HRV-guided vs. pre-planned training at altitude in an elite wheelchair marathoner

This new paper is in collaboration with Santi Sanz-Quinto and colleagues from his dissertation work. The case study compares HRV-guided vs. pre-planned training at altitude in an elite wheelchair marathoner with CMT.

Influence of Training Models at 3,900-m Altitude on the Physiological Response and Performance of a Professional Wheelchair Athlete: A Case Study.

Abstract

This case study compared the effects of two training camps using flexible planning (FP) vs. inflexible planning (IP) at 3,860-m altitude on physiological and performance responses of an elite marathon wheelchair athlete with Charcot-Marie-Tooth disease (CMT). During IP, the athlete completed preplanned training sessions. During FP, training was adjusted based on vagally mediated heart rate variability (HRV) with specific sessions being performed when a reference HRV value was attained. The camp phases were baseline in normoxia (BN), baseline in hypoxia (BH), specific training weeks 1-4 (W1, W2, W3, W4), and Post-camp (Post). Outcome measures included the root mean square of successive R-R interval differences (rMSSD), resting heart rate (HRrest), oxygen saturation (SO2), diastolic blood pressure and systolic blood pressure, power output and a 3,000-m test. A greater impairment of normalized rMSSD (BN) was shown in IP during BH (57.30 ± 2.38% vs. 72.94 ± 11.59%, p = 0.004), W2 (63.99 ± 10.32% vs. 81.65 ± 8.87%, p = 0.005), and W4 (46.11 ± 8.61% vs. 59.35 ± 6.81%, p = 0.008). At Post, only in FP was rMSSD restored (104.47 ± 35.80%). Relative changes were shown in power output (+3 W in IP vs. +6 W in FP) and 3,000-m test (-7s in IP vs. -16s in FP). This case study demonstrated that FP resulted in less suppression and faster restoration of rMSSD and more positive changes in performance than IP in an elite wheelchair marathoner with CMT

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Modelling the HRV Response to Training Loads in Elite Rugby Sevens Players

New paper in collaboration with my colleagues Sean Williams, Dan Howells et al. Full-text link below.

Modelling the HRV Response to Training Loads in Elite Rugby Sevens Players

Key Points

  • A systems theory approach can be used to describe the variation in chronic HRV responses to training within elite Rugby Sevens players.
  • For the majority of athletes, model parameters can be used to accurately predict future responses to training stimuli.
  • Responses that diverge from the predicted values may serve as a useful flag for the investigation of changes in lifestyle factors.
  • Internal training load measures (sRPE) markedly outperformed external load measures (HSD) in predicting future HRV responses to training stimuli.

Abstract

A systems modelling approach can be used to describe and optimise responses to training stimuli within individuals. However, the requirement for regular maximal performance testing has precluded the widespread implementation of such modelling approaches in team-sport settings. Heart rate variability (HRV) can be used to measure an athlete’s adaptation to training load, without disrupting the training process. As such, the aim of the current study was to assess whether chronic HRV responses, as a representative marker of training adaptation, could be predicted from the training loads undertaken by elite Rugby Sevens players. Eight international male players were followed prospectively throughout an eight-week pre-season period, with HRV and training loads (session-RPE [sRPE] and high-speed distance [HSD]) recorded daily. The Banister model was used to estimate vagallymediated chronic HRV responses to training loads over the first four weeks (tuning dataset); these estimates were then used to predict chronic HRV responses in the subsequent four-week period (validation dataset). Across the tuning dataset, high correlations were observed between modelled and recorded HRV for both sRPE (r = 0.66 ± 0.32) and HSD measures (r = 0.69 ± 0.12). Across the sRPE validation dataset, seven of the eight athletes met the criterion for validity (typical error <3% and Pearson r >0.30), compared to one athlete in the HSD validation dataset. The sRPE validation data produced likely lower mean bias values, and most likely higher Pearson correlations, compared to the HSD validation dataset. These data suggest that a systems theory approach can be used to accurately model chronic HRV responses to internal training loads within elite Rugby Sevens players, which may be useful for optimising the training process on an individual basis.

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Podcast Interview: HRV in football and rugby

I recently had the pleasure of discussing HRV in football and rugby on the Rugby Renegade Podcast. Soundcloud and iTunes links below.

 

iTunes link: https://itunes.apple.com/zw/podcast/rugby-renegade-podcast/id1102026866?mt=2

 

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Revisiting 60-s HRV recordings vs. Criterion in athletes

I’ve recently had the pleasure of peer-reviewing a few very well-written and carried out studies investigating duration requirements for stabilization preceding HRV recordings by different research groups. I look forward to seeing the published versions as the quality of the papers was very high.

In reviewing these papers it prompted me to reconsider what we all have been using as the criterion period. My colleagues and I have published 5 papers using a 5-min R-R sample preceded by a 5-min ‘stabilization’ period (10 min total duration) as the criterion (as has other groups), which is in line with traditional procedures. But I think we failed to address an important limitation of these procedures…

The issue is that the ‘traditional procedures’ were not devised for the purposes of establishing LnRMSSD specifically (rather, they needed to accommodate spectral analysis), nor were they devised for reflecting fatigue and adaptation to training programs. Therefore, for these specific purposes, it can be argued that the traditional procedures may not be as relevant, or at the very least, calls into question whether the 5-10 min period following the 0-5 min stabilization is in fact a criterion within this context.

Some things to consider:

  • 10 min is a long time to lay or sit still, especially for athletes who struggle to go 30-sec without checking their iPhone (I don’t think anyone disputes this). Are they more relaxed and stable in this situation or are they growing impatient and restless?
  • Are ANS responses and adaptation to training best measured in a completely relaxed state, or perhaps in response to a mild stimulus such as orthostasis (sitting or standing) (previous thoughts on this here)?
  • Should we be as skeptical with the ‘criterion’ recordings as much as as we are with 60-s recordings? How do we know if one is better than the other in the context of monitoring athletes? There’s now numerous studies by different groups showing the usefulness of 60-s measures for reflecting training responses, associating with fitness, etc.
  • Perhaps the question shouldn’t be regarding the optimal duration of the recording but rather, what is the shortest, most convenient procedure possible that still provides meaningful training status information? I don’t think an athlete or coach cares if their 60 sec HRV isn’t the same as the criterion if it’s still providing useful information.
  • I’m doubtful we would have completed any longitudinal training studies where HRV recordings were >60 sec on a near-daily basis. In my experience, >60 sec measures are not feasible with teams. Therefore, it’s ~60 s or we don’t bother.
  • Should future research instead try to determine what are the best ways to perform a ~60 sec HRV measure to limit noise from confounding factors? How can we improve the validity and reliability of 60-sec measures? How long from food/fluid ingestion should we wait? Can we obtain this with PPG rather than HR straps? What is the best position to measure in? etc.

To be clear, I still think that research evaluating stabilization requirements and comparing to the ‘criterion’ is absolutely meaningful and an important starting point. This was not intended to be critical, but rather to open discussion on future research directions.

 

 

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HRV responses to in-season training among D-1 college football players

During spring training camp, we found that Linemen demonstrate the greatest reductions in LnRMSSD at ~20 h post-training, followed by Mid-Skill and Skill, possibly reflecting inadequate cardiovascular recovery between consecutive-day sessions for the larger players, despite lower PlayerLoad values. (Full-text available here)

Our first follow-up study during the early  part of the competitive season found the same position-based trend, where Linemen demonstrated the greatest reductions in LnRMSSD at ~20 h post-training, followed by Mid-Skill and Skill. However, the magnitude of the reductions in LnRMSSD during the in-season were smaller relative to spring camp. We speculate that both reduced PlayerLoad values (15-22% lower than spring camp) and adaptation to intense preseason training in the heat and humidity during the preceding weeks account for the smaller LnRMSSD reductions observed during the early part of the competitive season. (Full-text available here)

Cardiac-Autonomic Responses to In-Season Training Among Division-1 College Football Players.

Despite having to endure a rigorous in-season training schedule, research evaluating daily physiological recovery status markers among American football players is limited. The purpose of this study was to determine if recovery of cardiac-autonomic activity to resting values occurs between consecutive-day, in-season training sessions among college football players. Subjects (n = 29) were divided into groups based on position: receivers and defensive backs (SKILL, n = 10); running backs, linebackers and tight-ends (MID-SKILL, n = 11) and linemen (LINEMEN, n = 8). Resting heart rate (RHR) and the natural logarithm of the root-mean square of successive differences multiplied by twenty (LnRMSSD) were acquired at rest in the seated position prior to Tuesday and Wednesday training sessions and repeated over three weeks during the first month of the competitive season. A position × time interaction was observed for LnRMSSD (p = 0.04), but not for RHR (p = 0.33). No differences in LnRMSSD between days was observed for SKILL (Tuesday = 82.8 ± 9.3, Wednesday = 81.9 ± 8.7, p > 0.05). Small reductions in LnRMSSD were observed for MID-SKILL (Tuesday = 79.2 ± 9.4, Wednesday = 76.2 ± 9.5, p < 0.05) and LINEMEN (Tuesday = 79.4 ± 10.5, Wednesday = 74.5 ± 11.5, p < 0.05). The individually averaged changes in LnRMSSD from Tuesday to Wednesday were related to PlayerLoad (r = 0.46, p = 0.02) and body mass (r = -0.39, p = 0.04). Cardiac-parasympathetic activity did not return to resting values for LINEMEN or MID-SKILL prior to the next training session. Larger reductions in LnRMSSD tended to occur in players with greater body mass despite having performed lower workloads, though some individual variability was observed. These findings may have implications for how coaches and support staff address training and recovery interventions for players demonstrating inadequate cardiovascular recovery between sessions.

Figure 1

 

Our next paper, currently in production, will feature HRV responses among positions throughout the entire preparatory and competitive season.

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Ultra-short SDNN:RMSSD ratio strongly relates with LF:HF ratio in athletes

Our latest methodological study suggests that the SDNN:RMSSD ratio derived from a 60-sec recording may be a more convenient alternative to the traditional LF:HF ratio in athletes.

Ultra-shortened time-domain HRV parameters at rest and following exercise in athletes: an alternative to frequency computation of sympathovagal balance

Purpose: The primary purpose of this study was to determine the accuracy of the standard deviation of normal-to-normal intervals (SDNN) to root mean square of successive normal-to-normal interval differences (RMSSD) ratio from 1-min recordings (SDNN:RMSSD1−min) compared to criterion recordings, as well as its relationship to low-frequency-to-high-frequency ratio (LF:HF) at rest and following maximal exercise in a group of collegiate athletes.

Method: Twenty athletes participated in the study. Heart rate variability (HRV) data were measured for 5 min before and at 5–10 and 25–30 min following a maximal exercise test. From each 5-min segment, the frequency-domain measures of HF, LF, and LF:HF ratio were analyzed. Time-domain measures of SDNN, RMSSD, and SDNN:RMSSD ratio were also analyzed from each 5-min segment, as well as from randomly selected 1-min recordings.

Result: The 1-min values of SDNN, RMSSD, and SDNN:RMSSD provided no significant differences and nearly perfect intra-class correlations (ICCs ranged from 0.97 to 1.00, p < 0.001 for all) to the criterion measures from 5-min recordings. In addition, SDNN, RMSSD, and SDNN:RMSSD from the 1-min segments provided very large to nearly perfect correlations (r values ranged from 0.71 to 0.97, p < 0.001 for all) to LF, HF, and LF:HF, respectively, at each time point.

Conclusion: The findings of the study suggest that ultra-shortened time-domain markers may be useful surrogates of the frequency-domain parameters for tracking changes in sympathovagal activity in athletes.

Full-text available on Research Gate: Link

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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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