Predicting physical activity behaviors in school-aged children

2007 ◽  
Author(s):  
H. Prapavessis ◽  
L. S. Foley ◽  
S. M. Burke ◽  
E. McGowan ◽  
R. Maddison ◽  
...  
2016 ◽  
Vol 48 ◽  
pp. 1049
Author(s):  
Ashlyn Schwartz ◽  
Aslynn Halvorson ◽  
Michael McClanahan ◽  
Gregory Grieco ◽  
Dawn Coe

2017 ◽  
Vol 26 (1) ◽  
pp. 97
Author(s):  
Monika Übner ◽  
Kandela Õun ◽  
Merle Mägi

In 2013/2014, a study on students’ health behaviour was conducted in Estonia. In 2016, a school-aged children’s lifestyle study was carried out in Pärnu City and Pärnu County. The survey explored the students’ relations with their family and friends, family affluence, physical activity, use of alcohol, tobacco, and cannabis. 2,512 respondents participated in the study, 48% of them were boys and 52% girls. The respondents mainly had a family with two biological parents and their family financial status was mostly “good”. About half of the respondents played computer games 0.5–3 hours a day, but those who were physically active spent less hours behind the computer and had higher family financial status. Respondents who were not physically very active met friends less frequently. In communication with friends, they used more social media. The questionnaire included several questions about risk behaviours. If the respondent had one bad habit, this was likely to lead to other bad habits, too.


2013 ◽  
Vol 167 (3) ◽  
pp. 223 ◽  
Author(s):  
Tala H. I. Fakhouri ◽  
Jeffery P. Hughes ◽  
Debra J. Brody ◽  
Brian K. Kit ◽  
Cynthia L. Ogden

2008 ◽  
Vol 20 (3) ◽  
pp. 342-356 ◽  
Author(s):  
Louise Foley ◽  
Harry Prapavessis ◽  
Ralph Maddison ◽  
Shauna Burke ◽  
Erin McGowan ◽  
...  

Two studies were conducted to predict physical activity in school-aged children. Study 1 tested the utility of an integrated model in predicting physical activity (PA) intention and behavior—the theory of planned behavior (TPB) and self-efficacy theory. Six hundred and forty-five New Zealand children (aged 11–13 years) completed measures corresponding to the integrated model and a self-reported measure of PA one week later. Perceived behavioral control (PBC) and subjective norm were the two strongest predictors of intentions. Task efficacy and barrier efficacy were the two strongest predictors of PA. A second study (Study 2) was conducted to determine whether the self-efficacy measures could discriminate objectively measured PA levels. Sixty-seven Canadian children (aged 11–13 years) completed task and barrier self-efficacy measures. The following week, children classified as ‘high’ (n = 11) and ‘lower’ (n = 7) for both task and barrier efficacy wore an Actical® monitor for seven consecutive days to provide activity-related energy expenditure (AEE) data. Results showed that children with high efficacy expended significantly greater AEE than their lower efficacious counterparts. Findings from these two studies provide support for the use of self-efficacy interventions as a potentially useful means of increasing PA levels among school-aged children.


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