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1.
The purpose of the study was to quantify the contributions of physical education, exergaming (active video games that also are a type of exercise), recess, lunch break and after-school time segments to children’s daily physical activity and sedentary behaviours. Participants were 138 second and third graders (71 girls) who attended 20-min recess and 75-min lunch time daily, 25-min regular physical education or exergaming-based classes being alternated daily. The after-school period was defined as 3:20–10:00pm. Physical activity was assessed via accelerometry and the dependent variables were children’s time spent in moderate-to-vigorous physical activity (MVPA), light physical activity and sedentary behaviour. Children’s percentages of time spent in MVPA (P < .001; except for the difference between exergaming and lunch break: P = .63), light physical activity (P < .001) and sedentary behaviour (P < .001) differed significantly across the time segments (i.e., physical education/exergaming, recess, lunch break and after-school). Additionally, children accumulated significantly more MVPA (t = 10.22, P < .001) but less light physical activity (t = ?3.17, P = .002) and sedentary behaviour (t = ?3.91, P < .001) in physical education than in exergaming. Overall, physical education was more effective in generating MVPA than other segments over the school day. The after-school segment holds potential as an avenue for promoting children’s MVPA, as this long period could be better utilised to organise structured physical activity.  相似文献   

2.
The aim was to assess the relationship between school rhythm and physical activity (PA) in adolescents. The study included 2024 adolescents (12.5–17.4 years). Participants wore an accelerometer for 7 days. A short school rhythm was defined as a short time at school with short recesses and less time in teaching per day (Group 1). A long school rhythm was defined as a longer time at school with more time in teaching and recess (Group 2). Adolescents in Group 1 performed less moderate to vigorous PA (MVPA) than those in Group 2 per week (P < .0001), especially during school days (recess: 3.9 ± 4.0 vs. 9.8 ± 7.9 min · day?1; P < .0001; teaching hours: 14.5 ± 9.8 vs. 19.1 ± 12.0 min · day?1; P < .0001). Adolescents in Group 1 were less likely to meet the PA recommendations than were adolescents in Group 2: 30.7% vs. 34.1% (P < .0001). During school days, the percentage of adolescents who spent more than 2 h · day?1 in sedentary activities was greater in the Group 1 (P < .001). Our results suggest that leisure-time out-of-school hours is used mainly for sedentary activities, and that school time provides a good opportunity for promoting PA.  相似文献   

3.
This study assessed children’s physical activity (PA) levels derived from wrist-worn GENEActiv and hip-worn ActiGraph GT3X+ accelerometers and examined the comparability of PA levels between the two devices throughout the segmented week. One hundred and twenty-nine 9–10-year-old children (79 girls) wore a GENEActiv (GAwrist) and ActiGraph GT3X+ (AGhip) accelerometer on the left wrist and right hip, respectively, for 7 days. Mean minutes of light PA (LPA) and moderate-to-vigorous PA (MVPA) per weekday (whole-day, before-school, school and after-school) and weekend day (whole-day, morning and afternoon–evening) segments were calculated, and expressed as percentage of segment time. Repeated measures analysis of variance examined differences in LPA and MVPA between GAwrist and AGhip for each time segment. Bland–Altman plots assessed between-device agreement for LPA and MVPA for whole weekday and whole weekend day segments. Correlations between GAwrist and AGhip were weak for LPA (r = 0.18–0.28), but strong for MVPA (r = 0.80–0.86). LPA and MVPA levels during all weekday and weekend day segments were significantly higher for GAwrist than AGhip (p < 0.001). The largest inter-device percent difference of 26% was observed in LPA during the school day segment. Our data suggest that correction factors are needed to improve raw PA level comparability between GAwrist and AGhip.  相似文献   

4.
This study compared children’s physical activity (PA) levels, the prevalence of children meeting current guidelines of ≥60 minutes of daily moderate to vigorous PA (MVPA), and PA-health associations using individually calibrated (IC) and empirical accelerometer cutpoints. Data from 75 (n = 32 boys) 10–12 year old children were included in this study. Clustered cardiometabolic (CM) risk, directly measured cardiorespiratory fitness (CRF), anthropometric and 7 day accelerometer data were included within analysis. PA data were classified using Froude anchored IC, Evenson et al. (Evenson, K. R., Catellier, D. J., Gill, K., Ondrak, K. S., &; McMurray, R. G. (2008). Calibration of two objective measures of physical activity for children. Journal of Sports Sciences, 26(14), 1557–1565. doi:10.1080/02640410802334196) (Ev) and Mackintosh et al. (Mackintosh, K. A., Fairclough, S. J., Stratton, G., &; Ridgers, N. D. (2012). A calibration protocol for population-specific accelerometer cutpoints in children. PLoS One, 7(5), e36919. doi:10.1371/journal.pone.0036919) (Mack) cutpoints. The proportion of the cohort meeting ≥60mins MVPA/day ranged from 37%-56% depending on the cutpoints used. Reported PA differed significantly across the cutpoint sets. IC LPA and MPA were predictors of CRF (LPA: standardised β = 0.32, p = 0.002, MPA: standardised β = 0.27 p = 0.013). IC MPA also predicted BMI Z-score (standardised β = ?0.35, p = 0.004). Ev VPA was a predictor of BMI Z-score (standardised β = ?0.33, p = 0.012). Cutpoint choice has a substantial impact on reported PA levels though no significant associations with CM risk were observed. Froude IC cutpoints represent a promising approach towards classifying children’s PA data.  相似文献   

5.
This study examined differences in physical activity (PA) estimates provided from raw and counts processing methods. One hundred and sixty-five children (87 girls) wore a hip-mounted ActiGraph GT3X+ accelerometer for 7 days. Data were available for 129 participants. Time in moderate PA (MPA), vigorous PA (VPA) and moderate-vigorous PA (MVPA) were calculated using R-package GGIR and ActiLife. Participants meeting the wear time criteria for both processing methods were included in the analysis. Time spent in MPA (?21.4 min.d?1, 95%CI ?21 to ?20) and VPA (?36 min.d?1, 95%CI ?40 to ?33) from count data were higher (P < 0.001) than raw data. Time spent in MVPA between the two processing methods revealed significant differences (All P < 0.001). Bland-Altman plots suggest that the mean bias for time spent in MPA, VPA and MVPA were large when comparing raw and count methods. Equivalence tests showed that estimates from raw and count processing methods across all activity intensities lacked equivalence. Lack of equivalence and poor agreement between raw and count processing methods suggest the two approaches to estimate PA are not comparable. Further work to facilitate the comparison of findings between studies that process and report raw and count physical activity data may be necessary.  相似文献   

6.
ABSTRACT

Purpose: The purpose of this study was to examine whether structured physical activity (PA) in a family-based community exercise program affects PA of young children and parents. Method: Twenty-two children (mean ± SD; age, 4.9 ± 2.1 years) and their parents (age, 34.3 ± 7.6 years) participated in unstructured PA sessions followed by either short- or long-duration structured PA sessions, while wearing an ActiGraph GT9X activity monitor on their right hip to estimate PA. Independent t-tests compared children’s and parents’ PA during short- and long-structured PA sessions. Paired t-tests compared short- versus long-structured PA sessions. A mixed model ANOVA compared PA during unstructured versus structured sessions and between children and parents. Results: Children spent proportionately more time in moderate-to-vigorous PA (MVPA) and had higher accelerometer counts/min than parents during short-structured PA (children:60.9 ± 18.8% vs. parents:17.7 ± 6.8%, children:3870 ± 742 vs. parents:1836 ± 556 counts/min, p < .05) and long-structured PA (children:61.1 ± 20.1% vs. parents:12.6 ± 4.9%, children:3415 ± 758 vs. parents:1604 ± 633 counts/min, p < .05). No statistical differences were found between short- and long-structured PA sessions for proportion of time spent in MVPA or counts/min for children or parents (all, p > .05). Children spent proportionally more time in MVPA and had higher counts/min during unstructured PA compared to structured PA (unstructured MVPA:54.4 ± 3.9% vs. structured MVPA:38.2 ± 4.2%, unstructured counts/min:3830 ± 222 vs. structured counts/min:2768 ± 239 counts/min; p < .05). Conclusions: Children were more active than parents during both the unstructured and structured PA sessions. However, unstructured PA sessions resulted in 63–77% and 10–11% of PA recommendations for children and adults, respectively. Family-based exercise programming can provide an opportunity for children and their parents to attain MVPA during the week.  相似文献   

7.
Participation in youth sport is assumed to promote and contribute towards more physically active lifestyles among children and adolescents. The aim of this study was to examine inter-participant variability in objectively measured habitual physical activity (PA) behaviours and sedentary time among youth sport participants and their implications for health. One-hundred-and-eighteen male youth sport footballers (Mean ± s = 11.72 ± 1.60) wore a GT3X accelerometer for 7 days. Average daily PA [min · day?1, in light (LPA), moderate (MPA), vigorous (VPA) and combined moderate-to-vigorous (MVPA)] and sedentary time were calculated. Participants’ body mass index adjusted for age and sex (BMI–standard deviation score), per cent body fat (BF%), waist circumference and cardiorespiratory fitness were assessed. Results revealed that variability in daily PA behaviours and sedentary time (min · day?1) was associated with BMI–standard deviation score [VPA (?), MVPA (?)], BF% [sedentary time (+), VPA (?), MVPA (?)], waist circumference [sedentary time (+), LPA (?)] and cardiorespiratory fitness [sedentary time (?), MPA (+), VPA (+), MVPA (+)]. Whilst sedentary time and MVPA were not related to health outcomes independent of one another, associations with markers of adiposity and cardiorespiratory fitness were stronger for sedentary time. Sedentary time was also significantly positively related to waist circumference independent of VPA. Results demonstrate inter-participant variability in habitual PA and sedentary time among youth sport participants which holds implications for their health. Thus, promoting PA and, in particular, reducing sedentary time may contribute towards the prevention of adverse health consequences associated with a physically inactive lifestyle for children and adolescents active in the youth sport context.  相似文献   

8.
ABSTRACT

Purpose: Most built environment studies have quantified characteristics of the areas around participants’ homes. However, the environmental exposures for physical activity (PA) are spatially dynamic rather than static. Thus, merged accelerometer and global positioning system (GPS) data were utilized to estimate associations between the built environment and PA among adults. Methods: Participants (N = 142) were recruited on trails in Massachusetts and wore an accelerometer and GPS unit for 1–4 days. Two binary outcomes were created: moderate-to-vigorous PA (MVPA vs. light PA-to-sedentary); and light-to-vigorous PA (LVPA vs. sedentary). Five built environment variables were created within 50-meter buffers around GPS points: population density, street density, land use mix (LUM), greenness, and walkability index. Generalized linear mixed models were fit to examine associations between environmental variables and both outcomes, adjusting for demographic covariates. Results: Overall, in the fully adjusted models, greenness was positively associated with MVPA and LVPA (odds ratios [ORs] = 1.15, 95% confidence interval [CI] = 1.03, 1.30 and 1.25, 95% CI = 1.12, 1.41, respectively). In contrast, street density and LUM were negatively associated with MVPA (ORs = 0.69, 95% CI = 0.67, 0.71 and 0.87, 95% CI = 0.78, 0.97, respectively) and LVPA (ORs = 0.79, 95% CI = 0.77, 0.81 and 0.81, 95% CI = 0.74, 0.90, respectively). Negative associations of population density and walkability with both outcomes reached statistical significance, yet the effect sizes were small. Conclusions: Concurrent monitoring of activity with accelerometers and GPS units allowed us to investigate relationships between objectively measured built environment around GPS points and minute-by-minute PA. Negative relationships between street density and LUM and PA contrast evidence from most built environment studies in adults. However, direct comparisons should be made with caution since most previous studies have focused on spatially fixed buffers around home locations, rather than the precise locations where PA occurs.  相似文献   

9.
The GT3X+ worn at the wrist promotes greater compliance than at the hip. Minutes in SB and PA calculated from raw accelerations at the hip and wrist provide contrasting estimates and cannot be directly compared.

Wear-time for the wrist (15.6 to 17.4 h.d?1) was greater than the hip (15.2 to 16.8 h.d?1) across several wear-time criteria (all P < 0.05). Moderate-strong associations were found between time spent in SB (r = 0.39), LPA (r = 0.33), MPA (r = 0.99), VPA (r = 0.82) and MVPA (r = 0.81) between the two device placements (All P < 0.001). The wrist device detected more minutes in LPA, MPA, VPA and MVPA whereas the hip detected more SB (all P = 0.001). Estimates of time in SB and all activity outcomes from the wrist and hip lacked equivalence.

One hundred and eighty-eight 9–12-year-old children wore a wrist- and hip-mounted accelerometer for 7 days. Data were available for 160 (hip) and 161 (wrist) participants. Time spent in SB and PA was calculated using GGIR.

This study examined the compliance of children wearing wrist- and hip-mounted ActiGraph GT3X+ accelerometers and compared estimates of sedentary behaviour (SB) and physical activity (PA) between devices.  相似文献   

10.
ABSTRACT

This study validated sedentary behaviour (SB), moderate-to-vigorous physical activity (MVPA) and vigorous physical activity (VPA) accelerometer cut-points in 5–7-year-old children. Participants (n = 49, 55% girls) wore an ActiGraph GT9X accelerometer, recording data at 100 Hz downloaded in 1 s epochs, on both wrists and the right hip during a standardised protocol and recess. Cut-points were generated using ROC analysis with direct observation as a criterion. Subsequently, cut-points were optimised using Confidence intervals equivalency analysis and then cross-validated in a cross-validation group. SB cut-points were 36 mg (Sensitivity (Sn) = 79.8%, Specificity (Sp) = 56.8%) for non-dominant wrist, 39 mg (Sn = 75.4%, Sp = 70.2%) for dominant wrist and 20 mg (Sn = 78%, Sp = 50.1%) for hip. MVPA cut-points were 189 mg (Sn = 82.6%, Sp = 78%) for non-dominant wrist, 181 mg (Sn = 79.1%, Sp = 76%) for dominant wrist and 95 mg (Sn = 79.3%, Sp = 75.6%) for hip. VPA cut-points were 536 mg (Sn = 75.1%, Sp = 68.7%) for non-dominant wrist, 534 mg (Sn = 67.6%, Sp = 95.6%) for dominant wrist and 325 mg (Sn = 78.2%, Sp = 96.1%) for hip. All placements demonstrated adequate levels of accuracy for SB and PA assessment.  相似文献   

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