Tag Archives: Poise

Are To Do Lists Really Useful?

I was grinding through my daily “To Do” list, moving and consolidating the various seemingly urgent items, when I wondered if such a practice was really effective. I was spending a lot of time tracking what needed to be done on these lists. I was using online tools, notebooks, and scraps of paper. But I had never examined the practice itself and was wondering. Are To Do lists really useful?


These lists played a significant role in my day. I would start with a list early in the morning and use it until evening.  It seems we all have a complex relationship with our To Do lists. Psychologists study why we use them, and why we don’t follow them. In the tradition of Quantified Self, I decided to study my own relationship with my To Do lists.

My Question

Was my practice of making and following a To Do list really useful?

What I Did

Over the course of 21 days I logged my To Do’s as they arose. I captured my frame of mind as I first thought of the item and my feeling after I had explored it. I also logged what would happen if I did not complete the item, and what I thought was the underlying goal behind the item. I call these underlying goals Source Code Stories. I was looking for Source Code Stories that were driving the impulse to do something. For example, if the To Do was to get a report to my colleague, the Source Code Story could be that I wanted to be seen as responsive.

How I Did It

I used a Google Form and my iPhone to create a mini-survey. The format was similar to my Upset log that I have described here before. When I felt the urge to do something and log it on a To Do list, the first entry on the Form was my state of mind on a scale of 1 to 5. The next entry was the item. The third was what would happen if I did not complete the item. The fourth entry captured what I thought I was trying to achieve, which was the indicator of the Source Code Story. The fifth and final entry again rated my state of mind from 1 to 5. Using this simple survey I was able to capture 105 To Do impulses over the course of 21 days.

What I Learned

My habit of creating To Do lists was not really useful. The majority of impulses to get something done came from a stressed state and the impact of not following these impulses on my situation was negligible. My To Do lists were capturing non-essential impulses that were not driving big outcomes.  What was least useful is the existence of the list kept dragging my attention back to these small matters, robbing me of attention of being available for more creative, larger outcomes.

Looking at my state of mind when To Do’s arise, I found that 79.1% of the time I was in a negative mental state. That meant I was worried or in a state where my mind was racing. Only 20.9% of the time was I in a positive state considering creative things to do. Here is an image of my state during To Do’s:

Are To Do Lists Really Useful

Looking at what would happen if I did not take action on the To Do’s, I found that nearly all of the recorded To Do’s had no immediate impact on the situation if I did not do them. Over half, if not done, would have no impact at all. For a portion eventually I would be reminded to do the item by another person or I could do it later. Here is the breakdown of results if I did not pursue the To Do’s:

Are To Do Lists Really Useful

Only 3 of the 105 logged impulses would result in something creative and interesting that I was initiating coming to a halt. That meant that the creation of the To Do list was not driving big, creative outcomes at all. It was rooting action back in the long list of stress based low level tactical activity.

Looking at my state of mind, when I felt I had an obligation to another person my average start mood was 2.35. When the To Do was a mechanical item like getting a car washed, the average start mood was 2.9. There was a statistically significant difference in state of mind between those To Do’s that were obligations to a person versus getting something mechanical done.

This tied into my Source Code Stories. Looking at the most prominent of these stories, I found that having people appreciate my effort and seeing me as knowledgeable were the most numerous. Other stories like being organized or balancing my checkbook lagged far behind.

Looking at my Source Code Stories and counting the number of To Do’s by type, I saw that despite that fact that connecting with people had a disproportionate importance for me, 63% of my To Do lists were tactical, mechanical items. Are to do lists really useful? I’ve found they aren’t for me.

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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.

Using Stress App While Getting Tooth Drilled At Dentist

That title does not lie. I wore a Polar H7 Heart Rate Sensor paired with SweetwaterHRV’s SweetBeat Life to the dentist where I was to have a cavity filled. I measured the entire session and have pulled the data for the specific five minutes where the drill was going and that burning smell filled the room. To that measurement in a moment.

I also visited my stockbroker today to review the state of affairs. I wore the same heart rate sensor and had SweetBeat Life reading my vitals. At one point in the discussion he asked me to make a long term decision that could effect the fortunes of my children and their children’s children. I have the data from that five minute discussion as well. To that in a moment.

As a baseline I have uploaded five minutes of a coherent session that I did some time back. It is the most perfectly relaxed and coherent session I have experienced. The data was exported and put into Kubios HRV freeware. Here is what that report looks like:


You can see at the green arrow the waveform of my heart rate. It looks nearly like a perfect sine wave, smooth and relatively even. The blue arrow shows a nice distribution of heart frequencies which indicates good variability. And the orange arrow shows the Low Frequency to High Frequency ratio (LF/HF) of my system as 9.5. This is a smooth, relaxed state.

For comparison I pulled five minutes of a difficult work session. I don’t know what was happening that day, I was just out of sorts while doing email and tweeting. Here are those readings:


You can see the waveform (green arrow) is much flatter, there is a much smaller distribution of frequencies (blue arrow) and LF/HF ratio (orange arrow) is 1.056. This is a stress state and the metrics are very clear that there is very little variability in the heart rate.

To our friend the stockbroker. Though I spent an hour in the appointment there was one five minute period where I was “on the spot” with respect to a decision. Here is the picture:


The waveform is chaotic at points (green arrow), but there are clear sine waves for a portion of the period. The distribution of frequencies (blue arrow) is quite broad and the LF/HF ratio is 8.046. This is nearly the same ratio as in the relaxed state. So though I felt somewhat out of sorts at moments mostly because I never was any good at picking stocks, I was generally in a Poised state during that meeting.

The Dentist came as quite a surprise. There were periods where I was very concerned that I would feel intense pain. Fortunately I have an excellent dentist and things turned out to be quick and painless. The data shows I was not nearly as upset on a physiological level as I thought I would be:


To remind you, this five minutes was when the drill was going. The waveform (green arrow) is a more jagged than a relaxed waveform, but there is some variability. See the difficult session above to see a waveform with little variability. The frequency distribution (blue arrow) was much broader than I would have expected. And the LF/HF ratio was 4.350, not as low as the difficult session at all. In fact, the LF power which indicates a mixture of the Sympathetic Nervous System (Fight/Flight) and the Parasympathetic Nervous System (Rest/Digest) was equal at the dentist and the stockbroker.

So the conclusion here could be that having a bad day doing email is far worse than visiting the stockbroker or the dentist.  If you are banging away at email , feeling out of sorts and find yourself in San Francisco, head over to Dr. Shek and have him the get the drill out!

Improvement Results In Upset Recovery

I am pleased to be presenting the results of this study at the QuantifiedSelf Meetup in San Francisco on 28 July.  As I have been crunching the numbers I thought I would give a sneak peek at the summary results.

Starting on 24 June and I sat for 71 sessions using the Heartmath Pro and a digitial recorder. I would do work on a computer and record verbally what I was thinking when the Heartmath indicated I was dropping out of cardiac coherence (Upset). I was measuring interruptions to coherency which roughly correspond to being distracted or Upset while working.

As the sessions progressed I developed recovery techniques to try and move back to coherence (Poise) as quickly as possible. I wanted to see if I could reduce the amount of time I was Upset during the sessions. The data shows that my techniques did reduce the amount of time I spent Upset in each session over the course of the study:


What this graph means is that the % of time I spent Upset per session went from close to 35% at the baseline to 12% at completion. In time terms, that means in each 60 minutes of work I was able to add back 13 minutes and 48 seconds of productive time. When in Poise my thoughts are more focussed, clearer and my output higher.

As an example of how Poise made for more a more productive mental state, while working on multiple administrative issues I needed to recall a mailing address. In every session when I was in Poise I could do this with ease. During one particular session I was in Upset and I could not remember the address at all. I remember thinking “I know this information” but I had to look it up on my computer. When I returned to Poise I could again easily remember the address. This is one anecdote that reflects my qualitative experience of how clear my mind was when in either Upset or Poise.

My second question was if the number of Upsets were decreasing per session. The techniques slightly improved the average time between upsets, but not enough to account for the overall improvement show in the first graph:


The average time between Upsets in each session went from 2 minutes to 2.5 minutes over the course of the study. My qualitative experience was that the sessions had a steady diet of distractions and upsets that would occur before I was even aware of it and the machine had to alert me that I had triggered. My pre-conscious awareness was knocking me into an Upset state at a pretty steady rate.

What accounted for the improvement? The techniques I developed to effectively react to the Upset look to be the driver. The length of time I spent in the Upset state once I detected it dropped over the course of the sessions:


Awareness and consistent response in this study was particularly effective in improving how fast I could reverse an Upset. The methods were based on the two navigational impulses I discussed in my earlier post on Feeling Lost or in the Wrong Place. I have now moved to measuring two sessions daily as training to embed the ability to reverse Upsets with a guide of keeping the % of session time in Upset at around 10%. So far the results have been positive.

Look forward to seeing you at the QS SF Meetup!


Heart EM Spectrum Working & Uninterrupted Focus

Did two sessions today, one to focus for 20 minutes without any activity, the other to measure how effectively I could return to Poise after detecting an upset using the Hearmath Pro. The device also took readings of the frequency spectrum of my system and the average of my system EM power emissions over the length of the session looked like this:



By Working I mean working in a quiet environment with no immediate physical danger while editing a document. While doing the uninterrupted session I was sitting in the exact same place. Instead of editing a document I was simply focussing on paced breathing. Both sessions were of similar length, 20 and 25 minutes respectively.

Each session had a spike at the .1Hz range, which is associated with the entrainment frequency of heart rate coherence. That means when your heart and breath are entrained you see the system emit a pretty steady .1Hz.

Of interest is during the Work session, the lower frequencies associated with the sympathetic (fight-flight) nervous system also showed a spike. During that session I had 2 navigational “Lost” feelings and 13 “Wrong Place” feelings. The average recovery for both was around 18 seconds.

I can not draw any conclusion from this but it certainly gives an other dimension to the training in that I can see the shape of sessions on an EM level and connect that to the shape of the emotions as they occur while I navigate through my mental territory of the work session.

Anatomy of a Freakback

I had a classic Freakback experience while doing some free form meditation with the Heartmath device. I was sitting quietly and had achieved a Poised state while watching my Heart Rate Variability (HRV) measurement on the screen. At 2:02 I saw the line jump up and can hear myself on the audio saying “What the heck?”


After that I can hear myself trying to figure out whether the device is reading correctly (as indicated by the red lines) then getting irritated that I have sat for a minute watching the reading go wild. Finally realizing I am Freakbacking, I start doing paced breathing and emerge back to Poise about 88 seconds later.

This session took place in a room with no distractions. I was relaxed, had nowhere else to be and was entirely set up for an extended period in a state of Poise. A distraction of some sort sent my HRV up and my attachment to that not happening did the rest.

Knowing how to avoid this reaction while taking on board real time data is a critical Quantified Self skill. Reacting to the notification “you are in Upset” with more Upset is and ironic and unwanted outcome.  User Experience designers should also take note – designing interactions that pull a user deeper into being mesmerised by their reading can destroy the purpose of your design.

Closeout of Upset Study

Having just spend the last three days in Finland at the Upgraded Life Festival I had a chance to both network with an interesting group of people and bring my first measured Upset study to a conclusion. As is always the case with these studies there are always a few conclusions that are unexpected. I measured 699 Upsets over 11 days using the Heartmath Pro on my Mac. My largest takeaway is that I believe is the trend intellectually is just not so when I look at the data. Three examples:

Belief #1 – I thought that the number of Upset triggers during each session was keeping pretty steady at about one every two minutes.

Fact – Not so. Over the 11 days of data I collected the number of upsets per minute was steadily declining as shown in this graph.


Other than a really poor session on day 5 the trend shows the number or triggers dropping.

Belief #2 – I was improving my ability to return to Poise steadily over the course of the study. This graph seems to say that.


Fact – Not so. The introduction of the paced breathing was the single structural driver of the change. When I looked at the data for those Upset recoveries that occurred after introducing paced breathing the trend actually showed that Upset recovery was steadily increasing, not decreasing.


Belief #3 – By logging and recovering from Upsets that are completely Self Induced, ie I was sitting and imagining a poor outcome sometime in the future, I was improving my recovery time for that type of Upset.

Fact – Not so. After logging 160 specific Self Induced Upsets of that type after I introduced paced breathing, the recovery time remained flat as shown in this graph.



So the high level takeaway is that the introduction of the physical act of paced breathing was effective but none of the other elements of the process had a significant effect on the recoveries.

Pushing Poise Outward

I spent 1.5 hours today working on getting myself back to Poise and added some imagery to see if it intensified the improvements but I feel the need to push a little harder. Rather than seeing how far down I can screw the return to Poise while working at my computer by myself, I want to take this into the realm of dealing with other people directly. So I am now going to start measuring my Upset reactions while dealing with others in person, by Skype or on the phone.



I started down this path with the 800 Number project which failed because trying to read from just heart rate and self report did not really yield much useful information. I did try multiple calls and logged them but was not satisfied it was going to go anywhere. Now with Heartmath and the satisfactory results of the previous look at respiration I am going to start exploring this again.

I feel pretty strongly that all the tech companies are going to introduce a large number of products that will help you return to a relaxed state while you sit quietly and count your breaths. Its a good thing and an excellent start point. But very soon it will not be cutting edge to sit relaxed for ten minutes a day and have your app turn green when your stress drops past a certain level.That will be center of the crowd. I want to push further afield.

To embed the learning from the past studies I am going to log 20 hours of returning the Poise while on a computer so I can lock in the sequence and have it at my disposal. Every five hours on the computer I want my 40 extra minutes of Poise so I am going to stick the basics and put in the miles.

In the meantime, I have a call with my bank in 30 minutes for which I have to get the kit set up so I can start measuring the baselines in that direction!

On we go.



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.

The Effect of Logging on the Return to Poise

In my post on Base Rate for HRV Recovery I outline the details of the Poised state versus the Upset state. What I am measuring is the effect of active remedies taken when the Upset occurs. The environment was working on the computer with as many variables as possible (sleep, exercise, food, etc) unchanged. I had the baseline data as described in the earlier post.

For the first test I wanted to see the effect on the amount of time it would take to return from Upset to Poise by simply and consciously logging the Upset. I used the Heartmath EmwavePro, Taplog and an Olympus VN-733PC digital recorder. I set the EmwavePro to make a low sound only when I my Heart Rate Variability (HRV) indicated I was in an Upset state. When I heard that, I would log the time on Taplog and speak what was on my mind into the Olympus recorder. At the end of each session I was then able to assemble a spreadsheet showing the Upset time, the length of time I was in the Upset and what I was thinking that may have triggered the Upset.

Over five hours of logging I had a list of 169 Upset events. Of those, 19 were about the process of doing the observations itself, like getting the devices to work. So 150 environmental triggers created upsets. In the baseline study the number was 124. So a generalization can be that while working on the computer some stimulus sends me from Poise to Upset every two minutes.

Each Upset had a different recovery time, which I have displayed in this chart showing the number of upsets by length of recovery time. I show both baseline and logging results.



Of interest is that there were an equal number of “long trigger” (over 61 seconds) Upset events and a large difference in the under 15 second group. There were nearly twice as many under 15 second events when logging than when doing nothing (baseline). I noticed that interruptions in attention cause short Upset spikes, and the act of logging was full of interrupted attention. Was that interruption worth it? Only if the total number of minutes in Upset was reduced. Here is the chart showing total number of minutes grouped by length of time.


This picture is much different. The similarity is on the low end of the scale, meaning even though there were double the number of Upset events while logging, the amount of time not Poised was not nearly as impactful on total time. When we get to those Upset events that were 46 seconds or over, the baseline number of seconds in Upset state are clearly higher though the number of triggers were similar.

So it does appear that logging tactically reduces the amount of time spend in Upset. How much? The total time in the 5 hours of baseline readings that were in Upset was 27.6% of the time (82 minutes out of 300). The total time in Upset for Logging was 26% of the time (78 minutes out of 300). So logging “saved” about 4.6 minutes out of the 300 total. Not an impressive result if we expect to have an active measure deliver us back more time in the Poised state.

Next I will be measuring what the literature considers the largest lever we have – respiration.