ArticleCell reports. Medicine2022
Consumer-grade wearables identify changes in multiple physiological systems during COVID-19 disease progression.
Article in Cell reports. Medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 12 papers.
What it found
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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.
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.
Wearable Assisted Viral Evidence (WAVE) Study A Decentralized, Prospective Study Exploring the Relationship Between Passively-collected Data From Wearable Activity Devices and Respiratory Viral Infections
Predict + Protect: A Randomized Controlled Trial Exploring the Effectiveness of a Predictive Health Education Intervention on the Adoption of Protective Behaviors Related to Influenza-like Illness (ILI)
Who cites it
12 citing papers in PubMed.
- Digital Biometric Measures in Long COVID: A Secondary Analysis of the STOP-PASC Randomized Clinical Trial.JAMA network open · 2025Trial
- Article
- Biorhythms derived from consumer wearables predict postoperative complications in children.Science advances · 2025Article
- The real-world association between digital markers of circadian disruption and mental health risks.NPJ digital medicine · 2024Article
- Autocorrelation of daily resting heart rate: A novel metric of postoperative recovery.International journal of medical informatics · 2024Article
- Wearable Devices and Explainable Unsupervised Learning for COVID-19 Detection and Monitoring.Diagnostics (Basel, Switzerland) · 2023Article
- Applying machine learning to consumer wearable data for the early detection of complications after pediatric appendectomy.NPJ digital medicine · 2023Article
- Efficient assessment of real-world dynamics of circadian rhythms in heart rate and body temperature from wearable data.Journal of the Royal Society, Interface · 2023Article
- CovidRhythm: A Deep Learning Model for Passive Prediction of Covid-19 Using Biobehavioral Rhythms Derived From Wearable Physiological Data.IEEE open journal of engineering in medicine and biology · 2023Article
- Integrative Analysis of Rhythmicity: From Biology to Urban Environments and Sustainability.International journal of environmental research and public health · 2022Review
- Reducing chronic disease may just be a walk in the park.Cell reports. Medicine · 2022Article
- Rest-activity rhythmicity as digital biomarkers of postoperative recovery and complications in children.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Consumer-grade wearables are needed to track disease, especially in the ongoing pandemic, as they can monitor patients in real time. We show that decomposing heart rate from low-cost wearable technologies into signals from different systems can give a multidimensional description of physiological changes due to COVID-19 infection. We find that the separate physiological features of basal heart rate, heart rate response to physical activity, circadian variation in heart rate, and autocorrelation of heart rate are significantly altered and can classify symptomatic versus healthy periods. Increased heart rate and autocorrelation begin at symptom onset, while the heart rate response to activity increases soon after symptom onset and increases more in individuals exhibiting cough. Symptom onset is associated with a blunting of circadian variation in heart rate, as measured by the uncertainty in the phase estimate. This work establishes an innovative data analytic approach to monitor disease progression remotely using consumer-grade wearables.
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What OpenQuestion holds
Registered trials
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 OpenQuestion graph.