Visualisation and network analysis of physical activity and its determinants: Demonstrating opportunities in analysing baseline associations in the Let’s Move It trial

PEER REVIEWED VERSION NOW OPEN ACCESS AT https://doi.org/10.1080/21642850.2019.1646136. Supplementary website: https://git.io/fhxvu. Background: Let's Move It is a complex whole-school system intervention aiming to reduce sedentary behaviours (SB) and increase physical activity (PA) among adolescents in vocational schools, by targeting their environmental and psychosocial determinants. This paper describes participants' baseline characteristics in a cluster-randomised trial testing the Let's Move It intervention, and explores possibilities for visual data presentation, making use of recent developments in software and network analyses. We provide an example of a comprehensive research report with all analysis code and results in a readily accessible format, allowing other researchers to apply these tools to their own data. Methods: At baseline, 1166 adolescents in 57 classes at 6 school clusters, distributed across four educational tracks, participated the study. We measured PA and SB (with 7-day accelerometry), psychological and social constructs hypothesised to affect the intervention's effects on outcomes (with questionnaires), and body composition (with bioimpedance measurement). Data were visualised using various techniques, e.g., combining ridge plots and diamond plots. Network analysis was used to explore relations between psychological/social variables and outcomes. Results: Participants' mean age was 18.5 (Median = 18.0) years. On average, participants engaged in moderate-to-vigorous daily PA for 1h 5min(CI95: 0h 57min - 1h 13min), SB for 8h 44min(CI95: 8h 4min - 9h 24min), and interrupted their sitting 25.8 times (CI95: 23.5 - 28.0) per day on average. Cluster randomisation appeared to result in balanced distributions for baseline characteristics between intervention and control groups, but differences emerged across the four educational tracks. Self-reported behaviour change technique (BCT) use was low for many but not all techniques. A network analysis revealed direct relationships between PA and behavioural experiments, planning and autonomous motivation. Several BCTs were connected to PA via autonomous motivation. Conclusions: Data-visualisation and data exploration techniques (e.g. network analysis) can help reveal the dynamics involved in complex multi-causal systems -- a challenging task with traditional data presentations. The benefits of presenting complex data visually should encourage researchers to publish extensive analyses and descriptions as website supplements, which would increase the speed and quality of scientific communication, as well as help to address the crisis of reduced confidence in research findings.