Using distal temperature (wrist temperature with smartwatch / finger temperature with smart ring as Oura) to estimate core body temperature (CBT).
We can then use the wrist temperature shifts as circadian phase shift estimates in circadian phase management. For example when prescribing melatonin or/and light exposure to mitigate the effects of jet lag
Alternative download link:
https://www.dropbox.com/scl/fi/es7174291yws262rhr568/cbt_estimation.pdf?rlkey=846yeed1wrqsjgkx7kp8ccc2y&dl=0
4. Circadian rhythms and SCN
Hastings et al. (2018)
Suprachiasmatic Nucleus
(SCN) drives the intrinsic
timekeeping show, and then
the show will get entrained
(“synced”) to the outside by
cues (zeitgebers) such as light
Can’t really measure the SCN
activity in humans in vivo thus
melatonin secretion or/and
core body temperature used
as proxies for estimating
circadian phase
6. Phase Response Curve (PRC)
Light in the morning
makes you fall
asleep earlier
Light in the evening
(e.g. bright
smartphone) will
delay your
sleepiness
Melatonin in the
morning makes
you fall asleep
later
Melatonin in the
evening will
advance your
sleepiness
Need to estimated for
each person
Adapted from Lewy et al. (2010),
Khalsa et al. 2003
The circadian phase effect of the light/melatonin intervention depends on the time of the delivery in relation
to the intrinsic circadian phase of the person. E.g. bright light in the “circadian morning”, and melatonin in the
“circadian evening” will make you fall asleep earlier
7. Light and Melatonin in Jetlag
management recommendations
Melatonin & Light
https://www.mysportscience.com/post/preventing-jet-lag
8. Sleep and food intake also in humans to some extent as zeitgebers
Circadian Rhythm Variations and Nutrition in Children
https://doi.org/10.1055/s-0038-1670667
https://doi.org/10.1101/2020.12.31.424983
10. Phase from Wearables #1
https://doi.org/10.1016/j.coisb.2020.07.011
Knowledge of circadian phase is critical for timing interventions
for circadian rhythm disorders, medications, or predicting
alertness. Current gold-standard measures of circadian phase
are impractical for continuous or real-time
tracking. Mathematical modeling offers an alternative, whereby
ambulatory monitoring of environmental, behavioral, and/or
physiological variables can be used to predict circadian phase.
https://doi.org/10.1137/22M1509680
Accurately estimating circadian phase from wearable data remains
challenging, primarily due to the lack of methods that integrate minute-by-
minute wearable data with prior knowledge of the circadian phase. To
address this issue, we propose a framework that integrates multitime scale
physiological data and estimates the circadian phase, along with an
efficient implementation algorithm based on Bayesian inference and a new
state space estimation method called the level set Kalman filter.
11. Phase from Wearables #2
https://doi.org/10.1038/s41746-023-00865-0
“The present study suggests that CARE is an effective
wearable-based metric of circadian amplitude with a strong
genetic basis and clinical significance, and its adoption can
facilitate future circadian studies and potential intervention
strategies to improve circadian rhythms and cognitive
functions.”
https://doi.org/10.1093/sleep/zsad179
“We developed a personalized sleep intervention framework that analyzes
real-world sleep–wake patterns obtained from wearable devices to
maximize alertness during specific target periods. Our framework utilizes a
mathematical model that tracks the dynamic sleep pressure (homeostatic)
and circadian rhythm based on the user’s sleep history. “
12. Patents on Circadian Phase Estimation
Philips
https://patents.google.com/patent/US20180160944A1/en
Rensselaer Polytechnic Inbstitute (RPI), Troy, NY, USA
https://patents.google.com/patent/US20180160944A1/en
13. You need
feedback for “re-
entrainment
frameworks”
Phase Response Curves from
populations. You would actually want
to measure the effect of the “zeitgeber
intervention”
14. Phase Control Models #1
https://doi.org/10.1371/journal.pcbi.1000418
Frequent and repeated transmeridian travel is associated with long-term
cognitive deficits, and rodents experimentally exposed to repeated schedule
shifts have increased death rates. One approach to reduce the short-term
circadian, sleep–wake, and performance problems is to use mathematical
models of the circadian pacemaker to design countermeasures that rapidly
shift the circadian pacemaker to align with the new schedule. In this paper, the
use of mathematical models to design sleep–wake and countermeasure
schedules for improved performance is demonstrated.
The key schedule design inputs are endogenous circadian period length, desired
sleep–wake schedule, length of intervention, background light level, and
countermeasure strength. The method presented in this paper has direct
implications for designing jet lag, shift-work, and non-24-hour schedules,
including scheduling for extreme environments, such as in space, undersea, or in
polar regions
15. Phase Control Models #2
https://doi.org/10.1109/LCSYS.2021.3129475
“We augment the model predictive control (MPC)
algorithm to develop an anticipatory control algorithm,
which has advantages over MPC in achieving
scheduled phase shifts (as occurs with jet lag and
shiftwork). We further extend the algorithm in a model
switching control scheme to account for changes in
the light environment. Taken together, these two
enhancements to the standard MPC framework allow
for better control of the circadian oscillator in more
realistic environments by anticipating environmental
light changes.”
https://doi.org/10.1371/journal.pcbi.1008445
“Jet lag is a consequence of the misalignment of the body’s internal
circadian (~24-hour) clock during an adjustment to a new schedule.
Light is the clock’s primary synchronizer. We find that people rarely
follow the optimal schedules generated through mathematical
modeling entirely, but travelers who better followed the optimal
schedules reported more positive moods after their trips. Using the
data collected, we improve the optimal schedule predictions to
accommodate real-world constraints. We also develop a
scheduling algorithm that allows for the computation of
approximately optimal schedules "on-the-fly" in response to
disruptions.“
16. PRC Recap
Let’s say your “prescribed light”
was exposure on 5 consecutive
days at circadian time (CT) 7
designed to give you 5*1h phase
advance, but you forgot to do this
on 1 day before day 5.
The model by
Christensen et al. (2020)
accommodates this “disruption”
and adjust the day 5 exposure to
happen at CT 3 almost fixing the
issue (3*1h + 0h + 1*~2h)
But… even if person adhered to
prescription, was the daily phase
advance 1h if it was not measured
and verified we need some
→
sensing tech
CT 3 CT 7
1h
~2h
18. Melatonin Rhythm
Melatonin & Chronobiology – Chronobiology.com
Shift Right: Assessing Circadian Rhythm
Sleep Disorders and Associated Health
Concerns Based on Salivary Melatonin
and Dim Light Melatonin Onset (DLMO)
- salimetrics.com
19. Wrist temperature
to the rescue
+ Easy/non-invasive to measure
- Confounded by environmental factors
20. Core-Proximal-Distal (e.g. wrist, finger)
Hierarchy in Thermoregulation
Mean + SEM. (vertical
lines on the right) of
core body
temperature (CBT),
proximal and distal
skin temperatures
and subjective ratings
of sleepiness (KSS,
Karolinska Sleepiness
Scale) of 10 healthy
men (mean + SD: 25 +
4 yrs). Subjects
received either
melatonin (5 mg per
os; black lines) or
placebo (gray lines)
Kräuchi et al. (2006):
“Thermoregulatory
effects of melatonin
in relation to
sleepiness”
https://doi.org/10.3389/fnins.2019.00336
http://dx.doi.org/10.1589/jpts.35.330
21. Maybe you would prefer to use less sensors?
https://doi.org/10.1126/scitranslmed.aan4950
22. Wrist temperature needs some algorithms to go along
https://doi.org/10.1016/j.physbeh.2008.08.005
Cited by 275
https://doi.org/10.1371/journal.pone.0061142 -
Cited by 84
“In conclusion, the stepwise multiple regression
method allowed us to reduce the masking
influence on wrist temperature of recorded
variables (activity, body position, light exposure,
environmental temperature and sleep) by using
the independent term to unmask the endogenous
circadian component of the WT circadian rhythm.”
23. Use of wrist temperature in research studies
Timo Partonen et al. (2020, THL, Finland)
The interactions of the principal circadian clock with the homeostatic
sleep process create the time-sensitive window for easy falling
asleep in the evening, which is affected by a thermoregulatory
process. It has been hypothesized that the changes in skin and core
body temperatures before the sleep onset might play a direct role in
sleep regulation.
We found that wrist skin temperatures increased on average by
0.6°C in 10 min prior to the sleep onset and could be tracked robustly
along a slope of time
https://doi.org/10.1038/s41467-023-40977-5
Here, we investigated the association of wrist temperature amplitudes with
a future onset of disease in the UK Biobank one year after actigraphy.
A two-standard deviation (1.8° Celsius) lower wrist temperature amplitude
corresponded to hazard ratios of 1.91 (1.58-2.31 95% CI) for Non-alcoholic
fatty liver disease (NAFLD), 1.69 (1.53-1.88) for type 2 diabetes, 1.25 (1.14-1.37)
for renal failure, 1.23 (1.17-1.3) for hypertension, and 1.22 (1.11-1.33) for
pneumonia.
This work suggests peripheral thermoregulation as a digital biomarker.
25. Commercial consumer devices exist for this #1
How your wrist temperature is measured
Apple Watch Series 8 and Apple Watch Ultra have 2 temperature sensors—one on the back crystal,
near your skin, and another just under the display. While you sleep, Apple Watch samples your
temperature every five seconds. This design improves accuracy by reducing bias from the outside
environment.
Advanced algorithms then use this data to provide an aggregate for each night that you can view as
relative changes from your established baseline temperature in the Health app.
https://support.apple.com/en-us/102674
26. Commercial consumer devices exist for this #2
Fitbit Charge 4 now supports tracking of SpO2
on wrist, skin temperature variability
https://www.devdiscourse.com/article/technology/1506346-fitbit-charge-4-now-supports-tracking-of-spo2-on-wrist-skin-temperature-variability
https://www.scmp.com/lifestyle/gadgets/article/3098886/covid-19-detector-your-wrist-thats-what-fitbit-claiming-its-new
27. Commercial consumer devices exist for this #3
https://news.samsung.com/us/skin-temperature-based-cycle-tracking-now-available-samsung-galaxy-watch5-series
29. Ring worn either way by many people
“Developed algorithms using skin temperature
were tested to predict the start of menstruation
and ovulation. Nocturnal skin temperature based
on wearable ring showed potential for menstrual
cycle monitoring in real life conditions.”
Example skin temperature data with search limits
for tracking (a) start of menstruation and, (b)
ovulation. The narrow solid line represents the daily
temperature values. The thick solid line represents
the fitted menstrual cycle component and marks x
and + maximums and minimums of the fitted
component, respectively.
32. Menstrual Cycle Tracking #2
How many phases could you
detect with distal (finger/wrist)
temperature? For example
menstrual phase-based sports
training could benefit from
identifying as many as 7
different phases
Colenso-Semple et al. (2023): Current evidence
shows no influence of women's menstrual cycle
phase on acute strength performance or
adaptations to resistance exercise training
Johanna Ihalainen What a session on #endocrinology #femalephysiology and
#strength @JYUsport_health #ICST2023 True legends William J Kreamer Kirsty Sale
Ritva Mikkonen (Taipale) #endocrinology
33. Shift Workers and Occupational Health
https://doi.org/10.4324/9781003296966-10
https://doi.org/10.1101/2023.09.08.23295231
“Minimizing exposure to light at night and keeping regular
light-dark patterns that enhance circadian rhythms may
promote cardiometabolic health and longevity.”
Boivin et al. (2021) https://doi.org/10.1177/07487304211064218
34. Fatigue Management e.g. for Airline Pilots
easa.europa.eu
https://www.crunchbase.com/organization/clockwork-research
IATA Uses and Limitations of Biomathematical Fatigue Models
Sleep Science, Biomathematical Modeling, and Machine Learning
35. Elite and Recreational Athletes
https://doi.org/10.1519/JSC.0000000000004021
https://doi.org/10.1080/07420528.2022.2139186
https://doi.org/10.1249/JSR.0000000000000133
36. Jet Lag Management for frequent fliers
In post-covid times maybe less business people with money and desire to
optimize their physical and cognitive performance? Probably still a market
there with people liking to track their vitals?
You could probably
improve the UX of the light
intervention?
Use bright light glasses /
visors such as Luminette
and integrate that with
your sensing solution (e.g.
your smartwatch)?
This article is a summary of a YouTube video "Luminette 3 Light
Therapy Glasses & Timeshifter App Review" by JohnnyAction Tech
38. Children and school performance
https://doi.org/10.1093/sleep/zsac061
https://doi.org/10.3109/07420528.2015.1049271
https://doi.org/10.1136/bmjopen-2021-055716
https://doi.org/10.1080/07420528.2022.2117050