RESULTS: This study investigates the effect of iterations in sliding semi-landmarks and their results on the predictive ability in GM analyses of soft-tissue in 3D human face. Principal Component Analysis (PCA) is used for feature selection and the gender are predicted using Linear Discriminant Analysis (LDA) to test the effect of each relaxation state. The results show that the classification accuracy is affected by the number of iterations but not in progressive pattern. Also, there is stability at 12 relaxation state with highest accuracy of 96.43% and an unchanging decline after the 12 relaxation state.
CONCLUSIONS: The results indicate that there is a particular number of iteration or cycle where the sliding becomes optimally relaxed. This means the higher the number of iterations is not necessarily the higher the accuracy.
DESIGN AND MEASURES: Data were analysed from the Global School-Based Student Health Survey Timor-Leste (n = 3455). An ordered probit model was used to assess the effects of demographic, lifestyle, social, and psychological factors on different levels of worry-related sleep problems (i.e., no, mild and severe sleep problems).
RESULTS: School-going adolescents were more likely to face mild or severe worry-related sleep problems if they were older, passive smokers, alcohol drinkers and moderately active. School-going adolescents who sometimes or always went hungry were more likely to experience worry-related sleep problems than those who did not. Involvement in physical fights, being bullied, and loneliness were positively associated with the probability of having modest or severe worry-related sleep problems.
CONCLUSION: Age, exposure to second-hand smoke, alcohol consumption, physical activity, going hungry, physical fights, being bullied and loneliness are the important determining factors of adolescent worry-related sleep problems. Policymakers should pay special attention to these factors when formulating intervention measures.
METHODS: A nationwide data set was examined for this secondary data analysis. The dependent variable was the degree of risk, which was measured based on the number of high-risk behaviours in which adolescents participated. Age, gender, ethnicity, self-rated academic performance, family size, parental marital status and parental academic attainment were included as independent variables. Analyses stratified by educational level were conducted. Odds ratios (ORs) were calculated using ordered logit.
RESULTS: The most common high-risk behaviour among Malaysian adolescents was physical inactivity (35.97%), followed by smoking (13.27%) and alcohol consumption (4.45%). The majority of adolescents had low risks (52.93%), while only a small proportion had high risks (6.08%). Older age was associated with increased odds of having high risks (OR: 1.26). Male adolescents had higher odds of being in a high-risk category compared to female adolescents (OR: 1.28). Compared to Malays, Chinese adolescents had higher odds of being in a high-risk category (OR: 1.71), whereas Indian adolescents had lower odds (OR: 0.65). Excellent academic performance was associated with reduced odds of participating in high-risk behaviours (OR: 0.41).
CONCLUSION: Personal factors are important determinants of high-risk behaviours. This study provides a better understanding of those adolescent groups that are at greater risk.
PRACTICAL IMPLICATIONS: An intervention directed towards reducing participation in high-risk behaviours among adolescents who have both poor academic performance and less-educated parents may yield promising outcomes.
Patients and methods: A nationally representative data of adolescents that consists of 25399 respondents is used. The demographic (age, gender, education) and lifestyle (fruits and vegetables consumption, carbonated soft drink consumption, cigarette smoking, alcohol drinking, sex behaviour, participation in physical education class, obesity) determinants of physical activity are assessed using binomial regression.
Results: The results show that age is negatively associated with time spent in physical activity. However, being male and education levels are positively related to time spent in physical activity. Having unhealthy lifestyle and being obese are associated with low levels of physical activity. Physical education seems to promote participation in physical activity.
Conclusion: In conclusion, demographic and lifestyle factors play an important role in determining levels of physical activity among adolescents. In order to reduce the prevalence of physically inactive adolescents, policy makers should focus primarily on late adolescents, females, adolescents who engage in unhealthy lifestyle and seldom attend physical education classes, as well as obese adolescents.