Development of a personalized digital biomarker of vaccine-associated reactogenicity using wearable sensors and...

Effective response to vaccination requires activation of the innate immune system, triggering the synthesis of inflammatory cytokines. The degree of subjective symptoms related to this, referred to as reactogenicity, may predict their eventual immune response. However, the subjective nature of these symptoms is influenced by the nocebo effect, making it difficult to accurately quantify a person's physiologic response. The use of wearable sensors allows for the identification of objective evidence of physiologic changes a person experiences following vaccination, but as these changes are subtle, they can only be detected when an individual's pre-vaccination normal variability is considered.

We use a wearable torso sensor patch and a machine learning method of similarity-based modeling (SBM) to create a physiologic digital twin for 88 people receiving 104 COVID vaccine doses. By using each individual's pre-vaccine digital twin, we are able to effectively control for expected physiologic variations unique to that individual, leaving only vaccine-induced differences. We use these individualized differences between the pre- and post-vaccine period to develop a multivariate digital biomarker for objectively measuring the degree and duration of physiologic changes each individual experiences following vaccination.

Here we show that the multivariate digital biomarker better predicted systemic reactogenicity than any one physiologic data type and correlated with vaccine-induced changes in humoral and cellular immunity in a 20-person subset.

A digital biomarker is capable of objectively identifying an individual's unique response to vaccination and could play a future role in personalizing vaccine regimens.