Prevalence of Overweight/Obesity and Its Associated Factors among University Students from 22 Countries
by
,
Karl Peltzer
1,2,3,*
,
Supa Pengpid
1,4,
T. Alafia Samuels
5,
Neslihan Keser Özcan
6,
Carolina Mantilla
7,
Onja H. Rahamefy
8,
Mee Lian Wong
9 and
Alexander Gasparishvili
10 1
ASEAN Institute for Health Development, Madidol University, Salaya, Phutthamonthon, Nakhonpathom 73170, Thailand
2
Department of Psychology, University of the Free State, Bloemfontein 9300, South Africa
3
HIV/AIDS/STIs and TB (HAST) Research Programme, Human Sciences Research Council, Private Bag X41, Pretoria 0001, South Africa
4
Department of Research, University of Limpopo, Turfloop Campus, Sovenga 0727, South Africa
5
Faculty of Medical Sciences, University of the West Indies, Cave Hill Campus, P.O. Box 64, Bridgetown 11000, Barbados
6
Department of Midwifery, Faculty of Health Science, Istanbul University, Bakirkoy, Istanbul 34740, Turkey
7
Departamento de Fisioterapia, Universidad de Pamplona, Pamplona 0, Colombia
8
Research and Training Laboratory of Medical Biology, Faculty of Medicine, University of Antananarivo, Antananarivo 001, Madagascar
9
School of Public Health, National University of Singapore, 16, Medical Drive, Singapore 117597, Singapore
10
Centre for Sociological Studies, Moscow State University, Moscow 119991, Russia
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2014, 11(7), 7425-7441; https://doi.org/10.3390/ijerph110707425
Submission received: 3 June 2014
/
Revised: 10 July 2014
/
Accepted: 14 July 2014
/
Published: 21 July 2014
Abstract
:Obesity among young people increases lifetime cardiovascular risk. This study assesses the prevalence of overweight/obesity and its associated factors among a random sample of university students from 22 universities in 22 low, middle income and emerging economy countries. This cross-sectional survey comprised of a self-administered questionnaire and collected anthropometric measurements. The study population was 6773 (43.2%) males and 8913 (56.8%) females, aged 16 to 30 years (mean 20.8 years, SD = 2.6). Body mass index (BMI) was used for weight status. Among men, the prevalence of underweight was 10.8%, normal weight 64.4%, overweight 18.9% and obesity 5.8%, while among women, the prevalence of underweight was 17.6%, normal weight 62.1%, overweight 14.1% and obesity 5.2%. Overall, 22% were overweight or obese (24.7% men and 19.3% women). In multivariate regression among men, younger age, coming from a higher income country, consciously avoiding fat and cholesterol, physically inactivity, current tobacco use and childhood physical abuse, and among women older age, coming from a higher income country, frequent organized religious activity, avoiding fat and cholesterol, posttraumatic stress symptoms and physical childhood abuse were associated overweight or obesity. Several gender specific risk factors identified can be utilized in health promotion programmes.
1. Introduction
Globally, there is rising prevalence of overweight and obesity in both developing and developed countries [1]. The rate of obesity has tripled in developing countries over the past 20 years as they rapidly become more urbanized, with increased consumption of high calorie foods and adoption of a more sedentary lifestyle [1,2]. Some studies observed that first year university students have significant weight gain [3], followed by ongoing slow but steady increase in weight [4].
Studies among university students in developing countries show high prevalence of overweight and obesity: Africa (Nigeria: 10% [5]; Egypt: 25.3%–59.4% [6,7], South Africa: 10.8%–24% [8,9]; Asia (Bangladesh: 20.8% [10]; China: 2.9%–14.3% [11,12]; Malaysia: 20%–30.1% [13,14], Thailand: 31% [15], Pakistan: 13%–52.6% [16,17], and India: 11%–37.5% [18,19,20,21]; Latin America (Colombia: 12.4%–16.7% [22]; Mexico: 31.6% [23], the Middle and Near East (Saudi females: 47.9% [24], Oman: 28.2% [25] Kuwait: 42% [26,27], Iran 12.4% [28], and Turkey: 10%–47.4% [29,30]). Although there are prior studies on obesity/overweight in students in individual developing countries, this study collected data from 22 countries (low, lower middle, upper middle and high income countries) in different regions (Africa, Asia, The Caribbean and South America) using the same methodology, which allows for direct comparison across countries.
In a previous review [21], the following factors were identified to be associated with overweight and obesity among university students or (young) adults: (1) socio-demographic factors (mainly male gender, older age and higher socioeconomic status; (2) Social factors: lack of social support, capital and lack of religiousness; (3) Dietary behaviour: intakes of fiber, consumption of red meat, skip breakfast more often, high number of meals, snacking behaviour; and (4) Health risk behaviour: Physical inactivity, frequent alcohol use, and smoking; (5) Mental health and childhood abuse: poor mental health (depression, anxiety) and childhood physical abuse, sexual and verbal abuse.
The purpose of this study was to assess the prevalence of overweight/obesity and its associated factors among university students in 22 low and middle income and emerging economy countries.
2. Methods
2.1. Sample and Procedure
This cross-sectional study was carried out with a network of collaborators in participating countries (see Acknowledgments). The anonymous, self-administered questionnaire was developed in English, translated into the Arabic, Bahasa, French, Lao, Russian, Spanish, Thai and Turkish languages of the participating countries, then back-translated to English. In each country where translated questionnaires were used, they were pilot tested for face validity and understanding with 25 students who were not from the sample population. The study was initiated through personal academic contacts of the principal investigators. In 2013, these collaborators arranged for data to be collected by trained research assistants from intended 400 male and 400 female undergraduate university students aged 16–30 years in one or two universities located in the capital or other major cities. Classes of students from all years of study and across multiple faculties were recruited by timetable scheduling using stratified random sampling. Research assistants asked these classes of undergraduate students to complete the questionnaire at the end of a teaching session, followed by the anthropometric measurements. We included no incentive for participation, and there were no penalties for refusal. Written informed consent was obtained from participating students. The required ethics approvals were obtained by all participating institutions.
2.2. Measures
2.2.1. Anthropometric Measurements
Students were weighed and measured by trained researchers using standardised protocols [31]. 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 [32]. Body mass index (BMI) was calculated as weight in kg divided by height in metres squared. In South and East Asian participants overweight and obesity were defined as ≥23.0–27.4 kg/m2 and ≥27.5 kg/m2 respectively [33] and for the other countries ≥25.0–29.9 kg/m2 and ≥30 kg/m2, respectively [34].
Socio-demographic questions included age, gender, and marital status. 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%) [35].
2.2.2. Social Variables
Three items from the Social Support Questionnaire were utilized to assess perceived social support [36]. The questions were selected, as in previous research [37], to assess perceived tangible and emotional support: “If I were sick and needed someone to take me to a doctor I would have trouble finding someone (reversed); I feel that there is no one I can share my most private concerns and fears (reversed); and I feel a strong emotional bond with at least one other person”. Cronbach alpha for this social support index was 0.95. Organized religious activity was assessed with one item from the Duke University Religion Index (DUREL) [38].
2.2.3. Dietary Behaviour 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?” using the 24-h dietary recall data as the gold standard [39]. Cronbach alpha for this fruit and vegetable measure was 0.74. Insufficient FV consumption was defined as less than five servings of fruits and/or vegetables a day [39]. 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); (d) trying to eat foods that are high in fibre (yes, no); (e) frequency of having breakfast; (f) frequency of between-meal snacks and (g) number of meals a day [40].
2.2.4. Health Risk Behaviour
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 [41]. We categorized physical activity (short form) according to the official IPAQ scoring protocol [42] as low, moderate and high.
Tobacco use was assessed with the question: Do you currently use one or more of the following tobacco products (cigarettes, snuff, chewing tobacco, cigars, etc.)? Response options were “yes” or “no” [43]. Heavy alcohol consumption was measured by asking participants “how often do you have (for men) five or more and (for women) four or more drinks on one occasion?”
2.2.5. Mental Health and Abuse
We assessed depressive symptoms using the 10-item version of the Centres for Epidemiologic Studies Depression Scale (CES-D). Scores of 0–9, 10–14 and 15 or more indicative of mild, moderate and severe depressive [44]. The Cronbach alpha reliability coefficient of this 10-item scale was 0.74 in this study.
Breslau’s 7-item screener was used to identify Post traumatic stress disorder (PTSD) symptoms in the past month [45]. Items asked whether the respondent had experienced difficulties related to a traumatic experience (e.g., “Did you begin to feel more isolated and distant from other people?”). Participants who answered affirmatively to at least four of the questions were considered to have a positive screen for PTSD [45]. The Cronbach alpha reliability coefficient of this 7-item scale was 0.75 in this study.
Child abuse was assessed with two questions, regarding being physically abused as a child and being sexually abused as a child.
2.3. Data Analysis
The data were analysed using IBM SPSS Statistics for Windows (Version 20.0., Armonk, NY, USA). Sociodemographic factors, social and dietary variables, health risk behaviours, mental health, childhood abuse and BMI categories were calculated as percentages. Multivariate logistic regression was performed for men and women separately with overweight/obesity as the dependent variable. Potential multi-collinearity between variables was assessed with variance inflation factors, none of which exceeded the recommended critical value of 4.0 [46]. Socio-demographic characteristics, social, health risk behaviours, mental health and childhood abuse variables 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.
3. Results and Discussion
3.1. Sample Characteristics
The total sample included 15,746 undergraduate university students (mean age 20.8, SD 2.6, age range of 16–30 years) from 22 countries. The students were from faculties of education, humanities and arts, social sciences, business and law, science, engineering, manufacturing and construction, agriculture, health and welfare and services. Participation rate in most countries was over 90%.
Table 1 and Table 2 show the numbers of male and female university student participants by country, the mean age, measured height and weight, calculated BMI, and the proportion of students classified as underweight, normal weight, overweight and obese. Among men, the prevalence of underweight was 10.8%, normal weight 64.4%, overweight 18.9% and obesity 5.8%, while among women, the prevalence of underweight was 17.6%, normal weight 62.1%, overweight 15.1% and obesity 5.2%. Table 3 shows the prevalence of overweight/obesity by gender and socio-economic profile.
Table 1.
Reported mean age, measured height and weight, and calculated BMI and weight status among male university students from 22 low and middle income and emerging economy countries, 2013.
| Country | N | Age (years) (SD) | Height (m) (SD) | Weight (kg) (SD) | BMI | Under Weight | Normal Weight | Over Weight | Obesity |
|---|---|---|---|---|---|---|---|---|---|
| All | 6773 | 21.0 (2.6) | 1.73 (0.09) | 67.3 (13.2) | 22.5 (4.1) | 10.8 | 64.4 | 18.9 | 5.8 |
| Carribbean and Latin America | |||||||||
| Barbados 4 | 330 | 21.6 (2.6) | 1.76 (0.08) | 77.2 (17.5) | 24.9 (5.0) | 6.1 | 54.5 | 26.4 | 13.0 |
| Jamaica 3 | 158 | 20.9 (4.2) | 1.76 (0.12) | 73.0 (15.6) | 23.4 (4.2) | 3.8 | 69.0 | 21.5 | 5.7 |
| Colombia 3 | 357 | 21.4 (3.7) | 1.72 (0.06) | 69.3 (12.1) | 23.5 (3.6) | 4.5 | 66.1 | 24.4 | 5.0 |
| Venezuela 3 | 173 | 21.0 (2.7) | 1.74 (0.15) | 71.3 (13.4) | 23.3 (3.0) | 4.0 | 71.7 | 19.1 | 5.2 |
| Sub-Saharan Africa | |||||||||
| Ivory Coast 2 | 396 | 24.0 (2.5) | 1.72 (0.10) | 63.0 (8.1) | 21.3 (2.8) | 7.8 | 85.4 | 6.1 | 0.8 |
| Madagascar 1 | 387 | 20.3 (1.8) | 1.68 (0.06) | 58.7 (7.3) | 20.7 (2.3) | 16.0 | 79.3 | 4.4 | 0.3 |
| Mauritius 3 | 142 | 21.4 (1.1) | 1.75 (0.08) | 66.8 (14.0) | 21.7 (4.1) | 20.4 | 62.0 | 15.5 | 2.1 |
| Namibia 3 | 111 | 20.6 (1.1) | 1.73 (0.06) | 66.1 (11.7) | 22.0 (3.9) | 10.8 | 72.1 | 11.7 | 5.4 |
| Nigeria 2 | 439 | 21.1 (2.6) | 1.73 (0.11) | 65.2 (12.1) | 21.4 (2.8) | 11.8 | 77.4 | 10.0 | 0.7 |
| South Africa 3 | 315 | 22.3 (3.4) | 1.67 (0.20) | 64.7 (10.6) | 22.3 (3.5) | 9.8 | 73.0 | 13.3 | 3.8 |
| North Africa, Near East and central Asia | |||||||||
| Egypt 2 | 321 | 20.8 (1.3) | 1.75 (0.08) | 78.0 (13.9) | 25.4 (4.5) | 2.8 | 46.4 | 38.3 | 12.5 |
| Tunesia 3 | 295 | 23.7 (4.1) | 1.78 (0.07) | 74.8 (13.7) | 23.7 (4.1) | 4.1 | 67.1 | 23.4 | 5.4 |
| Turkey 3 | 398 | 20.7 (2.3) | 1.77 (0.07) | 73.4 (11.6) | 23.4 (3.4) | 3.0 | 73.1 | 19.6 | 4.3 |
| Russia 3 | 381 | 20.1 (1.9) | 1.79 (0.09) | 76.7 (11.0) | 23.7 (3.3) | 2.9 | 69.0 | 24.4 | 3.7 |
| Kyrgyzstan 1 | 349 | 21.3 (1.5) | 1.75 (0.06) | 67.6 (9.2) | 22.0 (2.7) | 6.6 | 82.5 | 9.2 | 1.7 |
| South Asia | |||||||||
| Bangladesh 1 | 358 | 21.6 (2.1) | 1.69 (0.08) | 67.8 (13.6) | 23.8 (4.9) | 6.1 | 47.2 | 30.4 | 16.2 |
| India 2 | 541 | 17.9 (0.6) | 1.67 (0.09) | 63.2 (11.9) | 22.7 (4.5) | 11.8 | 47.9 | 28.5 | 11.8 |
| Pakistan 2 | 319 | 20.2 (1.8) | 1.77 (0.11) | 57.2 (5.1) | 18.1 (1.9) | 61.1 | 36.7 | 2.2 | 0.0 |
| Southeast Asia | |||||||||
| Laos 2 | 260 | 22.6 (1.8) | 1.63 (0.07) | 56.7 (7.8) | 21.3 (2.8) | 8.1 | 68.1 | 19.2 | 4.6 |
| Philippines 2 | 196 | 18.4 (1.5) | 1.68 (0.22) | 60.9 (14.4) | 21.9 (5.0) | 19.9 | 46.4 | 22.4 | 11.2 |
| Singapore 4 | 328 | 21.9 (1.6) | 1.74 (0.07) | 66.4 (11.8) | 22.1 (4.0) | 8.8 | 61.9 | 23.8 | 5.5 |
| Thailand 3 | 219 | 20.4 (1.3) | 1.72 (0.07) | 64.5 (11.9) | 21.9 (3.8) | 13.7 | 58.0 | 19.6 | 8.7 |
Notes: 1 Low income country; 2 Lower middle income country; 3 Upper middle income country; 4 High income country (Source: World Bank, New Country Classifications, 2013 [47]).
Table 2.
Reported mean age, measured height and weight, and calculated BMI and weight status among female university students from 22 low and middle income and emerging economy countries, 2013.
| Country | N | Age (years) (SD) | Height (m) (SD) | Weight (kg) (SD) | BMI | Under Weight | Normal Weight | Over Weight | Obesity |
|---|---|---|---|---|---|---|---|---|---|
| All | 8913 | 20.7 (2.6) | 1.61 (0.09) | 56.9 (11.8) | 21.9 (4.2) | 17.6 | 62.1 | 15.1 | 5.2 |
| Carribbean and Latin America | |||||||||
| Barbados 4 | 246 | 21.6 (3.0) | 1.64 (0.07) | 68.4 (21.3) | 25.5 (7.7) | 11.8 | 47.6 | 22.0 | 18.7 |
| Jamaica 3 | 516 | 21.1 (4.9) | 1.64 (0.09) | 63.1 (15.3) | 23.4 (5.6) | 11.8 | 60.1 | 17.4 | 10.7 |
| Colombia 3 | 453 | 20.8 (2.8) | 1.59 (0.06) | 58.3 (9.2) | 23.0 (3.8) | 6.2 | 71.5 | 16.8 | 5.5 |
| Venezuela 3 | 271 | 20.5 (2.8) | 1.62 (0.07) | 57.6 (10.1) | 22.0 (3.6) | 13.7 | 68.3 | 13.7 | 4.4 |
| Sub-Saharan Africa | |||||||||
| Ivory Coast 2 | 381 | 23.5 (2.8) | 1.63 (0.06) | 58.3 (9.2) | 21.9 (3.2) | 12.9 | 70.3 | 14.7 | 2.1 |
| Madagascar 1 | 393 | 19.6 (1.5) | 1.56 (0.06) | 50.7 (7.7) | 20.8 (2.8) | 20.1 | 74.3 | 4.6 | 1.0 |
| Mauritius 3 | 319 | 20.7 (1.2) | 1.60 (0.07) | 52.3 (12.6) | 20.3 (4.6) | 37.6 | 51.4 | 6.6 | 4.4 |
| Namibia 3 | 317 | 22.1 (4.1) | 1.61 (0.12) | 58.9 (12.1) | 22.7 (5.7) | 16.7 | 61.8 | 14.5 | 6.9 |
| Nigeria 2 | 361 | 21.4 (2.7) | 1.62 (0.17) | 60.0 (16.4) | 21.7 (3.6) | 17.7 | 65.9 | 13.3 | 3.0 |
| South Africa 3 | 430 | 22.2 (22.2) | 1.57 (0.13) | 62.9 (13.5) | 25.1 (5.2) | 4.0 | 55.1 | 25.3 | 15.6 |
| North Africa, Near East and central Asia | |||||||||
| Egypt 2 | 375 | 20.3 (1.3) | 1.61 (0.11) | 63.2 (11.7) | 24.0 (4.0) | 4.8 | 61.3 | 25.3 | 8.5 |
| Tunesia 3 | 615 | 21.0 (1.7) | 1.65 (0.06) | 62.5 (9.9) | 23.0 (3.6) | 7.3 | 67.3 | 20.3 | 5.0 |
| Turkey 3 | 397 | 20.5 (2.1) | 1.64 (0.06) | 58.0 (9.5) | 21.5 (3.1) | 16.1 | 70.3 | 11.8 | 1.8 |
| Russia 3 | 404 | 19.8 (1.7) | 1.67 (0.06) | 57.3 (8.6) | 20.5 (2.9) | 24.0 | 69.3 | 5.9 | 0.7 |
| Kyrgyzstan 1 | 465 | 21.3 (1.4) | 1.64 (0.06) | 55.7 (8.3) | 20.7 (2.9) | 24.1 | 68.0 | 7.3 | 0.6 |
| South Asia | |||||||||
| Bangladesh 1 | 283 | 20.5 (1.8) | 1.63 (0.08) | 56.3 (10.4) | 21.2 (3.3) | 18.4 | 63.3 | 13.1 | 5.3 |
| India 2 | 259 | 18.0 (0.9) | 1.61 (0.08) | 56.6 (11.2) | 21.8 (4.1) | 13.5 | 56.8 | 22.0 | 7.7 |
| Pakistan 2 | 442 | 19.6 (1.9) | 1.58 (0.06) | 53.1 (5.8) | 21.3 (2.6) | 14.5 | 61.3 | 23.3 | 0.9 |
| Southeast Asia | |||||||||
| Laos 2 | 499 | 22.1 (1.9) | 1.56 (0.06) | 50.5 (8.8) | 20.8 (3.6) | 28.7 | 52.5 | 14.6 | 4.2 |
| Philippines 2 | 573 | 18.3 (1.3) | 1.54 (0.09) | 49.3 (10.6) | 20.6 (3.6) | 27.1 | 54.3 | 14.1 | 4.5 |
| Singapore 4 | 348 | 20.5 (1.4) | 1.61 (0.06) | 52.3 (8.7) | 20.2 (3.0) | 30.2 | 54.3 | 13.2 | 2.3 |
| Thailand 3 | 566 | 20.0 (1.2) | 1.61 (0.05) | 53.6 (9.7) | 20.8 (3.6) | 24.6 | 57.6 | 12.4 | 5.5 |
Notes: 1 Low income country; 2 Lower middle income country; 3 Upper middle income country; 4 High income country (Source: World Bank, New Country Classifications, 2013 [47]).
Table 3.
Prevalence characteristics of overweight/obesity participants by gender among university students from 22 low and middle income and emerging economy countries, 2013.
| Variable | All | Men | Women | Statistic |
|---|---|---|---|---|
| N (%) or M (SD) | N (%) or M (SD) | N (%) or M (SD) | p-value | |
| Socio-Demographics | ||||
| All | 3495 (22.0) | 1676 (24.7) | 1812 (20.3) | |
| Age in years | <0.001 | |||
| 16–19 | 1078 (21.8) | 513 (26.3) | 565 (18.9) | |
| 20–21 | 1091 (19.7) | 494 (21.6) | 597 (18.4) | 0.004 |
| 22 or more | 1186 (25.6) | 616 (26.7) | 566 (24.5) | |
| Wealth | ||||
| Wealthy | 184 (25.9) | 116 (31.9) | 68 (19.7) | |
| Quite well off | 1699 (22.2) | 813 (25.8) | 882 (19.7) | <0.001 |
| Not well off | 1309 (22.2) | 576 (23.5) | 730 (21.4) | |
| Poor | 272 (21.2) | 152 (21.8) | 120 (20.7) | |
| Country income classification | ||||
| Low income | 337 (15.0) | 223 (20.4) | 111 (9.7) | |
| Lower middle income | 1225 (22.8) | 590 (23.9) | 635 (22.8) | <0.001 |
| Upper middle income | 1311 (22.0) | 552 (24.5) | 756 (20.6) | |
| High income | 231 (40.0) | 130 (39.4) | 100 (40.7) | |
| Social variables | ||||
| Organized religious activity (Range 1–6) | 3.9 (1.6) | 3.8 (1.7) | 3.9 (1.6) | 0.815 |
| Social support (Range 3–12) | 9.1 (2.0) | 9.0 (2.1) | 9.3 (1.9) | <0.001 |
| Dietary variables | ||||
| Eats red meat at least once a day | 1527 (22.5) | 720 (25.0) | 806 (20.8) | <0.001 |
| Try to eat fiber | 1414 (24.1) | 583 (26.6) | 829 (22.7) | <0.001 |
| Avoids fat and cholesterol | 1496 (25.2) | 631 (28.2) | 863 (23.4) | <0.001 |
| Fruit and vegetables (<5 times/day) | 2500 (21.9) | 1209 (24.7) | 1289 (19.8) | <0.001 |
| Skipping breakfast | 1617 (22.3) | 737 (24.3) | 878 (20.9) | 0.013 |
| Number of meals a day | 2.7 (0.8) | 2.7 (0.8) | 2.6 (0.7) | <0.001 |
| Number of in-between snacks | 1.5 (0.9) | 1.5 (0.9) | 1.6 (0.9) | <0.001 |
| Health risk behaviour | ||||
| Physical activity | ||||
| Low | 1703 (22.7) | 706 (26.8) | 994 (20.7) | |
| Moderate | 700 (21.0) | 326 (24.4) | 370 (18.7) | <0.001 |
| High | 1092 (22.2) | 644 (23.0) | 448 (21.1) | |
| Current tobacco use | 517 (27.1) | 408 (29.8) | 109 (20.5) | <0.001 |
| Binge drinking (past month) | 508 (24.3) | 327 (26.4) | 181 (21.4) | <0.001 |
| Mental health and abuse | ||||
| Depression (severe) | 552 (25.8) | 249 (29.1) | 303 (23.7) | 0.094 |
| PTSD | 746 (23.9) | 333 (26.1) | 413 (22.6) | 0.010 |
| Child sexual abuse | 114 (28.1) | 46 (32.2) | 68 (26.1) | 0.179 |
| Child physical abuse | 216 (28.7) | 104 (28.5) | 112 (29.2) | 0.568 |
3.2. Prevalence of Overweight/Obesity
Overall, 22% were overweight or obese; men (24.7%) were significantly more overweight or obese than women (20.0%) (see Table 3).
3.3. Associations between Sociodemographics, Social, Dietary, Health Risk Behaviour, Mental Health, Childhood Abuse and Prevalence of Overweight/Obesity
Bivariate analysis among men found that younger age (16–19 years), being from a wealthy family background, coming from a higher income country, lack of organized religious activity, dietary behaviour (trying to eat fiber, and avoiding fat and cholesterol), health risk behaviour (physically inactivity, current tobacco use), poor mental health (depression) and childhood abuse (physical and sexual) were associated with overweight or obesity, while in multivariate regression analysis younger age (16–19 years), coming from a higher income country, dietary behaviour (avoiding fat and cholesterol), health risk behaviour (physically inactive, current tobacco use) and childhood physical abuse were associated with overweight and obesity.
Bivariate analysis among women found that older age (22 or more years), coming from a higher income country, frequent organized religious activity, dietary behaviour (trying to eat fibre, avoiding fat and cholesterol), poor mental health (depression symptoms, PTSD symptoms) and childhood abuse (physical and sexual) were associated with overweight and obesity, while in multivariate regression analysis older age (22 or more years), coming from a higher income country, frequent organized religious activity, dietary behaviour (avoiding fat and cholesterol), poor mental health (PTSD symptoms) and physical childhood abuse were associated with overweight or obesity (see Table 4).
Table 4.
Associations between BMI overweight/obesity and social, dietary, health behaviours, mental health and abuse in university students from 22 low and middle income and emerging economy countries, 2013.
| Variable | Men | Women | ||
|---|---|---|---|---|
| UOR (95% CI) | AOR (95% CI) | UOR (95% CI) | AOR (95% CI) | |
| Socio-Demographics | ||||
| Age in years | ||||
| 16–19 | 1.00 | 1.00 | 1.00 | 1.00 |
| 20–21 | 0.77 (0.67–0.89) *** | 0.76 (0.64–0.90) *** | 0.97 (0.87–1.10) | 0.90 (0.77–1.05) |
| 22 or more | 1.02 (0.90–1.17) | 0.94 (0.80–1.11) | 1.39 (1.22–1.59) *** | 1.38 (1.18–1.60) *** |
| Wealth | ||||
| Wealthy | 1.00 | 1.00 | 1.00 | |
| Quite well off | 0.74 (0.59–0.94) * | 0.90 (0.66–1.23) | 1.00 (0.76–1.32) | |
| Not well off | 0.66 (0.52–0.83) *** | 0.83 (0.61–1.14) | 1.11 (0.84–1.47) | --- |
| Poor | 0.59 (0.45–0.79) *** | 0.72 (0.50–1.03) | 1.06 (0.76–1.48) | |
| Country income classification | ||||
| Low income | 1.00 | 1.00 | 1.00 | 1.00 |
| Lower middle income | 1.22 (1.03–1.46) * | 3.53 (2.59–4.81) *** | 2.59 (1.95–3.31) *** | 3.25 (2.39–4.42) *** |
| Upper middle income | 1.27 (1.06–1.51) ** | 3.35 (2.46–4.550 *** | 2.38 (1.93–2.94) *** | 2.99 (2.21–4.05) *** |
| High income | 2.54 (1.95–3.31) *** | 6.44 (4.34–9.55) *** | 6.30 (4.57–8.68) *** | 8.87 (5.92–13.28) *** |
| Social variables | ||||
| Organised religious activity | ||||
| Low | 1.00 | 1.00 | 1.00 | 1.00 |
| Medium | 1.01 (0.87–1.17) | 1.05 (0.87–1.25) | 1.26 (1.09–1.45) *** | 1.19 (0.99–1.43) |
| High | 0.83 (0.72–0.96) * | 0.86 (0.71–1.03) | 1.21 (1.05–1.40) ** | 1.22 (1.03–1.47) * |
| Social support | ||||
| Low | 1.00 | 1.00 | ||
| Medium | 1.06 (0.92–1.24) | --- | 0.94 (0.81–1.10) | --- |
| High | 1.06 (0.92–1.23) | 0.87 (0.75–1.01) | ||
| Dietary variables | ||||
| Eats red meat at least once a day | 1.02 (0.91–1.14) | --- | 1.05 (0.94–1.16) | --- |
| Try to eat fiber | 1.16 (1.03–1.30) * | 1.02 (0.87–1.20) | 1.30 (1.19–1.44) *** | 1.14 (0.96–1.37) |
| Avoids fat and cholesterol | 1.33 (1.18–1.49) *** | 1.29 (1.11–1.51) *** | 1.38 (1.25–1.52) *** | 1.28 (1.12–1.46) *** |
| Fruit and vegetables (<5 times/day) | 0.94 (0.81–1.08) | --- | 0.98 (0.86–1.11) | --- |
| Skipping breakfast | 0.96 (0.86–1.07) | --- | 1.07 (0.97–1.19) | --- |
| Number of meals a day | 1.05 (0.98–1.13) | --- | 0.95 (0.89–1.02) | --- |
| Number of in-between snacks | 1.03 (0.97–1.10) | --- | 1.02 (0.96–1.09) | --- |
| Health risk behaviour | UOR (95% CI) | AOR (95% CI) | UOR (95% CI) | AOR (95% CI) |
| Physical activity | ||||
| Low | 1.00 | 1.00 | 1.00 | |
| Moderate | 0.88 (0.76–1.03) | 0.92 (0.77–1.11) | 0.88 (0.77–1.01) | --- |
| High | 0.82 (0.72–0.93) ** | 0.78 (0.67–0.91) *** | 1.02 (0.90–1.16) | |
| Current tobacco use | 1.33 (1.16–1.51) *** | 1.22 (1.03–1.45) * | 1.02 (0.82–1.27) | --- |
| Binge drinking (past month) | 1.11 (0.97–1.28) | --- | 1.08 (0.90–1.28) | --- |
| Mental health and abuse | ||||
| Depression symptoms (severe) | 1.29 (1.10–1.51) ** | 1.13 (0.93–1.39) | 1.26 (1.10–1.45) *** | 1.15 (0.96–1.37) |
| PTSD symptoms | 1.12 (0.97–1.29) | --- | 1.24 (1.09–1.40) *** | 1.23 (1.06–1.44) ** |
| Child sexual abuse | 1.53 (1.08–2.19) * | 1.29 (0.83–2.01) | 1.39 (1.05–1.85) ** | 1.03 (0.71–1.47) |
| Child physical abuse | 1.30 (1.02–1.64) * | 1.35 (1.01–1.80) * | 1.65 (1.32–2.08) *** | 1.72 (1.22–2.30) *** |
Notes: *** p <0.001; ** p < 0.01; * p < 0.05; UOR = Unadjusted Odds Ratio; AOR = Adjusted Odds Ratio; CI = Confidence Interval.
In sub-Saharan Africa and in Latin America and the Caribbean, female obesity exceeds male obesity both in the general populations and in this study (except for Colombia) reflecting the prevalence of female obesity in these regions. In contrast, in Asia and North Africa (Egypt and Tunisia), male obesity exceeds female obesity. This likely reflects the significant social disadvantage of women in Asia and North Africa. In this study, male obesity exceeded 10% in Bangladesh, India, Philippines, Egypt and Barbados, while female obesity rates exceeded 10% in South Africa, Jamaica and Barbados. Thus, Barbados was the only country in which both male and female obesity rates exceeded 10%, and with 61% tertiary enrollment, these rates reflect a significant national obesity problem, due to high levels of disposable income, diets rich in calorie dense foods and low levels of physical activity. In several countries, tertiary enrollment is low, and likely to be biased towards upper income groups as compared to countries with high rates of enrollment in tertiary education where these results are more likely to reflect the status of this age group in the wider society. Obesity is also associated with the per capita income of the country (Table 5), especially in countries where tertiary enrollment is high, thus a better representation of the general population of all 16 to 30 year olds in the population. The study did find that coming from a higher income country was significantly associated with prevalence of overweight/obesity.
The study found among university students from 22 low and middle income and emerging economy countries a prevalence of overweight or obese that compares with previous studies in transitional societies [5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]. In agreement with a number of studies mainly from Asia [14,15,22], it was found that male gender was associated with overweight/obesity in those populations.
| Country | % Tertiary Educaton/5 year Post Secondary Age Group (2011) | Per Capita Income | Infant Mortality Rate/1000 Live Births (2012) | Life Expectancy at Birth (2012) Males | Life Expectancy at Birth (2012) Females | % Obesity Males | % Obesity Females |
|---|---|---|---|---|---|---|---|
| All | 5.8 | 5.0 | |||||
| Asia | |||||||
| Bangladesh | 13 | 1815 | 33 | 69 | 71 | 16.2 | 5.3 |
| India | 23 | 3870 | 44 | 64 | 68 | 11.8 | 7.7 |
| Kyrgyzstan | 41 | 2360 | 24 | 66 | 73 | 1.7 | 0.6 |
| Laos | 17 | 2879 | 54 | 64 | 67 | 4.6 | 4.2 |
| Pakistan | 8 | 2741 | 69 | 64 | 66 | 0.0 | 0.9 |
| Philippines | 28 (2009) | 4339 | 24 | 65 | 72 | 11.2 | 4.5 |
| Russia | 75 (2009) | 23,589 | 9 | 63 | 75 | 3.7 | 0.7 |
| Singapore | n/a | 60,800 | 2 | 80 | 85 | 5.5 | 2.3 |
| Thailand | 53 | 9660 | 11 | 71 | 79 | 8.7 | 5.5 |
| Turkey | 61 | 18,551 | 12 | 72 | 78 | 4.3 | 1.8 |
| Africa | |||||||
| Egypt | 29 | 6614 | 18 | 69 | 74 | 12.5 | 8.5 |
| Ivory Coast | 8 (2010) | 2006 | 76 | 52 | 54 | 0.8 | 2.1 |
| Madagascar | 4 | 962 | 41 | 62 | 65 | 0.3 | 1.0 |
| Mauritius | 36 | 14,902 | 13 | 70 | 78 | 2.1 | 4.4 |
| Namibia | n/a | 7442 | 28 | 64 | 69 | 5.4 | 6.9 |
| Nigeria | n/a | 2620 | 78 | 53 | 55 | 0.7 | 3.0 |
| South Africa | n/a | 11,021 | 33 | 56 | 62 | 3.8 | 15.6 |
| Tunesia | 35 | 9636 | 14 | 74 | 78 | 5.4 | 5.0 |
| Carribbean and Latin America | |||||||
| Barbados | 61 | 26,488 | 17 | 75 | 81 | 13.0 | 18.7 |
| Jamaica | 28 | 5130 | 14 | 72 | 77 | 5.7 | 10.7 |
| Colombia | 43 | 10,436 | 15 | 76 | 83 | 5.0 | 5.5 |
| Venezuela | 78 (2009) | 13,267 | 13 | 72 | 80 | 5.2 | 4.4 |
Note: World Bank School enrollment, tertiary (% gross); Gross enrollment ratio. Tertiary (ISCED 5 and 6). Total is the total enrollment in tertiary education (ISCED 5 and 6), regardless of age, expressed as a percentage of the total population of the five-year age group following on from secondary school leaving. Source [48,49].
We found that among women, high prevalence of organised religious activity was associated with overweight/obesity. As in this study, recent research reported a higher BMI in women who were affiliated with a Christian religion [50]. Although weight loss programmes were run in some churches [51], weight management has rarely been addressed by religious lectures or in group discussions in developing countries [52]. This presents an opportunity for religious-based prevention programmes for youth at risk for obesity especially in urban areas. In this study an unexpected finding was that making a conscious effort to avoid fat and cholesterol and trying to eat fiber were associated with overweight/obesity. Other studies have also found this association between intake of fiber and obesity [28]. It is possible that in this study where intention (assessed by conscious effort) to avoid fats and cholesterol which was self-reported, this did not correspond to the actual eating pattern. On the other hand, students who were overweight or obese might have already adopted healthier eating behaviour in order to lose weight and be more accepted by their peers. Since the study design was cross-sectional, causality between dietary variables and overweight/obesity could not be established.
The finding in this study that lack of physical activity and tobacco use were associated with overweight/obesity has also been found in a number of previous studies [15,17,23,28] In this study, male students had a higher mean frequency of physical activity than female students, and physical inactivity was related to overweight/obesity among males but not among females. Other researchers did not find a link between physical inactivity and overweight/obesity either for male or female students despite showing that the men are more like likely to engage in physical exercise in their free time [53]. Others studies indicate that the relationship between BMI and physical activity occurs only among men [54]. Despite these differences the link between obesity and a sedentary lifestyle has been established.
We found associations between overweight/obesity and poor mental health (PTSD) among women and childhood physical abuse among both men and women. The relationship between overweight/obesity and mental health and childhood abuse have been confirmed in previous studies [55,56]. “These results demonstrate the long-term impact of childhood physical abuse on weight into adulthood and suggest that physically abused children may be at risk for other adverse health outcomes associated with increased weight. Health professionals need to understand this risk for physically abused children and researchers should identify and evaluate strategies for effective interventions.” [56].
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 centres could have resulted in different results. University students are not representative of young adults in general, especially so in countries where they are a small proportion of the university age population, and the overweight or 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 health behaviour practices and social and health status cannot be established in such studies. Further prospective studies are warranted to understand whether social and health behaviours lead to overweight or obesity or vice versa. Apart from anthropometric measurements, a limitation of the study was that all the other information collected in the study was based on self-reporting. It is possible that certain behaviours were under or over reported. Further, several concepts were only assessed with one item such as child abuse, some dietary items and religious involvement. Future studies should include full scales for each concept to be assessed. Finally, certain concepts identified in other studies to be related to overweight/obesity, such as body fat percentage, dietary history [14], family history of risk factors [24] should be included in future studies.
4. Conclusions
The study found a high prevalence of overweight/obesity among university students. Several gender specific health risk practices were identified that can be utilized in health promotion programmes. Universities need to address their obesogenic environment and the need for the university administration to promote healthy life styles as proposed by the WHO Global Strategy on Diet, Physical Activity and Health. Student themselves should be engaged in this process since they also need to be involved in promoting and living healthy lives.
Acknowledgments
Partial funding for this study was provided by the South African Department of Higher Education. The following colleagues participated in this student health survey and contributed to data collection (locations of universities in parentheses) Bangladesh: Gias Uddin Ahsan (Dhaka); Barbados: T. Alafia Samuels (Bridgetown); Colombia: Carolina Mantilla (Pamplona); Egypt: Alaa Abou-Zeid (Cairo); India: Krishna Mohan (Visakhapatnam); Ivory Coast: Issaka Tiembre (Abidjan); Jamaica: Caryl James (Kingston); Kyrgyzstan: Erkin M. Mirrakhimov (Bishkek); Laos: Vanphanom Sychareun (Vientiane); Madagascar: Onja H Rahamefy (Antananarivo); Mauritius: Hemant Kumar Kassean (Réduit, Moka); Namibia: Pempelani Mufune (Windhook); Nigeria: Solu Olowu (Ile-Ife); Pakistan: Rehana Reman (Karachi); Philippines: Alice Ferrer (Miagao); Russia: Alexander Gasparishvili (Moscow); Singapore: Mee Lian Wong (Singapore); South Africa: Tholene Sodi (Polokwane); Thailand: Tawatchai Apidechkul (Chiang Rai); Tunesia: Hajer Aounallah-Skhiri (Tunis); Turkey: Neslihan Keser Özcan (Istanbul); Venezuela: Yajaira M Bastardo (Caracas)
Author Contributions
Karl Peltzer and Supa Pengpid designed the study, analysed the data and wrote the first draft of the paper. T. Alafia Samuels, Neslihan Keser Özcan, Carolina Mantilla, Onja H. Rahamefy, Mee Lian Wong and Alexander Gasparishvili participated in data collection, writing and approving the final manuscript.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Haidar, Y.M.; Cosman, B.C. Obesity epidemiology. Clin. Colon Rectal Surg. 2011, 24, 205–210. [Google Scholar]
- Popkin, B.M.; Adair, L.S.; Ng, S.W. Global nutrition transition and the pandemic of obesity in developing countries. Nutr. Rev. 2012, 70, 3–21. [Google Scholar] [CrossRef]
- Vella-Zarb, R.A.; Elgar, F.J. The “freshman 5”: A meta-analysis of weight gain in the freshman year of college. J. Amer. Coll. Health 2009, 58, 161–166. [Google Scholar] [CrossRef]
- Gores, S.E. Addressing nutritional issues in the college-aged client: Strategies for the nurse practitioner. J. Amer. Acad. Nurse Pract. 2008, 20, 5–10. [Google Scholar] [CrossRef]
- Nwachukwu, D.C.; Nwagha, U.I.; Obikili, E.N.; Ejezie, F.E.; Okwuosa, C.N.; Nweke, M.L.; Ezeh, C.O. Assessment of body mass index and blood pressure among university students in, Enugu, South East, Nigeria. Niger. J. Med. 2010, 19, 148–152. [Google Scholar]
- Abolfotouh, M.A.; Bassiouni, F.A.; Mounir, G.M.; Fayyad, R.C. Health-related lifestyles and risk behaviours among students living in Alexandria University Hostels. East Mediterr. Health J. 2007, 13, 376–391. [Google Scholar]
- Bakr, E.M.; Ismail, N.A.; Mahaba, H.M. Impact of life style on the nutritional status of medical students at Ain Shams University. J. Egypt. Public Health Assoc. 2002, 77, 29–49. [Google Scholar]
- Cilliers, J.; Senekal, M.; Kunneke, E. The association between the body mass index of first-year female university students and their weight-related perceptions and practices, psychological health, physicalactivity and other physical health indicators. Public Health Nutr. 2006, 9, 234–243. [Google Scholar]
- Bodiba, P.; Madu, S.N.; Ezeokana, J.O.; Nnedum, O.A. The relationship between body mass index and self-concept among adolescent black female university students. Curationis 2008, 31, 77–84. [Google Scholar]
- Sultana, N.; Nahar, S.; Debnath, B.C.; Rahman, M.Z.; Naher, M.Q.; Malik, S.U.F. Prevalence of obesity among the medical students in Sylhet. Jalalabad Med. J. 2011, 8, 12–15. [Google Scholar]
- Sakamaki, R.; Toyama, K.; Amamoto, R.; Liu, C.J.; Shinfuku, N. Nutritional knowledge, food habits and health attitude of Chinese university students—A cross sectional study. Nutr. J. 2005, 4. [Google Scholar] [CrossRef] [Green Version]
- Jingya, B.; Ye, H.; Jing, W.; Xi, H.; Tao, H. Quantitative analysis and comparison of BMI among Han, Tibetan, and Uygur university students in Northwest China. Sci. World J. 2013. [Google Scholar] [CrossRef]
- Gopalakrishnan, S.; Ganeshkumar, P.; Prakash, M.V.; Amalraj, C.V. Prevalence of overweight/obesity among the medical students, Malaysia. Med. J. Malays. 2012, 67, 442–444. [Google Scholar]
- Boo, N.Y.; Chia, G.J.; Wong, L.C.; Chew, R.M.; Chong, W.; Loo, R.C. The prevalence of obesity among clinical students in a Malaysian medical school. Singap. Med. J. 2010, 51, 126–132. [Google Scholar]
- Banwell, C.; Lim, L.; Seubsman, S.A.; Bain, C.; Dixon, J.; Sleigh, A. Body mass index and health-related behaviours in a national cohort of 87,134 Thai Open University students. J. Epidemiol. Community Health 2009, 63, 366–372. [Google Scholar] [CrossRef]
- Chaudhry, M.A.; Ahmad, F.; Ashraf, M.Z. Frequency of overweight and obesity in students of medical College Lahore. Ann. Pak. Inst. Med. Sci. 2012, 8, 137–140. [Google Scholar]
- Hingorjo, M.R.; Syed, S.; Qureshi, M.A. Overweight and obesity in students of a dental college of Karachi: Lifestyle influence and measurement by an appropriate anthropometric index. J. Pak. Med. Assoc. 2009, 59, 528–532. [Google Scholar]
- Seo, D.C.; Torabi, M.R.; Jiang, N.; Fernandez-Rojas, X.; Park, B.H. Correlates of college students’ physical activity: Cross-cultural differences. Asia Pac. J. Public Health 2009, 21, 421–432. [Google Scholar] [CrossRef]
- Balhara, Y.P.; Mathur, S.; Kataria, D.K. Body shape and eating attitudes among female nursing students in India. East Asian Arch. Psychiatry 2012, 22, 70–74. [Google Scholar]
- Bhongir, A.V.; Nemani, S.; Reddy, P.S. Rural-urban epidemiologic transition of risk factors for coronary artery disease in collegestudents of Hyderabad and nearby rural area—A pilot study. J. Assoc. Physician. India 2011, 59, 222–226. [Google Scholar]
- Pengpid, S.; Peltzer, K. Prevalence of overweight/obesity and central obesity and its associated factors among a sample of university students in India. Obes. Res. Clin. Pract. 2014. [Google Scholar] [CrossRef]
- Vargas, M.; Becerra, F.; Prieto, E. Anthropometric evaluation of university students in Bogotá, Colombia. Rev. Salud Pública 2008, 10, 433–442. [Google Scholar] [CrossRef]
- Trujillo-Hernández, B.; Vásquez, C.; Almanza-Silva, J.R.; Jaramillo-Virgen, M.E.; Mellin-Landa, T.E.; Valle-Figueroa, O.B.; Pérez-Ayala, R.; Millán-Guerrero, R.O.; Prieto-Díaz-Chávez, E.; Newton-Sánchez, O. The frequency of risk factors associated with obesity and being overweight in university students from Colima, Mexico. Rev. Salud Publica 2010, 12, 197–207. [Google Scholar]
- Al Qauhiz, N.M. Obesity among Saudi Female University students: Dietary habits and health behaviors. J. Egypt. Public Health Assoc. 2010, 85, 45–59. [Google Scholar]
- Al-Kilani, H.; Waly, M.; Yousef, R. Trends of obesity and overweight among college students in Oman: A cross sectional study. Sultan Qaboos Univ. Med. J. 2012, 12, 69–76. [Google Scholar]
- Al-Isa, A.N. Factors associated with overweight and obesity among Kuwaiti college women. Nutr. Health 1998, 12, 227–233. [Google Scholar] [CrossRef]
- Al-Isa, A.N. Dietary and socio-economic factors associated with obesity among Kuwaiti college men. Br. J. Nutr. 1999, 82, 369–374. [Google Scholar]
- Nojomi, M.; Najamabadi, S. Obesity among university students, Tehran, Iran. Asia Pac. J. Clin. Nutr. 2006, 15, 516–520. [Google Scholar]
- Sanlier, N.; Yabanci, N. Relationship between body mass index, lipids and homocysteine levels in university students. J. Pak. Med. Assoc. 2007, 57, 491–495. [Google Scholar]
- Kutlu, R.; Memetoglu, M.E. Evaluation of cardiovascular risk factors among university students in Turkey: A cross-sectional survey. Russ. Open Med. J. 2013, 2. [Google Scholar] [CrossRef]
- Lee, R.D.; Nieman, D.C. Nutritional Assessment; Brown & Benchmark: Oxford, UK, 1993. [Google Scholar]
- Bastow, M.D. Anthropometrics revisited. Proc. Nutr. Soc. 1982, 41, 381–388. [Google Scholar] [CrossRef]
- WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004, 363, 157–163. [Google Scholar] [CrossRef]
- WHO. The International Classification of Adult Underweight, Overweight and Obesity according to BMI. Available online: http://apps.who.int/bmi/index.jsp?introPage=intro_3.html (accessed on 15 June 2014).
- Wardle, J.; Steptoe, A.; Bellisle, F.; Davou, B.; Reschke, K.; Lappalainen, R.; Fredrikson, M. Healthy dietary practices among European students. Health Psychol. 1997, 16, 443–450. [Google Scholar] [CrossRef]
- Brock, D.; Sarason, I.; Sarason, B.; Pierce, G. Simultaneous assessment of perceived global and relationship-specific support. J. Soc. Pers. Relationsh. 1996, 13, 143–152. [Google Scholar] [CrossRef]
- Simbayi, L.C.; Kalichman, S.; Strebel, A.; Cloete, A.; Henda, N.; Nqeketo, A. Internalized stigma, discrimination, and depression among men and women living with HIV/AIDS in Cape Town, South Africa. Soc. Sci. Med. 2007, 64, 1823–1831. [Google Scholar] [CrossRef]
- Koenig, H.G.; Meador, K.; Parkerson, G. Religion index for psychiatric research: A 5-item measure for use in health outcome studies. Amer. J. Psychiat. 1997, 154, 885–886. [Google Scholar]
- Hall, J.N.; Moore, S.; Harper, S.B.; Lynch, J.W. Global variability in fruit and vegetable consumption. Amer. J. Prev. Med. 2009, 36, 402–409. [Google Scholar] [CrossRef]
- Steptoe, A.; Wardle, J. Cognitive predictors of health behaviour in contrasting regions of Europe. Brit. J. Clin. Psychol. 1992, 31, 485–502. [Google Scholar] [CrossRef]
- Craig, C.L.; Marshall, A.L.; Sjöström, M.; Bauman, A.E.; Booth, M.L.; Ainsworth, B.E.; Pratt, M.; Ekelund, U.; Yngve, A.; Sallis, J.F.; et al. International physical activity questionnaire: 12-Country reliability and validity. Med. Sci. Sport. Exercise 2003, 35, 1381–1395. [Google Scholar] [CrossRef]
- International Physical Activity Questionnaire (IPAQ). IPAQ Scoring Protocol. Available online: https://sites.google.com/site/theipaq/ (accessed on 5 April 2014).
- Guidelines for Controlling and Monitoring the Tobacco Epidemic; World Health Organization (WHO): Geneva, Switzerland, 1998.
- Andresen, E.M.; Malmgren, J.A.; Carter, W.B.; Patrick, D.L. Screening for depression in well older adults: Evaluation of a short form of the CES-D (Center for Epidemiologic Studies Depression Scale). Amer. J. Prev. Med. 1994, 10, 77–84. [Google Scholar]
- Kimerling, R.; Ouimette, P.; Prins, A.; Nisco, P.; Lawler, C.; Cronkite, R.; Moos, R.H. Brief report: Utility of a short screening scale for DSM-IV PTSD in primary care. J. Gen. Intern. Med. 2006, 21, 65–67. [Google Scholar] [CrossRef]
- Fisher, J.C.; Mason, R.L. The analysis of multicollinear data in criminology. In Methods in Quantitative Criminology; Fox, J.A., Ed.; Academic Press: New York, NY, USA, 1981; pp. 99–125. [Google Scholar]
- World Bank. New Country Classifications, 2013. Available online: http://data.worldbank.org/news/new-country-classifications (accessed on 5 April 2014).
- The World Bank. GDP per capita, PPP (current international $). Available online: http://data.worldbank.org/indicator/SE.TER.ENRR (accessed on 10 May 2014).
- World Health Organization. Global Health Observatory Data Repository. Infant Mortality. Life Expectancy. Available online: http://apps.who.int/gho/data/node.main.688?lang=en (accessed on 10 May 2014).
- Kortt, M.A.; Dollery, B. Religion and BMI in Australia. J. Relig. Health 2014, 53, 217–228. [Google Scholar] [CrossRef]
- Yeary, K.H.K.; Cornell, C.E.; Moore, P.; Bursac, Z.; Prewitt, T.E.; West, D.S. Feasibility of an evidence-based weight loss intervention for a faith-based, rural, African American population. Prev. Chronic Dis. 2011, 8, A146. Available online: http://www.cdc.gov/pcd/issues/2011/nov/10_0194.htm (accessed on 17 July 2014). [Google Scholar]
- Ellison, C.G.; Levin, J.S. The religion-health connection: Evidence, theory, and future directions. Health Educ. Behav. 1998, 25, 700–720. [Google Scholar] [CrossRef]
- Arroyo, P.; Loria, A.; Fernandez, V.; Flegal, K.M.; Kuri-Morales, P.; Olaiz, G.; Tapia-Conyer, R. Prevalence of pre-obesity and obesity in urban adult Mexicans in comparison with other large surveys. Obes. Res. 2000, 8, 179–185. [Google Scholar] [CrossRef]
- Gómez, M.; Ruiz, F.; García, M.; Granero, A.; Piéron, M. Reasons mentioned by university student who practice physical and sporting activities. Rev. Latinoamer. Psicol. 2009, 41, 519–532. [Google Scholar]
- Pagoto, S.L.; Schneider, K.L.; Bodenlos, J.S.; Appelhans, B.M.; Whited, M.C.; Ma, Y.; Lemon, S.C. Association of post-traumaticstress disorder and obesity in a nationally representative sample. Obesity 2012, 20, 200–205. [Google Scholar] [CrossRef]
- Bentley, T.; Widom, C.S. A 30-year follow-up of the effects of child abuse and neglect on obesity in adulthood. Obesity 2009, 17, 1900–1905. [Google Scholar] [CrossRef]
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MDPI and ACS Style
Peltzer, K.; Pengpid, S.; Samuels, T.A.; Özcan, N.K.; Mantilla, C.; Rahamefy, O.H.; Wong, M.L.; Gasparishvili, A. Prevalence of Overweight/Obesity and Its Associated Factors among University Students from 22 Countries. Int. J. Environ. Res. Public Health 2014, 11, 7425-7441. https://doi.org/10.3390/ijerph110707425
AMA Style
Peltzer K, Pengpid S, Samuels TA, Özcan NK, Mantilla C, Rahamefy OH, Wong ML, Gasparishvili A. Prevalence of Overweight/Obesity and Its Associated Factors among University Students from 22 Countries. International Journal of Environmental Research and Public Health. 2014; 11(7):7425-7441. https://doi.org/10.3390/ijerph110707425
Chicago/Turabian StylePeltzer, Karl, Supa Pengpid, T. Alafia Samuels, Neslihan Keser Özcan, Carolina Mantilla, Onja H. Rahamefy, Mee Lian Wong, and Alexander Gasparishvili. 2014. "Prevalence of Overweight/Obesity and Its Associated Factors among University Students from 22 Countries" International Journal of Environmental Research and Public Health 11, no. 7: 7425-7441. https://doi.org/10.3390/ijerph110707425
APA StylePeltzer, K., Pengpid, S., Samuels, T. A., Özcan, N. K., Mantilla, C., Rahamefy, O. H., Wong, M. L., & Gasparishvili, A. (2014). Prevalence of Overweight/Obesity and Its Associated Factors among University Students from 22 Countries. International Journal of Environmental Research and Public Health, 11(7), 7425-7441. https://doi.org/10.3390/ijerph110707425
