Since launching HRV4Training, the easiest and most cost-effective solution to acquire high quality physiological data, in particular HR and HRV at rest, we published a fair amount of work. From the validation of the camera-based measurement, to acute day to day changes in physiology (heart rate and HRV) in response to training, to methods to estimate VO2max from workout data, methods to estimate running performance and the relation between HRV, training load and injury in Crossfit. Transparency and solid scientific grounds are what we believe in, which is why we started documenting to the public and validating our work since day zero.
Most importantly, HRV4Training gave the opportunity to universities not associated with us, to collect physiological data easily in the wild, potentially leading to additional insights. More and more universities have started publishing papers based on data collected using the app and web platform in the past few years, and we'd like to cover the last two that came to my attention recently. Both papers are authored by Sara Sherman, who was a master's student at the University of Alabama, under the supervision of Michael Esco, who needs no introductions.
Sara is currently pursuing a PhD at the University of Illinois-Chicago and will be providing her input alongside mine in this overview of her work.
The two studies authored by Sara cover two different topics, both investigated in a population of thirty-one NCAA Division I female rowers at the University of Alabama, Tuscaloosa.
Let's break down this section into measurement time and menstrual cycle, and discuss the two studies separately.
In this study, the authors looked at the relation between measurements taken at two different times. The first measurement occurred at the athlete’s home following waking (this is the setting we normally recommend in the app), the second upon arrival at the team’s boathouse immediately before practice.
The second measurement, is obviously affected by more potential confounding factors, as some time has passed in which we could be exposed to stressors that have a transient acute effect on physiology, and therefore we might lose the ability to capture baseline chronic stress, which is what we are interested in. Nonetheless, this is typically the setting in which other systems that are a little more cumbersome are used, and I can say from personal experience that elite teams often seem to struggle getting their players to remember to measure at home, hence sometimes there is no other way to do it than to measure at the facilities.
Pro tip: if you can't remember to do your measurement, a post-it on your mirror in the bathroom can go a long way (thanks Hugo for this!).
I'll let Sara tell us everything about her results, which are of great practical applicability:
As a former collegiate rower, I can understand the feeling of being “stuck in the mud”- doing everything as instructed, but still falling short of the anticipated result. This situation was often accompanied by coach in my ear, completely unsatisfied with my performance- leaving me confused, frustrated, and dreading practice the next day. My coach was phenomenal, and his training plan impressive; however, I am a 5-foot 4-inch brawny ex-ice hockey player of 17 years that did not resemble my lanky 6-foot(+) teammates. I loved embracing this aspect of the sport, but it was clear that I was adapting to the training plan in a completely different manner than the others. Upon retirement from the sport, and switching to a coaching role, I began to understand the predicament that my coach faced (and continues to face) in all those years ago. I found myself wondering how some of the athletes were able to find success and others completely flounder under the same training plan? Further, was there a way to objectively measure this training response? Most coaches at any level will be the first to admit the difficulty they experience in detecting if an athlete is aware of their full capabilities and training to maximize their performance, or just simply burnt out physically and/or mentally. This is ultimately what led to my thesis idea, and this project.Athletes have a million things on their mind when they wake up- possibly sleep deprived, sore, anxious, and thinking about all the things they have to fit into their day.
Compliance is always an issue in such real-world situations and may be one of the sources of frustration for coaches who are trying to gain a first-hand look into the autonomic response of their athletes from the given training stimuli. Often times, our first waking motion is to the bed stand to grab our mobile phone, making the HRV4Training application completely practical for this morning time crunch, for those who remember to take their two-minute HRV measure.
Extensive literature investigating autonomic function in athletes indicates that morning measures are preferred, but is equivocal as to what “morning” actually means. Most coaches prefer to have their athletes take the HRV measurements at their respective practice facilities under alike conditions, finding this results in greater compliance overall. However, it is unclear whether the perturbations which inevitably occur between the time of waking up to arrival at the practice facility are actually substantial enough to significantly alter their true HRV value, and possibly lead to false conclusions. Furthermore, there is no literature to date investigating within-and between-day reliability of short-term HRV (i.e., RMSSD mean (M) and CV) in a “real-world” situation among athletes and its association with athletic performance. Therefore, the aim of our study was compare HRV values when recorded immediately upon waking to the values recorded later in the morning, but still prior to practice. Additionally, we wanted to determine the associations of these HRV measures with common performance outcomes in competitive female rowers.
Thirty-nine (n= 39) volunteers from the University of Alabama Women’s Rowing team downloaded and became familiar with the HRV4Training photoplethysmography (PPG) smart-phone application during a three-week familiarization period over their winter-training season. Each recording was measured under the same conditions using a two-minute seated position in a quiet area inside the team’s boathouse, prior to their morning practice which took place at 06:00 AM Monday-Saturday.
During the week of data collection, the rowers were asked to complete two morning measurements using the same personal mobile device with the HRV4Training PPG application. The first HRV measurement (T1) was performed at the athlete’s home immediately after waking and elimination; wherein, they proceeded through their normal, pre-practice morning routine prior to arrival at the boathouse. The second HRV measurement (T2) was obtained within one-hour of the first measurement (T1) upon arriving at the on-campus boathouse for daily practice according to the same procedures used in the familiarization period, and earlier that morning. Two objective performance assessments were conducted on an indoor rowing ergometer on separate days within this week: timed 2000 meters and distance covered in 30 minutes. Additionally, rank was determined by the coaches based on performance for that week.
Of the 39 athletes who began the study, six were excluded due to compliance and injury. Our results yielded surprising, yet exciting findings: we did not observe any significant differences in RMSSDM and RMSSDCV between the two time points (i.e., T1 & T2), which had tight limits of agreement and strong correlations between one another. RMSSDM at both time points were moderately associated with athlete rank and 30-minute distance. A moderate correlation was also observed for RMSSDM and 2,000 meters at T1.
While significant associations were found for RMSSDM, the coefficient of variation (CV) of daily RMSSD, representing daily perturbations to cardiac-autonomic homeostasis, was not significantly correlated with any performance measures. This is not surprising given the daily fluctuations in training (i.e., type and volume) and lack of competitive races during this particular training period. Our findings suggest that HRV (i.e., RMSSD) can be measured in rowing athletes upon waking or later in the morning prior to training. However, when reviewing these data, it is important to consider the tradeoff between slightly decreased sensitivity of HRV measures completed later in the morning versus increased monitoring compliance. More real-life situation studies are warranted to investigate this unique relationship further.
The second study, investigates the relation between HRV, menstrual cycle and performance, at the cross-sectional level.
Before moving into the details of this study, I just want to spend a second to thank the authors for doing this kind of work. It is absolute nonsense how most research out there still ignores 50% of the population. Just to continue a little with the digression, I was reading this absurd article earlier this week on how menstrual cycle was discussed in the context of women astronauts, and would highly recommend reading it.
Anyhow, back to the study, I'll let once again Sara explain her findings:
Investigating autonomic function in female athletes is often considered “taboo” in the area of human performance. As such, the research surrounding the autonomic variations throughout cyclic physiological changes present uniquely to female athletes is ambiguous, leading researchers to shy away from this understudied population. It is clear that gender-specific hormones affect multiple functions within a woman’s body, but it is unclear the exact nature of these effects. This forces coaches and practitioners reading HRV and other markers of autonomic function literature to extrapolate findings from mainly male subjects onto their female athletes, only to become frustrated when the outcome varies in their population.
Our findings for this particular abstract were retrospective in nature, and were found one late night in the office by my own curiosity. I split the group of rowers that we had in our sample by merely one factor: the presence of a menstrual cycle. We did not ask anything regarding phase, or date, and we did have information on contraceptive methods, but we did not include this in this preliminary analysis. These are important of course and would most likely influence the variability, but again, we are not certain either way. However, I was thinking from a coach’s standpoint, what would be important for them to know? They are not going to ask phase or type or any specifics, they just want to know if their athlete is menstruating or is not and if that would influence their training outcome or schedule.
RMSSDCV, representing daily perturbations in cardiac-autonomic modulation, was investigated with rowing-specific performance variables throughout a week. Our findings were contrary to what we would have expected. Although no associations to performance were observed for RMSSDCV in those who were not experiencing a menstrual cycle (and the full sample, as I mentioned in the last study), large, significant bivariate correlations between RMSSDCV and performance (i.e., 30-minute ergometer test) were found within the group of women who were menstruating. Further, these larger RMSSDCV values (i.e., greater daily fluctuations) for those who were not menstruating lends questions for further research. While this analysis was performed on a smaller sample of women (n= 12 who were menstruating; n= 17 who were not menstruating), it is clear there is much more work to be done. Menstruation is not going to go away in female sports, so work in this area should not either.
Thanks Sara for your overview. As this is data I've also been looking at using the web platform, I'll give my two cents. Considering that this was a preliminary analysis at the cross-sectional level (meaning that we look for macro differences at the group level, not at changes within individuals), in this second study I would have indeed not expected strong relationships to show up.
This is of course different when we analyze several months of data within one individual, which is what you can do easily by measuring every morning using HRV4Training. This way you can potentially see more subtle patterns, for example a more clear relationships between resting physiology and menstruation, as relative changes with respect to your own historical data are always the most meaningful ones to look at.
Some anecdotal evidence here from Alessandra who consented to share her data for the sake of making this point:
You can see the cyclical nature of the HRV baseline and how often it dips in correspondence of the menstrual cycle annotations, obviously an important point for coaches and athletes to account for. Note also that dips are not always low points at the absolute level, but still sink the current baseline, which might be higher than a month earlier, hence the importance to measure every day and always analyze data with respect to your current baseline and normal values.
I'f you'd like to analyze your own data in the same way, with respect not only to menstrual cycle but also to all other available annotations, you can do so using HRV4Training Pro, our web platform, that you can find here.
I hope you've found the write-up useful, definitely some good practical insights from this research, and looking forward to seeing more studies published.
Register to the mailing list
and try the HRV4Training app!
1. Intro to HRV
2. How to use HRV, the basics
3. HRV guided training
4. The big picture
5. HRV and training load
6. HRV, strength & power
7. Overview in HRV4Training Pro
8. HRV in team sports
1. Context & Time of the Day
3. Paced breathing
4. Orthostatic Test
5. Slides HRV overview
6. rMSSD vs SDNN
7. Normal values and historical data
1a. Acute Changes in HRV
1b. Acute Changes in HRV (population level)
1c. Acute Changes in HRV & measurement consistency
1d. Acute Changes in HRV in endurance and power sports
2a. Interpreting HRV Trends
2b. HRV Baseline Trends & CV
3. Tags & Correlations
4. Ectopic beats & motion artifacts
5. HRV4Training Insights
6. HRV4Training & Sports Science
7. HRV & fitness / training load
8. HRV & performance
9. VO2max models
10. Repeated HRV measurements
11. VO2max and performance
12. HR, HRV and performance
13. Training intensity & performance
14. Publication: VO2max & running performance
15. Estimating running performance
16. Coefficient of Variation
17. More on CV and the big picture
18. Case study marathon training
19. Case study injury and lifestyle stress
20. HRV and menstrual cycle
21. Cardiac decoupling
22. FTP, lactate threshold, half and full marathon time estimates
Camera & Sensors
1. ECG vs Polar & Mio Alpha
2a. Camera vs Polar
2b. Camera vs Polar iOS10
2c. iPhone 7+ vs Polar
2d. Comparison of PPG sensors
3. Camera measurement guidelines
4. Validation paper
5. Android camera vs Chest strap
6. Zoom HRV vs Polar
7. Apple Watch and HRV
8. Scosche Rhythm24
9. Apple Watch
11. Samsung Galaxy
1. Features and Recovery Points
2. Daily advice
3. HRV4Training insights
4. Sleep tracking
5. Training load analysis
6a. Integration with Strava
6b. Integration with TrainingPeaks
6c. Integration with SportTracks
6d. Integration with Genetrainer
6e. Integration with Apple Health
6f. Integration with Todays Plan
7. HRV4T Coach advanced view
8. Acute HRV changes by sport
9. Remote tags in HRV4T Coach
10. VO2max Estimation
11. Acute stressors analysis
12. Training Polarization
13. Custom desirable range / SWC
14. Lactate Threshold Estimation
15. Functional Threshold Power(FTP) Estimation for cyclists
16. Aerobic Endurance analysis
17. Intervals Analysis
18. Training Planning
19. Integration with Oura
20. Aerobic efficiency and cardiac decoupling
1. HRV normal values
2. HRV by sport
3. HRV normalization by HR
4. HRV 101