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Monitoring exercise intensity in diabetes: applicability of “heart rate-index” to estimate oxygen consumption during aerobic and resistance training

  • A. L. Colosio
  • G. Spigolon
  • E. Bacchi
  • P. Moghetti
  • S. PogliaghiEmail author
Original Article
First Online:

Abstract

Purpose

Accurate quantification and monitoring of exercise “dose”, described by oxygen consumption (VO2), is necessary for exercise prescription and individualization. However, due to the complexity and elevated cost of direct, gold-standard methods, this is rarely done outside research laboratories. Heart rate-index (HRindex) is a new simple method to estimate VO2 in healthy and clinical populations. We tested the performance of HRindex to estimate VO2 in diabetic patients during aerobic (AT) and isotonic training (IT).

Methods

Data from 12 males (age: 64 ± 5 years; BMI: 26 ± 12) with type 2 diabetes were analysed. VO2 and heart rate were measured during one AT and one IT session. Furthermore, VO2 was indirectly estimated based on HRindex. Then, the correspondence between measured and estimated VO2 was evaluated by two-way RM-ANOVA, correlation and Bland–Altman analysis.

Results

Estimated average VO2 values during AT (1292 ± 366 ml/min) were not different from (p = 0.243) and highly correlated with (r = 0.87, p < 0.001) the measured values (1369 ± 417 ml/min), with a small bias and imprecision. Conversely during IT, HRindex overestimated VO2 compared to the actual measures (1048 ± 404 vs 667 ± 230 ml/min, p ≤ 0.001) and only a moderate correlation was found between values (r = 0.43, p ≤ 0.001), with a large bias and imprecision.

Conclusion

VO2 of aerobic exercises can be accurately estimated in diabetes patients using HRindex. During isotonic exercise, this method is not recommended for monitoring metabolic intensity due to large overestimation and imprecision. In aerobic exercise, HRindex offers a simple and valid alternative to the direct VO2 determination and may favour the applicability of time-resolved measures of exercise “dose”.

Keywords

Exercise prescription Oxidative metabolism Field methods Physical activity 

Abbreviations

ANOVA

Analysis of variance

AT

Aerobic training

BMI

Body mass index

estVO2

Estimated oxygen consumption

HbA1c

Glycated haemoglobin

HR

Heart rate

HRindex

Heart rate-index

IT

Isotonic training

MET

Metabolic equivalent

mVO2

Measured oxygen consumption

T2DM

Type 2 diabetes mellitus

T0

Exercise onset

VO2

Oxygen consumption

VO2max

Maximal oxygen consumption

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Notes

Author contributions

ACL and SP conceived and designed the study, GS and EB acquired the data. ACL and SP wrote the first draft of the manuscript. All authors interpreted the data, contributed to writing and revising the manuscript and approved the final version.

Funding

None.

Compliance with ethical standards

Conflict of interest

All the authors declare that they have no conflict of interest.

Ethical approval

This study was conducted in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments.

Informed consent

Written informed consent was obtained from all patients included in this study

References

  1. 1.
    American College of Sports Medicine (2017) ACSM’S guidelines for exercise testing and prescription, 10th edn. Wolters Kluwer, PhiladelphiaGoogle Scholar
  2. 2.
    Colberg SR, Sigal RJ, Fernhall B et al (2010) Exercise and type 2 diabetes: the American College of Sports Medicine and the American Diabetes Association: joint position statement. Diabetes Care 33:e147–e167.  https://doi.org/10.2337/dc10-9990 CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Bacchi E, Negri C, Zanolin ME et al (2012) Metabolic effects of aerobic training and resistance training in type 2 diabetic subjects: a randomized controlled trial (the RAED2 study). Diabetes Care 35:676–682.  https://doi.org/10.2337/dc11-1655 CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Garber CE, Blissmer B, Deschenes MR et al (2011) Quantity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults: guidance for prescribing exercise. Med Sci Sports Exerc 43:1334–1359.  https://doi.org/10.1249/MSS.0b013e318213fefb CrossRefPubMedGoogle Scholar
  5. 5.
    Bacchi E, Negri C, Trombetta M et al (2012) Differences in the acute effects of aerobic and resistance exercise in subjects with type 2 diabetes: results from the RAED2 randomized trial. PLoS One 7:e49937.  https://doi.org/10.1371/journal.pone.0049937 CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Peake JM, Kerr G, Sullivan JP (2018) A critical review of consumer wearables, mobile applications, and equipment for providing biofeedback, monitoring stress, and sleep in physically active populations. Front Physiol 9:1–19.  https://doi.org/10.3389/fphys.2018.00743 CrossRefGoogle Scholar
  7. 7.
    Achten J, Jeukendrup AE (2003) Heart rate monitoring: applications and limitations. Sport Med 33:517–538.  https://doi.org/10.2165/00007256-200333070-00004 CrossRefGoogle Scholar
  8. 8.
    Wicks JR, Oldridge NB, Nielsen LK, Vickers CE (2011) HR index—a simple method for the prediction of oxygen uptake. Med Sci Sports Exerc 43:2005–2012.  https://doi.org/10.1249/MSS.0b013e318217276e CrossRefPubMedGoogle Scholar
  9. 9.
    Haller JM, Fehling PC, Barr DA et al (2013) Use of the HR index to predict maximal oxygen uptake during different exercise protocols. Physiol Rep 1:e00124.  https://doi.org/10.1002/phy2.124 CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Colosio AL, Pogliaghi S (2018) Quantification of energy expenditure of military loaded runs: what is the performance of laboratory-based equations when applied to the field environment? J R Army Med Corps 164:253–258.  https://doi.org/10.1136/jramc-2017-000887 CrossRefPubMedGoogle Scholar
  11. 11.
    Colosio AL, Pedrinolla A, Da Lozzo G, Pogliaghi S (2018) Heart rate-index estimates oxygen uptake, energy expenditure and aerobic fitness in rugby players. J Sport Sci Med 17:633–639Google Scholar
  12. 12.
    Zuccarelli L, Porcelli S, Rasica L et al (2018) Comparison between slow components of HR and VO2 kinetics. Med Sci Sport Exerc 50:1649–1657.  https://doi.org/10.1249/MSS.0000000000001612 CrossRefGoogle Scholar
  13. 13.
    Lipscombe L, Booth G, Butalia S et al (2018) Diabetes Canada 2018 Clinical Practice Guidelines for the Prevention and Management of Diabetes in Canada: pharmacologic Glycemic Management of Type 2 Diabetes in Adults. Can J Diabetes 42:S88–S103.  https://doi.org/10.1016/j.jcjd.2017.10.021 CrossRefPubMedGoogle Scholar
  14. 14.
    MacDougall JD, Tuxen D, Sale DG et al (1985) Arterial blood pressure response to heavy resistance exercise. J Appl Physiol 58:785–790.  https://doi.org/10.1152/jappl.1985.58.3.785 CrossRefPubMedGoogle Scholar
  15. 15.
    Gotshall RW, Gootman J, Byrnes WC et al (1999) Noninvasive characterization of the blood pressure response to the double-leg press exercise. J Exerc Physiol 2:1–6Google Scholar
  16. 16.
    Spigolon G, Fontana FY, Bacchi E et al (2016) VO2/PO relationship in type 2 diabetic subjects. Med Sci Sport Exerc 48:607.  https://doi.org/10.1249/01.mss.0000486822.64860.1f CrossRefGoogle Scholar
  17. 17.
    Pogliaghi S, Bellotti C, Paterson DH (2014) “Tailored” submaximal step test for VO2max prediction in healthy older adults. J Aging Phys Act 22:261–268.  https://doi.org/10.1123/JAPA.2012-0171 CrossRefPubMedGoogle Scholar
  18. 18.
    Pogliaghi S, Dussin E, Tarperi C et al (2007) Calculation of oxygen uptake efficiency slope based on heart rate reserve end-points in healthy elderly subjects. Eur J Appl Physiol 101:691–696.  https://doi.org/10.1007/s00421-007-0545-1 CrossRefPubMedGoogle Scholar
  19. 19.
    Brzycki M (1993) Predicting a one-rep max from reps-to-fatigue. J Phys Educ Recreat Danc 64:88–90.  https://doi.org/10.1080/07303084.1993.10606684 CrossRefGoogle Scholar
  20. 20.
    McMurray RG, Soares J, Caspersen CJ, McCurdy T (2014) Examining variations of resting metabolic rate of adults: a public health perspective. Med Sci Sports Exerc 46:1352–1358.  https://doi.org/10.1249/MSS.0000000000000232 CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Tanaka H, Monahan KD, Seals DR (2001) Age-predicted maximal heart rate revisited. J Am Coll Cardiol 37:153–156.  https://doi.org/10.1016/S0735-1097(00)01054-8 CrossRefPubMedGoogle Scholar
  22. 22.
    Palatini P, Benetos A, Grassi G et al (2006) Identification and management of the hypertensive patient with elevated heart rate: statement of a European Society of Hypertension Consensus Meeting. J Hypertens 24:603–610.  https://doi.org/10.1097/01.hjh.0000217838.49842.1e CrossRefPubMedGoogle Scholar
  23. 23.
    Capelli C, Cautero M, Pogliaghi S (2011) Algorithms, modelling and V̇O2kinetics. Eur J Appl Physiol 111:331–342.  https://doi.org/10.1007/s00421-010-1396-8 CrossRefPubMedGoogle Scholar
  24. 24.
    Pogliaghi S, Terziotti P, Cevese A et al (2006) Adaptations to endurance training in the healthy elderly: arm cranking versus leg cycling. Eur J Appl Physiol 97:723–731.  https://doi.org/10.1007/s00421-006-0229-2 CrossRefPubMedGoogle Scholar
  25. 25.
    Esco MR, Olson MS, Williford HN et al (2012) Crossvalidation of two heart rate-based equations for predicting VO2max in white and black men. J Strength Cond Res 26:1920–1927.  https://doi.org/10.1519/JSC.0b013e318238e863 CrossRefPubMedGoogle Scholar
  26. 26.
    di Prampero PE (1986) The energy cost of human locomotion on land and in water. Int J Sports Med 7:55–72.  https://doi.org/10.1055/s-2008-1025736 CrossRefPubMedGoogle Scholar

Copyright information

© Italian Society of Endocrinology (SIE) 2019

Authors and Affiliations

  1. 1.Department of Neurosciences, Biomedicine and Movement SciencesUniversity of VeronaVeronaItaly
  2. 2.Department of Medicine, Section of Endocrinology, Diabetes and MetabolismUniversity and AOUI of VeronaVeronaItaly

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