Sleep 6.6 Future Innovations in Sleep Monitoring

Previews Labfront’s upcoming tools for multi-night sleep tracking, open-source metrics, and advanced visualization of circadian rhythms.

Video Breakdown:

00:00 Introduction to Labfront's Future Plans

00:49 Consumer Wearables and Data Processing

01:21 Open Source Packages and Algorithms

02:17 Correlation vs. Causation in Sleep Regularity

02:47 Assessing Behaviors and Circadian Rhythms

03:25 Future Steps and Visualization

For the future, what we really like to do at Labfront is to create a detailed actigraphy report. Again, this is a raster plot that was generated by an individual over the span of a month and you could see, the sleep patterns throughout the week. we can obviously generate nightly reports where you could talk about sleep efficiency, time in bed, et cetera.
But then we also are planning to generate multi night reports where it shows standard deviation of all the measures, onset offset, mid sleep duration, Inter daily stability because we can do that using actigraphy social jet lag index, SRI, CPD may be a little bit challenging 'cause the chronotype is a little complex to do, but we hope to do that.
And when it comes to consumer wearables, there is a algorithm, there's a whole stepwise process. You take the acceleration raw data, this gets converted to activity counts. Which is then converted to sleep/wake classification, then subsequently you can calculate your sleep regularity index.
And what's been done in the literature is, let's just say the UK Biobank used the Axivity wearable. This one unfortunately can only do about a week, and you have to ship it back in order to get back the data. It requires a hundred hertz of raw data. Use the open package GGIR, which is our program,
analysis system. The activity count is done almost every five seconds as although it's not entirely clear. And then you have a sleep/wake classification of 30 seconds. And then you can calculate the sleep regulation using sort of the two open packages, open source, packages using R or Python.
You could do the same for CamNTech, which also was done by other groups, and this was their stepwise process. But there's no reason why we can't do this using Garmins. And this is our intent to incorporate again. We're able to get raw data, we're able to get activity counts. We apply our, Cole-Kripke, our Sadeh method.
We can also incorporate the open package DGR as well, and then apply this and then produce the report for you, using the proven algorithms that's been published out there. The final two things, is again, the question of correlation versus causation. It's unclear whether low sleep regularity actually causes chronic disease, or whether this is just more of a correlation or not.
And for the future, then it would be good to see. Does instituting high sleep regularity actually improve or reverse ill health, and lead to healthiness or good wellbeing? And so that's gonna be, in the future. Then the other thing is we are able to assess sort of behaviors. Getting access to our internal circadian rhythms is a little more challenging, but there are options that are out there.
There is obviously salivary cortisol and salivary urinary melatonin, but that may be a little bit I guess invasive in terms of the amount of time that requires the day-to-day in order to do that, or an hour to hour even. But there are options for obtaining core and distal temperature, heart rate variability, which seems to correlate with this internal, circadian rhythms.
And we're gonna continue to evaluate that. And then next step is finding the best way to visualize the sleep regularity so that participants themselves can interpret the data and take the next step.

Course episodes

Sleep 6.1 Intro to Sleep Regularity

Sleep 6.2 The Clinical Importance of Sleep Regularity

Sleep 6.3 Physiological Mechanisms of Sleep Regularity

Sleep 6.4 Exploring the Factors Influencing Sleep Regularity

Sleep 6.5 Understanding Sleep Regularity Metrics

Sleep 6.6 Future Innovations in Sleep Monitoring