Physical activity (PA) is beneficial for the health of people living with HIV and AIDS (PLWHA).
The aim of this study was to determine if age, body weight, height, gender, waist-to-hip ratio (WHR), educational attainment, employment status, CD4+ cell count and body mass index (BMI) can predict overall PA among PLWHA of low socio-economic status (SES).
Participants in this study were HIV-infected patients on first-line antiretroviral therapy (ART) regimen offered by the South African National Department of Health, and those not on ART. Participants were conveniently sampled from a list at a community health care centre in Cape Town.
This study sample consisted of 978 HIV-infected South Africans. Physical activity data were collected using the Global Physical Activity Questionnaire. Backward multiple linear regression modelling was used to determine the relative influence of variables (age, body weight, height, gender, WHR, educational attainment, employment status, CD4+ count and BMI) on total moderate-to-vigorous PA. Alpha level was set at 0.05.
The mean age of the participants was 38.2 (standard deviation [SD] = 8.76) years for men and 33.9 (SD = 8.53) years for women. Physical activity was significantly higher in men (480.2 [SD = 582.9] min/week) than among women (369.35 [SD = 222.53] min/week). The results of the multiple linear regression showed that educational attainment (β = 0.127;
There is a need for PA programmes that are designed to (1) target women, (2) strengthen programmes for education and promotion of PA and (3) engage the unemployed into PA for PLWHA. Physical activity interventions for this particular group should be tailored for persons of low SES.
Physical activity (PA) is defined as any bodily movement produced by skeletal muscles that cause the utilisation of energy (Caspersen, Powell & Christenson
It has been suggested that PA is a cost-effective approach to prevent and manage chronic diseases (Giannini, Mohn & Chiarelli
Given that PA is a cost-effective adjunctive therapy for the management of HIV and/or AIDS, and that persons of low socio-economic status (SES) are disproportionately burdened by HIV/AIDS (Ogunmola, Oladosu & Olamoyegun
Individuals of low SES, especially those living in informal settlements or townships, have the highest prevalence of HIV in South Africa (Shisana & Simbayi
People living with HIV and AIDS experience the adverse effects of pharmacological therapy, which negatively affects fat metabolism (Grunfeld et al.
Resistance exercise has been found to be both safe and effective for improving muscle strength and body composition among PLWHA (Yarasheski et al.
Importantly, there is a need to understand factors that affect the PA behaviours of PLWHA to be able to design informed and context-sensitive PA interventions. There are, however, few quality studies concerning PA in HIV-positive persons, especially of low SES. Therefore, it is particularly important to understand the socio-economic correlates of PA for PLWHA of low SES because HIV and/or AIDS is a disease that is embedded in social and economic inequities (Perry
In terms of socio-demographic variables, Webel et al. (
In spite of a growing emphasis on the importance of PA in promoting health, PA among PLWHA in South Africa remains poorly researched. To the best of our knowledge, no study has investigated the influence of demographic and socio-economic determinants of PA among PLWHA of low SES.
The present study is a secondary analysis utilising data from a larger cross-sectional investigation (Dave et al.
Trained fieldworkers administered a researcher-generated questionnaire to the participants to obtain data on socio-demographic details, known diabetes risk factors, family history, medical history, smoking, alcohol and current medication. Subjects’ clinical records were reviewed, and information was extracted on prior weight, ART regimen, time on ART, CD4+ count, viral load, renal function, oral glucose tolerance test (OGTT), blood pressure measures, lipodystrophy and neuropathy examination. The clinical status and anthropometry data have been reported previously (Dave et al.
The PA data reported in this study were collected using the Global Physical Activity Questionnaire (GPAQ). The GPAQ consists of 16 questions designed to estimate an individual’s level of PA in three domains (work, transport and leisure time) and time spent in sedentary behaviour (Bull, Maslin & Armstrong
Data were analysed using the Statistical Package for Social Science (SPSS) version 23 (IBM, New York, USA). Frequency distributions were calculated for all predictors and the outcome variable. Sub-categories related to ‘employment’ were dummy coded to produce two groups that were coded as ‘1’ (employed: inclusive of employed individuals and full-time homemakers) and ‘0’ (unemployed: inclusive of unemployed individuals, pensioners and those on a disability grant). Educational attainment was coded as follows: (1) never went to school; (2) up to grade 7/primary schooling; (3) grades 8–10/form 1–4; (4) grades 11–12/form 5–6 and (5) tertiary/diploma.
Participants’ age, gender, educational attainment, employment status, body weight, height, WHR, CD4+ cell count and BMI were considered as potential predictors for PA. An independent sample
Permission to conduct the study was granted by the Senate Research Ethics Committee of the University of the Western Cape (registration number: 14/10/33).
The study sample consisted of 978 PLWHA. The mean age of the participants was 38.2 (standard deviation [SD] = 8.76) years for men and 33.9 (SD = 8.53) years for women. In terms of educational attainment, the average for the group was grades 11–12/form 5–6 (3.12 [SD = 0.92]). On average, most of the participants were employed (1.74 [SD = 0.48]). Women were significantly heavier in terms of BMI (27.5 [SD = 6.25] kg/m²) than their male counterparts (22.6 [SD = 3.46] kg/m²). CD4+ cell count was higher in women (369.35 [SD = 222.53] cells/µL) than in men (312.31 [SD = 234.27] cells/µL). Total moderate-to-vigorous PA in men (480.2 [SD = 582.9] min/week) was significantly higher than that of women (369.35 [SD = 222.53] min/week).
Physical characteristics of the participants mean (SD) (
Variables | Men |
Women |
Total |
|||||||
---|---|---|---|---|---|---|---|---|---|---|
Mean | SD | Mean | SD | Mean | SD | |||||
Age (year) | 218 | 38.2 | 8.76 | 760 | 33.9 | 8.53 | 978 | 34.9 | 8.76 | 0.000 |
Height (m) | 215 | 1.69 | 0.06 | 749 | 1.57 | 0.06 | 964 | 1.60 | 0.07 | 0.000 |
Weight (kg) | 216 | 65.2 | 11.0 | 748 | 68.7 | 16.5 | 964 | 67.9 | 15.2 | 0.000 |
BMI (kg/m²) | 215 | 22.6 | 3.46 | 748 | 27.5 | 6.25 | 963 | 26.4 | 6.10 | 0.000 |
WHR | 213 | 0.90 | 0.06 | 742 | 0.84 | 0.11 | 955 | 0.85 | 0.11 | 0.000 |
CD4+ | 185 | 312.31 | 234.27 | 673 | 369.35 | 222.53 | 858 | 357.0 | 226.2 | 0.003 |
TMVPA | 218 | 480.2 | 582.9 | 760 | 269.0 | 331.5 | 978 | 316.1 | 410.6 | 0.000 |
SD, standard deviation; TVMPA, total vigorous-to-moderate physical activity; BMI, body mass index; WHR, waist-to-hip ratio.
, Significant
, Significant
The bivariate correlation analysis showed that there was a significant relationship between total moderate-to-vigorous PA and height (
Pearson correlation matrix showing relationships between body weight, body mass index, waist-to-hip ratio, age, CD4 count, gender and moderate-to-vigorous physical activity (
Variables | Age | CD4+ | Weight | Height | BMI | WHR | Education | Employment | TMVPA |
---|---|---|---|---|---|---|---|---|---|
Pearson correlation | 1 | −0.038 | 0.092 |
0.073 |
0.067 |
0.204 |
−0.403 |
0.025 | 0.002 |
Sig. (2-tailed) | - | 0.272 | 0.004 | 0.024 | 0.036 | 0.000 | 0.000 | 0.439 | 0.955 |
978 | 858 | 964 | 964 | 963 | 955 | 978 | 978 | 978 | |
Pearson correlation | −0.038 | 1 | 0.138 |
−0.046 | 0.157 |
0.104 |
0.020 | −0.027 | −0.013 |
Sig. (2-tailed) | 0.272 | - | 0.000 | 0.184 | 0.000 | 0.003 | 0.558 | 0.431 | 0.712 |
858 | 858 | 847 | 847 | 846 | 838 | 858 | 858 | 858 | |
Pearson correlation | 0.092 |
0.138 |
1 | 0.215 |
0.909 |
0.154 |
0.071 |
−0.106 |
−0.004 |
Sig. (2-tailed) | 0.004 | 0.000 | - | 0.000 | 0.000 | 0.000 | 0.027 | 0.001 | 0.907 |
964 | 847 | 964 | 963 | 963 | 955 | 964 | 964 | 964 | |
Pearson correlation | 0.073 |
−0.046 | 0.215 |
1 | −0.201 |
0.133 |
−0.023 | −0.119 |
0.173 |
Sig. (2-tailed) | 0.024 | 0.184 | 0.000 | - | 0.000 | 0.000 | 0.471 | 0.000 | 0.000 |
964 | 847 | 963 | 964 | 963 | 954 | 964 | 964 | 964 | |
Pearson correlation | 0.067 |
0.157 |
0.909 |
−0.201 |
1 | 0.100 |
0.073 |
−0.051 | −0.074 |
Sig. (2-tailed) | 0.036 | 0.000 | 0.000 | 0.000 | - | 0.002 | 0.024 | 0.114 | 0.021 |
963 | 846 | 963 | 963 | 963 | 954 | 963 | 963 | 963 | |
Pearson correlation | 0.204 |
0.104 |
0.154 |
0.133 |
0.100 |
1 | −0.139 |
−0.005 | 0.019 |
Sig. (2-tailed) | 0.000 | 0.003 | 0.000 | 0.000 | 0.002 | - | 0.000 | 0.876 | 0.553 |
955 | 838 | 955 | 954 | 954 | 955 | 955 | 955 | 955 | |
Pearson correlation | −0.403 |
0.020 | 0.071 |
−0.023 | 0.073 |
−0.139 |
1 | −0.114 |
0.095 |
Sig. (2-tailed) | 0.000 | 0.558 | 0.027 | 0.471 | 0.024 | 0.000 | - | 0.000 | 0.003 |
978 | 858 | 964 | 964 | 963 | 955 | 978 | 978 | 978 | |
Pearson correlation | 0.025 | −0.027 | −0.106 |
−0.119 |
−0.051 | −0.005 | −0.114 |
1 | −0.132 |
Sig. (2-tailed) | 0.439 | 0.431 | 0.001 | 0.000 | 0.114 | 0.876 | 0.000 | - | 0.000 |
978 | 858 | 964 | 964 | 963 | 955 | 978 | 978 | 978 | |
Pearson correlation | 0.002 | −0.013 | −0.004 | 0.173 |
−0.074 |
0.019 | 0.095 |
−0.132 |
1 |
Sig. (2-tailed) | 0.955 | 0.712 | 0.907 | 0.000 | 0.021 | 0.553 | 0.003 | 0.000 | - |
978 | 858 | 964 | 964 | 963 | 955 | 978 | 978 | 978 |
TMVPA, total moderate-to-vigorous physical activity; BMI, body mass index; WHR, waist-to-hip ratio.
, Correlation is significant at the 0.05 level (2-tailed).
, Correlation is significant at the 0.01 level (2-tailed).
The results of the multiple linear regression analysis to predict total moderate-to-vigorous PA using age, CD4+ count, gender, height, body weight, BMI, WHR, education and employment showed that education, employment and gender significantly predicted total moderate-to-vigorous PA (
Multiple linear regression analysis to predict total moderate vigorous physical activity and body weight, body mass index, waist-to-hip ratio, age, CD4 count and gender (
Variable | Regression coefficients |
Model summary |
|||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
SE | β | ||||||||||
Criterion: TMVPA | - | - | - | - | - | 0.274 | 0.075 | 22.529 | 3 | 833 | 0.000 |
Intercept | 216.143 | 76.752 | 2.816 | 0.005 | - | - | - | - | - | - | |
Education | 56.599 | 15.298 | 0.127 | 3.700 | 0.000 | - | - | - | - | - | - |
Employment | −73.469 | 28.486 | −0.087 | −2.579 | 0.010 | - | - | - | - | - | - |
Gender | 235.485 | 34.439 | 0.235 | 6.838 | 0.000 | - | - | - | - | - | - |
TMVPA, total moderate-to-vigorous physical activity.
The aim of this study was to establish if age, body weight, height, gender, WHR, educational attainment, employment status, CD4+ cell count and BMI predicted PA among PLWHA of low SES. It was found that education, employment status and gender significantly predicted total moderate-to-vigorous PA.
Gender was found to have a greater effect on total moderate-to-vigorous PA than education and employment. Webel et al. (
Currently, in South Africa, there is substantial infrastructural development and men of low SES are employed in work that characteristically requires high levels of moderate-to-vigorous PA (Malambo et al.
For employment, one might argue that the results may be confounded by clinical status. However, the present results showed no correlation between CD4+ cell count and employment. Employment was also found to be a predictor of PA. Similar results have been reported in other studies with non-HIV-infected persons (Macassa et al.
Health insurance and health care also become more accessible with the availability of monetary resources (North Carolina Institute of Medicine Task Force on Prevention
Employed PLWHA of low SES are likely to afford health-enhancing opportunities (e.g. PA, a healthy diet and medical insurance), which often positively influence their quality of life, with regard to physical and mental health. In addition, job security might also enhance their self-esteem and self-efficacy to exercise, thus placing them in a better position to engage in PA. For employed PLWHA, job security is generally related to better mental quality of life (Rueda et al.
It has been reported that differences in income generally make the greatest disparities for health (North Carolina Institute of Medicine Task Force on Prevention
Educational attainment was a predictor of PA, with an increase in educational attainment associated with an increase in total moderate-to-vigorous PA. Brown and Roberts (
On the contrary, Frantz and Murenzi (
The findings of particular importance for this specific study were that for total moderate-to-vigorous PA, the predictors of PA, in order of their effect, were gender, educational attainment and employment. This finding reinforces the need to strengthen programmes of education and the promotion of PA. In addition, PA interventions should be aimed at creating conducive environments in the workplace that motivate people to engage in regular PA.
This study is one of the few that have examined the relationships between SES and PA among PLWHA of low SES in South Africa. The study explains how socio-demographic variables predict PA among PLWHA. This information may be used to design PA interventions that are aimed at engaging PLWHA of low SES in PA.
Use of different instruments in data collection hinders the comparison between studies. Furthermore, only a few studies have investigated the combined effects of demographic and socio-economic variables on PA among PLWHA of low SES, which further limits comparisons with other studies.
Future researchers should focus their research on the demographic and socio-economic predictors of domain-specific PA among PLWHA of low SES. It is important for interventionists to understand how the socio-demographic determinants of PA influence specific PA domains in order for them to use specific techniques that target PA domains of interest. Studies should also attempt to understand how these predictors relate with different PA intensities (i.e. low, moderate or vigorous PA).
The results of this study revealed that among PLWHA of low SES, gender, education and employment were significant predictors of PA. Accordingly, for this particular population, there is a need for PA programmes and policies that are designed to (1) target women, (2) strengthen programmes of education and the promotion of PA and (3) engage the unemployed in regular PA. Physical activity interventions for PLWHA should take cognisance of the health disparities that exist between persons of low SES and those of higher SES to be context sensitive.
The authors have declared that no competing interest exists.
All the authors have contributed equally to this work.
This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.
Data sharing is not applicable to this article as no new data were created or analysed in this study.
The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of any affiliated agency of the authors.