What the daily advice can tell you about your physical condition
The daily advice reflects acute changes in HRV. In other terms, it reflects how stressed your body is, with respect to the very recent past (i.e. the past week).
Note that this is different from the acute HRV changes analysis we discussed many times in this blog (see the first part of this post for a brief overview). The acute HRV changes analysis looks at day to day changes, i.e. what's your HRV change between yesterday and today, with respect to yesterday's training. It answers the question: how is my physiology changing after trainings of different intensities?
On the other hand, the advice aims at helping you in making small daily adjustments to your training program, by keeping in consideration not only how your score changed from yesterday's, but also with respect to your past 7 days moving average (i.e. the baseline). While considering only a week of data does not allow us to understand much about the big picture (see limitations section later), extending the day to day acute changes analysis by comparing your daily score to your recent past, does allow us to get a bit more perspective on "what is normal" in your specific case.
The daily advice should not revolutionize your training. You should have a clear plan and use the advice to make small adjustments that can optimize physical condition in the long run.
Some examples: you planned an intense workout and your HRV score is to the ground. Move it to tomorrow and do something easy today. Similarly, you planned your long run for tomorrow but today your physiology looks particularly good (maybe you had the best night of sleep), then go today instead of tomorrow. These are the small adjustments that you can do using the daily advice.
Here is an example using my data. I hardly train consecutive days, and I trained every day this week, with two very easy workouts and a more intense one. I had planned an intense workout for Friday and a day of rest for Thursday. However, I did have a good night of sleep (uncommon event) and a good HRV on Thursday morning, hence I moved the intense workout to Thursday and rest day to Friday:
On the following day, Friday morning, my physiology could definitely feel the effect of the intense workout and the need for recovery:
Nothing really changed in my plan for this week, but I made adjustments based on the daily advice, similarly to what was done in literature a few years back in one of the landmarks of HRV research (Kiviniemi et al.). By training harder when the body is better prepared, we can obtain long term benefits in physical condition and performance, as shown also in recent studies.
How the daily advice is computed
The main principle here is that to compute the daily advice, we look at deviations from your own baseline, since the only thing that matters is what is normal for you and how far you are from your normal values on a given day. Most of the time you'll be around your average, but when you are below or when you are below for two days in a row, the app will suggest to take it easy or rest because your body seems to be physiologically stressed with respect to your recent values. In particular, the daily advice always combines 1) your daily score 2) yesterday's score 3) your recent baseline, and implements a mechanism very similar to the one proposed by Kiviniemi et al. (you can read the full paper here).
Color coded bar
The most interesting part, is not really the message provided by the app, but the color coded bar. Why? Because the message fails to understand to what extent you are below or above what is normal to you. On the other hand, the color coded bar shows you exactly where you stand with respect to a normalization of your recent values. In other words, the closer you are to the middle of the bar, the less important the advice is, since you are pretty much around what is your average.
What are the limitations
While I strongly believe that this is the best way to analyze physiological stress (i.e. looking at deviations from your baseline), there are two issues with this approach. The first one was just discussed. The advice message does not take into account if your daily score is a lot worse than yesterday or just a bit worse than yesterday. Hopefully, the next update including the transparency area will make it more clear that small changes are not that relevant and sometimes you can ignore the message.
The second and more important issue is that when you look at deviations from your own baseline in a week of data, you loose perspective. If today I'm better than yesterday and also better than my baseline but I am in a week that is an absolute low when looking at it over a couple of months, it makes little sense to suggest an intense workout.
Your physiology might be in a better state than your recent past, but you need to look at longer term trends to capture your overall physiological condition. In the next section, we go over some additional features we prepared for you in HRV4Training so that you can go beyond the daily advice and make better decisions.
How you can overcome the limitations by looking at HRV trends
We've established one of the main limitations of the daily advice is the inability to go beyond what happened in the past week. How do we get a better understanding of our physical condition and the big picture?
Multiple studies recently highlighted the importance of looking at longer term trends, and as a matter of fact, not only at HRV trends, but also HR, coefficient of variation of your HRV scores, and all with respect to your training load. For a comprehensive overview of the latest research, you can have a look at this post.
To keep things simple, I would suggest to start by looking simply at HRV trends. You can do so in HRV4Training from the Insights page, once you have enough data:
Your HRV baseline trend, the first plot shown under HRV Trends, is a 7 days moving average showing the past 2 months of data. This should be a decent timeframe including multiple training cycles.
The app automatically detects the recent HRV trend, and determines if the trend is trivial or not, given your normal day to day variability in the past 60 days. A good example on how to use these trends more practically to guide your trainings can be found here. In general, what we want to see is HRV not going down. Both a stable trend and an increase are signs of good adaptations to your current training plan. Another case study of how HRV trends can be used to guide individual trainings was recent published on ithlete's blog, and I highly recommend reading it.
Apart from looking for a stable or upwards trend in HRV, there are other interesting parameters, and often combining them gives more perspective in the longer term, for example a stable HRV trend is also very good if it comes together with a reduction in HR over time. Another interesting parameter is the coefficient of variation of your HRV, i.e. if your baseline is 8, was it seven 8 values or was it all over the place and the average was 8? Interestingly this also has a double meaning depending on the other parameters. For example, if your HRV is good and your HR is good (low), then when the coefficient of variation is low (you had all similar values) it probably means that you adapted well to training and you might want to try some more intense workouts. However if your coefficient of variation is low when your HRV is low and your HR is getting higher, you are most likely overtrained and your body is not responding well.
A practical example of how to use the coefficient of variation can be found at the second half of this post. You can look at the same trends in HRV4Training Coach, which lets you also pick the timeframe between 30, 60 and 90 days and your favorite training load parameter:
In this post I went through a few points related to HRV4Training's advice, here is what you should remember:
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This blog is curated by
Marco Altini, founder of HRV4Training
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. HRV and training load
5. HRV, strength & power
6. Overview in HRV4Training Pro
7. 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