Prevalence and Determinants of Overweight and Obesity among the Catholic University of Eastern Africa Staff, Langata Campus, Nairobi, Kenya ()
1. Introduction
Low- and middle-income countries, including Kenya, are facing rising prevalence of obesity and obesity related cardiometabolic complications. Overweight and obesity are major risk factors for a number of non-communicable diseases (NCDs), including type 2 diabetes, cardiovascular diseases, some forms of cancers and increase medical expenditures. Overweight is defined as body mass index (BMI) ≥ 25 kg/m2 and obesity as BMI ≥ 30 kg/m2. Over recent years, rates of overweight and obesity have escalated rapidly in many parts of the world to epidemic proportions, mostly because of increased consumption of energy dense diets high in fats and sugars, compounded by declining physical activitylevels. The worldwide prevalence of obesity has nearly doubled between 1980 and 2008. In 2008, 10% of men and 14% of women in the world were obese (BMI ≥ 30 kg/m2), compared with 5% for men and 8% for women in 1980 [3] . Globally 1.9 billion adults aged 18 years and above are overweight, of these, over 650 million are obese. Evidence from the Kenya Demographic Health Survey (KDHS) (2008-09) indicated that there is increasing prevalence of overweight and obesity in Kenya. The proportion of women aged 15 - 49 who are overweight and obese has increased from 23% in 2003 to 25% in 2008-09 with uneven prevalence distribution between rural (20%) and urban areas (39%) [4] .
The global burden of overweight and obesity is increasing rapidly and will have significant social, economic and health consequences unless urgently addressed. Worldwide, 2.8 million people die each year as a result of being overweight and obesity [5] . Excess weight tends to increase low density lipoprotein (LDL) level, triglycerides and lowers high density lipoprotein (HDL) which predisposes a person to type 2 diabetes and cardiovascular diseases. The more fat there is in the body, the harder time insulin has in getting glucose into the cells. Conversely, minimizing fat intake and reducing body fat help insulin do its job much better [6] .
Teaching institutions like the Catholic University of Eastern Africa are important risk factors for overweight and obesity due to their lifestyles factors. Staff from universities usually suffer from lack of sufficient physical activity because of the nature of the work. People in such work environment spend too much time in office probably sitting the whole day and then drive back to home which is one of the major risk factors for obesity and related health problems. Although the prevalence of overweight and obesity in some developing countries including Kenya is extremely high, less attention has been given to mitigate the problem as more attention has been concentrated on infectious diseases and under-nutrition or malnutrition of children [7] [8] . If preventive measures are not put in place, the problem will affect millions of Kenyans and overburden the health care system. Therefore, there is a need to put measures in place to arrest the problem of overweight and obesity and to prevent the negative health and socio-economic consequences in the country. In order to address the burden of overweight and obesity, it is critically important to examine the problem and understand the local determinants of overweight and obesity; thus corrective strategic measures can be implemented. Hence, the aim of the study was to determine the factors associated with overweight and obesity among the Catholic University of Eastern Africa, Nairobi, Kenya (Table 1).
2. Methods and Materials
2.1. Study Setting
The study was carried out at the Catholic University of Eastern Africa (CUEA), Langata Campus which is located at Langata, Nairobi, Kenya. The institution in Kenya was established in 1984 as a private faith based University. The Institution offers several courses from Undergraduate to PhD programmes.
2.2. Study Design and Respondents
A descriptive, cross sectional study design was employed among three hundred and one (301) academic and nonacademic staff working at the University in two months period (April-May, 2018). During the study period, the University had
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Table 1. Categories of body mass index and waist circumference for adults.
415 staff and all of them were included in the study. However, after excluded some of the staff members who were away or on leave during the study period and those who did not want to participate in the study, 301 staff participated in the study.
2.3. Data Collection Tools
Data was collected using a pre-tested, WHO stepwise-structured questionnaire. Two research assistants having a bachelor’s degree in Nursing were trained to collect the data on: socio-demographic characteristics, physical activity, dietary intake patters and anthropometric measurements. The questionnaire had four sections. The first section was used to collect socio-demographic information. The second section was used to collect data on physical activity during work, transportation and leisure time in a typical week. The third section was used to collect information on dietary intake and eating habits. This was done using a food frequency questionnaire. The final section of the questionnaire was used to collect anthropometric parameters including weight, height, body mass index (BMI) and waist circumference (WC), measured using standard measurement units. Both BMI and waist circumference (WC) were used to determine the prevalence of overweight and obesity.
2.3.1. Physical Activity
The physical activity of the respondents was collected using the Global Physical Activity Questionnaire regarding type, frequency, duration and intensity of physical activity during work, transportation and leisure time in a typical week [9] . The tool is developed by the World Health Organization (WHO) for physical activity surveillance.
2.3.2. Dietary Intake Patterns
The food frequency questionnaire (FFQ) was used to capture information on the type and frequency of foods consumed by the respondents. The questionnaire focused on food recall on types of foods including fast foods, vegetables, fruits, beverages such as alcohol consumption, including the frequency of their consumption.
2.3.3. Anthropometric Measurements and Procedures
Physical measurements including weight, height, body mass index (BMI) and waist circumference (WC) were measured and recorded. Body weight in light clothes was measured to the nearest 0.1 kg using a Sohenle mechanical weighing scale. Height (in metres) to the nearest 0.5 cm was measured using a portable stadiometer, with subjects standing upright on a flat surface without shoes, the back of the heels and the occiput on the stadiometer. BMI was calculated as the ratio of weight in kilograms over height in meters squared [weight (kg)/height (m2)]. WC was taken at the midpoint between the lower margin of the last palpable rib and the top of the iliac crest (hip bone) (WHO, 2000). Men with WC of < 94 were classified as normal weight, 94 - 101.9 overweight and ≥102 cm obese. Women were also classified in same categories of <80, 80 - 87.9 and ≥88 cm for normal weight, overweight and obese, respectively.
2.3.4. Validity and Reliability
Validity of the questionnaire was examined by an expert in the field of nutrition and non-communicable diseases. He examined the questionnaire and provided feedback to the researcher. The recommendations and suggestions were incorporated in the final questionnaire.
The test re-test method was used to test reliability of the questionnaires in producing the same results. The pre-test sample comprised of 10 respondents who were randomly sampled outside the study area. Areas of the questionnaire that were found to be deficient were revised and the questionnaire adapted accordingly.
2.3.5. Pilot Study
The questionnaires were pre-tested for accuracy and clarity prior to the commencement of data collection on a sample of 21 adults (5% of the sample size) with similar characteristics to the actual sample, but who were not included in the final study.
2.4. Data Analyses
Completed questionnaires were checked, cleaned and coded before data entry. Computer software, (SPSS V. 22) was used to analyze the data. The data was descriptively analyzed into proportions and frequency tables, while one-Way ANOVA was used to determine relationships between various variables. A p value of less than 0.05 was considered to be significant.
2.5. Ethical Consideration
The ethical approval to conduct the study was obtained from Kenyatta National Hospital-University of Nairobi Ethical Review Committee (KNH-UoN ERC) (Approval number (UP 592/10/2017). The institutional permission was granted by the administration of the Catholic University of Eastern Africa. The consent was obtained from the respondents after explanation regarding the study both verbally and in written before data collection was commenced.
3. Results
3.1. Socio-Demographic Characteristics of Respondents
Of the respondents, 68.1% were in the age group of 31 - 50 years, 60.5% were males and 62.8% were married. Among the respondents, 94% had university/college degree and 52.8% were academic staff. The majority, (92.7%) were belongs to Christian religion and most, (66.4%), were residents of Nairobi (Table 2).
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Table 2. Socio-demographic characteristics of the respondents.
3.2. Physical Measurements (BMI and WC) of the Respondents
The prevalence of overweight (BMI = 25 - 29.9 kg/m2) and obesity (BMI ≥ 30 kg/m2) among the respondents were 47.5% and 36.3% respectively. Of the obese respondents, 30.2% had obesity I (BMI = 30.0 - 34.9 kg/m2), 3.4% obesity II (BMI = 35.0 - 39.9 kg/m2) and 2.7% obesity III (BMI ≥ 40.0 kg/m2). Using the European group for study of insulin resistance (EGIR, 2004) definition, majority, (79.8%) men and (76.3%) women had abnormal high waist circumference (WC) measurements (≥94 cm for men and ≥80 cm for women). Based on WC, the prevalence of overweight and obesity among men were 42.6% and 37.2% respectively. While among the women the prevalence of overweight and obesity were 56.8% and 19.5% respectively. Further analyses with One-Way ANOVA and independent samples T-test revealed that the mean BMI (F (2215) = 6.185, p = 0.006) and the mean WC (t = 2.021, df = 217, p = 0.004) were significantly higher among respondents aged ≤ 40 years old respectively. Additionally, the mean WC was significantly higher among male respondents compared to the female counterparts (t = 2.014, df = 216, p = 0.003) (Table 3).
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Table 3. Measurements of body mass index (BMI) and waist circumference (WC) of the respondents.
Analysis with: *One-Way ANOVA; **Independent Samples T test.
3.3. Relationship between Respondents’ Dietary Practice and Physical Measurements (WC and BMI)
Of the respondents, a good number had eaten processed foods, often at 16.9% and always at 14% respectively. Nearly half, (49.2%), of the respondents consumed fruits 1 - 2 days in a week and majority, (52.8%) consumed vegetables 3 - 4 days per week. A One-Way ANOVA test was used to establish the relationship between eating habits and anthropometric measurements. Respondents who consumed vegetables (F (2, 241) = 3.663, p = 0.022) and fruits (F (2, 207) = 10.743, p = 0.017) on daily basis were likely to have low WC. However, higher WC (F (2, 204) = 10.745, p = 0.015) and BMI (F (2, 207) = 12.026, p = 0.003) were significantly associated with daily consumption of fast foods (Table 4).
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Table 4. Relationship between fruits, vegetables and fast foods consumption and BMI.
Key abbreviations: WC = Waist circumference, BMI = Body circumference, Analysis with One-Way ANOVA.
3.4. Anthropometric Measurements in Relation to Physical Activities of the Respondents
Majority, (89.4%) and (87.7%) of the respondents’ work did not involve vigorous intensity and moderate intensity activities respectively. Of the respondents, most, (64.8%) did not involve in any vigorous intensity sports or recreation activities. Majority, (90%) of the respondents had spent seating for ≥5 hours per day. Further analysis revealed that respondents who had involved in vigorous intensity physical activity for ≥20 minutes for ≥3 days in a week had significantly lower mean BMI (F (2, 217) = 3.470, p = 0.025) and mean WC (F (2, 207) = 7.046, p = 0.002) compared to respondents who did not involve in any vigorous intensity physical activity for ≥20 minutes for ≥3 days in a week respectively. Additionally, respondents who had involved in moderate intensity physical activity for ≥30 minutes for ≥5 days in a week had significantly lower mean BMI (F (2, 211) = 5.194, p = 0.011) and mean WC (F (2, 140) = 3.479, p = 0.023) than respondents who did not involve in in moderate intensity physical activity for ≥30 minutes for ≥5 days in a week respectively (Table 5).
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Table 5. Anthropometric measurements in relation to physical activities of the respondents.
Analysis with: *One-Way ANOVA; **Independent Samples T test.
3.5. Relationship between Alcohol Consumption and Physical Measurements
Most, (61.1%) of the respondents consumed alcohol. Majority, (67.6%) of those who consumed alcohol, consumed ≥3 standard drinks in one drinking occasion. Further analyses with One-Way ANOVA and independent samples T-test revealed that both mean BMI (F (2191) = 3.282, p = 0.002) and mean WC (t = 2.051, df = 1169, p = 0.005) were significantly higher among respondents who had drunk ≥ 3 standard drinks per drinking occasion than respondents who had drunk less than 3 standard drinks per drinking occasion (Table 6).
4. Discussion
Our study findings revealed that age, gender, physical activity, consumption of fruits, vegetables, fast foods and alcohol were the variables significantly associated with overweight and obesity among the study respondents. The subsequent discussion will be based on these findings.
4.1. Relationship between Respondents’ Age and Gender and Physical Measurements
Using BMI, the prevalence of overweight and obesity among the respondents were 47.5% and 36.3% respectively. Of the obese respondents, 30.2% had obesity I, 3.4% obesity II and 2.7% obesity III. Using the European group for study of insulin resistance (EGIR, 2004) definition, majority, (79.8%) men and (76.3%) women had abnormal high waist circumference (WC) measurements (≥94 cm
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Table 6. Relationship between alcohol consumption and physical measurements.
Key abbreviations: BMI = Body mass index. WC = Waist circumference, Analysis with: *One-Way ANOVA; **Independent Samples T Test.
for men and ≥80 cm for women). Based on WC, the prevalence of overweight and obesity among men were 42.6% and 37.2% respectively. While among the women the prevalence of overweight and obesity were 56.8% and 19.5% respectively. The prevalence of overweight is higher in women at 56.8% compared to 42.6% in men, while, the prevalence of obesity is higher in men at 37.2% compared to 19.5% in women. In the current study, this prevalence of overweight/obese (42.6%) among Kenyan women is higher than the prevalence recorded in 2009 at 39% in Nairobi, the main capital city of Kenya [10] . The observed high prevalence rate of overweight/obese in this study could be due to the fact that the respondents were University staff with higher standards of living than the general population and also the nature of their work, sedentary behavior, might predispose them to overweight/obese than the general population.
Further analyses revealed that mean BMI and the mean WC were significantly higher among respondents aged ≤40 years old compared to respondents aged above 40 years. Additionally, the mean WC was significantly higher among male respondents compared to the female counterparts (p = 0.044). This finding is in agreement with many previous studies which found that young adults (below 40 years old) had abnormal higher BMI and WC compared to individuals above 40 years old [11] [12] [13] [14] . Young adults are more prone to obesity than older adults because of several reasons. Firstly, young adults eat a lot of fast foods and drink more alcohols than older adults which might put them at higher risk of obesity. People younger than 40s have higher chances of engaging in socialization and involving in unhealthy lifestyles (eating and drinking) than individuals above 40s, characteristics which might put them to overweight/obese. Fast foods are high in calories, salt and sugar, which are known risk factors for obesity [15] . WHO data revealed that consumption of processed foods and/or fast foods promotes obesity due to their high contents of salt, sugar and fats compared to low-energy foods such as fruits and vegetables [16] . Secondly, adults above 40 years old are more knowledgeable about lifestyle changes because they visit health facilities more frequently than young adults which might play a significant role in adapting healthier lifestyle behavior. Obesity has negative health consequences, both in increased morbidity and mortality as well as social stigma and limitations to healthy social functioning.
4.2. Relationship between Dietary Intake Patterns and Physical Measurements
The study revealed that daily consumption of vegetables and fruits were significantly associated with lower WC; while, higher WC and BMI were significantly associated with daily consumption of fast foods. Dietary intake has been cited as one of the leading forces in the development of overweight and obesity. Over the years, the diets of populations have been changing towards diets that favour the weight gain as traditional diets are gradually replaced with modern diets and this is particularly significant in Nairobi (the capital city of Kenya) where the study was carried out. The adoption of unhealthy eating habits such as increased consumption of soft drinks, sweets, fried and processed foods, favours an increase in the amount of body fat and thus obesity [10] . Fruits and vegetables on the other hand reduce weight because they have low energy density, high water, fiber, and low fat content. The high-fiber content is attributed to weight loss through the feeling of stomach fullness or enhances satiety without additional calories. On the other hand, fast foods, high calorie beverages and diet rich in calories, but lacking fruits and vegetables are associated with weight gain [15] [17] [18] [19] . WHO data revealed consumption of processed foods and/or fast foods promotes obesity due to their high contents of salt, sugar and fats compared to low-energy foods such as fruits and vegetables [16] . Consumption of unhealthy diets and changing lifestyles has resulted in increased levels of obesity, cardiovascular diseases, cancers and diabetes and represent a significant development challenge [10] .
4.3. Relationship between Physical Activity and Physical Measurements
Majority, (89.4%) and (87.7%) of the respondents’ work did not involve vigorous intensity and moderate intensity physical activities respectively. Of the respondents, most, (64.8%) did not involve in any vigorous intensity sports or recreation activities. Majority, (90%) of the respondents had spent seating for ≥5 hours per day. The WHO defines insufficient physical activity as less than five times 30 minutes of moderate physical activity per week, or less than three times 20 minutes of vigorous physical activity per week [20] . Further analysis revealed that respondents who had involved in vigorous intensity physical activity for ≥20 minutes for ≥3 days in a week had significantly lower mean BMI and mean WC compared to respondents who did not involve in any vigorous intensity physical activity for ≥20 minutes for ≥3 days in a week respectively. Additionally, respondents who had involved in moderate intensity physical activity for ≥30 minutes for ≥5 days in a week had significantly lower mean BMI and mean WC than respondents who did not involve in moderate intensity physical activity for ≥30 minutes for ≥5 days in a week respectively.
This finding is in line with other studies which found that engaging in physical activities reduces both BMI and WC [21] [22] [23] [24] . Physical activity contributes to energy consumption, prevents obesity, favors weight loss, and reduces the risk of developing cardiovascular diseases, type 2 diabetes mellitus and some forms of cancer [25] [26] . Adequate physical activity has been shown to have many health promoting properties and has a direct, independent role in reducing CVDs mortality. Inactivity on the other hand is one of the most important factors that have been known to fuel overweight and obesity.
Kenyans today rely more on personal and public vehicles to move even the shortest of distance. This is particularly significant for people, who work in teaching institutions; they usually drive from home to work place and then drive back to home after sitting the whole day in office, sedentary behaviors which predispose them to obesity. Physical inactivity is recognized as one of the four major risk factors for the four major Non-communicable diseases which contribute for multiple deaths and disability. People who do not involve in physical activity have a 20% - 30% increased risk of all-cause mortality compared to those who involve in at least 30 minutes of moderate intensity physical activity for ≥5 days in a week [WHO, 2009]. Research revealed that weekly engagement in 150 minutes of moderate intensity physical activity reduces the risk of ischaemic heart disease by approximately 30%, the risk of diabetes by 27% and the risk of breast and colon cancer by 21% - 25% [20] . Regular physical activity also helps the body cells take up glucose and thus lower blood glucose levels. Adequate physical activity can prevent the development of diabetes by reducing body weight, increasing insulin sensitivity and blood sugar control. It also helps control blood cholesterol and blood pressure. Conversely, insufficient physical activity decreases insulin sensitivity, which can lead to diabetes [27] .
4.4. Relationship between Alcohol Consumption and Physical Measurements
Most, (61.1%) of the respondents consume alcohol. Of those who consume alcohol, majority, (67.6%) consumed ≥3 standard drinks in one drinking occasion. Further analyses with One-Way ANOVA and independent samples T-test revealed that both mean BMI and mean WC were significantly higher among respondents who had drunk ≥3 standard drinks per drinking occasion as compared to respondents who drink less than 3 standard drinks per drinking occasion. The amount and frequency of alcohol consumption have been found to correlate with BMI and WC. A study using national surveys in the United States found that people who consumed ≥4 drinks per day had significantly higher BMI than those who consumed one drink per day [28] [29] [30] [31] . There are several reasons alcohol and weight gain are linked. Alcohol can cause weight gain simply because it has calories. If there is too much alcohol, it can also turn into fat in the liver, which then turns into fat which is likely to be stored as fat in the body and thus weight gaining. Alcohol is an appetite stimulant, so people may be more likely to eat more and also make poorer food choices if they’re drinking [32] [33] . Alcohol may also fail to trigger the physiologic mechanism that produces the feeling of fullness in the short term, long-term, frequent drinkers which may predispose heavy drinkers to overeating and thus weight gain [34] .
5. Conclusion
Our findings show that there is high prevalence of overweight and obesity among the respondents. Consumption of fast foods and excessive alcohol is predictors for obesity. While high consumption of fruits and vegetable and engaging in both moderate and vigorous physical activities is protective against obesity. Institution like the Catholic University of Eastern Africa should be involved in lifestyle modification including supervised weight loss programs leading to improved nutrition, physical activity and behavioral change.
Limitation of the Study
Biochemical profiles (lipid and blood glucose levels) should be checked to establish a comprehensive image on the general cardiovascular health status of the study population.
Acknowledgements
The authors are grateful for all study participants who took part in the study for their time.
Funding
No funding was obtained for this study.
Availability of Data and Materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Authors’ Contributions
Both authors were instrumental in conception, research development and study design. FO contributed in data collection. OT was instrumental in statistical analysis. OT contributed in discussion and manuscript revision. Both authors read and gave final approval for the manuscript’s submission and publication.
Ethics Approval and Consent to Participate
The ethical approval to conduct the study was obtained from Kenyatta National Hospital-University of Nairobi Ethical Review Committee (KNH-UoN ERC) (Approval number (UP 592/10/2017)). The institutional permission was granted by the administration of the Catholic University of Eastern Africa. The consent was obtained from the respondents after explanation regarding the study both verbally and in written before data collection was commenced.