Four Computer Science and Applications (CSA, Model 7164) accelerometers were validated against speed and heart rate in a field trial, consisting of two walking and two preset running speeds, and 3 min of running at freely chosen speeds. Fifteen children (9–11 years) were recruited from a suburban school in Denmark. Mean CSA output was calculated and converted to acceleration by calibration to sinusoidal accelerations in a mechanical setup, the latter variable being independent of frequency-based filtering. Mean CSA output and estimated acceleration both correlated significantly with speed (r 2 = 0.55 and r 2 = 0.76, respectively) and heart rate (r 2 = 0.60 and r 2 = 0.81, respectively), controlled for gender. ANOVA post hoc test failed to show significant differences in accelerometer output between running speeds. Inter-individual variability of CSA output and acceleration could not be explained by differences in step frequency in walking but running values correlated significantly with step frequency (r = −0.86 and r = −0.47 for CSA output and acceleration, respectively). Conversion of CSA output to average acceleration provides more precise estimates of intensity with less inter-individual variability than raw CSA output. Different running intensities, however, are generally not well differentiated with vertical accelerometry.
Søren Brage, Niels Wedderkopp, Lars Bo Andersen and Karsten Froberg
Andreas Wolff Hansen, Inger Dahl-Petersen, Jørn Wulff Helge, Søren Brage, Morten Grønbæk and Trine Flensborg-Madsen
The International Physical Activity Questionnaire (IPAQ) is commonly used in surveys, but reliability and validity has not been established in the Danish population.
Among participants in the Danish Health Examination survey 2007–2008, 142 healthy participants (45% men) wore a unit that combined accelerometry and heart rate monitoring (Acc+HR) for 7 consecutive days and then completed the IPAQ. Background data were obtained from the survey. Physical activity energy expenditure (PAEE) and time in moderate, vigorous, and sedentary intensity levels were derived from the IPAQ and compared with estimates from Acc+HR using Spearman’s correlation coefficients and Bland-Altman plots. Repeatability of the IPAQ was also assessed.
PAEE from the 2 methods was significantly positively correlated (0.29 and 0.49; P = 0.02 and P < 0.001; for women and men, respectively). Men significantly overestimated PAEE by IPAQ (56.2 vs 45.3 kJ/kg/day, IPAQ: Acc+HR, P < .01), while the difference was nonsignificant for women (40.8 vs 44.4 kJ/kg/day). Bland-Altman plots showed that the IPAQ overestimated PAEE, moderate, and vigorous activity without systematic error. Reliability of the IPAQ was moderate to high for all domains and intensities (total PAEE intraclass correlation coefficient = 0.58).
This Danish Internet-based version of the long IPAQ had modest validity and reliability when assessing PAEE at population level.
Matthew Pearce, Tom R.P. Bishop, Stephen Sharp, Kate Westgate, Michelle Venables, Nicholas J. Wareham and Søren Brage
Harmonization of data for pooled analysis relies on the principle of inferential equivalence between variables from different sources. Ideally, this is achieved using models of the direct relationship with gold standard criterion measures, but the necessary validation study data are often unavailable. This study examines an alternative method of network harmonization using indirect models. Starting methods were self-report or accelerometry, from which we derived indirect models of relationships with doubly labelled water (DLW)-based physical activity energy expenditure (PAEE) using sets of two bridge equations via one of three intermediate measures. Coefficients and performance of indirect models were compared to corresponding direct models (linear regression of DLW-based PAEE on starting methods). Indirect model beta coefficients were attenuated compared to direct model betas (10%–63%), narrowing the range of PAEE values; attenuation was greater when bridge equations were weak. Directly and indirectly harmonized models had similar error variance but most indirectly derived values were biased at group-level. Correlations with DLW-based PAEE were identical after harmonization using continuous linear but not categorical models. Wrist acceleration harmonized to DLW-based PAEE via combined accelerometry and heart rate sensing had the lowest error variance (24.5%) and non-significant mean bias 0.9 (95%CI: −1.6; 3.4) kJ·day−1·kg−1. Associations between PAEE and BMI were similar for directly and indirectly harmonized values, but most fell outside the confidence interval of the criterion PAEE-to-BMI association. Indirect models can be used for harmonization. Performance depends on the measurement properties of original data, variance explained by available bridge equations, and similarity of population characteristics.
Xanne Janssen, Dylan P. Cliff, John J. Reilly, Trina Hinkley, Rachel A. Jones, Marijka Batterham, Ulf Ekelund, Soren Brage and Anthony D. Okely
This study examined the classification accuracy of the activPAL, including total time spent sedentary and total number of breaks in sedentary behavior (SB) in 4- to 6-year-old children. Forty children aged 4–6 years (5.3 ± 1.0 years) completed a ~150-min laboratory protocol involving sedentary, light, and moderate- to vigorous-intensity activities. Posture was coded as sit/lie, stand, walk, or other using direct observation. Posture was classified using the activPAL software. Classification accuracy was evaluated using sensitivity, specificity and area under the receiver operating characteristic curve (ROC-AUC). Time spent in each posture and total number of breaks in SB were compared using paired sample t-tests. The activPAL showed good classification accuracy for sitting (ROC-AUC = 0.84) and fair classification accuracy for standing and walking (0.76 and 0.73, respectively). Time spent in sit/lie and stand was overestimated by 5.9% (95% CI = 0.6−11.1%) and 14.8% (11.6−17.9%), respectively; walking was underestimated by 10.0% (−12.9−7.0%). Total number of breaks in SB were significantly overestimated (55 ± 27 over the course of the protocol; p < .01). The activPAL performed well when classifying postures in young children. However, the activPAL has difficulty classifying other postures, such as kneeling. In addition, when predicting time spent in different postures and total number of breaks in SB the activPAL appeared not to be accurate.
Eero A. Haapala, Juuso Väistö, Aapo Veijalainen, Niina Lintu, Petri Wiklund, Kate Westgate, Ulf Ekelund, Virpi Lindi, Soren Brage and Timo A. Lakka
To investigate the relationships of objectively measured physical activity (PA) and sedentary time (ST) to arterial stiffness in prepubertal children.
Altogether 136 children (57 boys, 79 girls) aged 6–8-years participated in the study. Stiffness index (SI) was assessed by pulse contour analysis based on photoplethysmography. ST, light PA, moderate PA, and vigorous PA were assessed using combined acceleration and heart rate monitoring. We investigated the associations of ST (<1.5METs) and time spent in intensity level of PA above 2–7METs in min/d with SI using linear regression analysis. We studied the optimal duration and intensity of PA to identify children being in the highest quarter of SI using Receiver Operating Characteristics curves.
Moderate PA, vigorous PA, and cumulative time spent in PA above 3 (β=–0.279, p = .002), 4 (β =–0.341, P<0.001), 5 (β =–0.349, P<0.001), 6 (β =–0.312, P<0.001), and 7 (β =–0.254, p = .005) METs were inversely associated with SI after adjustment for age, sex, and monitor wear time. The cutoffs for identifying children being in the highest quarter of SI <68 min/d for PA exceeding 5 METs and <26 min/d for PA exceeding 6 METs.
Lower levels of PA exceeding 3–6 METs were related to higher arterial stiffness in children.
Christiana M.T. van Loo, Anthony D. Okely, Marijka Batterham, Tina Hinkley, Ulf Ekelund, Soren Brage, John J. Reilly, Gregory E. Peoples, Rachel Jones, Xanne Janssen and Dylan P. Cliff
To validate the activPAL3 algorithm for predicting metabolic equivalents (TAMETs) and classifying MVPA in 5- to 12-year-old children.
Fifty-seven children (9.2 ± 2.3y, 49.1% boys) completed 14 activities including sedentary behaviors (SB), light (LPA) and moderate-to-vigorous physical activities (MVPA). Indirect calorimetry (IC) was used as the criterion measure. Analyses included equivalence testing, Bland-Altman procedures and area under the receiver operating curve (ROC-AUC).
At the group level, TAMETs were significantly equivalent to IC for handheld e-game, writing/coloring, and standing class activity (P < .05). Overall, TAMETs were overestimated for SB (7.9 ± 6.7%) and LPA (1.9 ± 20.2%) and underestimated for MVPA (27.7 ± 26.6%); however, classification accuracy of MVPA was good (ROC-AUC = 0.86). Limits of agreement were wide for all activities, indicating large individual error (SB: −27.6% to 44.7%; LPA: −47.1% to 51.0%; MVPA: −88.8% to 33.9%).
TAMETs were accurate for some SB and standing, but were overestimated for overall SB and LPA, and underestimated for MVPA. Accuracy for classifying MVPA was, however, acceptable.
Priscila M. Nakamura, Grégore I. Mielke, Bernardo L. Horta, Maria Cecília Assunção, Helen Gonçalves, Ana M.B. Menezes, Fernando C. Barros, Ulf Ekelund, Soren Brage, Fernando C. Wehrmeister, Isabel O. Oliveira and Pedro C. Hallal
Physical inactivity is responsible for 7% of diabetes deaths worldwide, but little is known whether low levels of physical activity (PA) during adolescence increase the risk of diabetes in early adulthood. We evaluated the cross-sectional and longitudinal associations between PA throughout adolescence and HbA1c concentration in early adulthood.
HbA1c was measured by high performance liquid chromatography. PA was assessed by self-report at the ages of 11, 15, and 18 years and by accelerometry at the ages of 13 (subsample) and 18 years. The loss percentages of follow up were 12.5% at 11 years, 14.4% at 15 years, and 18.7% at 18 years.
At 18 years, boys showed higher HbA1c than girls. At age 18 years, accelerometrybased PA at 18 years was inversely related to HbA1c levels in boys. Self-reported leisure-time PA at ages 11, 15, and 18 were unrelated to HbA1c in both genders. PA at 13 years of age was unrelated to HbA1c among both genders. In trajectory analysis, PA and accelerometer PA trajectories were not associated with later HbA1c.
Objectively measured PA at 18 years was cross-sectionally inversely associated with HbA1c in boys only. No prospective associations were identified.
Chris Riddoch, Dawn Edwards, Angie Page, Karsten Froberg, Sigmund A. Anderssen, Niels Wedderkopp, Søren Brage, Ashley R. Cooper, Luis B. Sardinha, Maarike Harro, Lena Klasson-Heggebø, Willem van Mechelen, Colin Boreham, Ulf Ekelund, Lars Bo Andersen and The European Youth Heart Study Team
The aim of the European Youth Heart Study (EYHS) is to establish the nature, strength, and interactions between personal, environmental, and lifestyle influences on cardiovascular disease (CVD) risk factors in European children.
The EYHS is an international study measuring CVD risk factors, and their associated influences, in children. Relationships between these independent factors and risk of disease will inform the design of CVD interventions in children. A minimum of 1000 boys and girls ages 9 and 15 y were recruited from four European countries—Denmark, Estonia, Norway, and Portugal. Variables measured included physical, biochemical, lifestyle, psychosocial, and sociodemographic data.
Of the 5664 children invited to participate, 4169 (74%) accepted. Response rates for most individual tests were moderate to high. All test protocols were well received by the children.
EYHS protocols are valid, reliable, acceptable to children, and feasible for use in large, field-based studies.