Clustering of lifestyle factors and association with overweight in adolescents of the Kiel Obesity Prevention Study

Phenotyping children and adolescents with obesity using behavioral, psychological, and familial data

OBJECTIVE

This prospective cohort study aimed to empirically derive phenotypes of children and adolescents with overweight and obesity.

METHODS

Latent class analyses using Mplus were carried out in the Growing Up Today Study. Information on participants' weight status, disordered eating behaviors, body image and weight concerns, depressive symptoms, and pubertal timing, as well as and maternal weight status, were included in the latent class analyses, which were stratified by sex. Mixed-effects regression was used to examine associations of the obesity phenotypes with adult weight gain, between age 20 and 35 years, independent of weight at beginning of follow-up and duration of follow-up.

RESULTS

Among the girls, four obesity phenotypes were identified: 1) "early puberty"; 2) "mothers with obesity"; 3) "high weight concerns"; and 4) "mixed." Only three phenotypes were identified among the boys: 1) "high weight concerns"; 2) "mothers with obesity"; and 3) "mixed." Participants who had overweight or obesity in childhood or adolescence gained more weight in young adulthood than their leaner peers, but the patterns of weight gain in young adulthood varied by phenotype of obesity in childhood and adolescence.

CONCLUSIONS

These results support examining risk factors for and treatment outcomes by obesity phenotypes.

Lifestyle behaviors clusters in a nationwide sample of Spanish children and adolescents: PASOS study

BACKGROUND

Youth is a vulnerable period. To classify lifestyle behaviors and its relationship with health-related outcomes of Spanish children and adolescents.

METHODS

Cross-sectional study including 3261 children aged 7.5-17.5 y (52.8% females). Physical activity (PA), screen-time, sleep time, adherence to Mediterranean diet (MD), weight status (WS) by validated methods. Cluster analysis was run considering chronological age.

RESULTS

Six clusters were identified: C1: high screen time, low adherence to MD and sleep time (n = 431,13.20%); C2: high WS, medium adherence to MD,high sleep time, and low screen time (n = 466,14.30%); C3: young group with low screen time and high PA, adherence to MD and sleep (n = 537,16.40%); C4: worst profile regarding adherence to MD, PA, WS and sleep time (n = 609,18.70%); C5: low screen time and PA, high sleep time (n = 804,24.70%); C6: high PA and screen time, low WS (n = 414,12.70%). Mean absolute values were statistically different among PA levels, screen and sleep time, adherence to MD, age, and WS (all p < 0.001).

CONCLUSIONS

The most prevalent pattern was low levels of PA, MD, and screen time, and high sleep time. The second most prevalent was characterized by very low levels of PA, sleep time, and adherence to MD, and high screen time, and WS in adolescents.

IMPACT STATEMENT

The main identified lifestyle behavior was poor physical activity, low adherence to Mediterranean Diet and high screen and sleep time. Children should increase physical activity levels, adherence to Mediterranean diet, decrease screen and sleep the appropriate hours per day. Families, schools, and medical communities must work together to gloss over present and future diseases. Sleep time had not been previously included in cluster analysis with physical activity, sedentary behaviors, obesity, and nutritional status, thus the present data open a new perspective in Spanish population. Health policies should focus on promoting physical activity, Mediterranean diet, adequate sleep and reducing screen time.

Obesogenic behavior clusters associated with weight status among Brazilian students: a latent class analysis

This study aimed to examine the association between clusters of physical activity (PA), diet, and television viewing (TV) with weight status among a representative sample of Brazilian students. Data from the National Health School-based Survey (PeNSE) 2015 were analyzed (n = 16,521; mean age 14.8, standard deviation 0.03 year). PA (minutes/week spent in leisure-time, and commuting to/from school), TV (hours/day), and weekly consumption of deep-fried empanadas, candies, sodas, ultra-processed foods, fast foods, green salads or vegetables, and fruits were self-reported on the validated PeNSE questionnaire. Latent class analysis defined behavior classes, and binary logistic regression assessed the association between clustering and weight status. Six classes' types with positive and negative behaviors were identified. Adolescents belonging to the "low TV time and high healthy diet" class had higher chances of being overweight (including obesity) compared to their peers in the "moderate PA and mixed diet" class. No associations were found in the other clusters. Mixed classes with healthy and unhealthy behaviors characterized adolescents' lifestyles and these profiles were related to weight status.

Clustering of Obesogenic Behaviors Associated With Bullying Roles Among 100,794 Adolescents

Obesogenic behaviors have been individually associated with bullying during adolescence. However, physical activity, sedentary behavior, and diet manifest themselves in synergy and even behavioral profiles in which positive and negative behaviors coexist can be more positively associated with psychosocial outcomes. The present study aimed to analyze the association between clusters of obesogenic behaviors and different bullying roles in Brazilian adolescents. This cross-sectional study used data from the Brazilian School-based Health Survey-PENSE, 2015. A total of 100,794 male and female adolescents of the ninth-grade elementary school participated in the study. Students responded to an electronic questionnaire. Clusters of obesogenic behavior consisted of physical activity, exposure to sedentary behavior, and diet, and the different roles in bullying were: participant, victim, bully, and bully-victim. Binary logistic regression with 95% confidence intervals (CI) was used for data analysis (p < .05). Multiple adjustments and complex sampling procedures were employed. Adolescents in the cluster "Health-promoting sedentary behavior and Diet" had reduced chances of participating in bullying (odds ration [OR] = 0.70; 95% CI [0.64, 0.76]), of being a victim (OR = 0.87; 95% CI = 0.76-0.99), and being the bully (OR = 0.65; 95% CI [0.59, 0.71]); and those from the cluster "Health-promoting physical activity and Diet" had reduced chances of participating (OR = 0.81; 95% CI [0.76, 0.87]), being a victim of bullying (OR = 0.86; 95% CI [0.76, 0.98]), being the bully (OR = 0.79; 95% CI [0.72, 0.85]), and being a bully-victim (OR = 0.74; 95% CI [0.61, 0.90]), when compared to those from the "health-risk" cluster in the adjusted analysis. Clusters of obesogenic behavior may reduce adolescent bullying: victim, bully, and bully-victim benefit when exposed to healthier behavioral profiles. The school setting must recognize bullying as a problem and therefore simultaneously promote multi-component interventions to tackle physical activity, sedentary behavior, and eating behavior. Outcomes other than obesity should be acknowledged when promoting obesogenic behaviors.

Health-promoting domains and lifestyle of a sample of Brazilian adolescents

BACKGROUND

Reports indicate that the health habits of adults are strongly linked to the behaviors incorporated in adolescence. Therefore, it is essential to monitor the lifestyle of adolescents to promote their present and future health. This study aimed to identify differences in health-promoting domains according to demographic data and lifestyle behaviors, including physical activity, sedentary behavior, sleep duration, and food intake, in a sample of Brazilian adolescents.

MATERIALS AND METHODS

Cross-sectional school-based study, with the participation of 306 adolescents aged 14 to 18 years. A questionnaire was applied with structured questions to collect demographic data and lifestyle behaviors. To examine the health-promoting domains the Adolescent Health Promotion Scale (AHPS) was used. Data were analyzed using multivariate analysis.

RESULTS

Scores attributed to each of the health-promoting domains showed substantial variations according to sex, age, year of study, parents' schooling, and family economic class. After adjustment for covariables, the adolescent who presented significantly higher scores equivalent to the overall index of health promotion reported being more physically active (F = 4.848; P = 0.009), sleeping 6-8 hours/night (F = 2.328; P = 0.046), consuming fruit/vegetable more frequently (F = 3.168; P = 0.024), while sedentary behavior and intake of sweetened products/soft drinks have not shown any significant effect.

CONCLUSION

The findings confirmed the consistent positive influence of health-promoting domains assessed by AHPS on healthy lifestyle behaviors, suggesting in the intervention programs aimed at adopting healthy lifestyle approaches it is important enough to contemplate actions aimed at all the areas of health promotion with characteristics aimed at nutrition behavior, social support, health responsibility, life appreciation, exercise behavior, and stress management.

Clusters of Activity-Related Social and Physical Home Environmental Factors and Their Association With Children's Home-Based Physical Activity and Sitting

PURPOSE

Understanding which physical activity (PA) and sedentary behavior correlates cluster in children is important, particularly in the home, where children spend significant time. Therefore, this study aimed to assess clustering of physical and social activity-related factors at home, and whether these clusters are related to home-based sitting and PA in children. A secondary aim was to explore whether the clusters were associated with child, parent, and family characteristics.

METHODS

Altogether, 235 children (55% girls, mean age = 10.2 [0.7] y) and their parents took part. Physical (eg, PA and electronic media equipment, house and garden size, layout) and social (eg, activity preferences, priorities, parental rules) home environmental factors were obtained via the HomeSPACE-II audit and self-report, respectively. Principal component analysis was used to identify clusters of physical and social environmental factors. Backward regression analysis and partial correlations were used to examine relationships between clusters, children's device-measured home-based activity behaviors, and background characteristics.

RESULTS

The findings show that physical and social environment activity-related factors at home cluster. The clusters were associated with several background characteristics, with socioeconomic factors appearing to be particularly influential. The clusters were also associated with home-based activity behaviors in the hypothesized directions.

CONCLUSION

Interventions which target clusters of social and physical factors at home, especially among low-socioeconomic status families, are warranted.

Congruence between Physical Activity Patterns and Dietary Patterns Inferred from Analysis of Sex Differences in Lifestyle Behaviors of Late Adolescents from Poland: Cophylogenetic Approach

Global trends toward physical inactivity and the replacement of healthy dietary behaviors with unhealthy food consumption, particularly in late adolescence, were commonly observed. Thus, the aim of this work was twofold: (1) to examine congruence between PAPs and DPs by identifying sex differences in healthy and unhealthy behaviors and (2) to assess the explanation behind why certain participants were classified into clusters using BMI and fat mass covariates. Late adolescents (19-21 years of age, n = 107) were selected to study. PAPs and DPs were assessed with questionnaires (IPAQ, QEB) and body height, weight, and fat mass percentage were self-reported (the accuracy and reliability of self-reported data were empirically verified). A cophylogenetic approach with several clustering procedures (heatmaps and tanglegrams) was the way to solve the stated problems. Results showed that students achieved the minimal level of physical activity, lower than students from other regions in Poland, Czech Republic, or Germany. There was congruence between PAPs and DPs in some males' and females' behaviors. Significant similarities in sex patterns of behaviors were revealed (Mantel tests-PAPs: r = 0.70, p < 0.001; DPs: r = 0.84, p < 0.001). Similarities in PAPs were related to transportation cycling and school/work activities behaviors. Non-healthy triads in dietary behaviors were found: fast-food, sweetened beverages, and alcoholic drinks in the first, and sweets, fried meals, and yellow cheese in the second. One healthy diad was revealed: vegetables and fruits. Only females' dietary behaviors were reflected in body mass index (F = 3.19, p = 0.049), percentage of body fat (F = 3.87, p = 0.027), and fat mass index (F = 3.83, p = 0.028). The usefulness of the cophenetic approach in patterns study was verified. Sex similarities and specific disparities could help preparing targeted tailored intervention programs that improve healthy habits in late adolescents. This is especially important in relation to females, who more strongly reflected dietary behaviors in body composition.

The clustering of physical activity and screen time behaviours in early childhood and impact on future health-related behaviours: a longitudinal analysis of children aged 3 to 8 years

BACKGROUND

Meeting physical activity and screen time guidelines has been associated with improved health in children. Research has shown that lifestyle behaviours happen in combination and can be tracked into later life. Thus, a complex approach is needed to identify the effects of physical activity and screen time altogether. This study aims to identify clusters of both behaviours in a cohort of Irish 3-year-old children (n = 8833) and determine the association with sociodemographic characteristics and behaviours at age 5 and 7-8.

METHODS

Data from the "Growing Up in Ireland" study collected between 2010 and 2016 was used in this study. Two-step cluster analysis was used to understand how physical activity and recreational screen time behaviours group together among 3-year-old children. Binary logistic regressions were conducted to examine if cluster placement at age 3 determined physical activity and recreational screen time behaviours at age 5 and 7-8 years, while controlling for gender of child, gender, age and employment status of the primary caregiver.

RESULTS

Six clusters were identified in 9771 (49.3% female) 3-year-old children with the majority falling into a "High Active & Mixed Screen Time" (23.2%). Those in the "High Active & Mixed Screen Time" cluster at age 3 were more likely to engage in all physical activities reported at age 5 (p < 0.01) and age 7-8 (p < 0.01) when compared to a "Low Active & Screen Time Exceed" cluster. Children categorised in a "Moderate Active & Screen Time Below" and "Moderate Active & Screen Time Exceed" were more likely to engage in the same physical activities at age 5 and 7-8 (p < 0.05 - p < 0.01). However, children in the latter cluster were also more likely (p < 0.05) to play on a computer or tablet device.

CONCLUSIONS

This paper highlights the importance of establishing positive health-related behaviours during early childhood, as this predicts future engagement in health-promoting activities. Regardless of screen time level, being part of a cluster with moderate or high levels of physical activity positively influences a child's future physical activity at age 5 and again at age 7 -8 years. The multiple layers of influence on a child's development should be leveraged to support the adoption of health-enhancing behaviours.

Obesogenic clusters: excess body weight-related lifestyle behaviors in schoolchildren

abstract The objective of the study was to identify the existence of clusters in multiple lifestyle behaviors, including fruits/vegetables intake, sweetened products/soft drinks intake, sleep duration, physical activity and sedentary behavior. In sequence, the association between identified clusters and excess body weight in schoolchildren was examined. This was a cross-sectional school-based epidemiological study with approximately 17 thousand schoolchildren aged 4-20 years who participated in the Parana Health Project. Questionnaire was applied with structured questions to collect lifestyle behaviors. Excess body weight was identified through body mass index. Cluster analysis was performed to identify sex-specific clusters of multiple lifestyle behaviors. Analysis of covariance and logistic regression were used to analyze associations between clusters and excess body weight. Five clusters were identified in both sexes. Schoolchildren in the cluster characterized by lower fruits/vegetables intake and greater time spent in sedentary behavior showed close to twice the chance of having excess body weight compared to their peers in the reference cluster (girls: OR=1.98 [1.41-2.93]; boys: OR=1.94 [1.39-3.01]). In the case of schoolchildren in the cluster characterized by high sweetened products/soft drinks intake and shorter sleep duration, the chances of having excess body weight were 69% in girls (OR=1.69 [1.23-2.67]) and 73% in boys (OR=1.73 [1.25-2.91]). In conclusion, high fruits/vegetables intake, low sweetened products/soft drinks intake, longer sleep duration and less sedentary behavior was considered the most effective combination for maintaining a healthy body weight in the sample of schoolchildren.

Clustering of Physical Activity, Diet and Sedentary Behavior among Youth from Low-, Middle-, and High-Income Countries: A Scoping Review

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.

Obesogenic Clusters Associated with Weight Status in Brazilian Adolescents of the Movimente School-Base Intervention

Background: the relationship between behavior clusters and weight status, mainly in low- and middle-income countries, remains unclear. This study aimed to examine the association between profiles of physical activity (PA), diet and sedentary behavior (SB) with weight status in adolescents from a southern Brazilian city, according to sex. Methods: data from the Movimente Intervention study were analyzed (n = 812 / mean age 13.0 years (sd 1.04). Data on SB hours per day, PA minutes per week and weekly consumption frequencies of fruits, vegetables, salty snacks, candies and soda were self-reported on the validated Movimente questionnaire. Classes of healthy and unhealthy behaviors were derived by latent class analysis. Logistic regression analysis was used to estimate the associations between adolescents’ weight status and classes. Results: two classes were identified for the whole sample and for boys and girls. All classes had high probabilities of engaging high time in SB. Male adolescents in the unhealthy class had low probabilities of being active and high probability of consuming a low-quality diet. In contrast, girls’ healthiest profile presented lower probabilities of being active compared to boys’ healthiest profiles. No association was found between weight status and classes. Conclusion: All classes had at least one unhealthy behavior, for both the whole sample, and for girls and boys. Girls’ profiles were unhealthier compared to boys’ profiles. Hence, it is recommended that intervention strategies to change behaviors need to be distinct according to sex, targeting more than one obesogenic behavior at the same time.

Association of Diet, Physical Activity Guidelines and Cardiometabolic Risk Markers in Children

The aim was to identify different dietary and physical activity (PA) patterns in 5- to 14-year-old children with a high prevalence of overweight and obesity using cluster analysis based on their adherence to the Spanish Society of Community Nutrition dietary guidelines and levels of PA, and to determine their associations with age, sex, body composition, and cardiometabolic risk markers. In 549 children, hierarchical cluster analysis was used to identify subgroups with similar adherence to dietary recommendations and level of PA. Three clusters were identified: Cluster 1, with the lowest level of vigorous PA and adherence to dietary recommendations; Cluster 2, with the lowest levels of moderate and vigorous PA and the highest adherence to dietary recommendations; and Cluster 3, with the highest level of PA, especially vigorous PA and a medium level adherence to dietary recommendations. Cluster 3 had lower total body fat and higher lean body mass percentages than Cluster 2. Cluster 2 had lower high-density lipoprotein cholesterol and higher low-density lipoprotein cholesterol levels than Cluster 1. The results from our study suggest that it is important to consider adherence to PA recommendations together with adherence to dietary guidelines to understand patterns of obesogenic habits in pediatric populations with high prevalence of overweight and obesity.

Joint trajectories of life style indicators and their links to psychopathological outcomes in the adolescence

BACKGROUND

Rapid socio-economic development makes China a unique laboratory for examining how lifestyle changes affect adolescent mental health. This study aims to identify joint trajectories of modifiable lifestyle indicators during pubertal transition and its associations with psychopathological outcomes.

METHODS

A cohort of 1974 children aged 7-9 years were recruited in Anhui Province, China during March 2013. The assessment of lifestyle behaviors (screen time, physical activity, sleep duration and beverage intake) and depressive symptoms were conducted from Wave 1 to Wave 4 (2018). Suicide ideation, non-suicidal self-harm (NSSI) and alcohol use were self-reported at Wave 4. Longitudinal trajectories of lifestyle patterns were defined using group-based multi-trajectory models in 2019.

RESULTS

Four lifestyle trajectories were identified: persistent healthy (39.9%), suboptimal healthy (25.3%), unhealthy mitigation (17.2%), and persistent unhealthy (17.7%). Compared with persistent healthy group, the risk of subsequent suicide ideation [odds ratio (OR): 2.86, 95%CI: 2.15-3.81], depressive symptoms (OR: 2.16, 95%CI: 1.39-3.35), alcohol use (OR: 2.53, 95%CI: 1.78-3.61) and non-suicidal self-harm (OR: 1.35, 95%CI: 1.09-1.67) was significantly higher in persistent unhealthy group.

CONCLUSIONS

This study provided convincing evidence that unhealthy lifestyle trajectory during adolescence is associated with more than two-fold elevated odds for multiple domains of psychopathological outcomes over 5 years.

Clustering of multiple health risk behaviours and association with socio-demographic characteristics and psychological distress among adolescents in Ghana: A latent class analysis

This study examined the prevalence of multiple health risk behaviours, the clustering patterns of health risk behaviours, the association between socio-demographic characteristics, psychological distress and clusters and the relationship between number of health risk behaviours and psychological distress among adolescents in Ghana. Participants were senior high school (SHS) students aged 11-19 years who participated in the 2012 Global School-based Students Health Survey (n = 1763). Five health risk behaviours (smoking tobacco, inadequate fruit intake, inadequate vegetable intake, alcohol intake and physical inactivity) were measured. Participants were classified to be at risk if they indicated they smoked tobacco, did not eat fruit ≥ 2 times a day and vegetables ≥ 5 a day, drank alcohol during the past 30 days and did not engage in physical activity for ≥ 60 min per day during the past 7 days. Latent class analysis and latent regression were used to identify the clusters and factors associated with the clusters respectively. Multiple logistic regression was used to determine the relationship between number of health risk behaviours and psychological distress. The prevalence of multiple health risk behaviours (2 or more) was 94.8%. Two clusters emerged: Cluster 1 ("Poor nutrition, inactive, low substance use cluster"; 91%); Cluster 2 ("High Risk Cluster"; 9%). Using cluster 1 as a reference group, adolescents in the 11-15 years category had lower odds of belonging to cluster 2 (OR = 0.21 CI 0.05-0.91, ρ = 0.036) while those experiencing symptoms of depression had higher odds of belonging to cluster 2 (OR = 2.45 CI 1.45-4.14, ρ = 0.001). No significant relationship was found between number of health risk behaviour and psychological distress. Health risk behaviours cluster among adolescents with age and depression associated with the identified clusters. Early interventions that target these clusters are needed at the individual, school and community level to mitigate the burden of non-communicable diseases.

Clustering of health-related behaviours within children aged 11-16: a systematic review

OBJECTIVE

We aimed to systematically review and synthesise evidence on the clustering of a broad range of health-related behaviours amongst 11-16 year olds.

METHOD

A literature search was conducted in September 2019. Studies were included if they used cluster analysis, latent class analysis, prevalence odds ratios, principal component analysis or factor analysis, and considered at least three health-related behaviours of interest among 11-16 year olds in high-income countries. Health-related behaviours of interest were substance use (alcohol, cigarettes and other drug use) and other behavioural risk indicators (diet, physical activity, gambling and sexual activity).

RESULTS

The review identified 41 studies, which reported 198 clusters of health-related behaviours of interest. The behaviours of interest reported within clusters were used to define eight behavioural archetypes. Some included studies only explored substance use, while others considered substance use and/or other health-related behaviours. Consequently, three archetypes were comprised by clusters reporting substance use behaviours alone. The archetypes were: (1) Poly-Substance Users, (2) Single Substance Users, (3) Substance Abstainers, (4) Substance Users with No/Low Behavioural Risk Indicators, (5) Substance Abstainers with Behavioural Risk Indicators, (6) Complex Configurations, (7) Overall Unhealthy and (8) Overall Healthy.

CONCLUSION

Studies of youth health behavioural clustering typically find both a 'healthy' cluster and an 'unhealthy' cluster. Unhealthy clusters are often characterised by poly-substance use. Our approach to synthesising cluster analyses may offer a means of navigating the heterogeneity of method, measures and behaviours of interest in this literature.

Latent Class Analysis of Lifestyle Risk Factors and Association with Overweight and/or Obesity in Children and Adolescents: Systematic Review

Background: Multiple modifiable lifestyle factors are well-known contributors to many health problems. Objectives: This study aims to determine the association between latent class analysis (LCA) of modifiable lifestyle risk factors with being overweight and/or obese for children and/or adolescents. Methods: Articles were selected from six databases, without limitation regarding language or date. The review included studies that identify latent classes of modifiable lifestyle risk factors [e.g., physical activity (PA), diet, sedentary behavior (SB), and/or unhealthy behavior] by LCA to determine the association between latent classes with being overweight and/or obese. The methodology of the selected studies was evaluated using the JBI Critical Appraisal Checklist for Analytical Cross-Sectional Studies. Owing to the heterogeneity between latent classes of modifiable lifestyle risk factors with obesity and/or being overweight, the results are described narratively. Results: Using a selection process in two phases, nine articles were included. All of the included studies were of high methodological quality. The studies were conducted in six different countries: the USA, Brazil, Canada, Portugal, Italy, and Australia. Sample sizes ranged from 166 to 18.587 children and adolescents, and in terms of age (range 5-19 years). Across study clusters characterized by low consumption of fruit and vegetables, and high consumption of fatty foods, sugar snack foods, sweets, chips and fries, low PA (<1 hour each day), and high SB (screen time and TV >2 hours/day), sleep time (<10 hours/day) were positively associated with being overweight and/or obese. Conclusion: Overall there is good evidence to support that the modifiable lifestyle risk factors clustered together by LCA should be novel targets for the treatment of obesity and its associated comorbidities.

A composite measure of healthy lifestyle: A study from 38 countries and regions from Europe and North America, from the Health Behavior in School-Aged Children survey

INTRODUCTION

This study aimed to create a composite measure of a healthy lifestyle for adolescents, and analyze its relationship to sociodemographic factors.

METHODS

Data were from the Health Behavior in School-Aged Children 2014 International survey. Participants were 167 021 adolescents (48.2% boys and 51.8 girls), aged 10-16 years, from 38 countries.

RESULTS

Five healthy behaviors used in this study included engaging in ≥60 minutes of physical activity every day, daily consumption of fruit and vegetables, spending <2 hours daily immersed in screen-based behaviors, and abstinence from alcohol as well as from tobacco products. Only 1.9% (95% CI: 1.4%, 2.3%) of adolescents had a healthy lifestyle, achieving all five healthy behaviors. In contrast, 4.2% (95% CI: 3.7%, 4.6%) reported none of the healthy behaviors.

CONCLUSIONS

Despite the benefits of engaging in physical activity, engaging in low levels of screen-based activity, regular consumption of fruits and vegetables, and abstaining from alcohol and cigarettes, only 2% of adolescents could be classified as having a healthy lifestyle.

Clustering of lifestyle risk factors for non-communicable diseases in 304,779 adolescents from 89 countries: A global perspective

The precursors of non-communicable diseases (NCDs) are often manifested during childhood and adolescence with little knowledge about co-occurrence of their related lifestyle risk factors. To address this deficit, we estimated the prevalence and clustering of six major NCD-risk factors in adolescents around the world. Data from the Global School-based Student Health Survey, collected between 2007 and 2016, were analysed in 304,779 adolescents aged 11-17 years (52.2% females) from 89 countries. We compared the observed (O) to expected (E) prevalence ratios of 64 possible combinations of six risk factors to determine their clustering patterns. Overall, 82.4% (95% CI 82.1-82.7) of adolescents had ≥2 risk factors, while 34.9% (34.6-35.3) had ≥3. Adolescents aged 16-17 years, compared to those aged 11-13 years, had higher odds (OR 1.33; 95% CI 1.31-1.36) of reporting ≥3 risk factors. Risk factors clustered in multiple combinations and differed by sex. The clustering of physical inactivity and low fruit and vegetable intake was evident in both males (O/E 1.10; 95% CI 1.07-1.12) and females (1.08; 1.06-1.10). The co-occurrence of cigarette smoking, alcohol drinking, physical inactivity, and low fruit and vegetable intake was 165% greater in females (2.65; 2.28-3.07) and 110% greater in males (2.10; 1.90-2.32) than expected. Globally, adolescents exhibit multiple modifiable risk factors for future development of NCDs. Early gender-specific prevention strategies targeting clusters of lifestyle risk factors should be prioritised to help mitigate future burden of NCDs globally. Periodical collection of behavioural risk factor data should be encouraged to facilitate a sustainable global surveillance.

'Social screens' and 'the mainstream': longitudinal competitors of non-organized physical activity in the transition from childhood to adolescence

Background

Physical activity (PA) tends to decline during late childhood and adolescence. In Australia, this decline has been shown to occur particularly in non-organized PA (e.g. active play and informal sport). Using a social marketing approach, segments of youth may be identified and targeted based on their profile of alternative activities that compete with non-organized PA during the transition to adolescence. The objectives of this study were to identify and describe segments of youth whose participation in non-organized PA declined between 11 and 13 years, based on changes in other potential competing activities during this period.

Methods

Data were sourced from Waves 4 and 5 of the Longitudinal Study of Australian Children. Participation in non-organized PA and thirteen alternative activities (e.g. video games, homework, sleep) were measured using 24-h time-use diaries. Analyses were limited to participants whose non-organized PA had declined between 11 and 13 years (n = 1043). Two-stage cluster analysis was conducted and segments were described using chi-square and t-tests.

Results

Among the analytic sample, average non-organized PA participation declined by 87 min/day between 11y and 13y (p < 0.001). Two segments were identified (κ = 0.66). The ‘Social Screens’ segment (n = 143) had large increases in texting, emailing and social media use (+ 56 min/day, p < 0.001) and other internet use (+ 32 min/day, p < 0.001). Conversely, ‘the Mainstream’ segment (n = 900) had smaller increases in a wider range of activities, including other PA (organized PA, active transport, active chores/work) (+ 16.0 min/day, p < 0.001), homework/study (+ 9.5 min/day, p < 0.001) and electronic gaming (+ 6.7 min/day, p < 0.05). ‘Social Screens’ were more likely to attend public school, live in urban areas and have more advanced pubertal development (girls only). ‘The Mainstream’ were more likely to participate in PA and out-of-school activities.

Conclusions

The ‘Social Screens’ segment had a much larger increase in texting, emailing, social media and other internet use, and lower participation in overall PA and out-of-school activities, compared with ‘the Mainstream’. Future research may trial PA promotion strategies to replace benefits that this segment may seek in competing activities (e.g. social PA apps).

Prevalence and Trends of Overweight and Obesity in European Children From 1999 to 2016: A Systematic Review and Meta-analysis

IMPORTANCE

Studies of trends in excess weight among European children throughout the last few decades have rendered mixed results. Additionally, some studies were outdated, were based on self-reported weight and height, or included only a few European countries.

OBJECTIVE

To assess prevalence trends in measured overweight and obesity among children across Europe from 1999 to 2016 using a systematic methodology.

DATA SOURCES

MEDLINE, Embase, CINAHL, and Web of Science were searched from their inception until May 2018. Moreover, searches were conducted on health institutions' websites to identify studies not published in scientific journals.

STUDY SELECTION

The inclusion criteria were: (1) studies reporting the population-based prevalence of excess weight (overweight plus obesity) or obesity according to body mass index cutoffs proposed by the International Obesity Task Force; (2) cross-sectional or follow-up studies; and (3) studies including populations aged 2 to 13 years.

DATA EXTRACTION AND SYNTHESIS

Literature review and data extraction followed established guidelines. The Mantel-Haenszel method was used to compute the pooled prevalence estimates and their 95% CI whenever there was no evidence of heterogeneity (I2 < 50%); otherwise, the DerSimonian and Laird random-effects method was used. Subgroup analyses by study year, country, or European region (Atlantic, Iberian, Central, and Mediterranean) were conducted. Prevalence estimates were calculated as an aggregate mean, weighted by the sample size and the number of individuals in each study.

RESULTS

A total of 103 studies (477 620 children aged 2 to 13 years) with data from 28 countries were included. The combined prevalence of overweight and obesity in the Iberian region tended to decrease from 30.3% (95% CI, 28.3%-32.3%) to 25.6% (95% CI, 19.7%-31.4%) but tended to increase in the Mediterranean region from 22.9% (95% CI, 17.9%-27.9%) to 25.0% (95% CI, 14.5%-35.5%). No substantial changes were observed in Atlantic Europe or Central Europe, where the overweight and obesity prevalence changed from 18.3% (95% CI, 14.0%-23.9%) to 19.3% (95% CI, 17.7%-20.9%) and from 15.8% (95% CI, 13.4%-18.5%) to 15.3% (95% CI, 11.6%-20.3%), respectively.

CONCLUSIONS AND RELEVANCE

The prevalence of childhood overweight and obesity is very high, but trends have stabilized in most European countries. There are substantial between-country differences in the current levels and trends of overweight and obesity. The rising prevalence in some Mediterranean countries is worrisome.

TRIAL REGISTRATION

PROSPERO identifier: CRD42017056924.

Activity-related behavior typologies in youth: a systematic review

BACKGROUND

Clusters of adolescents differentiated by patterns of physical activity and sedentary behavior (activity-related typologies) are common. Understanding both the characteristics of adolescents and modifiable correlates of these typologies, can help to develop interventions for those most at risk. This systematic review aimed to synthesize the socio-demographic characteristics and modifiable correlates of activity-related behavioral typologies among adolescents.

METHODS

A systematic search of seven electronic databases was conducted to identify quantitative studies using person-oriented statistical approaches to identify activity-related behavioral typologies among 12-18 year-olds. This systematic review was registered in Prospero (registration number: CRD42016046879).

RESULTS

Thirty-six studies met the inclusion criteria and were classified according to three sub-themes based on behaviors included in the typologies (1. physical activity and sedentary behavior only; 2. physical activity, sedentary behavior and risk-related behaviors; 3. physical activity, sedentary behavior and diet). Studies were mostly cross-sectional and relied on self-report measures. Methods were considerably heterogeneous, however results revealed some consistency in typologies within specific groups. For example, typologies characterized by unhealthy behavior patterns (e.g., characterized by physical inactivity, high sedentary behavior and poor diet or high risk-related behaviors) comprised more older adolescents. With the exception of socio-demographics (age, sex, body mass index and socio-economic status), very few correlates have been studied to date (mostly school-related behavioral factors and intrapersonal influences), with evidence largely from typologies comprised of physical activity, sedentary behavior and diet.

CONCLUSIONS

More research is needed to assess a range of modifiable correlates associated with activity-related behavior typologies among adolescents. This will allow for more targeted interventions, to achieve long-lasting, positive behavior change in adolescent populations.

What Psychosocial Factors Determine the Physical Activity Patterns of University Students?

Background: Although levels of physical activity (PA) have been researched, no information on how university students organize their PA across different life domains is available. The purpose of this study is to explore if and how students organize their PA across transport and recreational domains, and to identify the psychosocial factors related to these patterns. Methods: Students from 31 Irish universities completed a supervised online survey measuring participant characteristics, psychosocial factors, and PA. Two-step cluster analysis was used to identify specific PA patterns in students. Binary logistic regressions identified factors associated with cluster membership while controlling for age, sex, household income, and perceived travel time to a university. Results: Analysis was performed on 6951 students (50.7% male; 21.51 [5.55] y). One Low Active cluster emerged. Four clusters containing a form of PA emerged including Active Commuters, Active in University, Active Outside University, and High Active. Increases in motivation and planning improved the likelihood of students being categorized in a cluster containing PA. Conclusion: One size does not fit all when it comes to students PA engagement, with 5 patterns identified. Health professionals are advised to incorporate strategies for increasing students' motivation, action planning, and coping planning into future PA promotion efforts.

Identification of health-related behavioural clusters and their association with demographic characteristics in Irish university students

Background

Students engage in risky health-related behaviours that influence their current and future health status. Health-related behaviours cluster among adults and differently based on sub-populations characteristics but research is lacking for university populations. Examining the clustering of health- related behaviours can inform our initiatives and strategies, while examining cluster members’ characteristics can help target those who can prosper most from health promotion efforts. This study examines the clustering of health-related behaviours in Irish university students, and investigates the relationship with students’ sex, age, field of study and accommodation type.

Methods

An online survey was completed by 5672 Irish university students (51.3% male; 21.60 ± 5.65 years) during 2014. Two-step cluster analysis was used to understand how health-related behaviours (physical activity, smoking, alcohol intake, drug use and dietary habits) cluster among male and female students. Binary logistic regressions were conducted to examine the likelihood of students falling into certain clusters based on their characteristics.

Results

Five cluster groups were identified in males and four in females. A quarter of males were categorised as ideal healthy with older students and those from certain fields of study having a higher likelihood of being classified in a low physical activity and poor diet (OR = 1.06–2.89), alcohol consumption (OR = 1.03–3.04), or smoking and drug use (OR = 1.06–2.73) cluster. Forty-five percent of females were categorised as ideal healthy with older females more likely to be in a low active and smoking cluster (OR = 1.03), and less likely to be in a convenience food cluster (OR = 0.96). Females from certain fields of study were also more likely to be classified in these clusters (OR = 1.59–1.76). Students living away from their family home had in increased likelihood of being in a cluster related to a higher frequency of alcohol consumption (OR = 1.72–3.05).

Conclusion

Health-related behaviours cluster among this population and need to be taken into account when designing multi-health interventions and policies. These findings can be used to target student groups at risk, leading to more efficient and successful health promotion efforts. The addition of modules providing information regarding health-related behaviours are advised in all fields of study.

Lifestyle Clusters in School-Aged Youth and Longitudinal Associations with Fatness: The UP&DOWN Study

OBJECTIVES

To identify lifestyle clusters in children and adolescents, to analyze associations between lifestyle clusters and body fat percentage (BFP) at baseline and 2 years later, and to examine if BFP at baseline is associated with BFP 2 years later.

STUDY DESIGN

This longitudinal study involved 1634 Spanish youth (804 girls) aged 8-18 years (mean, 12.45 ± 2.51 years). Cluster analysis was performed by including objectively measured sedentary time and physical activity and self-reported screen time and diet. The associations between cluster membership and BFP was analyzed through general linear models. All the analyses were separated by 3 age groups: older children, younger adolescents, and older adolescents.

RESULTS

Four clusters were identified in the 3 age groups: (1) healthy lifestyle cluster (high moderate-to-vigorous physical activity, low screen and total sedentary time), (2) predominantly sedentary cluster, (3) mainly screen time consumers cluster, and (4) nonhealthy lifestyle cluster (predominantly low moderate-to-vigorous physical activity and unhealthy diet). Participants belonging to the healthy lifestyle cluster showed significantly lower BFP at baseline and 2 years later compared with the other profiles. These differences remained significant when adjusted by BFP at baseline within the younger adolescents. Moreover, BFP at baseline positively predicted BFP 2 years later in all groups.

CONCLUSIONS

These findings identify distinct lifestyle patterns. These clusters could be useful to develop interventions to reduce overweight and obesity in children and adolescents.

Clustering of Excess Body Weight-Related Behaviors in a Sample of Brazilian Adolescents

The aim of the study was to identify the existence of clusters in multiple lifestyle behaviors, including consumption of fruits/vegetables, sugary products/soft drinks, physical activity and sedentary behavior. The association between identified clusters and excess body weight in a sample of adolescents from Dourados, Brazil, was examined. This is a cross-sectional school-based study involving 578 participants aged 12⁻18 of both sexes. Anthropometric measurements were performed and a questionnaire was applied with structured questions to collect data. Excess body weight was identified through body mass index. Cluster analysis was performed to identify sex-specific clusters of multiple lifestyle behaviors. Analysis of covariance and logistic regression were used to analyze associations between clusters and excess body weight. Six clusters were identified in both sexes. Girls and boys in the cluster characterized by greater time spent in sedentary behavior were 53% (OR = 1.53 [1.06⁻2.26]) and 63% (OR = 1.63 [1.12⁻2.35]) more likely to present excess body weight compared to their peers in the reference cluster. In the case of adolescents in the cluster characterized by high consumption of sugary products/soft drinks, girls were 47% more likely to be overweight (OR = 1.47 [1.05⁻2.13]) and boys were 51% more likely (OR = 1.51 [1.05⁻2.16]). High consumption of fruits/vegetables, low consumption of sugary products/soft drinks and less sedentary behavior was considered the most effective combination for the maintenance of a healthy weight.

Profiles of Physical Fitness Risk Behaviours in School Adolescents from the ASSO Project: A Latent Class Analysis

The aim of the present investigation was to describe profiles of adolescents’ fitness level, identify latent classes of fitness-related risk behaviours, and describe their sociodemographic and environmental predictors. In total, 883 adolescents (16.4 ± 1.4 years; 167.3 ± 10.4 cm; 62.8 ± 13.5 kg; 62.2% males) were assessed for personal and lifestyle information and for physical fitness components. Eleven possible fitness determinants and seven predictors were included. Latent class analysis (LCA) was used to determine fitness-related risk behaviours. Logistic regressions predicted class membership and assessed associations with fitness levels and fitness components. Five latent classes were recognised: 1—virtuous, 30.7% of respondents; 2—low physical activity/sport, 18.8%; 3—incorrect alcohol/food habits, 25.8%; 4—health risk/overweight, 15.9%; 5—malaise/diseases, 8.8%. Sex, age, parents’ overweightness/obesity and education, and school type predicted most classes significantly. Compared to class 1, class 2 had higher odds of having all poor fitness components except upper body maximal strength; class 4 had higher risk of low muscular endurance; and class 5 was likely to have lower maximal strength, muscular endurance, and speed/agility. Educating adolescents to reach a sufficient practice of PA/sport could help decreasing the risk of low health-related fitness more than discouraging them from using alcohol, addressing proper food behaviours and habits, and helping them understand their psychophysical malaise symptoms.

Exploring the use of technology pathways to access health information by Australian university students: a multi-dimensional approach

OBJECTIVE

Mobile wireless communication technologies (MWCT) and social media (SM) networks create pathways for accessing widely available information that are favoured particularly by younger generations. The purpose of this study was to explore the way university students use these communication technologies to access health information.

METHOD

Participants ( n = 696), who were recruited via an email invitation, completed an online self-reported survey that collected multi-dimensional data consisting of quantitative, qualitative and spatial statistics.

RESULTS

The results indicate that technology was used extensively to search for health information and resulted in positive changes in participants' behaviours; however they perceived lack of reliability of the information as a pressing issue.

CONCLUSION

The results suggest that appealing, persuasive and reliable MWCT and SM can be used extensively to promote health amongst Australian youth.

Association between Clustering of Lifestyle Behaviors and Health-Related Physical Fitness in Youth: The UP&DOWN Study

OBJECTIVES

To examine clustering of lifestyle behaviors in Spanish children and adolescents based on screen time, nonscreen sedentary time, moderate-to-vigorous physical activity, Mediterranean diet quality, and sleep time, and to analyze its association with health-related physical fitness.

STUDY DESIGN

The sample consisted of 1197 children and adolescents (597 boys), aged 8-18 years, included in the baseline cohort of the UP&DOWN study. Moderate-to-vigorous physical activity was assessed by accelerometry. Screen time, nonscreen sedentary time, Mediterranean diet quality, and sleep time were self-reported by participants. Health-related physical fitness was measured following the Assessing Levels of Physical Activity battery for youth. A 2-stage cluster analysis was performed based on the 5 lifestyle behaviors. Associations of clusters with fatness and physical fitness were analyzed by 1-way ANCOVA.

RESULTS

Five lifestyle clusters were identified: (1) active (n = 171), (2) sedentary nonscreen sedentary time-high diet quality (n = 250), (3) inactive-high sleep time (n = 249 [20.8%]), (4) sedentary nonscreen sedentary time-low diet quality (n = 273), and (5) sedentary screen time-low sleep time (n = 254). Cluster 1 was the healthiest profile in relation to health-related physical fitness in both boys and girls. In boys, cluster 3 had the worst fatness and fitness levels, whereas in girls the worst scores were found in clusters 4 and 5.

CONCLUSIONS

Clustering of different lifestyle behaviors was identified and differences in health-related physical fitness were found among clusters, which suggests that special attention should be given to sedentary behaviors in girls and physical activity in boys when developing childhood health prevention strategies focusing on lifestyles patterns.

Clustering of unhealthy lifestyle behaviours and associations with perceived and actual weight status among primary school children in China: A nationally representative cross-sectional study

Few studies have focused on clustering of unhealthy lifestyle behaviours among primary school children and potential associations with perceived and actual weight status. An index was constructed from adding up 13 unhealthy behaviours measured by survey responses. Multilevel linear regressions were used to analyse associations between child personal characteristics, perceived and actual weight status with the unhealthy lifestyle index among 11,157 children in primary schools across China. Parental and area factors were also taken into account, including education, weight status, physical activity, urban/rural and area socioeconomic circumstances. The unhealthy lifestyle index normally distributed, with 84.5% of children reporting between 2 and 6 unhealthy behaviours. Boys reported more unhealthy behaviours compared with girls (coefficient 0.32, 95%CI 0.26 to 0.37) and children in urban areas had fewer unhealthy behaviours than their rural counterparts (-0.29, 95%CI -0.56 to -0.03). An interaction revealed stronger 'protective' effects of living in cities for girls than boys, which were not explained by differences in child overweight/obesity. More unhealthy behaviours were characteristic of children in more affluent areas, and of those born to mothers and/or fathers with lower educational attainment. Children who perceived themselves as overweight or underweight both scored higher on the unhealthy lifestyle index. Unhealthy behaviours that could increase the risk of childhood obesity are common among Chinese primary school children, particularly among boys in cities, those in more affluent areas and with parents with lower education. There was no effect of actual weight status on number of unhealthy behaviours. Perceived, but not actual weight status, was also a significant correlate of unhealthy behaviours. Clustering of unhealthy lifestyle behaviours that could increase the risk of childhood obesity are common among Chinese primary school children, particularly among boys in cities, those in more affluent areas and with parents with lower education. Perceived, but not actual weight status, was also a significant correlate of unhealthy lifestyle. This has important implications for public health because understanding clustering of unhealthy lifestyle behaviours can be used to assist in the development of targeted obesity prevention initiatives.

Trends and correlates of overweight/obesity in Czech adolescents in relation to family socioeconomic status over a 12-year study period (2002-2014)

Background

This study examined a) trends in overweight/obesity, moderate-to-vigorous physical activity (MVPA), and screen time (ST) among Czech adolescents over a 12-year study period (2002–2014) in relation to family affluence (FA) and b) correlates of adolescent overweight/obesity from different FA categories.

Methods

A nationally representative sample of 18,250 adolescents (51.4% girls) aged 10.5–16.5 years was drawn from the Czech Health Behaviour in School-aged Children questionnaire-based surveys in 2002, 2006, 2010, and 2014. Using the FA scale, the socioeconomic status (SES) of the respondents’ families was assessed. SES-stratified trends in the prevalence of overweight/obesity meeting the MVPA (≥60 min/day), and ST (≤2 h/day) recommendations were assessed using logistic regression.

Results

A trend-related significant increase (p < 0.05) in the prevalence of overweight/obesity was observed in low−/medium-FA boys and medium−/high-FA girls. Unlike in high-FA adolescents, a significant decrease was revealed in the rates of meeting the MVPA recommendation in low-FA boys (28.9%₂₀₀₂ → 23.3%₂₀₁₄, OR = 0.75, 95% CI = 0.59–0.95, p < 0.05) and girls (22.3%₂₀₀₂ → 17.3%₂₀₁₄, OR = 0.73, 95% CI = 0.57–0.92, p < 0.01). A significant (p < 0.001) trend-related increase in excessive ST was evident in adolescents regardless of gender and FA category. Generally, girls and older adolescents had lower odds of overweight/obesity than boys and 11-year-old adolescents. While in the high-FA category of adolescents, achieving 60 min of MVPA daily and the absence of excessive ST on weekdays significantly (p < 0.01) reduced their odds of being overweight/obese, in low-FA adolescents this was not the case.

Conclusions

High rates of overweight/obesity and a poor level of daily MVPA among low-FA children provide disturbing evidence highlighting the necessity of public health efforts to implement obesity reduction interventions for this disadvantaged population.

Do socio-cultural factors influence college students' self-rated health status and health-promoting lifestyles? A cross-sectional multicenter study in Dalian, China

Background

An unhealthy lifestyle of college students is an important public health concern, but few studies have been undertaken to examine the role of socio-cultural differences.

Methods

For this cross-sectional comparative study, data on college students’ health-promoting lifestyles (HPL), as measured using the Health-Promoting Lifestyle Profile (HPLP-II) scale, and self-rated health status (SRH) as measured by Sub-Optimal Health Measurement Scale (SHMS V1.0) were collected from 829 college students.

Results

The sample of 829 college students included 504 (60.8%) Chinese and 325 (39.2%) international students. Chinese students had higher scores in overall health-promoting lifestyle (HPL) (P < 0.001, eta squared =0.113) and in all the six subclasses than their international counterparts. In relation to health status evaluation, the two groups varied in physiological health (P < 0.001, eta squared = 0.095) and social health (P = 0.020, eta squared = 0.007) but there was no significant difference in psychological health subscale (P = 0.156, eta squared = 0.002). HPL was predicted by financial status among the Chinese group and by student’s major, age and level of education in the international group. Body mass index (BMI) and financial status emerged as predictors of the three subscales of SHMS V1.0 in the Chinese group and also of physiological and psychological subscales in the international group. Gender was associated with psychological health in both groups. Smoking status was a predictor of psychological health in both groups and also of social health in the international group. The level of education emerged as a predictor of social health in the international group.

Regression analyses revealed a significant association between health status and healthy lifestyle (P < 0.001). In reference to participants with “excellent” lifestyle, participants with moderate lifestyle were at a 4.5 times higher risk of developing suboptimal health status (SHS) (OR: 4.5,95% CI:2.2-9.99) and those with a ‘general’ lifestyle were at a 3.2 times higher risk SHS (OR: 3.2, 95% CI: 1.5-7.18). Good and moderate HPLP-II levels of nutrition are associated with low risk of suboptimal health status (OR: 0,41 and 0,25, respectively). Participants in good and moderate HPLP-II levels of interpersonal relations are associated with higher risk of suboptimal health (OR:2,7 and 3,01 respectively) than those in excellent levels of HPLP-II.

Conclusion

Collectively, these findings provide a convincing body of evidence to support the role of socio-cultural factors as key determinants of the HPL and SRH of college students.

WHO European Childhood Obesity Surveillance Initiative: health-risk behaviours on nutrition and physical activity in 6-9-year-old schoolchildren

Objective

To assess to what extent eight behavioural health risks related to breakfast and food consumption and five behavioural health risks related to physical activity, screen time and sleep duration are present among schoolchildren, and to examine whether health-risk behaviours are associated with obesity.

Design

Cross-sectional design as part of the WHO European Childhood Obesity Surveillance Initiative (school year 2007/2008). Children’s behavioural data were reported by their parents and children’s weight and height measured by trained fieldworkers. Descriptive statistics and logistic regression analyses were performed.

Setting

Primary schools in Bulgaria, Lithuania, Portugal and Sweden; paediatric clinics in the Czech Republic.

Subjects

Nationally representative samples of 6–9-year-olds (n 15 643).

Results

All thirteen risk behaviours differed statistically significantly across countries. Highest prevalence estimates of risk behaviours were observed in Bulgaria and lowest in Sweden. Not having breakfast daily and spending screen time ≥2 h/d were clearly positively associated with obesity. The same was true for eating ‘foods like pizza, French fries, hamburgers, sausages or meat pies’ >3 d/week and playing outside <1 h/d. Surprisingly, other individual unhealthy eating or less favourable physical activity behaviours showed either no or significant negative associations with obesity. A combination of multiple less favourable physical activity behaviours showed positive associations with obesity, whereas multiple unhealthy eating behaviours combined did not lead to higher odds of obesity.

Conclusions

Despite a categorization based on international health recommendations, individual associations of the thirteen health-risk behaviours with obesity were not consistent, whereas presence of multiple physical activity-related risk behaviours was clearly associated with higher odds of obesity.

Who are the obese? A cluster analysis exploring subgroups of the obese

BACKGROUND

Body mass index (BMI) can be used to group individuals in terms of their height and weight as obese. However, such a distinction fails to account for the variation within this group across other factors such as health, demographic and behavioural characteristics. The study aims to examine the existence of subgroups of obese individuals.

METHODS

Data were taken from the Yorkshire Health Study (2010-12) including information on demographic, health and behavioural characteristics. Individuals with a BMI of ≥30 were included. A two-step cluster analysis was used to define groups of individuals who shared common characteristics.

RESULTS

The cluster analysis found six distinct groups of individuals whose BMI was ≥30. These subgroups were heavy drinking males, young healthy females; the affluent and healthy elderly; the physically sick but happy elderly; the unhappy and anxious middle aged and a cluster with the poorest health.

CONCLUSIONS

It is important to account for the important heterogeneity within individuals who are obese. Interventions introduced by clinicians and policymakers should not target obese individuals as a whole but tailor strategies depending upon the subgroups that individuals belong to.

Longitudinal associations of health-related behavior patterns in adolescence with change of weight status and self-rated health over a period of 6 years: results of the MoMo longitudinal study

Background

Promoting a healthy lifestyle especially in adolescents is important because health-related behaviors adopted during adolescence most often track into adulthood. Longitudinal studies are necessary for identifying health-related risk groups of adolescents and defining target groups for health-promoting interventions. Multiple health behavior research may represent a useful approach towards a better understanding of the complexity of health-related behavior. The aim of this study was to examine the longitudinal association of health-related behavior patterns with change of weight status and self-rated health in adolescents in Germany.

Methods

Within the framework of the longitudinal German Health Interview and Examination Survey for Children and Adolescents (KiGGS) and the Motorik-Modul (MoMo), four clusters of typical health-related behavior patterns of adolescents have been previously identified. Therefor the variables ‘physical activity’, ‘media use’ and ‘healthy nutrition’ were included. In the current study longitudinal change of objectively measured weight status (N = 556) and self-rated health (N = 953) in the four clusters was examined. Statistical analyses comprised T-tests for paired samples, McNemar tests, multinomial logistic regression analysis and two-way ANOVA with repeated measures.

Results

The prevalence of overweight increased in all four clusters. The health-related behavior pattern of low activity level with high media use and low diet quality had the strongest increase in prevalence of overweight, while the smallest and non-significant increase was found with the behavior pattern of a high physical activity level and average media use and diet quality. Only some significant relationships between health-related behaviour patterns and change in self-rated health were observed.

Conclusions

High-risk patterns of health-related behavior were identified. Further, cumulative as well as compensatory effects of different health-related behaviors on each other were found. The information gained in this study contributes to a better understanding of the complexity of health-related behavior and its impact on health parameters and may facilitate the development of targeted prevention programs.

The clustering of diet, physical activity and sedentary behavior in children and adolescents: a review

Diet, physical activity (PA) and sedentary behavior are important, yet modifiable, determinants of obesity. Recent research into the clustering of these behaviors suggests that children and adolescents have multiple obesogenic risk factors. This paper reviews studies using empirical, data-driven methodologies, such as cluster analysis (CA) and latent class analysis (LCA), to identify clustering patterns of diet, PA and sedentary behavior among children or adolescents and their associations with socio-demographic indicators, and overweight and obesity. A literature search of electronic databases was undertaken to identify studies which have used data-driven methodologies to investigate the clustering of diet, PA and sedentary behavior among children and adolescents aged 5–18 years old. Eighteen studies (62% of potential studies) were identified that met the inclusion criteria, of which eight examined the clustering of PA and sedentary behavior and eight examined diet, PA and sedentary behavior. Studies were mostly cross-sectional and conducted in older children and adolescents (≥9 years). Findings from the review suggest that obesogenic cluster patterns are complex with a mixed PA/sedentary behavior cluster observed most frequently, but healthy and unhealthy patterning of all three behaviors was also reported. Cluster membership was found to differ according to age, gender and socio-economic status (SES). The tendency for older children/adolescents, particularly females, to comprise clusters defined by low PA was the most robust finding. Findings to support an association between obesogenic cluster patterns and overweight and obesity were inconclusive, with longitudinal research in this area limited. Diet, PA and sedentary behavior cluster together in complex ways that are not well understood. Further research, particularly in younger children, is needed to understand how cluster membership differs according to socio-demographic profile. Longitudinal research is also essential to establish how different cluster patterns track over time and their influence on the development of overweight and obesity.

Clustering of children's obesity-related behaviours: associations with sociodemographic indicators

BACKGROUND/OBJECTIVES

Research suggests obesity-related behaviours cluster together in children and adolescents, but how these cluster patterns differ by sociodemographic indicators remains unclear. Furthermore, few studies examining clustering of behaviours have included younger children or an objective measure of physical activity (PA) and sedentary behaviour. Therefore, the aim of this study was to examine clustering patterns of diet, PA and sedentary behaviour in 5- to 6- and 10- to 12-year-old children, and their cross-sectional associations with sociodemographic indicators.

SUBJECTS/METHODS

In this cross-sectional study, data from the baseline wave (2002/2003) of the Health Eating and Play study (HEAPS) were used. Questionnaires were completed by parents of Australian children aged 5-6 (n=362) and 10-12 years (n=610). Children wore accelerometers for up to 7 days. K-medians cluster analysis identified groups of children with similar diet, PA and sedentary behaviours. Chi-square tests assessed cluster differences by gender, maternal education and marital status.

RESULTS

For each age group, three reliable and meaningful clusters were identified and labelled 'most healthy', 'energy-dense (ED) consumers who watch TV' and 'high sedentary behaviour/low moderate-to-vigorous PA (MVPA)'. Clusters varied by sociodemographic indicators. For example, a higher proportion of older girls comprised the 'high sedentary behaviour/low MVPA' cluster (χ(2)=22.4, P<0.001). Among both age groups, the 'ED consumers who watch TV' cluster comprised more children with lower educated mothers (younger children: χ(2)=34.9, P<0.001; older children: χ(2)=27.3, P<0.001).

CONCLUSIONS

Identification of cluster patterns of obesity-related risk factors in children, and across sociodemographic groups may assist the targeting of public health initiatives, to those most in need.

Healthy lifestyles of university students in China and influential factors

This study was conducted to analyze to what extent university students exhibit healthy lifestyles and which sociodemographic variables influence healthy lifestyles. 4809 university students randomly selected were measured by use of the Healthy Lifestyle Scale for University Students questionnaire. When controlling for the other variables, the total healthy lifestyles score was predicted by gender, grade, father's level of education, and type of institution; exercise behaviour was partially predicted by gender, grade, type of institution, and family monthly income; regular behaviour was modulated by gender, grade, type of institution, family monthly income, and father's educational level; nutrition behaviour was partially affected by type of institution, family monthly income, and father's educational level; health risk behaviour was modulated by gender, mother's level of education, and family monthly income; health responsibility was modulated by gender, grade, type of institution, and father's educational level; social support was modulated by gender, grade, and father's educational level; stress management was modulated by gender, grade, type of institution, and mother's education level; life appreciation was modulated by grade, type of institution, and mother's educational level. These influences should be taken into account in designing interventions for specific socio-demographic profiles that might be at higher risk for certain behaviours.

Relative contribution of physical activity, sedentary behaviors, and dietary habits to the prevalence of obesity among Kuwaiti adolescents

BACKGROUND

The increasing rate of obesity among Kuwaiti adolescents is associated with immediate and long term-risks to their health and well-being.

OBJECTIVE

To update data on the prevalence of overweight and obesity among Kuwaiti adolescents and to examine the relative contribution of selected lifestyle factors to overweight and obesity in this population.

METHODS

The present study is part of the Arab Teens Lifestyle Study (ATLS). A total of 906 adolescents (463 boys and 443 girls) aged between 14 and 19 years were selected from Kuwaiti schools by a multistage stratified randomization process. A validated questionnaire was used to collect data on physical activity, sedentary lifestyle, and eating habits. The International Obesity Task Force (IOTF) cutoff values for adolescents under 18 years of age were used to define overweight and obesity. Total energy expenditure was calculated using metabolic equivalent-minutes per week. A general linear model was used to establish the proportion of the variance (expressed in partial eta squared) in excess weight attributable to differences in eating habits and physical activity.

RESULTS

The prevalence of overweight and obesity was 50.5% in boys and 46.5% in girls. Among boys, moderate and vigorous activities were found to be significantly negatively associated with overweight and obesity (p < .05), whereas in girls, only those with not less than moderate activities were negatively associated with overweight and obesity (p < .05). Sedentary behaviors, time spent watching television, and time spent working on the computer were not significantly associated with obesity in either sex. Consumption of breakfast, vegetables, and fast foods (boys and girls) and potatoes, cakes and doughnuts, and sweets (girls only) was significantly associated with overweight and obesity (p < .05). In general, the partial eta square explained by physical activity was less than 3.6% in boys compared with less than 1.0% in girls, and eating habits explained less than 1.8% in boys compared with 2.5% in girls.

CONCLUSIONS

Physical activity explains a greater proportion of variation in body mass index than do eating habits, particularly in boys. Eating habits explain a greater proportion of variation in body mass index than does physical activity in girls. Prospective studies are needed to clarify the relative effects of sedentary behaviors on overweight in adolescents.

A cluster-analytic approach towards multidimensional health-related behaviors in adolescents: the MoMo-Study

BACKGROUND

Although knowledge on single health-related behaviors and their association with health parameters is available, research on multiple health-related behaviors is needed to understand the interactions among these behaviors. The aims of the study were (a) to identify typical health-related behavior patterns in German adolescents focusing on physical activity, media use and dietary behavior; (b) to describe the socio-demographic correlates of the identified clusters and (c) to study their association with overweight.

METHODS

Within the framework of the German Health Interview and Examination Survey for Children and Adolescents (KiGGS) and the "Motorik-Modul" (MoMo), 1,643 German adolescents (11-17 years) completed a questionnaire assessing the amount and type of weekly physical activity in sports clubs and during leisure time, weekly use of television, computer and console games and the frequency and amount of food consumption. From this data the three indices 'physical activity', 'media use' and 'healthy nutrition' were derived and included in a cluster analysis conducted with Ward's Method and K-means analysis. Chi-square tests were performed to identify socio-demographic correlates of the clusters as well as their association with overweight.

RESULTS

Four stable clusters representing typical health-related behavior patterns were identified: Cluster 1 (16.2%)--high scores in physical activity index and average scores in media use index and healthy nutrition index; cluster 2 (34.6%)--high healthy nutrition score and below average scores in the other two indices; cluster 3 (18.4%)--low physical activity score, low healthy nutrition score and very high media use score; cluster 4 (30.5%)--below average scores on all three indices. Boys were overrepresented in the clusters 1 and 3, and the relative number of adolescents with low socio-economic status as well as overweight was significantly higher than average in cluster 3.

CONCLUSIONS

Meaningful and stable clusters of health-related behavior were identified. These results confirm findings of another youth study hence supporting the assumption that these clusters represent typical behavior patterns of adolescents. These results are particularly relevant for the characterization of target groups for primary prevention of lifestyle diseases.

Beverage patterns among Canadian children and relationship to overweight and obesity

Sweetened beverage intake has risen in past decades, along with a rise in prevalence of overweight and obesity among children. Our objective was to examine the relationship between beverage intake patterns and overweight and obesity among Canadian children. Beverage intake patterns were identified by cluster analysis of data from the cross-sectional Canadian Community Health Survey 2.2. Intake data were obtained from a single 24-hour recall, height and weight were measured, and sociodemographic data were obtained via interview. Data on children and adolescents aged 2-18 years who met inclusion criteria (n = 10 038) were grouped into the following categories: 2-5 years (male and female), 6-11 years (female), 6-11 years (male), 12-18 years (female), and 12-18 years (male). χ² test was used to compare rates of overweight and obesity across clusters. Logistic regression was used to determine the association between overweight and obesity and beverage intake patterns, adjusting for potential confounders. Clustering resulted in distinct groups of who drank mostly fruit drinks, soft drinks, 100% juice, milk, high-fat milk, or low-volume and varied beverages (termed "moderate"). Boys aged 6-11 years whose beverage pattern was characterized by soft drink intake (553 ± 29 g) had increased odds of overweight-obesity (odds ratio 2.3, 95% confidence interval 1.2-4.1) compared with a "moderate" beverage pattern (23 ± 4 g soft drink). No significant relationship emerged between beverage pattern and overweight and obesity among other age-sex groups. Using national cross-sectional dietary intake data, Canadian children do not show a beverage-weight association except among young boys who drink mostly soft drinks, and thus may be at increased risk for overweight or obesity.

Exploring overweight, obesity and their behavioural correlates among children and adolescents: results from the Health-promotion through Obesity Prevention across Europe project

OBJECTIVES

The Health-promotion through Obesity Prevention across Europe (HOPE) project aims to bring the European scientific knowledge on overweight, obesity and their determinants together and use the expertise of researchers across Europe to contribute to tackling the obesity epidemic.

DESIGN/SUBJECTS/RESULTS

This special issue of Public Health Nutrition presents important results from one of the work packages of the HOPE project that aims at gaining and integrating knowledge on the determinants of nutrition, physical activity and obesity among schoolchildren and adolescents (aged 10-18 years) in different European regions. It includes contributions from Northern Europe (Norway), Central and Eastern Europe (Germany, Poland and the Czech Republic), Southern Europe (Greece) and Western Europe (Belgium and The Netherlands), as well as an overview of the availability of good-quality data on prevalence rates and trends in overweight (including obesity) among adolescents in European Union (EU) countries. The studies that are included report prevalence differences, data on relevant nutrition and physical activity behaviours, as well as potential physical and environmental behavioural determinants.

CONCLUSION

These papers provide further evidence on differences in obesity and overweight prevalence among different EU regions and countries, and contribute to the further exploration of risk factors that may or should be addressed in obesity prevention efforts for school-aged children and adolescents in EU countries.