|Dans une revue||
Mov Sport Sci/Sci Mot
|Publié en ligne||20 mai 2020|
Concurrent strength and sprint training increases resting metabolic rate in masters road cyclists
L’entraînement simultané en force et en sprint augmente le métabolisme de repos chez des cyclistes masters sur route
Southern Cross University, School of Health and Human Sciences,
2 Central Queensland University, School of Medical and Applied Sciences, Rockhampton, Australia
3 Bond University, Institute of Health and Sport, Gold Coast, Australia
4 MeasureUp, Sydney, Australia
5 Toi Ohomai Institute of Technology, Faculty of Education, Health, Nursing and Social Science, Tauranga, New Zealand
6 University of Jyvaskyla, Faculty of Sport and Health Sciences, Gerontology Research Centre, Jyvaskyla, Finland
7 University of Newcastle, School of Environmental and Life Sciences, Ourimbah, Australia
8 University of Sydney, Physical Activity, Lifestyle, Ageing and Wellbeing Faculty Research Group, Sydney, Australia
* Corresponding author: Luke.DelVecchio@scu.edu.au
Accepted: 25 March 2020
High-intensity concurrent sprint and strength training has been shown to provide a strong physiological training stimulus in young adult endurance athletes. However, the effect in veteran endurance athletes remains unknown. This study examined if replacing a portion of endurance training with concurrent sprint and strength training influenced resting metabolic rate (RMR) and lean mass (LM) in veteran endurance cyclists. Eighteen well-trained male veteran road cyclists (55.2 ± 8.4 years; 7.9 ± 1.1 training hrs/wk; 323 ± 53 Wpeak) were allocated to a concurrent strength and sprint training group (CT, n = 9) or control group (CON, n = 9). The CT group completed a 12-weeks of sprint and strength training while the CON group maintained their normal endurance training. RMR and LM were measured before and after the 12-week training intervention. CT training significantly (p < 0.05) increased both RMR (+14.2%, 1600 ± 244 to 1828 ± 207 kcal/day) and LM (+2.0%, 61.8 ± 5.5 to 63.1 ± 5.4 kg) pre to post-intervention. No significant changes from pre- to post-training were observed in the CON group. These findings suggest replacing a portion of endurance training with sprint and strength training may preserve, and even increase, LM and RMR in veteran road cyclists.
L’entraînement en sprint et en force à haute intensité constitue un puissant stimulus physiologique et, est couramment utilisé dans les programmes d’entraînement des jeunes athlètes d’endurance adultes. Les adaptations potentielles de l’ajout d’exercices de sprint/de force au régime d’entraînement des athlètes d’endurance vieillissants sont mal connues. La présente étude examine si le remplacement d’une partie de l’entraînement en endurance par un entraînement simultané en force et en sprint, influence le métabolisme au repos (RMR) et la masse maigre (LM) chez les athlètes d’endurance masters. Dix-huit cyclistes sur route bien entraînés (55.2 ± 8,4 ans; 7,9 ± 1,1 heures d’entraînement/semaine; Pmax = 323 ± 53 W) ont été affectés à un groupe d’entraînement en force et sprint (CT, n = 9), ou à un groupe de contrôle groupe (CON, n = 9). Le groupe CT a réalisé un entraînement spécifique de 12 semaines alors que le groupe CON maintenait son entraînement d’endurance normal. RMR et LM ont été mesurés avant et après l’entraînement. L’entraînement par a augmenté de façon significative le RMR de 14.2% (1600 ± 244 à 1828 ± 207 kcal/jour, p < 0,05) et la LM de 2.0% (61.8 ± 5.5 à 63,1 ± 5,4 kg, p < 0,05) entre avant et après l’entraînement. Aucun changement significatif entre avant et après l’entraînement n’a été observé dans le groupe CON. Ces données suggèrent que remplacer une partie de l’entraînement en endurance par des sprints et des exercices de force pourrait aider à préserver, voire même augmenter, la masse maigre et le métabolisme de repos chez les cyclistes masters.
Key words: masters athlete / cyclist / resistance training / endurance training / sprinting / basal metabolism / body composition / DXA scan
Mots clés : athlètes masters / exercices d’endurance / métabolisme basal / composition corporelle / DEXA
© ACAPS, 2020
Despite other widely known performance and health benefits of endurance training in healthy older adults (Oja, Kelly, Pedisic, Titze, Bauman, Foster, & Stamatakis, 2017), previous research has reported master athletes (individuals typically over the age of 35 who train [exercise] on a regular basis to compete in organized competitive sport) involved in endurance sports retain age-related losses in lean mass (LM) and muscular strength when compared to sedentary, age-matched adults (Del Vecchio, Stanton, Macgregor, Doering, Korhonen, & Reaburn, 2016; Harridge, Magnusson, & Saltin, 1997). This loss of muscle mass, in addition to compromising exercise performance and function, may predispose veteran endurance athletes to similar age-related declines in muscular function experienced to that which are sedentary individuals, such as sarcopenia (Tanaka, 2017).
Resting metabolic rate (RMR) is known to decline with advancing age (Lemmer, Ivey, Ryan, Martel, Hurlbut, Metter, & Hurley, 2001). For example, Luhrmann et al. (2010) investigated 513 individuals with an average age of 67.4 ± 5.5 years and reported that RMR decreased by 34.1 Kj (4.1–5.2%) per year in males and noted a further decrease in a 10-year follow-up. The age-related decrease in RMR is associated with reductions in mitochondrial membrane proton permeability (Wilson & Morley, 2003), Na+-K+ pump activity (Dolezal & Potteiger, 1998; Luhrmann, Bender, Edelmann-Schafer, & Neuhauser-Berthold, 2009).
Preventing an age-related decline in RMR is important because a low RMR is a risk factor for future weight gain (Johnstone, Murison, Duncan, Rance, & Speakman, 2005), sarcopenia and frailty (Soysal, Ates Bulut, Yavuz, & Isik, 2019). A decline of RMR may also be a negative consequence during periods of detraining in older athletes. Furthermore, the loss of LM with age may in turn lead to further, cyclical reductions in RMR in a cyclical manner (Luhrmann et al., 2009).
Previous research has suggested that high levels of aerobic capacity and endurance training undertaken by veteran athletes may improve RMR over time. For example, in a previous study by Sullo et al. (2004) veteran athletes with high aerobic power exhibited 9.4% higher RMR than age-matched veteran athletes with lower aerobic power. In contrast, the results of previous studies examining the effects of endurance training on RMR in sedentary older adults are equivocal. Some research suggests endurance training maintains (van Pelt, Dinneno, Seals, & Jones, 2001) or increases RMR in healthy older adults (Poehlman & Danforth, 1991), whilst other research shows endurance training does not impact positively on RMR (Antunes, Santos, Boscolo, Bueno, & Mello, 2005).
Given RMR may be influenced by sympathetic tone (Curry, Somaraju, Hines, Groenewald, Miles, Joyner, & Charkoudian, 2013) and the maintenance of LM (Antunes et al., 2005) others have suggested strength training interventions which increase sympathetic tone and LM may reduce the age-related decline in RMR among healthy older adults (Pratley, Nicklas, Rubin, Miller, Smith, Smith, & Goldberg, 1994). For example, Campbell et al. (1994) have reported significant 6.8% increases in RMR following 12 and 16 weeks of strength training in sedentary older adults.
Collectively, the above data suggest both endurance and strength training may increase RMR in older adults. However, the effect of concurrent strength sprint and endurance training on RMR, particularly in well-trained veteran athletes that may have an above average RMR, is not yet known. Therefore, the purpose of this study was to examine the effects of 12 weeks concurrent sprint and strength training on RMR and LM in veteran road cyclists. Investigating the effects of concurrent sprint and strength training in veteran road cyclists is important because further adaptations in RMR among aging endurance athletes who have not previously engaged in sprint or strength training may be possible.
Eighteen healthy male veteran endurance road cyclists aged 40 years and older with no background of strength training were recruited and provided written informed consent. All participants were required to be currently involved in regular cycling training, completing ≥8.0 hours endurance training per week and competing in Veteran/Masters competition for a minimum of two years (Tab. 1). All participants underwent a pre-exercise screening to ensure they had no established cardiovascular, metabolic or respiratory disease (nor) signs (or) symptoms that would exclude them from participating in intensive exercise training (Norton, Norton, & Australia, 2011). The study was approved by the Central Queensland University Human Research Ethics Committee.
Random allocation of participants into training groups was not possible due to work and family commitments that limited their availability to participate in the CT program. As a result, participants were allocated to either a control group (CON, n = 9) or concurrent strength and sprint training group (CT, n = 9) based upon their availability.
Physical and training characteristics of participants.
Initially, peak power output (VO2peak) was determined by a ramp test on an electronically-braked cycle ergometer, completed between three and five days prior to initial RMR and LM testing. Subsequently, participants were instructed to attend the laboratory following an overnight fast, avoid consuming caffeinated beverages for at least 12 hours, and to abstain from physical activity for at least 48 hours prior to the testing sessions. Adherence to these guidelines was confirmed before all testing took place. Testing sessions were carried out between 07:00 and 09:00 hours in the following order:
standard anthropometric measures whereby stature and body mass were measured (Seca, Birmingham, UK);
dual energy X-ray absorptiometry (DXA) assessment for LM.
Following initial testing, the CT group completed a 12-week exercise intervention while the CON group continued their normal endurance-training regime, which did not include any sprint or strength training. Post-intervention, the experimental testing was again completed as previously undertaken. Each measurement was performed by the same trained researchers on both occasions at approximately the same time of the day.
A graded maximal exercise test to measure peak power output (Wpeak) was completed on an electrically-braked, computer-controlled cycle ergometer (Velotron Dynafit Pro, RaceMate, Seattle, USA). Expired gas analysis (O2, CO2 and ventilation) was undertaken during the graded maximal exercise test using a Fitmate Pro (Cosmed, Rome, Italy). The FitMate Pro has been previously shown to be valid and reliable in measuring both peak oxygen consumption and RMR (Nieman, Austin, Benezra, Pearce, McInnis, Unick, & Gross, 2006). The cycle protocol started following a 5-minute warm-up at a self-selected cadence at 50 watts. Following the warm-up period, the graded incremental exercise test started at 50 watts with work increments increased by 15 W · min−1 until volitional exhaustion. Participants were required to maintain a pedalling cadence of 90 rpm throughout the test. Wpeak was calculated from the last completed work rate, plus the fraction of time spent in the final non-completed work rate multiplied by 25 watts (Hawley & Noakes, 1992).
RMR measurements took place between 07:00 and 09:00 hours and were measured using the FitMate™ metabolic system (Cosmed, Rome, Italy). Upon arrival, participants assumed a supine position for 20 minutes in a quiet room at a temperature between 22 °C to 24 °C (Compher, Frankenfield, Keim, & Roth-Yousey, 2006). During the procedure, participants were relaxed and stable with a face mask sealed around their nose and mouth to measure oxygen consumption for 15 minutes. RMR was then calculated using the modified Weir equation (Amirkalali, Hosseini, Heshmat, & Larijani, 2008):
Dual energy X-ray absorptiometry (DXA) (Hologic Discovery-W, Bedford, MA) was used to measure LM as it is recognized as the gold standard for body composition (Buckinx, Landi, Cesari, Fieding, Visser, Engelke, & Kanis, 2018; Scafoglieri & Clarys, 2018). A certified clinical densitometrist (CM) performed all DXA data collection and analysis procedures. Prior to each measurement session, the DXA was calibration to assess and maintain the measurement precision and accuracy of the scanner. During the procedure, participants were motionless in a supine position on a padded exam table, while an X-ray fan array passed above the table. LM and FM was determined using manufacturer-supplied software (APEX version 4.0, Hologic Discovery). All participants were scanned according to Australian Institute of Sport best practice protocols for a total body scan (Nana, Slater, Hopkins, & Burke, 2012).
The CT group who was completing five weekly endurance cycling training sessions; replaced four of these sessions with two groups, track sprint-cycling training sessions, and two morning gym-based group strength training sessions per week. All four training sessions were supervised by the same accredited strength and conditioning coach (LD). Strength training sessions were conducted on alternate days to the track sprint training days. During each strength training session, participants completed exercises in the following order: double- and single-leg hopping (2–3 sets of 10–20 hops), box jumps, leg press throws, single-leg leg presses, seated hip flexions, leg curls, leg extensions, seated calf-raises, supine hip extensions, chest presses, bench rows, abdominal curl ups and lower back extensions. Recovery was two minutes between sets, and exercises were strictly controlled with the strength training sessions lasting approximately 90 minutes. The progressive strength training program utilised training intensities that ranged from 50 to 95% of one-repetition maximum and was periodised to reduce both the potential for overtraining and to optimise neuromuscular adaptation (Tabs. 2a, 2b, 2c) (Del Vecchio, Stanton, Reaburn, Macgregor, Meerkin, Villegas, & Korhonen, 2019). Participants completed electronic training logs describing all training parameters (number of repetitions, sets, loads, track and road training distances, track sprint cycling times) to monitor progress and to provide feedback and motivation for maximal effort during the entire training program (Tab. 3) (Del Vecchio et al., 2019). The CON group were asked to maintain their normal endurance cycling training for the 12-weeks intervention period.
Weeks 1–4 hypertrophy phase.
Weeks 5–8 strength phase.
Weeks 9–12 power phase.
Flying 200-m Track Cycling Program.
Data were analysed using SPSS (version 22.0, SPSS, Armonk, NY) and are reported as mean ± standard deviation (SD). Normality was assessed by Shapiro Wilk’s test and skewness and kurtosis z-score (Kim, 2013). Two-way repeated measures ANOVA were used to determine group (CT, CON) × time (Pre, Post) interactions, or main effects where no interaction effect was observed. If an interaction effect was noted, Student’s t-tests were used to for post-hoc analysis. A Pearson’s correlational analysis between changes in LM and relative RMR was performed. Alpha was accepted at p < 0.05. t-tests (2-tailed) were used to compare the within-group changes (pre to post) and Cohen’s d effect sizes were calculated to compare the differences (pre to post) between groups. Threshold values for small, moderate and large effects were 0.2, 0.5 and 0.8, respectively (Sullivan & Feinn, 2012).
A total of 18 (CT = 9, CON = 9) healthy male veteran road cyclists aged 55.2 (±8.4 years) volunteered and completed the study. There was no dropout of participants during the study. There were no significant between-group differences between age, height, body mass index, VO2peak, peak watts or training hours (Tab. 3). There were significant differences (p < 0.05) in CT pre to post in both mass (+2.2%) and percent fat (-2%). There were no other significant changes in participant’s demographics pre- to post-test in CT or CON.
The overall training adherence rate in the CT group was 83%. The CT participants completed 85% (±4) of the track-sprint cycling sessions and 82% (±5) of the strength training sessions across the 12-week period. The CON group completed 100% of their usual training sessions. Neither group reported any adverse effects or events associated with their training or the testing procedures.
Following completion of the 12 weeks training, CT significantly increased total body mass by 2% compared to CON who demonstrated a negligible, non-significant weight loss (-0.3%). There was a significant two-way interaction between group and time (F(1, 16) = 8.323, p = 0.011). Post-hoc analysis revealed that only the CT group had a significant increase in body mass over the 12 week intervention (T(8) = 3.427, p = 0.009; d = 0.24) (Tab. 4).
Body mass and resting metabolic rate following 12-week intervention.
There was a significant (p = 0.15) decrease in percent body fat in the CT group (-4.7%) following the 12 weeks of training however there was no change (p = 0.85) in adiposity seen in the CON group.
The CT group demonstrated a significant (p < 0.05) increase in LM over the 12 weeks of 2% compared to the CON group, which actually lost a non-significant amount of LM over the same period (-0.9%). There was a significant two-way interaction between group and time (F(1, 16) = 5.589, p = 0.031). Post-hoc analysis revealed that only the CT group increased lean mass (+1.8 kg) over the intervention (T(8) = 3.296, p = 0.011; d = 0.24). In contrast, there was a small, non-significant, loss (-0.3 kg) in lean mass within the CON (T(8) = -0.876, p = 0.407; d = -0.09).
There was a significant increase in RMR in the CT group (+14%) following the concurrent strength and sprint training. In contrast, the CON actually demonstrated a decrease in RMR over the 12-week intervention period (-12%). There was a significant two-way interaction between group and time (F(1, 16) = 7.215, p = 0.016). Post-hoc analysis revealed that the CT group increased absolute RMR by 227 kcal/day (+14%) over the intervention period (T(8) = 3.691, p = 0.006; d = 1.00). In contrast, a non-significant decrease of similar magnitude was observed (-220 kcal/day) within the CON (T(8) = 1.422, p = 0.193; d = -0.80).
The aim of this study was to examine the effects of 12 weeks concurrent sprint and strength training on RMR and LM in veteran road cyclists. The results of this study show for the first time that replacing a portion of endurance training with concurrent strength and sprint training significantly increased RMR, and that this increase in RMR was accompanied by a significant increase in LM.
According to Kennis et al. (2014), middle-aged men typically lose 1 to 2% of their LM per year after the age of 50 years. However, in our active participants who underwent CT training for 12 weeks, we found a significant increase in LM of 2% (∼1.2 kg), whereas CON had a negligible, age-related decline of 0.9% (∼0.6 kg). A meta-analysis of 49 studies on the effect of resistance training on LM in older men and women (age range 50–83 years, n = 1328) revealed that training for an average of 20.5 ± 9.1 weeks (2.8 ± 0.4 times per week) resulted in a significant 1.1 kg increase in LM (Peterson, Sen, & Gordon, 2011). Although the CT participants in the present study completed only 12 weeks of concurrent strength and sprint training, it appears the stimulus was adequate to induce significant increases in LM.
Candow et al. (2011) investigated the effects of strength training on LM and strength in 17 active, older men aged 60–71 years. They reported 3.4% (∼2.0 kg) increase in LM following 22 weeks of training, the effect size was small (0.30), which is similar to the small effect size we found in our CT group following 12 weeks of combined sprint cycling and strength training.
We also observed a significant positive correlation between the change in LM and RMR in the CT group. Previous researchers (Dolezal & Potteiger, 1998; Pratley et al., 1994) have reported increases in lean mass in young and middle-aged males who completed strength training with commensurate increases in RMR. Dolezal & Potteiger (1998) concluded that both resistance training and/or endurance training may significantly increase RMR in younger individuals. The present study is the first to observe the same finding in veteran athletes undertaking CT training consisting of endurance, sprint and strength training.
In the present investigation, RMR was increased significantly by 14% in the CT group, and was considered a large effect following 12 weeks of CT. This finding is consistent with the results of two previous studies, which have reported strength training increased RMR in healthy older adults (Lemmer et al., 2001; Pratley et al., 1994). Lemmer et al. (2001) reported RMR was increased by 9% following 24 weeks of strength training in a group of previously inactive (>6 months) healthy, older males (n = 11, 65–75 years). Earlier, Pratley et al. (1994) reported RMR significantly increased by 7.7% following 16 weeks of strength training in a group of previously sedentary (>6 months), healthy, older males (n = 13, 50–65 years). However, our study is the first to have investigated the effects of CT on RMR in veteran road cyclists. The present findings suggest a further positive effect of CT in elevating RMR above aerobic training alone.
Increases in RMR have also been observed following aerobic exercise training in healthy older but untrained adults. For instance, Poehlman & Danforth (1991) reported a 10% increase in RMR following 8-weeks of endurance training in a group of healthy older, but untrained, adults (n = 19, 64.0 ± 1.6 years). In contrast to the population studied by Poehlman & Danforth (1991), the veteran road cyclists observed in the current study were highly aerobically fit. Piacentini et al. (2013) investigated the effects of a concurrent strength and endurance training program in master endurance runners (mean age: 44 years). They found following six weeks of training resulted in 17% improvement in one rep max (1RM) in the maximal strength training group and 10% improvement in strength in the resistance group however, no change in either groups resting metabolic rate (0.0%). The authors found a negligible (2%) improvement over the six weeks in fat free mass (FFM) and commented that FFM was largely related to resting metabolic rate.
In masters endurance athletes, previous cross-sectional studies have indicated aerobic power is positively correlated with a higher RMR (Sullo, Cardinale, Brizzi, Fabbri, & Maffulli, 2004; van Pelt, Jones, Davy, Desouza, Tanaka, Davy, & Seals, 1997). In the present investigation, the increases seen in LM may also account for the increase seen in RMR in our CT group. Indeed, Campbell et al. (1994) reported that 12 weeks of resistance training was associated with an increase in LM and RMR in previously untrained, healthy older males and females aged 56 to 80 years of age. Moreover, Westcott (2012) has further added significant increases in RMR have been seen following several weeks of resistance training and acutely, an increase in RMR of up to 9% can be seen following a single session of resistance training. Collectively, the results of the present and previous studies suggest that elevating aerobic fitness leads to increases in RMR in older individuals. Indeed, the current study showed that replacing endurance training sessions with CT led to even further changes in RMR in veteran road cyclists who have not previously engaged in systematic strength or sprint training.
To the best of our knowledge, no other studies to date have examined the effect of CT on RMR in healthy older adults or veteran endurance athletes. However, in healthy younger adults and obese, middle-aged adults, concurrent strength and endurance training has been shown to significantly increase RMR (Dolezal & Potteiger, 1998; Medeiros Nda, de Abreu, Colato, de Lemos, Ramis, Dorneles, & Dani, 2015). For instance, Dolezal & Potteiger (1998) reported 10-weeks of concurrent strength and endurance training significantly increased RMR by 4.6% a group of healthy younger adults (n = 10, 20.1 ± 1.6 years). Furthermore, Medeiros Nda et al. (2015) reported a 2.7% increase in RMR following 26 sessions of concurrent strength and endurance training, in a group of obese, middle-aged adults (n = 12, 45.3 ± 10.4 years). The 14% increase in RMR observed in the current study is surprisingly higher than the results reported by Dolezal & Potteiger (1998) and Medeiros Nda et al. (2015). These results might be explained by the different combination of training stimulus. In the present study we combined strength training with sprint training, which may promote summative physiological adaptations, including the observed increase in LM (Cantrell, Schilling, Paquette, & Murlasits, 2014).
At present the effects of concurrent strength and sprint training is unknown in master athletes. However, progressive resistance training to improve endurance cycling performance has been previously evaluated in both younger and older healthy adults (Bastiaans, van Diemen, Veneberg, & Jeukendrup, 2001; Izquierdo, Hakkinen, Ibanez, Anton, Garrues, Ruesta, & Gorostiaga, 2003; Loveless, Weber, Haseler, & Schneider, 2005; Ronnestad & Mujika, 2014; Widrick, Trappe, Costill, & Fitts, 1996). Loveless et al. (2005) reported that eight weeks of leg strength training significantly improved cycling peak power in young (mean age 25 y) males. Izquierdo et al. (2003) found a similar response following 16 weeks of strength training in older (64–74 y) adults. As noted in our manuscript, we did not find a significant improvement in peak power output, as has been seen in younger cyclists. This finding was surprising however this was commensurate with no change in VO2peak. The Velotron manual cites an accuracy of +0.1% and Astorino & Cottrell (2012) demonstrated the Velotron has a high reliability (r = 0.90). We therefore believe our testing methodology was valid and reliable. We believe the lack of improvement in peak power output may be related to the intensity and/or duration of sprint training, which may not have been of sufficient stimulus to evoke an improvement in peak power output. The track-cycling program was not watts based, rather intensity based upon distance and speed. Therefore, it is quite possible that the ST group actually was training at a lower intensity, such that it did not improve the post-intervention peak power output (i.e., watts).
Our study had a number of strengths. Our participants in the CT group had no previous strength training experience and the mean peak power output of both groups is rated in the 70th percentile of all cyclist in the cycling analytics data base (Cycling Analytics, 2019). Lean mass was assessed by DXA, which is recognized as the gold standard technique for assessing LM. Additionally, we assessed RMR using a technique, which has been shown to be both valid and reliable. We do acknowledge the following limitations to the current study. Firstly, the specialised population (only male master athletes) and limited participant numbers of this group, limited the statistical power of this study. Secondly, only two RMR measurements were taken during the 12-week study, more frequent measurements may have improved the reliability of our findings Thirdly, the non-randomised design may have affected the outcome. Finally, we acknowledge that a larger sample would be justified given the observed relatively large inter-individual variability in RMR response, especially in the CON group.
In conclusion, the findings of the present study suggest that 12 weeks of CT have beneficial effects on RMR and LM. While single-mode training such as endurance or strength training has been shown to increase RMR in healthy older adults, the present study has identified CT increases both RMR and LM in veteran road cyclists. Replacing a portion of endurance training with CT may be of benefit to the veteran road cyclist, by further stimulating RMR, thus allowing for the intake of more nutrients, whilst maintaining or even increasing lean mass.
We would like to extend our sincere thanks to Professor Pat O’Shea, friend, mentor and avid master athlete for instilling a passion for research; you are sincerely missed but not forgotten.
Authors contribution statement. All authors contributed to the design and implementation of the research, to the analysis of the results and to the writing of the manuscript.
- Amirkalali, B., Hosseini, S., Heshmat, R., & Larijani, B. (2008). Comparison of Harris Benedict and Mifflin-ST Jeor equations with indirect calorimetry in evaluating resting energy expenditure. Indian Journal of Medical Sciences, 62(7), 283–290. doi:10.4103/0019-5359.42024. [CrossRef] [PubMed] [Google Scholar]
- Antunes, H., Santos, R., Boscolo, R., Bueno, O., & Mello, M. (2005). Analysis of resting metabolic rate and body composition in elderly males before and after six months of endurance exercise. Revista Brasileira de Medicina do Esporte, 11, 71–75. [CrossRef] [Google Scholar]
- Astorino, T.A., & Cottrell, T. (2012). Reliability and validity of the velotron racermate cycle ergometer to measure anaerobic power. International Journal of Sports Medicine, 33(3), 205–210. doi:10.1055/s-0031-1291219. [CrossRef] [PubMed] [Google Scholar]
- Bastiaans, J.J., van Diemen, A.B., Veneberg, T., & Jeukendrup, A.E. (2001). The effects of replacing a portion of endurance training by explosive strength training on performance in trained cyclists. European Journal of Applied Physiology, 86(1), 79–84. doi:10.1007/s004210100507. [CrossRef] [PubMed] [Google Scholar]
- Buckinx, F., Landi, F., Cesari, M., Fieding, R.A., Visser, M., Engelke, K., & Kanis, J.A. (2018). The authors reply: “Dual energy X-ray absorptiometry: gold standard for muscle mass?” by Scafoglieri et al. Journal of Cachexia, Sarcopenia and Muscle, 9(4), 788–790. doi:10.1002/jcsm.12329. [CrossRef] [PubMed] [Google Scholar]
- Campbell, W.W., Crim, M.C., Young, V.R., & Evans, W.J. (1994). Increased energy requirements and changes in body composition with resistance training in older adults. American Journal of Clinical Nutrition, 60(2), 167–175. doi:10.1093/ajcn/60.2.167. [CrossRef] [Google Scholar]
- Candow, D.G., Chilibeck, P.D., Abeysekara, S., & Zello, G.A. (2011). Short-term heavy resistance training eliminates age-related deficits in muscle mass and strength in healthy older males. Journal of Strength and Conditioning Research, 25(2), 326–333. doi:10.1519/JSC.0b013e3181bf43c8. [CrossRef] [PubMed] [Google Scholar]
- Cantrell, G.S., Schilling, B.K., Paquette, M.R., & Murlasits, Z. (2014). Maximal strength, power, and aerobic endurance adaptations to concurrent strength and sprint interval training. European Journal of Applied Physiology, 114(4), 763–771. doi:10.1007/s00421-013-2811-8. [CrossRef] [PubMed] [Google Scholar]
- Compher, C., Frankenfield, D., Keim, N., & Roth-Yousey, L. (2006). Best practice methods to apply to measurement of resting metabolic rate in adults: a systematic review. Journal of the American Dietetic Association, 106(6), 881–903. doi:10.1016/j.jada.2006.02.009. [CrossRef] [PubMed] [Google Scholar]
- Curry, T.B., Somaraju, M., Hines, C.N., Groenewald, C.B., Miles, J.M., Joyner, M.J., & Charkoudian, N. (2013). Sympathetic support of energy expenditure and sympathetic nervous system activity after gastric bypass surgery. Obesity (Silver Spring), 21(3), 480–485. doi:10.1002/oby.20106. [Google Scholar]
- Cycling Analytics. (2019). How does your cycling power output compare? Availaible from https://www.cyclinganalytics.com/blog/2018/06/how-does-your-cycling-power-output-compare. [Google Scholar]
- Del Vecchio, L., Stanton, R., Macgregor, C., Doering, T., Korhonen, M.T., & Reaburn, P. (2016). Lower limb muscular strength and power characteristics of Veteran road cyclists and age-matched sedentary controls. Gazzetta Medica Italiana, 175, 123–129. [Google Scholar]
- Del Vecchio, L., Stanton, R., Reaburn, P., Macgregor, C., Meerkin, J., Villegas, J., & Korhonen, M.T. (2019). Effects of combined strength and sprint training on lean mass, strength, power, and sprint performance in masters road cyclists. Journal of Strength and Conditioning Research, 33(1), 66–79. doi:10.1519/jsc.0000000000001960. [CrossRef] [PubMed] [Google Scholar]
- Dolezal, B.A., & Potteiger, J.A. (1998). Concurrent resistance and endurance training influence basal metabolic rate in nondieting individuals. Journal of Applied Physiology (1985), 85(2), 695–700. doi:10.1152/jappl.19188.8.131.525. [CrossRef] [Google Scholar]
- Harridge, S., Magnusson, G., & Saltin, B. (1997). Life-long endurance-trained elderly men have high aerobic power, but have similar muscle strength to non-active elderly men. Aging, 9(1–2), 80–87. doi:10.1007/bf03340131. [PubMed] [Google Scholar]
- Hawley, J.A., & Noakes, T.D. (1992). Peak power output predicts maximal oxygen uptake and performance time in trained cyclists. European Journal of Applied Physiology and Occupational Physiology, 65(1), 79–83. doi:10.1007/bf01466278. [CrossRef] [PubMed] [Google Scholar]
- Izquierdo, M., Hakkinen, K., Ibanez, J., Anton, A., Garrues, M., Ruesta, M., & Gorostiaga, E.M. (2003). Effects of strength training on submaximal and maximal endurance performance capacity in middle-aged and older men. Journal of Strength and Conditioning Research, 17(1), 129–139. doi:10.1519/1533-4287(2003)017<0129:eostos>2.0.co;2. [PubMed] [Google Scholar]
- Johnstone, A.M., Murison, S.D., Duncan, J.S., Rance, K.A., & Speakman, J.R. (2005). Factors influencing variation in basal metabolic rate include fat-free mass, fat mass, age, and circulating thyroxine but not sex, circulating leptin, or triiodothyronine. American Journal of Clinical Nutrition, 82(5), 941–948. doi:10.1093/ajcn/82.5.941. [CrossRef] [Google Scholar]
- Kennis, E., Verschueren, S., Van Roie, E., Thomis, M., Lefevre, J., & Delecluse, C. (2014). Longitudinal impact of aging on muscle quality in middle-aged men. Age (Dordr), 36(4), 9689. doi:10.1007/s11357-014-9689-1. [CrossRef] [Google Scholar]
- Kim, H.Y. (2013). Statistical notes for clinical researchers: assessing normal distribution (2) using skewness and kurtosis. Restorative Dentistry & Endodontics, 38(1), 52–54. doi:10.5395/rde.2013.38.1.52. [CrossRef] [PubMed] [Google Scholar]
- Lemmer, J.T., Ivey, F.M., Ryan, A.S., Martel, G.F., Hurlbut, D.E., Metter, J.E., & Hurley, B.F. (2001). Effect of strength training on resting metabolic rate and physical activity: age and gender comparisons. Medicine and Science in Sports and Exercise, 33(4), 532–541. doi:10.1097/00005768-200104000-00005. [CrossRef] [PubMed] [Google Scholar]
- Loveless, D.J., Weber, C.L., Haseler, L.J., & Schneider, D.A. (2005). Maximal leg-strength training improves cycling economy in previously untrained men. Medicine and Science in Sports and Exercise, 37(7), 1231–1236. doi:10.1249/01.mss.0000170071.33880.f8. [CrossRef] [PubMed] [Google Scholar]
- Luhrmann, P.M., Bender, R., Edelmann-Schafer, B., & Neuhauser-Berthold, M. (2009). Longitudinal changes in energy expenditure in an elderly German population: a 12-year follow-up. European Journal of Clinical Nutrition, 63(8), 986–992. doi:10.1038/ejcn.2009.1. [CrossRef] [PubMed] [Google Scholar]
- Luhrmann, P.M., Edelmann-Schafer, B., & Neuhauser-Berthold, M. (2010). Changes in resting metabolic rate in an elderly German population: cross-sectional and longitudinal data. The Journal of Nutrition, Health & Aging, 14(3), 232–236. doi:10.1007/s12603-010-0055-4. [CrossRef] [PubMed] [Google Scholar]
- Medeiros Nda, S., de Abreu, F.G., Colato, A.S., de Lemos, L.S., Ramis, T.R., Dorneles, G.P., & Dani, C. (2015). Effects of concurrent training on oxidative stress and insulin resistance in obese individuals. Oxidative Medicine and Cellular Longevity, 2015, 697181. doi:10.1155/2015/697181. [Google Scholar]
- Nana, A., Slater, G.J., Hopkins, W.G., & Burke, L.M. (2012). Effects of daily activities on dual-energy X-ray absorptiometry measurements of body composition in active people. Medicine and Science in Sports and Exercise, 44(1), 180–189. doi:10.1249/MSS.0b013e318228b60e. [CrossRef] [PubMed] [Google Scholar]
- Nieman, D.C., Austin, M.D., Benezra, L., Pearce, S., McInnis, T., Unick, J., & Gross, S.J. (2006). Validation of Cosmed’s FitMate in measuring oxygen consumption and estimating resting metabolic rate. Research in Sports Medicine, 14(2), 89–96. doi:10.1080/15438620600651512. [CrossRef] [Google Scholar]
- Norton, K.I., Norton, L., & Australia, F. (2011). Pre-exercise screening: guide to the Australian adult pre-exercise screening system. Australia: Exercise and Sports Science Australia. [Google Scholar]
- Oja, P., Kelly, P., Pedisic, Z., Titze, S., Bauman, A., Foster, C., & Stamatakis, E. (2017). Associations of specific types of sports and exercise with all-cause and cardiovascular-disease mortality: a cohort study of 80,306 British adults. British Journal of Sports Medicine, 51(10), 812–817. doi:10.1136/bjsports-2016-096822. [CrossRef] [PubMed] [Google Scholar]
- Peterson, M.D., Sen, A., & Gordon, P.M. (2011). Influence of resistance exercise on lean body mass in aging adults: a meta-analysis. Medicine and Science in Sports and Exercise, 43(2), 249–258. doi:10.1249/MSS.0b013e3181eb6265. [CrossRef] [PubMed] [Google Scholar]
- Piacentini, M.F., De Ioannon, G., Comotto, S., Spedicato, A., Vernillo, G., & La Torre, A. (2013). Concurrent strength and endurance training effects on running economy in master endurance runners. Journal of Strength and Conditioning Research, 27(8), 2295–2303. doi:10.1519/JSC.0b013e3182794485. [CrossRef] [PubMed] [Google Scholar]
- Poehlman, E.T., & Danforth, E., Jr. (1991). Endurance training increases metabolic rate and norepinephrine appearance rate in older individuals. American Journal of Physiology, 261(2 Pt 1), E233–E239. doi:10.1152/ajpendo.1991.261.2.E233. [Google Scholar]
- Pratley, R., Nicklas, B., Rubin, M., Miller, J., Smith, A., Smith, M., & Goldberg, A. (1994). Strength training increases resting metabolic rate and norepinephrine levels in healthy 50- to 65-yr-old men. Journal of Applied Physiology (1985), 76(1), 133–137. doi:10.1152/jappl.19184.108.40.206. [CrossRef] [Google Scholar]
- Ronnestad, B.R., & Mujika, I. (2014). Optimizing strength training for running and cycling endurance performance: A review. Scandinavian Journal of Medicine and Science in Sports, 24(4), 603–612. doi:10.1111/sms.12104. [CrossRef] [Google Scholar]
- Scafoglieri, A., & Clarys, J.P. (2018). Dual energy X-ray absorptiometry: gold standard for muscle mass? Journal of Cachexia, Sarcopenia and Muscle, 9(4), 786–787. doi:10.1002/jcsm.12308. [CrossRef] [PubMed] [Google Scholar]
- Soysal, P., Ates Bulut, E., Yavuz, I., & Isik, A.T. (2019). Decreased basal metabolic rate can be an objective marker for sarcopenia and frailty in older males. Journal of the American Medical Directors Association, 20(1), 58–63. doi:10.1016/j.jamda.2018.07.001. [CrossRef] [PubMed] [Google Scholar]
- Sullivan, G.M., & Feinn, R. (2012). Using effect size-or why the P value is not enough. Journal of Graduate Medical Education, 4(3), 279–282. doi:10.4300/jgme-d-12-00156.1. [CrossRef] [PubMed] [Google Scholar]
- Sullo, A., Cardinale, P., Brizzi, G., Fabbri, B., & Maffulli, N. (2004). Resting metabolic rate and post-prandial thermogenesis by level of aerobic power in older athletes. Clinical and Experimental Pharmacology and Physiology, 31(4), 202–206. doi:10.1111/j.1440-1681.2004.03979.x. [CrossRef] [Google Scholar]
- Tanaka, H. (2017). Aging of competitive athletes. Gerontology, 63(5), 488–494. doi:10.1159/000477722. [CrossRef] [PubMed] [Google Scholar]
- van Pelt, R.E., Dinneno, F.A., Seals, D.R., & Jones, P.P. (2001). Age-related decline in RMR in physically active men: relation to exercise volume and energy intake. American Journal of Physiology: Endocrinology and Metabolism, 281(3), E633–E639. doi:10.1152/ajpendo.2001.281.3.E633. [CrossRef] [Google Scholar]
- van Pelt, R.E., Jones, P.P., Davy, K.P., Desouza, C.A., Tanaka, H., Davy, B.M., & Seals, D.R. (1997). Regular exercise and the age-related decline in resting metabolic rate in women. Journal of Clinical Endocrinology and Metabolism, 82(10), 3208–3212. doi:10.1210/jcem.82.10.4268. [Google Scholar]
- Westcott, W.L. (2012). Resistance training is medicine: effects of strength training on health. Current Sports Medicine Reports, 11(4), 209–216. doi:10.1249/JSR.0b013e31825dabb8. [CrossRef] [PubMed] [Google Scholar]
- Widrick, J.J., Trappe, S.W., Costill, D.L., & Fitts, R.H. (1996). Force-velocity and force-power properties of single muscle fibers from elite master runners and sedentary men. American Journal of Physiology, 271(2 Pt 1), C676–C683. doi:10.1152/ajpcell.1996.271.2.C676. [CrossRef] [Google Scholar]
- Wilson, M.M., & Morley, J.E. (2003). Invited review: aging and energy balance. Journal of Applied Physiology (1985), 95(4), 1728–1736. doi:10.1152/japplphysiol.00313.2003. [CrossRef] [Google Scholar]
Cite this article as: Delvecchio L, Reaburn P, Meerkin J, Korhonen MT, Borges N, Macgregor C, & Climstein M (2020) Concurrent strength and sprint training increases resting metabolic rate in masters road cyclists. Mov Sport Sci/Sci Mot, https://doi.org/10.1051/sm/2020007
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