Exploring Nocturnal Recovery through Linear Regression: How HRV and Time During Sleep Connect

Imagine we're looking at your HRV during one night of sleep. We want to understand how your HRV (heart rate variability) changes as time passes during sleep and how that relates to your nocturnal recovery rate.

As you’ll see in the Vital Science app when you tap on the HRV tile, there’s a graph with the following metrics:

HRV Axis (y): This is the vertical line on a graph. On the bottom of this line, we have low HRV values, and on the top, we have high HRV values. This line represents the range of HRV values.

Time During Sleep Axis (x): This is the horizontal line that represents time. It starts from the beginning of the person's sleep and goes on to reach around 24 hours. This line shows the progression of time as you sleep through the night.

Now, as you sleep, we'll collect your HRV data at different points in time. Let's say we measure your HRV every 5 minutes. So, as time passes, we'll mark dots on the graph at different heights to show your HRV value every 5 minutes.

Imagine that your HRV starts at a relatively low point when you go to bed. This means the dot is lower on the HRV axis. As the night goes on and time passes, we might notice that the dots start moving upward the HRV axis, showing that your HRV is increasing as you sleep.

Now, let's draw a line of best fit. This line will show us the trend of your HRV during the night. If the line slopes upward, it means that your HRV is increasing as time goes on.

Now let’s explore the connection to nocturnal recovery rate:

If the line slopes downward, suggesting that HRV is decreasing, it might mean that your body is working harder during sleep to recover. This might indicate that your recovery rate is not as good, and your body is still under stress even during sleep.

Conversely, if the line slopes upward, it could mean that your HRV is increasing as you sleep, which might indicate that your body is recovering well during the night. This suggests a better nocturnal recovery rate.

So, by looking at the line on the graph that represents your HRV changing over time during sleep, we can get an idea of how well your body is recovering during the night. It's like a visual way to understand how your HRV and recovery rate are connected while you sleep.

Now, let's connect the method by which that best fit line is drawn, via linear regression, to the scenario of tracking HRV and its relationship with time during sleep.

Remember, in the above example, the HRV values were plotted on the vertical axis, and time during sleep was on the horizontal axis.

When you perform linear regression with this data, the goal is to find the best-fitting straight line that represents the average change in HRV as time progresses during sleep. This line helps you understand the average pattern of HRV change over the night.

Biostrap uses a color coding system to help you understand the slope of the line. Red indicates a downward trajectory, again indicating a poor overnight recovery. Yellow indicates a moderately flat line, indicating HRV is not changing much overnight. Green indicates that HRV increased substantially throughout the night, suggesting good recovery.

The linear regression line doesn't necessarily pass through each individual data point. Instead, it tries to capture the overall trend in the data. This trend can help you understand the relationship between HRV and time during sleep more clearly.

In summary, linear regression helps you draw a straight line that summarizes the general pattern between HRV and time during sleep. This line gives you a clearer picture of how your body is recovering overnight.