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Research article
Mattienne R van der Kamp
Medisch Spectrum Twente
Corresponding Author
ORCiD: https://orcid.org/0000-0003-3053-2342
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Asthma is one of the most common chronic diseases in childhood. Regular follow-up of physiological parameters in the home setting, in relation to asthma symptoms, can provide complementary quantitative insights into the dynamics of the asthma status. Despite considerable interest in asthma home-monitoring in children, there is a paucity of scientific evidence, especially on multi-parameter monitoring approaches. Therefore, the aim of this study is to investigate whether asthma control can be accurately assessed in the home situation by combining parameters from respiratory physiology sensors.
Methods: Sixty asthmatic and 30 non-asthmatic children were enrolled in the observational WEARCON-study. Asthma control was assessed according to GINA guidelines by the paediatrician. All children were also evaluated during a 2-week home-monitoring period with wearable devices; a physical activity tracker, a handheld spirometer, smart inhalers, and an ambulatory electrocardiography device to monitor heart and respiratory rate. Multiple logistic regression analysis was used to determine which diagnostic measures were associated asthma control.
Results: 24 of the 27 uncontrolled asthmatic children and 29 of the 32 controlled asthmatic children could be accurately identified with this model. The final model showed that a larger variation in pre-exercise lung function (OR=1.34 95%-CI 1.07-1.68), an earlier wake-up-time (OR=1.05 95%-CI 1.01-1.10), more reliever use (OR=1.11 95%-CI 1.03-1.19) and a longer respiratory rate recovery time (OR=1.12 95%-CI 1.05-1.20) were significant contributors to the probability of having uncontrolled asthma.
Conclusions: Home-monitoring of physiological parameters correlates with paediatrician assessed asthma control. The constructed multivariate model identifies 88.9% of all uncontrolled asthmatic children, indicating a high potential for monitoring of asthma control. This may allow healthcare professionals to assess asthma control at home.
Trial registration: Netherlands Trail Register, NL6087. Registered 14 February 2017, https://www.trialregister.nl/trial/6087
Keywords
asthma control, ambulatory monitoring, eHealth, physiology sensors, wearable electronic devices, paediatrics, telemedicine, multivariate analysis, spirometry, inhaler use
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View the published article at BMC Medical Informatics and Decision Making.
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Posted 08 Jun, 2020
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Editor assigned
On 02 Jun, 2020
Submission checks complete
On 01 Jun, 2020
Editor invited
On 01 Jun, 2020
No comments provided
Editorial decision: Major revision
On 05 May, 2020
Review #4 received
Received 04 May, 2020
Review #3 received
Received 01 May, 2020
Reviewer #4 agreed
On 28 Apr, 2020
Reviewer #3 agreed
On 08 Apr, 2020
Review #2 received
Received 05 Apr, 2020
Reviewer #2 agreed
On 03 Apr, 2020
Review #1 received
Received 03 Apr, 2020
Reviewer #1 agreed
On 31 Mar, 2020
Reviewers invited
Invitations sent on 12 Mar, 2020
Editor assigned
On 05 Mar, 2020
Submission checks complete
On 28 Feb, 2020
Editor invited
On 28 Feb, 2020
First submitted
On 21 Feb, 2020
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Subject Areas
Medical Informatics
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See the published version of this preprint at BMC Medical Informatics and Decision Making.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Mattienne R van der Kamp
Medisch Spectrum Twente
Corresponding Author
ORCiD: https://orcid.org/0000-0003-3053-2342
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Medical Informatics and Decision Making.
Version 2
Posted 08 Jun, 2020
No community comments so far
Editor assigned
On 02 Jun, 2020
Submission checks complete
On 01 Jun, 2020
Editor invited
On 01 Jun, 2020
No comments provided
Editorial decision: Major revision
On 05 May, 2020
Review #4 received
Received 04 May, 2020
Review #3 received
Received 01 May, 2020
Reviewer #4 agreed
On 28 Apr, 2020
Reviewer #3 agreed
On 08 Apr, 2020
Review #2 received
Received 05 Apr, 2020
Reviewer #2 agreed
On 03 Apr, 2020
Review #1 received
Received 03 Apr, 2020
Reviewer #1 agreed
On 31 Mar, 2020
Reviewers invited
Invitations sent on 12 Mar, 2020
Editor assigned
On 05 Mar, 2020
Submission checks complete
On 28 Feb, 2020
Editor invited
On 28 Feb, 2020
First submitted
On 21 Feb, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Medical Informatics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Medical Informatics and Decision Making.
Background: Asthma is one of the most common chronic diseases in childhood. Regular follow-up of physiological parameters in the home setting, in relation to asthma symptoms, can provide complementary quantitative insights into the dynamics of the asthma status. Despite considerable interest in asthma home-monitoring in children, there is a paucity of scientific evidence, especially on multi-parameter monitoring approaches. Therefore, the aim of this study is to investigate whether asthma control can be accurately assessed in the home situation by combining parameters from respiratory physiology sensors.
Methods: Sixty asthmatic and 30 non-asthmatic children were enrolled in the observational WEARCON-study. Asthma control was assessed according to GINA guidelines by the paediatrician. All children were also evaluated during a 2-week home-monitoring period with wearable devices; a physical activity tracker, a handheld spirometer, smart inhalers, and an ambulatory electrocardiography device to monitor heart and respiratory rate. Multiple logistic regression analysis was used to determine which diagnostic measures were associated asthma control.
Results: 24 of the 27 uncontrolled asthmatic children and 29 of the 32 controlled asthmatic children could be accurately identified with this model. The final model showed that a larger variation in pre-exercise lung function (OR=1.34 95%-CI 1.07-1.68), an earlier wake-up-time (OR=1.05 95%-CI 1.01-1.10), more reliever use (OR=1.11 95%-CI 1.03-1.19) and a longer respiratory rate recovery time (OR=1.12 95%-CI 1.05-1.20) were significant contributors to the probability of having uncontrolled asthma.
Conclusions: Home-monitoring of physiological parameters correlates with paediatrician assessed asthma control. The constructed multivariate model identifies 88.9% of all uncontrolled asthmatic children, indicating a high potential for monitoring of asthma control. This may allow healthcare professionals to assess asthma control at home.
Trial registration: Netherlands Trail Register, NL6087. Registered 14 February 2017, https://www.trialregister.nl/trial/6087
Figure 1
Figure 2
Figure 3
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