Monitoring collegiate soccer players during a congested match schedule: Heart rate variability versus subjective wellness measures
Introduction
Monitoring soccer players' physical fatigue is of paramount importance for practitioners aiming to optimize training loads [1,2] and to prevent possible fatigue-induced injuries [3,4]. A myriad of measures, including blood and salivary hormones [5,6], short-duration maximal performances [7,8], submaximal heart rate measures [9], resting heart rate variability indices [10,11], and self-reported measures [12,13], have been examined by researchers to monitor fatigue in soccer players.
While collecting laboratory-based biomarkers is of special interest among the scientific community, sports scientists working in club settings are always looking to find more time-efficient and easy-to-use measures to monitor athletes' training status (fitness and/or fatigue) [14]. The aim of collecting such measures is to avoid situations such as overtraining, to mitigate injury risks, and to optimize human performance [1,4]. Therefore, the interest in more practical measures of fatigue monitoring has recently been growing [2,12,15,16], with an aim to help practitioners to solve problems arising from real-world scenarios (i.e., club settings).
Subjective self-reported measures are among the top variables commonly used in practice as a useful monitoring tool [1]. More specifically, the Hooper index and/or its subsets (i.e., sleep quality, stress, fatigue, and muscle soreness) [17] have recently been shown as a promising measure of monitoring in soccer players [8,12,13,[18], [19], [20]]. Indeed, self-reported measures are not only pertinent to high-load versus low-load training phases [13,18,20,21] but are also associated with training load fluctuations [8,20] in soccer players.
Despite laboratory-based objective measures (e.g., blood and salivary hormones), field-based alternatives, like heart rate variability obtained using smart-phone applications, have recently received special attention among practitioners for monitoring soccer players' training status [11,15,16,22]. The natural logarithm of the square root of the mean of the sum of the squares of differences between adjacent normal R-R intervals (Ln rMSSD) has been recommended as a strong heart rate variability index for monitoring athletes' training status [2], and its sensitivity to training adaptations [22] and training-induced fatigue [8,19] has been evidenced among soccer players.
Recent studies have shown that Hooper index and heart rate variability show promising practical applications for monitoring soccer players' training status. Hooper index has been shown to be influenced by match-induced loads in academy soccer players [12]. Moalla et al. [20] observed that all Hooper index subsets, including perceived sleep, stress, fatigue and muscle soreness are moderately related to daily training load in professional soccer players. Fessi et al. [18] also observed that all subsets of the Hooper index show higher values (detrimental change) in high-load versus low-load phases (pre-season vs. in-season). The use of Ln rMSSD not only has practical applications for monitoring training-induced adaptations [11] but is also a practical measure to detect fatigue [10,16] in soccer players. Thorpe et al. [8] showed that fluctuations in Ln rMSSD are significantly related to fluctuations in external load (i.e., total high-intensity running distance) in elite soccer players.
The importance of monitoring players' training status and their sensitivity to high-load periods is even greater during congested fixtures [21,23]. Usually, congested fixtures are characterized by a specific high-load period with matches occurring every three days or less. Higher internal or external training loads have been observed when comparing regular with congested fixtures in some studies examining session-ratings of perceived exertion (sRPE) [13] or performance variables such as frequency of accelerations [24]. However, while changes in both wellness and heart rate variability measures to changes in training load have been usually investigated in preseason preparations [11,16], high-load versus low-load periods [19,22], and in-season phases [8], their sensitivity to congested match schedules has not been compared yet. Determining the most sensitive measure of congested match schedules helps practitioners select the best option for their monitoring purposes. Therefore, the aims of this study were a) to examine within-group changes in wellness and heart rate variability measures and b) to compare their sensitivity to a congested match schedule in collegiate soccer players.
Section snippets
Experimental approach
Soccer players' wellness measures were collected after four consecutive days of low-load (training weak) and high-load (congested match schedule) phases, at the same time (i.e., ~10 a.m.). Heart rate variability was also collected by players using a smart-phone application based on their individual waking times (i.e., 7:00 to 9;00 a.m.) on the same testing days. Wellness measures were also reported by players at the same time they provided heart rate variability data. The results of both heart
Results
Daily sRPE for low-load (i.e., training days) and high-load (i.e., match days) phases are shown in Fig. 1. Mean subjective (i.e., Hooper index) and objective (i.e., heart rate variability) measures of monitoring that were collected on days after low-load (first four days) and high-load (second four days) phases are also shown in Fig. 1. Accumulated sRPE showed an almost certain large increase (+148.4%, [87.3; 229.5%]), effect size (ES, 2.16 [1.49; 2.82]) from 827.1 ± 261.7 to 2026 ± 562.7 AU. A
Discussion
The aims of this study were a) to examine within-group changes in wellness and heart rate variability measures and b) to compare their sensitivity to a congested match schedule in collegiate soccer players. The main finding of the present study was that, while wellness measures moderately changed due to a large increase in mean sRPE, heart rate variability indices only showed a trivial change over four consecutive days of monitoring. Relationships between changes in the Hooper index and some of
Conclusions
In conclusion, based on the findings from this study, it is suggested to use subjective wellness measures instead of heart rate variability indices when “one time point” is efficient option available to monitor soccer players' training status during congested match schedules. The results found more sensitiveness of this measure to variations of load than HRV. Hooper index and its subsets including quality of sleep, stress, fatigue and soreness are very time-efficient and easy-to-use measures to
Acknowledgements
The authors thank all players for their participation in this experiment.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Declarations of interest
None.
References (42)
Internal training load and its longitudinal relationship with seasonal player wellness in elite professional soccer
Physiol. Behav.
(2017)- et al.
Heart rate variability and psychometric responses to overload and tapering in collegiate sprint-swimmers
J. Sci. Med. Sport
(2017) Influence of fatigue, stress, muscle soreness and sleep on perceived exertion during submaximal effort
Physiol. Behav.
(2013)- et al.
Training load and player monitoring in high-level football: current practice and perceptions
Int. J. Sports Physiol. Perform.
(2016) Monitoring training status with HR measures: do all roads lead to Rome?
Front. Physiol.
(2014)Risk factors, testing and preventative strategies for non-contact injuries in professional football: current perceptions and practices of 44 teams from various premier leagues
Br. J. Sports Med.
(2014)Monitoring training load to understand fatigue in athletes
Sports Med.
(2014)Extensive monitoring through multiple blood samples in professional soccer players
J. Strength Cond. Res.
(2013)Salivary cortisol in top-level professional soccer players
Eur. J. Appl. Physiol.
(2009)Game-induced fatigue patterns in elite female soccer
J. Strength Cond. Res.
(2010)
Monitoring fatigue during the in-season competitive phase in elite soccer players
Int. J. Sports Physiol. Perform.
The reliability and validity of a submaximal warm-up test for monitoring training status in professional soccer players
J. Strength Cond. Res.
Smartphone-derived heart-rate variability and training load in a women's soccer team
Int. J. Sports Physiol. Perform.
Evaluating individual training adaptation with smartphone-derived heart rate variability in a collegiate female soccer team
J. Strength Cond. Res.
Ground travel-induced impairment of wellness is associated with fitness and travel distance in young soccer players
Kinesiology
Houston, we still have a problem
Int. J. Sports Physiol. Perform.
Heart rate variability and training load among National Collegiate Athletic Association Division 1 college football players throughout spring camp
J. Strength Cond. Res.
Individual heart rate variability responses to preseason training in high level female soccer players
J. Strength Cond. Res.
Monitoring overtraining in athletes
Sports Med.
Changes of the psychophysical state and feeling of wellness of professional soccer players during pre-season and in-season periods
Res. Sports Med.
Relationship between daily training load and psychometric status of professional soccer players
Res. Sports Med.
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