A Dual Process Model to predict Adolescents’ Screen Time and Physical Activity

OBJECTIVE Many adolescents report a lack of physical activity (PA) and excess screen time (ST). Psychological theories aiming to understand these behaviours typically focus on predictors of only one behaviour. Yet, behaviour enactment is often a choice between options. This study sought to examine predictors of PA and ST in a single model. Variables were drawn from dual process models, which portray behaviour as the outcome of deliberative and automatic processes. DESIGN 411 Finnish vocational school students (age 17-19) completed a survey, comprising variables from the Reasoned Action Approach (RAA) and automaticity pertaining to PA and ST, and self-reported PA and ST four weeks later. MAIN OUTCOME MEASURES Self-reported time spent on PA and ST and their predictors. RESULTS PA and ST correlated negatively (r = -.17, p = .03). Structural equation modelling revealed that intentions and habit for PA predicted PA while ST was predicted by intentions and habit for ST and negatively by PA intentions. RAA-cognitions predicted intentions. CONCLUSION PA and ST and their psychological predictors seem to be weakly interlinked. Future studies should assess more behaviours and related psychological influences to get a better picture of connections between different behaviours. HighlightsPhysical activity and screen time are largely mutually exclusive classes of behaviours and might therefore be related in terms of their psychological predictors.411 adolescent vocational school students self-reported variables from the Reasoned Action Approach and behavioural automaticity related to physical activity and leisure time screen time behaviours as well as those behaviours.Structural equation modelling revealed expected within-behaviour predictions but, against expectations, no strong connections between the two behaviour classes in terms of their predictors. Only intentions to engage in physical activity negatively predicted screen time.Future research should aim to measure a wider range of mutually exclusive classes of behaviours that cover a large share of the day to uncover relations between behaviours and their respective predictors.