Family social norm Internet useb My family members think that I spend too much time using the Internet Friends social norm Internet useb My friends think that I spend too much time using the Internet Self-efficacy reducing Internet use (sum of different items)c Turn off the computer even when I am doing something funny Limit my leisure-time Internet use to one hour a day Plan ahead of time how much time I will spend on leisure-time Internet use daily a Size of the largest TV was positively scored on a five-point scale (1-5), ranging from 39 cm to 139 cm b pros, cons, family social norm and friends social norm were positively scores on a five-point scale (1-5), ranging from ‘strongly disagree’ to ‘strongly agree’ c self-efficacy was positively scored on a five-point scale (1-5), ranging from ‘I think this is very difficult’ to ‘I do not think this is difficult at all’ r = Pearson correlation coefficient *p < 0.05; **p < 0.01; ***p < 0.001 with more TV viewing time (p = 0.014) while more pros (p < 0.001) and higher self-efficacy about reducing TV viewing time were related to less TV viewing time (p < 0.001). Results of the moderated regression analyses showed that none of the socio-demographic factors (gen- der, age, education) significantly moderated the associa- tions between the psychosocial and home-environmental factors and TV viewing time (all p > 0.05). Associations of socio-demographic, home-environmental and psychosocial variables with leisure-time Internet use (Table 4) In total, 34% of the variance in leisure-time Internet use was explained by the independent variables. Educational level was positively associated with leisure-time Internet use (p = 0.015), while employment status was negatively related to Internet use (p = 0.001). The number of compu- ters was the only home-environmental factor that was positively associated with leisure-time Internet use (p = 0.020). Concerning the psychosocial factors, perception of higher social norm from family towards Internet use (p = 0.011) and more cons (p = 0.002) were related to more leisure-time Internet use. Moreover, more pros (p = 0.009) and higher self-efficacy about reducing leisure-time Inter- net use were associated with less Internet use (p < 0.001). Results of the moderated regression analyses showed that none of the socio-demographic factors (gender, age, edu- cation) significantly moderated the associations between the psychosocial and home-environmental factors and lei- sure-time Internet use (all p > 0.05). Discussion This was the first study to investigate the concurrent contribution of socio-demographic, home-environmental and psychosocial factors to explain variance in domestic screen time in adults. Generally, the included factors explained a large amount (34% and 39%) of the variance in TV viewing and leisure-time Internet use. By conduct- ing analyses that examined sociodemographic attributes as both predictors of the main effects, and as moderators of the relationships of home environmental and psycho- social factors with domestic screen time, we were able to examine specifically whether these associations differed by gender, age and educational attainment. The absence of interaction effects suggested that similar psychosocial and home-environmental factors can be targeted in Table 3 Sedentary behaviors of the sample by gender, age and educational level min/day TV viewing time min/day leisure-time Internet use Page 6 of 10 0.40*** 0.26*** -0.47*** -0.30*** -0.54*** -0.51*** Mean (SD) 128.83 (78.88) 128.02 (74.96) 165.95 (80.35) 100.02 (59.96) 112.65 (72.57) 137.59 (41.94) Median (IQR) 120.00 (120.00) 120.00 (102.86) 180.00 (94.29) 94.29 (81.43) 120.00 (77.14) 137.14 (102.86) Mean (SD) 53.59 (53.17) 34.67 (37.74) 44.31 (54.95) 43.13 (39.20) 46.46 (46.10) 41.94 (46.91) Median (IQR) 38.57 (40.71) 25.71 (36.43) 30.00 (57.36) 30.00 (45.00) 30.00 (40.71) 30.00 (49.82) Van Dyck et al. BMC Public Health 2011, 11:668 http://www.biomedcentral.com/1471-2458/11/668 Table 2 Bivariate correlations of home-environmental and psychosocial factors with sedentary behaviors (Continued) Socio-demographic Factors Gender Men (n = 198) Women (n = 221) Educational level No college/univ (n = 181) College/univ (n = 238) Age 20 - 45 years (n = 169) 46 - 65 years (n = 250) IQR = inter quartile range SD = standard deviation Note: medians and inter quartile ranges were reported because the data were strongly skewed