Literature DB >> 27652827

Identifying adults' valid waking wear time by automated estimation in activPAL data collected with a 24 h wear protocol.

Elisabeth A H Winkler1, Danielle H Bodicoat, Genevieve N Healy, Kishan Bakrania, Thomas Yates, Neville Owen, David W Dunstan, Charlotte L Edwardson.   

Abstract

The activPAL monitor, often worn 24 h d-1, provides accurate classification of sitting/reclining posture. Without validated automated methods, diaries-burdensome to participants and researchers-are commonly used to ensure measures of sedentary behaviour exclude sleep and monitor non-wear. We developed, for use with 24 h wear protocols in adults, an automated approach to classify activity bouts recorded in activPAL 'Events' files as 'sleep'/non-wear (or not) and on a valid day (or not). The approach excludes long periods without posture change/movement, adjacent low-active periods, and days with minimal movement and wear based on a simple algorithm. The algorithm was developed in one population (STAND study; overweight/obese adults 18-40 years) then evaluated in AusDiab 2011/12 participants (n  =  741, 44% men, aged  >35 years, mean  ±  SD 58.5  ±  10.4 years) who wore the activPAL3™ (7 d, 24 h d-1 protocol). Algorithm agreement with a monitor-corrected diary method (usual practice) was tested in terms of the classification of each second as waking wear (Kappa; κ) and the average daily waking wear time, on valid days. The algorithm showed 'almost perfect' agreement (κ  >  0.8) for 88% of participants, with a median kappa of 0.94. Agreement varied significantly (p  <  0.05, two-tailed) by age (worsens with age) but not by gender. On average, estimated wear time was approximately 0.5 h d-1 higher than by the diary method, with 95% limits of agreement of approximately this amount  ±2 h d-1. In free-living data from Australian adults, a simple algorithm developed in a different population showed 'almost perfect' agreement with the diary method for most individuals (88%). For several purposes (e.g. with wear standardisation), adopting a low burden, automated approach would be expected to have little impact on data quality. The accuracy for total waking wear time was less and algorithm thresholds may require adjustments for older populations.

Entities:  

Year:  2016        PMID: 27652827     DOI: 10.1088/0967-3334/37/10/1653

Source DB:  PubMed          Journal:  Physiol Meas        ISSN: 0967-3334            Impact factor:   2.833


  61 in total

1.  Characteristics of a protocol to collect objective physical activity/sedentary behaviour data in a large study: Seniors USP (understanding sedentary patterns).

Authors:  P M Dall; D A Skelton; M L Dontje; E H Coulter; S Stewart; S R Cox; R J Shaw; I Čukić; C F Fitzsimons; C A Greig; M H Granat; G Der; I J Deary; Sfm Chastin
Journal:  J Meas Phys Behav       Date:  2018-03

2.  Validity of Two Awake Wear-Time Classification Algorithms for activPAL in Youth, Adults, and Older Adults.

Authors:  Jordan A Carlson; Fatima Tuz-Zahra; John Bellettiere; Nicola D Ridgers; Chelsea Steel; Carolina Bejarano; Andrea Z LaCroix; Dori E Rosenberg; Mikael Anne Greenwood-Hickman; Marta M Jankowska; Loki Natarajan
Journal:  J Meas Phys Behav       Date:  2021-04-22

3.  Association Between Accelerometer-Derived Physical Activity Measurements and Brain Structure: A Population-Based Cohort Study.

Authors:  Fabienne A U Fox; Kersten Diers; Hweeling Lee; Andreas Mayr; Martin Reuter; Monique M B Breteler; N Ahmad Aziz
Journal:  Neurology       Date:  2022-08-02       Impact factor: 11.800

4.  Does sedentary time increase in older adults in the days following participation in intense exercise?

Authors:  Nikola Goncin; Andrea Linares; Meghann Lloyd; Shilpa Dogra
Journal:  Aging Clin Exp Res       Date:  2020-03-04       Impact factor: 3.636

5.  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

6.  Patterns of Sitting, Standing, and Stepping After Lower Limb Amputation.

Authors:  Matthew J Miller; Jennifer M Blankenship; Paul W Kline; Edward L Melanson; Cory L Christiansen
Journal:  Phys Ther       Date:  2021-02-04

7.  How Accurate and Precise Can We Measure the Posture and the Energy Expenditure Component of Sedentary Behaviour with One Sensor?

Authors:  Roman P Kuster; Wilhelmus J A Grooten; Victoria Blom; Daniel Baumgartner; Maria Hagströmer; Örjan Ekblom
Journal:  Int J Environ Res Public Health       Date:  2021-05-27       Impact factor: 3.390

8.  Descriptive Epidemiology of Interruptions to Free-Living Sitting Time in Middle-Age and Older Adults.

Authors:  Jennifer M Blankenship; Elisabeth A H Winkler; Genevieve N Healy; Paddy C Dempsey; John Bellettiere; Neville Owen; David W Dunstan
Journal:  Med Sci Sports Exerc       Date:  2021-12-01       Impact factor: 5.411

9.  Modelling the Reallocation of Time Spent Sitting into Physical Activity: Isotemporal Substitution vs. Compositional Isotemporal Substitution.

Authors:  Gregory J H Biddle; Joseph Henson; Stuart J H Biddle; Melanie J Davies; Kamlesh Khunti; Alex V Rowlands; Stephen Sutton; Thomas Yates; Charlotte L Edwardson
Journal:  Int J Environ Res Public Health       Date:  2021-06-08       Impact factor: 3.390

10.  The PERSonalized Glucose Optimization Through Nutritional Intervention (PERSON) Study: Rationale, Design and Preliminary Screening Results.

Authors:  Anouk Gijbels; Inez Trouwborst; Kelly M Jardon; Gabby B Hul; Els Siebelink; Suzanne M Bowser; Dilemin Yildiz; Lisa Wanders; Balázs Erdos; Dick H J Thijssen; Edith J M Feskens; Gijs H Goossens; Lydia A Afman; Ellen E Blaak
Journal:  Front Nutr       Date:  2021-06-30
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