Application status and development prospects of smart wearable devices in cardiovascular diseases_West China Medical Journal

Authors：

 HUO Chang ¹ , LI Yiming ²

1. Department of Cardiology, West China Chunxi Hospital, Sichuan University and the Fourth People’s Hospital of Sichuan Province, Chengdu, Sichuan 610016, P. R. China;
2. Department of Cardiology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;

Corresponding author：

HUO Chang, Email: 2488066421@qq.com

Keywords：

Smart wearable device; artificial intelligence; cardiovascular disease; sensor

DOI：

10.7507/1002-0179.202404059

Video：

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Cardiovascular disease has caused a huge burden of disease worldwide, and the rapid advancement of smart wearable devices has provided new means for early diagnosis, real-time monitoring, and event prevention of cardiovascular disease. Smart wearable devices can be classified into various categories based on detection signals and physical carrier types. Based on an overview of the composition of such devices, this article further introduces the current cutting-edge research and related market products related to smart blood pressure monitoring, electrocardiogram monitoring, and ultrasound monitoring. It also discusses the future development and challenges of such devices, aiming to provide evidence support for the research and development of smart wearable devices in the diagnosis and treatment of cardiovascular diseases in the future.

Citation： HUO Chang, LI Yiming. Application status and development prospects of smart wearable devices in cardiovascular diseases. West China Medical Journal, 2024, 39(7): 1140-1144. doi: 10.7507/1002-0179.202404059 Copy

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2.	国家心血管病中心. 中国心血管健康与疾病报告: 2022. 北京: 中国协和医科大学出版社, 2023.
3.	Balakumar P, Maung-U K, Jagadeesh G. Prevalence and prevention of cardiovascular disease and diabetes mellitus. Pharmacol Res, 2016, 113: 600-609.
4.	Sana F, Isselbacher EM, Singh JP, et al. Wearable devices for ambulatory cardiac monitoring: JACC state-of-the-art review. J Am Coll Cardiol, 2020, 75(13): 1582-1592.
5.	Grand View Research. Healthcare analytics market size, share, trends report 2030. California: Grand View Research, 2024.
6.	Mizuno A, Changolkar S, Patel MS. Wearable devices to monitor and reduce the risk of cardiovascular disease: evidence and opportunities. Annu Rev Med, 2021, 72: 459-471.
7.	An BW, Shin JH, Kim SY, et al. Smart sensor systems for wearable electronic devices. Polymers (Basel), 2017, 9(8): 303.
8.	Nahavandi D, Alizadehsani R, Khosravi A, et al. Application of artificial intelligence in wearable devices: opportunities and challenges. Comput Methods Programs Biomed, 2022, 213: 106541.
9.	Kario K, Shimbo D, Tomitani N, et al. The first study comparing a wearable watch-type blood pressure monitor with a conventional ambulatory blood pressure monitor on in-office and out-of-office settings. J Clin Hypertens (Greenwich), 2020, 22(2): 135-141.
10.	Zweiker R, Schumacher M, Fruhwald FM, et al. Comparison of wrist blood pressure measurement with conventional sphygmomanometry at a cardiology outpatient clinic. J Hypertens, 2000, 18(8): 1013-1018.
11.	Kurylyak Y, Lamonaca F, Grimaldi D. A neural network-based method for continuous blood pressure estimation from a PPG signal. In: 2013 IEEE International instrumentation and measurement technology conference (I2MTC). New York: Institute of Electrical and Electronics Engineers, 2013: 280-283.
12.	Lazazzera R, Belhaj Y, Carrault G. A new wearable device for blood pressure estimation using photoplethysmogram. Sensors (Basel), 2019, 19(11): 2557.
13.	Islam SMS, Cartledge S, Karmakar C, et al. Validation and acceptability of a cuffless wrist-worn wearable blood pressure monitoring device among users and health care professionals: mixed methods study. JMIR Mhealth Uhealth, 2019, 7(10): e14706.
14.	Chiang PH, Wong M, Dey S. Using wearables and machine learning to enable personalized lifestyle recommendations to improve blood pressure. IEEE J Transl Eng Health Med, 2021, 9: 2700513.
15.	Li J, Jia H, Zhou J, et al. Thin, soft, wearable system for continuous wireless monitoring of artery blood pressure. Nat Commun, 2023, 14(1): 5009.
16.	Eom H, Lee D, Han S, et al. End-to-end deep learning architecture for continuous blood pressure estimation using attention mechanism. Sensors (Basel), 2020, 20(8): 2338.
17.	Song K, Chung K, Chang JH. Cuffless deep learning-based blood pressure estimation for smart wristwatches. IEEE Trans Instrum Meas, 2020, 69: 4292-4302.
18.	Fox K, Ford I, Steg PG, et al. Heart rate as a prognostic risk factor in patients with coronary artery disease and left-ventricular systolic dysfunction (BEAUTIFUL): a subgroup analysis of a randomised controlled trial. Lancet, 2008, 372(9641): 817-821.
19.	Singh N, Moneghetti KJ, Christle JW, et al. Heart rate variability: an old metric with new meaning in the era of using mhealth technologies for health and exercise training guidance. part two: prognosis and training. Arrhythm Electrophysiol Rev, 2018, 7(4): 247-255.
20.	Dagher L, Shi H, Zhao Y, et al. Wearables in cardiology: here to stay. Heart Rhythm, 2020, 17(5): 889-895.
21.	Karlsson JS, Wiklund U, Berglin L, et al. Wireless monitoring of heart rate and electromyographic signals using a smart T-shirt. Berlin: ResearchGate, 2008: 59.
22.	Isakadze N, Martin SS. How useful is the smartwatch ECG?. Trends Cardiovasc Med, 2020, 30(7): 442-448.
23.	Guo Y, Wang H, Zhang H, et al. Mobile photoplethysmographic technology to detect atrial fibrillation. J Am Coll Cardiol, 2019, 74(19): 2365-2375.
24.	Niu Y, Wang H, Wang H, et al. Diagnostic validation of smart wearable device embedded with single-lead electrocardiogram for arrhythmia detection. Digit Health, 2023, 9: 20552076231198682.
25.	Perez MV, Mahaffey KW, Hedlin H, et al. Large-scale assessment of a smartwatch to identify atrial fibrillation. N Engl J Med, 2019, 381(20): 1909-1917.
26.	Walker AL, Muhlestein JB. Smartphone electrocardiogram monitoring: current perspectives. Adv Health Care Technol, 2018: 15-24.
27.	Goldenthal IL, Sciacca RR, Riga T, et al. Recurrent atrial fibrillation/flutter detection after ablation or cardioversion using the AliveCor KardiaMobile device: iHEART results. J Cardiovasc Electrophysiol, 2019, 30(11): 2220-2228.
28.	Sohn K, Dalvin SP, Merchant FM, et al. Utility of a smartphone based system (cvrPhone) to predict short-term arrhythmia susceptibility. Sci Rep, 2019, 9(1): 14497.
29.	La TG, Le LH. Flexible and wearable ultrasound device for medical applications: a review on materials, structural designs, and current challenges. Adv Mater Technol, 2022, 7: 2100798.
30.	Chen S, Qi J, Fan S, et al. Flexible wearable sensors for cardiovascular health monitoring. Adv Healthc Mater, 2021, 10(17): e2100116.
31.	Seetharam K, Raina S, Sengupta PP. The role of artificial intelligence in echocardiography. Curr Cardiol Rep, 2020, 22(9): 99.
32.	Shomaji S, Dehghanzadeh P, Roman A, et al. Early detection of cardiovascular diseases using wearable ultrasound device. IEEE Consum Electron Mag, 2019, 8: 12-21.
33.	Hu H, Huang H, Li M, et al. A wearable cardiac ultrasound imager. Nature, 2023, 613(7945): 667-675.
34.	Lim GB. A wearable ultrasonic device to image cardiac function. Nat Rev Cardiol, 2023, 20(4): 212.
35.	Carpenter A, Frontera A. Smart-watches: a potential challenger to the implantable loop recorder?. Europace, 2016, 18(6): 791-793.
36.	Bayoumy K, Gaber M, Elshafeey A, et al. Smart wearable devices in cardiovascular care: where we are and how to move forward. Nat Rev Cardiol, 2021, 18(8): 581-599.
37.	Olhede SC, Wolfe PJ. The growing ubiquity of algorithms in society: implications, impacts and innovations. Philos Trans A Math Phys Eng Sci, 2018, 376(2128): 20170364.

1. Timmis A, Townsend N, Gale CP, et al. European Society of Cardiology: cardiovascular disease statistics 2019. Eur Heart J, 2020, 41(1): 12-85.
2. 国家心血管病中心. 中国心血管健康与疾病报告: 2022. 北京: 中国协和医科大学出版社, 2023.
3. Balakumar P, Maung-U K, Jagadeesh G. Prevalence and prevention of cardiovascular disease and diabetes mellitus. Pharmacol Res, 2016, 113: 600-609.
4. Sana F, Isselbacher EM, Singh JP, et al. Wearable devices for ambulatory cardiac monitoring: JACC state-of-the-art review. J Am Coll Cardiol, 2020, 75(13): 1582-1592.
5. Grand View Research. Healthcare analytics market size, share, trends report 2030. California: Grand View Research, 2024.
6. Mizuno A, Changolkar S, Patel MS. Wearable devices to monitor and reduce the risk of cardiovascular disease: evidence and opportunities. Annu Rev Med, 2021, 72: 459-471.
7. An BW, Shin JH, Kim SY, et al. Smart sensor systems for wearable electronic devices. Polymers (Basel), 2017, 9(8): 303.
8. Nahavandi D, Alizadehsani R, Khosravi A, et al. Application of artificial intelligence in wearable devices: opportunities and challenges. Comput Methods Programs Biomed, 2022, 213: 106541.
9. Kario K, Shimbo D, Tomitani N, et al. The first study comparing a wearable watch-type blood pressure monitor with a conventional ambulatory blood pressure monitor on in-office and out-of-office settings. J Clin Hypertens (Greenwich), 2020, 22(2): 135-141.
10. Zweiker R, Schumacher M, Fruhwald FM, et al. Comparison of wrist blood pressure measurement with conventional sphygmomanometry at a cardiology outpatient clinic. J Hypertens, 2000, 18(8): 1013-1018.
11. Kurylyak Y, Lamonaca F, Grimaldi D. A neural network-based method for continuous blood pressure estimation from a PPG signal. In: 2013 IEEE International instrumentation and measurement technology conference (I2MTC). New York: Institute of Electrical and Electronics Engineers, 2013: 280-283.
12. Lazazzera R, Belhaj Y, Carrault G. A new wearable device for blood pressure estimation using photoplethysmogram. Sensors (Basel), 2019, 19(11): 2557.
13. Islam SMS, Cartledge S, Karmakar C, et al. Validation and acceptability of a cuffless wrist-worn wearable blood pressure monitoring device among users and health care professionals: mixed methods study. JMIR Mhealth Uhealth, 2019, 7(10): e14706.
14. Chiang PH, Wong M, Dey S. Using wearables and machine learning to enable personalized lifestyle recommendations to improve blood pressure. IEEE J Transl Eng Health Med, 2021, 9: 2700513.
15. Li J, Jia H, Zhou J, et al. Thin, soft, wearable system for continuous wireless monitoring of artery blood pressure. Nat Commun, 2023, 14(1): 5009.
16. Eom H, Lee D, Han S, et al. End-to-end deep learning architecture for continuous blood pressure estimation using attention mechanism. Sensors (Basel), 2020, 20(8): 2338.
17. Song K, Chung K, Chang JH. Cuffless deep learning-based blood pressure estimation for smart wristwatches. IEEE Trans Instrum Meas, 2020, 69: 4292-4302.
18. Fox K, Ford I, Steg PG, et al. Heart rate as a prognostic risk factor in patients with coronary artery disease and left-ventricular systolic dysfunction (BEAUTIFUL): a subgroup analysis of a randomised controlled trial. Lancet, 2008, 372(9641): 817-821.
19. Singh N, Moneghetti KJ, Christle JW, et al. Heart rate variability: an old metric with new meaning in the era of using mhealth technologies for health and exercise training guidance. part two: prognosis and training. Arrhythm Electrophysiol Rev, 2018, 7(4): 247-255.
20. Dagher L, Shi H, Zhao Y, et al. Wearables in cardiology: here to stay. Heart Rhythm, 2020, 17(5): 889-895.
21. Karlsson JS, Wiklund U, Berglin L, et al. Wireless monitoring of heart rate and electromyographic signals using a smart T-shirt. Berlin: ResearchGate, 2008: 59.
22. Isakadze N, Martin SS. How useful is the smartwatch ECG?. Trends Cardiovasc Med, 2020, 30(7): 442-448.
23. Guo Y, Wang H, Zhang H, et al. Mobile photoplethysmographic technology to detect atrial fibrillation. J Am Coll Cardiol, 2019, 74(19): 2365-2375.
24. Niu Y, Wang H, Wang H, et al. Diagnostic validation of smart wearable device embedded with single-lead electrocardiogram for arrhythmia detection. Digit Health, 2023, 9: 20552076231198682.
25. Perez MV, Mahaffey KW, Hedlin H, et al. Large-scale assessment of a smartwatch to identify atrial fibrillation. N Engl J Med, 2019, 381(20): 1909-1917.
26. Walker AL, Muhlestein JB. Smartphone electrocardiogram monitoring: current perspectives. Adv Health Care Technol, 2018: 15-24.
27. Goldenthal IL, Sciacca RR, Riga T, et al. Recurrent atrial fibrillation/flutter detection after ablation or cardioversion using the AliveCor KardiaMobile device: iHEART results. J Cardiovasc Electrophysiol, 2019, 30(11): 2220-2228.
28. Sohn K, Dalvin SP, Merchant FM, et al. Utility of a smartphone based system (cvrPhone) to predict short-term arrhythmia susceptibility. Sci Rep, 2019, 9(1): 14497.
29. La TG, Le LH. Flexible and wearable ultrasound device for medical applications: a review on materials, structural designs, and current challenges. Adv Mater Technol, 2022, 7: 2100798.
30. Chen S, Qi J, Fan S, et al. Flexible wearable sensors for cardiovascular health monitoring. Adv Healthc Mater, 2021, 10(17): e2100116.
31. Seetharam K, Raina S, Sengupta PP. The role of artificial intelligence in echocardiography. Curr Cardiol Rep, 2020, 22(9): 99.
32. Shomaji S, Dehghanzadeh P, Roman A, et al. Early detection of cardiovascular diseases using wearable ultrasound device. IEEE Consum Electron Mag, 2019, 8: 12-21.
33. Hu H, Huang H, Li M, et al. A wearable cardiac ultrasound imager. Nature, 2023, 613(7945): 667-675.
34. Lim GB. A wearable ultrasonic device to image cardiac function. Nat Rev Cardiol, 2023, 20(4): 212.
35. Carpenter A, Frontera A. Smart-watches: a potential challenger to the implantable loop recorder?. Europace, 2016, 18(6): 791-793.
36. Bayoumy K, Gaber M, Elshafeey A, et al. Smart wearable devices in cardiovascular care: where we are and how to move forward. Nat Rev Cardiol, 2021, 18(8): 581-599.
37. Olhede SC, Wolfe PJ. The growing ubiquity of algorithms in society: implications, impacts and innovations. Philos Trans A Math Phys Eng Sci, 2018, 376(2128): 20170364.

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Application status and development prospects of smart wearable devices in cardiovascular diseases

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