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Cited by 21Cardiovascular and exercise physiologySports Performance and TrainingPhysical Activity and Health

Are wearable heart rate measurements accurate to estimate aerobic energy cost during low-intensity resistance exercise?

Open access
Reis, Victor M., Vianna, Jeferson M., Barbosa, Tiago M., Garrido, Nuno, Vilaça Alves, Jose, Carneiro, André L., Aidar, Felipe J., Novaes, Jefferson·2019·PLOS ONE
DOI 10.1371/journal.pone.0221284PMC PMC6705857
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Abstract

The aim of the present study was to assess the accuracy of heart rate to estimate energy cost during eight resistance exercises performed at low intensities: half squat, 45° inclined leg press, leg extension, horizontal bench press, 45° inclined bench press, lat pull down, triceps extension and biceps curl. 56 males (27.5 ± 4.9 years, 1.78 ± 0.06 m height, 78.67 ± 10.7 kg body mass and 11.4 ± 4.1% estimated body fat) were randomly divided into four groups of 14 subjects each. Two exercises were randomly assigned to each group and subjects performed four bouts of 4-min constant-intensity at each assigned exercise: 12%, 16%, 20% and 24% 1-RM. Exercise and intensity order were random. Each subject performed no more than 2 bouts in the same testing session. A minimum recovery of 24h was kept between sessions. During testing VO2 was measured with Cosmed K4b2 and heart rate was measured with Polar V800 monitor. Energy cost was calculated from mean VO2 during the last 30-s of each bout by using the energy equivalent 1 ml O2 = 5 calorie. Linear regressions with heart rate as predictor and energy cost as dependent variable were build using mean data from all subjects. Robustness of the regression lines was given by the scatter around the regression line (Sy.x) and Bland-Altman plots confirmed the agreement between measured and estimated energy costs. Significance level was set at p≤0.05. The regressions between heart rate and energy cost in the eight exercises were significant (p<0.01) and robustness was: half squat (Sy.x = 0,48 kcal·min-1), 45° inclined leg press (Sy.x = 0,54 kcal·min-1), leg extension (Sy.x = 0,59 kcal·min-1), horizontal bench press (Sy.x = 0,47 kcal·min-1), 45° inclined bench press (Sy.x = 0,54 kcal·min-1), lat pull down (Sy.x = 0,28 kcal·min-1), triceps extension (Sy.x = 0,08 kcal·min-1) and biceps curl (Sy.x = 0,13 kcal·min-1). We conclude that during low-intensity resistance exercises it is possible to estimate aerobic energy cost by wearable heart rate monitors with errors below 10% in healthy young trained males.

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  • Heart rate measurements from wearable devices are often less accurate during physical activity or cyclic wrist motions.

    “Several studies have demonstrated that HR measurements from wearable devices are often less accurate during physical activity or cyclic wrist motions.”

    From Investigating sources of inaccuracy in wearable optical heart rate sensors (2020) · cites this paper as [39]

    The Discussion reports that some wearable HR devices had errors above 10% when used to predict energy cost during higher-intensity resistance exercise, while this study found errors below 10% at low intensity. That supports a narrower, activity-dependent concern about energy-cost estimates, not the claim that HR measurements themselves are often less accurate during physical activity; the paper does not address cyclic wrist motions. Our copy provides no page numbers for these passages. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Wearable devices have higher error during activity than at rest.

    “Our analyses reinforces the growing body of research demonstrating that wearable devices have higher error during activity than at rest.8,11,35,38,39”

    From Investigating sources of inaccuracy in wearable optical heart rate sensors (2020) · cites this paper as [39]

    The Results and Conclusions report estimation errors during low-intensity resistance exercise, but do not compare them with errors at rest. A search for resting measurements and an activity-versus-rest accuracy comparison found no passage addressing that comparison in our copy. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 3

  1. [5]Oliveira JC, Baldissera V, Simões HG, Aguiar AP, Azevedo P, Poian P et al Identification of the lactate threshold and the blood glucose threshold in resistance exercise. Braz J Sports Med. 2006; 12: 333– 338.
  2. [6]Rocha RM, Bomfim DL, Nascimento TB, Moreira SR, Simões HG. Variation in the incremental workload method does not change the lactate threshold determination in resistance exercise. Braz J Sports Med. 2010; 16: 282– 285.
  3. [9]Kraemer WJ, Fry AC. Strength testing: development and evaluation of methodology In: Maud P and Nieman DC, editors. Fitness and sports medicine: a health-related approach. Palo Alto: Bull Publishing; 1995 pp. 115– 138.

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Authors
Reis, V. M., Vianna, J. M., Barbosa, T. M., Garrido, N., Vilaça Alves, J., Carneiro, A. L., Aidar, F. J., & Novaes, J.
Affiliations
  • Nanyang Technological University (SG)
  • University of Trás-os-Montes and Alto Douro (PT)
  • Universidade Estadual de Montes Claros (BR)
  • Universidade Federal de Sergipe (BR)
  • Universidade Federal de Juiz de Fora (BR)
ORCID
Venue
PLOS ONE
Publisher
Public Library of Science (PLoS)
Published
2019-08-22
Volume
14
Issue
8
Pages
e0221284
ISSN
1932-6203 (ISSN-L)
DOI
10.1371/journal.pone.0221284
PMC
PMC6705857
OpenAlex
W2969220010
Cited by
21
References
16
Citations by year
2026: 2 · 2024: 4 · 2023: 3 · 2022: 1 · 2021: 5 · 2020: 6
FWCI
3.87
Type
journal article
Language
en
Update notices
correction (2020-03-05)
Open access
Licence
Locations
  • PLoS ONE (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Norte 2020 (NanoSTIMA, NORTE-01-0145- FEDER-000016)
Topics
  • Health Sciences › Medicine › Complementary and alternative medicine › Cardiovascular and exercise physiology
  • Health Sciences › Medicine › Orthopedics and Sports Medicine › Sports Performance and Training
  • Health Sciences › Medicine › Physiology › Physical Activity and Health
Keywords
heart rate monitoring, wearable heart rate monitor, resistance training, low-intensity exercise, energy expenditure estimation, VO2, exercise intensity, half squat, bench press, leg press, triceps extension, biceps curl, lat pull-down, leg extension, energy equivalents, Bland-Altman agreement, smartwatches
MeSH terms
Wearable Electronic Devices, Adult, Energy Metabolism, Heart Rate, Humans, Male, Monitoring, Physiologic, Oxygen Consumption, Linear Models, Muscle, Skeletal, Resistance Training

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