The Impact of Diet, Physical Activity and Weight Perception on Body Mass Index
(BMI) In Patients of Indian Origin
A predisposing factor for the increasing tide of diabetes mellitus (DM), is the is the increasing global prevalence of obesity . Alarmingly, over 66 million Indians are already documented to have DM, with this number steadily on the rise . The body-mass index (BMI) measurement is a validated and well utilized tool for determining whether individuals are normal in terms of weight, overweight or obese . Using this formula, alarmingly in 2010, 14% of women and 10% of men worldwide were estimated to be obese (BMI ≥30kg/m2). This is a stark increase from 1980, when only 8% of women and 5% of men were thought to be in this category . This concomitant rise in weight has prompted the World Health Organization (WHO) Member States to formulate a Global Action Plan that emphasizes the need to halt the rise in obesity and diabetes .
The reasons underlying the development of obesity are multiple and complex. The effects of diet and exercise on weight loss are well documented . However, before any intervention can be successful, there must be a self-perception of weight appropriateness . Weight perception is an established motivating factor behind weight control behaviors such as diet and exercise . In reality, there seems to be a mismatch between actual weight and perceived weight across all ethnicities. For example, data from the National Health and Nutrition Examination Survey (NHANES) indicates that approximately 30% of children/adolescents in the United States of America, between the ages of 8-15 years, misperceive their weight status. Subgroup analysis indicates that this disparity is higher amongst Mexican-American (34%) and non-Hispanic black (34.4%) children, vis-à-vis their white counterparts (27.7%) . Limited data exists in the developing world. In 2013, a study by Agrawal et al in women in urban India identified that 25% of overweight women and 10% of obese women perceived themselves as having normal weight .
Understanding the factors that positively or negatively impact BMI are vital in the fight against obesity. This is certainly crucial in the Indian context, as Asians have been found to have a higher risk of developing co-morbid conditions like diabetes at a lower BMI, as opposed to their Caucasian counterparts. Variables such as diet, physical activity, smoking, alcohol use and underlying genetics all contribute to this . The Indian Diabetes Prevention Program is proof of the fact that simple interventions like lifestyle modification (LSM) can decrease the progression to type 2 DM, in those with impaired glucose tolerance .
The aim of this cross-sectional study in middle-aged adults was to understand the impact of diet, physical activity and self-perception of body weight on BMI in an Indian context.
This study was conducted at the Preventive Health Program (PHP) of the Max Super Specialty Hospital, Saket, New Delhi. The primary outcome was to assess the impact diet and physical activity on BMI. The secondary outcome was to assess link between self-perception of body weight and BMI. We included all males and females between 40-60 years with a BMI >18kg/ m2 and excluded all known patients with T2DM and those unwilling to participate. The study received approval from both the Scientific and Ethics committee of Max Hospital and was carried out from March 2015 to May 2015.
150 individuals met the inclusion and exclusion criteria. Out of these, biochemical data for 5 was not available as was the physical examination for 2 individuals. Subsequently, 143 individuals were enrolled and gave informed consent. Subjects were given a paper based case report form (CRF), a physical activity questionnaire (Rapid Assessment of Physical Activity [RAPA] ) and 24 hour diet recall sheet. The RAPA questionnaire classified activity levels as Active, Under Active or Sedentary. The 24-hour recall sheet recorded data on all food items the individuals consumed on two individual weekdays and one weekend day prior to enrollment/PHP visit. The total amount of energy was calculated and its average was compared with the recommended dietary allowance (RDA). The dietician collecting all details remained blinded to the subject’s medical history, in order to alleviate bias.
The patient population was subdivided based on revised Asian specific guidelines for BMI (BMI 18-22.9= normal weight, BMI 23-24.9= overweight and BMI ≥25= obese) . Self-perception of weight was assessed by asking the patient which BMI category they felt they were in, either in person or via a telephonic interview. One way Analysis of variance to compare quantitative parameters (such as calorie intake, protein intake, fat intake and other dietary components) across weightgroups and Chi-Square test for qualitative parameters (such as mild, moderate and heavy physical activity).
The study was carried out over a period of 3 months and enrolled 150 subjects, out of which 7 opted out. Out of the remaining 143, it was only possible to collect weight perception data on 118. The demographic profile of the cohort is given in Table 1 and biochemical profile is given in Table 2. Notably, 49 individuals had a normal BMI (out of which 5 had type 2 diabetes mellitus [T2DM]), 46 were overweight (out of which 5 had T2DM) and 48 were obese (out of which 4 had T2DM). A definite trend towards increased energy intake was noted in the overweight and obese group (Table 1). Whilst no strong association was seen between carbohydrate/protein intake and increasing BMI, there was a statistically significant correlation between increased dietary fat/fiber, and increasing weight.
No correlation was seen between those following a non-vegetarian diet vs. a vegetarian diet vis-a-vis BMI (p-value 0.336). Interestingly, whilst there was increase in healthy food snacking behavior in the normal weight group (24.5%), there was no difference in junk food intake across the groups. Furthermore, no clear link could be drawn between individuals in the normal, overweight and obese BMI groups and eating out in a restaurant (75.5% vs. 71.7% vs 66.7%).
In terms of lifestyle, table 1 shows a comparable data on all relevant parameters which we have analyzed. We found that there were a significantly higher number of subjects in the obese group (22.9%) taking alcohol on a regular or occasional basis as compared to normal (2%) and overweight (10.9%) subjects (p-value 0.010).
Television watching hours had no significant association with BMI, however more people eating snacks while watching television were found to be obese (33.3%) than overweight (28.3%) and normal (32.7%). The RAPA assessment indicated that 28.6% subjects were active in the normal weight group while only 16.7% were active in the obese group and 23.9% in
Table 1. Demographic Parameters of study population among the three groups.
Figure 1. Weight Perception of the Study Population.
overweight group. This difference was highly significant with p-value=0.000, and links a lack of physical activity with obesity. Out of the total sample population, were able to obtain data on 118 subjects in terms of perception of body weight. In the normal BMI group, the majority (82.9%) of people perceived their weight correctly.
14% wanted to further reduce their weight (Table 3). In the overweight group, almost half (47.2%) thought that their weight was in fact normal. Whilst 33.3% of this group wanted to maintain their BMI, 11.1% wanted to increase it. Surprisingly, all of individuals in the obese category thought their weight to be more than their actual BMI. Only 63.4% of the obese cohort was aware of what a BMI is, as opposed to 75% of those in overweight group. Of those who felt that their weight was greater than it actually is, only 15.9% were found to have an active lifestyle. Only 29.4% of individuals perceiving their weight to be normal displayed an active lifestyle.
Table 3. Body Weight Perception of the Study Population.
According to the World Health Organization, there has been an alarming increase in the global prevalence of obesity. In 1980, 5% of men and 8% of women were found to be obese (BMI ≥30kg/m2). However, in 2008, this figure almost doubled as 10% of men and 14% of women fit this classification. At the same time, 35% of adults above 20 years of age were noted to be overweight (BMI ≥ 25 kg/m2). This has a direct impact on morbidity and mortality, as being overweight or obese results in 2.8 million people deaths a year and causes an estimated 35.8 million (2.3%) of global Disability-Adjusted Life Years (DALYs) .
Recently, the prevalence of obesity in an urban adult population in New Delhi was reported to be 50%. Unfortunately, the rural population is also at risk. Data using Asian cutoffs for obesity, from rural Tamil Nadu, gives a prevalence of 32.8% in males and 38.2% in females .This is of great concern, as accompanying this rise in weight, has been the steady increase in the prevalence of diabetes. Recently, in a cohort study from Chennai, Anjana et al reported the incidence rate of progression to type 2 diabetes mellitus (T2DM) in those with prediabetes to be as high as 78.9 per 1000 person-years (95% CI 68.0–90.9). Out of 1007 individuals with normal glucose tolerance at baseline, 209 developed T2DM after 9.1 years (incidence rate of 22.2 per 1,000 person-years [95% CI 19.4–25.4]) . Dutta et al reported a similar conversion rate in a cohort of 144 individuals with prediabetes from Calcutta (71.52 per 1,000 person-years [95% CI 56.76–97.29]) . This rate of progression is truly alarming as it is in tune with that seen in small, homogenous, sheltered populations (i.e. Pima Indians) .
Although our study did not show a strong correlation between diet and increasing weight, from this study, there seemed to be an increasing trend in overall energy intake in the overweight and obese groups. On the other hand, the overall physical activity score of the entire cohort was extremely poor. The obese subset was found to be the most inactive with almost 4/5 of the subjects reporting themselves as being underactive. Of great relevance is the fact that almost 50% of individuals in the overweight group perceive their BMI to be within normal parameters. Furthermore, it is surprising that the entire obese population perceives its BMI to be greater than it actually is. Our study adds to the literature in several ways. First of all, it reinforces that crucial fact that obese Indian patients seem to have a greater caloric/energy intake than their non-obese/ overweight counterparts and are also the least active. Furthermore, 50% of adult overweight Indians perceive their BMI to be within normal parameters and 100% of the obese feel that their weight is greater than what it is (Table 3). This has enormous social and cultural implications. Are we as healthcare professionals failing to educate the general public with regards to maintaining a healthy lifestyle and keeping one’s weight in check? Or, are we as a society creating a sense of low self-esteem and poor body image in those who are obese, thereby causing them to feel worse than they actually are and prompting depression, overeating and a host of other unhealthy behaviors? The world of chronic disease management is now heavily engaged in bringing about change by motivational interviewing . Only by understanding how Indian patients perceive their body weight and by recognizing the dietary and lifestyle patterns of normal, overweight and obese individuals, will we be able to connect with, motivate and inspire behavior change in our patients. Any positive and sustainable change the decreases weight, will in turn serve to decrease the prevalence of other co-morbidities such as diabetes mellitus.
We have several limitations to this study. First of all, this was a small sample size and not all of the original cohort could be included when assessing an individual’s perception of their BMI. Secondly, this was a one visit study, we could only collect data on diet & physical activity at the time of enrolment and no follow-up was done. Thirdly, this was a single center study and may not represent the regional differences in diet, physical activity and weight perception that exist in the Indian sub-continent.
Undoubtedly, India faces troubled times ahead as the burden of non-communicable diseases like diabetes and obesity rise day by day. The need of the hour is for ethnic specific data to understand how we can better educate in a positive manner, how we can better implement lifestyle change and how we can better reach the community at the grass roots level. Aside from focusing on diet and exercise, we must target the patients’ perception of body weight in a constructive and healthy manner, to ensure that individuals remain motivated, engaged and invested in their own health.
This study has received no financial or editorial support.
Author Disclosure Statement
No competing financial interests exist.
Compliance with Ethical Standards
There is no potential conflict of interest involved in this research.
This study protocol, CRF and Informed Consent Form were approved by the Institutional Ethics Committee of Max Super Speciality Hospital (A Unit of Devki Devi Foundation, prior to initiation of study. Proper informed consent was taken from each every study participant.
Disclosure: No funding source to disclose or conflicts of interest present.
3. Finucane MM et al. National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9.1 million participants. The Lancet. 2011, 337(9765): 557-567.
6. Agrawal P, Gupta K, Mishra V et al. A study on body-weight perception, future intention and weight-management behaviour among normal-weight, overweight and obese women in India. Public Health Nutr. 2014, 17(4): 884-895.
9. Ramachandran A, Snehalatha C, Mary S et al. The Indian Diabetes Prevention Programme shows that lifestyle modification and metformin prevent type 2 diabetes in Asian Indian subjects with impaired glucose tolerance (IDPP-1). Diabetologia. 2006, 49(2): 289-297.
10. University of Washington. Rapid Assessment of Physical Activity. University of Washington Health Promotion Research Center, © 2006. Funded in part by the Centers for Disease Control. Reproduced with permission.
11. Misra A. Ethnic-Specific Criteria for Classification of Body Mass Index: A Perspective for Asian Indians and American Diabetes Association Position Statement. Diabetes Technol Ther. 2015, 17(9): 667-671.
14. Anjana RM, Shanthi Rani CS, Deepa M et al. Incidence of diabetes and prediabetes and predictors of progression among Asian Indians: 10-year follow-up of the Chennai Urban Rural Epidemiology Study (CURES). Diabetes Care. 2015, 38(8): 1441-1448.
16. Hardcastle SJ, Taylor AH, Bailey MP et al. Effectiveness of a motivational interviewing intervention on weight loss, physical activity and cardiovascular disease risk factors: a randomised controlled trial with a 12-month post-intervention follow-up. Int J Behav Nutr Phys Act. 2013, 10: 40.