Factors associated with executive function among adolescents aged 12 to 16 years in Petaling Perdana, Selangor, Malaysia

Adolescence represents one of the most dynamic and influential periods in human life stages, with profound changes of physiological, cognitive, and psychosocial developments. Individual differences of successful transition across adolescence are critically determined by their self-regulatory abilities, known as executive function (EF). EF refers to a group of high-order cognitive skills that underlie planning, organizing and self-regulation which are responsible for achievement of goal-directed behaviours. There are three dissociable yet interrelated domains of EF, namely inhibition, working memory (WM) and cognitive flexibility (CF). Proficiency of these domains affect multiple aspects of adolescents’ well being. Given that considerable reorganization of EF in adolescence could be highly responsive to different biological and environmental factors, this study aimed to examine the factors associated with EF among adolescents aged 12 to 16 years in Petaling Perdana, Selangor. A total of 513 adolescents (41.1% males, 58.9% females) with a mean age of 14.08 ± 1.32 years from two randomly selected government secondary schools in Petaling Perdana participated in this study. Mothers of the adolescents completed a self-administered questionnaire to obtain information on socioeconomic factors, while adolescents completed another set of self-administered questionnaire on their demographic information, meal consumption pattern, eating behaviours, physical activity (PA) and sleep quality. Anthropometric measurements (weight, height and waist circumference) and blood pressure of the adolescents were measured by the researcher. Physical fitness of the adolescents was assessed using a modified Harvard-step test. Three different neurocognitive tests were used to measure each domain of EF, including Stroop Colour-Word test, Digit-span test, and Trail-making test that measured inhibition, WM and CF, respectively. Nearly one third of the adolescents were found to be overweight or obese (32.6%), while 14.0% and 18.5% were abdominally obese, based on their waist circumference and waist-to-height ratio, respectively. About one in ten of the adolescents were hypertensive (11.9%), while 19.1% were at pre-hypertensive stage. Birth data showed that one in ten of the adolescents were born with low birth weight (11.4%) and more than half of them were pre-term births (56.5%). Unhealthy lifestyle behaviours were prevalent in adolescents in this study. Among all three main meals, the rate of breakfast skipping was the highest (69.0%), followed by dinner (47.4%) and lunch (47.2%). Although more than half of the adolescents had moderate PA level (55.0%), only 6.2% reported high level of PA. Likewise, only 9.0% of them attained good physical fitness level. Besides, majority of them were shown to have poor sleep quality (72.5%). In term of EF, the mean interference score (inhibition), WM score, and task-switching score (CF) of adolescents were 1.79 ± 6.68, 17.28 ± 3.74, and 27.92 ± 19.58, respectively. Based on the available categorization given, 7.6% and 13.3% of the adolescents had poor levels of inhibition and CF. Results of multiple linear regression revealed that higher monthly household income (β=0.078, p=0.048) and lower A Body Shape Index (β=-0.155, p=0.001) significantly predicted better inhibition, in which these variables explained for 3.7% of variance in inhibition. Better WM was significantly predicted by older age of adolescents (β=0.132, p=0.003), higher monthly household income (β=0.117, p=0.018) and more years of father’s education (β=0.129, p=0.008). It was also found that lower BMI-for-age (β=-0.099, p=0.025) and food responsiveness (β=-0.096, p=0.029), higher frequency of dinner intake (β=0.105, p=0.017), higher PA level (β=0.084, p=0.045) and better sleep quality (β=0.192, p<0.001) significantly predicted higher level of WM. Collectively, these variables explained for 12.7% of variance in WM. Lastly, female adolescents (β=-0.107, p=0.017), those who were at older age (β=-0.166, p<0.001), more regular dinner intake (β=-0.090, p=0.042), and lower emotional undereating (β=0.097, p=0.032) significantly predicted better CF. In relative to normal birth weight, very low birth weight significantly predicted poorer CF (β=0.125, p=0.005). These variables together explained for 7.5% of variance in CF. In conclusion, a number of demographic and socioeconomic, physiological and lifestyle factors were found to be associated with EF among adolescents in this study. The proficiency of EF could be enhanced by improving socio-economic status such as parent’s education and family income; while reducing risk of low birth weight via promotion of perinatal care and maternal health. Healthy lifestyle including regular meal intakes especially dinner, active in PA and good sleep quality should be advocated and established for life-long benefits and better EF. In view of the relatively small variance for EF found in this study, other factors such as dietary intakes, appetitive hormones, home environment and parent’s mental health might possibly associated with EF of adolescents which required further investigations. The transition during adolescence appears as a valuable window for intervention to boost EF beyond preschool years. Therefore, a multidisciplinary approach involving parents, health professionals, government agencies, and school authorities should be established to foster healthy development of EF in adolescence and thereafter.

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