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Daskalaki E, Parkinson A, Brew-Sam N, Hossain MZ, O'Neal D, Nolan CJ, Suominen H. The Potential of Current Noninvasive Wearable Technology for the Monitoring of Physiological Signals in the Management of Type 1 Diabetes: Literature Survey. J Med Internet Res 2022; 24:e28901. [PMID: 35394448 PMCID: PMC9034434 DOI: 10.2196/28901] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 12/06/2021] [Accepted: 12/23/2021] [Indexed: 11/13/2022] Open
Abstract
Background Monitoring glucose and other parameters in persons with type 1 diabetes (T1D) can enhance acute glycemic management and the diagnosis of long-term complications of the disease. For most persons living with T1D, the determination of insulin delivery is based on a single measured parameter—glucose. To date, wearable sensors exist that enable the seamless, noninvasive, and low-cost monitoring of multiple physiological parameters. Objective The objective of this literature survey is to explore whether some of the physiological parameters that can be monitored with noninvasive, wearable sensors may be used to enhance T1D management. Methods A list of physiological parameters, which can be monitored by using wearable sensors available in 2020, was compiled by a thorough review of the devices available in the market. A literature survey was performed using search terms related to T1D combined with the identified physiological parameters. The selected publications were restricted to human studies, which had at least their abstracts available. The PubMed and Scopus databases were interrogated. In total, 77 articles were retained and analyzed based on the following two axes: the reported relations between these parameters and T1D, which were found by comparing persons with T1D and healthy control participants, and the potential areas for T1D enhancement via the further analysis of the found relationships in studies working within T1D cohorts. Results On the basis of our search methodology, 626 articles were returned, and after applying our exclusion criteria, 77 (12.3%) articles were retained. Physiological parameters with potential for monitoring by using noninvasive wearable devices in persons with T1D included those related to cardiac autonomic function, cardiorespiratory control balance and fitness, sudomotor function, and skin temperature. Cardiac autonomic function measures, particularly the indices of heart rate and heart rate variability, have been shown to be valuable in diagnosing and monitoring cardiac autonomic neuropathy and, potentially, predicting and detecting hypoglycemia. All identified physiological parameters were shown to be associated with some aspects of diabetes complications, such as retinopathy, neuropathy, and nephropathy, as well as macrovascular disease, with capacity for early risk prediction. However, although they can be monitored by available wearable sensors, most studies have yet to adopt them, as opposed to using more conventional devices. Conclusions Wearable sensors have the potential to augment T1D sensing with additional, informative biomarkers, which can be monitored noninvasively, seamlessly, and continuously. However, significant challenges associated with measurement accuracy, removal of noise and motion artifacts, and smart decision-making exist. Consequently, research should focus on harvesting the information hidden in the complex data generated by wearable sensors and on developing models and smart decision strategies to optimize the incorporation of these novel inputs into T1D interventions.
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Affiliation(s)
- Elena Daskalaki
- School of Computing, College of Engineering and Computer Science, The Australian National University, Canberra, Australia
| | - Anne Parkinson
- Department of Health Services Research and Policy, Research School of Population Health, College of Health and Medicine, The Australian National University, Canberra, Australia
| | - Nicola Brew-Sam
- Department of Health Services Research and Policy, Research School of Population Health, College of Health and Medicine, The Australian National University, Canberra, Australia
| | - Md Zakir Hossain
- School of Computing, College of Engineering and Computer Science, The Australian National University, Canberra, Australia.,School of Biology, College of Science, The Australian National University, Canberra, Australia.,Bioprediction Activity, Commonwealth Industrial and Scientific Research Organisation, Canberra, Australia
| | - David O'Neal
- Department of Medicine, University of Melbourne, Melbourne, Australia.,Department of Endocrinology and Diabetes, St Vincent's Hospital Melbourne, Melbourne, Australia
| | - Christopher J Nolan
- Australian National University Medical School and John Curtin School of Medical Research, College of Health and Medicine, The Autralian National University, Canberra, Australia.,Department of Diabetes and Endocrinology, The Canberra Hospital, Canberra, Australia
| | - Hanna Suominen
- School of Computing, College of Engineering and Computer Science, The Australian National University, Canberra, Australia.,Data61, Commonwealth Industrial and Scientific Research Organisation, Canberra, Australia.,Department of Computing, University of Turku, Turku, Finland
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Byun S, Kim AY, Jang EH, Kim S, Choi KW, Yu HY, Jeon HJ. Entropy analysis of heart rate variability and its application to recognize major depressive disorder: A pilot study. Technol Health Care 2020; 27:407-424. [PMID: 31045557 PMCID: PMC6597986 DOI: 10.3233/thc-199037] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
BACKGROUND The current method to evaluate major depressive disorder (MDD) relies on subjective clinical interviews and self-questionnaires. OBJECTIVE Autonomic imbalance in MDD patients is characterized using entropy measures of heart rate variability (HRV). A machine learning approach for screening depression based on the entropy is demonstrated. METHODS The participants experience five experimental phases: baseline (BASE), stress task (MAT), stress task recovery (REC1), relaxation task (RLX), and relaxation task recovery (REC2). The four entropy indices, approximate entropy, sample entropy, fuzzy entropy, and Shannon entropy, are extracted for each phase, and a total of 20 features are used. A support vector machine classifier and recursive feature elimination are employed for classification. RESULTS The entropy features are lower in the MDD group; however, the disease does not have a significant effect. Experimental tasks significantly affect the features. The entropy did not recover during REC1. The differences in the entropy features between the two groups increased after MAT and showed the largest gap in REC2. We achieved 70% accuracy, 64% sensitivity, and 76% specificity with three optimal features during RLX and REC2. CONCLUSION Monitoring of HRV complexity changes when a subject experiences autonomic arousal and recovery can potentially facilitate objective depression recognition.
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Affiliation(s)
- Sangwon Byun
- Department of Electronics Engineering, Incheon National University, Incheon 22012, Korea
| | - Ah Young Kim
- Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea
| | - Eun Hye Jang
- Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea
| | - Seunghwan Kim
- Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea
| | - Kwan Woo Choi
- Department of Psychiatry, Depression Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea.,Department of Psychiatry, Korea University Anam Hospital, Korea University College of Medicine, Seoul 02841, Korea
| | - Han Young Yu
- Bio-Medical IT Convergence Research Division, Electronics and Telecommunications Research Institute, Daejeon 34129, Korea
| | - Hong Jin Jeon
- Department of Psychiatry, Depression Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea
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