Impact of activity outcome and measurement instrument on estimates of youth compliance with physical activity guidelines: a cross-sectional study.

BACKGROUND: The national physical activity guidelines (PAG) in many countries recommend that youth accumulate 60 min or more of moderate-to-vigorous physical activity (MVPA) daily (PAG-MVPA). A daily target of ≥ 11,500 steps/day has been proposed as a step count alternative to this guideline (PAG-Steps). Contemporary activity monitors are capable of estimating both MVPA and steps, but it is not clear how these units compare when used to evaluate compliance with the national PAG. The purpose of this study was to compare prevalence estimates of meeting the PAG-MVPA and PAG-Steps using two commonly used monitors, the ActiGraph (AG) and SenseWear Armband (SWA). METHODS: A sample of 69 children (25 girls and 44 boys) aged 9-16 years each wore a wrist-mounted AG and a SWA over a one-week period. Days with ≥10 h of wear time for both monitors were included in the analysis. Estimates of time spent in MVPA were obtained using the Crouter equation for the AG and from proprietary algorithms for the SWA. Step counts for the AG and SWA were directly obtained from the respective software. The prevalence of meeting the PAG-MVPA and PAG-Steps was compared within each monitor, using Cohen's kappa (κ) statistic. Agreement was similarly assessed between monitors using each guideline individually. RESULTS: When assessed with the AG, the prevalence of meeting PAG was substantially higher for the PAG-MVPA (87.2 %) than for the PAG-Steps (54.2 %), with fair classification agreement (κ = 0.30) between the two guidelines. Higher prevalence rates were also observed for the PAG-MVPA (83.6 %) than for the PAG-Steps (33.8 %) when assessed using the SWA, but the prevalence rates and classification agreement (κ = 0.18) were lower than the values from the AG. Classification agreement between AG and SWA was lower for the PAG-MVPA (κ = 0.42) than for the PAG-Steps (κ = 0.55). CONCLUSIONS: The results show differential patterns of compliance with the PAG-MVPA and PAG-Steps, as assessed by the AG and SWA. Additional research is needed to directly evaluate and compare findings from public health research based on different guidelines and measurement methods

Impact of activity outcome and measurement instrument on estimates of youth compliance with physical activity guidelines: a cross-sectional study

Abstract Background The national physical activity guidelines (PAG) in many countries recommend that youth accumulate 60 min or more of moderate-to-vigorous physical activity (MVPA) daily (PAG-MVPA). A daily target of ≥ 11,500 steps/day has been proposed as a step count alternative to this guideline (PAG-Steps). Contemporary activity monitors are capable of estimating both MVPA and steps, but it is not clear how these units compare when used to evaluate compliance with the national PAG. The purpose of this study was to compare prevalence estimates of meeting the PAG-MVPA and PAG-Steps using two commonly used monitors, the ActiGraph (AG) and SenseWear Armband (SWA). Methods A sample of 69 children (25 girls and 44 boys) aged 9–16 years each wore a wrist-mounted AG and a SWA over a one-week period. Days with ≥10 h of wear time for both monitors were included in the analysis. Estimates of time spent in MVPA were obtained using the Crouter equation for the AG and from proprietary algorithms for the SWA. Step counts for the AG and SWA were directly obtained from the respective software. The prevalence of meeting the PAG-MVPA and PAG-Steps was compared within each monitor, using Cohen’s kappa (κ) statistic. Agreement was similarly assessed between monitors using each guideline individually. Results When assessed with the AG, the prevalence of meeting PAG was substantially higher for the PAG-MVPA (87.2 %) than for the PAG-Steps (54.2 %), with fair classification agreement (κ = 0.30) between the two guidelines. Higher prevalence rates were also observed for the PAG-MVPA (83.6 %) than for the PAG-Steps (33.8 %) when assessed using the SWA, but the prevalence rates and classification agreement (κ = 0.18) were lower than the values from the AG. Classification agreement between AG and SWA was lower for the PAG-MVPA (κ = 0.42) than for the PAG-Steps (κ = 0.55). Conclusions The results show differential patterns of compliance with the PAG-MVPA and PAG-Steps, as assessed by the AG and SWA. Additional research is needed to directly evaluate and compare findings from public health research based on different guidelines and measurement methods

Accuracy of the Cosmed K5 portable calorimeter

Purpose The purpose of this study was to assess the accuracy of the Cosmed K5 portable metabolic system dynamic mixing chamber (MC) and breath-by-breath (BxB) modes against the criterion Douglas bag (DB) method. Methods Fifteen participants (mean age±SD, 30.6±7.4 yrs) had their metabolic variables measured at rest and during cycling at 50, 100, 150, 200, and 250W. During each stage, participants were connected to the first respiratory gas collection method (randomized) for the first four minutes to reach steady state, followed by 3-min (or 5-min for DB) collection periods for the resting condition, and 2-min collection periods for all cycling intensities. Collection periods for the second and third methods were preceded by a washout of 1–3 min. Repeated measures ANOVAs were used to compare metabolic variables measured by each method, for seated rest and each cycling work rate. Results For ventilation (VE) and oxygen uptake (VO2), the K5 MC and BxB modes were within 2.1 l/min (VE) and 0.08 l/min (VO2) of the DB (p≥0.05). Compared to DB values, carbon dioxide production (VCO2) was significantly underestimated by the K5 BxB mode at work rates ≥150W by 0.12–0.31 l/min (p\u3c0.05). K5 MC and BxB respiratory exchange ratio values were significantly lower than DB at cycling work rates ≥100W by 0.03–0.08 (p\u3c0.05). Conclusion Compared to the DB method, the K5 MC and BxB modes are acceptable for measuring VE and VO2 across a wide range of cycling intensities. Both K5 modes provided comparable values to each other

Validity of a Global Positioning System-Based Algorithm and Consumer Wearables for Classifying Active Trips in Children and Adults

Accepted author manuscript version reprinted, by permission, from [Journal for the Measurement of Physical Behaviour, 2021, Volume 4: Issue 4: pp. 321–332, https://doi.org/10.1123/jmpb.2021-0019. © Human Kinetics, Inc.Objective: To investigate the convergent validity of a global positioning system (GPS)-based and two consumer-based measures with trip logs for classifying pedestrian, cycling, and vehicle trips in children and adults. Methods: Participants (N = 34) wore a Qstarz GPS tracker, Fitbit Alta, and Garmin vivosmart 3 on multiple days and logged their outdoor pedestrian, cycling, and vehicle trips. Logged trips were compared with device-measured trips using the Personal Activity Location Measurement System (PALMS) GPS-based algorithms, Fitbit’s SmartTrack, and Garmin’s Move IQ. Trip- and day-level agreement were tested. Results: The PALMS identified and correctly classified the mode of 75.6%, 94.5%, and 96.9% of pedestrian, cycling, and vehicle trips (84.5% of active trips, F1 = 0.84 and 0.87) as compared with the log. Fitbit and Garmin identified and correctly classified the mode of 26.8% and 17.8% (22.6% of active trips, F1 = 0.40 and 0.30) and 46.3% and 43.8% (45.2% of active trips, F1 = 0.58 and 0.59) of pedestrian and cycling trips. Garmin was more prone to false positives (false trips not logged). Day-level agreement for PALMS and Garmin versus logs was favorable across trip modes, though PALMS performed best. Fitbit significantly underestimated daily cycling. Results were similar but slightly less favorable for children than adults. Conclusions: The PALMS showed good convergent validity in children and adults and were about 50% and 27% more accurate than Fitbit and Garmin (based on F1). Empirically-based recommendations for improving PALMS’ pedestrian classification are provided. Since the consumer devices capture both indoor and outdoor walking/running and cycling, they are less appropriate for trip-based research

Accelerometer and self-reported measures of sedentary behaviour and associations with adiposity in UK youth

This study used accelerometer and self-report measures of overall sedentary time (ST) and screen time behaviours to examine their respective associations with adiposity among UK youth. Participants (Year groups 5, 8, and 10; n=292, 148 girls) wore the SenseWear Armband Mini accelerometer for eight days and completed the Youth Activity Profile, an online report tool designed to estimate physical activity and ST.Stature, body mass and waist circumference were measured to classify adiposity outcomes (overweight/obese and central obesity). One-way between groups ANOVA and adjusted linear, logistic and multinomial logistic regression analyses were conducted. There was a significant main effect of age on total ST across the whole week (F(2, 289)=41.64, p≤0.001). ST increased monotonically across Year 5 (581.09±107.81 min·dˉ¹), 8 (671.96±112.59 min·dˉ¹) and 10 (725.80±115.20 min·dˉ¹), and all pairwise comparisons were significant at p≤0.001. A steep age-related gradient to mobile phone use was present (p≤0.001). ST was positively associated with adiposity outcomes independent of moderate-to-vigorous intensity physical activity (MVPA; p≤0.001). Engaging in >3 hours of video gaming daily was positively associated with central obesity (OR=2.12, p≤0.05) but not after adjustment for MVPA. Results further demonstrate the importance of reducing overall ST to maintain healthy weight status among UK youth

Calibration of the Online Youth Activity Profile Assessment for School-Based Applications

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.This article is published as Welk, G. J., Saint-Maurice, P. F., Dixon, P. M., Hibbing, P. R., Bai, Y., McLoughlin, G. M., & Pereira da Silva, M. (2021). Calibration of the Online Youth Activity Profile Assessment for School-Based Applications. Journal for the Measurement of Physical Behaviour, 4(3), 236-246. doi:https://doi.org/10.1123/jmpb.2020-0048. © 2021 The Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, CC BY-NC-ND 4.0, which permits the copy and redistribution in any medium or format, provided it is not used for commercial purposes, no modifications are made, appropriate credit is given, and a link to the license is provided. See http://creativecommons.org/licenses/by-nc-nd/4.0

Calibration and Validation of the Youth Activity Profile as a Physical Activity and Sedentary Behaviour Surveillance Tool for English Youth

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, 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 algorithm

Challenges in microbial ecology: building predictive understanding of community function and dynamics.

The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth's soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development with mathematical model building. We discuss specific examples where model-experiment integration has already resulted in important insights into MC function and structure. We also highlight key research questions that still demand better integration of experiments and models. We argue that such integration is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved

Accuracy of the Cosmed K5 portable calorimeter.

PURPOSE:The purpose of this study was to assess the accuracy of the Cosmed K5 portable metabolic system dynamic mixing chamber (MC) and breath-by-breath (BxB) modes against the criterion Douglas bag (DB) method. METHODS:Fifteen participants (mean age±SD, 30.6±7.4 yrs) had their metabolic variables measured at rest and during cycling at 50, 100, 150, 200, and 250W. During each stage, participants were connected to the first respiratory gas collection method (randomized) for the first four minutes to reach steady state, followed by 3-min (or 5-min for DB) collection periods for the resting condition, and 2-min collection periods for all cycling intensities. Collection periods for the second and third methods were preceded by a washout of 1-3 min. Repeated measures ANOVAs were used to compare metabolic variables measured by each method, for seated rest and each cycling work rate. RESULTS:For ventilation (VE) and oxygen uptake (VO2), the K5 MC and BxB modes were within 2.1 l/min (VE) and 0.08 l/min (VO2) of the DB (p≥0.05). Compared to DB values, carbon dioxide production (VCO2) was significantly underestimated by the K5 BxB mode at work rates ≥150W by 0.12-0.31 l/min (p<0.05). K5 MC and BxB respiratory exchange ratio values were significantly lower than DB at cycling work rates ≥100W by 0.03-0.08 (p<0.05). CONCLUSION:Compared to the DB method, the K5 MC and BxB modes are acceptable for measuring VE and VO2 across a wide range of cycling intensities. Both K5 modes provided comparable values to each other