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Validation of fitness tracker for sleep measures in women with asthma.

Authors
  • Castner, Jessica1, 2, 3
  • Mammen, Manoj J4, 5
  • Jungquist, Carla R6
  • Licata, Olivia7, 8
  • Pender, John J6
  • Wilding, Gregory E9
  • Sethi, Sanjay5
  • 1 a The Rockefeller University Heilbrunn Family Center for Research Nursing , New York , NY , USA.
  • 2 b University at Buffalo , Buffalo , NY , USA.
  • 3 c Castner Incorporated , Grand Island , New York , NY , USA.
  • 4 d Department of Biomedical Informatics, Jacobs School of Medicine and Biomedical Sciences , University at Buffalo , Buffalo , NY , USA.
  • 5 e Department of Medicine, Jacobs School of Medicine and Biomedical Sciences , University at Buffalo , Buffalo , NY , USA.
  • 6 f School of Nursing , University at Buffalo , Buffalo , NY , USA.
  • 7 g Department of Materials Design and Innovation, School of Engineering and Applied Sciences , University at Buffalo , Buffalo , NY , USA.
  • 8 h Department of Biomedical Engineering, School of Engineering and Applied Sciences , University at Buffalo , Buffalo , NY , USA.
  • 9 i Department of Biostatistics, School of Public Health and Health Professions , University at Buffalo , Buffalo , NY , USA.
Type
Published Article
Journal
Journal of Asthma
Publisher
Informa UK (Taylor & Francis)
Publication Date
Jul 01, 2019
Volume
56
Issue
7
Pages
719–730
Identifiers
DOI: 10.1080/02770903.2018.1490753
PMID: 29972657
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Nighttime wakening with asthma symptoms is a key to assessment and therapy decisions, with no gold standard objective measure. The study aims were to (1) determine the feasibility, (2) explore equivalence, and (3) test concordance of a consumer-based accelerometer with standard actigraphy for measurement of sleep patterns in women with asthma as an adjunct to self-report. Panel study design of women with poorly controlled asthma from a university-affiliated primary care clinic system was used. We assessed sensitivity and specificity, 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. Analysis included 424 938 minutes, 738 nights, and 833 unique sleep segments from 47 women. The fitness tracker demonstrated 97% sensitivity and 40% specificity to identify sleep. Between device equivalence for total sleep time (15 and 42-minute threshold) was demonstrated by sleep segment. Concordance improved for wake counts and sleep efficiency when adjusting for a linear trend. There were important differences in total sleep time, efficiency, and wake count measures when comparing individual sleep segments versus 24-hour measures of sleep. Fitbit overestimates sleep efficiency and underestimates wake counts in this population compared to actigraphy. 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. Fitness trackers offer an accessible and inexpensive method to quantify sleep patterns in the home environment as an adjunct to subjective reports, and require further informatics development.

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