Danladi I Musa1, Abel L Toriola, Makama A Monyeki, Badamasi Lawal. 1. Human Performance Laboratory, Department of Human Kinetics and Health Education, Benue State University, Makurdi, Nigeria Department of Sport, Rehabilitation and Dental Sciences, Tshwane University of Technology, Pretoria, South Africa Physical Activity, Sport and Recreation Niche Area, School of Biokinetics, Recreation and Sport Science, North-West University, Potchefstroom, South Africa Department of Physical and Health Education, Bayero University, Kano, Nigeria.
Abstract
OBJECTIVE: To evaluate demographic variation in the prevalence of overweight (OW) and obesity (OB) among 3240 children and adolescents (girls: n = 1714; boys: n = 1526) aged 9-16 years attending primary and secondary schools in Benue State of Nigeria. METHODS: Participants' anthropometric characteristics (body weight, stature, body mass index: BMI and lean body mass: LBM) were determined using standard protocols. OW and OB were estimated using International Obesity task Force diagnostic criteria. Data were analysed with one-way anova and binary logistic regression method. RESULTS: Overall, 88.5%, 9.7% and 1.8% of the adolescents had normal BMI and were OW and obese, respectively. Prevalence of OW was higher among girls (20.3%) than boys (16.2%), whereas a relatively higher incidence of OB was noted among the boys (3.5%). Girls in urban areas had a significantly higher BMI (t524 = 3.61, P = 0.002) than their rural peers, but the rural girls were more significantly OW than their urban counterparts (BMI: t1186 = 2.506). Logistic regression models assessing the influence of age, gender and location on OW/OB in children (α(2) (3, N = 1014) = 6.185, P = 0.103) and adolescents (α(2) (3, N = 2226) = 1.435, P = 0.697) did not turn up significant results. In the gender-specific analysis, the younger boys' model was also not significant (α(2) (2, N = 488) = 1.295, P = 0.523) in contrast to the girls' (α(2) (2, N = 526) = 15.637, P = 0.0005), thus discriminating between OW and healthy weight among the children. Overall, the model explained 2.9-4.4% of the variance in weight status and correctly classified 76.8% of the cases. Age wise, the model yielded a significant odds ratio of 1.49, suggesting that the likelihood of being OW increases by a factor of 1.5 with a unit increase in age. Also, the likelihood of an urban girl becoming OW or obese was 0.57 times that of a rural girl. CONCLUSIONS: In general, girls in urban areas had higher prevalence of OW and OB than girls in rural settings. Among the boys, similar but less marked trends were found, except that the rural boys tended to be more OW on average than their peers in urban areas. In view of its public health significance, it is important to periodically evaluate the prevalence of weight disorders in children and adolescents so that appropriate preventative strategies can be instituted.
OBJECTIVE: To evaluate demographic variation in the prevalence of overweight (OW) and obesity (OB) among 3240 children and adolescents (girls: n = 1714; boys: n = 1526) aged 9-16 years attending primary and secondary schools in Benue State of Nigeria. METHODS:Participants' anthropometric characteristics (body weight, stature, body mass index: BMI and lean body mass: LBM) were determined using standard protocols. OW and OB were estimated using International Obesity task Force diagnostic criteria. Data were analysed with one-way anova and binary logistic regression method. RESULTS: Overall, 88.5%, 9.7% and 1.8% of the adolescents had normal BMI and were OW and obese, respectively. Prevalence of OW was higher among girls (20.3%) than boys (16.2%), whereas a relatively higher incidence of OB was noted among the boys (3.5%). Girls in urban areas had a significantly higher BMI (t524 = 3.61, P = 0.002) than their rural peers, but the rural girls were more significantly OW than their urban counterparts (BMI: t1186 = 2.506). Logistic regression models assessing the influence of age, gender and location on OW/OB in children (α(2) (3, N = 1014) = 6.185, P = 0.103) and adolescents (α(2) (3, N = 2226) = 1.435, P = 0.697) did not turn up significant results. In the gender-specific analysis, the younger boys' model was also not significant (α(2) (2, N = 488) = 1.295, P = 0.523) in contrast to the girls' (α(2) (2, N = 526) = 15.637, P = 0.0005), thus discriminating between OW and healthy weight among the children. Overall, the model explained 2.9-4.4% of the variance in weight status and correctly classified 76.8% of the cases. Age wise, the model yielded a significant odds ratio of 1.49, suggesting that the likelihood of being OW increases by a factor of 1.5 with a unit increase in age. Also, the likelihood of an urban girl becoming OW or obese was 0.57 times that of a rural girl. CONCLUSIONS: In general, girls in urban areas had higher prevalence of OW and OB than girls in rural settings. Among the boys, similar but less marked trends were found, except that the rural boys tended to be more OW on average than their peers in urban areas. In view of its public health significance, it is important to periodically evaluate the prevalence of weight disorders in children and adolescents so that appropriate preventative strategies can be instituted.
Authors: Stella K Muthuri; Claire E Francis; Lucy-Joy M Wachira; Allana G Leblanc; Margaret Sampson; Vincent O Onywera; Mark S Tremblay Journal: PLoS One Date: 2014-03-27 Impact factor: 3.240
Authors: Paula Braitstein; Samuel Ayaya; Winstone M Nyandiko; Allan Kamanda; Julius Koech; Peter Gisore; Lukoye Atwoli; Rachel C Vreeman; Corey Duefield; David O Ayuku Journal: PLoS One Date: 2013-07-26 Impact factor: 3.240