A modifiable factors-based model for detecting inactive individuals: are the European assessment tools fit for purpose?

Autor:	Mayo X; Observatory of Healthy & Active Living of Spain Active Foundation, Centre for Sport Studies, King Juan Carlos University, Madrid, Spain., Iglesias-Soler E; Performance and Health Group, Department of Physical Education and Sport, Faculty of Sports Sciences and Physical Education, University of A Coruna, A Coruña, Spain., Liguori G; University of Rhode Island, Kingston, RI, USA., Copeland RJ; Advanced Wellbeing Research Centre, College of Health, Wellbeing and Life Sciences, Sheffield Hallam University, Sheffield, UK.; The National Centre for Sport and Exercise Medicine, Sheffield, UK., Clavel I; Performance and Health Group, Department of Physical Education and Sport, Faculty of Sports Sciences and Physical Education, University of A Coruna, A Coruña, Spain.; Galician Sport Foundation, Galician Government, Santiago, Spain., Del Villar F; Observatory of Healthy & Active Living of Spain Active Foundation, Centre for Sport Studies, King Juan Carlos University, Madrid, Spain., Jimenez A; Observatory of Healthy & Active Living of Spain Active Foundation, Centre for Sport Studies, King Juan Carlos University, Madrid, Spain.; GO fit LAB, Ingesport, Madrid, Spain.
Jazyk:	angličtina
Zdroj:	European journal of public health [Eur J Public Health] 2022 Nov 29; Vol. 32 (6), pp. 894-899.
DOI:	10.1093/eurpub/ckac116
Abstrakt:	Background: The lack of systematic factors affecting physical inactivity (PIA) challenges policymakers to implement evidence-based solutions at a population level. The study utilizes the Eurobarometer to analyse PIA-modifiable variables. Methods: Special Eurobarometer 412 physical activity (PA) data were analysed (n = 18 336), including 40 variables along with the International PA Questionnaire. PIA was used as the dependent variable. Variables considered were alternatives to car, places, reasons and barriers to engaging in PA, memberships to clubs and categorical responses about the agreement extent with the area, provision of activities and local governance statements. Logistic regression was used to identify variables contributing to PIA. Beta values (β), standard errors, 95% confidence intervals, the exponentiation for odds ratio and Cox & Snell and Nagelkerke R2 were indicated. Results: The resulting model correctly identified 10.7% inactives and 96.9% of actives (R2 of Nagelkerke: 0.153). Variables contributing to the detection of PIA were (P ≤ 0.01): having a disability or an illness, not having friends to do sport with, lacking motivation or interest in and being afraid of injury risk. 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. Conclusions: The model reported a limited ability to detect modifiable factors affecting PIA, identifying a small percentage of inactive individuals correctly. New questions focused on understanding inactive behaviour are needed to support the European PA public health agenda. (© The Author(s) 2022. Published by Oxford University Press on behalf of the European Public Health Association.)
Databáze:	MEDLINE
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