1
|
Köhler C, Bartschke A, Fürstenau D, Schaaf T, Salgado-Baez E. The Value of Smartwatches in the Healthcare Sector for Monitoring, Nudging, and Predicting: Viewpoint on 25 Years of Research. J Med Internet Res 2024. [PMID: 39356287 DOI: 10.2196/58936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/03/2024] Open
Abstract
UNSTRUCTURED We propose a categorization of smartwatch use in the healthcare sector into three key functional domains: monitoring, nudging, and predicting. Monitoring involves using smartwatches within medical treatments to track health data, nudging pertains to individual use for health purposes outside a particular medical setting, and predicting involves aggregated user data to train machine learning algorithms to predict health outcomes. Each domain offers unique contributions to healthcare, yet there is a lack of nuanced discussion in existing research. Our paper not only provides an overview of recent technological advancements in consumer smartwatches but also explores the three domains in detail, culminating in a comprehensive summary that anticipates the future value and impact of smartwatches in healthcare. By dissecting the interconnected challenges and potentials, we aim to enhance the understanding and effective deployment of smartwatches in value-based healthcare.
Collapse
Affiliation(s)
- Charlotte Köhler
- Department for Data Science & Decision Support, European University Viadrina, Frankfurt (Oder), DE
| | - Alexander Bartschke
- Core Unit Digital Medicine & Interoperability, Berlin Institute of Health @ Charité, Charité - Universitätsmedizin Berlin, Berlin, DE
| | - Daniel Fürstenau
- Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Charitéplatz 1, Berlin, DE
- School of Business & Economics, Freie Universität Berlin, Berlin, DE
| | - Thorsten Schaaf
- Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Charitéplatz 1, Berlin, DE
| | - Eduardo Salgado-Baez
- Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Charitéplatz 1, Berlin, DE
- Department of Anesthesiology & Intensive Care Medicine, Charité - Universitätsmedizin Berlin, Berlin, DE
| |
Collapse
|
2
|
de Zambotti M, Goldstein C, Cook J, Menghini L, Altini M, Cheng P, Robillard R. State of the science and recommendations for using wearable technology in sleep and circadian research. Sleep 2024; 47:zsad325. [PMID: 38149978 DOI: 10.1093/sleep/zsad325] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 12/21/2023] [Indexed: 12/28/2023] Open
Abstract
Wearable sleep-tracking technology is of growing use in the sleep and circadian fields, including for applications across other disciplines, inclusive of a variety of disease states. Patients increasingly present sleep data derived from their wearable devices to their providers and the ever-increasing availability of commercial devices and new-generation research/clinical tools has led to the wide adoption of wearables in research, which has become even more relevant given the discontinuation of the Philips Respironics Actiwatch. Standards for evaluating the performance of wearable sleep-tracking devices have been introduced and the available evidence suggests that consumer-grade devices exceed the performance of traditional actigraphy in assessing sleep as defined by polysomnogram. However, clear limitations exist, for example, the misclassification of wakefulness during the sleep period, problems with sleep tracking outside of the main sleep bout or nighttime period, artifacts, and unclear translation of performance to individuals with certain characteristics or comorbidities. This is of particular relevance when person-specific factors (like skin color or obesity) negatively impact sensor performance with the potential downstream impact of augmenting already existing healthcare disparities. However, wearable sleep-tracking technology holds great promise for our field, given features distinct from traditional actigraphy such as measurement of autonomic parameters, estimation of circadian features, and the potential to integrate other self-reported, objective, and passively recorded health indicators. Scientists face numerous decision points and barriers when incorporating traditional actigraphy, consumer-grade multi-sensor devices, or contemporary research/clinical-grade sleep trackers into their research. Considerations include wearable device capabilities and performance, target population and goals of the study, wearable device outputs and availability of raw and aggregate data, and data extraction, processing, and analysis. Given the difficulties in the implementation and utilization of wearable sleep-tracking technology in real-world research and clinical settings, the following State of the Science review requested by the Sleep Research Society aims to address the following questions. What data can wearable sleep-tracking devices provide? How accurate are these data? What should be taken into account when incorporating wearable sleep-tracking devices into research? These outstanding questions and surrounding considerations motivated this work, outlining practical recommendations for using wearable technology in sleep and circadian research.
Collapse
Affiliation(s)
- Massimiliano de Zambotti
- Center for Health Sciences, SRI International, Menlo Park, CA, USA
- Lisa Health Inc., Oakland, CA, USA
| | - Cathy Goldstein
- Sleep Disorders Center, Department of Neurology, University of Michigan-Ann Arbor, Ann Arbor, MI, USA
| | - Jesse Cook
- Department of Psychology, University of Wisconsin-Madison, Madison, WI, USA
| | - Luca Menghini
- Department of Psychology and Cognitive Science, University of Trento, Trento, Italy
| | - Marco Altini
- Department of Human Movement Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Philip Cheng
- Sleep Disorders and Research Center, Henry Ford Health, Detroit, MI, USA
| | - Rebecca Robillard
- School of Psychology, University of Ottawa, Ottawa, ON, Canada
- Canadian Sleep Research Consortium, Canada
| |
Collapse
|
3
|
Zeng Z, Li L, Hu L, Wang K, Li L. Smartwatch measurement of blood oxygen saturation for predicting acute mountain sickness: Diagnostic accuracy and reliability. Digit Health 2024; 10:20552076241284910. [PMID: 39351311 PMCID: PMC11440541 DOI: 10.1177/20552076241284910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2024] [Accepted: 09/02/2024] [Indexed: 10/04/2024] Open
Abstract
Objective This study aims to assess the accuracy and stability of smartwatches in predicting acute mountain sickness (AMS). Methods In locations exceeding an altitude of 2500 m, a cohort of 42 subjects had their Lake Louise AMS self-assessment score, blood oxygen saturation (SpO2), heart rate, and perfusion index measured using smartwatches, with the data seamlessly conveyed to the Huawei Cloud. Results A significant decrease in SpO2 was observed in individuals positive for AMS compared to those negative (p < 0.05), with the mild AMS group exhibiting significantly lower SpO2 levels than the non-AMS group (p < 0.05). Furthermore, SpO2 emerged as a significant, independent predictor of AMS [β=-0.086, p < 0.01, OR (95% CI) = 0.92 (0.87-0.97)], indicating that each unit increase in SpO2 decreases the probability of AMS occurrence by 8.6%. Conclusion The Huawei smartwatches have demonstrated efficacy in diagnosing and foretelling AMS at elevations exceeding 4000 m, showcasing significant reliability and high precision in SpO2 measurement.
Collapse
Affiliation(s)
- Zhengyang Zeng
- Department of Physical Education, Chengdu Technological University, Yibin, Sichuan, China
- School of Physical Education, China University of Geosciences (Wuhan), Hubei, China
| | - Lili Li
- School of Physical Education, China University of Geosciences (Wuhan), Hubei, China
| | - Li'ao Hu
- School of Physical Education, China University of Geosciences (Wuhan), Hubei, China
| | - Kang Wang
- School of Physical Education, China University of Geosciences (Wuhan), Hubei, China
| | - Lun Li
- School of Physical Education, China University of Geosciences (Wuhan), Hubei, China
| |
Collapse
|
4
|
Charlton PH, Allen J, Bailón R, Baker S, Behar JA, Chen F, Clifford GD, Clifton DA, Davies HJ, Ding C, Ding X, Dunn J, Elgendi M, Ferdoushi M, Franklin D, Gil E, Hassan MF, Hernesniemi J, Hu X, Ji N, Khan Y, Kontaxis S, Korhonen I, Kyriacou PA, Laguna P, Lázaro J, Lee C, Levy J, Li Y, Liu C, Liu J, Lu L, Mandic DP, Marozas V, Mejía-Mejía E, Mukkamala R, Nitzan M, Pereira T, Poon CCY, Ramella-Roman JC, Saarinen H, Shandhi MMH, Shin H, Stansby G, Tamura T, Vehkaoja A, Wang WK, Zhang YT, Zhao N, Zheng D, Zhu T. The 2023 wearable photoplethysmography roadmap. Physiol Meas 2023; 44:111001. [PMID: 37494945 PMCID: PMC10686289 DOI: 10.1088/1361-6579/acead2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 04/04/2023] [Accepted: 07/26/2023] [Indexed: 07/28/2023]
Abstract
Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.
Collapse
Affiliation(s)
- Peter H Charlton
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, CB1 8RN, United Kingdom
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
| | - John Allen
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5RW, United Kingdom
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
| | - Raquel Bailón
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Stephanie Baker
- College of Science and Engineering, James Cook University, Cairns, 4878 Queensland, Australia
| | - Joachim A Behar
- Faculty of Biomedical Engineering, Technion Israel Institute of Technology, Haifa, 3200003, Israel
| | - Fei Chen
- Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, 518055 Guandong, People’s Republic of China
| | - Gari D Clifford
- Department of Biomedical Informatics, Emory University, Atlanta, GA 30322, United States of America
- Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States of America
| | - David A Clifton
- Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, United Kingdom
| | - Harry J Davies
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, United Kingdom
| | - Cheng Ding
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States of America
- Department of Biomedical Engineering, Emory University, Atlanta, GA 30322, United States of America
| | - Xiaorong Ding
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, People’s Republic of China
| | - Jessilyn Dunn
- Department of Biomedical Engineering, Duke University, Durham, NC 27708-0187, United States of America
- Department of Biostatistics & Bioinformatics, Duke University, Durham, NC 27708-0187, United States of America
- Duke Clinical Research Institute, Durham, NC 27705-3976, United States of America
| | - Mohamed Elgendi
- Biomedical and Mobile Health Technology Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, 8008, Switzerland
| | - Munia Ferdoushi
- Department of Electrical and Computer Engineering, University of Southern California, 90089, Los Angeles, California, United States of America
- The Institute for Technology and Medical Systems (ITEMS), Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America
| | - Daniel Franklin
- Institute of Biomedical Engineering, Translational Biology & Engineering Program, Ted Rogers Centre for Heart Research, University of Toronto, Toronto, M5G 1M1, Canada
| | - Eduardo Gil
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Md Farhad Hassan
- Department of Electrical and Computer Engineering, University of Southern California, 90089, Los Angeles, California, United States of America
- The Institute for Technology and Medical Systems (ITEMS), Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America
| | - Jussi Hernesniemi
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33720, Finland
- Tampere Heart Hospital, Wellbeing Services County of Pirkanmaa, Tampere, 33520, Finland
| | - Xiao Hu
- Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, 30322, Georgia, United States of America
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, 30322, Georgia, United States of America
- Department of Computer Sciences, College of Arts and Sciences, Emory University, Atlanta, GA 30322, United States of America
| | - Nan Ji
- Hong Kong Center for Cerebrocardiovascular Health Engineering (COCHE), Hong Kong Science and Technology Park, Hong Kong, 999077, People’s Republic of China
| | - Yasser Khan
- Department of Electrical and Computer Engineering, University of Southern California, 90089, Los Angeles, California, United States of America
- The Institute for Technology and Medical Systems (ITEMS), Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, United States of America
| | - Spyridon Kontaxis
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Ilkka Korhonen
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33720, Finland
| | - Panicos A Kyriacou
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
| | - Pablo Laguna
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Jesús Lázaro
- Biomedical Signal Interpretation and Computational Simulation (BSICoS) Group, Aragon Institute of Engineering Research (I3A), IIS Aragon, University of Zaragoza, E-50018 Zaragoza, Spain
- CIBER-BBN, Instituto de Salud Carlos III, C/Monforte de Lemos 3-5, E-28029 Madrid, Spain
| | - Chungkeun Lee
- Digital Health Devices Division, Medical Device Evaluation Department, National Institute of Food and Drug Safety Evaluation, Ministry of Food and Drug Safety, Cheongju, 28159, Republic of Korea
| | - Jeremy Levy
- Faculty of Biomedical Engineering, Technion Israel Institute of Technology, Haifa, 3200003, Israel
- Faculty of Electrical and Computer Engineering, Technion Institute of Technology, Haifa, 3200003, Israel
| | - Yumin Li
- State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, People’s Republic of China
| | - Chengyu Liu
- State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, People’s Republic of China
| | - Jing Liu
- Analog Devices Inc, San Jose, CA 95124, United States of America
| | - Lei Lu
- Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, United Kingdom
| | - Danilo P Mandic
- Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, United Kingdom
| | - Vaidotas Marozas
- Department of Electronics Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania
- Biomedical Engineering Institute, Kaunas University of Technology, 44249 Kaunas, Lithuania
| | - Elisa Mejía-Mejía
- Research Centre for Biomedical Engineering, City, University of London, London, EC1V 0HB, United Kingdom
| | - Ramakrishna Mukkamala
- Department of Bioengineering and Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Meir Nitzan
- Department of Physics/Electro-Optic Engineering, Lev Academic Center, 91160 Jerusalem, Israel
| | - Tania Pereira
- INESC TEC—Institute for Systems and Computer Engineering, Technology and Science, Porto, 4200-465, Portugal
- Faculty of Engineering, University of Porto, Porto, 4200-465, Portugal
| | | | - Jessica C Ramella-Roman
- Department of Biomedical Engineering and Herbert Wertheim College of Medicine, Florida International University, Miami, FL 33174, United States of America
| | - Harri Saarinen
- Tampere Heart Hospital, Wellbeing Services County of Pirkanmaa, Tampere, 33520, Finland
| | - Md Mobashir Hasan Shandhi
- Department of Biomedical Engineering, Duke University, Durham, NC 27708-0187, United States of America
| | - Hangsik Shin
- Department of Digital Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, 05505, Republic of Korea
| | - Gerard Stansby
- Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
- Northern Vascular Centre, Freeman Hospital, Newcastle upon Tyne, NE7 7DN, United Kingdom
| | - Toshiyo Tamura
- Future Robotics Organization, Waseda University, Tokyo, 1698050, Japan
| | - Antti Vehkaoja
- Finnish Cardiovascular Research Center Tampere, Faculty of Medicine and Health Technology, Tampere University, Tampere, 33720, Finland
- PulseOn Ltd, Espoo, 02150, Finland
| | - Will Ke Wang
- Department of Biomedical Engineering, Duke University, Durham, NC 27708-0187, United States of America
| | - Yuan-Ting Zhang
- Hong Kong Center for Cerebrocardiovascular Health Engineering (COCHE), Hong Kong Science and Technology Park, Hong Kong, 999077, People’s Republic of China
- Department of Biomedical Engineering, City University of Hong Kong, Hong Kong, 999077, People’s Republic of China
| | - Ni Zhao
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong
| | - Dingchang Zheng
- Research Centre for Intelligent Healthcare, Coventry University, Coventry, CV1 5RW, United Kingdom
| | - Tingting Zhu
- Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, United Kingdom
| |
Collapse
|
5
|
Bin KJ, De Pretto LR, Sanchez FB, De Souza E Castro FPM, Ramos VD, Battistella LR. Digital Platform for Continuous Monitoring of Patients Using a Smartwatch: Longitudinal Prospective Cohort Study. JMIR Form Res 2023; 7:e47388. [PMID: 37698916 PMCID: PMC10523215 DOI: 10.2196/47388] [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: 03/17/2023] [Revised: 08/01/2023] [Accepted: 08/02/2023] [Indexed: 09/13/2023] Open
Abstract
BACKGROUND Since the COVID-19 pandemic, there has been a boost in the digital transformation of the human society, where wearable devices such as a smartwatch can already measure vital signs in a continuous and naturalistic way; however, the security and privacy of personal data is a challenge to expanding the use of these data by health professionals in clinical follow-up for decision-making. Similar to the European General Data Protection Regulation, in Brazil, the Lei Geral de Proteção de Dados established rules and guidelines for the processing of personal data, including those used for patient care, such as those captured by smartwatches. Thus, in any telemonitoring scenario, there is a need to comply with rules and regulations, making this issue a challenge to overcome. OBJECTIVE This study aimed to build a digital solution model for capturing data from wearable devices and making them available in a safe and agile manner for clinical and research use, following current laws. METHODS A functional model was built following the Brazilian Lei Geral de Proteção de Dados (2018), where data captured by smartwatches can be transmitted anonymously over the Internet of Things and be identified later within the hospital. A total of 80 volunteers were selected for a 24-week follow-up clinical trial divided into 2 groups, one group with a previous diagnosis of COVID-19 and a control group without a previous diagnosis of COVID-19, to measure the synchronization rate of the platform with the devices and the accuracy and precision of the smartwatch in out-of-hospital conditions to simulate remote monitoring at home. RESULTS In a 35-week clinical trial, >11.2 million records were collected with no system downtime; 66% of continuous beats per minute were synchronized within 24 hours (79% within 2 days and 91% within a week). In the limit of agreement analysis, the mean differences in oxygen saturation, diastolic blood pressure, systolic blood pressure, and heart rate were -1.280% (SD 5.679%), -1.399 (SD 19.112) mm Hg, -1.536 (SD 24.244) mm Hg, and 0.566 (SD 3.114) beats per minute, respectively. Furthermore, there was no difference in the 2 study groups in terms of data analysis (neither using the smartwatch nor the gold-standard devices), but it is worth mentioning that all volunteers in the COVID-19 group were already cured of the infection and were highly functional in their daily work life. CONCLUSIONS On the basis of the results obtained, considering the validation conditions of accuracy and precision and simulating an extrahospital use environment, the functional model built in this study is capable of capturing data from the smartwatch and anonymously providing it to health care services, where they can be treated according to the legislation and be used to support clinical decisions during remote monitoring.
Collapse
Affiliation(s)
- Kaio Jia Bin
- Instituto de Medicina Física e Reabilitação, Hospital das Clínicas, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Lucas Ramos De Pretto
- Instituto de Medicina Física e Reabilitação, Hospital das Clínicas, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Fábio Beltrame Sanchez
- Instituto de Medicina Física e Reabilitação, Hospital das Clínicas, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | | | - Vinicius Delgado Ramos
- Instituto de Medicina Física e Reabilitação, Hospital das Clínicas, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| | - Linamara Rizzo Battistella
- Instituto de Medicina Física e Reabilitação, Hospital das Clínicas, Faculdade de Medicina da Universidade de São Paulo, São Paulo, Brazil
| |
Collapse
|
6
|
Liu JH, Shih CY, Huang HL, Peng JK, Cheng SY, Tsai JS, Lai F. Evaluating the Potential of Machine Learning and Wearable Devices in End-of-Life Care in Predicting 7-Day Death Events Among Patients With Terminal Cancer: Cohort Study. J Med Internet Res 2023; 25:e47366. [PMID: 37594793 PMCID: PMC10474512 DOI: 10.2196/47366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 07/02/2023] [Accepted: 07/14/2023] [Indexed: 08/19/2023] Open
Abstract
BACKGROUND An accurate prediction of mortality in end-of-life care is crucial but presents challenges. Existing prognostic tools demonstrate moderate performance in predicting survival across various time frames, primarily in in-hospital settings and single-time evaluations. However, these tools may fail to capture the individualized and diverse trajectories of patients. Limited evidence exists regarding the use of artificial intelligence (AI) and wearable devices, specifically among patients with cancer at the end of life. OBJECTIVE This study aimed to investigate the potential of using wearable devices and AI to predict death events among patients with cancer at the end of life. Our hypothesis was that continuous monitoring through smartwatches can offer valuable insights into the progression of patients at the end of life and enable the prediction of changes in their condition, which could ultimately enhance personalized care, particularly in outpatient or home care settings. METHODS This prospective study was conducted at the National Taiwan University Hospital. Patients diagnosed with cancer and receiving end-of-life care were invited to enroll in wards, outpatient clinics, and home-based care settings. Each participant was given a smartwatch to collect physiological data, including steps taken, heart rate, sleep time, and blood oxygen saturation. Clinical assessments were conducted weekly. The participants were followed until the end of life or up to 52 weeks. With these input features, we evaluated the prediction performance of several machine learning-based classifiers and a deep neural network in 7-day death events. We used area under the receiver operating characteristic curve (AUROC), F1-score, accuracy, and specificity as evaluation metrics. A Shapley additive explanations value analysis was performed to further explore the models with good performance. RESULTS From September 2021 to August 2022, overall, 1657 data points were collected from 40 patients with a median survival time of 34 days, with the detection of 28 death events. Among the proposed models, extreme gradient boost (XGBoost) yielded the best result, with an AUROC of 96%, F1-score of 78.5%, accuracy of 93%, and specificity of 97% on the testing set. The Shapley additive explanations value analysis identified the average heart rate as the most important feature. Other important features included steps taken, appetite, urination status, and clinical care phase. CONCLUSIONS We demonstrated the successful prediction of patient deaths within the next 7 days using a combination of wearable devices and AI. Our findings highlight the potential of integrating AI and wearable technology into clinical end-of-life care, offering valuable insights and supporting clinical decision-making for personalized patient care. It is important to acknowledge that our study was conducted in a relatively small cohort; thus, further research is needed to validate our approach and assess its impact on clinical care. TRIAL REGISTRATION ClinicalTrials.gov NCT05054907; https://classic.clinicaltrials.gov/ct2/show/NCT05054907.
Collapse
Affiliation(s)
- Jen-Hsuan Liu
- Department of Family Medicine, National Taiwan University Hospital Hsin-Chu Branch, Hsin-Chu, Taiwan
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
- Department of Family Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
| | - Chih-Yuan Shih
- Department of Family Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
- Department of Family Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Hsien-Liang Huang
- Department of Family Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
- Department of Family Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Jen-Kuei Peng
- Department of Family Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
- Department of Family Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Shao-Yi Cheng
- Department of Family Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
- Department of Family Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Jaw-Shiun Tsai
- Department of Family Medicine, National Taiwan University Hospital, National Taiwan University, Taipei, Taiwan
- Department of Family Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Feipei Lai
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan
| |
Collapse
|
7
|
Petek BJ, Al-Alusi MA, Moulson N, Grant AJ, Besson C, Guseh JS, Wasfy MM, Gremeaux V, Churchill TW, Baggish AL. Consumer Wearable Health and Fitness Technology in Cardiovascular Medicine: JACC State-of-the-Art Review. J Am Coll Cardiol 2023; 82:245-264. [PMID: 37438010 PMCID: PMC10662962 DOI: 10.1016/j.jacc.2023.04.054] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 04/26/2023] [Accepted: 04/28/2023] [Indexed: 07/14/2023]
Abstract
The use of consumer wearable devices (CWDs) to track health and fitness has rapidly expanded over recent years because of advances in technology. The general population now has the capability to continuously track vital signs, exercise output, and advanced health metrics. Although understanding of basic health metrics may be intuitive (eg, peak heart rate), more complex metrics are derived from proprietary algorithms, differ among device manufacturers, and may not historically be common in clinical practice (eg, peak V˙O2, exercise recovery scores). With the massive expansion of data collected at an individual patient level, careful interpretation is imperative. In this review, we critically analyze common health metrics provided by CWDs, describe common pitfalls in CWD interpretation, provide recommendations for the interpretation of abnormal results, present the utility of CWDs in exercise prescription, examine health disparities and inequities in CWD use and development, and present future directions for research and development.
Collapse
Affiliation(s)
- Bradley J Petek
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Performance Program, Massachusetts General Hospital, Boston, Massachusetts, USA; Knight Cardiovascular Institute, Oregon Health and Science University, Portland, Oregon, USA
| | - Mostafa A Al-Alusi
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Nathaniel Moulson
- Division of Cardiology and Sports Cardiology BC, University of British Columbia, Vancouver, British Columbia, Canada
| | - Aubrey J Grant
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Performance Program, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Cyril Besson
- Swiss Olympic Medical Center, Lausanne University Hospital (CHUV), Lausanne, Switzerland; Institute for Sport Science, University of Lausanne (ISSUL), Lausanne, Switzerland
| | - J Sawalla Guseh
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Performance Program, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Meagan M Wasfy
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Performance Program, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Vincent Gremeaux
- Swiss Olympic Medical Center, Lausanne University Hospital (CHUV), Lausanne, Switzerland; Institute for Sport Science, University of Lausanne (ISSUL), Lausanne, Switzerland
| | - Timothy W Churchill
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Performance Program, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Aaron L Baggish
- Division of Cardiology, Massachusetts General Hospital, Boston, Massachusetts, USA; Cardiovascular Performance Program, Massachusetts General Hospital, Boston, Massachusetts, USA; Swiss Olympic Medical Center, Lausanne University Hospital (CHUV), Lausanne, Switzerland; Institute for Sport Science, University of Lausanne (ISSUL), Lausanne, Switzerland.
| |
Collapse
|
8
|
Tele-Medicine: The Search of the Holy Grail. Arch Bronconeumol 2023:S0300-2896(23)00026-1. [PMID: 36803936 DOI: 10.1016/j.arbres.2023.01.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 01/27/2023] [Accepted: 01/30/2023] [Indexed: 02/10/2023]
|
9
|
Wijsenbeek MS, Moor CC, Johannson KA, Jackson PD, Khor YH, Kondoh Y, Rajan SK, Tabaj GC, Varela BE, van der Wal P, van Zyl-Smit RN, Kreuter M, Maher TM. Home monitoring in interstitial lung diseases. THE LANCET. RESPIRATORY MEDICINE 2023; 11:97-110. [PMID: 36206780 DOI: 10.1016/s2213-2600(22)00228-4] [Citation(s) in RCA: 27] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 06/08/2022] [Accepted: 06/08/2022] [Indexed: 11/05/2022]
Abstract
The widespread use of smartphones and the internet has enabled self-monitoring and more hybrid-care models. The COVID-19 pandemic has further accelerated remote monitoring, including in the heterogenous and often vulnerable group of patients with interstitial lung diseases (ILDs). Home monitoring in ILD has the potential to improve access to specialist care, reduce the burden on health-care systems, improve quality of life for patients, identify acute and chronic disease worsening, guide treatment decisions, and simplify clinical trials. Home spirometry has been used in ILD for several years and studies with other devices (such as pulse oximeters, activity trackers, and cough monitors) have emerged. At the same time, challenges have surfaced, including technical, analytical, and implementational issues. In this Series paper, we provide an overview of experiences with home monitoring in ILD, address the challenges and limitations for both care and research, and provide future perspectives. VIDEO ABSTRACT.
Collapse
Affiliation(s)
- Marlies S Wijsenbeek
- Centre of Excellence for Interstitial Lung Diseases and Sarcoidosis, Department of Respiratory Medicine, Erasmus MC, University Medical Center, Rotterdam, Netherlands.
| | - Catharina C Moor
- Centre of Excellence for Interstitial Lung Diseases and Sarcoidosis, Department of Respiratory Medicine, Erasmus MC, University Medical Center, Rotterdam, Netherlands
| | - Kerri A Johannson
- Department of Medicine and Snyder Institute for Chronic Diseases, University of Calgary, Calgary, AB, Canada
| | - Peter D Jackson
- Department of Pulmonary and Critical Care Medicine, Virginia Commonwealth University, Richmond, VA, USA
| | - Yet H Khor
- Central Clinical School, Monash University, Melbourne, VIC, Australia; Department of Respiratory and Sleep Medicine, Austin Health, Heidelberg, VIC, Australia
| | - Yasuhiro Kondoh
- Department of Respiratory Medicine and Allergy, Tosei General Hospital, Seto, Japan
| | - Sujeet K Rajan
- Department of Chest Medicine, Bombay Hospital Institute of Medical Sciences, Bhatia Hospital, Mumbai, India
| | - Gabriela C Tabaj
- Department of Respiratory Medicine, Cetrángolo Hospital, Buenos Aires, Argentina
| | - Brenda E Varela
- Department of Respiratory Medicine, Hospital Alemán, Buenos Aires, Argentina
| | - Pieter van der Wal
- Patient expert, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
| | - Richard N van Zyl-Smit
- Division of Pulmonology and University of Cape Town Lung Institute, Department of Medicine, University of Cape Town and Groote Schuur Hospital, Cape Town, South Africa
| | - Michael Kreuter
- Center for Interstitial and Rare Lung Diseases and Interdisciplinary Center for Sarcoidosis, Thoraxklinik, University Hospital Heidelberg, Germany; German Center for Lung Research, Heidelberg, Germany; Department of Pneumology, RKH Clinics Ludwigsburg, Ludwigsburg, Germany
| | - Toby M Maher
- Division of Pulmonary, Critical Care and Sleep Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; National Heart and Lung Institute, Imperial College London, London, UK
| |
Collapse
|
10
|
Shei RJ, Holder IG, Oumsang AS, Paris BA, Paris HL. Wearable activity trackers-advanced technology or advanced marketing? Eur J Appl Physiol 2022; 122:1975-1990. [PMID: 35445837 PMCID: PMC9022022 DOI: 10.1007/s00421-022-04951-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 04/04/2022] [Indexed: 11/26/2022]
Abstract
Wearable devices represent one of the most popular trends in health and fitness. Rapid advances in wearable technology present a dizzying display of possible functions: from thermometers and barometers, magnetometers and accelerometers, to oximeters and calorimeters. Consumers and practitioners utilize wearable devices to track outcomes, such as energy expenditure, training load, step count, and heart rate. While some rely on these devices in tandem with more established tools, others lean on wearable technology for health-related outcomes, such as heart rhythm analysis, peripheral oxygen saturation, sleep quality, and caloric expenditure. Given the increasing popularity of wearable devices for both recreation and health initiatives, understanding the strengths and limitations of these technologies is increasingly relevant. Need exists for continued evaluation of the efficacy of wearable devices to accurately and reliably measure purported outcomes. The purposes of this review are (1) to assess the current state of wearable devices using recent research on validity and reliability, (2) to describe existing gaps between physiology and technology, and (3) to offer expert interpretation for the lay and professional audience on how best to approach wearable technology and employ it in the pursuit of health and fitness. Current literature demonstrates inconsistent validity and reliability for various metrics, with algorithms not publicly available or lacking high-quality validation studies. Advancements in wearable technology should consider standardizing validation metrics, providing transparency in used algorithms, and improving how technology can be tailored to individuals. Until then, it is prudent to exercise caution when interpreting metrics reported from consumer-wearable devices.
Collapse
Affiliation(s)
- Ren-Jay Shei
- Indiana University Alumni Association, Indiana University, 1000 E 17th Street, Bloomington, IN, 47408, USA.
| | - Ian G Holder
- Department of Sports Medicine, Pepperdine University, Malibu, CA, USA
| | - Alicia S Oumsang
- Department of Sports Medicine, Pepperdine University, Malibu, CA, USA
| | - Brittni A Paris
- Department of Sports Medicine, Pepperdine University, Malibu, CA, USA
| | - Hunter L Paris
- Department of Sports Medicine, Pepperdine University, Malibu, CA, USA
| |
Collapse
|