than men have chosen to participate in the study). Due to the cross-sectional design of our study, it is not possible to separate the effect of age on sitting time from the possible effect of generation. However, our results are supported by two other studies also reporting an association between age
Search Results
Accelerometer-Assessed Prolonged Sitting During Work and Leisure Time and Associations With Age, Body Mass Index, and Health: A Cross-Sectional Study
Inger Mechlenburg, Marianne Tjur, and Kristian Overgaard
Simultaneous Validation of Count-to-Activity Thresholds for Five Commonly Used Activity Monitors in Adolescent Research: A Step Toward Data Harmonization
Gráinne Hayes, Kieran Dowd, Ciaran MacDonncha, and Alan Donnely
cross-compare the results between devices or studies. For example, Van Hecke et al. highlight that across the youth PA literature, five different count-to-activity thresholds ranging from >1,000 counts/min to >3,000 counts/min were used to define moderate- to vigorous-intensity PA measured with
Inter-Brand, -Dynamic Range, and -Sampling Rate Comparability of Raw Accelerometer Data as Used in Physical Behavior Research
Annelinde Lettink, Wessel N. van Wieringen, Teatske M. Altenburg, Mai J.M. Chinapaw, and Vincent T. van Hees
.e., temporal comparability), we can perform lagged cross-correlation analysis. In contrast, the frequency domain is about describing oscillations in time series by mapping their frequencies, providing insight into the periodic patterns occurring in the data. By applying Fast Fourier Transformation, the acceleration signal
Calibration of the Online Youth Activity Profile Assessment for School-Based Applications
Gregory J. Welk, Pedro F. Saint-Maurice, Philip M. Dixon, Paul R. Hibbing, Yang Bai, Gabriella M. McLoughlin, and Michael Pereira da Silva
sample included 717 participants (68% of the initial sample). Statistical Analysis The statistical analyses involved separate calibration and cross-validation phases. The retained sample was randomly split into separate calibration ( n = 359; 50%) and cross-validation data sets ( n = 358; 50%) to
Distinguishing Passive and Active Standing Behaviors From Accelerometry
Robert J. Kowalsky, Herman van Werkhoven, Marco Meucci, Tyler D. Quinn, Lee Stoner, Christopher M. Hearon, and Bethany Barone Gibbs
raises that lifted both the heels simultaneously off the ground about 4–6 in., (d) alternating the rocking of the knees forward and back while keeping both feet flat on the floor next to each other, and (e) alternating a foot crossed over in front of the other foot so that one foot was flat and the
Translation of the International Physical Activity Questionnaire to Maltese and Reliability Testing
Karl Spiteri, Kate Grafton, John Xerri de Caro, and David Broom
Organization experts whose aim was to develop a tool to encourage cross-country comparisons ( Bauman et al., 2009 ). The importance of IPAQ is its use in large-scale surveys like the European Physical Activity Surveillance System ( Rütten et al., 2003 ) and Eurobarometer ( Sjöström, Oja, Hagströmer, Smith
A Transparent Method for Step Detection Using an Acceleration Threshold
Scott W. Ducharme, Jongil Lim, Michael A. Busa, Elroy J. Aguiar, Christopher C. Moore, John M. Schuna Jr., Tiago V. Barreira, John Staudenmayer, Stuart R. Chipkin, and Catrine Tudor-Locke
repeated k -fold cross-validation was performed ( k = 5 with 10 repetitions). For this analysis, the data set ( n = 75) was divided into five equally sized groups of participants ( n = 15). The mean optimal threshold across four of the groups ( n = 60) was treated as the “training set,” and the
Depressive Symptoms Are Associated With Accelerometer-Measured Physical Activity and Time in Bed Among Working-Aged Men and Women
Pauliina Husu, Kari Tokola, Henri Vähä-Ypyä, Harri Sievänen, and Tommi Vasankari
with (a) less PA assessed in terms of light PA, MVPA, and number of steps and (b) more SB, standing, and TIB. Methods Study Population This study is based on the cross-sectional, population-based FinFit2017 study, which is a multifactorial study on PA, fitness, and health conducted with a stratified
Sequential Activity Patterns and Outcome-Specific, Real-Time, and Target Group-Specific Feedback: The SPORT Algorithm
Nathalie Berninger, Gill ten Hoor, Guy Plasqui, and Rik Crutzen
inferential analyses were performed using R (version 3.4.1). The outcomes of the models were evaluated using five-fold cross-validation, where data sets are divided into five groups of nearly equal number of data points. Each group was taken as test data set in one of the five iterations. The remaining
A Machine Learning Classifier for Detection of Physical Activity Types and Postures During Free-Living
Kerstin Bach, Atle Kongsvold, Hilde Bårdstu, Ellen Marie Bardal, Håkon S. Kjærnli, Sverre Herland, Aleksej Logacjov, and Paul Jarle Mork
and single accelerometer setups. LOOCV was thereafter performed to determine the performance of the classifier. See text for more details. B = back; T = thigh; a = annotated; F = features; LOOCV = leave-one-out cross-validation; XGBoost = extreme gradient boosting. The labeled accelerometer data were
