ReviewAnnual review of biomedical engineering2022
Detection and Monitoring of Viral Infections via Wearable Devices and Biometric Data.
Review in Annual review of biomedical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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.
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.
Who cites it
17 citing papers in PubMed.
- Feasibility of wearable sensor signals and self-reported symptoms to prompt at-home testing for acute respiratory viruses in the USA (DETECT-AHEAD): a decentralised, randomised controlled trial.The Lancet. Digital health · 2024Trial
- Safety Monitoring of High-Risk Antibiotics Using Artificial Intelligence: A Narrative Review with Focus on Real-World Evidence.Life (Basel, Switzerland) · 2026Review
- AI-driven big data analysis and predictive modeling of infectious disease immunity: from correlates to causal, multiscale understanding.Archives of microbiology · 2026Review
- Remote Patient Monitoring and Connected Technologies in Inflammatory Bowel Disease.Gastroenterology & hepatology · 2025Article
- Digital Health Technologies in Pediatric Infectious Disease and the Perspective of Patients and Healthcare Professionals: A Review.Health science reports · 2025Article
- Interactions between sleep, inflammation, immunity and infections: A narrative review.Immunity, inflammation and disease · 2024Review
- Detection of Common Respiratory Infections, Including COVID-19, Using Consumer Wearable Devices in Health Care Workers: Prospective Model Validation Study.JMIR formative research · 2024Article
- Remote digital health technologies for improving the care of people with respiratory disorders.The Lancet. Digital health · 2024Review
- Prediction and detection of side effects severity following COVID-19 and influenza vaccinations: utilizing smartwatches and smartphones.Scientific reports · 2024Article
- Article
- The 2023 wearable photoplethysmography roadmap.Physiological measurement · 2023Review
- Disrupting the infectious disease ecosystem in the digital precision health era innovations and converging emerging technologies.Antimicrobial agents and chemotherapy · 2023Article
- Bioenergy-Based Closed-Loop Medical Systems for the Integration of Treatment, Monitoring, and Feedback.Small science · 2023Article
- Review of strategies to investigate low sample return rates in remote tobacco trials: A call to action for more user-centered design research.Addiction neuroscience · 2023Article
- Aflatoxin BToxins · 2022Article
- Sensor-based surveillance for digitising real-time COVID-19 tracking in the USA (DETECT): a multivariable, population-based, modelling study.The Lancet. Digital health · 2022Article
- A survey of COVID-19 detection and prediction approaches using mobile devices, AI, and telemedicine.Frontiers in artificial intelligence · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Mounting clinical evidence suggests that viral infections can lead to detectable changes in an individual's normal physiologic and behavioral metrics, including heart and respiration rates, heart rate variability, temperature, activity, and sleep prior to symptom onset, potentially even in asymptomatic individuals. While the ability of wearable devices to detect viral infections in a real-world setting has yet to be proven, multiple recent studies have established that individual, continuous data from a range of biometric monitoring technologies can be easily acquired and that through the use of machine learning techniques, physiological signals and warning signs can be identified. In this review, we highlight the existing knowledge base supporting the potential for widespread implementation of biometric data to address existing gaps in the diagnosis and treatment of viral illnesses, with a particular focus on the many important lessons learned from the coronavirus disease 2019 pandemic.
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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.