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?:abstract
  • Reviews of physical inactivity (PIA) have not consistently identified systematic determinants influencing such behavior. Associations in subjective rather than objective measures may be important to consider when designing effective policy targeting PIA. PURPOSE: To analyze predictive variables that could influence PIA and how these factors may inform PIA-reducing policy. METHODS: Data from the 2014 Special Eurobarometer 412 (n = 27,919) were analyzed, including 40 separate variables and the International Physical Activity Questionnaire (IPAQ) for determining physical activity (PA) in MET-min per week. Variables included alternatives to car, places, reasons, and barriers to engaging in PA, memberships to clubs, and categorical responses regarding the extent of agreement with statements about the area, provision of activities, and local governance. A logistic regression model with a likelihood ratio statistic and a backward stepwise method was used to identify what variables contributed to PIA, which was defined as a “low” level based on IPAQ score. PIA was used as the dependent variable (0 = PA and 1 = PIA). Beta values (β) and standard errors (SE) are reported and Nagelkerke R2 is indicated. A priori alpha level was set at 0.05. RESULTS: The model for detecting PIA (χ2 = 2,023; p < 0.001; R2 of Nagelkerke= 0.153) was able to identify 10.7% of the inactive and 96.9% of the active people (74.5% of the total sample). The variables contributing to the detection of PIA were (p ≤ 0.01): having a disability or an illness (β = 0.521, SE = 0.052), not having friends to do sport with (β = 0.314, SE = 0.089), lacking motivation or interest (β = 0.407, SE = 0.04), and being afraid of the risk of an injury (β = 0.190, SE = 0.073). Additionally, totally agreeing, tend to agree, and tend to disagree regarding the extent of local providers offering enough opportunities to be more active also contributed to the model (β = 0.302-433, SE = 1.353-1.542). CONCLUSIONS: Overall, the model was effective for detecting PA but not PIA. However, in the proportion where PIA was detected, key subjective factors influencing PIA began to emerge. Greater insight into these subjective mediators will be helpful in drafting effective policy around active living, and therefore better correlates should be included in future public health surveillance efforts. (xsd:string)
?:author
?:comment
  • https://doi.org/10.1249/01.mss.0000561200.36359.a0. (Eurobarometer) (xsd:string)
?:dataSource
  • Eurobarometer-Bibliography (xsd:string)
?:dateModified
  • 2019 (xsd:gyear)
?:datePublished
  • 2019 (xsd:gyear)
?:doi
  • 10.1249/01.mss.0000561200.36359.a0 ()
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?:fromPage
  • 230 (xsd:string)
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?:inLanguage
  • english (xsd:string)
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?:issn
  • 0195-9131 ()
?:issueNumber
  • 6 (xsd:string)
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?:name
  • A Modifiable Factors-based Model for Detecting Physically Inactive Individuals Using the Eurobarometer Survey: 910 Board #144 May 29 2:00 PM - 3:30 PM (xsd:string)
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  • article (xsd:string)
?:reference
?:sourceInfo
  • Bibsonomy (xsd:string)
  • In Medicine & Science in Sports & Exercise, 51(6), 230-231, 2019 (xsd:string)
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  • EB - Standard and Special Eurobarometer (xsd:string)
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  • 2019 (xsd:string)
  • EB80.2 (xsd:string)
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  • Eurobarometer (xsd:string)
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  • SpecialEB412 (xsd:string)
  • ZA5877 (xsd:string)
  • article (xsd:string)
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  • 231 (xsd:string)
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  • 51 (xsd:string)