Skip to main content

Current and Potential Applications of Wearables in Sports Cardiology

Abstract

Purpose of the review

Commercial wearable biosensors are commonly used among athletes and highly active individuals, although their value in sports cardiology is not well established. In this review, we discuss the evidence for the current applications of wearables and provide our outlook for promising future directions of this emerging field.

Recent findings

The integration of routine assessment of physiological parameters, activity data, and features such as electrocardiogram recording has generated excitement over a role for wearables to help diagnose and monitor cardiovascular disease. Presently, however, there are significant challenges limiting their routine clinical use. While studies suggest that wearable-derived data may help guide training, evidence for the use of wearables in guiding exercise regimens for individuals with cardiovascular disease is lacking. Further, there is a paucity of data to demonstrate its efficacy in detecting exercise-related arrhythmias or conditions associated with sudden cardiac death. Nevertheless, future technological developments may lead to a greater potential for wearables to aid in sports cardiology practice.

Summary

The ability to collect vast amounts of physiological information can help athletes personalize training regimens. However, interpretation of these data and separating the signal from the noise are paramount, especially when used in a clinical setting. While there are currently no standardized approaches for the use of wearable-derived data in sports cardiology, we outline three domains in which they could guide the care of athletes in the future: (1) optimizing athletic performance (2) guiding exercise in athletes with known cardiovascular disease, and (3) screening for cardiovascular disease.

This is a preview of subscription content, access via your institution.

Fig. 1.
Fig. 2.
Fig. 3.

Availability of data and materials

Not applicable.

References and Recommended Reading

  1. Liu S. Number of connected wearable devices worldwide from 2016 to 2022. 2019.

  2. Samydurai K. Technology: a key to patient satisfaction. Manag Heal Care Exec. 2016.

  3. Bloss CS, Wineinger NE, Peters M, Boeldt DL, Ariniello L, Kim JY, Sheard J, Komatireddy R, Barrett P, Topol EJ. A prospective randomized trial examining health care utilization in individuals using multiple smartphone-enabled biosensors. Peer J. 2016;4:e1554.

  4. Nielsen JC, Lin YJ, Oliveira Figueiredo MJ, de, Sepehri Shamloo A, Alfie A, Boveda S, Dagres N, Toro D Di, Eckhardt LL, Ellenbogen K, Hardy C, Ikeda T, Jaswal A, Kaufman E, Krahn A, Kusano K, Kutyifa V, Lim HS, Lip GYH, Nava-Townsend S, Pak HN, Diez GR, Sauer W, Saxena A, Svendsen JH, Vanegas D, Vaseghi M, Wilde A, Bunch TJ, Buxton AE, Calvimontes G, Chao TF, Eckardt L, Estner H, Gillis AM, Isa R, Kautzner J, Maury P, Moss JD, Nam GB, Olshansky B, Pava Molano LF, Pimentel M, Prabhu M, Tzou WS, Sommer P, Swampillai J, Vidal A, Deneke T, Hindricks G et al. European Heart Rhythm Association (EHRA)/Heart Rhythm Society (HRS)/Asia Pacific Heart Rhythm Society (APHRS)/Latin American Heart Rhythm Society (LAHRS) expert consensus on risk assessment in cardiac arrhythmias: use the right tool for the right outcome. Hear Rhythm. 2020;17:e269–316.

    Article  Google Scholar 

  5. Väliaho ES, Kuoppa P, Lipponen JA, Martikainen TJ, Jäntti H, Rissanen TT, Kolk I, Castrén M, Halonen J, Tarvainen MP. Wrist band photoplethysmography in detection of individual pulses in atrial fibrillation and algorithm-based detection of atrial fibrillation. EP Eur. 2019;21:1031–8.

    Google Scholar 

  6. Brasier N, Raichle CJ, Dörr M, Becke A, Nohturfft V, Weber S, Bulacher F, Salomon L, Noah T, Birkemeyer R. Detection of atrial fibrillation with a smartphone camera: first prospective, international, two-centre, clinical validation study (DETECT AF PRO). Ep Eur. 2019;21:41–7.

    Google Scholar 

  7. Verbrugge FH, Proesmans T, Vijgen J, Mullens W, Rivero-Ayerza M, Van HH, Vandervoort P, Nuyens D. Atrial fibrillation screening with photo-plethysmography through a smartphone camera. EP Eur. 2019;21:1167–75.

    Google Scholar 

  8. Kamiŝalić A, Fister I, Turkanović M, Karakatiĉ S. Sensors and functionalities of non-invasive wrist-wearable devices: a review. Sensors (Switzerland). 2018;18. Available at: https://pubmed.ncbi.nlm.nih.gov/29799504/. Accessed March 30, 2021.

  9. Shelley KH. Photoplethysmography: beyond the calculation of arterial oxygen saturation and heart rate. Anesth Analg. 2007;105. Available at: https://pubmed.ncbi.nlm.nih.gov/18048895/. Accessed April 3, 2021.

  10. Li X, Dunn J, Salins D, Zhou G, Zhou W, Schüssler-Fiorenza Rose SM, Perelman D, Colbert E, Runge R, Rego S, Sonecha R, Datta S, McLaughlin T, Snyder MP. Digital health: tracking physiomes and activity using wearable biosensors reveals useful health-related information. PLoS Biol. 2017;15:e2001402.

  11. Gillinov S, Etiwy M, Wang R, Blackburn G, Phelan D, Gillinov AM, Houghtaling P, Javadikasgari H, Desai MY. Variable accuracy of wearable heart rate monitors during aerobic exercise. Med Sci Sport Exerc. 2017;49:1697–703.

    Article  Google Scholar 

  12. Etiwy M, Akhrass Z, Gillinov L, Alashi A, Wang R, Blackburn G, Gillinov SM, Phelan D, Marc Gillinov A, Houghtaling PL, Javadikasgari H, Desai MY. Accuracy of wearable heart rate monitors in cardiac rehabilitation. Cardiovasc Diagn Ther. 2019;9:262–271. Available at: https://pubmed.ncbi.nlm.nih.gov/31275816/. Accessed March 30, 2021.

  13. Pasadyn SR, Soudan M, Gillinov M, Houghtaling P, Phelan D, Gillinov N, Bittel B, Desai MY. Accuracy of commercially available heart rate monitors in athletes: a prospective study. Cardiovasc Diagn Ther. 2019;9:379–385. Available at: https://pubmed.ncbi.nlm.nih.gov/31555543/. Accessed March 30, 2021.

  14. Dagher L, Shi H, Zhao Y, Marrouche NF. Wearables in cardiology: here to stay. Hear Rhythm. 2020;17:889–895. Available at: https://pubmed.ncbi.nlm.nih.gov/32354455/. Accessed March 30, 2021.

  15. Gil MÁC. Standard and precordial leads obtained with an apple watch. Ann Intern Med. 2020;172:436–437. Available at: https://pubmed.ncbi.nlm.nih.gov/31766051/. Accessed March 30, 2021.

  16. Yang CC, Hsu YL. A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors. 2010;10:7772–7788. Available at: www.mdpi.com/journal/sensors.

  17. Karlen W, Raman S, Ansermino JM, Dumont GA. Multiparameter respiratory rate estimation from the photoplethysmogram. IEEE Trans Biomed Eng. 2013;60:1946–1953. Available at: https://pubmed.ncbi.nlm.nih.gov/23399950/. Accessed April 3, 2021.

  18. Pimentel MAF, Johnson AEW, Charlton PH, Birrenkott D, Watkinson PJ, Tarassenko L, Clifton DA. Toward a robust estimation of respiratory rate from pulse oximeters. IEEE Trans Biomed Eng. 2017;64:1914–1923. Available at: https://pubmed.ncbi.nlm.nih.gov/27875128/. Accessed April 3, 2021.

  19. Charlton PH, Birrenkott DA, Bonnici T, Pimentel MAF, Johnson AEW, Alastruey J, Tarassenko L, Watkinson PJ, Beale R, Clifton DA, Birrenkott DA, Pimentel MAF, Tarassenko L, Clifton DA, Johnson AEW, Alastruey J, Watkinson PJ. Breathing rate estimation from the electrocardiogram and photoplethysmogram: a review. IEEE Rev Biomed Eng. 2018;11.

  20. Charlton PH, Bonnici T, Tarassenko L, Alastruey J, Clifton DA, Beale R, Watkinson PJ. Extraction of respiratory signals from the electrocardiogram and photoplethysmogram: technical and physiological determinants. Physiol Meas. 2017;38:669–690. Available at: https://pubmed.ncbi.nlm.nih.gov/28296645/. Accessed April 3, 2021.

  21. Crouter SE, Clowers KG, Bassett DR. A novel method for using accelerometer data to predict energy expenditure. J Appl Physiol. 2006;100:1324–1331. Available at: https://pubmed.ncbi.nlm.nih.gov/16322367/. Accessed April 3, 2021.

  22. Lin CW, Yang YTC, Wang JS, Yang YC. A wearable sensor module with a neural-network-based activity classification algorithm for daily energy expenditure estimation. IEEE Trans Inf Technol Biomed. 2012;16:991–998. Available at: https://pubmed.ncbi.nlm.nih.gov/22875251/. Accessed April 3, 2021.

  23. Rothney MP, Neumann M, Béziat A, Chen KY. An artificial neural network model of energy expenditure using nonintegrated acceleration signals. J Appl Physiol. 2007;103:1419–1427. Available at: https://pubmed.ncbi.nlm.nih.gov/17641221/. Accessed April 3, 2021.

  24. Uth N, Henrik AE, Ae S, Overgaard K, Pedersen PK. Estimation of _ V O 2max from the ratio between HR max and HR rest-the Heart Rate Ratio Method.

  25. Abut F, Akay MF. Machine learning and statistical methods for the prediction of maximal oxygen uptake: recent advances. Med Devices Evid Res. 2015;8:369–379. Available at: https://doi.org/10.2147/MDER.S57281.

  26. Malhotra A, Dhutia H, Finocchiaro G, Gati S, Beasley I, Clift P, Cowie C, Kenny A, Mayet J, Oxborough D, Patel K, Pieles G, Rakhit D, Ramsdale D, Shapiro L, Somauroo J, Stuart G, Varnava A, Walsh J, Yousef Z, Tome M, Papadakis M, Sharma S. Outcomes of cardiac screening in adolescent soccer players. N Engl J Med. 2018;379:524–34.

    Article  Google Scholar 

  27. Strik M, Caillol T, Daniel Ramirez F, Abu-Alrub S, Marchand H, Welte N, Ritter P, Haïssaguerre M, Ploux S, Bordachar P. Validating QT-Interval Measurement Using the Apple Watch ECG to enable remote monitoring during the COVID-19 pandemic circulation https://www.ahajournals.org/journal/circ. Circulation. 2020;142:416–418.

  28. Perez MV, Mahaffey KW, Hedlin H, Rumsfeld JS, Garcia A, Ferris T, Balasubramanian V, Russo AM, Rajmane A, Cheung L. Large-scale assessment of a smartwatch to identify atrial fibrillation. N Engl J Med. 2019;381:1909–17.

    Article  Google Scholar 

  29. Guo Y, Wang H, Zhang H, Liu T, Liang Z, Xia Y, Yan L, Xing Y, Shi H, Li S. Mobile photoplethysmographic technology to detect atrial fibrillation. J Am Coll Cardiol. 2019;74:2365–75.

    Article  Google Scholar 

  30. Abdulla J, Nielsen JR. Is the risk of atrial fibrillation higher in athletes than in the general population? A systematic review and meta-analysis. Eur Eur pacing, arrhythmias, Card Electrophysiol J Work groups Card pacing, arrhythmias, Card Cell Electrophysiol Eur Soc Cardiol. 2009;11:1156–9.

    Google Scholar 

  31. Maron BJ, Zipes DP, Kovacs RJ. Eligibility and disqualification recommendations for competitive athletes with cardiovascular abnormalities: preamble, principles, and general considerations: a scientific statement from the American Heart Association and American College of Cardiology. J Am Coll Cardiol. 2015;66:2343–9.

    Article  Google Scholar 

  32. Ommen SR, Mital S, Burke MA, Day SM, Deswal A, Elliott P, Evanovich LL, Hung J, Joglar JA, Kantor P, Kimmelstiel C, Kittleson M, Link MS, Maron MS, Martinez MW, Miyake CY, Schaff H V., Semsarian C, Sorajja P. AHA/ACC guideline for the diagnosis and treatment of patients with hypertrophic cardiomyopathy: executive summary: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation. 2020;142:533–557. Available at: https://pubmed.ncbi.nlm.nih.gov/33215938/. Accessed April 7, 2021.

  33. Anderson L, Sharp GA, Norton RJ, Dalal H, Dean SG, Jolly K, Cowie A, Zawada A, Taylor RS. Home-based versus centre-based cardiac rehabilitation. Cochrane database Syst Rev. 2017;6:CD007130.

  34. Seshadri DR, Thom ML, Harlow ER, Gabbett TJ, Geletka BJ, Hsu JJ, Drummond CK, Phelan DM, Voos JE. Wearable technology and analytics as a complementary toolkit to optimize workload and to reduce injury burden. Front Sport Act Living. 2021;2. Available at: https://pubmed.ncbi.nlm.nih.gov/33554111/. Accessed March 30, 2021.

  35. Gabbett TJ. The training—injury prevention paradox: should athletes be training smarter and harder? Br J Sport Med. 2016;50:273–280. Available at: https://bjsm.bmj.com/content/50/5/273.

  36. Halson SL, Jeukendrup AE. Does overtraining exist? An analysis of overreaching and overtraining research. Sport Med. 2004;34:967–981. Available at: https://pubmed.ncbi.nlm.nih.gov/15571428/. Accessed March 30, 2021.

  37. Kiviniemi AM, Hautala AJ, Kinnunen H, Tulppo MP. Endurance training guided individually by daily heart rate variability measurements. Eur J Appl Physiol. 2007;101:743–751. Available at: https://pubmed.ncbi.nlm.nih.gov/17849143/. Accessed March 30, 2021.

  38. Kiviniemi AM, Hautala AJ, Kinnunen H, Nissilä J, Virtanen P, Karjalainen J, Tulppo MP. Daily exercise prescription on the basis of hr variability among men and women. Med Sci Sports Exerc. 2010;42:1355–1363. Available at: https://pubmed.ncbi.nlm.nih.gov/20575165/. Accessed March 30, 2021.

  39. Vesterinen V, Nummela A, Heikura I, Laine T, Hynynen E, Botella J, Häkkinen K. Individual endurance training prescription with heart rate variability. Med Sci Sports Exerc. 2016;48:1347–1354. Available at: https://pubmed.ncbi.nlm.nih.gov/26909534/. Accessed March 30, 2021.

  40. Javaloyes A, Sarabia JM, Lamberts RP, Moya-Ramon M. Training prescription guided by heart-rate variability in cycling. Int J Sports Physiol Perform. 2019;14:23–32. Available at: https://pubmed.ncbi.nlm.nih.gov/29809080/. Accessed March 30, 2021.

  41. Plews DJ, Laursen PB, Kilding AE, Buchheit M. Heart-rate variability and training-intensity distribution in Elite rowers. Int J Sports Physiol Perform. 2014;9:1026–1032. Available at: https://pubmed.ncbi.nlm.nih.gov/24700160/. Accessed March 30, 2021.

  42. Fischer A, Forsberg F, Lapisa M. SB-M & undefined. Integrating mems and ics. Nature.com. 2015. Available at: https://www.nature.com/articles/micronano20155. Accessed March 31, 2021.

  43. Hsieh AC, Hwang T, Chang MT, Tsai MH, Tseng CM, Li HC. TSV redundancy: Architecture and design issues in 3D IC. In: Proceedings -Design, Automation and Test in Europe, DATE. 2010;166–171.

  44. Wu B, Kumar A, Ramaswami S. 3D IC Stacking Technology. 1 edition. Place of publication not identified: McGraw-Hill Education; 2011. Available at: https://www.amazon.com/3D-IC-Stacking-Technology-Banqiu/dp/007174195X.

  45. Farahbakhsh N, Venditti RA, Jur JS. Mechanical and thermal investigation of thermoplastic nanocomposite films fabricated using micro- and nano-sized fillers from recycled cotton T-shirts. Cellulose. 2014;21:2743–2755. Available at: https://doi.org/10.1007/s10570-014-0285-4.

  46. Xu R, Lee JW, Pan T, Ma S, Wang J, Han JH, Ma Y, Rogers JA, Huang Y. Designing thin, ultrastretchable electronics with stacked circuits and elastomeric encapsulation materials. Adv Funct Mater. 2017;27:1604545. Available at: https://doi.org/10.1002/adfm.201604545.

  47. Xu S, Zhang Y, Jia L, Mathewson KE, Jang K-I, Kim J, Fu H, Huang X, Chava P, Wang R, Bhole S, Wang L, Na YJ, Guan Y, Flavin M, Han Z, Huang Y, Rogers JA. Soft microfluidic assemblies of sensors, circuits, and radios for the skin. Science. 2014;344:70–74. Available at: http://www.ncbi.nlm.nih.gov/pubmed/24700852.

  48. Araki H, Kim J, Zhang S, Banks A, Crawford KE, Sheng X, Gutruf P, Shi Y, Pielak RM, Rogers JA. Materials and device designs for an epidermal UV colorimetric dosimeter with near field communication capabilities. Adv Funct Mater. 2017;27:1604465. Available at: https://doi.org/10.1002/adfm.201604465.

  49. Webb RC, Pielak RM, Bastien P, Ayers J, Niittynen J, Kurniawan J, Manco M, Lin A, Cho NH, Malyrchuk V, Balooch G, Rogers JA. Thermal transport characteristics of human skin measured in vivo using ultrathin conformal arrays of thermal sensors and actuators. Ugaz VM, ed. PLoS One. 2015;10:e0118131. Available at: https://doi.org/10.1371/journal.pone.0118131.

  50. Yildiz O, Stano K, Faraji S, Stone C, Willis C, Zhang X, Jur JS, Bradford PD. High performance carbon nanotube – polymer nanofiber hybrid fabrics. Nanoscale. 2015;7:16744–16754. Available at: http://pubs.rsc.org/en/content/articlelanding/2015/nr/c5nr02732b.

Download references

Funding

J.J.H. is supported by a career development grant from the NIH (1K08HL151961-01).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jeffrey J. Hsu MD, PhD.

Ethics declarations

Conflict of Interest

Prashant Rao declares that he has no conflict of interest. Dhruv R. Seshadri declares that he has no conflict of interest. Jeffrey J. Hsu declares that he has no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Topical Collection on Sports Cardiology

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Rao, P., Seshadri, D.R. & Hsu, J.J. Current and Potential Applications of Wearables in Sports Cardiology. Curr Treat Options Cardio Med 23, 65 (2021). https://doi.org/10.1007/s11936-021-00942-1

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11936-021-00942-1

Keywords

  • Wearables
  • Sports cardiology
  • Athlete
  • Cardiovascular performance
  • Recovery