Van Dyck et al. BMC Public Health 2011, 11:668 http://www.biomedcentral.com/1471-2458/11/668 in the home to be positively associated with TV viewing time in adolescent girls. It might be that in adults, having TVs versus having no TVs shows a stronger association with SB than just the number of TVs. Since only a very small percentage of the present sample (2.9%) had no TV in the home, this could possibly explain the non-signifi- cant results. Moreover, the location of the TV (bedroom vs other location) might also be important in the associa- tion with TV viewing time [41]. Overall, no strong evi- dence was found for the importance of home- environmental factors to explain variance in domestic screen time in this study. Psychosocial factors were strongly associated with domestic screen time. Self-efficacy about reducing screen time and perceiving pros and cons to reduce screen time were the most consistent correlates. No other adult studies examining these factors were identified, but in adolescents, self-efficacy and decisional balance (pros - cons) have been found to be important correlates of total sedentary time [26,42]. When examining the psychosocial constructs on item level, several consistent correlations with the out- come measures emerged. For self-efficacy, “confidence in being able to limit TV viewing time/Internet use to one hour a day” was strongly correlated to the outcome measures, while “thinking that TV viewing/Internet use is boring” and “feeling lazy and sluggish if watching TV/surf- ing on Internet for many hours” were the most strongly related pros. Concerning the cons, all items had strong correlations with TV time and Internet use. This specific information can be helpful to decide on which specific constructs one could focus when developing tailored SB- reducing interventions. Family social norm was associated with leisure-time Internet use, but not in the expected direction. A possible explanation for this unexpected result could be the phrasing of the questions on social norms. The questions might assess behavior rather than attitudes towards behavior. Consequently, the amount of domestic screen time might determine the responses to these ques- tions, biasing the results. Reformulation of these questions should be considered in further studies. Furthermore, the questions to assess social norm consisted of only one item for family and one item for friends and were derived from questions to measure social norm towards physical activ- ity; thus, our findings might have differed if this factor would have been assessed using multiple items. When comparing the present findings to what has been reported in studies examining correlates of PA in adults, similar patterns can be identified. Self-efficacy is one of the most consistent correlates of PA, together with per- ceived benefits and barriers and social support [1]. More- over, psychosocial factors (because they can be assessed in ways that closely match the behaviors of concern) usually explain a larger proportion of the variance in PA than do environmental attributes [43]. These similarities Page 8 of 10 suggest that the use of behavior-specific correlates may lead to some findings that are comparable across differ- ent behavioral domains. This is interesting for future interventions, since focusing on self-efficacy and bene- fits/barriers towards health behaviors (including both PA and SB) might lead to positive effects on different outcomes. The first study limitation was the cross-sectional study design, inducing that no inferences on causality can be made. Second, both outcome measures and potential cor- relates were self-reported, so the measures may suffer from social desirability and underestimation. Third, since the study sample was somewhat more likely to be highly- educated and employed compared to the Belgian popula- tion, generalizability of the results may be limited. Fourth, some possibly important correlates (e.g. number of TVs in the bedroom, enjoyment of SB, social support towards decreasing SB) were not included in the questionnaire or were only assessed with a limited number of questions (as was the case for how we assessed social norm from family and friends). These factors should be investigated more thoroughly in the future. Moreover, the validity of ques- tions to assess social norm from family and friends towards domestic screen time needs to be examined. Strengths of this study included the use of validated psy- chosocial questionnaires and the inclusion of correlates of multiple domains. In general, these findings are promising, with high pro- portions of variance in domestic screen time explained by mainly socio-demographic and psychosocial correlates. Future research should keep this focus on different SB (not only TV viewing and leisure-time Internet use, but also automobile-sitting and job-related sitting) and on multiple levels of determinants, because a wide range of correlates can be important for different types of SB. Moreover, within the ecological perspective [2], other cor- relates like modeling, enjoyment of SB and neighborhood factors could be included in future correlation models to increase the explained variance of SB [44]. According to the behavioral epidemiology framework of Owen and col- leagues [18] research should try to improve the measure- ment of SB (increased focus on objective measurement tools like accelerometers) and to strengthen the findings of behavioral determinants studies and intervention trials to reduce these behaviors. Conclusions In summary, the present findings show that educational level, age, self-efficacy about reducing screen time and pros and cons for reducing screen time were the most important correlates of TV viewing time and leisure-time Internet use in a sample of Belgian adults. Based on these findings, tailored interventions focusing mainly on psy- chosocial factors in specific population subgroups might