Tag Archives: Fight/Flight Recovery

Fort-Six Meditations

I was pulling data yesterday preparing my speech for the Quantified Self Conference & Expo. I have been collecting heart rate variability (HRV) readings since October during conversations with work colleagues. My hypothesis was that I could train myself to be like a conversational ninja and outwit people using my physiology.


I had to ensure I could bring myself to a relaxed state by practicing sitting in a meditative state each morning for five minutes. l talk about the value of this in my tutorial post “By Yourself – Basic Training.”  I wanted to train myself to get to calm in five minutes or less.

For these sessions, I use Heartmath emWave pro because it has a very clear interface. It uses an ear clip that ties to software on my laptop and this is the dashboard I see during the session:


I can see my HRV wave on the top part of the screen and get a score on how I am doing on the bottom. I don’t recommend the emWave pro based on its high price. You can use a phone-based app for the same five-minute session. But I have one so I use it.

In prepping the speech, I pulled the data on 46 meditative sessions to see if I was getting fight/flight readings when I was purposely downshifting my physiology. These sessions were spread out over multiple months and consisted of 17,872 heart beats. I analyzed these beats looking for fight/flight incidents using a technique I outline in my HRV Tutorial. The number of fight/flight incidents?


That’s right, zero. Over all of those sessions, I did not have a single incident of extended fight/flight during those sessions. So I had in my Basic Training learned how to bring myself to a relaxed and refreshed state very consistently.

I’ll be talking how I wove this training into my conversational experiences as part of my speech for the conference. I’ll also be rehearsing this speech this Wednesday at the first Denver Quantified Self Meetup. If you are in the area stop on by.

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Superpower Series: The Working Session

You can use measurements of your heart rate variability (HRV) to improve how effectively you concentrate when you work. When engaged in thoughtful work on your own and your prefrontal cortex is fully engaged your heart rate variability will be high enough that you will not show sustained stress. My experience applying techniques that kept me engaged yielded more output and I felt more relaxed when the session was over.

I had explored work session hygiene techniques in past work  that I called returning to poise. In those sessions I had discovered that I was more engaged and less stressed when:

  • I set aside a fixed period of time from 25 to 30 minutes,
  •  there was only one topic I focussed on for that period,
  • when I was distracted I used steady breathing to bring my attention back to my task,
  • the task at hand was the “right one” and no thoughts of being elsewhere intruded.

Here are four working sessions and how the measurements corresponded with how effectively I used the hygiene techniques. In all sessions I was working in the same office at roughly the same time of day. The topic was the same in all sessions, and I was working alone in the office on my computer doing planning for organizational alignment.

In the first session, I worked without using any of the hygiene factors. I simply put on the heart rate belt and worked. This is the graph of the session:


You can see periodic stress points, where my sympathetic nervous system was firing and it is probable that my prefrontal cortex was not in full gear. I was not doing my best thinking. I logged that I was not sure there was not more important work I should have been doing. This distracted me, and I did not see good results.

Contrast this to a second session of similar length where the topic was important, I had the time set aside and was focussed. You can see the graph here:


Far fewer periods where I was in fight/flight mode. It appears that my belief in the importance of the task reduced the amount of stress. In a 40 minute session again my concentration was high based on the belief I was working on the most important task possible and that I was in the “right place at the right time.”


You can see that even for a longer session the number of fight/flight events were singular and fewer in number. The other hygiene techniques all were in place. The reason for the 40 minutes session was that I engaged enough that I blew right through the time limit.

Finally, I was able to have all the hygiene factors in place for a shorter session and in that I had no fight/flight incidents at all. Here is the graph:


So when working alone it is possible to improve your concentration by developing techniques to keep yourself focussed. When so focussed, your HRV will reflect that you are physiologically in an state of complete engagement. And you will see much improved work output.

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.

Upsets Are Smaller Than They Appear

I had a good opportunity to unpack an Upset in real time. I was with another person and they did something that caught me off guard in a negative way. I was wearing my Polar H7 heart rate belt and was measuring my heart rate variability using Heart Rate Variability Logger app by Marco Altini. Here is a graph showing 30 second snapshots of my rMSSD, a measure of the variability of my heart rate.


I know from previous readings that a reading above 48 of my rMSSD is a reading that indicates low stress, an rMSSD below 48 indicates stress. The event that caught me off guard happened at the “13” time mark on the chart. As it unfolded I chose not to react to the situation and take stock. When it happened my rMSSD was at 51, a comfortable stress free reading. As I sat quietly and processed the event you can see my rMSSD drop to 47, not too bad, then pop back up to 58 which is quite relaxed. When I decided on some level to show I was displeased, shown in the chart at time hack “22,” my rMSSD dropped to 27. This is a reading comparable to being in the dentist chair or firing a shotgun at a range.

So my decision to react and show my displeasure was the stress inducing action, not the original event itself. My decision to be visibly confrontational created the deep negative rMSSD reaction. I think this event has measured my fight/flight response. The response occurred a full four minutes after the original event. So I chose to enter that state.

Once I walked away I maintained my state of Upset. On being clear of the situation you can see the opening reading is still in a somewhat stressful state, but 30 seconds after that I was back in a state of no stress.


After I was clear I was still mentally considering my reaction and the event, but I was not physiologically in a confrontational state as measured by my heart rate variability.

So a few insights come from this event. First, the actual event was not the physiological trigger. My interpretation, arrived at a full four minutes after the fact was the trigger. Once into it, the time I was in fight/flight was quite short. Though I felt I was still in an Upset state I had emerged from it a full minute or two earlier.

Interpretation is a choice, Upset results from interpretation. Conflict creates fight/flight, removing oneself from conflict seems to reduce fight/flight reaction. Regardless of the mental rehearsals before and after, an Upset unfolding in real time is quite a bit smaller than it appears when you look to the data.

Doc’s Needle Better Than Dentist Drill

Finishing off my service provider stress-off I had the opportunity to take a reading while getting shots at the Doctor. I used the Polar H7, SweetBeat Life and Kubios to pull the data together. I was quite surprised to see that getting a needle stuck in me was not nearly as stressful at having the tooth drilled, and other then the moment of anticipation prior to the jab I was quite relaxed.


You can see at time had 2 I was anticipating the jab and the jab itself happened between 3 and 4. Afterward I was quite pleased when it was over. So looking across service providers my stress leader board looks like this:


When relaxed my average pNN50 was just a bit higher than getting a needle at the doctor. Doctors win gold, hairdressers win silver and dentists get a distant bronze. I use average pNN50 read at 30 second intervals to compare because the length of time I was in an anticipatory state and the time of treatment was different for each provider.

I can only speculate why the stress levels were not what I was expecting. I would have guessed a doctor’s jab would be nearer the dentists drill. Two factors may have influenced the readings, both related to mental state. First, the length of time was different for each. The doctor’s needle was very fast. The haircut had not pain, but it was 45 minutes long. And I was in the dentists chair for an hour during which 20 minutes was drilling. So mentally I was working on different time horizons.

Second, the certainty of outcome and context was different. In the doctors office my wife was with me, we were joking around and discussing our upcoming trip to Vietnam. We know the doctor and the atmosphere was quite convivial. The jab was going to be quick and done. So my relaxed state was consistent with those environmental factors. The haircut was with a long time and trusted provider and sadly I had to tell him at the end I was moving from London to San Francisco and would no longer be seeing him. So the readings tail off at the end where I said goodbye. Finally, the dentist was putting in a filling “to see how it goes” with the possibility that if they drilled and found the tooth in too bad a shape more detailed, longer and painful work was going to have necessary. So the anticipation in that chair was very high stakes.

Despite my calling this a description of the effect of the doctor and dentist, what we are actually seeing is the physiological output of my expectations. Where this journey has taken us is where it began. My interpretation of the situation fires my physiological stress. With the doctor, dentist and hairdresser I was sitting and they were using a sharp object to bring on some physical improvement. In each case my own interpretation of what was happening and what was about to happen triggered my reaction, so we continue to show that all stress has a powerful subjective element.

Machine Learning & Stress Prediction

Seeking a reliable way to measure when I was am in a relaxed and focussed frame of mind and when I was feeling stress I looked again at the data I had collected for several sessions. I used the data from the readings when I was getting my tooth drilled and when I was giving a speech as a reliable reading of when I was in a stress state. I also used sessions of high coherence while using Heartmath Pro as a baseline for when I was in a relaxed state. I used this as a training set which I fed into an Artificial Neural Network using a freeware package called WEKA.

The machine learning algorithm came back with a 98.3% accuracy on the ability to classify whether I was relaxed or stressed. Now that I had an algorithm I could run it on other data to see how it looked. First I ran the data for a meeting I had with a colleague that was very positive. Running the data I was not surprised to see that I was relaxed during the majority of the session. Twice I dropped into stress in a 53 minute meeting. That was consistent with my experience.


Running the algorithm on a second meeting gave me a different outcome. I remember the meeting well and the conversation was not combative, but the topic was more difficult. This result was more of a stress state with a four time periods where I was in Poise. Again this was consistent with my experience of the meeting.


I cut the data in the training set and the meeting data lists into 30 second increments. This is the shortest period of time needed to get a meaningful set of heart rate variability data.  It also allows me to use my previous work in returning to Poise as a comparison. The next step is to run the algorithm on the data from different situations.

One Big Reset

Not a lot of exciting news as I gather mounds of data for my next study. With a focus on the small increments within a working session or a meeting with another person I sometimes don’t report bigger wins in reducing actual Upsets.

Last night I had a belief that a good night of sleep was important and when the dog woke me up at 2.30am because she wanted some water I triggered an Upset. In this case I was irritated enough that getting back to sleep was a problem. In a quintessential Freakback being Upset about not sleeping made it hard to get back to sleep.

I pulled out my HeartmathPro and did a session to 500 points. Heartmath awards you points per second based on your coherence score, the higher earning more points. I do 500 points a day to keep myself reminded of what coherence feels like and I have been improving over time. Here is a graph of the time it has taken to earn 500 points in each of the last 46 sessions:

Slide1The trend line is down overall and you can see that some sessions take a long time, some are quite short. With practice I no longer need a breath pacer, I can breath along with the displayed RR interval curve and maintain high coherence.

Last night though I was irritated at the dog, and irritated I could not sleep, when I sat and did the breathing I had a record breaking session and finished 500 points in just over 10 minutes. This is the fastest time I have ever recorded. Here are the waveforms of the session as displayed by the Kubios software:


The top is the total session and the bottom the breakout of the first five minutes. That is a nearly perfect session. When I finished the irritation was completely gone. I set the device aside and fell asleep immediately.

That is a great ending and a victory for breath pacing and coherence. And puzzling in that I would have guessed that I would have taken a longer time to earn the points given I was irritated. Not so. My mind shifted to Poise almost immediately as seen on the waveforms and all trace of the irritation was gone.

As a sleep aid and way to reverse Upset emotions coherence is tough to beat.

My recent study I measured Upsets using Heartmath over 11 days, 699 total entries. I usually sat for 1 hour session where I tweeted, worked on this blog or did email. As I did so I would make an audio recording and say what was on my mind when the device said I was in a state of Upset. When reviewing the data I saw I had a really poor session on day five.


Most of the entries were about my feeling that the work I was doing was not productive and I also saw I was trying to do too many things at once. I was constantly shifting my focus between different activities and worrying about getting other tasks done.

So after seeing that I changed how I organized my work sessions. I created simple work plans for the session where I would only focus on one thing at a time. To fill the hour I wrote a short list of things to work on so my “switching stress” would be zero. This had a significant effect on the number of Upsets where I was unsure what to do next.



I also saw that Upsets around time pressure dropped.




These are where I would work on one item and create and Upset by thinking that I should get this task completed more quickly. It was often driven by thinking I had other things to do.

So the insight for me is that the mind is quite adept at creating distractions and when I sit to engage in an activity the context I create beforehand around the goals for the activity can either worsen or improve Upsets around time pressure and the belief about productivity.

This very tactical work planning will be the basis for my next set of observations.

Improve with Practice

After having discovered the benefits of using paced breathing to bring myself out of the Upset state, I have practiced 391 shifts from Upset to Poise using paced breathing. The original baseline was 124 Upset events and only logging the events, which had little effect, was 169. So of the 684 shifts from Upset to Poise just over half have been using breathing. Is the average recovery time improving? Here is a graph of all 684 shifts:



Over time the trend in the amount of time is takes to recover (black line) is decreasing. It becomes even more stark when looking at the sessions pre and post the use of respiration. When in baseline (no intervention) or only logging the average recovery time from an Upset was 33 seconds. Using respiration to assist in recovery dropped the average recovery time to 17.5 seconds.

So while I have experienced that practice improves the ability to reduce the recovery time, the the big change was including respiration as a tool in the process.

Effect of Respiration on the Return to Poise

I have three comparable sets of data measuring recovery time from the Upset state. Each are five hours in length. I began with a look at my base rate for recovery which meant that I used no technique to change my Upset state at all. I just measured and let me mind do its thing. Then I looked at logging what was on my mind during the Upset state. I did this by typing into Taplog what I was thinking in that moment. The result was some change, but not a significant improvement. Over a five hour period the time spent in Upset decreased by only 4.6 minutes. My third five hour period was measuring the effect of respiration on the Upset state.

What I did in each session was use Heartmath Pro to measure my HRV. I had the software set to indicate with a tone when I entered the Upstate state. When I heard the noise, I muted the computer, noted the time on a sheet of paper and picked up my Android phone which had the Breath Pacer app open and ready. I spoke what was on my mind which was captured on an Olympus VN-711PC digital recorder that I had recording the entire session. I then focussed only on breathing at 5.8 breaths per minute which is programmed into the App. Once the software indicated I had returned to Poise by showing a green light, I would turn off the Paced Breathing App, unmute the computer and resume work. I had learned through trial and error that those specific actions avoided any Freakback in which attention gets rooted in gaming the software causing added stress versus relaxing the mind and returning to Poise.

After each one hour session I combined the data manually into a spreadsheet with columns for time, reason for Upset, and length of time in Upset. The resulting difference in recovery time is shown here:

 Stacked comparison

Respiration as a recovery tool was far more effective. Versus logging, it provided 35 minutes more time in the Poise state for a 300 minute period. Versus baseline it delivered 40 minutes more. Looking at the difference from another view, this graph compares the number of incidents based on the length of time it took to recover from each incident:


There is a clear movement of the Respiration based responses into shorter recovery times. There was not a single incident over 60 seconds, and only three over 45. The number of incidents for logging and respirations responses in total was 169 and 165 respectively, so the recovery methods did not effect the number of triggers.

So as a recovery method from the Upset state, respiration alone is a powerful candidate in the building of a Personal Performance strategy. This is not a revolutionary finding, but it is gratifying to see mathematically play out in my own studies.