Evidence map›Paper›PMID 37147083›Full record

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.

Amir Hadid, Emily G McDonald, Matthew P Cheng, Jesse Papenburg, Michael Libman, Phillipe C Dixon, Dennis Jensen

Registry-linked trialOpen access · greenAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.1field-weighted citation impact, top 12% of its field
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.

NCT05290792 nacompletednot on this map

Use of Wearable Sensors for Early Detection and Tracking of Viral Respiratory Tract Infections: The WE SENSE Study

TypeinterventionalSponsorEmily McDonaldRan2021 to 2022Enrolled56ConditionsViral Respiratory Tract Infection, InfluenzaArmsAdministration of FluMist (Live Attenuated Influenza Vaccine)
3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Large language models for bioinformatics.Quantitative biology (Beijing, China) · 2026
    Review
  3. Article
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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

7 authors at 4 institutions in 1 country.

Amir HadidClinical Exercise and Respiratory Physiology Laboratory, Department of Kinesiology and Physical Education, Faculty of Education, McGill University, Montréal, Quebec, Canada; McGill Sport Science Research Institute, Faculty of Education, McGill University, Montréal, Quebec, Canada. Electronic address: Amir.hadid@mcgill.ca.
Emily G McDonaldResearch Institute of the McGill University Health Centre, Division of General Internal Medicine, Department of Medicine, Montreal, Quebec, Canada.
Matthew P ChengResearch Institute of McGill University Health Center, Department of Medicine, Divisions of Infectious Diseases and Medical Microbiology, Infectious Diseases and Immunity in Global Health Program, Montréal, Quebec, Canada.
Jesse PapenburgDivision of Pediatric Infectious Diseases, Department of Pediatrics, Montreal Children's Hospital, McGill University Health Centre, Montreal, Quebec, Canada.
Michael LibmanResearch Institute of McGill University Health Center, Department of Medicine, Divisions of Infectious Diseases and Medical Microbiology, Infectious Diseases and Immunity in Global Health Program, Montréal, Quebec, Canada.
Phillipe C DixonSchool of Kinesiology and Physical Education, Faculty of Medicine, Université de Montréal, Montréal, Quebec, Canada; Research Center of the Sainte-Justine University Hospital (CRCHUSJ), Montreal, Quebec, Canada.
Dennis JensenClinical Exercise and Respiratory Physiology Laboratory, Department of Kinesiology and Physical Education, Faculty of Education, McGill University, Montréal, Quebec, Canada; McGill Sport Science Research Institute, Faculty of Education, McGill University, Montréal, Quebec, Canada; Research Institute of the McGill University Health Centre, Translational Research in Respiratory Diseases Program and Respiratory Epidemiology and Clinical Research Unit, Montréal, Quebec, Canada.
McGill University Health Centre · CAMcGill University · CAMontreal Children's Hospital · CAUniversité de Montréal · CA

Funding

NCEZID CDC HHS U50 CK000189
6 · The paper itself

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.

Indexed as

COVID-19Wearable Electronic DevicesAdultAlgorithmsHumansLongitudinal StudiesProspective StudiesEarly detectionInflammatory biomarkersMachine learningPhysiomarkersRespiratory tract infections

Identifiers

PMID37147083
PMCPMC10049920
OpenAlexW4361285897

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.