Some, but not all studies, report that independent of MVPA time, sedentary time is associated with poor health outcomes, while LPA time is associated with favorable health outcomes.
Therefore, it can be hypothesized that TAC, which includes SB and LPA, are more dependent on wear time than MVPA.Īs diverse activity metrics can now be calculated using accelerometer data beyond MVPA, which was traditionally the sole focus of PA research when using PA questionnaires, more recent studies have examined the health effects of these diverse accelerometer data-derived metrics.
Given that children spend ≥90% of waking time in lower intensity activities such as SB and LPA, longer wear time during waking time would capture more SB and LPA. That metric may require additional processing to standardize the metric for a comparison. However, a wear time-dependent PA metric has a limitation when comparing the results across studies that have different wear times. Thus, researchers often implement additional approaches (e.g., adjust for wear time in statistical modeling ) to standardize/adjust PA metrics within a study population. Although researchers apply an accelerometer data inclusion criterion, for example, at least 8 wear hours per day, to estimate a PA level that can reflect all-day PA, still there are wide variations in wear time from 8 h to 24 h per day. Also, TAC could be more dependent on wear time than MVPA.Īccelerometer wear time is a key variable that could significantly impact the accelerometer-derived PA metrics. For example, it is probable that TAC is mostly explained by accelerometer counts collected during MVPA. Despite the conceptual distinctions between TAC and PA intensity metrics, there is a knowledge gap in how TAC is related to time spent in individual PA intensity categories and if wear time affects TAC differently than the individual PA intensity categories. TAC can be conceptualized as a proxy of the total PA volume that encompasses the frequency, intensity, and duration of activity bouts. Daily accumulated accelerometer counts (total activity counts TAC) has also been suggested as a metric of total PA volume. Using accelerometer data, several important public health-related PA metrics, including time spent in sedentary behavior (SB), light-intensity PA (LPA), moderate-intensity PA (MPA), vigorous-intensity PA (VPA), and moderate- to vigorous-intensity PA (MVPA), can be estimated. Due to the moderate to high correlation between some PA metrics, potential collinearity should be addressed when including multiple PA metrics together in statistical modeling.Īccelerometers have become a widely used tool to assess physical activity (PA) levels among children. MVPA appears to be comparable across different wear durations and studies when wear time is ≥8 h/day. TAC is mostly explained by MVPA, while it could be more dependent on wear time, compared to MVPA. VPA was moderately correlated with MPA ( r = .58 99% CI = .57. Wear time-adjusted correlation between SB and LPA was also very high ( r = −.96 99% CI = -.96, − 95). MVPA was very highly correlated with TAC ( r = .91 99% CI = .91 to. TAC was approximately 22X10 3 counts higher ( p < 0.01) with longer wear time (13 to 18 h/day) as compared to shorter wear time (8 to < 13 h/day), while MVPA was similar across the wear time categories. Correlation coefficients between wear time, sedentary behavior (SB), light-intensity PA (LPA), moderate-intensity PA (MPA), vigorous-intensity PA (VPA), moderate- and vigorous-intensity PA (MVPA), and total activity counts (TAC) were calculated. MethodsĪccelerometer data from 24,316 children aged 5 to 18 years were extracted from the International Children’s Accelerometer Database (ICAD) 2.0. The aim of this study was to examine the relationships among the metrics derived from accelerometers in children. It also has significant implications for comparing PA metrics across studies and fitting a statistical model to examine their health effects. Knowledge about the relationships between these different metrics can improve our understanding of children’s PA behavioral patterns. Using the accelerometer data, several PA metrics can be estimated. Accelerometers are widely used to assess child physical activity (PA) levels.
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