Sleep Correlations using Google BigQuery

Sara Robinson is an advocate for Google Machine Learning and an OuraRing user. She has posted a helpful tutorial on her blog explaining the process she used to gather two months worth of sleep and activity data using Oura’s API and upload it to Google’s BigQuery. The beautiful thing about Sara’s  guide is that it shows how we can have some confidence that small lifestyle changes are increasing aspects of our healthspan.
The results of Sara’s analysis can be summarized as going to bed at a consistent early time resulted in better REM sleep and a lower heart rate. Exercise can promote more deep sleep. While these results might seem to be intuitive it is reassuring to see this small data set confirm that. Both forms of sleep REM or DEEP are critical to performance. If you have not already heard Matthew Walkers three part interview on the Peter Attia Drive Podcast this will quickly explain why biohackers are obsessing over the total, REM and DEEP sleep. Matthews book “Why We Sleep” is also an essential read and a NYT bestseller.
While our fitness trackers are excellent of keeping tally of our daily health metrics and rest they provide very little in the way of correlating certain activities. It is easy for me to download my lowest resting heart rate for the last three years, but extremely difficult for me to confirm which lifestyle change made it so low. Today the tools we need to analyze diet macros, caloric intake, fasting windows, heart rate variability, blood glucose and fitness tracker collected health data are all available, but in isolated forms. We are seeing applications such as Cronometer which is really a food tracker, but allows all the health data to be synced into one dashboard. MementoLabs is trying to do the same by creating group experiments and collecting data from many popular quantifiedself devices which can be cross referenced. It is good to see that Google’s BigQuery is another helpful utility that can be used to merge data across different health apps and fitness tracker. Those with the patience to configure it can add to their selftracking toolbox.

As 2022 starts, let’s take some time to share our goals for CircuitPython in 2022. Just like past years (full summary 2019, 2020, and 2021), we’d like everyone in the CircuitPython community to contribute by posting their thoughts to some public place on the Internet. Here are a few ways to post: a video on YouTub, a post on the CircuitPython forum, a blog post on your site, a series of Tweets, a Gist on GitHub. We want to hear from you. When you post, please add #CircuitPython2022 and email to let us know about your post so we can blog it up here.

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