Literature DB >> 35365696

Equivalency of four research-grade movement sensors to assess movement behaviors and its implications for population surveillance.

Jairo H Migueles1,2, Pablo Molina-Garcia3, Lucia V Torres-Lopez3, Cristina Cadenas-Sanchez3,4, Alex V Rowlands5,6,7, Ulrich W Ebner-Priemer8,9, Elena D Koch9, Andreas Reif10, Francisco B Ortega11,12,13.   

Abstract

the benefits of physical activity (PA) and sleep for health, accurate and objective population-based surveillance is important. Monitor-based surveillance has potential, but the main challenge is the need for replicable outcomes from different monitors. This study investigated the agreement of movement behavior outcomes assessed with four research-grade activity monitors (i.e., Movisens Move4, ActiGraph GT3X+, GENEActiv, and Axivity AX3) in adults. Twenty-three participants wore four monitors on the non-dominant wrist simultaneously for seven days. Open-source software (GGIR) was used to estimate the daily time in sedentary, light, moderate-to-vigorous PA (MVPA), and sleep (movement behaviors). The prevalence of participants meeting the PA and sleep recommendations were calculated from each monitor's data. Outcomes were deemed equivalent between monitors if the absolute standardized difference and its 95% confidence intervals (CI95%) fell within ± 0.2 standard deviations (SD) of the mean of the differences. The participants were mostly men (n = 14, 61%) and aged 36 (SD = 14) years. Pairwise confusion matrices showed that 83-87% of the daily time was equally classified into the movement categories by the different pairs of monitors. The between-monitor difference in MVPA ranged from 1 (CI95%: - 6, 7) to 8 (CI95%: 1, 15) min/day. Most of the PA and sleep metrics could be considered equivalent. The prevalence of participants meeting the PA and the sleep guidelines was 100% consistent across monitors (22 and 5 participants out of the 23, respectively). Our findings indicate that the various research-grade activity monitors investigated show high inter-instrument reliability with respect to sedentary, PA and sleep-related estimates when their raw data are processed in an identical manner. These findings may have important implications for advancement towards monitor-based PA and sleep surveillance systems.
© 2022. The Author(s).

Entities:  

Mesh:

Year:  2022        PMID: 35365696      PMCID: PMC8975935          DOI: 10.1038/s41598-022-09469-2

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  35 in total

1.  Trends in Step-determined Physical Activity among Japanese Adults from 1995 to 2016.

Authors:  Tomoko Takamiya; Shigeru Inoue
Journal:  Med Sci Sports Exerc       Date:  2019-09       Impact factor: 5.411

2.  Accelerometer-measured moderate-to-vigorous physical activity of Canadian adults, 2007 to 2017.

Authors:  Janine Clarke; Rachel Colley; Ian Janssen; Mark S Tremblay
Journal:  Health Rep       Date:  2019-08-21       Impact factor: 4.796

Review 3.  Epidemiologic analyses with error-prone exposures: review of current practice and recommendations.

Authors:  Pamela A Shaw; Veronika Deffner; Ruth H Keogh; Janet A Tooze; Kevin W Dodd; Helmut Küchenhoff; Victor Kipnis; Laurence S Freedman
Journal:  Ann Epidemiol       Date:  2018-09-18       Impact factor: 3.797

Review 4.  The pandemic of physical inactivity: global action for public health.

Authors:  Harold W Kohl; Cora Lynn Craig; Estelle Victoria Lambert; Shigeru Inoue; Jasem Ramadan Alkandari; Grit Leetongin; Sonja Kahlmeier
Journal:  Lancet       Date:  2012-07-21       Impact factor: 79.321

5.  Raw Accelerometer Data Analysis with GGIR R-package: Does Accelerometer Brand Matter?

Authors:  Alex V Rowlands; Tom Yates; Melanie Davies; Kamlesh Khunti; Charlotte L Edwardson
Journal:  Med Sci Sports Exerc       Date:  2016-10       Impact factor: 5.411

Review 6.  Evolution of accelerometer methods for physical activity research.

Authors:  Richard P Troiano; James J McClain; Robert J Brychta; Kong Y Chen
Journal:  Br J Sports Med       Date:  2014-04-29       Impact factor: 13.800

7.  Accelerometer-assessed Physical Activity in Epidemiology: Are Monitors Equivalent?

Authors:  Alex V Rowlands; Evgeny M Mirkes; Tom Yates; Stacey Clemes; Melanie Davies; Kamlesh Khunti; Charlotte L Edwardson
Journal:  Med Sci Sports Exerc       Date:  2018-02       Impact factor: 5.411

8.  Comparison of physical behavior estimates from three different thigh-worn accelerometers brands: a proof-of-concept for the Prospective Physical Activity, Sitting, and Sleep consortium (ProPASS).

Authors:  Patrick Crowley; Jørgen Skotte; Emmanuel Stamatakis; Mark Hamer; Mette Aadahl; Matthew L Stevens; Vegar Rangul; Paul J Mork; Andreas Holtermann
Journal:  Int J Behav Nutr Phys Act       Date:  2019-08-16       Impact factor: 6.457

9.  Cohort profile: the Women's Health Accelerometry Collaboration.

Authors:  Kelly R Evenson; John Bellettiere; Carmen C Cuthbertson; Chongzhi Di; Rimma Dushkes; Annie Green Howard; Humberto Parada; Benjamin T Schumacher; Eric J Shiroma; Guangxing Wang; I-Min Lee; Andrea Z LaCroix
Journal:  BMJ Open       Date:  2021-11-29       Impact factor: 3.006

10.  Cardiovascular Risk and Disease Among Masters Endurance Athletes: Insights from the Boston MASTER (Masters Athletes Survey To Evaluate Risk) Initiative.

Authors:  Kayle Shapero; James Deluca; Miranda Contursi; Meagan Wasfy; Rory B Weiner; Gregory D Lewis; Adolph Hutter; Aaron L Baggish
Journal:  Sports Med Open       Date:  2016-08-09
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.