Let's clarify common misconceptions about sleep stages and explore the science behind them.

While wearables offer a convenient way to track sleep, they often fall short when it comes to accurately measuring sleep stages. Polysomnography remains the gold standard for sleep studies, but it is impractical for everyday use. Our algorithm, while not perfect, is more aligned with scientific data and provides actionable insights to improve sleep quality. Ultimately, we are committed to delivering accurate and meaningful results based on physiological data.

The concept of sleep stages dates back to the early 1900s, with researchers such as Henri Piéron and Nathaniel Kleitman making significant contributions. Kleitman, along with his student Aserinsky, discovered rapid eye movement (REM) sleep in the 1950s, providing evidence that the brain remains active during sleep. Kleitman also conducted experiments on himself, including living in a cave for 32 days to study his own sleep cycles. These early studies laid the foundation for our understanding of sleep stages.

In the 1950s, polysomnography (PSG) was developed as a method for tracking sleep stages by monitoring brain activity, heart rate, and other physiological signals. PSG remains the gold standard for sleep studies but is cost-prohibitive, averaging $2,000 per night in a sleep lab. By the 1970s, researchers began using actigraphs, early wrist-worn devices, which later evolved into modern sleep trackers like Apple Watches, Oura Rings, and Fitbits. This advancement, coupled with marketing efforts, has made sleep tracking more widespread.

Sleep occurs in cycles lasting approximately 90 minutes, and individuals typically experience 4 to 6 cycles per night. These cycles vary in duration and are influenced by factors such as age, sleep quality, and even alcohol consumption.

There are four primary stages of sleep, three of which are classified as non-REM (NREM) stages:

Research indicates that approximately 75% of sleep is spent in NREM stages, with the largest portion in Stage 2. A typical sleep cycle follows this sequence: Stage 1 → Stage 2 → Stage 3 → Stage 2 → REM, with each cycle lasting 90 to 110 minutes. Early cycles contain more deep sleep, while later cycles involve longer REM periods.

An average night of sleep consists of:

We analyzed sleep data from approximately 1,000 individuals wearing Apple Watches over 20 nights. The results showed significant difference between wearable data and scientific expectations:

This discrepancy is largely due to the limitations of wearable technology in accurately measuring sleep stages.

The primary reason wearables struggle to track sleep stages accurately is that they rely on indirect measurements, such as heart rate and movement. In contrast, polysomnography measures brain waves (EEG), muscle activity (EMG), eye movement (EOG), and heart activity (ECG), providing a comprehensive view of the body’s physiological state during sleep.

Wearables have several limitations:

These discrepancies occur frequently, and users may observe mismatches between their heart rate, movement data, and reported sleep stages over several nights.

In response to these inaccuracies, we have developed an algorithm that more closely aligns with physiological markers with scientific data on sleep stages. For example, it identifies light sleep when elevated heart rate and body movement are present, deep sleep when heart rate significantly decreases, and REM sleep when there is a notable increase in heart rate variability combined with minimal physical movement. Based on data from nearly 10,000 users collected in March 2024, it yielded the following averages:

While not perfect, these results are much closer to the scientifically accepted breakdown. Our focus is on using physiological markers such as heart rate variability and movement patterns to offer actionable insights rather than relying strictly on traditional sleep stage labels.

While our algorithm does not match the precision of a sleep lab, it consistently provides reliable results. We are continually refining it to offer users more accurate sleep stage tracking. Our primary objective, however, is to deliver actionable insights into sleep quality. Instead of fixating on perfect sleep stage tracking, we focus on large-scale analytics that help users improve their sleep quality in meaningful ways.

Despite the limitations of sleep stage tracking, we include them because user feedback indicates a strong expectation for this feature. Competitors also offer sleep stage tracking, and its familiarity has created a demand. However, unlike other apps, we prioritize accuracy and focus on more meaningful metrics, such as sleep onset, wake times, heart rate patterns, and awakenings, giving you actionable insights based on physiological data.