**Introduction**
Rhythms characterise the metabolism of all living things. Superimposition of rhythms of different periods creates a dynamic environment that is coordinated by an endogenous clock machinery to maintain healthy homeostasis.
It is our belief that, given the complexity of the systems involved, it is inappropriate and insufficient to extrapolate information about the state of an organism based on single time point measurements of single analytes.
We propose that simultaneous, longitudinal assessment of multiple internal processes, measured during normal daily activity, will provide a richer, more meaningful understanding of the internal biological state.
Pilot experimental work indicates:
1. Automated microdialysis detects robust, dynamic rhythms of circadian hormones in human subcutaneous tissue,
3. Rhythms in temperature and cardiac output can be determined with high accuracy using non-invasive 'wearable' technology (Smarr, Burnett, Mesri, Pister, & Kriegsfeld, 2016).
**Methods**
Healthy participants aged 18-38 will complete 48-hour ambulatory microdialysis sampling sessions. During each session, information will be collected in the following ways:
* continuous measurement of free tissue concentrations of melatonin and cortisol (U-RHYTHM microdialysis sampling device)
* continuous interstitial glucose monitoring (CGMS; FreeStyle Libre or similar subcutaneous sensor)
* ambient light exposure (CamNtech MotionWatch or similar)
* actigraphy, skin temperature, skin conductance, and heart rate (CamNtech watch, Oura ring)
* meta data in the form of self-reported sleep, physical activity and meal information
**Analysis approach**
Tissue concentrations of hormones will be analysed in the microdialysis samples using liquid chromatography-mass spectrometry. Concentration changes in subcutaneous fluid samples across different periods and times of the day will be analysed using appropriate statistical methods, e.g. two-way ANOVA.
To assess rhythmicity, techniques such as cosinor-based rhythmometry methods will be applied. Data collected from the wearable devices will be analysed using mathematical algorithms that explore relationships between endogenous physiological rhythms (e.g. hormone levels in subcutaneous fluid) and dynamic data collected by the peripheral sensor devices.
**Reference**
Smarr, B. L., Burnett, D. C., Mesri, S. M., Pister, K. S. J., & Kriegsfeld, L. J. (2016). A Wearable Sensor System with Circadian Rhythm Stability Estimation for Prototyping Biomedical Studies. IEEE Transactions on Affective Computing, 7(3), 220-230.
