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  • Author: Andrew D. Govus x
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Andrew D. Govus, Aaron Coutts, Rob Duffield, Andrew Murray and Hugh Fullagar

Context: The relationship between pretraining subjective wellness and external and internal training load in American college football is unclear. Purpose : To examine the relationship of pretraining subjective wellness (sleep quality, muscle soreness, energy, wellness Z score) with player load and session rating of perceived exertion (s-RPE-TL) in American college football players. Methods: Subjective wellness (measured using 5-point, Likert-scale questionnaires), external load (derived from GPS and accelerometry), and s-RPE-TL were collected during 3 typical training sessions per week for the second half of an American college football season (8 wk). The relationship of pretraining subjective wellness with player load and s-RPE training load was analyzed using linear mixed models with a random intercept for athlete and a random slope for training session. Standardized mean differences (SMDs) denote the effect magnitude. Results: A 1-unit increase in wellness Z score and energy was associated with trivial 2.3% (90% confidence interval [CI] 0.5, 4.2; SMD 0.12) and 2.6% (90% CI 0.1, 5.2; SMD 0.13) increases in player load, respectively. A 1-unit increase in muscle soreness (players felt less sore) corresponded to a trivial 4.4% (90% CI −8.4, −0.3; SMD −0.05) decrease in s-RPE training load. Conclusion: Measuring pretraining subjective wellness may provide information about players’ capacity to perform in a training session and could be a key determinant of their response to the imposed training demands American college football. Hence, monitoring subjective wellness may aid in the individualization of training prescription in American college football players.

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Alan J. Metcalfe, Paolo Menaspà, Vincent Villerius, Marc Quod, Jeremiah J. Peiffer, Andrew D. Govus and Chris R Abbiss

Purpose:

To describe the within-season external workloads of professional male road cyclists for optimal training prescription.

Methods:

Training and racing of 4 international competitive professional male cyclists (age 24 ± 2 y, body mass 77.6 ± 1.5 kg) were monitored for 12 mo before the world team-time-trial championships. Three within-season phases leading up to the team-time-trial world championships on September 20, 2015, were defined as phase 1 (Oct–Jan), phase 2 (Feb–May), and phase 3 (June–Sept). Distance and time were compared between training and racing days and over each of the various phases. Times spent in absolute (<100, 100–300, 400–500, >500 W) and relative (0–1.9, 2.0–4.9, 5.0–7.9, >8 W/kg) power zones were also compared for the whole season and between phases 1–3.

Results:

Total distance (3859 ± 959 vs 10911 ± 620 km) and time (240.5 ± 37.5 vs 337.5 ± 26 h) were lower (P < .01) in phase 1 than phase 2. Total distance decreased (P < .01) from phase 2 to phase 3 (10911 ± 620 vs 8411 ± 1399 km, respectively). Mean absolute (236 ± 12.1 vs 197 ± 3 W) and relative (3.1 ± 0 vs 2.5 ± 0 W/kg) power output were higher (P < .05) during racing than training, respectively.

Conclusion:

Volume and intensity differed between training and racing over each of 3 distinct within-season phases.

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Helen G. Hanstock, Andrew D. Govus, Thomas B. Stenqvist, Anna K. Melin, Øystein Sylta and Monica K. Torstveit

Intensive training periods may negatively influence immune function, but the immunological consequences of specific high-intensity training (HIT) prescriptions are not well defined.

Purpose:

This study explored whether three different HIT prescriptions influence multiple health-related biomarkers and whether biomarker responses to HIT were associated with upper respiratory illness (URI) risk.

Methods:

Twenty-five male cyclists and triathletes were randomised to three HIT groups and completed twelve HIT sessions over four weeks. Peak oxygen consumption (V̇O2peak) was determined using an incremental cycling protocol, while resting serum biomarkers (cortisol, testosterone, 25(OH)D and ferritin), salivary immunoglobulin-A (s-IgA) and energy availability (EA) were assessed before and after the training intervention. Participants self-reported upper respiratory symptoms during the intervention and episodes of URI were identified retrospectively.

Results:

Fourteen athletes reported URIs, but there were no differences in incidence, duration or severity between groups. Increased risk of URI was associated with higher s-IgA secretion rates (odds ratio=0.90, 90% CI:0.83-0.97). Lower pre-intervention cortisol and higher EA predicted a 4% increase in URI duration. Participants with higher V̇O2peak reported higher total symptom scores (incidence rate ratio=1.07, 90% CI:1.01-1.13).

Conclusions:

Although multiple biomarkers were weakly associated with risk of URI, the direction of associations between s-IgA, cortisol, EA and URI risk were inverse to previous observations and physiological rationale. There was a cluster of URIs within the first week of the training intervention, but no samples were collected at this time-point. Future studies should incorporate more frequent sample time-points, especially around the onset of new training regimes, and include athletes with suspected or known nutritional deficiencies.