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Javier Molina-García, James F. Sallis and Isabel Castillo

Background:

Commuting to university represents an opportunity to incorporate physical activity (walking or biking) into students’ daily routines. There are few studies that analyze patterns of transport in university populations. This cross-sectional study estimated energy expenditure from active commuting to university (ACU) and examined sociodemographic differences in findings.

Methods:

The sample included 518 students with a mean age of 22.4 years (59.7% female) from 2 urban universities in Valencia, Spain. Time spent in each mode of transport to university and sociodemographic factors was assessed by self-report.

Results:

Nearly 35% of the students reported walking or biking as their main mode of transport. ACU (min/wk) were highest for walkers (168) and cyclists (137) and lowest for motorbike riders (0.0) and car drivers (16). Public transport users, younger students, low socioeconomic status students, and those living ≤ 2 km from the university had higher energy expenditure from active commuting than comparison groups. Biking was highest among those living 2–5 km from the university.

Conclusions:

Our findings suggest that active commuting and public transit use generated substantial weekly energy expenditure, contributed to meeting physical activity recommendations, and may aid in obesity prevention.

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Eileen K. Nehme, Adriana Pérez, Nalini Ranjit, Benjamin C. Amick III and Harold W. Kohl III

Background:

Transportation bicycling is a behavior with demonstrated health benefits. Population-representative studies of transportation bicycling in United States are lacking. This study examined associations between sociodemographic factors, population density, and transportation bicycling and described transportation bicyclists by trip purposes, using a US-representative sample.

Methods:

This cross-sectional study used 2009 National Household Travel Survey datasets. Associations among study variables were assessed using weighted multivariable logistic regression.

Results:

On a typical day in 2009, 1% of Americans older than 5 years of age reported a transportation bicycling trip. Transportation cycling was inversely associated with age and directly with being male, with being white, and with population density (≥ 10,000 vs < 500 people/square mile: odd ratio, 2.78, 95% confidence interval, 1.54–5.05). Those whose highest level of education was a high school diploma or some college were least likely to bicycle for transportation. Twenty-one percent of transportation bicyclists reported trips to work, whereas 67% reported trips to social or other activities.

Conclusions:

Transportation bicycling in the United States is associated with sociodemographic characteristics and population density. Bicycles are used for a variety of trip purposes, which has implications for transportation bicycling research based on commuter data and for developing interventions to promote this behavior.

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Xihe Zhu and Justin A. Haegele

Purpose: This study aims to (a) examine elementary school students’ health-related fitness knowledge growth under one curriculum condition and (b) examine the impacts of student/school-level factors on health-related fitness knowledge and its growth rate in physical education. Method: We used an observational, longitudinal repeated-measures design, and conducted analyses on an existing dataset. Participants were 7,479 third, fourth, and fifth graders (48.9% girls) from 152 elementary schools. Measures were a knowledge test and sex at the student level, and socioeconomic data, academic performance, and student–faculty ratio at the school level. We ran three-level hierarchical linear models on the data. Results: Fitness knowledge growth was found to form a quadratic curve from third through fifth grades. School-level academic performance was positively associated with fitness knowledge. Sex was not associated with fitness knowledge or knowledge growth rate. Discussion: These findings contribute to the understanding of health-related fitness knowledge growth among elementary students.

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Sunday Azagba and Mesbah Fathy Sharaf

Background:

In spite of the substantial benefits of physical activity for healthy aging, older adults are considered the most physically inactive segment of the Canadian population. This paper examines leisure-time physical inactivity (LTPA) and its correlates among older Canadian adults.

Methods:

We use data from the Canadian Community Health Survey with 45,265 individuals aged 50–79 years. A logistic regression is estimated and separate regressions are performed for males and females.

Results:

About 50% of older Canadian adults are physically inactive. Higher odds of physical inactivity are found among current smokers (OR = 1.52, CI = 1.37–1.69), those who binge-drink (OR = 1.24, CI = 1.11–1.39), visible minorities (OR = 1.60, CI = 1.39–1.85), immigrants (OR = 1.13, CI = 1.02–1.25), individuals with high perceived life stress (OR = 1.48, CI = 1.31–1.66). We also find lower odds of physical inactivity among: males (OR = 0.89, CI = 0.83 to 0.96), those with strong social interaction (OR = 0.71, CI = 0.66–0.77), with general life satisfaction (OR = 0.66, CI = 0.58–0.76) and individuals with more education. Similar results are obtained from separate regressions for males and females.

Conclusions:

Identifying the correlates of LTPA among older adults can inform useful intervention measures.

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Lilian G. Perez, Terry L. Conway, Adrian Bauman, Jacqueline Kerr, John P. Elder, Elva M. Arredondo and James F. Sallis

environmental and sociodemographic factors has come from single country studies whose findings are limited by the samples and context under study. Differences in methodology across studies can also contribute to inconsistencies. Multicountry studies that employ comparable measures and protocols across sites can

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Seigo Mitsutake, Ai Shibata, Kaori Ishii, Shiho Amagasa, Hiroyuki Kikuchi, Noritoshi Fukushima, Shigeru Inoue and Koichiro Oka

.8 6.9 11.4 6.6 .166 Abbreviations: PC, personal computer; SB, sedentary behavior; TV, television. a One-way analysis of variance. Table  2 presents the differences between sociodemographic factors, exercise habits, chronic diseases, and total SB time among the 4 clusters. The proportion of

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Katie E. Cherry, Jennifer Silva Brown, Sangkyu Kim and S. Michal Jazwinski

Social behaviors are associated with health outcomes in later life. The authors examined relationships among social and physical activities and health in a lifespan sample of adults (N = 771) drawn from the Louisiana Healthy Aging Study (LHAS). Four age groups were compared: younger (21–44 years), middle-aged (45–64 years), older (65–84 years), and oldest-old adults (85–101 years). Linear regression analyses indicated that physical activity, hours spent outside of the house, and social support were significantly associated with selfreported health, after controlling for sociodemographic factors. Number of clubs was significantly associated with objective health status, after controlling for sociodemographic factors. These data indicate that social and physical activities remain important determinants of self-perceived health into very late adulthood. Implications of these data for current views on successful aging are discussed.

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Giovâni F. Del Duca, Leandro Martin Totaro Garcia, Shana Ginar da Silva, Kelly Samara da Silva, Elusa S. Oliveira, Mauro V. Barros and Markus V. Nahas

Background:

Physical inactivity in each domain (leisure, work, commuting, and household) is not completely independent. This study aimed to describe the clustering of physical inactivity in different domains and its association with sociodemographic factors among Brazilian industrial workers.

Methods:

This was a cross-sectional, population-based study using data from 23 Brazilian states and the Federal District collected via questionnaires between 2006 and 2008. Physical inactivity in each domain was defined as nonparticipation in specific physical activities. Clustering of physical inactivity was identified using the ratio of the observed (O) and expected (E) percentages of each combination. Multinomial logistic regression was used to identify sociodemographic factors with the outcome.

Results:

Among the 44,477 interviewees, most combinations exceeded expectations, particularly the clustering of physical inactivity in all domains among men (O/E = 1.37; 95% CI: 1.30; 1.44) and women (O/E = 1.47; 95% CI: 1.36; 1.60). Physical inactivity in 2 or more domains was observed more frequently in women, older age groups, individuals living without a partner, and those with higher education and income levels.

Conclusions:

Physical inactivity tends to be observed in clusters regardless of gender. Women and workers with higher income levels were the main factors associated with to be physically inactive in 2 or more domains.

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Alan Nevill, Paul Donnelly, Simon Shibli, Charlie Foster and Marie Murphy

Background:

The association between health and deprivation is of serious concern to many health promotion agencies. The purpose of the current study was to assess whether modifiable behaviors of physical activity (PA), sports participation, diet, smoking and body mass index (BMI) can help to explain these inequalities in a sample of 4653 respondents from Northern Ireland.

Methods:

The study is based on a cross-sectional survey of Northern Irish adults. Responses to a self-rated health question were dichotomized and binary logistic regression was used to identify the health inequalities between areas of high, middle or low deprivation. These differences were further adjusted for other sociodemographic factors and subsequently for various modifiable behaviors of PA, sports participation, diet, smoking, and BMI.

Results:

Respondents from high and middle areas of deprivation are more likely to report poorer health. As soon as sociodemographic factors and other modifiable behaviors were included, these inequalities either disappeared or were greatly reduced.

Conclusion:

Many inequalities in health in NI can be explained by the respondents’ sociodemographic characteristics that can be further explained by introducing information about respondents who meet the recommended PA guidelines, play sport, eat 5 portions of fruit and vegetables, and maintain an optimal BMI.

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Renata Moraes Bielemann, Andreia Morales Cascaes, Felipe Fossati Reichert, Marlos R. Domingues and Denise Petrucci Gigante

Background:

The aim of this study was to assess physical activity (PA) patterns (intensity and prevalence) in children according to demographic, socioeconomic, and familiar characteristics.

Methods:

In 2010, a cross-sectional study of 239 children aged 4–11 was conducted, in Pelotas, Southern Brazil. PA was measured by accelerometry and classified in different intensities. Insufficient physical activity was defined as less than 60 min/day of moderate-to-vigorous PA. Descriptive analyses of accelerometry-related variables were presented. Multivariate Poisson regression models were used to estimate the association between physical insufficient PA and covariates.

Results:

For both sexes, around 65% of the registered time was spent in sedentary activities and less than 20 min/day in vigorous activity. Age and economic status were inversely associated to PA in all categories of PA. Moderate and vigorous activities means were higher in boys than in girls. The prevalence of insufficient PA was 34.5% in girls and 19.5% in boys.

Conclusions:

We found important differences in physical activity patterns according to sex and economic status, as well as a significant decline in time spent in moderate-to-vigorous PA with increasing age. Understanding the relationship between these sociodemographic factors is important to tackle low levels of PA.