Clustering of health-related behaviors among early and mid-adolescents in Tuscany: results from a representative cross-sectional study

A Cross-Sectional Study of Oral Health Status and Behavioral Risk Indicators among Non-Smoking and Currently Smoking Lithuanian Adolescents

The purpose of this study was to evaluate oral health status, behavioral risk indicators, and the impact of smoking on oral health among Lithuanian adolescents. This representative cross-sectional study was conducted among 15-year-old Lithuanian adolescents. The method of multistage cluster sampling was used. A total of 1127 adolescents met the inclusion criteria. Two originally created self-reported questionnaires were used in this study. Dental caries, periodontal status, and oral hygiene status were evaluated by four trained researchers. A p-value ≤ 0.05 was set to indicate statistically significant differences. Statistical analysis included Mann-Whitney, Kruskal-Wallis, and Spearman correlation tests. Out of all the participants, 9.6% self-reported being a current tobacco smoker. The mean PI value was 1.14 ± 0.69 among all the participants. Currently smoking adolescents had more active caries lesions (D-S) than those who did not smoke (13.2 ± 16.4 vs. 9.8 ± 10.7, p = 0.023). Considering periodontal status, non-smoking adolescents had significantly lower mean PSR index scores than current smokers (0.52 ± 0.51 vs. 0.61 ± 0.50, p = 0.0298). Tobacco smoking and the consumption of energy drinks were significantly associated (OR = 3.74, 95% CI 2.66-5.26, p < 0.001) among participants. Currently smoking adolescents tended to have improper dietary habits, especially a higher consumption of energy drinks; thus, they were more likely to have active dental caries lesions, as well as poorer periodontal status, than their non-smoking peers.

Screen time among school-aged children of aged 6-14: a systematic review

BACKGROUND

Screen time refers to the time an individual spends using electronic or digital media devices such as televisions, smart phones, tablets or computers. The purpose of this study was to conduct systematic review to analyze the relevant studies on the length and use of screen time of school-aged children, in order to provide scientific basis for designing screen time interventions and perfecting the screen use guidelines for school-aged children.

METHODS

Screen time related studies were searched on PubMed, EMBASE, Clinical Trials, Controlled Trials, The WHO International Clinical Trials Registry Platform, the Cochrane Central Register of Controlled Trials, CNKI, and Whipple Journal databases from January 1, 2016 to October 31, 2021. Two researchers independently screened the literature and extracted the data, and adopted a qualitative analysis method to evaluate the research status of the length and usage of screen time of school-aged students.

RESULTS

Fifty-three articles were included. Sixteen articles studied screen time length in the form of continuous variables. Thirty-seven articles studied screen time in the form of grouped variables. The average screen time of schoolchildren aged 6 to 14 was 2.77 h per day, and 46.4% of them had an average screen time ≥ 2 h per day. A growth trend could be roughly seen by comparing studies in the same countries and regions before and after the COVID-19 outbreak. The average rates of school-aged children who had screen time within the range of ≥ 2 h per day, were 41.3% and 59.4% respectively before and after January 2020. The main types of screen time before January 2020 were watching TV (20 literatures), using computers (16 literature), using mobile phones/tablets (4 literatures). The mainly uses of screens before January 2020 were entertainment (15 literatures), learning (5 literatures) and socializing (3 literatures). The types and mainly uses of screen time after January 2020 remained the same as the results before January 2020.

CONCLUSIONS

Excessive screen time has become a common behavior among children and adolescents around the world. Intervention measures to control children's screen use should be explored in combination with different uses to reduce the proportion of non-essential uses.

Health lifestyles during adolescence: clustering of health behaviours and social determinants in Italian adolescents

BACKGROUND

Health behaviours in adolescence have a high impact on both current and future health. The literature claims a focus on social determinants and shows that these behaviours are associated. The concept of 'health lifestyles' should be considered to account for co-occurring health behaviours and determinants interactions. The aim of this study is to increase our understanding of adolescents' health lifestyles and to (i) cluster adolescents according to a comprehensive number of health behaviours and (ii) describe these groups according to sociodemographic characteristics, perceptions of life contexts (family, school, peers or neighbourhood) and perceived physical and psychosocial health conditions.

METHODS

In Italy, 906 15-year-old students participated in the cross-national Health Behaviour of School-aged Children study. Clusters were identified by applying the KAMILA clustering method and compared using analysis of variance and chi-squared tests.Results: Four clusters were identified: 'substance consumers', 'media lovers', 'active students' and 'passive students'. Each cluster exhibited different characteristics related to health behaviours and social determinants.

CONCLUSIONS

Interesting associations between health behaviours were identified, which showed the relevance of considering the adolescents' overall lifestyles. The description of each cluster permitted the identification of risks and protective factors, which may be important for designing effective health promotion activities.

Analysis of the mandibular canal course using unsupervised machine learning algorithm

Objectives

Anatomical structure classification is necessary task in medical field, but the inevitable variability of interpretation among experts makes reliable classification difficult. This study aims to introduce cluster analysis, unsupervised machine learning method, for classification of three-dimensional (3D) mandibular canal (MC) courses, and to visualize standard MC courses derived from cluster analysis in the Korean population.

Materials and methods

A total of 429 cone-beam computed tomography images were used. Four sites in the mandible were selected for the measurement of the MC course and four parameters, two vertical and two horizontal parameters were measured per site. Cluster analysis was carried out as follows: parameter measurement, parameter normalization, cluster tendency evaluation, optimal number of clusters determination, and k-means cluster analysis. The 3D MC courses were classified into three types with statistically significant mean differences by cluster analysis.

Results

Cluster 1 showed a smooth line running towards the lingual side in the axial view and a steep slope in the sagittal view. Cluster 2 ran in an almost straight line closest to the lingual and inferior border of mandible. Cluster 3 showed the pathway with a bent buccally in the axial view and an increasing slope in the sagittal view in the posterior area. Cluster 2 showed the highest distribution (42.1%), and males were more widely distributed (57.1%) than the females (42.9%). Cluster 3 comprised similar ratio of male and female cases and accounted for 31.9% of the total distribution. Cluster 1 had the least distribution (26.0%) Distributions of the right and left sides did not show a statistically significant difference.

Conclusion

The MC courses were automatically classified as three types through cluster analysis. Cluster analysis enables the unbiased classification of the anatomical structures by reducing observer variability and can present representative standard information for each classified group.

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.

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.

Patterns of substance use among adolescents: A systematic review

PURPOSE

This review characterizes empirically derived patterns of multiple (multi-) substance use among adolescents. A secondary objective was to examine the extent to which mental health symptomatology was included in the empirical analyses examining substance use patterns.

METHODS

Eligible studies included those that used cluster-based approaches, included the assessment of at least two different substances, and were based on study samples with mean ages between 11 and 18 years. 4665 records were screened including 461 studies for full-text screening.

RESULTS

70 studies were included with common clusters being: low use, single or dual substance use, moderate general multi-use, and high multi-use. The most common patterns of single or multi-substance use were: alcohol only, alcohol with cannabis and/or tobacco, and use of alcohol, tobacco, and cannabis with and without other drugs. Lower socioeconomic status, older age, and male gender were consistent predictors of multi-use clusters. Only 37 % of studies compared differences in levels of mental health across clusters with symptoms consistently associated with a greater likelihood of multi-use. Only 29 % of studies included mental health indicators in cluster-based analyses, with over half identifying distinct mental health and substance use clusters. Fit indices in cluster analyses and measurement properties of substance use were heterogeneous and inconsistently reported across studies.

CONCLUSIONS

Distinct patterns of substance use were derived but methodological differences prevented direct comparison and reduced capacity to generalize across studies. There is a need to establish standardized methodological approaches to identify robust patterns of substance use to enhance etiological, prognostic, and intervention research.

Clustering of Six Key Risk Behaviors for Chronic Disease among Adolescent Females

Chronic diseases are the leading cause of disability and mortality globally. In Australia, females are at heightened risk. This research explored the prevalence, patterns, and correlates of six key risk behaviors (physical inactivity, poor diet, recreational screen time, inadequate sleep, alcohol use, and smoking) among adolescent females and whether knowledge of health guidelines was associated with adherence. Adolescent females completed an anonymous online questionnaire (N = 687; Mage = 13.82). Logistic regression assessed the association between knowledge and adherence. A Latent Class Analysis (LCA) and three-step procedure identified risk behavior clusters and their correlates. Despite positive health self-ratings (77% good/very good), most participants reported insufficient moderate-to-vigorous physical activity (MVPA; 89%), vegetable intake (89%), and excessive screen time (63%). Knowledge of guidelines was associated with adherence for MVPA, vegetable intake, sleep, and alcohol abstinence. Three classes emerged: "moderate risk" (76%), "relatively active, healthy eaters" (19%), and "excessive screen users" (5%). These risk-behavior clusters were associated with perceived value of academic achievement and physical wellbeing. Adolescent females commonly perceive they are in good health, despite engaging in unhealthy behaviors. Public health interventions should utilize effective behavior change strategies, adopt a multiple health behavior change approach (MHBC), and be tailored to specific risk profiles and values among females.

Clustering of health-related behaviours and its relationship with individual and contextual factors in Portuguese adolescents: results from a cross-sectional study

BACKGROUND

Health behaviours are shaped early in life and tend to occur in complex specific patterns. We aimed to characterise these patterns among Portuguese adolescents and their association with individual and contextual factors.

METHODS

This study was based in the Portuguese 2009/10 survey of Health Behaviour in School-Aged Children Study, comprising 4036 adolescents. Individuals were grouped using two-step cluster analysis based on 12 behaviours regarding diet, physical activity, screen use and substance use. The association between clusters and individual and contextual factors was analysed using multinomial regression.

RESULTS

The median age was 13,6, and 54% were female. Overweight and obesity were highly prevalent (25%). We identified four behavioural clusters: "Active screen users", "Substance users", "Healthy" and "Inactive low fruit and vegetable eaters". Sociodemographics varied across clusters. The "Substance users" and "Active screen users" clusters were associated with poor family communication, academic performance and school attachment and violent behaviours, and the "Inactive low fruit and vegetable eaters" were associated with lower socioeconomic status.

CONCLUSION

The understanding of these health-compromising patterns and their social determinants is of use to Public Health, allowing tailored health-promoting interventions. Further research is needed to understand how cluster membership evolves and its influence on nutritional status.

Young people, young adults and binge drinking

Introduction and purpose

The consumption of alcohol among young people and young adults has undergone, in recent decades, a sharp upsurge with the increasingly frequent intake of large quantities of alcohol. The aim of our study was to investigate socio-demographic, economic and behavioural factors that have a major impact on the voluntary alcohol habit in young people.

Methods

The survey was conducted via administration of an anonymous questionnaire based on “the WHO Alcohol Use Disorders Identification Test”, disseminated on-line, to young people and young adults (aged 18 to 35).

Results

We examined a sample of 365 subjects. Consumption of wine and beer were predominant followed by super-alcohol mixes. We found correlations between alcohol use and the following variables: marital status (p < 0.001), parental education (mother p < 0.05; father p < 0.001), income level (p < 0.05), physical activity (p < 0.05) and voluptuous habits (smoke and coffee: p < 0.001.The 5% of men and 1% of women had car accidents during the previous year due to alcohol use and 15% said they did not remember what happened in an alcoholic evening once or twice a month.

Conclusions

The phenomenon of alcohol consummation is deeply ingrained in our reality, with dangerous episodes of binge drinking in young adults with a higher prevalence in the female sex.

Breakfast Is Brain Food? The Effect on Grade Point Average of a Rural Group Randomized Program to Promote School Breakfast

BACKGROUND

Adolescents frequently miss breakfast which may impact cognitive, academic, and health outcomes. This analysis describes the effect of a trial to assess school level changes to increase breakfast consumption on grade point average (GPA).

METHODS

Sixteen rural Minnesota high schools were randomized to a policy and environmental change intervention or delayed intervention (control) group. Baseline screening identified, randomly selected and enrolled 9th and 10th grade students who eat breakfast ≤3 times per school week for assessment. Mean unweighted GPA was provided by 13 schools for 636 students. Student-level and administrative data were used for sociodemographic and free or reduced-price meals (FRPM). Linear mixed models and latent class analysis (LCA) were used to assess change in GPA.

RESULTS

Students were 54% female, 76% white, and 34% received FRPM. Unweighted cumulative GPA mean = 2.82 (0.78) at baseline. There was no significant intervention effect on GPA postintervention or 1-year follow-up. LCA revealed two classes: "higher" (N = 495) and "lower" (N = 141) resource. There was an intervention effect among low-resource students from baseline to 1-year post only among the control condition (delayed intervention).

CONCLUSIONS

In combination with the full study results, increasing breakfast consumption may have an impact particularly for low resource students.

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.

Prevalence and co-occurrence of unhealthy lifestyle habits and behaviours among secondary school students in Tuscany, central Italy

OBJECTIVES

Unhealthy habits acquired during adolescence may persist in adulthood and eventually increase the risk of chronic illnesses.

STUDY DESIGN

We reported on a survey conducted in 2013-2015 among secondary school students in Tuscany, central Italy.

METHODS

We compared the prevalence of self-reported lifestyle characteristics and overweight/obesity between genders and age groups (14-16 vs 17-21 years). We partitioned each gender- and age-specific stratum into groups based on cigarette smoking and engagement in sport activities, and compared the prevalence of other unhealthy lifestyles across groups using Poisson regression.

RESULTS

Overall, 2167 students (53.3% males, mean age 16.8 years) were included. Males were more frequently overweight/obese than females. Cigarette smoking increased with age and did not differ by gender. Males were more likely to engage in sport activities, drink alcoholic beverages and adopt other unhealthy lifestyle habits, whereas females reported a more frequent use of painkillers. Cigarette smoking was the single lifestyle characteristic most consistently associated with other unhealthy habits.

CONCLUSIONS

The prevalence and patterns of co-occurrence of unhealthy lifestyle habits varied by gender and age group among secondary school students in Italy. Our findings should be taken into account when planning public health initiatives aiming to combat obesity and tackle unhealthy lifestyles among secondary school students in Italy.

Clustering of diet, physical activity and sedentary behavior among Brazilian adolescents in the national school - based health survey (PeNSE 2015)

BACKGROUND

There is a lack of evidence regarding clusters of health-related behaviors among adolescents from low, lower-middle, and upper-middle income countries. This study aimed to identify clustering patterns of health-related behaviors (diet, physical activity [PA] and sedentary behavior [SB]) and association with sociodemographic variables among a population-based sample of Brazilian adolescents.

METHODS

Cross-sectional data from the 2015 National School-Based Health Survey (PeNSE). A total of 102,072 (females: 51.7%) students in ninth-grade (age: 14.3 ± 1.1 years-old) enrolled in public and private schools were investigated in this study. Healthy and unhealthy diet, PA and SB were measured using a validated questionnaire. Two-step cluster analysis was conducted to identify lifestyle patterns. The methodology for complex analysis and weighting was used to inferential statistical procedures. Multinomial logistic regression assessed associations between sociodemographic factors and the clusters.

RESULTS

Three reliable and meaningful clusters were identified and labelled as follows: (1) health-promoting SB and diet (32.6%); (2) health-promoting PA and diet (44.9%), and (3) health-risk (22.5%). Compared to boys, girls were less likely to be in clusters 1 (OR = 0.85; 95% CI = 0.78-0.93, p < 0.001) and 2 (OR = 0.43; 95% CI = 0.40-0.46, p < 0.001) than the health-risk cluster. Higher socioeconomic status was positively associated with health-promoting PA and diet, and negatively related to health-promoting SB and diet. Older adolescents were more likely to be in cluster 1 than in cluster 3, compared to younger adolescents.

CONCLUSION

Approximately one-quarter of the population (health-risk cluster) reported engaging in multiple risk behaviors. Interventions may need to be tailored to specific adolescent groups, especially considering sociodemographic differences.