METHODS: The development process follows the systematic steps recommended by the Active Healthy Kids Global Alliance was used. Nationally representative data from 2016 to 2021, government reports and unpublished data were reviewed and consolidated by a panel of experts. Letter grades were assigned based on predefined benchmarks to 12 indicators including 10 core physical activity indicators that are common to Global Matrix 4.0 and two additional indicators (Diet and Weight Status). The current grading was then compared against those obtained in 2016.
RESULTS: Four of six indicators in the Daily Behaviors category received D- or C grades [Overall Physical Activity, Active Transportation and Diet (D-); Sedentary Behaviors (C)], which remains poor, similar to the 2016 report card. School indicator was graded for the Settings and Sources of Influence category, which showed an improvement from grade B (2016) to A- (2022). As for the Strategies and Investments category, B was again assigned to the Government indicator. Two new indicators were added after the 2016 Report Card, and they were graded B (Physical Fitness) and B- (Weight Status). Four indicators (Organized Sports and Physical Activity, Active Play, Family and Peers, and Community and Environment) were again graded Incomplete due to a lack of nationally representative data.
CONCLUSION: The 2022 Report Card revealed that Malaysian children and adolescents are still caught in the "inactivity epidemic". This warrants more engagement from all stakeholders, public health actions, and timely research, to comprehensively evaluate all indicators and drive a cultural shift to see Malaysian children and adolescents moving more every day.
METHODS: Data from 939 preschoolers aged 3-6 years (mean age = 4.83 ± 0.04 years, 53.7% boys) from the Second South East Asian Nutrition Surveys (SEANUTS II) Malaysia study was analyzed. Socio-demography, physical activity, sedentary behaviors, and sleep were parent-reported via questionnaire. Associations between adherence of 24-hMG and sociodemographic factors were analyzed using complex samples logistic regression.
RESULTS: Only 12.1% of preschoolers adhered to the overall 24-hMG, and 67.1%, 54.7%, and 42.7% of preschoolers adhered to physical activity, sleep, and sedentary behavior guidelines, respectively; while 6.8% did not meet any guidelines. Compared to 3-4-year olds, preschoolers aged 5-6 years had higher odds of adhering to physical activity guidelines, sedentary behavior guidelines, and overall 24-hMG, but lower odds of adhering to sleep guidelines. Chinese and Indian preschoolers were more likely to adhere to sedentary behavior guidelines than Malay preschoolers; however, Chinese preschoolers had lower odds of adhering to physical activity guidelines. Paternal tertiary education was associated with a higher likelihood of adherence to sleep guidelines.
CONCLUSION: Our findings suggest that adherence to 24-hMG among Malaysian preschoolers is associated with age, ethnicity, and paternal education level. This underscores the importance of targeted interventions and health awareness program to promote healthy movement behaviors, particularly among children under 5, ethnic minorities, and educationally disadvantaged families.
DESIGN: Food choice was assessed using the validated New Zealand Adolescent FFQ. Principal components analysis was used to determine dietary patterns. Trained research assistants measured participants' height and body mass. Cardiorespiratory fitness was assessed in a subset of participants using the multistage 20 m shuttle run. The level and stage were recorded, and the corresponding VO2max was calculated. Differences in mean VO2max according to sex and BMI were assessed using t tests, while associations between cardiorespiratory fitness and dietary patterns were examined using linear regression analyses adjusted for age, sex, school attended, socio-economic deprivation and BMI.
SETTING: Secondary schools in Otago, New Zealand.
SUBJECTS: Students (n 279) aged 14-18 years who completed an online lifestyle survey during a class period.
RESULTS: Principal components analysis produced three dietary patterns: 'Treat Foods', 'Fruits and Vegetables' and 'Basic Foods'. The 279 participants who provided questionnaire data and completed cardiorespiratory fitness testing had a mean age of 15·7 (sd 0·9) years. Mean VO2max was 45·8 (sd 6·9) ml/kg per min. The 'Fruits and Vegetables' pattern was positively associated with VO2max in the total sample (β=0·04; 95%CI 0·02, 0·07), girls (β=0·06; 95% CI 0·03, 0·10) and boys (β=0·03; 95% CI 0·01, 0·05).
CONCLUSIONS: These results indicate that increase in cardiorespiratory fitness was associated with a healthier dietary pattern, suggesting both should be targeted as part of a global lifestyle approach. Longitudinal studies are needed to confirm this association in relation to health outcomes in New Zealand adolescents.
METHODS: A total of 381 children (mean age 9.7 [1.6] y, 57% girls) provided 24-hour wrist-worn GENEActiv accelerometry data which captured time spent for sleep, SB, light PA and moderate to vigorous PA (MVPA). Indicators of adiposity were derived from anthropometry and bioelectrical impedance analysis: body-mass-index-for-age, waist circumference, waist-to-height ratio, percent body fat, and body mass index. The composition of 4-part movement behaviors was expressed as isometric log-ratio coordinates which were entered into regression models. Isotemporal substitution analysis was used to assess changes in adiposity indicators when reallocating time between movement behaviors.
RESULTS: Relative to other movement behaviors, time spent on MVPA was significantly associated with waist circumference, waist-to-height ratio, percent body fat, and fat mass index. A 15-minute one-to-one reallocation from other movement behaviors to MVPA predicted lower body-mass-index-for-age (-0.03 to -0.11), smaller waist circumference (-0.67 to -1.28 cm), lower waist-to-height ratio (-0.004 to -0.008), percent body fat (-0.87% to -1.47%), and fat mass index (-0.23 to -0.42). Replacing SB and light PA with sleep or MVPA was associated with lower adiposity.
CONCLUSIONS: The overall composition of movement behavior was significantly associated with the adiposity of Malaysian schoolchildren. Promoting MVPA and sleep and reducing SB and light PA are important for prevention of childhood obesity.
METHODS: An online health survey was conducted between May to July 2017 among employees from 47 private companies located in urban Malaysia. A total of 5235 respondents completed the 20-min online employee health survey on a voluntary basis. Chi-Square or Fisher's exact tests were used to determine association between income with demographic and categorical factors of absenteeism and presenteeism. Multivariate linear regression was used to identify factors predicting absenteeism and presenteeism.
RESULTS: More than one third of respondents' monthly income were less than RM4,000 (35.4%), 29.6% between RM4,000-RM7,999 and 35.0% earned RM8,000 and above. The mean age was 33.8 years (sd ± 8.8) and 49.1% were married. A majority were degree holders (74.4%) and 43.6% were very concerned about their financial status. Mean years of working was 6.2 years (sd ± 6.9) with 68.9% satisfied with their job. More than half reported good general physical health (54.5%) (p = 0.065) and mental health (53.5%) (p = 0.019). The mean hours of sleep were 6.4 h (sd ± 1.1) with 63.2% reporting being unwell due to stress for the past 12 months. Mean work time missed due to ill-health (absenteeism) was 3.1% (sd ± 9.1), 2.8% (sd ± 9.1) and 1.8% (sd ± 6.5) among employees whose monthly income was less than RM4,000, RM4,000-RM7,999 and over RM8,000 respectively (p = 0.0066). Mean impairment while working due to ill-health (presenteeism) was 28.2% (sd ± 25.3), 24.9% (sd ± 25.5) and 20.3% (sd ± 22.9) among employees whose monthly income was less than RM4,000, RM4,000-RM7,999 and over RM8,000 respectively (p
OBJECTIVE: This study aims to determine the accuracy of MEDAL in assessing the dietary intake of Malaysian school children, using photographs of the children's meals taken by their parents as an objective reference.
METHODS: A convenience sample of 46 children aged 10 to 11 years recorded their daily meals in MEDAL for 4 days (2 weekdays and 2 weekend days). Their parents took photographs of the meals and snacks of their children before and after consumption during the 4-day period and sent them along with a brief description of food and drinks consumed via an instant SMS text messaging app. The accuracy of the children's reports of the food they had consumed was determined by comparing their MEDAL reports to the photographs of the food sent by their parents.
RESULTS: Overall, the match, omission, and intrusion rates were 62% (IQR 46%-86%), 39% (IQR 16%-55%), and 20% (IQR 6%-44%), respectively. Carbohydrate-based items from the food categories "rice and porridge"; "breads, spreads, and cereals"; and "noodles, pasta, and potatoes" were reported most accurately (total match rates: 68%-76%). "Snack and dessert" items were omitted most often (omission rate: 54%). Furthermore, side dishes from "vegetables and mushrooms," "eggs and tofu," "meat and fish," and "curry" food groups were often omitted (omission rates: 42%-46%). Items from "milk, cheese, and yogurt"; "snacks and desserts"; and "drinks" food groups intruded most often (intrusion rates: 37%-46%). Compared to the items reported by the boys, those reported by the girls had higher match rates (69% vs 53%) and lesser omission rates (31% vs 49%; P=.03, respectively).
CONCLUSIONS: In conclusion, children aged 10 to 11 years can self-report all their meals in MEDAL, although some items are omitted or intruded. Therefore, MEDAL is a tool that can be used to assess the dietary intake of Malaysian school children.
DESIGN: Body weight and length/height were measured. The LMS method was used for calculating smoothened body-weight- and BMI-for-age percentile values. The standardized site effect (SSE) values were used for identifying large differences (i.e. $\left| {{\rm SSE}} \right|$ >0·5) between the pooled SEANUTS sample and the remaining pooled SEANUTS samples after excluding one single country each time, as well as with WHO growth references.
SETTING: Malaysia, Thailand, Vietnam and Indonesia.
SUBJECTS: Data from 14 202 eligible children.
RESULTS: The SSE derived from the comparisons of the percentile values between the pooled and the remaining pooled SEANUTS samples were indicative of small/acceptable (i.e. $\left| {{\rm SSE}} \right|$ ≤0·5) differences. In contrast, the comparisons of the pooled SEANUTS sample with WHO revealed large differences in certain percentiles.
CONCLUSIONS: The findings of the present study support the use of percentile values derived from the pooled SEANUTS sample for evaluating the weight status of children in each SEANUTS country. Nevertheless, large differences were observed in certain percentiles values when SEANUTS and WHO reference values were compared.
DESIGN: Data on length/height-for-age percentile values were collected. The LMS method was used for calculating smoothened percentile values. Standardized site effects (SSE) were used for identifying large or unacceptable differences (i.e. $\mid\! \rm SSE \!\mid$ >0·5) between the pooled SEANUTS sample (including all countries) and the remaining pooled SEANUTS samples (including three countries) after weighting sample sizes and excluding one single country each time, as well as with WHO growth references.
SETTING: Malaysia, Thailand, Vietnam and Indonesia.
SUBJECTS: Data from 14202 eligible children were used.
RESULTS: From pair-wise comparisons of percentile values between the pooled SEANUTS sample and the remaining pooled SEANUTS samples, the vast majority of differences were acceptable (i.e. $\mid\! \rm SSE \!\mid$ ≤0·5). In contrast, pair-wise comparisons of percentile values between the pooled SEANUTS sample and WHO revealed large differences.
CONCLUSIONS: The current study calculated length/height percentile values for South East Asian children aged 0·5-12 years and supported the appropriateness of using pooled SEANUTS length/height percentile values for assessing children's growth instead of country-specific ones. Pooled SEANUTS percentile values were found to differ from the WHO growth references and therefore this should be kept in mind when using WHO growth curves to assess length/height in these populations.
METHODS: A total of 2406 Malaysian children aged 5 to 12 years, who had participated in the South East Asian Nutrition Surveys (SEANUTS), were included in this study. Cognitive performance [non-verbal intelligence quotient (IQ)] was measured using Raven's Progressive Matrices, while socioeconomic characteristics were determined using parent-report questionnaires. Body mass index (BMI) was calculated using measured weight and height, while BMI-for-age Z-score (BAZ) and height-for-age Z-score (HAZ) were determined using WHO 2007 growth reference.
RESULTS: Overall, about a third (35.0%) of the children had above average non-verbal IQ (high average: 110-119; superior: ≥120 and above), while only 12.2% were categorized as having low/borderline IQ ( 3SD), children from very low household income families and children whose parents had only up to primary level education had the highest prevalence of low/borderline non-verbal IQ, compared to their non-obese and higher socioeconomic counterparts. Parental lack of education was associated with low/borderline/below average IQ [paternal, OR = 2.38 (95%CI 1.22, 4.62); maternal, OR = 2.64 (95%CI 1.32, 5.30)]. Children from the lowest income group were twice as likely to have low/borderline/below average IQ [OR = 2.01 (95%CI 1.16, 3.49)]. Children with severe obesity were twice as likely to have poor non-verbal IQ than children with normal BMI [OR = 2.28 (95%CI 1.23, 4.24)].
CONCLUSIONS: Children from disadvantaged backgrounds (that is those from very low income families and those whose parents had primary education or lower) and children with severe obesity are more likely to have poor non-verbal IQ. Further studies to investigate the social and environmental factors linked to cognitive performance will provide deeper insights into the measures that can be taken to improve the cognitive performance of Malaysian children.