Sleep 1.2 Data Integration Across Devices

Labfront integrates wearables like Garmin, Movesense & Dexcom for research. Learn about data collection, sleep tracking & future integrations.

Video Breakdown:

0:00 - Commonly Used Wearables

0:53 - Available Raw Data

1:35 - Sleep Measures & Data Sources

1:58 - Sleep Staging with PPG & HRV

3:10 - Future Integrations

Now for data integration across the devices, to let you know Labfront is a device-agnostic platform and we do incorporate multiple devices.
But the most commonly used wearable is the Vivosmart 5, which does provide data that is obtained from the PPG as well as the accelerometer. So you get all this information from the VivoSmart 5. We also can, you can go up higher end to the higher end levels of Garmin and those also in addition obtain information on temperature.
The Movesense also provides EKG and the 9 axis IMU capabilities. And, and we also work with Dexcom. So just to give you an idea of what type of raw data that you can get, so you can get the beat-to-beat interval, the continuous beat-to-beat interval that I talked about, not the actual raw PPG, but the actual beat-to-beat interval, and again, you can get the raw accelerometer data as well. And it's all sort of labeled here for you, the raw data for the IMU from the Movesense devices as well.
The temperature actually is an interesting one, which is available in Venu 3 and some of the higher end Garmins. Unlike any of the other devices, the temperature is actually an actual value. It's not a deviation from a five day or 10 day average. So this could be potentially useful for some of your research cases.
So let's go through some of the sleep measures that I've talked about previously. So in order to sort of classify by doing sleep versus wake, you can use the Garmin watches or the Movesense using actigraphy from this information, or you could use heart rate variability, or even some of the magnetometer information, which gives you some information about your posture, whether you're upright or downright as well.
For sleep staging, again, as I mentioned, the watches were able to abduce sleep stagings differently than the research, the traditional research actigraphy because of the addition of PPG, and specifically heart rate variability. So you can theoretically do actigraphy and heart rate variability to do sleep staging.
And I'll actually, respiration is an important part to it as well, the consistency of the respiration. And I hope that at some point in the future webinars, I'll go into this in detail as well. For sleep regularity, actigraphy is primarily used on a day to day basis. And for circadian rhythms, temperature is useful.
But again, this is with a caveat. This is a distal temperature as opposed to peripheral temperature as opposed to central or core temperature. And I'll talk about that later as it relates to circadian rhythms in future talks. But you can use actigraphy, heart rate variability, and possibly glucose in assessing circadian rhythms, and this is an active area of ongoing research, and people are increasingly using this to establish the underlying intrinsic circadian rhythms.
But as I stated, we are a device-agnostic system, and we're continually exploring ways to integrate multiple devices. We're, for instance, looking into incorporating EEG, core temperature, EDA and GSR, raw PPG. And in fact, including environmental sensors, such as, you know, eco be for, you know, the indoor temperature or calibrate to assess the amount of light exposure that the participants have right before bed or even the pink air, which assesses the quality of the air, you know, whether there's micro particulates in the air as well.

Course episodes

Sleep 1.1 Overview of Series and Making Consumer Devices Research-Worthy

Sleep 1.2 Data Integration Across Devices

Sleep 1.3 Implementation Science and UX Design

Sleep 1.4 Performance Evaluation of Consumer Devices

Sleep 1.5 Research Transparency and Commitment to Open Science

Sleep 1.6 Exploration of Novel Approaches and Analytics