OBJECTIVE: We investigated the association between measures and determinants of obesity in African women. METHODS: For a cross-sectional study of adult black women in the North West Province, South Africa, we used a stratified sample of 1040 volunteers from 37 randomly selected sites in the province according to the level of urbanization. We analyzed the association between measures of obesity, namely body mass index (BMI), waist circumference, waist-to-hip ratio, triceps and subscapular skinfolds, and socioeconomic factors, dietary intakes, and physical activity. RESULTS: The rate of obesity (BMI > 30) in the sample was 28.6%. We found a significant positive association between household income and measures of obesity. After exclusion of underreporters and adjustments for age, smoking, and household income, we found significant positive correlations between total energy intake, fat intake, and BMI. Physical activity index (derived from a subset of 530 subjects) correlated negatively with BMI and waist circumference. Subjects in the highest third of physical activity were less likely to be obese (odds ratio-0.38, 95% confidence interval-0.22-0.66). CONCLUSIONS: Women with higher incomes and lower physical activity were at the greatest risk of increased BMI. Physical inactivity showed the strongest association with measures of obesity in this study.
OBJECTIVE: We investigated the association between measures and determinants of obesity in African women. METHODS: For a cross-sectional study of adult black women in the North West Province, South Africa, we used a stratified sample of 1040 volunteers from 37 randomly selected sites in the province according to the level of urbanization. We analyzed the association between measures of obesity, namely body mass index (BMI), waist circumference, waist-to-hip ratio, triceps and subscapular skinfolds, and socioeconomic factors, dietary intakes, and physical activity. RESULTS: The rate of obesity (BMI > 30) in the sample was 28.6%. We found a significant positive association between household income and measures of obesity. After exclusion of underreporters and adjustments for age, smoking, and household income, we found significant positive correlations between total energy intake, fat intake, and BMI. Physical activity index (derived from a subset of 530 subjects) correlated negatively with BMI and waist circumference. Subjects in the highest third of physical activity were less likely to be obese (odds ratio-0.38, 95% confidence interval-0.22-0.66). CONCLUSIONS:Women with higher incomes and lower physical activity were at the greatest risk of increased BMI. Physical inactivity showed the strongest association with measures of obesity in this study.
Authors: A E Schutte; S Botha; C M T Fourie; L F Gafane-Matemane; R Kruger; L Lammertyn; L Malan; C M C Mels; R Schutte; W Smith; J M van Rooyen; L J Ware; H W Huisman Journal: J Hum Hypertens Date: 2017-03-23 Impact factor: 3.012
Authors: Francis Barchi; Millan A AbiNader; Samantha C Winter; Lena M Obara; Daniel Mbogo; Bendettah M Thomas; Brittany Ammerman Journal: Int J Environ Res Public Health Date: 2021-02-27 Impact factor: 3.390
Authors: Adewale L Oyeyemi; Babatunde O Adegoke; Adetoyeje Y Oyeyemi; Benedicte Deforche; Ilse De Bourdeaudhuij; James F Sallis Journal: Int J Behav Nutr Phys Act Date: 2012-03-27 Impact factor: 6.457