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Kyalo M N, Chege P, Kamuhu R. Overweight and Obesity and Associated Factors among Adolescent Students in Public Day Secondary Schools in Machakos County, Kenya. JNFS 2024; 9 (4) :744-754
URL: http://jnfs.ssu.ac.ir/article-1-849-en.html
URL: http://jnfs.ssu.ac.ir/article-1-849-en.html
Department of Foods, Nutrition, and Dietetics; Kenyatta University, Kenya.
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1 Department of Foods, Nutrition, and Dietetics; Kenyatta University, Kenya.
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Overweight and Obesity and Associated Factors among Adolescent Students in Public Day Secondary Schools in Machakos County, Kenya
Mary Ndanu Kyalo; MSc*1, Peter Chege; PhD1 & Regina Kamuhu1; PhD1
Mary Ndanu Kyalo; MSc*1, Peter Chege; PhD1 & Regina Kamuhu1; PhD1
1 Department of Foods, Nutrition, and Dietetics; Kenyatta University, Kenya.
| ARTICLE INFO | ABSTRACT | |
| ORIGINAL ARTICLE | Background: Overweight and obesity have increased significantly worldwide, placing major economic problems on health systems. This study aims to determine socio-economic characteristics, dietary practices, physical activity levels, nutrition status, predictors of overweight and obesity, and their relationships among adolescents. Methods: The study adopted a cross-sectional analytical study design. 401 adolescents were randomly selected from five secondary day schools using a proportionate-to-size sampling method. Results: Based on the findings, one third (36.3%) of the adolescents were between ages 17-18, with more girls (55.9%) than boys. The number of meals consumed per day among adolescents was 3.34±1.24. Individual dietary diversity score among adolescents was 4.21±1.61. Based on Body Mass Index (BMI), the prevalence of overweight and obesity among boys was 5.2% and 1.8%, respectively, and for girls, it was 7.3% and 3.6%, respectively. Using bioelectrical impedance analysis, the prevalence rates for males were 8.8% and 4.1%, respectively, while for females the rates were 10.1% and 5.1%. Regression analysis revealed that the most significant predictors were energy intake and physical activity, with P˂0.05 based on BMI and bioelectrical impedance analysis. Conclusion: There is a significant relationship between nutritional status and energy intake, the number of meals, dietary diversity score, and physical activity. Physical activity and dietary practices were the most important predictors of overweight and obesity among adolescents. The study recommends nutrition education and public health interventions in schools’ curricula. | |
| Article history: Received:10 Mar 2023 Revised: 16 Jul 2023 Accepted: 28 Jul 2023 |
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| *Corresponding author: marykyalo7@gmail.com Department of Foods, Nutrition, and Dietetics; Kenyatta University, Kenya. Postal code: 43844-00100 Tel: + 25 4721866934 |
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| Keywords: Adolescent; Body mass index; Overweight; Cross-sectional studies; Electric impedance. |
Introduction
Overweight/obese means an unnecessary increase in body fat, creating harmful health consequences. Being overweight and obese predisposes one to non-communicable diseases and is considered the fifth leading risk factor for killer diseases worldwide (Lobstein et al., 2015). Studies have shown that 10% of children and adolescents are overweight or obese worldwide (De Schutter et al., 2014). The causes of overweight and obesity include genetics, physical inactivity, and excess energy intake (Akinlade et al., 2014).
Obesity is a risk factor for insulin resistance, hypertension, atherosclerosis, some cancers, gallbladder disease, disabilities, and decreased quality of life in adolescents (Sylvetsky-Meni et al., 2015). Obese adolescents are prone to stigmatization and low self-esteem (Ashdown-Franks et al., 2019, Caleyachetty et al., 2018). Students in public day secondary schools in Kisumu, Kenya, were more likely to suffer from obesity or being overweight due to low physical activity and high-fat, sugar, and salt diets (Okoth et al., 2015).
Many studies have shown that for overweight and obesity prevention, people should modify their eating practices and physical activity (Williamson et al., 2020). The results of a cross-sectional study in designing an effective intervention program may increase obesity prevention among adolescents (Towner et al., 2015). Data on adolescents' overweight/obesity is limited in Kenya (Hüls et al., 2021). Therefore, this study aims to assess the predictors of nutritional status among adolescent students in public day secondary schools in Machakos Sub-County.
Materials and Methods
Obesity is a risk factor for insulin resistance, hypertension, atherosclerosis, some cancers, gallbladder disease, disabilities, and decreased quality of life in adolescents (Sylvetsky-Meni et al., 2015). Obese adolescents are prone to stigmatization and low self-esteem (Ashdown-Franks et al., 2019, Caleyachetty et al., 2018). Students in public day secondary schools in Kisumu, Kenya, were more likely to suffer from obesity or being overweight due to low physical activity and high-fat, sugar, and salt diets (Okoth et al., 2015).
Many studies have shown that for overweight and obesity prevention, people should modify their eating practices and physical activity (Williamson et al., 2020). The results of a cross-sectional study in designing an effective intervention program may increase obesity prevention among adolescents (Towner et al., 2015). Data on adolescents' overweight/obesity is limited in Kenya (Hüls et al., 2021). Therefore, this study aims to assess the predictors of nutritional status among adolescent students in public day secondary schools in Machakos Sub-County.
Materials and Methods
Research design and participants
The researcher adopted a cross-sectional analytical design method. This study was conducted in Machakos Sub-County, Machakos County. No research has been done on adolescents’ overweight in Machakos County (Towner et al., 2015). The adolescent students (13-19) attending public day secondary schools in Machakos Sub-County. This study only included boys and girls aged 13-19 attending public day secondary schools in Machakos Sub-County. The study excluded students who were sick, were transferred from other counties six months before the commencement of the study, and those who did not sign the consent forms to contribute to the study.
Sample size and sampling
The required sample size was calculated based on the standard formula by Cochran (1963) as cited by Fisher (Fisher et al., 1998) as n=(Z².pq)/d². Finally, 401 adolescences randomly were selected.
Data collection
Data collection
The questionnaire was administered to the students at school in classrooms. The informed verbal consent was obtained in the classroom from each respondent. After that, the Physical Activity Questionnaire for Adolescents (PAQ-A) was administered. Dietary intake data was gathered through a food frequency questionnaire and the 24-Hour Recall Questionnaire. The 24-hour recall comprised inquiring the respondents to report on the entire food and drink intake for the preceding 24 hours (the previous day), from foods consumed for breakfast in the morning to the last foods at night before bed.
From a list of 12 regularly consumed food groups, a seven-day food frequency questionnaire was used to gather information on the frequency of consumption in the preceding seven days. Individual dietary diversity score was done using individual dietary diversity score (IDDS) composed of 12 food groups. It was adapted to assess the quality and variety of the diet consumed by the household members, emphasizing the adolescent students by adding the number of food groups they consumed in the last 24 hours into scores.
Anthropometric measurements of weight, height, and body fat percentage were collected. Weight was measured using a bathroom scale (VON model HMSB 13 CB) to the nearest 100 grams calibrated at the beginning of each session. Stadiometer was used to measure height to the nearest 0.1 centimeters. The participants were measured while standing straight with their backs on the height board mounted on the wall. The BIA scale (Beurer model BF 750) measured body fat. Students were asked to remove shoes and socks or any heavy clothing; then, body fat was taken twice for each student and was recorded. Finally, the mean value was calculated.
Data analyses
Except for the 24-hour recall data, all other data were entered and analyzed using the SPSS computer software version 20. Data from 24 hours recall and food frequency was analyzed using Nutri- Survey for windows (2007). WHO AnthroPlus software was applied to analyze anthropometric data in z-scores for those under 18 and their body mass index (BMI) (Epi-Info), which was then exported to SPSS. The indicators of overweight and obesity used were BMI above 25 kg/m² for overweight and BMI above 30 kg/m² for obesity. For BIA indicator, male overweight adolescents were categorized as 17.1-22% and female overweight adolescents as 23.1-30%. Obesity for male adolescents was classified as ˃22.1%, whereas for female adolescents, it was ˃30.1% (Jebb, 2004). The data on physical activity were categorized into very little, little, moderate, often, and very often, per PAQ-A guidelines. Then, using SPSS, chi-square was used to measure associations between physical activity levels and overweight/obesity. Multiple logistical regression analysis was done to determine how overweight/obesity is predicted by physical activity and dietary practices, and thus identify the significant predictors in the study population. Significance levels were then determined at a 95% confidence interval, and a P-value of < 0.05 was used.
Results
About 44.1% of the students included in the study were male, and 55.9% were female. About 35.3% of the students were between 15-16, with mean ± SD age of 16.55±1.8 (Table 1). Over half (68%) of households comprised four to six members. Most adolescent students lived with their parents (74.7%), and 72.9% of their parents were married. Most adolescent parents/guardians (54.4%) had completed secondary education. Most of the teenage students’ parents/guardians (37.4%) were entrepreneurs, with 27.8% of parents/ guardians depending on farming. The majority (67%) of the households involved in the study obtained food by purchasing from the market, and 31.2% got food from their farm production. Over half (55.9%) of the participants consumed three meals daily: breakfast, lunch at school, and supper at home. On average, the study population consumed 3.34±1.24 meals a day.
Results
About 44.1% of the students included in the study were male, and 55.9% were female. About 35.3% of the students were between 15-16, with mean ± SD age of 16.55±1.8 (Table 1). Over half (68%) of households comprised four to six members. Most adolescent students lived with their parents (74.7%), and 72.9% of their parents were married. Most adolescent parents/guardians (54.4%) had completed secondary education. Most of the teenage students’ parents/guardians (37.4%) were entrepreneurs, with 27.8% of parents/ guardians depending on farming. The majority (67%) of the households involved in the study obtained food by purchasing from the market, and 31.2% got food from their farm production. Over half (55.9%) of the participants consumed three meals daily: breakfast, lunch at school, and supper at home. On average, the study population consumed 3.34±1.24 meals a day.
The study result indicated that cereals were most regularly consumed at school and home (up to 6 days by 88.1%). Over 95% consumed sugar/sweets for ≥ 6 days. An oil/fat-rich diet for ≥ 6 days was consumed in a week by 84.5% of respondents (Table 2).
The respondents consumed a mean IDDS of 4.21±1.61. About a third of the respondents consumed five food groups (37.9%), with half (50.8%) not meeting the recommended number of at least five food groups daily. The study results showed that 88.3% of boys and 92.4% of girls met the recommended daily allowance (RDA) for energy (Table 3).
There was a significant difference in the physical activity levels of male and female adolescents (P˂0.05) throughout the week, with females being less active than male adolescents (Table 4).
Judged by BMI for age z-scores, 81.5% of the total participants (had a normal weight, 6.4%, were overweight, and 2.3%, were obese (Table 5). Based on BIA, 81.3% of male adolescents had normal weights, while 8.8% were overweight and 4.1% were obese. As for the female adolescents, 74.7% were of normal weight, 10.1% overweight, and 5.1% were obese (Table 5). Based on BMI for age z-scores, most participants of different ages were of normal weight. Those who were 13-14 (7.5%) and 19 (8.8%) were overweight. The obese were 17-18 (4.3%) and 19 (3.5%, Table 6). There were significant relationships between nutritional status as measured by BMI and BIA with household income, gender, number of meals, energy intake, dietary diversity score, and physical activity level (P<0.05, Table 7). Regression analysis was conducted to assess the relationship between dependent variables (BMI and BIA) and independent variables, dietary intake and physical activity. Both energy intake and physical activity were the most significant predictors of overweight and obesity based on BMI and BIA with P˂0.05 (Table 8).
Discussion
This study documented that the majority (54.4%) of the adolescent student's parents/guardians had completed secondary education. Low educational level limits their ability to engage in professional occupations, ending up doing small-scale businesses. Similarly, a study in Tanzania also reported that adolescent fathers (54.8%) and mothers (38%) had secondary education levels with low-socio-economic backgrounds (Tluway et al., 2018).
A study in Ghana found that adolescents from low or high socioeconomic status were either overweight/obese, with the majority staying within the town (Amoh and Appiah-Brempong, 2017). These results were consistent with the findings in research from Nigeria, which also found that obesity/overweight existed among adolescents staying near town who were from low and high social classes (Pediatric project, 2016). This study found similar results. The research findings established that the participants' occupation, expenditure on food, and location influenced obesity and overweight.
Judged by BMI for age z-scores, 81.5% of the total participants (had a normal weight, 6.4%, were overweight, and 2.3%, were obese (Table 5). Based on BIA, 81.3% of male adolescents had normal weights, while 8.8% were overweight and 4.1% were obese. As for the female adolescents, 74.7% were of normal weight, 10.1% overweight, and 5.1% were obese (Table 5). Based on BMI for age z-scores, most participants of different ages were of normal weight. Those who were 13-14 (7.5%) and 19 (8.8%) were overweight. The obese were 17-18 (4.3%) and 19 (3.5%, Table 6). There were significant relationships between nutritional status as measured by BMI and BIA with household income, gender, number of meals, energy intake, dietary diversity score, and physical activity level (P<0.05, Table 7). Regression analysis was conducted to assess the relationship between dependent variables (BMI and BIA) and independent variables, dietary intake and physical activity. Both energy intake and physical activity were the most significant predictors of overweight and obesity based on BMI and BIA with P˂0.05 (Table 8).
Discussion
This study documented that the majority (54.4%) of the adolescent student's parents/guardians had completed secondary education. Low educational level limits their ability to engage in professional occupations, ending up doing small-scale businesses. Similarly, a study in Tanzania also reported that adolescent fathers (54.8%) and mothers (38%) had secondary education levels with low-socio-economic backgrounds (Tluway et al., 2018).
A study in Ghana found that adolescents from low or high socioeconomic status were either overweight/obese, with the majority staying within the town (Amoh and Appiah-Brempong, 2017). These results were consistent with the findings in research from Nigeria, which also found that obesity/overweight existed among adolescents staying near town who were from low and high social classes (Pediatric project, 2016). This study found similar results. The research findings established that the participants' occupation, expenditure on food, and location influenced obesity and overweight.
| Table 2. Number of times various food groups were consumed among adolescents at Machakos Sub-County. | ||||||
| Variable | Times a week | |||||
| =<2 | 3-5 | 6 | ||||
| N | % | N | % | N | % | |
| Cereals | 3 | 0.77 | 43 | 11.0 | 342 | 88.1 |
| Pulses, legumes & nuts | 35 | 9.0 | 105 | 27.0 | 248 | 63.9 |
| Meat | 85 | 21.9 | 229 | 59.0 | 74 | 19.0 |
| Fish | 201 | 51.8 | 185 | 47.6 | 2 | 0.5 |
| Eggs | 95 | 24.4 | 237 | 61.0 | 56 | 14.4 |
| Green leafy and other vegetables | 24 | 6.1 | 77 | 19.8 | 287 | 73.9 |
| Fats and oils | 7 | 1.8 | 53 | 13.6 | 328 | 84.5 |
| Fruits | 67 | 17.2 | 137 | 35.3 | 184 | 47.4 |
| Tubers/roots | 42 | 10.8 | 257 | 66.2 | 89 | 22.9 |
| Milk and milk products | 15 | 3.8 | 49 | 12.6 | 324 | 83.5 |
| Sugar /sweets | 4 | 1.0 | 2 | 3.09 | 372 | 95.8 |
| Spices /condiments | 7 | 1.8 | 303 | 78.0 | 78 | 20.1 |
| Table 3. Nutrient intake among the adolescents at Machakos Sub-County. | ||||||
| Nutrient | Recommended daily allowance | Mean intake | % of adequate intake | |||
| Male | Female | Male | Female | Male | Female | |
| Energy (Kcal) | 3000 | 2200 | 3124±218 | 2314±271 | 88.3 | 92.4 |
| Protein (g) | 59 | 44 | 62.4±4.5 | 53.4±5.7 | 75.2 | 79.3 |
| Carbohydrates(g) | 130 | 130 | 148.3±12.9 | 141.3±9.5 | 92.4 | 94.2 |
| Fat (g) | 16 | 11 | 17.5±0.68 | 14.3±0.84 | 91.5 | 95.8 |
| Table 4. Participation in physical activity level over 5 days period among adolescents at Machakos Sub-County . | ||||||||||||||||
| Physical activity | P- valueb | |||||||||||||||
| Very little | Little | Moderate | Often | Very often | ||||||||||||
| Male | Female | Total | Male | Female | Total | Male | Female | Total | Male | Female | Total | Male | Female | Total | ||
| Mon | 10 (5.8) | 29 (13.4)a | 22 (5.7) | 51 (29.8) | 62 (28.6) | 200 (51.5) | 52 (30.4) | 60 (27.6) | 118 (30.4) | 40 (23.4) | 40 (18.4) | 35 (9.0) | 18 (10.8) | 26 (11.9) | 13 (3.4) | ˂0.001 |
| Tue | 12 (7.0) | 27 (12.4) | 10 (2.6) | 47 (27.5) | 58 (26.7) | 204 (52.6) | 50 (29.2) | 59 (27.2) | 120 (30.9) | 40 (23.4) | 38 (17.5) | 38 (9.8) | 22 (12.9) | 35 (16.1) | 16 (4.1) | ˂0.001 |
| Wed | 10 (5.8) | 25 (11.5) | 23 (5.9) | 51 (29.8) | 65 (29.9) | 207 (53.4) | 49 (28.7) | 62 (28.6) | 114 (29.4) | 37 (21.6) | 39 (17.9) | 29 (7.5) | 24 (14.0) | 26 (11.9) | 15 (3.9) | ˂0.003 |
| Thur | 23 (13.5) | 20 (9.2) | 31 (7.9) | 49 (28.1) | 69 (31.8) | 198 (51.0) | 43 (25.1) | 60 (27.6) | 116 (29.9) | 42 (24.6) | 38 (17.5) | 30 (7.7) | 14 (8.2) | 30 (13.8) | 13 (3.4) | ˂0.001 |
| Fri | 19 (11.1) | 22 (10.1) | 15 (3.9) | 50 (29.2) | 68 (31.3) | 203 (52.3) | 38 (22.2) | 65 (29.9) | 124 (31.9) | 46 (26.9) | 32 (14.7) | 34 (8.8) | 18 (10.5) | 30 (13.8) | 12 (3.1) | ˂0.001 |
| a: n(%); b:ANOVA test. | ||||||||||||||||
| Table 5. Nutritional status of the adolescent students by sex based on body mass index and bioelectrical impedance analysis at Machakos Sub-County. | ||||||
Variable |
Male | Female | Total | |||
| N | % | N | % | N | % | |
| Category /Based on body mass index Underweight |
14 |
8.2 |
24 |
11.1 |
38 |
9.8 |
| Normal | 145 | 84.8 | 169 | 78 | 316 | 81.5 |
| Overweight | 9 | 5.2 | 16 | 7.3 | 25 | 6.4 |
| Obese | 3 | 1.8 | 8 | 3.6 | 9 | 2.3 |
| Category /Based on bioelectrical impedance analysis | ||||||
| Underweight | 10 | 5.8 | 22 | 10.1 | 32 | 8.2 |
| Normal | 139 | 81.3 | 162 | 74.7 | 301 | 77.6 |
| Overweight | 15 | 8.8 | 22 | 10.1 | 37 | 9.5 |
| Obese | 7 | 4.1 | 11 | 5.1 | 18 | 4.6 |
| Table 6. Nutritional status of adolescent students by age (year) based on body mass index at Machakos. | ||||||||
| Weight status | Age groups | |||||||
| 13-14 | 15-16 | 17-18 | 19 | |||||
| N | % | N | % | N | % | N | % | |
| Underweight | 15 | 28.5 | 12 | 8.8 | 9 | 6.4 | 2 | 3.5 |
| Normal | 34 | 64.0 | 117 | 85.4 | 117 | 83.0 | 48 | 84.2 |
| Overweight | 4 | 7.5 | 7 | 5.1 | 9 | 6.4 | 5 | 8.8 |
| Obese | 0 | 0.0 | 1 | 0.7 | 6 | 4.2 | 2 | 3.5 |
| Table 7. Relationships between demographic and socio-economic characteristics and overweight/obesity status of the adolescent students at Machakos Sub-County. | |||||
| Variable | Body mass index | Bioelectrical impedance analysis | |||
| Statistical test | P-value | Statistical test | P-value | ||
| Demographic and socio-economic characteristics Household income |
Pearson=0.20 |
0.014 |
Pearson=0.62 |
0.009 |
|
| Gender | Chi- square | 0.013 | Chi- square | 0.011 | |
| Dietary practices Number of meals |
Pearson=0.43 |
0.016 |
Pearson=0.53 |
0.012 |
|
| Energy intake | Pearson=0.64 | 0.005 | Pearson=0.67 | 0.0091 | |
| Individual dietary diversity score | Pearson=0.31 | 0.007 | Pearson=0.35 | 0.006 | |
| Physical activity | Pearson=0.56 | 0.021 | Pearson=0.57 | 0.001 | |
| Table 8. Multiple linear regressions showing how energy intake and physical activity predict body mass index and bioelectrical impedance analysis among adolescent students at Machakos Sub-County. | ||||
| Variable | Body mass index | Bioelectrical impedance analysis | ||
| Β | P-value | Β | P-value | |
| Energy | 0.012 | 0.002 | 0.009 | 0.031 |
| Physical activity | -0.024 | 0.001 | 0.011 | 0.021 |
| R² | ˂0.001 | ˂0.001 | ||
Discussion
This study documented that the majority (54.4%) of the adolescent student's parents/guardians had completed secondary education. Low educational level limits their ability to engage in professional occupations, ending up doing small-scale businesses. Similarly, a study in Tanzania also reported that adolescent fathers (54.8%) and mothers (38.0%) had secondary education levels with low-socio-economic backgrounds (Tluway et al., 2018).
A study in Ghana found that adolescents from low or high socioeconomic status were either overweight/obese, with the majority staying within the town (Amoh and Appiah-Brempong, 2017). These results were consistent with the findings in research from Nigeria, which also found that obesity/overweight existed among adolescents staying near town who were from low and high social classes (Pediatric project, 2016). This study found similar results. The research findings established that the participants' occupation, expenditure on food, and location influenced obesity and overweight.
This study indicated that all the students obtained their lunch from school. Diet source in this study influenced their diet intake leading to a low prevalence of overweight /obesity among adolescents. Similarly, studies in Nigeria and Kenya indicated the source of diet for adolescent students, which influences the frequency of food consumption and the number of meals taken daily. Ready access to foods may lead to a sufficient/ excessive nutrient intake. These affect adolescents’ nutritional status (Pediatric project, 2016). Most day school students eat the lunch prepared at school (Bereket et al., 2017, Okoth et al., 2015). The 24-hour recall collected information regarding the number of meals consumed by the respondent daily. Regular and adequate meals among adolescents help them meet their increased nutrient requirements for emotional, physical, and psychological growth (Kumah et al., 2015). In this study, more than half (55.9%) of the adolescents consumed three meals daily, with few consuming snacks. The schools only provided lunch daily and snacks one to two days a week. This led to a higher number of normal weights and a lower prevalence of overweight/obesity. This was in contrast to US and European studies, where most overweight and obese adolescents consumed at least four meals and more than two snacks daily (Garrido-Miguel et al., 2017, Ogden et al., 2014). The study findings revealed that students who were overweight and obese consumed more significant food portions for lunch in school which were rich in cereals, legumes, and fats/oils daily in the seven days, with consumption of meat and eggs for less than three days a week. Increased sugar intake was significantly associated with overweight and obesity. The outcomes of this research were in agreement with the results of a study conducted in rural parts of Machakos county, Kenya, that found most communities in the rural areas frequently consumed cereals and cereal products, sweetened beverages, fats/oils, and sugar food groups (Ndolo, 2019).
A study showed a high cereal, fats, and sugar intake among overweight adolescents (Gebrie et al., 2018). The majority of the adolescent students cited that local production (31.2%) and low market prices (67.0%) were primary reasons for the moderate consumption of leafy green vegetables and fruits (47.4%). The average consumption of fruits and vegetables was significantly related to the low prevalence of overweight and obesity among the students. The result of this study was in agreement with a study in Nigeria in which 56.5% consumed fruits, and in contrast, fewer adolescents (15.0%) consumed vegetables (Lateef et al., 2016). Other studies in Tanzania similarly reported adolescents' low fruit and vegetable intake (Chinenere, 2014, Nakiranda, 2015).
The dietary diversity score effectively obtained data on the number of food groups’ intake by participants daily. In this study, 37.9% of adolescents consumed about five food groups out of twelve. A similar study in Kenya, Nigeria, and Ethiopia indicated cases of skipping meals by adolescents due to food insecurity ((Pediatric project, 2016). In this study, less than half of the students consumed five food groups, with the majority consuming less than five food groups daily.
The study found that most adolescents met the RDA for energy, fat, carbohydrates, and protein requirements. Similarly, the outcomes of this study were supported by the research carried out in Nigeria in which most of the respondents assessed consumed proteins, energy, fats, and carbohydrates adequately (Adebusoye et al., 2021). In this study, higher percentages of normal BMI were reported as 84.8% for males, and 78.0% for females and for normal BIA, it was 81.3% for males, and 74.7% for females.
This study indicated that 51.5% of the adolescents had little physical activity, and 30.4% had moderate physical activity. On the other hand, very few (3.4%) were involved in physical activity very often. These findings agreed with the study's results in Kenya and Nigeria, which indicated that most adolescents in rural areas were more active compared to those in urban areas who were overweight and had low physical activity levels (Adeniyi et al., 2016). Physical inactivity among students in this study contributed to overweight and obesity.
In this study, based on BMI for age, 6.4% of adolescents were overweight, while 2.3% were obese. These outcomes are related to a study conducted in South Africa in which the prevalence of overweight and obesity among teenagers in day high schools were 5.7% and 3.8%, respectively based on BMI for age (Rossouw et al., 2012).
BMI and BIA were used in this study to measure obesity levels. However, they were not the gold standard for estimating obesity/overweight but have been used before in further studies (Wan et al., 2014). Studies found BIA to be more precise in giving higher percentages of obesity than BMI (Kamanu, 2019). BMI is at its best in measuring fatness but does not differentiate fat from muscles, bones, and lean body mass (Ganu, 2019). So, this means a person can have a healthy BMI but still have excess fats around the stomach, which can increase the possibility of heart disease, high blood pressure, and type 2 diabetes (Craig et al., 2014).
The prevalence of overweight and obesity was higher based on BIA; 8.8% of the male adolescents and 10.1% of the females were overweight, and 4.1% of males and 5.1% of females were obese. In this study, the prevalence of overweight and obesity by BMI was consistent with past studies compared to BIA (Okoth et al., 2015, Wan et al., 2014).
Overall, the most significant predictors of overweight and obesity in this study using BMI and BIA were energy intake and physical activity, with a P-value of ˂0.05. These results were similar to a study in Kenya, where the most significant predictors of overweight and obesity among adolescents were increased energy intake and physical activity (Jimoh, 2016). A study in Kenya observed that increased energy intake predicted overweight and obesity compared to other factors among adolescents, similar to the results of this study (Fongar et al., 2019).
As in other studies, in this study, the researchers observed a difference in prevalence regarding sex in overweight/obesity, where girls experienced a higher prevalence than boys (Muhihi et al., 2012, Rossouw et al., 2012). The study findings reported an association between parental education and occupation status and overweight or obesity using BMI (P=0.014) and BIA (P=0.009) among adolescents, as has been reported in other school-based studies in urban Tanzania, Kenya, Ghana, and Brazil (Amidu et al., 2013, Diouf et al., 2018, Mwaikambo et al., 2015). The hypothesis that there was no significant association between socioeconomic characteristics and overweight and obesity was rejected.
A study in Indonesia reported that intake of energy involving excessive consumption of refined sweets, sugars, refined foods, and fats was associated with overweight/obesity using BMI (P=0.003) among Indonesian adolescents (Oddo et al., 2019). Similarly, these findings revealed that increased intake of sugar and refined cereals high in energy was linked to overweight/obese students using BMI (P=0.005) in the study.
A study in Kenya observed that most adolescent students usually consumed fewer fruits and vegetables, which was highly linked to an increased prevalence of overweight and obesity (Kanaiza, 2016). In contrast, this study noted a moderate intake of fruits and vegetables that was significantly linked to a low prevalence of overweight and obesity among adolescents. Students who exceeded RDA for energy, fat, carbohydrates, and proteins were overweight and obese in this study. The study results aligned with the findings conducted in Ethiopia and Kenya, which established that dietary practice might be positively and negatively associated with adolescent students' nutritional status (Gali et al., 2017).
A study in Kenya pointed out that physical inactivity among adolescents was significantly associated with overweight and obesity using BMI (P=0.006) and BIA (P=0.031) (Ssewanyana et al., 2018). Similarly, this study observed that overweight and obese students were prevalent among most adolescents regarding little or moderate physical activity. Therefore, there was a significant relationship between physical activity and overweight/obesity using BMI (P=0.021) and BIA (P=0.001). The finding of this study agreed with and confirmed several studies that found overweight and obesity were associated with physical activity levels (Bereket et al., 2017, Matsudo et al., 2016, Pediatric project, 2016).
The accessibility of students at schools provided easy contact for the respondents, a major strength for the study. In addition, Kenyatta University and the schools provided the necessary infrastructure, materials and equipment required for the study. However, the assessment of dietary practices was limited to a recall of food consumed, thus an estimated rather than the actual measurement.
This study documented that the majority (54.4%) of the adolescent student's parents/guardians had completed secondary education. Low educational level limits their ability to engage in professional occupations, ending up doing small-scale businesses. Similarly, a study in Tanzania also reported that adolescent fathers (54.8%) and mothers (38.0%) had secondary education levels with low-socio-economic backgrounds (Tluway et al., 2018).
A study in Ghana found that adolescents from low or high socioeconomic status were either overweight/obese, with the majority staying within the town (Amoh and Appiah-Brempong, 2017). These results were consistent with the findings in research from Nigeria, which also found that obesity/overweight existed among adolescents staying near town who were from low and high social classes (Pediatric project, 2016). This study found similar results. The research findings established that the participants' occupation, expenditure on food, and location influenced obesity and overweight.
This study indicated that all the students obtained their lunch from school. Diet source in this study influenced their diet intake leading to a low prevalence of overweight /obesity among adolescents. Similarly, studies in Nigeria and Kenya indicated the source of diet for adolescent students, which influences the frequency of food consumption and the number of meals taken daily. Ready access to foods may lead to a sufficient/ excessive nutrient intake. These affect adolescents’ nutritional status (Pediatric project, 2016). Most day school students eat the lunch prepared at school (Bereket et al., 2017, Okoth et al., 2015). The 24-hour recall collected information regarding the number of meals consumed by the respondent daily. Regular and adequate meals among adolescents help them meet their increased nutrient requirements for emotional, physical, and psychological growth (Kumah et al., 2015). In this study, more than half (55.9%) of the adolescents consumed three meals daily, with few consuming snacks. The schools only provided lunch daily and snacks one to two days a week. This led to a higher number of normal weights and a lower prevalence of overweight/obesity. This was in contrast to US and European studies, where most overweight and obese adolescents consumed at least four meals and more than two snacks daily (Garrido-Miguel et al., 2017, Ogden et al., 2014). The study findings revealed that students who were overweight and obese consumed more significant food portions for lunch in school which were rich in cereals, legumes, and fats/oils daily in the seven days, with consumption of meat and eggs for less than three days a week. Increased sugar intake was significantly associated with overweight and obesity. The outcomes of this research were in agreement with the results of a study conducted in rural parts of Machakos county, Kenya, that found most communities in the rural areas frequently consumed cereals and cereal products, sweetened beverages, fats/oils, and sugar food groups (Ndolo, 2019).
A study showed a high cereal, fats, and sugar intake among overweight adolescents (Gebrie et al., 2018). The majority of the adolescent students cited that local production (31.2%) and low market prices (67.0%) were primary reasons for the moderate consumption of leafy green vegetables and fruits (47.4%). The average consumption of fruits and vegetables was significantly related to the low prevalence of overweight and obesity among the students. The result of this study was in agreement with a study in Nigeria in which 56.5% consumed fruits, and in contrast, fewer adolescents (15.0%) consumed vegetables (Lateef et al., 2016). Other studies in Tanzania similarly reported adolescents' low fruit and vegetable intake (Chinenere, 2014, Nakiranda, 2015).
The dietary diversity score effectively obtained data on the number of food groups’ intake by participants daily. In this study, 37.9% of adolescents consumed about five food groups out of twelve. A similar study in Kenya, Nigeria, and Ethiopia indicated cases of skipping meals by adolescents due to food insecurity ((Pediatric project, 2016). In this study, less than half of the students consumed five food groups, with the majority consuming less than five food groups daily.
The study found that most adolescents met the RDA for energy, fat, carbohydrates, and protein requirements. Similarly, the outcomes of this study were supported by the research carried out in Nigeria in which most of the respondents assessed consumed proteins, energy, fats, and carbohydrates adequately (Adebusoye et al., 2021). In this study, higher percentages of normal BMI were reported as 84.8% for males, and 78.0% for females and for normal BIA, it was 81.3% for males, and 74.7% for females.
This study indicated that 51.5% of the adolescents had little physical activity, and 30.4% had moderate physical activity. On the other hand, very few (3.4%) were involved in physical activity very often. These findings agreed with the study's results in Kenya and Nigeria, which indicated that most adolescents in rural areas were more active compared to those in urban areas who were overweight and had low physical activity levels (Adeniyi et al., 2016). Physical inactivity among students in this study contributed to overweight and obesity.
In this study, based on BMI for age, 6.4% of adolescents were overweight, while 2.3% were obese. These outcomes are related to a study conducted in South Africa in which the prevalence of overweight and obesity among teenagers in day high schools were 5.7% and 3.8%, respectively based on BMI for age (Rossouw et al., 2012).
BMI and BIA were used in this study to measure obesity levels. However, they were not the gold standard for estimating obesity/overweight but have been used before in further studies (Wan et al., 2014). Studies found BIA to be more precise in giving higher percentages of obesity than BMI (Kamanu, 2019). BMI is at its best in measuring fatness but does not differentiate fat from muscles, bones, and lean body mass (Ganu, 2019). So, this means a person can have a healthy BMI but still have excess fats around the stomach, which can increase the possibility of heart disease, high blood pressure, and type 2 diabetes (Craig et al., 2014).
The prevalence of overweight and obesity was higher based on BIA; 8.8% of the male adolescents and 10.1% of the females were overweight, and 4.1% of males and 5.1% of females were obese. In this study, the prevalence of overweight and obesity by BMI was consistent with past studies compared to BIA (Okoth et al., 2015, Wan et al., 2014).
Overall, the most significant predictors of overweight and obesity in this study using BMI and BIA were energy intake and physical activity, with a P-value of ˂0.05. These results were similar to a study in Kenya, where the most significant predictors of overweight and obesity among adolescents were increased energy intake and physical activity (Jimoh, 2016). A study in Kenya observed that increased energy intake predicted overweight and obesity compared to other factors among adolescents, similar to the results of this study (Fongar et al., 2019).
As in other studies, in this study, the researchers observed a difference in prevalence regarding sex in overweight/obesity, where girls experienced a higher prevalence than boys (Muhihi et al., 2012, Rossouw et al., 2012). The study findings reported an association between parental education and occupation status and overweight or obesity using BMI (P=0.014) and BIA (P=0.009) among adolescents, as has been reported in other school-based studies in urban Tanzania, Kenya, Ghana, and Brazil (Amidu et al., 2013, Diouf et al., 2018, Mwaikambo et al., 2015). The hypothesis that there was no significant association between socioeconomic characteristics and overweight and obesity was rejected.
A study in Indonesia reported that intake of energy involving excessive consumption of refined sweets, sugars, refined foods, and fats was associated with overweight/obesity using BMI (P=0.003) among Indonesian adolescents (Oddo et al., 2019). Similarly, these findings revealed that increased intake of sugar and refined cereals high in energy was linked to overweight/obese students using BMI (P=0.005) in the study.
A study in Kenya observed that most adolescent students usually consumed fewer fruits and vegetables, which was highly linked to an increased prevalence of overweight and obesity (Kanaiza, 2016). In contrast, this study noted a moderate intake of fruits and vegetables that was significantly linked to a low prevalence of overweight and obesity among adolescents. Students who exceeded RDA for energy, fat, carbohydrates, and proteins were overweight and obese in this study. The study results aligned with the findings conducted in Ethiopia and Kenya, which established that dietary practice might be positively and negatively associated with adolescent students' nutritional status (Gali et al., 2017).
A study in Kenya pointed out that physical inactivity among adolescents was significantly associated with overweight and obesity using BMI (P=0.006) and BIA (P=0.031) (Ssewanyana et al., 2018). Similarly, this study observed that overweight and obese students were prevalent among most adolescents regarding little or moderate physical activity. Therefore, there was a significant relationship between physical activity and overweight/obesity using BMI (P=0.021) and BIA (P=0.001). The finding of this study agreed with and confirmed several studies that found overweight and obesity were associated with physical activity levels (Bereket et al., 2017, Matsudo et al., 2016, Pediatric project, 2016).
The accessibility of students at schools provided easy contact for the respondents, a major strength for the study. In addition, Kenyatta University and the schools provided the necessary infrastructure, materials and equipment required for the study. However, the assessment of dietary practices was limited to a recall of food consumed, thus an estimated rather than the actual measurement.
Conclusion
In the present study, most male and female adolescents met RDA for energy, proteins, carbohydrates, and fat. The level of physical activity by students was low. The probability of females being overweight and obese was higher than their male counterparts. The most significant predictors of overweight and obesity were increased energy intake and low physical activity. Significant relationships existed between nutritional status and gender, energy intake, number of meals, dietary diversity score, and physical activity. It is also recommended that other studies be conducted in other semi-urban areas in Kenya to acquire sufficient information on diet intake patterns, physical activity levels, and nutritional status among adolescents in public day secondary and private secondary schools.
Acknowledgment
The authors would like to thank Dr. Chege and Dr. Kamuhu for their professional guidance and encouragement during the research.
Ethics approval and consent to participate
A permit and ethical clearance were obtained from Kenyatta University. Research permit was obtained from National Council for Science, Technology, and Innovation. Research authorization was given from the county commissioner, the Sub- County director of education, and respective school principals. Informed verbal consent was obtained from each respondent before leading the interview. The investigator assured respondents that involvement in the study was voluntary, and that confidentiality was guaranteed.
Ethics approval and consent to participate
A permit and ethical clearance were obtained from Kenyatta University. Research permit was obtained from National Council for Science, Technology, and Innovation. Research authorization was given from the county commissioner, the Sub- County director of education, and respective school principals. Informed verbal consent was obtained from each respondent before leading the interview. The investigator assured respondents that involvement in the study was voluntary, and that confidentiality was guaranteed.
Conflict of interests
The authors declared no conflict of interests.
Funding
The authors supported this research.
Authors’ contributions
Ndanu Kyalo M designed the experiment, collected and, analyzed the data, and wrote the manuscript. Chege P and Kamuhu R read, corrected, and approved the manuscript. All authors read and approved the final version of the manuscript.
References
Adebusoye B, Leonardi-Bee J, Phalkey R & Chattopadhyay K 2021. Proportion of school attending adolescents meeting the recommended moderate-to-vigorous physical activity level and its predictors in Lagos, Nigeria. International journal of environmental research and public health. 18 (20): 10744.
Adeniyi AF, et al. 2016. Results from Nigeria’s 2016 report card on physical activity for children and youth. Journal of physical activity and health. 13 (s2): S231-S236.
Akinlade A, Afolabi W, Oguntona E & Agbonlahor M 2014. Prevalence of obesity among adolescents in senior secondary schools in Oyo state, Nigeria. Age. 12 (14): 219.
Amidu N, et al. 2013. Determinants of childhood obesity among basic school children aged 6–12 years in Tamale Metropolis. Journal of medical and biomedical sciences. 2 (3): 26-34.
Amoh I & Appiah-Brempong E 2017. Prevalence and risk factors of obesity among senior high school students in the Adansi North district of Ghana. International journal of community medicine and public health. 4 (10): 3762-3769.
Ashdown-Franks G, et al. 2019. Leisure-time sedentary behavior and obesity among 116,762 adolescents aged 12-15 years from 41 low-and middle-income countries. Obesity. 27 (5): 830-836.
Bereket AH, Beyero M, Fikadu AR & Bosha T 2017. Risk factors for overweight and obesity in private high school adolescents in Hawassa city, Southern Ethiopia: a case-control study. Food public health. 7 (2): 29-34.
Caleyachetty R, et al. 2018. The double burden of malnutrition among adolescents: analysis of data from the Global School-Based Student Health and Health Behavior in School-Aged Children surveys in 57 low-and middle-income countries. American journal of clinical nutrition. 108 (2): 414-424.
Chinenere FD 2014. Overweight and obesity among adolescents: burden and associated factors-a case study of Babati, Manyara, Tanzania. Muhimbili University of Health and Allied Sciences.
Craig E, Bland R, Ndirangu J & Reilly J 2014. Use of mid-upper arm circumference for determining overweight and overfatness in children and adolescents. Archives of disease in childhood. 99 (8): 763-766.
De Schutter A, Lavie CJ & Milani RV 2014. The impact of obesity on risk factors and prevalence and prognosis of coronary heart disease—the obesity paradox. Progress in cardiovascular diseases. 56 (4): 401-408.
Diouf A, et al. 2018. Body mass index vs deuterium dilution method for establishing childhood obesity prevalence, Ghana, Kenya, Mauritius, Morocco, Namibia, Senegal, Tunisia and United Republic of Tanzania. Bulletin of the World Health Organization. 96 (11): 772.
Fisher A, et al. 1998. Cross sectional study of exhaled nitric oxide levels following lung transplantation. Thorax. 53 (6): 454-458.
Fongar A, Gödecke T & Qaim M 2019. Various forms of double burden of malnutrition problems exist in rural Kenya. BMC public health. 19: 1-9.
Gali N, Tamiru D & Tamrat M 2017. The emerging nutritional problems of school adolescents: overweight/obesity and associated factors in Jimma town, Ethiopia. Journal of pediatric nursing. 35: 98-104.
Ganu D 2019. Prevalence and contributing factors for overweight and obesity in children 0-18 years in sub saharan Africa for 20-year period: systematic review. ACTA scientific nutritional health. 3 (12): 67-73.
Garrido-Miguel M, et al. 2017. Prevalence and trends of thinness, overweight and obesity among children and adolescents aged 3–18 years across Europe: a protocol for a systematic review and meta-analysis. BMJ open. 7 (12): e018241.
Gebrie A, Alebel A, Zegeye A, Tesfaye B & Ferede A 2018. Prevalence and associated factors of overweight/obesity among children and adolescents in Ethiopia: a systematic review and meta-analysis. BMC obesity. 5 (1): 1-12.
Hüls A, et al. 2021. Polygenic risk for obesity and its interaction with lifestyle and sociodemographic factors in European children and adolescents. International journal of obesity. 45 (6): 1321-1330.
Jebb S 2004. Obesity: causes and consequences. Women's health medicine. 1 (1): 38-41.
Jimoh O 2016. Food Consumption Pattern, Physical Activity and Overweight and Obesity Among Secondary School Students in Kwara State, Nigeria. Kenyatta University: Nairobi, Kenya.
Kamanu TW 2019. Nutrition Knowledge, Dietary Practices And Nutrition Status Of Secondary School Adolescents (13-18 Years) In Ruiru Sub County, Kenya. University of Nairobi.
Kanaiza C 2016. Eating habits and their association with nutritional status of school going adolescent girls in Kisumu city, Kenya. Maseno University.
Kumah D, Akuffo K, Abaka-Cann J, Affram D & Osae E 2015. Prevalence of overweight and obesity among students in the Kumasi metropolis. Journal of nutrition and metabolism. 2015 (1): 613207.
Lateef O, Njogu E, Kiplamai F, Haruna U & Lawal R 2016. Determinants of overweight and obesity among adolescent students in public secondary schools in Kwara State, Nigeria. Current research in nutrition and food science journal. 4 (2): 96-106.
Lobstein T, et al. 2015. Child and adolescent obesity: part of a bigger picture. Lancet. 385 (9986): 2510-2520.
Matsudo VKR, et al. 2016. Socioeconomic status indicators, physical activity, and overweight/obesity in Brazilian children. Revista Paulista de Pediatria. 34: 162-170.
Muhihi AJ, et al. 2012. Obesity, overweight, and perceptions about body weight among middle-aged adults in Dar es Salaam, Tanzania. International scholarly research notices. 2012 (1): 368520.
Mwaikambo SA, Leyna GH, Killewo J, Simba A & Puoane T 2015. Why are primary school children overweight and obese? A cross sectional study undertaken in Kinondoni district, Dar-es-salaam. BMC public health. 15: 1-10.
Nakiranda R 2015. Prevalence and trends of undernutrition, overweight and obesity among children and adolescents in Kenya, Tanzania and Uganda.
Ndolo M 2019. Food security in the semi-arid Machakos county: a case study of Mwala sub-county. In Kenya University.
Oddo VM, Roshita A & Rah JH 2019. Potential interventions targeting adolescent nutrition in Indonesia: a literature review. Public health nutrition. 22 (1): 15-27.
Ogden CL, Carroll MD, Kit BK & Flegal KM 2014. Prevalence of childhood and adult obesity in the United States, 2011-2012. Journal of the American Medical Association. 311 (8): 806-814.
Okoth M, Ochola S, Onywera V & Steyn N 2015. Determinants of overweight and obesity in Kenyan adolescents in public and private schools. African journal for physical health education, recreation and dance. 21 (1.2): 261-272.
Pediatric project 2016. Prevalence of overweight and obesity in apparently healthy school children in Tarauni local goverment area of Kano stat.
Rossouw HA, Grant CC & Viljoen M 2012. Overweight and obesity in children and adolescents: The South African problem. South African journal of science. 108 (5): 1-7.
Ssewanyana D, Abubakar A, van Baar A, Mwangala PN & Newton CR 2018. Perspectives on Underlying Factors for Unhealthy Diet and Sedentary Lifestyle of Adolescents at a Kenyan Coastal Setting. Front public health. 6: 11.
Sylvetsky-Meni AC, Gillepsie SE, Hardy T & Welsh JA 2015. The impact of parents' categorization of their own weight and their child's weight on healthy lifestyle promoting beliefs and practices. Journal of obesity. 2015: 307381.
Tluway FD, Leyna GH & Mmbaga EJ 2018. Magnitude and factors associated with overweight and obesity among adolescents in in semi-rural area of Babati District, Tanzania. Tanzania journal of health research. 20 (2).
Towner EK, Reiter-Purtill J, Boles RE & Zeller MH 2015. Predictors of caregiver feeding practices differentiating persistently obese from persistently non-overweight adolescents. Appetite. 84: 120-127.
Wan CS, et al. 2014. Bioelectrical impedance analysis to estimate body composition, and change in adiposity, in overweight and obese adolescents: comparison with dual-energy x-ray absorptiometry. BMC Pediatrics. 14: 249.
Williamson VG, et al. 2020. The Influence of Socioeconomic Status on Snacking and Weight among Adolescents: A Scoping Review. Nutrients. 12 (1).
Adeniyi AF, et al. 2016. Results from Nigeria’s 2016 report card on physical activity for children and youth. Journal of physical activity and health. 13 (s2): S231-S236.
Akinlade A, Afolabi W, Oguntona E & Agbonlahor M 2014. Prevalence of obesity among adolescents in senior secondary schools in Oyo state, Nigeria. Age. 12 (14): 219.
Amidu N, et al. 2013. Determinants of childhood obesity among basic school children aged 6–12 years in Tamale Metropolis. Journal of medical and biomedical sciences. 2 (3): 26-34.
Amoh I & Appiah-Brempong E 2017. Prevalence and risk factors of obesity among senior high school students in the Adansi North district of Ghana. International journal of community medicine and public health. 4 (10): 3762-3769.
Ashdown-Franks G, et al. 2019. Leisure-time sedentary behavior and obesity among 116,762 adolescents aged 12-15 years from 41 low-and middle-income countries. Obesity. 27 (5): 830-836.
Bereket AH, Beyero M, Fikadu AR & Bosha T 2017. Risk factors for overweight and obesity in private high school adolescents in Hawassa city, Southern Ethiopia: a case-control study. Food public health. 7 (2): 29-34.
Caleyachetty R, et al. 2018. The double burden of malnutrition among adolescents: analysis of data from the Global School-Based Student Health and Health Behavior in School-Aged Children surveys in 57 low-and middle-income countries. American journal of clinical nutrition. 108 (2): 414-424.
Chinenere FD 2014. Overweight and obesity among adolescents: burden and associated factors-a case study of Babati, Manyara, Tanzania. Muhimbili University of Health and Allied Sciences.
Craig E, Bland R, Ndirangu J & Reilly J 2014. Use of mid-upper arm circumference for determining overweight and overfatness in children and adolescents. Archives of disease in childhood. 99 (8): 763-766.
De Schutter A, Lavie CJ & Milani RV 2014. The impact of obesity on risk factors and prevalence and prognosis of coronary heart disease—the obesity paradox. Progress in cardiovascular diseases. 56 (4): 401-408.
Diouf A, et al. 2018. Body mass index vs deuterium dilution method for establishing childhood obesity prevalence, Ghana, Kenya, Mauritius, Morocco, Namibia, Senegal, Tunisia and United Republic of Tanzania. Bulletin of the World Health Organization. 96 (11): 772.
Fisher A, et al. 1998. Cross sectional study of exhaled nitric oxide levels following lung transplantation. Thorax. 53 (6): 454-458.
Fongar A, Gödecke T & Qaim M 2019. Various forms of double burden of malnutrition problems exist in rural Kenya. BMC public health. 19: 1-9.
Gali N, Tamiru D & Tamrat M 2017. The emerging nutritional problems of school adolescents: overweight/obesity and associated factors in Jimma town, Ethiopia. Journal of pediatric nursing. 35: 98-104.
Ganu D 2019. Prevalence and contributing factors for overweight and obesity in children 0-18 years in sub saharan Africa for 20-year period: systematic review. ACTA scientific nutritional health. 3 (12): 67-73.
Garrido-Miguel M, et al. 2017. Prevalence and trends of thinness, overweight and obesity among children and adolescents aged 3–18 years across Europe: a protocol for a systematic review and meta-analysis. BMJ open. 7 (12): e018241.
Gebrie A, Alebel A, Zegeye A, Tesfaye B & Ferede A 2018. Prevalence and associated factors of overweight/obesity among children and adolescents in Ethiopia: a systematic review and meta-analysis. BMC obesity. 5 (1): 1-12.
Hüls A, et al. 2021. Polygenic risk for obesity and its interaction with lifestyle and sociodemographic factors in European children and adolescents. International journal of obesity. 45 (6): 1321-1330.
Jebb S 2004. Obesity: causes and consequences. Women's health medicine. 1 (1): 38-41.
Jimoh O 2016. Food Consumption Pattern, Physical Activity and Overweight and Obesity Among Secondary School Students in Kwara State, Nigeria. Kenyatta University: Nairobi, Kenya.
Kamanu TW 2019. Nutrition Knowledge, Dietary Practices And Nutrition Status Of Secondary School Adolescents (13-18 Years) In Ruiru Sub County, Kenya. University of Nairobi.
Kanaiza C 2016. Eating habits and their association with nutritional status of school going adolescent girls in Kisumu city, Kenya. Maseno University.
Kumah D, Akuffo K, Abaka-Cann J, Affram D & Osae E 2015. Prevalence of overweight and obesity among students in the Kumasi metropolis. Journal of nutrition and metabolism. 2015 (1): 613207.
Lateef O, Njogu E, Kiplamai F, Haruna U & Lawal R 2016. Determinants of overweight and obesity among adolescent students in public secondary schools in Kwara State, Nigeria. Current research in nutrition and food science journal. 4 (2): 96-106.
Lobstein T, et al. 2015. Child and adolescent obesity: part of a bigger picture. Lancet. 385 (9986): 2510-2520.
Matsudo VKR, et al. 2016. Socioeconomic status indicators, physical activity, and overweight/obesity in Brazilian children. Revista Paulista de Pediatria. 34: 162-170.
Muhihi AJ, et al. 2012. Obesity, overweight, and perceptions about body weight among middle-aged adults in Dar es Salaam, Tanzania. International scholarly research notices. 2012 (1): 368520.
Mwaikambo SA, Leyna GH, Killewo J, Simba A & Puoane T 2015. Why are primary school children overweight and obese? A cross sectional study undertaken in Kinondoni district, Dar-es-salaam. BMC public health. 15: 1-10.
Nakiranda R 2015. Prevalence and trends of undernutrition, overweight and obesity among children and adolescents in Kenya, Tanzania and Uganda.
Ndolo M 2019. Food security in the semi-arid Machakos county: a case study of Mwala sub-county. In Kenya University.
Oddo VM, Roshita A & Rah JH 2019. Potential interventions targeting adolescent nutrition in Indonesia: a literature review. Public health nutrition. 22 (1): 15-27.
Ogden CL, Carroll MD, Kit BK & Flegal KM 2014. Prevalence of childhood and adult obesity in the United States, 2011-2012. Journal of the American Medical Association. 311 (8): 806-814.
Okoth M, Ochola S, Onywera V & Steyn N 2015. Determinants of overweight and obesity in Kenyan adolescents in public and private schools. African journal for physical health education, recreation and dance. 21 (1.2): 261-272.
Pediatric project 2016. Prevalence of overweight and obesity in apparently healthy school children in Tarauni local goverment area of Kano stat.
Rossouw HA, Grant CC & Viljoen M 2012. Overweight and obesity in children and adolescents: The South African problem. South African journal of science. 108 (5): 1-7.
Ssewanyana D, Abubakar A, van Baar A, Mwangala PN & Newton CR 2018. Perspectives on Underlying Factors for Unhealthy Diet and Sedentary Lifestyle of Adolescents at a Kenyan Coastal Setting. Front public health. 6: 11.
Sylvetsky-Meni AC, Gillepsie SE, Hardy T & Welsh JA 2015. The impact of parents' categorization of their own weight and their child's weight on healthy lifestyle promoting beliefs and practices. Journal of obesity. 2015: 307381.
Tluway FD, Leyna GH & Mmbaga EJ 2018. Magnitude and factors associated with overweight and obesity among adolescents in in semi-rural area of Babati District, Tanzania. Tanzania journal of health research. 20 (2).
Towner EK, Reiter-Purtill J, Boles RE & Zeller MH 2015. Predictors of caregiver feeding practices differentiating persistently obese from persistently non-overweight adolescents. Appetite. 84: 120-127.
Wan CS, et al. 2014. Bioelectrical impedance analysis to estimate body composition, and change in adiposity, in overweight and obese adolescents: comparison with dual-energy x-ray absorptiometry. BMC Pediatrics. 14: 249.
Williamson VG, et al. 2020. The Influence of Socioeconomic Status on Snacking and Weight among Adolescents: A Scoping Review. Nutrients. 12 (1).
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