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Hugh H.K. Fullagar, Andrew Govus, James Hanisch and Andrew Murray

Purpose:

To investigate the recovery time course of customized wellness markers (sleep, soreness, energy, and overall wellness) in response to match play in American Division I-A college football players.

Methods:

A retrospective research design was used. Wellness data were collected and analyzed for 2 American college football seasons. Perceptions of soreness, sleep, energy, and overall wellness were obtained for the day before each game (GD–1) and the days after each game (GD+2, GD+3, and GD+4). Standardized effect-size (ES) analyses ± 90% confidence intervals were used to interpret the magnitude of the mean differences between all time points for the start, middle, and finish of the season, using the following qualitative descriptors: 0–0.19 trivial, 0.2–0.59 small, 0.6–1.19 moderate, 1.2–1.99 large, <2.0 very large.

Results:

Overall wellness showed small ES reductions on GD+2 (d = 0.22 ± 0.09, likely [94.8%]), GD+3 (d = 0.37 ± 0.15, very likely), and GD+4 (d = 0.29 ± 0.12, very likely) compared with GD–1. There were small ES reductions for soreness between GD–1 and GD+2, GD+3, and GD +4 (d = 0.21 ± 0.09, likely, d = 0.29 ± 0.12, very likely, and 0.30 ± 0.12, very likely, respectively). Small ES reductions were also evident between GD–1 and GD+3 (d = 0.21 ± 0.09, likely) for sleep. Feelings of energy showed small ESs on GD+3 (d = 0.27 ± 0.11, very likely) and GD+4 (d = 0.22 ± 0.09, likely) compared with GD–1.

Conclusion:

All wellness markers were likely to very likely worse on GD+3 and GD+4 than on GD–1. These findings show that perceptual wellness takes longer than 4 d to return to pregame levels and thus should be considered when prescribing training and/or recovery.

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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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Amelia Carr, Kerry McGawley, Andrew Govus, Erik P. Andersson, Oliver M. Shannon, Stig Mattsson and Anna Melin

This study investigated the energy, macronutrient, and fluid intakes, as well as hydration status (urine specific gravity), in elite cross-country skiers during a typical day of training (Day 1) and a sprint skiing competition the following day (Day 2). A total of 31 (18 males and 13 females) national team skiers recorded their food and fluid intakes and urine specific gravity was measured on Days 1 and 2. In addition, the females completed the Low Energy Availability in Females Questionnaire to assess their risk of long-term energy deficiency. Energy intake for males was 65 ± 9 kcal/kg on Day 1 versus 58 ± 9 kcal/kg on Day 2 (p = .002) and for females was 57 ± 10 on Day 1 versus 55 ± 5 kcal/kg on Day 2 (p = .445). Carbohydrate intake recommendations of 10–12 g·kg−1·day−1 were not met by 89% of males and 92% of females. All males and females had a protein intake above the recommended 1.2–2.0 g/kg on both days and a postexercise protein intake above the recommended 0.3 g/kg. Of the females, 31% were classified as being at risk of long-term energy deficiency. In the morning of Day 1, 50% of males and 46% of females were dehydrated; on Day 2, this was the case for 56% of males and 38% of females. In conclusion, these data suggest that elite cross-country skiers ingested more protein and less carbohydrate than recommended and one third of the females were considered at risk of long-term energy deficiency. Furthermore, many of the athletes were dehydrated prior to training and competition.

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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.