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Abe had an issue with staying up too late. The early morning hours often found him on his couch, working on his laptop.
The problem is that he simply lost track of time. To help make his bedtime unforgettable, Abe built a reminder he could not ignore. He wrote a simple app that uses colors to gently prod him to get ready for bed and installed it on an old android phone that he mounted on the wall in his living room. When the screen first lights up in the evening, the colors are blue (“bedtime is coming.”) and increasingly become red (“bedtime is here.”). When he long-presses the screen, it means that he is ready to sleep, and the phone responds by lighting up with a celebratory array of colors.
It was a simple intervention, but did it work? Abe thought so. But the skepticism of friends spurred him to dig into the data to make sure. The problem was that his simple app didn’t record any data. He had an idea, though. For the past year, a webcam connected to a Raspberry Pi had been recording his living room. Abe used the light levels of the video stream as a proxy for his bedtime. When the light levels dropped, it meant that he had gone to bed. This proved to be a reliable indicator because, as Abe says, “I’m always the last one to sleep, and the last light I turn off is always the living room light.”
Would this work for you? Possibly not, but that’s not the point. It is an excellent example of a person building a solution that is specifically designed for his personality, and also how meaning can be found in the unlikeliest of datasets. In the video, you will find out how much sleep Abe saved and learn more about how he set up his device and ran the analysis.
As you may know, we’re very interested in how HealthKit is shaping and extending the reach of personal self-tracking data. Last week, during Apple’s quarterly earnings call, Tim Cook mentioned that “There’s also been incredible interest in HealthKit, with over 600 developers now integrating it into their apps.” (emphasis mine).
@fat32io just a heads up. All the data in HealthKit is NOT backed up into iCloud. Unless encrypted local backup all data lost on reset
— Daniel Yates (@astralpilgrim) February 3, 2015
@astralpilgrim yikes, didn’t know that.
— fat32io (@fat32io) February 3, 2015
@fat32io yup. Lost all my data history last week doing a reset. Spoke to apple Genius Bar who told me about it.
— Daniel Yates (@astralpilgrim) February 3, 2015
@fat32io it’s the encryption that is key. They said its due to future sharing of health data with docs etc, requires encryption
— Daniel Yates (@astralpilgrim) February 3, 2015
For those of you that are unfamiliar with backup options for your iOS device. Here’s a quick gif to walk you through the process of encrypting your iOS backup so that you can restore your HealthKit data if anything happens to your device:
Enjoy these articles, examples, and visualizations!
OpenNotes: ’This is not a software package, this is a movement’ by Mike Milliard. I’ve been following the OpenNotes project for the last few years. There is probably no better source of meaningful personal data than a medical record and it’s been interesting to see how this innovative project has spread from a small trial in 2010 to millions of patients. This interview with Tom Delbanco, co-director of the OpenNotes project, is a great place to learn more about this innovative work.
Beyond Self-Tracking for Health – Quantified Self by Deb Wells. It was nice to see this flattering piece about the Quantified Self movement show up on the HIMSS website. For those of you looking to connect our work and the broader QS community with trends in healthcare and health IT you should start here.
So Much Data! How to Share the Wealth for Healthier Communities by Alonzo L. Plough. A great review of the new book, What Counts: Harnessing Data for America’s Communities, published by the Federal Reserve Bank of San Francisco and the Urban Institute. The book is available to read online and in pdf format.
The Ultimate Guide to Sleep Tracking by Jeff Mann. A great place to start if you’re interested in tracking sleep or just want to learn more about sleep tracking in general.
What RunKeeper data tells us about travel behavior by Eric Fischer. We linked to the recent collaboration between Runkeeper and Mapbox that resulted in an amazing render of 1.5 million activities a few weeks ago. The folks over at Mapbox aren’t just satisfied with making gorgeous maps though. In this post, Eric, a data artist and software developer at Mapbox dives into the data to see what questions he can answer.
General Wellness: Policy for Low Risk Devices – Draft Guidance for Industry and Food and Drug Administration Staff . On Friday, January 16, 2015, the Food and Drug Administration released a draft of their current approach to regulating “low risk products that promote a healthy lifestyle.” These guidelines point to a stance that will allow many of the typical self-tracking tools currently in use today to remain outside the regulations normally associated with medical devices. (A quick overview of this document is also available from our friends at MobiHealthNews)
The Great Caffeine Conundrum. A wonderfully thorough post about using the scientific process, statistics, and self-tracking data (Jawbone UP) to answer a seemingly simple question, “Does eliminating caffeine consumption help me sleep better?”
Four Years of Quantified Reading by Shrivats Iyer. Shrivels has been tracking his reading for the last four years. In this post he explains his process and some of the data he’s collected, with a special emphasis on what he’s learned from his 2014 reading behavior.
Pretty Colors by Chanlder Abraham. Chandler spent his holiday break exploring his messaging history and creating some amazing visualizations. Above you see a representation of his messaging history with the 25 most contacted people since he’s began collecting data in 2007.
Heart Rate During Marriage Proposal by Reddit user ao11112. Inspired by another similar project, this ingenious individual convinced his now fiancé to wear a hear rate monitor during a hike. Unbeknownst to her, he also proposed. This is her annotated heart rate profile.
Help CDC Visualize Vital Statistics by Paula A. Braun. The CDC has a new project based on the idea that better visualization can make the data they have more impactful. If you’re a data visualizer or design consider downloading the CDC Vital Statistics Data and joining #vitalstatsviz.
From the Forum
We hope you enjoy this week’s list!
Big Data in the 1800s in surgical science: A social history of early large data set development in urologic surgery in Paris and Glasgow by Dennis J Mazur. An amazing and profoundly interesting research paper tracing the use of “large numbers” in medical science. Who knew that is all began with bladder stones!
Civil Rights, Big Data, and our Algorithmic Future by Aaron Rieke, David Robinson and Harlan Yu. A very thorough and thoughtful report on the role of data in civil and social rights issues. The report focuses on four areas: Financial Inclusion, Jobs, Criminal Justice, and Government Data Collection and Use.
Caution in the Age of the Quantified Self by J. Travis Smith. If you’ve been following the story of self-tracking, data privacy, and data sharing this article won’t be all that surprising. Still, I can’t help but read with fascination the reiteration of tracking fears, primarily a fear of higher insurance premiums.
Patient Access And Control: The Future Of Chronic Disease Management? by Dr. Kaveh Safavi. This article is focused on providing and improving access and control of medical records for patients, but it’s only a small mental leap to take the arguments here and apply them all our personal data. (Editors note: If you haven’t already, we invite you to take some time and read our report: Access Matters.)
Perspectives of Patients with Type 1 or Insulin-Treated Type 2 Diabetes on Self-Monitoring of Blood Glucose: A Qualitative Study by Johanna Hortensius, Marijke Kars, and Willem Wierenga, et al. Whether or not you have experience with diabetes you should spend some time reading about first hand experiences with self-monitoring. Enlightening and powerful insights within.
Building a Sleep Tracker for Your Dog Using Tessel and Twilio by Ricky Robinett. Okay, maybe not strictly a show&tell here, but this was too fun not to share. Please, if you try this report back to us!
Digging Into my Diet and Fitness Data with JMP by Shannon Conners, PhD. Shannon is a software development manager at JMP, a statical software company. In this post she describes her struggle with her weight and her experience with using a BodyMedia Fit to track her activity and diet for four years. Make sure to take some time to check out her amazing poster linked below!
The following two visualizations are part of Shannon Conners’ excellent poster detailing her analysis of data derived from almost four years of tracking (December 2010 through July 2014). The poster is just excellent and these two visualizations do not do it justice. Take some time to explore it in detail!
Tracking Energy use at home by reddit user mackstann.
“The colors on the calendar represent the weather, and the circles represent how much power was used that day. The three upper charts are real-time power usage charts, over three different time spans. I use a Raspberry Pi and an infrared sensor that is taped onto my electric meter. The code is on github but it’s not quite up to date (I work on it in bits and pieces as time permits I have kids).”
Yesterday we posted our first opening plenary talk from the 2014 Quantified Self Europe Conference. Today we are happy to post our second talk from the opening plenary session.
Kaiton Williams is PhD student at Cornell in the department of Information Science. Over the last few years he’s been interested in how people use technology to understand and create the stories of themselves. As we were exploring our 2014 Quantified Self Europe Conference registrants to see what they were involved in we were immediately drawn to Kaiton’s paper from the 2013 CHI Personal Informatics Workshop, The Weight of Things Lost. We asked Kaiton to talk about his experience with self-tracking and the mental and social tension inherent in the numerical definition of life. Kaiton’s plenary talk is available below as is a transcript of the talk.
First, thank you all for welcoming me here. I do take it as a privilege to be here. This is a surreal, and a little bit frightening, experience for me. It feels in many ways like the end of a pilgrimage.
I’m a Ph.D. candidate at Cornell University and over the last few years, I’ve been working to understand how we’re harnessing our devices, our applications and our algorithms to figure out just who, when, what, & why we are. I’m particularly interested in the ideologies and values that inform the things we discuss in rooms like this one, and go on to create and use.
I’m going to talk a little but about my experience with self-tracking and self-transformation and how it brought me here to this room, and then I’ll pose some ideas and questions on how we might use personal experiences like mine as a platform from which to influence the developing relationships between companies, markets, health, and our data.
And while my talk is fancily titled in your program as “The Weight of Things Lost,” I really could have gone with “All I Wanted Was a Flat Stomach and Six- Pack Abs” It was this, more than any high-minded investigation into technology, or a community, or our practices, that got me started and kept me going.
My story began about 28 months and over 1.3M calories ago. Like many tales, it began at Christmas. I was experimenting with a polaroid camera one day, and as I watched my picture develop, I realized how out of shape I had gotten. Even though I thought I was in control of my diet and getting enough exercise I had been slowly but steadily gaining weight without paying much attention. I looked, in my own estimation, terrible. Granted, as physical problems go, this was a minor calamity but I wanted to do something about it. But I realized that I didn’t know how exactly to go about it. I wasn’t sure what good goals where or even what I was capable of. And I definitely had little formal idea of how to manage my consumption to meet them.
2 ½ years later and this remains something that I consider with a fair amount of irony. I was among a group of researchers who had been critical of the persuasive and reductive logic that powered many of the popular diet control and tracking systems. But now I found myself in need of them.
This was the time and place of my first conflict. As a researcher now seeking to modify my body how could I participate in systems like these and still champion the resistance against them? Would I be taking them down from the inside?
Maybe after I got my 6 pack, washboard abs. THEN, then it would be down with the tyranny of rational digital systems and self-surveillance.
What I told myself was that I would be able to develop a personal, inside understanding that was tied to a real personal need. Surely this was better than just critical analysis lobbed in from the outside? So I swallowed my pride and looked for help. And, as it turned out, there were many apps for that.
In the months that followed I assembled & auditioned a shifting conglomerate of tracking apps, sensors, and databases. I scheduled full body density scans, blood panels, and metabolic breath tests. It didn’t take very long before I began to read my life through the prism of my tools and data. I had found new units of measure, new ways of marking my time, my mind, and my body. For 18 months, not a single day passed where I did not enter, in almost excruciating detail, what I had eaten and planned to eat. My tools were my oracles, and I consulted with them regularly.
Their effect was strong even though I knew intellectually that I was reacting to numbers, colors and graphs based on rough estimates, or provisional theories. I knew that, by describing my body as a precise system that would go out of sync based on small discrepancies, an industry benefited by positioning their tools and systems as indispensable and necessary guide in my life.
But once I began to see successes, I felt a strong sense of fidelity to my system; an ordained from Logos desire to keep the record true. And, over the months I steadily made my life more calculable by streamlining my diet to in turn streamline how I input data into my tools. I avoided complex recipes and prioritized foods that best fit the capabilities of my databases and sensors.
Halfway in, I spent the better part of one morning trying to figure out what happens to the calories in baking powder once baked into a cake. For that matter actually, I swore off cake.
Surprisingly though, I found a freedom & spiritual joy in this calculation and control, and ample room in its reduction. It was, reassurance itself. Together, my conglomerate and I had constructed a digital model of my self that I fully bought into and managed. I was managing myself, it seems now, by proxy.
I became worried about going it alone though. What would I do without my systems? How would I maintain the goals that I had developed and now hit? I think a lot about the transformation.
The numbers showing my weight and fitness level fill me with as much pleasure as fear. Can I maintain this state without help from my system? And even if I do cast these systems aside, would doing so really lead to any better engagement with my self? What happens if these tools are no longer supported, or if the people behind them make business or ethical decisions that I can no longer support?
And this is how I ended up here: to get your help in answering the questions.
I had begun this journey this to feel in better control of my self and to be healthy and fit. I definitely feel healthier but am I really in control? It is this last move, from personal questions to broader political ones, that concerns me the most— particularly when being healthy no longer seems to mean just avoiding being sick but continuously optimizing our selves. Self-tracking habits are becoming mainstream and I believe that how we are globally perceiving and contesting our possibilities is being reshaped through discussions and design decisions made at conferences like this one.
Our conversations are already embracing holistic ideas of well-being that stretch beyond the easily quantifiable, but we should also incorporate and question how our personhood and our work is increasingly being defined not just by ourselves, but by an array of others that includes entrepreneurs, governments, institutions and corporations that are all building on our desire to optimize our selves. If we understand that the work done in this community affects practices in the wider world, how can we begin to explicitly shape those relationships?
I think we can use our diverse store of personal knowledge to construct platforms for doing just that. Focusing on personal experience doesn’t have to be seen as a retreat from focusing on others, but instead can be a strong foundation from which to develop empathy for the experiences of others and to understand their implications for our joint lives.
And so to close, I’d like to pose these questions to you:
If our new abilities to measure and track our selves are forming the basis of what it means to be modern, healthy and connected, how can we use personal experiences like mine and the ones we’ll hear this weekend, to tackle not just the question of what does the collection and availability of data means for n=1/just me, but what it might mean for others? Particularly, others who might not be in the same circumstances, or might not have the same ability or availability to join this community? How do we incorporate the perspectives of the many who can’t participate here, are overlooked and marginalized, but whose lives will eventually be affected by practices that spiral out from ours?
Can we transform our wealth of personal and experiential data into a platform for improving our connection to those around us and to the broader world?
Welcome to the sixth and final part of the QS book on mood tracking that Robin Barooah and I wrote. This chapter has some thoughts on what the future of mood tracking might look like. Thanks for being on this journey with us!
At this point, you should have a good understanding of the nuances and methods of tracking mood. You could stop reading here and be well-versed and ready to go. If you want a peek into some possible new ways to track mood in future, read on.
Passive Body Position and Movement
What if your mood could be measured without you having to do anything or enter any data? Would this be helpful, or is the act of reflecting on your mood the useful part? We mentioned a few existing examples earlier, like tracking what music you listen to, and your voice patterns. Here are a few other efforts happening:
A sensor called LUMOback can be stuck on your back to detect your posture throughout the day and report to you via your smartphone if you are slouching. They don’t specifically talk about mood tracking as an application for this, but posture is a known sign of mood. When we’re depressed, we don’t stand up tall.
Other experimental ways to passively capture mood include keystroke logging, which involves detecting how quickly and actively you are typing on your keyboard, and using your webcam to take random pictures or continuous video of yourself while you’re at your computer. Moritz Stefaner did a project in which he automated hourly webcam pictures of himself. He then had 13585 of the pictures analyzed for mood, with the following result.
A lot of his “sad” photos are really just him concentrating, mislabeled as sadness. but Moritz’s project shows the potential power of the cheap, universally available webcam as a passive mood tracking device.
Reverse Mood Tracking
A fascinating way of using mood tracking in a clinical setting has been pioneered by Dr. Alan Greene. He was kind enough to share his story with us here:
“Most mood trackers I know tend to notice, record, and track their moods in order to gain insights about themselves. I’ve come to also do the reverse: track my moods to gain insight about others.
It all started when I walked through a door.
Welcome to part 5 of the QS book on mood tracking that Robin Barooah and I wrote. This chapter has some tips that we’ve found helpful for getting started with mood tracking. Enjoy!
Once you’ve been tracking mood for a while, and have a good baseline established, it’s time to play. What if you could influence the factors that shape your mood? What if you had a trusted buddy to confide in, to make your tracking more robust? If we know ourselves better, we can make choices that help us to make the most of our lives. We’ll explore how and why to experiment with and share your mood in this chapter.
There’s a concept called heutagogy that applies nicely to self-tracking activities. Heutagogy is basically the idea that people direct their own learning, using personal experiences to update their models of themselves and the world around them. Stewart Hase and Chris Kenyon, who came up the term, write that “people only change in response to a very clear need… involving confusion, dissonance, fear, or intense desire.”
At Quantified Self, we usually see intense desire as a motivator, but fear creeps in too, often for health concerns. If you do want to change your mood, it’s helpful to know how others with similar motivations have gone about doing it, to get some ideas and approaches to adapt to your needs.
Welcome to part 4 of the QS book on mood tracking that Robin Barooah and I wrote. This chapter has some tips that we’ve found helpful for getting started with mood tracking. Enjoy!
The excitement of starting a tracking project can lead to a classic newbie behavior of tracking too many things at once. This can get tiring and confusing, so it’s important to be mindful of keeping it simple and not overdoing it. This chapter offers some tips and insights for getting started with the practicalities of mood tracking.
Keep it Simple
Ernesto Ramirez of Quantified Self Labs wrote a “QS 101” post on lessons learned from self-tracking:
“Lesson #1: Something is better than nothing. Engaging yourself in some experiment, no matter how flawed it may be, is better than never starting. The best way to learn is to do. So go out and do something!
Lesson #2: When you decide to start something, try and do the simplest thing that you think might give you some insight. It’s great to have ambitious ideas, but keeping it simple ensures your experiment is manageable.
Lesson #3: Mistakes are worthwhile. Some of our best knowledge comes from learning from our failures, so don’t be afraid of failing. By keeping it simple you also keep the mistakes small and manageable.
Lesson #4: Seek help from others. We have a great network of individuals around the world who are ready and willing to help you on your tracking journey. Find a Quantified Self meetup in your area and don’t be afraid to ask for help!”
Here is a brief roundup of some of the things we’ve either collected or written about tracking mood since we first started paying attention to mood tracking back in 2008.
Get Your Mood On
Alex Carmichael and Robin Barooah have recently completed work on an excellent book detailing their experiences and knowledge gained from years of mood tracking. We’ve already posted the first three chapters of their book and are excited to bring you more in the upcoming weeks.
- Why Measure Mood and How It Can Help
- How is Mood Measured?
- Preparing Your Mental State for Self-Tracking
- DIY Mood Tracking
- Mood Sharing and Experimentation
- Exploring the Future of Mood Tracking
Mood Tracking Show & Tell Talks
Mood tracking is also a popular presentation topic at our worldwide Meetup groups. Here are a few of the talks from the last year that discuss personal mood tracking projects.
- Remko Siemerink on Mood and Music: Remko Siemerink tells his personal story of health insights through accidental lifelogging. He has bipolar disorder, and has been using last.fm over the past 7 years to track his music listening and compare it with his friends’ music patterns.
- Marie Dupuch on Mood Tracking With Colors: Marie created a rating scale based on colors as a visual metric, and a self-reported quantifiable metric, to gauge her mood over periods of time. This led her to have more awareness and provided the information she needed to make confident choices in her own life.
- Erik Kennedy on Tracking Happiness: Erik was interested in what makes him happy so he started tracking it. After categorizing hundreds of events he shares what makes him happy, what doesn’t, and some very thoughtful takeaways.
Mood Measuring Tools
Here’s a list of some of the mood measuring tools we’ve covered in the past and used in our personal lives. This list is by no means complete so if you use a mood tracker we don’t mention be sure to add it in the comments!
- Moodscope: A simple online tracker and support system. Make sure to watch founder Jon Cousin’s show & tell talk about how and why he created Moodscope.
- Moodpanda: Track your mood online or with mobile apps (iOS and Android). Read our Toolmaker Talk with founder Ross Larter here.
- Expereal: A new visual mood rating and journal application. Read our Toolmaker Talk with founder Jonathan Cohen here.
- MoodJam: An online tool to track your mood using colors and keywords. Watch founder Ian Li talk about the latest version of MoodJam at a QS Pittsburgh Meetup.
The Science of Mood Measurement
For deeper background on the scholarly work and controversy about how mood is measured here’s a long post by QS Founder Gary Wolf: Measuring Mood: Current Research and New Ideas.
What do you do to track mood? What have you learned?
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.