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.

What explains the socioeconomic status gap in activity? Educational differences in determinants of physical activity and screentime.

BACKGROUND: Designing evidence-based interventions to address socioeconomic disparities in health and health behaviours requires a better understanding of the specific explanatory mechanisms. We aimed to investigate a comprehensive range of potential theoretical mediators of physical activity (PA) and screen time in different socioeconomic status (SES) groups: a high SES group of high school students, and a low SES group of vocational school students. The COM-B system, including the Theoretical Domains Framework (TDF), was used as a heuristic framework to synthesise different theoretical determinants in this exploratory study. METHODS: Finnish vocational and high school students (N = 659) aged 16-19, responded to a survey assessing psychological, social and environmental determinants of activity (PA and screen time). These determinants are mappable into the COM-B domains: capability, opportunity and motivation. The outcome measures were validated self-report measures for PA and screen time. The statistical analyses included a bootstrapping-based mediation procedure. RESULTS: Regarding PA, there were SES differences in all of the COM-B domains. For example, vocational school students reported using less self-monitoring of PA, weaker injunctive norms to engage in regular PA, and fewer intentions than high school students. Mediation analyses identified potential mediators of the SES-PA relationship in all of three domains: The most important candidates included self-monitoring (CI95 for b: 0.19-0.47), identity (0.04-0.25) and material resources available (0.01-0.16). However, SES was not related to most determinants of screentime, where there were mainly gender differences. Most determinants were similarly related with both behaviours in both SES groups, indicating no major moderation effect of SES on these relationships. CONCLUSIONS: This study revealed that already in the first years of educational differentiation, levels of key PA determinants differ, contributing to socioeconomic differences in PA. The analyses identified the strongest mediators of the SES-PA association, but additional investigation utilising longitudinal and experimental designs are needed. This study demonstrates the usefulness of combining constructs from various theoretical approaches to better understand the role of distinct mechanisms that underpin socioeconomic health behaviour disparities.