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

Abstract

Background: Visualisations and readily-accessible web-based supplementary files can improve data reporting and transparency. In this paper, we make use of recent developments in software and psychological network analysis to describe the baseline cohort of a trial testing the Let's Move It intervention, which aimed to increase physical activity (PA) and reduce sedentary behaviours (SB) among vocational school students. Methods: At baseline, 1166 adolescents, distributed across 6 school clusters and four educational tracks, completed measures of PA and SB, theoretical predictors of these behaviours, and body composition. Within a comprehensive website supplement, which includes all code and analyses, data were tabulated and visualised, and network analyses explored relations between predictor variables and outcomes. Results: Average daily moderate-to-vigorous PA was 65 min (CI95: 57min-73 min), and SB 8h44 min (CI95: 8h04min-9h24 min), with 25.8 (CI95: 23.5-28.0) interruptions to sitting. 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, and several BCTs were connected to PA via autonomous motivation. Visualisation uncovered a case of Simpson's paradox. 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. We hope that this example will serve as a template for other investigators to improve upon in the future.

Keywords: Exercise; behaviour change; physical activity; school-based intervention; sedentary behaviour.

Figure 1.

Stacked bar plot drawn with R package ggplot (Wickham et al. (2018), code available at https://git.io/fptlp), showing proportions of accelerometer-measured physical activity (PA) in relation to measurement time, averaged over genders, arms and educational tracks. Nur = Practical nurse, HRC = Hotel, restaurant and catering, BA = Business and administration, IT = Information and communications technology.

Figure 2.

Raincloud ridge plot combined with a diamond plot, drawn with R packages ggridges (Wilke & ggridges, 2018) and userfriendlyscience (Peters, Verboon, and Green (2018), code available at https://git.io/fjLBG), showing hours of accelerometer-measured moderate-to-vigorous physical activity for different educational tracks. Midpoints of diamonds indicate means, endpoints 95% credible intervals (see (Heino, Vuorre, & Hankonen, 2018) for interpretation). Individual observations are presented under the density curves, with random scatter on the y-axis to ease inspection. Nur = Practical nurse, HRC = Hotel, restaurant and catering, BA = Business and administration, IT = Information and communications technology.

Figure 3.

Diamond comparison plot drawn with R package ufs (Peters (2019), code available at https://git.io/fjLBB), showing means (middle of diamonds), 99% confidence intervals (endpoints of diamonds) and individual answers (dots) separated by gender and arm. Rightmost plots show heuristic effect sizes for differences in means (transformed to Pearson’s r). ICC is not accounted for in any plot.

Figure 4.

Histogram drawn with R package ggridges (Wilke and ggridges (2018), code available at https://git.io/fpOLj), showing self-reported use of frequency-dependent BCTs (1 = Not once … 6 = Daily).

Figure 5.

Histogram drawn with R package ggridges (Wilke and ggridges (2018), code available at https://git.io/fjLBE), showing self-reported use of agreement-dependent BCTs (1 = Not at all true … 6 = Completely true).

Figure 6.

Mixed graphical model with LASSO regularisation and model selection by EBIC. Network models estimated and drawn with packages mgm (Haslbeck, 2019) and qgraph (Epskamp et al. (2019), code available at https://git.io/fpOXV). Blue lines indicate positive relationships. Plot shows the conditional dependence relationships between the variables of interest (edges which connect nodes), which can be interpreted akin to partial correlations. Pies depict means as proportion of theoretical maximum (in the case of accelerometer-measured moderate-to-vigorous physical activity (MVPA), mean as proportion of highest observed value); behaviour change technique (BCT) use and controlled motivation are dichotomised (see Methods). Node colours distinguish the three types of nodes; MPVA (blue), motivation (yellow), and BCT use (green).