Heart Rate Variability in College Football Players throughout Preseason Camp in the Heat

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Here’s a quick look at our latest study examining cardiac-autonomic responses to preseason camp in the heat among college football players. The free full text can be accessed here: Heart rate variability in college football players throughout preseason camp in the heat IJSM. 

Intensive training periods tend to increase RHR and decrease HRV, reflecting stress and fatigue. However, adaptations to heat exposure (e.g., plasma volume expansion) tend to have the opposite effects. So we wanted to see what happens when players were exposed to both intense training and intense heat stress during preseason camp.

Despite increases in perceived fatigue throughout the 2-week period, RHR and HRV reflected responses consistent with heat acclimation.

HRV initially decreased in linemen, then peaked after a day of rest. Non-linemen faired a little better with smaller decrements in perceived fatigue and more frequent day-to-day improvements in RHR and HRV.

These results indicate that heart rate parameters and perceived fatigue are independent markers of training status, and that desirable cardiovascular adaptations can occur in the presence of soreness and fatigue.

This is especially important for tech companies who try to infer recovery status from HRV alone. As HRV improved throughout camp, an app’s algorithm would report to coaches that players are well-recovered. Given that no player feels well-recovered during preseason camp in the heat, the technology suddenly loses credibility for being wrong and will likely be dismissed.

This is unfortunate because the heart rate parameters are likely reflecting important adaptations that may indicate better tolerance to training in the heat, a reduced exercising heart rate, and improved fitness. Thus, I encourage users to ignore “recovery” scores and interpret the data in appropriate context.

ABSTRACT 

We aimed to characterize cardiac-autonomic responses to a 13-day preseason camp in the heat among an American college football team. Players were categorized as linemen (n=10) and non-linemen (n=18). RHR, natural logarithm of the root-mean square of successive differences multiplied by twenty (LnRMSSD), and subjective wellbeing (LnWellness) were acquired daily. Effect sizes±90% confidence interval showed that for linemen, LnRMSSD decreased (moderate) on day 2 (71.2±10.4) and increased (moderate) on day 12 (87.1±11.2) relative to day 1 (77.9±11.2) while RHR decreased (small–moderate) on days 6, 7, and 12 (67.7±9.3–70.4±5.5 b∙min-1) relative to day 1 (77.1±10.1 b∙min-1). For non-linemen, LnRMSSD increased (small–large) on days 3–5, 7, 12, and 13 (83.4±6.8–87.6±8.5) relative to day 1 (80.0±6.5) while RHR decreased (small–large) on days 3–9, 12, and 13 (62.1±5.2–67.9±8.1 b∙min-1) relative to day 1 (70.8±6.2 b∙min-1). Decrements in LnWellness were observed on days 4–10 and 13 for linemen (moderate) and on days 6–9, 12, and 13 for non-linemen (small–moderate). Despite reductions in LnWellness, cardiac-autonomic parameters demonstrated responses consistent with heat-acclimation, which possibly attenuated fatigue-related decrements.

Heart Rate Variability, Neuromuscular and Perceptual Recovery Following Resistance Training

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Our latest study…

Heart Rate Variability, Neuromuscular and Perceptual Recovery Following Resistance Training

We quantified associations between changes in heart rate variability (HRV), neuromuscular and perceptual recovery following intense resistance training (RT). Adult males (n = 10) with >1 year RT experience performed six sets to failure with 90% of 10 repetition maximum in the squat, bench press, and pull-down. Changes (∆) from pre- to immediately (IP), 24 and 48 h post-RT were calculated for neuromuscular performance markers (counter-movement jump peak power and mean concentric bench press and squat velocity with load corresponding to 1.0 m∙s−1) and perceived recovery and soreness scales. Post-waking natural logarithm of the root-mean square of successive differences (LnRMSSD) in supine and standing positions were recorded pre-RT (5 day baseline), IP and two mornings post-RT. All parameters worsened at IP (p < 0.05). LnRMSSD measures were not different from baseline by 24 h. Neuromuscular markers were not different from pre-RT by 48 h. Perceptual measures remained suppressed at 48 h. No significant associations among ∆ variables were observed (p = 0.052–0.978). These data show varying timeframes of recovery for HRV, neuromuscular and perceptual markers at the group and individual level. Thus, post-RT recovery testing should be specific and the status of one metric should not be used to infer that of another.

Full-text is available here: https://www.mdpi.com/2075-4663/7/10/225

HRV Monitoring During Strength and High-Intensity Interval Training Overload Microcycles

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Thanks to Christoph Schneider for inviting my collaboration on this new study from his PhD work. The full-text can be viewed here.

Heart Rate Variability Monitoring During Strength and High-Intensity Interval Training Overload Microcycles

Abstract

Objective: In two independent study arms, we determine the effects of strength training (ST) and high-intensity interval training (HIIT) overload on cardiac autonomic modulation by measuring heart rate (HR) and vagal heart rate variability (HRV).

Methods: In the study, 37 well-trained athletes (ST: 7 female, 12 male; HIIT: 9 female, 9 male) were subjected to orthostatic tests (HR and HRV recordings) each day during a 4-day baseline period, a 6-day overload microcycle, and a 4-day recovery period. Discipline-specific performance was assessed before and 1 and 4 days after training.

Results: Following ST overload, supine HR, and vagal HRV (Ln RMSSD) were clearly increased and decreased (small effects), respectively, and the standing recordings remained unchanged. In contrast, HIIT overload resulted in decreased HR and increased Ln RMSSD in the standing position (small effects), whereas supine recordings remained unaltered. During the recovery period, these responses were reversed (ST: small effects, HIIT: trivial to small effects). The correlations between changes in HR, vagal HRV measures, and performance were weak or inconsistent. At the group and individual levels, moderate to strong negative correlations were found between HR and Ln RMSSD when analyzing changes between testing days (ST: supine and standing position, HIIT: standing position) and individual time series, respectively. Use of rolling 2–4-day averages enabled more precise estimation of mean changes with smaller confidence intervals compared to single-day values of HR or Ln RMSSD. However, the use of averaged values displayed unclear effects for evaluating associations between HR, vagal HRV measures, and performance changes, and have the potential to be detrimental for classification of individual short-term responses.

Conclusion: Measures of HR and Ln RMSSD during an orthostatic test could reveal different autonomic responses following ST or HIIT which may not be discovered by supine or standing measures alone. However, these autonomic changes were not consistently related to short-term changes in performance and the use of rolling averages may alter these relationships differently on group and individual level.

 

Daily Heart Rate Variability before and after Concussion in an American College Football Player

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Our latest paper is a case report demonstrating substantial changes in HRV following concussion in a college football player. The full text can be accessed here. The main findings were:

1: The post-concussion HRV trend appeared similar to trends commonly associated with training fatigue. Therefore, staff should investigate the possibility of an unreported concussion when similar trends are observed in athletes. 

2. Alterations in HRV persisted well beyond return to play clearance. This may have implications for clinical treatment and return to play considerations.

3. Since HRV demonstrated greater daily fluctuation post-concussion, isolated (i.e., single time-point) HRV recordings are likely inadequate for assessing persisting effects on the autonomic nervous system. Thus, near-daily HRV assessment may be required.

4. The convenient methodology used to monitor HRV (60-second finger-pulse plethysmography on a mobile application) can feasibly be implemented with an entire roster of athletes.

Abstract

This case report demonstrates the effects of sport-related concussion (SRC) on heart rate variability (HRV) in an American college football player. Daily measures of resting, ultra-short natural logarithm of the root mean square of successive differences (LnRMSSD), subjective wellbeing, and Player Load were obtained each training day throughout a 4-week spring camp and 4 weeks of preseason training. SRC occurred within the first 2 weeks of the preseason. During spring camp and preseason pre-SRC, the athlete demonstrated minimal day-to-day fluctuations in LnRMSSD, which increased post-SRC (LnRMSSD coefficient of variation pre-SRC ≤ 3.1%, post-SRC = 5.8%). Moderate decrements in daily-averaged LnRMSSD were observed post-SRC relative to pre-SRC (Effect Size ± 90% Confidence Interval = −1.12 ± 0.80), and the 7-day rolling average fell below the smallest worthwhile change for the remainder of the preseason. LnRMSSD responses to SRC appeared similar to trends associated with stress and training fatigue. Therefore, performance and sports medicine staff should maintain regular communication regarding player injury and fatigue status so that HRV can be interpreted in the appropriate context. Detection and monitoring of autonomic dysregulation post-SRC may require near-daily assessment, as LnRMSSD showed greater daily fluctuations rather than chronic suppression following the head injury.

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New Podcast Episode: HRV monitoring in team sports

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Thanks to Dr. Marc Bubbs for having me on the show to discuss HRV in team sports. Details and episode links below.

In Season 3, Episode 7 Dr. Bubbs interviews Dr. Andrew Flatt PhD to discuss applications of heart rate variability (HRV) monitoring in team sport athletes. Dr. Flatt reviews the basic physiology of HRV, how pre-season testing can inform your training and recovery plans, how in-season monitoring influences decision making, and new findings on HRV results in larger athletes, such as linemen in American football. Dr. Flatt also discusses how the “other 22-hours” in the day – sleep, long-haul travel, mental and emotional stress, etc. – impact the nervous system and HRV measures, and finally provides some practical tips on how to collect HRV measurements, validated apps, and red flags to avoid when interpreting results.

Summary of This Episode

7:00 – What Is HRV?

7:45 – What impacts HRV scores in athletes

11:00 – Pre-season HRV trends in team sport athletes

13:20 – Olympic vs. national level swimmer HRV values

25:00 – In-season monitoring in collegiate football players

33:00 – Strategies for improving recovery as competitive season progresses

40:00 – Monitoring – a tool to start a conversation

46:30 – Different “apps” to implement with clients

57:00 – Evolution of research in HRV and team sports

 

https://www.stitcher.com/podcast/dr-bubbs-performance-podcast/e/58826132?autoplay=true

Monitoring Ultra-Short HRV and HR-Running Speed Index in an Elite Soccer Player: A Case Study

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Here’s a recent case study featuring HRV changes in a pro soccer player throughout preseason training. Thanks to my colleague Alirezza Rabbani for inviting my collaboration on this one.

The main findings were that post-waking HRV and the HR-Running Speed Index (an indicator of aerobic fitness) during the warm-up jog progressively improved and were highly correlated. Additionally, we found that 1-min HRV measures were no different than 5-min HRV measures. The paper is free open-access at the link below.

Monitoring Ultra-Short Heart Rate Variability and Heart Rate-Running Speed Index in an Elite Soccer Player: A Case Study

Case study HRV RSI

Monitoring Ultra-Short Heart Rate Variability and Heart Rate-Running Speed Index in an Elite Soccer Player: A Case Study

New Study: Effects of consecutive domestic and international tournaments on HRV in an elite rugby sevens team

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A quick summary of our latest paper follows the full-text link and abstract below…

Effects of consecutive domestic and international tournaments on HRV in an elite rugby sevens team

JSAMS title HRV rugby

Rugby seven players often compete in 2-day tournaments over consecutive weekends, leaving only 5 days to recover and prepare for the next tournament. Many tournaments are held internationally, adding substantial travel demands to the already-taxing nature of the sport. We hypothesized that the added stress of travel for an international tournament may result in greater decrements in HRV relative to a local tournament.

The main findings were that despite no significant difference in match-physical demands (high speed running, total distance and sRPE), significant reductions in LnRMSSD were observed only in response to the international tournament (see figure below). Despite non-significant p values at other time-points, individual analysis showed that ~80% of the players showed meaningful reductions in LnRMSSD relative to baseline following the local tournament and on the day of travel (see bottom of figure). Thus, it seems that HRV was still affected by these events, though to a lesser magnitude than the international tournament.

The decrements in LnRMSSD at the international tournament were preceded by significant decrements in perceived sleep quality and energy levels reported on or after a chaotic travel day. The travel day involved an earlier than usual wake-time and a missed flight connection. This forced the team to complete the travel by bus, delaying their hotel arrival to 3 am.

Given that HRV is sensitive to a variety of physiological, psychological and environmental factors, we cannot say for certain that travel stress accounted for the differences in HRV responses, though it seems likely. We state: “LnRMSSD responses to the international tournament were therefore likely influenced by a combination of variables associated with, but not limited to altered sleep, a disrupted travel itinerary and the process of relocation which interacted with the physical and psychological stress associated with tournament-play.”

Differences in collision/body contact loads between tournaments may have contributed to differences in LnRMSSD responses, but were not assessed. Additionally, this was the first pair of consecutive tournaments for this team in at least six weeks, which may have served as a relatively novel stimulus. Therefore, we’re not sure if similar LnRMSSD responses would be observed when the team was re-familiarized with consecutive tournaments or when travel isn’t so hectic.

JSAMS Fig 1 HRV rugby 7 consecutive tournaments Flatt et al.

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

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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

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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

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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