Your Sleep Tracker Might Be Making Your Sleep Worse
Orthosomnia — sleep disruption caused by anxiety about sleep-tracking data — is a documented clinical phenomenon. Here's what it is, why it happens, and what to do about it.
In this article3 sections
Orthosomnia entered the medical literature in 2017, coined by researchers Kelly Baron, Sabra Abbott, Nancy Jootsen, Natalie Manalo, and Rebecca Mullen at Rush University Medical Center. The term describes a specific clinical presentation: sleep complaints driven not by actual sleep problems but by a patient’s anxiety about what their tracking device reports.
The tracker becomes the source of the problem it was supposed to solve.
How It Happens
Consumer-grade sleep wearables estimate sleep stages through movement and heart rate data. Algorithms for distinguishing “asleep” from “awake” are reasonably accurate. Algorithms for sleep stage classification — REM percentage, deep sleep duration — perform inconsistently. Menghini and colleagues (Sleep Medicine Reviews, 2021) documented error rates of 20–40% for individual sleep stage classification in validated wearables.
The device doesn’t know it got your REM percentage wrong. It reports a number as fact. You wake up, see a low sleep score, and start the day expecting to feel bad — even if your actual sleep was physiologically adequate.
Then the optimization loop begins: earlier bedtime (which, if you’re not tired, produces lying-awake time and reduces sleep efficiency). Avoiding activity before bed (which removes the temperature drop and adenosine buildup that actually help you fall asleep). Checking the score first thing each morning (which elevates cortisol before you’ve had a moment to notice how you actually feel).
The feedback loop runs backward. The watching makes the thing worse.
The Counterintuitive Finding
Baron et al.’s original orthosomnia patients had disrupted their sleep specifically through tracker-focused optimization. The clinical intervention was not adding better habits — it was removing the tracker. Most patients improved within weeks.
This doesn’t mean sleep trackers are useless. They’re useful for identifying large, stable patterns: whether sleep is consistently short over months, whether a particular behavior correlates with clearly worse nights across many data points. That’s a different use case than nightly score-checking.
The distinction matters: tracking input patterns (bedtime, wake time, alcohol, exercise) is actionable. You can change inputs. Optimizing for output numbers — particularly with devices whose stage accuracy is imprecise — tends to generate anxiety without producing insight.
The Practical Conclusion
If looking at your sleep score makes you anxious, and you check it before assessing how you actually feel — the data is working against you.
One test: spend two weeks not looking at your sleep data. Track only whether you fell asleep and woke at your intended times. Notice whether sleep quality, as you experience it, changes.
Most people either find no difference (in which case, the data was noise) or find improvement (in which case, the watching was the problem).
Dani spent four months trying to improve her sleep score. The number stayed erratic; her anxiety about it didn’t. She left the tracker on but stopped reading the sleep data in January. By March, she described mornings as “the least complicated they’ve been in a year.” She had made no other changes. Would committing to just one consistent wake time help you more than the sleep score data? DontSnooze does exactly that.
Related: why 4 AM wakings happen and what actually helps and how sleep efficiency is measured — and why it matters more than hours.