Van Dyck et al. BMC Public Health 2011, 11:668 http://www.biomedcentral.com/1471-2458/11/668 Page 7 of 10 Table 4 Multiple linear regressions on contribution of multidimensional correlates to TV viewing and leisure-time Internet use Dependent variable TV viewing time Adj R2 0.39 Leisure-time Internet use 0.34 Significant correlates Sociodemographic factors Age Educational level Body Mass Index Home-environmental factors Size of largest TV set Psychosocial factors Pros reducing TV viewing Cons reducing TV viewing Self-efficacy reducing TV viewing Sociodemographic factors Educational level Employment status Home-environmental factors Number of computers Psychosocial factors Family social norm Pros reducing Internet use Cons reducing Internet use Self-efficacy reducing Internet use Standardized b 0.189*** -0.148** 0.096* 0.107* -0.177*** 0.155* -0.241*** 0.112* -0.154** 0.102* 0.161* -0.116** 0.187** -0.285*** Adj = adjusted *p < 0.05; **p < 0.01; ***p < 0.001 different subgroups (men and women, younger and mid- dle-aged adults, lower- and more highly-educated adults). Detailed interpretation of the correlates showed that educational level and age were the strongest socio- demographic correlates of TV viewing time. Middle-aged and lower-educated adults watched more TV than younger and higher-educated adults. These findings are consistent with other adult studies [22,32-34] and consequently, one could plead for special attention to lower-educated and older adults when developing interventions to reduce TV viewing. A possible explanation for the findings could be that lower-educated adults, who spend most of their work- ing day in manual tasks compensate by watching more TV in leisure-time. Previous study results have shown that occupational PA moderates the relationship between socio- economic status and leisure-time PA. Adults engaging in more PA during work were less active during leisure-time [35-37]. Consequently, they might have higher levels of lei- sure-time sitting, including TV viewing. Middle-aged adults might have more time to watch TV because they are less occupied (with childcare, work, etc) than are younger adults. Moreover, PA levels decrease with increasing age [1], so middle-aged adults might replace PA partly with more TV viewing time. Concerning leisure-time Internet use, lower-educated adults spent less time using Internet, which is opposite to the results of an Australian study [14]. Financial aspects may play a role: less-educated adults may have less financial resources and possibly have priorities other than buying a computer with Internet connection for leisure-time use. Our findings support this assumption: the prevalence of having no computer with Internet access at home was higher in less-educated adults compared to higher-educated adults (13.3% and 4.6% respectively, c2 = 9.9, p = .002). Unemployed and retired adults spent more time using Internet in the present study; this could poten- tially be explained by the higher discretionary time they have available [38]. BMI was less consistently associated with the outcome measures. Only for TV viewing, a posi- tive association was found. This was unexpected, as other studies in adults consistently showed strong positive asso- ciations of BMI with TV viewing time and leisure-time Internet use [14,22,33]. The non-significant association between BMI and internet use might be partly explained by the lower prevalence of this behavior compared with TV viewing. Moreover, several other factors besides screen time, like snacking, total caloric intake and PA might be more strongly related to BMI [39]. Regarding the home-environmental factors, positive associations were found between the size of the largest TV set and TV viewing time and between the number of computers and leisure-time Internet use, although the beta-values were small. However, no association was found between the number of TVs and TV viewing time. Roemmich and colleagues [40] found the number of TVs