scholarly journals Validity and Calibration of the Youth Activity Profile

PLoS ONE ◽  
2015 ◽  
Vol 10 (12) ◽  
pp. e0143949 ◽  
Author(s):  
Pedro F. Saint-Maurice ◽  
Gregory J. Welk
Author(s):  
Gregory J. Welk ◽  
Pedro F. Saint-Maurice ◽  
Philip M. Dixon ◽  
Paul R. Hibbing ◽  
Yang Bai ◽  
...  

A balance between the feasibility and validity of measures is an important consideration for physical activity (PA) research—particularly in school-based research with youth. The present study extends previously tested calibration methods to develop and test new equations for an online version of the youth activity profile (YAP) tool, a self-report tool designed for school applications. Data were collected across different regions and seasons to develop more robust, generalizable equations. The study involved a total of 717 youth from 33 schools (374 elementary [ages 9–11 years], 224 middle [ages 11–14 years], and 119 high school [ages 14–18 years]) in two different states in the United States. Participants wore a Sensewear monitor for a full week and then completed the online YAP at school to report PA and sedentary behaviors in school and at home. Accelerometer data were processed using an R-based segmentation program to compute PA and sedentary behavior levels. Quantile regression models were used with half of the sample to develop item-specific YAP calibration equations, and these were cross validated with the remaining half of the sample. Computed values of mean absolute percentage error ranged from 15 to 25% with slightly lower error observed for the middle school sample. The new equations had improved precision compared with the previous versions when tested on the same sample. The online version of the YAP provides an efficient and effective way to capture school level estimates of PA and sedentary behaviors in youth.


Author(s):  
Stuart J. Fairclough ◽  
Danielle L. Christian ◽  
Pedro F. Saint-Maurice ◽  
Paul R. Hibbing ◽  
Robert J. Noonan ◽  
...  

Self-reported youth physical activity (PA) is typically overestimated. We aimed to calibrate and validate a self-report tool among English youth. Four-hundred-and-two participants (aged 9–16 years; 212 boys) wore SenseWear Armband Mini devices (SWA) for eight days and completed the self-report Youth Activity Profile (YAP) on the eighth day. Calibration algorithms for temporally matched segments were generated from the YAP data using quantile regression. The algorithms were applied in an independent cross-validation sample, and student- and school-level agreement were assessed. The utility of the YAP algorithms to assess compliance to PA guidelines was also examined. The school-level bias for the YAP estimates of in-school, out-of-school, and weekend moderate-to-vigorous PA (MVPA) were 17.2 (34.4), 31.6 (14.0), and −4.9 (3.6) min·week−1, respectively. Out-of-school sedentary behaviour (SB) was over-predicted by 109.2 (11.8) min·week−1. Predicted YAP values were within 15%–20% equivalence of the SWA estimates. The classification accuracy of the YAP MVPA estimates for compliance to 60 min·day−1 and 30 min·school-day−1 MVPA recommendations were 91%/37% and 89%/57% sensitivity/specificity, respectively. The YAP generated robust school-level estimates of MVPA and SB and has potential for surveillance to monitor compliance with PA guidelines. The accuracy of the YAP may be further improved through research with more representative UK samples to enhance the calibration process and to refine the resultant algorithms.


2019 ◽  
Author(s):  
Stuart J Fairclough ◽  
Danielle L Christian ◽  
Pedro F Saint-Maurice ◽  
Paul R Hibbing ◽  
Robert J Noonan ◽  
...  

Abstract Background Calibration algorithms applied to the Youth Activity Profile (YAP) self-report questionnaire in the US have accurately estimated moderate-to-vigorous physical activity (MVPA) and sedentary behaviour (SB). However, the efficacy of the calibration algorithms may vary when applied to different populations. We aimed to: (1) assess the accuracy of US-generated YAP calibration algorithms for MVPA and SB with English youth, (2) validate English-specific YAP calibration algorithms, (3) examine their potential surveillance utility to assess compliance to MVPA guidelines. Methods Four primary schools and five secondary schools were recruited. Four-hundred-and-two participants (aged 9-16 years; 212 boys) wore SenseWear Armband Mini devices (SWA) for eight days and completed the YAP on the eighth day. For aim (1) the original US calibration algorithms were applied to the YAP scores, which were matched to SWA-estimated in-school, out-of-school, and weekend MVPA and out-of-school SB data. For aim (2) new calibration algorithms for the equivalent time-segments were generated from the English YAP data using quantile regression. The algorithms were applied in an independent cross-validation sample, and individual- and group-level agreement were assessed using bias, mean absolute percent error (MAPE) and equivalency tests, respectively. For aim (3) the utility of the English YAP algorithms to assess compliance to MVPA guidelines was examined using kappa, sensitivity, and specificity. Results Agreement between the US calibration algorithms and SWA estimates of MVPA and SB was poor. Group-level MAPE for the English YAP-estimates of in-school, out-of-school, and weekend MVPA ranged from 3.6% to 17.3%. Bias for these estimates were 17.2 (34.4), 31.6 (14.0), and -4.9 (3.6) min·week-1, respectively. Out-of-school SB was over-predicted by 109.2 (11.8) min·week-1 (MAPE=11.8%). Predicted YAP values were within 15%-20% equivalence of the SWA estimates. Classification accuracy of the English YAP MVPA estimates for compliance to 60 min·day-1 and 30 min·school-day-1 MVPA recommendations were 91%/37% and 89%/57% sensitivity/specificity, respectively. Conclusions The English YAP generated robust group-level estimates of MVPA and SB and has potential for surveillance to monitor compliance with MVPA guidelines. The YAP’s accuracy may be further improved through research work with more representative UK samples to enhance the calibration process and to refine the resultant algorithms.


2016 ◽  
Vol 48 ◽  
pp. 313
Author(s):  
Pedro F. Saint-Maurice ◽  
Paul Hibbling ◽  
Yang Bai ◽  
Gregory J. Welk

2020 ◽  
pp. 1-7
Author(s):  
José Manuel Segura-Díaz ◽  
Yaira Barranco-Ruiz ◽  
Romina G. Saucedo-Araujo ◽  
María Jesús Aranda-Balboa ◽  
Cristina Cadenas-Sanchez ◽  
...  

2017 ◽  
Vol 52 (6) ◽  
pp. 880-887 ◽  
Author(s):  
Pedro F. Saint-Maurice ◽  
Youngwon Kim ◽  
Paul Hibbing ◽  
April Y. Oh ◽  
Frank M. Perna ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document