Editorial: Physiological, anatomical and sport performance adaptations to concentrated training periods in athletes
Bibliographic record
Abstract
Understanding the dose-response to exercise has become the focal point of a sport scientists' responsibility. The complex physiology of an athlete requires practitioners to develop a comprehensive "fingerprint", unique to the athletes' personal response to exercise, training methodologies, and periodization. The dose-response to exercise as described by (Banister et al., 1992) typically involves an initial, brief period of fatigue, that can develop into long-term improvement in biological function if appropriate rest and recovery is provided. This model is largely based on Selye's explanation of the fundamental reaction to experiencing whole body stress over a continuous period, whereby positive adaptations or exhaustion can occur depending on the sustained stress experienced (Selye, 1950). The "dose" of exercise can be quantified into a training load metric using either wearable technology (Perrotta et al., 2018) or a subjective rating of perceived exertion (Perrotta et al., 2017;Perrotta & Warburton, 2018), that can be compared to the resulting physiological "response" (Perrotta et al., 2019;Perrotta, 2020). When examined together, practitioners can make informed decisions when prescribing impending training sessions depending on the "response" to the "dose" of exercise. For example, wearable devices that included GPS technology can accurately measure speed and distance of a training session (i.e. the "dose"). Combined with information from a simple heart rate monitor (i.e. the "response"), coaches and practitioners can periodically evaluate the heart rate vs. speed relationship in the field as an indirect marker of exercise economy/efficiency and may indirectly serve to indicate states of transient or long-lasting fatigue (Fletcher & Tomkins-Lane, 2021;Smith et al., 2002). Analyzing the exercise dose-response relationship can be valuable to coaches, athletes and practitioners. In a 2012 survey of coaches and sport science support staff, Taylor et al., (2012) showed that 91 per cent of respondents used some form of a training monitoring system. In this survey, 70 per cent of these respondents indicated the focus was on 'load quantification' and the monitoring of fatigue or recovery to "prevent overtraining, reduce injuries, monitor the effectiveness of the training programs and ensure maintenance of performance" (Taylor et al., 2012). This Research Topic of Frontiers in Physiology and Sports and Active Living, contains four manuscripts where the primary purpose focused on analyzing the exercise dose-response relationship to improve athletic development. Three original articles (Perrotta et combination of the two offered greater strength and power gains. They demonstrate that all three strength intervention strategies equally improved upper and lower body strength and power, with only significant group differences found between peak power output at 30% of 1 repetition max. This may suggest that unilateral or a combination unilateral and bilateral strength training regime is superior to bilateral training alone, at least for this specific strength and power measure. This special topic edition provides coaches and practitioners novel insight into the dose-repose to various forms of exercise, and the interplay between physiological function and human performance. Although adaptations from exercise are often evaluated using a pre and post mesocycle approach, this form of assessment is retrospective in nature, and limits the understanding of athlete's response over duration of the training period. Unremitting developments in wearable technology allow integrative support staff to monitoring athletes physiological response during and post exercise (i.e. dose-response) in real-time. This immediate feedback allows evidence-based decisions towards adapting present training sessions to mitigate fatigue, or include additional exercise, to ensure athletic development over the course of each mesocycle. Sports scientists working within a team environment are encouraged to published comprehensive data sets that display the utility of wearable technology for examining the dose-response from daily training in elite athletes. Taking together, this information can develop consensus statements towards establishing best practice guidelines for integrative support staff working within professional, national and provincial sporting organizations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.024 | 0.017 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".