Prediction model development of women's daily asthma control using fitness tracker sleep disruption.
Jessica Castner, Carla R Jungquist, Manoj J Mammen, John J Pender, Olivia Licata, Sanjay Sethi
Author Information
Jessica Castner: The Rockefeller Heilbrunn Family Center for Research Nursing Nurse Scholar, New York, NY, USA; University at Buffalo, Buffalo, NY, USA; Castner Incorporated, Grand Island, NY 14072, USA. Electronic address: jcastner@castnerincorp.com.
Carla R Jungquist: University at Buffalo School of Nursing, Buffalo, NY, USA.
Manoj J Mammen: Department of Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.
John J Pender: University at Buffalo School of Nursing Graduate, Buffalo, NY, USA.
Olivia Licata: Department of Biomedical Engineering & Department of Materials Design and Innovation, University at Buffalo School of Engineering and Applied Sciences, Buffalo, NY, USA.
Sanjay Sethi: Department of Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY, USA.
BACKGROUND: Night-time wakening with asthma symptoms is an important indicator of disease control and severity, with no gold-standard objective measurement. OBJECTIVE: The study objective was to use fitness tracker sleep data to develop predictive models of daily disease control-related asthma-specific wakening and FEV in working-aged women with poorly controlled asthma. METHODS: 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 FEV. Data were analyzed using generalized linear mixed models. RESULTS: Our models demonstrated predictive value (AUC=0.77) for asthma-specific night-time wakening and good predictive value (AUC=0.83) for daily FEV CONCLUSIONS: Fitness tracker sleep efficiency and wake counts demonstrate clinical utility as predictive of asthma-specific night-time wakening and daily FEV Fitness tracker sleep data demonstrated predictive capability for daily asthma outcomes.
