06 Aug Self-Tracking: Problems and Possibilities for an Emerging Industry
Self-tracking, or collecting personal data to achieve quantitative self-knowledge, is a reoccurring topic of interest both in my work and personal life. I have spent a good deal of time reflecting on understanding self through patterns, on using data to achieve a higher quality of life, and on how to motivate users to self-track.
At GroupVisual.io, I’ve helped design several tools for self-tracking— from health-related apps that monitor sleep and mood patterns of pregnant women to professional development apps that encourage employees to track their training hours and learning efforts. Of course, these self-tracking products are just means to an end. For the pregnancy app, the “end goal” was quite lofty: to intercept post-partum depression by detecting and alleviating common problems while pregnant. For the professional development app, the “end goal” was to encourage employees to take advantage of their resources, realize their potential and become more valuable workers. It’s a challenge, though, to know the lasting impact (if any) that these tools have on users. Like many mHealth and self-tracking tech pursuits, we have yet to gather significant evidence on how effective and useful these products are.
In the Quantified Self community, an international group of self-trackers referred to as QSers, the question of efficacy and usefulness is widely discussed. Numerous challenges, spanning from data quality issues to the imprecision of tracking subjective data, have caused QSers frustration, or to stop tracking entirely. In his QS meetup talk, “Why I Track,” Buster Bensor provides an interesting look at meaningful metrics and cognitive dissonance in self-tracking. The community provides a wealth of information on user experience for people like me, developing the very tools they are using.
In my own life, I’ve tracked many different things—mostly for fun or out of curiosity. I’ve tracked my mood, sleep, steps, spending, places I’ve been, caffeine and alcohol intake, books started and finished, and range of colors worn each day. During my tracking pursuits, I’ve also encountered several problems, such as:
- Logging data on my own is often tedious.
- Correcting data that was inaccurately logged by devices is also a pain.
- Unusual patterns in data are often just caused by inaccuracies.
- If I forget to log data one day, I tend to stop logging for a while.
- Subjective data like mood is nearly impossible to get right.
- Tracking apps never seem to be as adaptable to my personal preferences as I’d like (not enough options for mood, specific food I ate isn’t listed).
- Logging data is distracting from being truly “present” in life.
These are all pretty typical user complaints. I’ve heard them echoed in app store reviews, user testing within my work, and in Quantified Self stories. Therefore, these are issues that every product designer needs to be thinking about and dealing with.
Wearable tech is currently a $20 billion industry, forecasted to grow to $70 billion by 2025. For self-tracking health technology alone, the BBC reported the global market reached nearly $3.2 billion in 2014.
I bring this up because as more people engage with self-tracking, more people will experience these problems. People who don’t have experience interpreting data may not detect when unusual patterns are just a fluke in the system. People who aren’t comfortable with technology will be more likely to “give up” quicker, creating more waste and missed opportunities.
It is the job of designers, developers, researchers and product owners to acknowledge and address these problems. When we do, we will be creating meaningful work. Self-tracking has the potential to motivate major changes in health and fitness, and engender deeper self-understanding. It’s important for those of us involved in product creation to remember this, and put it at the forefront of our development.