Oxygen Consumption (VO2) and Surface Electromyography (sEMG): Comparison
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Please note this is a comparison between Version 3 by Camila Xu and Version 2 by Camila Xu.

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
4.108~9.675
0.000 *
2 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)Parameter Estimate (ß) SE 95% CI (Lower~Upper) p
Trained (n = 6) p-Value
Group Model Parameter Estimate (ß) SE 95% CI (Lower~Upper) p
Within Subject

Pre vs. Post
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
LMD
sEMG-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 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
9.613~13.789
0.000
RLES
sEMG-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
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 VO
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.

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