|
 
 |
| ORIGINAL ARTICLE |
|
| Year : 2022 | Volume
: 10
| Issue : 4 | Page : 65-69 |
|
Prevalence and lifestyle-related risk factors of obesity: A descriptive cross-sectional study among bankers in central part of Saurashtra region, Gujarat, India
Nikita Maheshbhai Savani1, Rajendra B Chauhan2, Rajesh K Chudasama2
1 Department of Community Medicine, Shantabaa Medical College, Amreli, Gujarat, India
2 Department of Community Medicine, P.D.U. Medical College, Rajkot, Gujarat, India
| Date of Submission | 15-Jun-2022 |
| Date of Decision | 04-Aug-2022 |
| Date of Acceptance | 16-Aug-2022 |
| Date of Web Publication | 14-Sep-2022 |
Correspondence Address:
Nikita Maheshbhai Savani
G/12, Doctor Quarter, Shantaba Medical College Camus, Lathi Road, Amreli - 365 601, Gujarat
India
 Source of Support: None, Conflict of Interest: None
DOI: 10.4103/mjhs.mjhs_12_22
Background: Obesity is known to be the first wave of an outlined cluster of noncommunicable diseases called “New World Syndrome” creating a huge socioeconomic and public health burden in poorer countries. In India, obesity has emerged as a crucial health problem, specifically in urban areas, paradoxically coexisting with undernutrition imparting “Double Burden of Disease Obesity”.
Objective: To study the prevalence and associated factors of obesity among nationalized and private sectors' bank employees.
Materials and Methods: A descriptive cross-sectional study was conducted. There were around 160 total branches of banks in Rajkot. After explaining the purpose of the study, regional head of 70 banks gave permission to conduct the study. Employees working in nationalized and private banks and those who gave consent to take participation were included in this study. Total 800 bank employees participated in the present study.
Results: Out of all employees, 36.4% and 11.5% of employees were found to be overweight and obese, respectively. Among all, 23.4% of employees had addiction. Out of all, 88% of employees took homemade lunch, while 28.3% employees skip their lunch. Around 82.2% took junk food as snacks. More than half of the employees were not doing any type of exercise at the time of the present study.
Conclusion: Marital status, education, source of lunch food, and exercise were found to be significantly associated with obesity.
Keywords: Bank employees, obesity, prevalence, risk factors
How to cite this article:
Savani NM, Chauhan RB, Chudasama RK. Prevalence and lifestyle-related risk factors of obesity: A descriptive cross-sectional study among bankers in central part of Saurashtra region, Gujarat, India. MRIMS J Health Sci 2022;10:65-9 |
How to cite this URL:
Savani NM, Chauhan RB, Chudasama RK. Prevalence and lifestyle-related risk factors of obesity: A descriptive cross-sectional study among bankers in central part of Saurashtra region, Gujarat, India. MRIMS J Health Sci [serial online] 2022 [cited 2023 Oct 4];10:65-9. Available from: http://www.mrimsjournal.com/text.asp?2022/10/4/0/356073 |
| Introduction | |  |
Obesity is known to be the first wave of an outlined cluster of noncommunicable diseases called “New World Syndrome” creating a huge socioeconomic and public health burden in poorer countries. The New World Syndrome is responsible for disproportionately high levels of morbidity and mortality.[1] Obesity has reached a pandemic proportion with a global prevalence nearly more than tripled since 1975. Thirty-nine percent of adults aged 18 years and above were overweight, and 13% were obese in 2016.[2] In India, obesity is emerging as a crucial health problem, specifically in urban areas. The severity of obesity can induce morbidities such as prehypertension and hypertension.[3]
The increased incidence of overweight and obesity is provoked by the consumption of high-calories food, physical inactivity, the sedentary nature of many types of jobs, and means of transportation. Because occupation is linked to socioeconomic and lifestyle characteristics such as physical inactivity and a sedentary lifestyle, it is thought to be a predictor of obesity.[4]
Low physical activity during occupation has also been influenced by the economic transition, which now includes more sedentary tasks than in the past. Despite this, the World Health Organization (WHO) considers the workplace to be an ideal location for the prevention and management of diet-related disorders because it can encourage healthy lifestyle choices.[5]
Because of the nature of their jobs, bank employees spend a lot of time doing sedentary activities, and their socioeconomic level may influence their adaptability to less physical activity. For the following reasons, it is necessary to determine the risk factors for overweight and obesity in this occupational group because bankers play an essential role in country's finance sector.[6]
Unfortunately, data on the prevalence of overweight and obesity among working individuals are scarce.[7] Bank employees improper eating habits, physical inactivity, disturbed sleep pattern, excessive use of desktop and mobile phones, job stress, and increasing competition are mainly responsible for their lifestyle-related diseases.[8] The objective was to study the prevalence and associated factors of obesity among nationalized and private sectors' bank employees.
| Materials and Methods | | |
A descriptive cross-sectional study was carried out during 2016-2019 at Rajkot City, Gujarat. Sample size was calculated from the study done by Assudani et al.[9] among bank employees of Vadodara, Gujarat, in 2014, which showed 41% prevalence for obesity. Using the formula N = 4PQ/L2,[10] taking 10% allowable error (L) and using 95% confidence level, a sample size of 576 bank employees was calculated for the study. Ethical permission was taken from the ethical committee of the institute.
There were around 160 total branches of banks in Rajkot. After explaining the purpose of the study, regional head of 70 banks gave permission to conduct the study.
Inclusion criteria
Employees working in nationalized and private banks having desk job (8 hours) and those who gave consent to take participation were included in this study.
Exclusion criteria
Peons and security guard were excluded from the study because their nature of work is different.
The data were collected in preformed, pretested, and semistructured questionnaire by interview technique. The list of employees was collected from the head of 70 banks and then complete list of employees was prepared. Participants were selected using systemic random sampling. The study was carried out as per their convenient time. After obtaining written consent from each participant consequently, the interview was taken. The questionnaire included sociodemographic detail, status of comorbidity, their addiction history, eating habits, type of diet they consumed, and their physical activity status. Persons doing physical exercise as per the WHO norms were considered regular.[11] As per WHO a person doing at least 150–300 minutes of moderate-intensity aerobic physical activity; or at least 75–150 min of vigorous-intensity aerobic physical activity; or an equivalent combination of moderate- and vigorous-intensity activity at least on 5 days of the week is considered regular/adequate physical activity. Type of snack consumption and lunch skipping habits was assessed.
After completion of forms, anthropometric measurements such as height, weight, and waist circumference were taken. A digital weighing scale to the nearest 100 g was used to measure weight. The scale was calibrated using standard weights. Height was measured in centimeters by a stadiometer with 0.5 cm accuracy.[12] Waist circumference was measured at a level half way between the costal margin and iliac crest at the level of umbilicus, measured in horizontal plane, with the subject standing. The measurement was taken to the nearest 0.1 cm at the end of a normal expiration, without the tape compressing the skin.[13] The WHO body mass index (BMI) classification[14] used as an indicator in the present study for overweight and obesity classification. Most of the studies used it over the world especially for surveys, because of its relative ease of measurement and fair accuracy. Descriptive variables were described as percentage, and statistical association was described through the Chi-square test. Microsoft Office Excel version 2010 was used for analysis.
| Results | | |
Total 800 bank employees participated in the present study who were working in various nationalized and private banks. In the present study, age of employees was ranging from 20 years to 59 years. More than two-third of the employees (563 [70.4%]) were belonging to the age group of 20 to 39 years. Female: male ratio of employees was 1:3.6. Among all participates, 78.6% were male and 21.4% were female. In the present study, 422 employees were from nationalized and 378 employees were from private banks. Total 577 (72.1%) employees were married, while one-fourth of the employees were unmarried, followed by separated and divorced/widowed. More than half of the employees, 517 (64.6%), worked more than 8 hours. Distribution as per the type of family showed that nuclear family (679 [84.9%]) was found more compared to joint family. Among employees participated in the study, more than 90% of employees were from socioeconomic class I (as per Modified Prasad classification), followed by other classes.
Among all, total 187 (23.4%) employees had addiction of tobacco (smoking and smokeless) at the time of the study. 78.95% of employees were vegetarian at the time of study. Out of all, 88% of employees took homemade lunch. Looking on habit of skipping lunch, 226 (28.3%) employees skipped their lunch and from those more than half skip lunch frequently in a week. Among all employees, 225 (28.1%) employees had habit of taking snacks in between meals. More employees (182 [82.2%]) took junk food in snacks followed by fruits (17.8%). More than half of the employees, 471 (58%), were not doing any type of exercise at the time of the present study. Out of total males, 35.9% had waist circumference ≥90 cm, while out of total females, 60.8% had waist circumference ≥80 cm. Hence, 41.2% of employees had central obesity.
The prevalence of overweight and obesity was reported to be 36.4% and 11.5% respectively [Table 1]. In the age group of 20–29 years, maximum prevalence (31.5%) of obesity was observed. This difference in the prevalence of obesity with respect to age group was not statistically significant. The difference in sex with respect to obesity was not statically significant (P > 0.05). The statistically significant difference was found as per their education level and marital status with respect obesity on applying Chi-square test. No statistically significant difference was found between factors like employees; Average duration of working hour, type of family, socioeconomic class and obesity found (P > 0.05) [Table 2]. | Table 1: Prevalence of obesity and distribution as per body mass index status (World Health Organization classification) among employees (n=800)
Click here to view |
 | Table 2: Bivariable analysis of various sociodemographic risk factors by status of obesity (n=800)
Click here to view |
Looking on status of obesity, 27 (29.3%) obese had a history of addiction at the time of the study. No statically significant difference was observed between normal and obese for having addiction and different diet patterns, while source of lunch food and exercise had statistically significant difference among obese and normal [Table 3]. | Table 3: Bivariable analysis according to modifiable risk factors and status of obesity (n=800)
Click here to view |
| Discussion | | |
Obesity is characterized as a global public health issue that affects both developed and developing countries. According to a systematic review by Ahirwar et al.,[15] the prevalence of obesity has risen quickly from 1998 to 2018, because of sedentary lifestyles and high-calorie food consumption. Obesity creates various health issues, some of them are linked to cardiovascular disease. As a result, it is time to concentrate on the problem and take the required steps to solve it.
Only few data are available for the prevalence and associated risk factors of overweight and obesity among working adults. Therefore, this study was conducted. Even though bank employees are considered high-risk groups, they are neglected most of the time.[6]
The prevalence of overweight and obesity was reported to be 36.4% and 11.5%, respectively, in the present study. A study conducted by Assudani et al. (2014)[9] found 34% prevalence of overweight and 7% of obesity among bank employees of Vadodara city. Another study conducted by Proper et al. (2010) among Dutch bank employees reported 27.8% overweight and 6.8% obese.[16] A study conducted by Ganesh Kumar et al. (2013) in Mangalore, Karnataka, among bank employees found similar findings.[17]
Obesity found almost same in all age groups except in 40–49 years of age group. In the age group of 40–49 years, it was found less (14.1%). Possible reasons for that are they could be more cautious, good eating habits, more physically active, or having more healthy lifestyle compared to other age groups. Shah et al. (2015) found majority of obese employees to be (73%) in age group of more than 50 years.[18] The difference observed among age group and obesity in both the studies was found to be statistically not significant (P > 0.05). Singh et al. (2015) conducted a study to find out cardiometabolic risk factors in bank employees of Jammu; they also did not find a significant association between age group and obesity.[19]
In the present study, more males (81.5%) found obese as compared to females (18.5%). This difference of prevalence between males and females with respect to obesity was not found to be statistically significant, while Mummery et al. (2005) had found a statistically significant difference among white-collar workers of Australia.[20] Mummery et al. found different findings than the present study. it could be possible due to different study settings. A study conducted by Nakanishi et al. (2001) among Japanese white-collar workers also reported no significant association between working hours and status of obesity like the present study.[21]
Similar to the present study, no statistically significant difference was observed between obesity and addiction by Gasperin et al. (2014) among Austrian bank employees.[22] Obesity was found more among physically inactive group. Chau et al. (2012) reported more obesity among employees who were not doing exercise regularly as compared to those who were doing regular exercise.[23]
Some of the respondents said that they are having healthy eating habits like consumption of homemade lunch, limited outside food in their daily routine, and they are consuming fruits and vegetables on daily basis despite this they were overweight and obese. Therefore, what factors contributed to their obesity raises a question for further research.
| Conclusion | | |
Obesity was reported more in younger age group (20–29 years) and males (81.5%). Age and sex were not found significantly associated with obesity. Education and marital status were significantly associated with obesity, while working hours was not significantly associated with obesity. Type of family, socioeconomic class, and addiction were not significantly associated with obesity. Statistically significant association was found between source of lunch food and obesity, while no statistical significant association was found between type of diet, snacks, and obesity. Exercise has a significant association with obesity.
Recommendation
Regular calculation of BMI is encouraged. Promoting healthy lifestyles and lifestyle modifications related to the behavioral risk factors is recommended in reducing and controlling the prevalence of obesity. Adopting healthy eating habits and regular physical exercise could be strategy for risk factor control. There is a need for information, education, communication, and behavior change in bank employees for the prevention of obesity and its consequences.
Limitation
Bias in measurement of height, weight, or waist circumference can misclassify the subjects into BMI groups. This can arise due to mistake of investigator at the time of data collection, data entry, or data analysis. Subjective variation in the answers regarding personal habits and other questions may occur. This study was limited to bank employees, which are vulnerable group. Therefore, we cannot generalize this finding to other population other than working adults.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
| References | | |
| 1. | Obesity: Preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser 2000;894:i-253.  |
| 2. | |
| 3. | Subramanian SV, Kawachi I, Smith GD. Income inequality and the double burden of under- and overnutrition in India. J Epidemiol Community Health 2007;61:802-9. |
| 4. | Amoh I, Appiah-Brempong E. Prevalence and risk factors of obesity among senior high school students in the Adansi North district of Ghana. Int J Community Med Public Health 2017;4:3762-9. |
| 5. | Ogunjimi LO, Ikorok MM, Olayinka O. Prevalence of obesity among Nigeria nurses: The Akwa Ibom state experience. Int NGO J 2010;5:45-9. |
| 6. | Zubery D, Kimiywe J, Martin HD. Prevalence of overweight and obesity, and Its associated factors among health-care workers, teachers, and bankers in Arusha city, Tanzania. Diabetes Metab Syndr Obes 2021;14:455-65. |
| 7. | Ajayi IO, Adebamowo C, Adami HO, Dalal S, Diamond MB, Bajunirwe F, et al. Urban-rural and geographic differences in overweight and obesity in four sub-Saharan African adult populations: A multi-country cross-sectional study. BMC Public Health 2016;16:1126. |
| 8. | |
| 9. | Assudani A, Sheth M, Jain N. Indirect determinants of obesity in bank employees of urban Vadodara – A cross sectional study. Int J Appl Biol Pharm Technol 2014;5:5-12. |
| 10. | Lwanga SK, Lemeshow S. Sample Size Determination in Health Studies a Practicle Manual. Geneva: World Health Organization; 1991. P. 38. Available from: https://apps.who.int/iris/handle/10665/40062. [Last accessed on 2022 Jun 22]. |
| 11. | |
| 12. | Hirani S, Kuril BM, Lone DK, Ankushe RT, Doibale MK. Obesity prevalence and its relation with some sociodemographic factors in bank employee of Aurangabad city, Maharashtra, India. Int J Community Med Public Health 2017;3:1628-35. Available from: http://www.ijcmph.com/index.php/ijcmph/article/view/496. [Last accessed on 2022 Jun 22]. |
| 13. | |
| 14. | Obesity Task Force. International Association for the Study of Obesity. The Asia – Pacific perspective: Redefining obesity and its treatment 2000. Available from: https://apps.who.int/iris/handle/10665/206936. [Last accessed on 2022 Jun 22]. |
| 15. | Ahirwar R, Mondal PR. Prevalence of obesity in India: A systematic review. Diabetes Metab Syndr 2019;13:318-21. |
| 16. | Proper KI, Hildebrandt VH. Overweight and obesity among Dutch workers: Differences between occupational groups and sectors. Int Arch Occup Environ Health 2010;83:61-8. |
| 17. | Kumar SG, Unnikrishnan B, Nagaraj K. Self-reported chronic diseases and occupational health risks among bank employees of Southern Karnataka City, India. Indian J Community Med 2013;38:61-2. [ PUBMED] [Full text] |
| 18. | Shah K, Narasannavar AB, Angolkar M. Prevalance of risk factors of cardio vascular disease among bank employees of Belagavi city a cross-cross sectional study. Int J Curr res 2015;7:18558-61. |
| 19. | Singh O, Gupta M, Khajuria V. Cardiometabolic risk factors in bank employees. Natl J Physiol Pharm Pharmacol 2015;5:258-62. |
| 20. | Mummery WK, Schofield GM, Steele R, Eakin EG, Brown WJ. Occupational sitting time and overweight and obesity in Australian workers. Am J Prev Med 2005;29:91-7. |
| 21. | Nakanishi N, Yoshida H, Nagano K, Kawashimo H, Nakamura K, Tatara K. Long working hours and risk for hypertension in Japanese male white collar workers. J Epidemiol Community Health 2001;55:316-22. |
| 22. | de Oliveira Fontes Gasperin L, Neuberger M, Tichy A, Moshammer H. Cross-sectional association between cigarette smoking and abdominal obesity among Austrian bank employees. BMJ Open 2014;4:e004899. |
| 23. | Chau JY, van der Ploeg HP, Merom D, Chey T, Bauman AE. Cross-sectional associations between occupational and leisure-time sitting, physical activity and obesity in working adults. Prev Med 2012;54:195-200. |
[Table 1], [Table 2], [Table 3]
|
Search Pubmed for