Changes in lifestyle that emanates from urbanization have triggered a shift in determinants of health leading to an increase in noncommunicable diseases (NCDs) in low-income countries (LICs) [
Physical activity (PA) is known to prevent and treat a number of NCDs, subsequently improving the overall life expectancy [
In light of the health benefits and based on evidence, WHO has put forward global recommendations on PA necessary to maintain health and prevent diseases [
The global physical activity questionnaire (GPAQ) is one of the tools used in WHO stepwise approach to surveillance (STEPS survey). This tool offers an opportunity to collect comprehensive information on PA. Despite some practical and validity challenges that may pose limitations on data interpretation and inference, GPAQ has a high degree of reproducibility, making it a good tool for continuous PA monitoring [
We therefore analyzed PA data from the 2012 WHO-STEPS survey to explore the social-economic determinants of PA in Tanzania, particularly focusing on rural-urban differences. This analysis will not only provide baseline evidence for surveillance, but also instill knowledge that will guide policy and help in executing tailored PA promotions in specific groups.
The whole STEP survey employed a sample of 5770 representative citizens of Tanzania that was selected using multistage cluster and random sampling procedures. Districts served as primary sampling units. For physical activity analysis a subsample of 5398 people (2183 and 2400 males and females, resp.) with complete and valid physical activity (PA) data was drawn from the whole survey sample and included in the current analysis. The sample population was stratified according to age, gender, residential setting, employment status, income, and education levels.
Data collection was done using the modified WHO stepwise approach to NCD risk factors Surveillance Instrument [
The questionnaire captured all the necessary and important social-demographic information including behavioral measures. In addition the questionnaire also contained measures of socioeconomic status, diet, and PA that were also self-reported. All physical measurements were done according to standardized methods outlined in the STEPS survey manual [
Physical activity was assessed using the global physical activity questionnaire (GPAQ). This questionnaire collects information on PA participation in three settings (or domains): activity at work (work or occupational physical activity), travel to and from places (active travel), and on leisure time physical activity (recreational physical activities), as well as sedentary behavior.
The amount spent doing physical activity was quantified using Metabolic Equivalent of Task (MET), which is the ratio of a person’s working metabolic rate relative to the resting metabolic rate. One MET is defined as the energy cost of sitting quietly and is equivalent to a caloric consumption of 1 kcal/kg/hour. It is estimated that, compared to sitting quietly, a person’s caloric consumption is four times as high when being moderately active and eight times as high when being vigorously active. Therefore, when calculating a person’s overall energy expenditure 4 METs were assigned to the time spent in moderate activities and 8 METs to the time spent in vigorous activities [
Low levels of physical activity were defined as <600 MET-minutes per week and high levels of physical activity were defined as defined as ≥3000 MET-minutes per week. Low levels of physical activity (<600 MET-minutes/week) were considered as insufficient physical activity.
Ethical clearance was obtained from the ethical committee of National Institute for Medical Research of Tanzania and all necessary permissions sought from relevant authorities. The study was conducted maintaining all possible ethical considerations including obtaining written informed consent from the study subjects. The respondents had the right to refuse to answer any question without providing the reason for their decisions and could withdraw from the study at any time. The information was dealt with highest confidentiality and used only for this study.
The data from the field was downloaded from the personal digital assistant (PDA) using Epi data version 3.1 software which was then exported on MS-Excel and Statistical Package for Social Sciences version 20 for windows (SPSS Inc., Chicago, USA) for cleaning and cross-checking inconsistencies and outliers and analysis. Descriptive analysis was presented in tables and figures. Associations between variables (dependent and independent variables) were tested using Chi-squire. Sophisticated survey data analysis was performed to obtain population estimates and their 95% confidence intervals. Differences or association between variables were considered statistically significant if
Out of the 5680 participants included in the STEPS survey, 5398 (95.04%) had complete and valid physical activity (PA) data and therefore were included in the current analysis. The majority of participants included came from rural areas and were self-employed (75.99% and 70%, resp.). Only 10.3% of these participants had attained more than a primary education (secondary, 7.5%; tertiary 2.8 %). The social-demographic characteristics of the participants are summarised in Table
Distribution of study population according to settings/residence.
Demographic factor | Setting | ||
---|---|---|---|
Total, |
Rural, |
Urban, |
|
|
|||
25–34 | 1866 (32.9) | 1381 (32.0) | 485 (35.6) |
35–44 | 1658 (29.2) | 1279 (29.6) | 379 (27.8) |
45–54 | 1252 (22.0) | 977 (22.6) | 275 (20.2) |
55–64 | 904 (15.9) | 679 (15.7) | 225 (16.5) |
|
|||
Female | 2622 (46.2) | 2229 (51.6) | 829 (60.8) |
Male | 3058 (53.8) | 2087 (48.4) | 535 (39.2) |
|
|||
Employed | 477 (8.4) | 310 (7.2) | 167 (12.3) |
Self-employed | 4000 (70.4) | 3190 (73.9) | 810 (59.5) |
Nonpaid | 381 (6.7) | 331 (7.7) | 50 (3.7) |
Homemaker | 648 (11.4) | 403 (9.3) | 245 (18.0) |
Others (retired, students, unemployed, etc.) | 171 (3.0) | 81 (1.9) | 90 (6.6) |
Missing | 3 (0.1) | ||
|
|||
None/not completed primary school | 1745 (30.7) | 1430 (33.1) | 315 (23.1) |
Primary school | 3349 (59.0) | 2572 (59.6) | 777 (57.0) |
Secondary | 425 (7.5) | 223 (5.2) | 202 (14.8) |
Tertiary | 161 (2.8) | 91 (2.1) | 70 (5.1) |
The distribution of the study population by categories of physical activity (based on WHO criteria using MET-minutes per week) is shown in Table
Distribution of study population according to physical activity status and social economic categories (proportions).
Demographic factor | Physical activity status |
|
||
---|---|---|---|---|
Total, |
≥600 MET/week, |
<600 MET/week, |
||
|
||||
25–34 | 1787 (33.10) | 1726 (96.6) | 61 (3.4) | 0.002 |
35–44 | 1590 (29.5) | 1552 (97.6) | 38 (2.4) | |
45–54 | 1188 (22.0) | 1154 (97.1) | 34 (2.9) | |
55–64 | 833 (15.4) | 789 (94.7) | 44 (5.3) | |
|
||||
Female | 2901 (53.7) | 2781 (95.9) | 120 (4.1) |
|
Male | 2497 (46.3) | 2440 (97.7) | 57 (2.3) | |
|
||||
Employed | 452 (8.4) | 423 (93.6) | 29 (6.4) | |
Self-employed | 3825 (70.9) | 3744 (97.9) | 81 (2.1) | |
Nonpaid | 367 (6.8) | 360 (98.1) | 7 (1.9) | |
Homemaker | 607 (11.2) | 569 (93.7) | 38 (6.3) | |
Others (retired, students, unemployed, etc.) | 146 (2.7) | 124 (84.9) | 22 (15.1) | |
|
||||
None/not completed primary school | 1653 (30.6) | 1596 (96.6) | 57 (3.4) |
|
Primary school | 3195 (59.2) | 3111 (97.4) | 84 (2.6) | |
Secondary | 396 (7.3) | 370 (93.4) | 26 (6.6) | |
Tertiary | 154 (2.9) | 144 (93.5) | 10 (6.5) |
Meets WHO physical activity recommendations (>600 MET/week). Do not meet WHO physical activity recommendations (<600 MET/week).
Among the three domains of PA, the prevalence of physical inactivity was the highest in the recreation category (recreation: 71.9%, transport: 10.6%, and work-related: 8.9%), and in general there was a tendency for less participation in moderate to vigorous recreational PA (recreation-MVPA) among rural, compared to urban dwellers. Compared to those living in rural, significantly more people in the urban settings did not engage in active travel (AT) (9.6% in rural versus 13.8% in urban,
Proportional of participants reported not to engage in transport, recreational, and work physical activities (PA) by social-demographic characteristics
Demographic factor | Transport PA, |
Vigorous/moderate recreational PA, |
Vigorous/moderate work, PA, |
---|---|---|---|
|
|||
25–34 | 179 (9.6) | 1214 (65.1) | 141 (7.6) |
35–44 | 165 (10.0) | 1195 (72.1) | 114 (6.9) |
45–54 | 128 (10.2) | 938 (74.9) | 121 (9.7) |
55–64 | 132 (14.6) |
738 (81.6) |
134 (14.8) |
|
|||
Female | 397 (13.0) |
2394 (78.3) |
243 (7.9) |
Male | 207 (7.9) | 1691 (64.5) | 267 (10.2) |
|
|||
Rural | 416 (9.6) | 3115 (72.2) | 300 (7.0) |
Urban | 188 (13.8) |
970 (71.1) | 210 (15.4) |
|
|||
Employed | 61 (12.8) | 284 (59.5) | 80 (16.8) |
Self-employed | 367 (9.2) | 2859 (71.5) | 281 (7.0) |
Nonpaid | 40 (10.5) | 317 (83.2) |
19 (5.0) |
Homemaker | 97 (15.0) | 498 (83.2) |
78 (12.0) |
Others (retired, students, unemployed, etc.) | 37 (21.6) |
125 (73.1) | 50 (29.2) |
|
|||
None/not completed primary school | 205 (11.7) | 1356 (77.7) |
160 (9.2) |
Primary school | 314 (9.4) | 2392 (71.4) | 256 (7.6) |
Secondary | 58 (13.6) | 243 (57.2) | 65 (15.3) |
Tertiary | 27 (16.8) |
94 (58.4) | 29 (18.0) |
Figure
Factors associated with low levels of physical activity (≤600 MET).
Demographic factor | High (≥3000 MET/minutes/week), |
Odds ratio | |
---|---|---|---|
Unadjusted |
Adjusted |
||
|
|||
Rural | 83 (2.0) | 1 | 1 |
Urban | 94 (7.4) | 3.9 (2.9–5.2) |
2.9 (2.1–3.9) |
|
|||
Nonpaid | 7 (1.9) | 1 | 1 |
Employed | 29 (6.4) | 3.5 (1.5–8.1) |
3.0 (1.2–7.2) |
Self-employed | 81 (2.1) | 1.1 (0.5–2.4) | 1.1 (0.5–2.4) |
Homemaker | 38 (6.3) | 3.4 (1.5–7.8) |
2.2 (0.9–5.0) |
Others (retired, students, unemployed, etc.) | 22 (15.1) | 9.1 (3.8–21.9) |
5.0 (2.0–12.5) |
|
|||
None/not completed primary school | 57 (3.4) | 1 | 1 |
Primary school | 84 (2.6) | 0.8 (0.5–1.1) | 0.9 (0.6–1.3) |
Secondary | 26 (6.6) | 2.0 (1.2–3.2) |
1.3 (0.8–2.3) |
Tertiary | 10 (6.5) | 1.9 (1.0–3.9) | 0.9 (0.4–1.9) |
|
|||
25–34 | 61 (3.4) | 1 | 1 |
35–44 | 38 (2.4) | 0.7 (0.5–1.0) | 0.8 (0.5–1.2) |
45–54 | 34 (2.9) | 0.8 (0.5–1.3) | 0.9 (0.6–1.4) |
55–64 | 44 (5.3) |
1.6 (1.1–2.3) |
1.4 (0.9–2.3) |
|
|||
Male | 57 (2.3) | 1 | 1 |
Female | 120 (4.1) | 1.9 (1.3–2.5) |
1.6 (1.1–2.3) |
Factors associated with highest level of physical activity (≥3000 MET).
Demographic factor | High (≥3000 MET/minutes/week), |
Odds ratio | |
---|---|---|---|
Unadjusted |
Adjusted |
||
|
|||
Urban | 928 (72.6) | 1 | 1 |
Rural | 3670 (89.1) |
3.1 (2.6–3.6) |
2.4 (2.0–2.9) |
|
|||
Retired, students, unemployed, and so on | 93 (63.7) | 1 | 1 |
Employed | 321 (71.0) | 1.4 (0.9–2.1) | 1.0 (0.7–1.5) |
Homemakers | 456 (75.1) | 1.7 (1.2–2.5) |
1.4 (0.9–2.2) |
Self-employed | 3389 (88.6) | 4.4 (3.1–6.3) |
2.6 (1.8–3.8) |
Nonpaid | 338 (92.1) |
6.6 (4.0–11.0) |
3.8 (2.2–6.4) |
|
|||
Tertiary | 107 (69.5) | 1 | 1 |
Secondary | 291 (73.5) | 1.2 (0.8–1.8) | 0.9 (0.6–1.4) |
Primary | 2777 (86.9) |
2.9 (2.0–4.2) | 1.4 (0.9–2.1) |
Non | 1423 (86.1) | 2.7 (1.9–3.9) | 1.5 (1.0–2.3) |
|
|||
55–64 | 669 (80.3) | 1 | 1 |
45–54 | 1027 (86.4) | 1.6 (1.2–2.0) |
1.5 (1.2–2.0) |
35–44 | 1378 (86.7) |
1.6 (1.3–2.0) |
1.5 (1.2–2.0) |
25–34 | 1524 (85.3) | 1.4 (1.1–1.8) |
1.5 (1.2–1.9) |
|
|||
Female | 2382 (82.1) | 1 | 1 |
Male | 2216 (85.2) |
1.7 (1.5–2.0) |
1.6 (1.3–1.9) |
A dose-response relationship between physical activity levels (METS-minutes per week) and a number of cardio metabolic parameters.
Physical inactivity is one of the main instigates for the raising problem of noncommunicable diseases (NCDs) globally [
Our findings indicate that overall majority of study participants achieved the WHO-recommended PA levels (expressed as METS-minutes/week) and were considered sufficiently active. The proportion of sufficiently active participants in our study is higher than that reported from a pooled analysis of data from 22 African countries (Tanzania not included) [
Based on WHO-recommended PA cut-off (<600 MET-minutes/week), we found a higher prevalence of insufficient PA in urban compared to rural settings, and participants from rural areas had the highest levels of PA. The observed differences in PA status in rural and urban settings have also been reported previously and reflect the rural-urban differences in NCDs prevalence [
The ongoing urbanization in most developing countries has also steered the emergency of semiurban/semirural areas, mostly in places that are ordinarily classified as rural settings [
We also found a significant association between employment and insufficient PA. Employed people presented the highest proportion of those who were insufficiently active. As expected our study found the highest proportion of employed participants in urban than in rural settings. Furthermore, participants with higher social-economic status (postprimary education and/or employed) were less likely to engage in AT and/or work-MVPA, had the highest prevalence of insufficient PA, and were more prevalent in urban than in rural areas. Based on these observations, the relatively higher prevalence of insufficient PA in urban areas is likely due to lower participation in AT and work-MVPA among urban dwellers.
On the other hand our data show that, in addition to a significant participation in AT, people living in rural areas were more likely than their urban counterparts, to be involved in work-MVPA, and had the lowest overall prevalence of insufficient PA. Being self-employed which was ubiquitous in rural settings was significantly associated with the highest PA levels (>3000 MET-minutes/week), another possible reason for the favorable PA profile seen in rural areas. Together, these observations show that while rural societies tend to maintain traditional lifestyles characterized by manual work and active travel, urban societies in Africa are continually adopting western lifestyles, with a substantial reliance on passive ways of living [
In line with the above observations, a comprehensive analysis of PA data from 22 African countries in the year 2011 showed a dominant contribution of AT and work-MVPA in the overall MVPA [
This particular engagement in recreational-MVPA among employed people and/or urban dwellers may reflect their efforts in trying to create PA opportunities missed during work and commuting, by participating in recreational activities. Participating in recreational activities requires motivation and may be affected by awareness of benefits and accessibility to recreational facilities, including income [
Several studies have reported consistent gender differences in the overall PA levels in Africa, with women reporting lower levels than men [
These data came from a survey that was conducted following all the necessary criteria and procedures. However, overrepresentation of rural population (very active population) might have significantly contributed in pooling the absolute values for the overall PA levels towards the higher side.
In addition challenges affecting the validity of GPAQ in middle and low-income countries have been previously reported [
Despite some limitations, this manuscript provides one of the first detailed reports on PA including the associated social-economic antecedents in rural and urban Tanzania. Given an appropriate sampling that was used to select study areas and participants, these data came from a representative Tanzanian population. It therefore provides baseline information that will be used as a reference for monitoring changes in PA status and trends in the country and Africa at large. One of the biggest strengths of this study is the use of GPAQ, which is a globally standardized tool in assessing PA. The inclusion of both rural and urban population is also another strength, as it offers an opportunity to evaluate the health effects of urbanization, a common phenomenon in all African countries.
In conclusion, despite a favorable PA status observed in this study, rural-urban differences in the overall levels of PA exist and reflect the existing differences in the prevalence of major NCDs in rural and urban Tanzania. Social-economic factors such as gender, employment, and education statuses significantly modulate PA and are the reasons for the observed differences. Despite some slight variations in the preferences leading to a more engagement in recreational-MVPA among educated and employed urban dwellers, AT and work-MVPA are the main contributors to the overall PA levels across all groups in Tanzania. Promoting physical activity in this setting therefore should be context specific but specifically involves rigorous promotion of AT (walking and cycling) and considers work place exercise programs.
The authors declare no conflicts of interest.
Fredirick L. Mashili, Gibson B. Kagaruki, and Mary T. Mayige conceptualized and planned for the analysis. Fredirick L. Mashili led the data analysis, interpretation, and discussion of the findings as well as writing of the manuscript. Gibson B. Kagaruki did the analysis and contributed to writing and review of the manuscript. Joseph Mbatia, Alphoncina Nanai, Grace Saguti, Sarah Maongezi, Ayoub Magimba, Joseph Mbatia, Mathias Kamugisha, Eric Mgina, Clement N. Mweya, and Ramaiya Kaushik reviewed the manuscript and contributed to the discussion.
The authors acknowledge the support by the Lown Scholars Program at the Harvard T.H. Chan School of Public Health, through a grant that facilitated Dr. Mashili’s training, mentorship, and time to lead data analysis and writing of this manuscript.