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Demographic characteristics and type/frequency of physical activity participation in a large sample of 21,603 Australian people

Overview of attention for article published in BMC Public Health, June 2018
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  • Good Attention Score compared to outputs of the same age (68th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

blogs
1 blog

Citations

dimensions_citation
22 Dimensions

Readers on

mendeley
78 Mendeley
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Title
Demographic characteristics and type/frequency of physical activity participation in a large sample of 21,603 Australian people
Published in
BMC Public Health, June 2018
DOI 10.1186/s12889-018-5608-1
Pubmed ID
Authors

Rochelle M. Eime, Jack T. Harvey, Melanie J. Charity, Rayoni Nelson

Abstract

Regular physical activity (PA) is imperative for good health and there are many different ways that people can be active. There are a range of health, PA and sport policies aiming to get more people active more often. Much research has been directed towards understanding the determinants of inactivity and PA. However, it is important to understand the differences not only between inactive and active people, but also between activity contexts (for example participation in sport compared to non-sport activities), in order to align policies and strategies to engage market segments who have different participation preferences and accessibility. The aim of this study was to investigate demographic correlates of the propensity to be physically inactive or active within different contexts, and at different levels of frequency of participation. Data from the Australian Exercise, Recreation and Sport Survey was used for this analysis. This included information on the type, frequency and duration of leisure-time PA for Australians aged 15 years and over. Reported PA participation in the two-week period prior to the survey was used to allocate respondents into three categories: no PA, non-sport PA only, and sport. Subsequently, sport participants were further categorised according to frequency of participation. Potential demographic correlates included sex, age, education, employment, marital status, language spoken, having a condition that restricts life, children, and socio-economic status. The survey included 21,603 people. Bivariate chi-squared analysis showed that there were significant differences between the profiles of leisure-time PA participation across all demographic variables, except the variable languages spoken at home. Ordinal regression analysis showed that the same demographic variables were also correlated with the propensity to engage in more organised and competitive PA contexts, and to participate more frequently. People who were female, older, married or had a disability were less likely to participate in sport. Therefore when designing PA opportunities to engage those who are inactive, particularly those that are organised by a club or group, we need to ensure that appropriate strategies are developed, and tailored sport products offered, to ensure greater opportunities for increased diversity of participation in sport.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 78 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 9 12%
Student > Master 8 10%
Student > Doctoral Student 8 10%
Student > Ph. D. Student 5 6%
Professor > Associate Professor 4 5%
Other 13 17%
Unknown 31 40%
Readers by discipline Count As %
Sports and Recreations 17 22%
Nursing and Health Professions 7 9%
Social Sciences 4 5%
Psychology 4 5%
Biochemistry, Genetics and Molecular Biology 2 3%
Other 8 10%
Unknown 36 46%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 June 2018.
All research outputs
#5,828,208
of 23,088,369 outputs
Outputs from BMC Public Health
#5,830
of 15,048 outputs
Outputs of similar age
#100,893
of 329,782 outputs
Outputs of similar age from BMC Public Health
#175
of 310 outputs
Altmetric has tracked 23,088,369 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 15,048 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.0. This one has gotten more attention than average, scoring higher than 59% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 329,782 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 310 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.