SummaryDiet, physical activity, sedentary behaviour and sleep are typically examined independently with childhood adiposity; however, their combined influence remains uncertain. This review aims to systematically summarize evidence on the clustering of these behaviours through lifestyle patterns and evaluate associations with adiposity in children aged 5–12 years. Search strategies were run in six databases. Twenty‐eight papers met the inclusion criteria, six of which included all four behaviours. A range of lifestyle patterns were identified (healthy, unhealthy and mixed). Mixed patterns were most frequently reported. Unhealthy patterns comprising low physical activity and high sedentary behaviour were also frequently observed. Mixed patterns comprising healthy diets, low physical activity and high sedentary behaviour were more commonly seen in girls, whereas boys were more physically active, similarly sedentary and had unhealthier diets. Children from lower socio‐economic backgrounds tended to more frequently display unhealthy patterns. Unhealthy lifestyle patterns were more often associated with adiposity risk than healthy and mixed patterns. With few studies including all four behaviours, it is difficult to establish a clear picture of their interplay and associations with adiposity. Nonetheless, reliance on lifestyle patterns is likely more beneficial than individual behaviours in targeting adiposity and improving understanding of how these behaviours influence health.

Background Behavioural patterns are typically derived using unsupervised multivariate methods such as principal component analysis (PCA), latent profile analysis (LPA) and cluster analysis (CA). Comparability and congruence between the patterns derived from these methods has not been previously investigated, thus it’s unclear whether patterns from studies using different methods are directly comparable. This study aimed to compare behavioural patterns derived across diet, physical activity, sedentary behaviour and sleep domains, using PCA, LPA and CA in a single dataset. Methods Parent-report and accelerometry data from the second wave (2011/12; child age 6-8y, n = 432) of the HAPPY cohort study (Melbourne, Australia) were used to derive behavioural patterns using PCA, LPA and CA. Standardized variables assessing diet (intake of fruit, vegetable, sweet, and savoury discretionary items), physical activity (moderate- to vigorous-intensity physical activity [MVPA] from accelerometry, organised sport duration and outdoor playtime from parent report), sedentary behaviour (sedentary time from accelerometry, screen time, videogames and quiet playtime from parent report) and sleep (daily sleep duration) were included in the analyses. For each method, commonly used criteria for pattern retention were applied. Results PCA produced four patterns whereas LPA and CA each generated three patterns. Despite the number and characterisation of the behavioural patterns derived being non-identical, each method identified a healthy, unhealthy and a mixed pattern. Three common underlying themes emerged across the methods for each type of pattern: (i) High fruit and vegetable intake and high outdoor play (“healthy”); (ii) poor diet (either low fruit and vegetable intake or high discretionary food intake) and high sedentary behaviour (“unhealthy”); and (iii) high MVPA, poor diet (as defined above) and low sedentary time (“mixed”). Conclusion Within this sample, despite differences in the number of patterns derived by each method, a good degree of concordance across pattern characteristics was seen between the methods. Differences between patterns could be attributable to the underpinning statistical technique of each method. Therefore, acknowledging the differences between the methods and ensuring thorough documentation of the pattern derivation analyses is essential to inform comparison of patterns derived through a range of approaches across studies.

Background: Evidence for longitudinal associations between childhood weight status and academic achievement remains unclear due to considerable heterogeneity in study design, measures of academic achievement and appropriate categorization of weight status.Objective: To examine longitudinal associations between childhood weight status (underweight, healthy weight, overweight/obese) and academic achievement in the transition from preschool to primary (elementary) school among Australian school children. Methods: Data were from the Healthy Active Preschool and Primary Years study. Height and weight, for calculating BMI were measured at baseline (preschool age 3-5 years; 2008/9) and follow-up (primary school age 6-8 years; 2011/12). Academic achievement was measured at age 9 years. Results: No associations between BMI z-score or weight category in the preschool years and later NAPLAN scores were found for boys. For girls, having a higher BMI z-score (B = À13.68, 95%CI: À26.61, À0.76) and being affected by overweight

Identifying correlates of behavioural patterns are important to target population sub-groups at increased health risk. The aim was to investigate correlates of behavioural patterns comprising four behavioural domains in children. Data were from the HAPPY study when children were 6–8 years (n = 335) and 9–11 years (n = 339). Parents reported correlate and behavioural data (dietary intake, physical activity, sedentary behaviour, and sleep). Behavioural data were additionally captured using accelerometers. Latent profile analysis was used to derive patterns. Patterns were identified as healthy, unhealthy, and mixed at both time points. Multinomial logistic regression tested for associations. Girls were more likely to display healthy patterns at 6–8 years and display unhealthy and mixed patterns at 9–11 years than boys, compared to other patterns at the corresponding ages. Increased risk of displaying the unhealthy pattern with higher age was observed at both timepoints. At 9–11 years, higher parental working hours were associated with lower risk of displaying mixed patterns compared to the healthy pattern. Associations observed revealed girls and older children to be at risk for unhealthy patterns, warranting customisation of health efforts to these groups. The number of behaviours included when deriving patterns and the individual behaviours that dominate each pattern appear to be drivers of the associations for child level, but not for family level, correlates.

A positive perception of motor skills is important for physical activity participation. The aim was to investigate which modifiable factors predict children’s perceived motor skills. Mothers completed questionnaires when their child was 3.5 and 5 years old. At 5 years old, the children’s perceived motor competence (PMC) was assessed. Separate linear regression models (up to 300 children) examined which factors at each time point predicted children’s PMC, adjusted for relevant confounders. Multivariate models were then run with factors associated (p < 0.10) with perception. At 3.5 years, the time spent with same age and older children (both higher tertiles) and parental physical activity facilitation (sum of facilitation in last month, e.g., taking child to park) were initially associated with higher perception. Dance/gymnastics participation were associated with lower perceptions. Other child behaviours, maternal beliefs, play equipment, and swimming lessons were non-significant. In the final prospective model (n = 226), parental physical activity facilitation when child was 3.5 years old was the only factor to predict PMC. No factors were significant for the cross-sectional analyses at 5 years. Perceptions are formed based on past experiences which may explain why factors at 3.5 years rather than current experiences (when children were 5 years old) were associated with childhood perceptions.