Kanon Uchiyama, Peter Peeling, Shona L. Halson, Machar Reid, Karen Wallman, Jennifer Walsh, Simon Thomas, Olivier Girard
{"title":"重击者,轻睡眠者:职业橄榄球联盟球员睡眠结构的碰撞频率和运动负荷","authors":"Kanon Uchiyama, Peter Peeling, Shona L. Halson, Machar Reid, Karen Wallman, Jennifer Walsh, Simon Thomas, Olivier Girard","doi":"10.1002/ejsc.70052","DOIUrl":null,"url":null,"abstract":"<p>To assess whether certain players are more vulnerable to postmatch sleep disturbances by examining the relationship between match demands—collision frequency and locomotor load—and sleep in professional male rugby union players. A linear mixed-effects regression examined the relationship between match variables and sleep in 13 rugby players across three matches. Match variables included six physical demand variables derived from video analysis and GPS data (collision frequency, total distance, high-speed distance, sprint distance, acceleration load and fast acceleration count) and two contextual variables (location and kick-off time). Sleep variables collected via home-based polysomnography included total sleep time, sleep efficiency, sleep onset/offset, sleep onset latency, wake after sleep onset, number of awakenings and sleep stages (light, deep and rapid eye movement sleep [REM], evaluated both by proportion [%] and time [min]). Each match collision decreased total sleep time (<i>β</i> = −4 ± 1 min and <i>p =</i> 0.006) and REM sleep (time: <i>β</i> = −2 ± 0 min and <i>p <</i> 0.001; proportion: <i>β</i> = −0.6 ± 0.2% and <i>p</i> = 0.021). Conversely, every 500 m increase in locomotor load (total distance) increased REM sleep (time: <i>β</i> = +6 ± 2 min and <i>p =</i> 0.014; proportion: <i>β</i> = +2.7 ± 0.6% and <i>p</i> = 0.002). Every 100 m increase in high-speed distance was associated with decreased REM sleep time (<i>β</i> = −7 ± 3 min and <i>p =</i> 0.020). Match demands, including collision frequency and locomotor load, were associated with changes in postmatch sleep architecture in professional rugby players, particularly REM sleep. Furthermore, greater number of collisions was associated with reduced sleep quantity. Practitioners can leverage GPS and video analysis data to tailor additional sleep strategies aimed at improving postmatch sleep based on individual match demands.</p>","PeriodicalId":93999,"journal":{"name":"European journal of sport science","volume":"25 9","pages":""},"PeriodicalIF":3.0000,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ejsc.70052","citationCount":"0","resultStr":"{\"title\":\"Heavy Hitters, Light Sleepers: Collision Frequency and Locomotor Load on Sleep Architecture in Professional Rugby Union Players\",\"authors\":\"Kanon Uchiyama, Peter Peeling, Shona L. Halson, Machar Reid, Karen Wallman, Jennifer Walsh, Simon Thomas, Olivier Girard\",\"doi\":\"10.1002/ejsc.70052\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>To assess whether certain players are more vulnerable to postmatch sleep disturbances by examining the relationship between match demands—collision frequency and locomotor load—and sleep in professional male rugby union players. A linear mixed-effects regression examined the relationship between match variables and sleep in 13 rugby players across three matches. Match variables included six physical demand variables derived from video analysis and GPS data (collision frequency, total distance, high-speed distance, sprint distance, acceleration load and fast acceleration count) and two contextual variables (location and kick-off time). Sleep variables collected via home-based polysomnography included total sleep time, sleep efficiency, sleep onset/offset, sleep onset latency, wake after sleep onset, number of awakenings and sleep stages (light, deep and rapid eye movement sleep [REM], evaluated both by proportion [%] and time [min]). Each match collision decreased total sleep time (<i>β</i> = −4 ± 1 min and <i>p =</i> 0.006) and REM sleep (time: <i>β</i> = −2 ± 0 min and <i>p <</i> 0.001; proportion: <i>β</i> = −0.6 ± 0.2% and <i>p</i> = 0.021). Conversely, every 500 m increase in locomotor load (total distance) increased REM sleep (time: <i>β</i> = +6 ± 2 min and <i>p =</i> 0.014; proportion: <i>β</i> = +2.7 ± 0.6% and <i>p</i> = 0.002). Every 100 m increase in high-speed distance was associated with decreased REM sleep time (<i>β</i> = −7 ± 3 min and <i>p =</i> 0.020). Match demands, including collision frequency and locomotor load, were associated with changes in postmatch sleep architecture in professional rugby players, particularly REM sleep. Furthermore, greater number of collisions was associated with reduced sleep quantity. 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引用次数: 0
摘要
通过研究职业橄榄球联盟男子运动员的比赛需求——碰撞频率和运动负荷——与睡眠之间的关系,评估某些运动员是否更容易受到赛后睡眠障碍的影响。线性混合效应回归研究了13名橄榄球运动员在三场比赛中的比赛变量和睡眠之间的关系。匹配变量包括来自视频分析和GPS数据的6个物理需求变量(碰撞频率、总距离、高速距离、冲刺距离、加速度载荷和快速加速度计数)和2个上下文变量(位置和开球时间)。通过家庭多导睡眠仪收集的睡眠变量包括总睡眠时间、睡眠效率、睡眠开始/偏移、睡眠开始潜伏期、睡眠开始后醒来、醒来次数和睡眠阶段(浅眼动睡眠、深眼动睡眠和快速眼动睡眠,按比例[%]和时间[分钟]进行评估)。每次匹配碰撞使总睡眠时间(β = - 4±1 min, p = 0.006)和快速眼动睡眠时间(β = - 2±0 min, p < 0.001;比例:β = - 0.6±0.2%,p = 0.021)减少。相反,运动负荷(总距离)每增加500 m,快速眼动睡眠增加(时间:β = +6±2 min, p = 0.014;比例:β = +2.7±0.6%,p = 0.002)。高速距离每增加100 m, REM睡眠时间减少(β =−7±3 min, p = 0.020)。比赛需求,包括碰撞频率和运动负荷,与职业橄榄球运动员赛后睡眠结构的变化有关,尤其是快速眼动睡眠。此外,更多的碰撞与睡眠时间减少有关。从业者可以利用GPS和视频分析数据来定制额外的睡眠策略,旨在根据个人比赛需求改善赛后睡眠。
Heavy Hitters, Light Sleepers: Collision Frequency and Locomotor Load on Sleep Architecture in Professional Rugby Union Players
To assess whether certain players are more vulnerable to postmatch sleep disturbances by examining the relationship between match demands—collision frequency and locomotor load—and sleep in professional male rugby union players. A linear mixed-effects regression examined the relationship between match variables and sleep in 13 rugby players across three matches. Match variables included six physical demand variables derived from video analysis and GPS data (collision frequency, total distance, high-speed distance, sprint distance, acceleration load and fast acceleration count) and two contextual variables (location and kick-off time). Sleep variables collected via home-based polysomnography included total sleep time, sleep efficiency, sleep onset/offset, sleep onset latency, wake after sleep onset, number of awakenings and sleep stages (light, deep and rapid eye movement sleep [REM], evaluated both by proportion [%] and time [min]). Each match collision decreased total sleep time (β = −4 ± 1 min and p = 0.006) and REM sleep (time: β = −2 ± 0 min and p < 0.001; proportion: β = −0.6 ± 0.2% and p = 0.021). Conversely, every 500 m increase in locomotor load (total distance) increased REM sleep (time: β = +6 ± 2 min and p = 0.014; proportion: β = +2.7 ± 0.6% and p = 0.002). Every 100 m increase in high-speed distance was associated with decreased REM sleep time (β = −7 ± 3 min and p = 0.020). Match demands, including collision frequency and locomotor load, were associated with changes in postmatch sleep architecture in professional rugby players, particularly REM sleep. Furthermore, greater number of collisions was associated with reduced sleep quantity. Practitioners can leverage GPS and video analysis data to tailor additional sleep strategies aimed at improving postmatch sleep based on individual match demands.