Zoom recording of the full session is here: QS Show&Tell Talks 6.13.23

You can view slides and find out more about the presenters below.

Sara Riggare: Moderator

Gary Wolf: Did My Metabolism Improve? Link to slides.

Steven Jonas: How Bad is Snacking after Dinner? Link to slides.

Martijn de Groot: Learning to Change My Food Intake. Link to slides.

Thomas Blomseth Christiansen: Ruling Out Pre-Diabetes in Long Covid. Link to slides

Jakob Eg Larsen: Subjective and Objective measures of Blood Glucose. Link to slides.

About Personal Science: Around the world, people use self research and “quantified self” methods to ask and answer questions about their own lives. Topics and motivations are diverse — managing health conditions, self improvement, mental health, or simple curiosity. What connects these is that each person is doing their own research to answer their own questions. We can learn from each other.

About the QS Show&Tell: We use a simple format of short talks to share what we’ve discovered about ourselves with our own data. In these five to seven minute talks we’ll describe “What I did, how did I do it, and what did I learn?” Data will be shown.

About Nutrisense: A number of startups have launched in the past few years to support people using minimally invasive blood glucose monitors to learn about their diet and metabolism. One of these companies is Nutrisense. We recently partnered with Nutrisense to explore using the CGM and their meal and activity app to make our own blood glucose discoveries; in this hour of short talks we’ll share what we’ve learned.

We also welcome you to join our weekly Thursday self research chats, where folks share ongoing work, ideas, and tools. More information is here: Weekly Self-Research Meeting Notes.

The post CGM Show&Tell June 13 2023 appeared first on Quantified Self.

Use this form to get a meeting link if you want to attend.
Date: June 13, 2023
Time: 7:00 AM PDT / 4:00 PM CEST
Where: Online — use this form to get a meeting link.

PRESENTATIONS

Sara Riggare: Moderator

Gary Wolf: Did My Metabolism Improve?

Steven Jonas: How Bad is Snacking after Dinner?

Martijn de Groot: Learning to Change My Food Intake

Thomas Blomseth Christiansen: Ruling Out Pre-Diabetes in Long Covid

Jakob Eg Larsen: Subjective and Objective measures of Blood Glucose.

About Personal Science: Around the world, people use self research and “quantified self” methods to ask and answer questions about their own lives. Topics and motivations are diverse — managing health conditions, self improvement, mental health, or simple curiosity. What connects these is that each person is doing their own research to answer their own questions. We can learn from each other.

About the QS Show&Tell: We use a simple format of short talks to share what we’ve discovered about ourselves with our own data. In these five to seven minute talks we’ll describe “What I did, how did I do it, and what did I learn?” Data will be shown.

About Nutrisense: A number of startups have launched in the past few years to support people using minimally invasive blood glucose monitors to learn about their diet and metabolism. One of these companies is Nutrisense. We recently partnered with Nutrisense to explore using the CGM and their meal and activity app to make our own blood glucose discoveries; in this hour of short talks we’ll share what we’ve learned.

We also welcome you to join our weekly Thursday self research chats, where folks share ongoing work, ideas, and tools. More information is here: Weekly Self-Research Meeting Notes.

The post New Show&Tell Event: Tracking Blood Glucose appeared first on Quantified Self.

His doctor replied: “I don’t know. Nobody knows. Rob, you’re an astronaut.”

All self-researchers asking questions that don’t attract scientific interest are astronauts in this sense. They are forced to explore beyond the limit of medical knowledge because nobody else will ever care enough about their questions to make the empirical observations needed for discovery.

In her book released this week, We the Scientists: How a Daring Team of Parents and Doctors Forged a New Path for Medicine, Amy Dockser Marcus tells the story of a group of families who forced research attention onto a rare genetic syndrome, Niemann-Pick disease type C (NPC). The disease interferes with cholesterol transport in cells and most children diagnosed die before the age of twenty. With their medical allies, including some leading research geneticists, the activist families accelerated the study of promising drugs to treat NPC, and, though a cure is still beyond the horizon, the new forms of collaboration they tried may have a chance of changing the culture of drug research — though this is far from certain.

The scientist who first brought families and researchers together was Christopher Austin, then the director of the National Center for Advancing Translational Sciences; now he is the CEO of Vesalius Therapeutics. A familiar metaphor for exploring beyond known territory is a ship at sea. This image is taken from a post titled “Losing Sight of Land,” by Noubar Afeyan, the CEO of Flagship Pioneering, the VC company that funded Vesalius and recruited Dr. Austin.

Vesalius emphasizes that its focus is on common illnesses. There is an irony here: while there’s an inarguable rational for pointing the most powerful tools of discovery at the most common diseases, this is also the problem that We the Scientists starts with: when what you have is very rare, clinical research looks away.

And yet, the very notion of common illnesses may not survive the approach either of the patient-activists Dockser Marcus writes about or of the new techniques Austin and colleagues are developing at Vesalius. That’s because rarity is common.

NPC is rare in the sense that it is a known genetic disorder of a few thousand children. Something like Alzheimer’s disease, or Parkinson’s, or diabetes, or depression, affects tens or hundreds of millions. But these differences in scale are illusory. NPC is a narrowly defined condition with a known cause. Each of the other health problems I listed is a disease with widely varying expressions, causes, and treatment regimes. When you take into account the personal challenges of dosing, side effects, diet, sleep, and other activities that affect symptoms, the group size of people facing common diseases resolves downward toward a resolution of one.

This is not to downplay the crisis of rare disease, in the sense that Dockser Marcus and her protagonists mean it. Nor is it to undervalue the general tools clinical research delivers to manage common components of common diseases; for instance, Levodopa for Parkinson’s. It’s just to say that, in reading We the Scientists, I felt a strong empathy for the frustration and intensity of the families whose children were left outside the known boundaries of clinical research. They were sick and in need of help, but medicine knows almost nothing about how to help them. That’s what’s common. Everybody dealing with complex illness — or even with seemingly simple problems like recovering from injury — soon reaches the limit of clinical knowledge.

As new tools of rapid analysis of candidate drugs, rapid trials of treatments in small groups, and targeted therapies based on individuals deliver — hopefully — useful interventions, questions about the value and effects of these therapies will not decrease but multiply. (For instance, here’s a self-research report about a self-research project about dosing Parkinson’s medication. Here’s another one.)

New knowledge unveils new ignorance. The seafaring metaphor is inadequate. Sailors have it easy. They move in two dimensions. We’re astronauts.

The post Astronauts appeared first on Quantified Self.

Trained at Columbia College and Harvard University, where he studied with BF Skinner, Allen’s field is the experimental analysis of behavior. As a professor of psychology at Reed College (now professor emeritus), he noticed that few behavioral psychologists studied themselves in the same way they studied their animal subjects. Allen suspected that if he applied the scientific method to his own life, he would learn and be able to help others more.

Could the Heart Do What the Mind Wants?

Allen’s self-experiments began in the early 1970’s. He asked himself silly questions and tried to answer them. There were many failures, but that was okay. Failures in science are important. An early experiment that he regarded as a failure was the attempt to answer a question: If he could manually take over his breathing which is otherwise an automatic process, could he exert manual control over his heart rate as well? With breathing rate, he had feedback: it’s easy to tell whether breathing is fast or slow. What if he had feedback about his heart rate? Could he exert influence over it just as he could influence his breathing?

He used a stethoscope to get immediate feedback from his heart, and after wearing it for four days he discovered that, in a way, he could control his heart rate: Quick breaths would increase his heart rate, while holding his breath caused it to slow down. But he considered this result to be a failure, since he was looking for direct control of heart rate, rather than indirect control via breathing.

Another of his early experiments involved questioning why we sleep on a flat surface. Is it just a force of habit? What happens if we are at an angle? Getting buy in from his wife Martha, he used plywood and some bricks to prop up one side of the bed. First, they slept with their heads elevated and found they slid off the bed. With their feet elevated, Allen found that he woke up with a headache. These results were not surprising, but they gave him confidence that he wasn’t sleeping flat due to habit alone. (There are some people who argue that sleeping at an incline improves health. Why not conduct your own test?)

Why Confirmation Bias Is Not an Issue

Allen notes that failures are important because they demonstrate that the experimenter’s expectations did not determine the result. Self-experiment is not merely an excuse to practice wishful thinking or to demonstrate the power of “confirmation bias.” There may be a couple reasons why this is so. For one, the experimenter has an incentive to get a true result. If it really works, we’ll benefit from it. And, there is no incentive to fake a result. There’s no promotion or tenure waiting if the experimenter fudges a result to make it look positive.

A(B) Method for Self-Experimentation

Allen followed a classic model for his experimentation. He posed questions and observed phenomena and thought of possible explanations. Then he would devise an experiment to test his explanation. His experiments usually followed an AB model. He would do activity A for a set period of time (a day, perhaps more), followed by a B period without the intervention. He would repeat this interval, ABABAB, as many times as needed.

He notes that his methods are primarily experimental, befitting his professional background, while most in the Quantified Self community are observational. It’s possible to bridge the two, he notes, citing a former student of his, the late Seth Roberts, a prominent contributor to the early Quantified Self community. Seth monitored certain aspects of himself (mental acuity, for example) over long periods of time. When the results fluctuated unexpectedly, he hypothesized a few reasons why and switched from an observational mode to an experimental mode to test those ideas. Seth credited this method for helping himself generate more ideas than he otherwise would.

Generating New Ideas

Allen had his own method for generating ideas. While a grad student, he went on walks through the basement hallways of Harvard’s Memorial Hall, often passing Nobel Prize winner George von Bekezy, perambulating in the opposite direction. One day, von Bekezy said to Allen, “You walk, yeah?” in a heavy Hungarian accent. Allen nodded. “Good ideas come from walking.” Allen noticed that his ideas didn’t just come in hallways, but on long hikes as well. Movement seemed to generate ideas, but it could also go in the other direction. He noticed that if a good idea popped in his head while working at a desk, it would prompt him to jump up from his seat and pace back and forth.

This Brain Moves

Allen proceeded to test the effects of movement on his cognitive abilities. He tested memory at first. He had flashcards with faces on one side and names on the other. His A condition would be to run two miles or swim 20 laps and then review 20 of the cards recording how many he got right. The B condition would be to spend the same amount of time working at his desk before reviewing the cards. The effect was clear. His ability to memorize was better after activity.

But how does one test idea generation? Allen’s method was to spend 15 minutes moving around in a “quasi-dance” manner and noted any ideas he had on a notecard, writing the date and the condition on the back side, in this case, “move”. He then compared those cards to ones generated during a 15 minute period sitting at a desk.

He repeated these AB intervals over the course of weeks, accumulating piles of cards. Months later he went through the cards and evaluated the quality of the ideas, looking at whether or not they were good and how creative they were. He didn’t know which conditions they were, since “sit” and “move” were written on the back side. He calculated the number of subjectively judged “good” ideas for each condition. Again, he noticed there were clear differences. Movement helped.

Movement also helped with reading. Allen rigged a book holder out of an old backpack and through his testing found out that he surprisingly reads faster while moving and retains more. But was moving always better? Allen looked at his problem solving abilities in the move and sit conditions, using a similar method that he used for testing idea generation. He found that moving tended to make problem solving easier, with one significant exception: problems involving mathematical reasoning were more difficult to do while moving.

If A Then B

Allen also looked at how he could apply behavioral concepts to his life, like contingencies. A contigency is the relationship between two events, one being a consequent of another. It’s a simple model of “If A then B”.

Some things were difficult for Allen and Martha to do, even though they wanted to do them. Martha wanted to dance more, Allen wanted to write. So they agreed to a simple contingency: If she dances for 15 minutes and Allen fails to write, then he cooks and does the dishes. Vice versa if he writes and she doesn’t dance. If they both do their thing or fail to do their thing, they share as normal. Even though this wasn’t a terribly aversive contingency, Allen reports that it worked like magic on their behavior. Today, there are apps that now help you to set up contingencies, such as Beeminder. However, Allen wonders if his and Martha’s use of interpersonal contingencies has a more powerful effect. Something worth testing!

Learn More

To learn more about Allen, read his paper from 1981 advocating for self-experiment as a method of discovery. You can also watch his Quantified Self talk about his career, historical self-experimenters, and what his students have learned from their own self-experiments.

The post Allen Neuringer’s Decades of Self-Experimentation appeared first on Quantified Self.

There’s a lot to like about this this new community trial of potassium for weight loss from Slime Mold Time Mold.

The idea comes from a previous, much more difficult community trial they organized: Eat nothing but potatoes, oil, and seasonings for 30 days. Many people lost weight on this diet, and analysis of the aggregated data showed some big declines. But why?

One of SMTM’s ideas is that the potassium in the potatoes may reduce the obesogenic effects of lithium. (For details, see the original post and lengthy comments.) If potassium is the active ingredient in the potato diet, maybe KCl alone would work, sans the tasty but relentless accompanying of potatoes, potatoes, and more potatoes.

My expectation is the the potassium/lithium theory won’t last. It rests on a fragile chain of guesses both about the role of lithium in obesity and the relationship between lithium and potassium in the body. These guesses are informed guesses, which I admire, but the possibility space here is very large, and it would be pretty surprising if they turned out to be correct. (My preferred candidate for why the P diet works is that it removes flavor novelty and intensity, and this itself lowers the lipostat. If that’s the case, there is no “active ingredient” and any dull monodiet would work, although when it comes to monodiets potatoes are a good choice since they are very nutritious.)

Still, I really like this community trial even though I doubt the lithium theory. If you tell me to test some speculative, plausible, but probably ineffective health intervention, I’m going to think carefully about how much it costs to me do the test. What’s the trade-off? How much money, time, and, most importantly, risk am I taking on here?

Well, testing a KCl supplement is easy. And the payoff if the longshot comes in is insanely high: it would be a stunning surprise. Plus, as a fan of n-of-1 experiments, I like that Time Mold Slime Mold tried the KCl supplement and it worked for them. From n-of-1 to n-of-many-1s, isn’t that how it’s supposed to work in our QS science fiction utopia?

Twelve years ago Eri Gentry and some other QS participants tried this with the Butter Mind study. The study design had a flaw: measuring the effects was complex. Seth Roberts, whose n-of-1 study had inspired the community trial, argued it confirmed his ideas. But I think the result wasn’t as decisive as Seth believed. The reason we didn’t learn more — or inspire others to follow up—is that even with the help of the free assays at Quantified Mind, the small magnitude of cognitive changes, and the sensitivity of the instruments to the measurement context, makes for a taxing analysis, and opens door for conflicting interpretations.

The design of the Potassium For Weight Loss study, by contrast, is a marvel of simplicity. There is not going to be a problem measuring changes in weight. And the effect predicted is very large. So: the trial is likely to yield an answer. I’m very curious to find out if the answer is yes or no.

In short, the potassium trial: 1) is about something important. 2) is easy to participate in. 3) does not involve much risk. 4) is likely to yield a clear result. 5) could shock the world.

Let’s do it.

You can sign up here: https://slimemoldtimemold.com/2022/10/15/low-dose-potassium-community-trial-sign-up-now/.

The post Community Trial of Potassium for Weight Loss appeared first on Quantified Self.

Summarizing Anne Wright’s description of how a person coping with chronic health issues progresses through the process of self-research.

Sara Riggare created this animation inspired by Anne Wright’s essay, Reflections on the BodyTrack Project. We were highly influenced by Wright’s work, which we cite in the introductory section of our book Personal Science: Learning to Observe.

We’ve made an almost finished draft of the book available in an “early access” edition via LeanPub so that people with a strong interest in self-tracking and personal science can read it. Please let us know what you think. LeanPub permits sell the early access draft using a “pay-what-you-wish” system.. Please know that all royalties from the sale of this edition will go toward supporting QS community resources.

eBook Link: Personal Science

The post Anne Wright & Personal Science appeared first on Quantified Self.

On the other hand, I’m experienced at recovering from sports injuries.  I had 7 concussions by sophomore year in college.  Ten years ago I was read my last rites after a surfing accident.  I’ve learned I’m psychologically resilient, and that being engaged and creative during the recovery process can help you beat the odds. So I went ahead. I had the surgery. 

It’s now eight months later, and I’m certain I made the right decision.  From this end of the experience, I can look back and see the effects of certain choices I made about how to approach the challenge, and I want to share what I learned from using integrative approach, including a strong element of personal science. 

First, I gave some thought to my highest level goals. They were: 

1. Be stronger and healthier than before my skiing accident. 
2. Don’t overdo it and cause a setback. 

Next, I thought carefully about what to track, taking advantage of the practical advice about personal science that I’ve found in the Quantified Self community. I decided to keep simple daily log with a record structured like this:   

1. Focus for the day
2. How does today compare to the day before
3. The day’s key issue
4. Anecdotes

I used a numerical rating for the comparison of today with yesterday, with 1 meaning worse, 2 meaning equal, and 3 meaning better. I limited myself to three anecdotes per day.

These self-assessments represented my foreground phenomena. They were the factors I was paying the most conscious attention to, that I was most curious about, and that I wanted to see improve. 

In addition, I used data from my Apple watch Series 6, my Oura ring, my Calm app, and the clinical data from my care team. These represented my background phenomena, data that I might use to understand more deeply what was affecting my improvement and the issues I was facing. 

Once I organized my Personal Science approach, I went to the various teams of professionals at Barton Hospital, in Lake Tahoe, where the operation and recovery would be managed: surgical team, internist, fitness coaches and physical therapists.  All four teams were encouraging. In these pre-surgery conversations, I was told that the stronger I went into surgery, the more likely I would have a quick recovery. So I did the recommended workouts, trying hard to be ready.

Surgery was on May 18.  My average Resting Heart Rate (RHR) that night was 53.   My RHR spiked right after the surgery and was highest in the first two weeks. Up 28%.  Also, I was miserable from pain, weakness and inactivity. My RHR did not drop back down to 53 for the first time until June 28. It was not consistently in the 50s until July. My struggles are unambiguously illustrated in the graph above.  

In the first two weeks after surgery, there were seven days where I felt worse than the day before and seven days where I was about the same.  There were zero days when I felt better. This is not to say that I did not improve. Clearly the wounds were healing. But recovery can be complicated; the healing process itself felt very bad.  

Postoperative hypoxemia

On the day of the operation I spent 4 extra hours in recovery while my blood oxygen levels went back up and stabilized.  That night, my Apple watch showed blood oxygen readings in the 90+%.  Three days later I wrote “wheezy, shallow, high pitched sounds”, and my blood oxygen levels ranged from 76-97%.  The next day “more wheezes” oxygen 78-93%.  My blood oxygen did not start to have consistent readings in the 90s for more than two weeks after the operation.  Postoperative hypoxemia, or low blood oxygen level, is a known risk of surgery. Maybe it was the lingering effects of anesthesia, maybe the 6,100’ elevation, maybe the opioid painkillers; whatever the cause, I had it.

On the fourth day of my recovery I stretched the opioids out to every five and a half hours instead of the prescribed four.  My notes record me attempting to manage the tradeoff between “Oxy feeling” and “pain.” I did not like this drug. Seven days after the surgery, my physical therapist mentioned to me that my thinking was fuzzy.  I went to the surgeon’s team and asked if I could be taken off the Oxy and, instead, try medical marijuana. They approved the plan. My brain fog cleared overnight.

The surgeon had me in physical therapy the day after the surgery.  Although I felt horrible overall, my knee was making progress.  A key metric is “flexion.” That is, how much you can bend your knee. To measure flexion, you lie face down on a table and somebody lifts up on your ankle until you scream. On the third day after surgery, I got to 90°. But for full recovery the ankle has to be pressed all the way up into your buttocks, reaching 120°. This seemed like a long way. But the physical therapist told me that 90° was very good for right after surgery, and he thought my pre surgery exercises were paying off.  I watched this number carefully. I reached 95° six days after surgery, 115° in week 3 and 120° at week 4.  This was uplifting progress, and good to see, especially when I wasn’t feeling well overall.

I found it very useful to set a focus daily. My focus on the majority of days was either “reduce swelling” or “ice and elevate.” How much focus does it take to ice a knee? A lot, because I had to do it every three hours.  Along with wearing compression socks.  Sleeping with a pillow between my legs.  Trying not to limp with crutches.  Trying not to limp without crutches.  Trying not to limp with a brace.  Trying not to limp without a brace.  Focus! Keeping this record of what I was working on daily kept me engaged when often I just wanted to retreat.

On the tenth day I started my limited upper body workouts.  At first, I did these workouts sitting down.  I was not comfortable.  Distractions became increasingly useful to keep the pain out of my mind.  I had to set intentions to keep busy: keeping my logs, singing, song writing, photo processing and ukulele playing were ways to cope. Eventually, on week three, I could take my first walk outdoors, and ride a stationary bike for five minutes. Because the ACL tear was on my left leg, I was able to drive, and I took my first trip on my own in the car during week four. I ran a few errands, then came back and took two involuntary naps.  My leg was still very sore, twitches and pains were on going as the muscles reconnected and the neurons fired. I tried a variety of things to maintain my mental health, including meditation and conscious breathing.

Unexpected sadness

With the surgeon’s permission, I went to San Francisco to be an usher in a memorial service for a friend at Grace Cathedral.  There, I felt crushing sadness.  Along with the emotions, came pain. I had no ice, and on the six days surrounding the travel, I felt worse on five. Was I slipping into depression? But, looking at my log, I could see that even as my days seemed to be getting worse, my resting heart rate was improving.  It helped to have this data to encourage me. 

After a month, I no longer needed to wear my brace except when someone might hit me.  And they let me do exercises standing up, very carefully.  I even got to walk downstairs, one foot in front of the other. Scary! My incisions started hurting as the rest of my leg and body got more exercise.   But my log showed my progress.

During week six I walked 10,000 steps for the first time, kayaked for 20 minutes and played ukulele.  I felt like my recovery might be accelerating. But then my sister in law of 55 years died in South Carolina.  I had to be there with my brother.  Throughout this trip, my resting heart rate continued to improve even though my subjective experience was worse day after day. I was glad I went to South Carolina but I paid a price.

Acceleration

When I came back I found out that the acceleration of my recovery was real.  Two days after my return,  I walked 5 miles and kayaked for 40 minutes.  As the summer went by, I found myself in the third month since the surgery. During week nine, I had six worse days, six days about the same and ten days better.  I was steadily improving at last. Certain that I would be ok, I decided I no longer needed my personal science log.

Although my recovery was not over, and I still had many months ahead, I decided to ask my physical therapist to assess my progress in comparison with others who have had the same surgery.  He said there is a range of outcomes: some go very well.  Some do not.  I am clearly on the very well side. He added that my pace of recovery would be still be considered good even if I were 35 instead of 70.

A big benefit of my personal science project is that it made me feel more engaged in my recovery.  The medical team liked that I was thinking and planning from the beginning.  Taking notes every day helped me be more aware what was going on, taught me that the issues of today were not necessarily going to be the issues of the next day.  

I also benefited from noticing my progress. And as I tried to score the day or add anecdotes, I could put my feelings and symptoms in context. Generally I was getting better as I went.  My resting heart rate data was especially useful for this, because it seemed somewhat independent of my subjective feeling. It made the healing visible.

Finally, the process of logging everyday also reminded me if I had maintained my focus, and reminded me to care about recovery even the midst of my sadness and loss.

The structured phase of my recovery is now complete. In my last physical therapy session, two people played three ball catch with me while I stood one legged on a half exercise ball.  It wasn’t pretty, but it sure was fun.

The post Recovering from ACL Surgery appeared first on Quantified Self.

This long post is an attempt to provoke QS readers to approach the question of measuring mood in some new ways. As self-researchers, you won’t be surprised to discover that measuring mood has been the subject of controversy. Those of you who are also academics won’t be surprised to know that the controversy has at times become hostile. But in it are clues to some of the problems immediately confronting anybody who is trying to track how they feel.

Let’s start the story in the 1980’s, when psychologists began to increasingly use a two dimensional model to describe and measure mood. The key paper is James A. Russell’s “A Circumplex Model of Affect,” published in 1980 in the Journal of Personality and Social Psychology. [Abstract] The circumplex model describes emotional space as a two dimensional grid. On the x-axis is “pleasantness/unpleasantness,” sometimes called “valence.” On the y-axis is “arousal,” or “activation.” Arranged in a rough circle around this two dimensional space are the varieties of human feeling, like this

There are lots variations on this circumplex. 

Source: (Framework of product experience, Pieter Desmet* and Paul Hekkert, IJDesign, Vol 1, No 1 (2007)

The nice thing about this model is that you can track mood with a mood checklist containing any number of  terms, but whether you use more terms or fewer, this circumplex pattern is generally going to emerge. If you are only interested in the high level constructs, rather than in all the component descriptors, you should be able to approximate your mood with two questions: How happy do you feel? How energetic do you feel?

Or maybe not. In 1988, David Watson, Lee Anna Clark, and Auke Tellegen published a paper introducing the Positive and Negative Affect Schedule (PANAS), based on the idea that positive and negative affect should be separately tracked because they vary independently. In other words, it is possible to feel good and bad at the same time. To force mood tracking onto a bipolar two-dimension grid, when the supposed poles are not really opposites, is to risk distorting important observations.

Here is an example of a PANAS checklist, from a survey designed to measure emotions. You are asked to report to what extent you have felt this way during the time period being measured (right now, past few hours, past week, etc.).

———————————————————————————————————–

1                              2                       3                            4                        5
very slightly          a little            moderately             quite a bit           extremely
or not at all

______ cheerful      ______ sad      ______ active    ______ angry at self
______ disgusted   ______ calm     ______ guilty    ______ enthusiastic
______ attentive     ______ afraid    ______ joyful    ______ downhearted
______ bashful       ______ tired      ______ nervous ______ sheepish
______ sluggish     ______ amazed ______ lonely    ______ distressed
______ daring        ______ shaky    ______ sleepy    ______ blameworthy
______ surprised    ______ happy   ______ excited   ______ determined
______ strong         ______ timid     ______ hostile    ______ frightened
______ scornful      ______ alone    ______ proud     ______ astonished
______ relaxed       ______ alert      ______ jittery      ______ interested
______ irritable       ______ upset    ______ lively      ______ loathing
______ delighted    ______ angry    ______ ashamed   ____ confident
______ inspired      ______ bold      ______ at ease   ______ energetic
______ fearless      ______ blue      ______ scared    ______ concentrating
______ disgusted   ______ shy       ______ drowsy     ______ dissatisfied with self
with self
_________________________________________________________________

The conflict between the circumplex model and the PANAS scales launched the bipolarity controversy. For a short taste of the polemics you can read Russell and James M Carroll’s The Phoenix of Bipolarity: Reply to Watson and Tellegen (1999). In this short essay, which served as a final salvo on the first stage of the controversy, the circumplex seemed to gain victory. It turned out that, in a wide range of measurement contexts, data from the PANAS showed evidence of bipolarity. Items from the poles of the circumplex descriptors had strong correlations, and the descriptors arranged themselves roughly around the circle. The opposition between happiness and sadness was not an artifact of the mode. Watson and Tellegen appeared to concede the most controversial points, and bipolarity was saved. 

But this victory was not unambiguously satisfactory. Anybody with an ounce of reflective capacity can think of many examples of mixed emotion. Your good day comes to an end. You speak a foreign language enthusiastically, aware that lack of practice insures continued incompetence. You give successful aid to a beloved family member you’re seriously worried about. The intellectual critique given by Russell and Carroll to Watson and Tellegen may be definitive; and yet both positive and negative feelings about their triumph coexist. Must we really deny our own ambivalence? Since the topic at hand is subjective experience, why can’t we subjects get the last word?

In fact, this problem with the circumplex model has also been noticed by professionals, and looked into, and confirmed. Among the researchers to insist upon the independence of positive and negative feelings is John T. Cacioppo, who instead recommends the Evaluative Space model; you can read more about it in his paper: Relationship Between Attitudes and Evaluative Space; A Critical Review, With Emphasis on the Separability of Positive and Negative Substrates. [PDF]. Cacioppo proposed an architecture of the emotional space that looks like this. Please start redesigning your mood trackers now.

In a 2001 paper called Can People Feel Happy and Sad at the Same Time? [Abstract], Jeff T. Larsen, A. Peter McGraw, and Cacioppo used some experiments to reassert the claim that happiness and sadness can sometimes co-occur. Yes, happiness and sadness may usually correlate negatively; but, if they sometimes correlate positively, then they cannot be polar opposites. Another researcher, Eshkol Rafaeli (whose web site contains many interesting references), has put the label affective synchrony on the phenomenon of simultaneous happiness and sadness, and has done some work suggesting that all people are not alike when it comes to affective synchrony. Some people experience happiness and sadness as bipolar opposites; others have more experiences of mixed emotions. (See Rafaeli’s Affective Synchrony: Individual Differences in Mixed Emotions. [PDF]) 

At this point, I hope you are both happy and sad. Happy, that so much research on this question has been done for you. Sad, because this research remains contradictory where it is not confusing.

What would a good mood tracking protocol look like? Is there a clear, promising direction? If you only look at one of the references attached to this post, I recommend Lisa Feldman Barrett’s Solving the Emotion Paradox: Categorization and the Experience of Emotion [PDF]. Barrett offers both a wide-ranging review of the state of the art in modeling human emotion; more importantly, her theory of emotion offers some clear and plausible guidance for self-research.

Barrett divides emotion into two parts. Core affect is the totality of a person’s state that is available for emotional processing. The physiology of core affect is shared among animals. All of us carry out some degree of assessment along the lines of good and bad; and all of us vary in our level of energy. The fact that human descriptions of emotion clump together along the two dimensions of the circumplex reflects the structure of our core affect. This suggests that the circumplex is perfectly valid for many potential self-tracking experiments.

Let’s say we want to quit smoking, but we know that the process of quitting will make us tense and unhappy. Perhaps it would be prudent to have a way to observe our emotions that can show us patterns in the pain of quitting and even project a finish line beyond which these effects are likely to be negligible. Here the circumplex model ought to work. We are concerned about some basic elements of our emotional life — feelings of distress — in which our categories do not have to do very much subtle work. “These feelings” writes Feldman, “are primitive (psychologically irreducible) and universal…” But there are other situations in which the circumplex will fail us. Wherever the aim of our experiment is to understand and/or alter the more complex structure of our mood, we shouldn’t start with a measurement system that dramatically simplifies this structure.

Why should we be interested in the structure our moods? Here, the second part of Barrett’s paper is  interesting. In her model of emotion, core affect is the material on which emotion works, but the experience of emotion – the inner experience, as well as most of the repertoire of outwardly emotional behavior – comes from the act of categorizing core affect, giving it a label such as “anger,” “sadness” or “fear.” This does not mean that the emotion is not experienced until you are conscious of putting a name on it. You don’t have to quietly mutter “anger” in order to feel anger. But it does suggest that anger is a concept that you begin learning in fancy and may continue to extend and revise throughout life. The repeated experience of labeling a combination of core affect and the context in which it occurs as “anger” trains you in how to be angry and how to recognize anger. Barrett describes emotions as simulations, in the sense that they take an experience of core affect, plus the situation in which it occurs, and compute an appropriate result:

“….conceptual knowledge about emotion constitutes expertise about how to deal with your own internal state – experienced as “an emotion” – and the situation or event that you believe caused that emotion in the first place. In this sense, emotional categorization is functional. Situated conceptualizations may be thought of as an inference about what will make for successful self-regulation or goal achievement…”

This is a theory of emotion that could be articulated by a robot, and it strikes me as entirely plausible. Here are the practical implications:

“…conceptualizing core affect as emotion, like conceptualizing in general, is a skill… This skill for wielding conceptual knowledge about emotion might be considered a core aspect of emotional intelligence… It is a skill to simulate the most appropriate or effective representation, or even to know when to inhibit a simulated conceptualization that has been incidentally primed. Presumably, this skill not only can be measured, it can also be trained.”

This suggests that we can revise our emotional architecture through experiments in description.

When I was just out of my teens I worked in a fancy restaurant under the close supervision of its chef-owner. He wanted us to sell wine to the customers, so he let us taste everything and trained us to describe it. The obscurity of the distinctions permissible in describing wine are notorious, but my sophomoric witticisms about buttery charcoal and leather buzzing with flies did not make him laugh. After a minute he said: “You are being asked to remember these wines. These adjectives are your labels. You’re welcome to make up your own, but then I can’t teach you and nobody will understand you.” Then he pulled the toque from his head and trampled it underfoot. No, that last part didn’t happen, but I did experience something like enlightenment, and the jargon became meaningful, allowing me to meet the demands of the situation. In the scheme Barrett describes, emotions are a kind of inner jargon, triggering a cascade of associations and providing labels that make it possible to form an intentional response.

Barrett’s theory of emotion opens the door for another type of self-tracking than is permitted by the circumplex, tracking that asks questions like: How many emotions do I have? What is the range of my emotions? Do I meet various experiences with well tuned emotional responses, or have my feelings become rigid and stereotyped? Her paper suggests that we can improve our emotional structure, increasing the  granularity of emotional experiences by enriching our vocabulary and learning to apply it to previously unnoticed patterns in affect and context. 

Today mood tracking takes two forms. One uses a scale to rate mood according to it’s valence. The other uses text to label mood with adjectives. This is the bipolarity debate, operationalized. Which tracking method you want to use will depend on your goals. 

I’ll end with an idea about how this second type of mood tracking could work. First, let’s revisit two projects already underway: Ka-Ping Yee’s time allocation diary; and Atish Mehta’s Happy Factor. At the first QS Show&Tell, Ping showed us his time-allocation diary, which he keeps using a widget that stays open on his screen. He can enter some text into the box, where it automatically gets a time/date stamp. He often adds a keyword, so that he can graph his activities by category. Such a system is simple and flexible, and is not dependent on fixed categories; Ping can always start and maintain a new category simply by using a new word in his short entries.

Meanwhile, Atish has created a Facebook app that randomly queries users with a text message and asks them to rate their happiness. The ratings get a time/date stamp, and allow for the entry of a short note.

A mood tracking system to investigate emotional architecture might fruitfully combine these two methods. The ability to perform randomly timed queries is powerful. (For more on this, see “The Descriptive Experience Sampling Method” (PDF) by Russell T. Hurlburt and Sarah A. Akhter. I also discuss this method in a review of Describing Inner Experience by Hurlburt and philosopher Eric Schwitzgebel.) Right now Happy Factor asks only about happiness. Asking a constrained question such as “how happy are you” is only useful in the context of other data; to make use of Happy Factor as currently designed requires exporting the data and combining it with data about some other  dimension of your life, in order to give it meaning. But think about if the question were unconstrained: “what emotion are you experiencing right now?” Suddenly, the descriptive landscape our mood becomes accessible. [Starting at about minute 12:00 in the video above, some of these ideas are batted around in the discussion, along with other interesting prospects.]

Once the structure of our moods became accessible for visualization, experiments and interventions become possible. The type of experiments or interventions that might be interesting is left as an exercise for the reader.

NOTES
I have used mood, feeling, and emotion interchangeably. Though they are not the same, similar questions of measurement apply.  A good recent summary of the controversy over the circumplex is given in: Causes and Consequences of Feelings (Studies in Emotion and Social Interaction) (Paperback) by Leonard Berkowitz. The book is expensive but I’m happy to share the key pages if anybody needs them.

Revised August 2021

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You’re invited to attend the 2nd Annual Steven Keating Memorial Self-Research Talks. This ninety minute online meeting will present 4 first person self-research talks with discussion and Q&A.

Time: August 12, 2021, 10:00 am Pacific Time (US and Canada)/19:00 CDT (Europe)

This meeting will take place online in Zoom. We’ll have our regular show&tell format where people share what they learned from their self-tracking projects. Presentations will be in English. You must RSVP to be admitted to the meeting. Please use this link: Keating Memorial Show&Tell RSVP.

Where do our talks come from? They come from you! Do you have a personal self-tracking story to share? Please let us know by filling out the form here when you RSVP. 

This event is being cross-posted with other Quantified Self meetups.

To learn more about Steven Keating, please read this blog post by Mad Price Ball.

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Over the last few years, we’ve had a number of talks at Quantified Self meetings on tracking the effects of hallucinogenic drugs. Using very different frameworks and approaches for tracking the effects of different drugs and doses, Ahnjili ZhuParris and Janet Chang attempt to figure out what’s happening in their minds.

The image at the top, from the talk by Ahnjili ZhuParris, shows the relation between dosage and anxiety. The drugs 2C-B, cannabis, and ketamine consistently increased her anxiety no matter what the dose. But the drugs LSD, Ritalin, and psilocybin had a dose-dependent effect on anxiety. At smaller doses, her anxiety was reduced, but at larger doses her anxiety was amplified.

The image below, from the talk by Janet Chang, summarizes her experience of microdosing psilocybin of mood, productivity, and anxiety across four different phases of her tests. She found that when taken psilocybin mushrooms the relationship between mood, anxiety, and productivity could be quite different at doses that differed by as little as as a few tenths of a gram.

Full materials about both these interesting talks are available in the QS Show&Tell archive. You will find the videos of the talks (unfortunately without the Q&A), as well as slides and transcripts.

Finding My Psychedelic Sweet Spot Using R by Ahnjili ZhuParris

Sub-Perceptual Psilocybin Dosing by Janet Chang

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