Parasympathetic Flatline Talking versus Listening

I wanted to look at how often I entered the Parasympathetic Flatline while in a 1:1 conversation with a colleague by phone. For the discussion I read by heart beat intervals using a Polar H7 heart rate belt and the Heart Rate Variability Logger app for iOS. I also recorded my side of the meeting on a smart phone. When the meeting was complete I downloaded my heart beat intervals via csv file and pulled them into excel. Once in excel I used a formula mechanism I created that graphs segments where more than 10 consecutive interbeat intervals are less than 17 milliseconds apart.

During the 60 minutes session I measured 4,451 heart beats and the intervals between them. Of those intervals, 14% were in groups of consecutive intervals that were close together, meaning during 14% of the meeting I was in what I call Parasympathetic Flatline. This measured the periods where I was in fight/flight mode during the discussion.

Here is a vizualization of the meeting:

Slide1

In the session the forty-seven stress events triggered. Of these, 22 of 47 occurred when I was talking and presenting information to my colleague. 25, or 53%, occurred when I was listening to my colleague.  When listening to the recording, it is clear that the stress event, even when occurring when I am talking, begin when I was no agreeing with my colleagues response or trying to move him to a different position. The stress response was a result of not liking the direction the conversation was going.

Again physiology has shown that anticipating and trying to shape another person’t response is the source of stress in a 1:1 interaction. I once thought presenting my own opinion was a source of stress but that has turned out not to be the case. The stress, it appears, is not agreeing with someone else presenting their opinion.

Added clarification: From Twitter, fellow QS’er Gustavo (@GGlusman) asked the percentage of time I was talking versus not. Pushed by the question I went back beat by beat and looked at the session. As I reported above 736 beats were “in stress ” meaning that those beats were in a grouping with more than ten beats that occurred with a difference in beat interval less in 17 milliseconds to the adjacent beat. Of those beats in stress, I found that 238 were while I was talking and the remainder while listening. So that means 38.5% of the stress beats occurred while I was talking, 61.5% while I was listening. Impatience while listening was clearly more stress creating than flapping my gums. Thanks to Gustavo for asking the clarifying question!

Using the Parasympathetic Flatline

Using the Parasympathetic Flatline I analyzed a discussion with a colleague. I was following up on a topic that was not controversial. We had discussed this topic about a month prior. The colleague and I get along in a positive way.  So this should have been a relatively stress free and short meeting.

I used Marco Altini’s Heart Rate Variability Logger and a Polar H7 heart rate monitor to gather the base data while recording the meeting with my smart phone. Pulling that data into a spreadsheet I used my Parasympathetic Flatline model to determine at what point in the meeting I was experiencing physiological stress. I pulled the recording, the heart rate variability readings and the transcript into a timeline graph.

Slide1

What we see here is when I talked (green), when my colleague talked (blue) and when I was experiencing a physiological event of what I call Parasympathetic Flatline (red), or stress. There are specific points in the discussion when I was amped up, but they were not what I expected.

I had a hypothesis that I was entering these states when I was putting myself “out there,” however I had one moment about 3/4 of the way through the meeting where I really pushed the boundary of a sensitive topic but I did not experience stress. I was synched with my colleague and the discussion did not trigger stress at that point.

There were four points in the discussion, however, that I entered a heightened state when I wanted the conversation to go in a different direction. My agenda was not being followed. In the first two, early in the conversation, I wanted to hear the answer to the question more directly. I was impatient. In the second two, I had the information I needed and wanted to wrap up.

It appears that difficult topics are not stress inducing when discussing them with a colleague when we are in synch, but my overall judgements about the progress of the discussion seem to trigger an aroused state. It is our judgements about the situation that may be the source of stress.

Testing the Parasympathetic Flatline

Last week I proposed measuring a Parasympathetic Flatline (PS Flatline) where at least ten successive heart beat intervals were close together. This differs from using 30 second averages for rMSSD, a time based measure of average intervals. I wanted to to a side by side comparison to see which of these methods more accurately could point me to moments when I triggered a shutdown of my Parasympathetic Nervous System which allowed the fight or flight reaction to run things.

I measured several meetings where I was able to audio record the proceedings while measuring my heart rate. I used my Android phone to record the audio, a Polar H7 heart rate belt to pick up my heart rate and Marco Altini’s Heart Rate Variability Logger to capture the data. Afterward, I downloaded the csv files for both the rr intervals and the 30 second rMSSD.

The meeting was 55 minutes long and included one person in the room with me and one person on the phone. We were discussing a topic I was comfortable with and an activity I had experience doing. I was briefing my colleagues on a time schedule and details. I was in a relaxed state going into the meeting and did a short two minute breathing reset using BreatheSync prior to the meeting.

In previous studies I had determined that a 30 second rMSSD under 48 was probably a stress state. I had used several stress events to create this baseline. When looking at this meeting, however, I was disappointed to see that using this method definitely overstated the number of stress states. The graph below shows blue bars where I had a 30 second rMSSD under 48 and you can see it shows I was in that state for much of the meeting.

Slide1

This was not my experience of the meeting. I had the facts to hand, we had a good discussion and overall it was a friendly, informative discussion. So the 30 second rMSSD had not done the job for this meeting. I then looked at the PS Flatline that would show when my beat to beat intervals were less than 17 milliseconds for 10 consecutive beats. Here is what the meeting looked like using the PS Flatline.

Slide2

Listening to the audio I could definitely see that this method caught moments that had me shifting into high gear. I chose this meeting because there was a point at the fifteen minute mark where my information was completely wrong and I felt the flush of embarrassment. This came through accurately in the chart. It also showed several moments where I was trying to calculate sums with an audience and I needed to shift into gear and focus. You can see on the chart different elements of the meeting I heard in the audio and my memory of what was happening.

So the PS Flatline approach seems to be much more accurate though I have to do further analysis on other meetings to ensure it is catching all of the stress trigger events. I have 10 additional meetings recorded with audio and HRV so i will start crunching those number next.

Finding the Parasympathetic Flat Line

From the start I have wanted to pinpoint the moment I was stressing out and identify the causes. I started with logging Upsets, then moved to using different devices to read Heart Rate Variability (HRV). I was always bothered by the lack of precision in how the devices give insight into HRV. They never said “you were stressed from the 15th to the 45th second.” Rather, they gave an average score over a longer and generalized period of time. I want to nail down the specific time my physiology starts and stops going berserk. To understand precisely when this is happening I have to look at  RR Interbeat intervals (pictured below) and find those periods in the readings where I have multiple consecutive intervals with very little variability.

Slide1

When in stress mode the distance between successive beats for multiple beats remains very nearly the same. This occurs when the Parasympathetic Nervous System (rest & digest) flat lines and lets the Sympathetic Nervous System (fight or flight) run the show. Here are graphs of my RR Intervals for a similar time period using slow breathing to create a calm state described in last week’s blog post and a session on an elliptical where I was exercising and my heart rate was 145 beats per minute.

Slide2

You can see that the RR Intervals vary while calm, and there is no variability at all while exercising. While running the Sympathetic Nervous System has the hammer down. In relaxation the Parasympathetic Nervous Systems is braking the machine and providing periodic slowdown. That means that even while resting and digesting our RR interbeat intervals are close to the same values for 32% of the time (red circled areas).

Slide4

When reading HRV the fundamental output is the RR interval. All analysis is derived from that one string of numbers which are simply the number of milliseconds between beats. So it is straightforward to find periods in readings where those intervals are close together. Looking at the raw data I hypothesized that the Parasympathetic Nervous System is flat lined when the variance is small for 10 consecutive beat to beat intervals.

Slide5

I then looked at how a rule of 10 consecutive intervals would work for my readings of the calm state and while exercising. The maximum number of low variance intervals in the calm session reading was 7 consecutive beats, and while exercising there was no variance in more that 5 beats. So if I gave each interval a value of “1” if it was in a group of of 10 ore more intervals with low variance and a value of “0” if it was not in such a group, the graphs of the calm and exercising sessions would be as seen below. No intervals are in a group of 10 low variance readings in the calme state, all intervals in are a group of 10 or more for exercising.

Slide6

The second half of the calculation is the definition of “low variance.” I proposed in my post on HRV and Stress Free State that 25 milliseconds was low. So I took the rule set that the I was in berserk status when 10 consecutive intervals were under 25 ms and graphed if for a meeting I participated in last week. That graph shows more of the meeting in high vibration than I remember and didn’t quite look right to me. I lowered the number to 15 milliseconds and the amount of unrestrained Sympathetic activity seemed to get too small. Not a very rigorous sensitivity analysis I realize, but I have to pick a start point that seems to somewhat resemble what I remember happening.

Slide7

 

I set the calculation for 17 seconds and the graph started to look like general stress cadence of the meeting as I remembered it. Fortunately, I had audio recorded the meeting!  So I went back and listened to those portions where it looked like my Parasympathetic Nervous System had stepped aside.

Slide8

 

In those three portions of the recording I could hear in my voice that I was in a state of high vibration. In the first case I was presenting something and I sounded unsure of myself. In the second I sounded confident, but my cadence was noticeably slower and it sounded like I was searching for words at times. In the final case I actually said “I don’t understand your question” and there was a bit of confusion.

So I have a base that I am now going to start running data through to see if I can validate the 10 consecutive beat, 17 millisecond ruleset. If it starts identifying points of stress with precision I will have a framework that can help me start creating preparation regimes for 1:1 interactions based on precise knowledge of stressor that flatline my Parasympathetic Nervous System.

 

Stress Free State Through Lens of HRV

I have been collecting data on my Heart Rate Variability (HRV) to determine how to improve my personal performance and reduce stress. In doing so I have come to doubt the usefulness of “stress measurements” touted by some wearable devices and apps. They claim to be able to measure your physiology and indicate when you are stressed. My experience has been that the readings and the physiology is much more complex than a simple “red, amber, green” meter can indicate. It came to a head recently when I had a nicely engaged conversation with a colleague and my physiology readings indicated signs of stress. In fact, the experience had not been stressful at all. We were working hard on solving a problem and I felt engaged.

So I wanted to get a baseline by looking at data where I knew there was no stress at all. I had an HRV reading from an eight minute meditation session where I was completely relaxed and had nothing to distract me. The graph below shows how my heart rate stayed in the range from sixty-four to seventy-four beats per minute, a normal relaxed range for me.

Slide1

The heart rate is negatively correlated to HRV, meaning that the body when in a state of arousal or stress will elevate the heart rate and naturally lower the amount of variability between beats. The variability in heart rate is examined by looking at the intervals between beats. Here is a graphic showing the “R-R Intervals” and how they vary. You can see in this example how the heart speeds up and slows down beat by beat.

interbeat intervals

For the eight minute session my RR interval steadily rose and fell. In this graphic I display the distance between beats for the entire period. You can see the distance increase and decrease regularly.

Slide2

What you see here is my heart rate following my inhalations and exhalations. When we breathe in our heart accelerates and variability decreases. When we breathe out our heart decelerates and variability increases. During this exercise I used a breath pacer set for one breath every ten seconds. This graphic shows one ten second interval where I took one full breath in and out. You can see how the intervals drop on inhalation, rise on exhalation.

Slide3

When all of the interval changes are put side by side you can see that in a stress free state the changes are regular. The following graph shows the changes both positive and negative in the differences in intervals during the entire eight minute session. Notice that on the outward breath, when the variability is positive, that the scale of the changes are larger than on the inward breath when the variability is negative.This means that the breathing outward has a powerful effect on the overall measured averages of HRV readings.

Slide4

The averages of the readings, expressed best in the time based measure rMSSD, show for this session a settling at at rMSSD of 55 which is relaxed as any average reading for me indicates no stress when above 48.

Slide5

But this reading does not give a good picture of what is actually happening physiologically. It looks like in a relaxed state the rMSSD stays above 48 and implies that if I drop below 48 I will be stressed. So a natural reaction would be to try and devise methods that would keep the rMSSD above 48. But this is an average and obscures how variable the underlying heart beats are.

Looking at those successive heart beats where the interval change is “close” or under 25 milliseconds. These beats would indicate a stress response for a lot of the session, but we know this period was stress free. In this graph you see the blue lines are those intervals under 25 milliseconds, and the white spaces are those intervals over 25.

Slide6

My conclusion is this. In the drive to make wearable devices and apps more palatable to a mass audience the natural tendency is to bring the simplest interface to indicate stress or relaxation, be it a color or a score. This is useful for directing relaxation activity for beginners, and as the quantified self enthusiast digs into the underlying data much more nuanced picture emerges. What the last graph shows is the in the most relaxed and meditative state possible my beat to beat “score” was low for 41% of the beats. The fact is that these low score beats are part of healthy variability that goes both high and low. It isn’t right to see it as “59% good and 41% bad” –  it more correct to understand the mix of highs and lows, short term variability and the incredible speed with which the body becomes aroused and can calm itself. Sadly, the Apple Watch will not solve your stress problem. Data collection, repeated measurements and comparison with experience is the only way forward.

How Meetings Go – Physiologically

I have been measuring my rMSSD during work meetings to see what factors impact my performance when engaging face to face with others. I recently had two meetings with the same group of people on the same topic about a week apart. Before each meeting I also took my blood sugar to see if there was any information to be gleaned there. Here are the charts:

Slide1My blood sugar was abnormally high at 135 before the first meeting. I can’t account for it as I had salmon for lunch two hours before. My average rMSSD was much lower at 28.7 which is a reading of high stress. My stress point for rMSSD is 48, when I am below that reading it is an indicator that I am in stress. My experience during the meeting was of being overly excited and I breathed regularly during the meeting.

During the second meeting I was careful about my food choices for the day and had a good blood glucose level of 105. My average rMSSD for the meeting was higher at 38.8. I felt more in the zone in the second meeting physiologically. We were digging into more details in the second meeting and I felt challenged at a few points, and you can see where the reading drops to a very low rMSSD at a few points.

If asked I would have said the first meeting was more successful based on the discussion. And I would have said the second meeting was more challenging. However physiologically the second meeting was far less stressful than the first. When less stressed I imagine my actual performance was better. So my perception of the outcome was very different than the physiological reality.

Next step is to measure outcomes and see if the results correlate with the physiological state occurring during the meetings. I can’t rely on my perception of the situation so further readings will determine outcomes with physiology.

Blood Glucose, Heart Rate Variability and Face to Face Interaction

My Quantified Self Lessons Learned in 2014

I started the year wanting to explore how I could use technology to understand when stress was or was not occurring. I was interested in if self reported stress was reliable and if there were techniques I could practice that would reverse stress in increasingly shorter periods.

Looking for technology that could help identify when I was stressed was an exercise in buying a lot of technology and trying to find anything that would actually work. I looked at galvanic skin response, different watch iterations and ended up settling on heart rate variability (HRV) as a way to understand when I was relaxed or stressed.

As I began looking at different states of stress using HRV I measured myself while meditating, getting a tooth drilled at dentist, while giving a public speech, and getting a haircut. Each of these gave me the range of when I was stressed and not and gave me a baseline for further studies. I think the takeaway here is the boring baseline building work is necessary for real insight.

I learned that when it came to returning to poise from a state of upset, I could improve with practice and that a key technique was respiration. The ability breath well, which takes a bit of practice, was the key to busting stress. So stress, like fitness, was a state that could be altered with progressive practice. That was my assumption at the beginning of the work.

What was less obvious was how much thought and belief plays a part in how much stress I experience. Early on during my self reporting studies I found that a surprisingly high percentage of stress was self induced. Most stress was due to a discrepancy between what I thought was proper and what what happening. Even deeper, I found that my reactions were not complex reactions, but that emotion is navigation. Whether I was feeling in the right location or out of place determined whether I was calm or stressed.

I thought I could use technology to measure stress then solve for it through techniques, but that model turned out to be incorrect. It turns out my thoughts drove a stream of stressful reactions (or not) and that knowing when I am in a state of stress or not helped me change the underlying construct. And that is what takes me into the new year.

My Quantified Self Gear 2014

I have steered clear of reviewing products because I think simply buying products has very little to do with Quantified Self. And I thought it good for me to review what I used and how useful some of it was. My premise for my QS work in 2014 was to use technology to train myself to be happier.  I had used several Garmin products to successfully train for a half Ironman. Why couldn’t I train myself to be happier?

I pulled everything out of my wearables storage drawer and took this photo of everything I bought in 2014:

QSGear

The items:

I started with a Pebble smartwatch that my wife had given to me as a birthday gift. $99 from the original Kickstarter campaign. I love it and still use it daily with one app called Motiv8 that tracks activity.

Google Glass. What can I say. I fancied myself as an Explorer with $1500 burning a hole in my pocket. I once looked up the population of the state of New Jersey on it and sent my son an email saying “Hi this is my talking to my Google Glass.” That about sums it up. It has not been charged up for about 8 months now. It was so deep in the drawer it did not make the picture above and I just now remembered having it. Enough said.

Zensorium’s Tinke. Billing itself as a stress and fitness measurement device, I purchased one at the Quantified Self Europe conference in Amsterdam for over $100. Its readings made no sense to me and it went into the drawer pretty quickly.

Heartmath’s emWave2 & emWave Pro. This was over $400 worth of gear and if you follow this blog or my QS speeches at all I did get a lot of use out of both products. I conducted multiple experiments and accrued 183,843 “coherence points” – which is quite a few hours of cardiac coherence. In the end I grew out of it as coherence was not my ultimate goal. I think this product is way overpriced and was useful.

Neurosky Mindwave & Mindwave Mobile. Over $200 in cost, I could never get either headset to work consistently. I took some readings but any attempt to get the devices to reliably produce output was frustrated by bluetooth connectivity issues  of some sort. A big disappointment from Neurosky.

Emfit sleep monitor. I met the Emfit team at the QS EU conference and they helpfully offered me a free trial of their product. A combination of wireless connectivity issues and my move from London to San Francisco resulted in my never getting it working.

Mio heart rate band. Very slick implementation and a comfortable wrist band that uses pulse oximetry. I loved the idea, and it was not useful for heart rate variability experiments. The accuracy was not good enough so into the drawer it went. I paid over $100 for it.

After visiting with a friend who worked at Basis I dutifully bought the first version of the watch for around $150. I liked a lot of the ideas but did not really take to the interface or the gamification element of the online account. By the time I bought it I had eliminated pulse oximetry as reliable source of heart rate data. I gave it to a friend and he likes it.

Fitbit flex. I ended up buying two for $99 each because the first one gave out and stopped charging. The second one was spotty on charging as well. I used the product for 10 months and got a lot of value from it. In the end, the inability to charge it and a policy change that eliminated active minutes as a goal had me put it in the drawer. I replaced it with the $79 Garmin Vivofit because I do like to monitor my daily activity. So far that seems to be working out.

Sweetbeatlife & the VitalConnect Patch. Sweetbeat Life is an app that takes heart rate data from either a belt or the VitalConnect Patch. The patch seemed novel as it was convenient and comfortable. And it did not stay adhered on my chest for more than a few sessions. It was a breathtaking $199 for a set of 10 patches. I did not understand the real cost until the first patch fell off after the second use. Really cool and really expensive. I went back to the old reliable Polar H7 heart rate belt for a nice price of $80.  And one belt will last the whole year.

One thing that is not clearly stated is that you need top end smartphones to use apps associate with all this hardware. Neurosky, Fitbit, VitalConnect Patch and even my much loved Pebble need a phone with Bluetooth LE. I had an older version Android phone without Bluetooth LE so I needed to buy an iPod5 for iOS only apps and devices $199. And for Android I had to buy another device with LE so I bought a Nexus 7 tablet for $245.

So a quick add up gives me approximately $3,500 worth of gear of which 42% of that is the Google Glass. What did that expenditure do for me? It taught me through brute force that picking an area of Quantified Self to study and focussing there is 90% wikipedia work and networking with other people who have knowledge. 10% is hardware. And ultimately the majority of value came from about $500 worth of the gear I bought (Heartmath Pro, Polar H7, iPod5). The rest helped me understand some things but were not good value for money. For the Quantified Self, as in life, money cannot buy you happiness.

My Quantified Self 2014 in Review

I had a good Quantified Self year this year. As a long time logger and casual athlete I have always logged my personal data in some form. This year with the support of the Quantified Self community I was able to explore two specific areas. First, I moved stress tracking from self reporting to the use of wearable devices. Though I bought a few more devices than I would have liked I found that heart rate variability measurements using $65 worth of equipment was sufficient to track stress. Second, I was able to pull out insights about consciousness and heart rate variability that set the stage for future studies.

I explored 20 ideas this year that I organized into five umbrella studies. I started looking at the data I had collected through self reporting of “Upset Events.” I followed that up with a look at Upset intensity given different situations. After seeing the limits of self reporting I started using different devices to measure stress, settling on Heartmath used during working session. Using the device I discovered Freakback can have an effect on results. After learning how to work through that I completed a first study on how I recovered from Upsets.

As I was conducting these studies I had an emerging idea that emotion is navigation. The regularity of emotional shifts seemed like “sighting” as I worked through different ideas. As I worked on this idea I found that Heartmath was too limited in what it measures. Heart Rate Variability has a more direct measurement in rMSSD. I dropped Heartmath and started using Sweetwater HRV’s SweetbeatLife to monitor rMSSD. Using this tool I started measuring stressful events like getting a tooth drilled and firing a shotgun. I played with machine learning and straight statistical regression and determined my “stress point” when read by rMSSD. This provides me a tool to study a variety of situations going forward.

Along the way I gave five Quantied Self meetup talks, 2 in London, 1 in Amsterdam and 2 in the Bay Area. In London and Amsterdam I did my talk We Never Fight on Wednesdays, and in London my followup Don’t Just Stand There. In the Bay Area I presented my talk Every Other Minute where I talked about the navigation impulse. And finally my Bay Area presentation on heart rate variability and Flow. These talks went well and I am set up to give a presentation at the QS Global conference (QS15) in June.

Some of the 20 ideas did not pan out. My work on 800 numbers went nowhere. Ideas about reading my heart rate while doing The Work by Byron Katie did not have sufficient detail to be interesting. Several other ideas blew up on the launchpad. However, I’m pleased with the progress this year. In my next post I will talk about the lessons I have learned during this work.

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