George Mason University, Frank Pettrone Center for Sports Performance, Fairfax, Virginia.
Department of Exercise Science and Athletic Training, Springfield College, Springfield, Massachusetts.
J Strength Cond Res. 2021 May 1;35(5):1182-1186. doi: 10.1519/JSC.0000000000003997.
Fields, JB, Lameira, DM, Short, JL, Merrigan, JM, Gallo, S, White, JB, and Jones, MT. Relationship between external load and self-reported wellness measures across a collegiate men's soccer preseason. J Strength Cond Res 35(5): 1182-1186, 2021-Monitoring athlete training load is important to training programming and can help balance training and recovery periods. Furthermore, psychological factors can affect athlete's performance. Therefore, the purpose was to examine the relationship between external load and self-reported wellness measures during soccer preseason. Collegiate men soccer athletes (n = 20; mean ± SD age: 20.3 ± 0.9 years; body mass: 77.9 ± 6.8 kg; body height: 178.87 ± 7.18cm; body fat: 10.0 ± 5.0%; V̇o2max: 65.39 ± 7.61ml·kg-1·min-1) participated. Likert scale self-assessments of fatigue, soreness, sleep, stress, and energy were collected daily in conjunction with the Brief Assessment of Mood (vigor, depression, anger, fatigue, and confusion). Total distance (TD), player load (PL), high-speed distance (HSD, >13 mph [5.8 m·s-1]), high inertial movement analysis (IMA, >3.5 m·s-2), and repeated high-intensity efforts (RHIEs) were collected in each training session using positional monitoring (global positioning system/global navigation satellite system [GPS/GNSS]) technology. Session rate of perceived exertion (sRPE) was determined from athlete's post-training rating (Borg CR-10 Scale) and time of training session. Multilevel models revealed the bidirectional prediction of load markers on fatigue, soreness, sleep, energy, and sRPE (p < 0.05). Morning ratings of soreness and fatigue were predicted by previous afternoon's practice measures of TD, PL, HSD, IMA, RHIE, and sRPE. Morning soreness and fatigue negatively predicted that day's afternoon practice TD, PL, HSD, IMA, RHIE, and sRPE. Morning ratings of negative mood were positively predicted by previous day's afternoon practice HSD. In addition, negative morning mood states inversely predicted HSD (p = 0.011), TD (p = 0.002), and PL (p < 0.001) for that day's afternoon practice. Using self-reported wellness measures with GPS/GNSS technology may enhance the understanding of training responses and inform program development.
菲尔德斯、J·B、拉梅拉、D·M、肖特、J·L、梅里根、J·M、加洛、S、怀特、J·B 和琼斯、M·T。大学生男子足球赛季前训练中外部负荷与自我报告的健康指标之间的关系。J 力量与调节研究 35(5):1182-1186，2021 年——监测运动员的训练负荷对于训练计划很重要，有助于平衡训练和恢复周期。此外，心理因素会影响运动员的表现。因此，目的是研究足球赛季前外部负荷与自我报告的健康指标之间的关系。大学生男子足球运动员（n=20；平均±标准差年龄：20.3±0.9 岁；体重：77.9±6.8kg；身高：178.87±7.18cm；体脂：10.0±5.0%；最大摄氧量：65.39±7.61ml·kg-1·min-1）参加了研究。在与Brief Assessment of Mood（活力、抑郁、愤怒、疲劳和困惑）相结合的情况下，每天都会收集疲劳、酸痛、睡眠、压力和能量的李克特量表自我评估。在每次训练中，使用位置监测（全球定位系统/全球导航卫星系统[GPS/GNSS]）技术收集总距离（TD）、球员负荷（PL）、高速距离（HSD，>13mph[5.8m·s-1]）、高惯性运动分析（IMA，>3.5m·s-2）和重复高强度努力（RHIE）。会话感知努力（sRPE）是从运动员的训练后评分（Borg CR-10 量表）和训练时间确定的。多水平模型显示，负荷标志物对疲劳、酸痛、睡眠、能量和 sRPE 的双向预测（p<0.05）。早上的酸痛和疲劳评分可由前一天下午的 TD、PL、HSD、IMA、RHIE 和 sRPE 预测。早上的酸痛和疲劳会对当天下午的 TD、PL、HSD、IMA、RHIE 和 sRPE 产生负面影响。早上的负面情绪评分可由前一天下午的 HSD 预测。此外，负面的早晨情绪状态会对当天下午的 HSD（p=0.011）、TD（p=0.002）和 PL（p<0.001）产生相反的预测。使用 GPS/GNSS 技术的自我报告的健康指标可能会增强对训练反应的理解，并为项目开发提供信息。
