Literature DB >> 25202847

Ability of thigh-worn ActiGraph and activPAL monitors to classify posture and motion.

Jeremy A Steeves1, Heather R Bowles, James J McClain, Kevin W Dodd, Robert J Brychta, Juan Wang, Kong Y Chen.   

Abstract

PURPOSE: This study compared sitting, standing, and stepping classifications from thigh-worn ActiGraph and activPAL monitors under laboratory and free-living conditions.
METHODS: Adults wore both monitors on the right thigh while performing activities (six sitting, two standing, nine stepping, and one cycling) and writing on a whiteboard with intermittent stepping under laboratory conditions (n = 21) and under free-living conditions for 3 d (n = 18). Percent time correctly classified was calculated under laboratory conditions. Between-monitor agreement and weighted κ were calculated under free-living conditions.
RESULTS: In the laboratory, both monitors correctly classified 100% of standing time and >95% of the time spent in four of six sitting postures. Both monitors demonstrated misclassification of laboratory stool sitting time (ActiGraph 14% vs. activPAL 95%). ActivPAL misclassified 14% of the time spent sitting with legs outstretched; ActiGraph was 100% accurate. Monitors were >95% accurate for stepping, although ActiGraph was less so for descending stairs (86%), ascending stairs (92%), and running at 2.91 m·s(-1) (93%). Monitors classified whiteboard writing differently (ActiGraph 83% standing/15% stepping vs. activPAL 98% standing/2% stepping). ActivPAL classified 93% of cycling time as stepping, whereas ActiGraph classified <1% of cycling time as stepping. During free-living wear, monitors had substantial agreement (86% observed; weighted κ = 0.77). Monitors classified similar amounts of time as sitting (ActiGraph 64% vs. activPAL 62%). There were differences in time recorded as standing (ActiGraph 21% vs. activPAL 27%) and stepping (ActiGraph 15% vs. activPAL 11%).
CONCLUSIONS: Differences in data processing algorithms may have resulted in the observed disagreement in posture and activity classification between thigh-worn ActiGraph and activPAL. Despite between-monitor agreement in classifying sitting time under free-living conditions, ActiGraph appears to be more sensitive to free-living upright walking motions than activPAL.

Entities:  

Mesh:

Year:  2015        PMID: 25202847      PMCID: PMC6330899          DOI: 10.1249/MSS.0000000000000497

Source DB:  PubMed          Journal:  Med Sci Sports Exerc        ISSN: 0195-9131            Impact factor:   5.411


  38 in total

1.  Deviation between self-reported and measured occupational physical activity levels in office employees: effects of age and body composition.

Authors:  Katharina Wick; Oliver Faude; Susanne Schwager; Lukas Zahner; Lars Donath
Journal:  Int Arch Occup Environ Health       Date:  2015-10-28       Impact factor: 3.015

2.  Classifying sitting, standing, and walking using plantar force data.

Authors:  Kohle J Merry; Evan Macdonald; Megan MacPherson; Omar Aziz; Edward Park; Michael Ryan; Carolyn J Sparrey
Journal:  Med Biol Eng Comput       Date:  2021-01-08       Impact factor: 2.602

3.  Influence of Estradiol Status on Physical Activity in Premenopausal Women.

Authors:  Edward L Melanson; Kate Lyden; Ellie Gibbons; Kathleen M Gavin; Pamela Wolfe; Margaret E Wierman; Robert S Schwartz; Wendy M Kohrt
Journal:  Med Sci Sports Exerc       Date:  2018-08       Impact factor: 5.411

4.  Accuracy of inclinometer functions of the activPAL and ActiGraph GT3X+: A focus on physical activity.

Authors:  Hyun-Sung An; Youngwon Kim; Jung-Min Lee
Journal:  Gait Posture       Date:  2016-10-18       Impact factor: 2.840

5.  Momentary intentions and perceived behavioral control are within-person predictors of sedentary leisure time: preliminary findings from an ecological momentary assessment study in adolescents.

Authors:  Shayan Ebrahimian; Jennifer Zink; Chih-Hsiang Yang; Qihan Yu; Kellie Imm; Michele Nicolo; Genevieve F Dunton; Britni R Belcher
Journal:  J Behav Med       Date:  2022-04-01

6.  Using Graph Representation Learning to Predict Salivary Cortisol Levels in Pancreatic Cancer Patients.

Authors:  Guimin Dong; Mehdi Boukhechba; Kelly M Shaffer; Lee M Ritterband; Daniel G Gioeli; Matthew J Reilley; Tri M Le; Paul R Kunk; Todd W Bauer; Philip I Chow
Journal:  J Healthc Inform Res       Date:  2021-04-21

7.  Application of Convolutional Neural Network Algorithms for Advancing Sedentary and Activity Bout Classification.

Authors:  Supun Nakandala; Marta M Jankowska; Fatima Tuz-Zahra; John Bellettiere; Jordan A Carlson; Andrea Z LaCroix; Sheri J Hartman; Dori E Rosenberg; Jingjing Zou; Arun Kumar; Loki Natarajan
Journal:  J Meas Phys Behav       Date:  2021-02-25

8.  Physical Activity and Sedentary Time Among Mothers of School-Aged Children: Differences in Accelerometer-Derived Pattern Metrics by Demographic, Employment, and Household Factors.

Authors:  Bridgette Do; Jennifer Zink; Tyler B Mason; Britni R Belcher; Genevieve F Dunton
Journal:  Womens Health Issues       Date:  2022-04-28

9.  Comparing the activPAL software's Primary Time in Bed Algorithm against Self-Report and van der Berg's Algorithm.

Authors:  J B Courtney; K Nuss; K Lyden; K K Harrall; D H Glueck; A Villalobos; R F Hamman; J R Hebert; T G Hurley; J Leiferman; K Li; K Alaimo; J S Litt
Journal:  Meas Phys Educ Exerc Sci       Date:  2020-12-28

Review 10.  Assessment of Physical Activity in Adults Using Wrist Accelerometers.

Authors:  Fangyu Liu; Amal A Wanigatunga; Jennifer A Schrack
Journal:  Epidemiol Rev       Date:  2022-01-14       Impact factor: 4.280

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