Sleep Trackers for Sleep Analysis: What They Can Do and How to Analyze Your Sleep Stages Like in a Sleep Lab

Today, there are countless sleep trackers and ways to measure your sleep quality from the comfort of your home. But how accurate are these results? And how can you analyze your sleep stages almost like in a professional sleep lab?

From Smartwatches to Rings: What Sleep Trackers Actually Measure

Whether it’s a smartwatch, app, ring, or another wearable device — the sleep tech market is booming. More and more people are trying to improve their nighttime recovery. According to the research firm Magna, the sleep tech market in Germany, Austria, and Switzerland is expected to grow by 15% by 2028.

Fitness trackers worn on the wrist can count your steps and guide your workouts — and many now claim to “measure” your sleep. In the morning, users can see when they went to bed and woke up, and how much time they spent in each sleep phase.

But there’s a catch: unless these trackers are paired with more advanced sensors, the sleep phase data is often inaccurate. Apart from bed- and wake times, the data they provide can feel random, especially when compared to clinical sleep lab results [1, 2].

Why Sleep Measurement Is So Tricky

The problem lies in how sleep is defined: clinical sleep analysis relies primarily on brain activity, measured using EEG — something a wrist-worn device simply can’t capture. Instead, fitness trackers infer sleep stages from movement patterns: since we move less while sleeping — and are even paralyzed during REM sleep — the absence of motion is used as a proxy for sleep. But this method has significant limitations.

So take those charts with a grain of salt — believing them too literally can lead to misinterpretation.

Tracking Deep Sleep: How It Works More Accurately

Tracking becomes more meaningful when a device includes physiological measures like heart rate or heart rate variability (HRV) [3, 4]. These are processed by trained algorithms — often using AI — to classify sleep stages. The better the sensor and the algorithm, the more reliable the results.

One example is sleep², an app trained on data from thousands of patients in real sleep labs in Salzburg, Austria. The algorithm has been peer-reviewed and scientifically validated [5]. It uses patterns in heart activity to detect REM sleep and other stages — allowing accurate classification based solely on your heartbeat.

Developed by researchers at the University of Salzburg, sleep² partners with Polar, a leader in heart rate sensor technology. Their Verity Sense sensor is worn on the upper arm and is barely noticeable at night. You simply wear it while you sleep and connect it to the app before bedtime.

The app then analyzes your sleep phases (deep, light, REM, wake), calculates how long it took you to fall asleep, your nighttime wake periods, and gives you insights into sleep efficiency and overall sleep quality. While it still doesn’t measure brainwaves, it comes remarkably close to lab-grade accuracy.

Tracking Sleep Is One Thing — But What Do You Do With the Data?

Fair question. In traditional sleep labs, sleep is often measured for just one night — and even then, appointments can be hard to get. The advantage of sleep tracking at home is clear: you can measure as often as needed, over multiple nights, to detect patterns and changes.

sleep² doesn’t stop at measurement: it also offers personalized tips and training to help you improve your sleep over time. Many other trackers just leave you with raw data, unsure of what to do next.

“Don’t I Know How I Slept Without a Tracker?”

Absolutely! Often, your subjective feeling is more important than any measurement. But objective data can still be valuable — for example, to check if you really fell asleep faster, woke up less, or improved sleep efficiency. Sometimes, it's reassuring to learn that you slept more than you thought.

The Importance of Listening to Your Own Body

However, tracking can also cause stress, warns sleep researcher and sleep² advisor Dr. Christine Blume. For instance, if your device says you slept poorly, you might believe it — and then feel tired all day, even if you’re not. This can become a self-fulfilling prophecy, she says.

That’s why it’s essential to listen to how you feel and not rely solely on numbers. “That’s incredibly important when it comes to sleep,” says Blume. In fact, people with insomnia often underestimate how much they slept — that’s part of the disorder itself.

In therapy, the goal is to help people feel that their sleep is restorative, even if the objective measurement doesn’t always match up.

How to Actually Improve Your Sleep

Measurement alone doesn’t lead to better sleep. What matters is how you interpret the data and whether you change your habits — for example, by improving your diet, getting more exercise, and practicing good sleep hygiene.

“If there’s a problem, behavioral change is key,” says Prof. Manuel Schabus, sleep researcher and founder of sleep². “Work on relaxing before bedtime — that’s one of the most important things.”

sleep² Tips

• Most fitness trackers are inaccurate unless they include metrics like heart rate or HRV.

• Your feeling matters — and in case of doubt, it’s more important than numbers.

• Objective data can still offer helpful insights — and give context to how you feel.

REM Sleep

REM sleep (short for Rapid Eye Movement) is a sleep stage characterized by quick horizontal eye movements from left to right. During this phase, the skeletal muscles become completely relaxed — essentially rendering the body temporarily paralyzed. Meanwhile, brain activity (EEG) resembles that of the waking state. REM sleep is also when the most vivid dreams occur. In adults, REM sleep accounts for approximately 20 to 25 percent of total nightly sleep, with its proportion increasing in the second half of the night.

Sleep Efficiency

Sleep efficiency tells you how “efficiently” you use your time in bed. It’s always expressed as a percentage and is calculated based on the time you actually slept compared to the time you spent in bed.
Example: If you’re in bed for 8 hours but only sleep for 6, your sleep efficiency is 6/8 = 75%.
A good sleep efficiency is typically considered to be 80–85% or higher.

References

1. Ameen, M. S., Cheung, L. M., Hauser, T., Hahn, M. A., & Schabus, M. (2019). About the accuracy and problems of consumer devices in the assessment of sleep. Sensors, 19(19), 4160.
   https://doi.org/10.3390/s19194160

2. Chinoy, E. D., Cuellar, J. A., Huwa, K. E., Jameson, J. T., Watson, C. H., Bessman, S. C., Hirsch, D. A., Cooper, A. D., Drummond, S. P. A., & Markwald, R. R. (2020). Performance of seven consumer sleep-tracking devices compared with polysomnography. Sleep, 44(5), zsaa291.
   https://doi.org/10.1093/sleep/zsaa291

3. Sridhar, N., Shoeb, A., Stephens, P., Kharbouch, A., Shimol, D. B., Burkart, J., Ghoreyshi, A., & Myers, L. (2020). Deep learning for automated sleep staging using instantaneous heart rate. npj Digital Medicine, 3(1), 106.
   https://doi.org/10.1038/s41746-020-0291-x

4. de Zambotti, M., Cellini, N., Goldstone, A., Colrain, I. M., & Baker, F. C. (2019). Wearable sleep technology in clinical and research settings. Medicine and Science in Sports and Exercise, 51(7), 1538–1557.
   https://doi.org/10.1249/MSS.0000000000001947