1. THE EFFECT OF TRAINING STATUS ON HEART RATE VARIABILITY IN DIVISION-1 COLLEGIATE SWIMMERS
Andrew A. Flatt, Bjoern Hornikel and Michael R. Esco
Department of Kinesiology, University of Alabama, Tuscaloosa, AL
Abstract
Methods
Results
12 National level (NAT, n = 4 female) and 12 Conference level (CONF, n = 5 female) D-1 Collegiate swimmers (n =24 in total) participated in this study. (Descriptors in Results section)
Over 4 weeks, subjects completed daily HRV measures in the seated position after waking with a validated smartphone application and pulse-wave finger sensor (app) utilizing a 55-second recording. Subjects also completed a questionnaire via the app where they rated perceived levels of sleep quality, muscle soreness, mood, stress and fatigue on a 9-point scale.
The HR parameters evaluated by the app include resting HR (RHR) and lnRMSSD. The 4-week mean for RHR (RHRm) and lnRMSSD (lnRMSSDm) in addition to the coefficient of variation for RHR (RHRcv) and lnRMSSD (lnRMSSDcv) were determined for comparison. Wellness parameters were also averaged between groups and compared.
Independent t-tests and effect sizes ± 90% confidence limits (ES± 90% CL) were used for comparisons (Table 1). Pearson’s correlation (r) was used to quantify the relationship between lnRMSSDm and lnRMSSDcv (Figure 2).
Figure 1. Scatterplot representing the relationship between the mean and coefficient of variation of log transformed root mean square of successive RR interval differences.
Figure 2. HRV trend of a NAT (black line) compared with a CONF (gray line) level swimmer.
Resting heart rate variability (HRV) fluctuates on a daily basis in response to physical and psychological stressors and may provide useful information pertaining to fatigue and adaptation. However, there is limited research comparing HRV profiles between athletes of the same sport who differ by training status. PURPOSE: The purpose of this study was to compare resting heart rate (RHR) parameters between national and conference level Division-1 Collegiate swimmers and to determine if any differences were related to psychometric indices. METHODS: Twenty-four subjects were categorized as national (NAT, n = 12, 4 female) or conference level competitors (CONF, n=12, 5 female). Over 4 weeks, daily HRV was measured in the seated position by the subjects after waking and elimination with a validated smartphone application and pulse-wave finger sensor (app) utilizing a 55-second recording period. Subjects then completed a questionnaire on the app where they rated perceived levels of sleep quality, muscle soreness, mood, stress and fatigue on a 9-point scale. The HR parameters evaluated by the app include RHR and the log-transformed root-mean square of successive RR interval differences multiplied by 20 (lnRMSSD). The 4-week mean for RHR (RHRm) and lnRMSSD (lnRMSSDm) in addition to the coefficient of variation (CV) for RHR (RHRcv) and lnRMSSD (lnRMSSDcv) were determined for comparison. In addition, psychometric parameters were also averaged between groups and compared. Independent t-tests and effect sizes ± 90% confidence limits (ES± 90% CL) were used to compare the HR and psychometric parameters. RESULTS: NAT was moderately taller (184.9 ± 10.0 vs ± 12.5 cm; p = 0.06, ES ± 90% CL = 0.83 ± 0.70) and moderately heavier (80.4 ± 9.7 vs ± 11.9 kg; p = 0.26, ES ± 90% CL = 0.48 ± 0.67) than CONF, though not statistically significant. The results comparing HR and psychometrics are displayed in Table 1. lnRMSSDm and lnRMSSDcv was moderately higher and lower, respectively, in NAT compared to CONF (p<0.05). CONCLUSION: Higher training status is associated with moderately higher lnRMSSDm and lower lnRMSSDcv compared to those of lower training status. This was observed despite no significant difference in perceived stressors that may affect HR parameters. PRACTICAL APPLICATION: Training status appears to be a determinant of daily HRV and its fluctuation. This may be because higher level athletes are more fit and recover faster from training, resulting in a more stable HRV pattern. This information can be useful to practitioners when interpreting HRV trends in athletes. For example, an increase in HRV with reduced daily fluctuation may indicate improvements in an athletes training status. Alternatively, an athlete with high training status demonstrating reduced HRV and greater daily fluctuation may be showing signs of fatigue or loss of fitness depending on the context of the current training phase and program.
lnRMSSDcv
lnRMSSDm
lnRMSSDx20
Days
Intro & Purpose
Results
NAT was moderately taller (184.9 ± 10.0 vs ± 12.5 cm; p = 0.06, ES ± 90% CL = 0.83 ± 0.70) and moderately heavier (80.4 ± 9.7 vs ± 11.9 kg; p = 0.26, ES ± 90% CL = 0.48 ± 0.67) than CONF, though not statistically significant.
Table 1. Comparison statistics for cardiac-autonomic and wellness parameters.
Conclusions & Practical Applications
Vagally-mediated heart rate variability (HRV) (e.g., the logarithm of the root mean square of successive RR intervals, lnRMSSD) reflects parasympathetic influence of the heart and is used as an objective physiological marker for monitoring fatigue and adaptation in athletes.
The mean lnRMSSD represents average vagal activity over a period of time whereas the coefficient of variation of lnRMSSD represents its daily fluctuation and is used to reflect perturbations to cardiac-autonomic homeostasis.
Parasympathetic reactivation following exercise is effected by numerous factors including exercise intensity, plasma catecholamine's, metabolite accumulation, body temperature, and fluid balance. Parasympathetic reactivation following exercise is accelerated in higher fit individuals.
Factors known to effect daily changes in lnRMSSD parameters include fitness, training load, fatigue and life style factors such as sleep quality and perceived stress.
The purpose of this study was to determine the effect of training status on cardiac-autonomic activity among D-1 collegiate swimmers. A secondary objective was to determine the association between average vagal activity and its daily fluctuation.
Higher training status is associated with moderately higher lnRMSSDm and lower lnRMSSDcv compared with those of lower training status. These differences were observed despite no significant difference in perceived stressors and a standardized training load and structure.
This may be because higher level athletes are more fit and recover faster from training, resulting in a more stable HRV pattern with less daily fluctuation. However, genetics and unaccounted for life style factors may also contribute to these differences.
This information may be useful to practitioners when interpreting HRV trends in athletes. For example, an increase in HRV with reduced daily fluctuation may indicate improvements in an athletes training status. Alternatively, an athlete with high training status demonstrating reduced HRV and greater daily fluctuation may be showing signs of fatigue or loss of fitness depending on the context of the current training phase and program.
Greater vagal activity was associated with smaller fluctuations in daily lnRMSSD. Increasing vagal activity may reduce its daily fluctuation and thus result in less homeostatic perturbation in response to training. Modifying training and life style factors to increase HRV may therefore contribute to improved adaptation.
Table 1. Comparison of cardiac-autonomic and wellness parameters between NAT and CONF.
NAT
CONF P
ES±90%CL
Qualitative Inference
lnRMSSDm
88.2 ± 7.9
82.3 ± 5.5
0.04
0.86 ± 0.70
Moderate
lnRMSSDcv
6.1 ± 2.4
8.4 ± 2.6
0.03
-0.90 ± 0.70
RHRm
61.9 ± 8.2
64.4 ± 5.4
0.39
-0.36 ± 0.68
Unclear
RHRcv
8.5 ± 2.8
10.3 ± 2.9
0.14
-0.62 ± 0.64
Sleep
6.3 ± 0.8
6.2 ± 0.8
0.74
0.13 ± 0.67
Fatigue
5.0 ± 1.0
5.4 ± 0.9
0.26
-0.42 ± 0.68
Soreness
4.9 ± 5.1
5.1 ± 0.9
0.63
-0.05 ± 0.67
Stress
5.7 ± 0.8
6.0 ± 1.0
0.37
-0.33 ± 0.68
Mood
6.0 ± 0.9
0.35
-0.35 ± 0.68