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Consumption of Convenience Store Food and Obesity in Urban Preschool-going Children: A Cross-Sectional study in Nepal

Article Information

Short Title: Obesity in urban preschool-going children

Parvez Ahmed Ph.D.1*, Alisha Bohora BSc.PH2, Nawzia Yasmin MPH1, Nazmul Alam Ph.D.3, Mahim Eaty BSc.PH 4

1State University of Bangladesh, Dhaka, Bangladesh

2Aarus Lifestyle Hospital, Lalitpur-10, Nepal

3Asian University for Women, Bangladesh

4PLAN International, Bangladesh

*Corresponding Author: Dr. Parvez Ahmed, Assistant Professor, State University of Bangladesh, 77 Satmasjid Road, Dhanmondi, Dhaka, 1205, Bangladesh

Received: 07 May 2022; Accepted: 16 May 2022; Published: 27 May 2022

Citation: Parvez Ahmed, Alisha Bohora, Nawzia Yasmin, Nazmul Alam, Mahim Eaty. Consumption of Convenience Store Food and Obesity in Urban Preschool-going Children: A Cross-Sectional Study in Nepal. Journal of Food Science and Nutrition Research 5 (2022): 520-528.

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Abstract

Background

Childhood obesity is a complex condition and the associated factors are not well understood. This paper aims to explore the relationship of behavioral factors including consumption of convenience stores food for lunch at school and obesity in urban preschool-going children.

Methodology and principal findings

A cross-sectional study was conducted among 351 randomly selected preschool-going children in Montessori-based schools in Kathmandu, Nepal. Parental socio-economic information, lifestyle, and behavioral information of children including lunch food source and physical activity was collected. The Body Mass Index (BMI) of the children was measured and the cut-off point of the World Health Organization was used to categorize children into underweight, normal, overweight, and obese according to age and sex. Multinomial logistic regression analysis was conducted to assess the association. Odds of obesity increased for consumption of convenience store food for lunch [Adjusted Odds Ratio (OR) = 2.758, 95% CI=0.567-13.415] in comparison to home-cooked food. Odds also increased moderately for physical inactivity (AOR=1.951, 95% CI=0.199-19.152).

Conclusion

The findings suggest that consumption of convenience store food for lunch at school is associated with obesity in urban preschool-going children in Nepal. The findings can help to explain the information and knowledge gaps regarding obesity in preschool-going children in Nepal.

Keywords

Public health, Obesity, Preschool child, Cross-sectional study, Determinants, Nepal

Public health articles; Obesity articles; Preschool child articles; Cross-sectional study articles; Determinants articles; Nepal articles

Article Details

1. Introduction

Obesity among children has long-term effects on mortality and morbidity. Overweight and obese children are likely to maintain their status into adulthood and are at higher risks for developing cardiovascular diseases, stroke, hypertension, metabolic diseases such as type 2 diabetes, osteoarthritis, respiratory diseases, and certain cancers [1,2]. Although the health consequences of obesity are mostly manifested during adulthood, the factors underlying the disease conditions commonly originate during childhood [3]. Obesity in children has been associated with a wide range of factors including genetics, lifestyle, and behavioral factors such as diet patterns including fast food and takeaway meals, reduced or poor sleep quality, sedentary lifestyle, reduced physical activity, and their interaction [4-8]. However, these factors overlap each other which makes childhood obesity a complex condition. Childhood obesity is a global health problem [9]. The highest prevalence rates of childhood obesity have been observed in developed countries; however, its prevalence is increasing in developing countries as well including Nepal, particularly in urban settings [10-12]. This might be due to rapid urbanization and economic transitions such as subsistence to market in Nepal. According to the national census, the urban population in Nepal increased to 17% of the total population in 2011 from 13.9% in 2001 [13]. Economic transition and rapid urbanization influence lifestyle and behavioral factors such as changes in dietary habits including takeaway meals or fast food [14].

Nepal has been facing challenges of under-nutrition among under-5 children for decades. The coexistence of under-nutrition and obesity among preschool-going children can give rise to the double burden of malnutrition in Nepal. However, research on obesity among preschool-going children in Nepal associated with lifestyle and behavioral related factors has so far been limited. This study aims to fill the information and knowledge gaps in this area.

2. Methods

2.1 Study setting and design

A cross-sectional study was carried out in Montessori-based schools in Kathmandu in Nepal. The data collection process was carried out from June 1st 2018 to January 31st 2019.

2.2 Participants and Sampling

Participants of the study were preschool-going children aged 3-5 years. The sample size was determined by using a single population proportion formula with the following assumptions: level of significance (α) = 5% (at a confidence level of 95%), marginal error d =10%, and p=50% (as the prevalence of obesity among preschool going children in Nepal is unknown). The Z value is 1.96 (n = sample size, P = proportion, and d = marginal error)

n= z2 * p * (1-p)/d2=384

A simple random sampling technique was used to select the children following STROBE guidelines (https://www.strobe-statement.org/index.php?id=available-checklists).A total of 351 children participated in the study after obtaining consent.

2.3 Data collection

After getting permission from the school authority, structured questionnaires were distributed to a total of 351 children and respective mothers, with instructions about the questionnaire by the principal investigator. Each child's weight and height measurements were taken using a digital weighing machine and standard measuring tape respectively.

2.4 Variables

The questionnaire included demographic information including age and sex of the children, socioeconomic information including parental socio-economic status, lifestyle and behavioral information including the source of food for lunch at school, sedentary time, and physical activity. Anthropometric measurements including the height and weight of the children were measured with the consent of the parents. Body Mass Index (BMI) as weight in kg per square of height in meter was calculated. The World Health Organization (WHO) cut-off point according to age and sex was used to measure the underweight, normal, overweight, and obesity [15,16]. BMI less than the 5th percentile were categorized as Underweight, 5th percentile to less than the 85th percentile as Healthy weight, 85th to less than the 95th percentile as overweight, equal to or greater than the 95th percentile as obese. The Source of lunch food was divided into three categories. Source of food for children who were carrying home cook food to school or mothers bringing home-cooked food during lunchtime at school was categorized as home-cooked food, children having school cafeteria food at lunch was categorized as school food, and source of food for children having convenience store food during lunch at school brought by mothers was categorized as convenience store food. Physical activity was divided into three groups and the sedentary time limit was divided into two groups according to the Physical Activity and Exercise Guidelines for all infants, toddlers, and preschoolers by the Ministry of Health of Australia [17]. Children in the last 7 days, who did 3 hours of various physical activities spread throughout the day each day at school and home, including energetic play such as running – playing tips, ball games or races at the park, kicking, throwing, and jumping – at the park or by creating fun obstacle courses at home, dancing, skipping were classified into group I. Children in the last 7 days, who did 3 hours of various physical activities spread throughout the day each day at school and home, including 60 minutes of energetic play such as running – playing tips, ball games or races at the park, kicking, throwing, and jumping – at the park or by creating fun obstacle courses at home, dancing, skipping were classified into group II. Children without any physical activities in the last 7 days were classified into group III. Sedentary time more than or equal to one hour a day was classified into group I, and less than 1 hour into group II. Data were entered and analyzed in SPSS. Multinomial regression analysis was done to estimate risk after potential explanatory variables adjusted for each of the other. Healthy weight status was the reference group in the analysis.

2.5 Ethical consideration

The study was approved by the Research and Ethics Committee of the Asian University for Women and the study followed the Helsinki declaration. Consent of participants was obtained before the interview.

3. Result

Table 1 describes the characteristics of children attending preschool according to the lunch food source. A substantial difference was observed in the distribution of known or putative determinants. For example, 38% of the children from high income family were having convenience store food which was only 3.3% in the children from the low income families. Table 2 shows the prevalence of BMI status of preschool-going children according to the characteristics of the participants. The prevalence of obesity varied according to all putative determinants. Prevalence of obesity was 34.2% in children having home-cooked food for lunch while it was 36.8% among children having convenience store food for lunch and 28.9% among children having school cafeteria food. A similar difference was observed for overweight. Table 3 shows multinomial regression analysis. Preschool-going children who were having convenience store food were found almost 3 times at risk of obesity in comparison to children having home-cooked food (OR=2.758, 95% CI=0.567-13.415). Among other lifestyle factors, children with no physical activity were found at risk in comparison to children with no activity (OR=1.951, 95% CI=0.199-19.152). Among the socioeconomic variables, children from high-income families were found at risk (OR=1.822, 95% CI=0.479-6.928) in comparison to low-income families. In terms of gender, odds increased for female children (OR=1.791, 95% CI=0.799-4.015) in comparison to male children. However, the risks were not significant statistically.

Characteristics

Source of lunch

Home food

School cafeteria

Convenience store

Total

Gender

Female

95(57.2%)

30(46.9%)

44(36.4%)

169(48.1%)

Male

71(42.8%)

34(53.1%)

77(63.6%)

182(51.9%)

X2 =10.843,df=2,p=0.002

Socioeconomic status

Low

154(92.8%)

57(89.1%)

4(3.3%)

215(61.3%)

Middle

7(4.2%)

6(9.4%)

71(58.7%)

84(23.9%)

High

5(3.0%)

1(1.6%)

46(38.0%)

52(14.8%)

X2 =262.165,df=4, p=0.000

Physical activity

3 hours including 1-hour energy play

8(4.8%)

2(3.1%)

3(2.5%)

13(3.7%)

No activity

75(45.2%)

38(59.4%)

73(60.3%)

186(53.0%)

3 hours without 1-hour energy play

83(50.0%)

24(37.5%)

45(37.2%)

152(43.3%)

X2=8.000,df=4,p=o.498

Sedentary time

< 1 hours

83(50.0%)

26(40.6%

46(38.0%)

155(44.2%

>1 hours

83(50.0%

38(59.4%

75(62.0%

196(55.8%

X2= 6.628,df=2, p=0.092

Table 1: Characteristics of urban preschool-going children according to the lunch food source (n=351).

Table icon

Table 2: BMI status of preschool going children according to Lifestyle, behavioral and socioeconomic characteristics

Variables

Overweight

Obesity

Adjusted Odds Ratio

95% CI

Adjusted Odds Ratio

95% CI

Age

1.045

1.006 1.087

1.034

0.976- 1.095

Gender

Male

Reference

-

-

-

Female

1.696

0.978- 2.940

1.791

0.799- 4.015

Family socioeconomic status

High

Reference

-

-

-

Middle

0.841

0.227- 3.120

0.363

0.063- 2.079

Low

1.112

0.429 2.882

1.822

0.479- 6.928

Physical activity

3 hours of physical activity with 1 hour of energy play

reference

-

-

-

No activity

1.381

0.222- 0.656

1.951

0.199- 19.152

3 hours of physical activity with no energy play

0.592

0.154 2.275

0.674

0.066- 6.838

Leisure sedentary time

<1h/day

Reference

-

-

-

≥ 1h/day

0.378

0.219-0.653

0.618

0.275- 1.392

Lunch food

Home

Reference

-

-

-

School cafeteria

0.383

0.116 -1.262

1.225

0.276 5.435

Convenience store

0.486

0.134 -1.760

2.758

0.567 13.415

Table 3: Logistic regression analysis of Overweight/Obesity in preschool-going children

4. Discussion

The school-based cross-sectional was conducted to assess prevalence and factors associated with obesity in preschool-going children. The outcome information of the study was defined by the WHO Body Mass Index cut-off points according to age and sex. The source population included the preschool children attending the school selected through simple random sampling. The study used anthropometric measurements to measure the weight and height of the children, and the school registry for age. Results of this cross-sectional study show variation in the prevalence of obesity according to behavioral factors including lunch food sources, physical activity, and sedentary time. Among the obese preschool children 34.2% were having home-cooked for lunch, 28.9% were having school cafeteria food for lunch, and 36.8%were consuming convenience store food for lunch. After adjusting the potential confounders, children having convenience store food were found almost 3 fold at risk of being obese compared to the children having home-cooked food [Adjusted Odds Ratio (OR) = 2.758, 95% CI=0.567-13.415]. The Association of obesity with consumption food purchased from convenience is consistent with previous literature [18,19]. Many unhealthy foods are sold in convenience stores, such as sugar-sweetened beverages (SSB) and energy-dense snacks while limited healthy food is provided in convenience stores [20]. Parents buy unhealthy cheap food such as sugar-sweetened beverages and energy-dense snacks on the way to school due to poor socioeconomic status or lack of knowledge, increasing the intake of energy among children [21]. Energy intake that exceeds energy expenditure results in obesity among children. Majority of the obese preschool-going children (68.4%) were found without any physical activity in the last 7 days. The study explored preschool-going children who were not engaged in any physical activity are at risk of obesity in comparison to children with 3 hours of physical activity including I hour of energy play (Odds Ratio=1.951, 95% CI=0.199-19.152). Children who were not engaged in any physical activity are more likely to be overweight and obese than those who were engaged in physical activity, consistent with previous findings [22,23]. Physical activity increases total energy expenditure, which benefits staying in energy balance and lowers risks of overweight and obesity in children [24]. The study explored children who are from low socioeconomic status (SES) are at risk of being obese in comparison to children from high socioeconomic status (SES) families (OR=1.822, 95% CI=0.479-6.928). This finding corroborates with the result of cross-sectional studies conducted before in the USA [25,26]. Families from low socioeconomic status (SES) are more likely to live in low SES communities. Low SES communities may have fewer physical activity facilities, particularly few free activity resources, than high SES communities, thus limiting access to opportunities for physical activity that can contribute to obesity in children. Many leisure activities may also be cost-prohibitive for low-income parents due to membership, participation, or equipment fees [27]. Furthermore, families with low income can’t afford healthy food and tend to buy cheap junk food [28]. In the present study, girls were found at risk in comparison to boys (OR=1.961, 95% CI=0.510-7.543). In terms of sex differences, a study in India reported that boys are generally more likely to participate in physical activities than girls and at lower risk of obesity in comparison to girls. [29] However, in contrast to this study, a study in Vietnam among preschool-going children explored preschool- going boys are at risk of obesity in comparison to girls [30].

Limitation

The present study is subjected to some limitations. First, the survey was a cross-sectional study that did not define the causal relationship; second, the authenticity of lunch food source was not verified and the amount of calorie consumption was not measured; lastly, as the information is provided by mothers and was not monitored, there is a likelihood of recall bias. Studies with large sample sizes or longitudinal designs are needed to explore the causal relationship of overweight.

4. Conclusion

Current findings suggest that consumption of convenience store food for lunch at school is associated with obesity in urban preschool-going children in Nepal. The findings can help to explain the information and knowledge gaps regarding obesity in urban preschool-going children in Nepal.

Acknowledgment

Authors like to acknowledge the Asian University for Women, Chittagong for technical supports.

Funding

None

Conflict of interest

None

References

  1. Bhave S, Bavdekar A, Otiv M. IAP National Task Force for Childhood Prevention of Adult Diseases: Childhood Obesity. IAP National Task Force for Childhood Prevention of Adult Diseases: Childhood Obesity. Indian Pediatr 41 (2004): 559-575.
  2. Must A, Strauss RS. Risks and consequences of childhood and adolescent obesity. Int J Obes Relat Metab Disord 23 (1999): 2-11
  3. Raut BK, Jha MK, Baidya D, et al. Determination of risk factors associated with childhood obesity and the correlation with adult obesity- A cross-sectional study from Nepal. Am J Health Res 2 (2014): 134-139.
  4. Garver WS, Newman SB, Gonzales-Pacheco DM, et al. The genetics of childhood obesity and interaction with dietary macronutrients. Genes Nutr 8 (2013): 271-287.
  5. Jimenez-Cruz A, Bacardi-Gascon M, Pichardo-Osuna A, et al. Infant and toddlers' feeding practices and obesity amongst low-income families in Mexico. Asia Pac J Clin Nutr 19 (2010): 316-323.
  6. Chaput JP. Is sleep deprivation a contributor to obesity in children? Eat Weight Disord 21 (2016): 5-11.
  7. Xi B, Wang C, Wu L, et al. Influence of physical inactivity on associations between single nucleotide polymorphisms and genetic predisposition to childhood obesity. Am J Epidemiol 173 (2011): 1256-1262.
  8. Janssen I, Katzmarzyk PT, Boyce WF, et al. Comparison of overweight and obesity prevalence in school-aged youth from 34 countries and their relationships with physical activity and dietary patterns. Obes Rev 6 (2005): 123-132.
  9. Wang Y, Lobstein T. Worldwide trends in childhood overweight and obesity. Int J Pediatr Obes 1 (2006): 11-25.
  10. Worldwide prevalence of obesity. International Obesity Task Force (2007).
  11. De Onis M, Blossner M, Borghi E. Global prevalence and trends of overweight and obesity among preschool children. Am J Clin Nutr 92 (2010): 1257-1264.
  12. Koirala M, Khatri RB, Khanal V, et al. Prevalence and factors associated with childhood overweight/obesity of private school children in Nepal. Obes Res Clin Pract 9 (2015): 220-227.
  13. Central Bureau of Statistics. Preliminary result of census 2011. Kathmandu: National Planning Commission (2011).
  14. World Health Organization. Obesity: preventing and managing the global epidemic: report of a WHO consultation on obesity. Geneva (1997).
  15. World Health Organization. Child Growth Indicator, Body- Mass- Index- for- age-boys (2018).
  16. World Health Organization. Child Growth Indicator, Body- Mass- Index- for- age-girls (2018).
  17. Physical activity and exercise guidelines for all Australians. For infants, toddlers and preschoolers (birth to 5 years) (2018).
  18. Thompson OM, Ballew C, Resnicow K, Must A, Bandini LG, Cyr H, Dietz WH. Food purchased away from home as a predictor of change in BMI z-score among girls. Int J Obes Relat Metab Disord 28 (2004): 282-289.
  19. Gebauer H, Laska MN. Convenience stores surrounding urban schools: an assessment of healthy food availability, advertising, and product placement. J Urban Health 88 (2011): 616-622.
  20. Lucan SC, Karpyn A, Sherman S. Storing empty calories and chronic disease risk: snack-food products, nutritive content, and manufacturers in Philadelphia corner stores. J Urban Health 87 (2010): 394-409.
  21. Borradaile KE, Sherman S, Vander Veur SS, et al. Snacking in children: the role of urban corner stores. Pediatrics 124 (2009): 1293-1298.
  22. Kohl HW, Hobbs KE. Development of physical activity behaviors among children and adolescents. Pediatrics 101 (1998): 549-554.
  23. Tremblay MS, Willms JD. Is the Canadian childhood obesity epidemic related to physical inactivity? Int J Obes Relat Metab Disord 27 (2003): 1100-1105.
  24. Tanaka C, Janssen X, Pearce, M, et al. Bidirectional Associations Between Adiposity, Sedentary Behavior, and Physical Activity: A Longitudinal Study in Children. J Phys Act Health 7 (2018): 1-9.
  25. Assari S. Family Income Reduces Risk of Obesity for White but Not Black Children. Children (Basel) 5 (2018): 73.
  26. Jin Y, Jones-Smith JC. Associations between family income and children's physical fitness and obesity in California, 2010-2012. Prev Chronic Dis 12 (2015): 12-17.
  27. Gordon-Larsen P, Nelson MC, Page P, et al. Inequality in the built environment underlies key health disparities in physical activity and obesity. Pediatrics 117 (2006): 417-424.
  28. Rehm CD, Monsivais P, Drewnowski A. The quality and monetary value of diets consumed by adults in the United States. Am J Clin Nutr 94 (2011): 1333-1339.
  29. Kaur S, Sachdev HP, Dwivedi SN, et al. Prevalence of overweight and obesity amongst school children in Delhi, India. Asia Pac J Clin Nutr 17 (2008): 592-596.
  30. Dieu HT, Dibley MJ, Sibbritt DW, et al. Trends in overweight and obesity in pre-school children in urban areas of Ho Chi Minh City, Vietnam, from 2002 to 2005. Public Health Nutr 12 (2009): 702-709.

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