Predicting Energy Expenditure of an Acute Resistance Exercise Bout in Men and Women : Medicine & Science in Sports & Exercise

Journal Logo

APPLIED SCIENCES

Predicting Energy Expenditure of an Acute Resistance Exercise Bout in Men and Women

LYTLE, JASON ROBERT1; KRAVITS, DANIELLE M.1; MARTIN, STEVEN E.1; GREEN, JOHN S.1; CROUSE, STEPHEN F.1; LAMBERT, BRAD S.2

Author Information
Medicine & Science in Sports & Exercise 51(7):p 1532-1537, July 2019. | DOI: 10.1249/MSS.0000000000001925
  • Free

Abstract

The American College of Sports Medicine (ACSM) provides exercise guidelines that recommend individuals expend a minimum of 1000 kcal·wk−1 for maintenance of general health (1). These guidelines incorporate regular aerobic exercise in addition to resistance exercise (RE) 2–3 d·wk−1 (2). The RE component of these guidelines consists of total body RE, including 8–12 repetitions per set at an intensity of 60%–80% of the individual’s one-repetition maximum (1RM). This protocol is suggested for the health benefits associated with RE (1). Some of these benefits include improvements in glycemic control, risk factors associated with metabolic syndrome, body composition, and increases in muscular strength (3–5). In addition, research indicates that RE alone and in combination with aerobic exercise can aid in weight loss and weight maintenance (4,6–8). Despite the ACSM recommendations for general health being based on caloric expenditure, the caloric cost of RE is not accounted for within the ACSM guidelines (1). The ACSM provides prediction equations for the estimation of the kilocalorie expenditure of various aerobic exercises (1). However, to the authors’ knowledge, an individualized prediction equation to estimate the caloric expenditure of RE has not been identified.

Because caloric expenditure is often considered when developing an exercise program for general health or weight loss, it would be beneficial to include the energy requirements for both RE as well as aerobic exercise. More specifically, when considering weight management interventions, caloric intake and expenditure are key aspects to take into account. Thus, the ability to practically predict caloric cost of individual RE and/or RE bouts would be of great utility to health and fitness practitioners.

The estimated caloric cost of RE using indirect calorimetry varies considerably in the published literature. In a review by Meirelles and Gomes (9), the caloric cost of a complete RE session was found to range widely from 65 to 540 kcal. The ambiguous nature of these findings could be a result of varied protocols within the RE bout. For example, some RE studies use 1RM percentage to designate the exercise weight, and these intensities range from 40% to 70% 1RM (9). Other studies use multiple RM to estimate a 1RM to determine the exercise weight. RM values range from 10 to 15 repetitions. The rest interval between exercise sets is another inconstant component within the literature. These range from no, to very little rest (interval training), to 2-min intervals between sets. In some studies, the time between exercise sets was not reported at all (9). Contrary to this, studies conducted using the ACSM protocol for RE seemed to provide more consistent results. In a study involving 12 women who engaged in an RE bout similar to that recommended by the ACSM (10 exercises, 3 sets × 10 reps, 70% 1RM), the mean caloric expenditure was 155 kcal (8). In another study using an ACSM protocol, with a slightly lower volume (8 exercises, 1 set × 15RM) than the aforementioned study, a mean caloric expenditure of 135.20 ± 16.6 and 81.7 ± 11.1 kcal was found for men and women, respectively (10). Disparities in these findings (9) are likely due to differences in total exercise volume (TV), duration, and intensity, as well as the metabolic cost to perform differing types of RE (multiple joint exercises compared with single joint exercises). Although the caloric expenditure results from RE protocols similar to ACSM guidelines have been found to be comparable, these observed mean kilocalorie expenditures have not been used to create individualized predictions of caloric cost. This would be useful for individuals who do not have access to indirect calorimetry or might be using different exercise durations or volumes. Certain factors may influence energy expenditure during RE. Because age, gender, body weight/body composition, and duration of the RE bout are factors that affect resting energy expenditure, it can be hypothesized that these would also contribute to energy expenditure during RE (11). In aerobic exercise bouts, heart rate (HR) is often used to predict caloric cost (12). This is based on the linear increase in HR and oxygen consumption as aerobic intensity increases (13). However, because of the fluctuation in HR during RE, this may not be a practical means to assess energy expenditure. In addition, excess post–oxygen consumption (EPOC) of RE may also need to be considered. Study findings related to the EPOC after RE have been mixed (14–17). In light of this, further investigation is needed to determine whether the EPOC of traditional RE is a significant factor in total caloric cost.

A practical prediction equation, similar to that of ACSM aerobic exercise equations, would be useful to quantify the caloric expenditure of RE. In light of previous evidence and current field needs, the purpose of the present study was to develop a regression equation to predict net kilocalorie expenditure for an RE bout as well as the individual lifts that follow the current ACSM guidelines of health and fitness (3 sets of 8–12 reps at ~50%–70% 1RM) (1). We hypothesized that this could be accomplished with variables such as aerobic capacity, height, weight, body composition, age, and TV as prediction variables.

METHODS

All procedures performed were approved by the institutional review board for human subject research, and all study subjects provided informed consent before participation.

Participants

Fifty-two healthy, active subjects were studied (27 men, 25 women, 20–58 yr, 174.1 ± 10.5 cm, 188.7 ± 42.6 kg, V˙O2max 36.8 ± 9.2 mL·kg−1⋅min−1) (Table 1). Subjects were asked to complete a Health and Lifestyle History questionnaire to identify possible risk factors for heart disease, skeletal muscle injury, orthopedic risk, and any contraindications to heavy exercise. All subjects were screened to exclude those with evidence of medical contraindications to heavy exercise. Written informed consent was obtained from all subjects before participation.

T1
TABLE 1:
Body composition demographics and performance statistics.

Preliminary testing

Height, age, body weight, lean mass, %fat, fat-free mass, and maximal oxygen uptake (V˙O2max) were obtained for all subjects. Body composition values were acquired via dual-energy x-ray absorptiometry (DEXA) scans (Lunar Prodigy, GE®, Boston, MA). V˙O2max was determined using a maximal graded exercise test (GXT) on a standard treadmill with 12-lead electrocardiogram. The Bruce protocol (18) was used for the GXT. Printouts of the electrocardiogram, along with blood pressure, were taken before exercise, during every stage of exercise, and at minutes 1, 3, and 5 of the recovery after exercise. Breath-by-breath gas analysis was also used during the GXT to obtain each subject’s V˙O2max and to monitor each subject’s RER (Ultima; MGC Diagnostics®, St. Paul, MN). Requirements for determining V˙O2max were as follows: subjects had to achieve 1) >90% age-predicted maximal HR; 2) an RER of >1.2 (V˙CO2/V˙O2); 3) an RPE via the Borg scale >17 (1,19); and (4) a plateauing of V˙O2 with increasing workload and duration at maximal exercise. Of the total subject pool, nine volunteers agreed to participate in additional testing, which consisted of assessment of resisting metabolic rate (RMR) and EPOC. RMR was measured for a volunteer subgroup of nine participants (3 women, 6 men, 22 ± 1 yr, 177.0 ± 11 cm, 80.3 ± 18.6 kg). Subjects sat reclined in a phlebotomy chair in a temperature-controlled room, with the lights dimmed, while breath-by-breath measures of V˙O2, V˙CO2, and RER were continuously recorded for 1 h using a calibrated metabolic cart (CPX Express, MGC Diagnostics®). Subjects were asked to fast overnight for 10 h and avoid all exercise for 72 h before the collection of the RMR (see below for EPOC measurement).

At least 72 h after V˙O2max and RMR assessments and 1 wk before their experimental training bout, subjects were strength tested to determine their 3–5RM on Keiser® resistance training equipment. Keiser® equipment is a pneumatic exercise device that uses compressed air to add pounds of resistance similar to a plate loaded system. A thorough description of Keiser® equipment has been previously reported (20). The strength test was preceded by a resistance training equipment familiarization session using light, submaximal resistance. Seven common resistance training exercises were tested in the following order: leg press, chest press, leg curl, lateral pull down, leg extension, triceps push down, and biceps curl. For warm-up, the subjects performed 3 min of light aerobic activity on a cycle ergometer followed by stretching and several warm-up sets of RE. After the warm-up sets, maximal strength was assessed using a 3–5RM whereby 1RM was calculated for each exercise using previously published procedures (21). The data obtained from the maximum strength assessments were used to create each subject’s exercise prescription for the RE bout.

Metabolic measurements

One week after preliminary testing, subjects performed one acute total body RE bout based on the ACSM guidelines (1) using Keiser® resistance training equipment. Exercise order was the same as the strength assessment order: leg press, chest press, leg curl, lateral pull down, leg extension, triceps push down, and biceps curl. The warm-up set was 60% of 1RM followed by two to three working sets at 70% of 1RM. Subjects were instructed to attempt to achieve a maximum of 12 repetitions on each set. If the subjects were unable to complete a minimum of 8 repetitions on their second working set, they were instructed not to attempt a third set. Each set began every 2 min. The average exercise time for each set was 30 ± 6 s, leaving approximately 90 s of recovery time before the subsequent set. Breath-by-breath measures of V˙O2, V˙CO2, and RER were continuously recorded 30 min before and during exercise using a calibrated metabolic cart (CPX Express, MGC Diagnostics®). In addition, EPOC was measured for the subgroup (n = 9) who underwent the preliminary RMR measures. EPOC was measured using breath-by-breath analysis for 1 h after the RE in the same environmental conditions as the RMR collection (CPX Express, MGC Diagnostics®). These data were then partitioned for each individual exercise as well as the total bout. The recovery time between sets was included in the individual and total exercise prediction equations. Energy cost in kilocalories was calculated from total oxygen uptake and mean RER for resting, each individual exercise, total exercise bout, and postexercise measurements. For RER values exceeding 1.0, a constant of 5.047 kcal per LO2 was used to approximate energy expenditure (22). Net caloric expenditure was defined as caloric expenditure resulting from physical activity that exceeded predicted resting caloric expenditure over time. For example, if measured total caloric expenditure was 200 kcal over a period of 1 h, but the subject’s resting caloric expenditure was calculated to be 100 kcal, their net caloric expenditure from exercise would be 100 kcal. In the case of our subjects, resting caloric expenditure was approximated using an RER of 0.7 (4.686 kcal·min−1 per LO2) and a V˙O2 of 3.5 mL·kg−1⋅min−1 over the period they were exercising (22).

Statistical design

Multiple linear regression was used to predict net kilocalorie expenditure of the full RE bout as well as the individual exercises within the bout (Fig. 1). A backward stepwise removal was applied to determine the best model for prediction based on the highest adjusted R2 with the lowest level of collinearity and variance inflation. A best-fit model was identified for each individual exercise as well as complete RE bout. An unpaired two-tailed t-test was used to determine whether a difference in energy cost existed between males and females. For this analysis, net kilocalorie expenditure, net kilocalorie expenditure, and net kilocalorie expenditure normalized to lean mass were examined. A paired two-tailed t-test was used to compare metabolic gas exchange every 2 min for 1 h after the RE bout to measures taken during RMR testing. Type I error for all analyses was set at P ≤ 0.05.

F1
FIGURE 1:
Linear regression graph to determine predicted net kilocalorie expenditure equation for an acute bout of RE.

RESULTS

Regression results for the total RE bout and the individual RE are shown in Table 2. The stepwise removal identified height, weight, age, gender, lean body mass, fat mass, and TV (sets × reps × wt) as the independent/predictor variables (P < 0.05). An example equation derived from the results table can also be found in the Table 2 legend. Analysis revealed a significant difference in net caloric expenditure between men and women (men, 161.2 ± 65.9 kcal; women, 87.6 ± 39.0 kcal; P < 0.05); however, this difference was not observed when normalizing to lean mass (men, 2.8 ± 1.0 kcal·kg−1 LM; women, 2.4 ± 1.3 kcal·kg−1 LM; NS). Oxygen consumption was found to be significantly elevated above RMR for up to 20 min after the RE bout (Fig. 2). The average exercise bout length ranged from 42 to 56 min (51.3 ± 6.6 min) depending on achievable workload (3 or 4 sets). After 20 min, oxygen consumption returned to resting levels. This EPOC accounted for a net additional 7.43 kcal compared with resting values. The RER postexercise was not found to be significantly different from rest at any time. The mean RER levels were measured to be 0.86 ± 0.03 and 0.84 ± 0.04 for baseline resting RER and post-RE RER, respectively.

T2
TABLE 2:
Best-fit model, including regression coefficients, R 2, and standard error, for each individual exercise and the total RE bout.
F2
FIGURE 2:
Values are represented as means ± SD for V˙O2 mL·kg−1⋅min−1 measured at rest and 30 min after total body RE. *Significant difference between resting V˙O2 and postexercise V˙O2 (P < 0.05).

DISCUSSION

The ACSM guidelines recommend a caloric expenditure of at least 1000 kcal·wk−1 for general health through aerobic training and 2–3 d·wk−1 of RE (1). Caloric expenditure for aerobic exercises can be calculated via metabolic equations provided by the ACSM guidelines (1). Currently, there are no known metabolic equations to predict the energy expenditure of RE accurately. Such equations would be useful for a variety of health and fitness professionals, who are interested in caloric expenditure as it relates to weight loss or weight management. Although it is well known that the caloric expenditure from resistance training is considerably less from aerobic training, our results show it is not trivial. In the current study, the net caloric cost of an RE bout was successfully predicted using multiple linear regression with height, age, fat mass, lean mass, and TV providing the best-fit model for prediction (adjusted R2 = 0.773; Table 2). Net caloric cost was selected, rather than total caloric cost, to minimize variability associated with the total time of the exercise bout. By removing the resting component of caloric cost, the error associated with differing total exercise times is minimized. Because of the novelty of the current research, additional published equations for comparison are lacking. However, currently accepted prediction equations for the caloric cost of aerobic exercise have yielded similar correlation values (23,24).

The literature regarding caloric cost of EPOC after RE is widely variable. Some research suggests that the EPOC after RE lasts 36 h (25), and others showed a return to resting values in under 20 min (14,16,17). In our current study, the EPOC was measured to be a total of 7.4 kcal, and the metabolic rate (V˙O2) returned to baseline levels within 20 min postexercise. In addition, no significant difference was found between a 30-min postexercise caloric cost and a 30-min resting caloric expenditure. The postexercise caloric cost was measured to be 48.9 kcal, whereas resting was 41.5 kcal. On the basis of these results, the effect of EPOC with regard to caloric expenditure was deemed negligible. This is beneficial to note because of the potential for caloric cost from an initial lift carrying over to the next. On the basis of the current results and other literature (14,16,17), the EPOC between RE in succession would most likely be minimal.

For practical application, a personal trainer can implement our equations to account for the energy required during the RE component of a weight loss program. This would be additional to the kilocalorie expenditure during any aerobic component. These combined values would then account for the total exercise-related caloric expenditure. Overall, this value would be used to determine the caloric intake that the client can consume and still lose weight.

We caution the reader of the limitations of our study. Our age range is limited to 20 to 58 yr, and thus we cannot verify the accuracy if our equations are applied to younger or older individuals. However, other published prediction equations have used similar age demographics (24,26). Second, because of the exclusive use of Keiser® RE equipment, the direct application of our prediction equation to other RE systems with different mechanical loading systems may lead to inaccuracies. However, the similarities in the major muscle groups exercised and the mechanics of the movement patterns consistent across most RE training equipment suggest the general feasibility of our approach. To our knowledge, only one study has assessed the difference in 1RM between Keiser® and free weight RE, which found the difference in to be less than 10% (27). Despite this, future studies using other RE training systems will still be required to support or refute this contention. It is noteworthy that the total RE caloric expenditure of the subjects in our study was comparable with that found in a previous study involving a similar exercise bout using free weight equipment (8). Binzen et al. (8) reported that oxygen consumption was measured to be 2.4 kcal·min−1 for a 45-min RE bout in resistance-trained women. The intensities were also similar to ours at 70% of 1RM. Lastly, a potential limitation is the use of DEXA for body composition analysis, which may not be widely accessible. However, if a DEXA machine is not available for assessment, skinfold values have been used to estimate DEXA composition values for men, women, and athletic populations in the literature (28–30). Finally, our current results indicate that a significant difference exists between men and women with regard to the caloric expenditure of RE. However, these gender differences appear to be due primarily to differences in lean body mass.

In conclusion, we have developed a novel and accurate prediction equation to estimate the net caloric cost of individual RE and of an RE bout consisting of seven different RE consistent with the ACSM recommendation for health and fitness (1). The predictor variables for the regression equations were gender, height, age, fat mass, lean mass, and TV. These estimates of RE caloric cost will be immediately useful for practitioners in prescribing RE for weight management and weight reduction in the context of general health and fitness.

No funding was received for this research. The authors declare no conflict of interest.

The results of the study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation. The results of the present study do not constitute endorsement by the ACSM.

REFERENCES

1. Medicine ACoS, Magal M. ACSM’s Guidelines for Exercise Testing and Prescription. 10th ed. Wolters Kluwer; 2017. 472 p.
2. Garber CE, Blissmer B, Deschenes MR, et al. American College of Sports Medicine Position Stand: quantity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults: guidance for prescribing exercise. Med Sci Sports Exerc. 2011;43(7):1334–59.
3. Campbell WW, Crim MC, Young VR, Evans WJ. Increased energy requirements and changes in body composition with resistance training in older adults. Am J Clin Nutr. 1994;60(2):167–75.
4. Donnelly JE, Blair SN, Jakicic JM, Manore MM, Rankin JW, Smith BK. American College of Sports Medicine Position Stand: appropriate physical activity intervention strategies for weight loss and prevention of weight regain for adults. Med Sci Sports Exerc. 2009;41(2):459–71.
5. Castaneda C, Layne JE, Munoz-Orians L, et al. A randomized controlled trial of resistance exercise training to improve glycemic control in older adults with type 2 diabetes. Diabetes Care. 2002;25(12):2335–41.
6. Avila JJ, Gutierres JA, Sheehy ME, Lofgren IE, Delmonico MJ. Effect of moderate intensity resistance training during weight loss on body composition and physical performance in overweight older adults. Eur J Appl Physiol. 2010;109(3):517–25.
7. Azizi M, Baledi R. The comparison effect of aerobic and resistance training onregional and abdominal fat reduction and some of the heart risk factors among 30 to 45 years old healthy females. Int J Collab Res Intern Med Public Health. 2012;4(4):309–17.
8. Binzen CA, Swan PD, Manore MM. Postexercise oxygen consumption and substrate use after resistance exercise in women. Med Sci Sports Exerc. 2001;33(6):932–8.
9. Meirelles CdM, Gomes PSC. Acute effects of resistance exercise on energy expenditure: revisiting the impact of the training variables. Rev Bras Med Esporte. 2004;10(2):122–30.
10. Phillips WT, Ziuraitis JR. Energy cost of single-set resistance training in older adults. J Strength Cond Res. 2004;18(3):606–9.
11. Zakeri I, Puyau MR, Adolph AL, Vohra FA, Butte NF. Normalization of energy expenditure data for differences in body mass or composition in children and adolescents. J Nutr. 2006;136(5):1371–6.
12. Achten J, Jeukendrup AE. Heart rate monitoring—applications and limitations. Sports Med. 2003;33(7):517–38.
13. Rennie KL, Hennings SJ, Mitchell J, Wareham NJ. Estimating energy expenditure by heart-rate monitoring without individual calibration. Med Sci Sports Exerc. 2001;33(6):939–45.
14. Kang J, Hoffman JR, Im J, et al. Evaluation of physiological responses during recovery following three resistance exercise programs. J Strength Cond Res. 2005;19(2):305–9.
15. Melby C, Scholl C, Edwards G, Bullough R. Effect of acute resistance exercise on postexercise energy-expenditure and resting metabolic-rate. J Appl Physiol. 1993;75(4):1847–53.
16. Mukaimoto T, Ohno M. Effects of circuit low-intensity resistance exercise with slow movement on oxygen consumption during and after exercise. J Sports Sci. 2012;30(1):79–90.
17. Thornton MK, Rossi SJ, McMillan JL. Comparison of two different resistance training intensities on excess post-exercise oxygen consumption in African American women who are overweight. J Strength Cond Res. 2011;25(2):489–96.
18. Bruce RA, Kusumi F, Hosmer D. Maximal oxygen intake and nomographic assessment of functional aerobic impairment in cardiovascular disease. Am Heart J. 1973;85(4):546–62.
19. Borg G. Borg’s Perceived Exertion and Pain Scales. Champaign (IL): Human Kinetics; 1998.
20. Keiser DL. Exercising device including linkage for control of muscular exertion required through exercising stroke. US Patent No 4. 1980;227:689.
21. Haff GG, Triplett NT. Essentials of Strength Training and Conditioning 4th Edition. Champaign (IL): Human Kinetics; 2015. 752 p.
22. Crouse SF, Oden G. Clinical Exercise Physiology Laboratory Manual: Physiological Assessments in Health Disease and Sport Performance. Kendall Hunt Publishing Company; 2018. 278 p.
23. Berry MJ, Storsteen JA, Woodard CM. Effects of body mass on exercise efficiency and V˙O2 during steady-state cycling. Med Sci Sports Exerc. 1993;25(9):1031–7.
24. Latin RW, Berg K, Kissinger K, Sinnett A, Parks L. The accuracy of the ACSM stair-stepping equation. Med Sci Sports Exerc. 2001;33(10):1785–8.
25. Schuenke MD, Mikat RP, McBride JM. Effect of an acute period of resistance exercise on excess post-exercise oxygen consumption: implications for body mass management. Eur J Appl Physiol. 2002;86(5):411–7.
26. Latin RW, Berg KE, Mellion MB. The accuracy of the ACSM cycle ergometry equation. Med Sci Sports Exerc. 1992;24(2):272–6.
27. Frost DM, Cronin JB, Newton RU. A comparison of the kinematics, kinetics and muscle activity between pneumatic and free weight resistance. Eur J Appl Physiol. 2008;104(6):937–56.
28. Ball SD, Altena TS, Swan PD. Comparison of anthropometry to DXA: a new prediction equation for men. Eur J Clin Nutr. 2004;58(11):1525.
29. Ball SD, Swan PD, Desimone R. Comparison of anthropometry to dual energy X-ray absorptiometry: a new prediction equation for women. Res Q Exerc Sport. 2004;75(3):248–58.
30. Oliver JM, Lambert BS, Martin SE, Green JS, Crouse SF. Predicting football players’ dual-energy x-ray absorptiometry body composition using standard anthropometric measures. J Athl Trains. 2012;47(3):257–63.
Keywords:

ENERGY EXPENDITURE; RESISTANCE EXERCISE; PREDICTION EQUATION

Copyright © 2019 by the American College of Sports Medicine