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Chelsea L. Kracht, Elizabeth K. Webster and Amanda E. Staiano

whether these sociodemographic differences in individual guidelines translate into differences in meeting the 24-Hour Movement Guidelines. Therefore, the purpose of this investigation was to assess sociodemographic differences (ie, age, sex, race, poverty level, and weight status) in meeting the 24-Hour

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

PA. 7 – 9 Of the possible interactions across levels, those involving environmental factors remain the least understood. Examining interactions between environmental- and individual-level characteristics of residents (sociodemographics) can help inform interventions targeting environments to promote

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Robin C. Puett, Dina Huang, Jessica Montresor-Lopez, Rashawn Ray and Jennifer D. Roberts

of play activity in children. However, attributes of the built environment are often linked to the sociodemographic characteristics of the neighborhood residents. 8 Prior research has reported conflicting results concerning the interrelationships between socioeconomic status, race

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Estela Farías-Torbidoni, Demir Barić and Sebastià Mas-Alòs

different PAs have demonstrated that the types of recreational activities are important criteria for visiting particular protected natural settings and that they might reflect differences in visitors’ sociodemographic profiles, behavioral characteristics, and needs and attitudes. 8 , 9 However, there are

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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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Jennifer D. Roberts, Lindsey Rodkey, Rashawn Ray and Brian E. Saelens

.4278/0890-1171-22.2.107 10.4278/0890-1171-22.2.107 18019887 9. Oliver M , Badland H , Mavoa S , et al . Environmental and socio-demographic associates of children’s active transport to school: a cross-sectional investigation from the URBAN Study . Int J Behav Nutr Phys Act . 2014 ; 11 : 70 . 10

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Kaori Ishii, Ai Shibata and Koichiro Oka

Background:

Although physical activity is associated with a lower risk of colon cancer, few studies have described the physical activity required for colon cancer prevention in various sociodemographic subgroups. The current study examined the prevalence and sociodemographic correlates of attaining the 2 recommended physical activity criteria for colon cancer prevention among Japanese adults.

Methods:

The sample included 5322 Japanese adults aged 20 to 79 years. Seven sociodemographic attributes (eg, gender, age, education level, employment status) and the International Physical Activity Questionnaire were assessed via an Internet-based survey. The odds of meeting each physical activity criterion by sociodemographic variables were calculated.

Results:

Overall, 23.8% of the study population met the criterion of ≥ 420 minutes of moderate-intensity activity, and 6.4% met the criterion of ≥ 210 minutes of vigorous activity. Being male, highly educated, employed, living with another person, being married and having a higher household income were significantly correlated with the attainment of recommendations.

Conclusions:

Participants who met the 2 activity recommendations differed in gender, education level, employment status, marital status, living conditions, and household income. The findings of the current study imply that strategies to promote more intense physical activity in all demographic groups may be necessary.

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Babatunde O.A. Adegoke and Adewale L. Oyeyemi

Background:

This study assessed the prevalence of physical inactivity and the influence of sociodemographic variables on physical activity categories, highlighting the correlates of physical inactivity in Nigerian young adults.

Methods:

A representative sample of young adults age 16 to 39 years (n = 1006) from a Nigerian University were categorized using the International Physical Activity Questionnaire as physically inactive, moderately active, and highly active. Prevalence rates were computed for the activity categories and the independent associations of sociodemographic correlates on each category were determined using the multinomial logistic regression.

Results:

Physical inactivity prevalence was 41%. More likely to be inactive were females (OR = 1.93; CI: 1.49−2.49), those of Hausa ethnicity (OR = 2.29; CI: 1.08−5.84), having BMI > 30 kg/m2 (OR = 2.88; CI: 1.16−7.17), and those whose parents’ annual income was < 180,000 NAIRA (OR = 1.69; CI: 1.04−2.95). Less likely to be moderately active were females (OR = 0.71; CI: 0.61−0.95), those with BMI between 25.0 to 29.9 kg/m2 (OR = 0.46; CI: 0.23−0.92), and those of Hausa ethnicity (OR = 0.17; CI: 0.04−0.74).

Conclusion:

Important sociodemographic variables that can contribute to the preliminary analysis of correlates of physical inactivity among Nigerian young adults were identified.

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Dartagnan P. Guedes, Jaime Miranda Neto, Vitor Pires Lopes and António José Silva

Background:

This study investigated the association between sociodemographic and behavioral factors and health standards based on physical fitness component scores in a sample of Brazilian schoolchildren.

Methods:

A sample of 1457 girls and 1392 boys aged 6 to 18 years performed a test battery of 5 items: 1) sit-and-reach, 2) curl-up, 3) trunk-lift, 4) push-up, and 5) progressive endurance run (PACER). The cut-off scores for gender and age suggested by the FitnessGram were adopted.

Results:

The findings showed that the sociodemographic and behavioral factors significantly associated with the ability of schoolchildren of meeting the health standards varied according to the fitness test. In the 5 tests used girls presented lower chance of meeting the health standards. Age and socioeconomic class were negatively associated with the performance in all physical tests. Schoolchildren aged ≤ 9 years or from families of lowest socioeconomic class presented approximately twice the chance of meeting the health standards than those aged ≥ 15 years and from more privileged families, specifically in the push-up (OR = 2.40; 95% CI 2.01–2.82) and PACER (OR = 2.18; 95% CI 1.84–2.54) tests.

Conclusions:

Interventions to promote health-related physical fitness should not only consider gender and age of schoolchildren, but also selected sociodemographic and behavioral factors, especially socioeconomic class and leisure activities.

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Rachel Cole, Eva Leslie, Adrian Bauman, Maria Donald and Neville Owen

Background:

Walking is integral to strategies to promote physical activity. We identified socio-demographic variations in walking for transport, and for recreation or exercise.

Methods:

Representative population data (n = 3392) from Australia were collected using computer assisted telephone interviewing, to examine adults’ participation in moderate- or brisk-paced walking for transport and walking for recreation or exercise; walking “sufficient” to meet the current public health guideline (≥ 150 min/wk); and, the contributions of total walking to meeting the guideline for total physical activity.

Results:

Rates of sufficient walking for transport (10% for men, 9% for women) were lower than those for walking for recreation or exercise (14% for both genders). Few socio-demographic differences emerged. Men over age 60 y were significantly less likely (OR = 0.40) to walk for transport; men age 45 to 59 y were more likely (OR = 1.56) to walk for recreation or exercise. Walking contributed more toward meeting the current public health guideline among women (15% to 21%) than among men (6% to 8%).

Conclusions:

There is potential for socially equitable increases in participation, through a focus on both walking for transport and on walking for recreation or exercise; attention to gender differences would be helpful.