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A3642 - Validation of Fitness Tracker for Sleep Measures in Women with Asthma
Author Block: J. Castner1, M. J. Mammen2, C. Jungquist3, O. Licata4, J. J. Pender4, G. E. Wilding5, S. Sethi6; 1On file, Buffalo, NY, United States, 2Department of Medicine, SUNY at Buffalo, Buffalo, NY, United States, 3School of Nursing, University at Buffalo, Buffalo, NY, United States, 4University at Buffalo, Buffalo, NY, United States, 5Biostatistics, University at Buffalo, Buffalo, NY, United States, 6Medicine, University at Buffalo, Buffalo, NY, United States.
Introduction Night-time wakening with asthma symptoms is a key indicator of disease severity and control. Currently, standard assessments and therapy are based on the self-report of asthma-related wakening. Inexpensive and commonly available fitness trackers, such as the Fitbit, are promising for long-term, objective assessment of sleep-wake patterns in the home environment. The purpose of this study was to 1) determine the feasibility, 2) explore equivalence, and 3) test concordance of a consumer-based accelerometer with standard actigraphy for the objective measurement of sleep patterns in women with asthma. The hypothesis was the two devices would be equivalent within a 10% threshold level and demonstrate concordance with 90% of measures within the standard limits of agreement. Methods Panel study design of women with poorly controlled asthma was used. We assessed the equivalence and concordance of sleep time, sleep efficiency, and wake counts between the consumer-based accelerometer Fitbit Charge™ and Actigraph wGT3X+. We linked data between devices for comparison both automatically by 24-hour period and manually by sleep segment. Results A total of 723 nights (77.6%), and 854 unique sleep segments, from 47 women were included with a per participant average of 15.38 nights (SD=4.37, range=6-27). At a 45 minute (10%) threshold, equivalence was only demonstrated between device sleep segments for total sleep time. 89-97% of measures were within the standard limits of agreement on the Bland Altman concordance plots. Concordance improved for wake counts and sleep efficiency when adjusting for a linear trend. Conclusion If used, clinicians and researchers should consider Fitbit overestimates sleep efficiency and underestimates wake counts in this population compared to actigraphy. Proprietary algorithms process scores that are equivalent for total time per sleep segment, and have differences in all scores processed by 24-hour period as well as efficiency and wake count by sleep segment. Low levels of systematic bias indicate the potential for raw measurements from the devices to achieve equivalence and concordance with additional processing, algorithm modification, and modeling. There were important differences in the devices when comparing by specific sleep segment, rather than by 24-hour period.