Cardiovascular Journal of Africa: Vol 30 No 5 (October 2019)

CARDIOVASCULAR JOURNAL OF AFRICA • Volume 30, No 5, September/October 2019

AFRICA

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compromising and health-enhancing behaviours among the

youth. Themale gender was reported to be an important predictor

of health-compromising behaviours.

On the other hand, the

female gender served both as a control and as an instigator of

healthy behaviour.

These findings have been supported in the

literature in terms of a female gender preoccupation with body

weight management and body image.

For example, Fan

et al

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.

A recent South

African study reported that women were less likely to engage

in physical activity than men.

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,

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.

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 ( 

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

This

compendium quantifies the energy cost of a variety of physical

activities determined through self-report.

The METS were

then converted into  

max values by multiplying by 3.5 and

expressing in millilitres of oxygen per kilogram body weight per

minute.

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.