Understanding the Dynamics of Squat Training: Effects on Energy Expenditure, Oxygen Consumption, and Heart Rate in Young, Healthy Adults

Understanding the Dynamics of Squat Training: Effects on Energy Expenditure, Oxygen Consumption, and Heart Rate in Young, Healthy Adults | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Understanding the Dynamics of Squat Training: Effects on Energy Expenditure, Oxygen Consumption, and Heart Rate in Young, Healthy Adults Indya del-Cuerpo, Pedro Delgado-Floody, Daniel Jerez-Mayorga, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5394309/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract The main purpose of this study was to assess the changes in energy expenditure (EE), oxygen volume (VO 2 ), 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, VO 2 , 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 findings have important implications for the design of effective and personalized strength training programs. Earth and environmental sciences/Environmental sciences Health sciences/Cardiology Figures Figure 1 Figure 2 INTRODUCTION Strength training has been shown to have numerous benefits, 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 fitness 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 (VO 2 ), heart rate (HR), and velocity (V) while performing exercise 5 . Additionally, examining metrics such as EE, VO 2 , 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 specific 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, VO 2 , 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 specific physiological variables. However, there is a lack of research specifically focusing on how physiological variables vary depending on the number of sets, repetitions, and rest time 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 groups 13 . Understanding the effects of different squat training protocols on physiological variables such as EE, VO 2 , 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 specific goals and target populations. Second, it can provide evidence-based guidance for optimizing training efficiency 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 field of exercise science 15 . In the realm of strength training, the manipulation of training intensity has long been recognized as a pivotal factor influencing physiological adaptations and performance outcomes 16 . Specifically, 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, VO 2 , 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, VO 2 , 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 This study employed a repeated measures approach to assess variations in EE, VO 2 , 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/m 2 . 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 specific 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 confirm 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 specifically 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/m 2 ) 23.5 3.0 24.6 3.4 22.6 2.4 Procedures The study involved five 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 influence 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 specific 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 outfitted with a gas analyzer mask, and gas analysis commenced while they remained seated in a relaxed posture for five 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 five-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 five-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 VO 2 ), and the amount of carbon dioxide generated as a metabolic byproduct, which was expelled into the atmosphere (CO 2 – VCO 2 ). The sequence of exercises was arranged in a random fashion, with a five-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 . During the first 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 five 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 final 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 five 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. 2 7 . 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 specifically 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 affixed 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 Descriptive data are presented as mean ± standard deviation (SD). Normal distribution of the data (Shapiro–Wilk test) and homogeneity of variances (Levene test) were confirmed (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 (ω 2 ) 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 significance was set at p < 0.05. The JASP statistics package (version 0.11.1) was used for the statistical analyses. RESULTS There are significant differences for the variables of EE at 50% 1RM (p = 0.001; ω 2 = 0.012) and 75% 1RM (p = 0.001; ω 2 = 0.008) in the comparison of three series S1 vs S2 vs S3. The post hoc analysis using Holm's correction revealed that EE significantly 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. 2 a). There are significant differences for the variables of VO 2 at 50% 1RM (p = 0.001; ω 2 = 0.012) and 75% 1RM (p = 0.001; ω 2 = 0.008) in the comparison of three series S1 vs S2 vs S3. The post hoc analysis using Holm's correction revealed that VO 2 significantly 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. 2 b). There are significant differences for the variables of HR at 50% 1RM (p < 0.001; ω 2 = 0.119) and 75% 1RM (p < 0.001; ω 2 = 0.030) in the comparison of three series S1 vs S2 vs S3. The post hoc analysis using Holm's correction revealed that HR significantly 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. 2 c). There are significant 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 significantly 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) (Table 2 ). 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; ω 2 = 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; ω 2 = 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, VO 2 , 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, VO 2 , HR, and V tended to decrease in both protocols as the sets were performed. This finding holds practical significance, as it suggests an adaptive response in energy utilization and cardiovascular demand over the course of multiple repetitions within a set, potentially influencing training strategies for improved efficiency and performance optimization Taken together, these findings suggest that this trend may be indicative of several factors. First, participants may experience an improvement in movement efficiency as they become more familiar with exercise 34 . This can lead to decreases in EE and VO 2 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, VO 2 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 specific 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 weight corresponded to 145–150% of the 1RM (eccentric contractions). Subsequently, both VO 2 and EE decreased significantly. 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 scientific 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 findings 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 findings are significant 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 intensity and volume of training based on individual goals and athlete capabilities, (b) movement efficiency: observing the trend of decreasing EE and VO 2 throughout the sets highlights the importance of proper technique in performance. Encouraging efficient techniques can help athletes conserve energy and improve performance over time. (c) Monitoring progress and performance: observing changes in HR and VO 2 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 findings provide a solid scientific foundation for decision-making in strength-training program designs. Exercise professionals and coaches can use this information to maximize the benefits 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 findings of this study showed that all the variables measured (EE, VO 2 , 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 findings 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 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 final version of the manuscript. All authors read and approved the final version of the manuscript. DATA AVAILABILITY STATEMENT The data supporting the findings 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. 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Beginning Weight Training: The Safe and Effective Way (WCB/McGraw-Hill, 1989). Campbell, M. J. & Machin, D. Medical Statistics: A Commonsense Approach (J. Wiley, 1999). 10.1080/15438620600651512 Roman-Viñas, B. et al. International physical activity questionnaire: reliability and validity in a Spanish population. Eur. J. Sport Sci. 10 , 297–304 (2010). Nieman, D. C. et al. Validation of Cosmed’s FitMate™ in measuring oxygen consumption and estimating resting metabolic rate. Res. Sports Med. 14 , 89–96 (2006). Brisswalter, J. & Tartaruga, M. P. Comparison of COSMED’S FitMate™ and K4b2 metabolic systems reliability during graded cycling exercise. Scand. J. Clin. Lab. Invest. 74 , 722–724 (2014). Campbell, B. et al. Inter-and intra-day test-retest reliability of the Cosmed Fitmate ProTM indirect calorimeter for resting metabolic rate. J. Int. Soc. Sports Nutr. 11 , 1–2 (2014). Cohen, J. Statistical Power Analysis for the Behavioral Sciences (Academic, 2013). Sale, D. G. Neural adaptation to resistance training. Med. Sci. Sports Exerc. 20 , S135–S145 (1988). Seliger, V., Dolejš, L., Karas, V. & Pachlopnikova, I. Adaptation of trained athletes’ energy expenditure to repeated concentric and eccentric muscle contractions. Int. Z. für angewandte Physiologie einschließlich Arbeitsphysiologie . 26 , 227–234 (1968). Winder, W. W., Hagberg, J. M., Hickson, R. C., Ehsani, A. A. & McLane, J. A. Time course of sympathoadrenal adaptation to endurance exercise training in man. J. Appl. Physiol. 45 , 370–374 (1978). Nystoriak, M. A. & Bhatnagar, A. Cardiovascular effects and benefits of exercise. Front. Cardiovasc. Med. 5 , 135 (2018). Pareja-Blanco, F., Sánchez-Medina, L., Suárez-Arrones, L. & González-Badillo, J. J. Effects of velocity loss during resistance training on performance in professional soccer players. Int. J. Sports Physiol. Perform. 12 , 512–519 (2017). Sanchez-Medina, L. & González-Badillo, J. J. Velocity loss as an indicator of neuromuscular fatigue during resistance training. Med. Sci. Sports Exerc. 43 , 1725–1734 (2011). Pontzer, H. et al. Constrained total energy expenditure and metabolic adaptation to physical activity in adult humans. Curr. Biol. 26 , 410–417 (2016). Westerterp, K. R. Control of energy expenditure in humans. Eur. J. Clin. Nutr. 71 , 340–344 (2017). Reimers, A. K., Knapp, G. & Reimers, C. D. Effects of exercise on the resting heart rate: a systematic review and meta-analysis of interventional studies. J. Clin. Med. 7 , 503 (2018). Carter, J. B., Banister, E. W. & Blaber, A. P. Effect of endurance exercise on autonomic control of heart rate. Sports Med. 33 , 33–46 (2003). Périard, J. D., Travers, G. J. S., Racinais, S. & Sawka, M. N. Cardiovascular adaptations supporting human exercise-heat acclimation. Auton. Neurosci. 196 , 52–62 (2016). Dos Santos, W. D. N. et al. Resistance training performed to failure or not to failure results in similar total volume, but with different fatigue and discomfort levels. J. Strength. Conditioning Res. 35 , 1372–1379 (2021). González-Badillo, J. J. et al. Short-term recovery following resistance exercise leading or not to failure. Int. J. Sports Med. 295–304 (2015). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 29 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 31 Dec, 2024 Reviews received at journal 30 Dec, 2024 Reviews received at journal 25 Dec, 2024 Reviewers agreed at journal 20 Dec, 2024 Reviewers agreed at journal 20 Dec, 2024 Reviews received at journal 17 Dec, 2024 Reviewers agreed at journal 29 Nov, 2024 Reviewers invited by journal 29 Nov, 2024 Editor assigned by journal 21 Nov, 2024 Editor invited by journal 11 Nov, 2024 Submission checks completed at journal 10 Nov, 2024 First submitted to journal 05 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5394309","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":381235566,"identity":"a0368d6b-4b35-409d-bb97-904faa7a3863","order_by":0,"name":"Indya del-Cuerpo","email":"","orcid":"","institution":"University of Granada","correspondingAuthor":false,"prefix":"","firstName":"Indya","middleName":"","lastName":"del-Cuerpo","suffix":""},{"id":381235567,"identity":"9a111a99-3fa5-4d81-a14f-f841dc7e0a62","order_by":1,"name":"Pedro Delgado-Floody","email":"data:image/png;base64,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","orcid":"","institution":"Universidad de La Frontera","correspondingAuthor":true,"prefix":"","firstName":"Pedro","middleName":"","lastName":"Delgado-Floody","suffix":""},{"id":381235568,"identity":"f8c7f03c-38d9-4c1d-b3fa-0ac349fafa0a","order_by":2,"name":"Daniel Jerez-Mayorga","email":"","orcid":"","institution":"University of Granada","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"Jerez-Mayorga","suffix":""},{"id":381235569,"identity":"586954b5-ad79-492f-a66e-7ef8fa94bd15","order_by":3,"name":"Felipe Caamaño-Navarrete","email":"","orcid":"","institution":"Universidad Autónoma de Chile","correspondingAuthor":false,"prefix":"","firstName":"Felipe","middleName":"","lastName":"Caamaño-Navarrete","suffix":""},{"id":381235570,"identity":"34ceb0ed-5eb2-46ce-9cb2-db56cf1809bc","order_by":4,"name":"Mauricio Aliquintui-Flores","email":"","orcid":"","institution":"Universidad de La Frontera","correspondingAuthor":false,"prefix":"","firstName":"Mauricio","middleName":"","lastName":"Aliquintui-Flores","suffix":""},{"id":381235571,"identity":"b84d65a2-e095-4819-a4a3-7a95af511893","order_by":5,"name":"Luis Javier Chirosa-Ríos","email":"","orcid":"","institution":"University of Granada","correspondingAuthor":false,"prefix":"","firstName":"Luis","middleName":"Javier","lastName":"Chirosa-Ríos","suffix":""}],"badges":[],"createdAt":"2024-11-05 09:53:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5394309/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5394309/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-04427-0","type":"published","date":"2025-07-29T16:21:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70116966,"identity":"1986e6b1-352d-474d-a44b-e091b9bc34fb","added_by":"auto","created_at":"2024-11-28 13:30:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":136558,"visible":true,"origin":"","legend":"\u003cp\u003eProtocol measurement of the squat exercise.\u003c/p\u003e","description":"","filename":"figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5394309/v1/02f5980bd10dd91b9f9774fa.png"},{"id":70116965,"identity":"24107f6a-887f-4f0a-b85a-20ade246c65a","added_by":"auto","created_at":"2024-11-28 13:30:09","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":712979,"visible":true,"origin":"","legend":"\u003cp\u003eEE, VO2, and HR measurements obtained during both protocols for the three series.\u003c/p\u003e","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5394309/v1/0d55c4d37910c155e94761cf.jpg"},{"id":88268196,"identity":"eae2de2e-c699-46a4-b9e3-3afdda242168","added_by":"auto","created_at":"2025-08-04 16:49:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1456019,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5394309/v1/5b047159-44a1-454c-9bc6-d2dd5009e2f9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Understanding the Dynamics of Squat Training: Effects on Energy Expenditure, Oxygen Consumption, and Heart Rate in Young, Healthy Adults","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eStrength training has been shown to have numerous benefits, including improved muscle mass and bone density, increased metabolic rate, and decreased risk of chronic disease \u003csup\u003e1\u003c/sup\u003e. Squats are widely used in conditioning and rehabilitation protocols to enhance strength and engage both the anterior and posterior chain \u003csup\u003e2\u003c/sup\u003e. Furthermore, this exercise is not only incorporated into fitness routines \u003csup\u003e3\u003c/sup\u003e, but can also be incorporated into daily activities such as climbing stairs, lifting shopping bags, and rising from a seated position \u003csup\u003e4\u003c/sup\u003e. To optimize the effectiveness of strength training and, concretely, squat training, it is important to measure key metrics including energy expenditure (EE), oxygen volume (VO\u003csub\u003e2\u003c/sub\u003e), heart rate (HR), and velocity (V) while performing exercise \u003csup\u003e5\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAdditionally, examining metrics such as EE, VO\u003csub\u003e2\u003c/sub\u003e, 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 \u003csup\u003e6\u003c/sup\u003e. These parameters serve as critical indicators of the body\u0026apos;s response to the applied resistance and provide insights into the metabolic and cardiovascular systems\u0026apos; adaptations \u003csup\u003e7\u003c/sup\u003e. By quantifying these variables, we can establish a foundation for tailored training strategies, aligning exercise regimens with specific 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 \u003csup\u003e8\u003c/sup\u003e. This level of insight is particularly valuable for athletes, trainers, and health professionals seeking to optimize training protocols and enhance overall performance.\u003c/p\u003e\n\u003cp\u003eAs far as we know, there aren\u0026apos;t many studies that assess physiological variables such as EE, VO\u003csub\u003e2\u003c/sub\u003e, HR, and V collectively comparing different squat training protocols, but they have been individually studied during strength training \u003csup\u003e9\u0026ndash;11\u003c/sup\u003e. These studies have provided valuable insights into the effects of varying the training parameters on specific physiological variables. However, there is a lack of research specifically focusing on how physiological variables vary depending on the number of sets, repetitions, and rest time established in each training protocol \u003csup\u003e12\u003c/sup\u003e. 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 groups\u0026nbsp;\u003csup\u003e\u003cspan lang=\"EN-US\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnderstanding the effects of different squat training protocols on physiological variables such as EE, VO\u003csub\u003e2\u003c/sub\u003e, HR, and V is of great importance for several reasons \u003csup\u003e14\u003c/sup\u003e. First, it can shed light on the metabolic demands and cardiovascular stress imposed by each protocol \u003csup\u003e7\u003c/sup\u003e, helping trainers and coaches to tailor training programs to specific goals and target populations. Second, it can provide evidence-based guidance for optimizing training efficiency and effectiveness, ensuring that individuals maximize their potential gains while minimizing the risk of overexertion or injury \u003csup\u003e8\u003c/sup\u003e. 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 field of exercise science \u003csup\u003e15\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn the realm of strength training, the manipulation of training intensity has long been recognized as a pivotal factor influencing physiological adaptations and performance outcomes \u003csup\u003e16\u003c/sup\u003e. Specifically, 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 \u003csup\u003e17\u003c/sup\u003e. 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 \u003csup\u003e18\u003c/sup\u003e. 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.\u003c/p\u003e\n\u003cp\u003eDespite numerous studies on squat training \u003csup\u003e11,19\u0026ndash;21\u003c/sup\u003e, there is still a lack of understanding of how physiological variables such as EE, VO\u003csub\u003e2\u003c/sub\u003e, 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, VO\u003csub\u003e2\u003c/sub\u003e, 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.\u003c/p\u003e"},{"header":"METHODS","content":"\u003ch3\u003eExperimental approach to the problem\u003c/h3\u003e\n\u003cp\u003eThis study employed a repeated measures approach to assess variations in EE, VO\u003csub\u003e2\u003c/sub\u003e, 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.\u003c/p\u003e\n\u003ch3\u003eSubjects\u003c/h3\u003e\n\u003cp\u003eThe study involved 29 Sports Science students, consisting of 13 males and 16 females, with an average age of 24.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6 years, height of 1.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 m, body mass of 68.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.9 kg, and BMI of 23.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0 kg/m\u003csup\u003e2\u003c/sup\u003e. 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 specific 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 (N\u0026ordm;. 2182/CEIH/2021) and was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003eIn the initial interaction with the participants to confirm 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 specifically 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 \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive characteristics of sample study according to gender.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMen (n\u0026thinsp;=\u0026thinsp;13)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eWomen (n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" align=\"left\"\u003e\n \u003cp\u003eAnthropometrics parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\n \u003ch2\u003eProcedures\u003c/h2\u003e\n \u003cp\u003eThe study involved five 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\u0026deg;C and humidity of 60%.\u003c/p\u003e\n \u003cp\u003eTo standardize participants\u0026apos; 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 influence 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 specific 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 \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, total EE was determined using the corresponding activity factor, and body fat percentage was estimated using the Foulkner formula \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eParticipants followed the researcher\u0026apos;s instructions upon arrival at the study. They were then outfitted with a gas analyzer mask, and gas analysis commenced while they remained seated in a relaxed posture for five 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 \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Following this, they engaged in a five-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 five-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 VO\u003csub\u003e2\u003c/sub\u003e), and the amount of carbon dioxide generated as a metabolic byproduct, which was expelled into the atmosphere (CO\u003csub\u003e2\u003c/sub\u003e \u0026ndash; VCO\u003csub\u003e2\u003c/sub\u003e). The sequence of exercises was arranged in a random fashion, with a five-minute break provided between each set. Previous studies \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e have established the test-retest reliability of FEMD for squat exercises. The study protocol is illustrated in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eDuring the first 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\u0026apos; squat 1RM by following the protocol explained in del-Cuerpo (2023) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eOnce 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 five repetitions). The last repetition will be considered the individual\u0026apos;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 final repetition served as the participant\u0026apos;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 five repetitions). The last repetition was regarded as the participant\u0026apos;s 1RM. Finally, (d) if the participant exceeds 120 kg (the device\u0026apos;s load limit), we record the total number of repetitions they can perform and estimate the 1RM using Lombardi\u0026apos;s Eq.\u0026nbsp;2\u003csup\u003e7\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe 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 \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. 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 \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eEE during both protocols was measured using the FitMate\u0026trade; metabolic system (Cosmed, Rome, Italy), which is a dependable and validated metabolic analyzer specifically 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 \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. 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 affixed to the patient\u0026apos;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\u0026apos;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.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eDescriptive data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Normal distribution of the data (Shapiro\u0026ndash;Wilk test) and homogeneity of variances (Levene test) were confirmed (P\u0026thinsp;\u0026gt;\u0026thinsp;.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 (\u0026omega;\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) 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 \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The JASP statistics package (version 0.11.1) was used for the statistical analyses.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eThere are significant differences for the variables of EE at 50% 1RM (p\u0026thinsp;=\u0026thinsp;0.001; \u0026omega;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.012) and 75% 1RM (p\u0026thinsp;=\u0026thinsp;0.001; \u0026omega;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.008) in the comparison of three series S1 vs S2 vs S3. The post hoc analysis using Holm's correction revealed that EE significantly increased for 50% 1RM protocol between S1 and S3 (S1: 21.53 (5.52) vs S3: 23.26. (5.70), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and for 75% 1RM protocol between S1 and S2 (S1: 16.37 (3.92) vs S2: 17.19 (4.95), p\u0026thinsp;=\u0026thinsp;0.006) and between S1 and S3 (S1: 16.37 (3.92) vs S3: 17.33 (4.59), p\u0026thinsp;=\u0026thinsp;0.002) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere are significant differences for the variables of VO\u003csub\u003e2\u003c/sub\u003e at 50% 1RM (p\u0026thinsp;=\u0026thinsp;0.001; \u0026omega;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.012) and 75% 1RM (p\u0026thinsp;=\u0026thinsp;0.001; \u0026omega;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.008) in the comparison of three series S1 vs S2 vs S3. The post hoc analysis using Holm's correction revealed that VO\u003csub\u003e2\u003c/sub\u003e significantly increased for 50% 1RM protocol between S1 and S3 (S1: 10.75 (1.66) vs S3: 11.18 (1.72), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and for 75% 1RM protocol between S1 and S2 (S1: 8.71 (1.07) vs S2: 9.11 (1.43), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and between S1 and S3 (S1: 8.71 (1.07) vs S3: 9.20 (1.34), p\u0026thinsp;=\u0026thinsp;0.002) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e\n\u003cp\u003eThere are significant differences for the variables of HR at 50% 1RM (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u0026omega;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.119) and 75% 1RM (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u0026omega;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.030) in the comparison of three series S1 vs S2 vs S3. The post hoc analysis using Holm's correction revealed that HR significantly increased for 50% 1RM protocol between S1 and S2 (S1: 92.61 (11.72) vs S2: 100.68 (14.42), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), between S1 and S3 (S1: 92.61 (11.72) vs S3: 104.97 (15.07), p\u0026thinsp;\u0026lt;\u0026thinsp;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\u0026thinsp;=\u0026thinsp;0.003) and between S1 and S3 (S1: 82.82 (8.43) vs S3: 86.88 (9.69), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ec).\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n\u003cp\u003eThere are significant differences for the variables of V at 50% 1RM (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u0026omega;2\u0026thinsp;=\u0026thinsp;0.007) and 75% 1RM (p\u0026thinsp;=\u0026thinsp;0.033; \u0026omega;2\u0026thinsp;=\u0026thinsp;0.004) in the comparison of three series S1 vs S2 vs S3. The post hoc analysis using Holm's correction revealed that V significantly increased for 50% 1RM protocol between S1 and S2 (S1: 76.39 (21.59) vs S2: 79.26 (22.48), p\u0026thinsp;=\u0026thinsp;0.007) and between S1 and S3 (S1: 76.39 (21.59) vs S3: 80.98 (22.51), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and for 75% 1RM protocol between S1 and S3 (S1: 67.13 (17.73) vs S3: 70.24 (17.37), p\u0026thinsp;\u0026lt;\u0026thinsp;0.028) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eV measurements were obtained during both protocols for the three series.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eS1\u003c/p\u003e\n\u003cp\u003eMean (SD)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eS2\u003c/p\u003e\n\u003cp\u003eMean (SD)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eS3\u003c/p\u003e\n\u003cp\u003eMean (SD)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eANOVA\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eV\u003c/p\u003e\n\u003cp\u003e(cm/s)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e50% 1RM\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e76.39 (21.59)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e79.26 (22.48)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e80.98 (22.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eF (2.00, 56.00)\u0026thinsp;=\u0026thinsp;12.13; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u0026omega;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e75% 1RM\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e67.13 (17.73)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e68.71 (17.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e70.24 (17.37)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eF (2.00, 56.00)\u0026thinsp;=\u0026thinsp;3.64; p\u0026thinsp;=\u0026thinsp;0.033; \u0026omega;\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eS1: serie 1; S2: serie 2; S3: serie 3; SD: standard deviation; V: velocity.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003e\u0026nbsp;\u003c/h3\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe main purpose of this study was to assess the changes in EE, VO\u003csub\u003e2\u003c/sub\u003e, 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, VO\u003csub\u003e2\u003c/sub\u003e, HR, and V tended to decrease in both protocols as the sets were performed. This finding holds practical significance, as it suggests an adaptive response in energy utilization and cardiovascular demand over the course of multiple repetitions within a set, potentially influencing training strategies for improved efficiency and performance optimization\u003c/p\u003e \u003cp\u003eTaken together, these findings suggest that this trend may be indicative of several factors. First, participants may experience an improvement in movement efficiency as they become more familiar with exercise \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. This can lead to decreases in EE and VO\u003csub\u003e2\u003c/sub\u003e over the course of the series \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Additionally, the decrease in HR may be related to cardiovascular adaptation that occurs in response to the strength-training stimulus \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. 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 \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite 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, VO\u003csub\u003e2\u003c/sub\u003e 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 specific training program in different population groups. Thus, we believe that this is the novelty of this study.\u003c/p\u003e \u003cp\u003eRegarding EE and VO2, to the best of our knowledge, one of the few articles found dates back to 1968. Seliger et al. (1968) \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e 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\u0026ndash;95% of the 1RM (concentric contraction). The other half was trained only by lowering dumbbells, whose weight corresponded to 145\u0026ndash;150% of the 1RM (eccentric contractions). Subsequently, both VO\u003csub\u003e2\u003c/sub\u003e and EE decreased significantly. 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) \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, 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 \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWith regard to HR, a similar phenomenon occurs. The variation in HR after exercise has been widely studied, and there is abundant scientific literature indicating that individuals adapted to exercise show a lower resting heart rate and cardiac hypertrophy \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, 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) \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, 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 \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. 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 \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn 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) \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e 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. \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e 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 \u0026times; 10 and 3 \u0026times; 12, there were reductions in the MPV of 45% and 46%, respectively. Similarly, Gonzalez-Badillo et al. \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e and Pareja-Blanco et al. \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e observed MPV reductions of 44% in protocols involving 3 \u0026times; 8 \u003csup\u003e46\u003c/sup\u003e and 3 \u0026times; 12 \u003csup\u003e38\u003c/sup\u003e sets, with 5 minutes of rest between sets during the full squat in trained men. These findings 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.\u003c/p\u003e \u003cp\u003eThe practical implications of these findings are significant 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 intensity and volume of training based on individual goals and athlete capabilities, (b) movement efficiency: observing the trend of decreasing EE and VO\u003csub\u003e2\u003c/sub\u003e throughout the sets highlights the importance of proper technique in performance. Encouraging efficient techniques can help athletes conserve energy and improve performance over time. (c) Monitoring progress and performance: observing changes in HR and VO\u003csub\u003e2\u003c/sub\u003e throughout training can serve as an indicator of progress. Regular monitoring can help adjust training strategies according to the changing needs of the athletes.\u003c/p\u003e \u003cp\u003eTaken together, these findings provide a solid scientific foundation for decision-making in strength-training program designs. Exercise professionals and coaches can use this information to maximize the benefits 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.\u003c/p\u003e \u003cp\u003eNevertheless, 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.\u003c/p\u003e \u003cp\u003eIn conclusion, the main findings of this study showed that all the variables measured (EE, VO\u003csub\u003e2\u003c/sub\u003e, 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 findings 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.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHORS\u0026rsquo; CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eICR 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 final version of the manuscript. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are available and can be shared upon reasonable request to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICS DECLARATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach participant received detailed information on the study\u0026apos;s specifics, objectives, and potential risks, and provided informed consent. The study protocol was approved by the Committee on Human Research of the University of Granada (N\u0026ordm;. 2182/CEIH/2021) and was conducted in accordance with the Declaration of Helsinki.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBergmann, J., Kramer, A. \u0026amp; Gruber, M. Repetitive hops induce postactivation potentiation in triceps surae as well as an increase in the jump height of subsequent maximal drop jumps. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e8\u003c/b\u003e, e77705 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eComfort, P. \u0026amp; Kasim, P. Optimizing squat technique. \u003cem\u003eStrength. Cond J.\u003c/em\u003e \u003cb\u003e29\u003c/b\u003e, 10\u0026ndash;13 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchlegel, P. \u0026amp; Fialov\u0026aacute;, D. 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Sports Med.\u003c/em\u003e 295\u0026ndash;304 (2015).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5394309/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5394309/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe main purpose of this study was to assess the changes in energy expenditure (EE), oxygen volume (VO\u003csub\u003e2\u003c/sub\u003e), 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, VO\u003csub\u003e2\u003c/sub\u003e, 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 findings have important implications for the design of effective and personalized strength training programs.\u003c/p\u003e","manuscriptTitle":"Understanding the Dynamics of Squat Training: Effects on Energy Expenditure, Oxygen Consumption, and Heart Rate in Young, Healthy Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-28 13:30:04","doi":"10.21203/rs.3.rs-5394309/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-01-01T03:52:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-30T13:01:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-25T19:56:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160224204040742322665137146497162150713","date":"2024-12-20T15:15:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"26339530188554555436866650552289941738","date":"2024-12-20T09:54:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-17T19:17:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"313022752375341173460228431556981017657","date":"2024-11-30T03:51:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-29T14:08:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-21T09:46:43+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-11T10:47:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-11T04:36:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-11-05T09:47:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"461b127f-6f65-4c93-b00e-34484ac0dfa4","owner":[],"postedDate":"November 28th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":40606470,"name":"Earth and environmental sciences/Environmental sciences"},{"id":40606471,"name":"Health sciences/Cardiology"}],"tags":[],"updatedAt":"2025-08-04T16:41:25+00:00","versionOfRecord":{"articleIdentity":"rs-5394309","link":"https://doi.org/10.1038/s41598-025-04427-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-29 16:21:07","publishedOnDateReadable":"July 29th, 2025"},"versionCreatedAt":"2024-11-28 13:30:04","video":"","vorDoi":"10.1038/s41598-025-04427-0","vorDoiUrl":"https://doi.org/10.1038/s41598-025-04427-0","workflowStages":[]},"version":"v1","identity":"rs-5394309","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5394309","identity":"rs-5394309","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}