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Matti Hyvärinen, Sarianna Sipilä, Janne Kulmala, Harto Hakonen, Tuija H. Tammelin, Urho M. Kujala, Vuokko Kovanen and Eija K. Laakkonen

). However, compared with accelerometer-based methods, clearly less knowledge exists about the relationship between single self-report questions for physical activity assessment and physical performance including muscular fitness. The few studies that have been conducted have focused on the older adults

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Melissa Bittner

Edited by Michael Horvat, Luke Kelly, Martin Block, and Ron Croce. Published 2019 by Human Kinetics, Champaign, IL. $67.00 , 280 pp., ISBN 978-1-4925-4380-0 Developmental and Adapted Physical Activity Assessment , by Michael Horvat, Luke Kelly, Martin Block, and Ron Croce, now in its second

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David R. Paul, Ryan McGrath, Chantal A. Vella, Matthew Kramer, David J. Baer and Alanna J. Moshfegh

overweight adults . J Phys Act Health . 2015 ; 12 ( 5 ): 680 – 685 . PubMed doi:10.1123/jpah.2013-0269 10.1123/jpah.2013-0269 24834467 16. Melanson EL Jr , Freedson PS . Physical activity assessment: a review of methods . Crit Rev Food Sci Nutr . 1996 ; 36 ( 5 ): 385 – 396 . PubMed doi:10

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Kelley Pettee Gabriel, Adriana Pérez, David R. Jacobs Jr, Joowon Lee, Harold W. Kohl III and Barbara Sternfeld

, differed by cardiovascular health indicator. Furthermore, based on underlying mathematical properties, R 2 values were higher when using a physical activity assessment strategy that incorporated both self-report and accelerometer data, including dimension reduction (ie, composite score) and no dimension

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Natalie Kružliaková, Paul A. Estabrooks, Wen You, Valisa Hedrick, Kathleen Porter, Michaela Kiernan and Jamie Zoellner

.053967 10.1136/jech.2006.053967 19. Sallis JF , Haskell WL , Wood PD , et al . Physical activity assessment methodology in the Five-City Project . Am J Epidemiol . 1985 ; 121 ( 1 ): 91 – 106 . PubMed doi:10.1093/oxfordjournals.aje.a113987 3964995 10.1093/oxfordjournals.aje.a113987 20. Wendel

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Jeremy A. Steeves, Catrine Tudor-Locke, Rachel A. Murphy, George A. King, Eugene C. Fitzhugh, David R. Bassett, Dane Van Domelen, John M. Schuna Jr and Tamara B. Harris

, Rickenbach M , Wietlisbach V , Tullen B , Schutz Y . Physical activity assessment using a pedometer and its comparison with a questionnaire in a large population survey . Am J Epidemiol . 1995 ; 142 ( 9 ): 989 – 999 . PubMed ID: 7572981 doi:10.1093/oxfordjournals.aje.a117748 10

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Stephen Zwolinsky, James McKenna, Andy Pringle, Paul Widdop, Claire Griffiths, Michelle Mellis, Zoe Rutherford and Peter Collins

Background:

Increasingly the health impacts of physical inactivity are being distinguished from those of sedentary behavior. Nevertheless, deleterious health prognoses occur when these behaviors combine, making it a Public Health priority to establish the numbers and salient identifying factors of people who live with this injurious combination.

Methods:

Using an observational between-subjects design, a nonprobability sample of 22,836 participants provided data on total daily activity. A 2-step hierarchical cluster analysis identified the optimal number of clusters and the subset of distinguishing variables. Univariate analyses assessed significant cluster differences.

Results:

High levels of sitting clustered with low physical activity. The Ambulatory & Active cluster (n = 6254) sat for 2.5 to 5 h·d−1 and were highly active. They were significantly younger, included a greater proportion of males and reported low Indices of Multiple Deprivation compared with other clusters. Conversely, the Sedentary & Low Active cluster (n = 6286) achieved ≤60 MET·min·wk−1 of physical activity and sat for ≥8 h·d−1. They were the oldest cluster, housed the largest proportion of females and reported moderate Indices of Multiple Deprivation.

Conclusions:

Public Health systems may benefit from developing policy and interventions that do more to limit sedentary behavior and encourage light intensity activity in its place.

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Bethany Barone Gibbs, Wendy C. King, Kelliann K. Davis, Amy D. Rickman, Renee J. Rogers, Abdus Wahed, Steven H. Belle and John Jakicic

Background:

Sedentary behavior (SED) has been measured almost exclusively by self-reported total SED or television time in longitudinal studies. This manuscript aimed to compare self-reported vs. objectively measured SED.

Methods:

Among overweight and obese young adults enrolled in a weight loss trial, baseline SED was assessed by 3 methods: 1) a questionnaire assessing 8 common SEDs (SEDQ), 2) 1 question assessing SED from the Global Physical Activity Questionnaire (SEDGPAQ), and 3) a monitor worn on the arm (SEDOBJ). In addition, television time (SEDTV) was isolated from the SEDQ. SED measures were compared using Spearman’s correlations, signed-rank tests, and Bland-Altman plots.

Results:

In 448 participants, SEDQ and SEDGPAQ were only weakly associated with SEDOBJ (rs = 0.21; P < .001, rs = 0.32; P < .001, respectively). Compared with SEDOBJ, SEDQ more often overestimated SEDOBJ (median difference: 1.1 hours/day; P < .001), while SEDGPAQ more often underestimated SEDOBJ (median difference: –0.7 hours/day; P < .001). The correlation between SEDTV and SEDOBJ was not significantly different from 0 (rs = 0.08; P = .08).

Conclusions:

SEDQ and SEDGPAQ were weakly correlated with, and significantly different from, SEDOBJ in overweight and obese young adults. SEDTV was not related to SEDOBJ. The poor associations of self-reported and objectively measured SED could affect interpretation and comparison across studies relating SED to adverse health outcomes.

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Miguel A. Calabro, Gregory J. Welk, Alicia L. Carriquiry, Sarah M. Nusser, Nicholas K. Beyler and Charles E. Matthews

Purpose:

The purpose of this study was to examine the validity of a computerized 24-hour physical activity recall instrument (24PAR).

Methods:

Participants (n = 20) wore 2 pattern-recognition activity monitors (an IDEEA and a SenseWear Pro Armband) for a 24-hour period and then completed the 24PAR the following morning. Participants completed 2 trials, 1 while maintaining a prospective diary of their activities and 1 without a diary. The trials were counterbalanced and completed within a week from each other. Estimates of energy expenditure (EE) and minutes of moderate-to-vigorous physical activity (MVPA) were compared with the criterion measures using 3-way (method by gender by trial) mixed-model ANOVA analyses.

Results:

For EE, pairwise correlations were high (r > .88), and there were no differences in estimates across methods. Estimates of MVPA were more variable, but correlations were still in the moderate to high range (r > .57). Average activity levels were significantly higher on the logging trial, but there was no significant difference in the accuracy of self-report on days with and without logging.

Conclusions:

The results of this study support the overall utility of the 24PAR for group-level estimates of daily EE and MVPA.