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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] [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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Knell G, Li Q, Morales-Marroquin E, Drope J, Gabriel KP, Shuval K. Physical Activity, Sleep, and Sedentary Behavior among Successful Long-Term Weight Loss Maintainers: Findings from a U.S. National Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18115557. [PMID: 34067414 PMCID: PMC8196944 DOI: 10.3390/ijerph18115557] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 05/17/2021] [Accepted: 05/18/2021] [Indexed: 11/25/2022]
Abstract
Despite adults’ desire to reduce body mass (weight) for numerous health benefits, few are able to successfully lose at least 5% of their starting weight. There is evidence on the independent associations of physical activity, sedentary behaviors, and sleep with weight loss; however, this study provided insight on the combined effects of these behaviors on long-term body weight loss success. Hence, the purpose of this cross-sectional study was to evaluate the joint relations of sleep, physical activity, and sedentary behaviors with successful long-term weight loss. Data are from the 2005–2006 wave of the National Health and Examination Survey (NHANES). Physical activity and sedentary behavior were measured with an accelerometer, whereas sleep time was self-reported. Physical activity and sleep were dichotomized into meeting guidelines (active/not active, ideal sleep/short sleep), and sedentary time was categorized into prolonged sedentary time (4th quartile) compared to low sedentary time (1st–3rd quartiles). The dichotomized behaviors were combined to form 12 unique behavioral combinations. Two-step multivariable regression models were used to determine the associations between the behavioral combinations with (1) long-term weight loss success (≥5% body mass reduction for ≥12-months) and (2) the amount of body mass reduction among those who were successful. After adjustment for relevant factors, there were no significant associations between any of the independent body weight loss behaviors (physical activity, sedentary time, and sleep) and successful long-term weight loss. However, after combining the behaviors, those who were active (≥150 min MVPA weekly), regardless of their sedentary time, were significantly (p < 0.05) more likely to have long-term weight loss success compared to the inactive and sedentary referent group. These results should be confirmed in longitudinal analyses, including investigation of characteristics of waking (type, domain, and context) and sleep (quality metrics) behaviors for their association with long-term weight loss success.
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Affiliation(s)
- Gregory Knell
- Department of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston (UTHealth), Houston, TX 77030, USA;
- Center for Pediatric Population Health, The University of Texas Health Science Center at Houston (UTHealth), Dallas, TX 75390, USA
- Children’s Health Andrews Institute for Orthopaedics and Sports Medicine, Plano, TX 75024, USA
- Correspondence: ; Tel.: +01-972-546-2943
| | - Qing Li
- Department of Intramural Research, American Cancer Society, Atlanta, GA 30303, USA;
| | - Elisa Morales-Marroquin
- Department of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston (UTHealth), Houston, TX 77030, USA;
- Center for Pediatric Population Health, The University of Texas Health Science Center at Houston (UTHealth), Dallas, TX 75390, USA
| | - Jeffrey Drope
- Department of Health Policy and Administration, School of Public Health, University of Illinois at Chicago, Chicago, IL 60608, USA;
| | - Kelley Pettee Gabriel
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL 35294, USA;
| | - Kerem Shuval
- The Cooper Institute, Dallas, TX 75230, USA;
- Department of Epidemiology, School of Public Health, University of Haifa, Haifa 3498838, Israel
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