Prediction model development of women's daily asthma control using fitness tracker sleep disruption.

Night-time wakening with asthma symptoms is an important indicator of disease control and severity, with no gold-standard objective measurement.The study objective was to use fitness tracker sleep data to develop predictive models of daily disease control-related asthma-specific wakening and FEV₁ in working-aged women with poorly controlled asthma.A repeated measures panel design included data from 43 women with poorly controlled asthma. Two components of asthma control were the primary outcomes, measured daily as (1) self-reported asthma-specific wakening and (2) self-administered spirometry to measure FEV₁. Data were analyzed using generalized linear mixed models.Our models demonstrated predictive value (AUC=0.77) for asthma-specific night-time wakening and good predictive value (AUC=0.83) for daily FEV_1. CONCLUSIONS: Fitness tracker sleep efficiency and wake counts demonstrate clinical utility as predictive of asthma-specific night-time wakening and daily FEV_1. Fitness tracker sleep data demonstrated predictive capability for daily asthma outcomes.

Duke Scholars

Author Jessica Patience Castner Nicholas Institute for Energy, Environment & Sustainability