ArticleContemporary clinical trials2023
The WE SENSE study protocol: A controlled, longitudinal clinical trial on the use of wearable sensors for early detection and tracking of viral respiratory tract infections.
Article in Contemporary clinical trials, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05290792 (Use of Wearable Sensors for Early Detection and Tracking of Viral Respiratory Tract Infections), which is not on this map. Cited by 6 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.
Use of Wearable Sensors for Early Detection and Tracking of Viral Respiratory Tract Infections: The WE SENSE Study
Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Post-vaccine inflammation tracking: validation of a novel individualized digital inflammatory biomarker relative to serum biomarkers.NPJ digital medicine · 2026Article
- Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026Review
- Detection of Common Respiratory Infections, Including COVID-19, Using Consumer Wearable Devices in Health Care Workers: Prospective Model Validation Study.JMIR formative research · 2024Article
- Day-to-Day Variability in Measurements of Respiration Using Bioimpedance from a Non-Standard Location.Sensors (Basel, Switzerland) · 2024Article
- Article
- Artificial intelligence in respiratory care: Current scenario and future perspective.Annals of thoracic medicineArticle
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 at 4 institutions in 1 country.
Funding
Abstract
backgroundViral respiratory tract infections (VRTI) are extremely common. Considering the profound social and economic impact of COVID-19, it is imperative to identify novel mechanisms for early detection and prevention of VRTIs, to prevent future pandemics. Wearable biosensor technology may facilitate this. Early asymptomatic detection of VRTIs could reduce stress on the healthcare system by reducing transmission and decreasing the overall number of cases. The aim of the current study is to define a sensitive set of physiological and immunological signature patterns of VRTI through machine learning (ML) to analyze physiological data collected continuously using wearable vital signs sensors.
methodsA controlled, prospective longitudinal study with an induced low grade viral challenge, coupled with 12 days of continuous wearable biosensors monitoring surrounding viral induction. We aim to recruit and simulate a low grade VRTI in 60 healthy adults aged 18-59 years via administration of live attenuated influenza vaccine (LAIV). Continuous monitoring with wearable biosensors will include 7 days pre (baseline) and 5 days post LAIV administration, during which vital signs and activity-monitoring biosensors (embedded in a shirt, wristwatch and ring) will continuously monitor physiological and activity parameters. Novel infection detection techniques will be developed based on inflammatory biomarker mapping, PCR testing, and app-based VRTI symptom tracking. Subtle patterns of change will be assessed via ML algorithms developed to analyze large datasets and generate a predictive algorithm.
conclusionThis study presents an infrastructure to test wearables for the detection of asymptomatic VRTI using multimodal biosensors, based on immune host response signature. CliniclTrials.govregistration:NCT05290792.
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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.