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Sandi L. Pruitt and Andrew E. Springer

Background:

Evidence of an association between employment and physical activity (PA) in youth has been mixed, with studies suggesting both positive and negative associations. We examined the association between employment and PA among U.S. high school students as measured by self-reported overall PA, vigorous exercise, and participation in school athletic teams.

Methods:

We employed a secondary analysis using weighted linear regression to a sample of black and white 10th grade (n = 12073) and 12th grade students (n = 5500) drawn from the nationally representative cross-sectional 2004 Monitoring the Future Study.

Results:

Overall, 36.5% of 10th and 74.6% of 12th grade students were employed. In multivariable analyses, 10th graders working >10 hours a week reported less overall PA and exercise and those working >20 hours a week reported less participation in team sports. Among 12th graders, any level of employment was associated with lower rates of team sports; those working >10 hours a week reported less overall PA; and those working >20 hours reported less exercise.

Conclusions:

Employment at and above 10 hours per week is negatively associated with PA. Increasing work intensity may shed light on the decline of PA as adolescents grow older and merits further attention in research.

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Andrew E. Springer, Deanna M. Hoelscher and Steven H. Kelder

Background:

Geographic differences in the prevalence of physical activity (PA) have been found among adults in the US; similar studies have not been conducted among adolescents.

Methods:

Using nationally representative cross-sectional data from the CDC’s 2003 Youth Risk Behavior Survey, we estimated the prevalence of PA and sedentary behaviors by metropolitan status and geographic region.

Results:

The prevalence of PA was lowest and prevalence of sedentary behavior highest for urban students. Students from the South reported the lowest prevalence of PA and the highest prevalence of TV watching, while students from the West generally reported the highest PA prevalence and lowest sedentary behavior prevalence. Prevalence differences ranged from < 1.0% to > 15%, with most differences falling between 5% and 10%.

Conclusions:

Findings mirror regional variations previously observed in adult PA. We need to understand factors that contribute to lower PA in youth living in the South and in urban settings.

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Deanna M. Hoelscher, Cristina Barroso, Andrew Springer, Brian Castrucci and Steven H. Kelder

Background:

Few studies have compared physical activity (PA) and sedentary activity (SA) by grade and ethnicity, specifically including elementary school students. A cross-sectional probability-based design was used to provide data by ethnicity (African American, Hispanic, and White/Other), gender, and grade (4th, 8th, and 11th) from 2000 to 2002.

Methods:

Two validated questionnaires (elementary and secondary) assessed self-reported PA and SA. Point-prevalence estimates and 95% confidence intervals were computed.

Results:

Over 70% of students reported vigorous PA on ≥3 days/week, but <50% participated in daily physical education. A significant percentage (30% to 50%) of students reported ≥3 hours per day in SA. Fourth-grade boys and girls reported equal PA; however, 8th and 11th grade girls reported lowered vigorous PA. African American 8th- and 11th-grade boys reported the highest PA, but African American children also reported the highest prevalence of SA.

Conclusions:

Findings from this study highlight the disparities in physical and sedentary activities by gender, grade, and race/ethnicity, and the need to address these differences with programs and policy. In general, grade level and gender differences were more striking and consistent than racial/ethnic differences.

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Deanna M. Hoelscher, Andrew Springer, Tiffni H. Menendez, Peter W. Cribb and Steven H. Kelder

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Katherine A. Skala, Andrew E. Springer, Shreela V. Sharma, Deanna M. Hoelscher and Steven H. Kelder

Background:

Physical education (PE) classes provide opportunities for children to be active. This study examined the associations between specific environmental characteristics (teacher characteristics; class size, duration and location; and lesson context) and elementary school-aged children’s moderate-to-vigorous activity (MVPA) during PE.

Methods:

Environmental characteristics and student activity levels were measured in 211 third-, fourth-, and fifth-grade PE classes in 74 Texas public schools using SOFIT direct observation.

Results:

Students engaged in less than half their PE class time in MVPA (38%), while approximately 25% of class time was spent in classroom management. Percent time in MVPA was significantly higher in outdoor classes compared with indoors (41.4% vs. 36.1%, P = .037). Larger (P = .044) and longer (P = .001) classes were negatively associated with percentage of MVPA and positively correlated with time spent in management (P < .001).

Conclusions:

Findings suggest that children’s activity may be influenced by environmental factors such as class size, location, and lesson contexts. These findings hold important policy implications for PE class organization and the need for strategies that maximize children’s MVPA. Further research is needed to test the causal association of these factors with student MVPA.

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Andrew E. Springer, Steven H. Kelder, Nalini Ranjit, Heather Hochberg-Garrett, Sherman Crow and Joanne Delk

Background:

Marathon Kids® (MK) is a community and school-based program that promotes running, walking, and healthy eating in elementary school children. This study assessed the impact of MK on self-reported physical activity (PA), fruit and vegetable consumption (FVC), and related psycho-social factors in a sample of low-income, 4th- and 5th-grade students in Texas (n = 511). Intervention strategies included structured school running time, behavioral tracking, celebratory events, and rewards.

Methods:

A quasi-experimental design with 5 intervention (MK) and 3 comparison schools was employed. Students were assessed at baseline in the fall and at 3 time points during 2008 to 09. Mixed-effect regression methods were used to model pooled means, adjusting for baseline and sociodemographic variables.

Results:

MK students reported a higher mean time of running in past 7 days compared with non-MK students (mean = 4.38 vs. 3.83, respectively. P = .002), with a standardized effect size of 0.16. Mean times of FVC (P = .008), athletic identity self-concept (P < .001), PA outcome expectations (P = .007), and PA and FVC self-efficacy (P < .001 and P = .02, respectively) were also higher in MK students. Fewer differences in social support were observed.

Conclusion:

Findings provide further evidence on the importance of community and school partnerships for promoting PA and healthy eating in children.

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Susan Park, Lindsay P. Toth, Paul R. Hibbing, Cary M. Springer, Andrew S. Kaplan, Mckenzie D. Feyerabend, Scott E. Crouter and David R. Bassett

It has become common to wear physical activity monitors on the wrist to estimate steps per day, but few studies have considered step differences between monitors worn on the dominant and non-dominant wrists. Purpose: The purpose of this study was to compare four step counting methods on the dominant versus non-dominant wrist using the Fitbit Charge (FC) and ActiGraph GT9X (GT9X) across all waking hours of one day. Methods: Twelve participants simultaneously wore two monitors (FC and GT9X) on each wrist during all waking hours for an entire day. GT9X data were analyzed with three step counting methods: ActiLife algorithm with default filter (AG-noLFE), ActiLife algorithm with low-frequency extension (AG-LFE), and the Moving Average Vector Magnitude (AG-MAVM) algorithm. A 2-way repeated measures ANOVA (method × wrist) was used to compare step counts. Results: There was a significant main effect for wrist placement (F(1,11) = 11.81, p = .006), with the dominant wrist estimating an average of 1,253 more steps than the non-dominant wrist. Steps differed between the dominant and non-dominant wrist for three of the step methods: AG-noLFE (1,327 steps), AG-LFE (2,247 steps), AG-MAVM (825 steps), and approached statistical significance for FC (613 steps). No significant method x wrist placement interaction was found (F(3,9) = 2.62, p = .115). Conclusion: Findings suggest that for step counting algorithms, it may be important to consider the placement of wrist-worn monitors since the dominant wrist location tended to yield greater step estimates. Alternatively, standardizing the placement of wrist-worn monitors could help to reduce the differences in daily step counts across studies.