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Assessment of the Feasibility of Using Noninvasive Wearable Biometric Monitoring Sensors to Detect Influenza and the Common Cold, 2021, Grzesiak et al

Discussion in 'Epidemics (including Covid-19, not Long Covid)' started by Andy, Aug 29, 2022.

  1. Andy

    Andy Committee Member

    Messages:
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    Location:
    Hampshire, UK
    Full title: Assessment of the Feasibility of Using Noninvasive Wearable Biometric Monitoring Sensors to Detect Influenza and the Common Cold Before Symptom Onset

    Key Points

    Question Can noninvasive, wrist-worn wearable devices detect acute viral respiratory infection and predict infection severity before symptom onset?

    Findings In a cohort study of 31 participants inoculated with H1N1 and 18 participants with rhinovirus, infection detection and severity prediction models trained using data on wearable devices were able to distinguish between infection and noninfection with 92% accuracy for H1N1 and 88% accuracy for rhinovirus and were able to distinguish between mild and moderate infection 24 hours prior to symptom onset with 90% accuracy for H1N1 and 89% accuracy for rhinovirus.

    Meaning This study suggests that the use of wearable devices to identify individuals with presymptomatic acute viral respiratory infection is feasible; because wearable devices are common in the general population, using them for infection screening may help limit the spread of contagion.

    Abstract
    Importance Currently, there are no presymptomatic screening methods to identify individuals infected with a respiratory virus to prevent disease spread and to predict their trajectory for resource allocation.

    Objective To evaluate the feasibility of using noninvasive, wrist-worn wearable biometric monitoring sensors to detect presymptomatic viral infection after exposure and predict infection severity in patients exposed to H1N1 influenza or human rhinovirus.

    Design, Setting, and Participants The cohort H1N1 viral challenge study was conducted during 2018; data were collected from September 11, 2017, to May 4, 2018. The cohort rhinovirus challenge study was conducted during 2015; data were collected from September 14 to 21, 2015. A total of 39 adult participants were recruited for the H1N1 challenge study, and 24 adult participants were recruited for the rhinovirus challenge study. Exclusion criteria for both challenges included chronic respiratory illness and high levels of serum antibodies. Participants in the H1N1 challenge study were isolated in a clinic for a minimum of 8 days after inoculation. The rhinovirus challenge took place on a college campus, and participants were not isolated.

    Exposures Participants in the H1N1 challenge study were inoculated via intranasal drops of diluted influenza A/California/03/09 (H1N1) virus with a mean count of 106 using the median tissue culture infectious dose (TCID50) 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 TCID50 assay.

    Main Outcomes and Measures The primary outcome measures included cross-validated performance metrics of random forest models to screen for presymptomatic infection and predict infection severity, including accuracy, precision, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve (AUC).

    Results A total of 31 participants with H1N1 (24 men [77.4%]; mean [SD] age, 34.7 [12.3] years) and 18 participants with rhinovirus (11 men [61.1%]; mean [SD] age, 21.7 [3.1] years) were included in the analysis after data preprocessing. Separate H1N1 and rhinovirus detection models, using only data on wearble devices as input, were able to distinguish between infection and noninfection with accuracies of up to 92% for H1N1 (90% precision, 90% sensitivity, 93% specificity, and 90% F1 score, 0.85 [95% CI, 0.70-1.00] AUC) and 88% for rhinovirus (100% precision, 78% sensitivity, 100% specificity, 88% F1 score, and 0.96 [95% CI, 0.85-1.00] AUC). The infection severity prediction model was able to distinguish between mild and moderate infection 24 hours prior to symptom onset with an accuracy of 90% for H1N1 (88% precision, 88% sensitivity, 92% specificity, 88% F1 score, and 0.88 [95% CI, 0.72-1.00] AUC) and 89% for rhinovirus (100% precision, 75% sensitivity, 100% specificity, 86% F1 score, and 0.95 [95% CI, 0.79-1.00] AUC).

    Conclusions and Relevance This cohort study suggests that the use of a noninvasive, wrist-worn wearable device to predict an individual’s response to viral exposure prior to symptoms is feasible. Harnessing this technology would support early interventions to limit presymptomatic spread of viral respiratory infections, which is timely in the era of COVID-19.

    Open access, https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2784555
     
    RedFox, Sean, Amw66 and 2 others like this.
  2. Sean

    Sean Moderator Staff Member

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    7,044
    Location:
    Australia
    Not too much of a burden then?

    Just asking.
     
    Trish and Peter Trewhitt like this.
  3. Wonko

    Wonko Senior Member (Voting Rights)

    Messages:
    6,674
    Location:
    UK
    So only 8% of people will be stuffed when they file for sick pay if this thing says 'not sick', when they are?

    That's nice.

    Coz if there is one thing I've noticed about humans it's that if there is a device to make decisions for them, they 'tend' to follow it blindly.

    Much like todays announcement that stains do not cause pain - as the media put it, when the study actually said that they do, in - 11 out of 1000 people, over control - that's over 1% of all the people taking statins, which if GPs had there way would be 100% of the adult population, and 1% of them pain likely caused by the only variable that's different from the control group, statins - but the headlines is exactly the opposite - that statins have been proved not to cause pain.

    People like simple things, infallible decisions - doesn't matter if they are true.

    8% of sick people just became malingers - official (or will be if stuff like this is adopted)
     
    Trish and Peter Trewhitt like this.
  4. Peter Trewhitt

    Peter Trewhitt Senior Member (Voting Rights)

    Messages:
    3,637
    I worry that when we do have an agreed clinically useable diagnostic biomarker for ME that similarly a sub group will end up being left out in the cold. There is a reasonable chance that ME is not a homogenous group, but rather several related or overlapping conditions. Given as large a number of people as those with ME has struggled to combat medical gaslighting, how much worse will it be for a small subgroup who have been clinically shown not to have ‘ME’.

    Having said that, ‘biometric monitoring sensors’ are immensely important in progressing ME research and evaluating an treatment effectiveness. We just have to keep pointing it out if this happens, that ‘you got it wrong for the bigger grouping, so don’t now keep getting it wrong for the smaller left out grouping’. Also the BPS advocates and the dysfunctional ‘Functional’ disorders believers will just say, we never denied a biological component but it is the psychogenic aspect needs treating too.
     
    RedFox, shak8, Wonko and 1 other person like this.
  5. Trish

    Trish Moderator Staff Member

    Messages:
    51,872
    Location:
    UK
    This seems like a fascinating and potentially useful development, but it requires compliance of the sort we no longer see even from some people who know they have tested positive for Covid. I'm just imagining phoning up my employer and saying sorry I can't come to work today, my wearable device tells me I'm developing a cold or flu or something.
     
  6. BrightCandle

    BrightCandle Senior Member (Voting Rights)

    Messages:
    338
    I think if the narrative around illness changed and people were told to rest due to the chance of developing chronic illness after a link was established in research then it could be quite valuable. But it would take generations of that message and understanding getting deeper and treating our children in this way for it slowly but surely pervading into our society. I can't see it happening in the next 80 years but its got a chance of being a change if the causality can be established and these devices prove pervasively useful diagnostics.
     
    Sean, Trish and Peter Trewhitt like this.

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