To identify asthma-related physiological changes observed by wearable devices in real-world conditions by monitor multiple sensing modalities (e.g., heart rate (HR), heart rate variability (HRV), activity level, spirometry, coughing sounds) in order to find reliable signatures of impending asthma exacerbation and systematically explore any challenges on the use of wearable technologies.
The study aims to identify asthma-related physiological changes observed by wearable devices in real-world conditions. The investigators aim to monitor multiple sensing modalities (e.g., heart rate (HR), heart rate variability (HRV), activity level, spirometry, coughing sounds) in order to find reliable signatures of impending asthma exacerbation and systematically explore any challenges on the use of wearable technologies. A variety of off-the-shelf devices as well as the prototype HET platform developed by the NCSU ASSIST center will be used for data collection. The investigators plan to engage adolescents (ages 14-18) for a period of up to four months of monitoring. They will be asked to wear the wrist monitoring device for at least 8 hours daily and chest monitoring devices for at least 12 hours a week, to take daily measurements using a spirometer, and answer weekly questionnaires online and virtual interviews (at 1-week,1-month and 3-months) about their asthma control and experiences with the devices. Wearable devices are increasingly popular with young people and are capable of providing dynamically calculated up-to-the-minute measurements of a number of physiologic parameters, including heart rate, heart rate variability, respiratory rate, activity levels, and cough. The cough sounds the investigators hope to capture during a forced-cough recording and while sleeping can be used as biomarkers for early detection of exacerbation. These changes could be used to predict an asthma exacerbation, and provide the wearer with instant feedback allowing the user to intervene early and prevent progression to more severe symptoms. Young adults are likely to adopt wearable technologies to facilitate chronic disease management, making this an ideal age group to examine the utility of wearable devices to detect early physiologic predictors of an impending exacerbation.
Study Type
OBSERVATIONAL
Enrollment
20
UNC Children's Raleigh Clinic
Raleigh, North Carolina, United States
RECRUITINGIdentify the correlation between continuous physiological measurements, inhaler usage and lung function outcomes from spirometry
Physiological measurements obtained from wearable devices (including motion, heart rate and heart rate variability) and from acoustic sensors (including audio from cough) will be correlated with outcomes from the daily spirometry measurements and inhaler use. Hand-crafted and data-driven features will be extracted for physiological measurements. Standard statistical tests will be used to determine significant correlations.
Time frame: Continuously from baseline and up to 4 months
Develop a predictive model for asthma exacerbation and drop in lung function based on physiological measurements and inhaler usage
Results from the primary outcome will be used to build a data-driven predictive model for the detection of asthma exacerbation (as reported by the participants in their weekly survey) and drop in lung function (as measured by the spirometer). Standard metrics capturing specificity and sensitivity of the model will be used for evaluation. These models will be continuously developed after the completion of data collection from the first cohort.
Time frame: Continuously from baseline and up to 4 months
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