Body composition, water turnover and physical activity among women in Narok County, Kenya

BACKGROUND: In developing countries where access to water and food is not guaranteed, women may have to travel long distances or engage in intense physical activities to gather food. This may compromise their water requirements and overall nutritional status. The aim of the study was to determine water turnover, physical activity and body composition among women in Kenya and to describe the differences between rural and urban Kenyan women. METHODS: Thirty women from Narok County who were not pregnant at the time of the study were recruited. Body mass index (BMI) was calculated by dividing weight in kilograms by height in meters squared. Deuterium dilution was used to determine total body water (TBW) and water turnover was measured from deuterium elimination. Fat-free mass (FFM) was calculated by assuming a constant hydration fraction of 73.2%. Accelerometers (Actigraph GT3X) were used to assess physical activity and expressed as Vector magnitude counts per day (VM/day). Simple and multiple linear regressions were used to define the determinants of water turnover. RESULTS: Mean BMI was 23.4 +/- 4.1 and 21.5 +/- 3.8 among rural and urban women respectively. The prevalence of overweight (BMI > 25 kg/m(2)) was 24.1% and of underweight (BMI < 18.4 kg/m2)) was 25%. The mean total body water (TBW) was 29.3 +/- 4.2 liters (L) and water turnover was 3.2 +/- 0.8 liters per day (L/day). Water loss was positively associated with BMI (R(2) = .45, p < 0.001, n = 28) and Fat mass index (FMI) (R(2) = .41, p < 0.001, n = 28). Water loss was also positively associated with physical activity (PA) (R(2) = .25, p < 0.05, n = 22). Multiple regression analysis showed that physical activity in addition to BMI in the model explained an additional 15% of the variation in water turnover (r(2) = 0.53, p < 0.05; r(2) = 0.15, p < 0.05, n = 22) compared to BMI alone (r(2) = 0.38, p < 0.005 n = 22). CONCLUSION: BMI together with physical activity were the strongest predictors of water loss.