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71.
The purpose of the current study was to determine metabolic thresholds and subsequent activity intensity cutoff points for the ActiGraph GT1M with various epochs spanning from 5 to 60 sec in young children. Twenty-two children, aged 4 to 9 years, performed 10 different activities including locomotion and play activities. Energy expenditure was measured with indirect calorimetry. Thresholds and cutoff points were determined through receiver operating characteristic curves. The lower metabolic threshold was 6.19 kcal·kg?1·h?1 for moderate and 9.28 kcal·kg?1·h?1 for vigorous intensity. The cutoff points for the GT1M accelerometer appear to be lower than those for the previous model (7164). For 5-sec epochs, a cutoff point of 143 counts resulted for moderate intensity and of 208 counts for vigorous intensity activity. Whether short or long epochs were chosen when collecting data to determine cutoff points, does not appear to have an influence on the resulting cutoff values. Similarly, comparable results are seen when analyses are based on locomotion only as opposed to a wide range of activities including children's play.  相似文献   
72.
Abstract

In this study, we evaluated agreement among three generations of ActiGraph? accelerometers in children and adolescents. Twenty-nine participants (mean age = 14.2 ± 3.0 years) completed two laboratory-based activity sessions, each lasting 60 min. During each session, participants concurrently wore three different models of the ActiGraph? accelerometers (GT1M, GT3X, GT3X+). Agreement among the three models for vertical axis counts, vector magnitude counts, and time spent in moderate-to-vigorous physical exercise (MVPA) was evaluated by calculating intraclass correlation coefficients and Bland-Altman plots. The intraclass correlation coefficient for total vertical axis counts, total vector magnitude counts, and estimated MVPA was 0.994 (95% CI = 0.989–0.996), 0.981 (95% CI = 0.969–0.989), and 0.996 (95% CI = 0.989–0.998), respectively. Inter-monitor differences for total vertical axis and vector magnitude counts ranged from 0.3% to 1.5%, while inter-monitor differences for estimated MVPA were equal to or close to zero. On the basis of these findings, we conclude that there is strong agreement between the GT1M, GT3X, and GT3X+ activity monitors, thus making it acceptable for researchers and practitioners to use different ActiGraph? models within a given study.  相似文献   
73.
ABSTRACT

This study used the intensity gradient (IG) and average acceleration metrics to describe children’s activity profiles and explore associations with body mass index (BMI) z-score. Two hundred and forty-six children (n = 138 girls) aged 9.6 ± 1.4 years wore a wrist-mounted ActiGraph wGT3X-BT accelerometer for 7 days on their non-dominant wrist. Physical activity (PA) metrics captured included: the IG which describes the intensity distribution of accelerations across the 24 h monitoring period; average acceleration which provides a measure of the volume of activity; total moderate-to-vigorous PA (MVPA) and inactive time. Acceleration was averaged over 5s epochs. Finally, BMI z-score was calculated for each participant. Average acceleration was negatively associated with BMI z-score (p < 0.05) independent of age and gender but not IG. The IG was negatively associated with BMI z-score independent of potential correlates and average acceleration. Total MVPA was not associated with BMI-z score. The IG and average acceleration metrics may be used to explore the independent or cumulative effects of the volume and intensity distribution of activity upon measures of health and well-being in children to inform specific activity recommendations.  相似文献   
74.
Purpose

The aim of this study was to test the hypothesized structural paths in Bandura's social-cognitive theory (SCT) model on adolescent girls' physical activity following a 12-month physical activity and dietary intervention to prevent obesity.

Method

We conducted a 12-month follow-up study of 235 adolescent girls (M age = 13.2 years, SD = 0.4) from 12 secondary schools located in low-income communities. At baseline, participants completed SCT scales related to physical activity (i.e., self-efficacy, intention, parental support, and outcome expectations). At baseline and 12-month follow-up (postintervention), participants wore accelerometers for 7 days. Structural equation modeling was used to determine if Time 1 measures predicted physical activity at 12-month follow-up after adjusting for baseline activity.

Results

The model explained 28% and 34% of the variance in physical activity and intention, respectively. Model fit indexes indicated the data were a good fit to the model; however, only self-efficacy was associated with physical activity at 12 months. There was no support for intention or outcome expectations as proximal determinants of behavior. Self-efficacy was associated with outcome expectations and parental support; however, only outcome expectations predicted intention.

Conclusions

Current findings indicate a large proportion of the variance for physical activity and intention remains unexplained and that the proposed pathways in the SCT model were not fully supported. Future model testing may need to consider augmentation or integration of theoretical models, which may include ecological components if we are to advance our understanding of physical activity behavior in this subgroup of the adolescent population.  相似文献   
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