We developed an artificial neural network (ANN) to predict mole fractions in the extractive distillation of an n-hexane and ethyl acetate mixture, which are common organic solvents in chemical and pharmaceutical manufacturing. The ANN was trained on 250 data pairs from simulations in DWSIM software. The training dataset consisted of four inputs: Feed flow inlet (T1-F), Feed Stream Mass Flow temperature pressure (FM1-F), Make-up stream mass flow (FM2-MU), and ERC tower reflux ratio (RR-ERC). The ANN demonstrated the ability to forecast four output variables (neurons): Mole fraction of n-hexane in the distillate of EDC (XHE-EDC), Mole fraction of N-methyl-2 pyrrolidone in the bottom of EDC (XNMP-EDC), Mole fraction of ethyl acetate in the distillate of ERC (XEA-ERC), and Mole fraction of N-methyl-2 pyrrolidone in the bottom of ERC (XNMP-ERC).The ANN architecture contained 80 hidden neurons. Bayesian regularization training yielded high prediction accuracy (MSE = 2.56 × 10–7, R = 0.9999). ANOVA statistical validation indicated that ANN could reliably forecast mole fractions. By integrating this ANN into process control systems, manufacturers could enhance product quality, decrease operating expenses, and mitigate composition variability risks. This data-driven modeling approach may also optimize energy consumption when combined with genetic algorithms. Further research will validate predictions onsite and explore hybrid energy optimization technologies.
Citation: Daniel Chuquin-Vasco, Dennise Chicaiza-Sagal, Cristina Calderón-Tapia, Nelson Chuquin-Vasco, Juan Chuquin-Vasco, Lidia Castro-Cepeda. Forecasting mixture composition in the extractive distillation of n-hexane and ethyl acetate with n-methyl-2-pyrrolidone through ANN for a preliminary energy assessment[J]. AIMS Energy, 2024, 12(2): 439-463. doi: 10.3934/energy.2024020
| [1] | Supa Pengpid, Karl Peltzer . The Prevalence of Underweight, Overweight/Obesity and Their Related Lifestyle Factors in Indonesia, 2014–15. AIMS Public Health, 2017, 4(6): 633-649. doi: 10.3934/publichealth.2017.6.633 |
| [2] | Sameer Badri Al-Mhanna, Alexios Batrakoulis, Abdulrahman M. Sheikh, Abdulaziz A. Aldayel, Abdulwali Sabo, Mahaneem Mohamed, Hafeez Abiola Afolabi, Abdirizak Yusuf Ahmed, Sahra Isse Mohamed, Mehmet Gülü, Wan Syaheedah Wan Ghazali . Impact of COVID-19 lockdown on physical activity behavior among students in Somalia. AIMS Public Health, 2024, 11(2): 459-476. doi: 10.3934/publichealth.2024023 |
| [3] | Walid El Ansari, Khalid A Khalil, Derrick Ssewanyana, Christiane Stock . Behavioral risk factor clusters among university students at nine universities in Libya. AIMS Public Health, 2018, 5(3): 296-311. doi: 10.3934/publichealth.2018.3.296 |
| [4] | Amir Alakaam, Amanda Willyard . Eating habits and dietary acculturation effects among international college students in the United States. AIMS Public Health, 2020, 7(2): 228-240. doi: 10.3934/publichealth.2020020 |
| [5] | MaríaVictorinaAguilarVilas, GabrielaRubalcava, AntonioBecerra, MaríaCarmenMartínezPara . Nutritional Status and Obesity Prevalence in People with Gender Dysphoria. AIMS Public Health, 2014, 1(3): 137-146. doi: 10.3934/publichealth.2014.3.137 |
| [6] | Simone K. Malik, Jerome Kouame, Mory Gbane, Madikiny Coulibaly, Michèle D. Ake, Odile Ake . Prevalence of abdominal obesity and its correlates among adults in a peri-urban population of West Africa. AIMS Public Health, 2019, 6(3): 334-344. doi: 10.3934/publichealth.2019.3.334 |
| [7] | Naime Altay, Ebru Kılıcarslan Toruner, Ebru Akgun-CITAK . Determine the BMI levels, self-concept and healthy life behaviours of children during a school based obesity training programme. AIMS Public Health, 2020, 7(3): 535-547. doi: 10.3934/publichealth.2020043 |
| [8] | Martin Burtscher, Grégoire P Millet, Jeannette Klimont, Johannes Burtscher . Differences in the prevalence of physical activity and cardiovascular risk factors between people living at low (<1,001 m) compared to moderate (1,001–2,000 m) altitude. AIMS Public Health, 2021, 8(4): 624-635. doi: 10.3934/publichealth.2021050 |
| [9] | Tyler C. Smith MS PhD, Besa Smith MPH PhD . Understanding the Early Signs of Chronic Disease by Investigating the Overlap of Mental Health Needs and Adolescent Obesity. AIMS Public Health, 2015, 2(3): 487-500. doi: 10.3934/publichealth.2015.3.487 |
| [10] | Moazzam Tanveer, Ejaz Asghar, Umar Tanveer, Nadeem Roy, Asifa Zeba, Sameer Badri Al-Mhanna, Xiaoran Ma, Alexios Batrakoulis . Association of nutrition behavior and food intake with overweight and obesity among school-aged children and adolescents in Pakistan: a cross-sectional study. AIMS Public Health, 2024, 11(3): 803-818. doi: 10.3934/publichealth.2024040 |
We developed an artificial neural network (ANN) to predict mole fractions in the extractive distillation of an n-hexane and ethyl acetate mixture, which are common organic solvents in chemical and pharmaceutical manufacturing. The ANN was trained on 250 data pairs from simulations in DWSIM software. The training dataset consisted of four inputs: Feed flow inlet (T1-F), Feed Stream Mass Flow temperature pressure (FM1-F), Make-up stream mass flow (FM2-MU), and ERC tower reflux ratio (RR-ERC). The ANN demonstrated the ability to forecast four output variables (neurons): Mole fraction of n-hexane in the distillate of EDC (XHE-EDC), Mole fraction of N-methyl-2 pyrrolidone in the bottom of EDC (XNMP-EDC), Mole fraction of ethyl acetate in the distillate of ERC (XEA-ERC), and Mole fraction of N-methyl-2 pyrrolidone in the bottom of ERC (XNMP-ERC).The ANN architecture contained 80 hidden neurons. Bayesian regularization training yielded high prediction accuracy (MSE = 2.56 × 10–7, R = 0.9999). ANOVA statistical validation indicated that ANN could reliably forecast mole fractions. By integrating this ANN into process control systems, manufacturers could enhance product quality, decrease operating expenses, and mitigate composition variability risks. This data-driven modeling approach may also optimize energy consumption when combined with genetic algorithms. Further research will validate predictions onsite and explore hybrid energy optimization technologies.
Globally, the prevalence of obesity has more than doubled from 1980 to 39% of adults (≥18 years) being overweight and 13% being obese in 2014 [1]. In the Association of Southeast Asian Nations (ASEAN) region, the adult (>20 years) prevalence of overweight (BMI ≥ 25–29.99 kg2) and obesity (BMI ≥ 30 kg2) ranged from in Vietnam (13.6% and 1.5%, respectively, among men, and 12.3% and 1.7%, respectively, among women) to in Malaysia (43.8% and 11.4% among men, and 48.6% and 16.7% among women) [2]. Globally and in the Southeast Asian region, obesogenic behaviors such as the consumption of "energy-dense foods that are high in fat, salt and sugars but low in vitamins, minerals and other micronutrients" and physical inactivity and sedentary behavior have increased, contributing to overweight and obesity, including in young adults [3]. Obesity is a major risk factor for a number of non-communicable diseases, such as "diabetes mellitus, cardiovascular disease, hypertension and stroke, and certain forms of cancer" leading to the "increased risk of premature death to serious chronic conditions that reduce the overall quality of life" [4]. It is important to understand factors driving the obesity epidemic in ASEAN so as to better design health interventions [5].
Emerging adulthood is an important period in health behavior development as many of the health behaviors may change due to changing influences of parents, peers, social contexts and identity development [6,7,8]. Health risk behaviors such as dietary behavior, physical activity and sedentary behavior and the prevalence of obesity in university students has not been well studied in ASEAN [9,10,11,12]. Therefore, the aim of this study was to quantify the prevalence of obesity and obesity-related factors (dietary behaviors and physical activity levels) in a cross-sectional study of ASEAN undergraduate students.
This was a cross-sectional study using a self-administered questionnaire and anthropometric measurements in university students in eight ASEAN countries. The questionnaire utilized for data collection was developed in English, then translated and back translated into the languages (Bahasa, Lao, Myanmar, Thai, and Vietnamese) of the participating countries. In every participating study country, the questionnaire was pre-tested on a sample of 20–30 university students for validity; these results did not form part of the final sample. Some of the instruments used in this study have previously been validated, such as the International Physical Activity Questionnaire (IPAQ). In each study country, undergraduate students were surveyed in classrooms (inclusion criteria were present on the day of the survey) selected through a cluster random sample procedure (one university department randomly selected from each faculty as a primary sampling unit, and for each selected department randomly ordered undergraduate courses). Informed consent was obtained from participating students, and the study was conducted from 2014 to 2015. Participation rates were in all countries more than 90%, except for Indonesia 69% and Myanmar 73%. Ethics approvals for the study protocol were obtained from all participating institutions.
Anthropometric measurements. Students' anthropometric measurements were recorded by trained researchers using standardized protocols [13]. Standing height was measured to the nearest 0.1 cm without shoes, using a stadiometer. Participants wearing light clothes, were weighed to the nearest 0.01 kg, on a load-cell-operated digital scale which was first calibrated using a standard weight and re-checked daily [14]. Waist circumference was measured around the waist through a point one third of the distance between the xiphoidprocess and the umbilicus, using a non-stretchable tape measure [15]. Body mass index (BMI) was calculated as weight in kg divided by height in meters squared. BMI was classified according to Asian criteria: normal weight (18.5 to <23.0), overweight (23.0 to <25.0) and 25+ as obese [16]. Central obesity was defined as Waist Circumference (WC) 90 cm or more in men and 80 cm or more in women [17,18]. Waist-to-height ratio (WHtR) was defined as their waist circumference divided by their height, both measured in the same units; elevated WHtR was defined as >0.5 [19].
Socio-demographic questions included age, gender and year of study. Socioeconomic background was assessed by relative family income within each country as wealthy (within the highest 25%), quite well off (within the 50% to 75% range), not very well off (within the 25% to 50% range), or quite poor (within the lowest 25%); students were classified as wealthy if they considered themselves as wealthy or quite well off and poor if they considered themselves as coming from a not very well off or quite poor family background [20].
Dietary behavior variables: Fruit and vegetable (FV) consumption was assessed with two questions "How many servings of fruit do you eat on a typical day?" and "How many servings of vegetables do you eat on a typical day?" (One standard serving = 80 g) [21]. Cronbach alpha for this fruit and vegetable measure was 0.76. Additional dietary variables included: (a) frequency of consumption of red meat (daily, 2–3 times a week, once a week, less than once a week, never); (b) trying to avoid eating foods that contain fat and cholesterol (yes, no); (c) trying to eat foods that are high in fiber (yes, no); (d) frequency of having breakfast; (e) frequency of between-meal snacks and (f) number of meals a day [20].
Physical activity was assessed using the self-administered International Physical Activity Questionnaire (IPAQ) short version, for the last 7 days (IPAQ-S7S). We used the instructions given in the IPAQ manual for reliability and validity, which is detailed elsewhere [22]. We categorized physical activity (short form) according to the official IPAQ scoring protocol [23] as low, moderate and high.
Sitting time was measured with the IPAQ short form [23]: "During the last 7 days, how much time did you usually spend sitting on a weekday? Include time spent at work, at home, while doing course work and during leisure time. This may include time spent sitting at a desk, visiting friends, reading, or sitting or lying down to watch television" [23]. The question was answered in terms of hours and minutes. The average sitting time per day in minutes was calculated, and 480 minutes (8 hours) were classified as high sitting time or sedentary behavior [24].
The data were analyzed using IBM SPSS Statistics for Windows (Version 20.0. Armonk, NY: IBM Corp, USA). Sociodemographic factors, dietary variables, physical activity levels, and obesity categories were calculated as percentages. Multivariable logistic regression was performed with three obesity indicators (BMI obesity, high WC and high WHtR) as the dependent variables. Potential multi-collinearity between variables was assessed with variance inflation factors, none of which exceeded (1.8) critical value. Socio-demographic characteristics, dietary variables, physical activity levels and sedentary behavior were taken as independent variables. P < 0.05 was considered significant. The country was entered as the primary sampling unit for survey analysis in STATA in order to achieve accurate CIs, given the clustered nature of the data.
The sample included 6783 (35.5% male and 64.5% female) university students (Mean age: 20.5, SD = 2.0) from eight ASEAN countries. The sample size differed by country, ranging from 333 in Myanmar to 1583 in Thailand. Classifying students into wealthy and poor family background, most (62.4%) considered themselves as poor, especially in Laos (95.3%) and Vietnam (96.6%). Overall, the number of students was more or less equally distributed across the study years from 1 to 4 or 5, while students from Laos and Myanmar had higher proportions of students in higher years of study (see Table 1).
| Variable | Indonesia | Laos | Malaysia | Myanmar | Philippines | Singapore | Thailand | Vietnam | All |
| N | N | N | N | N | N | N | N | N | |
| Total | 969 | 759 | 1022 | 333 | 765 | 677 | 1583 | 765 | 6783 |
| Men | 283 | 260 | 519 | 201 | 194 | 347 | 1289 | 402 | 2357 |
| Women | 686 | 499 | 503 | 132 | 571 | 328 | 294 | 363 | 4514 |
| M (SD) | M (SD) | M (SD) | M (SD) | M (SD) | M (SD) | M (SD) | M (SD) | M (SD) | |
| Age | 19.4 (2.5) | 22.3 (1.8) | 20.7 (1.5) | 20.1 (1.2) | 18.7 (1.0) | 21.2 (1.6) | 20.0 (1.3) | 21.3 (1.7) | 20.5 (2.0) |
| % | % | % | % | % | % | % | % | % | |
| Wealthy family background Year of study | 37.3 | 4.7 | 58.8 | 29.7 | 39.9 | 60.7 | 27.9 | 3.4 | 37.6 |
| 1 | 23.4 | 0.0 | 37.9 | 0.0 | 28.4 | 29.8 | 36.9 | 19.5 | 27.5 |
| 2 | 22.2 | 0.0 | 27.7 | 21.3 | 27.5 | 27.8 | 16.3 | 19.2 | 20.1 |
| 3 | 32.6 | 63.6 | 15.1 | 0.0 | 26.6 | 29.0 | 32.6 | 19.6 | 27.6 |
| 4 or 5 | 21.8 | 36.4 | 19.4 | 78.7 | 17.5 | 13.4 | 14.2 | 41.7 | 24.9 |
| M = Mean; SD = Standard deviation. | |||||||||
DownLoad: CSVOverall, the consumption of meals were on average 2.7 per day, the number of between meal snacks were on average 1.6, the mean number of fruit servings per day 1.4 and the mean number of vegetable servings per day 1.8. More than half (53.2%) of students did not have regularly their breakfast (not almost every day), 58.7% had at least once a day red meat, 40.2% avoided eating foods high in fat and cholesterol and 48.4% reported trying to eat foods high in fiber. Half of the students (49.9%) had low physical activity, 33.6% moderate and 16.6% high physical activity. Sedentary behavior (sitting of eight or more hours) was reported by 31.5% of the students. The mean body weight for men was 62.3 kg and for women 51.0 kg, the mean height for men was 168 cm and for women 157 cm and the mean BMI was 22.2 for men and 20.8 for women. The prevalence of BMI overweight among men was 12.3% and among women 8.4%, the prevalence of BMI obesity among men was 24.2% and among women 9.3%, the prevalence of high waist-height ratio was among men16.6% and among women 12.1%, and high waist circumference among men was 9.4% and among women 10.1%. Among the different study countries, BMI obesity was the highest in Malaysia (25.6% for men and 15.8% for women, followed by Myanmar (24.2% for men and 15.4% for women) and Indonesia (23.3% for men and 10.6% for women). The highest prevalence of high waist/height ratio was in Indonesia (30.9% for men and 11.9% for women) and Malaysia (21.4% among men and 20.1% for women) and the Philippines (20.9% among men and 10.6% among women). Similarly, the prevalence of high waist circumference was the highest in Indonesia (23.6% among men and 8.5% among men) and Malaysia (14.5% among men and 17.7% among women) (see Table 2).
| Variable | Indonesia | Laos | Malaysia | Myanmar | Philippines | Singapore | Thailand | Vietnam | All |
| M (SD) | M (SD) | M (SD) | M (SD) | M (SD) | M (SD) | M (SD) | M (SD) | M (SD) | |
| Behavior | |||||||||
| No meals/day | 2.6 (0.7) | 2.9 (0.5) | 2.7 (0.5) | 2.6 (0.7) | 2.7 (0.6) | 2.9 (0.7) | 2.6 (0.7) | 2.8 (0.6) | 2.7 (0.7) |
| No snacks/day | 1.8 (1.1) | 1.4 (1.1) | 1.4 (1.0) | 2.2 (1.0) | 1.7 (0.9) | 1.2 (1.0) | 2.1 (1.1) | 1.1 (0.8) | 1.6 (1.1) |
| No servings of fruits/day | 1.4 (0.8) | 1.2 (0.8) | 1.0 (0.8) | 1.8 (1.1) | 1.1 (0.8) | 1.3 (0.9) | 1.8 (1.0) | 1.6 (1.1) | 1.4 (0.9) |
| No servings of vegetables/day | 1.6 (0.9) | 1.5 (0.9) | 1.6 (0.9) | 1.5 (0.7) | 1.3 (1.0) | 1.7 (0.9) | 2.1 (1.1) | 2.7 (1.0) | 1.8 (1.0) |
| % | % | % | % | % | % | % | % | % | |
| Not almost daily breakfast | 59.5 | 54.8 | 49.5 | 32.5 | 46.4 | 37.5 | 70.6 | 49.2 | 53.2 |
| Read meat at least once a day | 20.6 | 42.7 | 52.7 | 96.0 | 64.7 | 79.6 | 62.2 | 70.1 | 58.7 |
| Avoiding fatty foods and cholesterol | 46.3 | 29.5 | 37.7 | 43.1 | 28.6 | 43.7 | 49.5 | 33.7 | 40.2 |
| Trying to eat fiber | 70.3 | 27.8 | 41.8 | 3.9 | 31.9 | 38.3 | 62.6 | 75.5 | 48.4 |
| Physical activity Low Moderate High | 56.3 34.6 9.1 | 49.9 21.6 28.5 | 39.9 45.8 14.3 | 87.5 12.5 0.0 | 41.7 38.6 19.7 | 38.7 38.6 19.7 | 51.0 31.4 17.6 | 33.3 41.2 25.5 | 49.9 33.6 16.6 |
| Sitting 8 hours or more a day | 20.4 | 31.0 | 17.5 | 0.0 | 57.0 | 34.5 | 19.5 | 52.8 | 31.5 |
| Anthropometry 1 | |||||||||
| Weight (kg)-Male | 65.4 (14.1) | 56.7 (7.7) | 66.4 (14.9) | 68.9 (12.9) | 61.0 (13.9) | 66.4 (11.8) | 64.5 (11.9) | 59.7 (7.8) | 62.3 (12.8) |
| Weight (kg)-Female | 51.5 (11.5) | 50.5 (8.8) | 53.5 (11.4) | 53.4 (13.5) | 49.4 (10.4) | 52.4 (8.6) | 53.6 (9.7) | 48.0 (5.3) | 51.0 (9.9) |
| Height (cms)-Male | 169.4 (7.6) | 163.3 (6.8) | 169.9 (6.5) | 170.2 (8.8) | 166.0 (6.9) | 173.6 (6.6) | 171.7 (6.2) | 167.9 (5.2) | 168.9 (7.0) |
| Height (cms)-Female | 156.6 (6.2) | 155.7 (6.4) | 157.0 (6.1) | 157.6 (7.2) | 154.1 (5.6) | 160.9 (5.7) | 160.7 (5.5) | 155.6 (5.2) | 157.2 (6.2) |
| Waist (cms)-Male | 77.2 (19.2) | 74.7 (5.4) | 77.8 (12.1) | 67.7 (17.0) | 76.1 (10.3) | 79.3 (8.7) | 78.5 (8.8) | 75.0 (7.0) | 76.2 (10.7) |
| Waist (cms)-Female | 69.7 (10.5) | 71.2 (7.1) | 71.6 (9.5) | 63.5 (9.7) | 68.3 (7.4) | 67.6 (7.4) | 71.0 (8.1) | 68.9 (5.7) | 69.7 (8.2) |
| BMI-Male | 22.7 (4.7) | 21.3 (2.8) | 22.9 (4.9) | 23.5 (7.3) | 22.1 (4.6) | 22.1 (4.0) | 21.4 (3.7) | 21.2 (2.5) | 22.2 (4.6) |
| BMI-Female | 20.9 (4.1) | 20.8 (3.6) | 21.7 (4.3) | 21.5 (5.4) | 20.7 (3.5) | 20.2 (2.9) | 20.7 (3.5) | 19.8 (2.0) | 20.8 (4.3) |
| % | % | % | % | % | % | % | % | % | |
| BMI overweight-Male | 14.8 | 12.7 | 12.5 | 14.4 | 14.4 | 17.1 | 11.2 | 14.9 | 12.3 |
| BMI overweight-Female | 8.0 | 8.4 | 11.2 | 7.5 | 10.2 | 9.8 | 7.3 | 5.2 | 8.4 |
| BMI obesity-Male | 23.3 | 11.2 | 25.6 | 24.2 | 19.6 | 12.2 | 14.3 | 7.4 | 24.2 |
| BMI obesity-Female | 10.6 | 10.4 | 15.8 | 15.4 | 8.4 | 5.8 | 9.9 | 1.0 | 9.3 |
| High WHtR-Male | 30.9 | 9.3 | 21.4 | 14.3 | 20.9 | 17.4 | 12.3 | 12.3 | 16.6 |
| High WHtR-Female | 11.9 | 14.5 | 20.1 | 5.0 | 10.6 | 2.2 | 11.8 | 6.1 | 12.1 |
| High WC-Male | 23.6 | 2.8 | 14.5 | 8.2 | 10.7 | 11.7 | 7.4 | 3.9 | 9.4 |
| High WC-Female | 8.5 | 10.9 | 17.7 | 6.4 | 7.5 | 3.3 | 13.4 | 3.6 | 10.1 |
| 1Waist circumference data were only available for a subset of students from Indonesia (N = 231), Singapore (N = 201), and Thailand (N = 479); M: Mean; SD: Standard Deviation; BMI (Body Mass Index) ≥ 25 kg/m2= obesity; WHtR (Waist-to-height ratio of) > 0.5 = High; WC (Waist Circumference high) ≥80 cms in females, ≥90 cms in males. | |||||||||
DownLoad: CSVIn multivariable adjusted logistic regression analysis, frequently having snacks, infrequent red meat consumption, avoidance of eating fatty foods and cholesterol, being male and coming from a wealthy family background was associated with BMI obesity. Fewer meals a day, frequent snack consumption, avoidance of eating fatty foods and cholesterol and being male was associated with high waist-height ratio, and frequent snack consumption and avoidance of eating fatty foods and cholesterol was associated with high waist circumference. In addition, fewer meals a day, fewer servings of vegetables a day and being male was associated with BMI overweight. Physical activity and sedentary behavior was not significantly associated with any obesity measure (see Table 3).
| Variable | BMI Overweight (23.0 to < 25.0 kgm2) | BMI Obesity (≥25 kgm2) | High Waist-Height Ratio ( > 0.50) | High Waist circumference (≥90 for men and ≥80 for women) |
| AOR (95% CI) | AOR (95% CI) | AOR (95% CI) | AOR (95% CI) | |
| Behavior covariates | ||||
| Not almost daily breakfast | 1.03 (0.83, 1.28) | 1.06 (0.85, 1.57) | 0.93 (0.72, 1.21) | 1.09 (0.81, 1.47) |
| Number of meals/day | 0.78 (0.66, 0.92) ** | 0.87 (0.73, 1.02) | 0.76 (0.62, 0.93) ** | 0.81 (0.64, 1.01) |
| Number of snacks/day | 0.91 (0.82, 1.01) | 1.16 (1.05, 1.28) ** | 1.17 (1.04, 1.32) * | 1.16 (1.01, 1.33) * |
| Number of servings of fruits/day | 1.06 (0.95, 1.19) | 0.94 (0.83, 1.08) | 0.99 (0.85, 1.15) | 0.95 (0.80, 1.13) |
| Number of servings of vegetables/day | 0.85 (0.77, 0.95) ** | 0.99 (0.88, 1.11) | 0.95 (0.83, 1.08) | 0.88 (0.75, 1.03) |
| Eating red meat at least once a day | 1.07 (0.87, 1.32) | 0.78 (0.63, 0.96) * | 0.98 (0.77, 1.26) | 0.89 (0.67, 1.17) |
| Avoiding fatty foods and cholesterol | 0.98 (0.79, 1.22) | 1.54 (1.24, 1.92) *** | 1.46 (1.08, 1.54) ** | 1.52 (1.14, 2.04) ** |
| Trying to eat fiber | 1.10 (0.89, 1.37) | 1.18 (0.95, 1.48) | 1.24 (0.95, 1.60) | 1.27 (0.95, 1.72) |
| Physical activity | ||||
| Low | 1 (Reference) | 1 (Reference) | 1 (Reference) | 1 (Reference) |
| Moderate | 1.10 (0.88, 1.39) | 1.11 (0.88, 1.41) | 1.16 (0.88, 1.54) | 1.06 (0.77, 1.44) |
| High | 1.11 (0.85, 1.47) | 0.99 (0.75, 1.32) | 1.27 (0.92, 1.74) | 0.84 (0.58, 1.24) |
| Sitting 8 hours or more a day | 0.89 (0.71, 1.12) | 1.04 (1.05, 1.28) | 1.16 (0.90, 1.50) | 1.27 (0.95, 1.70) |
| Sociodemographics | ||||
| Male gender (base: female) | 1.70 (1.38, 2.10) *** | 2.13 (1.80, 2.77) *** | 1.90 (1.48, 2.43) *** | 1.23 (0.92, 1.64) |
| Year of study | ||||
| One | 1 (Reference) | 1 (Reference) | 1 (Reference) | 1 (Reference) |
| Two | 1.07 (0.79, 1.46) | 0.89 (0.64, 1.23) | 0.93 (0.63, 0.93) | 0.69 (0.43, 1.09) |
| Three | 1.02 (0.77, 1.35) | 1.19 (0.90, 1.58) | 1.14 (0.81, 1.60) | 1.02 (0.70, 1.49) |
| Four or five | 1.10 (0.83, 1.46) | 1.19 (0.89, 1.59) | 1.07 (0.76, 1.51) | 1.01 (0.69, 1.48) |
| Wealthy (base: poor) | 1.07 (0.86, 1.32) | 1.29 (1.04, 1.60)* | 1.22 (0.94, 1.58) | 1.24 (0.92, 1.66) |
| AOR: Adjusted Odds Ratio, CI: Confidence Interval; *** P < 0.001; ** P < 0.01; * P < 0.05. | ||||
DownLoad: CSVThe study found in a large sample of undergraduate university students across eight ASEAN countries a high prevalence of general obesity (24.2% among men and 9.3% among women). This prevalence is probably higher than in a previous study of university students from 22 developing countries that found 18.9% general overweight and 5.8% general obesity among men and 15.1% overweight and 5.2% obesity among women (with a cut off of ≥27 kg2 for obesity in Asian students and ≥30 kg2 for non-Asian students) [9]. In individual ASEAN countries such as Malaysia and Thailand a similar prevalence of general obesity was found among university students in previous studies [10,11,12,25] compared to this study. Apart from in Laos and Myanmar, male obesity exceeded female obesity in the other countries (Indonesia, Malaysia, Philippines, Singapore Thailand and Vietnam) and overall in this university student population. The preponderance of obesity among male university students was also found among previous studies among university students in Malaysia and Thailand [10,11,12]. However, in previous national estimates of obesity (≥30 BMI) among adults over 20 years female obesity exceeded in ASEAN countries (Indonesia, Laos, Malaysia, Myanmar, Thailand and Vietnam) male obesity [2]. It is possible that emerging adult men who are attending university education are more vulnerable to transitional changes than men in the general population are and develop more likely obesity than women. Compared to first year students, higher study year studies had higher (but not significantly higher) levels of obesity in this study, as found in a recent meta-analysis of weight gain in university students [26]. This study found that students coming from a wealthier family background were more likely to have general obesity than students from poorer family backgrounds were were. This finding was also confirmed in a large sample of open-university students in Thailand [27].
The study found that frequent snacking behavior was associated with all three obesity indicators measured, as also found among university students in Pakistan [28]. The question on in between meals snacking behavior did not specify the type of snack in this study, and different definitions of snacking may influence the estimated probability of obesity [29]. In this study, making a conscious effort to avoid fat and cholesterol was associated with obesity. Other studies have also found this association [5,9]. It is possible that in this study where intention (assessed by conscious effort) to avoid fats and cholesterol which was self-reported, did not correspond to the actual eating pattern [9]. On the other hand, students who were overweight or obese might have already adopted healthier eating behavior in order to lose weight and be more accepted by their peers [9]. Since the study design was cross-sectional, causality between dietary variables and obesity could not be established. In this study, a higher number of meals a day were protective of high WHtR, which is conforming to results from several studies [30,31]. It is likely that eating frequency impacts obesity through a combination of different mechanisms affecting a decrease in hunger, an increase of satiety responses and a reduction binge eating [31]. Frequent (daily) red meat consumption was in this study protective from BMI obesity, while high consumption of meat was positively associated with body mass index in a national study in Finland [32] and abdominal obesity in China [33]. Although a large proportion of students (53.2%) had been having their breakfast irregularly, this study did, unlike in a previous meta-analysis of studies in Asia and Pacific regions [34], not find an association between skipping breakfast consumption and obesity.
Contrary to some previous studies [12,28], this study did not find an association between lack of physical activity, sedentary behavior and obesity, as also found in other studies [5]. Yet, the low levels of physical activity and high levels of sedentary behavior found in this study are cause for concern, as low physical activity is a major risk factor for cardiovascular disease [35].
Study limitations
This study had several limitations. The investigation was carried out with students from one university in each country, mostly in urban centers and inclusion of other centers could have resulted in different results. University students are not representative of young adults in general, and the obesity prevalence and its associated factors may be different in other sectors of the population. The study was a cross-sectional study and the temporal relationships between behavior practices and obesity cannot be established in such studies; further longitudinal studies are needed. Apart from anthropometric measurements, a limitation of the study was that all the other information collected was based on self-reporting. Longitudinal studies are required to investigate the obesogenic factors in this university population in ASEAN.
The study found a high prevalence of obesity among male and female university students across eight ASEAN countries. Specific dietary behaviors but not physical activity nor sedentary behavior were found to be associated with obesity, which can be utilized in health promotion programs addressing this university student population.
Partial funding for this study was provided by the South African Department of Higher Education. The following colleagues participated in this ASEAN student health survey and contributed to the design and data collection and are considered co-authors (locations of universities in parentheses): Indonesia: Erna Rochmawati (Yogyakarta), Indri Hapsari Susilowati (Jakarta); Laos: Vanphanom Sychareun (Vientiane); Malaysia: Wah Yun Low (Kuala Lumpur); Myanmar: Hla Hla Win (Yangon); Philippines: Alice Ferrer (Miagao); Singapore: Mee Lian Wong (Singapore); Thailand: Niruwan Turnbull (Maha Sarakham), Tawatchai Apidechkul (Chiang Rai); Vietnam: Thang Nguyen Huu (Hanoi).
The authors declare no conflict of interest.
| [1] |
Feng ZF, Shen WF, Rangaiah GP, et al. (2020) Design and control of vapor recompression assisted extractive distillation for separating n-hexane and ethyl acetate. Sep Purif Technol 240: 116655. https://doi.org/10.1016/j.seppur.2020.116655 doi: 10.1016/j.seppur.2020.116655
|
| [2] |
Acosta J, Arce A, Martı́nez-Ageitos J, et al. (2002) Vapor-Liquid equilibrium of the ternary system ethyl Acetate+ Hexane+ acetone at 101.32 kPa. J Chem Eng Data 47: 849–854. https://doi.org/10.1021/je0102917 doi: 10.1021/je0102917
|
| [3] |
Yang Kong Z, Yeh Lee H, Sunarso J (2022) The evolution of process design and control for ternary azeotropic separation: Recent advances in distillation and future directions. Sep Purif Technol 284: 120292. https://doi.org/10.1016/j.seppur.2021.120292 doi: 10.1016/j.seppur.2021.120292
|
| [4] |
Gerbaud V, Rodriguez-Donis I, Hegely L, et al. (2019) Review of extractive distillation. Process design, operation, optimization and control. Chem Eng Res Des 141: 229–271. https://doi.org/10.1016/j.cherd.2018.09.020 doi: 10.1016/j.cherd.2018.09.020
|
| [5] |
Iqbal A, Akhlaq Ahmad S, Ojasvi (2019) Design and control of an energy-efficient alternative process for separation of dichloromethane-methanol binary azeotropic mixture. Sep Purif Technol 219: 137–149. https://doi.org/10.1016/j.seppur.2019.03.005 doi: 10.1016/j.seppur.2019.03.005
|
| [6] |
Zhu Z, Yu X, Ma Y, et al. (2020) Efficient extractive distillation design for separating binary azeotrope via thermodynamic and dynamic analyses. Sep Purif Technol 238: 116425. https://doi.org/10.1016/j.seppur.2019.116425 doi: 10.1016/j.seppur.2019.116425
|
| [7] |
Yang X, Ward J (2018) Design of a pressure-swing distillation process for the separation of n-hexane and ethyl acetate using simulated annealing. Comp Aid Chem Eng 44: 121–126. https://doi.org/10.1016/B978-0-444-64241-7.50015-X doi: 10.1016/B978-0-444-64241-7.50015-X
|
| [8] |
Lü L, Zhu L, Liu H, et al. (2018) Comparison of continuous homogenous azeotropic and pressure-swing distillation for a minimum azeotropic system ethyl acetate/n-hexane separation. Chin J Chem Eng 26: 2023–2033. https://doi.org/10.1016/j.cjche.2018.02.002 doi: 10.1016/j.cjche.2018.02.002
|
| [9] |
Li Y, Sun T, Ye Q, et al. (2021) Investigation on energy-efficient extractive distillation for the recovery of ethyl acetate and 1, 4-dioxane from industrial effluent. J Clean Prod 329: 129759. https://doi.org/10.1016/j.jclepro.2021.129759 doi: 10.1016/j.jclepro.2021.129759
|
| [10] |
Shi F, Gao J, Huang X (2016) An affine invariant approach for dense wide baseline image matching. Int J Distrib Sens Netw, 12. https://doi.org/10.1177/1550147716680826 doi: 10.1177/1550147716680826
|
| [11] |
Sun L, Liang F, Cui W (2021) Artificial neural network and its application research progress in chemical process. Asian J Res Comput Sci 12: 177–185. https://doi.org/10.9734/ajrcos/2021/v12i430302 doi: 10.9734/ajrcos/2021/v12i430302
|
| [12] | Singh RP, Heldman DR (2008) Introduction to food engineering. A volume in Food science and technology, Fifth Eds., California: Academic Press. Available from: https://www.sciencedirect.com/book/9780123985309/introduction-to-food-engineering. |
| [13] |
Elgibaly A, Ghareeb M, Kamel S, et al. (2021) Prediction of gas-lift performance using neural network analysis. AIMS Energy 9: 355–378. https://doi.org/10.3934/energy.2021019 doi: 10.3934/energy.2021019
|
| [14] |
Kandil A, Khaled S, Elfakharany T (2023) Prediction of the equivalent circulation density using machine learning algorithms based on real-time data. AIMS Energy 11: 425–453. https://doi.org/10.3934/energy.2023023 doi: 10.3934/energy.2023023
|
| [15] |
Hamdi M, El Salmawy H, Ragab R (2023) Optimum configuration of a dispatchable hybrid renewable energy plant using artificial neural networks: Case study of Ras Ghareb, Egypt. AIMS Energy 11: 171–196. https://doi.org/10.3934/energy.2023010 doi: 10.3934/energy.2023010
|
| [16] |
Aly A, Saleh B, Bassuoni M, et al. (2019) Artificial neural network model for performance evaluation of an integrated desiccant air conditioning system activated by solar energy. AIMS Energy 7: 395–412. https://doi.org/10.3934/energy.2019.3.395 doi: 10.3934/energy.2019.3.395
|
| [17] |
Zhang Z, Zhao J (2017) A deep belief network based fault diagnosis model for complex chemical processes. Comput Chem Eng 107: 395–407. https://doi.org/10.1016/j.compchemeng.2017.02.041 doi: 10.1016/j.compchemeng.2017.02.041
|
| [18] |
Yazdizadeh M, Jafari Nasr M, Safekordi A (2016) A new catalyst for the production of furfural from bagasse. RSC Adv 61: 55778–55785. https://doi.org/10.1039/c6ra10499a doi: 10.1039/c6ra10499a
|
| [19] |
Esonye C, Dominic Onukwuli O, Uwaoma Ofoefule A (2019) Optimization of methyl ester production from Prunus amygdalus seed oil using response surface methodology and artificial neural networks. Renewable Energy 130: 61–72. https://doi.org/10.1016/j.renene.2018.06.036 doi: 10.1016/j.renene.2018.06.036
|
| [20] |
Ge X, Wang B, Yang X, et al. (2021) Fault detection and diagnosis for reactive distillation based on convolutional neural network. Comput Chem Eng 145: 107172. https://doi.org/10.1016/j.compchemeng.2020.107172 doi: 10.1016/j.compchemeng.2020.107172
|
| [21] |
de Araújo Neto AP, Sales FA, Brito RP (2021) Controllability comparison for extractive dividing-wall columns: ANN-based intelligent control system versus conventional control system. Chem Eng Proc Intens 160: 108271. https://doi.org/10.1016/j.cep.2020.108271 doi: 10.1016/j.cep.2020.108271
|
| [22] |
Inyang V, Lokhat D (2022) Propionic acid recovery from dilute aqueous solution by emulsion liquid membrane (ELM) technique: optimization using response surface methodology (RSM) and artificial neural network (ANN) experimental design. Sep Scie Tech 57: 284–300. https://doi.org/10.1080/01496395.2021.1890774 doi: 10.1080/01496395.2021.1890774
|
| [23] | DWSIM (2020) DWSIM—The open source chemical process simulator. Available from: https://dwsim.org. |
| [24] |
Chuquin-Vasco D, Parra F, Chuquin-Vasco N, et al. (2021) Prediction of methanol production in a carbon dioxide hydrogenation plant using neural networks. Energies 14: 1–18. https://doi.org/10.3390/en14133965 doi: 10.3390/en14133965
|
| [25] |
Dimian AC, Bildea CS, Kiss AA (2014) Introduction in process simulation. Comput Aided Chem Eng 35: 35–71. https://doi.org/10.1016/B978-0-444-62700-1.00002-4 doi: 10.1016/B978-0-444-62700-1.00002-4
|
| [26] | Kiss AA (2013) Advanced distillation technologies: Design, control and applications. 1 Eds., Noida, India: Wiley. https://doi.org/10.1002/9781118543702 |
| [27] |
Soave G, Gamba S, Pellegrini L (2010) SRK equation of state: predicting binary interaction parameters of hydrocarbons and related compounds. Fluid Phase 299: 285–293. https://doi.org/10.1016/j.fluid.2010.09.012 doi: 10.1016/j.fluid.2010.09.012
|
| [28] |
Hu Y, Wang J, Tan C, et al. (2017) Further improvement of fluidized bed models by incorporating zone method with Aspen Plus interface. Energy Proc 105: 1895–1901. https://doi.org/10.1016/j.egypro.2017.03.556 doi: 10.1016/j.egypro.2017.03.556
|
| [29] |
Mlazi NJ, Mayengo M, Lyakurwa G, et al. (2024) Mathematical modeling and extraction of parameters of solar photovoltaic module based on modified Newton-Raphson method. Results Phys 57: 107364. https://doi.org/10.1016/j.rinp.2024.107364 doi: 10.1016/j.rinp.2024.107364
|
| [30] |
Chen Y, Song L, Liu Y, et al. (2020) A review of the artificial neural network models for water quality prediction. Appl Sci 10: 5776. https://doi.org/10.3390/app10175776 doi: 10.3390/app10175776
|
| [31] |
Singh V, Gupta I, Gupta H (2005) ANN based estimator for distillation-inferential control. Chem Eng Proc Int 44: 785–795. https://doi.org/10.1016/j.cep.2004.08.010 doi: 10.1016/j.cep.2004.08.010
|
| [32] | Pedregosa F, Varaquaux G, Gramfort A, et al. (2011) Scikit-learn: machine learning in Python. J Mach Lear Res. Available from: https://www.jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf. |
| [33] |
Bloice M, Holzinger A (2016) A tutorial on machine learning and data science tools with python. Lect Not Comp Sci 9605: 435–480. https://doi.org/10.1007/978-3-319-50478-0_22 doi: 10.1007/978-3-319-50478-0_22
|
| [34] |
Purna Pushkala S, Panda R (2023) Design and analysis of reactive distillation for the production of isopropyl myristate. Clean Chem Eng 5: 100090. https://doi.org/10.1016/j.clce.2022.100090 doi: 10.1016/j.clce.2022.100090
|
| [35] |
Elsheikh M, Ortmanns Y, Hecht F, et al. (2023) Control of an industrial distillation column using a hybrid model with adaptation of the range of validity and an ANN-based soft sensor. Chem Ing Tech 95: 1114–1124. https://doi.org/10.1002/cite.202200232 doi: 10.1002/cite.202200232
|
| [36] |
Neves T, De Araújo Neto A, Sales F, et al. (2021) ANN-based intelligent control system for simultaneous feed disturbances rejection and product specification changes in extractive distillation process. Sep Purif Technol 259: 118404. https://doi.org/10.1016/j.seppur.2020.118104 doi: 10.1016/j.seppur.2020.118104
|
| [37] |
Kayri M (2016) Predictive abilities of bayesian regularization and levenberg-marquardt algorithms in artificial neural networks: A comparative empirical study on social data. Math Comp App 21: 20. https://doi.org/10.3390/mca21020020 doi: 10.3390/mca21020020
|
| [38] |
Bharati S, Atikur Rahman M, Podder P, et al. (2021) Comparative performance analysis of neural network base training algorithm and neuro-fuzzy system with SOM for the purpose of prediction of the features of superconductors. Int Syst Des App 1181: 69–79. https://doi.org/10.1007/978-3-030-49342-4_7 doi: 10.1007/978-3-030-49342-4_7
|
| [39] |
Mohan Saini L (2008) Peak load forecasting using bayesian regularization, resilient and adaptive backpropagation learning based artificial neural networks. Elec Power Syst Res 78: 1302–1310. https://doi.org/10.1016/j.epsr.2007.11.003 doi: 10.1016/j.epsr.2007.11.003
|
| [40] |
Wang L, Wu B, Zhu Q, et al. (2020) Forecasting monthly tourism demand using enhanced backpropagation neural network. Neural Process Lett 52: 2607–2636. https://doi.org/10.1007/s11063-020-10363-z doi: 10.1007/s11063-020-10363-z
|
| [41] |
Zeng Y, Zeng Y, Choi B, et al. (2017) Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network. Energy 127: 381–396. https://doi.org/10.1016/j.energy.2017.03.094 doi: 10.1016/j.energy.2017.03.094
|
| [42] |
Suphawan K, Chaisee K (2021) Gaussian process regression for predicting water quality index: A case study on ping river basin, thailand. AIMS Environ Sci 8: 268–282. https://doi.org/10.3934/environsci.2021018 doi: 10.3934/environsci.2021018
|
| [43] |
Wang L, Wu B, Zhu Q, et al. (2020) Forecasting monthly tourism demand using enhanced backpropagation neural network. Neural Process Lett 52: 2607–2636. https://doi.org/10.1007/s11063-020-10363-z doi: 10.1007/s11063-020-10363-z
|
| [44] |
Zhang L, Sun X, Gao S (2022) Temperature prediction and analysis based on improved GA-BP neural network. AIMS Environ Sci 9: 735–753. https://doi.org/10.3934/environsci.2022042 doi: 10.3934/environsci.2022042
|
| [45] |
Suliman A, Omarov B (2018) Applying bayesian regularization for acceleration of levenberg marquardt based neural network training. Int J Int Mult Art Inte 5: 68. https://doi.org/10.9781/ijimai.2018.04.004 doi: 10.9781/ijimai.2018.04.004
|
| [46] |
Garoosiha H, Ahmadi J, Bayat H (2019) The assessment of Levenberg-marquardt and bayesian Framework training algorithm for prediction of concrete shrinkage by the artificial neural network. Cogent Eng 6: 1609179. https://doi.org/10.1080/23311916.2019.1609179 doi: 10.1080/23311916.2019.1609179
|
| [47] |
Kim R, Min J, Lee J, et al. (2023) Development of bayesian regularized artificial neural network for airborne chlorides estimation. Constr Build Mater 383: 131361. https://doi.org/10.1016/j.conbuildmat.2023.131361 doi: 10.1016/j.conbuildmat.2023.131361
|
| [48] |
Yalamanchi K, Kommalapati S, Pal P, et al. (2023) Uncertainty quantification of a deep learning fuel property prediction model. Appl Energy Comb Sci 16: 100211. https://doi.org/10.1016/j.jaecs.2023.100211 doi: 10.1016/j.jaecs.2023.100211
|
| [49] |
Jog S, Vázquez D, Santos L, et al. (2023) Hybrid analytical surrogate-based process optimization via bayesian symbolic regression. Comput Chem Eng 182: 108563. https://doi.org/10.1016/j.compchemeng.2023.108563 doi: 10.1016/j.compchemeng.2023.108563
|
| [50] |
Abiodun O, Jantan A, Omolara A, et al. (2018) State of the art in artificial neural network applications: A survey. Heliyon 4: E00938. https://doi.org/10.1016/j.heliyon.2018.e00938 doi: 10.1016/j.heliyon.2018.e00938
|
| [51] | Tgarguifa A, Bounahmidi T, Fellaou S (2020) Optimal design of the distillation process using the artificial neural networks method. 2020 1st International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), Meknes, Morocco, 1–6. Available from: https://ieeexplore.ieee.org/document/9092266. |
| [52] |
Kyono K, Hashimoto T, Nagai Y, et al. (2018) Analysis of endometrial microbiota by 16S ribosomal RNA gene sequencing among infertile patients: a single-center pilot study. Reprod Med Biol 17: 297–306. https://doi.org/10.1002/rmb2.12105 doi: 10.1002/rmb2.12105
|
| [53] |
Talaei Khoei T, Ould Slimane H, Kaabouch N (2023) Deep learning: Systematic review, models, challenges, and research directions. Neural Comput Appl 35: 23103–23124. https://doi.org/10.1007/s00521-023-08957-4 doi: 10.1007/s00521-023-08957-4
|
| [54] |
Boger Z (1997) Experience in industrial plant model development using large-scale artificial neural networks. Inf Sci 101: 203–216. https://doi.org/10.1016/S0020-0255(97)00010-8 doi: 10.1016/S0020-0255(97)00010-8
|
| [55] |
Acevedo L, Uche J, Del-Amo A (2018) Improving the distillate prediction of a membrane distillation unit in a trigeneration scheme by using artificial neural networks. Water 10: 310. https://doi.org/10.3390/w10030310 doi: 10.3390/w10030310
|
| [56] |
Shin Y, Smith R, Hwang S (2020) Development of model predictive control system using an artificial neural network: A case study with a distillation column. J Clean Prod 277: 124124. https://doi.org/10.1016/j.jclepro.2020.124124 doi: 10.1016/j.jclepro.2020.124124
|
| [57] |
Magdy Saady M, Hassan Essai M (2022) Hardware implementation of neural network-based engine model using FPGA. Alex Eng J 61: 12039–12050. https://doi.org/10.1016/j.aej.2022.05.035 doi: 10.1016/j.aej.2022.05.035
|
| [58] |
Hasimi L, Zavantis D, Shakshuki E, et al. (2024) Cloud computing security and deep learning: An ANN approach. Proc Comp Sci 231: 40–47. https://doi.org/10.1016/j.procs.2023.12.155 doi: 10.1016/j.procs.2023.12.155
|
 energy-12-02-020 Supplementary.pdf |
 |
| 1. | Sophia Rose, Fillah Fithra Dieny, Nuryanto Nuryanto, A. Fahmy Arif Tsani, The Correlation between Waist-to-Height Ratio (WHtR) and Second to Fourth Digit Ratio (2D:4D) with an Increase in Metabolic Syndrome Scores in Obese Adolescent Girls, 2020, 17, 25163507, em211, 10.29333/ejgm/7872 | |
| 2. | Oscar Castro, Jason Bennie, Ineke Vergeer, Grégoire Bosselut, Stuart J.H. Biddle, Correlates of sedentary behaviour in university students: A systematic review, 2018, 116, 00917435, 194, 10.1016/j.ypmed.2018.09.016 | |
| 3. | Nebojša Mitić, Ljiljana Popović, Assessment of the risk status of diseases associated with overweight in students of the University of Pristina - Kosovska Mitrovica, 2019, 48, 0350-8773, 5, 10.5937/pramed1904005M | |
| 4. | Seo Ah Hong, Karl Peltzer, Kyi Tun Lwin, La Seng Aung, Clemens Fürnsinn, The prevalence of underweight, overweight and obesity and their related socio-demographic and lifestyle factors among adult women in Myanmar, 2015-16, 2018, 13, 1932-6203, e0194454, 10.1371/journal.pone.0194454 | |
| 5. | Jamil Ahmed, Faisal Alnasir, Ahmed Jaradat, Amer Jebril Al Marabheh, Randah Ribhi Hamadeh, Association of Overweight and Obesity with High Fast Food Consumption by Gulf Cooperation Council Medical Students, 2019, 58, 0367-0244, 495, 10.1080/03670244.2019.1613986 | |
| 6. | Souad Benaich, Slimane Mehdad, Zineb Andaloussi, Saber Boutayeb, Meryem Alamy, Hassan Aguenaou, Khalid Taghzouti, Weight status, dietary habits, physical activity, screen time and sleep duration among university students, 2020, 0260-1060, 026010602096086, 10.1177/0260106020960863 | |
| 7. | Karl Peltzer, Supa Pengpid, The Prevalence and Social Determinants of Hypertension among Adults in Indonesia: A Cross-Sectional Population-Based National Survey, 2018, 2018, 2090-0384, 1, 10.1155/2018/5610725 | |
| 8. | Che Wan Mohamed Radzi, Hashem Salarzadeh Jenatabadi, Ayed Alanzi, Mohd Mokhtar, Mohd Mamat, Nor Abdullah, Analysis of Obesity among Malaysian University Students: A Combination Study with the Application of Bayesian Structural Equation Modelling and Pearson Correlation, 2019, 16, 1660-4601, 492, 10.3390/ijerph16030492 | |
| 9. | Djordje Stevanovic, Mina Poskurica, Jovan Jovanovic, Miodrag Sreckovic, Vladimir Zdravkovic, Mileta Poskurica, Vladimir Miloradovic, Nela Djonovic, Nutritional Status Disorders in Student Population, 2019, 0, 2335-075X, 10.2478/sjecr-2019-0027 | |
| 10. | Ranjan Chaudhuri, Sheshadri Chatterjee, Alkis Thrassou, Demetris Vrontis, Antecedents of obesity among Indian youth: from consumer psychological and lifestyle perspective, 2023, 125, 0007-070X, 1026, 10.1108/BFJ-10-2021-1151 | |
| 11. | Solomon Gebretsadik Bereka, Ayele Worku Demisse, Genanew Kassie Getahun, Prevalence of abdominal obesity and associated risk factors among women civil servants in Addis Ababa, Ethiopia, 2021: an institution-based study, 2022, 8, 2055-0928, 10.1186/s40795-022-00613-9 | |
| 12. | Tohru Kobayashi, Wenjing Zhao, Shigekazu Ukawa, Kenji Wakai, Kazuyo Tsushita, Takashi Kawamura, Masahiko Ando, Akiko Tamakoshi, Association between frequency of snacking and all‐cause mortality among community‐dwelling young‐old adults: An age‐specific prospective cohort study, 2021, 21, 1444-1586, 697, 10.1111/ggi.14209 | |
| 13. | Jing Su, Qingting Li, Ping Mao, Hua Peng, Huiwu Han, James Wiley, Jia Guo, Jyu-Lin Chen, Does the Association of Sedentary Time or Fruit/Vegetable Intake with Central Obesity Depend on Menopausal Status among Women?, 2022, 19, 1660-4601, 10083, 10.3390/ijerph191610083 | |
| 14. | Gerleison Ribeiro Barros, Sueyla Ferreira da Silva dos Santos, Alynne Christian Ribeiro Andaki, Thiago Ferreira de Sousa, Association between Physical Activity and Sitting Time Related to Excess Body Weight in Brazilian University Students, 2023, 41, 2504-3137, 102, 10.1159/000530858 | |
| 15. | Sharina Barkiah Muhamad Azhar, Divya Vanoh, Keerthana Sree Ganggaya, Food Choice Motives, Physical Activity Level and Body Mass Index Status Among Undergraduates Students: A Cross-sectional Study, 2023, 19, 1675-8544, 151, 10.47836/mjmhs.19.6.20 | |
| 16. | Nebojša Mitić, Ljiljana Popović, Assessment of the risk status of diseases associated with overweight in students of the University of Pristina - Kosovska Mitrovica, 2019, 48, 2560-3310, 5, 10.5937/pramed1904005m | |
| 17. | Body Image Perception and Its Association with Food Intake among Undergraduate Students in Kuala Lumpur, Malaysia, 2022, 26, 23563664, 10.7454/msk.v26i2.1354 |
Figures(13) / Tables(12)
Daniel Chuquin-Vasco, Dennise Chicaiza-Sagal, Cristina Calderón-Tapia, Nelson Chuquin-Vasco, Juan Chuquin-Vasco, Lidia Castro-Cepeda. Forecasting mixture composition in the extractive distillation of n-hexane and ethyl acetate with n-methyl-2-pyrrolidone through ANN for a preliminary energy assessment[J]. AIMS Energy, 2024, 12(2): 439-463. doi: 10.3934/energy.2024020
| Variable | Indonesia | Laos | Malaysia | Myanmar | Philippines | Singapore | Thailand | Vietnam | All |
| N | N | N | N | N | N | N | N | N | |
| Total | 969 | 759 | 1022 | 333 | 765 | 677 | 1583 | 765 | 6783 |
| Men | 283 | 260 | 519 | 201 | 194 | 347 | 1289 | 402 | 2357 |
| Women | 686 | 499 | 503 | 132 | 571 | 328 | 294 | 363 | 4514 |
| M (SD) | M (SD) | M (SD) | M (SD) | M (SD) | M (SD) | M (SD) | M (SD) | M (SD) | |
| Age | 19.4 (2.5) | 22.3 (1.8) | 20.7 (1.5) | 20.1 (1.2) | 18.7 (1.0) | 21.2 (1.6) | 20.0 (1.3) | 21.3 (1.7) | 20.5 (2.0) |
| % | % | % | % | % | % | % | % | % | |
| Wealthy family background Year of study | 37.3 | 4.7 | 58.8 | 29.7 | 39.9 | 60.7 | 27.9 | 3.4 | 37.6 |
| 1 | 23.4 | 0.0 | 37.9 | 0.0 | 28.4 | 29.8 | 36.9 | 19.5 | 27.5 |
| 2 | 22.2 | 0.0 | 27.7 | 21.3 | 27.5 | 27.8 | 16.3 | 19.2 | 20.1 |
| 3 | 32.6 | 63.6 | 15.1 | 0.0 | 26.6 | 29.0 | 32.6 | 19.6 | 27.6 |
| 4 or 5 | 21.8 | 36.4 | 19.4 | 78.7 | 17.5 | 13.4 | 14.2 | 41.7 | 24.9 |
| M = Mean; SD = Standard deviation. | |||||||||
DownLoad: CSV| Variable | Indonesia | Laos | Malaysia | Myanmar | Philippines | Singapore | Thailand | Vietnam | All |
| M (SD) | M (SD) | M (SD) | M (SD) | M (SD) | M (SD) | M (SD) | M (SD) | M (SD) | |
| Behavior | |||||||||
| No meals/day | 2.6 (0.7) | 2.9 (0.5) | 2.7 (0.5) | 2.6 (0.7) | 2.7 (0.6) | 2.9 (0.7) | 2.6 (0.7) | 2.8 (0.6) | 2.7 (0.7) |
| No snacks/day | 1.8 (1.1) | 1.4 (1.1) | 1.4 (1.0) | 2.2 (1.0) | 1.7 (0.9) | 1.2 (1.0) | 2.1 (1.1) | 1.1 (0.8) | 1.6 (1.1) |
| No servings of fruits/day | 1.4 (0.8) | 1.2 (0.8) | 1.0 (0.8) | 1.8 (1.1) | 1.1 (0.8) | 1.3 (0.9) | 1.8 (1.0) | 1.6 (1.1) | 1.4 (0.9) |
| No servings of vegetables/day | 1.6 (0.9) | 1.5 (0.9) | 1.6 (0.9) | 1.5 (0.7) | 1.3 (1.0) | 1.7 (0.9) | 2.1 (1.1) | 2.7 (1.0) | 1.8 (1.0) |
| % | % | % | % | % | % | % | % | % | |
| Not almost daily breakfast | 59.5 | 54.8 | 49.5 | 32.5 | 46.4 | 37.5 | 70.6 | 49.2 | 53.2 |
| Read meat at least once a day | 20.6 | 42.7 | 52.7 | 96.0 | 64.7 | 79.6 | 62.2 | 70.1 | 58.7 |
| Avoiding fatty foods and cholesterol | 46.3 | 29.5 | 37.7 | 43.1 | 28.6 | 43.7 | 49.5 | 33.7 | 40.2 |
| Trying to eat fiber | 70.3 | 27.8 | 41.8 | 3.9 | 31.9 | 38.3 | 62.6 | 75.5 | 48.4 |
| Physical activity Low Moderate High | 56.3 34.6 9.1 | 49.9 21.6 28.5 | 39.9 45.8 14.3 | 87.5 12.5 0.0 | 41.7 38.6 19.7 | 38.7 38.6 19.7 | 51.0 31.4 17.6 | 33.3 41.2 25.5 | 49.9 33.6 16.6 |
| Sitting 8 hours or more a day | 20.4 | 31.0 | 17.5 | 0.0 | 57.0 | 34.5 | 19.5 | 52.8 | 31.5 |
| Anthropometry 1 | |||||||||
| Weight (kg)-Male | 65.4 (14.1) | 56.7 (7.7) | 66.4 (14.9) | 68.9 (12.9) | 61.0 (13.9) | 66.4 (11.8) | 64.5 (11.9) | 59.7 (7.8) | 62.3 (12.8) |
| Weight (kg)-Female | 51.5 (11.5) | 50.5 (8.8) | 53.5 (11.4) | 53.4 (13.5) | 49.4 (10.4) | 52.4 (8.6) | 53.6 (9.7) | 48.0 (5.3) | 51.0 (9.9) |
| Height (cms)-Male | 169.4 (7.6) | 163.3 (6.8) | 169.9 (6.5) | 170.2 (8.8) | 166.0 (6.9) | 173.6 (6.6) | 171.7 (6.2) | 167.9 (5.2) | 168.9 (7.0) |
| Height (cms)-Female | 156.6 (6.2) | 155.7 (6.4) | 157.0 (6.1) | 157.6 (7.2) | 154.1 (5.6) | 160.9 (5.7) | 160.7 (5.5) | 155.6 (5.2) | 157.2 (6.2) |
| Waist (cms)-Male | 77.2 (19.2) | 74.7 (5.4) | 77.8 (12.1) | 67.7 (17.0) | 76.1 (10.3) | 79.3 (8.7) | 78.5 (8.8) | 75.0 (7.0) | 76.2 (10.7) |
| Waist (cms)-Female | 69.7 (10.5) | 71.2 (7.1) | 71.6 (9.5) | 63.5 (9.7) | 68.3 (7.4) | 67.6 (7.4) | 71.0 (8.1) | 68.9 (5.7) | 69.7 (8.2) |
| BMI-Male | 22.7 (4.7) | 21.3 (2.8) | 22.9 (4.9) | 23.5 (7.3) | 22.1 (4.6) | 22.1 (4.0) | 21.4 (3.7) | 21.2 (2.5) | 22.2 (4.6) |
| BMI-Female | 20.9 (4.1) | 20.8 (3.6) | 21.7 (4.3) | 21.5 (5.4) | 20.7 (3.5) | 20.2 (2.9) | 20.7 (3.5) | 19.8 (2.0) | 20.8 (4.3) |
| % | % | % | % | % | % | % | % | % | |
| BMI overweight-Male | 14.8 | 12.7 | 12.5 | 14.4 | 14.4 | 17.1 | 11.2 | 14.9 | 12.3 |
| BMI overweight-Female | 8.0 | 8.4 | 11.2 | 7.5 | 10.2 | 9.8 | 7.3 | 5.2 | 8.4 |
| BMI obesity-Male | 23.3 | 11.2 | 25.6 | 24.2 | 19.6 | 12.2 | 14.3 | 7.4 | 24.2 |
| BMI obesity-Female | 10.6 | 10.4 | 15.8 | 15.4 | 8.4 | 5.8 | 9.9 | 1.0 | 9.3 |
| High WHtR-Male | 30.9 | 9.3 | 21.4 | 14.3 | 20.9 | 17.4 | 12.3 | 12.3 | 16.6 |
| High WHtR-Female | 11.9 | 14.5 | 20.1 | 5.0 | 10.6 | 2.2 | 11.8 | 6.1 | 12.1 |
| High WC-Male | 23.6 | 2.8 | 14.5 | 8.2 | 10.7 | 11.7 | 7.4 | 3.9 | 9.4 |
| High WC-Female | 8.5 | 10.9 | 17.7 | 6.4 | 7.5 | 3.3 | 13.4 | 3.6 | 10.1 |
| 1Waist circumference data were only available for a subset of students from Indonesia (N = 231), Singapore (N = 201), and Thailand (N = 479); M: Mean; SD: Standard Deviation; BMI (Body Mass Index) ≥ 25 kg/m2= obesity; WHtR (Waist-to-height ratio of) > 0.5 = High; WC (Waist Circumference high) ≥80 cms in females, ≥90 cms in males. | |||||||||
DownLoad: CSV| Variable | BMI Overweight (23.0 to < 25.0 kgm2) | BMI Obesity (≥25 kgm2) | High Waist-Height Ratio ( > 0.50) | High Waist circumference (≥90 for men and ≥80 for women) |
| AOR (95% CI) | AOR (95% CI) | AOR (95% CI) | AOR (95% CI) | |
| Behavior covariates | ||||
| Not almost daily breakfast | 1.03 (0.83, 1.28) | 1.06 (0.85, 1.57) | 0.93 (0.72, 1.21) | 1.09 (0.81, 1.47) |
| Number of meals/day | 0.78 (0.66, 0.92) ** | 0.87 (0.73, 1.02) | 0.76 (0.62, 0.93) ** | 0.81 (0.64, 1.01) |
| Number of snacks/day | 0.91 (0.82, 1.01) | 1.16 (1.05, 1.28) ** | 1.17 (1.04, 1.32) * | 1.16 (1.01, 1.33) * |
| Number of servings of fruits/day | 1.06 (0.95, 1.19) | 0.94 (0.83, 1.08) | 0.99 (0.85, 1.15) | 0.95 (0.80, 1.13) |
| Number of servings of vegetables/day | 0.85 (0.77, 0.95) ** | 0.99 (0.88, 1.11) | 0.95 (0.83, 1.08) | 0.88 (0.75, 1.03) |
| Eating red meat at least once a day | 1.07 (0.87, 1.32) | 0.78 (0.63, 0.96) * | 0.98 (0.77, 1.26) | 0.89 (0.67, 1.17) |
| Avoiding fatty foods and cholesterol | 0.98 (0.79, 1.22) | 1.54 (1.24, 1.92) *** | 1.46 (1.08, 1.54) ** | 1.52 (1.14, 2.04) ** |
| Trying to eat fiber | 1.10 (0.89, 1.37) | 1.18 (0.95, 1.48) | 1.24 (0.95, 1.60) | 1.27 (0.95, 1.72) |
| Physical activity | ||||
| Low | 1 (Reference) | 1 (Reference) | 1 (Reference) | 1 (Reference) |
| Moderate | 1.10 (0.88, 1.39) | 1.11 (0.88, 1.41) | 1.16 (0.88, 1.54) | 1.06 (0.77, 1.44) |
| High | 1.11 (0.85, 1.47) | 0.99 (0.75, 1.32) | 1.27 (0.92, 1.74) | 0.84 (0.58, 1.24) |
| Sitting 8 hours or more a day | 0.89 (0.71, 1.12) | 1.04 (1.05, 1.28) | 1.16 (0.90, 1.50) | 1.27 (0.95, 1.70) |
| Sociodemographics | ||||
| Male gender (base: female) | 1.70 (1.38, 2.10) *** | 2.13 (1.80, 2.77) *** | 1.90 (1.48, 2.43) *** | 1.23 (0.92, 1.64) |
| Year of study | ||||
| One | 1 (Reference) | 1 (Reference) | 1 (Reference) | 1 (Reference) |
| Two | 1.07 (0.79, 1.46) | 0.89 (0.64, 1.23) | 0.93 (0.63, 0.93) | 0.69 (0.43, 1.09) |
| Three | 1.02 (0.77, 1.35) | 1.19 (0.90, 1.58) | 1.14 (0.81, 1.60) | 1.02 (0.70, 1.49) |
| Four or five | 1.10 (0.83, 1.46) | 1.19 (0.89, 1.59) | 1.07 (0.76, 1.51) | 1.01 (0.69, 1.48) |
| Wealthy (base: poor) | 1.07 (0.86, 1.32) | 1.29 (1.04, 1.60)* | 1.22 (0.94, 1.58) | 1.24 (0.92, 1.66) |
| AOR: Adjusted Odds Ratio, CI: Confidence Interval; *** P < 0.001; ** P < 0.01; * P < 0.05. | ||||
DownLoad: CSV| Variable | Indonesia | Laos | Malaysia | Myanmar | Philippines | Singapore | Thailand | Vietnam | All |
| N | N | N | N | N | N | N | N | N | |
| Total | 969 | 759 | 1022 | 333 | 765 | 677 | 1583 | 765 | 6783 |
| Men | 283 | 260 | 519 | 201 | 194 | 347 | 1289 | 402 | 2357 |
| Women | 686 | 499 | 503 | 132 | 571 | 328 | 294 | 363 | 4514 |
| M (SD) | M (SD) | M (SD) | M (SD) | M (SD) | M (SD) | M (SD) | M (SD) | M (SD) | |
| Age | 19.4 (2.5) | 22.3 (1.8) | 20.7 (1.5) | 20.1 (1.2) | 18.7 (1.0) | 21.2 (1.6) | 20.0 (1.3) | 21.3 (1.7) | 20.5 (2.0) |
| % | % | % | % | % | % | % | % | % | |
| Wealthy family background Year of study | 37.3 | 4.7 | 58.8 | 29.7 | 39.9 | 60.7 | 27.9 | 3.4 | 37.6 |
| 1 | 23.4 | 0.0 | 37.9 | 0.0 | 28.4 | 29.8 | 36.9 | 19.5 | 27.5 |
| 2 | 22.2 | 0.0 | 27.7 | 21.3 | 27.5 | 27.8 | 16.3 | 19.2 | 20.1 |
| 3 | 32.6 | 63.6 | 15.1 | 0.0 | 26.6 | 29.0 | 32.6 | 19.6 | 27.6 |
| 4 or 5 | 21.8 | 36.4 | 19.4 | 78.7 | 17.5 | 13.4 | 14.2 | 41.7 | 24.9 |
| M = Mean; SD = Standard deviation. | |||||||||
| Variable | Indonesia | Laos | Malaysia | Myanmar | Philippines | Singapore | Thailand | Vietnam | All |
| M (SD) | M (SD) | M (SD) | M (SD) | M (SD) | M (SD) | M (SD) | M (SD) | M (SD) | |
| Behavior | |||||||||
| No meals/day | 2.6 (0.7) | 2.9 (0.5) | 2.7 (0.5) | 2.6 (0.7) | 2.7 (0.6) | 2.9 (0.7) | 2.6 (0.7) | 2.8 (0.6) | 2.7 (0.7) |
| No snacks/day | 1.8 (1.1) | 1.4 (1.1) | 1.4 (1.0) | 2.2 (1.0) | 1.7 (0.9) | 1.2 (1.0) | 2.1 (1.1) | 1.1 (0.8) | 1.6 (1.1) |
| No servings of fruits/day | 1.4 (0.8) | 1.2 (0.8) | 1.0 (0.8) | 1.8 (1.1) | 1.1 (0.8) | 1.3 (0.9) | 1.8 (1.0) | 1.6 (1.1) | 1.4 (0.9) |
| No servings of vegetables/day | 1.6 (0.9) | 1.5 (0.9) | 1.6 (0.9) | 1.5 (0.7) | 1.3 (1.0) | 1.7 (0.9) | 2.1 (1.1) | 2.7 (1.0) | 1.8 (1.0) |
| % | % | % | % | % | % | % | % | % | |
| Not almost daily breakfast | 59.5 | 54.8 | 49.5 | 32.5 | 46.4 | 37.5 | 70.6 | 49.2 | 53.2 |
| Read meat at least once a day | 20.6 | 42.7 | 52.7 | 96.0 | 64.7 | 79.6 | 62.2 | 70.1 | 58.7 |
| Avoiding fatty foods and cholesterol | 46.3 | 29.5 | 37.7 | 43.1 | 28.6 | 43.7 | 49.5 | 33.7 | 40.2 |
| Trying to eat fiber | 70.3 | 27.8 | 41.8 | 3.9 | 31.9 | 38.3 | 62.6 | 75.5 | 48.4 |
| Physical activity Low Moderate High | 56.3 34.6 9.1 | 49.9 21.6 28.5 | 39.9 45.8 14.3 | 87.5 12.5 0.0 | 41.7 38.6 19.7 | 38.7 38.6 19.7 | 51.0 31.4 17.6 | 33.3 41.2 25.5 | 49.9 33.6 16.6 |
| Sitting 8 hours or more a day | 20.4 | 31.0 | 17.5 | 0.0 | 57.0 | 34.5 | 19.5 | 52.8 | 31.5 |
| Anthropometry 1 | |||||||||
| Weight (kg)-Male | 65.4 (14.1) | 56.7 (7.7) | 66.4 (14.9) | 68.9 (12.9) | 61.0 (13.9) | 66.4 (11.8) | 64.5 (11.9) | 59.7 (7.8) | 62.3 (12.8) |
| Weight (kg)-Female | 51.5 (11.5) | 50.5 (8.8) | 53.5 (11.4) | 53.4 (13.5) | 49.4 (10.4) | 52.4 (8.6) | 53.6 (9.7) | 48.0 (5.3) | 51.0 (9.9) |
| Height (cms)-Male | 169.4 (7.6) | 163.3 (6.8) | 169.9 (6.5) | 170.2 (8.8) | 166.0 (6.9) | 173.6 (6.6) | 171.7 (6.2) | 167.9 (5.2) | 168.9 (7.0) |
| Height (cms)-Female | 156.6 (6.2) | 155.7 (6.4) | 157.0 (6.1) | 157.6 (7.2) | 154.1 (5.6) | 160.9 (5.7) | 160.7 (5.5) | 155.6 (5.2) | 157.2 (6.2) |
| Waist (cms)-Male | 77.2 (19.2) | 74.7 (5.4) | 77.8 (12.1) | 67.7 (17.0) | 76.1 (10.3) | 79.3 (8.7) | 78.5 (8.8) | 75.0 (7.0) | 76.2 (10.7) |
| Waist (cms)-Female | 69.7 (10.5) | 71.2 (7.1) | 71.6 (9.5) | 63.5 (9.7) | 68.3 (7.4) | 67.6 (7.4) | 71.0 (8.1) | 68.9 (5.7) | 69.7 (8.2) |
| BMI-Male | 22.7 (4.7) | 21.3 (2.8) | 22.9 (4.9) | 23.5 (7.3) | 22.1 (4.6) | 22.1 (4.0) | 21.4 (3.7) | 21.2 (2.5) | 22.2 (4.6) |
| BMI-Female | 20.9 (4.1) | 20.8 (3.6) | 21.7 (4.3) | 21.5 (5.4) | 20.7 (3.5) | 20.2 (2.9) | 20.7 (3.5) | 19.8 (2.0) | 20.8 (4.3) |
| % | % | % | % | % | % | % | % | % | |
| BMI overweight-Male | 14.8 | 12.7 | 12.5 | 14.4 | 14.4 | 17.1 | 11.2 | 14.9 | 12.3 |
| BMI overweight-Female | 8.0 | 8.4 | 11.2 | 7.5 | 10.2 | 9.8 | 7.3 | 5.2 | 8.4 |
| BMI obesity-Male | 23.3 | 11.2 | 25.6 | 24.2 | 19.6 | 12.2 | 14.3 | 7.4 | 24.2 |
| BMI obesity-Female | 10.6 | 10.4 | 15.8 | 15.4 | 8.4 | 5.8 | 9.9 | 1.0 | 9.3 |
| High WHtR-Male | 30.9 | 9.3 | 21.4 | 14.3 | 20.9 | 17.4 | 12.3 | 12.3 | 16.6 |
| High WHtR-Female | 11.9 | 14.5 | 20.1 | 5.0 | 10.6 | 2.2 | 11.8 | 6.1 | 12.1 |
| High WC-Male | 23.6 | 2.8 | 14.5 | 8.2 | 10.7 | 11.7 | 7.4 | 3.9 | 9.4 |
| High WC-Female | 8.5 | 10.9 | 17.7 | 6.4 | 7.5 | 3.3 | 13.4 | 3.6 | 10.1 |
| 1Waist circumference data were only available for a subset of students from Indonesia (N = 231), Singapore (N = 201), and Thailand (N = 479); M: Mean; SD: Standard Deviation; BMI (Body Mass Index) ≥ 25 kg/m2= obesity; WHtR (Waist-to-height ratio of) > 0.5 = High; WC (Waist Circumference high) ≥80 cms in females, ≥90 cms in males. | |||||||||
| Variable | BMI Overweight (23.0 to < 25.0 kgm2) | BMI Obesity (≥25 kgm2) | High Waist-Height Ratio ( > 0.50) | High Waist circumference (≥90 for men and ≥80 for women) |
| AOR (95% CI) | AOR (95% CI) | AOR (95% CI) | AOR (95% CI) | |
| Behavior covariates | ||||
| Not almost daily breakfast | 1.03 (0.83, 1.28) | 1.06 (0.85, 1.57) | 0.93 (0.72, 1.21) | 1.09 (0.81, 1.47) |
| Number of meals/day | 0.78 (0.66, 0.92) ** | 0.87 (0.73, 1.02) | 0.76 (0.62, 0.93) ** | 0.81 (0.64, 1.01) |
| Number of snacks/day | 0.91 (0.82, 1.01) | 1.16 (1.05, 1.28) ** | 1.17 (1.04, 1.32) * | 1.16 (1.01, 1.33) * |
| Number of servings of fruits/day | 1.06 (0.95, 1.19) | 0.94 (0.83, 1.08) | 0.99 (0.85, 1.15) | 0.95 (0.80, 1.13) |
| Number of servings of vegetables/day | 0.85 (0.77, 0.95) ** | 0.99 (0.88, 1.11) | 0.95 (0.83, 1.08) | 0.88 (0.75, 1.03) |
| Eating red meat at least once a day | 1.07 (0.87, 1.32) | 0.78 (0.63, 0.96) * | 0.98 (0.77, 1.26) | 0.89 (0.67, 1.17) |
| Avoiding fatty foods and cholesterol | 0.98 (0.79, 1.22) | 1.54 (1.24, 1.92) *** | 1.46 (1.08, 1.54) ** | 1.52 (1.14, 2.04) ** |
| Trying to eat fiber | 1.10 (0.89, 1.37) | 1.18 (0.95, 1.48) | 1.24 (0.95, 1.60) | 1.27 (0.95, 1.72) |
| Physical activity | ||||
| Low | 1 (Reference) | 1 (Reference) | 1 (Reference) | 1 (Reference) |
| Moderate | 1.10 (0.88, 1.39) | 1.11 (0.88, 1.41) | 1.16 (0.88, 1.54) | 1.06 (0.77, 1.44) |
| High | 1.11 (0.85, 1.47) | 0.99 (0.75, 1.32) | 1.27 (0.92, 1.74) | 0.84 (0.58, 1.24) |
| Sitting 8 hours or more a day | 0.89 (0.71, 1.12) | 1.04 (1.05, 1.28) | 1.16 (0.90, 1.50) | 1.27 (0.95, 1.70) |
| Sociodemographics | ||||
| Male gender (base: female) | 1.70 (1.38, 2.10) *** | 2.13 (1.80, 2.77) *** | 1.90 (1.48, 2.43) *** | 1.23 (0.92, 1.64) |
| Year of study | ||||
| One | 1 (Reference) | 1 (Reference) | 1 (Reference) | 1 (Reference) |
| Two | 1.07 (0.79, 1.46) | 0.89 (0.64, 1.23) | 0.93 (0.63, 0.93) | 0.69 (0.43, 1.09) |
| Three | 1.02 (0.77, 1.35) | 1.19 (0.90, 1.58) | 1.14 (0.81, 1.60) | 1.02 (0.70, 1.49) |
| Four or five | 1.10 (0.83, 1.46) | 1.19 (0.89, 1.59) | 1.07 (0.76, 1.51) | 1.01 (0.69, 1.48) |
| Wealthy (base: poor) | 1.07 (0.86, 1.32) | 1.29 (1.04, 1.60)* | 1.22 (0.94, 1.58) | 1.24 (0.92, 1.66) |
| AOR: Adjusted Odds Ratio, CI: Confidence Interval; *** P < 0.001; ** P < 0.01; * P < 0.05. | ||||
