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Winslow B, Mills E. Future of service member monitoring: the intersection of biology, wearables and artificial intelligence. BMJ Mil Health 2024; 170:412-414. [PMID: 36702525 DOI: 10.1136/military-2022-002306] [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: 11/03/2022] [Accepted: 01/15/2023] [Indexed: 01/28/2023]
Abstract
While substantial investment has been made in the early identification of mental and behavioural health disorders in service members, rates of depression, substance abuse and suicidality continue to climb. Objective and persistent measures are needed for early identification and treatment of these rising health issues. Considerable potential lies at the intersection of biology, wearables and artificial intelligence to provide high accuracy, objective monitoring of mental and behavioural health in training, operations and healthcare settings. While the current generation of wearable devices has predominantly targeted non-military use cases, military agencies have demonstrated successes in monitoring and diagnosis via off-label uses. Combined with context-aware and individualised algorithms, the integration of wearable data with artificial intelligence allows for a deeper understanding of individual-level and group-level mental and behavioural health at scale. Emerging digital phenotyping approaches which leverage ubiquitous sensing technology can provide monitoring at a greater scale, lower price point and lower individual burden by removing the need for additional body-worn technology. The intersection of this technology will enable individualised strategies to promote service member mental and physical health, reduce injury, and improve long-term well-being and deployability.
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Affiliation(s)
| | - E Mills
- Design Interactive Inc, Orlando, Florida, USA
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Wang JJ, Liu SH, Tsai CH, Manousakas I, Zhu X, Lee TL. Signal Quality Analysis of Single-Arm Electrocardiography. SENSORS (BASEL, SWITZERLAND) 2023; 23:5818. [PMID: 37447668 DOI: 10.3390/s23135818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 06/15/2023] [Accepted: 06/21/2023] [Indexed: 07/15/2023]
Abstract
The number of people experiencing mental stress or emotional dysfunction has increased since the onset of the COVID-19 pandemic, as many individuals have had to adapt their daily lives. Numerous studies have demonstrated that mental health disorders can pose a risk for certain diseases, and they are also closely associated with the problem of mental workload. Now, wearable devices and mobile health applications are being utilized to monitor and assess individuals' mental health conditions on a daily basis using heart rate variability (HRV), typically measured by the R-to-R wave interval (RRI) of an electrocardiogram (ECG). However, portable or wearable ECG devices generally require two electrodes to perform bipolar limb leads, such as the Einthoven triangle. This study aims to develop a single-arm ECG measurement method, with lead I ECG serving as the gold standard. We conducted static and dynamic experiments to analyze the morphological performance and signal-to-noise ratio (SNR) of the single-arm ECG. Three morphological features were defined, RRI, the duration of the QRS complex wave, and the amplitude of the R wave. Thirty subjects participated in this study. The results indicated that RRI exhibited the highest cross-correlation (R = 0.9942) between the single-arm ECG and lead I ECG, while the duration of the QRS complex wave showed the weakest cross-correlation (R = 0.2201). The best SNR obtained was 26.1 ± 5.9 dB during the resting experiment, whereas the worst SNR was 12.5 ± 5.1 dB during the raising and lowering of the arm along the z-axis. This single-arm ECG measurement method offers easier operation compared to traditional ECG measurement techniques, making it applicable for HRV measurement and the detection of an irregular RRI.
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Affiliation(s)
- Jia-Jung Wang
- Department of Biomedical Engineering, I-Shou University, Kaohsiung 84001, Taiwan
| | - Shing-Hong Liu
- Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung 413310, Taiwan
| | - Cheng-Hsien Tsai
- Department of Biomedical Engineering, I-Shou University, Kaohsiung 84001, Taiwan
| | - Ioannis Manousakas
- Department of Biomedical Engineering, I-Shou University, Kaohsiung 84001, Taiwan
| | - Xin Zhu
- Division of Information Systems, School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu City 965-8580, Japan
| | - Thung-Lip Lee
- Department of Cardiology, E-Da Hospital, Kaohsiung 84001, Taiwan
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Ben Itzhak S, Ricon SS, Biton S, Behar JA, Sobel JA. Effect of temporal resolution on the detection of cardiac arrhythmias using HRV features and machine learning. Physiol Meas 2022; 43. [PMID: 35506573 DOI: 10.1088/1361-6579/ac6561] [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: 12/02/2021] [Accepted: 04/07/2022] [Indexed: 11/11/2022]
Abstract
Objective.Arrhythmia is an abnormal cardiac rhythm that affects the pattern and rate of the heartbeat. Wearable devices with the functionality to measure and store heart rate (HR) data are growing in popularity and enable diagnosing and monitoring arrhythmia on a large scale. The typical sampling resolution of HR data available from non-medical grade wearable devices varies from seconds to several minutes depending on the device and its settings. However, the impact of sampling resolution on the performance and quality of arrhythmia detection has not yet been quantified.Approach.In this study, we investigated the detection and classification of three arrhythmias, namely atrial fibrillation, bradycardia, tachycardia, from down-sampled HR data with various temporal resolution (5-, 15-, 30- and 60 s averages) in 1 h segments extracted from an annotated Holter ECG database acquired at the University of Virginia Heart Station. For the classification task, a total of 15 common heart rate variability (HRV) features were engineered based on the HR time series of each patient. Three different types of machine learning classifiers were evaluated, namely logistic regression, support vector machine and random forest.Main results.A decrease in temporal resolution drastically impacted the detection of atrial fibrillation but did not substantially affect the detection of bradycardia and tachycardia. A HR resolution up to 15 s average demonstrated reasonable performance with a sensitivity of 0.92 and a specificity of 0.86 for a multiclass random forest classifier.Significance.HRV features extracted from low resolution long HR recordings have the potential to increase the early detection of arrhythmias in undiagnosed individuals.
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Affiliation(s)
| | | | - Shany Biton
- Biomedical Engineering Faculty, Technion-IIT, Haifa, Israel
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Davies B. 'Personal Health Surveillance': The Use of mHealth in Healthcare Responsibilisation. Public Health Ethics 2021; 14:268-280. [PMID: 34899983 PMCID: PMC8661076 DOI: 10.1093/phe/phab013] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
There is an ongoing increase in the use of mobile health (mHealth) technologies that patients can use to monitor health-related outcomes and behaviours. While the dominant narrative around mHealth focuses on patient empowerment, there is potential for mHealth to fit into a growing push for patients to take personal responsibility for their health. I call the first of these uses 'medical monitoring', and the second 'personal health surveillance'. After outlining two problems which the use of mHealth might seem to enable us to overcome-fairness of burdens and reliance on self-reporting-I note that these problems would only really be solved by unacceptably comprehensive forms of personal health surveillance which applies to all of us at all times. A more plausible model is to use personal health surveillance as a last resort for patients who would otherwise independently qualify for responsibility-based penalties. However, I note that there are still a number of ethical and practical problems that such a policy would need to overcome. The prospects of mHealth enabling a fair, genuinely cost-saving policy of patient responsibility are slim.
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Affiliation(s)
- Ben Davies
- Uehiro Centre for Practical Ethics, University of Oxford
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Behar JA, Liu C, Zigel Y, Laguna P, Clifford GD. Editorial on Remote Health Monitoring: from chronic diseases to pandemics. Physiol Meas 2021; 41:100401. [PMID: 33393486 DOI: 10.1088/1361-6579/abbb6d] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Affiliation(s)
- Barbara Pavlova
- Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada.,Nova Scotia Health Authority, Halifax, Nova Scotia, Canada
| | - Rudolf Uher
- Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada.,Nova Scotia Health Authority, Halifax, Nova Scotia, Canada
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Bauer M, Glenn T, Geddes J, Gitlin M, Grof P, Kessing LV, Monteith S, Faurholt-Jepsen M, Severus E, Whybrow PC. Smartphones in mental health: a critical review of background issues, current status and future concerns. Int J Bipolar Disord 2020; 8:2. [PMID: 31919635 PMCID: PMC6952480 DOI: 10.1186/s40345-019-0164-x] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 10/24/2019] [Indexed: 02/06/2023] Open
Abstract
There has been increasing interest in the use of smartphone applications (apps) and other consumer technology in mental health care for a number of years. However, the vision of data from apps seamlessly returned to, and integrated in, the electronic medical record (EMR) to assist both psychiatrists and patients has not been widely achieved, due in part to complex issues involved in the use of smartphone and other consumer technology in psychiatry. These issues include consumer technology usage, clinical utility, commercialization, and evolving consumer technology. Technological, legal and commercial issues, as well as medical issues, will determine the role of consumer technology in psychiatry. Recommendations for a more productive direction for the use of consumer technology in psychiatry are provided.
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Affiliation(s)
- Michael Bauer
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Medical Faculty, Technische Universität Dresden, Fetscherstr. 74, 01307, Dresden, Germany.
| | - Tasha Glenn
- ChronoRecord Association, Fullerton, CA, USA
| | - John Geddes
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - Michael Gitlin
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Paul Grof
- Mood Disorders Center of Ottawa, Ottawa, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Lars V Kessing
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Scott Monteith
- Michigan State University College of Human Medicine, Traverse City Campus, Traverse City, MI, USA
| | - Maria Faurholt-Jepsen
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Emanuel Severus
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Medical Faculty, Technische Universität Dresden, Fetscherstr. 74, 01307, Dresden, Germany
| | - Peter C Whybrow
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles (UCLA), Los Angeles, CA, USA
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