Sleep is often discussed as a meaningful health target. People always ask:
- How do I get more sleep?
- Am I fully recovered?
- What is the ideal amount of sleep for me?
While answers to these questions are highly individualized—at the end of the day—any analysis of sleep, recovery, and related data should always come back to how sleep data are used in practice.
The question becomes this: which data influence your behavior to make a meaningful change in your life?
Sleep data are everywhere
The great thing about modern technology? We can measure everything.
The bad thing about modern technology? We can measure everything.
Sleep apps, aids, and algorithms abound. Purposeful analysis of these sleep data is what matters, not just that it CAN be measured.
Where to start with sleep data?
Most trackers have some native software that track sleep data. How those data are validated are up to these companies—Apple, Fitbit, Whoop, and popular apps (Sleep Score, Sleep Cycle).
When it comes to gathering sleep data, that part is easy. However, you are limited to their sleep definitions, displays, and outputs.
The difficult part? Generating relevant analyses that make sense to you, the individual.
5 Ways To Analyze Sleep Data
Among the countless ways to analyze data, these 5 graph are tailored to individual client needs.
Daily Hours Slept
Weekly Average Hours Slept
When averaging sleep data by week, you lose variability and there less to see…
Bed Time Start vs. Average Hours Slept
As bed gets later (x-axis), hours slept get shorter (y-axis).
Weekly Average Hours Slept vs. Weekly Average Steps
Maybe this is a fatigue factor? Correlation? But convincing evidence nonetheless. As this client became more consistent with physical activity (last 4 weeks), average hours slept increased as well.
Hours Slept By Bed Time Start with Trend Line
A scatterplot like this allows you to quickly assess which bed times produce the most sleep by individual day. As bed time to bed increased (12 = midnight, 13= 1am, etc…), hours slept decreased. With this client, she woke up at the same time, but the time to bed varied which affected sleep duration.
