Tag Archives: research

New study: Association between Subjective Indicators of Recovery Status and Heart Rate Variability among Divison-1 Sprint-Swimmers

Posted on by
1

Our latest study investigates the relationship between subjective indicators of recovery status and HRV among NCAA Division 1 sprint-swimmers. The main findings were:

1) Perceived sleep quality showed the strongest relationship with post-waking LnRMSSD.

2) LnRMSSD demonstrated stronger associations with subjective parameters than resting heart rate.

We report both group and individual relationships. The full text is available here.

Association between Subjective Indicators of Recovery Status and Heart Rate Variability among Divison-1 Sprint-Swimmers

Abstract

Heart rate variability (HRV) is a physiological marker of training adaptation among athletes. However, HRV interpretation is challenging when assessed in isolation due to its sensitivity to various training and non-training-related factors. The purpose of this study was to determine the association between athlete-self report measures of recovery (ASRM) and HRV throughout a preparatory training period. Ultra-short natural logarithm of the root mean square of successive differences (LnRMSSD) and subjective ratings of sleep quality, fatigue, muscle soreness, stress and mood were acquired daily for 4 weeks among Division-1 sprint-swimmers (n = 17 males). ASRM were converted to z-scores and classified as average (z-score −0.5–0.5), better than average (z-score > 0.5) or worse than average (z-score < −0.5). Linear mixed models were used to evaluate differences in LnRMSSD based on ASRM classifications. LnRMSSD was higher (p < 0.05) when perceived sleep quality, fatigue, stress and mood were better than average versus worse than average. Within-subject correlations revealed that 15 of 17 subjects demonstrated at least one relationship (p < 0.05) between LnRMSSD and ASRM variables. Changes in HRV may be the result of non-training related factors and thus practitioners are encouraged to include subjective measures to facilitate targeted interventions to support training adaptations.

Figure 1 Effect Size SPORTS jpeg

Figure 1

Effect sizes ± 90% confidence interval for resting heart rate parameters relative to subjective categorization.

Effects of varying training load on HRV and running performance among an Olympic rugby sevens team

Posted on by
2

This study is the first of a few collaborations between Dan Howells and I involving HRV in elite rugby sevens players. Here we evaluated HRV and running performance responses  to peak training loads during preparation for the 2016 Olympic games. A practical summary follows the abstract below.

Effects of varying training load on heart rate variability and running performance among an Olympic rugby sevens team

JSAMS Abstract Flatt Howells HRV rugby sevens

How do elite seven’s players respond to substantial increments in training load? Based on previous studies, we’d expect the weekly LnRMSSD mean (LnRMSSDm) to decrease and the coefficient of variation (LnRMSSDcv) to increase relative to baseline. We’ve observed this in collegiate soccer players and sprint-swimmers.

In contrast to this hypothesis, the players showed no change in LnRMSSDmean throughout two weeks of intensified training relative to a baseline week of low loads. LnRMSSDcv demonstrated a small increase during the first week of increased load (expected response) but then showed a moderate decrease during the second week of increased load, which involved greater loads than the previous week (unexpected response).

No change (or an increase) in LnRMSSDm and a reduction in LnRMSSDcv is typically observed when training loads are reduced. Less training stress results in less fluctuations in LnRMSSD. However, these players demonstrated less fluctuation in LnRMSSD despite significant increments in training load.

The discrepancy here appears to be related to how players are tolerating and adapting to the training load. We often assume that increased loads will result in fatigue accumulation and temporary negative responses. However, these elite players demonstrated no reductions in subjective indicators of recovery status during the weeks of increased load. Additionally, there was no significant decrement in running performance (maximum aerobic speed) mid-way through the intensified microcycles.

Thus, the preservation of autonomic activity (no change in LnRMSSDm) and less fluctuations (reduced LnRMSSDcv) seem to reflect a postive coping response to the training. In fact, individuals who demonstrated the lowest LnRMSSDcv during week 1 of increased load showed the most favorable changes in running performance (r = -0.74).

This is yet another study that demonstrates that reduced fluctuations in LnRMSSD (i.e., decrease in LnRMSSDcv) is associated with positive training responses in athletes.

The Practical Implications of the study were:

•When evaluated as a group, LnRMSSDcv may be a more sensitive training response marker than LnRMSSDm during training load variations among elite players.

•LnRMSSDcv did not display a linear dose–response relationship with training load. Rather, LnRMSSDcv seems to reflect an adaptive physiological response to the imposed training stimulus which may be useful for identifying individuals responding undesirably to training.

•Elite rugby players presenting large day-to-day fluctuations in LnRMSSD in response to training load variation should be monitored closely for performance decrements, particularly when nearing important competitions.

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

Posted on by
Reply

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

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

Posted on by
Reply

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.

Revisiting 60-s HRV recordings vs. Criterion in athletes

Posted on by
1

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.

 

 

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

Posted on by
Reply

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_Division1_College_Football_Players)

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.

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

Posted on by
Reply

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

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

Posted on by
1

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. Full-text link:

HRV AND TRAINING LOAD AMONG NCAA DIVISION 1 FOOTBALL PLAYERS THROUGHOUT SPRING CAMP
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

Ultra-short HRV in youth female basketball players

Posted on by
Reply

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

 

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

Posted on by
2

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.

Full text link:

HRV and psychometric responses to overload and taper in collegiate sprint-swimmers 

OBJECTIVES:

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.

DESIGN:

Observational.

METHODS:

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.

RESULTS:

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).

CONCLUSIONS:

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.

swim-trend-hrvtraining-blog

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.