Wearable-derived skin temperature dynamics during sleep reveal cardiovascular perfusion deficits through mechanistic modeling.

Classical statistics are commonly used to find differences between distributions of average skin temperature across populations. However, skin temperature is affected by many endogenous (within body) and exogenous (outside body) factors, and these factors induce causal changes in longitudinal skin temperature that can obfuscate the interpretation of average population differences. Moreover, interpretations are increasingly difficult to make when using temperature signals sampled longitudinally in uncontrolled settings. A potential way to better handle the inherent complexity of skin temperature dynamics in uncontrolled settings is to explicitly account for the effects of causal factors on the short- and long-term trajectories of temperature. In this work, we find that a physics-informed model of skin temperature and activity during sleep accounts for significantly more variance than an equally parsimonious linear model. Furthermore, this model enables separation of cohorts with cardiovascular conditions that are known to affect skin thermoregulation, an important improvement over classic statistical modeling.