The DOI matches its Crossref record; 77 of the 77 references it cites were found in a registry or an index.
Maximizing Muscle Hypertrophy: A Systematic Review of Advanced Resistance Training Techniques and Methods
Open accessAbstract
Background: Effective hypertrophy-oriented resistance training (RT) should comprise a combination of mechanical tension and metabolic stress. Regarding training variables, the most effective values are widely described in the literature. However, there is still a lack of consensus regarding the efficiency of advanced RT techniques and methods in comparison to traditional approaches. Methods: MEDLINE and SPORTDiscus databases were searched from 1996 to September 2019 for all studies investigating the effects of advanced RT techniques and methods on muscle hypertrophy and training variables. Thirty articles met the inclusion criteria and were consequently included for the quality assessment and data extraction. Results: Concerning the time-efficiency of training, the use of agonist–antagonist, upper–lower body supersets, drop and cluster sets, sarcoplasma stimulating training, employment of fast, but controlled duration of eccentric contractions (~2s), and high-load RT supplemented with low-load RT under blood flow restriction may provide an additional stimulus and an advantage to traditional training protocols. With regard to the higher degree of mechanical tension, the use of accentuated eccentric loading in RT should be considered. Implementation of drop sets, sarcoplasma stimulating training, low-load RT in conjunction with low-load RT under blood flow restriction could provide time-efficient solutions to increased metabolic stress. Conclusions: Due to insufficient evidence, it is difficult to provide specific guidelines for volume, intensity of effort, and frequency of previously mentioned RT techniques and methods. However, well-trained athletes may integrate advanced RT techniques and methods into their routines as an additional stimulus to break through plateaus and to prevent training monotony.
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Cites
Progression Models in Resistance Training for Healthy Adults
(2009) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e3181915670 · Cited by 4,332
Progression Models in Resistance Training for Healthy Adults
(2002) · Medicine and Science in Sports and Exercise · doi:10.1097/00005768-200202000-00027 · Cited by 2,979
Designing Resistance Training Programs
Fleck, S. J., & Kraemer, W. J. (2014) · Human Kinetics eBooks · doi:10.5040/9781718225107 · Cited by 1,360
The mechanisms of muscle hypertrophy and their application to resistance training
Schoenfeld, B. J. (2010) · Journal of Strength and Conditioning Research · doi:10.1519/jsc.0b013e3181e840f3 · Cited by 1,214 · cited 2×
“Three major factors are emphasized in the conventional hypertrophy model: mechanical tension, metabolic stress, and muscle damage [55].”
BackgroundBlood Flow Restriction Exercise: Considerations of Methodology, Application, and Safety
Patterson, S. D., Hughes, L., Warmington, S., Burr, J., Scott, B. R., Owens, J., Abe, T., Nielsen, J. L., Libardi, C. A., Laurentino, G., et al. (2019) · Frontiers in Physiology · doi:10.3389/fphys.2019.00533 · Cited by 917
Schoenfeld, B. J., Ogborn, D., & Krieger, J. W. (2017) · Journal of Sports Sciences · doi:10.1080/02640414.2016.1210197 · Cited by 671
Resistance exercise load does not determine training-mediated hypertrophic gains in young men
Mitchell, C. J., Churchward-Venne, T. A., West, D. W. D., Burd, N. A., Breen, L., Baker, S. K., & Phillips, S. M. (2012) · Journal of Applied Physiology · doi:10.1152/japplphysiol.00307.2012 · Cited by 627
Bird, S. P., Tarpenning, K. M., & Marino, F. E. (2005) · Sports Medicine · doi:10.2165/00007256-200535100-00002 · Cited by 533
Burd, N. A., West, D. W. D., Staples, A. W., Atherton, P. J., Baker, J. M., Moore, D. R., Holwerda, A. M., Parise, G., Rennie, M. J., Baker, S. K., et al. (2010) · PLoS ONE · doi:10.1371/journal.pone.0012033 · Cited by 531
Schoenfeld, B. J. (2013) · Sports Medicine · doi:10.1007/s40279-013-0017-1 · Cited by 495
Cited by
Brinlee, A. W., Dickenson, S. B., Hunter-Giordano, A., & Snyder-Mackler, L. (2022) · Sports Health A Multidisciplinary Approach · doi:10.1177/19417381211056873 · Cited by 212
Lasevicius, T., Ugrinowitsch, C., Schoenfeld, B. J., Roschel, H., Tavares, L. D., De Souza, E. O., Laurentino, G., & Tricoli, V. (2018) · European Journal of Sport Science · doi:10.1080/17461391.2018.1450898 · Cited by 180
A systematic review of the effects of different resistance training volumes on muscle hypertrophy.
Baz-Valle, E., Balsalobre-Fernández, C., Alix-Fages, C., & Santos-Concejero, J. (2022) · Journal of Human Kinetics · doi:10.2478/hukin-2022-0017 · Cited by 83
Brun, J.-F., Myzia, J., Varlet-Marie, E., Raynaud de Mauverger, E., & Mercier, J. (2022) · Nutrients · doi:10.3390/nu14081605 · Cited by 48
Vieira, F. T., de Oliveira, G. S., Gonçalves, V. S. S., Neri, S. G. R., de Carvalho, K. M. B., & Dutra, E. S. (2022) · PLOS ONE · doi:10.1371/journal.pone.0269699 · Cited by 36
Metadata
- Authors
- Krzysztofik, M., Wilk, M., Wojdała, G., & Gołaś, A.
- Venue
- International Journal of Environmental Research and Public Health
- Publisher
- MDPI AG
- Published
- 2019-12-04
- Volume
- 16
- Issue
- 24
- Pages
- 4897
- ISSN
- 1660-4601 (ISSN-L)
- OpenAlex
- W2993530164
- Cited by
- 264
- References
- 77
- FWCI
- 5.39
- Type
- journal article
- Language
- en
- Open access
- gold
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- Krzysztofik, M., Wilk, M., Wojdała, G., & Gołaś, A. (2019). Maximizing Muscle Hypertrophy: A Systematic Review of Advanced Resistance Training Techniques and Methods. International Journal of Environmental Research and Public Health. https://doi.org/10.3390/ijerph16244897
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