Comparison of Heart-Rate-Variability Recording With Smartphone Photoplethysmography, Polar H7 Chest Strap, and Electrocardiography

in International Journal of Sports Physiology and Performance
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Purpose: To establish the validity of smartphone photoplethysmography (PPG) and heart-rate sensor in the measurement of heart-rate variability (HRV). Methods: 29 healthy subjects were measured at rest during 5 min of guided breathing and normal breathing using smartphone PPG, a heart-rate chest strap, and electrocardiography (ECG). The root mean sum of the squared differences between R–R intervals (rMSSD) was determined from each device. Results: Compared to ECG, the technical error of estimate (TEE) was acceptable for all conditions (average TEE CV% [90% CI] = 6.35 [5.13; 8.5]). When assessed as a standardized difference, all differences were deemed “trivial” (average standard difference [90% CI] = 0.10 [0.08; 0.13]). Both PPG- and heart-rate-sensor-derived measures had almost perfect correlations with ECG (R = 1.00 [0.99; 1.00]). Conclusion: Both PPG and heart-rate sensors provide an acceptable agreement for the measurement of rMSSD when compared with ECG. Smartphone PPG technology may be a preferred method of HRV data collection for athletes due to its practicality and ease of use in the field.

Plews, Scott, Wood, Kilding, and Laursen are with Sports Performance Research Inst New Zealand (SPRINZ), Auckland University of Technology, Auckland, New Zealand. Altini is with ACTLab, University of Passau, Passau, Germany.

Plews (daniel.plews@AUT.ac.nz) is corresponding author.
  • 1.

    Appelboom G, Camacho E, Abraham ME, et al. Smart wearable body sensors for patient self-assessment and monitoring. Arch Public Health. 2014;72:28. PubMed doi:10.1186/2049-3258-72-28

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 2.

    Crawford K. The age of the analyzed athlete: big data helps high-performance sports stars improve their game. 2015. http://www.ca.com/us/rewrite/articles/application-economy/the-age-of-the-analyzed-athlete.html

    • Export Citation
  • 3.

    Buchheit M. Monitoring training status with HR measures: do all roads lead to Rome? Front Physiol. 2014;5:73. PubMed doi:10.3389/fphys.2014.00073

  • 4.

    Task Force of The European Society of Cardiology and The North American Society of Pacing and Electrophysiology. Heart rate variability. Eur Heart J. 1996;17:354381.

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5.

    Plews DJ, Laursen PB, Kilding AE, Buchheit M. Heart rate variability in elite triathletes, is variation in variability the key to effective training? A case comparison. Eur J Appl Physiol. 2012;112:37293741. PubMed doi:10.1007/s00421-012-2354-4

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6.

    Plews DJ, Laursen PB, Kilding AE, Buchheit M. Evaluating training adaptation with heart-rate measures: a methodological comparison. Int J Sports Physiol Perform. 2013;8:688691. PubMed doi:10.1123/ijspp.8.6.688

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 7.

    Plews DJ, Laursen PB, Le Meur Y, Hausswirth C, Kilding AE, Buchheit M. Monitoring training with heart rate-variability: how much compliance is needed for valid assessment? Int J Sports Physiol Perform. 2014;9:783790. PubMed doi:10.1123/ijspp.2013-0455

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8.

    Bagha S, Shaw L. A real time analysis of PPG signal for measurement of SpO2 and pulse rate. Int J Comput Appl. 2011;36:4550.

  • 9.

    Altini M, Amft O. HRV4Training: large-scale longitudinal training load analysis in unconstrained free-living settings using a smartphone application. Conf Proc IEEE Eng Med Biol Soc. 2016;2016:26102613. PubMed

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10.

    Lee J, Reyes BA, McManus DD, Mathias O, Chon KH. Atrial fibrillation detection using a smart phone. Conf Proc IEEE Eng Med Biol Soc. 2012;2012:11771180. PubMed

    • Search Google Scholar
    • Export Citation
  • 11.

    Voss A, Wessel N, Sander A, Malberg H, Dietz R. Influence of low sampling rate on heart rate variability analysis based on nonlinear dynamics. Paper presented at: Computers in Cardiology; September 13–18, 1995. Vienna, Austria.

    • Export Citation
  • 12.

    Lippman N, Stein KM, Lerman BB. Comparison of methods for removal of ectopy in measurement of heart rate variability. Am J Physiol. 1994;267:411418. PubMed

    • Search Google Scholar
    • Export Citation
  • 13.

    Sacknoff D, Gleim G, Stachenfeld N, Glace B, Coplan N. Suppression of high-frequency power spectrum of heart rate variability in well-trained endurance athletes. Circulation. 1992;86:I-658.

    • Search Google Scholar
    • Export Citation
  • 14.

    Flatt AA, Esco MR. Heart rate variability stabilization in athletes: towards more convenient data acquisition. Clin Physiol Funct Imaging. 2015:36(5):331336. doi:10.1111/cpf.12233

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 15.

    Hopkins WA. Analysis of validity by linear regression (Excel spreadsheet). A New View of Statistics: Internet Society for Sport Science. 2000. http://Sportsci.org.

    • Export Citation
  • 16.

    Plews DJ, Laursen PB, Stanley J, Kilding AE, Buchheit M. Training adaptation and heart rate variability in elite endurance athletes: opening the door to effective monitoring. Sports Med. 2013;43:773781. PubMed doi:10.1007/s40279-013-0071-8

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 17.

    Plews DJ, Laursen PB, Kilding AE, Buchheit M. Heart-rate variability and training-intensity distribution in elite rowers. Int J Sports Physiol Perform. 2014;9:10261032. PubMed doi:10.1123/ijspp.2013-0497

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 18.

    Schafer A, Vagedes J. How accurate is pulse rate variability as an estimate of heart rate variability? A review on studies comparing photoplethysmographic technology with an electrocardiogram. Int J Cardiol. 2013;166:1529. PubMed doi:10.1016/j.ijcard.2012.03.119

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19.

    Esco MR, Flatt AA, Nakamura FY. Agreement between a smart-phone pulse sensor application and ECG for determining lnRMSSD [published online ahead of print June 27, 2016]. J Strength Cond Res. PubMed doi:10.1519/JSC.0000000000001519

    • PubMed
    • Search Google Scholar
    • Export Citation
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