METHODS: A systematic search of Medline via the PubMed, Science Direct, Cochrane Review and Web of Science databases was conducted for studies on the associations between diet and PA factors and cardio-metabolic risk factors among Malaysian adolescents aged 13-18 years that were published until 31 August 2017. The search results were independently screened and extracted by two reviewers.
RESULTS: From over 2,410 references retrieved, 20 full texts articles were screened as potentially relevant. Seventeen (16 cross-sectional and one intervention) met the inclusion criteria for data extraction and analysis. All 17 studies were rated as poor quality and the majority had made insufficient adjustment for confounders. As regards the effect of diet and PA on cardio-metabolic health, the intakes of energy (n = 4) and macronutrients (n = 3) and meal frequency (n = 5) were the most commonly studied dietary factors, while the PA score and level were the most commonly studied PA factors. In addition, BMI and body weight were the most common cardio-metabolic health outcomes. The studies showed that obese and overweight adolescents consume significantly more energy and macronutrients. They are also more likely to skip their daily meals compared to their normal weight peers. In most studies, the direction of the PA effect on body weight was unclear. Some studies found that higher PA is associated with a lower risk of overweight and obesity. However, the associations are often small or inconsistent, with few studies controlling for confounding factors.
CONCLUSIONS: This review identified a lack of evidence and well-conducted prospective studies on the effect of diet and PA on cardio-metabolic health of Malaysian adolescents.
METHODS: For this pooled analysis, we used a database of cardiometabolic risk factors collated by the Non-Communicable Disease Risk Factor Collaboration. We applied a Bayesian hierarchical model to estimate trends from 1985 to 2019 in mean height and mean BMI in 1-year age groups for ages 5-19 years. The model allowed for non-linear changes over time in mean height and mean BMI and for non-linear changes with age of children and adolescents, including periods of rapid growth during adolescence.
FINDINGS: We pooled data from 2181 population-based studies, with measurements of height and weight in 65 million participants in 200 countries and territories. In 2019, we estimated a difference of 20 cm or higher in mean height of 19-year-old adolescents between countries with the tallest populations (the Netherlands, Montenegro, Estonia, and Bosnia and Herzegovina for boys; and the Netherlands, Montenegro, Denmark, and Iceland for girls) and those with the shortest populations (Timor-Leste, Laos, Solomon Islands, and Papua New Guinea for boys; and Guatemala, Bangladesh, Nepal, and Timor-Leste for girls). In the same year, the difference between the highest mean BMI (in Pacific island countries, Kuwait, Bahrain, The Bahamas, Chile, the USA, and New Zealand for both boys and girls and in South Africa for girls) and lowest mean BMI (in India, Bangladesh, Timor-Leste, Ethiopia, and Chad for boys and girls; and in Japan and Romania for girls) was approximately 9-10 kg/m2. In some countries, children aged 5 years started with healthier height or BMI than the global median and, in some cases, as healthy as the best performing countries, but they became progressively less healthy compared with their comparators as they grew older by not growing as tall (eg, boys in Austria and Barbados, and girls in Belgium and Puerto Rico) or gaining too much weight for their height (eg, girls and boys in Kuwait, Bahrain, Fiji, Jamaica, and Mexico; and girls in South Africa and New Zealand). In other countries, growing children overtook the height of their comparators (eg, Latvia, Czech Republic, Morocco, and Iran) or curbed their weight gain (eg, Italy, France, and Croatia) in late childhood and adolescence. When changes in both height and BMI were considered, girls in South Korea, Vietnam, Saudi Arabia, Turkey, and some central Asian countries (eg, Armenia and Azerbaijan), and boys in central and western Europe (eg, Portugal, Denmark, Poland, and Montenegro) had the healthiest changes in anthropometric status over the past 3·5 decades because, compared with children and adolescents in other countries, they had a much larger gain in height than they did in BMI. The unhealthiest changes-gaining too little height, too much weight for their height compared with children in other countries, or both-occurred in many countries in sub-Saharan Africa, New Zealand, and the USA for boys and girls; in Malaysia and some Pacific island nations for boys; and in Mexico for girls.
INTERPRETATION: The height and BMI trajectories over age and time of school-aged children and adolescents are highly variable across countries, which indicates heterogeneous nutritional quality and lifelong health advantages and risks.
FUNDING: Wellcome Trust, AstraZeneca Young Health Programme, EU.
METHODS AND STUDY DESIGN: Collective food data from MyHeARTs 2012 database were used to construct the MyUM Adolescent FFQ. Seventy-eight participants between 13 and 15 years old in 2014 were selected through convenient sampling for test-retest study. They completed the MyUM Adolescent FFQ twice, with an interval period of one week. One hundred and fifty-six MyHeARTs study participants who were 15 years old in 2014 were randomly selected for this comparative valid-ity study. They completed a 7-day diet history (7DDH) and subsequently completed the self-administered MyUM Adolescent FFQ.
RESULTS: Pearson's correlations between the FFQ and 7DDH for all macronutrients were statistically significant. Energy-adjusted correlations for protein, carbohydrate, and fat were 0.54, 0.63 and 0.49 respectively. Most of the micronutrients and minerals, were statistically correlated ranging from 0.31 to 0.49 after energy adjustment. Cross-classification analyses revealed that more than 70 percent of adolescents were classified into either the same or adjacent quartile of nutrient intake when comparing data of 7DDH and FFQ. No serious systematic bias was evident in the Bland-Altman plots.
CONCLUSION: The 200-item FFQ developed for Malaysian adolescents has moderate to good comparative validity for assessment of macronutrient and micronutrient intake.
MATERIALS AND METHODS: Two thousand seven hundred students were randomly selected by proportional stratified sampling. Analyses on 1,736 non-smoking students revealed that prevalence of adolescents susceptible to smoking was 16.3%.
RESULTS: Male gender (aOR=2.05, 95%CI= 1.23-3.39), poor academic achievement (aOR 1.60, 95%CI 1.05-2.44), ever-smoker (aOR 2.17, 95%CI 1.37-3.44) and having a smoking friend (aOR 1.76, 95%CI 1.10-2.83) were associated with susceptibility to smoking, while having the perception that smoking prohibition in school was strictly enforced (aOR 0.55, 95%CI 0.32-0.94), and had never seen friends smoking in a school compound (aOR 0.59, 95%CI 0.37-0.96) were considered protective factors
CONCLUSIONS: These results indicate that follow-up programmes need to capitalise on the modifiable factors related to susceptibility to smoking by getting all stakeholders to be actively involved to stamp out smoking initiation among adolescents.
MATERIALS AND METHODS: Data were collected in two waves from a cohort of 2,552 adolescents aged 12-13 years old studying in 15 secondary schools based in Kinta, Perak. A multistage sampling method was used to select the schools and a self-administered structured questionnaire was applied to help categorize the participants into five different smoking stages. Nonsmokers were divided into never smokers and susceptible never smokers. Ever-smokers were categorized as experimenters, current smokers or ex-smokers.
RESULTS: Among the participants 46.8% were Malay, 33.5% Chinese and 17.1% Indians. At baseline, we had 85.3% non-smokers and 14.6% ever smokers. Incidence of adverse transition among all our participants was 24.1%, with a higher value among male participants (16.8%). A higher proportion of susceptible never smokers and experimenters progressed to current smoking stage compared to never smokers.
CONCLUSIONS: This study highlights the changes and patterns of adverse transition among adolescents. Male adolescents, those who are susceptible to smoking and those who had already tried experimenting with cigarettes have a higher chance of escalating to a higher smoking stage.
MATERIALS AND METHODS: Thos longitudinal study started in February 2011 and the subjects were 2552 form one students aged between twelve to thirteen years of from 15 government secondary schools of Kinta, Perak. Data on demographic, parental, school and peer factors were collected using a self-administered questionnaire. We examined the effects of peer, school and parental factors on the five stages of smoking; never smokers, susceptible never smokers, experimenters, current smokers and ex-smokers, at baseline.
RESULTS: In the sample, 19.3% were susceptible never smokers, 5.5% were current smokers 6% were experimenters and 3.1% were ex-smokers. Gender, ethnicity, best friends' smoking status, high peer pressure, higher number of relatives who smoked and parental monitoring were found to be associated with smoking stages. Presence of parent-teen conflict was only associated with susceptible never smokers and experimenters whereas absence of home discussion on smoking hazards was associated with susceptible never smokers and current smokers.
CONCLUSIONS: We identified variations in the factors associated with the different stages of smoking. Our results highlight that anti-smoking strategies should be tailored according to the different smoking stages.
METHODS: The 70-item QOLQA measuring five QOL domains (physical, psychological, independence, social and environmental) was administered to a random sample of 1363 school-children aged 10-15 years, representative of the ethnic composition of Singapore adolescents (Chinese 72%, Malays 20% and Indians 8%).
RESULTS: Indians reported the highest overall QOL (mean 3.71 +/- SD 0.54) compared to Chinese (3.59 +/- 0.43), p < 0.05, and Malays (3.58 +/- 0.44), p < 0.05. In particular, Indians had significantly higher psychological QOL scores (3.73 +/- 0.61) compared to Chinese (3.55 +/- 0.54), p < 0.01. On the other hand, Chinese scored highest on physical and independence domains (3.97 +/- 0.54), p < 0.01 compared to Malays (3.82 +/- 0.55). There were no statistically significant gender differences in QOL scores. QOL declined significantly from age 10 to 15 for overall score, psychological, physical (p < 0.01) and environmental (p < 0.05). Lower socio-economic status and the self-report of a significant health problem were significantly associated with lower overall QOL and most domains. These ethnic differences persisted after adjusting for differences in socio-economic and health status. Psychometric properties and known group construct validity appeared to be similar across different ethnic groups, but compared to Chinese (r = 0.39) or Malays (r = 0.39), Indians showed a higher correlation of psychological scores with physical score (r = 0.59) and with other domain scores.
CONCLUSION: Significant ethnic differences in reported adolescent quality of life among Chinese, Malays and Indians in Singapore that are independent of socioeconomic and health status suggest important cultural differences.