Blog post by Marco Altini Daily measurements and 7-days moving average for lying down (left) and sitting (right) measurements A few months ago after an interesting chat with an elite team and Andrew Flatt, I started measuring my HRV in the morning while both 1) lying down and 2) sitting. Normally, I have always been measuring while lying down in bed, but Andrew brought up some interesting points which I thought were worth investigating further, at least in my own data. In particular, the reason for doing so, is the following: adding a little stress (e.g. sitting or standing instead of lying down), might better capture your physiological response (or capacity to deal with stress for the day). It's a physiological challenge (more on this later). In terms of the protocol, i would up and measure while lying down in bed, using HRV4Training (phone camera for one minute). At that point, I would sit, and after a few seconds, I'd measure also while sitting (another minute with the camera). Between measurement, the time would be rather short, something like 30 seconds. Let's look at the data. First of all, I'd like to stress how simple measures of agreement might be inadequate, e.g. correlation (often reported) does not tell us anything about deviations from normality. Correlation between daily measurements (left) and 7-days moving averages (right) for lying down and sitting data While we can have a look at day-to-day correlation and baseline correlations, as shown above, a better way to understand if we are capturing the same information is to use the data the way it should be used for decision making: monitoring deviations from our historical data and normal values (acute drops, etc.). How do we do that? We can use part of the data to compute the normal values (40-60 days), then look at baseline and acute drops. Here both measures show very similar responses: highly suppressed HRV on the same days, baseline reaching the bottom of the normal range the same period, etc. - it is quite clear that both lying down and sitting are capturing the same trends (which would result in the same advice, especially for acute drops and baseline changes, the two key factors in HRV-guided training): To sum up: in my data, both measures are very similar, especially in the longer term (baseline correlation) as well as in terms of acute drops (single days below the normal range, highlighted in blue in the previous image). What differed? Lying down seems like a squeezed version of sitting. Paraphrasing Andrew, applying a little stress (e.g. sitting) might better uncover the physiological response response, and this in turn, might explain the higher day to day variability when sitting. Note that this doesn't mean HRV will be lower (quite the contrary). Anecdotally I've heard from quite a few people that "when switching to night data, my physiology never changes" (unless sick, or drinking too much, e.g. very large stressors) Do measurements while lying down, and in particular night measurements simply lead to less day-to-day variability or is this associated to the measurement being less responsive to stressors, due to lack of a physiological challenge? In my opinion, both positions are valid and rather similar, as shown by the data here. While some have quite a dogmatic approach ("never measure in this or that position"), it is clear that the same processes are captured. However, there are also differences, and it is important to think it through and consider potential mechanisms and what could be best in an individual case. Most likely, the level of the athlete, baseline HRV (e.g. on the low vs high side of population values), type of sport practiced by the athlete (e.g. endurance or not), as well as other factors such as the application of interest (daily guidance or monitoring long term trends, performance or health oriented) and practical considerations (what modality makes it easier for the athlete to collect daily measurements consistently) should play a role in this decision. One minute of your time might be worth the insights after all. I hope you have found this blog useful, feel free to follow up here on Twitter. Lying down (left) and sitting (right), including HRV4Training's detected trend
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Marco Altini, founder of HRV4Training Blog Index The Ultimate Guide to HRV 1: Measurement setup 2: Interpreting your data 3: Case studies and practical examples How To 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 HRV Measurements Best Practices 1. Context & Time of the Day 2. Duration 3. Paced breathing 4. Orthostatic Test 5. Slides HRV overview 6. Normal values and historical data 7. HRV features Data Analysis 1a. Acute Changes in HRV (individual level) 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 8. CorSense 9. Samsung Galaxy App Features 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 Other 1. HRV normal values 2. HRV normalization by HR 3. HRV 101 |