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ORIGINAL ARTICLE
Year : 2022  |  Volume : 26  |  Issue : 4  |  Page : 261-265
 

Obesity burden and physical activity pattern among doctors in South India


1 Department of Community Medicine, Government Medical College, Thiruvananthapuram, Kerala, India
2 Government Medical College, Kollam, Kerala, India

Date of Submission10-Mar-2022
Date of Decision11-Apr-2022
Date of Acceptance24-May-2022
Date of Web Publication24-Dec-2022

Correspondence Address:
Dr. Anjana Nalina Kumari Kesavan Nair
Sreevilas Near GVHSS, Parassala, Kerala
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijoem.ijoem_67_22

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  Abstract 


Context: A career as a doctor makes him prone to develop health issues like obesity and obesity-related noncommunicable diseases. Aims: This study aims to find the burden and determinants of obesity among Modern Medicine doctors in Kerala. Settings and Design: We conducted a cross-sectional study among 240 doctors working in South Kerala from 2018 to 2019. Methods and Material: The sample size was calculated using a formula and stratified random sampling was done for the selection of study participants. An interviewer-administered structured questionnaire was used for data collection. Physical activity was measured using International Physical Activity Questionnaire. Statistical Analysis: Data were entered in MS Excel and was analyzed using Statistical Package for Social Sciences version 26.0. The significance of association was tested using the χ2 test. Binary logistic regression was done to predict the factors associated with overweight and obesity. Results: Out of 240 study participants, 128 (54%) were females and 112 (46%) were males. Among the 240 doctors, 54% (114) were either overweight or obese. A low level of physical activity was reported among 54.5% of doctors. Male gender odds ratio (OR) = 2.8 (95% confidence interval [CI] = 1.29-6.06), nuclear family OR = 2.7 (95% CI = 1.32-5.42), daily hours of sleep <6 hours OR = 4.92 (95% CI = 2.29-10.5), history of obesity among parents OR = 3.54 (95% CI = 1.04-12.02), reported the presence of private practice OR = 3.34 (95% CI = 1.25-8.96), and holding a graduation degree alone were found to be significantly associated with obesity. Conclusions: The study found that majority of the doctors (55%) were either overweight or obese. Awareness and behavior change communication among doctors on modifiable risk factors like having adequate sleep and reducing the hours spent in private practice is needed to reduce the burden of obesity among doctors.


Keywords: Doctors, India, obesity burden, physical activity


How to cite this article:
Nair AN, Lawrence T, Indu PS. Obesity burden and physical activity pattern among doctors in South India. Indian J Occup Environ Med 2022;26:261-5

How to cite this URL:
Nair AN, Lawrence T, Indu PS. Obesity burden and physical activity pattern among doctors in South India. Indian J Occup Environ Med [serial online] 2022 [cited 2023 Feb 2];26:261-5. Available from: https://www.ijoem.com/text.asp?2022/26/4/261/364943





  Introduction Top


According to the World Health Organization, the global burden of obesity has tripled from 1975 probably due to sedentary lifestyles and unhealthy eating habits.[1–3] This excessive fat accumulation kills more people than malnutrition.[3] Higher body mass index (BMI) accounted for 40 million deaths annually.[4] In India, the prevalence of obesity varies between 11.8% and 31.3%, whereas in Kerala, it is around 40%.[5]

The stressful life of doctors results in the premature onset of obesity and related noncommunicable diseases.[6] Physicians' excessive work hours are associated with physical inactivity and inadequate sleep.[7] This study explores to address the determinants of obesity among the doctor population of Kerala.


  Subjects and Methods Top


We conducted a cross-sectional study among 240 doctors from January 2018 to September 2019, working in various hospitals in the southernmost district of Kerala. Doctors working in various government and private institutions with at least 1 year of experience and age of the doctor should be <65 years were the inclusion criteria. Doctors who did not give consent due to their lack of interest to participate in the study or those who could not participate due to a busy outpatient department were excluded from the study. Pregnant doctors and those with disabilities were also excluded from the study. We estimated the sample size using the formula for the estimation of proportion. With a prevalence assumption of 40%,[8] relative precision of 15%, and 95% confidence level (CI), it was estimated to be 240. We did stratified random sampling for the selection of study participants. The list of government hospitals in the Thiruvananthapuram district was obtained from the District Medical Officer's office in Thiruvananthapuram. The hospitals were classified as primary health centers, family health centers, community health centers, Taluk headquarters, district hospitals, and specialty hospitals. The Government Medical College of the district was also included. The private hospitals were classified as primary clinics, specialty, multispecialty hospitals, and multispecialty tertiary care hospitals. Three private medical colleges are present in the Thiruvananthapuram district. The strata identified were government and private. From the list, hospitals were selected from each strata using simple random sampling. Permission was obtained from the Director of Health Services before starting the study. Hospital authorities were informed about the study on the previous day. Permission from the Administrative Medical Officer or Medical Officer in Charge was obtained for government institutions. The permission from the Managing Director and the superintendent was obtained for private hospitals. From the list of doctors available in the institution on the day of visit for data collection, doctors were selected randomly by using simple random sampling using computer-generated random number tables. An interviewer-administered structured questionnaire was used to capture data on sociodemographic details, occupational characteristics, perceptions regarding obesity, and physical activity patterns. Anthropometric measurements were made using calibrated instruments.

The prevalence of overweight and obesity was estimated based on the World Health Organization Asia Pacific Guidelines of Obesity. According to the criteria, the BMI (kg/m2) of <18.5 is considered to be underweight, 18.5 to 22.9 is considered to be normal weight, 23 to 24.9 is considered overweight, and more than 25 is obese. Physical activity was measured by using International Physical Activity Questionnaire (IPAQ). This tool measures the activities of study participants in the past 7 days in 4 parts-job related, transportation-related, housework, maintenance and caring for family, recreation, sports, and leisure-time physical activity.[9] Metabolic equivalents (METs) were used to calculate the intensity of physical activities. MET minutes represent the amount of energy expended carrying out physical activity. A MET is a multiple of your estimated resting energy expenditure. According to the scoring criteria of IPAQ long-form, individuals were categorized as those having low physical activity scores, moderate physical activity scores, and high physical activity scores. Those participants having scores < 600 METs were categorized as low, between 600 and 3000 METs as moderate, and more than 3000 METs were classified as a high level of physical activity. Data were entered using MS Excel and analyzed using SPSS version 26.0 (Armonk, NY: IBM Corp). Normally distributed quantitative variables were expressed as mean and standard deviation. Skewed quantitative variables were expressed as median and interquartile range. Categorical variables were expressed as proportions. Bivariable analysis using χ2 test was performed and the odds ratio (OR) and 95% CI were computed. For quantitative variables, an independent sample t test was done. Correlation analysis was done to find out the association between 2 quantitative variables. Binary logistic regression was done for modeling determinants of obesity. A P =0.05 was used to indicate statistical significance.

Ethical considerations

The data collection process started after getting clearance from the human ethics committee of our institution (IEC: 13/03/2017/MCT). Informed consent from the participants was obtained before the administration of the questionnaire. Privacy during data collection was ensured for all the study participants. Confidentiality of data was maintained throughout the study.


  Results Top


The mean age of the study population was 38.95 (9.28) years, minimum and maximum ages were 25 and 65 years, respectively. Out of 240 study participants, 128 (54%) were females and 112 (46%) were males. The majority of the study participants were married and had a nuclear type of family. Out of the total 240 study participants, 114 (60%) were working in the government sector and 96 (40%) were working in the private sector. Almost equal participation of study participants was ensured from primary, secondary, and tertiary health care levels. The majority of study participants were working as general practitioners. The average working hours per day was 7.4 (±1.8) hours. The minimum and maximum hours of working were 6 hours and 16 hours, respectively. The mean sleep hour was 6.5 (±1) hours. Among the 240 doctors, 46.7% had any one of the comorbidities mentioned below in [Table 1].
Table 1: Demographic and occupational characteristics of doctors (n=240)

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The mean BMI of the 240 study participants was 26.24 ± 4.18 kg/m2. The observed minimum BMI was 17.8 kg/m2 and the maximum BMI was 45.9 kg/m2. Among the 240 doctors, 54% (114) were either overweight or obese. The mean waist circumference was 89.33 (10.34) cm and the mean hip circumference was 96.11 (10.86) cm. Proportions of different categories of BMI are provided in [Table 2].
Table 2: Obesity burden among health care providers (n=240)

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Male gender OR = 2.8 (95% CI = 1.29-6.06), nuclear family OR = 2.7 (95% CI = 1.32-5.42), holding a degree of MBBS only OR-2.64 (95% CI- 1.21-5.71), and family type were found to be significantly associated with overweight and obesity. Daily hours of sleep < 6 hours OR = 4.92 (95% CI = 2.29-10.5), history of obesity among parents OR = 3.54 (95% CI = 1.04-12.02), and reported presence of private practice OR = 3.34 (95% CI = 1.25-8.96) were found to be significantly associated with overweight and obesity. Results of univariate analysis are given in [Table 3].
Table 3: Determinants of overweight and obesity among doctors

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Of the 240 doctors, 234 (97.5%) knew their own body weight. Among the study participants, 140 (58%) of the doctors perceive themselves as either overweight or obese. More than half of the doctors, that is, 136 (56.7%) did not perform efforts to maintain body weight. The median physical activity score was 495 (interquartile range = 200, 1039.5) METs. The minimum score was zero and the maximum score was 4761 METs. Out of the 240 study participants, 96 (40%) of the study participants reported that they used to have regular physical activity and 38 (40%) had a companion, while doing exercise. Male and female doctors were equally physically inactive. Lack of motivation (39%), family responsibility (38%), and lack of time (37%) were the major reasons for not engaging in regular physical activity. Lack of space, fear of street dogs, bad weather, and physical illness were the other reasons. The physical activity pattern of doctors is given in [Table 4].
Table 4: Physical activity pattern among doctors (n=240)

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Sleep, history of obesity among parents, presence of private practice, and nuclear type of family were found to be significantly associated with obesity. Binary logistic regression was done to create a model to predict overweight or obesity, which is the outcome variable in question. Results of binary logistic regression are given in [Table 5].
Table 5: Results of binary logistic regression

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  Discussion Top


The prevalence of obesity (55%) in this study was almost double the prevalence of overweight (29%). According to a study conducted by the Indian Council of Medical Research-India diabetes study (ICMR-INDIAB), the prevalence of obesity in India varies between 10% and 30% across India among the general population, which include samples from both urban and rural population.[10] The prevalence of obesity among doctors in the United State is on the rise with surgeons holding the top position in the list followed by family physicians, ophthalmologists, and dermatologists down the bottom.[11] In a study conducted among doctors at the University of Brunei Darussalam, the prevalence of overweight and obesity was found to be 37% and 17%, respectively.[12] A study done by Mahmood et al.,[13] on predictors of obesity among postgraduate trainee doctors, observed that the prevalence of overweight was 31.6% and obesity was 28.2%. The study also pointed out that the prevalence of obesity had increased during the past decade in all age groups among medical professionals. Obesity is a health problem in both males and females. In a study conducted in Mexico among physicians, the prevalence of overweight and obesity was found to be higher in males.[12] But some studies made an observation that females were more obese compared with males.[6],[14],[15]

The waist hip ratio is a measure of cardiovascular and cardiometabolic risk.[16],[17] Waist circumference is a surrogate of abdominal obesity.[18] According to ICMR-INDIAB study 2015, the prevalence rate of central obesity varies between 16.9% and 36.3%.[10]. In a study done in Mexico among physicians, the prevalence of abdominal obesity in males and females was found to be 73% and 63%, respectively. Similar findings were obtained in this study. Primary care physicians had a similar or higher risk of cardiovascular diseases than their patients.[19] Cardiovascular events at a younger age is one of the major contributors of mortality among doctors in India.[20]

A major contributing factor for increased BMI as obtained in this study is lack of physical activity. More than half of the study participants had a low level of physical activity. Both female and male professionals were found to be physically inactive. A significant negative correlation was obtained between physical activity and BMI. In a population-based survey conducted in South India, the prevalence of physical inactivity was 49.7%,[21] which is higher than the global prevalence of 21.4%.[22] In a study conducted by ICMR-INDIAB, the prevalence of physical inactivity is 65%.[23] Males were more physically active than females; rural people were more physically active than the urban population and physical activity was higher with increasing age.[23]

Cappuccio et al.,[24] found that reduced sleep is a risk factor for obesity in adults as well as children. Both short duration and long sleep duration are associated with a greater risk of mortality.[25] In the present study, short sleep duration was found to be significantly associated with obesity. Even though doctors know the need for practicing good dietary habits, engaging in physical activity, and maintaining adequate BMI, their occupation makes them prone to stress, lack of time to engage in physical activity, and follow unhealthy dietary practices. This area needs to be explored more to find out strategies to reduce the burden of mortality and morbidity due to obesity.


  Conclusion Top


The majority of the doctors (55%) were either overweight or obese. Nearly half of the doctors were physically inactive. Male gender, short sleep duration (<6 hours daily), history of obesity in parents, and presence of private practice were found to be significantly associated with obesity. Even though doctors had a perception that they are either overweight or obese, this study shows that the practices are lacking. Promoting behavior change related to the identified modifiable risk factors of obesity is needed to halt the burden of obesity-related noncommunicable diseases among doctors.

Limitations of the study

  1. The study was done among doctors in a selected geographic area of the state due to feasibility issues.
  2. Physical activity measurements using electronic devices like accelerometers would give a more accurate estimate of METs.
  3. Even though we identified some of the risk factors of obesity since it was a cross-sectional study, the casualty could not be proven. Moreover, further studies may be needed to explore the other factors associated with doctor obesity.


Acknowledgments

We extend our sincere gratitude to all the participants who were involved in the study. We extend our sincere thanks to all the staff in the department.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

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    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]



 

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