Want to Know What Really Makes You Happy? Try Tracking It
May 19, 2020
Throughout our careers and lives, the big decisions we have to make usually lead back to a single, overriding concern: What really makes me happy? Too often we try to answer these questions without knowing or understanding the real data from our lives. Our self-analysis devolves into speculation or wishful thinking.
Over the past month or so, I’ve been collaborating with Harvard Business Review to develop a quick self-test to determine individual readiness for understanding your own data through the world of auto-analytics. Auto-analytics is a method of using new self-tracking tools to help answer key professional (and personal) questions: How do I boost my productivity? Am I in the right career? How can I improve my work routines by altering my health habits, like sleep and exercise?
To get a sense of how auto-analytics can be used enrich our decision making, I recommend three distinct approaches:
1. Quantifying reflection
It is the practice of spending a few moments each evening to rate (or rank) that day on a numerical scale, and also to provide qualitative information on daily activities. This method not only begins to habituate reflection but also creates a repository of personal data to inform decisions on which sorts of behaviors to embrace or avoid.
Author Ashish Mukharji’s use of this method shows that we don’t have to be a professional philosophers or positive psychologists to think systematically about happiness. For the past three years he’s been rating his days on a scale of 1-10, also jotting down some associated thoughts, “a restaurant, movie … whatever made that day special.”
Through this exercise he has learned that his average happiness is a seven and he has uncovered some unexpected sources of happiness. For example, in the experience of accomplishment, “actually getting to a goal” is less apt to make him happy than the process of working toward that goal.
With his personal data in hand, he now resolves his existential puzzles with small, practical interventions — idiosyncratic methods to lift his daily happiness. For instance, no matter how much fun he might be having at night, he retires early to avoid missing sleep, since feeling tired invariably makes him unhappy, according to the data.
2. Theory testing
It uses auto-analytics tools as a way to quantify happiness in terms of an established model. Take the well-known study on happiness by academic Carol Ryff, which includes a theory of psychological well-being. Ryff posited that well-being could be measured on a model with six factors: self-acceptance, personal growth, purpose, mastery, autonomy, and positive relations with others. Researcher and statistician Konstanin Augemberg decided to test Ryff’s theory in this short case study. Using the programmable rTracker app on his mobile phone, Augemberg sampled himself three times a day on sliding scale with a simple question: How happy do you feel right now? He also rated himself at that moment on the six factors in Ryff’s model.
After a month, he ran the analysis of his data. “Out of 6 [variables] only 4 turned out to be predictive of happiness; the most influential of those were mastery and autonomy — being in control of the situation and being independent,” he found.
A good model like Ryff’s may have broad appeal, but as Augemberg’s experiment demonstrates, all of its six factors may not be relevant to each individual — an overly complicated model may be likened to a universal remote control with superfluous buttons. Through this experiment, Augemberg was able to remove the extra two components, allowing him to better focus on those factors that, according to his data, directly influenced his happiness. He observes, “n=me, so the model may work differently for others.”
3. Experience sampling
It gently nudges users at random intervals throughout the day to log how they’re feeling. Over time, the method creates a detailed happiness dashboard so participants can make fact-based decisions or change their habits based on their numbers.
Auto-analytics tools in this area, like trackyourhappiness, represent a new type of research approach, one that advances both scientific learning and individual progress toward happiness.
An interesting dimension of trackyourhappiness is its measurement of mind-wandering. The tool helps people work out tough questions like this one: As I’m performing a task I consider unpleasant, say collating monthly business travel expenses, is it better to focus on the task at hand or to imagine something more pleasant while mindlessly grinding through it?
To resolve this type of conundrum, users are asked three questions at various points throughout the day: (1) How do you feel now? which they answer on a sliding scale from “very bad” to “very good”; (2) What are you doing?; and (3) Are you thinking about something other than what you’re currently doing?, to which they can answer “no,” “yes — something unpleasant,” “yes — something neutral” or “yes — something pleasant.”
After using the tool for a while, most begin to discover through data that they are much more happy when they are focused on the present than when not. As lead researcher Matt Killingsworth’s analysis of more than 15,000 users shows, people are measurably less happy when they are mind-wandering, no matter what they are doing. “For example, people don’t really like commuting to work very much, it’s one of their least enjoyable activities. And yet they are substantially happier when they are only focused on their commute than when their mind is going off to something else,” he says in his research presentation.
A possible cause for the negative effects of mind-wandering may be that our minds most often wander to worrisome topics like job stability or declining sales this month. Yet on the flip side, the data also show that even when we are imagining something neutral or pleasant, we are slightly less happy when our mind is diverted from its main task than we are when it is attentive.
Killingsworth sums up: “If mind-wandering were a slot machine it would be like having a chance to lose $50, $20, or $1. You’d never want to play.”
Each of the three approaches is really about adding a dose of science, gathering, and acting on data to inform personal change. Whether you’re interested in addressing your happiness, your work productivity, or something else important, you can begin this data-gathering process by taking this short assessment.
This article was first published on HBR.org
By: H. James Wilson is a managing director of Information Technology and Business Research at Accenture Research. Follow him on Twitter @hjameswilson. He is a coauthor, with Paul Daugherty, of Human + Machine: Reimagining Work in the Age of AI (Harvard Business Review Press).