Salivary Biosensing Opportunities for Predicting Cognitive and Physical Human Performance

Abstract

Advancements in biosensing technologies have introduced opportunities for non-invasive, real-time monitoring of salivary biomarkers, enabling progress in fields ranging from personalized medicine to public health. Identifying and prioritizing the most critical analytes to measure in saliva is essential for estimating physiological status and forecasting performance in applied contexts. This study examined the value of 12 salivary analytes, including hormones, metabolites, and enzymes, for predicting cognitive and physical performance outcomes in military personnel (N = 115) engaged in stressful laboratory and field tasks. We calculated a series of features to quantify time-series analyte data and applied multiple regression techniques, including Elastic Net, Partial Least Squares, and Random Forest regression, to evaluate their predictive utility for five outcomes of interest: the ability to move, shoot, communicate, navigate, and sustain performance under stress. Predictive performance was poor across all models, with R-squared values near zero and limited evidence that salivary analytes provided stable or meaningful performance predictions. While certain features (e.g., post-peak slopes and variance metrics) appeared more frequently than others, no individual analyte emerged as a reliable predictor. These results suggest that salivary biomarkers alone are unlikely to provide robust insights into cognitive and physical performance outcomes. Future research may benefit from combining salivary and other biosensor data with contextual variables to improve predictive accuracy in real-world settings.

Keywords: biosensor; cognitive performance; cortisol; enzymes; hormones; metabolites; physical performance; stress.

Figure 1

Example participant time series (in minutes) data points reflecting the calculation of slope-based features: Slope (start to peak) and Slope (peak to end).

Figure 2

Scatter plots displaying the relationship between a single z-scored feature and the z-scored target with a simple linear regression line fit to each (with corresponding r values and p-values). Note. The features apparent in these plots were the top five most highly selected features, on average, across models for each domain.

Figure 3

Elbow plots displaying the relationship between the number of clusters, k, and the sum of squared distances between each point and its closest centroid.