Overreaching and overtraining in strength sports and resistance training: A scoping review

Bell, Lee; Ruddock, Alan; Maden‐Wilkinson, Thomas M.; Rogerson, David

doi:10.1080/02640414.2020.1763077

Cited by 31 publications

(51 citation statements)

References 60 publications

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“…For resistance training to be effective, it is important to quantify the stress imposed during training [ 7 ]. If the training load is insufficient then adaptation might not occur, whereas excessive or sudden increases in stress might result in injury, poor performance, or non-functional overreaching [ 8 , 9 ]. Therefore, although loading paradigms can be prescribed based upon a variety of methods, using evidence-based methods might help guide training with objective data, possibly increasing the effectiveness of training.…”

Section: Introductionmentioning

confidence: 99%

Prediction of One Repetition Maximum Using Reference Minimum Velocity Threshold Values in Young and Middle-Aged Resistance-Trained Males

Fernandes

Dingley

García-Ramos

et al. 2021

Behavioral Sciences

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Background: This study determined the accuracy of different velocity-based methods when predicting one-repetition maximum (1RM) in young and middle-aged resistance-trained males. Methods: Two days after maximal strength testing, 20 young (age 21.0 ± 1.6 years) and 20 middle-aged (age 42.6 ± 6.7 years) resistance-trained males completed three repetitions of bench press, back squat, and bent-over-row at loads corresponding to 20–80% 1RM. Using reference minimum velocity threshold (MVT) values, the 1RM was estimated from the load-velocity relationships through multiple (20, 30, 40, 50, 60, 70, and 80% 1RM), two-point (20 and 80% 1RM), high-load (60 and 80% 1RM) and low-load (20 and 40% 1RM) methods for each group. Results: Despite most prediction methods demonstrating acceptable correlations (r = 0.55 to 0.96), the absolute errors for young and middle-aged groups were generally moderate to high for bench press (absolute errors = 8.2 to 14.2% and 8.6 to 20.4%, respectively) and bent-over-row (absolute error = 14.9 to 19.9% and 8.6 to 18.2%, respectively). For squats, the absolute errors were lower in the young group (5.7 to 13.4%) than the middle-aged group (13.2 to 17.0%) but still unacceptable. Conclusion: These findings suggest that reference MVTs cannot accurately predict the 1RM in these populations. Therefore, practitioners need to directly assess 1RM.

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Section: Introductionmentioning

confidence: 99%

Prediction of One Repetition Maximum Using Reference Minimum Velocity Threshold Values in Young and Middle-Aged Resistance-Trained Males

Fernandes

Dingley

García-Ramos

et al. 2021

Behavioral Sciences

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“…For the amateur athlete, considerations must also be given to employment, study, family, travel and financial circumstances, which all place considerable demands on time required to comply with high-intensity conditioning programs. Failure to respect these demands can lead to maladaptation, non-functional overreaching and possibly overtraining [27,28].…”

Section: Competition Schedulementioning

confidence: 99%

High-Intensity Conditioning for Combat Athletes: Practical Recommendations

et al. 2021

Self Cite

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Combat sports have been practiced for millennia and today are predominant sports at the Olympic games, with international organizations that host world, continental and national championships at amateur standard. There are also an increasing number of professional combat sports with global audiences. The growing popularity of professional combat sports and their importance at the Olympic games have led to an increase in scientific studies that characterize the physical, physiological, nutritional, biomechanical and training strategies of combat sports athletes. These studies characterize combat sports as high-intensity sports which require training strategies to develop the high-intensity capabilities of athletes. Therefore, the aim of this article is to (i) summarize the physiological demands of combat sports; (ii) present the primary considerations required to program high-intensity conditioning for athletes; (iii) define and present key high-intensity conditioning methods; and (iv) provide guidance for scientists and coaches to help prepare athletes under common but differing circumstances.

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“…In strength training, concept of personalised training (referred also as autoregulation [276]) has existed for some time, in which the training load and recovery status of an individual athlete are continuously monitored, and the exercise continuously adapted by a human coach (subjective decision making) using the monitored parameters output [61,135,537].…”

Section: Precision Strength Trainingmentioning

confidence: 99%

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

Teikari¹,

Pietrusz²

2021

Preprint

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In strength training, personalised strength training (autoregulation) approaches have been used to individualise exercise programs with monitoring an for dynamic adjustment based on their responses to training. While this transition from tradition-based training to evidence-based training framework has been an improvement in training practices, we argue that the future of strength training will also incorporate deep learning models powered by data. We refer to this data-driven framework as precision strength training inspired by the similar modeling frameworks used in precision medicine. In contrast to current personalised training in which the acquired athlete data is often subject to human expert decision-making, we are anticipating the rise of human-in-the-loop systems with an augmented coach who will be doing decisions collaboratively with the machine. Similar to other precision frameworks, such as precision health, we envision such a future to take decades to be realised and we focus here on practical short-term targets on a way to long-term realisation. In this chapter, we will review the measurement technology needed for continuous data acquisition from an individual during training/physical activity, how to acquire these datasets for the development of such systems and, how a proof-of-concept system could be developed for powerlifting training with applicability to general strength and conditioning (S&C) and physical rehabilitation purposes. Additionally, we will evaluate how the user experience (UX) of the system feedback and visualisation could be designed.

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Overreaching and overtraining in strength sports and resistance training: A scoping review

Cited by 31 publications

References 60 publications

Prediction of One Repetition Maximum Using Reference Minimum Velocity Threshold Values in Young and Middle-Aged Resistance-Trained Males

Prediction of One Repetition Maximum Using Reference Minimum Velocity Threshold Values in Young and Middle-Aged Resistance-Trained Males

High-Intensity Conditioning for Combat Athletes: Practical Recommendations

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

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