# Longitudinal wearable sensor data enhance precision of Long COVID detection

## TL;DR

Wearable heart rate data combined with symptoms can accurately predict Long COVID, improving diagnosis and patient outcomes.

## Contribution

Combining wearable heart rate data with symptoms significantly improves Long COVID detection accuracy compared to using either alone.

## Key findings

- A combined model of heart rate and symptom data achieved 95.1% ROC-AUC and 85.9% PR-AUC for Long COVID prediction.
- Heart rate features derived from multiple analytical categories captured dynamic changes over time.
- Using wearable data offers a non-invasive, objective tool for diagnosing Long COVID.

## Abstract

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. To bridge this gap, we investigated the potential of utilizing wearable sensor data to aid in the diagnosis of Long COVID. We analyzed longitudinal heart rate (HR) data from 126 individuals with acute SARS-CoV-2 infections to develop machine learning models capable of predicting Long COVID status using derived HR features, symptom features, or a combination of both feature sets. The HR features were derived across six analytical categories, including time-domain, Poincaré nonlinear, raw signal, Kullback-Leibler (KL) divergence, variational mode decomposition (VMD), and the Shannon energy envelope (SEE), enabling the capture of heart rate dynamics over various temporal scales and the quantification of day-to-day shifts in HR distributions. The symptom features used in the final models included chest pain, vomiting, excessive sweating, memory loss, brain fog, heart palpitations, and loss of smell. The combined HR- and symptom-feature model demonstrated robust predictive performance, achieving an area under the Receiver Operating Characteristic curve (ROC-AUC) of 95.1% and an area under the Precision-Recall curve (PR-AUC) of 85.9%. These values represent a significant improvement of approximately 5% in both the ROC-AUC and PR-AUC over the symptoms-only model. At the population level, this improvement in discrimination could lead to clinically meaningful reductions in misclassification and improved patient outcomes, achieved through a non-invasive diagnostic tool. These findings suggest that wearable HR data could be used to derive an objective biomarker for Long COVID, thereby enhancing diagnostic precision.

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 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.