). 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
Matti Hyvärinen, Sarianna Sipilä, Janne Kulmala, Harto Hakonen, Tuija H. Tammelin, Urho M. Kujala, Vuokko Kovanen and Eija K. Laakkonen
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
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
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
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
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
Stephen Zwolinsky, James McKenna, Andy Pringle, Paul Widdop, Claire Griffiths, Michelle Mellis, Zoe Rutherford and Peter Collins
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.
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.
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.
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.
Miguel A. Calabro, Gregory J. Welk, Alicia L. Carriquiry, Sarah M. Nusser, Nicholas K. Beyler and Charles E. Matthews
The purpose of this study was to examine the validity of a computerized 24-hour physical activity recall instrument (24PAR).
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.
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.
The results of this study support the overall utility of the 24PAR for group-level estimates of daily EE and MVPA.
Bethany Barone Gibbs, Wendy C. King, Kelliann K. Davis, Amy D. Rickman, Renee J. Rogers, Abdus Wahed, Steven H. Belle and John Jakicic
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.
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.
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).
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.