3 Quick Tips On How to use Wearables to Track Sleep

"Rest when you need to, but never quit."

- John Wooden

Introduction

The pursuit of better sleep has gained substantial momentum, as has recovery in general. With the rise of wearable technology, individuals can now track various biometrics including their sleep patterns. However, while these devices offer a wealth of information, it’s essential to approach the data they provide critically. This article proposes three simple tips to help you avoid misuse; ignore aggregate readiness scores, narrow monitoring to resting heart rate (RHR) and heart/pulse rate variability (H/PRV), and respect the limitations of sleep stage tracking.

Figure 1: Summary of the three quick tips: 1) Ignore aggregate readiness scores; 2) Establish a baseline; and 3) Don't solely focus in on sleep stage classification.

Tip #1: Ignore Aggregate Readiness Scores

Wearable devices from various manufacturers often provide an aggregate readiness score that summarizes your “overall readiness” for the day. This is a sum of multiple factors such as sleep duration, previous day activity level, and other stress correlates/indicators. It’s crucial to recognize that the algorithms powering these scores differ among companies. Each brand has their proprietary algorithm that monitor and analyze your data that is not supportive of cross comparisons. For instance: company A’s ring utilizes a combination of factors, including heart rate, activity levels, and sleep patterns to calculate your readiness score; company B’s chest strap primarily focuses on recovery metrics influenced by your H/PRV; and company C’s watch employs various considerations around overall heart rate trends and activity levels.

Because these algorithms utilize different data points, the scores you receive may not only be inconsistent but also lack practical relevance across platforms. While it might be enticing to rely on an aggregate score to respect the complexity of readiness, it often fails to provide meaningful insights tailored to your individual circumstances. Not excluding sleep-centric metrics.

Instead of focusing on readiness scores, it is advisable to pay attention to key metrics relevant to your personal health and sleep performance. Consider tracking RHR, H/PRV, sleep duration, sleep efficiency, and subjective quality of rest. This approach allows for a more personalized understanding of your sleep dynamics over time (1,2). Addressing limitations that inhibit the product you choose to use and its algorithm. RHR and H/PRV are two critical metrics for assessing your overall health, recovery, and related sleep performance. When used effectively, they can reveal a wealth of insight (3,4). 

RHR is a good barometer of cardiovascular health. A lower resting heart rate often indicates stronger heart function and better cardiovascular fitness. Coincidently, H/PRV can be an indicator of the interplay between the autonomic nervous system’s sympathetic and parasympathetic branches. For example, high H/PRV (parasympathetic > sympathetic) is typically associated with greater physical resilience and recoverability (3,4).

Tip #2: Make No Decisions for Four-Weeks

Before starting any routine, it's essential to collect data over a period of at least four-weeks. This timeframe allows you to identify your baseline under varying conditions, such as fluctuating stress exposure and various lifestyle changes. Once you have established a baseline, you can begin monitoring acute (short-term) versus chronic (long-term) trends in these metrics.

Acute Trends: Short-term changes can indicate how well you are recovering from exercise or coping with stress. For instance, if your RHR spikes following an intense workout or stressful event >5-10bpm, it may suggest that your body is recovering from the stress stimulus experienced.

Chronic Trends: Over several weeks, you may notice patterns in your metrics that can inform decisions about training intensity, recovery, or potential lifestyle stressors. A consistently elevated RHR may serve as a warning sign, suggesting that you may be becoming ill, need to adjust your training regime, improve your sleep hygiene and/or environment, or manage cumulative stress.

As you gain insights into your own patterns, you empower yourself to make informed decisions toward better health and sleep. Providing yourself the opportunity to experiment with intervention strategies and assess for improvements. Collecting wearable heart rate and sleep data for at least four weeks before making decisions is essential for establishing a reliable personal baseline. Unlike population studies that analyze trends across large groups, your data is n=1—you are the only data point that matters. This means your normal patterns may look very different from average study participants, making it critical to gather enough data to understand what’s typical for you.

A four-week period allows you to capture a broad range of variables: workdays, weekends, stress levels, exercise habits, and recovery periods. With this data, you can begin to identify personal trends and variability, such as how much your RHR fluctuates day-to-day or how your sleep latency is impacted by certain behaviours. More importantly, it allows you to determine what changes are statistically meaningful against normal day-to-day noise.

Without enough data, it’s easy to overreact to short-term deviations. A single bad night’s sleep or an elevated HR could mean nothing—or it could indicate a trend. Only by collecting consistent data over time can you confidently assess whether a deviation is significant and worth acting on. Patience allows for accuracy, and accuracy leads to better decisions about your health and performance.

Tip #3: Do NOT Monitor Sleep Stages with Wearables

One of the most enticing features of many wearable devices is the ability to track sleep stages—such as NREM light and deep sleep plus REM sleep. While this information appears beneficial, it is essential to understand the limitations of wearable sleep tracking.

The most accurate method for measuring sleep stages is polysomnography (PSG), which is typically conducted in a sleep lab. This method utilizes multiple sensors to monitor brain waves, eye movements, and muscle activity, providing a comprehensive picture of sleep architecture.

Wearables, on the other hand, often use simplified algorithms based on heart rate trends, movement patterns (e.g. wrist movement), and other indirect measures. As a result, their accuracy in determining sleep stages is limited. Studies have shown that while wearables may provide some insight into overall sleep duration and sleep latency, but they are inconsistent at classifying sleep stages even in a controlled environment. The Oura Gen3 is seemingly industry leading, with average accuracy ranging from 75.5% (light NREM sleep) to 90.6% (REM) (1,2). At the very least, if using a product that is industry leading consider the limits of agreement published and the controlled environment it’s being used in to mimic (e.g. product placement).

The inaccuracies in sleep stage tracking can lead to misconceptions about your sleep quality. If you heavily rely on wearables to analyze your sleep stages, you may become fixated on metrics that don't accurately reflect how rested you feel upon waking. Moreover, you might make unnecessary changes to your lifestyle or sleep environment based on spurious data, which could ultimately hinder rather than help your sleep performance. This is called Orthoinsomnia; obsessing or trying to overcorrect sleep. To avoid it’s important to understand these products are not created to replace medical devices and interpretation, but rather be a relatively low-cost flagging opportunity.

Instead of monitoring sleep stages, consider concentrating on overall sleep duration, sleep latency, and subjective quality of sleep. This simple approach can provide more valuable insights into your sleep without the complications of relying on potentially faulty stage classifications.

Conclusion

Wearable technology has made tremendous strides in personal health monitoring, including sleep tracking. However, while using these devices it’s vital to adopt a critical approach toward the data they provide. Ignoring aggregate readiness scores allows for a more personalized analysis of your circumstances. Collecting a baseline allows for actionable insights considering your personal circumstances. Finally, recognizing the limitations of wearable sleep stage tracking allows you to draw more accurate data to inform decision making and can prevent unhealthy relationships with products.

Consult your medical doctor if you have any concerns about your sleep.

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References

1. Svensson, T., et al. (2024). Validity and reliability of the Oura Ring Generation 3 (Gen3) with Oura sleep staging algorithm 2.0 (OSSA 2.0) when compared to multi-night ambulatory polysomnography: a validation study of 96 participants and 421,045 epochs. Sleep Med., 115, 251-263.

2. Robbins, R., et al. (2024). Accuracy of three commercial wearable devices for sleep tracking in healthy adults. Sensors, 24(20), 6532.

3. Kinnunen, H., et al. (2020). Feasible assessment of recovery and cardiovascular health: accuracy of nocturnal HR and HRV assessed via ring PPG in comparison to medical grade ECG. Physiolo. Meas., 41(4).

4. Molina, G. E., et al. (2021). Post-exercise heart rate recovery and its speed are associated with cardiac autonomic responsiveness following orthostatic stress test in men. Scandinavian cardiovascular journal, 55(4), 220–226.

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