HRV and Training Load Among NCAA D-1 Football Players Throughout Spring Camp

For our first study with football, we wanted to determine if cardiovascular recovery from training varied among positional groups (i.e., Skill, Mid-Skill and Linemen). We also looked at some longitudinal relationships between cardiac-autonomic and training load parameters throughout spring camp.

We found that Linemen take longer to recover between training sessions than the other positions. This may have important implications for the competitive season because despite differences in recovery time among positional groups, football teams train on a fixed schedule. This may make Linemen more susceptible to developing signs and symptoms of overtraining, getting hurt or sick, etc. Fortunately, we captured data from the competitive season, too. That paper is forthcoming.

The purpose of this study was to determine whether recovery of cardiac-autonomic activity to baseline occurs between consecutive-day training sessions among positional groups of a collegiate football team during Spring camp. A secondary aim was to evaluate relationships between chronic (i.e., 4-week) heart rate variability (HRV) and training load parameters. Baseline HRV (lnRMSSD_BL) was compared with HRV after ∼20 hours of recovery before next-day training (lnRMSSDpost20) among positional groups composed of SKILL (n = 11), MID-SKILL (n = 9), and LINEMEN (n = 5) with a linear mixed model and effect sizes (ES). Pearson and partial correlations were used to quantify relationships between chronic mean and coefficient of variation (CV) of lnRMSSD (lnRMSSD_chronic and lnRMSSDcv, respectively) with the mean and CV of PlayerLoad (PL_chronic and PL_cv, respectively). A position × time interaction was observed for lnRMSSD (p = 0.01). lnRMSSD_BL was higher than lnRMSSDpost20 for LINEMEN (p < 0.01; ES = large), whereas differences for SKILL and MID-SKILL were not statistically different (p > 0.05). Players with greater body mass experienced larger reductions in lnRMSSD (r = -0.62, p < 0.01). Longitudinally, lnRMSSDcv was significantly related to body mass (r = 0.48) and PL_chronic (r = -0.60). After adjusting for body mass, lnRMSSDcv and PL_chronic remained significantly related (r = -0.43). The ∼20-hour recovery time between training sessions on consecutive days may not be adequate for restoration of cardiac-parasympathetic activity to baseline among LINEMEN. Players with a lower chronic training load throughout camp experienced greater fluctuation in lnRMSSD (i.e., lnRMSSDcv) and vice versa. Thus, a capacity for greater chronic workloads may be protective against perturbations in cardiac-autonomic homeostasis among American college football players.
LnRMSSD Spring Camp Football
Full-text on RG Link
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Ultra-short HRV in youth female basketball players

Here’s a look at our latest methodological paper in collaboration with Dr. Fabio Nakamura and colleagues investigating the suitability of ultra-short (1-min) HRV measures in athletes.

Previous studies of ours on this specific topic are linked below:

Ultra-short-term heart rate variability indexes at rest and post-exercise in athletes: evaluating the agreement with accepted recommendations.

Heart rate variability stabilization in athletes: towards more convenient data acquisition.

Assessing Shortened Field-Based Heart-Rate-Variability-Data Acquisition in Team-Sport Athletes.

Ultra-Short-Term Heart Rate Variability is Sensitive to Training Effects in Team Sports Players.

Intraday and Interday Reliability of Ultra-Short-Term Heart Rate Variability in Rugby Union Players.

Adequacy of the Ultra-Short-Term HRV to Assess Adaptive Processes in Youth Female Basketball Players

Heart rate variability has been widely used to monitor athletes’ cardiac autonomic control changes induced by training and competition, and recently shorter recording times have been sought to improve its practicality. The aim of this study was to test the agreement between the (ultra-short-term) natural log of the root-mean-square difference of successive normal RR intervals (lnRMSSD – measured in only 1 min post-1 min stabilization) and the criterion lnRMSSD (measured in the last 5 min out of 10 min of recording) in young female basketball players. Furthermore, the correlation between training induced delta change in the ultra-short-term lnRMSSD and the criterion lnRMSSD was calculated. Seventeen players were assessed at rest pre- and post-eight weeks of training. Trivial effect sizes (-0.03 in the pre- and 0.10 in the post- treatment) were found in the comparison between the ultra-short-term lnRMSSD (3.29 ± 0.45 and 3.49 ± 0.35 ms, in the pre- and post-, respectively) and the criterion lnRMSSD (3.30 ± 0.40 and 3.45 ± 0.41 ms, in the pre- and post-, respectively) (intraclass correlation coefficient = 0.95 and 0.93). In both cases, the response to training was significant, with Pearson’s correlation of 0.82 between the delta changes of the ultra-short-term lnRMSSD and the criterion lnRMSSD. In conclusion, the lnRMSSD can be calculated within only 2 min of data acquisition (the 1st min discarded) in young female basketball players, with the ultra-short-term measure presenting similar sensitivity to training effects as the standard criterion measure.

ultrashort HRV bball


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HRV and Wellness responses to overload and taper in sprint-swimmers

Here’s a brief overview of our latest study capturing daily HRV and wellness ratings throughout overload and tapering in collegiate sprint swimmers preceding a championships competition.

The majority of research in the area has primarily focused on endurance athletes. It’s been a goal of mine for a while now to examine HRV responses in athletes participating in anaerobic events such as short-distance swimming.

The athletes completed wellness questionnaires and recorded HRV daily via smartphone and validated pulse-wave finger sensor (seated position) after waking. The observation period lasted 6-weeks which included 1 week of baseline, 2 weeks of overload and a progressive 3-week taper. The overload was characterized by a substantial increase in training intensity while overall volume varied by up to only 20%. Of the group, 2 athletes went on to compete in the 2016 Olympic summer games.


Heart rate variability and psychometric responses to overload and tapering in collegiate sprint-swimmers

Full-text on Research Gate 


The purpose of this study was to evaluate cardiac-parasympathetic and psychometric responses to competition preparation in collegiate sprint-swimmers. Additionally, we aimed to determine the relationship between average vagal activity and its daily fluctuation during each training phase.




Ten Division-1 collegiate sprint-swimmers performed heart rate variability recordings (i.e., log transformed root mean square of successive RR intervals, lnRMSSD) and completed a brief wellness questionnaire with a smartphone application daily after waking. Mean values for psychometrics and lnRMSSD (lnRMSSDmean) as well as the coefficient of variation (lnRMSSDcv) were calculated from 1 week of baseline (BL) followed by 2 weeks of overload (OL) and 2 weeks of tapering (TP) leading up to a championship competition.


Competition preparation resulted in improved race times (p<0.01). Moderate decreases in lnRMSSDmean, and Large to Very Large increases in lnRMSSDcv, perceived fatigue and soreness were observed during the OL and returned to BL levels or peaked during TP (p<0.05). Inverse correlations between lnRMSSDmean and lnRMSSDcv were Very Large at BL and OL (p<0.05) but only Moderate at TP (p>0.05).


OL training is associated with a reduction and greater daily fluctuation in vagal activity compared with BL, concurrent with decrements in perceived fatigue and muscle soreness. These effects are reversed during TP where these values returned to baseline or peaked leading into successful competition. The strong inverse relationship between average vagal activity and its daily fluctuation weakened during TP.

While group responses are certainly meaningful, the individual responses provide more meaningful information to practitioners. The figure below shows the individual trends from 3 athletes that exemplify 3 common training responses I’ve observed in a variety of athletes.


Subject B (middle) has the smallest CV at baseline and subsequently handles the overload very well, with minimal reductions in lnRMSSD. This indicates that Subject B is in great shape and could probably handle greater loads.

Subject C (bottom) displays what I would consider to be a very expected response to overloading. There is a considerable increase in daily lnRMSSD fluctuation (i.e., increased CV) and progressive but small decrease in the trend. I interpret this type of response to indicate that the loads are sufficient enough to provoke the fatigue/recovery process but not so high that HRV becomes suppressed. This is possibly indicative of a load/dose of training that is high but within the overall recovery capacity of the athlete.

Subject A (top) has the highest CV of the group at baseline and subsequently responds the least favorably to the overload. lnRMSSD pretty much crashes almost immediately and remains suppressed for several days (boxed data points). The coach pulled back on subject A due to high fatigue, reduced performance, decrements in pulse-rate recovery between sets, etc. The trend immediately improves until about 1-week out from competition at which point loads again were further reduced. Ultimately, this athlete improved upon previous best times at competition from that year, suggesting that the interventions were effective.

The main take-home would be that the typical response to intensified training includes a reduction and greater daily fluctuation in HRV, along with decrements in wellness scores. Athletes demonstrating different responses (i.e., minimal change in HRV trend or conversely chronic suppression of HRV) may be coping better or worse than expected. Coaches should then investigate and address factors contributing to the poor response.


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The effect of training status on HRV in D-1 collegiate swimmers

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.


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.

table swim HRV comarison

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. HRV trend swim comparison

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New Study: Agreement between a smart-phone pulse sensor application and ECG for determining lnRMSSD

Here’s a brief overview of our latest study, in press with JSCR.  We compared the ithlete finger sensor with ECG in supine, seated and standing positions. We are continuing our testing with other popular smartphone HRV apps in the near future. Thanks to the Summer 2015 Alabama S&C interns for making up a large portion of the participants in this study.

Agreement between a smart-phone pulse sensor application and ECG for determining lnRMSSD


The purpose of this study was to determine the agreement between a smartphone pulse finger sensor (SPFS) and electrocardiography (ECG) for determining ultra-short-term heart rate variability (HRV) in three different positions. Thirty college-aged men (n = 15) and women (n = 15) volunteered to participate in this study. Sixty second heart rate measures were simultaneously taken with the SPFS and ECG in supine, seated and standing positions. lnRMSSD was calculated from the SPFS and ECG. The lnRMSSD values were 81.5 ± 11.7 via ECG and 81.6 ± 11.3 via SPFS (p = 0.63, Cohen’s d = 0.01) in the supine position, 76.5 ± 8.2 via ECG and 77.5 ± 8.2 via SPFS (p = 0.007, Cohen’s d = 0.11) in the seated position, and 66.5 ± 9.2 via ECG and 67.8 ± 9.1 via SPFS (p < 0.001, Cohen’s d = 0.15) in the standing positions. The SPFS showed a possibly strong correlation to the ECG in all three positions (r values from 0.98 to 0.99). In addition, the limits of agreement (CE ± 1.98 SD) were -0.13 ± 2.83 for the supine values, -0.94± 3.47 for the seated values, and -1.37 ± 3.56 for the standing values. The results of the study suggest good agreement between the SPFS and ECG for measuring lnRMSSD in supine, seated, and standing positions. Though significant differences were noted between the two methods in the seated and standing positions, the effect sizes were trivial.

Full Text on Research Gate

FS EKG data

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New Study: Intra- and inter-day reliability of ultra-short-term HRV in elite rugby union players

Here’s a look at our latest study in collaboration with Fabio Nakamura and colleagues, now in press with JSCR (Abstract below). In this study, HRV was recorded as a team at the training facility, not immediately after waking. This is the approach that many coaches are interested in using given the issue with compliance when trying to get athletes to perform HRV measures on their own at home after waking. Controlled and supervised measures at the facility appear promising, at least in these high level athletes.

It’s important to understand that autonomic activity is constantly making adjustments to physical, chemical and perceived psychological stimuli. Thus, HRV is inherently not the most reliable metric. However, training status/fitness appear to have a strong affect on day to day variation in HRV. More fit athletes recover faster/tolerate training better and thus tend to show less deviation from baseline compared to less fit athletes, of which will experience much greater homeostatic disruption from training and greater day to day variation. I strongly believe that the amount of daily fluctuation (i.e., lnRMSSDcv) is a very useful indication of fitness, stress and training adaptation.

We currently have a paper in production looking at the effect of training status on HRV. In the mean time, compare the trends below of an Olympic level and a conference level athlete, both short-distance swimmers (similar age and physical characteristics) across 4 consecutive weeks of training.

lnrmssd compareIntra- and inter-day reliability of ultra-short-term heart rate variability in rugby union players.

The aim of this study was to examine the intra-day and inter-day reliability of ultra-short-term vagal-related heart rate variability (HRV) in elite rugby union players. Forty players from the Brazilian National Rugby Team volunteered to participate in this study. The natural log of the root mean square of successive RR interval differences (lnRMSSD) assessments were performed on four different days. HRV was assessed twice (intra-day reliability) on the first day and once per day on the following three days (inter-day reliability). The RR interval recordings were obtained from 2-min recordings using a portable heart rate monitor. The relative reliability of intra- and inter-day lnRMSSD measures were analyzed using the intraclass correlation coefficient (ICC). The typical error of measurement (absolute reliability) of intra- and inter-day lnRMSSD assessments were analyzed using the coefficient of variation (CV). Both intra-day (ICC = 0.96; CV = 3.99%) and inter-day (ICC = 0.90; CV = 7.65%) measures were highly reliable. The ultra-short-term lnRMSSD is a consistent measure for evaluating elite rugby union players, in both intra- and inter-day settings. This study provides further validity to using this shortened method in practical field conditions with highly trained team sports athletes.

Full text on Research Gate

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New Study: Individual HRV responses to preseason training in D-1 women’s soccer players

Here’s a brief look at a new paper of ours in press with JSCR. This is a very small study that we submitted as “Research Note” that looked at changes in HRV (via finger pulse sensor) and training load (via Polar Team2) across preseason training in D-1 women’s soccer players.

Individual HRV responses to preseason training in D-1 women’s soccer players


The purpose of this study was to track changes in training load (TL) and recovery status indicators throughout a 2-week preseason and to interpret the meaning of these changes on an individual basis among 8 Division-1 female soccer players. Weekly averages for heart ratevariability (lnRMSSD), TL and psychometrics were compared with effect sizes (ES) and magnitude based inferences. Relationships were determined with Pearson correlations. Group analysis showed a very likely moderate decrease for total training load (TTL) (TTL week 1 = 1203 ± 198, TTL week 2 = 977 ± 288; proportion = 1/2/97, ES = -0.93) and a likely small increase in lnRMSSD (week 1 = 74.2 ± 11.1, week 2 = 78.1 ± 10.5; proportion = 81/14/5, ES = 0.35). Fatigue demonstrated a very likely small improvement (week 1 = 5.03 ± 1.09, week 2 = 5.51 ± 1.00; proportion = 95/4/1; ES = 0.45) while the other psychometrics did not substantially change. A very large correlation was found between changes in TL and lnRMSSD (r = -0.85) while large correlations were found between lnRMSSD and perceived fatigue (r = 0.56) and soreness (r = 0.54). Individual analysis suggests that 2 subjects may benefit from decreased TL, 2 subjects may benefit from increased TL and 4 subjects may require no intervention based on their psychometric and lnRMSSD responses to the TL. Individual weekly changes in lnRMSSD varied among subjects and related strongly with individual changes in TL. Training intervention based on lnRMSSD and wellness responses may be useful for preventing the accumulation of fatigue in female soccer players.


Full Text on Research Gate

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