When implementing HRV monitoring with a new team, the coach will be quick to point out the inter-individual variability in the athletes’ trends. Some athletes are showing high scores and some are low. Some are showing considerable daily fluctuation while others show very consistent numbers. Or, some show substantial fluctuation during this period but minimal fluctuation during that period. This can be confusing and difficult to interpret, but with some context, the trends (and changes therein) can usually be explained.

Greater fitness levels are associated with higher resting HRV and faster parasympathetic reactivation following exercise. This likely contributes to the smaller coefficient of variation  (CV) we (and others) have observed in athletes with higher VO2max and intermittent running performance. So if we were to categorize athletes of the same sport based on competitive level (i.e., training status), we should see group differences between their average lnRMSSD and CV. What makes our approach different from previous work is the longer observation period (1 month), the use of a finger sensor (PPG) and smartphone application using ultra-short HRV recordings for daily data acquisition and inclusion of the CV in the analysis. This was presented at the NSCA National Conference in New Orleans this July. Full manuscript in production soon.

THE EFFECT OF TRAINING STATUS ON HEART RATE VARIABILITY IN DIVISION-1 COLLEGIATE SWIMMERS

Andrew A. Flatt, Bjoern Hornikel, Michael R. Esco

University of Alabama, Tuscaloosa, AL

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. 175.5 ± 12.5 cm; p = 0.06, ES ± 90% CL = 0.83 ± 0.70) and heavier (80.4 ± 9.7 vs. 75.2 ± 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.

This figure shows a year of data from two athletes (Olympic level on top vs. Conference level on bottom) to provide a nice visual representation of their trend differences.