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Hong W. Advances and Opportunities of Mobile Health in the Postpandemic Era: Smartphonization of Wearable Devices and Wearable Deviceization of Smartphones. JMIR Mhealth Uhealth 2024; 12:e48803. [PMID: 38252596 PMCID: PMC10823426 DOI: 10.2196/48803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Revised: 11/08/2023] [Accepted: 12/20/2023] [Indexed: 01/24/2024] Open
Abstract
Mobile health (mHealth) with continuous real-time monitoring is leading the era of digital medical convergence. Wearable devices and smartphones optimized as personalized health management platforms enable disease prediction, prevention, diagnosis, and even treatment. Ubiquitous and accessible medical services offered through mHealth strengthen universal health coverage to facilitate service use without discrimination. This viewpoint investigates the latest trends in mHealth technology, which are comprehensive in terms of form factors and detection targets according to body attachment location and type. Insights and breakthroughs from the perspective of mHealth sensing through a new form factor and sensor-integrated display overcome the problems of existing mHealth by proposing a solution of smartphonization of wearable devices and the wearable deviceization of smartphones. This approach maximizes the infinite potential of stagnant mHealth technology and will present a new milestone leading to the popularization of mHealth. In the postpandemic era, innovative mHealth solutions through the smartphonization of wearable devices and the wearable deviceization of smartphones could become the standard for a new paradigm in the field of digital medicine.
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Affiliation(s)
- Wonki Hong
- Department of Digital Healthcare, Daejeon University, Daejeon, Republic of Korea
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Scheid JL, Reed JL, West SL. Commentary: Is Wearable Fitness Technology a Medically Approved Device? Yes and No. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6230. [PMID: 37444078 PMCID: PMC10341580 DOI: 10.3390/ijerph20136230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 05/08/2023] [Accepted: 06/22/2023] [Indexed: 07/15/2023]
Abstract
Wearable technologies, i.e., activity trackers and fitness watches, are extremely popular and have been increasingly integrated into medical research and clinical practice. To assist in optimizing health, wellness, or medical care, these devices require collaboration between researchers, healthcare providers, and wearable technology companies in order to clarify their clinical capabilities and educate consumers on the utilities and limitations of the wide-ranging wearable devices. Interestingly, activity trackers and fitness watches often track both health/wellness and medical information within the same device. In this commentary, we will focus our discussions regarding wearable technology on (1) defining and explaining the technical differences between tracking health, wellness, and medical information; (2) providing examples of health and wellness compared to medical tracking; (3) describing the potential medical benefits of wearable technology and its applications in clinical populations; and (4) elucidating the potential risks of wearable technology. We conclude that while wearable devices are powerful and informative tools, further research is needed to improve its clinical applications.
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Affiliation(s)
- Jennifer L. Scheid
- Department of Physical Therapy, Daemen University, Amherst, NY 14226, USA
| | - Jennifer L. Reed
- Exercise Physiology and Cardiovascular Health Lab, Division of Cardiac Prevention and Rehabilitation, University of Ottawa Heart Institute, Ottawa, ON K1Y 4W7, Canada
- School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, ON K1Y 4W7, Canada
- School of Human Kinetics, Faculty of Health Sciences, University of Ottawa, Ottawa, ON K1Y 4W7, Canada
| | - Sarah L. West
- Department of Kinesiology, Trent University, Peterborough, ON K9L 0G2, Canada
- Department of Biology, Trent University, Peterborough, ON K9L 0G2, Canada
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Windisch P, Schröder C, Förster R, Cihoric N, Zwahlen DR. Accuracy of the Apple Watch Oxygen Saturation Measurement in Adults: A Systematic Review. Cureus 2023; 15:e35355. [PMID: 36974257 PMCID: PMC10039641 DOI: 10.7759/cureus.35355] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/22/2023] [Indexed: 02/25/2023] Open
Abstract
The purpose of this review is to summarize the research on the accuracy of oxygen saturation (spO2) measurements using the Apple Watch (Apple Inc., Cupertino, California). The Medline and Google Scholar databases were searched for papers evaluating the spO2 measurements of the Apple Watch vs. any kind of ground truth and records were analyzed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The five publications with 973 total patients that met the inclusion criteria all used the Apple Watch Series 6 and described 95% limits of agreement of +/- 2.7 to 5.9% spO2. However, outliers of up to 15% spO2 were reported. Only one study had patient-level data uploaded to a public repository. The Apple Watch Series 6 does not show a strong systematic bias compared to conventional, medical-grade pulse oximeters. However, outliers do occur and should not cause concern in otherwise healthy individuals. The impact of race on measurement accuracy should be investigated.
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Affiliation(s)
- Paul Windisch
- Department of Radiation Oncology, Kantonsspital Winterthur, Winterthur, CHE
| | - Christina Schröder
- Department of Radiation Oncology, Kantonsspital Winterthur, Winterthur, CHE
| | - Robert Förster
- Department of Radiation Oncology, Kantonsspital Winterthur, Winterthur, CHE
| | - Nikola Cihoric
- Department of Radiation Oncology, Inselspital, University Hospital of Bern, Bern, CHE
| | - Daniel R Zwahlen
- Department of Radiation Oncology, Kantonsspital Winterthur, Winterthur, CHE
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Takano A, Ono K, Nozawa K, Sato M, Onuki M, Sese J, Yumoto Y, Matsushita S, Matsumoto T. Wearable Sensor and Mobile App-based mHealth Approach for Investigating Substance Use and Related Factors in Daily Life: Protocol for an Ecological Momentary Assessment Study (Preprint). JMIR Res Protoc 2022; 12:e44275. [PMID: 37040162 PMCID: PMC10131735 DOI: 10.2196/44275] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 03/05/2023] [Accepted: 03/09/2023] [Indexed: 03/11/2023] Open
Abstract
BACKGROUND Digital health technologies using mobile apps and wearable devices are a promising approach to the investigation of substance use in the real world and for the analysis of predictive factors or harms from substance use. Moreover, consecutive repeated data collection enables the development of predictive algorithms for substance use by machine learning methods. OBJECTIVE We developed a new self-monitoring mobile app to record daily substance use, triggers, and cravings. Additionally, a wearable activity tracker (Fitbit) was used to collect objective biological and behavioral data before, during, and after substance use. This study aims to describe a model using machine learning methods to determine substance use. METHODS This study is an ongoing observational study using a Fitbit and a self-monitoring app. Participants of this study were people with health risks due to alcohol or methamphetamine use. They were required to record their daily substance use and related factors on the self-monitoring app and to always wear a Fitbit for 8 weeks, which collected the following data: (1) heart rate per minute, (2) sleep duration per day, (3) sleep stages per day, (4) the number of steps per day, and (5) the amount of physical activity per day. Fitbit data will first be visualized for data analysis to confirm typical Fitbit data patterns for individual users. Next, machine learning and statistical analysis methods will be performed to create a detection model for substance use based on the combined Fitbit and self-monitoring data. The model will be tested based on 5-fold cross-validation, and further preprocessing and machine learning methods will be conducted based on the preliminary results. The usability and feasibility of this approach will also be evaluated. RESULTS Enrollment for the trial began in September 2020, and the data collection finished in April 2021. In total, 13 people with methamphetamine use disorder and 36 with alcohol problems participated in this study. The severity of methamphetamine or alcohol use disorder assessed by the Drug Abuse Screening Test-10 or the Alcohol Use Disorders Identification Test-10 was moderate to severe. The anticipated results of this study include understanding the physiological and behavioral data before, during, and after alcohol or methamphetamine use and identifying individual patterns of behavior. CONCLUSIONS Real-time data on daily life among people with substance use problems were collected in this study. This new approach to data collection might be helpful because of its high confidentiality and convenience. The findings of this study will provide data to support the development of interventions to reduce alcohol and methamphetamine use and associated negative consequences. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/44275.
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Affiliation(s)
- Ayumi Takano
- Department of Mental Health and Psychiatric Nursing, Tokyo Medical and Dental University, Tokyo, Japan
| | - Koki Ono
- Department of Clinical Information Engineering, The University of Tokyo, Tokyo, Japan
| | - Kyosuke Nozawa
- Department of Mental Health and Psychiatric Nursing, Osaka University, Osaka, Japan
| | | | | | | | - Yosuke Yumoto
- National Hospital Organization Kurihama Medical and Addiction Center, Yokosuka, Japan
| | - Sachio Matsushita
- National Hospital Organization Kurihama Medical and Addiction Center, Yokosuka, Japan
| | - Toshihiko Matsumoto
- Department of Drug Dependence Research, National Center of Neurology and Psychiatry, Tokyo, Japan
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Development of a low-cost wearable device for Covid-19 self-quarantine monitoring system. PUBLIC HEALTH IN PRACTICE 2022; 4:100299. [PMID: 35996362 PMCID: PMC9387171 DOI: 10.1016/j.puhip.2022.100299] [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: 09/15/2021] [Revised: 07/08/2022] [Accepted: 07/08/2022] [Indexed: 01/03/2023] Open
Abstract
Objectives The objective of this study is to develop a Bluetooth-based low-cost wearable device for a self-quarantine monitoring system. Study design The designed wearable device focuses on data transmission via Bluetooth, integration of tracking, tracing, and fencing into a single system, and low energy usage from its battery. Methods We design a wearable device using smartphone equipped with GPS, a communication module, Bluetooth low energy (BLE) and a high-capacity battery as a solution for low-cost device with excellent efficiency. We divide the designed system into two parts, the client and the server parts. The client parts are wearable device attached to the individual being monitored and the mobile phone as GPS and telecommunications module. Whereas the server parts are user interface, digital map, notification system, and backend database. Then, the whole system was tested in laboratory and field scale. Results We tested functions of integrated device such as wearable device, mobile applications, and server for laboratory scale test. Then, performing field test with geofencing, communication module, battery, web interface, and resource computing usage. The field test was conducted on a small scale with a limited number of trial patients. We found that the designed wearable device was successfully implemented for both self-quarantine and centralized quarantine requirements. The majority of the components used met the specifications and functioned properly as well. Conclusions A BLE-enabled wearable device can be used for tracking self-quarantine patients. The laboratory and field scale tests demonstrate that the designed wearable device functions properly and meets the requirements. We anticipate that this low-cost wearable device is effective in limiting Covid-19 virus spread and preventing the formation of a new Covid-19 virus-infected cluster.
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Mayer C, Tyler J, Fang Y, Flora C, Frank E, Tewari M, Choi SW, Sen S, Forger DB. Consumer-grade wearables identify changes in multiple physiological systems during COVID-19 disease progression. Cell Rep Med 2022; 3:100601. [PMID: 35480626 PMCID: PMC9017023 DOI: 10.1016/j.xcrm.2022.100601] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 11/04/2021] [Accepted: 03/20/2022] [Indexed: 11/29/2022]
Abstract
Consumer-grade wearables are needed to track disease, especially in the ongoing pandemic, as they can monitor patients in real time. We show that decomposing heart rate from low-cost wearable technologies into signals from different systems can give a multidimensional description of physiological changes due to COVID-19 infection. We find that the separate physiological features of basal heart rate, heart rate response to physical activity, circadian variation in heart rate, and autocorrelation of heart rate are significantly altered and can classify symptomatic versus healthy periods. Increased heart rate and autocorrelation begin at symptom onset, while the heart rate response to activity increases soon after symptom onset and increases more in individuals exhibiting cough. Symptom onset is associated with a blunting of circadian variation in heart rate, as measured by the uncertainty in the phase estimate. This work establishes an innovative data analytic approach to monitor disease progression remotely using consumer-grade wearables. We separate wearable heart rate into cardiopulmonary, circadian, and other signals Parameters from different physiological systems enable disease tracking Individual signals change in distinct ways around COVID-19 symptom onset Together, the parameter changes can distinguish healthy from infection periods
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Affiliation(s)
- Caleb Mayer
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jonathan Tyler
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA.,Division of Pediatric Hematology/Oncology, Department of Pediatrics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Yu Fang
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Christopher Flora
- Division of Hematology and Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Elena Frank
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Muneesh Tewari
- Division of Hematology and Oncology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA.,Rogel Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA.,Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.,Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Sung Won Choi
- Division of Pediatric Hematology/Oncology, Department of Pediatrics, University of Michigan, Ann Arbor, MI 48109, USA.,Rogel Comprehensive Cancer Center, University of Michigan, Ann Arbor, MI 48109, USA
| | - Srijan Sen
- Michigan Neuroscience Institute, University of Michigan, Ann Arbor, MI 48109, USA
| | - Daniel B Forger
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA.,Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
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