If success leaves clues, then there was something to learn from what Dan Howells & staff did to prepare GB 7s for the 2016 Olympics where they advanced to the gold medal final with an undefeated record.
After sorting through the data (HRV, wellness, training load) and having several video and email conversations with Dan, we decided to share the story of their Olympic expedition.
Prior to analyzing the data or obtaining specific details from Dan, I anticipated substantial decrements in status markers in response to a full day in transit (travel fatigue/jet lag, etc.), pre-tournament (arousal/anxiety), & throughout the tournament (match fatigue, sleep loss).
However, data showed minimal effects of travel (decrements mostly in non-starters), no evidence of pre-competitive anxiety (values improved pre-match), & intra-tournament decrements (small in magnitude) comparable to a previous domestic tournament.
Essentially, the data suggest that the team travelled across multiple time zones, adjusted to a foreign environment, and competed successfully on the worlds biggest stage with hardly any indication of stress or fatigue. Incredible!
Although we can’t say for sure that the strategies employed by staff can explain the findings (no control group, unfortunately), we felt that the details were worth sharing.
The paper discusses various proactive and reactive interventions that were used to support training adaptation, manage travel and competition related stress/fatigue, and aid recovery in players.
I’m very grateful to Dan and staff for the collaboration and for being open with these details. There is tremendous vulnerability in giving everyone access to how you do things. Thank you, Dan. You shared tremendous insights that many coaches and players can benefit from.
As part of my PhD work at Alabama, we tracked HRV in football players from day 1 of preseason training through to the national championship. A practical summary of some key findings follow the full-text link below.
Fluctuations in HRV are expected throughout a season. However, chronically suppressed values are cause for concern. Sustained parasympathetic hypoactivity is associated with various pathological conditions and is a hallmark of stress and impaired recovery in athletes.
We learned from spring camp that day-to-day HRV recovery was delayed in linemen vs. the smaller and more aerobically fit skill players. Thus, we anticipated that linemen would be more susceptible to attenuated HRV throughout the season.
HRV started to decline by week 6 of the competitive period for linemen. A couple notable events occurred here: 1) the first of 5 consecutive SEC match-ups vs Top 25 nationally-ranked opponents and 2) the week of mid-term exams for many players.
Although significant group-level reductions for linemen weren’t observed until later, key players showed descending HRV by mid-season, in the absence of changes in PlayerLoad. Suppressed HRV preceded illness and injury in 2 starters. Temporary rest restored HRV.
Group-level reductions occurred during an intensive camp-style preparation period for the college football playoffs following the SEC championship. Most players took a hit to their HRV, but linemen were hit the hardest. Note magnitudes of the effect sizes in the table below.
HRV remain suppressed for linemen through prep weeks for the national semi-final and the national championship. Smaller decrements (non-significant) were observed for skill players. In addition to accumulating physical stress, psycho-emotional factors (pre-competitive anxiety, pressure to perform, media attention, etc) likely contributed.
Although we emphasize the toll of a season on linemen, some skill players also showed suppressed values. The table below shows the rate of change in HRV for all players. 25% of skill and 63% of linemen showed sig. descending HRV patterns throughout the season.
Linemen experience hypertension, arterial stiffening, and pathologic LV hypertrophy following 1 or more seasons. These maladaptations are possibly preceded by ANS imbalance. We hypothesize that larger players showing the worst HRV profiles suffer the greatest decrement in cardiovascular health markers.
If so, intervening when a decreasing HRV pattern is observed may not only be relevant to performance (limiting fatigue, injury-, and infection-risk), it may also help mitigate the cardiovascular toll of playing football at such a high level. Seeking funding to explore this in the future.
The findings highlight potential deficiencies in or greater taxation to the coping capacity of linemen vs. smaller players. Factors hypothesized to contribute to more prevalent ANS imbalance in linemen and potential implications for health and performance are summarized below.
Linemen need careful attention and monitoring. We need strategies to prevent ANS imbalance from occurring (load management, aerobic capacity, treatment of health conditions like sleep apnea, etc) and we need restorative methods to implement if it occurs.
Tracking HRV with a mobile app was inexpensive and easy. Time-demand from players was ~3 min/week while waiting to get taped. Though sub-optimal relative to post-waking measures, this approach enabled timely detection of descending patterns, which may be useful for guiding interventions relevant to player health and wellbeing.
Though a better understanding of the health and performance ramifications of suppressed HRV in football players is needed, a descending pattern may serve as an easily identifiable red flag requiring attention from performance and medical staff.
When first getting started with tracking HRV in athletes, the inter-individual variation in trend characteristics can be confusing. Some athletes will display very high values and others will show lower values. Likewise, some will show quite stable values while others display substantial day-to-day variation. Naturally, the following question arises: why do some athletes have higher and more stable values than others?
Collegiate swim rosters typically include a mixed roster of athletes (males and females with a broad range of experience and skill). In this investigation we compared HRV trend characteristics between the national-level (including 6 Olympians) and conference-level sprint-swimmers throughout 4 weeks of standardized preparatory training. We also obtained details of individual training history.
The main findings were that national-level swimmers had higher and more stable HRV (higher mean LnRMSSD, lower LnRMSSD coefficient of variation) than their conference-level teammates. Differences in trend characteristics were attributable to a greater history of training and competing among the national-level swimmers (i.e., greater training age).
Whether these findings can be explained by greater aerobic fitness (we don’t think so), greater familiarity with training (possibly), or chronic physiological adaptations (possibly) among the higher-level swimmers is unclear.
The findings may be of some practical use for coaches when interpreted with previous work (see links below). For example, preliminary expectations with HRV monitoring should be that higher-level swimmers will display higher and more stable values throughout training and vice-versa for lower-level athletes. This may be interpreted to mean that the higher-level athletes could tolerate greater loads or that the lower-level athletes may need reduced loads. However, it is unclear if these training modifications would offer any performance/adaptation advantage. In addition, a higher-level athlete showing lower and less-stable values may be cause for concern (fatigue, stress, detraining, etc. depending on context). Whereas a lower-level athlete displaying higher and more stable values is likely adapting well to the training.
We’ve previously assessed how overload and tapering impact HRV in sprint-swimmers here.
We’ve previously assessed associations between subjective indicators of recovery and daily HRV in sprint-swimmers here.
Here’s our latest study comparing 1 min vs 5 min HRV throughout a 4-week camp in international-level girls field hockey players. Values were highly correlated, showed similar responses to load, & similar associations with fitness. Practically same insight, 80% less time. Thanks to Drs. Gonzalez-Fimbres and Hernandez-Cruz for the collaboration.
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.
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.
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.
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
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.
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 trendappeared 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.
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.
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.
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.
Effect sizes ± 90% confidence interval for resting heart rate parameters relative to subjective categorization.