MyNon24Sleep - A self-study of the circadian rhythm and its altering factors

Published on by Stephen Karl Larroque

Longitudinal data acquisition over years of various biomarkers to monitor sleep and the circadian rhythm as part of a self-experiment on the non-24 circadian rhythm disorder (with sight). This is the longest running continuously biomarkers-monitored circadian rhythm sleep-wake experiment done to date as of March 2022, with one year of 24/7 recordings of various circadian and sleep biomarkers. Think MyConnectome but for the study of sleep and circadian processes.

The aim of this dataset is to serve as a pilot study to demonstrate the possibility of conducting very long 24/7 remote decentralized clinical trials (RDCTs), and for this preliminary data to hopefully provide some insights for both circadian rhythm disorders management and the fundamental processes and interactions of the circadian rhythm and sleep processes, since circadian rhythm disorders provide an exceptional mean to study the factors affecting the circadian rhythm which is impossible or unethical to do with typical sleepers. Indeed, although either a constant routine protocol or a forced desynchrony protocol, both aiming to produce a freerunning sleep-wake pattern are common approaches to study circadian rhythm and sleep processes in humans and animals, it would be unethical to maintain human subjects under the conditions necessary to make them freerun for a long period of time, and hence most (all?) such experiments are limited in duration to one or two weeks at most. Whereas here, the base state of the subject's circadian rhythm is freerunning, and hence this provides an invaluable opportunity to study the factors that can affect the circadian rhythm under various conditions for very long timespans. A primary goal is to determine a reliable measure of the circadian rhythm, unaffected by the sleep-wake patterns or other confounding environmental factors.

This dataset includes the following biomarkers for v1 (1st year of data collection, from 1st March 2021 to 1st March 2022):

* Manually curated sleep diary to track sleep-wake patterns and provide additional context with tags and free-form comments over controlled factors.

* Core body temperature measuments (non-invasive) tracked continuously 24/7 at 1Hz using the GreenTEG CORE device.

* ECG and 3-axis trunk actigraphy tracked continuously 24/7 at 130Hz and 100Hz respectively using Polar H10.

* 6-axis actigraphy tracked continuously 24/7 at 100Hz using Axivity AX6.

* Skin temperature at various body sites tracked continuously 24/7 with a sampling rate of one sample every 5min using Maxim Thermocron iButtons.

* MRI dataset, including structural MRI (MPRAGE/FLAWS), sub-second functional MRI (EPI BOLD fMRI), multi-shell diffusion MRI (DWI/DTI), FLAIR 3D, SWI 3D and PC-ASL 3D.

* Whole-genome sequence (WGS) with clinical grade 30x sampling.

Changes for v2 (2nd year of data collection, from March 2022 to March 2023):

* Intensity and color of light exposure using a necklace photosensor.

MyNon24Sleep v2 is published at

More information on the experiment's design, the wearable devices used for data acquisition and some data analysis tools can be found under an open-source licence in the Wearadian GitHub repository:

It is recommended to use data starting in january 2021, as this is when the data acquisition procedure and devices finished being ironed out, so that the dataset from this date onward should be the cleanest and most pertinent for the study of the circadian rhythm and sleep patterns. This time period includes the following datasets:

* ibutton-left-nondominant-upper-arm-cotton-sports-wristband-5min (data before January 2021 can be used, but it won't be matched with other devices since they were modified).

* ibutton-left-nondominant-upper-arm-cotton-sports-wristband-dorsal-interior-5min (started acquisition 2nd March 2021).

* ibutton-left-nondominant-upper-arm-cotton-sports-wristband-ventral-exterior-5min (started acquisition 2nd March 2021).

* ecg-polar-h10 (start with set2 to start from 21th January 2021 onward, but data before in set1 can be used, although it won't be matched with other devices or with older versions).

* axivity-ax6-left-forearm-nondominant (started acquisition in March 2021).

* greenteg-core-axillary (from 21th January 2021 onward, data before used another attachment system and hence was much more noisy).

* sleep-diary-sleepmeter (data before 21th January 2021 can be used, but it won't be matched with other devices since they were modified, still it can be useful to map the natural sleep-wake pattern prior or using other less effective entrainment procedures than bright light therapy + melatonin).

Older data can still be used by keep in mind the noise will be greater (eg, GreenTEG CORE device not being attached in the way to be in optimal skin contact at all time) or the signal (eg, iButtons placement on or close to the trunk, which is unable to reflect circadian rhythm oscillations).

Start with the sleep diary (sleep-diary-sleepmeter) and core body temperature (greenteg-core-axillary) measurements as they are the most reliable indicators of the sleep-wake patterns and the circadian rhythm.

As a starting point, to assess where is the circadian rhythm, here are 3 potential approaches:

* Either use the period of lowest temperature in the greenteg-core-axillary dataset, as this sensor measures core body temperature this should be the most accurate way to assess the circadian rhythm.

* Either from the sleep-diary dataset, threshold sleep sessions by duration: sleep sessions lasting more than 6h30 are almost certainly in phase with the circadian rhythm, whereas sleep sessions lasting less than 6h are likely out of phase.

* Either from the sleep-diary dataset, filter sleep sessions by tag: LikelyInPhase, LikelyPartiallyInPhase, LikelyOutOfPhase. These tags represent the subjective assessment by the subject (who is also the experimenter) of whether he was sleeping in phase or out of phase with his circadian rhythm for this sleep session. The most reliable use of this dataset is to only keep the LikelyInPhase sleep sessions, consider the circadian rhythm night phase to overlap with these sleep sessions, and to interpolate the circadian rhythm phase in-between for the other days where this tag is missing. Assuming the circadian rhythm moves in a continuous way (no discrete step shifts), this approach should faithfully map the circadian rhythm progressive shifts (or lack thereof during entrainment therapy).

Note: some data may be missing for some sensors during short periods from one to 14 days due to unforeseen failures of devices (almost all devices failed at some point at least once).

Additional infos about the subject:

* Height: 1m74.

* Weight: Between 60 and 77.5 kg during the whole experiment period (regularly updated weight measures have been added as a dataset).

For ECG data, it was split into multiple parts zip files because of size constraints and into multiple sessions (session 1, session 2, etc) that are consecutive with one another (ie, session 2 follows session 1). Please make sure you use the 7zip software to unzip them. Please also make sure to download all parts of a session before trying to unzip it, and unzip by sellecting the part ending with .zip.001 (the first part), not other parts. If all parts are in the same folder, you should be able to open the zip right away and access the files inside, otherwise if it takes some time to uncompress, it is likely because you are missing a part and 7zip is recalculating so that you can still access the files you have in the parts you downloaded.

Although all the datasets are officially licensed as CC-BY 4.0 by FigShare because other CC options are not available for non-institutional accounts, the author hereby declares all datasets in the MyNon24 project to be dual-licensed under CC-BY-SA 4.0 in addition to CC-BY 4.0 at the user's choice.

Cite items from this project

3 Biotech
3D Printing in Medicine
3D Research
3D-Printed Materials and Systems
AAPG Bulletin
AAPS PharmSciTech
Abhandlungen aus dem Mathematischen Seminar der Universität Hamburg
ABI Technik (German)
Academic Medicine
Academic Pediatrics
Academic Psychiatry
Academic Questions
Academy of Management Discoveries
Academy of Management Journal
Academy of Management Learning and Education
Academy of Management Perspectives
Academy of Management Proceedings
Academy of Management Review

cite all items