Blog post by Massimiliano 'Massi' Milani & intro by Marco Altini
Me and Alessandra had the pleasure to meet Massi in person when we were in Europe last October, the day after his 10 km PR (33:16). Massi is writing almost on a weekly basis for an italian Blog, TheRunningPitt, curated by GIanmarco Pitteri, another sub 2:30 marathon runner. I highly recommend to all Italian runners this blog, as it's full of great advice on all things running from nutrition to technology. Through TheRunningPitt, I learnt about Massi's statistical mindset and R skills, and I started bothering him to write something for us, so without further ado, here it is. Thank you again Massi for your story (and plots!).
Planning is often the difference between success and failure. As a business manager, I am always seeking new and innovative ways to raise the bar and metrics for success. Over the last seven years I have found distance running to be an invaluable tool for creative planning and individual inspiration. I started running to relieve stress, increase energy, and control my weight, but it quickly turned into an appreciation for not just the physical, but also the mental and professional benefits that can come from a daily 50-minute solo run in the dark. However, having entered distance running as a novice with no performance expectations, I quickly discovered that my competitive nature easily translated to my amateur running. Although I made significant improvements year over year with increased intensity and improving times, eventually the reality of my late start in running and creeping middle age had caused a plateau in my performance. To break through this plateau, I looked for, and found, the next technological edge.
I started using the HRV4 training app at the end of 2015. In my six years of running I had had no significant injuries and constant time improvements from 3h30 marathon in 2010 to 2h37 in 2015. I was worried that I had optimized my training and performance and as I approached 44 years of age, there was no further room for improvement. But by finding the right technology I was able to get that additional edge.
My initial foray into the training application found many competitors along with the HRV4Trainng app in the “optimization recovery arena,” including Elite HRV and Ithlete. At first, I saw the value proposition of the app as the ability to use a camera instead of the Polar H7 chest strap. However, this turned out to be no more than a secondary feature, with the primary benefit being a fantastic dashboard, incredible statistics, and frequent, almost weekly, updates. I signed up to be a beta tester and have been using the heart rate strap instead of the camera because I am more familiar with it and it allows me to be more relaxed during measurements. In the following charts, I am using Rmssd as the metric for HRV for consistency purposes.
My major constraint for training was (and is) traveling, as I am in planes 2-3 times a week for business. This presented a significant obstacle in optimizing performance and towards next step competition between a three day a week training schedule and daily/multi-daily conditioning. It is not just the time restraints of travel, but more importantly the energy involved with plane travel, wait times, and poor sleeping conditions in hotels that will affect even the strongest athletes, let alone an amateur runner such as myself. After tracking my experiences for one year and accumulating more than 350 data points, my HRV was lowered by an average of 11 points, or 3%, on travel days.
Even at my level, despite limited time, sticking to a training schedule was vitally important with all daily constraints and training loads evaluated with careful precision. I followed the 80/20 method for the easy days and Steve Magness for the quality runs, but I adapted my training based on the responsive advice from the app. Not surprisingly, the major bottleneck due to my lifestyle was traveling. Three weeks before my last marathon, I traveled 11 hours in economy to South Africa, and I had to limit my training duration and intensity to avoid overtraining. When traveling, I did one or two quality sessions per week; when at home, I tried to do two or three. I also added double-long runs during the weekend to compensate for weeks when I was not able to properly train during the week. In those instances, on the weekends, I did back to back running, mostly 16 miles on Saturday and 20-22 miles on Sunday with half mile alterations. I also cut back on the weekends if the app told me to reduce intensity.
A feature I found particularly useful was the ability to import data from Strava to assess my training load and export the HRV measurement to TrainingPeaks. Early in the beta the app’s developer, Marco, implemented the "training load" feature, which was another key piece of the puzzle and provided two very useful features. First, it provides you with a visual representation of your load without major spreadsheet computing. Previously, I had to download data from Strava, run calculations offline with R, and then determine if my load was sufficient for progress or if I was risking overtraining or injury. Time is a major constraint for all of us and having a tool that does the calculations for you is invaluable. Additionally, the app correlates the training load with multiple variables and you can get clear statistics on your potential overtraining.
Running has become a passion of mine, and I take it seriously, but I am not a professional and I do not want it to exclude other pleasures in life. Because of my travels I am able to sample wines from many regions, and I do not want to sacrifice this joy for minor incremental increases in performance. However, what I have discovered is quite the opposite. That is, a single glass of wine the night before (my preference is Brunello di Montalcino) has no impact on my morning HRV, and helps to settle my mind for the evening in preparation for training the next day. Of course, both common sense and experience proves that too much drinking will have a significant impact on HRV, and I tried to limit those times to when it was "necessary" for social reasons to go beyond a glass or two to weekdays, avoiding any excess drinking near the weekends to ensure my ability to complete the intense long runs on those days.
Another important element of my training was rest and sleep. It is no surprise that getting the right amount of sleep makes a significant difference between recovery and overtraining. But as with most things, having a vague idea is not nearly as effective as being able to quantify the numbers and results, which is where the app comes in. On one hand, my life did not allow for a fixed sleeping pattern. On the other hand, I tried to adapt when I went to bed, depending on the following morning’s training and work responsibilities, which led to scattered hours and sleep. With the app's help, I realized that my HRV, holding constant all other parameters, increased by ten points with an additional 60 minutes of sleep. I wish I could sleep more! During Q2 of 2016, I used a Bayesian algorithm that, based on iPhone movement, automatically proposed sleeping times. It was a good start, but suboptimal, since activity trackers or GPS watches are much better at analyzing such information. The new version of the application solved this problem by allowing you to update your sleeping hours from Apple Health. Therefore, if you have a Garmin, Fit Bit, or Apple Watch, you will be able to update automatically in the morning. This is a wonderful Christmas gift from Marco: the less we need to input in the morning, the better we will be.
Another interesting aspect of HRV4Training is the ability to download user data. In the early days, the dashboard was limited, while user content was still extremely rich. In principle, 99% of the analysis is now possible in the dashboard, but the system still allows the user to download data, both via email in CSV format or by upload to Dropbox, for additional statistical processing. The process works well, and I recommend using it if you are interested in analyzing additional patterns. Additional features have been implemented, like Vo2max calculations, with quite reasonable accuracy.
I consider eating the most integral element of my overall training plan. When I turned 41, I was no longer able to improve my resistance and I was overly tired after each intense training session. After a poor performance in my latest Boston Marathon, I decided to adopt a low carb/high fat dietary regime. This required replacing pasta with salmon, bread with parmesan, cakes with mascarpone cheese, and including lots of vegetables and tomatoes in my meals. Consequently, in a few weeks I had lost weight, improved my energy level, increased my running economy, and reduced my recovery time.
To push my boundaries, three years later I did something unusual: after my best marathon time (2h29), I ran a second marathon two weeks later in 2h35, running with only water and no carbs whatsoever. I never would have been able to do this under HCLF training. I am still learning the best way to eat and I know that HRV and Keto adaptations are connected, so although we still need time to understand the precise relationship, I am confident that science and technology will provide the answers we need in due time.
Every morning I would wake up, take my heart rate strap, and measure my HRV. Initially the system was very rigid and black and white, suggesting training was only dependent on a mechanical number, most likely current HRV measurement, as well its baseline evolution. I initially liked this analytical approach, but I also adjusted my training needs according to my feeling on any given day. Because of that, I developed a small table which included some adjusted advice. Now, with the help of top research scientists in the field, HRV4Training has become more flexible and the advice is more realistic. Going forward, I am expecting the ability to get a training plan based on current HRV, or at least to pick and choose among several predefined options. Am I dreaming?
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The Ultimate Guide to HRV
1: Measurement setup
2: Interpreting your data
3: Case studies and practical examples
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. 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
23. Training Monotony
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. Scosche Rhythm24
7. Apple Watch
9. 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. Acute HRV changes by sport
8. Remote tags in HRV4T Coach
9. VO2max Estimation
10. Acute stressors analysis
11. Training Polarization
12. Lactate Threshold Estimation
13. Functional Threshold Power(FTP) Estimation for cyclists
14. Aerobic Endurance analysis
15. Intervals Analysis
16. Training Planning
17. Integration with Oura
18. Aerobic efficiency and cardiac decoupling
1. HRV normal values
2. HRV normalization by HR
3. HRV 101