DOI: https://doi.org/10.21203/rs.3.rs-1395828/v1
Dietary patterns and physical activity, in addition to countervailing social norms, that influence health and nutritional well-being of adolescents are increasingly becoming important in recent times. This study was conducted to compare the level of physical activity and its relationship with nutritional status (BMI) among rural and urban adolescents in the Northern Region. The study employed an analytical cross-sectional study design. The study used random sampling technique to sample 520 adolescents. The study administered a questionnaire on the respondents and the data analyzed using Statistical Package for Social Sciences version 23.0 and Microsoft word excels 2013. The study showed that, 9.4% of the urban dwellers were too thin for their age relative to 5.4% of the rural folks. Overweight/obese prevalence was 7.9% among the rural adolescents compare to 8.3% of the adolescents living in the urban areas. From the results, 52.9% of the urban adolescents had low physical activity compared to 43.9% of the rural adolescents. A multinomial logistic regression with reference to people engaging in moderate physical activities reveals that male’s adolescent are more than two (2) times (RRR: 2.24, 95% CI: 1.133-4.404, P-value: 0.02) more likely to engage in high physical activities than female adolescents. Adolescents whose fathers had up-to primary education only were found to be more than 13% less likely to engage in moderately physical activities than those whose fathers attended junior high school (RRR; 0.134, 95% CI; 0.0022-0.827, P-value; 0.03). Based on the results, it was concluded that adolescents were moderately engaged in some form of physical activity and this could explain why overweight/obesity was low among the study population. The study recommends that, parents and guardians as well as health workers and teachers encourage adolescents to continue with daily physical activities in order to sustain their physical activity levels.
Globally, non-communicable diseases (NCDs) are a major health burden and with health behaviours such as tobacco cessation, healthy dietary choices, and low alcohol consumption and or physical activity all proven effective in the prevention and treatment of NCDs; however, limited global responsiveness has been given to the significance of an active lifestyle in disease prevention (Das and Horton, 2016).
Contemporary research findings indicate adolescent and adult physical activity levels have decreased over the past years in both developed and developing countries (McVeigh & Meiring, 2014), leading to concerns relating to several health risks (Monyeki, 2013). Physical activity according to WHO (2015), involves any kind of movement that is instigated by energy- producing muscular function.
Regular engagement in moderate to vigorous intensity physical activity has been shown to guard the individual against chronic diseases and conditions such as obesity, diabetes and coronary heart diseases (Spengler & Woll, 2013). Physical activity has also been shown to provide psychological advantages such as self-confidence and self-image and to prevent breast and colon cancer (WHO, 2015). Likewise, physical activity has proved to be a key determinant of energy expenditure and thus fundamental to energy balance and weight control (WHO, 2010).
According to existing evidence, the world is experiencing a decreasing trend in physical activity levels (Swinburn et al., 2011). Physical inactivity accounted for more than 3 million preventable deaths in 2009 and was recognized as the fourth leading risk factor for non- communicable diseases (Booth et al., 2008). According to world health organization (WHO, 2011), it became the third top risk factor for non- communicable diseases following an increase to 3.2 million. In 2010, physical inactivity and dietary risk factors together accounted for 10.0% of global DALYs (Lim et al., 2012). The prevalence of physical inactivity among adolescents aged 11–17 years was 81% in 2010 according to the global action plan report on physical activity 2017. Adolescent girls were less active than adolescent boys, with 84% vs. 78% not meeting WHO recommendations (WHO, 2019).
Urbanization is emphasized periodically as a factor influencing physical activity, sedentary behaviour and weight status of adolescents. Intuitively, individuals living in urban centers are presumed to be less active with lower levels of physical activity and higher overweight and obesity than those in rural areas, (Springer et al., 2006; Liu et al., 2008; Ismailov and Leatherdale, 2010).
However, studies dealing with the effects of urbanization on physical activity, sedentary behaviour and weight status have not been completely consistent (Cicognani et al., 2008). Potential confounders such as local cultural and social factors, climate and methods of assessment in addition to diverse definition of rural and urban; also makes it difficult to generalize socio-geographic variation in activity and sedentary behaviours associated with weight outcomes across countries. The weight outcomes associated with urban-rural residence may also vary across geographic regions.
There is evidence of an increase in the world’s population living in urban areas (Barreto, 2000). This has been obvious in Ghana with over 50% of the population living in urban centres (GSS, 2014). According to WHO (2015), urban- rural disparity is one of the factors influencing an individual’s behavioural development.
In general, rural communities have limited access to health care, lower preventable morbidity and mortality rates, and low per capita health and professional expertise, especially when compared with urban communities (Muula, 2007).
It has been reported that 80% of 13–15 year olds individuals do not meet the current physical activity recommendation of 60 minutes of moderate to vigorous physical activity per day and thus; the need for more physical activity surveillance data from Africa ( Hallal et al., 2012). According to WHO the problem of NCDs has increased in Africa, and is anticipated to rise further if no action is taken early (WHO, 2011).
The high prevalence of physical inactivity and the influence it has on the current nutritional outcome (BMI) of adolescents is unknown making it difficult to find solutions to the health, economic and psychological consequences it comes with.
There is a gap of data in rural – urban comparative studies that establishes a link between the nutritional status (BMI) of adolescents and the levels of physicals activities they engage in. In Ghana, most studies in this subject turn to dwell more on urban communities to the neglect of rural communities. The reality is that, there could be significant difference in nutritional status and amounts of physical activities undertaken among urban and rural settings. This study therefore, aimed at comparing the level of physical activity and its relationship with nutritional status (BMI) among rural and urban adolescents in the Northern Region.
The proposed conceptual framework to determine the association between physical activity and nutritional status of adolescents is the ecological model which indicates the different dimensions of associations of physical activity and nutritional status of adolescents (Fig. 1).
The study was under taken in Tamale and Kumbungu districts of the northern region. These two districts were selected to represent urban and rural communities respectively. Tamale has majority of adolescents’ parents working in the government sector whilst in Kumbungu, most parents of adolescents are engaged in the agricultural sectors, unskilled manual labour and belongs to lower socio-economic class.
Analytical cross-sectional study design was employed to achieve the objectives of the study with quantitative approach.
The study population was made up of adolescents within 12–17 years. In all, 520 adolescents comprising 260 each from Tamale and Kumbungu was obtained using the expression below;
N = (P1 x (100-P1) + P2 x (100-P2)) x f(α, β) / ( P2 - P1)2 (Pocock, 1984)
Where n = required sample size
P2 = estimated prevalence of overweight and obesity combined in urban Tamale = 17.3% (Amidu et al., 2013)
PI = estimated prevalence of overweight and obesity combined in rural Kumbungu = 5.4% Expecting an estimated percentage point difference between P2 and P1 to be 11.9%,
Tamale and Kumbungu were selected to represent urban and rural communities respectively. A two-stage cluster sampling method was used to select communities from both districts. Six clusters each was selected randomly from each district, each selected cluster was then divided into four segments (north, south, west and east) and with the help of the assemblyman in each selected cluster a list of households in each segment was then made. Random sampling using the lottery method was finally used to select households with adolescents falling within the age category for the study.
A questionnaire was used to collect data on socio economic and demographic characteristics, dietary diversity and frequency of consumption of energy dense foods of adolescents. Anthropometric measurements such as weight and height were measured to the nearest 0.1kg and 0.1 meters respectively using Seca scale and stadiometer respectively. International physical activity questionnaire (IPAQ) was adapted and used to generate data on physical activity. A food frequency questionnaire was also used to obtain data on the pattern of consumption of energy dense foods.
Data Collection Procedure
Adolescents’ anthropometric data was collected using stadiometer and Seca scale. Each respondent’s weight was measured twice and average weight determined. Respondents were weighed one after the other wearing light clothing; their feet placed side by side and pointing straight with their hands by their sides with no heavy object on them. Each respondent height was measured twice and average height determined. Heights were measured when the respondents stood erect with heels, shoulder, back and head all resting on the wall. Data on level of Physical activity was collected with reference to the international physical activity questionnaire (IPAQ) guidelines.
A food frequency questionnaire comprising list of energy dense foods, frequency of food intake, and snacks of the adolescents was administered in order to determine the pattern of consumption of energy dense foods.
Data analysis was done in two folds according to place of residence; urban and rural.
Consumption patterns of energy dense foods were categorized using principal component analysis (PCA). This method was used to convert consumption of each individual respondent into an index. The indexes were then categorized by generating z- scores and further using quantiles to group the consumption pattern into often (75th quantile and above), sometimes (50th quantile) and rarely consumed (25th quantile).
The height and weight measurements obtained were entered into the Anthroplus software to determine the BMI of the respondents using WHO (2007) BMI for age and sex cut-offs. The BMI for age were categorized into underweight, normal weight, overweight/ obese. Each category of the BMI for age was expressed as percentages to determine the prevalence of overweight/ obesity. Levels of physical activity were categorized according to international physical activity questionnaire (IPAQ) guidelines. IPAQ based physical activity intensity on metabolic equivalent score per minutes per week (METs-minutes/week). The categories were, low physical activity intensity (< 600METs), moderate physical activity intensity (600-3000METs), and high physical activity intensity (> 3000 METs) for persons.
The data was analysed using Statistical Package for Social Science (SPSS) version 25. Descriptive statistics such as means, frequencies, and percentages were computed and presented in tables for categorical variables while means were generated for continuous variables. Chi-square tests were used to test for association between levels of physical activity and nutritional status as well as level of physical activity and socio- demographic characteristics of adolescents and their guardians. A multinomial logistic regression analysis was used further to determine the contribution of physical activity to nutritional status of adolescents. A p- value < 0.05 was accepted as significant. Exposure variables that were statistically significant in the bivariate, Chi-square test were further analysed in a multinomial logistic regression model. Moderate physical activity was used as the reference category. For each exposure variable, the relative risk for the reference group was assigned as the relative risk ration of 1.00.
The sex distribution of the respondents was uneven as the proportion of males and females was 48.9% and 51.1% respectively. The majority (77.6%) of respondents were below the ages of 16 (12–15 years). About 57% of the adolescents were staying with their parent's whiles the remaining 43.4% were living with guardians.
Almost 70% of mothers were into trading with about 23% of the mothers being involved in one craftmanship or another. However, only 4.4% of the mothers were into farming whiles 3.6% were into public service (Table 1).
|
Variables |
Categories |
Number |
Percentage |
|---|---|---|---|
|
Type of dwelling |
Rural |
278 |
50.0 |
|
Urban |
278 |
50.0 |
|
|
Sex of respondents |
Male |
272 |
48.9 |
|
Female |
248 |
51.1 |
|
|
Age of respondents |
<= 15 |
432 |
77.6 |
|
16+ |
124 |
22.4 |
|
|
Level of Education |
Primary |
123 |
22.1 |
|
Junior High School |
433 |
77.9 |
|
|
Do you live with your parents or Guardian |
Parent |
315 |
56.6 |
|
Guardian |
241 |
43.4 |
|
|
Father’s Educational Status |
Primary |
24 |
4.3 |
|
JHS |
58 |
10.4 |
|
|
Secondary |
48 |
8.6 |
|
|
Tertiary |
75 |
13.4 |
|
|
No Education |
110 |
19.7 |
|
|
Father’s Occupation |
Farming |
83 |
14.9 |
|
Trading |
62 |
11.1 |
|
|
Craftsman |
62 |
11.1 |
|
|
Public Servant |
72 |
12.9 |
|
|
Other |
36 |
6.4 |
|
|
Mother’s Educational Status |
Primary |
48 |
8.6 |
|
JHS |
54 |
9.7 |
|
|
Secondary |
21 |
3.7 |
|
|
Tertiary |
10 |
1.8 |
|
|
No Education |
182 |
32.7 |
|
|
Mother’s Occupation |
Farming |
12 |
4.4 |
|
Trading |
184 |
67.6 |
|
|
Craftsman |
65 |
23.8 |
|
|
Public Servant |
10 |
3.6 |
|
|
Other |
1 |
0.3 |
|
|
Household size |
<= 3 |
35 |
6.3 |
|
4–6 |
233 |
41.9 |
|
|
7+ |
288 |
51.7 |
|
|
Wealth Index |
Poor |
139 |
25.0 |
|
Average |
391 |
70.3 |
|
|
Rich |
26 |
4.6 |
The study grouped respondents’ physical activity level based on the guidelines of the international physical activity questionnaire. The results showed that 21% of the adolescents interweaved were engaged in high physical activity level while about half (48.4%) had low physical activity levels (Table 2).
|
Indicator |
Category |
Number |
Percent |
|---|---|---|---|
|
Physical Activity Category |
Low |
269 |
48.4 |
|
Moderate |
170 |
30.6 |
|
|
High |
117 |
21.0 |
Nutritional status of study participants
The nutritional status of the adolescents in this study was assessed using their Body Mass Index (BMI)-for-Age. In general, 8.1% of the study population were overweight/obese while 7.4% of the study participants were thin. The results further showed that more than half of the study participants (55.2%) sometimes consume energy dense-foods, whereas more than a quarter (29.3%) consumed energy dense-foods often (Table 3). The study showed that 43.7 percent of adolescents consumed four to five varieties of food daily. 27.2 percent of the adolescents consumed less than three food items per day whereas 29.1 percent of the adolescents consumed more than six food items per day.
|
Indicator |
Category |
Number |
% |
|---|---|---|---|
|
BMI for Age Z-scores |
Thinness |
41 |
7.4 |
|
Normal |
470 |
84.5 |
|
|
Overweight/Obese |
45 |
8.1 |
|
|
Individual Dietary Diversity |
Low |
151 |
27.2 |
|
Medium |
243 |
43.7 |
|
|
High |
162 |
29.1 |
|
|
Frequency of consumption of Energy-dense foods |
Often |
163 |
29.3 |
|
Sometimes |
307 |
55.2 |
|
|
Rarely |
86 |
15.5 |
The results showed that About 53% of the adolescents residing in urban areas had low physical activity compared to 43% of those living in rural areas. Also, about 54% of male adolescents had low physical activity levels compared to females (48%). The study also showed 38.5% of male participants had high physical activity levels compared to 61.5% of female participants. The difference observed in physical activity level between male participants and female participants was statistically significant (p = 0.036).
The study further revealed that 26% of the study participants whose fathers were public servants had low physical activity levels compared to 24.5% of the study participants whose fathers were farmers. A statistically significant association (p = 0.025) was observed between father’s occupation and level of participants’ physical activity.
About 62% of the study participants whose mothers had secondary education had low physical activity compared to about 40% each of adolescents whose mothers had primary and no education respectively. There was statistically significant association between mother’s education and level of physical activity (p = 0.000).
The study however did not show any statistically significant association between levels of physical activity of participants and mother’s occupation, father’s educational status, socio-economic status and nutritional status even though there was an observed difference in the prevalence of low physical activity levels as shown in Table 4 below.
|
Variable |
Categories |
Physical Activity Level |
Test Statistic |
||
|
Low |
Moderate |
High |
|||
|
Urban/Rural |
Urban |
147 (52.9) |
81 (29.1) |
50 (18.0) |
χ2 = 5.170 p = 0.075 |
|
Rural |
122 (43.0) |
89 (32.0) |
67 (25) |
||
|
Sex |
Male |
141 (51.9) |
86 (31.6) |
45 (16.5) |
χ2 = 6.627 p = 0.036 * |
|
Female |
128(45.0) |
84 (29.6) |
72 (29.4) |
||
|
Age |
<= 15 |
209 (48.4) |
137 (31.7) |
86 (19.9) |
χ2 = 2.007 p = 0.367 |
|
16+ |
60 (48.4) |
33 (26.6) |
31(25) |
||
|
Household size |
<= 3 |
14 (40) |
11 (31) |
10 (29) |
χ2 = 8.694 p = 0.069 |
|
4–6 |
128 (54.9) |
67 (28.8) |
38 (16.3) |
||
|
7+ |
127 (44.1) |
92 (31.9) |
69 (24) |
||
|
Wealth Index (SES) |
Poor |
73 (52.5) |
44 (31.7) |
22 (15.8) |
χ2 = 6.184 p = 0.186 |
|
Average |
188 (48.1) |
115 (29.4) |
88 (22.5) |
||
|
Rich |
8 (30.8) |
11 (42.3) |
7 (26.9) |
||
|
Frequency of consuming energy-dense foods |
Often |
78 (47.9) |
58 (35.6) |
27 (16.5) |
χ2 = 6.641 p = 0.156 |
|
Sometimes |
152 (49.5) |
90 (29.3) |
65 (21.2) |
||
|
Rarely |
39 (45.4) |
22 (25.5) |
25 (29.1) |
||
|
Nutritional Status |
Thinness |
20 (48.8) |
14 (34.1) |
7 (17.1) |
χ2 = 1.103 p = 0.894 |
|
Normal |
225 (47.9) |
143 (30.4) |
102 (21.7) |
||
|
Overweight/Obese |
24 (53.3) |
13 (28.9) |
8 (17.8) |
||
|
Level of education |
Primary |
61 (49.6) |
43 (35.0) |
19 (15.4) |
χ2 = 7.690 p = 0.262 |
|
Junior High School |
208 (48.0) |
127 (29.3) |
98 (22.7) |
||
|
Do you live with your parents or guardian |
Live with parent |
149 (47.3) |
103 (32.7) |
63 (20) |
χ2 = 1.622 p = 0.444 |
|
Live with guardian |
120(49.8) |
67 (27.8) |
54 (22.4) |
||
|
Father's educational status |
Primary |
12 (50.0) |
10 (41.7) |
2 (8.3) |
χ2 = 12.622 p = 0.126 |
|
JHS |
36 (62.1) |
13 (22.4) |
9 (15.5) |
||
|
Secondary |
23 (47.9) |
12 (25) |
13 (27.9) |
||
|
Tertiary |
35 (46.7) |
26 (34.7) |
14 (18.6) |
||
|
No education |
43 (39.1) |
42 (38.2) |
25 (22.7) |
||
|
Father's occupation |
Farming |
37 (44.6) |
30 (36.1) |
16 (19.3) |
χ2 = 17.553 p = 0.025 * |
|
Trading |
36 (58.1) |
19 (30.6) |
7 (11.3) |
||
|
Crafts man |
29 (46.7) |
21 (33.9) |
12 (19.4) |
||
|
Public servant |
38 (52.7) |
21 (29.2) |
13 (18.1) |
||
|
Mother's educational status |
Primary |
33 (68.8) |
14 (29.1) |
1 (2.1) |
χ2 = 30.359 p = 0.000 * |
|
JHS |
25 (46.3) |
13 (24.1) |
16 (29.6) |
||
|
Secondary |
13 (61.9) |
8 (38.1) |
0 (0.0) |
||
|
Tertiary |
4 (40.0) |
6 (60.0) |
0 (0.0) |
||
|
No education |
74 (40.7) |
62 (34.0) |
46 (25.3) |
||
|
Mother's occupation |
Farming |
5 (41.7) |
4 (33.3) |
3 (25) |
χ2 = 6.297 p = 0.790 |
|
Trading |
88 (47.8) |
57 (31.0) |
39 (21.2) |
||
|
Housewife only |
31 (47.7) |
23 (35.4) |
11 (16.9) |
||
|
Seamstress |
20 (46.5) |
13 (30.2) |
10 (23.3) |
||
|
Public servant |
5 (50.0) |
5 (50.0) |
0 (0.0) |
||
|
Other |
0 (0.0) |
1 (100) |
0 (0.0) |
||
The cross tabulation and bivariate chi square analysis performed showed that, there was difference in the prevalence of thinness as 9.4% of adolescents who reside in urban areas were thin compare to 5.4% of the adolescents who reside in rural areas. The study also revealed that similar prevalence of overweight/obese among urban (8.3%) and rural (7.9%) were observed. However, no statistically significant association between nutritional status (BMI) and the location of adolescents was observed.
The results also showed that 5.6% of the adolescents who were 16 years or older were overweight/obesity compared to 8.8% of the adolescents who were 15 years or below.
It was also observed that, the 10.8% of the adolescents who were from poor households were overweight/obese compared to 7.2% and 7.7% of the adolescents from average and rich households respectively. There was also no significant association between household size, frequency of consumption of energy dense food, mothers and fathers’ educational status and mothers and fathers’ occupation although there was an observed difference in the prevalence (Table 5).
|
Variable |
Categories |
BMI-for-Age Z-scores |
Test-Statistic |
||
|
Thinness |
Normal |
Overweight / Obese |
|||
|
Urban/Rural |
Urban |
26 (9.4) |
229 (82.4) |
23 (8.2) |
χ2 = 3.280 p = 0.194 |
|
Rural |
15 (5.4) |
241 (86.7) |
22 (7.9) |
||
|
Sex |
Male |
20 (7.4) |
230 (84.6) |
22 (8.1) |
χ2 = 0.000 p = 1.000 |
|
Female |
21 (7.3) |
240 (84.5) |
23 (8.1) |
||
|
Age |
<= 15 |
31 (7.2) |
363 (84.0) |
38 (8.8) |
χ2 = 1.344 p = 0.511 |
|
16+ |
10 (8.1) |
107 (86.3) |
7 (5.6) |
||
|
Individual Dietary Diversity Score |
Low |
12 (7.9) |
126 (83.4) |
13 (8.6) |
χ2 = 3.578 p = 0.466 |
|
Medium |
22 (9.1) |
201 (82.7) |
20 (8.2) |
||
|
High |
7 (4.3) |
143 (88.3) |
12 (7.4) |
||
|
Household size |
<= 3 |
2 (5.7) |
32 (91.4) |
1 (2.9) |
χ2 = 2.668 p = 0.615 |
|
4–6 |
15 (6.5) |
200 (85.8) |
18 (7.7) |
||
|
7+ |
24 (8.3) |
238 (82.6) |
26 (9.0) |
||
|
Wealth Index (SES) |
Poor |
12 (8.6) |
112 (80.6) |
15 (10.8) |
χ2 = 4.372 p = 0.358 |
|
Average |
29 (7.4) |
334 (85.4) |
28 (7.2) |
||
|
Rich |
0 (0.0) |
24 (92.3) |
2 (7.7) |
||
|
Frequency of consuming energy-dense foods |
Often |
14 (8.6) |
138 (84.7) |
11 (6.7) |
χ2 = 1.261 p = 0.868 |
|
Sometimes |
20 (6.5) |
260 (84.7) |
27 (8.8) |
||
|
Rarely |
7 (8.1) |
72 (83.8) |
7 (8.1) |
||
|
Level of education |
Primary |
11 (8.9) |
100 (81.3) |
12 (9.8) |
χ2 = 1.262 p = 0.532 |
|
Junior High School |
30 (6.9) |
370 (85.5) |
33 (7.6) |
||
|
Do you live with your parents or guardian |
Live with parent |
23 (7.3) |
261 (82.9) |
31 (9.8) |
χ2 = 2.989 p = 0.224 |
|
Live with guardian |
18 (7.5) |
209 (86.7) |
14 (5.8) |
||
|
Father's educational status |
Basic Education (Primary - JHS) |
3 (3.7) |
70 (85.4) |
9 (11.0) |
χ2 = 8.713 p = 0.190 |
|
Secondary |
8 (16.7) |
37 (77.1) |
3 (6.3) |
||
|
Tertiary |
4 (5.3) |
63 (84.0) |
8 (10.7) |
||
|
No education |
8 (7.3) |
91 (82.7) |
11 (10.0) |
||
|
Father's occupation |
Informal Occupation |
15 (7.2) |
173 (83.6) |
19 (9.2) |
χ2 = 0.818 p = 0.936 |
|
Formal Occupation |
5 (6.9) |
58 (80.6) |
9 (12.5) |
||
|
Other |
3 (8.3) |
30 (83.3) |
3 (8.3) |
||
|
Mother's educational status |
Basic Education (Primary - JHS) |
12 (11.8) |
80 (78.4) |
10 (9.8) |
χ2 = 6.300 p = 0.390 |
|
Secondary |
0 (0.0) |
18 (85.7) |
3 (14.3) |
||
|
Tertiary |
0 (0.0) |
9 (90.0) |
1 (10.0) |
||
|
No education |
11 (6.0) |
154 (84.6) |
17 (9.3) |
||
|
Mother's occupation |
Informal occupation |
23 (7.6) |
251 (82.6) |
30 (9.9) |
χ2 = 1.031 p = 0.905 |
|
Formal occupation |
0 (0.0) |
9 (90.0) |
1 (10.0) |
||
|
Other |
0 (0.0) |
1 (100.0) |
0 (0.0) |
||
A multinomial logistic regression analysis revealed that male adolescent were more than two (2) times (RRR: 2.24, 95% CI: 1.133–4.404, P-value: 0.02) more likely to engage in high physical activities than female adolescents. Adolescents whose fathers had up-to primary education only were about 87% less likely to engage in moderately physical activities than those whose fathers attended junior high school (RRR; 0.13, 95% CI; 0.0022–0.827, P-value; 0.03) (Table 6).
|
Physical Activity Level |
Variable |
Category |
RRR |
P>|Z| |
[95% Conf. Interval] |
|
|---|---|---|---|---|---|---|
|
Low |
location |
rural |
Ref |
|||
|
urban |
0.770 |
0.491 |
0.366 |
1.619 |
||
|
Body Mass Index |
Underweight |
0.812 |
0.684 |
0.297 |
2.218 |
|
|
Normal |
Ref |
|||||
|
Overweight |
0.871 |
0.75 |
0.373 |
2.034 |
||
|
sex |
female |
Ref |
||||
|
male |
0.357 |
0.571 |
0.503 |
1.461 |
||
|
age |
≤ 15 |
Ref |
||||
|
≥ 16 |
1.752 |
0.134 |
0.841 |
3.653 |
||
|
level of education |
primary |
0.691 |
0.328 |
0.330 |
1.450 |
|
|
Junior High School |
Ref |
|||||
|
Senior High School |
0.471 |
0.406 |
0.079 |
2.786 |
||
|
Fathers’ educational status |
Primary |
0.733 |
0.606 |
0.225 |
2.385 |
|
|
Junior High School |
1.470 |
0.434 |
0.561 |
3.855 |
||
|
Senior High School |
Ref |
|||||
|
Tertiary |
0.701 |
0.432 |
0.289 |
1.701 |
||
|
No education |
0.570 |
0.206 |
0.239 |
1.361 |
||
|
socio-economic status of parent |
poor |
1.010 |
0.974 |
0.557 |
1.833 |
|
|
Average |
Ref |
|||||
|
rich |
0.571 |
0.419 |
0.147 |
2.221 |
||
|
cons |
2.332 |
0.055 |
0.982 |
5.539 |
||
|
Moderate |
(Base outcome) |
|||||
|
High |
location |
rural |
Ref |
|||
|
urban |
0.992 |
0.986 |
0.387 |
2.539 |
||
|
Body Mass Index |
Underweight |
0.723 |
0.611 |
0.207 |
2.521 |
|
|
Normal |
Ref |
|||||
|
Overweight |
0.789 |
0.687 |
0.250 |
2.497 |
||
|
sex |
female |
Ref |
||||
|
male |
2.234 |
0.02 |
1.133 |
4.404 |
||
|
age |
≤ 15 |
Ref |
||||
|
≥ 16 |
2.144 |
0.083 |
0.904 |
5.086 |
||
|
level of education |
primary |
0.858 |
0.757 |
0.327 |
2.255 |
|
|
Junior High School |
Ref |
|||||
|
Senior High School |
0.000 |
0.982 |
0.000 |
|||
|
Fathers’ educational status |
Primary |
0.134 |
0.03 |
0.022 |
0.827 |
|
|
Junior High School |
0.568 |
0.358 |
0.171 |
1.894 |
||
|
Senior High School |
Ref |
|||||
|
Tertiary |
0.404 |
0.095 |
0.140 |
1.171 |
||
|
No education |
0.391 |
0.075 |
0.139 |
1.099 |
||
|
socio-economic status of parent |
poor |
0.617 |
0.258 |
0.267 |
1.425 |
|
|
Average |
Ref |
|||||
|
rich |
1.508 |
0.573 |
0.362 |
6.290 |
||
|
cons |
0.898 |
0.839 |
0.317 |
2.542 |
||
Generally, the prevalence of low physical activity stands at 48.4% among adolescents in both Kumbungu district and Tamale metropolis which is lower than the national prevalence of 87.9% (Global Status report on NCDs 2014).
The findings of this study also revealed a high rate of low physical activity among urban adolescents compared to rural adolescents. This could be due to largely geographical factors, high physical activity demanding lifestyle in rural areas. However, this observation was higher than the 29.1% and 25.2% reported in a study on rural- urban differences, overweight status and physical inactivity among US children aged 10–17 years (Liu et al., 2008; McCormack & Meendering 2016).
The rural – urban disparity in physical activity could be attributed to the fact that farming remains the main economic activity in the rural sector of the northern region and most of the rural adolescents engage themselves in it whereas urban adolescents tend to focus on less physically active endeavors such as sitting in stores of parents on week days and weekends. It could also be ascribed to the fact that majority of rural adolescent walk long distance to get to school where as urban adolescents are mostly transported through cars and motorbikes to school.
Findings from this study on body mass index (BMI) showed a combined overweight/obesity prevalence for all participants as 8.1 percent with the prevalence of thinness being 7.4 percent. Previous studies reported prevalence contrary to this finding. For example, research in Nigeria on the prevalence of overweight, obesity and thinness among urban school aged children reported a combined prevalence of overweight /obesity as 14.2 percent and thinness as 13.0 percent (Ene- Obong et al., 2012). The low prevalence of overweight/ obesity recorded in this study indicates that overweight/ obesity still remains lower in rural areas even though it is becoming a problem of public health concern worldwide.
This study found higher prevalence (55.2%) of consumption of energy dense foods among study participants. A similar study in Ghana revealed that Ghanaian adolescents consumed high energy foods (Nti Brown & Danquah, 2013).
Rathi and colleagues (2017) established that, adolescents in Kolkata frequently consumed energy dense, nutrient- poor foods and sugar – sweetened beverages and these puts them at risk of developing chronic degenerative diseases.
The study showed that 43.7 percent of adolescents consumed four to five varieties of food daily. 27.2 percent of the adolescents consumed less than three food items per day whereas 29.1 percent of the adolescents consumed more than six food items per day.
The low dietary diversity among urban adolescents could be ascribed to the patronage of western diet of fast foods which is mainly energy dense foods.
The current study could not establish any association between physical activity level and nutritional status (BMI) among study population. The finding from this study is at variance with the findings from Alkahtari et al. (2015) in Saudi Arabia which shows a significant relationship between physical activity level and BMI status of adolescents.
Findings from the present study is however in line with the findings from a similar study conducted by Bazhan et al. (2013) who found no statistical association between having normal BMI and engaging in physical activity.
The multinomial logistic regression established an association between physical activity level and sex of adolescents. The results showed male adolescents are more likely to have low levels of physical activity compared to female adolescents, which is different from Dodu and colleagues (2013), and Pelzer and Pengpid (2011) who found that girls were more likely to have low physical activity than boys.
The study observed that father’s occupation and mother’s educational status had significant effects on the physical activity levels of adolescents as adolescents whose fathers were formally employed had lower chances of being physically active compared to adolescents whose fathers were employed at the formal sector. This could be attributed to the fact that most formal employees have access to motorized means of transport and so limits the walk time to school of their children. The results on the other hand showed that, adolescents whose mothers had some level of formal education had lower chances of being physically inactive compared to adolescent whose mothers had no formal education.
The findings are supported by Wilson (2007) who suggested that adolescents from parents with high educational attainment and income indulged more in moderate and vigorous physical activities compared to adolescents from parents with low educational attainment and income level. Duncan and colleagues (2004) and McCormack & Meendering (2016). also established that adolescents from lower economic strata experience greater barriers to activity than adolescents from higher economic status.
Linda (2002) and Kirby, Levin & Inchley (2011) indicated that parents who show interest in their children’s activity levels increase the likelihood of children’s prolonged involvement in physical activity. Also, Muthuri et al (2016) revealed that adolescents whose mothers attained higher education level had lower chances of being physically active. Higher parental education was also shown to have negative influence on child and adolescent physical activity level in a study conducted by Ferreira and colleagues (2007).
The findings of this study should be interpreted with caution as cross sectional study design has inherent limitations such as recall and reporting bias. However, efforts were made in the present study to minimize the effect these bias on the findings by probing.
The study showed evidence of double burden of malnutrition as thinness and overweight/obesity rates were similar. Rural urban difference in physical activity level was also evident as adolescents in the rural areas were found to be more active than those in the urban settings.
It is important to strengthen physical education in schools particularly in urban settings in order to ensure sound growth and development and to reduce poor health and quality of life later in life. The school health programme in Ghana should also use behaviour modification communication to ensure sound lifestyle such as optimal dietary habits among adolescents particularly those in school.
WHO World Health Organization
RRR Relative Risk Ratio
BMI Body Mass Index
CI Confidence Interval
CVD Cardiovascular Disease
GHS Ghana Health Service
GSS Ghana Statistical Service
NCDs Non-communicable Diseases
SSA Sub-Saharan Africa
Ethical approval and consent to participate:
Ethical Review Committee (ERC) of Navrongo Health Research Centre granted ethical approval for this study with ethics approval ID: NHRCIRB350.
Informed Consent was sought from participants by first getting a verbal informed consent from their parents and then informed consent form was then signed by the adolescents who could read and write. For those who could not read-write, the informed consent form was thump printed after the information concerning the study was translated into the local language.
All the methods used in this study were performed in accordance with the relevant guidelines and regulations
Consent for publication:
Not applicable
Data and materials availability:
The dataset for this study is available on the University for Development Studies repository (udsspace repository) and can be access on the URL http://udsspace.uds.edu.gh/handle/123456789/3453 (physicalactivity2022)
Competing interest:
The authors have no competing interest to declare
Funding:
There was no funding for this research
Authors contribution:
A.A., R.Y. and Z.Y.I Conceived and executed the research.
R.Y. and Z.Y.I. analyzed the data. A.A., R.Y. and Z.Y.I drafted and proofread the manuscript
A.A., R.Y. and Z.Y.I approved the manuscript for publication
Acknowledgement:
Not applicable