The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Training for intense exercise performance: high‐intensity or high‐volume training?
Open accessAbstract
Performance in intense exercise events, such as Olympic rowing, swimming, kayak, track running and track cycling events, involves energy contribution from aerobic and anaerobic sources. As aerobic energy supply dominates the total energy requirements after ∼75 s of near maximal effort, and has the greatest potential for improvement with training, the majority of training for these events is generally aimed at increasing aerobic metabolic capacity. A short‐term period (six to eight sessions over 2–4 weeks) of high‐intensity interval training (consisting of repeated exercise bouts performed close to or well above the maximal oxygen uptake intensity, interspersed with low‐intensity exercise or complete rest) can elicit increases in intense exercise performance of 2–4% in well‐trained athletes. The influence of high‐volume training is less discussed, but its importance should not be downplayed, as high‐volume training also induces important metabolic adaptations. While the metabolic adaptations that occur with high‐volume training and high‐intensity training show considerable overlap, the molecular events that signal for these adaptations may be different. A polarized approach to training, whereby ∼75% of total training volume is performed at low intensities, and 10–15% is performed at very high intensities, has been suggested as an optimal training intensity distribution for elite athletes who perform intense exercise events.
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Cites
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Gibala, M. J., & McGee, S. L. (2008) · Exercise and Sport Sciences Reviews · doi:10.1097/jes.0b013e318168ec1f · Cited by 682
Skeletal Muscle Fiber Type: Influence on Contractile and Metabolic Properties
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Autonomic Recovery after Exercise in Trained Athletes: Intensity and Duration Effects
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“The rate at which these adaptations occur is variable (Vollaard et al., 2009) and appears to depend on the volume, intensity and frequency of the training.”
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Cited by
Physiological adaptations to low-volume, high-intensity interval training in health and disease
Gibala, M. J., Little, J. P., MacDonald, M. J., & Hawley, J. A. (2012) · The Journal of Physiology · doi:10.1113/jphysiol.2011.224725 · Cited by 1,744
Buchheit, M., & Laursen, P. B. (2013) · Sports Medicine · doi:10.1007/s40279-013-0029-x · Cited by 1,490
Self-citationPlews, D. J., Laursen, P. B., Stanley, J., Kilding, A. E., & Buchheit, M. (2013) · Sports Medicine · doi:10.1007/s40279-013-0071-8 · Cited by 617
Self-citationPsychological skills training as a way to enhance an athlete's performance in high-intensity sports
Birrer, D., & Morgan, G. (2010) · Scandinavian Journal of Medicine & Science in Sports · doi:10.1111/j.1600-0838.2010.01188.x · Cited by 342
Strategies to Improve Running Economy
Barnes, K. R., & Kilding, A. E. (2015) · Sports Medicine · doi:10.1007/s40279-014-0246-y · Cited by 245
The Physiological Capacity of the World’s Highest Ranked Female Cross-country Skiers
SANDBAKK, Ø., HEGGE, A. M., LOSNEGARD, T., SKATTEBO, Ø., TØNNESSEN, E., & HOLMBERG, H.-C. (2016) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000000862 · Cited by 110
Effects of varying the step duration on the determination of lactate thresholds in elite rowers
Bourdon, P. C., Woolford, S. M., & Buckley, J. D. (2018) · International Journal of Sports Physiology and Performance · doi:10.1123/ijspp.2017-0258 · Cited by 21
Feng, Y., Li, D., Liu, Y., & Tang, D. (2025) · Journal of Human Kinetics · doi:10.5114/jhk/205427 · Cited by 3
“MICT can activate PGC ‐ 1 α through calcium/calmodulin ‐ dependent protein kinase II (CaMKII) and the phosphatase calcineurin A (CnA), thereby regulating various downstream targets (Furrer et al., 2023; Laursen, 2010).”
Metadata
- Authors
- Laursen, P. B.
- Venue
- Scandinavian Journal of Medicine & Science in Sports
- Publisher
- Wiley
- Published
- 2010-09-14
- Volume
- 20
- Issue
- s2
- Pages
- 1-10
- ISSN
- 0905-7188 (ISSN-L)
- 1600-0838
- OpenAlex
- W2116865489
- Cited by
- 444
- References
- 7
- FWCI
- 16.97
- Type
- journal article
- Language
- en
- Open access
- bronze
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.