Changes in body mass index and its association with socio-demographic characteristics between 2010 and 2016 in Singapore

doi:10.21203/rs.3.rs-2459601/v1

Download PDF

Research Article

Changes in body mass index and its association with socio-demographic characteristics between 2010 and 2016 in Singapore

https://doi.org/10.21203/rs.3.rs-2459601/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background

Epidemiological studies have observed an increase in the prevalence of obesity in both western and Asian countries. This study aims to compare the distribution of body mass index (BMI) in the general population of Singapore between 2010 and 2016, and to explore the socio-demographic risk factors associated with it.

Methods

Data for this study were extracted from two national-wise studies in 2010 and 2016, two population-based, cross-sectional epidemiological studies. BMI cut-off scores were used as an indicator to assess obesity in this study, and the data included in the analysis was self-reported by the respondents.

Results

Overall, the study observed decreasing prevalence in underweight and normal weight categories; and an increasing prevalence in overweight and obesity categories in the Singapore adult population between 2010 and 2016. Age, gender, ethnicity, marital status, and educational level were found to be significantly associated with BMI categories.

Conclusion

The observed increase in the population’s BMI between 2010 and 2016 may lead to an increase in the incidence of chronic diseases in Singapore which would have an impact on the quality of life and the health care system. Our study findings add to the existing local literature BMI provides data for evidence-based policymaking on health-related interventions and programme planning.

Epidemiology

body mass index

obesity

obese

overweight

Overweight and obesity are known to be important contributors to adverse health consequences. (1, 2) At an individual level, they are associated with various physical diseases, including cardiovascular diseases, (3) several types of cancers, and diabetes mellitus. (4) Having excessive body weight not only affects an individual’s physical health but could also lead to poor mental health, (5) lowers one’s quality of life and reduces life expectancy. (6) At the population level, overweight and obesity are a public health concern, resulting in excessive health care costs to society.

The Global Burden of Disease Study Report (7) showed that the global prevalence of overweight increased from 26.5–39.0% from 1980 to 2015. The prevalence of obesity likewise rose from 7–12.5% from 1980 to 2015, representing an estimated increase of 50% and 80% respectively, with the American and European regions having the highest prevalence. In North America, the prevalence of overweight increased from 45.3–64.2% from 1980 to 2015, and the prevalence of obesity increased from 12.9–28.3% from 1980 to 2015. While in South East Asia, the prevalence of overweight increased from 10.9–24.3% from 1980 to 2015, and the prevalence of obesity increased from 1.7–6.2% from 1980 to 2015. (8, 9)

In relation to gender and socioeconomic status, the same report (9) found that the prevalence of overweight and obesity is higher in females than in males and that the gender difference remained persistent across time. Obesity is more common among middle-aged adults, especially wealthy females in low-income countries, whereas, among the disadvantaged groups in high-income countries, obesity affects both genders. (10)

However, Asians generally have a higher percentage of body fat than Caucasians of the same gender and age, and the risk of developing type 2 diabetes and cardiovascular diseases is higher among Asians than among Caucasians with the same body mass index (BMI) score (cut-off score at 25).(11) Thus, the current global cut-off points do not provide an adequate basis for many populations in Asia in relation to understanding or giving recommendations for those in the overweight and obese category. Thus, studies have suggested different categories for Asians which are at lower cut-offs. (12)

Singapore has a unique multi-ethnic composition, comprising Chinese (74.3%), Malays (13.4%), Indians (9.0%), and other ethnic groups (3.3%). (13) With evidence suggesting a different relationship between ethnic groups and body fat percentage, (14, 15) it is important to examine the changes in BMI across time in each ethnic group as it could have public health implications.

The current study aims to compare the distribution of BMI prevalence using Asian BMI cut-offs between 2010 and 2016 using self-reported height and weight measurements using data from the Singapore Mental Health Study 2010 (SMHS 2010) and Singapore Mental Health Study 2016 (SMHS 2016) (16, 17) to explore the changes and the socio-demographic risk factors in the Singapore adult population.

Respondents and Procedure

Both SMHS 2010 and SMHS 2016 were conducted using the same procedures. (17, 18) Both studies were cross-sectional, population-based epidemiological studies conducted among Singaporeans and Permanent residents aged 18 years and above living in Singapore. The sample was drawn from an administrative database of all citizens and permanent residents in Singapore and updated regularly.

An invitation letter was sent to each randomly selected participant/resident, followed by a personal home visit by a trained interviewer from the designated survey company to obtain their agreement to participate in the survey. Once participants agree, a trained interviewer will conduct face-to-face interviews at participants’ preferred time and venue. The survey was available in English, Chinese, and Malay languages, and the participant was asked to choose their preferred language before starting any study procedures. Residents who were having severe physical or mental conditions, were living in institutions or hospitals, were not able to speak the above-mentioned languages, or living overseas at the point of the survey, and those who were not contactable due to incomplete or incorrect addresses, were considered ineligible cases and were excluded from the study.

Ethics approval for this study was obtained by the National Healthcare Group Domain Specific Review Board (DSRB No.: 2015/01035). Written informed consent was obtained from all participants prior to the survey, participants could also choose their preferred language (English, Mandarin, or Malay) for the consent as well as the survey. Parental consent was also obtained for minors, i.e., those aged 18–20 years. All study procedures were performed in accordance with DSRB’s ethical guidelines and regulations.

Measures

Anthropometric measurements

Self-reported height and weight were collected from respondents as part of the data collected in the sociodemographic questionnaire.

Sociodemographic information

Data on gender (female, male), age, ethnicity (Chinese, Malay, Indian, and Others), marital status (single, married, divorced/ separated, or widowed), educational level (primary and below, secondary, vocational institute, pre-university/ junior college, diploma, and university), employment status (employed, unemployed and economically inactive) was collected.

Body Mass Index (BMI)

BMI is defined as the weight in kilograms divided by the square of the height in meters (BMI = body mass/(height)² ). The BMI classification scores were categorized according to World Health Organization (WHO) guidelines. Those having BMI scores of 30 and above were classified as obese, those with BMI of 25–29.9 were defined as overweight, 18.5–24.9 were considered to be in the normal range, and BMI below 18.5 was classified as underweight (WHO, 2000).

Statistical analysis

All estimates were weighted to ensure the results represented Singapore’s general population. To examine the associations between BMI categories and socio-demographic variables, chi-square (χ2) tests were used, followed by multinomial logistic regressions. A significant change in BMI categories between two surveys in sociodemographic groups was further tested in the pooled multinomial logistic regression analyses by adding interaction terms between the year of the study and each demographic variable with adjustment of sociodemographic factors. Year of survey (SMHS-2010 = 0, SMHS-2016 = 1), age groups, gender, ethnicity, marital status, education, and employment status were predictors in the regression model. Statistical significance was evaluated at the < 0.05 level using two-sided tests. All statistical analyses were performed using the Statistical Analysis Software (SAS) System version 9 (SAS Institute Inc, 2011).

Table I summarises the prevalence in 2010 and 2016 according to BMI categories. Table II presents the changes in BMI categories between 2010 and 2016 and the association between each category and socio-demographic characteristics.

Overall Prevalence

Overall, 6.7% were underweight in 2016 vs 8.4% in 2010, 36.5% were in the normal range in 2016 vs 39.9% in 2010, 38.6% were overweight in 2016 vs 35.1% in 2010, while 18.2% were obese in 2016 vs 16.7% in 2010.

BMI and associated factors across the two surveys

Age groups

BMI was significantly associated with age in both 2010 and 2016 (p < 0.001) (Table I). However, However, no statistically significant associations were observed between BMI categories and age across the two cohorts (Table II).

Gender

BMI was significantly associated with gender in both 2010 and 2016 (p < 0.001) (Table I). However, no statistically significant associations were observed between BMI categories and gender across the two cohorts (Table II).

Ethnicity

BMI was significantly associated with ethnicity in both 2010 and 2016 (p < 0.001) (Table I). Compared to Chinese ethnicity, those of Malay ethnicity were 1.4 times more likely to be in the obese category in 2016 than in 2010 (p = 0.02; OR = 1.4, 95% CI:1.1, 2.0) (Table II). In addition, those of Indian ethnicity were 1.4 times more likely to be in the obese category in 2016 than in 2010 (p = 0.03; OR = 1.4, 95% CI:1.04, 1.9) (Table II).

Marital Status

BMI was significantly associated with marital status in both 2010 and 2016 (p < 0.001; p < 0.001) (Table I). However, no statistically significant associations were observed between BMI categories and marital status across the two cohorts.

Education Level

BMI was significantly associated with education level in both 2010 and 2016 (p < 0.001) (Table I). As compared to respondents with a university degree, respondents with vocational education were 1.8 times more likely to be overweight in 2016 than in 2010 (p = 0.02; OR = 1.8, 95% CI:1.1, 2.9) (Table II); for those with pre-university/junior college certifications, they

were 0.4 times less likely to become obese in 2016 than in 2010 (p = 0.01; OR = 0.4, 95% CI:0.2, 0.8) (Table II).

Table I. Sociodemographic Characteristics and Body Mass Index (BMI) Categories in 2010 and 2016

Overall Sample		2010									2016
		Body Mass Index Categories
		Underweight		Normal Weight		Overweight		Obesity			Underweight		Normal Weight			Overweight		Obesity
		(< 18.5 kg/m²)		( = > 18.5 < 25 kg/m²)		( = > 25 < 30 kg/m²)		( = > 30 kg/m²)			(< 18.5 kg/m²)		( = > 18.5 < 25 kg/m²)			( = > 25 < 30 kg/m²)		( = > 30 kg/m²)
Sociodemographic		n	%	n	%	n	%	n	%	p value	n	%	n	%		n	%	n	%		p value
Characteristics		446	8.4	2141	39.9	2231	35.1	1473	16.7		289	6.7	1581	36.5		2061	38.6	1454	18.2
Age group	18–34	273	13.3	950	46	646	26.6	395	14.1	< 0.001	135	8.9	640	46.5		528	30.5	354	14.2		< 0.001
	35–49	97	6.4	705	39.3	913	37	593	17.3		58	6.2	346	31.5		566	41	457	21.2
	50–64	60	5.6	385	34.4	537	39.3	419	20.7		49	4.6	347	32.2		572	41.6	437	21.5
	65 and over	16	5.9	101	32.2	135	46	66	13.9		47	6.5	248		32.3	403	47.7	206		13.5
Gender	Male	146	4.4	968	34	1294	42	783	19.8	< 0.001	111	4.5	713		30.1	1225	45.4	737		20	< 0.001
Gender	Female	300	12.1	1173	45.5	937	28.6	690	13.7	< 0.001	178	8.8	868		43	836	31.8	717		16.4	< 0.001
Ethnicity	Chinese	185	9	849	42.4	669	34.8	273	13.8	< 0.001	119	7.3	655		40	646	39.1	218		13.6	< 0.001
	Malay	144	6.7	668	31.2	684	32	649	30		76	4.9	359		23	565	34.6	606		37.6
	Indian	108	5.9	534	28.7	766	39.9	497	25.4		72	4.4	408		24.6	656	38.6	538		32.5
	Others	9	4.2	90	36.1	112	41.5	54	18.1		22	4.8	159		34	194	41	92		20.2
Marital status	Never married	241	13.5	770	44.5	488	27.8	288	14.1	< 0.001	138	9.7	584		44.7	457	32	291		13.6	< 0.001
	Married	175	6	1222	38	1588	38.1	1091	17.8		113	4.9	849		32.6	1402	41.6	1028		20.9
	Divorced/separated	19	9.3	93	42.9	89	31.5	47	16.4		21	8.57	98		34.2	101	38	75		19.2
	Widowed	11	6.4	56	20.9	64	44.8	47	18		17	6.8	58		29.4	101	51.6	60		12.3
Education	Primary and below	12	6.1	66	31.9	69	42.3	56	19.7	< 0.001	49	8.4	188		28.3	299	41.8	211		21.6	< 0.001
	Secondary	48	6.4	227	34.1	293	39.5	245	20		68	5.2	365		32.8	557	40.7	461		21.4
	Pre-Uni./	123	8.2	572	37.6	670	35.3	540	18.9		20	8.7	108		45.3	106	35.7	61		10.3
	Junior College	123	8.2	572	37.6	670	35.3	540	18.9		20	8.7	108		45.3	106	35.7	61		10.3
	Diploma	65	10.9	236	35	222	31.9	183	22.3		46	5.9	333		40.2	349	35.9	269		18
	Vocational	113	8.9	535	45.3	435	30.4	248	15.3		33	5.14	109		25.8	174	42.5	162		26.6
	University	85	9.1	505	44	542	36.3	201	10.7		73	7.4	478		40.7	576	37.3	290		14.5
Employment	Employed	304	8.1	1487	38.8	1630	35.9	1043	17.2	0.2	170	5.6	1091		36.1	1457	39.3	1028		19	0.02
	Economically inactive	103	9	498	43.1	434	33.1	330	14.8		95	9.2	406		38	502	36.9	353		15.9
	Unemployed	30	11.9	100	40.7	93	29.7	62	17.8		24	10.4	84		35.6	101	36.9	73		17.1

Table II. Multinomial logistic regression models for changes in BMI categories between 2010 and 2016 periods

Sociodemographic factors		Underweight at 2016				Overweight at 2016				Obesity at 2016
		OR	95% CI		p value	OR	95% CI		p value	OR	95% CI		p value
Age group	18–34	Ref				Ref				Ref
	35–49	1.8	1.0	3.2	0.1	1.1	0.8	1.6	0.5	1.4	1.0	2.1	0.9
	50–64	1.2	0.6	2.4	0.6	0.9	0.6	1.2	0.4	0.8	0.5	1.3	0.4
	65 and over	1.6	0.6	3.9	0.3	0.9	0.5	1.5	0.6	0.8	0.4	1.7	0.7
Gender	Male	Ref				Ref				Ref
Gender	Female	0.7	0.4	1.1	0.1	1.0	0.7	1.2	0.7	1.0	0.8	1.3	0.7
Ethnicity	Chinese	Ref				Ref				Ref
	Malay	1.2	0.7	1.8	0.5	1.2	0.9	1.5	0.2	1.4	1.1	2.0	0.02
	Indian	1.0	0.7	1.5	1.0	0.9	0.7	1.2	0.6	1.4	1.0	1.9	0.03
	Others	1.4	0.6	3.5	0.5	1.0	0.7	1.5	1.0	1.3	0.8	2.2	0.3
Marital status	Never married	Ref				Ref				Ref
	Married	1.2	0.8	2.0	0.4	1.0	0.7	1.3	0.7	1.2	0.8	1.7	0.4
	Divorced/separated	1.6	0.6	4.4	0.4	1.1	0.6	2.1	0.8	1.1	0.5	2.5	0.8
	Widowed	1.3	0.3	5.0	0.7	1.0	0.4	2.0	0.9	0.6	0.2	1.7	0.3
Education	University	Ref				Ref				Ref
	Primary and below	2.0	0.7	5.8	0.2	1.0	0.5	1.9	1.0	0.7	0.3	1.4	0.3
	Secondary	0.9	0.5	1.9	0.9	1.0	0.7	1.6	0.7	0.7	0.4	1.2	0.2
	Pre-Uni./ Junior College	0.8	0.4	1.9	0.7	1.0	0.6	1.5	0.9	0.4	0.2	0.8	0.01
	Diploma	0.5	0.3	1.0	0.07	1.2	0.8	1.8	0.4	0.7	0.4	1.2	0.2
	Vocational	1.2	0.6	2.6	0.76	1.8	1.1	2.9	0.02	1.2	0.7	2.2	0.5
Employment	Economically inactive	Ref				Ref				Ref
	Employed	0.7	0.4	1.1	0.1	0.9	0.7	1.2	0.5	0.9	0.6	1.4	0.8
	Unemployed	0.8	0.3	2.1	0.7	1.1	0.6	2.1	0.8	0.8	0.4	1.9	0.7

Our findings showed an overall decrease in the underweight category (from 8.4% in 2010 to 6.7% in 2016) and in the normal weight category (from 39.9% in 2010 to 36.5% in 2016) and an increase in the overweight category (from 35.1% in 2010 to 38.6% in 2016) and obese category (from 16.7% in 2010 to 18.7% in 2016) in the Singapore adult population, which is consistent with the global trend that has been observed in other studies. In 2008, about 1·5 billion adults globally were overweight, and 502 million adults were obese. While the epidemic started in high income-countries later, a similar increase was seen in middle and low-income countries (mostly in high socioeconomic countries)(10). In 2014, 39% of adults were overweight. 11% of men and 15% of women worldwide were obese. In the South East Asia region, the prevalence of overweight and obese were 22% and 5%, respectively (6).

From a systemic perspective, rising socio-economic status affects the population's lifestyle. People tend to be more sedentary due to longer working hours and are less able to devote time to vigorous physical activities, especially as jobs have become more office-based. (19) Furthermore, the convenience of transportation also plays a part in encouraging sedentary behavior. (10) A study conducted in China found that obesity prevalence is likely to increase among families that own auto-motor vehicles. (20) In Singapore, the car population, has increased from 2010 to 2016 (21) suggests that this could be another contributing factor.

Increased calorie content of food has been used to explain the global rise in body weight and obesity. (22) Overconsumption of food is promoted in the form of increased portion size and more affordable fast food that are high in energy but poor in nutrition. (23) Unhealthy food is more accessible than healthy and nutritious food in the market. (24) Singapore’s active night-life culture, including late-night suppers, might also be associated with the current increasing trend. (25) Emerging literature suggests feeding times may have an effect on metabolism rate and hence influence the development of obesity.(26, 27)

The current study found that age was significantly associated with BMI in both 2010 and 2016. With the economic growth in Singapore in the past couple of decades, people have more access to more nutrition-dense food resulting in changes in their health status.(28) Many studies suggest that the direct reason being human basal metabolism decreases with age due to muscle loss and an increase in body fat. (29, 30)

Gender was also found to be significantly associated with BMI in both 2010 and 2016. This finding is in line with other studies conducted on differences in body mass index and body weight perceptions between the two genders. (31, 32) One explanation could be that females generally have a higher body fat composition than males. (33) A locally based population study found that females, compared to males, are less likely to exercise regularly. (19) Few other studies also found that young females are more sedentary than males because they enjoy socializing more, such as sitting and talking, which could be another possible explanation for current findings on the gender difference. (34, 35)

Compared to degree holders, diploma holders were found less likely to be underweight in 2016 than in 2010, while those with vocational education were more likely to be overweight in 2016 than in 2010. Study findings also suggested that students with pre-university/junior college qualifications are less likely to become obese in 2016 than in 2010. While the findings are consistent with existing literature on the significant association between educational levels and obesity-related behavior, (36) Results did not show a significant inverse relationship between the education levels and BMI categories. Changes between the two time periods for individuals with the same educational level might be explained due to the positive effects of ongoing national programmes in local schools; for example, healthy eating guidelines are provided to help students choose healthy food and beverages. (37) Information regarding healthy ingredients and their suppliers also comply on an official website for school canteen vendors’ reference. (38)

Limitation

Data were collected through self-report from the respondents, so the measurement was subjective, the weight could be under-reported due to embarrassment, and both height and weight could be misreported due to inaccurate recollection. However, the mean differences between self-reported and measured anthropometric values were insignificant in this population ⁽³⁹⁾. Thus, the use of self-reported anthropometric values should not affect the validity of the conclusions drawn in this study. Data did not include those below 18 years of age, which may be an important group to explore.

Obesity has become a serious public health concern globally and locally, resulting in multiple chronic diseases affecting an individual’s quality of life. Hence, the increasing prevalence of obesity in Singapore is burdening the healthcare system, costing the nation approximately 260 million per year on disease-related treatments and comorbidities. (39) In 2016, obesity-related expenditure rose to at least 400 million. (12) To alleviate the burden, several agencies have ramped up their efforts and rolled out ongoing national campaigns to promote an active lifestyle among the local residents. (40, 41) However, more needs to be done, such that agencies should develop programs that are culturally sensitive for each ethnic as well as an educational group.

Ethics approval and consent to participate

Ethics approval for this study was obtained by the National Healthcare Group Domain Specific Review Board. Written informed consent was obtained before starting any study procedure. Parental consent was also obtained for minors, i.e., those aged 18-20 years.

Consent for publication

Not applicable

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests

Funding

The study was supported by funding from the Ministry of Health, Singapore and Temasek Foundation.

Authors' contributions

YJZ wrote the manuscript draft, EA provided statistical analysis, MS reviewed the manuscript and provided critical comments, RS, SS, VJA, SAC reviewed the manuscript. All authors read and approved the final manuscript.

Acknowledgments

Not applicable

Collaboration PS. Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies. The Lancet. 2009 Mar 28;373(9669):1083–96.
Ezzati M, Lopez AD, Rodgers A, Vander Hoorn S, Murray CJL, Comparative Risk Assessment Collaborating Group. Selected major risk factors and global and regional burden of disease. Lancet Lond Engl. 2002 Nov 2;360(9343):1347–60.
Czernichow S, Kengne AP, Stamatakis E, Hamer M, Batty GD. Body mass index, waist circumference and waist-hip ratio: which is the better discriminator of cardiovascular disease mortality risk?: evidence from an individual-participant meta-analysis of 82 864 participants from nine cohort studies. Obes Rev Off J Int Assoc Study Obes. 2011 Sep;12(9):680–7.
Singh GM, Danaei G, Farzadfar F, Stevens GA, Woodward M, Wormser D, et al. The Age-Specific Quantitative Effects of Metabolic Risk Factors on Cardiovascular Diseases and Diabetes: A Pooled Analysis. PLOS ONE. 2013 Jul 30;8(7):e65174.
Anstey KJ, Cherbuin N, Budge M, Young J. Body mass index in midlife and late-life as a risk factor for dementia: a meta-analysis of prospective studies. Obes Rev Off J Int Assoc Study Obes. 2011 May;12(5):e426-437.
WHO. Obesity and overweight [Internet]. [cited 2020 Nov 9]. Available from: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight
Chooi YC, Ding C, Magkos F. The epidemiology of obesity. Metabolism. 2019;92:6–10.
Wang H, Wang J, Liu MM, Wang D, Liu YQ, Zhao Y, et al. Epidemiology of general obesity, abdominal obesity and related risk factors in urban adults from 33 communities of Northeast China: the CHPSNE study. BMC Public Health. 2012 Nov 12;12:967.
Chooi YC, Ding C, Chan Z, Choo J, Sadananthan SA, Michael N, et al. Moderate Weight Loss Improves Body Composition and Metabolic Function in Metabolically Unhealthy Lean Subjects. Obesity. 2018;26(6):1000–7.
Swinburn BA, Sacks G, Hall KD, McPherson K, Finegood DT, Moodie ML, et al. The global obesity pandemic: shaped by global drivers and local environments. Lancet Lond Engl. 2011 Aug 27;378(9793):804–14.
Weir CB, Jan A. BMI Classification Percentile And Cut Off Points. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2020 [cited 2020 Nov 9]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK541070/
HPB-MOH Clinical Practical Guideline: Obesity [Internet]. [cited 2020 Nov 9]. Available from: https://www.hpb.gov.sg/docs/default-source/pdf/obesity-cpg_main_for-online-30-aug.pdf?sfvrsn=2288eb72_0
Publications and Methodology [Internet]. Base. 2020 [cited 2020 Nov 9]. Available from: http://www.singstat.gov.sg/find-data/search-by-theme/population/population-and-population-structure/publications-and-methodology
Wang J, Thornton J, Russell M, Burastero S, Heymsfield S, Pierson R. Asians have lower body mass index (BMI) but higher percent body fat than do whites: Comparisons of anthropometric measurements. Am J Clin Nutr. 1994 Aug 1;60:23–8.
Gurrici S, Hartriyanti Y, Hautvast J, Deurenberg P. Relationship between body fat and body mass index: differences between Indonesians and Dutch Caucasians. Eur J Clin Nutr. 1998 Nov;52(11):779–83.
Chong SA, Abdin E, Vaingankar JA, Heng D, Sherbourne C, Yap M, et al. A population-based survey of mental disorders in Singapore. Ann Acad Med Singapore. 2012 Feb;41(2):49–66.
Subramaniam M, Abdin E, Vaingankar JA, Shafie S, Chua BY, Sambasivam R, et al. Tracking the mental health of a nation: prevalence and correlates of mental disorders in the second Singapore mental health study. Epidemiol Psychiatr Sci. 2019 Apr 5;1–10.
Subramaniam M, Vaingankar J, Heng D, Kwok KW, Lim YW, Yap M, et al. The Singapore Mental Health Study: an overview of the methodology. Int J Methods Psychiatr Res. 2012 Jun;21(2):149–57.
Win AM, Yen LW, Tan KHX, Lim RBT, Chia KS, Mueller-Riemenschneider F. Patterns of physical activity and sedentary behavior in a representative sample of a multi-ethnic South-East Asian population: a cross-sectional study. BMC Public Health. 2015 Apr 1;15:318.
Bell AC, Ge K, Popkin BM. The road to obesity or the path to prevention: motorized transportation and obesity in China. Obes Res. 2002 Apr;10(4):277–83.
Annual Car Population by CC Rating [Internet]. Data.gov.sg. [cited 2020 Nov 9]. Available from: https://data.gov.sg/dataset/annual-car-population-by-cc-rating
Rodgers A, Woodward A, Swinburn B, Dietz WH. Prevalence trends tell us what did not precipitate the US obesity epidemic. Lancet Public Health. 2018;3(4):e162–3.
Young LR, Nestle M. Expanding portion sizes in the US marketplace: implications for nutrition counseling. J Am Diet Assoc. 2003 Feb;103(2):231–4.
Bray GA, Nielsen SJ, Popkin BM. Consumption of high-fructose corn syrup in beverages may play a role in the epidemic of obesity. Am J Clin Nutr. 2004 Apr;79(4):537–43.
Cultural_Food_Practices-Singapore.pdf [Internet]. [cited 2020 Nov 9]. Available from: https://www.snda.org.sg/downloads/publications/Cultural_Food_Practices-Singapore.pdf
Wang W, Wu Z, Dai Z, Yang Y, Wang J, Wu G. Glycine metabolism in animals and humans: implications for nutrition and health. Amino Acids. 2013 Sep 1;45(3):463–77.
Garaulet M, Gómez-Abellán P. Timing of food intake and obesity: A novel association. Physiol Behav. 2014 Jul 1;134:44–50.
Freedman DS, Khan LK, Dietz WH, Srinivasan SR, Berenson GS. Relationship of childhood obesity to coronary heart disease risk factors in adulthood: the Bogalusa Heart Study. Pediatrics. 2001 Sep;108(3):712–8.
H S, F K. [Aging, basal metabolic rate, and nutrition]. Nihon Ronen Igakkai Zasshi Jpn J Geriatr. 1993 Jul 1;30(7):572–6.
Rezuş E, Burlui A, Cardoneanu A, Rezuş C, Codreanu C, Pârvu M, et al. Inactivity and Skeletal Muscle Metabolism: A Vicious Cycle in Old Age. Int J Mol Sci. 2020 Jan;21(2):592.
Veggi AB, Lopes CS, Faerstein E, Sichieri R. [Body mass index, body weight perception and common mental disorders among university employees in Rio de Janeiro]. Rev Bras Psiquiatr Sao Paulo Braz 1999. 2004 Dec;26(4):242–7.
Af Y, G Z, Mq W, Ca S, Bm H. Weight perception and weight control practice in a multiethnic sample of US adolescents. South Med J. 2009 Apr 1;102(4):354–60.
Deurenberg-Yap M, Schmidt G, van Staveren WA, Deurenberg P. The paradox of low body mass index and high body fat percentage among Chinese, Malays and Indians in Singapore. Int J Obes Relat Metab Disord J Int Assoc Study Obes. 2000 Aug;24(8):1011–7.
Marshall SJ, Biddle SJH, Sallis JF, McKenzie TL, Conway TL. Clustering of Sedentary Behaviors and Physical Activity among Youth: A Cross-National Study. Pediatr Exerc Sci. 2002 Nov 1;14(4):401–17.
Rousea PC, Biddleb SJH. Article hej Health Education Journal.
Ball K, Crawford D. Socioeconomic status and weight change in adults: a review. Soc Sci Med 1982. 2005 May;60(9):1987–2010.
obesity-cpg_main_for-online-30-aug.pdf [Internet]. [cited 2020 Nov 9]. Available from: https://www.hpb.gov.sg/docs/default-source/pdf/obesity-cpg_main_for-online-30-aug.pdf?sfvrsn=2288eb72_0
Eating Healthily at The School Canteen [Internet]. [cited 2020 Nov 9]. Available from: https://www.healthhub.sg/live-healthy/511/Healthy meals in school
Khoo J, Eng SK, Foo CS. Recommendations for Obesity Management from Singapore. J ASEAN Fed Endocr Soc. 2011;26(2):110–110.
Physical Activity [Internet]. Health Promotion Board. [cited 2020 Nov 10]. Available from: https://www.hpb.gov.sg/healthy-living/physical-activity
https://www.healthhub.sg/programmes [Internet]. [cited 2020 Nov 10]. Available from: https://www.healthhub.sg/programmes

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Changes in body mass index and its association with socio-demographic characteristics between 2010 and 2016 in Singapore

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Introduction

Methods

Respondents and Procedure

Anthropometric measurements

Sociodemographic information

Body Mass Index (BMI)

Statistical analysis

Results

Overall Prevalence

BMI and associated factors across the two surveys

Age groups

Gender

Ethnicity

Marital Status

Education Level

Discussion

Conclusion

Declarations

References

Additional Declarations

Status:

Version 1