Rob Duffield

Purpose : Sleep is important for athlete health and performance; however, the relationship between nutritional intake and sleep in elite male athletes is not well reported. This study described the relationship between dietary intake and sleep in male rugby league athletes. A secondary aim examined how evening dietary intake (≥6 PM), macronutrient and micronutrient composition, and dinner timing relates to sleep variables. Methods : Twenty-eight rugby league athletes had their dietary intake and sleep monitored for 7 consecutive days during preseason. Sleep (total sleep time [minutes], sleep efficiency [percentage], wake after sleep onset [minutes], and sleep onset latency [minutes]) was measured using actigraphy and sleep diaries. Dietary intake, including total daily and evening (>6 PM) energy, macronutrient, and micronutrient intake, was measured using a smartphone application (MealLogger and FoodWorks). Dietary data were paired with the corresponding nightly sleep outcomes to enable day-to-day associations between dietary intake and sleep behaviors. Furthermore, to investigate the influence of nutrient timing, all food and fluid intake consumed ≥6 PM was isolated and analyzed separately. Relationships between nutrient intake and sleep were assessed using linear mixed models. Results : Evening data showed for every 1 g and 1 g·kg ⁻¹ increase in sugar ≥6 PM, there was a significant decrease in sleep efficiency by −0.21% ( P = .03) and −0.22% ( P = .02), respectively. No other significant associations between intake or sleep outcomes were shown. Conclusions : Evening (≥6 PM) sugar intake in professional male rugby league athletes is associated with poorer sleep efficiency. Future research should explore how the manipulation of dietary intake could guide dietary recommendations to improve sleep in rugby league athletes.

This study used inertial measurement units (IMUs) to describe the accelerometry profiles of plyometric and resistance training, compare between sensor placements and gym session types, and evaluate the between-session reliability of accelerometry-derived metrics during plyometric training. Ten high-performance youth tennis players completed two identical plyometric sessions and one resistance session while wearing IMUs at four anatomical locations (thoracic spine, lumbar spine, left ankle, right ankle). Metrics included peak upward acceleration (PUpAcc), 1D upward PlayerLoad™ (1DPL-up), 2D PlayerLoad™ (2DPL), and 3D PlayerLoad™ (3DPL). All metrics were significantly higher at the ankles than the thoracic or lumbar sites (p < 0.05), consistent with signal attenuation across the kinetic chain. For vertical loading in isolation, 1DPL-up demonstrated stronger thoracic-ankle relationships (r = 0.47–0.70) although only one of four correlations was statistically significant, compared with PUpAcc (r = −0.67–0.27; all p > 0.05). At the thoracic site, between-session reliability during plyometric training was poor for 1DPL-up (ICC = 0.49) and moderate for PUpAcc (ICC = 0.53). All thoracic-derived metrics differentiated plyometric from resistance sessions (p < 0.05). These findings indicate that thoracic-mounted IMUs provide moderately reliable, session-sensitive information on peak vertical accelerometry loads in gym-based training. However, the limited inter-sensor agreement for PUpAcc highlights the need for further refinement to enhance practical utility. Overall, accelerometry appears feasible for integrating gym-based training loads into holistic daily training load monitoring.

This study retrospectively described the extent and type of travel experienced by female national team footballers and compared travel based on club locations. Details of 634 trips over a three-year period from 48 professional footballers in a women's national team were collated and confirmed via an online flight database. For each trip, travel variables included total travel and flight durations, number of time zones crossed, direction, number of overnights and arrival/departure times. Descriptive statistics reported team travel demands, whilst linear mixed models tested the relationship between club locations (Europe, North America, Australia) and travel variables. Post-hoc pairwise comparisons were conducted using estimated marginal means to assess differences between club locations (significance at p < 0.05). Travel demands frequently exceeded >10 h duration (66%) and >3 h time difference (77%), while 52% involved overnight travel. The nature of travel undertaken was broad, with trips ranging from 0.7–28.9 h travel time and 0.7–24.6 h flight time across four continents. Significant differences were observed by club location, as European-based players crossed more time zones than Australian-based players (p < 0.001) and travelled overnight more frequently than Australian- (p = 0.002) and North American-based (p = 0.020) players during return travel. European-based players completed more trips overall (Outbound: p < 0.001, Return: p = 0.002) and more eastward outbound trips than Australian-based players (p = 0.011). Travel for national team duties often requires extensive travel, which may induce jet lag and travel fatigue symptoms. Within this cohort, players based in Europe and North America faced the most demanding travel schedules, with increased exposure to long-haul flights, overnight travel, and time zone shifts.

Purpose : To assess the relationship between travel variables and perceived fatigue, sleep, stress, soreness, and overall wellness responses in female national team footballers. Methods : Details of 342 flights over a 3-year period from 45 professional footballers in a women’s national team were collated and confirmed with an online flight database; including duration, time zone difference, direction, trip type, and arrival/departure times. Perceptual ratings of fatigue, sleep, stress, and soreness were self-reported for up to 3 days posttravel. Linear mixed-effects models assessed associations between travel variables and perceptual responses as outcome variables. Post hoc pairwise comparisons were conducted with estimated marginal means to assess differences between categorical variables (significance at P < .05). Results : Larger time zone changes were associated with increased fatigue, poorer sleep, and worse total wellness scores ( P < .001). Eastward travel was associated with poorer perceived responses than westward, with higher fatigue ( P = .003), sleep ( P = .001), and total wellness ( P = .026). Morning arrivals were associated with better fatigue and sleep ratings than evening ( P < .001), while daytime departures observed reduced fatigue ( P = .013) and soreness ( P = .004) compared with morning. Return trips were associated with greater fatigue than outbound or transition trips ( P < .001). Most measures improved 2 days postarrival ( P < .001). However, mostly small or trivial effect sizes were observed, and all models had low explanatory power (marginal R ² = .001–.10). Conclusions : Larger time zone shifts, eastward travel, and evening arrivals had small associations with poorer fatigue, sleep, and total wellness, while westward travel and morning arrivals yielded small improvements. Overall, travel variables explained limited variance in wellness ratings, suggesting other factors also influence posttravel responses.

Cooperation plays a crucial role in individual and team performance in invasion sports, yet little is known about how individual factors like age and sex affect cooperation in football. This study examines the relationship between cooperative behaviour, and age and sex in youth football. Passing actions from 181 male and female U16 and U17 players across six talent selection matches were analysed to understand relationships between age, sex and cooperative passing network measures (betweenness centrality, closeness centrality and transitivity) using linear mixed effects models. Matches were held on FIFA regulation pitches with standard rules (i.e., 11v11) regulated by referees. A large association between age group and closeness centrality was observed in an initial model (partial η ² = 0.24, adjusted p < 0.001). However, when the model was adjusted for network size, this association was no longer evident, while network size demonstrated a large negative association with closeness centrality (partial η ² = 0.32, adjusted p < 0.001). In the updated model, no clear associations with age group or sex were observed for betweenness or closeness centrality. Small associations involving network size and the interaction of age group and sex were observed for transitivity, although this study lacked statistical power to detect associations of this magnitude. These contrasting findings highlight the importance of structural characteristics in the analysis of cooperative passing networks. Future studies that utilise convenience samples in which network size varies should account for network size statistically, while more controlled studies should aim to maintain consistent network size when comparing passing networks.

Play the ball (PTB) is an important tactical performance indicator in elite rugby league, referring to the action whereby a tackled ball carrier restarts play by rolling the ball underfoot to a teammate following a completed tackle. Despite its growing use in applied performance analysis, the accuracy of commercially coded PTB duration data has not been verified. This study aimed to assess the agreement between commercial and independently coded play the ball event durations from National Rugby League matches. A cross-sectional methodological design was employed using data from 1185 matches over six seasons (2018–2023). A stratified random sample of 372 PTB events was selected within eight standard deviation (SD) categories. Commercial coding of PTB duration was compared against independent coding with stopwatch during playback of official broadcast. Agreement was evaluated using intraclass correlation coefficients (ICCs), typical error of measurement (TEM), and Wilcoxon Signed Rank Tests for systematic bias. Agreement was poor for short-duration PTBs (<2.44 s; ICC = −0.03 to 0.32), with systematic underestimation by the commercial provider (e.g., < –3 SD: mean difference = −3.15 s; TEM = 2.51 s; p < 0.001). Agreement improved for moderate-to-long PTBs (≥2.48 s; ICC = 0.75–0.90), with strongest agreement observed in the 1–2 SD category (ICC = 0.90; TEM = 0.09 s; p = 0.60). Commercial PTB coding demonstrated acceptable agreement for moderate and longer durations but may misrepresent very rapid PTBs. Ongoing evaluation to ensure accurate PTB event coding is warranted given the importance of ruck speed in professional rugby league matchplay.

Objective : To describe sleep behaviors of male rugby league athletes and examine the association with self-reported sleep behaviors, attitudes, and knowledge. Methods : Twenty-two rugby league athletes completed 7 consecutive nights of sleep monitoring using wrist-worn ActiGraphy, with the Sleep Practices and Attitudes Questionnaire completed at the commencement of the monitoring period. ActiGraphy measures included time in-bed (hh:mm), time out-of-bed (hh:mm), duration in bed (minutes), sleep duration (minutes), sleep efficiency (percentages), sleep onset latency (minutes), wake after sleep onset (minutes), and number of awakenings. The Sleep Practices and Attitudes Questionnaire assessed self-reported sleep behaviors, sleep hygiene practices, and sleep knowledge. Linear regressions determined associations between objective sleep measures and subjective self-reported sleep behaviors, sleep knowledge, and sleep attitudes. Results : Sleep behaviors showed mean bedtime at 10:25 PM (45) minutes and wake time 6:30 AM (25) minutes, with 484 (41) minutes in bed. Sleep efficiency was 73% (5%), resulting in a sleep duration of 355 (42) minutes. In contrast, subjective data reported sleep duration as 7.5 (1.0) hours. A small association was observed between lower objective sleep efficiency and longer subjective sleep duration ( P < .05, r ² = .21). Earlier bedtimes related to better coping with chronic insomnia ( r ² = .23, P = .02) but showed small associations with greater in-bed activities ( r ² = .25, P < .05). Longer time in bed and increased sleep duration had small associations with poorer coping with chronic insomnia strategies ( r ² = .25 and r ² = .24, respectively; P < .05). No associations existed between sleep behaviors and sleep knowledge ( P < .05). Conclusions : Suboptimal sleep duration (<8 h) and efficiency (<85%) exist in this cohort of players, despite high sleep knowledge. Therefore, sleep education needs to consider influencing factors on sleep behaviors when designing sleep education programs.

Purpose: This study investigated the effects of pre-cooling and cooling breaks on thermoregulatory, hydration and running responses in football (soccer) players in moderate and hot temperatures. Methods: Forty male youth footballers participated in at least two of four matches, during which core body temperature ( T _core ), heart rate (HR), match running, hydration and perceptual responses were measured. Cooling breaks (CBs), consisting of ice-cold towels and drinks, were compared to drinking breaks (DBs), consisting of passive rest and a temperate drink, applied at the same timeframes. Both were used as pre-cooling for 10 min before the warm-up, before the pre-match, during half-time and during additional 3-min cooling breaks at the 25th minute of each half. Initially, 20 players performed two crossover matches in 25 °C wet-bulb globe temperature (WBGT) receiving cooling (CB ₂₅ ) and drinking (DB ₂₅ ). A second group of 20 players played a regular match in 25 °C WBGT with no breaks (NB ₂₅ ) and then a match in 33 °C WBGT during which they received either cooling (CB ₃₃ ) or drinking breaks (DB ₃₃ ). Results: In CB ₂₅ , players felt cooler ( p < 0.001) and less fatigued ( p < 0.045) than in DB ₂₅ , without differences in match running ( p > 0.20), HR _mean ( p > 0.35) or T _core ( p > 0.09). Players in CB ₂₅ sweated less ( p = 0.005) and drank less ( p = 0.002), resulting in no significant difference in body mass loss compared to DB ₂₅ . In CB ₃₃ , players had lower HR _mean ( p = 0.007), similar total distance ( p = 0.21), lower peak T _core ( p < 0.001) and lower body mass loss ( p = 0.007) compared to NB ₂₅ . In DB ₃₃ , players reduced moderate (12–18 km/h; p = 0.007) and high-speed running distance (18–24 km/h; p = 0.002) but had similar peak T _core ( p = 0.71) and body mass loss ( p = 0.95) to that in NB ₂₅ . Conclusions: In general, high T _core values and body mass losses were observed even when playing in moderate heat. Both drinking and cooling breaks attenuated the continuous T _core rise, but using cooling also improved player perceptions in moderate temperatures. In hotter temperatures, cooling breaks further lowered T _core and body mass loss compared to using only drinking breaks. Trial Regsistry: German Clinical Trials Register: DRKS00032208.