Adi Andrei wanted to combine artificial intelligence, psychology, art, and storytelling for the purpose of self-discovery of the subconscious mind. In the video below, Adi explains why he’s focused on this, how to go about entering the subconscious, and what he’s learned about hacking it. (Filmed by the London QS Show&Tell meetup group.)
To start a sparkling new year, I thought it would be fun and helpful to make a fresh list of all the QS-related tools, companies and projects out there.
I found 205, and I’m sure there are more, so please feel free to add your tool or ones that you know about to the comments. Thanks, and happy 2013!!
Institute for the Future
Johnson & Johnson
Lena Baby Monitor
Michael J Fox Foundation
My Fitness Pal
Personal Genome Project
Pew Internet Project
Remember the milk
Robert Wood Johnson Foundation
Seventh Sense Biosystems
Welcome to part 3 of the QS book on mood tracking that Robin Barooah and I wrote. This chapter explains how to prepare for your self-tracking journey. We hope it helps you in your future tracking adventures!
Before you start tracking something new, there’s an important first step you can do to lay the groundwork for a rewarding self-tracking experience. How you approach tracking your mood and looking at your results can make a significant difference in what you end up learning from it. So in this chapter we’ll explore how to cultivate a helpful mindset, accept what you discover about yourself, and keep your mind and body open to building intuition.
Cultivate The Right Mindset
When you look in the mirror, what goes through your mind? Do you judge that part of your body that you just can’t bring yourself to like? Do you gush with warm appreciation? Do you notice something out of place and calmly adjust it or make a mental note to investigate?
It’s an interesting exercise to do on its own, noticing what thoughts you have when you see a reflection of yourself. According to several recent studies, a healthy mindset involves being mindful of your thoughts without jumping into problem-solving mode, and being kind to yourself.
If you think about self-tracking as a different kind of mirror, the same logic applies. What you record about yourself is actually a very personal reflection of your inner world, and so the kind of self that you bring to bear on it will influence what effect the information has on you. If you bring a judgmental mindset to looking at your data, you will feel like you’re being judged. If you bring a curious attitude to it, it may be easier to see new patterns in what you’ve collected.
Many of the activities that make up our fast-paced modern life are easier to handle if we can make quick decisions to get results based on patterns of judgment we’ve learned through personal experience. An important principle behind the training that professional scientists receive is to learn to step back from this routine mode of thinking and consider what the data could be telling them that they haven’t noticed before.
Because we’re not used to working this way in our daily routines, it’s easy to fall back on our normal problem solving methods, and in the case of mood this often includes self-judgement.
Of course, it’s easy for us to suggest avoiding a problem-solving mindset when looking at your data, but practically speaking, how can we change our own minds?
One thing we’ve found to be effective is to name some alternative mindsets that we can cultivate. These are mindsets that most of us have experienced at one time or another, and there is nothing mysterious about them. Often just remembering that there’s another way of looking at things is enough to find a different perspective.
Here are some of the attributes we’ve found helpful when cultivating a mindset for looking at our own data:
You might find yourself starting out with reflexive judgments when you start looking at your data. “How can I be depressed again? What’s wrong with me?” This approach can lead to a painful feeling of defeat, and sometimes people give up tracking entirely soon after they begin.
If this judgmental voice comes up in your head, redirecting it towards being realistic and pragmatic can help. For example, if you’ve been depressed for much of your life, it’s pragmatic to realize that simply by recording your mood, you’re learning something, as opposed to expecting an instant cure.
This attitude can also help you weather and understand the ups and downs you will find in your mood, without necessarily trying to optimize for being up all the time. Each mood can be respected for what it is without wanting to only be happy. Another consideration is that extreme moods may interfere with tracking, and this should be expected rather than considered to be a failure.
So, a mindset of clarity can help you be more gentle with yourself, as well as not delude yourself. It’s important to look at your empirical evidence carefully, in order to avoid flights of fantasy, and to not make up negative stories about yourself. The evidence for the conclusions you reach may be in the data, or may be in obvious life history that you can remember, but you want to make sure what you conclude is based on facts, not judgments.
When you see your data, a common reaction is to want to compare yourself to others. “I hope my moods aren’t the most wildly swinging ones in the office!” This is a competitive flavor of the unkind, judgmental voice that can lead to depression. When this voice comes up, just observe your data without judgment, be very kind and gentle to yourself, and get curious.
For example, you might want to ask questions like whether your moods correlate to things like sleep or exercise, as the Optimism iPhone app does:
Long-time self-trackers have an almost insatiable curiosity. When a really good or really bad mood day happens, the analytical mind kicks in to try to see patterns. Comparing this time to previous times that were similar in some way, we try to figure out possible variables. “I took extra vitamin D this morning” or “I haven’t seen people for two days.” Then we can test these variables and see if the high or low mood is reproducible.
A benefit of this kind of self-experimentation is that it’s personalized. Rather than relying only on scientific studies that look at population averages, you can start to tease out individual ways in which you respond to your internal and external environments that may be different from conventional wisdom. More on experimentation in Chapter Four.
Finally, a good baseline attitude for life in general is one of compassion. We’ve found that self-compassion is an essential part of maintaining a tracking practice. A good technique for increasing self-compassion is to think of all the people out there who are in the same situation as you – anxious about a job interview, stressed out from dealing with fighting kids, completely in love, whatever it is. Think kind thoughts towards them, and it will help you be kind to yourself. Your data is what it is, and it’s ok. It’s nothing to be embarrassed about.
Closely related to compassion when trying to make sense of data about our own lives is the idea of humility. In our experience, mood tracking can lead to powerful and helpful insights, but they don’t always come quickly. It’s an inevitable part of the process to be confused and to not have answers when we’re learning and exploring. The reality is that a lot of the time, we just don’t know what our tracking data means, and there’s no reason why we should.
Humor is another powerful tool for developing compassion, rather than taking yourself or your data too seriously. As the Buddhist saying goes, quoted by Mihalyi Csikszentmihalyi, “Act always as if the future of the universe depends on what you do, and laugh at yourself for thinking that whatever you do makes any difference.”
Accept What You Discover
The practice of acceptance can be incredibly transformative. If you can accept yourself as you are, accept other people as they are, accept your data as it is, and accept situations around you, you will be free from secondary layers of emotion that prevent you from just dealing with whatever you need to deal with.
For example, let’s say you discover that every time you see your mother-in-law, you get angry followed by a week of depression. You have a choice here – layer frustration and resentment on top of the situation, or accept it and think about what your options are. Maybe you can talk to your spouse about finding a way to ease the trauma. It might not be as complex as you think to minimize the harm of the situation, but if you’re frustrated, you’re less likely to see the answer.
As Zen master Suzuki Roshi says, “It’s like putting a horse on top of a horse and then climbing on and trying to ride. Riding a horse is hard enough. Why add another horse?” Acceptance helps you just ride one horse at a time.
Also, feelings change us simply by being accepted and experienced even if we don’t have a plan for what to try in response to them.
Expectations come into play here. What we’ve learned is, the fewer expectations you have, about how you will respond to any particular experiment or about other people doing anything in particular, the easier life becomes. Keep moving strongly towards your inspiring intentions, just don’t expect anything to work out in the exact way you imagine.
Humans have been learning new skills for thousands of years, since long before the advent of modern science or even reason. Learning from our experiences is an innate gift we all have. The question is, which experiences do we learn from? We’d like to suggest that the experiences we learn from are the ones we pay attention to. There’s no need to take this at face value – it’s a question you can answer for yourself.
So the major gift that acceptance brings is that simply by trusting yourself to have your experiences and not trying to figure out what to change, you are still learning. Mood tracking can help us to pay attention to our mood and learn from it directly.
Build Your Intuition
This is one of the main benefits of self-tracking. A dedicated effort to look at something over time can help you to see patterns you didn’t know existed, and give you a greater awareness of yourself and how you function in the world. In the case of mood, building an intuitive understanding of how different triggers affect you makes it easier to manage and even change your mood.
A general principle we talk about at Quantified Self is: use a tool, learn a lesson, incorporate it into your life and body, then drop the tool because you don’t need it anymore. Of course, some people like to continue tracking for the sake of having data to look back on in the future, but sometimes it’s more helpful to track one or a few things deeply, until you’ve learned what you need to learn, then move on. There’s no hard and fast rule about when to be done with a particular form of tracking, but it’s worth periodically evaluating why you are tracking something and what you hope to gain from it, rather than continuing out of duty or habit.
Take the following mood chart, published by the Center for Quality Assessment and Improvement in Mental Health, which is operated by Tufts and Harvard Universities. It’s a great tool to start to be able to see your mood patterns, and can be especially useful for people with Bipolar Disorder, but you might not need to use it forever.
As part of preparing your mental state for tracking mood, recognize that by tracking you’ll get to know yourself better and that the learning isn’t just a list of facts – it affects how you feel about yourself, and you may not always have words to describe it.
For example, a person can know that she likes the taste of banana when he eats it without having to say “I like banana” in her head. Similarly, you know the mood of a painting, or a song, or someone’s expression, whether you say it to yourself or not. And after two years of mood tracking, Alex (from the story in the Introduction) can feel if she’s getting too depressed or manic and needs to change her behavior with some mood hacks to compensate. We’ll talk more about mood hacking in Chapter Four.
So armed with the right mindset of clarity, curiosity, and compassion, as well as a sense of acceptance and intuition, you’re now ready to start tracking mood. Tips for getting started are coming up in the next part of the book.
Amelia Greenhall, of QS Seattle, describes five simple and powerful self-tracking experiments she has been doing over the past few years that feel like getting gold stars. For example, she records everything she has learned, done, read, or accomplished each month. Check out Amelia’s insightful lessons in the video below.
J. Paul Neeley has done experiments on optimizing happiness, self-control, and most recently, puns! His mom and brother are great punsters, so he decided to measure how many puns happened over Thanksgiving weekend with his family. In the video below, J. Paul explains this fun experiment, shares what he learned about the pattern of puns, and warns that punning can be contagious! (Filmed by the London QS Show&Tell meetup group.)
Neil Bachelor has been tracking his daily learning for the past two and a half years, with 3,200 discrete learning events. One of his motivations for this is to create a data-based CV that reflects his real work and learning habits. Neil uses Faviki to bookmark things he’s learned. In the video below, he describes his process, shows different visualizations of his learning, and explains the challenges he faces in managing so much data. (Filmed by the London QS Show&Tell meetup group.)
Crystal Goh looks at brains every day, as part of her work in a brain and sleep imaging lab in Berkeley. She wanted to know how her brain was different from other brains, in a quantitative way. In the video below, Crystal explains voxel-based morphometry, normalization and standard deviation calculations, and the scary, revealing things she has learned about herself by seeing her brain scan! (Filmed by the San Francisco QS meetup group.)
Welcome to part 2 of the QS book on mood tracking that Robin Barooah and I wrote. This chapter walks through the various ways of measuring mood. Please enjoy, and share anything we’ve missed in the comments!
How Is Mood Measured?
When someone asks you how you’re feeling, how do you reply? With a number? A color? A dot on a two-axis grid? Probably not. Chances are, you answer with words, incorporating body language, facial expressions, and maybe a verbal description of events that led to your current mood.
The person who asked you pieces all that together into a reasonable idea in their own mind of how you must be feeling. But how can that idea be captured, recorded, compared to other people’s moods, or even to your earlier moods? Are there standard, reproducible ways to measure mood that are both widely applicable and personally relevant?
The answer is… maybe. Many attempts have been made to quantify mood, from psychological assessments to online color palettes to analyses of phone conversations. We’ll explore them here, and discuss some of the ongoing debates. Think of it as a journey through the wild landscape of the mood tracking space.
POMS (Profile of Mood States) – the gold standard
If you’re looking for a psychological assessment for measuring mood fluctuations that is used in clinical and research settings, POMS is your answer. The assessment consists of 65 emotion adjectives that are each rated on a five-point scale, where 0= not at all; 1=a little; 2=moderately; 3=quite a bit; and 4=extremely. The answers are then grouped into seven dimensions to give you an overview of your mood state:
A second form of POMS has also been developed specifically for looking at Bipolar Disorder. The dimensions are slightly different:
The downside of POMS is that the questions are not freely available, and you have to be a qualified psychological professional to order them.
Circumplex vs. Evaluative Space Model – are positive and negative moods opposite?
There is some debate among psychologists whether happiness and sadness are opposites on a spectrum or can exist concurrently. Can you be happy and sad at the same time?
The circumplex camp says no. They arrange emotions on a two-dimensional grid, with one axis moving from pleasantness to unpleasantness, also called valence, and the other axis moving from activation to deactivation, also called arousal. Depending on how positive and how energetic you feel, you should be able to place a dot on an appropriate part of the grid to record your current mood, and notice how the dots move around over time.
The evaluative space camp disagrees. They argue that while emotions are often experienced as opposites, there are situations or times in life when people experience both happiness and sadness. According to this model, you should measure your positive and negative emotions separately.
There have also been studies showing that different individuals have different ways of experiencing emotions. Some people experience a strong opposite effect, others can have multiple emotions at the same time that fluctuate independently, and still others have emotions that do depend on each other but not in an opposite way. So it’s not clear that there is a single method that will work for everyone.
The fact that scientists disagree on fundamental questions such as whether we can experience more than one mood at the same time, or whether we even all experience moods in the same way, is a major reason why we believe this area is so ripe for self-experimentation. Each of us has access to our own individual experience in a way that no scientist does, and we can answer these questions for ourselves and make use of the knowledge we gain, confident that what we’ve discovered applies to us.
Mood Scoring – Moodscope
For the numerically inclined, one quick way to get a daily number for your mood is to use the PANAS-based app Moodscope. PANAS stands for Positive Affect Negative Affect Schedule. Moodscope’s adaptation of the PANAS consists of ten questions for positive affect, or mood, and ten questions for negative affect, on a 0-3 scale. The scores are then combined into one number that represents your overall mood percentage, where 100% is extremely positive and 0% is extremely negative.
At Moodscope, you rate your mood once a day and are given graphs to see how it is changing over time. The questions are presented as cards, to make it fun and to increase the accuracy of your answers by introducing a bit of extra time to stop and reflect. What the cards and graphs look like are shown below.
Measuring your mood once a day is a great start, but you may find that you want a more nuanced view of how your moods change within a day. Moodscope also allows sharing your mood with a friend for support, and lets you add descriptive words and comments to each measurement. We’ll discuss sharing mood in Chapter Four.
Artistic Expression – Moodjam
In common language, people sometimes describe their moods in color, like “I’m feeling blue.” Ian Li, a graduate student at Carnegie Mellon University, built an app that takes that a step further. It’s called Moodjam, and it lets you choose up to ten colors to represent your mood at any time of day, annotate it, and share it publicly if you like.
The act of pausing to look inward and choose colors and words to describe how you’re feeling can be an inspiring, releasing part of your day. The result is a beautiful visual representation of your mood over time. Here’s what it looks like to record a mood and to see moods of other people at Moodjam.
Text Analysis – 750 words
Perhaps the most traditional way of recording mood as part of life events is to keep some kind of written journal or diary. The practice of writing free-flowing text can be cathartic and insightful. A modern version of the daily journal is a web app called 750 words. A beautifully simple interface encourages you to write 750 words every day, which are completely private.
One benefit of an online journal is that the text can be analyzed. 750 words uses sentiment analysis to break down what common moods or thoughts your chosen words reveal. Looking at the charts can give you new clues about what your typical thoughts are. However, the primary benefit may still be just in the act of writing, allowing your subconscious to find patterns and your intuition to develop.
Emotional Stroop Test
As it turns out, there is also a cognitive measurement that can objectively detect different emotions being experienced by a person. If you’ve ever seen those cognitive tests where the word “blue” is written in the color red, and you have to name the color instead of reading the word, that’s a Stroop test.
The emotional version of the Stroop test is to show people a series of words, some of which are emotional, and ask them to name the color of the word when it appears. If a person is feeling anxious, they will delay slightly before naming the color of the word “anxiety” compared to naming the color of an emotion they’re not experiencing or a non-emotional word. The delay in the response time indicates the level of emotion. No online version of this test is currently available.
So these are some of the active ways of tracking mood, where your input is required in some way. There are also a few passive ways emerging that are worth noting.
Voice Analysis – Cogito Health
Researchers at MIT have discovered that analyzing the spectral and temporal patterns of voice conversations can identify depression or psychological distress. A company called Cogito Health is commercializing this technology to help call centers become more effective as well as track behavioral health at a population level.
Presumably, the same technology could be made available to individuals to monitor their own phone calls. Imagine talking to a friend by phone and getting a text message signaling you that she is depressed, even if you can’t necessarily tell by the way she is talking. Faking a cheerful mood with each other would become more challenging! And you might become more empathetic friends.
Facial Recognition and Skin Conductance – Affectiva
What about measuring emotional states just by looking at people’s faces, or detecting arousal from skin conductance? Affectiva is a company working on both of these methods. Their Affdex system uses webcams to measure people’s reactions to marketing campaigns, as a way to detect whether consumers are engaged, surprised, confused, or turned off by their ads. A very commercial application to begin with, but a system like this made available to individuals could help you figure out things like whether checking email always leaves you in distress, especially when your cranky Aunt Edna writes to you.
Affectiva’s other product is called Q Sensor. It’s a wireless wristband that detects electrical activity on your skin as you go about your day. High activity means you’re excited or anxious, low activity means you’re bored or relaxed. It is currently being used for clinical and academic research, and is prohibitively expensive for many individuals, but it’s a fascinating signal of what’s coming down the road. One fascinating application is helping people with autism to communicate their internal states. Instead of seeming perfectly calm and then erupting into an unexpected meltdown, autistic individuals can use the Q Sensor to show their caregiver the rising stress level they feel well before meltdown occurs, and the caregiver is able to intervene with calming activities or a change in environment.
Music Patterns – Last.fm
The music we choose to listen to, and whether we choose to listen to music or not, can be other good indicators of how we’re feeling. At a Quantified Self meetup in Amsterdam, Remko Siemerink described how he discovered a pattern of listening to music intensely when he’s feeling good, and not listening to music when he is feeling depressed, usually in the summer.
Last.fm is an online radio station that tracks all the music you listen to, and provides an API for external services like LastGraph to display your music listening habits over time as beautiful charts. It could be a useful proxy for measuring mood.
Robin, one of the authors of this book, has been tracking his meditation practice for the past 3 years. His main goal in doing this was to help him get into the habit of a regular daily meditation practice. Unexpectedly, this turned out to be a rich source of information about his mood.
“I discovered that the periods of time when I wasn’t meditating corresponded with times when I was suffering from depression. These were long gaps, of a month or more, and it was very easy to remember how I’d been feeling and what was going on in my life during those times. I could see that the gaps corresponded with life events that altered my routines of work and connection with friends.
The surprise for me was that looking at the simple long term pattern of my meditation practice provided me with insights about major changes in my mood that I couldn’t see from looking at my daily mood diary.
I’ve now learned that skipping meditation for more than a couple of days is generally a warning sign that I’m at risk of falling into a depression. Medicine is starting to recognize meditation as one of the most effective treatments for depression, so it’s likely that the meditation itself is protecting me. Tracking helped me to see how disruption in my life both brought on depression and disrupted my meditation practice at the same time.
Since learning this, I’ve been able to take action when I start to notice the pattern – both by making meditation more of a priority in times of stress, and by recognizing that a few days of missed meditations means that I need to be more gentle with myself as I adapt to change.
I’ve known for a long time that meditation was very helpful for depression, but it wasn’t until I saw my pattern for myself that I really understood how important it was in my own life.”
These last two examples illustrate how behavior can be an indirect way to investigate mood, and how different methods of tracking can provide us with different kinds of insights. They also show how we can learn different things about our mood depending on the timescale we are looking at. It’s possible that you’re already doing something that could give you insight into your mood if you tracked it – maybe how often you shower, or how many text messages you send at what times, or your patterns of food consumption.
Whatever method you choose, whether active or passive, clinical or colorful, it helps to know how to go about using the tool. In the following chapters we’ll share some principles for how to think about mood tracking to maximize benefit to your life, followed by practical details for getting started.
Jules Goldberg is a snorer, and estimates that he has spent 1/8th of his life snoring. The noise was bothering his wife, so he built an app called SnoreLab to quantify his snoring (mild, loud, or epic?) and help him reduce it. In the video below, Jules shares how he identified where his snoring was coming from, remedies he tried, and which ones made it better and worse. (Filmed by the London QS Show&Tell meetup group.)
This post was kindly sent in by Peter Lewis and Florian Schumacher, translated from Arne Tensfeldt’s original post in German.
The first Show & Tell meetup of theBerlin International Quantified Self Group took place on November 22nd. The Berlin QS Group had been founded several weeks earlier for the English-speaking Berlin community. With around 70 participants, the meetup was the largest to date in Germany, having been announced and covered by a variety of Berlin blogs and news sites. The meeting began with the standard “three word introduction,” in which everyone present introduced themselves with just three of their interests or other descriptive words. The variety of chosen descriptions and interests reinforced the wide range of the QS movement and offered a good preview of the subjects that would be discussed over the rest of the evening.
After this introduction, Steve Dean (head of the NY Quantified Self Group) began with a keynote speech about the founding of the Quantified Self movement as well as his own experience in preparing for an Ironman Triathlon. Through the measurement of his resting pulse every morning, recommended by his trainer, he was able to predict when he would get sick from overtraining and reschedule his workouts to allow more rest at the right times. He then discussed a second self-experiment that was also shaped by his athletic pursuits: after the end of his intensive Ironman training, he suffered from an inflammation of his eyelids. After countless unsuccessful treatment attempts, he learned from careful self-tracking that the regular exposure to chlorine from swimming had been keeping this problem in check — and after a long break, resuming his visits to the pool led to a recovery from the infection. His slide presentation can be seen here.
Max Kossatz, CEO of Archify, showed the data he had gathered from his company’s newly developed browser plugin. Archify tracks each website that a user visits, saving all text content and capturing a screenshot. This leads to a type of digital “mindfile” which can be easily searched in the future. Max presented an analysis of his own online content in his personal project “My Online Life for the Last 8 Months.” In addition to showing his preferred sources of information, this data also made it possible to recognize patterns like the drastic reduction of online time during his vacation, or an increase in online activity during his preparation for important business events. His presentation can be seen here.
As the third speaker, Peter Lewis (co-organizer of the Berlin QS group) showed his experience with spaced repetition algorithms to optimize learning efficiency, which he had used in learning languages. As a starting point, he set up an experiment in which he (as a native English speaker) tried to acquire all the new vocabulary he found in a German novel — about 900 words — within a period of one month. He demonstrated the use of software that allowed him to track his progress through decks of digital flashcards. With an excursion into theory and algorithms, as well as practical explanations and tips on the current state of the technology, he gave a comprehensive overview of spaced repetition software applications and and the different ways to use them.
After the lectures, the attendees had the chance to view demos from some Berlin startups in the QS field and to make new contacts as well. The event was also recorded by the TV show Planetopia; their episode on QS aired on Monday, December 3rd.
The organizers of the Berlin group are already planning their next meeting: in January’s Show & Tell there will be numerous speakers on subjects like genome sequencing, health and biohacking, as well as another Demo Hour with projects and startups from the QS scene.