Journal of Obesity

Journal of Obesity / 2020 / Article

Research Article | Open Access

Volume 2020 |Article ID 9173075 | https://doi.org/10.1155/2020/9173075

Mohammed Ahmed, Abdu Seid, Adnan Kemal, "Does the Frequency of Watching Television Matters on Overweight and Obesity among Reproductive Age Women in Ethiopia?", Journal of Obesity, vol. 2020, Article ID 9173075, 7 pages, 2020. https://doi.org/10.1155/2020/9173075

Does the Frequency of Watching Television Matters on Overweight and Obesity among Reproductive Age Women in Ethiopia?

Academic Editor: Eliot Brinton
Received16 Mar 2020
Revised29 May 2020
Accepted30 Jun 2020
Published12 Aug 2020

Abstract

Background. Studies in developed countries have revealed an association of different magnitudes between watching television and the risk of being overweight and obese among reproductive age women. Even so, there is no evidence of such an association in the context of the Ethiopian population. Hence, the study aimed to assess the association between watching television with overweight and obesity in a nationally representative sample of Ethiopian women. Methods. A cross-sectional study was conducted by using secondary data analysis from 2016 Ethiopia demographic and health survey among women aged from 15 to 49 years. The samples were selected using a two-stage stratified cluster sampling technique. A total of 10,074 women were included in the analysis. The outcome variables were both overweight and obesity, whereas the main exposure variable was the frequency of watching television. Multivariate logistic regression analysis was performed for adjusting potential confounders. Adjusted odds ratio (AOR) with 95% confidence intervals was used to declare a statistically significant association. Results. The study found that watching television at least once a week was significantly associated with both overweight (AOR: 1.79; 95% CI: 1.20–2.73) and obesity (AOR: 3.76; 95% CI: 2.04–6.95). The study also divulged that the odds of overweight were higher among women aged 25–39 years (AOR: 2.17; 95% CI: 1.25–3.77) and 40–49 years (AOR: 2.69; 95% CI: 1.45–5.00), urban residents (AOR: 1.76; 95% CI:1.17–2.65), attended higher education (AOR:2.11; 95% CI: 1.22–3.65), and richest in the wealth index (AOR: 2.83; 95% CI:1.71–4.68). Similarly, the odds of obesity were higher among women aged 25–39 years and 40–49 years, attended higher education, and the richest in wealth index. Conclusions. The results from this study demonstrated that watching television at least once a week is associated with obesity among reproductive age women in Ethiopia. Therefore, a social behavioral change communication campaign needs to be taken to improve awareness regarding the harmful consequences of watching television for long hours. Further research studies should be conducted among men and adolescents to determine whether this positive association exists among that target population as well.

1. Background

Overweight and obesity are defined as abnormal or excessive fat accumulation that may impair health. Obesity is a worldwide public health problem, which affects both developed and developing countries [1]. Overweight and obesity are important risk factors for the development of noncommunicable diseases, including preeclampsia and eclampsia [2], cardiovascular diseases [3], hypertension [4], diabetes mellitus [5, 6], chronic kidney diseases [7], and cancer [8].

Reproductive aged women have higher rates of overweight and obesity and are more adversely affected by obesity-related complications than men [9]. This gender discrepancy is mainly owed to general weight gain during pregnancy, childbearing years, adverse lifestyle, or risk factors associated with pregnancy and the postpartum period [10]. Maternal obesity increases the risk of copious complications, including a higher risk of cesarean delivery, a higher incidence of anesthetic and postoperative complications, low Apgar scores, macrosomia, and neural tube defects [11, 12].

The association between watching television with overweight and obesity was evidenced by a different explanation such as hours spent in front of the television, decrease in the physical activity [13], a television-based food advertising influencing what and how people eat [14], and watching television leads to mindless eating or a lack of attention paid to consume due to external cues in the environment [15].

In the same vein, different studies revealed that watching television is positively associated with an overall increase in food intake [16, 17], particularly pizza [17], fast food, and high-calorie snacks [18] and is inversely associated with intakes of vegetables and fruits [19, 20]. Similarly, young adults consumed more calories from energy-dense foods when watching television than when listening to classical music [17].

Various studies in different countries showed that watching television frequently was associated with overweight and obesity among reproductive aged women [2124]. A previous study, which was conducted in Ethiopia, on the determinant of obesity and overweight among women [25] faced two drawbacks; first, the study was conducted without excluding women who gave birth in the last 2 months, which overestimated the prevalence of overweight and obesity as evidence showed that postpartum body weight is influenced by gestational weight gain and lactation [26, 27]. Second, both overweight and obesity were analyzed as one outcome, merged during their analysis, which leads to misclassification of the outcomes. Therefore, the current study endeavored to fill this dearth by investigating whether there is an association between the frequency of watching television with overweight and obesity among reproductive age women.

2. Methods

2.1. Data Source and Sampling Procedure

The current study uses secondary data from the 2016 Ethiopia demographic and health survey (EDHS). A detailed description of the study design and methodology of the survey were founded elsewhere [28]. A two-stage stratified cluster sampling was used. Since Ethiopia has nine regional states and two city administrations, stratification was done by separating each structural division into urban and rural areas, except Addis Ababa (entirely urban). Therefore, a total of 23 sampling strata have been created. Then, each stratum was again further divided into enumeration areas (EAs) or clusters prepared by the 2007 Population and Housing Census as a sampling frame. In the first stage, a total of 645 EAs were selected. Of which, 202 were from urban areas. In the second stage, a fixed number of 28 households per cluster were selected randomly from the household listing. A total of 15,683 women (15–49 years) were interviewed, making up response rates of 95%. The analytic sample for the current study consisted of women who were not pregnant and gave birth 2 months or more with a BMI of greater or equal to 18.5 Kg/m2 were included in the analysis (n = 10074).

2.2. Selection Criteria

The sample utilized in this study excluded (i) women who were pregnant at the time of the survey (n = 1122); (ii) women who gave birth less than 2 months preceding the date of the interview (n = 365); and (iii) women who had a BMI of less than 18.5 Kg/ m2 (n = 4037) (Figure 1).

2.3. Study Variables

The dependent variables of this study had two outcomes (overweight and obesity) measured by body mass index (BMI), which is expressed as weight in kilogram divided by height in m2. EDHS carries out anthropometric measurements such as height and weight for women aged 15–49 years, excluding women who are pregnant and women who gave birth within 2 months preceding the date of the interview as an indicator of woman’s nutritional status. As per WHO recommendations, the woman was categorized as normal when BMI was 18.5–24.9 kg/m2, overweight if BMI was 25–29.9 kg/m2, and obese if BMI was ≥30 kg/m2.

The exposure variable of main interest was the frequency of watching television (TV), which was measured based on three categories (not watching television at all/watching television less than once a week/and watching television at least once a week).

Based on reviewing different literature, the following covariates were deemed relevant to the topic and for inclusion in the study such as the age of the respondents(15–24 years/25–39/40–49 years), residence (urban/rural), educational level (no education/primary/secondary/higher), marital status (never in union/currently in union/formerly in union), occupation (unemployed/nonagricultural/agricultural), wealth index (poorest/poorer/middle/richer/richest), alcohol drinking(yes/no), and parity (one birth/2 to 4 births/greater than 4 births).

2.4. Data Analysis

The data were analyzed using SPSS version 20. All statistical procedures incorporated complex sampling design analysis applied in the 2016 EDHS. Frequencies and weighted percentages of the variables were utilized to describe the profile of the study participants. Rao–Scott adjusted chi-square statistic was used to examine the distribution between the outcome variables (overweight and obesity) and each of the independent variables. Multivariate logistic regression analysis was conducted to assess the association between watching television with overweight and obesity among women by adjusting the covariates. Adjusted odds ratios (AOR) with 95% confidence interval were used to declare a statistically significant association.

3. Results

3.1. Participant’s Characteristics

A total of 10,074 samples of reproductive age women were included and analyzed. About, 75.1% of respondents was rural residents, and 46.6% of the participants was found in the age between 25 and 39 years. Moreover, 46.8% of the respondents did not attend education. Only 17.5% of women watched television at least once per week. The prevalence of normal BMI, overweight, and obesity was 90.3%, 7.7%, and 2.0%, respectively (Table 1).


VariablesCategoryTotalBody mass index (BMI) value
NormalOverweightObese
n (wt.%)n (wt.%)n (wt.%)n (wt.%)

Age in years15–243867 (37.7)3555 (39.4)278 (24.3)34 (9.1)<0.001
25–394581 (46.6)3745 (45.5)628 (55.9)208 (58.3)
40–491626 (15.7)1234 (15.0)280 (19.8)112 (32.6)

ResidenceUrban3903 (24.9)2746 (20.7)861 (59.6)296 (82.8)<0.001
Rural6171 (75.1)5788 (79.3)325 (40.4)58 (17.2)

Educational statusNo education4283 (46.8)3863 (48.7)334 (31.5)86 (19.1)<0.001
Primary3427 (34.6)2922 (34.7)400 (33.3)105 (35.9)
Secondary1531 (12.6)1170 (11.8)262 (20.0)99 (22.4)
Higher833 (5.9)579 (4.8)190 (15.3)64 (22.6)

Marital statusNever in union2846 (26.9)2518 (27.6)278 (22.4)50 (14.1)<0.001
Currently in union6097 (63.2)5136 (63.1)717 (63.5)244 (67.3)
Formerly in union1131 (9.9)880 (9.4)191 (14.1)60 (18.6)

OccupationUnemployed4800 (47.4)4163 (48.4)486 (39.8)151 (30.8)<0.001
Nonagricultural3608 (31.8)2784 (29.3)634 (52.1)190 (65.5)
Agricultural1666 (20.8)1587 (22.3)66 (8.2)13 (3.6)

Wealth indexPoorest1974 (15.0)1854 (16.0)104 (6.4)16 (2.0)<0.001
Poorer1250 (17.0)1198 (18.2)41 (6.9)11 (3.5)
Middle1284 (18.6)1228 (20.0)50 (7.9)6 (0.4)
Richer1386 (20.0)1282 (20.9)90 (12.7)14 (7.7)
Richest4180 (29.4)2972 (25.0)901 (66.2)307 (86.3)

Parity11224 (16.8)991 (16.7)182 (19.2)51 (14.3)<0.001
2–42840 (41.5)2251 (40.3)417 (48.2)172 (64.4)
>42490 (41.6)2190 (43.0)226 (32.6)74 (21.4)

Drank alcoholNo6470 (62.8)5525 (63.2)735 (60.5)210 (56.6)0.25
Yes3604 (37.2)3009 (36.8)451 (39.5)144 (43.4)

Frequency of watching televisionNot at all6038 (69.9)5588 (73.6)382 (40.7)68 (17.5)<0.001
Less than once a week1251 (12.6)1029 (12.5)173 (13.5)49 (9.8)
At least once a week2785 (17.5)1917 (13.9)631 (45.8)237 (72.7)

n: frequency; wt.%: weight percentage; value <0.05 is based on the adjusted F, which is a variant of the second-order Rao–Scott adjusted chi-square statistic.
3.2. Factors Associated with Overweight and Obesity among Reproductive Age Woman in Ethiopia

All the variables were entered into multivariate logistic regression analysis. After adjusting for potential confounders by logistic regression, watching television at least once per week, age of the respondents (25–39 and 40–49 years), urban residence, attending higher education, and being rich in wealth index were positively associated with overweight and obesity. The study found that the odds of overweight and obesity was 1.79 (AOR: 1.79; 95% CI: 1.20–2.73) and 3.76 (AOR: 3.76; 95% CI: 2.04–6.95) times higher among women watching television at least once a week compared to women not watched at all, respectively.

In this study, the odds of overweight were higher among woman aged 25–39 years (AOR: 2.17; 95% CI: 1.25–3.77) and 40–49 years (AOR: 2.69; 95% CI: 1.45–5.00), urban residents (AOR: 1.76; 95% CI:1.17–2.6>5), attended higher education (AOR:2.11; 95% CI: 1.22–3.65), and richest in wealth index (AOR: 2.83; 95% CI:1.71–4.68). Likewise, the odds of obesity were higher among woman aged 25–39 years (AOR: 13.8; 95% CI: 5.47–34.9) and 40–49 years (AOR: 32.9; 95% CI: 12.1–89.7), attended higher education (AOR: 2.95; 95% CI: 1.25–6.97), and richest in wealth index (AOR: 4.23; 95% CI: 1.20–15.5) (Table 2).


VariablesCategoryOverweightObesity
COR (95% CI)AOR (95% CI)COR (95% CI)AOR (95% CI)

Age in years15–241111
25–391.99 (1.60–2.50)2.17 (1.25–3.77)5.57 (2.92–10.6)13.8 (5.47–34.9)
40–492.13 (1.64–2.78)2.69 (1.45–5.00)9.44 (5.47–16.3)32.9 (12.1–89.7)

ResidenceUrban5.64 (4.30–7.40)1.76 (1.17–2.65)18.5 (10.1–33.6)2.33 (0.75–7.18)
Rural1111

Educational statusNo education1111
Primary1.48 (1.19–1.85)1.18 (0.86–1.63)2.63 (1.51–4.58)2.00 (0.95–4.21)
Secondary2.62 (1.96–3.50)1.47 (0.89–2.43)4.84 (2.85–8.21)2.02 (0.95–4.28)
Higher4.95 (3.48–7.02)2.11 (1.22–3.65)12.1 (5.12–28.4)2.95 (1.25-6.97)

Marital statusNever in union1111
Married1.24 (0.97–1.58)1.52 (0.54–4.27)2.08 (1.40–3.10)0.56 (0.19–1.59)
Formerly in union1.85 (1.34–2.57)1.79 (0.63–5.15)3.88 (2.28–6.60)0.72 (0.21–2.45)

OccupationUnemployed1111
Nonagricultural2.16 (1.73–2.69)1.06 (0.78–1.44)3.50 (2.16–5.67)1.18 (0.74–1.89)
Agricultural0.44 (0.29–0.67)0.57 (0.37–0.88)0.25 (0.08–0.74)0.45 (0.14–1.43)

Wealth indexPoorest1111
Poorer0.96 (0.58–1.57)0.79 (0.43–1.50)1.55 (0.57–4.24)1.84 (0.64–5.30)
Middle0.99 (0.61–1.62)0.86 (0.49–1.52)0.17 (0.06–0.53)0.19 (0.06–0.63
Richer1.53 (0.99–2.37)1.26 (0.73–2.17)2.93 (1.07–8.06)2.19 (0.76–6.40)
Richest6.69 (4.60–9.72)2.83 (1.71-4.68)27.6 (13.7–55.2)4.23 (1.20-15.5)

Parity11111
2–41.52 (1.09–2.11)0.79 (0.48–1.32)1.72 (0.87–3.35)0.73 (0.29–1.81)
>41.58 (1.20–2.07)0.88 (0.63–1.24)3.21 (2.14–4.82)1.43 (0.89–2.26)

Drank alcoholNo1111
Yes1.12 (0.87–1.43)0.99 (0.75–1.32)1.31 (0.93–1.85)0.90 (0.56–1.43)

Watching televisionNot at all1111
Less than once a week1.94 (1.41–2.66)1.04 (0.63–1.73)3.28 (1.66–6.49)1.76 (0.78–3.95)
At least once a week5.95 (4.68–7.56)1.79 (1.20-2.73)22.0 (12.8–37.6)3.76 (2.04-6.95)

Significant association by AOR with 95% CI with a value of less than 0.05.

4. Discussion

The study found that watching television at least once a week was significantly associated with both overweight and obesity among women of reproductive age in Ethiopia. The finding is coherent with studies conducted in the United States, Australia, Myanmar, Bangladesh, and India [18, 2123, 29]. This may be due to watching television and displaces the physical activity, resulting in an overall decrease in energy expenditure [13].

The present study also showed that the odds of overweight were higher among women aged 25–39 years and 40–49 years, being urban residents, attending higher education, and richest in wealth index status. Likewise, the odds of obesity were also higher among woman aged 25–39 years and 40–49 years, attended higher education, and the richest in wealth index status.

The study has identified that the odds of overweight and obesity were higher among older women compared to younger women, which is consistent with findings from many other studies [3033]. This may be explained by the intake of more energy-dense food as well as having less physical activity increases with age [34] and the association of higher age with changes in body composition (fat mass rises and also fat-free mass declines) [35, 36]. Overweight was associated with being urban residents compared to its counterparts. This finding is consistent with studies performed in Bangladesh, Myanmar, Ghana, and India [21, 22, 3739]. This may be due to highly developed transportation, and technology results employment to be less labor-consuming, and higher coverage of electricity in urban areas in comparison to rural areas exposes the women to watch television frequently, by decreasing the physical activity.

The highest educational qualification has a strong positive association with women being overweight and obese in this study, which is also consistent with findings from other similar studies [30, 33, 40]. The expected reason for this might be higher educational levels which lead a woman into more sedentary occupations resulting in less physical activity.

Moreover, being the richest in wealth index status was associated with both overweight and obesity. This finding is supported by studies conducted in Bangladesh and India [3032]. This could be explained by the consumption of energy-dense food products and the intake of higher fat increases with the rise in income [41].

4.1. Strength and Limitation of the Study

As it was conducted on a national sample of Ethiopian women, the findings would contribute a lot to interventions aimed at preventing overweight and obesity, which is the risk factor for different noncommunicable diseases. The findings of this study have important implications for policymakers and other concerned bodies. However, there are some limitations to consider. First, as cross-sectional data were used, we cannot assign causations to any of the associations between the identified factors and the outcomes of interest.

5. Conclusions

From the finding of the study, it is possible to demonstrate that watching television is associated with obesity among reproductive aged women in Ethiopia. Therefore, a social behavioral change communication campaign needs to be taken to improve awareness regarding the harmful consequences of watching television for long hours. Further research should be conducted among men and adolescents to determine whether this positive association exists among that target population as well.

Abbreviations

AOR:Adjusted odds ratio
BMI:Body mass index
COR:Crude odds ratio
EDHS:Ethiopia demographic and health survey
SPSS:Statistical package for social science
TV:Television.

Data Availability

For this analysis, we used the USAID–DHS program 2016 Ethiopian demographic and health survey data set. To request the same or different data for another purpose, a new research project request should be submitted to the DHS program here: https://dhsprogram.com/data/Access-Instructions.cfm. The DHS program will normally review all data requests within 24–48 hours (during working days) and provide notification if access has been granted, or additional project information is needed before access can be granted. After receiving permission, the researcher can log in and select the specific data in the format they prefer.

Ethical Approval

The authors analyzed secondary data provided by 2016 Ethiopian demographic and health survey (EDHS). Ethical clearance for the EDHS 2016 data collection project was obtained from the ICF International’s Institutional Review Board (IRB). The survey ensured international ethical standards of confidentiality, anonymity, and informed consent. This study is based on publicly available, deidentified DHS data.

Conflicts of Interest

The authors declare no potential conflicts of interest to the research, authorship, and publication of this article.

Authors’ Contributions

MA conceived the research idea, conducted the data extraction, data analysis, and data interpretation, and wrote and reviewed the paper. AS and AK conducted data extraction and reviewed and approved the final manuscript. All authors have read and approved the manuscript.

Acknowledgments

The authors are grateful to the USAID–DHS program for providing access to 2016 Ethiopia demographic and health survey.

References

  1. WHO, Obesity and Overweight, WHO, Geneva, Switzerland, 2020, https://www.who.int/westernpacific/health-topics/obesity.
  2. L. Kanguru, A. McCaw-Binns, J. Bell et al., “The burden of obesity in women of reproductive age and in pregnancy in a middle-income setting: a population based study from Jamaica,” PLoS One, vol. 12, Article ID e0188677, 2017. View at: Publisher Site | Google Scholar
  3. P. Poirier, T. D. Giles, G. A. Bray et al., “Obesity and cardiovascular disease: pathophysiology, evaluation, and effect of weight loss,” Circulation, vol. 113, no. 6, pp. 898–918, 2006. View at: Publisher Site | Google Scholar
  4. S.-Z. Jiang, W. Lu, X.-F. Zong, H.-Y. Ruan, and Y. Liu, “Obesity and hypertension,” Experimental and Therapeutic Medicine, vol. 12, no. 4, pp. 2395–2399, 2016. View at: Publisher Site | Google Scholar
  5. A. Algoblan, M. Alalfi, and M. Khan, “Mechanism linking diabetes mellitus and obesity,” Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, vol. 7, pp. 587–589, 2014. View at: Google Scholar
  6. S. Y. Kim, L. England, H. G. Wilson, C. Bish, G. A. Satten, and P. Dietz, “Percentage of gestational diabetes mellitus attributable to overweight and obesity,” American Journal of Public Health, vol. 100, no. 6, pp. 1047–1052, 2010. View at: Publisher Site | Google Scholar
  7. C. P. Kovesdy, S. Furth, C. Zoccali et al., “Obesity and kidney disease: hidden consequences of the epidemic,” Clinical Kidney Journal, vol. 10, no. 1, pp. 1–8, 2017. View at: Publisher Site | Google Scholar
  8. K. Basen-Engquist and M. Chang, “Obesity and cancer risk: recent review and evidence,” Current Oncology Reports, vol. 13, no. 1, pp. 71–76, 2011. View at: Publisher Site | Google Scholar
  9. M. I. Cedergren, “Maternal morbid obesity and the risk of adverse pregnancy outcome,” Obstetrics & Gynecology, vol. 103, no. 2, pp. 219–224, 2004. View at: Publisher Site | Google Scholar
  10. E. P. Gunderson, “Childbearing and obesity in women: weight before, during, and after pregnancy,” Obstetrics and Gynecology Clinics of North America, vol. 36, no. 2, pp. 317–332, 2009. View at: Publisher Site | Google Scholar
  11. F. Galtier-Dereure, C. Boegner, and J. Bringer, “Obesity and pregnancy: complications and cost,” The American Journal of Clinical Nutrition, vol. 71, no. 5, pp. 1242S–1248S, 2000. View at: Publisher Site | Google Scholar
  12. S. Triunfo and A. Lanzone, “Impact of overweight and obesity on obstetric outcomes,” Journal of Endocrinological Investigation, vol. 37, no. 4, pp. 323–329, 2014. View at: Publisher Site | Google Scholar
  13. R. H. DuRant, T. Baranowski, M. Johnson, and W. O. Thompson, “The relationship among television watching, physical activity, and body composition of young children,” Pediatrics, vol. 94, no. 4, pp. 449–455, 1994. View at: Google Scholar
  14. J. A. Foster, S. A. Gore, and D. S. West, “Altering TV viewing habits: an unexplored strategy for adult obesity intervention?” American Journal of Health Behavior, vol. 30, no. 1, pp. 3–14, 2006. View at: Publisher Site | Google Scholar
  15. B. Wansink, “From mindless eating to mindlessly eating better,” Physiology & Behavior, vol. 100, no. 5, pp. 454–463, 2010. View at: Publisher Site | Google Scholar
  16. F. Bellisle, A. M. Dalix, and G. Slama, “Non food-related environmental stimuli induce increased meal intake in healthy women: comparison of television viewing versus listening to a recorded story in laboratory settings,” Appetite, vol. 43, no. 2, pp. 175–180, 2004. View at: Publisher Site | Google Scholar
  17. E. Blass, D. Anderson, H. Kirkorian, T. Pempek, I. Price, and M. Koleini, “On the road to obesity: television viewing increases intake of high-density foods,” Physiology & Behavior, vol. 88, no. 4-5, pp. 597–604, 2006. View at: Publisher Site | Google Scholar
  18. V. J. Cleland, M. D. Schmidt, T. Dwyer, and A. J. Venn, “Television viewing and abdominal obesity in young adults: is the association mediated by food and beverage consumption during viewing time or reduced leisure-time physical activity?” The American Journal of Clinical Nutrition, vol. 87, no. 5, pp. 1148–1155, 2008. View at: Publisher Site | Google Scholar
  19. R. Boynton-Jarrett, T. N. Thomas, K. E. Peterson, J. Wiecha, A. M. Sobol, and S. L. Gortmaker, “Impact of television viewing patterns on fruit and vegetable consumption among adolescents,” Pediatrics, vol. 112, no. 6, pp. 1321–1326, 2003. View at: Publisher Site | Google Scholar
  20. S. Feldman, M. E. Eisenberg, D. Neumark-Sztainer, and M. Story, “Associations between watching TV during family meals and dietary intake among adolescents,” Journal of Nutrition Education and Behavior, vol. 39, no. 5, pp. 257–263, 2007. View at: Publisher Site | Google Scholar
  21. B. Ghose, “Frequency of TV viewing and prevalence of overweight and obesity among adult women in Bangladesh: a cross-sectional study,” BMJ Open, vol. 7, no. 1, Article ID e014399, 2017. View at: Publisher Site | Google Scholar
  22. R. Das Gupta, I. H. Sajal, M. Hasan, I. Sutradhar, M. R. Haider, and M. Sarker, “Frequency of television viewing and association with overweight and obesity among women of the reproductive age group in Myanmar: results from a nationwide cross-sectional survey,” BMJ Open, vol. 9, no. 3, Article ID e024680, 2019. View at: Publisher Site | Google Scholar
  23. R. Das Gupta, S. S. Haider, I. Sutradhar et al., “Association of frequency of television watching with overweight and obesity among women of reproductive age in India: evidence from a nationally representative study,” PLoS One, vol. 14, no. 8, Article ID e0221758, 2019. View at: Publisher Site | Google Scholar
  24. M. A. B. Chowdhury, M. M. Adnan, and M. Z. Hassan, “Trends, prevalence and risk factors of overweight and obesity among women of reproductive age in Bangladesh: a pooled analysis of five national crosssectional surveys,” BMJ Open, vol. 8, Article ID e018468, 2018. View at: Publisher Site | Google Scholar
  25. Y. Yeshaw, S. A. Kebede, A. M. Liyew et al., “Determinants of overweight/obesity among reproductive age group women in Ethiopia: multilevel analysis of Ethiopian demographic and health survey,” BMJ Open, vol. 10, Article ID e034963, 2020. View at: Publisher Site | Google Scholar
  26. M. Haugen, L. B. Annie, W. Anna et al., “Associations of pre-pregnancy body mass index and gestational weight gain with pregnancy outcome and postpartum weight retention: a prospective observational cohort study,” BMC Pregnancy and Childbirth, vol. 14, p. 201, 2014. View at: Publisher Site | Google Scholar
  27. P. Erica, Gunderson, and B. Abrams, “Epidemiology of gestational weight gain and body weight changes after pregnancy,” Epidemiologic Reviews, vol. 22, no. 2, 2000. View at: Google Scholar
  28. Central Statistical Agency (CSA), Ethiopia and ICF.Ethiopia Demographic and Health Survey 2016, Central Statistical Agency (CSA), Addis Ababa, Ethiopia, 2016.
  29. L. A. Tucker and M. Bagwell, “Television viewing and obesity in adult females,” American Journal of Public Health, vol. 81, no. 7, pp. 908–911, 1991. View at: Publisher Site | Google Scholar
  30. H. Sarma, N. Saquib, M. M. Hasan et al., “Determinants of overweight or obesity among evermarried adult women in Bangladesh,” BMC Obesity, vol. 3, no. 1, p. 13, 2016. View at: Publisher Site | Google Scholar
  31. M. M. Khan and A. Krämer, “Factors associated with being underweight, overweight and obese among ever-married non-pregnant urban women in Bangladesh,” Singapore Medical Journal, vol. 50, no. 8, p. 804, 2009. View at: Google Scholar
  32. S. Subramanian, J. M. Perkins, and K. T. Khan, “Do burdens of underweight and overweight coexist among lower socioeconomic groups in India?” The American Journal of Clinical Nutrition, vol. 90, no. 2, pp. 369–376, 2009. View at: Publisher Site | Google Scholar
  33. S. Abrha, S. Shiferaw, and K. Y. Ahmed, “Overweight and obesity and its sociodemographic correlates among urban Ethiopian women: evidence from the 2011 EDHS,” BMC Public Health, vol. 16, no. 1, p. 636, 2016. View at: Publisher Site | Google Scholar
  34. E. Alemu, A. Atnafu, M. Yitayal, and K. Yimam, “Prevalence of overweight and/or obesity and associated factors among high school adolescents in Arada sub city, Addis Ababa, Ethiopia,” International Journal of Food Sciences and Nutrition, vol. 4, no. 2, p. 1, 2014. View at: Google Scholar
  35. D. Gallagher, M. Visser, R. E. De Meersman et al., “Appendicular skeletal muscle mass: effects of age, gender, and ethnicity,” Journal of Applied Physiology, vol. 83, no. 1, pp. 229–239, 1997. View at: Publisher Site | Google Scholar
  36. D. T. Villareal, C. M. Apovian, R. F. Kushner, and S. Klein, “Obesity in older adults: technical review and position statement of the American Society for Nutrition and NAASO, the Obesity Society,” Obesity Research, vol. 13, no. 11, pp. 1849–1863, 2005. View at: Publisher Site | Google Scholar
  37. T. Biswas, M. J. Uddin, A. A. Mamun et al., “Increasing prevalence of overweight and obesity in Bangladeshi women of reproductive age: findings from 2004 to 2014,” PLoS One, vol. 12, Article ID e0181080, 2017. View at: Publisher Site | Google Scholar
  38. R. Ofori-Asenso, A. A. Agyeman, A. Laar et al., “Overweight and obesity epidemic in Ghana—a systematic review and meta-analysis,” BMC Public Health, vol. 16, p. 1239, 2016. View at: Publisher Site | Google Scholar
  39. K. Gaur, K. Keshri, and W. Joe, “Does living in slums or non-slums influence women'\’s nutritional status? Evidence from Indian mega-cities,” Social Science & Medicine, vol. 77, pp. 137–146, 2013. View at: Publisher Site | Google Scholar
  40. M. E. Hoque, K. Z. Long, L. W. Niessen, and A. A. Mamun, “Rapid shift toward overweight from double burden of underweight and overweight among Bangladeshi women: a systematic review and pooled analysis,” Nutrition Reviews, vol. 73, no. 7, pp. 438–447, 2015. View at: Publisher Site | Google Scholar
  41. S. Du, T. A. Mroz, F. Zhai, and B. M. Popkin, “Rapid income growth adversely affects diet quality in China-particularly for the poor!,” Social Science & Medicine, vol. 59, no. 7, pp. 1505–1515, 2004. View at: Publisher Site | Google Scholar

Copyright © 2020 Mohammed Ahmed et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


More related articles

 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder
Views123
Downloads70
Citations

Related articles

We are committed to sharing findings related to COVID-19 as quickly as possible. We will be providing unlimited waivers of publication charges for accepted research articles as well as case reports and case series related to COVID-19. Review articles are excluded from this waiver policy. Sign up here as a reviewer to help fast-track new submissions.