for other variables, particularly categorical covariates such as ethnicity and sex. The results obtained are valuable from a public health perspective and could lead to new strategies for optimizing physical activity interventions in specific American subpopulations.
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Affiliation(s)
- Marcos Matabuena
- Department of Biostatistics, Harvard University, 677 Huntington Avenue, Building 2, MA 02115, United States
| | - Aritra Ghosal
- Department of Biostatistics, St. Jude Children's Research Hospital, 262 Danny Thomas Place, TN 38105, United States
| | - Wendy Meiring
- Department of Statistics and Applied Probability, University of California, South Hall, Santa Barbara, CA 93106, United States
| | - Alexander Petersen
- Department of Statistics, Brigham Young University, 223 TMCB, UT 84602, United States
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9
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Tang M, Powell J, Li X. Can Wearable Technology Help Guide Dieting Safety? RESEARCH SQUARE 2024:rs.3.rs-5619684. [PMID: 39711534 PMCID: PMC11661416 DOI: 10.21203/rs.3.rs-5619684/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2024]
Abstract
Background The safety of dietary interventions is often unmonitored. Wearable technology can track elevations in resting heart rate (RHR), a marker of physiologic stress, which may provide safety information that is incremental to self-reported data. Methods A single subject was placed on an isocaloric diet for four weeks. In weeks # 1 and 4, timing of food consumption was unregulated. In week #2, food was consumed during a three-hour feeding window (one-meal-a-day, OMAD). During week #3, food was consumed at six intervals, spaced three hours apart (6-meal diet). A Fitbit Versa™ was worn continuously, and questionnaires were administered twice daily. Results Meal frequency did not affect the subject's weight. Hunger scores from morning and night were widely split on OMAD and relatively constant on the 6-meal diet. Energy, happiness, irritability, and sleep scores were more favorable on the 6-meal diet than on OMAD. RHR extracted from the wearable device was lower during the 6-meal diet than during OMAD, especially in the late afternoon, evening, and nighttime (p<0.05). Lower RHR during the 6-meal diet corresponded to more favorable questionnaire scores. Conclusions Changes in RHR patterns acquired by wearable technology are promising indicators of physiologic stress during dietary interventions. Wearable technology can provide physiologic data that are complementary to questionnaire scores or timed manual measurements.
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Affiliation(s)
| | | | - Xiao Li
- Case Western Reserve University
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10
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Liang A, Zhao W, Lv T, Zhu Z, Haotian R, Zhang J, Xie B, Yi Y, Hao Z, Sun L, Luo A. Advances in novel biosensors in biomedical applications. Talanta 2024; 280:126709. [PMID: 39151317 DOI: 10.1016/j.talanta.2024.126709] [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: 12/25/2023] [Revised: 07/09/2024] [Accepted: 08/13/2024] [Indexed: 08/19/2024]
Abstract
Biosensors, devices capable of detecting biomolecules or bioactive substances, have recently become one of the important tools in the fields of bioanalysis and medical diagnostics. A biosensor is an analytical system composed of biosensitive elements and signal-processing elements used to detect various biological and chemical substances. Biomimetic elements are key to biosensor technology and are the components in a sensor that are responsible for identifying the target analyte. The construction methods and working principles of biosensors based on synthetic biomimetic elements, such as DNAzyme, molecular imprinted polymers and aptamers, and their updated applications in biomedical analysis are summarised. Finally, the technical bottlenecks and future development prospects for biomedical analysis are summarised and discussed.
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Affiliation(s)
- Axin Liang
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Weidong Zhao
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Tianjian Lv
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Ziyu Zhu
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Ruilin Haotian
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Jiangjiang Zhang
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Bingteng Xie
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Yue Yi
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Zikai Hao
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Liquan Sun
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China
| | - Aiqin Luo
- Key Laboratory of Molecular Medicine and Biotherapy, The Ministry of Industry and Information Technology, School of Life Science, Beijing Institute of Technology, Beijing, 100081, China.
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11
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Vo DK, Trinh KTL. Advances in Wearable Biosensors for Healthcare: Current Trends, Applications, and Future Perspectives. BIOSENSORS 2024; 14:560. [PMID: 39590019 PMCID: PMC11592256 DOI: 10.3390/bios14110560] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2024] [Revised: 11/15/2024] [Accepted: 11/16/2024] [Indexed: 11/28/2024]
Abstract
Wearable biosensors are a fast-evolving topic at the intersection of healthcare, technology, and personalized medicine. These sensors, which are frequently integrated into clothes and accessories or directly applied to the skin, provide continuous, real-time monitoring of physiological and biochemical parameters such as heart rate, glucose levels, and hydration status. Recent breakthroughs in downsizing, materials science, and wireless communication have greatly improved the functionality, comfort, and accessibility of wearable biosensors. This review examines the present status of wearable biosensor technology, with an emphasis on advances in sensor design, fabrication techniques, and data analysis algorithms. We analyze diverse applications in clinical diagnostics, chronic illness management, and fitness tracking, emphasizing their capacity to transform health monitoring and facilitate early disease diagnosis. Additionally, this review seeks to shed light on the future of wearable biosensors in healthcare and wellness by summarizing existing trends and new advancements.
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Affiliation(s)
- Dang-Khoa Vo
- College of Pharmacy, Gachon University, 191 Hambakmoe-ro, Yeonsu-gu, Incheon 21936, Republic of Korea;
| | - Kieu The Loan Trinh
- BioNano Applications Research Center, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si 13120, Republic of Korea
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12
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Bliss JW, Underwood WP, Carlson AM, Scott JM, Daly R, Li BT, Drilon A, Stetson P, Boutros PC, Jones LW. Case report: dynamic personalized physiological monitoring in lung cancer using wearable data. Front Oncol 2024; 14:1420888. [PMID: 39429470 PMCID: PMC11486691 DOI: 10.3389/fonc.2024.1420888] [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: 04/21/2024] [Accepted: 09/12/2024] [Indexed: 10/22/2024] Open
Abstract
Pretreatment prognostication, on-treatment monitoring, and early detection of physiological symptoms are considerable challenges in cancer. We describe the feasibility of high-resolution wearable data (steps per day, walking speed) to longitudinally profile physiological trajectories extracted from Apple Health data in three patients with lung cancer from diagnosis through cancer treatment after obtaining informed consent. We used descriptive statistics to describe our approach of building longitudinal physiological profiles. The wearable data monitoring period ranged from 58 to 135 weeks, with between 34,319 and 103,535 distinct digital physiological measures collected during this period-the equivalent to 41 measures per day/patient. Longitudinal profiling revealed that wearable data accurately captured physiological changes linked with clinical events such as surgery and hospitalizations as well as initiation (and cessation) of systemic cancer treatment in all three patients. These findings suggest that wearable devices could play a critical role in the management of lung cancer, although larger studies are needed to confirm these preliminary observations and validate their generalizability. Wearable devices hold significant promise for the development of personalized "digital biomarkers," which may enhance risk stratification and management in oncology.
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Affiliation(s)
- Joshua W. Bliss
- New York Presbyterian, Weill Cornell Medicine, New York, NY, United States
| | - Whitney P. Underwood
- Department of Medicine, Division of Solid Tumor Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Adele M. Carlson
- Department of Medicine, Division of Solid Tumor Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Jessica M. Scott
- New York Presbyterian, Weill Cornell Medicine, New York, NY, United States
- Department of Medicine, Division of Solid Tumor Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Robert Daly
- New York Presbyterian, Weill Cornell Medicine, New York, NY, United States
- Department of Medicine, Division of Solid Tumor Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Bob T. Li
- New York Presbyterian, Weill Cornell Medicine, New York, NY, United States
- Department of Medicine, Division of Solid Tumor Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Alexander Drilon
- New York Presbyterian, Weill Cornell Medicine, New York, NY, United States
- Department of Medicine, Division of Solid Tumor Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Peter Stetson
- New York Presbyterian, Weill Cornell Medicine, New York, NY, United States
- Department of Medicine, Division of Solid Tumor Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Paul C. Boutros
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Institute for Precision Health, University of California, Los Angeles, Los Angeles, CA, United States
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA, United States
| | - Lee W. Jones
- New York Presbyterian, Weill Cornell Medicine, New York, NY, United States
- Department of Medicine, Division of Solid Tumor Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
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13
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Wei JCJ, van den Broek TJ, van Baardewijk JU, van Stokkum R, Kamstra RJM, Rikken L, Gijsbertse K, Uzunbajakava NE, van den Brink WJ. Validation and user experience of a dry electrode based Health Patch for heart rate and respiration rate monitoring. Sci Rep 2024; 14:23098. [PMID: 39367187 PMCID: PMC11452725 DOI: 10.1038/s41598-024-73557-8] [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: 11/29/2023] [Accepted: 09/18/2024] [Indexed: 10/06/2024] Open
Abstract
Successful implementation of remote monitoring of vital signs outside of the hospital setting hinges on addressing three crucial unmet needs: longer-term wear, skin comfort and signal quality. Earlier, we developed a Health Patch research platform that uses self-adhesive dry electrodes to measure vital digital biomarkers. Here, we report on the analytical validation for heart rate, heart rate variability and respiration rate. Study design included n = 25 adult participants with data acquisition during a 30-minute exercise protocol involving rest, squats, slow, and fast cycling. The Shimmer3 ECG Unit and Cosmed K5, were reference devices. Data analysis showed good agreement in heart rate and marginal agreement in respiratory rate, with lower agreement towards higher respiratory rates. The Lin's concordance coefficient was 0.98 for heart rate and 0.56 for respiratory rate. Heart rate variability (RMSSD) had a coefficient of 0.85. Participants generally expressed a positive experience with the technology, with some minor irritation from the medical adhesive. The results highlighted potential of this technology for short-to-medium term clinical use for cardiorespiratory health, due to its reliability, accuracy, and compact design. Such technology could become instrumental for remote monitoring providing healthcare professionals with continuous data, remote assessment and enhancing patient outcomes in cardiorespiratory health management.
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Affiliation(s)
- Jonathan C J Wei
- Microbiology & Systems Biology, TNO (Netherlands Organisation for Applied Scientific Research), Leiden, The Netherlands
| | - Tim J van den Broek
- Microbiology & Systems Biology, TNO (Netherlands Organisation for Applied Scientific Research), Leiden, The Netherlands
| | - Jan Ubbo van Baardewijk
- Human Performance, TNO (Netherlands Organisation for Applied Scientific Research), Soesterberg, The Netherlands
| | - Robin van Stokkum
- Risk Analysis for Products in Development, TNO (Netherlands Organisation for Applied Scientific Research), Utrecht, The Netherlands
| | - Regina J M Kamstra
- Microbiology & Systems Biology, TNO (Netherlands Organisation for Applied Scientific Research), Leiden, The Netherlands
| | - Lars Rikken
- Holst Centre, TNO (Netherlands Organisation for Applied Scientific Research), Eindhoven, The Netherlands
| | - Kaj Gijsbertse
- Human Performance, TNO (Netherlands Organisation for Applied Scientific Research), Soesterberg, The Netherlands
| | | | - Willem J van den Brink
- Microbiology & Systems Biology, TNO (Netherlands Organisation for Applied Scientific Research), Leiden, The Netherlands.
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14
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Choi HI, Lee SJ, Choi JD, Kim G, Lee YS, Lee JY. Efficacy of Wearable Single-Lead ECG Monitoring during Exercise Stress Testing: A Comparative Study. SENSORS (BASEL, SWITZERLAND) 2024; 24:6394. [PMID: 39409434 PMCID: PMC11479017 DOI: 10.3390/s24196394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/13/2024] [Revised: 09/27/2024] [Accepted: 09/28/2024] [Indexed: 10/20/2024]
Abstract
BACKGROUND AND OBJECTIVES Few comparative studies have evaluated wearable single-lead electrocardiogram (ECG) devices and standard multi-lead ECG devices during exercise testing. This study aimed to validate the accuracy of a wearable single-lead ECG monitor for recording heart rate (HR) metrics during graded exercise tests (GXTs). METHODS A cohort of 50 patients at a tertiary hospital underwent GXT while simultaneously being equipped with wearable single- and conventional multi-lead ECGs. The concordance between these modalities was quantified using the intraclass correlation coefficient and Bland-Altman plot analysis. RESULTS The minimum and average HR readings between the devices were generally consistent. Parameters such as ventricular ectopic beats and supraventricular ectopic beats showed strong agreement. However, the agreement for the Total QRS and Maximum RR was not sufficient. HR measurements across different stages of the exercise test showed sufficient agreement. Although not statistically significant, the standard multi-lead ECG devices exhibited higher noise levels compared to the wearable single-lead ECG devices. CONCLUSIONS Wearable single-lead ECG devices can reliably monitor HR and detect abnormal beats across a spectrum of exercise intensities, offering a viable alternative to traditional multi-lead systems.
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Affiliation(s)
- Hyo-In Choi
- Division of Cardiology, Department of Internal Medicine, Sungkyunkwan University School of Medicine, Kangbuk Samsung Hospital, Seoul 03181, Republic of Korea; (H.-I.C.); (S.J.L.)
| | - Seung Jae Lee
- Division of Cardiology, Department of Internal Medicine, Sungkyunkwan University School of Medicine, Kangbuk Samsung Hospital, Seoul 03181, Republic of Korea; (H.-I.C.); (S.J.L.)
| | - Jong Doo Choi
- Seers Technology Co., Ltd., Seongnam-si 13558, Republic of Korea; (J.D.C.); (G.K.); (Y.-S.L.)
| | - GyungChul Kim
- Seers Technology Co., Ltd., Seongnam-si 13558, Republic of Korea; (J.D.C.); (G.K.); (Y.-S.L.)
| | - Young-Shin Lee
- Seers Technology Co., Ltd., Seongnam-si 13558, Republic of Korea; (J.D.C.); (G.K.); (Y.-S.L.)
| | - Jong-Young Lee
- Division of Cardiology, Department of Internal Medicine, Sungkyunkwan University School of Medicine, Kangbuk Samsung Hospital, Seoul 03181, Republic of Korea; (H.-I.C.); (S.J.L.)
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15
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Griffin AC, Mentch L, Lin FC, Chung AE. mHealth Physical Activity and Patient-Reported Outcomes in Patients With Inflammatory Bowel Diseases: Cluster Analysis. J Med Internet Res 2024; 26:e48020. [PMID: 39316795 PMCID: PMC11462094 DOI: 10.2196/48020] [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: 04/08/2023] [Revised: 06/05/2024] [Accepted: 07/03/2024] [Indexed: 09/26/2024] Open
Abstract
BACKGROUND Regular physical activity is associated with improved quality of life in patients with inflammatory bowel diseases (IBDs), although much of the existing research is based on self-reported data. Wearable devices provide objective data on many rich physical activity dimensions including steps, duration, distance, and intensity. Little is known about how patients with IBDs engage in these varying dimensions of exercise and how it may influence their symptom and disease-specific patient-reported outcomes (PROs). OBJECTIVE This study aims to (1) cluster physical activity patterns from consumer-grade wearable devices and (2) assess the relationship between the clusters and PROs in patients with IBDs. METHODS We conducted a cross-sectional and longitudinal cohort study among adults with IBDs in the Crohn's and Colitis Foundation IBD Partners cohort. Participants contribute physical activity data through smartphone apps or wearable devices in a bring-your-own-device model. Participants also complete biannual PRO questionnaires from the Patient-Reported Outcomes Measurement Information System short forms and IBD-specific questionnaires. K-means cluster analysis was used to generate physical activity clusters based on 3 key features: number of steps, duration of moderate to vigorous activity (minutes), and distance of activity (miles). Based on the clusters, we conducted a cross-sectional analysis to examine differences in mean questionnaire scores and participant characteristics using one-way ANOVA and chi-square tests. We also conducted a longitudinal analysis to examine individual cluster transitions among participants who completed multiple questionnaires, and mean differences in questionnaire scores were compared using 2-tailed paired sample t tests across 6-month periods. RESULTS Among 430 participants comprising 1255 six-week physical activity periods, we identified clusters of low (33.7%, n=423), moderate (46%, n=577), and high (20.3%, n=255) physical activity. Scores varied across clusters for depression (P=.004), pain interference (P<.001), fatigue (P<.001), sleep disturbance (P<.001), social satisfaction (P<.001), and short Crohn Disease Activity Index (P<.001), with those in the low activity cluster having the worst scores. Sociodemographic characteristics also differed, and those with low physical activity were older (P=.002), had higher BMIs (P<.001), and had longer disease durations (P=.02) compared to other clusters. Among 246 participants who completed at least 2 consecutive questionnaires consisting of 726 questionnaire periods, 67.8% (n=492) remained in the same cluster, and only 1.2% (n=9) moved to or from the furthest clusters of low and high activity across 6-month periods. CONCLUSIONS For patients with IBDs, there were positive associations between physical activity and PROs related to disease activity and psychosocial domains. Physical activity patterns mostly did not fluctuate over time, suggesting little variation in exercise levels in the absence of an intervention. The use of real-world data to identify subgroups with similar lifestyle behaviors could be leveraged to develop targeted interventions that provide support for psychosocial symptoms and physical activity for personalized IBD care.
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Affiliation(s)
- Ashley C Griffin
- VA Palo Alto Health Care System, Palo Alto, CA, United States
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, United States
| | - Lucas Mentch
- Department of Statistics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Feng-Chang Lin
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Arlene E Chung
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, United States
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16
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Park H, Metwally AA, Delfarah A, Wu Y, Perelman D, Rodgar M, Mayer C, Celli A, McLaughlin T, Mignot E, Snyder M. Lifestyle Profiling Using Wearables and Prediction of Glucose Metabolism in Individuals with Normoglycemia or Prediabetes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.09.05.24312545. [PMID: 39281757 PMCID: PMC11398605 DOI: 10.1101/2024.09.05.24312545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/18/2024]
Abstract
This study examined the relationship between lifestyles (diet, sleep, and physical activity) and glucose responses at a personal level. 36 healthy adults in the Bay Area were monitored for their lifestyles and glucose levels using wearables and continuous glucose monitoring (NCT03919877). Gold-standard metabolic tests were conducted to phenotype metabolic characteristics. Through the lifestyle data (2,307 meals, 1,809 nights, and 2,447 days) and 231,206 CGM readings from metabolically-phenotyped individuals with normoglycemia or prediabetes, we found: 1) eating timing was associated with hyperglycemia, muscle insulin resistance (IR), and incretin dysfunction, whereas nutrient intakes were not; 2) timing of increased activity in muscle IS and IR participants was associated with differential benefits of glucose control; 3) Integrated ML models using lifestyle factors predicted distinct metabolic characteristics (muscle, adipose IR or incretin dysfunction). Our data indicate the differential impact of lifestyles on glucose regulation among individuals with different metabolic phenotypes, highlighting the value of personalized lifestyle modifications.
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Affiliation(s)
- Heyjun Park
- Department of Genetics, Stanford University, Stanford, CA 94305, U.S.A
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, MD 21205, U.S.A
| | - Ahmed A. Metwally
- Department of Genetics, Stanford University, Stanford, CA 94305, U.S.A
| | - Alireza Delfarah
- Department of Genetics, Stanford University, Stanford, CA 94305, U.S.A
| | - Yue Wu
- Department of Genetics, Stanford University, Stanford, CA 94305, U.S.A
| | - Dalia Perelman
- Department of Genetics, Stanford University, Stanford, CA 94305, U.S.A
| | - Majid Rodgar
- Department of Genetics, Stanford University, Stanford, CA 94305, U.S.A
| | - Caleb Mayer
- Department of Genetics, Stanford University, Stanford, CA 94305, U.S.A
| | - Alessandra Celli
- Department of Genetics, Stanford University, Stanford, CA 94305, U.S.A
| | - Tracey McLaughlin
- Department of Medicine, Stanford University, Stanford, CA 94305, U.S.A
| | - Emmanuel Mignot
- Center for Sleep, Sciences and Medicine, Stanford University School of Medicine, Palo Alto, CA 94304, U.S.A
| | - Michael Snyder
- Department of Genetics, Stanford University, Stanford, CA 94305, U.S.A
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17
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AbiMansour JP, Kaur J, Velaga S, Vatsavayi P, Vogt M, Chandrasekhara V. Accuracy and role of consumer facing wearable technology for continuous monitoring during endoscopic procedures. Front Digit Health 2024; 6:1422929. [PMID: 39355612 PMCID: PMC11443421 DOI: 10.3389/fdgth.2024.1422929] [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: 04/24/2024] [Accepted: 08/22/2024] [Indexed: 10/03/2024] Open
Abstract
Background Consumer facing wearable devices capture significant amounts of biometric data. The primary aim of this study is to determine the accuracy of consumer-facing wearable technology for continuous monitoring compared to standard anesthesia monitoring during endoscopic procedures. Secondary aims were to assess patient and provider perceptions of these devices in clinical settings. Methods Patients undergoing endoscopy with anesthesia support from June 2021 to June 2022 were provided a smartwatch (Apple Watch Series 7, Apple Inc., Cupertino, CA) and accessories including continuous ECG monitor and pulse oximeter (Qardio Inc., San Francisco, CA) for the duration of their procedure. Vital sign data from the wearable devices was compared to in-room anesthesia monitors. Concordance with anesthesia monitoring was assessed with interclass correlation coefficients (ICC). Surveys were then distributed to patients and clinicians to assess patient and provider preferences regarding the use of the wearable devices during procedures. Results 292 unique procedures were enrolled with a median anesthesia duration of 34 min (IQR 25-47). High fidelity readings were successfully recorded with wearable devices for heart rate in 279 (95.5%) cases, oxygen in 203 (69.5%), and respiratory rate in 154 (52.7%). ICCs for watch and accessories were 0.54 (95% CI 0.46-0.62) for tachycardia, 0.03 (95% CI 0-0.14) for bradycardia, and 0.33 (0.22-0.43) for oxygen desaturation. Patients generally felt the devices were more accurate (56.3% vs. 20.0% agree, p < 0.001) and more permissible (53.9% vs. 33.3% agree, p < 0.001) to wear during a procedure than providers. Conclusion Smartwatches perform poorly for continuous data collection compared to gold standard anesthesia monitoring. Refinement in software development is required if these devices are to be used for continuous, intensive vital sign monitoring.
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Affiliation(s)
- Jad P AbiMansour
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, United States
| | - Jyotroop Kaur
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, United States
| | - Saran Velaga
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, United States
| | - Priyanka Vatsavayi
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, United States
| | - Matthew Vogt
- Department of Anesthesia and Perioperative Medicine, Mayo Clinic, Rochester, MN, United States
| | - Vinay Chandrasekhara
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, United States
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18
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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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19
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Zhu Y, Aimandi NB, Ul Alam MA. Enhancing Wearable based Real-Time Glucose Monitoring via Phasic Image Representation Learning based Deep Learning. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2024; 2024:1-4. [PMID: 40039837 DOI: 10.1109/embc53108.2024.10781542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2025]
Abstract
In the U.S., over a third of adults are pre-diabetic, with 80% unaware of their status. This underlines the need for better glucose monitoring to prevent type 2 diabetes and related heart diseases. Existing wearable glucose monitors are limited by the lack of models trained on small datasets, as collecting extensive glucose data is often costly and impractical. Our study introduces a novel machine learning method using modified recurrence plots in the frequency domain to improve glucose level prediction accuracy from wearable device data, even with limited datasets. This technique combines advanced signal processing with machine learning to extract more meaningful features. We tested our method against existing models using historical data, showing that our approach surpasses the current 87% accuracy benchmark in predicting real-time interstitial glucose levels.
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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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21
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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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22
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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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23
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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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24
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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: 6] [Impact Index Per Article: 6.0] [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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25
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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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26
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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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27
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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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28
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Allen B. The Promise of Explainable AI in Digital Health for Precision Medicine: A Systematic Review. J Pers Med 2024; 14:277. [PMID: 38541019 PMCID: PMC10971237 DOI: 10.3390/jpm14030277] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 02/14/2024] [Accepted: 02/24/2024] [Indexed: 03/26/2025] Open
Abstract
This review synthesizes the literature on explaining machine-learning models for digital health data in precision medicine. As healthcare increasingly tailors treatments to individual characteristics, the integration of artificial intelligence with digital health data becomes crucial. Leveraging a topic-modeling approach, this paper distills the key themes of 27 journal articles. We included peer-reviewed journal articles written in English, with no time constraints on the search. A Google Scholar search, conducted up to 19 September 2023, yielded 27 journal articles. Through a topic-modeling approach, the identified topics encompassed optimizing patient healthcare through data-driven medicine, predictive modeling with data and algorithms, predicting diseases with deep learning of biomedical data, and machine learning in medicine. This review delves into specific applications of explainable artificial intelligence, emphasizing its role in fostering transparency, accountability, and trust within the healthcare domain. Our review highlights the necessity for further development and validation of explanation methods to advance precision healthcare delivery.
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Affiliation(s)
- Ben Allen
- Department of Psychology, University of Kansas, Lawrence, KS 66045, USA
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29
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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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30
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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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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: 42] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [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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32
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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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Chen SF, Loguercio S, Chen KY, Lee SE, Park JB, Liu S, Sadaei HJ, Torkamani A. Artificial Intelligence for Risk Assessment on Primary Prevention of Coronary Artery Disease. CURRENT CARDIOVASCULAR RISK REPORTS 2023; 17:215-231. [DOI: 10.1007/s12170-023-00731-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/09/2023] [Indexed: 01/04/2025]
Abstract
Abstract
Purpose of Review
Coronary artery disease (CAD) is a common and etiologically complex disease worldwide. Current guidelines for primary prevention, or the prevention of a first acute event, include relatively simple risk assessment and leave substantial room for improvement both for risk ascertainment and selection of prevention strategies. Here, we review how advances in big data and predictive modeling foreshadow a promising future of improved risk assessment and precision medicine for CAD.
Recent Findings
Artificial intelligence (AI) has improved the utility of high dimensional data, providing an opportunity to better understand the interplay between numerous CAD risk factors. Beyond applications of AI in cardiac imaging, the vanguard application of AI in healthcare, recent translational research is also revealing a promising path for AI in multi-modal risk prediction using standard biomarkers, genetic and other omics technologies, a variety of biosensors, and unstructured data from electronic health records (EHRs). However, gaps remain in clinical validation of AI models, most notably in the actionability of complex risk prediction for more precise therapeutic interventions.
Summary
The recent availability of nation-scale biobank datasets has provided a tremendous opportunity to richly characterize longitudinal health trajectories using health data collected at home, at laboratories, and through clinic visits. The ever-growing availability of deep genotype-phenotype data is poised to drive a transition from simple risk prediction algorithms to complex, “data-hungry,” AI models in clinical decision-making. While AI models provide the means to incorporate essentially all risk factors into comprehensive risk prediction frameworks, there remains a need to wrap these predictions in interpretable frameworks that map to our understanding of underlying biological mechanisms and associated personalized intervention. This review explores recent advances in the role of machine learning and AI in CAD primary prevention and highlights current strengths as well as limitations mediating potential future applications.
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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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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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36
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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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37
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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: 2] [Impact Index Per Article: 1.0] [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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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: 41] [Impact Index Per Article: 20.5] [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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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: 0.5] [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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40
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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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41
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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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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 PMCID: PMC10300851 DOI: 10.3390/s23125752] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [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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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: 6] [Impact Index Per Article: 3.0] [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: 107] [Impact Index Per Article: 53.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [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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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: 2] [Impact Index Per Article: 1.0] [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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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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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: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/28/2023]
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50
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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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