Literature DB >> 29742990

Identifying and Quantifying the Unintended Variability in Common Systematic Observation Instruments to Measure Youth Physical Activity.

Robert G Weaver, Aaron Beighle, Heather Erwin, Michelle Whitfield, Michael W Beets, James W Hardin.   

Abstract

BACKGROUND: Direct observation protocols may introduce variability in physical activity estimates.
METHODS: Thirty-five physical education lessons were video recorded and coded using the System for Observing Fitness Instruction Time (SOFIT). A multistep process examined variability in moderate to vigorous physical activity (MVPA%; walking + vigorous/total scans). Initially, per-SOFIT protocol MVPA% (MVPA%SOFIT) estimates were produced for each lesson. Second, true MVPA% (mean MVPA% of all students using all observations, MVPA%true) estimates were calculated. Third, MVPA% (MVPA%perm) was calculated based on all permutations of students and observation order. Fourth, physical education lessons were divided into 2 groups with 5 lessons from each group randomly selected 10,000 times. Group MVPA%perm differences between the 10 selected lessons were compared with the MVPA%true difference between group 1 and group 2.
RESULTS: Across all lessons, 10,212,600 permutations were possible (average 291,789 combinations per lesson; range = 73,440-570,024). Across lessons, the average absolute difference between MVPA%true and MVPA%SOFIT estimates was ±4.8% (range = 0.1%-17.5%). Permutations, based on students selected and observation order, indicated that the mean range of MVPA%perm estimates was 41.6% within a lesson (range = 29.8%-55.9%). Differences in MVPA% estimates between the randomly selected groups of lessons varied by 32.0%.
CONCLUSION: MVPA% estimates from focal child observation should be interpreted with caution.

Entities:  

Keywords:  SOFIT; focal child; measurement

Mesh:

Year:  2018        PMID: 29742990     DOI: 10.1123/jpah.2017-0375

Source DB:  PubMed          Journal:  J Phys Act Health        ISSN: 1543-3080


  2 in total

1.  Physical education environment and student physical activity levels in low-income communities.

Authors:  Soyang Kwon; Sarah Welch; Maryann Mason
Journal:  BMC Public Health       Date:  2020-01-31       Impact factor: 3.295

2.  Automated High-Frequency Observations of Physical Activity Using Computer Vision.

Authors:  Jordan A Carlson; B O Liu; James F Sallis; J Aaron Hipp; Vincent S Staggs; Jacqueline Kerr; Amy Papa; Kelsey Dean; Nuno M Vasconcelos
Journal:  Med Sci Sports Exerc       Date:  2020-09
  2 in total

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