Activity-related behavior typologies in youth: a systematic review

Clusters of 24-hour movement behavior and diet and their relationship with health indicators among youth: a systematic review

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.

Comparison of children's physical activity profiles before and after COVID-19 lockdowns: A latent profile analysis

Physical activity is important for children's health, but moderate to vigorous physical activity (MVPA) declines with age. COVID-19 lockdowns resulted in reduced MVPA and increased sedentary time among children. Characterising children's activity patterns may help identify groups who are most likely to be inactive post-lockdown. Data were combined from a pre-COVID-19 cohort study on children aged 5-6 years (Year1: n = 1299), 8-9 years (Year4: n = 1223) and 10-11 years (Year6: n = 1296) and cross-sectional post-lockdown data from a natural experiment on 10-11-year-olds in 2021 (Year6-W1: n = 393) and 2022 (Year6-W2: n = 436). The proportions of time spent in MVPA, light physical activity (LPA) and sedentary time on weekdays and weekends were derived from accelerometer data. Latent class analysis was used to identify activity profiles pre and post-lockdown, and estimate pre-COVID-19 transitions between Year4 and Year6. We identified six pre-COVID-19 activity profiles in Year6, including a new profile characterised by very low MVPA and high sedentary time (19% of children). There was substantial movement between profiles at Year4 and Year6, with 45% moving to a profile with lower MVPA. Likelihood ratio tests suggested differences in Year6 activity profiles pre and post-lockdown, with a new post-lockdown profile emerging characterised by higher LPA. The percentage of children in the least active profiles (where under 20% meet UK physical activity guidelines), rose post-lockdown, from 34% pre-COVID-19 to 50% in 2021 and 40% in 2022. We also saw gender and socioeconomic gaps widen, and increased separation between high and low physical activity levels. Children's physical activity has changed post-COVID-19, in terms of who is being active and how. The impact varies by activity profile, which is influenced by gender and socio-economic position. A greater understanding of these differences and targeting of low active groups is needed to increase both individual and population levels of physical activity.

The clustering of multiple health and lifestyle behaviors among Swedish adolescents: a person-oriented analysis

Background

Knowledge of the distribution, prevalence, and clustering of multiple health and lifestyle related behaviors (HLBs) among adolescents can inform the development of effective health-promoting policies and interventions. We assessed the clustering of multiple HLBs among 11, 13 and 15-year-old Swedish adolescents and examined the socioeconomic and demographic correlates for the identified clusters.

Methods

We used data from the 2017/2018 Swedish Health Behaviour in School-aged children (HBSC) study to conduct sex and age-stratified latent class analysis (LCA). The LCA was based on five HLBs: eating behavior and habits (EBH), physical activity (PA), tobacco usage (TU), alcohol consumption (AC) and sleeping habits and patterns (SHPs). Multinomial logistic regression models were used to assess the associations between the identified clusters and the socioeconomic and demographic characteristics of adolescents and their parents.

Results

Health behaviors varied by sex and age. Four distinct clusters were identified based on sex: cluster 1 (Mixed eating behaviors and habits, physical activity and low alcohol consumption), cluster 2 (Healthy lifestyle behaviors), cluster 3 (Unhealthy lifestyle behaviors), and cluster 4 (Breakfast, low alcohol consumption and tobacco usage). In the age-stratified analyzes, three clusters were identified: cluster 1 (Unhealthy lifestyle behaviors), cluster 2 (Moderately healthy lifestyle behaviors) and cluster 3 (Healthy lifestyle behaviors). The multinomial analysis showed that sex, age, family situation and perceived family wealth were strong predictors of health behaviors. Unhealthy behaviors were most commonly associated with socioeconomic disadvantage, having a migrant background, and living in reconstructed families or single-parent households.

Conclusion

Health behaviors vary significantly based on socioeconomic and demographic circumstances. Targeted policies and intervention programs are necessary to improve HLBs among vulnerable and at-risk adolescents.

A systematic review of the clustering and correlates of physical activity and sedentary behavior among boys and girls

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.

A latent transition analysis of physical activity and screen-based sedentary behavior from adolescence to young adulthood

BACKGROUND

Distinct typologies of physical activity and screen-based sedentary behaviors are common during adolescence, but it is unknown how these change over time. This longitudinal study examined the stability of activity-related behavioral typologies over the transition out of secondary school.

METHODS

Year 11 students (penultimate school year) completed a self-report survey (baseline), which was repeated 2 years later (follow-up) (75% female, mean baseline age: 16.9 ± 0.4 years). Latent transition analysis identified typologies of physical activity and screen time behaviors and explored changes in typology membership between baseline and follow-up among those with complete data and who were not attending secondary school at follow-up (n = 803).

RESULTS

Three unique typologies were identified and labelled as: 1) Sedentary gamers (baseline: 17%; follow-up: 15%: high levels of screen behaviors, particularly video gaming); 2) Inactives (baseline: 46%; follow-up: 48%: low physical activities, average levels of screen behaviors); and 3) Actives (baseline: 37%; follow-up: 37%: high physical activities, low screen behaviors). Most participants remained in the same typology (83.2%), 8.5% transitioned to a typology with a more health-enhancing profile and 8.3% transitioned to a typology with a more detrimental behavioral profile.

CONCLUSIONS

The high proportion within the 'inactive' typology and the stability of typologies over the transition period suggests that public health interventions are required to improve activity-related behavior typologies before adolescents leave secondary school.

Ecological correlates related to adolescent movement behaviors: A latent class analysis

The ecological model has been widely used to help researchers understand the multiple influences in the physical activity (PA) and in the sedentary behaviors in isolated forms. To date, few correlates concerning the behavioral groupings of PA and sedentary behaviors have been studied. In this context, this study aimed to identify movement behaviors’ latent classes related to the different adolescents’ PA and sedentary time expressions, as well as their associations with individual, sociodemographic, family, and environmental correlates. This is a cross-sectional study with 309 students aged between 14 and 16. Latent Class Analysis was used to identify movement behavior classes based on light PA, moderate to vigorous PA, number of steps, sedentary time, and screen time (ST). An accelerometer was used to evaluate movement behaviors. The individual, sociodemographic, family, and environmental correlates were assessed by questionnaires. Three classes were identified: Class 1, "Active and Non-Sedentary" (8.10% of the sample), Class 2, "Active and Sedentary" (28.5%), and Class 3, "Inactive and Sedentary" (63.4%). Those with low fruit intake, low aerobic fitness, stressed and whose head of the family obtained an ‘elementary school’ level education were, respectively, 7.17, 3.59, 3.56, and 4.40 times more likely to belong to class 3 than class 1. Those with medium and high socioeconomic status were 82% and 83% less likely to belong to class 1 than classes 2 and 3, respectively. Adolescents who perceived the neighborhoods with the best access to diversified land use, street connectivity, walking/pedaling ease, and traffic safety attributes, were 84%, 85%, 82%, and 82%, respectively less likely to belong to class 1 than class 2. It is concluded that distinct correlates can be associated with the movement behaviors classes.

Typologies of Joint Family Activities and Associations With Mental Health and Wellbeing Among Adolescents From Four Countries

PURPOSE

This study aims to identify distinct typologies of joint family activities and the associations with mental health and wellbeing among adolescents across four countries from the World Health Organization European region.

METHODS

The 2017/2018 data from adolescents from Armenia (n = 3,977, M_age = 13.5 ± 1.6 years, 53.4% female), Czechia (n = 10,656, M_age = 13.4 ± 1.7, 50.1% female), Russia (n = 4,096, M_age = 13.8 ± 1.7, 52.4% female), and Slovakia (n = 3,282, M_age = 13.4 ± 1.5, 51.0% female) were collected in schools. The respondents self-reported their participation in joint family leisure-time activities, life satisfaction, psychological and somatic complaints, as well as a range of demographic and family situational factors. Stratified by countries, latent class analysis identified typologies of joint family activities, and logistic regression models explored cross-sectional associations with life satisfaction, and psychological and somatic complaints.

RESULTS

Three typologies were identified across each of the four countries, distinguished by low, moderate, and high levels of family engagement. Adolescents with higher family engagement generally reported greater life satisfaction and fewer psychological complaints compared to those with lower family engagement. Russian adolescents in the high family engagement typology reported fewer somatic complaints compared to those with low family engagement. In addition, adolescents from Czechia and Russia showing moderate family engagement also reported fewer psychological complaints compared to those in the low family engagement typology.

DISCUSSION

Our findings from four countries suggest that adolescents with high family engagement have greater life satisfaction and fewer psychological complaints, pointing toward a need for interventions to support family engagement among adolescents. Further research is needed to fully explore underlying mechanisms.

COVID-19: Effects of Pandemic Related Restrictions on Physical Activity, Screen Time, and Mental Well-being in German adolescents

Objective: To describe the impact of the COVID-19 pandemic-related restrictions (PR) in April and May 2020 on physical activity (PA), sedentary screen time (SST), and mental well-being (MWB) in German adolescents, and to analyze associations between these variables. Methods: The Münster District Government invited all secondary school students (aged 11-17) in the region to take part in the online survey that assessed PA, SST, and MWB. For data analysis, we calculated descriptive statistics and ran linear regression analysis. Results: 1,038 students (627 [60.4%] female; 14.18 [± 1.97] years) were included in the analysis. During the PR, a marked decline in overall PA (p < .001) and a significant increase (p < .001) in SST were observed. One-third of the students reported worrying more and being less satisfied with their lives since PR. A decrease in life satisfaction (ß = -.524, p < .001) as well as an increase in general worrying (ß = -.336, p = .015) were associated with a decrease in PA during PR. Conclusion: The results show that the restrictions led to a decrease in physical activity, which may have detrimental effects on the students' mental and physical health.

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.

Clustering of Physical Activity, Sleep, Diet, and Screen-Based Device Use Associated with Self-Rated Health in Adolescents

BACKGROUND

Little is known about how the interplay among health-related behaviors impacts self-rated health (SRH). We examined the clustering of physical activity (PA), sleep, diet, and specific screen-based device use, and the associations between the emergent clusters and SRH among Brazilian adolescents.

METHOD

The data used in this cross-sectional study were from the baseline of the Movimente Program. Self-reported data were analyzed. SRH was recorded as a 5-point scale (from poor to excellent). Daily duration of exposure to the computer, the television, the cell phone, and games; PA; sleep; and weekly consumption of fruits and vegetables and ultra-processed foods were included in a Two-Step cluster analysis. Multilevel ordered logistic regressions assessed the associations between the clusters and SRH.

RESULTS

The data of 750 students (girls: 52.8%, 13.1 ± 1.0 years) were analyzed. Good SRH was more prevalent (52.8%). Three clusters were identified: the Phubbers (50.53%; characterized by the longest cell phone use duration, shortest gaming and computer use, lowest PA levels, and low consumption of fruits and vegetables), the Gamers (22.80%; longest gaming and computer use duration, PA < sample average, highest intake of ultra-processed foods), and a Healthier cluster (26.67%; physically active, use of all screen-based devices < sample average, and healthier dietary patterns). For both Gamers (-0.85; 95% CI -1.24, -0.46) and Phubbers (-0.71; 95% CI -1.04, -0.38), it was found a decrease in the log-odds of being in a higher SRH category compared with the Healthier cluster.

CONCLUSION

Specific clusters represent increased health-related risk. Assuming the interdependence of health-related behaviors is indispensable for accurately managing health promotion actions for distinguishable groups.

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.

Longitudinal patterns of lifestyle behaviours in adolescence: a latent transition analysis

The interdependence among eating behaviour (EB), physical activity (PA) and sedentary time (ST) suggests simultaneously identifying homogeneous profiles and describing their changes. This study aimed to (1) identify cross-sectional lifestyle behaviour profiles and their 2-year changes among French school-age adolescents and (2) identify factors associated with these profiles and changes. Longitudinal data from adolescents who participated in the PRomotion de l'ALIMentation et de l'Activité Physique trial were used. PA and ST were assessed with the International Physical Activity Questionnaire and EB with a FFQ. Profiles at baseline and their changes were identified by latent transition analysis. Multinomial logistic regression models were used to identify factors associated with profiles and their changes. Among 2390 adolescents included (14-18 years), five baseline profiles that differed mainly in EB were identified: 'healthy diet and high PA (7·9 %)', 'big eater and moderate to high PA (23·8 %)', 'healthy diet and low PA (31·2 %)', 'restrictive diet and moderate PA (20·6 %)' and 'sugar products, nibbling and moderate PA (16·5 %)'. Young adolescents, those who were overweight or obese and socially advantaged, were more in the 'healthy diet and low PA' than others. Boys, older and socially less advantaged adolescents exhibited more 'unfavourable' than 'mixed' changes, while adolescents with overweight or obesity had less 'unfavourable' than 'mixed' changes. In conclusion, adolescents were twice the number in the least than the most favourable profile. Findings highlighted the importance of EB among adolescents and suggest taking adolescents' sociodemographic and weight characteristics into account in interventions aimed at acting on adolescents' behaviours.

Activity-related typologies and longitudinal change in physical activity and sedentary time in children and adolescents: The UP&DOWN Study

BACKGROUND

Children and adolescents can be distinguished by different typologies (clusters) of physical activity and sedentary behavior. How physical activity and sedentary behaviors change over time within different typologies is not known. This study examined longitudinal changes in physical activity and sedentary time among children and adolescents with different baseline typologies of activity-related behavior.

METHODS

In this longitudinal study (3 annual time points) of children (n = 600, age = 9.2 ± 0.4 years (mean ± SD), 50.3% girls) and adolescents (n = 1037, age = 13.6 ± 1.7 years, 48.4% girls), participants were recruited in Spain in 2011-2012. Latent class analyses identified typologies based on self-reported screen, educational, social and relaxing sedentary behaviors, active travel, muscle strengthening activity, and sport at baseline. Within each typology, linear mixed growth models explored longitudinal changes in accelerometer-derived moderate-to-vigorous physical activity and sedentary time, as well as time by class interactions.

RESULTS

Three typologies were identified among children ("social screenies", 12.8%; "exercisers", 61.5%; and "non-sporty active commuters", 25.7%) and among adolescents ("active screenies", 43.5%; "active academics", 35.0%; and "non-sporty active commuters", 21.5%) at baseline. Sedentary time increased within each typology among children and adolescents, with no significant differences between typologies. No changes in physical activity were found in any typology among children. In adolescents, physical activity declined within all typologies, with "non-sporty active commuters" declining significantly more than "active screenies" over 3 years.

CONCLUSION

These results support the need for intervention to promote physical activity and prevent increases in sedentary time during childhood and adolescence. Adolescents characterized as "non-sporty active commuters" may require specific interventions to maintain their physical activity over time.

Eating behaviour, physical activity, TV exposure and sleeping habits in five year olds: a latent class analysis

BACKGROUND

Diet, physical activity, sedentary behaviours, and sleep time are considered major contributory factors of the increased prevalence of childhood overweight and obesity. The aims of this study were to (1) identify behavioural clusters of 5 year old children based on lifestyle behaviours, (2) explore potential determinants of class membership, and (3) to determine if class membership was associated with body measure outcomes at 5 years of age.

METHODS

Data on eating behaviour, engagement in active play, TV watching, and sleep duration in 1229 5 year old children from the Cork BASELINE birth cohort study was obtained through in-person interviews with parent. Latent class analysis was used to identify behavioural clusters. Potential determinants of cluster membership were investigated using multinomial logistic regression. Associations between the identified classes and cardio metabolic body measures were examined using multivariate logistic and linear regression, with cluster membership used as the independent variable.

RESULTS

51% of children belonged to a normative class, while 28% of children were in a class characterised by high scores on food avoidance scales in combination with low enjoyment of food, and 20% experienced high scores on the food approach scales. Children in both these classes had lower conditional probabilities of engaging in active play for at least 1 hour per day and sleeping for a minimum of 10 h, and higher probability of watching TV for 2 hours or more, compared to the normative class. Low socioeconomic index (SEI) and no breastfeeding at 2 months were found to be associated with membership of the class associated with high scores on the food avoidance scale, while lower maternal education was associated with the class defined by high food approach scores. Children in the class with high scores on the food approach scales had higher fat mass index (FMI), lean mass index (LMI), and waist-to-height ratio (WtHR) compared to the normative class, and were at greater risk of overweight and obesity.

CONCLUSION

Findings suggest that eating behaviour appeared to influence overweight and obesity risk to a greater degree than activity levels at 5 years old. Further research of how potentially obesogenic behaviours in early life track over time and influence adiposity and other cardio metabolic outcomes is crucial to inform the timing of interventions.

Clustering of multiple lifestyle behaviors among migrant, left-behind and local adolescents in China: a cross-sectional study

BACKGROUND

Influence of migration on externalized behavioral problems (e.g., aggressive) among adolescents has been well assessed, yet lifestyle behaviors of migrant, left-behind and local adolescents have been largely overlooked by researchers and policy-makers. Therefore, this study aimed to identify clustering of multiple lifestyle behaviors and their associations with migrant status among Chinese adolescents.

METHODS

A cross-sectional survey was conducted in 2015 in Beijing, and Wuhu city (Anhui province). Adolescents self-reported age, gender, family economic status, migrant situation, and lifestyle behaviors (i.e., physical activity, screen time, sleep, smoke, soft-drink, alcohol, fruit and vegetable consumption) via a battery of validated questionnaires. Latent class analysis was conducted to identify behavioral clusters using Mplus 7.1. ANOVA, and multivariable logistic regression were used to examine associations between migrant situations and behavioral clusters using SPSS 22.

RESULTS

Three distinct behavioral clusters were exhibited among 1364 students (mean age: 13.41 ± 0.84 years): "low risk" (N = 847), "moderate risk" (N = 412) and "high risk" (N = 105). The "high-risk" cluster had the highest prevalence of adolescents not meeting healthy behavioral recommendations. There were no significant differences in the prevalence of high-risk lifestyle among migrant, left-behind, rural local and urban local adolescents. But migrant adolescents had the lowest prevalence of low-risk lifestyle, followed by left-behind, rural and urban local adolescents. Moreover, compared with urban local, migrant (OR = 2.72, 95%CI: 1.88,3.94), left-behind (OR = 2.28, 95%CI: 1.46, 3.55), and rural local (OR = 1.76, 95%CI:1.03,3.01) adolescents had a higher risk of moderate-risk lifestyle.

CONCLUSIONS

Clustering of assessed lifestyle behaviors differed by the migrant status. Particularly, migrant and left-behind adolescents were more likely to have moderate-risk lifestyle compared with their counterparts. Interventions that promote moderate to vigorous physical activity and consumption of fruits and vegetables simultaneously are needed among them.

Latent Profiles of Health Behaviors in Rural Children with Overweight and Obesity

OBJECTIVE

The objectives were to identify profiles of school-age children with overweight and obesity (OW/OB) from rural counties based on patterns of diet, activity, and sleep, to examine demographic predictors, and to examine whether profiles were differentially associated with psychosocial functioning.

METHODS

Participants included 163 children (Mage = 9.8) and parents. Children wore accelerometers to assess physical activity and sleep duration. Consumption of fruits and vegetables (F/V) and sugar-sweetened beverages (SSB) was assessed with a food frequency questionnaire. Self-report of emotional, social, and academic health-related quality of life (HRQOL), peer victimization, social skills, and social problem behaviors was collected, as well as parent-report of HRQOL. Latent variable mixture modeling (LVMM) was conducted.

RESULTS

Sleep did not significantly contribute to profile differentiation and was removed. Four profiles emerged: (a) Low F/V + Low SSB + Low activity, (b) Low F/V + Low SSB + Moderate activity, (c) High F/V + High SSB + Low activity, and (d) Moderate F/V + Moderate SSB + High activity. Older children were more likely to be in profile 1. After controlling for child age, parents of children in profile 1 reported significantly lower child social HRQOL than parents of children in profiles 2 and 4. Children in profile 4 reported experiencing significantly lower victimization than those in profile 3.

CONCLUSIONS

There are subgroups of rural children with OW/OB that engage in various combinations of healthy and unhealthy behaviors. LVMM has the potential to inform future interventions and identify needs of groups of children with OW/OB.

Correlates of dual trajectories of physical activity and sedentary time in youth: The UP & DOWN longitudinal study

Trajectories of physical activity and sedentary time (SED) may differ between subgroups of youth. The aim of this study was to identify group-based dual trajectories of physical activity and SED and explore individual, social, and environmental correlates of these trajectories. Longitudinal data (three time points, baseline 2011-2012) of Spanish youth (n = 1597, mean age = 11.94 ± 2.52, 50.9% boys) were used. Moderate-to-vigorous physical activity (MVPA) and SED were assessed objectively at each time point, and 21 potential correlates were self-reported at baseline. Parallel process growth mixture models identified shared categorical latent groups, adjusting for school and age. Multinomial logistic regression models identified baseline correlates of a given trajectory. Four shared categorical latent groups were identified: (1) stable MVPA and decreasing SED (4%); (2) stable MVPA and increasing SED (3%); (3) consistently higher MVPA (18%); and (4) stable low MVPA and slight increase in SED (75%). Multinomial logistic regression models with group 3 as reference found: negative affect (RRR = 0.90, 95% CI 0.84-0.97), parental screen-time rules (RRR = 1.15, 95% CI 1.00-1.33), and household media equipment (RRR = 1.17, 95% CI 1.05-1.30) predicted likelihood of group 1 membership; cons of reducing SED (RRR = 2.70, 95% CI 1.77-4.10) predicted likelihood of group 2 membership; and co-participation in physical activity with friends (RRR = 0.80, 95% CI 0.69-0.94), fathers' modeling of TV viewing (RRR = 1.22, 95% CI 1.02-1.47), and household media equipment (RRR = 1.16, 95% CI 1.02-1.31) predicted likelihood of group 4 membership. Results suggest that strategies to improve MVPA and SED behaviors among youth may need to be multifaceted, targeting all levels of influence.

Identifying patterns of movement behaviours in relation to depressive symptoms during adolescence: A latent profile analysis approach

Movement behaviour guideline adherence has been associated with lower depressive symptoms during adolescence, yet no studies have used person-centered approaches to examine this relationship. The purpose of the present study was to identify whether unique adolescent movement behaviour profiles exist, evaluate predictors of profile membership, and determine whether profile membership was associated with differences in depressive symptoms cross sectionally and longitudinally. This study involved secondary analysis of the public-use data from Wave 1 and Wave 2 of the National Study of Adolescent Health. Adolescents (N = 6436; 48% male) in grades 7 to 12 (M_age = 16.03 ± 1.75) completed measures to assess moderate-to-vigorous physical activity (MVPA), recreational screen time (ST), and sleep - collectively known as movement behaviours - and depressive symptoms. Latent profile analysis identified four profiles that had similar sleep patterns and were thus characterized by different levels of MVPA and ST: high MVPA/low ST (29%), high MVPA/high ST (4%), low MVPA/low ST (53%), and low MVPA/high ST (14%). Several socio-demographic variables were found to influence profile membership. After adjusting for covariates, findings revealed depressive symptoms were lowest among the high MVPA/low ST profile and this trend was evident one year later. Engaging in high levels of either MVPA or ST alone did not provide additive benefits for depressive symptoms compared to those who engaged in low levels of both MVPA and ST. These findings suggest intervention efforts should take an integrative approach to improve mental health outcomes among adolescents by considering each of the movement behaviours concurrently.

Contemporaneous trajectories of physical activity and screen time in adolescents

Adolescents often report low moderate-to-vigorous physical activity (MVPA) and high screen time. We modeled sex-specific MVPA and screen time trajectories during adolescence and identified contemporaneous patterns of evolution. Data were drawn from 2 longitudinal investigations. The Nicotine Dependence in Teens (NDIT) study included 1294 adolescents recruited at age 12-13 years who completed questionnaires every 3 months for 5 years. The Monitoring Activities of Teenagers to Comprehend their Habits (MATCH) study included 937 participants recruited at age 9-12 years who completed questionnaires every 4 months for 7 years. MVPA was measured as the number of days per week of being active for at least 5 min (NDIT) or 60 min (MATCH). In both studies, screen time was measured as the number of hours spent weekly in screen activities. In each study, sex-specific group-based trajectories were modeled separately for MVPA and screen time from grade 7 to 11. Contemporaneous patterns of evolution were examined in mosaic plots. In both studies, 5 MVPA trajectories were identified in both sexes, and 4 and 5 screen time trajectories were identified in boys and girls, respectively. All combinations of MVPA and screen time trajectories were observed. However, the contemporaneous patterns of evolution were favourable in 14%-31% of participants (i.e., they were members of the stable high MVPA and the lower screen time trajectories). Novelty: MVPA and screen time trajectories during adolescence and their combinations showed wide variability in 2 Canadian studies. Up to 31% of participants showed favourable contemporaneous patterns of evolution in MVPA and screen time. Using uniform methods for trajectory modeling may increase the potential for replication across studies.

Lifestyle Clusters and Cardiometabolic Risks in Adolescents: A Chinese School-Based Study Using a Latent Class Analysis Approach

Background: Unhealthy dietary and lifestyle behaviors are associated with a higher prevalence of non-communicable chronic diseases and higher mortality in adults. However, there remains some uncertainty about the magnitude of the associations between lifestyle behaviors and cardiovascular factors in adolescents. Methods: We conducted a school-based cross-sectional study of 895 Chinese adolescents aged 15-19 years. They participated in a questionnaire survey, physical examination, and blood sample collection. Latent class analysis (LCA) was used to identify heterogeneous subgroups of lifestyle behaviors. A set of 12 latent class indicators, which reflected lifestyle behaviors including dietary habits, physical activity, sleep duration, screen time, and pressure perception, were included in the analysis. Logistic regression analysis was performed to determine whether the derived classes were related to a cardiometabolic risk. Results: In total, 13.7 and 5.6% of the participants were overweight and obese, respectively, and 8.4 and 14.1% reported having pre-hypertension and hypertension, respectively. A two-class model provided the best fit with a healthy lifestyle pattern (65.8%) and a sub-healthy lifestyle pattern (34.2%). There were more female participants with a healthy lifestyle (56.2 vs. 43.8%), whereas there were more males with a sub-healthy lifestyle (45.4 vs. 54.6%), (all P = 0.002). Increased risk of cardiometabolic abnormality (BMI categories, blood pressure and lipids) was not significant across lifestyle patterns, except for waist circumference (70.5 vs 69.1 cm, P = 0.044). There was no significant difference in physical activity and intake of fruit and vegetable between the two patterns. Conclusion: Primary prevention based on lifestyle modification should target patterns of behaviors at high risk in adolescents. Due to the complex effect of lifestyle clusters on cardiometabolic risks, well-designed and prospective studies in adolescents are needed in the future.

Screen media activity does not displace other recreational activities among 9-10 year-old youth: a cross-sectional ABCD study®

Background

Screen media is among the most common recreational activities engaged in by children. The displacement hypothesis predicts that increased time spent on screen media activity (SMA) may be at the expense of engagement with other recreational activities, such as sport, music, and art. This study examined associations between non-educational SMA and recreational activity endorsement in 9–10-year-olds, when accounting for other individual (i.e., cognition, psychopathology), interpersonal (i.e., social environment), and sociodemographic characteristics.

Methods

Participants were 9254 youth from the Adolescent Brain Cognitive Development Study®. Latent factors reflecting SMA, cognition, psychopathology, and social environment were entered as independent variables into logistic mixed models. Sociodemographic covariates included age, sex, race/ethnicity, education, marital status, and household income. Outcome variables included any recreational activity endorsement (of 19 assessed), and specific sport (swimming, soccer, baseball) and hobby (music, art) endorsements.

Results

In unadjusted groupwise comparisons, youth who spent more time engaging with SMA were less likely to engage with other recreational activities (ps < .001). However, when variance in cognition, psychopathology, social environment, and sociodemographic covariates were accounted for, most forms of SMA were no longer significantly associated with recreational activity engagement (p > .05). Some marginal effects were observed: for every one SD increase in time spent on games and movies over more social forms of media, youth were at lower odds of engaging in recreational activities (adjusted odds ratio = 0·83, 95% CI 0·76–0·89). Likewise, greater general SMA was associated with lower odds of endorsing group-based sports, including soccer (0·93, 0·88–0·98) and baseball (0·92, 0·86–0·98). Model fit comparisons indicated that sociodemographic characteristics, particularly socio-economic status, explained more variance in rates of recreational activity engagement than SMA and other latent factors. Notably, youth from higher socio-economic families were up to 5·63 (3·83–8·29) times more likely to engage in recreational activities than youth from lower socio-economic backgrounds.

Conclusions

Results did not suggest that SMA largely displaces engagement in other recreational activities among 9–10-year-olds. Instead, socio-economic factors greatly contribute to rates of engagement. These findings are important considering recent shifts in time spent on SMA in childhood.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-020-09894-w.

Clustering of health risk behaviors among adolescents in Kilifi, Kenya, a rural Sub-Saharan African setting

BACKGROUND

Adolescents tend to experience heightened vulnerability to risky and reckless behavior. Adolescents living in rural settings may often experience poverty and a host of risk factors which can increase their vulnerability to various forms of health risk behavior (HRB). Understanding HRB clustering and its underlying factors among adolescents is important for intervention planning and health promotion. This study examines the co-occurrence of injury and violence, substance use, hygiene, physical activity, and diet-related risk behaviors among adolescents in a rural setting on the Kenyan coast. Specifically, the study objectives were to identify clusters of HRB; based on five categories of health risk behavior, and to identify the factors associated with HRB clustering.

METHODS

A cross-sectional survey was conducted of a random sample of 1060 adolescents aged 13-19 years living within the area covered by the Kilifi Health and Demographic Surveillance System. Participants completed a questionnaire on health behaviors which was administered via an Audio Computer-Assisted Self-Interview. Latent class analysis on 13 behavioral factors (injury and violence, hygiene, alcohol tobacco and drug use, physical activity, and dietary related behavior) was used to identify clustering and stepwise ordinal logistic regression with nonparametric bootstrapping identified the factors associated with clustering. The variables of age, sex, education level, school attendance, mental health, form of residence and level of parental monitoring were included in the initial stepwise regression model.

RESULTS

We identified 3 behavioral clusters (Cluster 1: Low-risk takers (22.9%); Cluster 2: Moderate risk-takers (67.8%); Cluster 3: High risk-takers (9.3%)). Relative to the cluster 1, membership of higher risk clusters (i.e. moderate or high risk-takers) was strongly associated with older age (p<0.001), being male (p<0.001), depressive symptoms (p = 0.005), school non-attendance (p = 0.001) and a low level of parental monitoring (p<0.001).

CONCLUSION

There is clustering of health risk behaviors that underlies communicable and non-communicable diseases among adolescents in rural coastal Kenya. This suggests the urgent need for targeted multi-component health behavior interventions that simultaneously address all aspects of adolescent health and well-being, including the mental health needs of adolescents.

Home-based screen time behaviors amongst youth and their parents: familial typologies and their modifiable correlates

Background

Excessive screen time behaviors performed by children and parents at home is a major public health concern. Identifying whether child and parent screen time behaviors cluster and understanding correlates of these familial clusters can help inform interventions for the whole family. This study characterized familial typologies of screen time behaviors and identified key modifiable correlates of these typologies.

Methods

Parents participating in the cross-sectional Sitting in the Home (SIT) study reported the duration (mins/day) they and their child (aged 11.2 ± 2.62 years) spent in six screen time behaviors at home (computer/laptop for home/work, computer/laptop for leisure, TV/videos/DVDs, tablet/smart phone for home/work, tablet/smart phone for leisure, and electronic games) and completed items related to 21 potential correlates framed by an adapted Social Cognitive Theory, Family Perspective. Latent Class Analysis was used to identify typologies based on parent and child data for the six behaviors. Multinomial logistic regression analysis assessed the relative risk of typology membership for each potential correlate, adjusting for child and parent age and sex.

Results

The sample comprised 542 parent-child dyads (parents: 40.7 ± 6.3 yrs., 94% female; children: 11.2 ± 2.6 yrs., 46% female). Three typologies were identified: 1) high computer/moderate TV (n = 197); 2) high TV/tablet/smartphone, low computer (n = 135); and 3) low-screen users (n = 210). ‘Low-screen users’ spent the least amount of time in all screen time behaviors (assigned as reference category). Greater child preference for screen time behaviors, parental support for screen time behaviors and frequency of homework requiring a tablet/laptop were associated with higher odds of being in the ‘high computer/moderate TV’ typology. The odds of being in the ‘high TV/tablet/smartphone, low computer’ typology were greater amongst children with a higher preference for screen time behaviors, and lower among more active parents.

Conclusions

Three familial typologies of screen time behaviors were identified. The findings highlight that screen time in the home can be influenced by the home environment, parental behaviours and role modelling, child preferences as well as school policies. Findings can inform the development of family screen time interventions, however more research exploring the influence of factors outside of the home is warranted.

Movement Behaviors and Perceived Loneliness and Sadness within Alaskan Adolescents

Physical activity, screen use, and sleep are behaviors that integrate across the whole day. However, the accumulative influence of meeting recommendations for these 24-h movement behaviors on the mental health of Alaskan adolescents has not been examined. The purpose of this study was to examine the associations between movement behaviors, loneliness, and sadness within Alaskan adolescents. Data were obtained from the 2019 Alaska Youth Risk Behavior Survey (YRBS). The number of adolescents participating in the 2019 Alaska YRBS was 1897. Associations between meeting recommendations for movement behaviors with loneliness and sadness were examined using weighted logistic regression models, adjusted for age, sex, race/ethnicity, and body mass index (BMI). Approximately 5.0% of the sample met recommendations for all three movement behaviors. Meeting 2 or 3 movement behavior recommendations was associated with lower odds of loneliness (odds ratio (OR) range = 0.23 to 0.44, p < 0.01). Additionally, meeting 1 to 3 movement behavior recommendations was associated with lower odds of sadness (OR range = 0.29 to 0.52, p < 0.05). Joint association analyses determined that these relationships were primarily driven by meeting the sleep recommendation for loneliness and meeting the screen use recommendation for sadness. The results support use of multiple movement-based behavior programming to attenuate feelings of loneliness and sadness within Alaskan adolescents.

One size does not fit all: identifying clusters of physical activity, screen time, and sleep behaviour co-development from childhood to adolescence

Purpose

Canada was the first to adopt comprehensive 24-h movement guidelines that include recommendations for physical activity, screen time and sleep to promote health benefits. No studies have investigated the concurrent development of these behaviours in youth. The objectives were to assess adherence to the Canadian 24-h movement guidelines for children and youth and estimate co-development of self-reported moderate-to-vigorous intensity physical activity (MVPA), screen time and sleep during 8-years from childhood to adolescence.

Methods

Nine hundred and twenty three participants of the MATCH study self-reported their MVPA, screen time and sleep duration at least twice over 8 years. MVPA and screen time were measured three times per year (24 cycles), and sleep was measured once per year (8 cycles). Guideline adherence was dichotomised as meeting each specific health behaviour recommendation or not. Multi-group trajectory modeling was used to identify unique trajectories of behavioural co-development. Analyses were stratified by sex.

Results

Between 10 and 39% of youth did not meet any recommendation at the various cycles of data collection. More than half of youth met only one or two recommendation, and roughly 5% of participants met all three recommendations at one or more study cycle throughout the 8 years of follow-up. Four different trajectories of behavioural co-development were identified for boys and for girls. For boys and girls, a complier (good adherence to the guideline recommendations; 12% boys and 9% girls), a decliner (decreasing adherence to the guideline recommendations; 23% boys and 18% girls) and a non-complier group (low adherence to the guideline recommendations; 42% boys and 42% girls) were identified. In boys, a MVPA-complier group (high MVPA-low screen time; 23%) was identified, whereas in girls a screen-complier group (moderate screen time-low MVPA; 30%) was identified.

Conclusions

There is a need to recognise that variations from general trends of decreasing MVPA, increasing screen time and decreasing sleep exist. Specifically, we found that although it is uncommon for youth to adhere to the Canadian 24-h movement guidelines, some youth displayed a high likelihood of attaining one or multiple of the behavioural recommendations. Further, patterns of adherence to the guidelines can differ across different sub-groups of youth.

Clusters of diet, physical activity, television exposure and sleep habits and their association with adiposity in preschool children: the EDEN mother-child cohort

Background

Despite the growing interest in the relation between adiposity in children and different lifestyle clusters, few studies used a longitudinal design to examine a large range of behaviors in various contexts, in particular eating- and sleep-related routines, and few studies have examined these factors in young children. The objectives of this study were to identify clusters of boys and girls based on diet, sleep and activity-related behaviors and their family environment at 2 and 5 years of age, and to assess whether the clusters identified varied across maternal education levels and were associated with body fat at age 5.

Methods

At 2 and 5 years, respectively, 1436 and 1195 parents from the EDEN mother-child cohort completed a questionnaire including behavioral data. A latent class analysis aimed to uncover gender-specific behavioral clusters. Body fat percentage was estimated by anthropometric and bioelectrical impedance measurements. Association between cluster membership and body fat was assessed with mutivariable linear regression models.

Results

At 2 years, two clusters emerged that were essentially characterized by opposite eating habits. At 5 years, TV exposure was the most distinguishing feature, but the numbers and types of clusters differed by gender. An association between cluster membership and body fat was found only in girls at 5 years of age, with girls in the cluster defined by very high TV exposure and unfavorable mealtime habits (despite high outdoor playing and walking time) having the highest body fat. Girls whose mother had low educational attainment were more likely to be in this high-risk cluster. Girls who were on a cluster evolution path corresponding to the highest TV viewing time and the least favorable mealtime habits from 2 to 5 years of age had higher body fat at 5 years.

Conclusions

Efforts to decrease TV time and improve mealtime routines may hold promise for preventing overweight in young children, especially girls growing up in disadvantaged families. These preventive efforts should start as early in life as possible, ideally before the age of two, and should be sustained over the preschool years.