Citation: Toni Varila, Henrik Romar, Tero Luukkonen, Ulla Lassi. Physical activation and characterization of tannin-based foams enforced with boric acid and zinc chloride[J]. AIMS Materials Science, 2019, 6(2): 301-314. doi: 10.3934/matersci.2019.2.301
| [1] | Truong Quang Dat, Le Nguyen Huong Giang, Nguyen Thi Tuong Loan, Vo Van Toan . The prevalence of malnutrition based on anthropometry among primary schoolchildren in Binh Dinh province, Vietnam in 2016. AIMS Public Health, 2018, 5(3): 203-216. doi: 10.3934/publichealth.2018.3.203 |
| [2] | Karl Peltzer, Supa Pengpid . The Association of Dietary Behaviors and Physical Activity Levels with General and Central Obesity among ASEAN University Students. AIMS Public Health, 2017, 4(3): 301-313. doi: 10.3934/publichealth.2017.3.301 |
| [3] | Sigma Bhattarai, Chet Kant Bhusal . Prevalence and associated factors of malnutrition among school going adolescents of Dang district, Nepal. AIMS Public Health, 2019, 6(3): 291-306. doi: 10.3934/publichealth.2019.3.291 |
| [4] | 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 |
| [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] | 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 |
| [7] | Robert D McGuigan, Jenny M Wilkinson . Obesity and Healthcare Avoidance: A Systematic Review. AIMS Public Health, 2015, 2(1): 56-63. doi: 10.3934/publichealth.2015.1.56 |
| [8] | Helen Mary Haines, Opie Cynthia, David Pierce, Lisa Bourke . Notwithstanding High Prevalence of Overweight and Obesity, Smoking Remains the Most Important Factor in Poor Self-rated Health and Hospital Use in an Australian Regional Community. AIMS Public Health, 2017, 4(4): 402-417. doi: 10.3934/publichealth.2017.4.402 |
| [9] | Henry V. Doctor, Sangwani Nkhana-Salimu . Trends and Determinants of Child Growth Indicators in Malawi and Implications for the Sustainable Development Goals. AIMS Public Health, 2017, 4(6): 590-614. doi: 10.3934/publichealth.2017.6.590 |
| [10] | 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 |
Globally, from 1975 to 2014, the prevalence of underweight decreased from 13.8% to 8.8% in adult men and from 14.6% to 9.7% in adult women, and the prevalence of obesity increased from 3.2% to 10.8% in adult men, and from 6.4% to 14.9% in adult women [1]. In Indonesia in 2013, the national adult (18 years and older) prevalence rate of underweight was 11.1% and the prevalence of overweight or obesity (≥25 kg m2) was 30.4% [2,3]. The prevalence of underweight and overweight among girls (15–18 years) in rural areas was 25% and 10%, respectively, and in urban areas 28% and 10%, respectively, in Indonesia [4]. Similar rates of underweight and overweight or obesity have been reported in countries of the Southeast Asian region. In Bangladesh (35 years and older), 30.4% were underweight and 23.5% had overweight or obesity (≥23 kg/m2) [5]; in Malaysia (18 years and above), 51.2% had overweight or obesity (≥25 kg/m2) [6]; in Thailand, 40.9% had overweight or obesity (≥23 kg/m2) [7]; and in Vietnam in 2005, 20.9% were underweight and 16.3% had overweight or obesity (≥23 kg/m2) [8]. As globally, a decrease in the prevalence of underweight and increase of overweight or obesity over the past 20 years have been reported in Indonesia [9] and other countries in the Southeast Asian region such as Vietnam [8] and Thailand [10].
Undernutrition in adulthood can lead to increased morbidity and mortality and other adverse outcomes [11]. Obesity is a major risk factor for a number of non-communicable diseases, such as cardiovascular disease, hypertension, stroke, diabetes mellitus, and specific forms of cancer leading to increased morbidity and premature mortality [12]. Factors related to adult underweight may include sociodemographic variables, such as being female [8,13,14], early adulthood (15–24 years) [14], older adults [5,14], having lower education [5,13,14,15], poorer economic background [5,14,15], not working [5], being married [15], being a non-Christian [15] and residing in rural areas [8]. For example in India, among "rural young (15–24 years) females from more educated villages had a higher likelihood of underweight relative to those in less educated villages; but for rural mature (>24 years) females the opposite was the case" [15]. Moreover, food insecurity, inadequate food intake, diets low in diversity and with insufficient nutrient density [4] and fear of being fat [16] may be associated with underweight.
Sociodemographic risk factors for having overweight or obesity may include being female [5,8], middle aged [5,14], higher education [5], higher economic status [5,14,15] and urban residence [5,8]. Obesogenic behaviors may include the consumption of energy-dense foods high in fat, salt and sugars [18,19], animal source foods [20], meat [21], fried food [22], inadequate fruit and vegetables consumption [23]. Fish intake [24], low meal frequency [25,26], physical activity [6,18,19] and smoking [27,28] have been found to be negatively related to obesity. Obesity is linked to a number of chronic conditions such as hypertension, type 2 diabetes mellitus, and dyslipidaemia [3,29]. Physical and mental health status risk factors for obesity include poor self-reported health status [28], depression [30,31], lower life satisfaction [32], poor sleep quality [33] and greater happiness [34].
There is a lack of more recent national data on the prevalence of underweight and overweight and obesity and its sociodemographic, behavioral and physical and mental health risk factors in Indonesia. It is important to understand factors driving the dual underweight and obesity burden in Indonesia.
Data were analysed from the "Indonesia Family Life Survey (IFLS-5)", a continuing demographic and health survey that began in 1993 and had since four rounds of data collection, with the fifth wave (IFLS-5) fielded in late 2014 and completed in 2015 [35]. The community surveys collected data on household, individual, and community level using a multistage stratified sampling [35]. The sampling frame of the first survey in 1993 was based on households from 321 enumeration areas (EAs) (20 households were randomly selected from each urban EA, and 30 households were selected from each rural EA) in 13 out of 27 Indonesian provinces that were selected representing 83% of the Indonesian population in 1993, more details in [35]. At household level, several randomly selected household members were asked to provide detailed individual information. Being a member of a selected household (15 years and above) was defined as members that reside in the same dwelling and share food from the same cooking pot [35]. In the IFLS-5, 16,204 households and 29509 (excluding 617 who were pregnant) 18 years and older individuals were interviewed with complete anthropometric measurements. In the IFLS-5 the household response rate was 90.5% [35]. Although the survey is longitudinal, we restricted our analysis to the IFLS-5 cross-sectional survey for persons 18 years and older, being the most recent national survey available assessing anthropometric measures.
Anthropometric measurements. Heights were measured to the nearest millimetre using a Seca plastic height board (model 213) [35]. Weights were taken to the nearest tenth of a kilogram using a Camry model EB1003 scale [35]. Body mass index (BMI) was calculated as weight in kg divided by height in metre squared and classified according to Asian criteria: underweight (<18.5kg/m2), normal weight (18.5 to < 23.0 kg/m2), overweight (23.0 to < 25.0 kg/m2), obesity (≥25 kg/m2), class Ⅰ obesity (25–29.9 kg/m2), and class Ⅱ obesity (≥30 kg/m2) [36].
Socio-demographic factor questions included age, sex, marital status, education, work status, religion, residential status, subjective socioeconomic background, and country region. Subjective economic status was assessed with the question "Please imagine a six-step ladder where on the bottom (the first step), stand the poorest people, and on the highest step (the sixth step), stand the richest people. On which [economic] step are you today?" The answers ranged from (1) poorest to (6) richest [35].
Childhood hunger was assessed with the question "Did you experience hunger in your childhood (from birth to 15 years)?" [35] (Coded as yes).
Fruit consumption was assessed with three questions on, "How many days in the past week, (1) Banana, (2) Mango and (3) Papaya had been eaten?" [32]
Food frequency consumption was assessed with questions on, "How many days in the past week did you eat, 1a) Green leafy vegetables? (1b) carrots? (2a) banana? (2b) papaya? (2c) mango? (3) meat? (4) dairy products? (5) fish? (6) fast food? (7) fried snacks (tempe, tahu, bakwan, etc.)? (8) sweet snacks (wajik, geplak, donuts, wafers, chocolate, etc.)? and 9) soft drinks (Coca cola, sprite, etc.)?" [35] Inadequate fruit consumptions was defined as less than 3 days a week, and inadequate vegetable consumption less than daily. Meal frequency was measured with the question, "Do you normally eat, …3 times a day, 2 times a day, 1 times per day, 5–6 times a week, etc.?" [35] Responses were coded as 1 = 3 times/day and 0 = 2 times/day or less.
Physical activity was assessed with a modified version of the "International Physical Activity Questionnaire (IPAQ) short version, for the last 7 days (IPAQ-S7S)". We used the instructions given in the IPAQ manual [37], and categorized physical activity according to the official IPAQ scoring protocol [38] as low, moderate and high.
Tobacco use was assessed with two questions: (1) "Have you ever chewed tobacco, smoked a pipe, smoked self-enrolled cigarettes, or smoked cigarettes/cigars?" (Yes, No) (2) "Do you still have the habit or have you totally quit?" (Still have, Quit) [35]. Responses were grouped into never, quitters and current tobacco users.
Self-reported health status was measured with the question, "In general, how is our health?" Response options were 1 = Very healthy, 2, Somewhat healthy, 3 = Somewhat unhealthy, and 4 = Unhealthy [35].
Diabetes or high blood sugar, High blood pressure, High cholesterol (total or LDL) were assessed with the question, "Has a doctor/paramedic/nurse/midwife ever told you that you had…?" (Yes, No) [35].
Three consecutive measurements of systolic and diastolic blood pressure (BP) were recorded with an Omron meter, HEM-7203, by regular trained interviewers on household members 15 years and older at home in a seated position [35]. Information was collected on awareness and treatment of hypertension. Average blood pressure was calculated arithmetically for the three measurements of each systolic and diastolic blood pressure. Blood pressure classification was done using JNC 7 algorithm [39]. Hypertension was defined as SBP ≥140 mm Hg and/or DBP ≥90 mm Hg and/or current use of antihypertensive medication. Normotension was defined as BP values < 120/80 mm Hg in individuals who were not taking antihypertensive medication [39].
Sleep disturbance was assessed with five items from the "Patient-Reported Outcomes Measurement Information System (PROMIS)" sleep disturbance measure [40]. A sample item was, "I had difficulty falling a asleep." Response options ranged from 1 = not at all to 5 = very much. (Cronbach's alpha = 0.68 in this study). Sleep disturbance was defined as a score of four or five on the averaged mean items.
The Centres for Epidemiologic Studies Depression Scale (CES-D: 10 items) was used to assess depressive symptoms, and scores 10 or more were classified as having depressive symptoms [41] (Cronbach's alpha = 0.71 in this study).
Happiness was assessed with the question, "Taken all things together how would you say things are these days – would you say you were very happy, happy, unhappy or very unhappy?" [35]
Descriptive statistics were used to describe the variables. Multinomial logistic regression analysis was computed to calculate the adjusted relative risk ratios (ARRR) with 95% confidence interval (CI) to determine the associations between socio-demographic, health risk behavior, physical and mental health risk status variables and underweight and overweight or obesity. Potential multi-collinearity between variables was assessed with variance inflation factors, none of which exceeded critical value of 4. P < 0.05 was considered significant. Missing values were excluded from the analysis. "Cross-section analysis weights were applied to correct both for sample attrition from 1993 to 2014, and then to correct for the fact that the IFLS1 sample design included over-sampling in urban areas and off Java. The cross-section weights are matched to the 2014 Indonesian population, again in the 13 IFLS provinces, in order to make the attrition-adjusted IFLS sample representative of the 2014 Indonesian population in those provinces." [35] Both the 95% confidence intervals and P-values were adjusted considering the survey design of the study. All analyses were done with STATA software version 13.0 (Stata Corporation, College Station, TX, USA).
The total sample included 29509 adults (median age 41.0 years, Inter Quartile Range (IQR) = 22.0, age range of 15–103 years) in Indonesia. The proportion of women was 52.1%, 55.8% had high school or higher education, 77.2% were married or cohabiting, 46.6% described themselves as having medium economic status and most (93.4%) belonged to Islamic religion. The majority of the participants (62.4%) worked for a salary, 52.0% were living in urban areas, and more than half (54.7%) lived in Java.
Overall, 11.2% measured to be underweight (13.5% among men and 9.1% among women), 39.8% normal weight (48.1% among men and 32.0% among women) and 49.0% had overweight or obesity (38.3% among men and 58.9% among women). Overall, 24.6% (CI = 24.1, 25.2) had class Ⅰ obesity (25–29.9 kg/m2) and 8.5% (CI = 8.1, 8.8) had class Ⅱ obesity (30 or more kg/m2). Among different age groups, underweight was the highest among 18–29 year-olds (16.7%) and those 70 years and older (29.8%), while overweight or obesity was the highest in the age group 30 to 59 years (more than 53%). The prevalence of underweight was significantly higher among male young adults (20.0%) than female 18–29 year-olds (13.2%), and in the age groups 30–49 years, while the prevalence of underweight in age groups 50 years and above was still higher among men than women but not significantly. Among the sexes, the prevalence of overweight or obesity was significantly higher in women than men in every age group (see Table 1).
| Sociodemographic variables | Sample N (%) | Underweight (<18.5 kg/m2) % | Normal weight (18.5 to < 23.0 kg/m2) % | Overweight/obesity (≥23 kg/m2) % |
| All | 29509 | 11.2 (10.8, 11.6) | 39.8 (39.1, 40.4) | 49.0 (48.4, 49.7) |
| Age in years | ||||
| 18-29 (all) | 8171 (22.7) | 16.7 (15.8, 17.7) | 49.0 (47.7, 50.2) | 34.3 (33.1, 35.5) |
| 18-29 (male) | 3769 (24.9) | 20.0 (18.6, 21.5) | 53.6 (51.8, 55.4) | 26.4 (24.9, 28.0) |
| 18-29 (female) | 4402 (20.7) | 13.2 (12.1, 14.3) | 43.8 (42.2, 45.5) | 43.0 (41.4, 44.6) |
| 30-39 (all) | 8265 (22.5) | 7.6 (7.0, 8.3) | 38.6 (37.4, 39.8) | 53.8 (52.6, 55.1) |
| 30-39 (male) | 4032 (21.8) | 10.7 (9.7.11.8) | 48.2 (46.4, 49.9) | 41.1 (39.4, 42.9) |
| 30-39 (female) | 4233 (23.1) | 4.9 (4.2, 5.7) | 30.3 (28.7, 31.8) | 64.9 (63.2, 66.5) |
| 40-49 (all) | 5622 (23.8) | 5.6 (5.0, 6.3) | 33.0 (31.7, 34.4) | 61.4 (60.0, 62.8) |
| 40-49 (male) | 2792 (22.2) | 7.5 (6.5, 8.7) | 43.4 (41.4, 45.5) | 49.0 (47.0, 51.1) |
| 40-49 (female) | 2830 (25.3) | 4.1 (3.3, 4.9) | 24.6 (22.9, 26.4) | 71.3 (69.5, 73.1) |
| 50-59 (all) | 3866 (16.8) | 8.6 (7.6, 9.6) | 36.5 (34.8, 38.2) | 54.9 (53.2, 56.6) |
| 50-59 (male) | 1773 (17.1) | 8.8 (7.4, 10.4) | 45.8 (43.3, 48.3) | 45.4 (42.9, 47.9) |
| 50-59 (female) | 2093 (16.4) | 8.4 (7.1, 9.8) | 27.5 (25.5, 29.7) | 64.1 (61.8, 66.3) |
| 60-69 (all) | 2120 (8.8) | 14.7 (13.1, 16.5) | 39.6 (37.3, 41.9) | 45.7 (43.4, 48.1) |
| 60-69 (male) | 1006 (9.0) | 15.9 (13.5, 18.5) | 48.4 (45.0, 51.8) | 35.8 (32.6, 39.1) |
| 60-69 (female) | 1114 (8.6) | 13.6 (11.5, 16.0) | 31.1 (28.2, 34.2) | 55.3 (52.1, 58.5) |
| 70 or more (all) | 1465 (5.5) | 29.8 (27.3, 32.5) | 46.0 (43.2, 48.9) | 24.2 (21.8, 26.7) |
| 70 or more (male) | 655 (5.0) | 32.3 (28.4, 36.4) | 49.4 (45.3, 53.6) | 18.3 (15.4, 21.6) |
| 70 or more (female) | 810 (5.9) | 27.9 (24.5, 31.5) | 43.4 (39.6, 47.2) | 28.7 (25.4, 32.3) |
| Sex | ||||
| Female | 15482 (52.1) | 9.1 (8.6, 9.6) | 32.0 (31.2, 32.9) | 58.9 (58.0, 59.8) |
| Male | 14027 (47.9) | 13.5 (12.9, 14.2) | 48.1 (47.2, 49.1) | 38.3 (37.4, 39.2) |
| Education | ||||
| None | 1540 (6.4) | 23.0 (20.7, 25.5) | 44.2 (41.4, 47.0) | 32.8 (30.2, 35.5) |
| Elementary | 9671 (37.8) | 11.2 (10.5, 11.9) | 40.1 (39.1, 41.2) | 48.7 (47.6, 49.8) |
| High school | 14022 (43.7) | 10.2 (9.7, 10.8) | 40.2 (39.3, 41.1) | 49.6 (48.7, 50.5) |
| Higher education | 4221 (12.1) | 8.5 (7.6, 9.5) | 34.6 (33.0, 36.2) | 56.9 (55.2, 58.6) |
| Marital status | ||||
| Unmarried | 4123 (11.9) | 21.3 (19.9, 22.7) | 52.5 (50.8, 54.3) | 26.1 (24.7, 27.7) |
| Married, cohabiting | 22533 (77.2) | 8.8 (8.4, 9.2) | 38.1 (37.3, 38.8) | 53.1 (52.6, 53.8) |
| Separated, divorced, widowed | 2853 (10.9) | 17.2 (15.7, 18.8) | 37.4 (35.5, 39.4) | 45.4 (43.4, 47.4) |
| Religion | ||||
| Islam | 25378 (93.4) | 10.8 (10.4, 11.2) | 39.6 (38.9, 40.3) | 49.6 (48.9, 50.3) |
| Other | 2848 (6.6) | 6.7 (5.7, 7.8) | 38.9 (36.8, 40.9) | 54.4 (52.3, 56.5) |
| Subjective economic background | ||||
| Poor | 7156 (25.6) | 12.6 (11.8, 13.5) | 44.5 (43.2, 45.9) | 42.8 (41.5, 44.1) |
| Medium | 13269 (46.6) | 10.0 (9.5, 10.6) | 39.6 (38.6, 40.5) | 50.4 (49.5, 51.4) |
| Rich | 7809 (27.9) | 9.4 (8.7, 10.2) | 35.0 (33.8, 36.2) | 55.6 (54.4, 56.9) |
| Work status | ||||
| Work for income | 18399 (62.4) | 10.7 (10.2, 11.2) | 42.4 (41.5, 43.1) | 47.0 (46.1, 47.8) |
| Search for job | 365 (1.2) | 22.6 (18.1, 27.8) | 51.4 (45.6, 56.8) | 26.1 (21.4, 31.3) |
| School attendance | 822 (2.1) | 25.0 (21.8, 28.5) | 50.8 (46.3, 50.6) | 24.2 (21.0, 27.7) |
| House keeping | 8055 (27.5) | 8.1 (7.5, 8.8) | 31.8 (30.7, 32.9) | 60.1 (58.9, 61.3) |
| Retired, sick, disabled, other | 1866 (6.8) | 22.1 (20.3, 24.6) | 42.6 (40.1, 45.1) | 35.0 (32.6, 37.5) |
| Residence | ||||
| Rural | 12177 (48.0) | 12.7 (12.1, 13.4) | 42.9 (41.9, 43.9) | 44.4 (43.4, 45.4) |
| Urban | 17332 (52.0) | 9.8 (9.3, 10.3) | 36.9, 36.1, 37.7) | 53.3 (52.5, 54.2) |
| Region | ||||
| Sumatra | 6714 (22.8) | 11.1 (10.8, 12.4) | 40.8 (39.6, 42.1) | 49.0 (47.8, 50.3) |
| Java | 16137 (54.7) | 11.2 (11.9, 12.9) | 39.3 (38.5, 40.1) | 49.4 (48.6, 50.3) |
| Main island groups1 | 6658 (22.6) | 12.5 (11.7, 13.4) | 41.2 (40.0, 42.5) | 46.3 (43.4, 45.9) |
| Note: 1Major Island groups: Bali, West Nusa Tenggara, South Kalimantan, and South Sulawesi. | ||||
DownLoad: CSVRegarding various health variables, 8.8% of participants reported to have experienced hunger when they were a child, 57.7% consumed fruits less than three days a week, 59.9% did not consume vegetables daily, and 68.2% had meals three times a day. Other dietary behavior included, having 4–7 days a week meat (10.4%), 4–7 days a week dairy products (11.6%), 4–7 days a week fish (29.1), 4–7 days a week fried snacks (29.4), 4–7 days a week sweet snacks (17.1%), 4–7 days a week soft drinks (1.7%), and on one or more days a week fast food consumption (9.4%). Almost half (46.4%) engaged in low physical activity and 33.8% were current tobacco users. Almost one in five (18.9) rated their health as "very healthy", 2.8% had diabetes, 33.5% hypertension, 4.9% high cholesterol (total or low-density lipoprotein), 3.0% sleep disturbance, 21.4% depression symptoms, and 11.8% rated themselves as being "very happy" (see Table 2).
| Health variables | Sample N (%) | Underweight (<18.5 kg/m2) % | Normal weight (18.5 to < 23.0 kg/m2) % | Overweight/obesity (≥23 kg/m2) % |
| Health risk behavior | ||||
| Childhood hunger (≤15 years) | 2412 (8.8) | 10.6 (9.3, 12.1) | 40.9 (38.7, 43.2) | 48.5 (46.2, 50.8) |
| Fruit consumption (<3 days/week) | 16462 (57.7) | 11.4 (10.8, 11.9) | 41.7 (40.8, 42.5) | 47.0 (46.1, 47.9) |
| Vegetable consumption (<daily/week) | 16263 (59.9) | 11.1 (10.6, 11.6) | 39.9 (39.0, 40.7) | 49.0 (48.2, 49.9) |
| Meals a day (3 times vs 2 or 1 time) | 19284 (68.2) | 10.4 (9.9, 10.9) | 40.8 (40.0, 41.8) | 48.9 (48.0, 49.7) |
| Meat (4–7 days/week) | 3115 (10.4) | 8.9 (7.8, 10.1) | 37.0 (35.1, 39.0) | 54.1 (52.1, 56.1) |
| Dairy products (4–7 days/week | 3469 (11.6) | 11.9 (10.7, 13.1) | 41.8 (40.0, 43.7) | 46.3 (44.4, 48.2) |
| Fish (4–7 days/week) | 9289 (29.1) | 9.5 (8.8, 10.2) | 39.4 (38.2, 40.6) | 51.1 (49.9, 52.3) |
| Fast food consumption (1–7 days/week) | 2982 (9.4) | 9.6 (8.4, 10.8) | 34.0 (32.1, 36.0) | 56.4 (54.4, 58.4) |
| Fried snacks (4–7 days/week) | 7236 (29.4) | 9.5 (8.7, 10.2) | 38.2 (37.0, 39.5) | 52.3 (51.0, 53.6) |
| Sweet snacks (4–7 days/week) | 5280 (17.1) | 11.0 (10.0, 12.0) | 39.6 (38.1, 41.1) | 49.4 (47.8, 50.9) |
| Soft drinks (4–7 days/week) | 574 (1.7) | 12.1 (9.4, 15.3) | 44.2 (39.5, 49.0) | 43.7 (39.0, 48.5) |
| Physical activity | ||||
| Low | 13591 (46.4) | 10.6 (10.1, 11.2) | 38.1 (37.1, 39.0) | 51.3 (50.4, 52.3) |
| Medium | 7483 (26.6) | 9.7 (8.9, 10.4) | 36.8 (35.5, 38.0) | 53.6 (52.3, 54.9) |
| High | 7160 (27.0) | 11.2 (10.4, 12.1) | 44.9 (43.6, 46.2) | 43.9 (42.6, 45.2) |
| Tobacco use | ||||
| Never | 18172 (61.1) | 9.1 (8.6, 9.5) | 33.2 (32.5, 34.0) | 57.7 (56.9, 58.5) |
| Quit | 1508 (5.2) | 13.1 (11.2, 15.1) | 38.3 (35.6, 41.1) | 48.7 (45.8, 51.5) |
| Current | 9829 (33.8) | 14.8 (14.0, 15.6) | 51.8 (50.7, 52.9) | 33.4 (33.4, 34.5) |
| Physical and mental health risk status | ||||
| Self-rated health status | 6581 (22.0) | 12.8 (11.9, 13.8) | 38.8 (37.5, 40.1) | 48.4 (47.0, 49.8) |
| Unhealthy | 17524 (59.1) | 10.9 (10.3, 11.4) | 40.2 (39.4, 41.0) | 48.9 (48.1, 49.8) |
| Somewhat healthy | 5404 (18.9) | 10.4 (9.5, 11.4) | 39.4 (38.0, 40.9) | 50.1 (48.6, 51.7) |
| Very healthy | ||||
| Diabetes | 704 (2.8) | 3.7 (2.4, 5.5) | 27.8 (24.3, 31.6) | 68.5 (64.6, 72.2) |
| Hypertension | 8807 (33.5) | 8.2 (7.6, 8.9) | 31.2 (30.1, 32.3) | 60.5 (59.4, 61.7) |
| High Cholesterol (Total or LDL) | 1310 (4.9) | 1.9 (1.3, 2.9) | 17.0 (14.9, 19.3) | 81.1 (78.7, 83.3) |
| Sleep disturbance | 913 (3.0) | 13.3 (11.0, 16.0) | 42.7 (39.1, 46.3) | 44.0 (40.4, 47.7) |
| Depression symptoms | 6420 (21.4) | 11.9 (11.0, 12.8) | 42.1 (40.7, 43.5) | 46.0 (44.6, 47.4) |
| Happiness | ||||
| Unhappy | 2499 (9.9) | 13.8 (12.4, 15.4) | 45.7 (43.5, 47.9) | 40.5 (38.4, 42.7) |
| Somewhat happy | 22065 (78.3) | 10.4 (9.9, 10.8) | 39.2 (38.4, 39.9) | 50.5 (49.7, 51.2) |
| Very happy | 3670 (11.8) | 8.8 (7.8, 9.9) | 37.0 (35.2, 38.8) | 54.2 (52.4, 56.0) |
| Note: LDL= Low-density lipoprotein. | ||||
DownLoad: CSVMen were less likely to have overweight or obesity than women (Relative Risk Ratio: RRR = 0.67, Confidence Interval: CI = 0.61, 0.73). Compared with 18 to 29 year olds, 30 to 39 year-olds were less likely and individuals 70 years and older (RRR = 2.04, CI = 1.62, 2.56) were more likely to be underweight, and compared with 18 to 29 year olds 30 to 59 year-olds were more likely to have overweight or obesity and persons 70 years and older were less likely to have overweight or obesity (RRR = 0.53, CI = 0.43, 0.65). Individuals with high school or higher education were less likely underweight (RRR = 0.89, CI = 0.80, 0.99) and were more likely to have overweight or obesity (RRR = 1.28, CI = 1.19, 1.37). Persons with a richer subjective economic status (RRR = 1.35, CI = 1.23, 1.47) were more often having overweight or obesity than with persons from a poor subjective economic status. The odds of being underweight were higher among participants searching for a job (RRR = 1.63, CI = 1.20, 2.22), those attending school (RRR = 1.65, CI = 1.32, 2.05) and the retired, sick and disabled (RRR = 1.23, CI = 1.01, 1.49), and the odds of having overweight or obesity were lower in those searching for a job (RRR = 0.72, CI = 0.53, 0.96), those attending school (RRR = 0.53, CI = 0.43, 0.65) and higher among those who were house keeping (RRR = 1.15, CI = 1.06, 1.24) than those working for an income. Urban residence was negatively (RRR = 0.90, CI = 0.81, 0.99) associated with underweight and positively (RRR = 1.25, CI = 1.18, 1.34) associated with having overweight or obesity. Compared to living in Sumatra, living in the major island groups was positively associated with underweight (RRR = 1.18, CI = 1.04, 1.35) and negatively associated with having overweight or obesity (RRR = 0.83, CI = 0.76, 0.90).
Regarding various health risk behaviors, none of them were associated with underweight. Frequent meat consumption (RRR = 1.19, CI = 1.07, 1.31), frequent fried snacks consumption (RRR = 1.09, CI = 1.01, 1.17) and fast food consumption (RRR = 1.29, 1.16, 1.43) were positively, and having three meals a day (RRR = 0.82, 0.77, 0.88), frequent consumption of dairy products (RRR = 0.82, CI = 0.75, 0.90), frequent sweet snacks consumption (RRR = 0.90, CI = 0.83, 0.98) and infrequent fruit consumption (OR = 0.87, CI = 0.82, 0.93) were negatively associated with having overweight or obesity. In addition, high physical activity (RRR = 0.86, CI = 0.80, 0.93) and current tobacco use (RRR = 0.54, CI = 0.49, 0.59) were negatively associated with having overweight or obesity.
In relation to physical and mental health status, having diabetes, hypertension and high cholesterol were negatively associated with underweight and positively associated with having overweight or obesity. Very healthy perceived health status (RRR = 1.12, CI = 1.04, 1.22) and perceived very happiness (RRR = 1.19, CI = 1.09, 1.31) were both positively and depressive symptoms (RRR = 0.90, CI = 0.83, 0.97) negatively associated with having overweight or obesity (see Table 3).
| Underweight ARRR | Overweight/obesity ARRR | |
| Sociodemographics | ||
| Age in years | ||
| 18–29 | 1 (Reference) | 1 (Reference) |
| 30–39 | 0.62 (0.54, 0.70)*** | 1.92 (1.77, 2.09)*** |
| 40–49 | 0.55 (0.47, 0.65)*** | 2.27 (2.07, 2.49)*** |
| 50–59 | 0.79 (0.67, 0.94)** | 1.65 (1.47, 1.84)*** |
| 60–69 | 1.27 (1.05, 1.55)* | 1.09 (0.95, 1.26) |
| 70 or more | 2.04 (1.62, 2.56)*** | 0.53 (0.43, 0.65)*** |
| Sex | ||
| Female | 1 (Reference) | 1 (Reference) |
| Male | 0.97 (0.84, 1.13) | 0.67 (0.61, 0.73)*** |
| Education | ||
| None/Elementary | 1 (Reference) | 1 (Reference) |
| High school/Higher education | 0.89 (0.80, 0.99)* | 1.28 (1.19, 1.37)*** |
| Subjective economic background | ||
| Poor | 1 (Reference) | 1 (Reference) |
| Medium | 0.92 (0.82, 1.03) | 1.24 (1.15, 1.34)*** |
| Rich | 0.98 (0.86, 1.12) | 1.35 (1.23, 1.47)*** |
| Work status | ||
| Work for income | 1 (Reference) | 1 (Reference) |
| Search for job | 1.63 (1.20, 2.22)** | 0.72 (0.53, 0.96)* |
| School attendance | 1.65 (1.32, 2.05)*** | 0.53 (0.43, 0.65)*** |
| House keeping | 0.98 (0.86, 1.13) | 1.15 (1.06, 1.24)*** |
| Retired, sick, disabled, other | 1.23 (1.01, 1.49)* | 0.97 (0.83, 1.14) |
| Urban residence (base=rural) | 0.90 (0.81, 0.99)* | 1.25 (1.18, 1.34)*** |
| Region | ||
| Sumatra | 1 (Reference) | 1 (Reference) |
| Java | 1.09 (0.97, 1.23) | 0.91 (0.84, 0.98)* |
| Main island groups | 1.18 (1.04, 1.35)* | 0.83 (0.76, 0.90)*** |
| Health risk behavior | ||
| Childhood hunger (≤15 years) (base=no) | 0.92 (0.77, 1.10) | 1.07 (0.96, 1.20) |
| Fruit consumption (<3 days/week) (base ≥3 days/week) | 1.04 (0.94, 1.15) | 0.87 (0.82, 0.93)*** |
| Vegetable consumption (<daily/week) (base=daily) | 1.08 (0.98, 1.19) | 1.02 (0.96, 1.09) |
| Meals a day (3 times) (base < 3 times) | 0.92 (0.84, 1.02) | 0.82 (0.77, 0.88)*** |
| Meat (4–7 days/week) (base= < 4 days/week) | 0.88 (0.74, 1.03) | 1.19 (1.07, 1.31)*** |
| Dairy products (4–7 days/week) (base= < 4 days/week) | 1.08 (0.95, 1.24) | 0.82 (0.75, 0.90)*** |
| Fish (4–7 days/week) (base= < 4 days/week) | 0.91 (0.81, 1.02) | 1.06 (0.98, 1.13) |
| Fast food consumption (1–7 days/week) (base= < 1 day) | 1.06 (0.90, 1.25) | 1.29 (1.16, 1.43)*** |
| Fried snacks (4–7 days/week) (base= < 4 days/week) | 0.94 (0.84, 1.06) | 1.09 (1.01, 1.17)* |
| Sweet snacks (4–7 days/week) (base= < 4 days/week) | 1.07 (0.94, 1.21) | 0.90 (0.83, 0.98)* |
| Soft drinks (4–7 days/week) (base= < 4 days/week) | 0.96 (0.71, 1.31) | 1.06 (0.84, 1.34) |
| Physical activity | ||
| Low | 1 (Reference) | 1 (Reference) |
| Medium | 0.99 (0.89, 1.12) | 1.01 (0.94, 1.09) |
| High | 0.94 (0.84, 1.06) | 0.86 (0.80, 0.93)*** |
| Tobacco use (current) | 1.12 (0.98, 1.28) | 0.54 (0.49, 0.59)*** |
| Physical and mental health risk status | ||
| Self-rated health (very healthy) (base=not very healthy) | 0.97 (0.86, 1.10) | 1.12 (1.04, 1.22)** |
| Diabetes | 0.53 (0.32, 0.87)* | 1.29 (1.04, 1.60)* |
| Hypertension | 0.67 (0.59, 0.76)*** | 2.13 (1.98, 2.30)*** |
| High Cholesterol (Total or LDL) | 0.49 (0.31, 0.79)** | 2.67 (2.24, 3.19)*** |
| Sleep disturbance | 1.15 (0.90, 1.48) | 0.86 (0.72, 1.02) |
| Depression symptoms | 1.04 (0.93, 1.16) | 0.90 (0.83, 0.97)** |
| Happiness (very happy) (base=not very happy) | 0.90 (0.77, 1.04) | 1.19 (1.09, 1.31)*** |
| Note: ARRR = Adjusted Relative Risk Ratio; LDL = Low-density lipoprotein; *** P < 0.001; ** P < 0.01; * P < 0.05. | ||
DownLoad: CSVThe results of this national study demonstrate the co-existence of dual burden of underweight (11.2%) and overweight/obesity (≥23 kg/m2) (49.0%) and (≥25 kg/m2) 31.0% among 18 years and older individuals in 2014–2015 in Indonesia. These figures are similar to the 2013 national adult (18 years and older) prevalence of underweight of 11.1% and the prevalence of overweight or obesity (≥25 kg/m2) 30.4% in Indonesia [2,3]. Largely similar rates of underweight and overweight or obesity have been reported in countries of the Southeast Asian region such as Bangladesh [5], Malaysia [6], Thailand [7] and Vietnam [8]. Further, the study showed a further decline in the prevalence of underweight from 14.4% in 2007 [14] to 11.2% in 2014–2015, and a further increase of overweight/obesity (≥25 kg/m2) from 17.9% in 2007 [14] to 31.0% (≥25 kg/m2) in 2014–2015 in Indonesia. This trend is consistent with a global decrease in the prevalence of underweight and increase of overweight or obesity over the past 20 years, which has also been observed in countries of the Southeast Asian region such as Vietnam [8] and Thailand [10].
The study found that the prevalence of underweight was the highest among 18 to 29 year-olds (16.8% overall, 20.0% in males and 13.2% in females) and those 70 years and older (29.8%). Previous studies also confirm the higher prevalence of underweight among young adults [14] and older adults [5,14]. In this study the prevalence of underweight was in bivariate analysis significantly higher among men than women, while previous studies found an opposite result [8,13,14]. It is not clear why the prevalence of underweight is higher in men than women, partially also explained by a lack of data reporting undernutrition in adult men [5]. Reasons for the high prevalence of underweight during early adulthood may be related to food insecurity [4] and fear of being fat [16]. Some studies report an increase of an underweight body ideal and in eating disorders in Southeast Asia, including Indonesia [42]. In a small study among university students in Indonesia 14.5% of male and 5.1% of female students reported disordered eating attitudes [43].
In agreement with previous studies [5,8,13,14,15], this study found an association between lower education, not working and living in rural areas with underweight. Unlike some previous studies [4,5,14,15], this study did not find an association between poorer economic background, the experience of childhood hunger and underweight. The association between participants attending school, retired, sick or disabled and underweight may be related to the higher prevalence of underweight among adolescents and older adults. Compared to persons living in Sumatra and Java, individuals living in major island groups (Bali, South Kalimantan, South Sulawesi and West Nusa Tenggara) had a higher prevalence of underweight. Hanandita and Tampubolon [14] found that undernutrition is spatially clustered within islands of Indonesia.
Regarding overweight or obesity, in consistence with previous studies [5,8,14,15], this study found that being female, being middle aged, having higher education, higher economic status and urban residence were associated with having overweight or obesity. Compared to persons living in Sumatra, individuals living in Java major island groups had a lower prevalence of overweight or obesity. Hanandita and Tampubolon [14] found that over-nutrition is spatially clustered within islands of Indonesia. Further, specific dietary behavior (infrequent meals, frequent meat, fried snacks and fast food consumption) was, as previously found [18,19,20,21,22,25,26], associated with having overweight or obesity. 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 [44]. Contrary to some studies [19,23], this study found that inadequate fruit consumption and frequent sweet snacks consumption were negatively associated with having overweight or obesity. These findings largely confirm a shift in dietary behavior towards increased consumption of calorie-dense foods containing refined carbohydrates, fats, red meats, and low fiber in Indonesia [45].
In line with previous studies [6,18,19,27,28], this study also found a negative association between physical activity, smoking and having overweight or obesity. In this study, however, the association between physical inactivity and overweight or obesity was only observed in men and not in women (in sex-stratified analysis, not shown). Similar results were found in the Malaysian national study [6]. A possible explanation for this result could be that a greater proportion of men than women engage in high physical activity producing an effect large enough to reduce obesity [6].
The study findings further suggest, as previously found [3,6,29], that cardiometabolic comorbities, including diabetes, hypertension and dyslipidaemia, were associated with having overweight or obesity. Contrary to a previous study on self-rated health status [28], and in agreement with a previous study on happiness [34], this study found an association between very healthy self-rated health status, very happy happiness status and having overweight or obesity. Moreover, unlike previous studies on depression [30,31], this study found a negative association between depression and having overweight or obesity. Some previous study [33] found an association between poor sleep quality and having obesity, while this study did not find such an association. Perhaps, overweight or obesity is further shaped by the ideal body image of fatness as a symbol of nurturance and affluence [14,34]. Sohn [34] proposes that the positive relationship between having obesity and happiness may be "explained by improved socioeconomic status and health enjoyed by the obese". In case Indonesians perceive overweight or obesity status as a signal of better health and greater happiness, it will be more difficult to target obesity in intervention programmes.
Study limitations
The study was a cross-sectional study and the temporal relationships between sociodemographic factors, health status and health risk behaviors and underweight and overweight or obesity cannot be established in such studies; further longitudinal studies are needed. Apart from anthropometric and blood pressure measurements, a limitation of the study was that all the other information, including physical activity, was collected based on self-reporting.
The paper found in a cross-sectional study a high prevalence of both underweight and overweight or obesity among 18 years and older individuals in 2014–2015 in Indonesia, with underweight decreasing and overweight or obesity increasing in the past 20 years, as assessed in previous other studies. Sociodemographic, health risk behavior and health status risk factors were identified which can guide much needed public health interventions to address both these conditions.
The research was conducted based on the IFLS-5 conducted by RAND (http://www.rand.org/labor/FLS/IFLS.html). We thank RAND for providing the access to the survey data and the study participants who provided the survey data.
The authors declare no conflict of interest.
| [1] |
Tondi G, Zhao W, Pizzi A, et al. (2009) Tannin-based rigid foams: A survey of chemical and physical properties. Bioresource Technol 100: 5162–5169. doi: 10.1016/j.biortech.2009.05.055
|
| [2] |
Tondi G, Pizzi A (2009) Tannin-based rigid foams: Characterization and modification. Ind Crop Prod 29: 356–363. doi: 10.1016/j.indcrop.2008.07.003
|
| [3] | Link M, Kolbitsch C, Tondi G, et al. (2011) Formaldehyde-free tannin based foams and their use as lightweight panels. BioResources 6: 4218–4228. |
| [4] |
Szczurek A, Fierro V, Pizzi A, et al. (2014) A new method for preparing tannin-based foams. Ind Crop Prod 54: 40–53. doi: 10.1016/j.indcrop.2014.01.012
|
| [5] | Li X, Pizzi A, Lacoste C, et al. (2013) Physical properties of tannin/furanic resin foamed with different blowing agents. BioResources 8: 743–752. |
| [6] |
Lacoste C, Basso MC, Pizzi A, et al. (2013) Bioresourced pine tannin/furanic foams with glyoxal and glutaraldehyde. Ind Crop Prod 45: 401–405. doi: 10.1016/j.indcrop.2012.12.032
|
| [7] |
Lacoste C, Basso MC, Pizzi A, et al. (2015) Natural albumin/tannin cellular foams. Ind Crop Prod 73: 41–48. doi: 10.1016/j.indcrop.2015.03.087
|
| [8] |
Antal MJ, Gronli M (2003) The art, science, and technology of charcoal production. Ind Eng Chem Res 42: 1619–1640. doi: 10.1021/ie0207919
|
| [9] | Tadda MA, Ahsan A, Shitu A, et al. (2016) A review on activated carbon: process, application and prospects. J Adv Civil Eng Pract Res 2: 7–13. |
| [10] |
Yorgun S, Vural N, Demiral H (2009) Preparation of high-surface area activated carbons from Paulownia wood by ZnCl2 activation. Micropor Mesopor Mat 122: 189–194. doi: 10.1016/j.micromeso.2009.02.032
|
| [11] |
Lillo-Rodenas MA, Cazorla-Amoros D, Lineras-Solano A (2003) Understanding chemical reactions between carbons and NaOH and KOH: An insight into the chemical activation mechanism. Carbon 41: 267–275. doi: 10.1016/S0008-6223(02)00279-8
|
| [12] | Mohammad-Khah A, Ansari R (2009) Activated charcoal: preparation, characterization and applications: a review article. Int J Chem Tech Res 1: 859–864. |
| [13] | Varila T, Bergna D, Lahti R, et al. (2017) Activated carbon production from peat using ZnCl2: Characterization and applications. BioResources 12: 8078–8092. |
| [14] |
Tondi G, Pizzi A, Delmotte L, et al. (2010) Chemical activation of tannin–furanic carbon foams. Ind Crop Prod 31: 327–334. doi: 10.1016/j.indcrop.2009.11.013
|
| [15] |
Meikleham NE, Pizzi A (1994) Acid- and alkali-catalyzed tannin-based rigid foams. J Appl Polym Sci 53: 1547–1556. doi: 10.1002/app.1994.070531117
|
| [16] |
Tondi G, Oo CW, Pizzi A, et al. (2009) Metal adsorption of tannin based rigid foams. Ind Crop Prod 29: 336–340. doi: 10.1016/j.indcrop.2008.06.006
|
| [17] |
Das S, Heasman P, Ben T, et al. (2017) Porous organic materials: Strategic design and structure–function correlation. Chem Rev 117: 1515–1563. doi: 10.1021/acs.chemrev.6b00439
|
| [18] |
Salminen JP, Karonen M (2011) Chemical ecology of tannins and other phenolics: we need a change in approach. Funct Ecol 25: 325–338. doi: 10.1111/j.1365-2435.2010.01826.x
|
| [19] | Natural chemistry research group from university of Turku (2014–2019): Tannin definition and structures. Available from: http://naturalchemistry.utu.fi. |
| [20] |
Pietta PG (2000) Flavonoids as Antioxidants. J Nat Prod 63: 1035–1042. doi: 10.1021/np9904509
|
| [21] |
Arnold TM, Targett NM (2002) Marine tannins: the importance of mechanistic framework for predicting ecological roles. J Chem Ecol 28: 1919–1934. doi: 10.1023/A:1020737609151
|
| [22] |
Kilkowski WJ, Gross GG (1999) Color reaction of hydrolyzable tannins with Bradford reagent, Coomassie brilliant blue. Phytochemistry 51: 363–366. doi: 10.1016/S0031-9422(98)00747-X
|
| [23] | Gross GG (1999) Biosynthesis, biodegradation, and cellular localization of hydrolyzable tannins, In: Lewis NG, Romeo JT, Towers GHN, Phytochemicals in Human Health Protection, Nutrition and Plant Defenses, New York: Plenum Press, 185–213. |
| [24] |
Barbehenn RV, Jones CP, Hagerman AE, et al. (2006) Ellagitannins have greater oxidative activities than condensed tannins and galloyl glucoses at high pH: potential impact on caterpillars. J Chem Ecol 32: 2253–2267. doi: 10.1007/s10886-006-9143-7
|
| [25] | Basso MC, Lagel MC, Pizzi A, et al. (2015) First tools for tannin-furanic foams design. BioResources 10: 5233–5241. |
| [26] |
Brunauer S, Emmett PH, Teller E (1938) Adsorption of gases in multimolecular layers. J Am Chem Soc 60: 309–319. doi: 10.1021/ja01269a023
|
| [27] |
Seaton NA, Walton JPRB, Quirke N (1989) A new analysis method for the determination of the pore size distribution of porous carbons from nitrogen adsorption measurements. Carbon 27: 853–861. doi: 10.1016/0008-6223(89)90035-3
|
| [28] |
Lastoskie C, Gubbins KE, Quirke N (1993) Pore size heterogeneity and the carbon slit pore: a density functional theory model. Langmuir 9: 2693–2702. doi: 10.1021/la00034a032
|
| [29] |
Hackley VA, Stefanial AB (2013) "Real-world" precision, bias, and between-laboratory variation for surface area measurement of a titanium dioxide nanomaterial in powder form. J Nanopart Res 15: 1742. doi: 10.1007/s11051-013-1742-y
|
| [30] |
Letellier M, Delgado-Sanchez C, Khelifa M, et al. (2017) Mechanical properties of model vitreous carbon foams. Carbon 116: 562–571. doi: 10.1016/j.carbon.2017.02.020
|
| [31] |
Santiago-Medina FJ, Delgado-Sánchez C, Basso MC, et al. (2018) Mechanically blown wall-projected tannin-based foams. Ind Crop Prod 113: 316–323. doi: 10.1016/j.indcrop.2018.01.049
|
| [32] |
Demiral H, Demiral I, Tümsek F, et al. (2008) Pore structure of activated carbon prepared from hazelnut bagasse by chemical activation. Surf Interface Anal 40: 616–619. doi: 10.1002/sia.2631
|
| [33] |
Bouchemal N, Belhachemi M, Merzougui Z, et al. (2009) The effect of temperature and impregnation ratio on the active carbon porosity. Desalin Water Treat 10: 115–120. doi: 10.5004/dwt.2009.828
|
| [34] |
Celzard A, Zhao W, Pizzi A, et al. (2010) Mechanical properties of tannin-based rigid foams undergoing compression. Mat Sci Eng A-Struct 527: 4438–4446. doi: 10.1016/j.msea.2010.03.091
|
| 1. | Jobert Richie Nansseu, Jean Jacques Noubiap, Jean Joel Bigna, Epidemiology of Overweight and Obesity in Adults Living in Cameroon: A Systematic Review and Meta‐Analysis, 2019, 27, 1930-7381, 1682, 10.1002/oby.22566 | |
| 2. | Gustavo Mora-García, Antonio Trujillo, Vanessa García-Larsen, Diet quality, general health and anthropometric outcomes in a Latin American population: evidence from the Colombian National Nutritional Survey (ENSIN) 2010, 2020, 1368-9800, 1, 10.1017/S1368980019005093 | |
| 3. | Yohanes Andy Rias, Adi Lukas Kurniawan, Ching Wen Chang, Christopher James Gordon, Hsiu Ting Tsai, Synergistic Effects of Regular Walking and Alkaline Electrolyzed Water on Decreasing Inflammation and Oxidative Stress, and Increasing Quality of Life in Individuals with Type 2 Diabetes: A Community Based Randomized Controlled Trial, 2020, 9, 2076-3921, 946, 10.3390/antiox9100946 | |
| 4. | Ho EVELYN, Ainor Farahin AZIZ, Sarina SARIMAN, Associations of Knowledge, Attitude and Practices of Food Label on Cardiovascular Diseases (CVD) Risk amongst University Students in Selangor, Malaysia, 2020, 66, 0301-4800, S275, 10.3177/jnsv.66.S275 | |
| 5. | Yohanes Andy Rias, Maria Dyah Kurniasari, Victoria Traynor, Shu Fen Niu, Bayu Satria Wiratama, Ching Wen Chang, Hsiu Ting Tsai, Synergistic Effect of Low Neutrophil–Lymphocyte Ratio With Physical Activity on Quality of Life in Type 2 Diabetes Mellitus: A Community-Based Study, 2020, 22, 1099-8004, 378, 10.1177/1099800420924126 | |
| 6. | Supa Pengpid, Manithong Vonglokham, Sengchanh Kounnavong, Vanphanom Sychareun, Karl Peltzer, The prevalence of underweight and overweight/obesity and its correlates among adults in Laos: a cross-sectional national population-based survey, 2013, 2020, 25, 1590-1262, 265, 10.1007/s40519-018-0571-5 | |
| 7. | Thin Zar Thike, Yu Mon Saw, Htin Lin, Khin Chit, Aung Ba Tun, Hein Htet, Su Myat Cho, Aye Thazin Khine, Thu Nandar Saw, Tetsuyoshi Kariya, Eiko Yamamoto, Nobuyuki Hamajima, Association between body mass index and ready-to-eat food consumption among sedentary staff in Nay Pyi Taw union territory, Myanmar, 2020, 20, 1471-2458, 10.1186/s12889-020-8308-6 | |
| 8. | Masako Yamada, Elsi Dwi Hapsari, Hiroya Matsuo, Behaviors toward Noncommunicable Diseases Prevention and Their Relationship with Physical Health Status among Community-dwelling, Middle-aged and Older Women in Indonesia, 2020, 17, 1660-4601, 2332, 10.3390/ijerph17072332 | |
| 9. | Yohanes Andy Rias, Christopher James Gordon, Shu Fen Niu, Bayu Satria Wiratama, Ching Wen Chang, Hsiu Ting Tsai, Secondhand Smoke Correlates with Elevated Neutrophil-Lymphocyte Ratio and Has a Synergistic Effect with Physical Inactivity on Increasing Susceptibility to Type 2 Diabetes Mellitus: A Community-Based Case Control Study, 2020, 17, 1660-4601, 5696, 10.3390/ijerph17165696 | |
| 10. | Biruk Shalmeno Tusa, Adisu Birhanu Weldesenbet, Sewnet Adem Kebede, Srinivas Goli, Spatial distribution and associated factors of underweight in Ethiopia: An analysis of Ethiopian demographic and health survey, 2016, 2020, 15, 1932-6203, e0242744, 10.1371/journal.pone.0242744 | |
| 11. | Supa Pengpid, Karl Peltzer, Overweight and Obesity among Adults in Iraq: Prevalence and Correlates from a National Survey in 2015, 2021, 18, 1660-4601, 4198, 10.3390/ijerph18084198 | |
| 12. | Esthika Dewiasty, Rina Agustina, Siti Rizny F. Saldi, Arvin Pramudita, Fenna Hinssen, Meutia Kumaheri, Lisette C. P. G. M. de Groot, Siti Setiati, Malnutrition Prevalence and Nutrient Intakes of Indonesian Community-Dwelling Older Adults: A Systematic Review of Observational Studies, 2022, 9, 2296-861X, 10.3389/fnut.2022.780003 | |
| 13. | Yanjun Ren, Jiajia Zhao, Thomas Glauben, Bente Castro Campos, Supermarket environment and nutrition outcomes: Evidence from rural China, 2022, 92, 07430167, 79, 10.1016/j.jrurstud.2022.03.019 | |
| 14. | Promit Ananyo Chakraborty, Animesh Talukder, Shams Shabab Haider, Rajat Das Gupta, Enamul Kabir, Prevalence and factors associated with underweight, overweight and obesity among 15-49-year-old men and women in Timor-Leste, 2022, 17, 1932-6203, e0262999, 10.1371/journal.pone.0262999 | |
| 15. | Supa Pengpid, Karl Peltzer, Underweight and overweight/obesity among adults in Afghanistan: prevalence and correlates from a national survey in 2018, 2021, 40, 2072-1315, 10.1186/s41043-021-00251-0 | |
| 16. | Mahalul Azam, Luluk Fadhoh Sakinah, Martha Irene Kartasurya, Arulita Ika Fibriana, Tania Tedjo Minuljo, Syed Mohamed Aljunid, Prevalence and determinants of obesity among individuals with diabetes in Indonesia, 2023, 11, 2046-1402, 1063, 10.12688/f1000research.125549.2 | |
| 17. | Iriyani Harun, Dodik Briawan, Hadi Riyadi, Ali Khomsan, Effects of Walking Exercise Program Based on Duration on the Body Composition and Lipid Profile in Overweight and Obesity Female College Students, 2022, 10, 23220007, 1130, 10.12944/CRNFSJ.10.3.26 | |
| 18. | Yi-Ching Lynn Ho, Vivian Shu Yi Lee, Moon-Ho Ringo Ho, Gladis Jing Lin, Julian Thumboo, Towards a Parsimonious Pathway Model of Modifiable and Mediating Risk Factors Leading to Diabetes Risk, 2021, 18, 1660-4601, 10907, 10.3390/ijerph182010907 | |
| 19. | Rupa Singh, Carolyn K. Nyamasege, Steven R. Hawks, Yukiko Wagatsuma, Health-Related Lifestyles in Relation to Body Mass Index Among Young and Middle-Aged Women in Japan, 2022, 3, 2688-4844, 887, 10.1089/whr.2022.0049 | |
| 20. | Mahalul Azam, Luluk Fadhoh Sakinah, Martha Irene Kartasurya, Arulita Ika Fibriana, Tania Tedjo Minuljo, Syed Mohamed Aljunid, Prevalence and determinants of obesity among individuals with diabetes in Indonesia, 2022, 11, 2046-1402, 1063, 10.12688/f1000research.125549.1 | |
| 21. | Rury Tiara Oktariza, Viskasari Pintoko Kalanjati, Ni Wajan Tirthaningsih, Body Mass Index, Waist-Hip Ratio and Fasting Blood Glucose Levels amongst the University Students, 2021, 57, 2599-056X, 53, 10.20473/fmi.v57i1.14661 | |
| 22. | Oyedolapo A Anyanwu, Sara C Folta, Fang Fang Zhang, Kenneth Chui, Virginia R Chomitz, Martha I Kartasurya, Elena N Naumova, A Cross-Sectional Assessment of Dietary Patterns and Their Relationship to Hypertension and Obesity in Indonesia, 2022, 6, 24752991, 6006010, 10.1093/cdn/nzac091 | |
| 23. | Geetu Singh, Renu Agrawal, Neelika Tripathi, Arti Verma, Overweight and obesity, the clock ticking in India? A secondary analysis of trends of prevalence, patterns, and predictors from 2005 to 2020 using the National Family Health Survey, 2023, 8, 2468-8827, 31, 10.4103/jncd.jncd_58_22 | |
| 24. | Salhah Alsulami, Mukhtiar Baig, Tauseef Ahmad, Nouf Althagafi, Eman Hazzazi, Razan Alsayed, Majd Alghamdi, Thikra Almohammadi, Obesity prevalence, physical activity, and dietary practices among adults in Saudi Arabia, 2023, 11, 2296-2565, 10.3389/fpubh.2023.1124051 | |
| 25. | Eric Nzirakaindi Ikoona, Mame Awa Toure, Amon Njenga, Lucy Namulemo, Ronald Kaluya, Kassim Kamara, Freddy Wathum Drinkwater Oyat, Judith Aloyo, David Lagoro Kitara, Prevalence and factors associated with underweight among 15–49-year-old women in Sierra Leone: a secondary data analysis of Sierra Leone demographic health survey of 2019, 2023, 23, 1472-6874, 10.1186/s12905-023-02358-4 | |
| 26. | Mahalul Azam, Luluk Fadhoh Sakinah, Martha Irene Kartasurya, Arulita Ika Fibriana, Tania Tedjo Minuljo, Syed Mohamed Aljunid, Prevalence and determinants of obesity among individuals with diabetes in Indonesia, 2023, 11, 2046-1402, 1063, 10.12688/f1000research.125549.3 | |
| 27. | Mahalul Azam, Luluk Fadhoh Sakinah, Martha Irene Kartasurya, Arulita Ika Fibriana, Tania Tedjo Minuljo, Syed Mohamed Aljunid, Prevalence and determinants of obesity among individuals with diabetes in Indonesia, 2023, 11, 2046-1402, 1063, 10.12688/f1000research.125549.4 | |
| 28. | Affandi Ismail, Chaikal Nuryakin, Who is chronically obese in Indonesia? The role of individual preferences, 2023, 0021-9320, 1, 10.1017/S0021932023000214 | |
| 29. | Diana Laila Ramatillah, Siew Hua Gan, Judith Novarticia, Gena Nafta Araminda, Michael Michael, Mohammad Elnaem, Rizki Alawuddin, Kashifullah Khan, Side Effects of CoronaVac® COVID-19 Vaccination: Investigation in North Jakarta District Public Health Center Communities in Indonesia, 2024, 24058440, e30087, 10.1016/j.heliyon.2024.e30087 | |
| 30. | Yasuaki Saijo, Eiji Yoshioka, Yukihiro Sato, Yuki Kunori, Tomoko Kanaya, Kentaro Nakanishi, Yasuhito Kato, Ken Nagaya, Satoru Takahashi, Yoshiya Ito, Hiroyoshi Iwata, Takeshi Yamaguchi, Chihiro Miyashita, Sachiko Itoh, Reiko Kishi, Malshani Lakshika Pathirathna, Maternal pre-pregnancy body mass index and related factors: A cross-sectional analysis from the Japan Environment and Children’s Study, 2024, 19, 1932-6203, e0304844, 10.1371/journal.pone.0304844 | |
| 31. | Karar Nadhum Jawad Musafer, Sarah Mohammed Alsheikh, Prevalence of obesity in Kirkuk governorate/Iraq, 2024, 6, 2582-1075, 85, 10.18231/j.ijrimcr.2024.056 | |
| 32. | Supa Pengpid, Karl Peltzer, Underweight and overweight/obesity among middle aged and older adults in India, 2021, 6, 2468-8827, 172, 10.4103/jncd.jncd_9_21 | |
| 33. | Fifi Retiaty, Nuri Andarwulan, Nurheni Sri Palupi, Fitrah Ernawati, Renata Kazimierczak, Dominika Średnicka-Tober, Contribution of Food, Energy, Macronutrients and Fiber Consumption Patterns to Obesity and Other Non-Communicable Disease Risks in the Indonesian Population, 2025, 17, 2072-6643, 1459, 10.3390/nu17091459 |
Figures(3) / Tables(4)
Toni Varila, Henrik Romar, Tero Luukkonen, Ulla Lassi. Physical activation and characterization of tannin-based foams enforced with boric acid and zinc chloride[J]. AIMS Materials Science, 2019, 6(2): 301-314. doi: 10.3934/matersci.2019.2.301
| Sociodemographic variables | Sample N (%) | Underweight (<18.5 kg/m2) % | Normal weight (18.5 to < 23.0 kg/m2) % | Overweight/obesity (≥23 kg/m2) % |
| All | 29509 | 11.2 (10.8, 11.6) | 39.8 (39.1, 40.4) | 49.0 (48.4, 49.7) |
| Age in years | ||||
| 18-29 (all) | 8171 (22.7) | 16.7 (15.8, 17.7) | 49.0 (47.7, 50.2) | 34.3 (33.1, 35.5) |
| 18-29 (male) | 3769 (24.9) | 20.0 (18.6, 21.5) | 53.6 (51.8, 55.4) | 26.4 (24.9, 28.0) |
| 18-29 (female) | 4402 (20.7) | 13.2 (12.1, 14.3) | 43.8 (42.2, 45.5) | 43.0 (41.4, 44.6) |
| 30-39 (all) | 8265 (22.5) | 7.6 (7.0, 8.3) | 38.6 (37.4, 39.8) | 53.8 (52.6, 55.1) |
| 30-39 (male) | 4032 (21.8) | 10.7 (9.7.11.8) | 48.2 (46.4, 49.9) | 41.1 (39.4, 42.9) |
| 30-39 (female) | 4233 (23.1) | 4.9 (4.2, 5.7) | 30.3 (28.7, 31.8) | 64.9 (63.2, 66.5) |
| 40-49 (all) | 5622 (23.8) | 5.6 (5.0, 6.3) | 33.0 (31.7, 34.4) | 61.4 (60.0, 62.8) |
| 40-49 (male) | 2792 (22.2) | 7.5 (6.5, 8.7) | 43.4 (41.4, 45.5) | 49.0 (47.0, 51.1) |
| 40-49 (female) | 2830 (25.3) | 4.1 (3.3, 4.9) | 24.6 (22.9, 26.4) | 71.3 (69.5, 73.1) |
| 50-59 (all) | 3866 (16.8) | 8.6 (7.6, 9.6) | 36.5 (34.8, 38.2) | 54.9 (53.2, 56.6) |
| 50-59 (male) | 1773 (17.1) | 8.8 (7.4, 10.4) | 45.8 (43.3, 48.3) | 45.4 (42.9, 47.9) |
| 50-59 (female) | 2093 (16.4) | 8.4 (7.1, 9.8) | 27.5 (25.5, 29.7) | 64.1 (61.8, 66.3) |
| 60-69 (all) | 2120 (8.8) | 14.7 (13.1, 16.5) | 39.6 (37.3, 41.9) | 45.7 (43.4, 48.1) |
| 60-69 (male) | 1006 (9.0) | 15.9 (13.5, 18.5) | 48.4 (45.0, 51.8) | 35.8 (32.6, 39.1) |
| 60-69 (female) | 1114 (8.6) | 13.6 (11.5, 16.0) | 31.1 (28.2, 34.2) | 55.3 (52.1, 58.5) |
| 70 or more (all) | 1465 (5.5) | 29.8 (27.3, 32.5) | 46.0 (43.2, 48.9) | 24.2 (21.8, 26.7) |
| 70 or more (male) | 655 (5.0) | 32.3 (28.4, 36.4) | 49.4 (45.3, 53.6) | 18.3 (15.4, 21.6) |
| 70 or more (female) | 810 (5.9) | 27.9 (24.5, 31.5) | 43.4 (39.6, 47.2) | 28.7 (25.4, 32.3) |
| Sex | ||||
| Female | 15482 (52.1) | 9.1 (8.6, 9.6) | 32.0 (31.2, 32.9) | 58.9 (58.0, 59.8) |
| Male | 14027 (47.9) | 13.5 (12.9, 14.2) | 48.1 (47.2, 49.1) | 38.3 (37.4, 39.2) |
| Education | ||||
| None | 1540 (6.4) | 23.0 (20.7, 25.5) | 44.2 (41.4, 47.0) | 32.8 (30.2, 35.5) |
| Elementary | 9671 (37.8) | 11.2 (10.5, 11.9) | 40.1 (39.1, 41.2) | 48.7 (47.6, 49.8) |
| High school | 14022 (43.7) | 10.2 (9.7, 10.8) | 40.2 (39.3, 41.1) | 49.6 (48.7, 50.5) |
| Higher education | 4221 (12.1) | 8.5 (7.6, 9.5) | 34.6 (33.0, 36.2) | 56.9 (55.2, 58.6) |
| Marital status | ||||
| Unmarried | 4123 (11.9) | 21.3 (19.9, 22.7) | 52.5 (50.8, 54.3) | 26.1 (24.7, 27.7) |
| Married, cohabiting | 22533 (77.2) | 8.8 (8.4, 9.2) | 38.1 (37.3, 38.8) | 53.1 (52.6, 53.8) |
| Separated, divorced, widowed | 2853 (10.9) | 17.2 (15.7, 18.8) | 37.4 (35.5, 39.4) | 45.4 (43.4, 47.4) |
| Religion | ||||
| Islam | 25378 (93.4) | 10.8 (10.4, 11.2) | 39.6 (38.9, 40.3) | 49.6 (48.9, 50.3) |
| Other | 2848 (6.6) | 6.7 (5.7, 7.8) | 38.9 (36.8, 40.9) | 54.4 (52.3, 56.5) |
| Subjective economic background | ||||
| Poor | 7156 (25.6) | 12.6 (11.8, 13.5) | 44.5 (43.2, 45.9) | 42.8 (41.5, 44.1) |
| Medium | 13269 (46.6) | 10.0 (9.5, 10.6) | 39.6 (38.6, 40.5) | 50.4 (49.5, 51.4) |
| Rich | 7809 (27.9) | 9.4 (8.7, 10.2) | 35.0 (33.8, 36.2) | 55.6 (54.4, 56.9) |
| Work status | ||||
| Work for income | 18399 (62.4) | 10.7 (10.2, 11.2) | 42.4 (41.5, 43.1) | 47.0 (46.1, 47.8) |
| Search for job | 365 (1.2) | 22.6 (18.1, 27.8) | 51.4 (45.6, 56.8) | 26.1 (21.4, 31.3) |
| School attendance | 822 (2.1) | 25.0 (21.8, 28.5) | 50.8 (46.3, 50.6) | 24.2 (21.0, 27.7) |
| House keeping | 8055 (27.5) | 8.1 (7.5, 8.8) | 31.8 (30.7, 32.9) | 60.1 (58.9, 61.3) |
| Retired, sick, disabled, other | 1866 (6.8) | 22.1 (20.3, 24.6) | 42.6 (40.1, 45.1) | 35.0 (32.6, 37.5) |
| Residence | ||||
| Rural | 12177 (48.0) | 12.7 (12.1, 13.4) | 42.9 (41.9, 43.9) | 44.4 (43.4, 45.4) |
| Urban | 17332 (52.0) | 9.8 (9.3, 10.3) | 36.9, 36.1, 37.7) | 53.3 (52.5, 54.2) |
| Region | ||||
| Sumatra | 6714 (22.8) | 11.1 (10.8, 12.4) | 40.8 (39.6, 42.1) | 49.0 (47.8, 50.3) |
| Java | 16137 (54.7) | 11.2 (11.9, 12.9) | 39.3 (38.5, 40.1) | 49.4 (48.6, 50.3) |
| Main island groups1 | 6658 (22.6) | 12.5 (11.7, 13.4) | 41.2 (40.0, 42.5) | 46.3 (43.4, 45.9) |
| Note: 1Major Island groups: Bali, West Nusa Tenggara, South Kalimantan, and South Sulawesi. | ||||
DownLoad: CSV| Health variables | Sample N (%) | Underweight (<18.5 kg/m2) % | Normal weight (18.5 to < 23.0 kg/m2) % | Overweight/obesity (≥23 kg/m2) % |
| Health risk behavior | ||||
| Childhood hunger (≤15 years) | 2412 (8.8) | 10.6 (9.3, 12.1) | 40.9 (38.7, 43.2) | 48.5 (46.2, 50.8) |
| Fruit consumption (<3 days/week) | 16462 (57.7) | 11.4 (10.8, 11.9) | 41.7 (40.8, 42.5) | 47.0 (46.1, 47.9) |
| Vegetable consumption (<daily/week) | 16263 (59.9) | 11.1 (10.6, 11.6) | 39.9 (39.0, 40.7) | 49.0 (48.2, 49.9) |
| Meals a day (3 times vs 2 or 1 time) | 19284 (68.2) | 10.4 (9.9, 10.9) | 40.8 (40.0, 41.8) | 48.9 (48.0, 49.7) |
| Meat (4–7 days/week) | 3115 (10.4) | 8.9 (7.8, 10.1) | 37.0 (35.1, 39.0) | 54.1 (52.1, 56.1) |
| Dairy products (4–7 days/week | 3469 (11.6) | 11.9 (10.7, 13.1) | 41.8 (40.0, 43.7) | 46.3 (44.4, 48.2) |
| Fish (4–7 days/week) | 9289 (29.1) | 9.5 (8.8, 10.2) | 39.4 (38.2, 40.6) | 51.1 (49.9, 52.3) |
| Fast food consumption (1–7 days/week) | 2982 (9.4) | 9.6 (8.4, 10.8) | 34.0 (32.1, 36.0) | 56.4 (54.4, 58.4) |
| Fried snacks (4–7 days/week) | 7236 (29.4) | 9.5 (8.7, 10.2) | 38.2 (37.0, 39.5) | 52.3 (51.0, 53.6) |
| Sweet snacks (4–7 days/week) | 5280 (17.1) | 11.0 (10.0, 12.0) | 39.6 (38.1, 41.1) | 49.4 (47.8, 50.9) |
| Soft drinks (4–7 days/week) | 574 (1.7) | 12.1 (9.4, 15.3) | 44.2 (39.5, 49.0) | 43.7 (39.0, 48.5) |
| Physical activity | ||||
| Low | 13591 (46.4) | 10.6 (10.1, 11.2) | 38.1 (37.1, 39.0) | 51.3 (50.4, 52.3) |
| Medium | 7483 (26.6) | 9.7 (8.9, 10.4) | 36.8 (35.5, 38.0) | 53.6 (52.3, 54.9) |
| High | 7160 (27.0) | 11.2 (10.4, 12.1) | 44.9 (43.6, 46.2) | 43.9 (42.6, 45.2) |
| Tobacco use | ||||
| Never | 18172 (61.1) | 9.1 (8.6, 9.5) | 33.2 (32.5, 34.0) | 57.7 (56.9, 58.5) |
| Quit | 1508 (5.2) | 13.1 (11.2, 15.1) | 38.3 (35.6, 41.1) | 48.7 (45.8, 51.5) |
| Current | 9829 (33.8) | 14.8 (14.0, 15.6) | 51.8 (50.7, 52.9) | 33.4 (33.4, 34.5) |
| Physical and mental health risk status | ||||
| Self-rated health status | 6581 (22.0) | 12.8 (11.9, 13.8) | 38.8 (37.5, 40.1) | 48.4 (47.0, 49.8) |
| Unhealthy | 17524 (59.1) | 10.9 (10.3, 11.4) | 40.2 (39.4, 41.0) | 48.9 (48.1, 49.8) |
| Somewhat healthy | 5404 (18.9) | 10.4 (9.5, 11.4) | 39.4 (38.0, 40.9) | 50.1 (48.6, 51.7) |
| Very healthy | ||||
| Diabetes | 704 (2.8) | 3.7 (2.4, 5.5) | 27.8 (24.3, 31.6) | 68.5 (64.6, 72.2) |
| Hypertension | 8807 (33.5) | 8.2 (7.6, 8.9) | 31.2 (30.1, 32.3) | 60.5 (59.4, 61.7) |
| High Cholesterol (Total or LDL) | 1310 (4.9) | 1.9 (1.3, 2.9) | 17.0 (14.9, 19.3) | 81.1 (78.7, 83.3) |
| Sleep disturbance | 913 (3.0) | 13.3 (11.0, 16.0) | 42.7 (39.1, 46.3) | 44.0 (40.4, 47.7) |
| Depression symptoms | 6420 (21.4) | 11.9 (11.0, 12.8) | 42.1 (40.7, 43.5) | 46.0 (44.6, 47.4) |
| Happiness | ||||
| Unhappy | 2499 (9.9) | 13.8 (12.4, 15.4) | 45.7 (43.5, 47.9) | 40.5 (38.4, 42.7) |
| Somewhat happy | 22065 (78.3) | 10.4 (9.9, 10.8) | 39.2 (38.4, 39.9) | 50.5 (49.7, 51.2) |
| Very happy | 3670 (11.8) | 8.8 (7.8, 9.9) | 37.0 (35.2, 38.8) | 54.2 (52.4, 56.0) |
| Note: LDL= Low-density lipoprotein. | ||||
DownLoad: CSV| Underweight ARRR | Overweight/obesity ARRR | |
| Sociodemographics | ||
| Age in years | ||
| 18–29 | 1 (Reference) | 1 (Reference) |
| 30–39 | 0.62 (0.54, 0.70)*** | 1.92 (1.77, 2.09)*** |
| 40–49 | 0.55 (0.47, 0.65)*** | 2.27 (2.07, 2.49)*** |
| 50–59 | 0.79 (0.67, 0.94)** | 1.65 (1.47, 1.84)*** |
| 60–69 | 1.27 (1.05, 1.55)* | 1.09 (0.95, 1.26) |
| 70 or more | 2.04 (1.62, 2.56)*** | 0.53 (0.43, 0.65)*** |
| Sex | ||
| Female | 1 (Reference) | 1 (Reference) |
| Male | 0.97 (0.84, 1.13) | 0.67 (0.61, 0.73)*** |
| Education | ||
| None/Elementary | 1 (Reference) | 1 (Reference) |
| High school/Higher education | 0.89 (0.80, 0.99)* | 1.28 (1.19, 1.37)*** |
| Subjective economic background | ||
| Poor | 1 (Reference) | 1 (Reference) |
| Medium | 0.92 (0.82, 1.03) | 1.24 (1.15, 1.34)*** |
| Rich | 0.98 (0.86, 1.12) | 1.35 (1.23, 1.47)*** |
| Work status | ||
| Work for income | 1 (Reference) | 1 (Reference) |
| Search for job | 1.63 (1.20, 2.22)** | 0.72 (0.53, 0.96)* |
| School attendance | 1.65 (1.32, 2.05)*** | 0.53 (0.43, 0.65)*** |
| House keeping | 0.98 (0.86, 1.13) | 1.15 (1.06, 1.24)*** |
| Retired, sick, disabled, other | 1.23 (1.01, 1.49)* | 0.97 (0.83, 1.14) |
| Urban residence (base=rural) | 0.90 (0.81, 0.99)* | 1.25 (1.18, 1.34)*** |
| Region | ||
| Sumatra | 1 (Reference) | 1 (Reference) |
| Java | 1.09 (0.97, 1.23) | 0.91 (0.84, 0.98)* |
| Main island groups | 1.18 (1.04, 1.35)* | 0.83 (0.76, 0.90)*** |
| Health risk behavior | ||
| Childhood hunger (≤15 years) (base=no) | 0.92 (0.77, 1.10) | 1.07 (0.96, 1.20) |
| Fruit consumption (<3 days/week) (base ≥3 days/week) | 1.04 (0.94, 1.15) | 0.87 (0.82, 0.93)*** |
| Vegetable consumption (<daily/week) (base=daily) | 1.08 (0.98, 1.19) | 1.02 (0.96, 1.09) |
| Meals a day (3 times) (base < 3 times) | 0.92 (0.84, 1.02) | 0.82 (0.77, 0.88)*** |
| Meat (4–7 days/week) (base= < 4 days/week) | 0.88 (0.74, 1.03) | 1.19 (1.07, 1.31)*** |
| Dairy products (4–7 days/week) (base= < 4 days/week) | 1.08 (0.95, 1.24) | 0.82 (0.75, 0.90)*** |
| Fish (4–7 days/week) (base= < 4 days/week) | 0.91 (0.81, 1.02) | 1.06 (0.98, 1.13) |
| Fast food consumption (1–7 days/week) (base= < 1 day) | 1.06 (0.90, 1.25) | 1.29 (1.16, 1.43)*** |
| Fried snacks (4–7 days/week) (base= < 4 days/week) | 0.94 (0.84, 1.06) | 1.09 (1.01, 1.17)* |
| Sweet snacks (4–7 days/week) (base= < 4 days/week) | 1.07 (0.94, 1.21) | 0.90 (0.83, 0.98)* |
| Soft drinks (4–7 days/week) (base= < 4 days/week) | 0.96 (0.71, 1.31) | 1.06 (0.84, 1.34) |
| Physical activity | ||
| Low | 1 (Reference) | 1 (Reference) |
| Medium | 0.99 (0.89, 1.12) | 1.01 (0.94, 1.09) |
| High | 0.94 (0.84, 1.06) | 0.86 (0.80, 0.93)*** |
| Tobacco use (current) | 1.12 (0.98, 1.28) | 0.54 (0.49, 0.59)*** |
| Physical and mental health risk status | ||
| Self-rated health (very healthy) (base=not very healthy) | 0.97 (0.86, 1.10) | 1.12 (1.04, 1.22)** |
| Diabetes | 0.53 (0.32, 0.87)* | 1.29 (1.04, 1.60)* |
| Hypertension | 0.67 (0.59, 0.76)*** | 2.13 (1.98, 2.30)*** |
| High Cholesterol (Total or LDL) | 0.49 (0.31, 0.79)** | 2.67 (2.24, 3.19)*** |
| Sleep disturbance | 1.15 (0.90, 1.48) | 0.86 (0.72, 1.02) |
| Depression symptoms | 1.04 (0.93, 1.16) | 0.90 (0.83, 0.97)** |
| Happiness (very happy) (base=not very happy) | 0.90 (0.77, 1.04) | 1.19 (1.09, 1.31)*** |
| Note: ARRR = Adjusted Relative Risk Ratio; LDL = Low-density lipoprotein; *** P < 0.001; ** P < 0.01; * P < 0.05. | ||
DownLoad: CSV| Sociodemographic variables | Sample N (%) | Underweight (<18.5 kg/m2) % | Normal weight (18.5 to < 23.0 kg/m2) % | Overweight/obesity (≥23 kg/m2) % |
| All | 29509 | 11.2 (10.8, 11.6) | 39.8 (39.1, 40.4) | 49.0 (48.4, 49.7) |
| Age in years | ||||
| 18-29 (all) | 8171 (22.7) | 16.7 (15.8, 17.7) | 49.0 (47.7, 50.2) | 34.3 (33.1, 35.5) |
| 18-29 (male) | 3769 (24.9) | 20.0 (18.6, 21.5) | 53.6 (51.8, 55.4) | 26.4 (24.9, 28.0) |
| 18-29 (female) | 4402 (20.7) | 13.2 (12.1, 14.3) | 43.8 (42.2, 45.5) | 43.0 (41.4, 44.6) |
| 30-39 (all) | 8265 (22.5) | 7.6 (7.0, 8.3) | 38.6 (37.4, 39.8) | 53.8 (52.6, 55.1) |
| 30-39 (male) | 4032 (21.8) | 10.7 (9.7.11.8) | 48.2 (46.4, 49.9) | 41.1 (39.4, 42.9) |
| 30-39 (female) | 4233 (23.1) | 4.9 (4.2, 5.7) | 30.3 (28.7, 31.8) | 64.9 (63.2, 66.5) |
| 40-49 (all) | 5622 (23.8) | 5.6 (5.0, 6.3) | 33.0 (31.7, 34.4) | 61.4 (60.0, 62.8) |
| 40-49 (male) | 2792 (22.2) | 7.5 (6.5, 8.7) | 43.4 (41.4, 45.5) | 49.0 (47.0, 51.1) |
| 40-49 (female) | 2830 (25.3) | 4.1 (3.3, 4.9) | 24.6 (22.9, 26.4) | 71.3 (69.5, 73.1) |
| 50-59 (all) | 3866 (16.8) | 8.6 (7.6, 9.6) | 36.5 (34.8, 38.2) | 54.9 (53.2, 56.6) |
| 50-59 (male) | 1773 (17.1) | 8.8 (7.4, 10.4) | 45.8 (43.3, 48.3) | 45.4 (42.9, 47.9) |
| 50-59 (female) | 2093 (16.4) | 8.4 (7.1, 9.8) | 27.5 (25.5, 29.7) | 64.1 (61.8, 66.3) |
| 60-69 (all) | 2120 (8.8) | 14.7 (13.1, 16.5) | 39.6 (37.3, 41.9) | 45.7 (43.4, 48.1) |
| 60-69 (male) | 1006 (9.0) | 15.9 (13.5, 18.5) | 48.4 (45.0, 51.8) | 35.8 (32.6, 39.1) |
| 60-69 (female) | 1114 (8.6) | 13.6 (11.5, 16.0) | 31.1 (28.2, 34.2) | 55.3 (52.1, 58.5) |
| 70 or more (all) | 1465 (5.5) | 29.8 (27.3, 32.5) | 46.0 (43.2, 48.9) | 24.2 (21.8, 26.7) |
| 70 or more (male) | 655 (5.0) | 32.3 (28.4, 36.4) | 49.4 (45.3, 53.6) | 18.3 (15.4, 21.6) |
| 70 or more (female) | 810 (5.9) | 27.9 (24.5, 31.5) | 43.4 (39.6, 47.2) | 28.7 (25.4, 32.3) |
| Sex | ||||
| Female | 15482 (52.1) | 9.1 (8.6, 9.6) | 32.0 (31.2, 32.9) | 58.9 (58.0, 59.8) |
| Male | 14027 (47.9) | 13.5 (12.9, 14.2) | 48.1 (47.2, 49.1) | 38.3 (37.4, 39.2) |
| Education | ||||
| None | 1540 (6.4) | 23.0 (20.7, 25.5) | 44.2 (41.4, 47.0) | 32.8 (30.2, 35.5) |
| Elementary | 9671 (37.8) | 11.2 (10.5, 11.9) | 40.1 (39.1, 41.2) | 48.7 (47.6, 49.8) |
| High school | 14022 (43.7) | 10.2 (9.7, 10.8) | 40.2 (39.3, 41.1) | 49.6 (48.7, 50.5) |
| Higher education | 4221 (12.1) | 8.5 (7.6, 9.5) | 34.6 (33.0, 36.2) | 56.9 (55.2, 58.6) |
| Marital status | ||||
| Unmarried | 4123 (11.9) | 21.3 (19.9, 22.7) | 52.5 (50.8, 54.3) | 26.1 (24.7, 27.7) |
| Married, cohabiting | 22533 (77.2) | 8.8 (8.4, 9.2) | 38.1 (37.3, 38.8) | 53.1 (52.6, 53.8) |
| Separated, divorced, widowed | 2853 (10.9) | 17.2 (15.7, 18.8) | 37.4 (35.5, 39.4) | 45.4 (43.4, 47.4) |
| Religion | ||||
| Islam | 25378 (93.4) | 10.8 (10.4, 11.2) | 39.6 (38.9, 40.3) | 49.6 (48.9, 50.3) |
| Other | 2848 (6.6) | 6.7 (5.7, 7.8) | 38.9 (36.8, 40.9) | 54.4 (52.3, 56.5) |
| Subjective economic background | ||||
| Poor | 7156 (25.6) | 12.6 (11.8, 13.5) | 44.5 (43.2, 45.9) | 42.8 (41.5, 44.1) |
| Medium | 13269 (46.6) | 10.0 (9.5, 10.6) | 39.6 (38.6, 40.5) | 50.4 (49.5, 51.4) |
| Rich | 7809 (27.9) | 9.4 (8.7, 10.2) | 35.0 (33.8, 36.2) | 55.6 (54.4, 56.9) |
| Work status | ||||
| Work for income | 18399 (62.4) | 10.7 (10.2, 11.2) | 42.4 (41.5, 43.1) | 47.0 (46.1, 47.8) |
| Search for job | 365 (1.2) | 22.6 (18.1, 27.8) | 51.4 (45.6, 56.8) | 26.1 (21.4, 31.3) |
| School attendance | 822 (2.1) | 25.0 (21.8, 28.5) | 50.8 (46.3, 50.6) | 24.2 (21.0, 27.7) |
| House keeping | 8055 (27.5) | 8.1 (7.5, 8.8) | 31.8 (30.7, 32.9) | 60.1 (58.9, 61.3) |
| Retired, sick, disabled, other | 1866 (6.8) | 22.1 (20.3, 24.6) | 42.6 (40.1, 45.1) | 35.0 (32.6, 37.5) |
| Residence | ||||
| Rural | 12177 (48.0) | 12.7 (12.1, 13.4) | 42.9 (41.9, 43.9) | 44.4 (43.4, 45.4) |
| Urban | 17332 (52.0) | 9.8 (9.3, 10.3) | 36.9, 36.1, 37.7) | 53.3 (52.5, 54.2) |
| Region | ||||
| Sumatra | 6714 (22.8) | 11.1 (10.8, 12.4) | 40.8 (39.6, 42.1) | 49.0 (47.8, 50.3) |
| Java | 16137 (54.7) | 11.2 (11.9, 12.9) | 39.3 (38.5, 40.1) | 49.4 (48.6, 50.3) |
| Main island groups1 | 6658 (22.6) | 12.5 (11.7, 13.4) | 41.2 (40.0, 42.5) | 46.3 (43.4, 45.9) |
| Note: 1Major Island groups: Bali, West Nusa Tenggara, South Kalimantan, and South Sulawesi. | ||||
| Health variables | Sample N (%) | Underweight (<18.5 kg/m2) % | Normal weight (18.5 to < 23.0 kg/m2) % | Overweight/obesity (≥23 kg/m2) % |
| Health risk behavior | ||||
| Childhood hunger (≤15 years) | 2412 (8.8) | 10.6 (9.3, 12.1) | 40.9 (38.7, 43.2) | 48.5 (46.2, 50.8) |
| Fruit consumption (<3 days/week) | 16462 (57.7) | 11.4 (10.8, 11.9) | 41.7 (40.8, 42.5) | 47.0 (46.1, 47.9) |
| Vegetable consumption (<daily/week) | 16263 (59.9) | 11.1 (10.6, 11.6) | 39.9 (39.0, 40.7) | 49.0 (48.2, 49.9) |
| Meals a day (3 times vs 2 or 1 time) | 19284 (68.2) | 10.4 (9.9, 10.9) | 40.8 (40.0, 41.8) | 48.9 (48.0, 49.7) |
| Meat (4–7 days/week) | 3115 (10.4) | 8.9 (7.8, 10.1) | 37.0 (35.1, 39.0) | 54.1 (52.1, 56.1) |
| Dairy products (4–7 days/week | 3469 (11.6) | 11.9 (10.7, 13.1) | 41.8 (40.0, 43.7) | 46.3 (44.4, 48.2) |
| Fish (4–7 days/week) | 9289 (29.1) | 9.5 (8.8, 10.2) | 39.4 (38.2, 40.6) | 51.1 (49.9, 52.3) |
| Fast food consumption (1–7 days/week) | 2982 (9.4) | 9.6 (8.4, 10.8) | 34.0 (32.1, 36.0) | 56.4 (54.4, 58.4) |
| Fried snacks (4–7 days/week) | 7236 (29.4) | 9.5 (8.7, 10.2) | 38.2 (37.0, 39.5) | 52.3 (51.0, 53.6) |
| Sweet snacks (4–7 days/week) | 5280 (17.1) | 11.0 (10.0, 12.0) | 39.6 (38.1, 41.1) | 49.4 (47.8, 50.9) |
| Soft drinks (4–7 days/week) | 574 (1.7) | 12.1 (9.4, 15.3) | 44.2 (39.5, 49.0) | 43.7 (39.0, 48.5) |
| Physical activity | ||||
| Low | 13591 (46.4) | 10.6 (10.1, 11.2) | 38.1 (37.1, 39.0) | 51.3 (50.4, 52.3) |
| Medium | 7483 (26.6) | 9.7 (8.9, 10.4) | 36.8 (35.5, 38.0) | 53.6 (52.3, 54.9) |
| High | 7160 (27.0) | 11.2 (10.4, 12.1) | 44.9 (43.6, 46.2) | 43.9 (42.6, 45.2) |
| Tobacco use | ||||
| Never | 18172 (61.1) | 9.1 (8.6, 9.5) | 33.2 (32.5, 34.0) | 57.7 (56.9, 58.5) |
| Quit | 1508 (5.2) | 13.1 (11.2, 15.1) | 38.3 (35.6, 41.1) | 48.7 (45.8, 51.5) |
| Current | 9829 (33.8) | 14.8 (14.0, 15.6) | 51.8 (50.7, 52.9) | 33.4 (33.4, 34.5) |
| Physical and mental health risk status | ||||
| Self-rated health status | 6581 (22.0) | 12.8 (11.9, 13.8) | 38.8 (37.5, 40.1) | 48.4 (47.0, 49.8) |
| Unhealthy | 17524 (59.1) | 10.9 (10.3, 11.4) | 40.2 (39.4, 41.0) | 48.9 (48.1, 49.8) |
| Somewhat healthy | 5404 (18.9) | 10.4 (9.5, 11.4) | 39.4 (38.0, 40.9) | 50.1 (48.6, 51.7) |
| Very healthy | ||||
| Diabetes | 704 (2.8) | 3.7 (2.4, 5.5) | 27.8 (24.3, 31.6) | 68.5 (64.6, 72.2) |
| Hypertension | 8807 (33.5) | 8.2 (7.6, 8.9) | 31.2 (30.1, 32.3) | 60.5 (59.4, 61.7) |
| High Cholesterol (Total or LDL) | 1310 (4.9) | 1.9 (1.3, 2.9) | 17.0 (14.9, 19.3) | 81.1 (78.7, 83.3) |
| Sleep disturbance | 913 (3.0) | 13.3 (11.0, 16.0) | 42.7 (39.1, 46.3) | 44.0 (40.4, 47.7) |
| Depression symptoms | 6420 (21.4) | 11.9 (11.0, 12.8) | 42.1 (40.7, 43.5) | 46.0 (44.6, 47.4) |
| Happiness | ||||
| Unhappy | 2499 (9.9) | 13.8 (12.4, 15.4) | 45.7 (43.5, 47.9) | 40.5 (38.4, 42.7) |
| Somewhat happy | 22065 (78.3) | 10.4 (9.9, 10.8) | 39.2 (38.4, 39.9) | 50.5 (49.7, 51.2) |
| Very happy | 3670 (11.8) | 8.8 (7.8, 9.9) | 37.0 (35.2, 38.8) | 54.2 (52.4, 56.0) |
| Note: LDL= Low-density lipoprotein. | ||||
| Underweight ARRR | Overweight/obesity ARRR | |
| Sociodemographics | ||
| Age in years | ||
| 18–29 | 1 (Reference) | 1 (Reference) |
| 30–39 | 0.62 (0.54, 0.70)*** | 1.92 (1.77, 2.09)*** |
| 40–49 | 0.55 (0.47, 0.65)*** | 2.27 (2.07, 2.49)*** |
| 50–59 | 0.79 (0.67, 0.94)** | 1.65 (1.47, 1.84)*** |
| 60–69 | 1.27 (1.05, 1.55)* | 1.09 (0.95, 1.26) |
| 70 or more | 2.04 (1.62, 2.56)*** | 0.53 (0.43, 0.65)*** |
| Sex | ||
| Female | 1 (Reference) | 1 (Reference) |
| Male | 0.97 (0.84, 1.13) | 0.67 (0.61, 0.73)*** |
| Education | ||
| None/Elementary | 1 (Reference) | 1 (Reference) |
| High school/Higher education | 0.89 (0.80, 0.99)* | 1.28 (1.19, 1.37)*** |
| Subjective economic background | ||
| Poor | 1 (Reference) | 1 (Reference) |
| Medium | 0.92 (0.82, 1.03) | 1.24 (1.15, 1.34)*** |
| Rich | 0.98 (0.86, 1.12) | 1.35 (1.23, 1.47)*** |
| Work status | ||
| Work for income | 1 (Reference) | 1 (Reference) |
| Search for job | 1.63 (1.20, 2.22)** | 0.72 (0.53, 0.96)* |
| School attendance | 1.65 (1.32, 2.05)*** | 0.53 (0.43, 0.65)*** |
| House keeping | 0.98 (0.86, 1.13) | 1.15 (1.06, 1.24)*** |
| Retired, sick, disabled, other | 1.23 (1.01, 1.49)* | 0.97 (0.83, 1.14) |
| Urban residence (base=rural) | 0.90 (0.81, 0.99)* | 1.25 (1.18, 1.34)*** |
| Region | ||
| Sumatra | 1 (Reference) | 1 (Reference) |
| Java | 1.09 (0.97, 1.23) | 0.91 (0.84, 0.98)* |
| Main island groups | 1.18 (1.04, 1.35)* | 0.83 (0.76, 0.90)*** |
| Health risk behavior | ||
| Childhood hunger (≤15 years) (base=no) | 0.92 (0.77, 1.10) | 1.07 (0.96, 1.20) |
| Fruit consumption (<3 days/week) (base ≥3 days/week) | 1.04 (0.94, 1.15) | 0.87 (0.82, 0.93)*** |
| Vegetable consumption (<daily/week) (base=daily) | 1.08 (0.98, 1.19) | 1.02 (0.96, 1.09) |
| Meals a day (3 times) (base < 3 times) | 0.92 (0.84, 1.02) | 0.82 (0.77, 0.88)*** |
| Meat (4–7 days/week) (base= < 4 days/week) | 0.88 (0.74, 1.03) | 1.19 (1.07, 1.31)*** |
| Dairy products (4–7 days/week) (base= < 4 days/week) | 1.08 (0.95, 1.24) | 0.82 (0.75, 0.90)*** |
| Fish (4–7 days/week) (base= < 4 days/week) | 0.91 (0.81, 1.02) | 1.06 (0.98, 1.13) |
| Fast food consumption (1–7 days/week) (base= < 1 day) | 1.06 (0.90, 1.25) | 1.29 (1.16, 1.43)*** |
| Fried snacks (4–7 days/week) (base= < 4 days/week) | 0.94 (0.84, 1.06) | 1.09 (1.01, 1.17)* |
| Sweet snacks (4–7 days/week) (base= < 4 days/week) | 1.07 (0.94, 1.21) | 0.90 (0.83, 0.98)* |
| Soft drinks (4–7 days/week) (base= < 4 days/week) | 0.96 (0.71, 1.31) | 1.06 (0.84, 1.34) |
| Physical activity | ||
| Low | 1 (Reference) | 1 (Reference) |
| Medium | 0.99 (0.89, 1.12) | 1.01 (0.94, 1.09) |
| High | 0.94 (0.84, 1.06) | 0.86 (0.80, 0.93)*** |
| Tobacco use (current) | 1.12 (0.98, 1.28) | 0.54 (0.49, 0.59)*** |
| Physical and mental health risk status | ||
| Self-rated health (very healthy) (base=not very healthy) | 0.97 (0.86, 1.10) | 1.12 (1.04, 1.22)** |
| Diabetes | 0.53 (0.32, 0.87)* | 1.29 (1.04, 1.60)* |
| Hypertension | 0.67 (0.59, 0.76)*** | 2.13 (1.98, 2.30)*** |
| High Cholesterol (Total or LDL) | 0.49 (0.31, 0.79)** | 2.67 (2.24, 3.19)*** |
| Sleep disturbance | 1.15 (0.90, 1.48) | 0.86 (0.72, 1.02) |
| Depression symptoms | 1.04 (0.93, 1.16) | 0.90 (0.83, 0.97)** |
| Happiness (very happy) (base=not very happy) | 0.90 (0.77, 1.04) | 1.19 (1.09, 1.31)*** |
| Note: ARRR = Adjusted Relative Risk Ratio; LDL = Low-density lipoprotein; *** P < 0.001; ** P < 0.01; * P < 0.05. | ||
