Evidence map›Paper›PMID 40790065›Full record

ArticleScientific reports2025

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

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

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Soheil BorhaniPhilips North America, Cambridge, MA, USA. Soheil.Borhani@philips.com.
Ikaro SilvaPhilips North America, Cambridge, MA, USA.
Robert J DamianoPhilips North America, Cambridge, MA, USA.
Ting FengPhilips North America, Cambridge, MA, USA.
Chunxue WangPhilips North America, Cambridge, MA, USA.
Luoluo LiuPhilips North America, Cambridge, MA, USA.
Emmanuele SalvatiPhilips North America, Cambridge, MA, USA.
Sara MarianiPhilips North America, Cambridge, MA, USA.
Bryan ConroyPhilips North America, Cambridge, MA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

COVID-19Wearable Electronic DevicesAdultFemaleHeart RateHumansMaleMiddle AgedMilitary PersonnelRetrospective StudiesSARS-CoV-2Chronic COVID-19 managementCOVID-19 infectionLong COVIDPhysiological biomarkersPost-Acute sequelae of SARS-CoV-2Wearable devices

Identifiers

PMID40790065
PMCPMC12340005

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.