Hourly activity
I recently experienced a “shock to the system” that freed me from some data slavery in which I had been unwittingly participating. This occurred when Google controversially rebranded the longstanding Fitbit app into Google Health, precipitating a continuing backlash on the r/fitbit subreddit and within the broader Fitbit community. The rebrand came with not only an unasked-for redesign that took a wrecking ball to the UX, but a slew of suddenly missing features and charts that were beloved by Fitbit users, loyal and casual alike. While this appears to be yet another in a long chain of “enshittification” moves by Google, the change unexpectedly relieved the grip that one of the charts had held on me, when I found that it was missing from the app update.
Most people are familiar with the widely-advertised 10,000 steps a day fitness goal, but Fitbit has another feature called Hourly Activity that lets the user choose a 14 hour window of the day during which to track how many of those hours registered at least 250 steps, marking the hour as “active”. I got a Fitbit Charge 3 from my brother as a gift back in 2019, and have since then been a particular fan of that chart as it gave a more even picture of how active I was across a particular day or week.
Sure, playing tennis one morning would take my step count well beyond 10,000, but I may have been sedentary for the rest of the day, resulting in perhaps only four or five active hours (my chosen window is 7am-9pm). By contrast, on an errand-filled Sunday, I may get 10+ active hours despite only clocking 7,000 steps. Steps measure the absolute activity for the day, while hourly activity measures the spread of the activity, and the general feeling of “wow, I was running around a lot today”.
I would often scroll back and see my weekly average of daily hourly activity, marvelling at an active week if my average was over 8 and lamenting a desk-bound one if it was below 6. It got to the point, however, where my awareness of this weekly average would sometimes influence my behaviour. As a religious Jew, I don’t wear my Fitbit on Shabbos from sunset on Friday until nightfall on Saturday, so that’s the time I conveniently choose for the watch’s weekly charge (it only actually needs a couple of hours). But once Shabbos is over, I would sometimes be eager to put the Fitbit back on to register hourly activity, especially in winter months when nightfall is earlier, giving me a few hours to move before the 9pm cutoff.
Falling prey to Goodhart’s law
In doing so, I had fallen for Goodhart’s law, which states that “when a measure becomes a target, it ceases to be a good measure”. This law describes the phenomenon where people focus so much on scoring high on a certain measure, that the measure is no longer useful because people’s behaviour has been too heavily influenced by it, and we don’t know if the thing that was originally being measured is actually improving or not.
Here’s a great example of this in the education field:
“… if a school decides to judge teachers by their students’ test scores, teachers might just teach to the test. The scores go up, but it doesn’t mean kids are learning better overall. The test score stops being a good sign of real learning because it’s now a target, not just a measure.”
The same thing applies to people who listen to music just to pump up their Spotify Wrapped numbers, or software engineers who increase the lines of code in their Git commits even when doing so provides no benefit or even worsens the overall codebase. Perhaps the worst form of this is “tokenmaxxing”, where developers are incentivised to spend as many AI tokens as possible, as some ludicrous productivity measure. This has been expounded upon by many, not least my friend Nik in his now famous essay about AI mania.
Either way, what had begun as a benign measure of daily movement had morphed into a behaviour that I found uncomfortable and dishonest. I didn’t like the urge to put my watch back on just to increase the hourly numbers. But I also didn't want to intentionally not put it on. It was one of those psychological conundrums where I could no longer act naturally because I was aware of some external motivating force.
However, when Fitbit’s hourly activity chart disappeared, I found that after only a few days of not seeing that data, and therefore no longer being allowed to check my average, I no longer cared about the measure altogether. Even when Google put the chart back after a month of public outrage, with all the intervening data still there, I realised that I had already been freed from the data slavery. I have barely glanced at it in the intervening months, and I have not been suffering.
No measure is fully resilient
In hindsight, I was simply lucky that this happened. A bad decision by Google led to a break in my regular data pattern big enough to dispel the fog, and after that, whatever nebulous part of me had cared up to that point had now thankfully drained away. But it does bring the bigger question to the fore: how do we escape from the data slavery in which many of us are trapped? We can’t just switch off our brains and simply forget that the metrics exist, thereby preventing our behaviour from becoming influenced to game the system. How, then, do we stop measures from inevitably becoming targets?
It is tempting to think that we should seek more resilient measures, which don’t suffer as much from Goodhart’s law. Most of the measures around us today are proxy measures, mere stand-ins for some hard-to-measure thing. It is clear that hourly activity is only a proxy for health and wellbeing from the fact that someone only needs 3,500 steps to register 14/14 active hours in a day. Say what you will about 10,000 steps being a measure of health, but 3,500 is certainly pretty sedentary. Such proxy measures are the easiest prey for Goodhart’s law, because our behaviour eventually reveals the disconnect between the goal of the measure and the measure itself. Hourly activity never actually was a measure for health and movement — it just took accidentally gaming the system to figure that out.
By contrast, a resilient measure would be interwoven with the goal itself. Even if people changed their behaviour to influence the measure, the overall goal would still be reached if the number went up. The issue with such measures is that they are hard to find, if they exist at all. I wanted to give some examples of resilient measures, but found myself immediately poking holes in them and seeing how they could be gamed. It may take a little longer, but eventually people will find a way to work towards the measure at the cost of the goal.
Quantification is not the way
The conclusion I’ve come to is that we try to quantify things that can’t be quantified, because it’s easier to have some number than nothing at all, or than really trying to probe a system. This phenomenon is aided by the proliferation of data all around us. With supercomputer number crunchers in our pockets, we can get advanced stats on anything instantly, and can be duped into thinking that we know something. But in this morass of data, finding measures that truly tell a story is hard work, and we don’t like that. So we take the shortcut, the spuriously-good measure, stand it up, put a nice hat on it, and then forget that it’s just a scarecrow whose sleeves are flapping in the wind.
Perhaps we can’t have everything packaged up for us with convenient facts and figures. Perhaps more patience is needed to extract real details, the real story of what’s going on.
Are you healthy? The answer won’t be found in your recent step count or sleep scores or hourly activity. Maybe a visit to the doctor would shed some light. And maybe the answer won’t be a single score but a three page report cannot be reduced into convenient numbers.
Is your engineering team doing well? It’s not a simple answer, and that’s okay. It might take talking to people, reading slowly through code with your eyes, looking at product sales reports, and spending time in the weeds to get a feel for what is really going on. The answer might not be something you can communicate in a few words, and again, that’s okay. With the right context and qualifications, some measures can be fine and indeed informative. Dispensing with measures altogether isn’t the point. It’s about remembering that they are just a part of the story.
Without easily-digestible numbers to profile everything, we won’t be able to easily compare and rank ourselves amongst our peers, and I believe this is a good thing. When we boil down life to salary or followers or steps, we lose much of what makes us human. This is what I was feeling when I wrote about counting the number of books you read in a year.
I may be a complete data nerd who majored in data science, loves spreadsheets, and named my Fantasy Premier League team Data FC under pressure from my friends, but I have shed some of the weight of data from my life. It’s something I’m actively working on, and I believe that a better world is one where we stop looking for data shortcuts and tell more comprehensive stories instead.