I Measured the Gap Between Waking Up and Getting Up

My alarm and my actual out-of-bed time are not the same event, and I’d never measured the distance between them until this spring. For six weeks I logged both — alarm-fire time from my phone, feet-on-floor time from a lamp plugged into a smart plug that I turned on the moment I stood up, timestamped by the plug’s own app. The average gap was seventeen minutes. The range was zero to fifty-four.

Here’s why I bothered setting this up at all: I didn’t think I had a problem. I thought of myself as someone who gets up when the alarm goes off, give or take a minute or two of stretching. The smart plug was supposed to confirm that, the way you check a number you already believe just to see it in writing. It did not go the way I expected.

The setup

The method is not sophisticated and I want to be honest about that rather than dress it up. A $12 smart plug, a lamp on the nightstand, and a phone alarm. When the alarm fired, I noted the time. When I actually stood up — not rolled over, not silenced the phone, but stood, both feet on the floor — I flipped the lamp on, and the plug’s app logged the exact timestamp. Thirty-eight mornings of data, six weeks, with two mornings excluded because I forgot to reset the plug the night before and the log was garbage.

It’s a crude instrument. It doesn’t capture what happened between the two timestamps — whether I was awake and deliberately delaying, or actually asleep again and the alarm had done nothing, which is closer to the specific stall covered in the piece on why getting out of bed is sometimes its own separate problem from waking up. I want to flag that limitation early because it matters for what I’m about to claim.

What seventeen minutes actually looked like

The average, across all thirty-eight mornings, was 17.4 minutes between alarm and feet-on-floor. That number alone isn’t that interesting — most people would probably guess something in that range if you asked them cold. What surprised me was the shape of the distribution, not the average.

It wasn’t a bell curve clustered around seventeen. It was closer to two separate clusters: a big group of mornings between two and eight minutes, and a smaller group between thirty and fifty-four minutes, with almost nothing in between. Days didn’t drift gradually from “fast” to “slow.” They were mostly one or the other, and the split tracked day-of-week almost perfectly. Every weekday morning with a specific first commitment — a call, a meeting, a train — landed in the fast cluster, usually under six minutes. Every morning with no fixed first event, weekends especially, landed in the slow cluster, several of them past forty minutes.

That’s not a discovery in the sense of being unpublished science — the general shape of “you get up faster when something concrete is waiting” is intuitive once you say it out loud, and it’s roughly the same finding someone else reached by moving their alarm 90 minutes earlier for a month and tracking what happened next, from a different angle. What I hadn’t appreciated is how binary it was for me specifically. I expected a gradient. I got a light switch.

The honest complication: I was probably measuring the wrong thing, partly

Here’s where I have to flag something that undercuts the tidiness of the result. Setting up a smart-plug experiment to measure how fast I get out of bed almost certainly changed how fast I got out of bed, at least on some mornings — I was aware, lying there, that I was being logged, which is not a neutral state to measure someone’s ordinary behavior in. The effect has a name: the term “Hawthorne effect” comes from Henry Landsberger’s 1958 reanalysis of a set of 1920s-30s productivity studies at Western Electric’s Hawthorne Works, where workers’ output seemed to improve simply because they knew they were being observed, regardless of what was actually changed about their conditions. Landsberger’s specific reinterpretation of that original data has since been contested by later researchers who’ve argued the original effect was smaller and messier than the popular version suggests — but the core idea, that measurement itself can change the behavior being measured, is well established well beyond that one contested dataset.

I can’t cleanly separate, in my own data, how much of the fast-cluster mornings were “I had somewhere to be” versus “I knew a lamp was about to timestamp me.” Probably both, tangled together, and I don’t have a clean way to pull them apart with a $12 smart plug and one person’s data. That’s the real limitation of this whole log: it’s an experiment I ran on myself, with a sample size of one, using an instrument that watches its own subject.

What actually changed after six weeks

I didn’t come out of this with a new morning routine. What changed is smaller and, I think, more useful: I stopped assuming “I got up when my alarm went off” was a meaningful description of my own mornings, because the data showed it plainly wasn’t, on roughly a third of them. Some of that slow-cluster time was fine — a slow Saturday morning costs nothing. Some of it wasn’t; two of the fifty-plus-minute mornings made me late enough that it mattered, and both were mornings where the only thing waiting for me was my own intention to get up, with nothing external checking whether I had.

That’s the piece I ended up acting on. Not “get up faster” as a general goal, which is vague enough to be useless, but specifically: the mornings that failed were the ones with no one and nothing positioned to notice if I didn’t get up. I added a friend into the loop for exactly those days — the ones with no fixed first commitment, where I’d shown my own data that I couldn’t be trusted to self-monitor. It’s not a smart-plug fix. It’s closer to the point the smart plug accidentally proved: on a morning with nothing external watching, seventeen minutes was a lucky average, not a reliable one.