Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
志維 彭
 + 1479 word(s) 1479 2022-02-17 03:39:47 |
2 revised
Camila Xu
 -5 word(s) 1474 2022-02-22 10:06:03 | |
3 revised
Camila Xu
 Meta information modification 1474 2022-02-22 10:06:34 |

Video Upload Options

We provide professional Video Production Services to translate complex research into visually appealing presentations. Would you like to try it?
No, upload directly Yes

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
彭, �. Oxygen Consumption (VO2) and Surface Electromyography (sEMG). Encyclopedia. Available online: https://encyclopedia.pub/entry/19734 (accessed on 15 November 2024).
彭 �. Oxygen Consumption (VO2) and Surface Electromyography (sEMG). Encyclopedia. Available at: https://encyclopedia.pub/entry/19734. Accessed November 15, 2024.
彭, 志維. "Oxygen Consumption (VO2) and Surface Electromyography (sEMG)" Encyclopedia, https://encyclopedia.pub/entry/19734 (accessed November 15, 2024).
彭, �. (2022, February 22). Oxygen Consumption (VO2) and Surface Electromyography (sEMG). In Encyclopedia. https://encyclopedia.pub/entry/19734
彭, 志維. "Oxygen Consumption (VO2) and Surface Electromyography (sEMG)." Encyclopedia. Web. 22 February, 2022.
Oxygen Consumption (VO2) and Surface Electromyography (sEMG)
Edit
This entry is adapted from the peer-reviewed paper 10.3390/ijerph19042233

Oxygen consumption (VO2) during strength training can be predicted through surface electromyography (sEMG) of local muscles. Surface electromyography (EMG; sEMG), one of the most common methods of measuring muscle activation, is an electrophysiological recording technique for detecting the electric potential across muscle fiber membranes.

GEE modeling oxygen consumption strength training

1. Introduction

According to the American College of Sports Medicine (ACSM) [1] and the American Heart Association (AHA), strength training is beneficial to one’s health [2]. It has several advantages, including increased strength and beneficial changes in body composition [1]. According to the ACSM, gaining health and fitness benefits from resistance training requires at least one set of eight to twelve repetitions of each of eight to ten exercises involving the major muscle groups [3] on two or more days per week [4].
Previous research has explored the acute metabolic demands during strength exercise. Variables such as the muscle mass [5], exercise speed [6][7][8], number of sets [9][10], number of repetitions [11][12], workload [13][14], training volume [15], and rest intervals [11][16][17] resulted in substantially greater increases in oxygen consumption (VO2) and energy expenditure (EE). Currently, oxygen consumption is commonly measured through indirect calorimetry, which has a stated accuracy of −2% to 4% [16]. However, acute physiological responses to skeletal muscle activation during moderate-strength exercises have not been thoroughly investigated.
Surface electromyography (EMG; sEMG), one of the most common methods of measuring muscle activation, is an electrophysiological recording technique for detecting the electric potential across muscle fiber membranes [17]. One recent research reported a relation between VO2 and muscle activity for squats and heel raises with 80% of one repetition maximum (1RM) in healthy male participants and observed an increase in oxygen uptake after 6 weeks of resistance exercises [18]. Another research investigated the mean correlation between the surface EMG amplitude and oxygen uptake for lower extremity muscles as 0.69~0.87 during treadmill walking in young males [19].
To the best of knowledge, no single research has found that oxygen consumption (VO2) is related to the sEMG of the various upper limb and lower limb muscles during strength exercises at 60% of 1RM. It is an unexplored field of research to find which muscle sEMG has a significant association in computing oxygen consumption in healthy populations. By determining this kind of relationship, better explaining which muscle activation can more or less predict VO2. Some muscles are highly active during the shoulder press, deadlift, and squat, but also observed other muscles which are not highly active during these strength workouts.

2. VO2 Models of Three Training Sessions (Group Models)

VO2 was predicted by the GEE model over the course of three training sessions but did not reach the level of significance. For the untrained group, right biceps femoris (RBFsEMG_rms) [p = 0.330; 95% CI = −0.532~1.586] predicted VO2, and for the trained group, left middle deltoid (LMDsEMG_rms) [p = 0.058; 95% CI = −0.010~0.607] predicted VO2 without attaining the level of significance. QIC values for the group models were 368 and 867 for the untrained and trained groups, respectively (Table 1).
Table 1. Generalized estimating equations for oxygen consumption (VO2; ml/min/kg) predictions for three training sessions (n = 11).
Group Model Parameter Estimate (ß) SE 95% CI (Lower~Upper) p
UTr Model 1 Intercept 9.068 0.530 8.030~10.107 0.000
(QIC 368) RBFsEMG-rms 0.527 0.541 −0.532~1.586 0.330
Tr Model 2 Intercept 11.134 0.703 9.757~12.511 0.000
(QIC 837) LMDsEMG-rms 0.298 0.157 −0.010~0.607 0.058
UTr, untrained (n = 5); Tr, trained (n = 6). SE, standard error; 95% CI, confidence interval; QIC, quasi-likelihood under an independence model criterion; RBFsEMG-rms, right biceps femoris muscle root mean square surface electromyography (sEMG); LMDsEMG-rms, left middle deltoid.

3. Oxygen Consumption (VO2) and Surface Electromyography (sEMG)

When utilizing GEE modeling to predict VO2 for three strength training exercises, including the shoulder press, deadlift, and squat with dumbbells in young participants. For the group models, the right biceps femoris predicted model for VO2 in the untrained group, while the left middle deltoid in the trained group without attaining the level of significance (Table 1). For the exercise models, the right and left middle deltoid, right and left biceps femoris, and left rectus femoris for the shoulder press, right and left lumbar erector spinae, and right biceps femoris for the deadlift and the right and left lumbar erector spinae, biceps femoris, and left middle deltoid for the squat significantly predicted the GEE models (Table 2a–c).
No single previous research predicted VO2 by sEMG of individual muscles during moderate-intensity strength exercises. Because there is not a lot of studies on GEE modeling during strength training exercises, it is difficult to compare computed models with previously available research. One research reported the association between VO2 and sEMG RMS during cycling exercise and reported global sEMG measured from vastus lateralis muscle as a good predictor of energy expenditure in trained cyclists [20]. In some previous studies, one research reported the relation between VO2 and sEMG responses of the anterior tibialis (TA), gastrocnemius medial (MG), gastrocnemius lateral (LG), and soleus muscles during different speeds of treadmill walking in young, healthy males; correlations between VO2 and sEMG were 0.69~0.87 for those muscles [19]. Another research conducted on young males reported the effect of 6 weeks of strength training exercises and whole-body vibration on changes in normalized VO2 and sEMG; they monitored the rectus femoris muscle during squats and lateral gastrocnemius during heel raises [18].
The main goal of the present research was to calculate GEE models for VO2 in two categories (1) group and (2) exercise types. For the group models, none of the groups significantly predicted VO2 from sEMG RMS of individual muscles, but for exercise type models, the shoulder press exercise showed significant relations of the right and left middle deltoid, right and left biceps femoris, and left rectus femoris with VO2 for the untrained group [QIC = 102, * p = 0.000], while in the trained group, only the left rectus femoris [QIC = 82, * p = 0.000] were significantly correlated with the VO2. Lower QIC and significant p-values for the trained group [QIC = 82 vs. 102 and * p = 0.000 vs. * 0.000] are suggestive of a better GEE model than that for the untrained group (Table 2a). For the deadlift and squat exercises, the untrained group models were more predictive than those of the trained group [QIC = 172 vs. 320 and * p = 0.000 vs. * 0.026] and [QIC = 76 vs. 348 and * p = 0.000 vs. * 0.001] (Table 2b,c) [21][22]. The reason why the correlations between the two groups differed lies in the fact that the untrained group participants had no previous experience of strength training, and their 60% 1RM was lower, so their muscle activation occurred differently than that in the trained group. Another factor that may have affected the results was the gender because the untrained group consisted mostly of female participants and trained group males.
Table 2. Generalized estimating equations for oxygen consumption (VO2; mL/min/kg) estimation for three training sessions (n = 11). (a) Shoulder Press. (b) Deadlift. (c) Squat.
(a)
Exercise Group Model Parameter Estimate (ß) SE 95% CI (Lower~Upper) p
Shoulder press UTr Model 1
(QIC 102)		 Intercept 3.489 1.009 1.512~5.466 0.001
LBFsEMG-rms −23.844 2.104 −27.967~−19.721 0.000 *
RMDsEMG-rms 0.942 0.204 0.543~1.341 0.000 *
RBFsEMG-rms 17.088 3.673 9.890~24.286 0.000 *
LMDsEMG-rms −0.737 0.143 −1.016~−0.457 0.000 *
LRFsEMG-rms −1.050 0.304 −1.646~−0.454 0.001 *
Tr Model 2
(QIC 82)		 Intercept 5.727 0.271 5.195~6.259 0.000
LRFsEMG-rms 7.685 1.814 4.131~11.240 0.000 *
(b)
Exercise Group Model Parameter Estimate (ß) SE 95% CI (Lower~Upper) p
Deadlift UTr Model 3
(QIC 172)		 Intercept 11.701 1.065 9.613~13.789 0.000
RLESsEMG-rms 9.366 1.425 6.573~12.159 0.000 *
LLESsEMG-rms −10.428 2.030 −14.407~−6.448 0.000 *
RBFsEMG-rms −2.086 0.459 −2.985~−1.186 0.000 *
Tr Model 4
(QIC 320)		 Intercept 9.314 1.339 6.689~11.939 0.000
LLESsEMG-rms 3.362 1.506 0.411~6.313 0.026 *
(c)
Exercise Group Model Parameter Estimate (ß) SE 95% CI (Lower~Upper) p
Squat UTr Model 5
(QIC 76)		 Intercept 10.328 0.875 8.612~12.043 0.000
LBFsEMG-rms −11.262 0.538 −12.318~−10.207 0.000 *
RLESsEMG-rms −3.318 0.514 −4.325~−2.312 0.000 *
LLESsEMG-rms 6.891 1.420 4.108~9.675 0.000 *
LMDsEMG-rms 0.653 0.126 0.406~0.901 0.000 *
RBFsEMG-rms 1.758 0.391 0.992~2.524 0.000 *
Tr Model 6
(QIC 348)		 Intercept 10.781 0.758 9.295~12.266 0.000
LLESsEMG-rms 0.494 0.155 0.191~0.797 0.001 *
UTr, untrained (n = 5); Tr, trained (n = 6). * Shows a significant difference p < 0.050. SE, standard error; 95% CI, confidence interval; QIC, quasi-likelihood under an independence model criterion; Root mean square surface electromyography (sEMG) of RMDsEMG-rms, right middle deltoid; LMDsEMG-rms, left middle deltoid; RLESsEMG-rms, right lumbar erector spinae; LLESsEMG-rms, left lumbar erector spinae; RBFsEMG-rms, right biceps femoris; LBFsEMG-rms, left biceps femoris; RRFsEMG-rms, right rectus femoris; LRFsEMG-rms, left rectus femoris.
Pre vs. post static muscle strength is shown in Table 3. Some of the muscles’ strength increased after six training sessions, but out of the eight muscles, not a single one reached a significant level, because 2-week trainings are not enough to increase muscle strength as adaptation in strength would require about 12 or more weeks of consecutive trainings. The changes in the VO2 and sEMG_rms after six trainings reported higher VO2 values in both groups, particularly during the squat and deadlift exercises, which may have been due to the greater training loads of these exercises. Meanwhile, the sEMG_rms amplitude was higher for the bilateral middle deltoid during the shoulder press, because it is the major muscle group that is active during that exercise. In the deadlift, the right and left middle deltoid, lumbar erector spinae, and biceps femoris were more active than the rectus femoris, and only the bilateral deltoid and rectus femoris were active during squatting.
Table 3. Two-way repeated measures ANOVA for manual muscle strength (MMT) (n = 11).
Parameters Untrained (n = 5) Trained (n = 6) p-Value
Within Subject
Pre vs. Post		 Between Groups
Right Middle Deltoid 85.08 ± 22.01 156.28 ± 20.09 0.926 0.041 *
Left Middle Deltoid 85.49 ± 17.82 136.28 ± 16.27 0.951 0.065
Right Lumbar Erector Spinae 130.35 ± 23.14 212.27 ± 21.12 0.016 * 0.028 *
Left Lumbar Erector Spinae 134.93 ± 18.87 207.78 ± 17.23 0.017 * 0.019 *
Right Rectus Femoris 281.61 ± 34.25 426.36 ± 31.27 0.170 0.012 *
Left Rectus Femoris 283.40 ± 34.01 408.43 ± 31.04 0.085 0.024 *
Right Biceps Femoris 208.81 ± 19.06 249.48 ± 17.40 0.569 0.149
Left Biceps Femoris 181.45 ± 22.73 256.76 ± 20.75 0.950 0.037 *
Mean ± standard error; participants; n = 11. Muscle strength was assessed through a dynamometer in newtons (N) two times before and after six training sessions. * Shows a significant difference p < 0.050. Mauchly’s sphericity and Greenhouse-Geisser Epsilon were equal to 1 for every muscle, so the assumption for the difference in equal variance was met.

References

  1. Kraemer, W.J.; Adams, K.; Cafarelli, E.; Dudley, G.A.; Dooly, C.; Feigenbaum, M.S.; Fleck, S.J.; Franklin, B.; Fry, A.C.; Hoffman, J.R.; et al. American College of Sports Medicine position stand. Progression models in resistance training for healthy adults. Med. Sci. Sports Exerc. 2002, 34, 364–380.
  2. Fletcher, G.F.; Balady, G.; Froelicher, V.F.; Hartley, L.H.; Haskell, W.L.; Pollock, M.L. Exercise standards: A statement for healthcare professionals from the American Heart Association. Circulation 1995, 91, 580–615.
  3. Pollock, M.L.; Gaesser, G.A.; Butcher, J.D.; Després, J.P.; Dishman, R.K.; Franklin, B.A.; Garber, C.E. ACSM position stand: The recommended quantity and quality of exercise for developing and maintaining cardiorespiratory and muscular fitness, and flexibility in healthy adults. Med. Sci. 1998, 30, 6.
  4. Garber, C.E.; Blissmer, B.; Deschenes, M.R.; Franklin, B.A.; Lamonte, M.J.; Lee, I.M.; Nieman, D.C.; Swain, D.P. Quantity and Quality of Exercise for Developing and Maintaining Cardiorespiratory, Musculoskeletal, and Neuromotor Fitness in Apparently Healthy Adults: Guidance for Prescribing Exercise. 2011. Available online: https://academiccommons.columbia.edu/doi/10.7916/D8CR5T2R (accessed on 1 February 2022).
  5. Kalb, J.S.; Hunter, G. Weight training economy as a function of intensity of the squat and overhead press exercise. J. Sports Med. Phys. Fit. 1991, 31, 154–160.
  6. Mazzetti, S.; Douglass, M.; Yocum, A.; Harber, M. Effect of explosive versus slow contractions and exercise intensity on energy expenditure. Med. Sci. Sports Exerc. 2007, 39.
  7. Haltom, R.W.; Kraemer, R.R.; Sloan, R.A.; Hebert, E.P.; Frank, K.; Tryniecki, J.L. Circuit weight training and its effects on excess postexercise oxygen consumption. Med. Sci. Sports Exerc. 1999, 31, 1613.
  8. Ballor, D.L.; Becque, M.D.; Katch, V.L. Metabolic responses during hydraulic resistance exercise. Med. Sci. Sports Exerc. 1987, 19, 363–367.
  9. Haddock, B.; Wilkin, L. Resistance training volume and post exercise energy expenditure. Int. J. Sports Med. 2006, 27, 143–148.
  10. Phillips, W.T.; Ziuraitis, J.R. Energy cost of the ACSM single-set resistance training protocol. J. Strength Cond. Res. 2003, 17, 350–355.
  11. Ratamess, N.A.; Falvo, M.J.; Mangine, G.T.; Hoffman, J.R.; Faigenbaum, A.D.; Kang, J. The effect of rest interval length on metabolic responses to the bench press exercise. Eur. J. Appl. Physiol. 2007, 100, 1–17.
  12. Wilmore, J.H.; Parr, R.B.; Ward, P.; Vodak, P.A.; Barstow, T.J.; Pipes, T.V.; Grimditch, G.; Leslie, P. Energy cost of circuit weight training. Med. Sci. Sports Exerc. 1978, 10, 75–78.
  13. Thornton, M.K.; Potteiger, J.A. Effects of resistance exercise bouts of different intensities but equal work on EPOC. Med. Sci. Sports Exerc. 2002, 34, 715–722.
  14. Hunter, G.; Blackman, L.; Dunnam, L.; Flemming, G. Bench press metabolic rate as a function of exercise intensity. J. Appl. Sports Sci. Res. 1988, 2, 1–6.
  15. Kang, J.; Hoffman, J.R.; Im, J.; Spiering, B.A.; Ratamess, N.A.; Rundell, K.W.; Nioka, S.; Cooper, J.; Chance, B. Evaluation of physiological responses during recovery following three resistance exercise programs. J. Strength Cond. Res. 2005, 19, 305–309.
  16. Croonen, F.; Binkhorst, R. Oxygen uptake calculated from expiratory volume and oxygen analysis only. Ergonomics 1974, 17, 113–117.
  17. Neto, W.K.; Vieira, T.L.; Gama, E.F. Barbell hip thrust, muscular activation and performance: A systematic review. J. Sports Sci. Med. 2019, 18, 198.
  18. Rosenberger, A.; Beijer, Å.; Schoenau, E.; Mester, J.; Rittweger, J.; Zange, J. Changes in motor unit activity and respiratory oxygen uptake during 6 weeks of progressive whole-body vibration combined with progressive, high intensity resistance training. J. Musculoskelet. Neuronal Interact. 2019, 19, 159–168.
  19. Watanabe, K.; Narouei, S. Association between Oxygen Consumption and Surface Electromyographic Amplitude and Its Variation within Individual Calf Muscles during Walking at Various Speeds. Sensors 2021, 21, 1748.
  20. Hug, F.; Decherchi, P.; Marqueste, T.; Jammes, Y. EMG versus oxygen uptake during cycling exercise in trained and untrained subjects. J. Electromyogr. Kinesiol. 2004, 14, 187–195.
  21. Piggott, B.; Müller, S.; Chivers, P.; Cripps, A.; Hoyne, G. Interdisciplinary sport research can better predict competition performance, identify individual differences, and quantify task representation. Front. Sports Act. Living 2020, 2, 14.
  22. Pan, W. Akaike’s information criterion in generalized estimating equations. Biometrics 2001, 57, 120–125.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Rehabilitation
Contributor MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
志維 彭
View Times: 555
Revisions: 3 times (View History)
Update Date: 22 Feb 2022
Table of Contents
    1000/1000
    Hot Most Recent
    ScholarVision Creations
    Feedback