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Liu F, Schrack J, Wanigatunga SK, Rabinowitz JA, He L, Wanigatunga AA, Zipunnikov V, Simonsick EM, Ferrucci L, Spira AP. Comparison of sleep parameters from wrist-worn ActiGraph and Actiwatch devices. Sleep 2024; 47:zsad155. [PMID: 37257489 PMCID: PMC10851854 DOI: 10.1093/sleep/zsad155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 05/03/2023] [Indexed: 06/02/2023] Open
Abstract
Sleep and physical activity, two important health behaviors, are often studied independently using different accelerometer types and body locations. Understanding whether accelerometers designed for monitoring each behavior can provide similar sleep parameter estimates may help determine whether one device can be used to measure both behaviors. Three hundred and thirty one adults (70.7 ± 13.7 years) from the Baltimore Longitudinal Study of Aging wore the ActiGraph GT9X Link and the Actiwatch 2 simultaneously on the non-dominant wrist for 7.0 ± 1.6 nights. Total sleep time (TST), wake after sleep onset (WASO), sleep efficiency, number of wake bouts, mean wake bout length, and sleep fragmentation index (SFI) were extracted from ActiGraph using the Cole-Kripke algorithm and from Actiwatch using the software default algorithm. These parameters were compared using paired t-tests, Bland-Altman plots, and Deming regression models. Stratified analyses were performed by age, sex, and body mass index (BMI). Compared to the Actiwatch, the ActiGraph estimated comparable TST and sleep efficiency, but fewer wake bouts, longer WASO, longer wake bout length, and higher SFI (all p < .001). Both devices estimated similar 1-min and 1% differences between participants for TST and SFI (β = 0.99, 95% CI: 0.95, 1.03, and 0.91, 1.13, respectively), but not for other parameters. These differences varied by age, sex, and/or BMI. The ActiGraph and the Actiwatch provide comparable absolute and relative estimates of TST, but not other parameters. The discrepancies could result from device differences in movement collection and/or sleep scoring algorithms. Further comparison and calibration is required before these devices can be used interchangeably.
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Affiliation(s)
- Fangyu Liu
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Jennifer Schrack
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
- Center on Aging and Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Sarah K Wanigatunga
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Jill A Rabinowitz
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Linchen He
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
- Department of Community and Population Health, College of Health, Lehigh University, Bethlehem, Pennsylvania, USA
| | - Amal A Wanigatunga
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
- Center on Aging and Health, Johns Hopkins University, Baltimore, Maryland, USA
| | - Vadim Zipunnikov
- Center on Aging and Health, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | | | - Luigi Ferrucci
- National Institute on Aging, National Institutes of Health, Baltimore, USA
| | - Adam P Spira
- Center on Aging and Health, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
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Abstract
Bioanalytical methods evolve throughout clinical development timelines, resulting in the need for establishing equivalency or correlation between different methods to enable comparison of data across different studies. This is accomplished by the conduct of cross validations and correlative studies to compare and describe the relationship. The incurred sample reanalysis acceptance criterion seems to be adopted universally for cross validations and correlative studies; however, this does not identify any trends or biases between the two methods (datasets) being compared. Presented here are graphing approaches suitable for comparing two methods and describing equivalence or correlation. This article aims to generate awareness on graphing techniques that can be adopted during cross validations and correlative studies.
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