CARDIOVASCULAR JOURNAL OF AFRICA • Volume 30, No 5, September/October 2019
AFRICA
263
compromising and health-enhancing behaviours among the
youth. Themale gender was reported to be an important predictor
of health-compromising behaviours.
10
On the other hand, the
female gender served both as a control and as an instigator of
healthy behaviour.
11
These findings have been supported in the
literature in terms of a female gender preoccupation with body
weight management and body image.
11
For example, Fan
et al
.
12
reported that among women, as body mass index increased, so
too did the level of participation in physical activity.
South African females have been reported to be overweight
and physically inactive compared to males.
13
A recent South
African study reported that women were less likely to engage
in physical activity than men.
14
The same study also reported
that gender, age, educational level, occupation and geographical
location were significantly associated with physical activity.
It is important to assess differences in physical activity
between urban and rural populations because it assists
researchers in the contextualisation of interventions in physical
activity in both rural and urban settings, especially in African
populations. This is because most deaths that are attributable
to physical inactivity have been reported in low- and middle-
income countries (LMICs).
15,16
Furthermore, research on physical
activity in LMICs is of importance to assist in understanding the
prevalence of physical inactivity globally.
17,18
Since South Africa, like most developing countries, is
experiencing nutritional, lifestyle and socio-economic changes,
which are complemented by an increase in the prevalence of
non-communicable diseases,
19
it is important to understand the
patterns of physical activity in South Africa. Understanding
the risk for physical inactivity related to urban–rural
sociodemographics may aid in identifying the pertinent areas
of focus in local environments, where change in physical activity
behaviour warrants attention.
20
Therefore, this study aimed to determine whether age, gender,
location and employment status could predict physical activity
among a sample of rural and urban South African adults.
Specifically, the aim was to inform physical activity interventions
aimed at reducing the risk for cardiovascular disease (CVD)
among adults by characterising the physical activity patterns
of behaviour among rural and urban South African adults.
A secondary aim was to determine the participants’ risk of
developing CVD, based on their physical activity patterns by
geographical location.
Methods
The study was carried out in a peri-urban community of black
South Africans in Langa, a predominantly sub-economic urban
African township near Cape Town in the Western Cape Province,
as well as in Mount Frere, a predominantly sub-economic rural
African township in the Eastern Cape Province. These sites were
purposely selected because of an existing cohort study, titled the
Prospective Rural Urban Epidemiological (PURE) study that
was undertaken in these communities by the School of Public
Health at the University of the Western Cape.
Participants in the current study were randomly sampled from
these townships, i.e. from the ‘zones’ and ‘hostels’. The intention
was to implement an intervention based on lifestyle modification
in this population, while not upsetting the longitudinal cohort
study in the process.
For the urban community (Langa), households were stratified
into three development areas, demarcated by the City of Cape
Town, which reflected the socio-economic status of the residents.
Using a street map obtained from the City of Cape Town, streets
were then randomly selected in each of the three areas. Once a
street was chosen, a systematic sample of every second house
was done for possible inclusion in the study.
For a household to be eligible, at least one member had to be
between the ages of 35 and 70 years, and this member also had
to continue living in the current home for the next four years.
Trained field workers approached all households for recruiting
eligible participants. All individuals, who were defined as one
‘who eats and sleeps in the household on most days of the week
and in [sic] most weeks of the year and [who] considered the
household as his/her primary place of habitation’, were eligible
for the study.
For the rural community (Mount Frere), the lack of
delineated streets disallowed the same sampling approach as for
the urban township. Therefore, a cluster sample of houses in the
community was undertaken according to the division of areas by
the clan heads. All households within the clusters were included
if there was a household member aged 30 to 70 years.
The Research Ethics Committee of the University of the
Western Cape approved the study with registration number
15/7/99. Participants gave their written informed consent after
the purpose of the study was explained to them.
Statistical analysis
Data were collected through face-to-face interviews using a
short researcher-generated questionnaire that obtained data
on the sociodemographic characteristics of the participants,
such as age, gender, educational level, employment status,
total household income and participation patterns in physical
activity. Physical activity was ascertained by asking the following
questions: (1) do you engage in physical activities; (2) if yes, what
are these activities, and (3) how much time do you spend doing
these activities. Data were collected from August to November
2016.
Data were analysed using the Statistical Package for Social
Science (SPSS) version 25 (IBM, New York, USA). Frequency
distributions were calculated for sociodemographic and physical
activity data. Descriptive statistics were performed to show
the means and standard deviations for age, physical activity
metabolic equivalent of task (MET) and predicted maximal
oxygen consumption (
.
VO
2
max) for both rural and urban
participants.
In this study, the METS for physical activity were obtained
by converting the participant responses to the question ‘What
are these activities?’ into specific activities based on the
Compendium of Physical Activities by Ainsworth
et al
.
21
This
compendium quantifies the energy cost of a variety of physical
activities determined through self-report.
21
The METS were
then converted into
.
VO
2
max values by multiplying by 3.5 and
expressing in millilitres of oxygen per kilogram body weight per
minute.
22
Percentages were calculated for gender, educational
level, employment status, total household income, engaging
in physical activity, MET categories for intensity of physical
activity, duration of physical activity and for the types of
activities.