While there are various avenues for performance improvement in college American football (AF), there is no comprehensive evaluation of the collective array of resources around performance, physical conditioning, and injury and training/game characteristics to guide future research and inform practitioners. Accordingly, the aim of the present review was to provide a current examination of these areas in college AF. Recent studies show that there is a wide range of body compositions and strength characteristics between players, which appear to be influenced by playing position, level of play, training history/programming, and time of season. Collectively, game demands may require a combination of upper- and lower-body strength and power production, rapid acceleration (positive and negative), change of direction, high running speed, high-intensity and repetitive collisions, and muscle-strength endurance. These may be affected by the timing of and between-plays and/or coaching style. AF players appear to possess limited nutrition and hydration practices, which may be disadvantageous to performance. AF injuries appear due to a multitude of factors—strength, movement quality, and previous injury—while there is also potential for extrinsic factors such as playing surface type, travel, time of season, playing position, and training load. Future proof-of-concept studies are required to determine the quantification of game demands with regard to game style, type of opposition, and key performance indicators. Moreover, more research is required to understand the efficacy of recovery and nutrition interventions. Finally, the assessment of the relationship between external/internal-load constructs and injury risk is warranted.
Hugh H.K. Fullagar, Robert McCunn and Andrew Murray
Hugh H.K. Fullagar, Andrew Govus, James Hanisch and Andrew Murray
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.
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.
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.
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.
Hugh H.K. Fullagar, Rob Duffield, Sabrina Skorski, Aaron J. Coutts, Ross Julian and Tim Meyer
While the effects of sleep loss on performance have previously been reviewed, the effects of disturbed sleep on recovery after exercise are less reported. Specifically, the interaction between sleep and physiological and psychological recovery in team-sport athletes is not well understood. Accordingly, the aim of the current review was to examine the current evidence on the potential role sleep may play in postexercise recovery, with a tailored focus on professional team-sport athletes. Recent studies show that team-sport athletes are at high risk of poor sleep during and after competition. Although limited published data are available, these athletes also appear particularly susceptible to reductions in both sleep quality and sleep duration after night competition and periods of heavy training. However, studies examining the relationship between sleep and recovery in such situations are lacking. Indeed, further observational sleep studies in team-sport athletes are required to confirm these concerns. Naps, sleep extension, and sleep-hygiene practices appear advantageous to performance; however, future proof-of-concept studies are now required to determine the efficacy of these interventions on postexercise recovery. Moreover, more research is required to understand how sleep interacts with numerous recovery responses in team-sport environments. This is pertinent given the regularity with which these teams encounter challenging scenarios during the course of a season. Therefore, this review examines the factors that compromise sleep during a season and after competition and discusses strategies that may help improve sleep in team-sport athletes.
Hugh H.K. Fullagar, Rob Duffield, Sabrina Skorski, David White, Jonathan Bloomfield, Sarah Kölling and Tim Meyer
The current study examined the sleep, travel, and recovery responses of elite footballers during and after long-haul international air travel, with a further description of these responses over the ensuing competitive tour (including 2 matches).
In an observational design, 15 elite male football players undertook 18 h of predominantly westward international air travel from the United Kingdom to South America (–4-h time-zone shift) for a 10-d tour. Objective sleep parameters, external and internal training loads, subjective player match performance, technical match data, and perceptual jet-lag and recovery measures were collected.
Significant differences were evident between outbound travel and recovery night 1 (night of arrival; P < .001) for sleep duration. Sleep efficiency was also significantly reduced during outbound travel compared with recovery nights 1 (P = .001) and 2 (P = .004). Furthermore, both match nights (5 and 10), showed significantly less sleep than nonmatch nights 2 to 4 and 7 to 9 (all P < .001). No significant differences were evident between baseline and any time point for all perceptual measures of jet-lag and recovery (P > .05), although large effects were evident for jet-lag on d 2 (2 d after arrival).
Sleep duration is truncated during long-haul international travel with a 4-h time-zone delay and after night matches in elite footballers. However, this lost sleep appeared to have a limited effect on perceptual recovery, which may be explained by a westbound flight and a relatively small change in time zones, in addition to the significant increase in sleep duration on the night of arrival after the long-haul flight.
Robert McCunn, Hugh H.K. Fullagar, Sean Williams, Travis J. Halseth, John A. Sampson and Andrew Murray
Purpose: American football is widely played by college student-athletes throughout the United States; however, the associated injury risk is greater than in other team sports. Numerous factors likely contribute to this risk, yet research identifying these risk factors is limited. The present study sought to explore the relationship between playing experience and position on injury risk in NCAA Division I college football players. Methods: Seventy-six male college student-athletes in the football program of an American NCAA Division I university participated. Injuries were recorded over 2 consecutive seasons. Players were characterized based on college year (freshman, sophomore, junior, or senior) and playing position. The effect of playing experience and position on injury incidence rates was analyzed using a generalized linear mixed-effects model, with a Poisson distribution, log-linear link function, and offset for hours of training exposure or number of in-game plays (for training and game injuries, respectively). Results: The overall rates of non-time-loss and time-loss game-related injuries were 2.1 (90% CI: 1.8–2.5) and 0.6 (90% CI: 0.4–0.8) per 1000 plays, respectively. The overall rates of non-time-loss and time-loss training-related injuries were 26.0 (90% CI: 22.6–29.9) and 7.1 (90% CI: 5.9–8.5) per 1000 h, respectively. During training, seniors and running backs displayed the greatest risk. During games, sophomores, juniors, and wide receivers were at greatest risk. Conclusions: Being aware of the elevated injury risk experienced by certain player groups may help coaches make considered decisions related to training design and player selection.
Jonathon J.S. Weakley, Dale B. Read, Hugh H.K. Fullagar, Carlos Ramirez-Lopez, Ben Jones, Cloe Cummins and John A. Sampson
Purpose: To investigate whether providing global positioning system feedback to players between bouts of small-sided games (SSGs) can alter locomotor, physiological, and perceptual responses. Methods: Using a reverse counterbalanced design, 20 male university rugby players received either feedback or no feedback during “off-side” touch rugby SSGs. Eight 5v5, 6 × 4-minute SSGs were played over 4 d. Teams were assigned to a feedback or no-feedback condition (control) each day, with feedback provided during the 2-min between-bouts rest interval. Locomotor, heart rate, and differential rating of perceived exertion of breathlessness and leg-muscle exertion were measured and analyzed using a linear mixed model. Outcomes were reported using effect sizes (ES) and 90% confidence intervals (CI), and then interpreted via magnitude-based decisions. Results: Very likely trivial to unclear differences at all time points were observed in heart rate and differential rating of perceived exertion measures. Possibly to very likely trivial effects were observed between conditions, including total distance (ES = 0.15; 90 CI, −0.03 to 0.34), high-speed distance (ES = −0.07; 90 CI, −0.27 to 0.13), and maximal sprint speed (ES = 0.11; 90% CI, −0.11 to 0.34). All within-bout comparisons showed very likely to unclear differences, apart from possible increases in low-speed distance in bout 2 (ES = 0.23; 90% CI, 0.01 to 0.46) and maximal sprint speed in bout 4 (ES = 0.21; 90% CI, −0.04 to 0.45). Conclusions: In this study, verbal feedback did not alter locomotor, physiological, or perceptual responses in rugby players during SSGs. This may be due to contextual factors (eg, opposition) or the type (ie, distance) or low frequency of feedback provided.