Blog post by Marco Altini
What does this mean? If it were true that paced breathing improves measurement repeatability, we would expect consecutive measurements taken while following a pacer to be closer, more consistent, compared to self-paced / free breathing. Is this the case? [Spoiler: no]
Literature has been inconclusive on this for a long time, however as we ran our own study recently to validate the camera based measurement, we also started investigating additional aspects that are still either unclear or controversial, for example how long a test should last or if paced breathing helps in getting more consistent data, see for example this blog post.
I will report part of the analysis below, so check out the section "systematic analysis in the lab: does it really help?" where we highlight how self-paced and paced breathing result in the same differences between consecutive measurements.
Another reason to use paced breathing, might be that we would expect people to be more consistent over time, because you are always guided the same way. This was the main point I made in the past to advocate for paced breathing. But is this a good point? What if with changes in fitness (or other) our resting breathing rate also changes, shall we force ourself to breathe at an unnatural rate? I don't have an answer, as gathering data to understand if over periods of months we change breathing rate and how that affects HRV would require a complex and very long study. However, breathing freely without constraints, considering that there are no differences in the data in terms of measures repeatability issues, might be a better way.
This consideration comes also after months and months of emails from users struggling with paced breathing. If we use paced breathing to make things easier and consistent, but many feel like they would not really breathe at the proposed frequencies (even if we had a broad range to pick from), I fear we might be introducing more problems than what we solve. Many reported "breathing like a robot", preferring different inhale and exhale timings, the pacer not being slow enough even at 6/minute, or being not fast enough even at 10/minute. Anyone can breathe naturally, and that might be a better way to go. Hence, the latest changes in which we removed paced breathing.
Finally, to support these ideas and recent changes, there is this great article put together by our friend Jason Moore at Elite HRV, for the HRV course he created with Greg Elliot, in which they also cover breathing, and mention paced breathing during readiness readings as one of the 8 biggest mistakes in HRV measurement. I grabbed a screenshot for the information relevant to us here:
Here Jason makes a very good point, as paced breathing might tend to push most people to breathe unnaturally, and while that might be exactly what we want in certain applications (deep breathing linked to mindfulness, yoga practice, meditation, etc.), this is definitely not what we want for our morning measurement, which should be taken without forcing our breathing in any way, as we want to capture the underlying physiological stress with respect to our normal values.
Let's move on to literature and data.
Literature findings: inconclusive
Paced breathing is one of those aspects where there is much controversy. In , HRV (SDNN and frequency power) was recorded during paced breathing at the relatively high frequencies of 15 and 18 breaths per minute. Features were the same compared to spontaneous breathing for 15 breaths per minute, and reduced for 18 breaths per minute. Thus, according to this study HRV should not be affected by paced breathing or be reduced in case of very high rates.
Normally, we expect increases in HRV when we measure at much lower rates, for example close to 0.1 Hz (or 6 breaths per minute), which is what is typically done during biofeedback exercises, to get the heart "in synch" with breathing (RSA). In  we have a meta analysis of many different HRV studies. According to the authors, paced breathing is one of the reasons behind different HRV values between studies, together with physical activity level, age, errors in correcting RR intervals, and many other factors. The point here being that rMSSD, our most relevant metric to monitor training load, was slightly higher during paced breathing. Similarly,  reports higher HRV (frequency domain features) with reduced breathing rates, analyzing breathing rates over a wide range (3 to 14 breaths per minute). The same is shown in . This makes sense, as we typically expect higher HRV for deep breathing, however the range covered in the study is very broad and way broader than any "natural breathing" variations.
On the contrary, in  the authors report pretty much the same rMSSD values across paced breathing rates between 6 and 15 per minute.  reports very small improvements in reliability when using paced breathing, while  says paced breathing is not necessary, as long as subjects are reminded to avoid irregular respiration (during a 5 minutes measurement).
The latest research by Flatt et al , also uses self-paced breathing, as the authors reported this to be the preferred method for their athletes. See full papers here and here. Finally, in  the authors found that for rMSSD, paced breathing and self-paced breathing are highly correlated, hence making the two methods interchangeable.
Now, this is rather inconclusive, even though the fact that the latest longitudinal research on HRV in athletes does not enforce paced breathing, letting athletes measure self-paced , points out at least the fact that scientists in the field think it should be done this way, or at least that it is not harmful to the study, i.e. to understand underlying physiological stress level. The studies by Flatt et al. and Saboul et al. are the most relevant here, as they are the closest to our use case as they measure HRV longitudinally.
Let's look at some data.
Systematic analysis in the lab: does it really help?
The data shown in this post were collected during a clinical validation study ran by Ben Scott,Daniel Plews and Paul Laursen. We are busy writing up a paper comparing ECG data to a Polar H7 and PPG data, but since we collected data under many different conditions, I will be using the same dataset to explore other relations, for example the one covered here between paced breathing and measurement repeatability.
The subset of the dataset used for this analysis consists of recordings from 26 participants and three sensor modalities (ECG, H7, camera). For each participant we have four measurement conditions, each one 5 minutes long: LB1: lying down with paced breathing, LNB1: lying down without paced breathing, SB1: sitting with paced breathing and SNB1: sitting without paced breathing.
I computed HRV (the usual rMSSD in ms) for each condition. In particular, I used the first 4 minutes of data to analyze differences in minutes 1 and 3 as well as between minutes 1-2 and 3-4, so between each repeated measure I left 1 minute. This is a bit of a random choice but I figured it was a more realistic setup to leave some time between consecutive measurements to analyze.
Getting practical: do we get more consistent results by using guided paced breathing?
I grouped paced breathing and self-paced breathing regardless of the measurement condition, to see if we could get higher repeatability (theoretically smaller differences between consecutive measurements) using paced breathing. I included all measurement setups (ECG, PPG and chest strap), simply to show that repeated measures are not even influenced by what you use, and these differences in consecutive measurements are simply due to natural physiological changes, as our physiology is never in the same exact state:
If there were a big difference between paced and self-paced breathing, for example if paced-breathing was much better at providing more repeatable measurements, we would expect the boxes on the left side to be much closer to zero, and the ones on the right much higher (higher = less repeatable, closer to zero = more repeatable). Instead, they are all pretty much the same, with the median (black horizontal line) around 10-12 ms, that's the difference you should expect when measuring twice in a row regardless of paced breathing modality, sensor modality and measurement duration, as we see the median is the same across all conditions.
No statistical test would detect a difference here, as differences between conditions are tiny, and the boxplots overlap almost perfectly across all conditions.
As we are digging more into data here, I will also include two plots that can be found in Saboul et al. , that clearly show the high correlation between the two methods, highlighting how it makes no difference for rMSSD to use paced breathing or not, while this is not the case for frequency domain features, as we alter the main breathing frequency when we force someone to breathe at a predefined rate:
Above we can see a case for a single person, while below we can see across the entire dataset acquired by the authors, that rMSSD is extremely well correlated between the two methods, so again, it makes no difference (and hence no improvement) if you use paced breathing or not. Interestingly enough some people are using the same paper to make the opposite point, but if you look at the data, the message is quite clear.
The authors themselves go as far as saying "Consequently, to perform more relevant longitudinal monitoring, the authors now prefer to use RMSSD or SD1 markers which are related to athletes’ fatigue (Kiviniemi et al., 2010; Plews et al., 2012) and provide the same variations whatever the breathing pattern".
But you said....
When I showed these data in the original post on measurement duration and measurement repeatability some time ago, I still advocated for paced breathing. My reason was that I would expect people to be more consistent over time, because you are always guided the same way. As I explained at the beginning of this article, there is no easy way to valiate this assumption, and the continuous feedback from users struggling with paced breathing, together with the recent post from the hrvcourse.com, made me reconsider our choice. We should not force our breathing to unnatural rates, and since each person is different, breathing naturally / self-paced might be the more meaningful solution.
Based on literature and on our analysis, while results are sometimes conflicting, I would derive the following:
As we moved to self-paced breathing, is this the only and best way to do it? Of course not. As we learn more by combining our efforts and trying to make better use of these data, we might update our guidelines again. To me, it really seems that as long as you feel comfortable with your breathing, it doesn't really matter if you breathe paced or self-paced.
However, from a user experience point of view, it seems that forcing a certain breathing rate might cause discomfort in quite some people, and therefore removing it might make things simpler and therefore provide more meaningful data.
If you are used to paced breathing, it might take a recording or two to get used not to have the pacer, but it should be easy enough. Simply try to relax and breathe naturally. Anecdotally, as I started doing this today, I scored the exact same score I scored yesterday (rMSSD 61ms today, yesterday 58ms, no training) - no big deal.
 Bernardi, Luciano, et al. "Effects of controlled breathing, mental activity and mental stress with or without verbalization on heart rate variability." Journal of the American College of Cardiology 35.6 (2000): 1462-1469.
 Nunan, David, Gavin RH Sandercock, and David A. Brodie. "A Quantitative Systematic Review of Normal Values for Short‐Term Heart Rate Variability in Healthy Adults." Pacing and Clinical Electrophysiology 33.11 (2010): 1407-1417.
 Song, Hye-Sue, and Paul M. Lehrer. "The effects of specific respiratory rates on heart rate and heart rate variability." Applied psychophysiology and biofeedback28.1 (2003): 13-23.
 Yildiz, M., and Y. Z. Ider. "Model based and experimental investigation of respiratory effect on the HRV power spectrum." Physiological Measurement27.10 (2006): 973.
 Guzik, Przemyslaw, et al. "Correlations between the Poincare plot and conventional heart rate variability parameters assessed during paced breathing." The Journal of Physiological Sciences 57.1 (2007): 63-71.
 Ginsburg, P., Bartur, G., Peleg, S., Vatine, J. J., & Katz-Leurer, M. (2011). Reproducibility of heart rate variability during rest, paced breathing and light-to-moderate intense exercise in patients one month after stroke. European Neurology, 66(2), 117-122
 Kobayashi, Hiromitsu. "Does paced breathing improve the reproducibility of heart rate variability measurements?." Journal of physiological anthropology28.5 (2009): 225-230.
 Flatt, Andrew A., Michael R. Esco, and Fabio Y. Nakamura. "Individual heart rate variability responses to preseason training in high level female soccer players." Journal of strength and conditioning research/National Strength & Conditioning Association (2016).
 Saboul, Damien, Vincent Pialoux, and Christophe Hautier. "The impact of breathing on HRV measurements: Implications for the longitudinal follow-up of athletes." European journal of sport science 13.5 (2013): 534-542.
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
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. 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