Ore, the same procedure was repeated separately for the psychosocial (eight models) and get MK-5172 environmental (eight models) variables. Second, socio-demographic variables, psychosocial variables, and environmental variables for which at least a trend towards a significant relationship (p<0.10) was observed in the previous step were (-)-Blebbistatin site included in one final model for each dependent variable (eight models). Consequently, fnins.2015.00094 the final models for the different dependent variables might include different socio-demographic, psychosocial and environmental variables. Furthermore, for each dependent variable, different socio-demographic, psychosocial and environmental variables might be included in the zero-inflated model compared to the negative binomial model. Only the results of the eight final models are reported in the results section and the tables. Self-efficacy was only included in the models for walking and cycling as this was only assessed for active transport. Concerning public transport, the four variables representing social modelling were included separately due to low internal consistency (Cronbach’s alpha<0.6). Distance to school and facilities at school were only included in the models for transport to school. To examine the correlates of transport to school, only participants living within a feasible active commuting distance of eight kilometres from school were included in analyses (n = 306) [21]. P-values < 0.05 were considered statistically significant.Results Sample characteristicsAfter exclusion of participants showing unrealistic data (i.e. 1000 minutes cycling/day) on nearly all items (n = 2) and participants younger than 16 years or older than 19 years (n = 49), 562 participants (562/1145 = 49.1 ) were included in the study. Table 2 presents socio-demographic characteristics, general transport data and data on transport to school and other destinations. In total, 54.6 of the sample was female, mean age was 17.8 (0.7) years and 71.6 was a high SES adolescent. Furthermore, 73.1 of the sample lived in a rural area. The participants that were included in the final sample did not differ significantly from those participants who did not complete the questionnaire entirely (n = 505) with regard to gender (54.4 female; p = 0.932) and SES (71.4 high SES; p = 0.951). However, participants who did not complete the questionnaire were on average younger (17.2 (0.9) years; p < 0.001). Mean scores of the psychosocial and environmental variables are shown in Table 1.Correlates of walkingTable 3 presents associations of psychosocial and environmental variables with walking. After controlling for socio-demographic variables, the logit model shows that older adolescents perceiving more social modelling for active transport had 29 lower odds of non-participation in walking to school. In other words, older adolescents perceiving more j.jebo.2013.04.005 social modelling for active transport were more likely to walk to school. The negative binomial model shows that among older adolescents who walked to school in the last week, those with a one-unit higher social norm towards active transport walked 15 minutes less to school. For walking to other destinations, the logit model shows that living in a densely built neighbourhood was associated with 21 lower odds of non-participation in walking to other destinations. Hence, older adolescents living in densely built neighbourhoods were more likely to walk to other destinations. The negative binomial model shows that among.Ore, the same procedure was repeated separately for the psychosocial (eight models) and environmental (eight models) variables. Second, socio-demographic variables, psychosocial variables, and environmental variables for which at least a trend towards a significant relationship (p<0.10) was observed in the previous step were included in one final model for each dependent variable (eight models). Consequently, fnins.2015.00094 the final models for the different dependent variables might include different socio-demographic, psychosocial and environmental variables. Furthermore, for each dependent variable, different socio-demographic, psychosocial and environmental variables might be included in the zero-inflated model compared to the negative binomial model. Only the results of the eight final models are reported in the results section and the tables. Self-efficacy was only included in the models for walking and cycling as this was only assessed for active transport. Concerning public transport, the four variables representing social modelling were included separately due to low internal consistency (Cronbach’s alpha<0.6). Distance to school and facilities at school were only included in the models for transport to school. To examine the correlates of transport to school, only participants living within a feasible active commuting distance of eight kilometres from school were included in analyses (n = 306) [21]. P-values < 0.05 were considered statistically significant.Results Sample characteristicsAfter exclusion of participants showing unrealistic data (i.e. 1000 minutes cycling/day) on nearly all items (n = 2) and participants younger than 16 years or older than 19 years (n = 49), 562 participants (562/1145 = 49.1 ) were included in the study. Table 2 presents socio-demographic characteristics, general transport data and data on transport to school and other destinations. In total, 54.6 of the sample was female, mean age was 17.8 (0.7) years and 71.6 was a high SES adolescent. Furthermore, 73.1 of the sample lived in a rural area. The participants that were included in the final sample did not differ significantly from those participants who did not complete the questionnaire entirely (n = 505) with regard to gender (54.4 female; p = 0.932) and SES (71.4 high SES; p = 0.951). However, participants who did not complete the questionnaire were on average younger (17.2 (0.9) years; p < 0.001). Mean scores of the psychosocial and environmental variables are shown in Table 1.Correlates of walkingTable 3 presents associations of psychosocial and environmental variables with walking. After controlling for socio-demographic variables, the logit model shows that older adolescents perceiving more social modelling for active transport had 29 lower odds of non-participation in walking to school. In other words, older adolescents perceiving more j.jebo.2013.04.005 social modelling for active transport were more likely to walk to school. The negative binomial model shows that among older adolescents who walked to school in the last week, those with a one-unit higher social norm towards active transport walked 15 minutes less to school. For walking to other destinations, the logit model shows that living in a densely built neighbourhood was associated with 21 lower odds of non-participation in walking to other destinations. Hence, older adolescents living in densely built neighbourhoods were more likely to walk to other destinations. The negative binomial model shows that among.