Clustering of diet, physical activity and sedentary behaviour and related physical and mental health outcomes: a systematic review

Abstract

Background Physical activity (PA), sedentary behaviour (SB) and diet play an important role in the physical and mental health of young people. Understanding how these behaviours cluster, and the impact of clusters on health is important for the development of public health interventions. This review examines the prevalence of clusters of PA, sedentary time, and dietary behaviours, and how clusters relate to physical and mental health indicators among children, adolescents and young adults. Methods Electronic (PubMed, Web of Science and Scopus) and manual searches were conducted for articles that were (i) observational studies including children, adolescents and/or young adults aged 5–24 years, (ii) examined the 'patterning', ‘clustering’, or ‘co-existence’ of each of PA, dietary behaviour and SB, and (iii) published in English up to and including July 2022. In addition to information on clustering, data on physical and mental health outcomes were extracted where reported. Included studies were assessed using the Cochrane risk of bias for observational studies. A narrative synthesis was conducted due to high heterogeneity. This review was registered with PROSPERO (CRD42021230976). Results Forty-nine cross-sectional studies and four prospective cohort studies from eighteen countries reporting data from 778,415 individual participants were included. A broad range of clusters (n = 172) were found (healthy, unhealthy, and mixed). Mixed clusters were common (n = 98), and clusters of high diet quality, low PA and high SB were more prevalent in girls, while mixed clusters of high PA, high SB and low diet quality were more prevalent in boys. Unhealthy clusters comprising low moderate to vigorous PA, low consumption of fruits and vegetables, and high screen time were prevalent, particularly in those from lower socioeconomic status families. Compared to those with healthy behavioural clusters, those with unhealthy and mixed clusters had a higher adiposity, higher risk of cardiovascular disease, poorer mental health scores, and lower cardiorespiratory fitness. Conclusions PA, SB and diet cluster in healthy, unhealthy and mixed patterns in young people that differ across sociodemographic characteristics. Unhealthy clusters are associated with poorer health outcomes. Intervention strategies targeting un-clustering multiple unhealthy behaviours should be developed and evaluated for their impact on health outcomes.

Sedentary Behaviors and Eating Habits in Active and Inactive Individuals: A Cross-Sectional Study in a Population from Southern Italy

“…Additionally, concerns about the cost of healthy food can lead to differences in food purchasing based on household income [ 41 ]. Commonly, unhealthy behavioral clusters comprising low levels of moderate to vigorous PA, low consumption of fruits and vegetables and high screen time are prevalent in individuals from lower socioeconomic status [ 42 ].…”

Role of Satisfaction with Life, Sex and Body Mass Index in Physical Literacy of Spanish Children

“…Cardiovascular diseases, hypertension, type II diabetes [9][10][11], and mental health issues [12], such as depressive and/or anxiety disorders [13,14], are some of the diseases related to sedentary and inactive lifestyles. Similarly, overweight and obesity are also highly correlated [15,16].…”

Health-Related Quality of Life in Relation to Health Behaviour Patterns among Canadian Children

“…Cluster analysis and latent class analysis (LCA) are two major data-driven, person-centered methods to investigate co-occurring patterns or clusters of health-related behaviours among various populations [ 10 , 21 ]. Previous studies using the individual-centered statistical method mostly applied traditional cluster analysis [ 10 , 21 ].…”

“…Cluster analysis and latent class analysis (LCA) are two major data-driven, person-centered methods to investigate co-occurring patterns or clusters of health-related behaviours among various populations [ 10 , 21 ]. Previous studies using the individual-centered statistical method mostly applied traditional cluster analysis [ 10 , 21 ]. In the past decade, LCA has been used to assess a wide range of health-related behaviours, such as physical activity, sedentary behaviour, diet or eating habits, alcohol consumption, tobacco use, and other behaviours [ 10 , 11 , 15 , 22 ].…”