Automatic detection of persistent physiological changes after COVID infection via wearable devices with potential for long COVID management

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can lead to post-acute sequelae of SARS-CoV-2 infection (PASC), or Long COVID, a chronic multisystemic condition with diverse symptoms and no objective diagnostic test. In this retrospective study, we developed a data-driven method to objectively detect persistent physiological changes using wearable device data in a large cohort of over 12,000 US military personnel. We analyzed physiological data from 663 symptomatic COVID-19 positive cases and 2,513 asymptomatic COVID-19 negative controls. Our method identified persistent physiological changes in 9.4% of COVID-19 positive individuals, most commonly manifesting as elevated nightly heart rate and reductions in some heart rate variability metrics. Our findings demonstrate that wearable technology can be used to objectively detect chronic physiological changes beyond the acute phase of COVID-19 illness. Although our method requires further clinical validation, it could potentially provide objective metrics to help standardize Long COVID diagnosis criteria.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-08-11
DOI
https://doi.org/10.1038/s41598-025-15208-0
Primary Topic
Long-Term Effects of COVID-19
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Automatic detection of persistent physiological changes after COVID infection via wearable devices with potential for long COVID management

Chunxue Wang, Ting Feng, Ikaro Silva, Robert J. Damiano et al.
Scientific Reports
Long-Term Effects of COVID-19
article

Automatic detection of persistent physiological changes after COVID infection via wearable devices with potential for long COVID management

Chunxue Wang, Ting Feng, Ikaro Silva, Robert J. Damiano, Soheil Borhani, Luoluo Liu, Sara Mariani, Emmanuele Salvati, Bryan Conroy
article en

Abstract

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can lead to post-acute sequelae of SARS-CoV-2 infection (PASC), or Long COVID, a chronic multisystemic condition with diverse symptoms and no objective diagnostic test. In this retrospective study, we developed a data-driven method to objectively detect persistent physiological changes using wearable device data in a large cohort of over 12,000 US military personnel. We analyzed physiological data from 663 symptomatic COVID-19 positive cases and 2,513 asymptomatic COVID-19 negative controls. Our method identified persistent physiological changes in 9.4% of COVID-19 positive individuals, most commonly manifesting as elevated nightly heart rate and reductions in some heart rate variability metrics. Our findings demonstrate that wearable technology can be used to objectively detect chronic physiological changes beyond the acute phase of COVID-19 illness. Although our method requires further clinical validation, it could potentially provide objective metrics to help standardize Long COVID diagnosis criteria.

Scientific ReportsVol. 15(1)
Philips (Finland) (FI), Philips (United States) (US), IPS Research (United States) (US)
U.S. Department of Defense, Defense Threat Reduction Agency
Good health and well-being
Openalex Percentile: Top 70%
Long-Term Effects of COVID-19
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Automatic detection of persistent physiological changes after COVID infection via wearable devices with potential for long COVID management — Chunxue Wang, Ting Feng, et al. · Scientific Reports (2025) | TGRS Research Map | TGRS