An interesting article published by Grzesiak and others in the Journal of the American Medical Association network open on September 29th 2021 looked at this issue. 

Currently, there exist no pre-symptomatic screening methods to identify individuals infected with a respiratory virus to prevent disease spread trajectory for resource allocation. 

This study evaluated the feasibility of using a non-invasive, wrist worn wearable biometric monitoring sensors to detect pre-symptomatic viral infection after exposure and predict infection severity in patients exposed to H1N1 influenza or human rhinoviruses. 

This cohort H1N1 viral challenge study was conducted during 2018.  Participants in the H1N1 challenge study were isolated in the clinic for a minimum eight days after inoculation.  The rhinovirus challenge took place on a college campus and participants were not isolated. 

Participants in the H1N1 challenge study were inoculated via intranasal drops of diluted influenza A with a mean count of 106 using median tissue culture infectious dose assay.  Participants in the rhinovirus challenge study were inoculated via intranasal drops of diluted human rhinovirus strain type 16 with a count of 100 using the tissue culture infectious dose assay.  

The primary outcome was to screen for pre-symptomatic infection and predict infection severity, including accuracy, precision, sensitivity and specificity. 

A total of 31 participants with H1N1 and 18 participants with rhinovirus were included in the analysis after data pre-processing.  Separate H1N1 and rhinovirus detection models using only data on wearable devices input were able to distinguish between infection and non-infection with accuracies of up to 92% for H1N1, 88% for rhinovirus.

The infection severity prediction model was able to distinguish between mild and moderate infections 24 hours prior to symptom onset with an accuracy of 90% for H1N1 and 89% for rhinovirus.  

Wearable biometric monitoring sensors have been shown to be useful in detecting infections before symptoms occur.  

Low cost and accessible technologies that record physiological measurements can empower underserved groups with new digital biomarkers.  

Digital biomarkers digitally collect data that are transformed into indicators of health and disease For example:

  • Resting heart rate 
  • Heart rate variability 
  • Accelerometer 
  • Electro dermal skin activity 
  • Skin temperature 

These can indicate a person’s infection status or predict if and when a person will become infected after exposure.  

Therefore, detecting abnormal bio signals using wearables could be the first step in identifying infections before symptom onset.  

This study developed digital biomarker models for early detection of infection and severity prediction after the pathogen exposure but before symptoms developed.  

The results highlighted the opportunity for the identification of early pre-symptomatic or asymptomatic infection that may support individual treatment decisions and public health interventions to limit the spread of viral infections.  

This cohort study suggested that the use of a non-invasive common wrist borne wearable device to predict an individual’s response to viral exposure prior to symptoms developing is feasible.  

Harnessing this technology would support early interventions to limit pre-symptomatic spread of viral respiratory infections, which would be timely in this era of COVID-19.  

The London General Practice, the leading London doctors’ clinic in Harley Street offers a full 24-hour service which involves face to face consultations and home visits.

Dr Paul Ettlinger

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