Longitudinal Wearable Sensor Data Enhance Precision of Long COVID Detection

Despite the millions of individuals struggling with persistent symptoms, Long COVID has remained difficult to diagnose due to limited objective biomarkers, often leading to underdiagnosis or even misdiagnosis. Wearable devices have recently emerged as powerful tools for real-time health monitoring of body functions, including heart rate (HR).

We investigated the utility of wearable HR data collected continuously over an extended period of time for identifying Long COVID patients. We collected both smartwatch data as well as daily/periodic symptom surveys from participants who had a prior SARS-CoV-2 infection. We used these data from the acute infection period to build machine learning models that identify those who will experience chronic symptoms of Long COVID.

When wearable HR data and symptom data are combined into a single model, the predictive performance improves significantly over models using HR data or symptom data alone.

We propose a workflow for how a clinician might use our machine learning model to aid clinical diagnosis. Overall, our findings suggest that wearable HR data could be used to derive an objective biomarker for Long COVID, thereby enhancing diagnostic precision.

Linked above, you can download the de-identified demographic data, resting heart rate data, and symptom survey response data for the participants used in our analyses.