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Understanding the Dynamics of Squat Training:
Effects on Energy Expenditure, Oxygen
Consumption, and Heart Rate in Young, Healthy
Adults
Indya del-Cuerpo
University of Granada
Pedro Delgado-Floody
Universidad de La Frontera
Daniel Jerez-Mayorga
University of Granada
Felipe Caamaño-Navarrete
Universidad Autónoma de Chile
Mauricio Aliquintui-Flores
Universidad de La Frontera
Luis Javier Chirosa-Ríos
University of Granada
Article
Keywords:
Posted Date: November 28th, 2024
DOI: https://doi.org/10.21203/rs.3.rs-5394309/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Read Full License
Additional Declarations: No competing interests reported.
Page 2/15
Abstract
The main purpose of this study was to assess the changes in energy expenditure (EE), oxygen volume
(VO2), heart rate (HR), and velocity (V) measurements obtained during three sets of each of two squat
training protocols in a group of healthy young adults. Twenty-nine students of Sports Sciences
volunteered to participate in this study. They attended the laboratory on four different days and
performed four sessions: two of 3 sets of 12 repetitions at 75% 1 repetition maximum (RM) and two of 3
sets of 30 repetitions at 50% 1RM while EE, VO2, HR and V was evaluated. The major outcomes of this
study indicated that EE, VO2, HR, and V tended to decrease in both protocols as the sets were
performed. Despite this, the creation of fresh insights regarding the assessment of different strengths
and metabolic variables can help illuminate the underlying causes of these distinctions. Furthermore,
these ndings have important implications for the design of effective and personalized strength training
programs.
INTRODUCTION
Strength training has been shown to have numerous benets, including improved muscle mass and bone
density, increased metabolic rate, and decreased risk of chronic disease 1. Squats are widely used in
conditioning and rehabilitation protocols to enhance strength and engage both the anterior and posterior
chain 2. Furthermore, this exercise is not only incorporated into tness routines 3, but can also be
incorporated into daily activities such as climbing stairs, lifting shopping bags, and rising from a seated
position 4. To optimize the effectiveness of strength training and, concretely, squat training, it is
important to measure key metrics including energy expenditure (EE), oxygen volume (VO2), heart rate
(HR), and velocity (V) while performing exercise 5.
Additionally, examining metrics such as EE, VO2, HR and V during strength training, particularly in the
context of squat exercises, offers a nuanced understanding of the physiological demands imposed on
the body 6. These parameters serve as critical indicators of the body's response to the applied resistance
and provide insights into the metabolic and cardiovascular systems' adaptations 7. By quantifying these
variables, we can establish a foundation for tailored training strategies, aligning exercise regimens with
specic performance goals and individual capabilities. Moreover, this comprehensive assessment aids
in preventing potential overexertion or injury, ensuring that training programs are not only effective but
also safe 8. This level of insight is particularly valuable for athletes, trainers, and health professionals
seeking to optimize training protocols and enhance overall performance.
As far as we know, there aren't many studies that assess physiological variables such as EE, VO2, HR,
and V collectively comparing different squat training protocols, but they have been individually studied
during strength training 9–11. These studies have provided valuable insights into the effects of varying the
training parameters on specic physiological variables. However, there is a lack of research specically
focusing on how physiological variables vary depending on the number of sets, repetitions, and rest time
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established in each training protocol 12. This presents a gap in the current literature as it leaves
unanswered questions regarding the potential differences in physiological responses between different
training protocols and their implications for optimizing training outcomes. Understanding how they vary
is crucial for prescribing different training programs to various population groups13.
Understanding the effects of different squat training protocols on physiological variables such as EE,
VO2, HR, and V is of great importance for several reasons 14. First, it can shed light on the metabolic
demands and cardiovascular stress imposed by each protocol 7, helping trainers and coaches to tailor
training programs to specic goals and target populations. Second, it can provide evidence-based
guidance for optimizing training eciency and effectiveness, ensuring that individuals maximize their
potential gains while minimizing the risk of overexertion or injury 8. Finally, by investigating these
variables collectively and directly comparing the two protocols, we can gain a comprehensive
understanding of their interplay and potential synergistic effects, which can further contribute to the
overall body of knowledge in the eld of exercise science 15.
In the realm of strength training, the manipulation of training intensity has long been recognized as a
pivotal factor inuencing physiological adaptations and performance outcomes 16. Specically, varying
the intensity, often expressed as a percentage of one-repetition maximum (1RM), exerts distinctive
effects on muscle recruitment, metabolic demands, and overall training stimulus 17. High-intensity
protocols with lower repetitions and greater external loads predominantly target maximal strength gains
and neural adaptations, while moderate to lower intensity, higher repetition schemes primarily contribute
to muscular endurance and hypertrophy 18. Therefore, investigating the physiological responses during
squat training across differing intensity paradigms not only augments our understanding of strength
training science but also provides practical insights for optimizing training strategies in diverse
populations.
Despite numerous studies on squat training 11,19–21, there is still a lack of understanding of how
physiological variables such as EE, VO2, HR, and V are related to the performance of this exercise.
Furthermore, we considered how these variables behave in two different squat exercise protocols.
Therefore, the main purpose of this study was to assess the changes in EE, VO2, HR, and V
measurements obtained during three sets of each of the two squat training protocols (30 repetitions at
50% 1RM and 12 repetitions at 75% 1RM) in a group of healthy young adults. Thus, the results of this
study could help better understand the differences and similarities between the two protocols and their
impact on the studied physiological variables.
METHODS
Experimental approach to the problem
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This study employed a repeated measures approach to assess variations in EE, VO2, HR, and V when
performing three series of two different acute squat exercise protocols using functional
electromechanical dynamometry (FEMD). Participants were familiarized, and their repetition maximum
(RM) was determined prior to the start of the study. Each participant visited the laboratory four times in a
two-week span, with a minimum of 48 h between visits, and performed three sets of 12 reps at 75% 1RM
and three sets of 30 reps at 50% 1RM during each session. The order of the protocols was randomized.
Subjects
The study involved 29 Sports Science students, consisting of 13 males and 16 females, with an average
age of 24.9 ± 4.6 years, height of 1.70 ± 0.1 m, body mass of 68.1 ± 12.9 kg, and BMI of 23.5 ± 3.0 kg/m2.
All participants were eligible to participate in the study by meeting the inclusion criteria, which required
having no medical conditions and at least one year of experience in muscle strength training. Before
participating in the study, each participant was informed of the specic details, objectives, and potential
risks involved and provided informed consent. The study protocol was approved by the Committee on
Human Research of the University of Granada (Nº. 2182/CEIH/2021) and was conducted in accordance
with the Declaration of Helsinki.
In the initial interaction with the participants to conrm their eligibility for the study, female participants
were queried about their menstrual cycle. This encompassed details such as the commencement and
conclusion dates of their most recent menstruation, length of their menstrual cycle, any instances of
intense discomfort or excessive bleeding, and use of hormonal contraceptives. Utilizing the data
provided by these participants, we specically assessed them during the luteal phase [32]. Additionally,
none of them relied on hormonal contraceptives, and only two reported experiencing severe pain and
heavy bleeding (Table 1).
Table 1
Descriptive characteristics of sample study according to gender.
Total (n = 29) Men (n = 13) Women (n = 16)
Mean SD Mean SD Mean SD
Age (years) 24.9 4.6 25.7 3.9 24.3 5.1
Anthropometrics parameters
BMI (kg/m2)23.5 3.0 24.6 3.4 22.6 2.4
Procedures
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The study involved ve separate sessions: one familiarization session and four experimental sessions.
Throughout these sessions, participants were instructed to get a minimum of 8 hours of sleep; avoid
smoking, alcohol, or caffeine 24 hours before testing; abstain from strenuous exercise for at least 12
hours before testing; and eat no less than an hour before the session. Additionally, the participants
arrived at the laboratory at the same time each day (within an hour window) and were exposed to similar
environmental conditions, with a temperature of approximately 22°C and humidity of 60%.
To standardize participants' nutritional conditions and eliminate any external factors that could affect
the results, the diet of all participants was regulated a week prior to and throughout the study. This
involved excluding any foods or beverages that could potentially inuence the outcome, such as caffeine
and supplements. A Nutrition and Dietetics graduate was tasked with creating an identical weekly diet
plan for all participants during the week leading up to the study and throughout the exercise period
tailored to their specic energy requirements. To determine these needs, various anthropometric
measurements were taken for all participants one week before the study and subsequently during the
following weeks. These measurements included weight (measured using a professional TANITA SC-240-
MA scale with a biological suite), height (measured using a portable Seca 213 Stadiometer), skinfold
measurements for the biceps, triceps, subscapular, abdominal, thigh, and mid-calf (measured using a
Holtein HOL-98610ND mechanical caliper), and arm and mid-thigh circumferences (measured using a
CESCORF measuring tape) by an ISAK level 1 anthropometrist. Basal EE was computed using the Harris-
Benedict formula 22, total EE was determined using the corresponding activity factor, and body fat
percentage was estimated using the Foulkner formula 23.
Participants followed the researcher's instructions upon arrival at the study. They were then outtted
with a gas analyzer mask, and gas analysis commenced while they remained seated in a relaxed posture
for ve minutes. Subsequently, they donated a vest equipped with a carabiner connected to an FEMD
cable. FEMD (Dynasystem, Model Research, Granada, Spain), a validated isokinetic multi-joint device that
enables us to assess the parameters of strength, speed, power, work, and impulse using a single device,
was used to conduct the half-squat 24,25. Following this, they engaged in a ve-minute warm-up on a
cycle ergometer at 60% of their reserve heart rate, succeeded by ten repetitions at 10% of their 1RM to
assess the exercise angle. After a ve-minute rest period, they performed three sets of 12 repetitions at
75% 1RM or 30 repetitions at 50% 1RM. Following completion, they were seated for ten minutes for post-
exercise gas analysis. Finally, the indirect calorimeter and vest were removed, and the animals were free
to leave the laboratory. EE was determined indirectly using a metabolic cart, which analyzed respiratory
gases (typically expired gases) to ascertain the volume of air passing through the lungs, the quantity of
oxygen extracted (referred to as oxygen consumption or VO2), and the amount of carbon dioxide
generated as a metabolic byproduct, which was expelled into the atmosphere (CO2 – VCO2). The
sequence of exercises was arranged in a random fashion, with a ve-minute break provided between
each set. Previous studies 24,26 have established the test-retest reliability of FEMD for squat exercises.
The study protocol is illustrated in Fig.1.
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During the rst laboratory visit, participants underwent a 60-minute session which aimed to help them
become familiar with the FEMD and determine their one-repetition maximum (1RM). This session
involved starting with a general warm-up comprising two sets of 10 squat repetitions with an initial load
of 10 kg, followed by increments of 2 kg on each repetition, and 40 s of rest between sets, and (b)
directly estimating the participants' squat 1RM by following the protocol explained in del-Cuerpo (2023)
24,26.
Once this is determined, the participant will have several options: (a) If the participant can perform more
than one repetition, pushing to the point of failure, a 5-minute rest period will follow. The initial load was
taken as the maximum load achievable, with subsequent increments of 1 kg until the resistance became
too challenging (up to a maximum of ve repetitions). The last repetition will be considered the
individual's 1RM. (b) If the participant was unable to complete any repetitions, a 2-minute rest period was
allowed. The initial load was set at 90% of the body weight for males and 70% of the body weight for
females. Further increments of 1 kg were applied until the resistance was too strong (up to a maximum
of 5 repetitions). The nal repetition served as the participant's 1RM. (c) If the individual can only
manage a single repetition, a 5-minute rest will follow. The initial load will remain the same as before,
with an additional 1 kg increment until the resistance is too formidable (up to a maximum of ve
repetitions). The last repetition was regarded as the participant's 1RM. Finally, (d) if the participant
exceeds 120 kg (the device's load limit), we record the total number of repetitions they can perform and
estimate the 1RM using Lombardi's Eq.27.
The FitMateTM metabolic system (Cosmed, Rome, Italy), a trustworthy and valid metabolic analyzer
developed to measure oxygen consumption and energy expenditure during rest and activity, has been
used to measure energy expenditure 14,28. The International Physical Activity Questionnaire (short
version) (IPAQ), a reliable tool for evaluating physical activity in adults between the ages of 18 and 69
years, was used to determine the level of physical activity for each participant 29.
EE during both protocols was measured using the FitMate™ metabolic system (Cosmed, Rome, Italy),
which is a dependable and validated metabolic analyzer specically designed for assessing oxygen
consumption and EE during both rest and exercise. This system captures breath-by-breath ventilation, as
well as measurements of expired oxygen and carbon dioxide 30–32. Notably, this indirect calorimeter
does not require a warm-up period and autonomously undergoes calibration before testing each subject.
Once the warm-up phase was completed, the mask was axed to the patient's face and remained in
position for an additional ten minutes post-test. If the mask was not properly secured, a warning was
displayed on the device's screen. All respiratory gas data were gathered and analyzed from the initiation
to the conclusion of the protocol. Notably, the use of this device did not hinder execution of the squat
protocol.
Statistical analyses
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Descriptive data are presented as mean ± standard deviation (SD). Normal distribution of the data
(Shapiro–Wilk test) and homogeneity of variances (Levene test) were conrmed (P > .05). For the main
analysis, a repeated-measures analysis of variance (ANOVA) was conducted using the Holm Post-Hoc
analysis. The Greenhouse-Geisser correction was used when the Mauchly sphericity test was violated.
Omega squared (ω2) was calculated for the ANOVA, where the values of the effect sizes 0.01, 0.06 and
above 0.14 were considered small, medium, and large, respectively 33. Statistical signicance was set at
p < 0.05. The JASP statistics package (version 0.11.1) was used for the statistical analyses.
RESULTS
There are signicant differences for the variables of EE at 50% 1RM (p = 0.001; ω2 = 0.012) and 75% 1RM
(p = 0.001; ω2 = 0.008) in the comparison of three series S1 vs S2 vs S3. The post hoc analysis using
Holm's correction revealed that EE signicantly increased for 50% 1RM protocol between S1 and S3 (S1:
21.53 (5.52) vs S3: 23.26. (5.70), p < 0.001) and for 75% 1RM protocol between S1 and S2 (S1: 16.37
(3.92) vs S2: 17.19 (4.95), p = 0.006) and between S1 and S3 (S1: 16.37 (3.92) vs S3: 17.33 (4.59), p =
0.002) (Fig.2a).
There are signicant differences for the variables of VO2 at 50% 1RM (p = 0.001; ω2 = 0.012) and 75%
1RM (p = 0.001; ω2 = 0.008) in the comparison of three series S1 vs S2 vs S3. The post hoc analysis
using Holm's correction revealed that VO2 signicantly increased for 50% 1RM protocol between S1 and
S3 (S1: 10.75 (1.66) vs S3: 11.18 (1.72), p < 0.001) and for 75% 1RM protocol between S1 and S2 (S1:
8.71 (1.07) vs S2: 9.11 (1.43), p < 0.001) and between S1 and S3 (S1: 8.71 (1.07) vs S3: 9.20 (1.34), p =
0.002) (Fig.2b).
There are signicant differences for the variables of HR at 50% 1RM (p < 0.001; ω2 = 0.119) and 75% 1RM
(p < 0.001; ω2 = 0.030) in the comparison of three series S1 vs S2 vs S3. The post hoc analysis using
Holm's correction revealed that HR signicantly increased for 50% 1RM protocol between S1 and S2 (S1:
92.61 (11.72) vs S2: 100.68 (14.42), p < 0.001), between S1 and S3 (S1: 92.61 (11.72) vs S3: 104.97
(15.07), p < 0.001), and between S2 and S3 (S2: 100.68 (14.42) vs S3: 104.97 (15.07) and for 75% 1RM
protocol between S1 and S2 (S1: 82.82 (8.43) vs S2: 85.45 (9.24), p = 0.003) and between S1 and S3 (S1:
82.82 (8.43) vs S3: 86.88 (9.69), p < 0.001) (Fig.2c).
There are signicant differences for the variables of V at 50% 1RM (p < 0.001; ω2 = 0.007) and 75% 1RM
(p = 0.033; ω2 = 0.004) in the comparison of three series S1 vs S2 vs S3. The post hoc analysis using
Holm's correction revealed that V signicantly increased for 50% 1RM protocol between S1 and S2 (S1:
76.39 (21.59) vs S2: 79.26 (22.48), p = 0.007) and between S1 and S3 (S1: 76.39 (21.59) vs S3: 80.98
(22.51), p < 0.001) and for 75% 1RM protocol between S1 and S3 (S1: 67.13 (17.73) vs S3: 70.24 (17.37),
p < 0.028) (Table2).
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Table 2
V measurements were obtained during both protocols for the three series.
Variable S1
Mean (SD)
S2
Mean (SD)
S3
Mean (SD)
ANOVA
V
(cm/s)
50%
1RM 76.39
(21.59) 79.26
(22.48) 80.98
(22.51) F (2.00, 56.00) = 12.13; p < 0.001;
ω2 = 0.007
75%
1RM 67.13
(17.73) 68.71
(17.27) 70.24
(17.37) F (2.00, 56.00) = 3.64; p = 0.033;
ω2 = 0.004
S1: serie 1; S2: serie 2; S3: serie 3; SD: standard deviation; V: velocity.
DISCUSSION
The main purpose of this study was to assess the changes in EE, VO2, HR, and V measurements
obtained during three sets of each of the two squat training protocols (30 repetitions at 50% 1RM and 12
repetitions at 75% 1RM) in a group of healthy young adults. The major outcomes of this study indicated
that EE, VO2, HR, and V tended to decrease in both protocols as the sets were performed. This nding
holds practical signicance, as it suggests an adaptive response in energy utilization and cardiovascular
demand over the course of multiple repetitions within a set, potentially inuencing training strategies for
improved eciency and performance optimization
Taken together, these ndings suggest that this trend may be indicative of several factors. First,
participants may experience an improvement in movement eciency as they become more familiar with
exercise 34. This can lead to decreases in EE and VO2 over the course of the series 35. Additionally, the
decrease in HR may be related to cardiovascular adaptation that occurs in response to the strength-
training stimulus 36,37. Regarding V, accumulated fatigue after each set tends to lead to a reduction in the
execution speed of the movement as well as an increase in the time taken to complete each set 38,39.
Despite this, having conducted an extensive review of the published literature on this topic, as far as we
know, there are no studies that assess EE, VO2 and HR during the different sets of the same training
protocol. Conversely, studies have been conducted on how these variables change after the application
of a specic training program in different population groups. Thus, we believe that this is the novelty of
this study.
Regarding EE and VO2, to the best of our knowledge, one of the few articles found dates back to 1968.
Seliger et al. (1968) 35 examined EE and VO2 in 15 athletes during 13 weeks of strength training. Half of
the athletes were trained in a traditional manner by lifting dumbbells, whose weight corresponded to 90–
95% of the 1RM (concentric contraction). The other half was trained only by lowering dumbbells, whose
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weight corresponded to 145–150% of the 1RM (eccentric contractions). Subsequently, both VO2 and EE
decreased signicantly. These results, although they do not assess how EE and VO2 vary in each of the
training sets, are similar to what is intended to be evaluated in this study, and they align with the results
obtained. This is because apart from the training adaptations mentioned previously, the participants'
level of training. According to Pontzer et al. (2016) 40, untrained participants experience an increase in EE
at low activity levels. In the case of trained individuals, such as those included in our study, who were
required to have a minimum of one year of strength training experience, EE tends to stabilize and even
decrease 41.
With regard to HR, a similar phenomenon occurs. The variation in HR after exercise has been widely
studied, and there is abundant scientic literature indicating that individuals adapted to exercise show a
lower resting heart rate and cardiac hypertrophy 42,43, but not as much during exercise. In HR during
exercise, to the best of our knowledge, the variation in HR during exercise has been less investigated.
Despite this, some studies have investigated it, such as the systematic review published by Periard et al.
(2016) 44, which sought to examine the cardiovascular adaptations that occur in parallel with improved
heat loss responses during exercise-heat acclimation. They realized that cardiovascular adaptations
supporting this challenge include a reduction in heart rate during exercise at a given work rate, among
other adaptations 44. These results align with those obtained in our study, indicating that the reduction in
HR during training at a sustained intensity is one of the cardiovascular adaptations generated by training
in trained individuals, such as those included in this study 37.
In the case of V, it is different, since studies have been conducted to investigate how these variable
changes during the execution of different strength-training protocols, observing and comparing its
variation between repetitions and between sets. For instance, Dos Santos et al. (2021) 45 examined the
immediate effects of performing four sets of high-velocity parallel squats, whether taken to the point of
momentary failure or not, and they showed notable reductions in both maximum and average velocity
loss, as well as power output loss. Likewise, Sanchez-Medina et al. 39 examined the reduction in V
following three sets of 10RM and three sets of 12RM loads, both with a 5-minute rest period between
sets, during the full back squat in trained male participants. They noted that after completing 3 × 10 and
3 × 12, there were reductions in the MPV of 45% and 46%, respectively. Similarly, Gonzalez-Badillo et al.
46 and Pareja-Blanco et al. 38 observed MPV reductions of 44% in protocols involving 3 × 8 46 and 3 × 12
38 sets, with 5 minutes of rest between sets during the full squat in trained men. These ndings align
with those of our study, in which we observed a tendency for V to decrease in both squat training
protocols. This is attributed to accumulated fatigue during exercise, which leads to a gradual reduction in
V in each set.
The practical implications of these ndings are signicant for both exercise professionals and trainers. It
could help exercise professionals, trainers, and athletes in different areas, such as (a) optimization of
strength training: understanding how the studied variables vary during squat training provides valuable
insights for designing effective and personalized strength training programs. Trainers can adjust the
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intensity and volume of training based on individual goals and athlete capabilities, (b) movement
eciency: observing the trend of decreasing EE and VO2 throughout the sets highlights the importance
of proper technique in performance. Encouraging ecient techniques can help athletes conserve energy
and improve performance over time. (c) Monitoring progress and performance: observing changes in HR
and VO2 throughout training can serve as an indicator of progress. Regular monitoring can help adjust
training strategies according to the changing needs of the athletes.
Taken together, these ndings provide a solid scientic foundation for decision-making in strength-
training program designs. Exercise professionals and coaches can use this information to maximize the
benets of training and enhance athletic performance. However, it is important to note that each
individual is unique and training adaptations may vary. Personalized approach and expert supervision are
recommended to achieve the best results.
Nevertheless, this study has certain limitations that warrant consideration in future investigations. The
participants consisted exclusively of young, healthy adults with 1RM below 160 kg. Consequently, future
studies should encompass diverse populations, including powerlifters, overweight or obese individuals,
and individuals with varying health conditions. Furthermore, this study focused on half squats, and
assessing the impact of full squats on EE could yield valuable insights. Moreover, it would have been
intriguing to incorporate accelerometer-based EE measurements to facilitate comparative analysis
between the two different assessment devices. Lastly, it would have been interesting to continue
assessing energy expenditure for at least an hour after completing the exercise, but this was not
possible due to the limited free time available to the participants. All of these limitations will be
considered in future research.
In conclusion, the main ndings of this study showed that all the variables measured (EE, VO2, HR, and V)
during both squat training protocols decreased as the sets were performed. Despite this, the creation of
fresh insights regarding the assessment of different strengths and metabolic variables can help
illuminate the underlying causes of these distinctions. Furthermore, these ndings have important
implications for the design of effective and personalized strength training programs. Future research
should further explore these phenomena in diverse populations and training contexts.
Declarations
ACKNOWLEDGEMENT
This work was supported by Spanish Ministry of Universities (FPU19/02030), and the High Council for
Sports (CSD); Spanish Ministry of Culture and Sports (09/UPB/23), and the project DIE22-0007,
Universidad de Granada.
AUTHORS’ CONTRIBUTIONS
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ICR lead the project, the methodology design, data collection, and the manuscript writing. DJM, PDF, and
MAF contribute to data analysis and manuscript review. LJCR revised the manuscript critically. All
authors read and approved the nal version of the manuscript. All authors read and approved the nal
version of the manuscript.
DATA AVAILABILITY STATEMENT
The data supporting the ndings of this study are available and can be shared upon reasonable request
to the corresponding author.
COMPETING INTERESTS STATEMENT
The author(s) declare no competing interests.
ETHICS DECLARATION
Each participant received detailed information on the study's specics, objectives, and potential risks,
and provided informed consent. The study protocol was approved by the Committee on Human
Research of the University of Granada (Nº. 2182/CEIH/2021) and was conducted in accordance with the
Declaration of Helsinki.
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Figures
Figure 1
Protocol measurement of the squat exercise.
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Figure 2
EE, VO2, and HR measurements obtained during both protocols for the three series.
