There is a lack of evidence regarding clusters of health-related behaviors among adolescents from low, lower-middle, and upper-middle income countries. This study aimed to identify clustering patterns of health-related behaviors (diet, physical activity [PA] and sedentary behavior [SB]) and association with sociodemographic variables among a population-based sample of Brazilian adolescents. Cross-sectional data from the 2015 National School-Based Health Survey (PeNSE). A total of 102,072 (females: 51.7%) students in ninth-grade (age: 14.3 ± 1.1 years-old) enrolled in public and private schools were investigated in this study. Healthy and unhealthy diet, PA and SB were measured using a validated questionnaire. Two-step cluster analysis was conducted to identify lifestyle patterns. The methodology for complex analysis and weighting was used to inferential statistical procedures. Multinomial logistic regression assessed associations between sociodemographic factors and the clusters. Three reliable and meaningful clusters were identified and labelled as follows: (1) health-promoting SB and diet (32.6%); (2) health-promoting PA and diet (44.9%), and (3) health-risk (22.5%). Compared to boys, girls were less likely to be in clusters 1 (OR = 0.85; 95% CI = 0.78–0.93, p < 0.001) and 2 (OR = 0.43; 95% CI = 0.40–0.46, p < 0.001) than the health-risk cluster. Higher socioeconomic status was positively associated with health-promoting PA and diet, and negatively related to health-promoting SB and diet. Older adolescents were more likely to be in cluster 1 than in cluster 3, compared to younger adolescents. Approximately one-quarter of the population (health-risk cluster) reported engaging in multiple risk behaviors. Interventions may need to be tailored to specific adolescent groups, especially considering sociodemographic differences.

Movement-related behaviors (physical activity [PA], sedentary behavior [SB], and sleep) and diet interact with each other and play important roles in health indicators in youth. This systematic review aimed to investigate how PA, SB, sleep, and diet cluster in youth by biological sex; and to examine which cluster are associated with health indicators. This study was registered in PROSPERO (number: CRD42018094826). Five electronic databases were assessed. Eligibility criteria allowed studies that included youth (aged 19 years and younger), and only the four behaviors {PA, SB, sleep, and diet (ultra-processed foods [UPF]; fruits and vegetables [FV])} analyzed by applying data-based cluster procedures. From 12,719 articles screened; 23 were included. Of these, four investigated children, and ten identified clusters by biological sex. Sixty-six mixed cluster were identified including, 34 in mixed-sex samples, 10 in boys and 11 in girls. The most frequent clusters in mixed-sex samples were “High SB UPF Low Sleep”, “Low PA High SB Satisfactory Sleep”, and “High PA”. The main difference in profiles according to sex was that girls’ clusters were characterized by high sleep duration, whereas boys’ clusters by high PA. There were a few associations found between cluster types and health indicators, highlighting that youth assigned to cluster types with low PA exhibited higher adiposity. In conclusion, the youth presented a range of clusters of behaviors, typically exhibiting at least one unhealthy behavior. Similar patterns were observed in both sexes with the biggest difference in time of sleep for girls and PA for boys. These findings underscore the importance of intervention strategies targeting multiple behaviors simultaneously to enhance health risk profiles and indicators in children and adolescents.

Background: The interaction between physical activity (PA), diet, and sedentary behavior (SB) plays an important role on health-related outcomes. This scoping review (Prospero CRD42018094826) aims to identify and appraise clusters of PA, diet, and SB among youth (0–19 years) according to country income. Methods: Five databases were searched. Fifty-seven articles met the inclusion criteria. Results: Fifty-five cluster types were identified, with greater variety in high-income than lower income countries. The most prevalent profiles were “High SB and consumption of sugar, salt, and beverages (SSB)” (n = 17) and “High PA” (n = 13–5), both of which presented in all income countries. The healthiest profile, “High PA and fruit and vegetables (F&V); Low SB and SSB” (n = 12), was present in upper-middle and high-income countries, while the unhealthiest “Low PA and F&V; High SB and SSB” (n = 6) was present only in high-income countries. Conclusions: High SB and unhealthy diet (SSB) were more prevalent in clusters, mainly in high-income countries. The results support the need for multi-component actions targeting more than one behavior at the same time.

Identifying the clustering and correlates of physical activity (PA) and sedentary behavior (SB) is very important for developing appropriate lifestyle interventions for children and adolescents. This systematic review (Prospero CRD42018094826) aimed to identify PA and SB cluster patterns and their correlates among boys and girls (0–19 years). The search was carried out in five electronic databases. Cluster characteristics were extracted in accordance with authors’ descriptions by two independent reviewers and a third resolved any disagreements. Seventeen studies met the inclusion criteria and the population age ranged from six to 18 years old. Nine, twelve, and ten cluster types were identified for mixed-sex samples, boys, and girls, respectively. While girls were in clusters characterized by “Low PA Low SB” and “Low PA High SB”, the majority of boys were in clusters defined by “High PA High SB” and “High PA Low SB”. Few associations were found between sociodemographic variables and all cluster types. Boys and girls in “High PA High SB” clusters had higher BMI and obesity in most of the tested associations. In contrast, those in the “High PA Low SB” clusters presented lower BMI, waist circumference, and overweight and obesity. Different cluster patterns of PA and SB were observed in boys and girls. However, in both sexes, a better adiposity profile was found among children and adolescents in “High PA Low SB” clusters. Our results suggest that it is not enough to increase PA to manage the adiposity correlates, it is also necessary to reduce SB in this population.

The association between dietary patterns and cardiometabolic risk factors is not well understood among adults in India, particularly among those at high risk for diabetes. For this study, we analyzed the data of 1007 participants (age 30–60 years) from baseline and year one and two follow-ups from the Kerala Diabetes Prevention Program using multi-level mixed effects modelling. Dietary intake was measured using a quantitative food frequency questionnaire, and dietary patterns were identified using principal component analysis. Two dietary patterns were identified: a “snack-fruit” pattern (highly loaded with fats and oils, snacks, and fruits) and a “rice-meat-refined wheat” pattern (highly loaded with meat, rice, and refined wheat). The “snack-fruit” pattern was associated with increased triglycerides (mg/dL) (β = 6.76, 95% CI 2.63–10.89), while the “rice-meat-refined wheat” pattern was associated with elevated Hb1Ac (percentage) (β = 0.04, 95% CI 0.01, 0.07) and central obesity (OR 1.16, 95% CI 1.01, 1.34). These findings may help inform designing dietary interventions for the prevention of diabetes and improving cardiometabolic risk factors in high-diabetes-risk individuals in the Indian setting.