Understanding the Relationship between Socio-Economic Status, Physical Activity and Sedentary Behaviour, and Adiposity in Young Adult South African Women Using Structural Equation Modelling.
Lisa K MicklesfieldRichard J MunthaliAlessandra PrioreschiRihlat Said-MohamedAlastair van HeerdenStephen TollmanKathleen KahnProfessor David DungerShane A NorrisPublished in: International journal of environmental research and public health (2017)
Socio-economic status (SES) is an important predictor of obesity, but how it is associated with differences in physical activity and sedentary behaviour is less clear. This cross-sectional study examined the association between SES (sum of household assets), physical activity and sedentary time, and how they predict adiposity. Socio-demographic, anthropometric, and physical activity data on rural (n = 509) and urban (n = 510) South African women (18-23 years) were collected. Overweight and obesity prevalence, and sedentary time, were higher; and moderate-vigorous intensity physical activity (MVPA) was lower, in the urban sample. Structural equation models (SEMs) were constructed for BMI and waist circumference. In the urban sample SES had a direct inverse effect on MVPA (ß; 95% CI, -41.69; -73.40 to -9.98), while in the rural sample SES had a direct effect on BMI (ß; 95% CI, 0.306; 0.03 to 0.59). In the pooled sample, SES had a direct inverse effect on MVPA (ß; 95% CI, -144; -170.34 to -119.04), and MVPA was directly associated with BMI (ß; 95% CI, 0.04; 0.01 to 0.08). The influence of SES, and the role of physical activity and sedentary time on adiposity differs between the urban and rural samples, and the importance of other environmental and behavioural factors must be considered in the development of obesity and the design of effective interventions.
Keyphrases
- physical activity
- body mass index
- weight gain
- insulin resistance
- south africa
- metabolic syndrome
- polycystic ovary syndrome
- type diabetes
- young adults
- sleep quality
- weight loss
- clinical trial
- pregnant women
- body composition
- high intensity
- depressive symptoms
- skeletal muscle
- high fat diet induced
- adipose tissue
- big data
- electronic health record
- machine learning
- climate change
- deep learning
- data analysis