The study was conducted at The Catholic University of Eastern Africa (CUEA), a private lead institution in Nairobi, Kenya. The University offers courses ranging from undergraduate to doctorate. The institution has seven undergraduate faculties including Arts & Social Sciences, Commerce, Education, Science, Law, and Theology. We involved all undergraduate students who were in session at the time of data collection.

This was a descriptive, cross-sectional study employed among undergraduate students (n = 245). The study was conducted between in 2019. During the study period, there were 5300 undergraduate students in the seven faculties. After calculating a proportionate sample from the seven faculties, a systematic random sampling technique was used to select 245 study participants. The 5300 number of undergraduate students was divided by the adjusted sample size (246) to get the sampling interval = 22. Therefore, every 22^nd undergraduate student from each faculty was included in the study until the required sample size was achieved. Students who were not available during the period of the study and those who declined to sign the consent were excluded from the study.

The Fischer’s formula below was used to calculate the sample size.

n = z 2 ( p ) ( q ) d 2 ,

where:

n = Sample size

Z = Normal deviation at the desired confidence interval. In this case it will be taken at 95%, Z value at 95% is 1.96.

P = Proportion of the population with the desired characteristic.

Q(1 − P) = Proportion of the population without the desired characteristic.

d² = Degree of precision; will be taken to be 5%.

The proportion of overweight and Obesity among undergraduate student in university colleges was taken to be 20% from a study carried out in Uganda (Waweru et al., 2016)

Therefore-

n = Z 2 P ( 1 − P ) d 2

n = (1.96)² × 0.2 × 0.8 ÷ (0.05)²

n = 3.8416 × 0.16 ÷ 0.0025

n = 0.614656 ÷ 0.0025 = 245.824

n = 246

The data were collected using a structured questionnaire adopted from the WHO STEP-wise approach to non-communicable disease risk factor surveillance [26] . The assessment comprised socio-demographic, lifestyle characteristics (dietary intake patterns, physical activity, alcohol intake, cigarette smoking), and anthropometric measurements.

Assessment of dietary intake patterns: Dietary intake patterns was assessed using food frequency questionnaires (FFQ) adopted from the WHO STEP-wise approach to non-communicable disease risk factor surveillance [26] . The respondents were asked to estimate the number of times per day/week they consumed particular food products and the amount usually eaten per food item by making comparisons with specified reference portions. Dietary intake patterns included frequency consumption of processed/fast foods, daily servings of fruit and vegetables, amount of sugars, and salts intake were captured.

Alcohol consumption and smoking status: Current alcohol consumption status including frequency and amount (number of standard drinks per day) was captured. Frequency was assessed by asking: “in the past, 30 days, 1 week, how often did you drink any alcoholic beverages?” We grouped responses into four categories (daily, 5 - 6 days per week, 3 - 4 days per week, 1 - 2 days per week, less than 1 day per week). The usual amount was assessed by the question: “on those days when you drank alcoholic beverages, on average, how many standard drinks (SDs) did you have?” We grouped responses into three categories (one, two, and more than two drinks per drinking occasion). Men who consumed more than two drinks per day and women more than one drink per day were classified as drinking above the WHO dietary guidelines [27] . We considered as one drink of alcohol: 1 can/bottle of beer (350 ml), 1 glass of wine (120 - 150 ml), or 1 shot of spirits (40 ml).

Assessments of Physical Activity: The Global Physical Activity Questionnaire (GPAQ) was used to measure the level of physical activity of the participants [28] . The frequency and duration of time spent engaging in physical activity are measured in 3 domains: activity at work, travel to and from places, and recreational activities. Energy expenditure, measured in metabolic equivalents (MET), was estimated using duration, intensity, and frequency of physical activities performed within 7 days. MET is the ratio of specific physical activity metabolic rates to the resting metabolic rate (1 MET = the energy cost of sitting quietly and is equivalent to a caloric consumption of 1 kcal/kg/hour). According to the GPAQ scoring protocol, the responses were converted to MET-minutes/ week: total minutes over the last 7 days spent on moderate, and vigorous activity were multiplied by 4.0, and 8.0, respectively, to obtain MET scores for each activity level. Respondents who scored ≥ 600 MET-minutes/week were categorized as active, while those who scored below 600 MET-minutes/week were categorized as inactive.

Anthropometric measurements: Bodyweight in light clothes was measured to the nearest 0.1 kg using a calibrated, Sohenle mechanical weighing scale. Height was measured using a portable stadiometer to the nearest 0.5 centimeters. BMI was calculated as weight in kilograms divided by the square of height in meters. According to the WHO [3] , individuals with a BMI ≥ 30 kg/m² are categorized as obese, 25.0 - 29.9 kg/m² as overweight, 18.5 - 24.9 kg/m² as normal, while those with less than 18.5 kg/m² as underweight. WC was taken at the midpoint between the lower margin of the last palpable rib and the top of the iliac crest (hip bone) [29] . Abdominal obesity was determined as a WC > 102 cm in men and >88 cm in women according to the WHO cutoff points and risk of metabolic complications for WC [3] .

Assessment tools were reviewed for content validity by experts in the field of cardiovascular disease to ascertain relevance and completeness. To measure the reliability of the questionnaire, a test re-test method was employed whereby, a repeat pre-test was carried out after two weeks, and Cohen’s kappa statistic was used to measure the level of agreement of the two results. The result of the repeated questions had a kappa value of 0.90 therefore, the questionnaire was considered reliable.

Ethical approval to conduct this study was obtained from Kenyatta National Hospital-University of Nairobi Ethical Review Committee (KNH-UoN ERC) (Approval number (UP89/02/2019). We also sought study approval from the National Commission for Science, Technology and Innovation (NACOSTI) (Approval number NACOSTI/P/19/53639/2870). The institutional permission was granted by the administration of the Catholic University of Eastern Africa. Consent was obtained from the study participants prior to data collection after explanation of the study’s aim and objectives.

Statistical analyses were performed using the statistical package for the Social Sciences (SPSS: version 22). Data were descriptively analyzed into means and proportions. The chi-square test of independence and binary logistic regression were employed to determine associations between the dependent (obesity) and various independents variables. A multiple logistic regression model with backward conditional was carried out to determine the variables that independently contributed to the occurrence of general and abdominal obesity. A p-value of less than 0.05 was considered to be significant.

The respondents (n = 245) were university students. Majority of them were single (95%, n = 233), females (64.1%, n = 157), with a mean age of 22.32 ± 4.0 (Mean ± SD) years (Table 1). The number of students who live in hostels was significantly (X² = 21.745, df = 2, p < 0.001) higher in females (78.6%) relative to males (21.4%). Most (83.7%, n = 205) of the respondents were physically inactive, with significantly (X² = 12.945, df = 2, p = 0.001) higher in females (68.8%) compared to males (31.2%). Almost half (47.8%, n = 99) of the respondents spent 4 - 6 hours in a day while sitted. Approximately one-fifth (19.6%, n = 48) of the respondents had general obesity with significantly (X² = 20.022, p < 0.001 higher in females (83.3%) than males (16.7%). While, about a quarter (27.8%, n = 68) of them had abdominal obesity, with substantially (X² = 47.056, df = 2, p < 0.001) higher in females (97.1%) than males (2.9%). Abdominal obesity was more prevalent than general obesity (Table 1).

Age, sex, accommodation status, physical activity, sedentary time, rate of eating fast/processed foods, and vegetables were significantly associated with general obesity. Students aged ≥ 20 years were at about 6-fold (OR, 3.858; 95% CI, 1.563 - 9.526, p = 0.003) increased risk of general obesity compared to those aged below 20 years. Female students were 3-times (OR = 3.42; 95% CI, 1.52 - 7.69, p = 0.003) more likely to suffer from general obesity compared to males. Students who lived with their parents (OR = 2.46; 95% CI, 1.08 - 5.57, p = 0.032) and in hostels (OR = 3.08; 95% CI, 1.42 - 6.67, p = 0.005) were at increased risk of general obesity compared to students who rented private houses. Physically inactive students were 5-times (OR, 5.49; 95% CI, 1.277 - 23.652, p = 0.022) more likely to develop general obesity compared to active individuals. Students who spent 7 or more hours sitting in a day were at about 7 times (OR = 6.939 (95% CI, 1.512 - 31.838) increased risk of general obesity relative to those who spent less sedentary time. Of the respondents, one-third (31.4%, n = 77) were alcohol consumers. However, there was no association between alcohol intake and general obesity. The odds of developing general obesity was higher among respondents who often (OR, 6.24; 95% CI, 2.09 - 18.63, p = 0.00) and sometimes (OR, 3.47; 95%

*Analysis with chi-square test of independence, **Analysis with independent t-test, ^ahad missing responses.

CI, 1.07 - 11.27, p = 0.038) consumed fast/processed foods compared to those who rarely consumed such foods. Respondents who did not daily consume vegetables were 2-times (OR, 2.33; 95% CI, 0.99 - 5.49, p = 0.054) more likely to develop general obesity than those who consumed vegetables on daily basis (Table 2).

Sex, accommodation status, physical activity, sedentary time, rate of eating fast/ processed foods, and vegetables were significantly associated with abdominal

obesity. Females had higher odds of abdominal obesity (OR, 31.19; 95% CI, 7.41 - 131.27, p < 0.001) compared to males. Students who used to live with their parents (OR, 4.38; 95% CI, 2.11 - 9.10, p < 0.001) and in hostels (OR, 3.58; 95% CI, 1.75 - 7.34, p < 0.001) were more likely to suffer from abdominal obesity relative to those students who rented private houses. Physically inactive students were at about 6-fold (OR = 5.726, 95% CI, 1.703 - 19.256, p = 0.005) increased risk of abdominal obesity compared to active students. The occurrence of abdominal obesity was higher among students who drank alcohol (35.1%) compared to those who never drank (24.4%), however, the difference was significant. Students who often (OR, 3.94; 95% CI, 1.70 - 9.10, p = 0.001) and sometimes (OR, 3.26; 95% CI, 1.33 - 7.96, p = 0.010) consumed fast/processed foods were more likely to develop central obesity compared to students who rarely consumed such foods. The odds of developing abdominal obesity were 2-times (OR, 2.16; 95% CI, 1.04 - 4.44, p = 0.037) higher among students who did not daily take vegetables compared to students who daily consumed vegetables (Table 3).

A multivariate logistic regression analysis was performed to assess the risk factors independently associated with general and abdominal obesity. Risk factors independently associated with general obesity were age ≥ 20 years (OR, 9.95;

95% CI, 3.09 - 32.08, p < 0.001), staying with parents (OR = 3.22; 95% CI, 1.09 - 9.58, p = 0.035), sedentary time (OR = 11.36; 95% CI, 2.08 - 61.96, p = 0.005) and often consumed processed/fast foods (OR = 7.83; 95% CI, 1.90 - 32.21, p = 0.004). Risk factors for abdominal obesity were being female (OR, 38.76; 95% CI, 5.07 - 296.54, p < 0.001), staying with parents (OR = 3.02; 95% CI, 1.14 - 7.99, p = 0.026) and sedentary time (OR = 6.55; 95% CI, 1.80 - 23.81, p = 0.004) (Table 4).