Predicting viral respiratory tract infections using wearable biosensor monitoring during 3-minute constant rate stair stepping tests

Background Viral Respiratory Tract Infections (VRTIs) are a major public health threat. Early detection and preventive measures are key to controlling their spread. Current machine learning approaches often depend on symptom onset, costly equipment, trained personnel, and slow results. This study aims to evaluate whether a machine learning algorithm using physiological data from wearable biosensors during a constant-rate stair-stepping task (3-min test, 2-min recovery) can predict inflammation levels, and to identify the most predictive indicators of VRTI. Methods 55 Healthy participants (27 males and 28 females) aged 18–59 years, were recruited and inoculated with a live influenza vaccine to induce an immune response, assessed via changes in circulating inflammatory biomarkers. Physiological markers, including breathing rate and heart rate, during a series of clinically controlled stair tests, were monitored by a wearable biosensor. These data were collected to develop a prediction model using gradient-boosting machine learning algorithms combined with hyperparameter tuning and a leave-one-subject-out method to train the models. Results The study developed a predictive model that accurately estimates inflammation levels in individuals. Features from heart rate variability (HRV) showed the greatest potential, with 70% sensitivity and 77% specificity, and physiological markers from controlled stair tests correlated with VRTI-related inflammatory responses. Impact The prediction model linked to stair-stepping tests offers clinicians and the public a tool for self-monitoring and early intervention. Using machine learning and physiological markers, especially HRV features, it can help guide timely treatments and reduce the impact of future outbreaks.