Journal of Physical Activity and Health, 2016, 13, 168 -176 http://dx.doi.org/10.1123/jpah.2014-0454 © 2016 Human Kinetics, Inc.
ORIGINAL RESEARCH
Physical Activity Level and Associated Factors Among Higher Secondary School Students in Banke, Nepal: A Cross-Sectional Study Susan Paudel, Narayan Subedi, and Suresh Mehata Background: This study was carried out to assess physical activity level and identify associated factors among higher secondary school students in Banke district, Nepal. Methods: A school-based, cross-sectional descriptive study was conducted among 405 students studying in grades 11 and 12 in 7 higher secondary schools selected randomly. A self-administered questionnaire based on the International Physical Activity Questionnaire was used to measure physical activity level. Results: Only 5% of students were found to be inactive, and domestic and transport-related activities were major contributors to total physical activity score. No significant difference existed for total physical activity and domain-specific and activity-specific scores across different age groups when males and females were tested separately. Being male (P = .046), lower economic status (P = .026), living at a distance of less than 30 minutes (P = .007), walking/cycling to school (P < .001), and studying in government school (P < .001) were associated with increased physical activity scores on multivariate logistic regression analysis. Conclusion: Routine activities such as transport and household chores contributed greatly to total physical activity among students. This study highlights the need for physical activity promotion interventions at school addressing the associated factors and a need for greater focus on leisure-time physical activities. Keywords: total physical activity score, adolescents
Regular physical activity (PA) is recommended for prevention of noncommunicable diseases1,2 and improvement of overall health.3 Among children and adolescents, it reduces symptoms of depression and stress, improves cardiorespiratory and muscular fitness, improves bone health, and decreases levels of body fat.4 Lack of physical inactivity has been identified as the fourth leading risk factor for global mortality5,6 and is estimated to be responsible for 6% of coronary heart disease, 7% of type 2 diabetes, 10% of colon cancer, and 10% of breast cancer.7 Adolescence has been identified as the appropriate time for adoption of PA behaviors,8 which are important to prevent or delay onset of chronic diseases during adulthood. Despite this, participation in PA is seen to decline dramatically during this age group,4,9 and girls are less active than boys.10,11 Globally, 80.3% of adolescents 13 to 15 years old do not meet the recommended levels, which is much higher than the level of inactivity among adults.12 In India, 63.2% of adolescents did not meet the recommendation of 60 minutes of activity per day.11 PA behavior is determined by different interpersonal, individual, and environmental factors13; study of these determinants is crucial to develop interventions for prevention of noncommunicable diseases and promotion of PA. The growing prevalence of noncommunicable diseases along with their risk factors have become a major concern for the world, affecting all countries and segments of population.5 Nepal is also facing a surging burden of noncommunicable diseases that now account for half of the burden of disease.14–16 The STEPwise approach to Surveillance (STEPs) chronic disease risk-factors survey carried out in 2013 in Nepal reported 3.5% of people to be at low PA levels (< 600 metabolic equivalents [METs] minutes per week),17 which is slightly lower than the results from the same study conducted in 2007–08.18 A cross-sectional study conducted in Paudel ([email protected]) is with Curtin University of Technology, Perth, Western Australia. Subedi and Mehata are with the Nepal Public Health Foundation, Kathmandu, Nepal. 168
Dharan, Nepal, among adult males found that 56% were engaged in moderate to heavy PA.19 However, there is inadequate information about levels of PA and factors associated with the practice among different population subgroups. This study was carried out to explore PA practices and its associated factors among high school students in Banke district, Nepal.
Methods Study Area A cross-sectional study was carried in higher secondary schools (HSSs) in the Nepalgunj municipality of Banke district, Nepal, as in our other study.20 The total population of the district was 491,313, which was 1.85% of total population of the country.21 The school enrollment rate in the district was 95%.22 There are 46 Village Development Committees (VDCs) and one municipality (Nepalgunj) in Banke. A VDC is the smallest administrative division in a district. Nepalgunj municipality is approximately 530 km from the capital city and is located near the southern border with Uttar Pradesh state of India. The city is a business center for nearby districts and is developing fast because of relocation of migrants from the midhill region of Nepal. The most common forms of short-distance public transport within the city are bicycle and rickshaw. Minibuses and microbuses are also commonly used.
Sampling A multistage random sampling technique was used to select study participants from 19 HSSs in Nepalgunj: 6 government schools and 13 private schools. First, of the 19 schools, about one third (7 HSS: 3 government schools and 4 private schools) were selected at random. Permission was not obtained for data collection in 3 schools, and these were replaced by 3 other randomly selected ones. Second, one section from either grade 11 or grade 12 was randomly
Physical Activity Level Among Students in Nepal 169
selected from each sample school. Altogether, 5 sections of grade 11 and 2 sections of grade 12, consisting of total 415 students, were included in the study. The total number of students in the 7 schools was 1100. Ten questionnaires were excluded during data analysis because of inconsistency or missing information; hence, only 405 questionnaires were subjected to further analysis.
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Data Collection The International Physical Activity Questionnaire–long form (IPAQLF) was used to collect data on the PA levels of students. The IPAQ has been developed to effectively measure the PA level in a population aged 15–69 years.23 The authors chose the adult version instead of the adolescent version of IPAQ because approximately 40% of the 15- to 19- year-olds in Nepal are economically active.24 Many students work part-time while carrying out their studies. Activities carried out at school during break time were included within the leisure-time-PA domain, whereas transportation to and from school were included within the transport-related-PA domain. Data were collected from September 4 through October 2, 2013. The data were collected by a researcher and 2 research assistants who had intermediate degrees in health science after a 1-day orientation on the questionnaire and the data-collection process. Students were provided a brief orientation of about an hour on the purpose of the study, the process of filling out the questionnaire, and the types of PAs. PA show cards were used to facilitate recall of activities during the past 7 days. Students completed the questionnaires in the classrooms of their respective schools in an exam setting under the supervision of their teacher and at least one member of the study team. Classes of more than 40 students were divided into 2 separate groups. The response rate was 98%. We planned to exclude students who had any form of physical disability related to their hands or legs that interfered with being active, but no such students were found during data collection.
PA Measurement The IPAQ-LF has been found to have acceptable validity to measure participants’ PA levels.25–27 The test-retest reliability coefficients of the questionnaires were acceptable, with Spearman correlation coefficients of 0.79 (P < .0001) between 0 and 8 days.28 Activities carried out for more than 10 minutes at a time during the past 7 days were measured in the study. PA was measured in 4 activity domains: leisure-time activities, job-related activities, domestic activities, and transport-related activities. For the calculation of a person’s total energy expenditure from different types of activities, walking was counted as 3.3 METs, cycling as 6 METs, moderate activities as 4 METs, and vigorous activities as 8 METs. MET is the ratio of a person’s working metabolic rate relative to their 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−1·h−1.23
Measures PA Level. Total PA was classified as follows:23
1. Low PA = < 600 MET min/wk (not meeting World Health Organization (WHO) recommendation) 2. Moderate PA = 600–3000 MET-min/wk, including any combination of walking, moderate-intensity, and vigorous-intensity activities.
3. High PA = At least 3000 MET-min/wk, including any combination of walking, moderate-intensity, and vigorous-intensity activities. Independent Variables. The sociodemographic characteristics assessed in the study were age, place of residence, parents’ education, and occupation and family type. Place of residence has been categorized as urban and rural; urban denotes a municipality, whereas rural denotes the VDCs. The variable family type indicates the type of family in which the student was living, categorized as nuclear and joint/extended. A nuclear family consisted of father, mother, and their own children; a joint/extended family consisted of family members other than father, mother, and their children (eg, grandchild, daughter-in-law, or other relative). Likewise, occupation of parents included activities such as agriculture, paid jobs, smallscale business, and labor carried out for earning and livelihood, which was measured in nominal scale. Occupation was categorized as business, job, agriculture, foreign employment, labor, and other. Environmental factors consisted of variables such as type of school, distance to school, availability of playground in school, regularly available extracurricular activities, neighborhood walkability, and presence of playground or parks near home. Schools were classified as government and private. Government schools were those run by the government and had cheaper fees; private schools were run by individuals or institutions, mostly for profit, and usually had higher fees than government schools. Perceived social-support-related factors consisted of 2 variables: perceived family support and perceived social support. Respondents were asked whether their family/friends would support them if they wanted to be physically active, and perceived support was recorded as “Yes” or “No.”
Data Analysis Data were entered in EpiData (The EpiData Association, Odense, Denmark) and analysis was carried out using IBM SPSS (Version 16; IBM, Armonk, NY). Calculation of total PA score in MET-min/ wk was based on the guidelines for data processing and analysis of IPAQ.23 To avoid overreporting, we cleaned and truncated PA scores. Total PA score was calculated by combining all 4 domains and classifying the result as low, moderate, or high. The outcome variable was categorical for the percentage of students who were below the recommended cut-off points and continuous for other analyses. For descriptive analysis, domain-specific and activityspecific PA subscores and means, medians, standard deviations, and interquartile ranges were calculated. Normality of data was checked using histograms, frequency curves, and Shapiro-Wilk test. Means and standard deviations were calculated for normally distributed variables; medians and interquartile ranges were calculated for variables with nonnormal distribution. Differences across different age groups in total, domainspecific, and activity-specific PA scores that were nonnormally distributed were tested separately for males and females using the Kruskal-Wallis test. Random effects linear regression was used to examine whether sociodemographic, environmental, and perceivedsocial-support-related factors were associated with MET-minutes of total PA per week. First, single factors were entered in the analysis model as sole predictors of PA, and unadjusted regression coefficient, 95% confidence interval (CI), and P values were calculated. Then, sociodemographic, environmental, and perceived-socialsupport-related factors were entered in 3 different models to get adjusted statistics. Significance of association was tested by 95% CI, and P values of < .05 were taken as significant.
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170 Paudel, Subedi, and Mehata
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Results Table 1 presents descriptive characteristics and PA levels of study population stratified by gender. The study sample of 405 consisted of 227 males (56%) and 178 females (44%). Mean age of all students was 17 ± 1.2 years. Students living in a nuclear family were more common (68%), and slightly higher among females than among males (71% vs 66%). Regarding parental education, the majority of fathers had secondary level education (41.5%), whereas about half of the mothers (52.6%) were without any formal schooling. Paid employment was most the common occupation of fathers; more female students than male students had fathers with paid employment (54% vs 41%). The majority of mothers were housewives (42%; note that information about parental employment is not shown in Table 1) Nearly 60% students were from private schools, and the percentage was higher for females than for males (63% vs 58%). Walking distance to school was 30 minutes or less for 86% of females and 79% of males. About 80% (4 in 5) of students, both male and female, walked or cycled to school on most days. Nearly half of the students had no playground in their school, and 79% of students had no extracurricular activities on a regular basis. Nine in 10 students had walking or playing space around their homes, whereas less than half had playgrounds or parks near their homes that could be used for leisure-time-related PAs. Around 90% of the students perceived that their family and friends supported them; this percentage was slightly higher in females than in males. Only 23 (5.7%) students were involved in income-generating jobs besides their studies, and 4% were involved in PAs in course of their work. Regarding PA level, about 4% of females and 5% of males did not meet WHO recommendations of at least 600 MET-min/wk of PA, whereas 47% female students and 60% male students had high PA levels. Percentage contribution of different domains and activity types to total PA score by gender is shown in Table 2. Overall, domestic and gardening-related activities (38%) and transport-related activities (36%) were major contributors to total MET-minutes per week of PA accumulated by the students. Females were found to be more engaged in domestic activities (53%), whereas males were more engaged in transport-related activities (39%). Contribution of leisure-time PAs was 24% and was lower in females than in males (13% vs 32%). Moderate activities were the greatest contributors for both male and female students. Vigorous activities contributed only 3% in females but was 12% (4 times higher) among males. The work domain contributed only 2% of the total MET-minutes per week of PA accumulated by the students. Table 3 presents PA measures relative to age and gender. A Kruskal-Wallis test revealed that no significant difference existed among total PA scores, domain-specific scores, and activity-specific scores across different age groups when tested separately for males and females. In addition, median MET-minutes per week across domestic activities were greater and transport-related METs were lower in females than in males across all age-groups. Among males, excluding those aged 18, domestic-activity-specific MET-minutes per week were decreasing whereas transport-related METs were increasing with age. Furthermore, engagement of females in leisuretime activities and walking was lower than in males across all age groups with the exception of those aged 15 and 20, respectively. There were large differences in terms of total PA and engagement in vigorous activities between male and female students throughout all age groups.
The linear regression analysis between sociodemographic, environment, and perceived-social-support-related factors is shown in Table 4. Analysis revealed that being male and of lower economic status was associated with increased PA. Likewise, students living at a distance of more than 30 minutes from their school and those who walked or cycled to school were more likely to have high MET-minutes per week of PA than those who lived near to school and those using vehicles. On the other hand, students studying in private school did less PA than those who studied in government schools. Surprisingly, those without support from friends were more likely to have low PA scores. However, no significant association was found with age of respondent, place of residence, parent’s education, occupation, presence of playground in school or near home, provision of extracurricular activities at school, and perceived family support.
Discussion This study was carried out to assess PA level and associated factors among HSS students in Banke district, Nepal. Five percent of students had low PA levels and did not meet the WHO recommendation of at least 600 MET-min/wk, whereas more than half (54%) had high PA levels. The WHO STEPs survey of noncommunicable diseases risk factors in Nepal found that 5.5% of the population had low levels of PA,18 which is nearly equal to the level of inactivity shown by the current study, but higher percentages of inactivity have been reported by studies conducted in high- and middle-income countries.29,30 Worldwide, 31% of adults (≥ 15 years old) are physically inactive, but the rate varies among regions and countries.12 In this study, domestic and transport-related activities were found to be the major contributors to total PA score, whereas recreation-related activities contributed only 24% of total METminutes per week of PA gained by the students. A similar study conducted in Nepal found a low contribution of transport- and leisure-time activities to total PA score.31 The reason for the greater engagement in transport-related activities found in the current study could be that cycles are more commonly used in terai districts in Nepal, including the study area, for short-distance transportation. Findings from other studies have also shown that PA in household and transport-related domains are the most common in low-income countries.13 The contribution of leisure-time activities and vigorous activities was even lower among females than among males in the study. This result is consistent with the findings from other studies that men are more likely to participate in vigorous-intensity activities than females are.12,20,32,33 Females tend to do light work and spent more time in household chores, whereas males spent more time in outdoor sports than females, which might be due to family restrictions placed on females for spending longer hours outside home along with the burden of household work. The study found a low contribution of work-related activities because only a few students were doing part-time jobs in addition to their studies and, among these, some were involved in sedentary jobs such as teaching junior students. However, the national data show a larger percentage (40%) to be economically active.24 This deviation may exist because more than half of the study respondents were from private schools, where there is greater pressure on studies. In the Nepalese context, it is usually the rich families that send their students to these schools, and hence the students are less likely to be engaged in part-time jobs. Some students helped their parents at farm, which is included in domestic-work domain in the study.
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Table 1 Descriptive Characteristics and Physical Activity Level of Study Population n (%) Characteristic
Frequency (%)
Female
Male
P
Rural
165 (40.7)
74 (41.6)
91 (40.0)
.763
Urban
240 (59.3)
104 (58.4)
136 (60.0)
Nuclear
275 (68.0)
126 (71.0)
149 (65.6)
Joint/Extended
130 (32.0)
52 (29.0)
78 (34.4)
Private
244 (60.2)
113 (63.5)
131 (57.7)
Government
161 (39.8)
65 (36.5)
96 (42.3)
≤ 30 min
333 (82.2)
153 (86.0)
180 (79.3)
> 30 min
72 (17.8)
25 (14.0)
47 (20.7)
Walking/cycling
347 (85.7)
146 (82.0)
201 (88.5)
Motorcycle/bus/rickshaw
58 (14.3)
32 (18.0)
26 (11.5)
Yes
220 (54.3)
95 (53.4)
125 (55.0)
No
185 (45.7)
83 (46.6)
102 (45.0)
Yes
85 (21.0)
43 (24.0)
42 (18.5)
No
320 (79.0)
135 (76.0)
185 (81.5)
Yes
368 (90.9)
165 (92.7)
203 (89.4)
No
37 (9.1)
13 (7.3)
24 (10.6)
Yes
188 (46.4)
83 (46.6)
105 (46.3)
No
217 (53.6)
95 (53.4)
122 (53.7)
Yes
370 (91.4)
166 (93.3)
204 (90.0)
No
35 (8.6)
12 (6.7)
23 (10.0)
Yes
355 (87.7)
162 (91.0)
193 (85.0)
No
50 (12.3)
16 (9.0)
34 (15.0)
19 (4.7)
7 (3.9)
12 (5.3)
Moderate
167 (41.2)
88 (49.4)
79 (34.8)
High
219 (54.1)
83 (46.6)
136 (59.9)
Sociodemographic factors Place of residence
Family type .271
Environmental factors
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Type of school .239
Distance to school .082
Mode of transport to school .063
Playground in school .734
Provision of extracurricular activities .165
Neighborhood walkability .257
Presence of playground/parks near home .940
Perceived social-support-related factors Perceived family support .228
Perceived peer support .069
Level of physical activity Low
.012
Note. Total respondents: n = 405; female respondents: n = 178; male respondents: n = 227.
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171
172
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38 24
Domestic and gardening Leisure time
64 8
Moderate Vigorous
Median, first and third quartile all are zero.
Note. Data are median and Interquartile range.
a
Domestic and yard activities Male Female Transportation-related activities Male Female Leisure-time activities Male Female Walking Male Female Moderate activities Male Female Vigorous activities Male Female Total Male Female
Variable and gender
3
75
22
13
53
33
1
Female (%)
12
56
32
32
26
39
3
Male (%)
780 (420–1410) 1590 (650–1590) 1188 (693–1980) 808 (528–1440 1236 (471–2829) 240 (0–679) 1089 (627–1311) 594 (66–1188) 1980 (1277–3592) 2310 (1245–2310) 0 (0–1320) 0a 3999 (2470–6461) 3120 (1885–4380)
693 (396–3780) 594 (561–1638) 693 (49–2079) 960 (0–1683) 1039 (396–1782) 594 (396–1588) 1840 (1260–4700) 2520 (630–3240) 0 (0–480) 0 (0–320) 3570 (2226–5378) 3314 (2047–5616)
3727 (1837–5327) 2295 (1354–4203)
0 (0–840) 0a
2105 (620–3780) 1755 (765–3885)
792 (280–1782) 297 (0–792)
742 (303–2162) 0 (0–476)
1125 (594–2160) 720 (358–1188)
551 (90–1150) 1150 (630–2520)
17-year-olds
3783 (1860–6292) 3428 (1740–5877)
0 (0–1680) 0a
2520 (630–4020) 1955 (1477–4938)
693 (132–1320) 495 (0–1336)
840 (99–2640) 0 (0–445)
1228 (594–2520) 1080 (594–1354)
700 (280–1432) 1785 (562–2520)
18-year-olds
5270 (2115–6839) 3930 (1455–5529)
0a 0a
3320 (840–4275) 2940 (1455–3945)
1287 (0–1551) 792 (0–1683)
636 (0–1984) 0 (0–594)
1413 (0–2214) 792 (0–891)
555 (0–3420) 2940 (1455–3585)
19-year-olds
Median Domain-Specific MET-minutes per week (Interquartile Deviation) 16-year-olds
1260 (760–1380) 1760 (592–2520)
15-year-olds
Table 3 Physical Activity Level Grouped by Age and Gender
28
Walking
Activity specific
2 36
Transport related
Total (%)
Work related
Domain specific
Characteristic
Table 2 Percentage Contribution to Total Physical Activity Score by Gender
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.666 .294
.060 .258
320 (0–1290) 0 (0–480) 3780 (2769–5544) 3138 (2061–4788)
.496 .717
.311 .347
.497 .070
.824 .493
.241 .414
P
3102 (1890–3780) 2060 (1095–3052)
247 (41–1138) 668 (198–1707)
697 (0–1792) 305 (0–1188)
1743 (1080–2160) 891 (294–2160)
675 (581–1620) 1020 (600–2040)
20-year-olds
Table 4 Linear Regression Coefficients for MET-Minutes per Week of Total Physical Activity Unadjusted
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Characteristic Sociodemographic factors Age of student 15–17 years 18–20 years Sex Female Male Place of residence Rural Urban Family type Nuclear Joint/Extended Father’s education Illiterate Literate Mother’s education Illiterate Literate Father’s occupation Business Paid employment Agriculture Mother’s occupation Paid employment Agriculture Housewife Economic status Low Medium High Environmental factors Type of school Government Private Distance to school ≤ 30 min > 30 min Mode of transport to school Walking/cycling Motorcycle/bus/rickshaw Playground in school No Yes Provision of extracurricular activities No Yes
Adjusted
b
95% CI
p
b
95% CI
p
388
Reference –181 to 956
.181
145
Reference –453 to 743
.633
810
Reference 315 to 1305
.001
545
Reference 10 to 1080
.046
–186
Reference –693 to 320
.47
–187
Reference –697 to 322
.470
80
Reference –453 to 613
.767
15
Reference –523 to 554
.955
137.1
Reference –416 to 690
.626
400
Reference –217 to 1018
.203
–425
Reference –922 to 72
.093
–257
Reference –842 to 328
.388
–464 544
Reference –960 to 31 –12 to 1099
.067 .055
–410 –178
Reference –1025 to 204 –977 to 621
.190 .662
420 –268
Reference –126 to 967 –772 to 236
.131 .297
40 –9
Reference –688 to 768 –616 to 596
.913 .974
128 –875
Reference –399 to 656 –1396 to –354
.633 .001
–226 –812
Reference –891 to 439 –1526 to –97
.505 .026
–1231
Reference –1725 to –737
< .001
–1158
Reference –1642 to –674
< .001
344
Reference –306 to 994
.299
863
Reference 233 to 1494
.007
–1807
Reference –2495 to –1118
< .001
–1758
Reference –2450 to –1066
< .001
359
Reference –140 to 857
.158
469
Reference –11 to 948
.055
170
Reference –441 to 780
.585
151
Reference –429 to 730
.609 (continued)
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174 Paudel, Subedi, and Mehata
Table 4 (continued) Unadjusted
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Characteristic Neighborhood walkability No Yes Playground/parks near home No Yes Perceived social-support-related factors Perceived family support Yes No Perceived peer support Yes No
Adjusted
b
95% CI
p
b
95% CI
p
–449
Reference –1312 to 413
.307
–535
Reference –1374 to 303
.210
230
Reference –269 to 728
.365
404
Reference –77 to 886
.100
–279
Reference –1164 to 607
.536
–453
Reference –1349 to 443
.321
764
Reference 11 to 1516
.047
832
Reference 67 to 1598
.033
Note. Adjusted for age, sex, family type, place of residence, economic status, father’s education an occupation, mother’s education and occupation, distance to school, mode of transport to school, playground in school, extracurricular activities at school, neighborhood walkability, playground/parks near home, perceived family support, perceived peer support. CI, confidence interval.
Regarding peer support, a negative association was found between support from friends and levels of engagement in PA, but an increase in PA with increased support from family and friends has been shown by other studies.4,34–38 Peer support is more important for leisure-time activities than for transport-related or domestic activities. In the context of Nepal, engagement in leisure-time PA is very low, as shown by the current study as well as other studies.20 Consequently, having peer support still might not have been associated with increased PA. Additional research is required to further explore this finding. We found that students in government schools were more likely to be engaged in PAs than students studying in private schools. This could possibly be because government schools in Nepal have comparatively more space, and almost all of them have large playgrounds. Furthermore, it was observed during field work for the current study that many private schools were run in small areas containing only school buildings, with very limited open space. Another possible reason could be more study pressure on students studying in private schools, resulting in less leisure time for games and sports. A systematic review conducted on environmental correlates of PA has also shown that the likelihood of PA is higher in public schools than private schools.9 Likewise, students who walked or cycled to school were more likely to have high PA levels compared with those who used a motorcycle, bus, or rickshaw; cycling and walking contributed a large share of total METs gained by the students, and more than 85% of the study participants cycled or walked to school every day. Significant association between age and PA was not found in the study. Age was also not significant in the study conducted in Iran30 but findings from a study conducted in Poland showed PA decreases among girls and PA increases among boys with age.29 A systematic review conducted on predicting factors of PA also found age and sex to be the most consistent demographic correlates of PA behavior in adolescents.35 No significant association existed between PA levels and neighborhood walkability and the presence of parks or playgrounds near home, although significant association has been shown by other studies conducted among different age groups.9,13,34,39,40
Strengths and Limitations Random selection of schools and sections in schools is one of the strongest aspects of the study. Furthermore, using a valid questionnaire, we have examined various sociodemographic, environmental, and perceived-social-support-related factors that have been found to be associated with PA among high school students. In addition, the use of IPAQ-LF has given a detailed breakdown of total PA scores across different domains and activity types. The findings of the study will add to the existing knowledge about PA levels and age and gender difference and correlates in Nepal and other developing countries with similar context. Sufficient attention has been paid to minimize recall bias and overreporting in the study. Despite this, the use of self-administered questionnaire and the use of adult version of IPAQ instead of the adolescent version remain a potential limitation. The cross-sectional nature of the study allows the findings to be used to examine associations but limits drawing inferences about causation. Because this study used the adult version of IPAQ, activities carried during physical education classes and recess have been included in the leisure-time-activity domain; this would have been included separately as school-related PA if the adolescent version had been used. This might have caused overreporting of the leisure time domain, although no significant effects are expected in the total PA score. Future studies using the adolescent version can shed further light on this.
Conclusion The study found a high prevalence of PA among high school students. Meanwhile, the prevalence of inactivity, which was 5%, is also quite high considering the setting in which the study was conducted. Total PA score was largely contributed by routine work rather than by recreation-related activities. In addition, engagement of females in such activities was much lower than in males. Further research needs to be carried out incorporating all determinates of PA as identified in the ecological model, and there is also a need to address the determinants of inactivity.
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Acknowledgments We thank Ritu Prasad Gartoulla, Amod Kumar Poudyal, and Khadga Bahadur Shrestha for their critical comments while we carried out the research.
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References 1. World Health Organization. Global Strategy on Diet, Physical Activity and Health. Geneva, Switzerland: World Health Organization; 2004. 2. Mathers C, Stevens G, Mascarenhas M. Global Health Risks: Mortality and Burden of Disease Attributable to Selected Major Risks. Geneva, Switzerland: World Health Organization; 2009. 3. Whitt-Glover MC, Taylor WC, Floyd MF, Yore MM, Yancey AK, Matthews CE. Disparities in physical activity and sedentary behaviors among US children and adolescents: prevalence, correlates, and intervention implications. J Public Health Policy. 2009;30(Suppl 1): S309–S334. PubMed doi:10.1057/jphp.2008.46 4. Lowry R, Lee SM, Fulton JE, Demissie Z, Kann L. Obesity and other correlates of physical activity and sedentary behaviors among US high school students. J Obes. 2013;2013:276318. PubMed doi:10.1155/2013/276318 5. World Health Organization. Global Recommendations for Physical Activity for Health. Geneva, Switzerland: World Health Organization; 2010. 6. Kohl HW 3rd, Craig CL, Lambert EV, et al. The pandemic of physical inactivity: global action for public health. Lancet. 2012;380:294–305. PubMed doi:10.1016/S0140-6736(12)60898-8 7. Lee IM, Shiroma EJ, Lobelo F, Puska P, Blair SN, Katzmaryk PT. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012;380:219–229. PubMed doi:10.1016/S0140-6736(12)61031-9 8. Majeed BM, Kubaisy WA, Hameed AAA. Knowledge, atti-
tudes, and practices of adolescent school girls towards dietary habits and physical activity 2003.
9. de Vet E, de Ridder DT, de Wit JB. Environmental correlates of physical activity and dietary behaviours among young people: a systematic review of reviews. Obes Rev. 2011;12:e130–e142. PubMed doi:10.1111/j.1467-789X.2010.00784.x 10. Saunders J, Hume C, Timperio A, Salmon J. Cross-sectional and longitudinal associations between parenting style and adolescent girls’ physical activity. Int J Behav Nutr Phys Act. 2012;9:141. PubMed doi:10.1186/1479-5868-9-141 11. Guthold R, Cowan MJ, Autenrieth CS, Kann L, Riley LM. Physical activity and sedentary behavior among schoolchildren: a 34-country comparison. J Pediatr. 2010;157:43–49. doi:10.1016/j. jpeds.2010.01.019 12. Hallal PC, Andersen LB, Bull FC, Guthold R, Haskell W, Ekelund U. Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet. 2012;380:247–257. PubMed doi:10.1016/S01406736(12)60646-1 13. Bauman AE, Reis RS, Sallis JF, Wells JC, Loos RJ, Martin BW. Correlates of physical activity: why are some people physically active and others not? Lancet. 2012;380(9838):258–271. PubMed doi:10.1016/ S0140-6736(12)60735-1 14. Ministry of Health and Population. Nepal Health Sector Program Implementation Plan II (NHSP-IP 2). Kathmandu, Nepal: Ministry of Health and Population; 2010 15. Nepal Health Research Council. Prevalence of Non-communicable Disease in Nepal: Hospital Based Study. Kathmandu, Nepal: Nepal Health Research Council; 2010. 16. Department of Health Services. Annual Report. Kathmandu, Nepal: Government of Nepal; 2011/12. 17. Nepal Health Research Council. WHO STEPS Surveillance: NonCommunicable Disease Risk Factors Survey Fact Sheet. Kathmandu, Nepal: Ministry of Health and Population; 2013. 18. Society for Local Integrated Development (SOLID) Nepal. WHO STEPS Surveillance: Non-Communicable Disease Risk Factors Survey. Kathmandu, Nepal: Ministry of Health and Population; 2008.
19. Vaidya AK, Pokharel PK, Nagesh S, Karki P, Kumar S, Majhi S. Association of obesity and physical activity in adult males of Dharan, Nepal. Kathmandu Univ Med J (KUMJ). 2006;4:192–197. PubMed 20. Paudel S, Subedi N, Bhandari R, Bastola R, Niroula R, Poudyal AK. Estimation of leisure time physical activity and sedentary behaviour among school adolescents in Nepal. BMC Public Health. 2014;14:637. PubMed doi:10.1186/1471-2458-14-637 21. Central Bureau of Statistics. National Population and Housing Census 2011. Kathmandu, Nepal: National Planning Commission; 2012. 22. Branch Office-Banke CBS. District Profile-Banke, Central Bureau of Statistics. CBS; 2005/06. 23. IPAQ Group. Guidelines for Data Processing and Analysis of the International Physical Activity Questionnaire (IPAQ) – Short and Long Forms. https://sites.google.com/site/theipaq/scoring-protocol/ scoring_protocol.pdf. Published November 2005. Updated November 4, 2010. Accessed November 10, 2013. 24. Ministry of Health and Population. Nepal Adolescent and Youth Survey 2010/11. Kathmandu, Nepal: Ministry of Health and Population; 2012. 25. Boon RM, Hamlin MJ, Steel GD, Ross JJ. Validation of the New Zealand Physical Activity Questionnaire (NZPAQ-LF) and the International Physical Activity Questionnaire (IPAQ-LF) with accelerometry. Br J Sports Med. 2010;44:741–746. PubMed doi:10.1136/ bjsm.2008.052167 26. Hagströmer M, Oja P, Sjöström M. The International Physical Activity Questionnaire (IPAQ): a study of concurrent and construct validity. Public Health Nutr. 2006;9:755–762. PubMed doi:10.1079/ PHN2005898 27. Graff-Iversen S, Anderssen SA, Holme IM, Jenum AK, Raastad T. An adapted version of the long International Physical Activity Questionnaire (IPAQ-L): construct validity in a low-income, multiethnic population study from Oslo, Norway. Int J Behav Nutr Phys Act. 2007;4:13. PubMed doi:10.1186/1479-5868-4-13 28. Maddison R, Ni Mhurchu C, Jiang Y, et al. International Physical Activity Questionnaire (IPAQ) and New Zealand Physical Activity Questionnaire (NZPAQ): a doubly labelled water validation. Int J Behav Nutr Phys Act. 2007;4:62. PubMed doi:10.1186/1479-58684-62 29. Bergier J, Kapka-Skrzypczak L, Bilinski P, Paprzycki P, Wojtyla A. Physical activity of Polish adolescents and young adults according to IPAQ: a population based study. Ann Agric Environ Med. 2012;19:109–115. PubMed 30. Shokrvash B, Majlessi F, Montazeri A, et al. Correlates of physical activity in adolescence: a study from a developing country. Glob Health Action. 2013;6:20327. PubMed doi:10.3402/gha.v6i0.20327 31. Vaidya A, Krettek A. Physical activity level and its sociodemographic correlates in a peri-urban Nepalese population: a cross-sectional study from the Jhaukhel-Duwakot health demographic surveillance site. Int J Behav Nutr Phys Act. 2014;11(1):39. PubMed doi:10.1186/14795868-11-39 32. Al-Hazzaa HM, Abahussain NA, Al-Sobayel HI, Qahwaji DM, Musaiger AO. Physical activity, sedentary behaviors and dietary habits among Saudi adolescents relative to age, gender and region. Int J Behav Nutr Phys Act. 2011;8:140. PubMed doi:10.1186/14795868-8-140 33. Allafi A, Al-Haifi AR, Al-Fayez MA, et al. Physical activity, sedentary behaviours and dietary habits among Kuwaiti adolescents: gender differences. Public Health Nutr. 2014;17:2045–2052. PubMed doi:10.1017/S1368980013002218 34. Physical activity. Healthy People 2020 Web site. http://www.healthypeople.gov/2020/topicsobjectives2020/overview.aspx?topicid=33. Accessed July 18 2013. 35. Park H, Kim N. Predicting factors of physical activity in adolescents: a systematic review. Asian Nurs Res (Korean Soc Nurs Sci). 2008;2:113–128. PubMed doi:10.1016/S1976-1317(08)60035-3 36. Motl RW, Dishman RK, Saunders RP, Dowda M, Pate RR. Perceptions of physical and social environment variables and self-efficacy as correlates of self-reported physical activity among adolescent girls. J Pediatr Psychol. 2007;32:6–12. PubMed doi:10.1093/jpepsy/ jsl001
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39. Solomon E, Rees T, Ukoumunne OC, Metcalf B, Hillsdon M. Personal, social, and environmental correlates of physical activity in adults living in rural south-west England: a cross-sectional analysis. Int J Behav Nutr Phys Act. 2013;10:129. PubMed doi:10.1186/1479-5868-10-129 40. Humpel N, Owen N, Leslie E. Environmental factors associated with adults’ participation in physical activity: a review. Am J Prev Med. 2002;22:188–199. PubMed doi:10.1016/S0749-3797(01)00426-3
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37. Ferreira I, van der Horst K, Wendel-Vos W, Kremers S, van Lenthe FJ, Brug J. Environmental correlates of physical activity in youth - a review and update. Obes Rev. 2007;8:129–154. PubMed doi:10.1111/j.1467789X.2006.00264.x 38. Seabra AF, Mendonca DM, Thomis MA, Malina RM, Maia JA. Correlates of physical activity in Portuguese adolescents from 10 to 18 years. Scand J Med Sci Sports. 2011;21:318–323. PubMed doi:10.1111/j.1600-0838.2009.01030.x
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ORIGINAL RESEARCH
Physical Activity Level and Associated Factors Among Higher Secondary School Students in Banke, Nepal: A Cross-Sectional Study Susan Paudel, Narayan Subedi, and Suresh Mehata Background: This study was carried out to assess physical activity level and identify associated factors among higher secondary school students in Banke district, Nepal. Methods: A school-based, cross-sectional descriptive study was conducted among 405 students studying in grades 11 and 12 in 7 higher secondary schools selected randomly. A self-administered questionnaire based on the International Physical Activity Questionnaire was used to measure physical activity level. Results: Only 5% of students were found to be inactive, and domestic and transport-related activities were major contributors to total physical activity score. No significant difference existed for total physical activity and domain-specific and activity-specific scores across different age groups when males and females were tested separately. Being male (P = .046), lower economic status (P = .026), living at a distance of less than 30 minutes (P = .007), walking/cycling to school (P < .001), and studying in government school (P < .001) were associated with increased physical activity scores on multivariate logistic regression analysis. Conclusion: Routine activities such as transport and household chores contributed greatly to total physical activity among students. This study highlights the need for physical activity promotion interventions at school addressing the associated factors and a need for greater focus on leisure-time physical activities. Keywords: total physical activity score, adolescents
Regular physical activity (PA) is recommended for prevention of noncommunicable diseases1,2 and improvement of overall health.3 Among children and adolescents, it reduces symptoms of depression and stress, improves cardiorespiratory and muscular fitness, improves bone health, and decreases levels of body fat.4 Lack of physical inactivity has been identified as the fourth leading risk factor for global mortality5,6 and is estimated to be responsible for 6% of coronary heart disease, 7% of type 2 diabetes, 10% of colon cancer, and 10% of breast cancer.7 Adolescence has been identified as the appropriate time for adoption of PA behaviors,8 which are important to prevent or delay onset of chronic diseases during adulthood. Despite this, participation in PA is seen to decline dramatically during this age group,4,9 and girls are less active than boys.10,11 Globally, 80.3% of adolescents 13 to 15 years old do not meet the recommended levels, which is much higher than the level of inactivity among adults.12 In India, 63.2% of adolescents did not meet the recommendation of 60 minutes of activity per day.11 PA behavior is determined by different interpersonal, individual, and environmental factors13; study of these determinants is crucial to develop interventions for prevention of noncommunicable diseases and promotion of PA. The growing prevalence of noncommunicable diseases along with their risk factors have become a major concern for the world, affecting all countries and segments of population.5 Nepal is also facing a surging burden of noncommunicable diseases that now account for half of the burden of disease.14–16 The STEPwise approach to Surveillance (STEPs) chronic disease risk-factors survey carried out in 2013 in Nepal reported 3.5% of people to be at low PA levels (< 600 metabolic equivalents [METs] minutes per week),17 which is slightly lower than the results from the same study conducted in 2007–08.18 A cross-sectional study conducted in Paudel ([email protected]) is with Curtin University of Technology, Perth, Western Australia. Subedi and Mehata are with the Nepal Public Health Foundation, Kathmandu, Nepal. 168
Dharan, Nepal, among adult males found that 56% were engaged in moderate to heavy PA.19 However, there is inadequate information about levels of PA and factors associated with the practice among different population subgroups. This study was carried out to explore PA practices and its associated factors among high school students in Banke district, Nepal.
Methods Study Area A cross-sectional study was carried in higher secondary schools (HSSs) in the Nepalgunj municipality of Banke district, Nepal, as in our other study.20 The total population of the district was 491,313, which was 1.85% of total population of the country.21 The school enrollment rate in the district was 95%.22 There are 46 Village Development Committees (VDCs) and one municipality (Nepalgunj) in Banke. A VDC is the smallest administrative division in a district. Nepalgunj municipality is approximately 530 km from the capital city and is located near the southern border with Uttar Pradesh state of India. The city is a business center for nearby districts and is developing fast because of relocation of migrants from the midhill region of Nepal. The most common forms of short-distance public transport within the city are bicycle and rickshaw. Minibuses and microbuses are also commonly used.
Sampling A multistage random sampling technique was used to select study participants from 19 HSSs in Nepalgunj: 6 government schools and 13 private schools. First, of the 19 schools, about one third (7 HSS: 3 government schools and 4 private schools) were selected at random. Permission was not obtained for data collection in 3 schools, and these were replaced by 3 other randomly selected ones. Second, one section from either grade 11 or grade 12 was randomly
Physical Activity Level Among Students in Nepal 169
selected from each sample school. Altogether, 5 sections of grade 11 and 2 sections of grade 12, consisting of total 415 students, were included in the study. The total number of students in the 7 schools was 1100. Ten questionnaires were excluded during data analysis because of inconsistency or missing information; hence, only 405 questionnaires were subjected to further analysis.
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Data Collection The International Physical Activity Questionnaire–long form (IPAQLF) was used to collect data on the PA levels of students. The IPAQ has been developed to effectively measure the PA level in a population aged 15–69 years.23 The authors chose the adult version instead of the adolescent version of IPAQ because approximately 40% of the 15- to 19- year-olds in Nepal are economically active.24 Many students work part-time while carrying out their studies. Activities carried out at school during break time were included within the leisure-time-PA domain, whereas transportation to and from school were included within the transport-related-PA domain. Data were collected from September 4 through October 2, 2013. The data were collected by a researcher and 2 research assistants who had intermediate degrees in health science after a 1-day orientation on the questionnaire and the data-collection process. Students were provided a brief orientation of about an hour on the purpose of the study, the process of filling out the questionnaire, and the types of PAs. PA show cards were used to facilitate recall of activities during the past 7 days. Students completed the questionnaires in the classrooms of their respective schools in an exam setting under the supervision of their teacher and at least one member of the study team. Classes of more than 40 students were divided into 2 separate groups. The response rate was 98%. We planned to exclude students who had any form of physical disability related to their hands or legs that interfered with being active, but no such students were found during data collection.
PA Measurement The IPAQ-LF has been found to have acceptable validity to measure participants’ PA levels.25–27 The test-retest reliability coefficients of the questionnaires were acceptable, with Spearman correlation coefficients of 0.79 (P < .0001) between 0 and 8 days.28 Activities carried out for more than 10 minutes at a time during the past 7 days were measured in the study. PA was measured in 4 activity domains: leisure-time activities, job-related activities, domestic activities, and transport-related activities. For the calculation of a person’s total energy expenditure from different types of activities, walking was counted as 3.3 METs, cycling as 6 METs, moderate activities as 4 METs, and vigorous activities as 8 METs. MET is the ratio of a person’s working metabolic rate relative to their 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−1·h−1.23
Measures PA Level. Total PA was classified as follows:23
1. Low PA = < 600 MET min/wk (not meeting World Health Organization (WHO) recommendation) 2. Moderate PA = 600–3000 MET-min/wk, including any combination of walking, moderate-intensity, and vigorous-intensity activities.
3. High PA = At least 3000 MET-min/wk, including any combination of walking, moderate-intensity, and vigorous-intensity activities. Independent Variables. The sociodemographic characteristics assessed in the study were age, place of residence, parents’ education, and occupation and family type. Place of residence has been categorized as urban and rural; urban denotes a municipality, whereas rural denotes the VDCs. The variable family type indicates the type of family in which the student was living, categorized as nuclear and joint/extended. A nuclear family consisted of father, mother, and their own children; a joint/extended family consisted of family members other than father, mother, and their children (eg, grandchild, daughter-in-law, or other relative). Likewise, occupation of parents included activities such as agriculture, paid jobs, smallscale business, and labor carried out for earning and livelihood, which was measured in nominal scale. Occupation was categorized as business, job, agriculture, foreign employment, labor, and other. Environmental factors consisted of variables such as type of school, distance to school, availability of playground in school, regularly available extracurricular activities, neighborhood walkability, and presence of playground or parks near home. Schools were classified as government and private. Government schools were those run by the government and had cheaper fees; private schools were run by individuals or institutions, mostly for profit, and usually had higher fees than government schools. Perceived social-support-related factors consisted of 2 variables: perceived family support and perceived social support. Respondents were asked whether their family/friends would support them if they wanted to be physically active, and perceived support was recorded as “Yes” or “No.”
Data Analysis Data were entered in EpiData (The EpiData Association, Odense, Denmark) and analysis was carried out using IBM SPSS (Version 16; IBM, Armonk, NY). Calculation of total PA score in MET-min/ wk was based on the guidelines for data processing and analysis of IPAQ.23 To avoid overreporting, we cleaned and truncated PA scores. Total PA score was calculated by combining all 4 domains and classifying the result as low, moderate, or high. The outcome variable was categorical for the percentage of students who were below the recommended cut-off points and continuous for other analyses. For descriptive analysis, domain-specific and activityspecific PA subscores and means, medians, standard deviations, and interquartile ranges were calculated. Normality of data was checked using histograms, frequency curves, and Shapiro-Wilk test. Means and standard deviations were calculated for normally distributed variables; medians and interquartile ranges were calculated for variables with nonnormal distribution. Differences across different age groups in total, domainspecific, and activity-specific PA scores that were nonnormally distributed were tested separately for males and females using the Kruskal-Wallis test. Random effects linear regression was used to examine whether sociodemographic, environmental, and perceivedsocial-support-related factors were associated with MET-minutes of total PA per week. First, single factors were entered in the analysis model as sole predictors of PA, and unadjusted regression coefficient, 95% confidence interval (CI), and P values were calculated. Then, sociodemographic, environmental, and perceived-socialsupport-related factors were entered in 3 different models to get adjusted statistics. Significance of association was tested by 95% CI, and P values of < .05 were taken as significant.
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Results Table 1 presents descriptive characteristics and PA levels of study population stratified by gender. The study sample of 405 consisted of 227 males (56%) and 178 females (44%). Mean age of all students was 17 ± 1.2 years. Students living in a nuclear family were more common (68%), and slightly higher among females than among males (71% vs 66%). Regarding parental education, the majority of fathers had secondary level education (41.5%), whereas about half of the mothers (52.6%) were without any formal schooling. Paid employment was most the common occupation of fathers; more female students than male students had fathers with paid employment (54% vs 41%). The majority of mothers were housewives (42%; note that information about parental employment is not shown in Table 1) Nearly 60% students were from private schools, and the percentage was higher for females than for males (63% vs 58%). Walking distance to school was 30 minutes or less for 86% of females and 79% of males. About 80% (4 in 5) of students, both male and female, walked or cycled to school on most days. Nearly half of the students had no playground in their school, and 79% of students had no extracurricular activities on a regular basis. Nine in 10 students had walking or playing space around their homes, whereas less than half had playgrounds or parks near their homes that could be used for leisure-time-related PAs. Around 90% of the students perceived that their family and friends supported them; this percentage was slightly higher in females than in males. Only 23 (5.7%) students were involved in income-generating jobs besides their studies, and 4% were involved in PAs in course of their work. Regarding PA level, about 4% of females and 5% of males did not meet WHO recommendations of at least 600 MET-min/wk of PA, whereas 47% female students and 60% male students had high PA levels. Percentage contribution of different domains and activity types to total PA score by gender is shown in Table 2. Overall, domestic and gardening-related activities (38%) and transport-related activities (36%) were major contributors to total MET-minutes per week of PA accumulated by the students. Females were found to be more engaged in domestic activities (53%), whereas males were more engaged in transport-related activities (39%). Contribution of leisure-time PAs was 24% and was lower in females than in males (13% vs 32%). Moderate activities were the greatest contributors for both male and female students. Vigorous activities contributed only 3% in females but was 12% (4 times higher) among males. The work domain contributed only 2% of the total MET-minutes per week of PA accumulated by the students. Table 3 presents PA measures relative to age and gender. A Kruskal-Wallis test revealed that no significant difference existed among total PA scores, domain-specific scores, and activity-specific scores across different age groups when tested separately for males and females. In addition, median MET-minutes per week across domestic activities were greater and transport-related METs were lower in females than in males across all age-groups. Among males, excluding those aged 18, domestic-activity-specific MET-minutes per week were decreasing whereas transport-related METs were increasing with age. Furthermore, engagement of females in leisuretime activities and walking was lower than in males across all age groups with the exception of those aged 15 and 20, respectively. There were large differences in terms of total PA and engagement in vigorous activities between male and female students throughout all age groups.
The linear regression analysis between sociodemographic, environment, and perceived-social-support-related factors is shown in Table 4. Analysis revealed that being male and of lower economic status was associated with increased PA. Likewise, students living at a distance of more than 30 minutes from their school and those who walked or cycled to school were more likely to have high MET-minutes per week of PA than those who lived near to school and those using vehicles. On the other hand, students studying in private school did less PA than those who studied in government schools. Surprisingly, those without support from friends were more likely to have low PA scores. However, no significant association was found with age of respondent, place of residence, parent’s education, occupation, presence of playground in school or near home, provision of extracurricular activities at school, and perceived family support.
Discussion This study was carried out to assess PA level and associated factors among HSS students in Banke district, Nepal. Five percent of students had low PA levels and did not meet the WHO recommendation of at least 600 MET-min/wk, whereas more than half (54%) had high PA levels. The WHO STEPs survey of noncommunicable diseases risk factors in Nepal found that 5.5% of the population had low levels of PA,18 which is nearly equal to the level of inactivity shown by the current study, but higher percentages of inactivity have been reported by studies conducted in high- and middle-income countries.29,30 Worldwide, 31% of adults (≥ 15 years old) are physically inactive, but the rate varies among regions and countries.12 In this study, domestic and transport-related activities were found to be the major contributors to total PA score, whereas recreation-related activities contributed only 24% of total METminutes per week of PA gained by the students. A similar study conducted in Nepal found a low contribution of transport- and leisure-time activities to total PA score.31 The reason for the greater engagement in transport-related activities found in the current study could be that cycles are more commonly used in terai districts in Nepal, including the study area, for short-distance transportation. Findings from other studies have also shown that PA in household and transport-related domains are the most common in low-income countries.13 The contribution of leisure-time activities and vigorous activities was even lower among females than among males in the study. This result is consistent with the findings from other studies that men are more likely to participate in vigorous-intensity activities than females are.12,20,32,33 Females tend to do light work and spent more time in household chores, whereas males spent more time in outdoor sports than females, which might be due to family restrictions placed on females for spending longer hours outside home along with the burden of household work. The study found a low contribution of work-related activities because only a few students were doing part-time jobs in addition to their studies and, among these, some were involved in sedentary jobs such as teaching junior students. However, the national data show a larger percentage (40%) to be economically active.24 This deviation may exist because more than half of the study respondents were from private schools, where there is greater pressure on studies. In the Nepalese context, it is usually the rich families that send their students to these schools, and hence the students are less likely to be engaged in part-time jobs. Some students helped their parents at farm, which is included in domestic-work domain in the study.
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Table 1 Descriptive Characteristics and Physical Activity Level of Study Population n (%) Characteristic
Frequency (%)
Female
Male
P
Rural
165 (40.7)
74 (41.6)
91 (40.0)
.763
Urban
240 (59.3)
104 (58.4)
136 (60.0)
Nuclear
275 (68.0)
126 (71.0)
149 (65.6)
Joint/Extended
130 (32.0)
52 (29.0)
78 (34.4)
Private
244 (60.2)
113 (63.5)
131 (57.7)
Government
161 (39.8)
65 (36.5)
96 (42.3)
≤ 30 min
333 (82.2)
153 (86.0)
180 (79.3)
> 30 min
72 (17.8)
25 (14.0)
47 (20.7)
Walking/cycling
347 (85.7)
146 (82.0)
201 (88.5)
Motorcycle/bus/rickshaw
58 (14.3)
32 (18.0)
26 (11.5)
Yes
220 (54.3)
95 (53.4)
125 (55.0)
No
185 (45.7)
83 (46.6)
102 (45.0)
Yes
85 (21.0)
43 (24.0)
42 (18.5)
No
320 (79.0)
135 (76.0)
185 (81.5)
Yes
368 (90.9)
165 (92.7)
203 (89.4)
No
37 (9.1)
13 (7.3)
24 (10.6)
Yes
188 (46.4)
83 (46.6)
105 (46.3)
No
217 (53.6)
95 (53.4)
122 (53.7)
Yes
370 (91.4)
166 (93.3)
204 (90.0)
No
35 (8.6)
12 (6.7)
23 (10.0)
Yes
355 (87.7)
162 (91.0)
193 (85.0)
No
50 (12.3)
16 (9.0)
34 (15.0)
19 (4.7)
7 (3.9)
12 (5.3)
Moderate
167 (41.2)
88 (49.4)
79 (34.8)
High
219 (54.1)
83 (46.6)
136 (59.9)
Sociodemographic factors Place of residence
Family type .271
Environmental factors
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Type of school .239
Distance to school .082
Mode of transport to school .063
Playground in school .734
Provision of extracurricular activities .165
Neighborhood walkability .257
Presence of playground/parks near home .940
Perceived social-support-related factors Perceived family support .228
Perceived peer support .069
Level of physical activity Low
.012
Note. Total respondents: n = 405; female respondents: n = 178; male respondents: n = 227.
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171
172
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38 24
Domestic and gardening Leisure time
64 8
Moderate Vigorous
Median, first and third quartile all are zero.
Note. Data are median and Interquartile range.
a
Domestic and yard activities Male Female Transportation-related activities Male Female Leisure-time activities Male Female Walking Male Female Moderate activities Male Female Vigorous activities Male Female Total Male Female
Variable and gender
3
75
22
13
53
33
1
Female (%)
12
56
32
32
26
39
3
Male (%)
780 (420–1410) 1590 (650–1590) 1188 (693–1980) 808 (528–1440 1236 (471–2829) 240 (0–679) 1089 (627–1311) 594 (66–1188) 1980 (1277–3592) 2310 (1245–2310) 0 (0–1320) 0a 3999 (2470–6461) 3120 (1885–4380)
693 (396–3780) 594 (561–1638) 693 (49–2079) 960 (0–1683) 1039 (396–1782) 594 (396–1588) 1840 (1260–4700) 2520 (630–3240) 0 (0–480) 0 (0–320) 3570 (2226–5378) 3314 (2047–5616)
3727 (1837–5327) 2295 (1354–4203)
0 (0–840) 0a
2105 (620–3780) 1755 (765–3885)
792 (280–1782) 297 (0–792)
742 (303–2162) 0 (0–476)
1125 (594–2160) 720 (358–1188)
551 (90–1150) 1150 (630–2520)
17-year-olds
3783 (1860–6292) 3428 (1740–5877)
0 (0–1680) 0a
2520 (630–4020) 1955 (1477–4938)
693 (132–1320) 495 (0–1336)
840 (99–2640) 0 (0–445)
1228 (594–2520) 1080 (594–1354)
700 (280–1432) 1785 (562–2520)
18-year-olds
5270 (2115–6839) 3930 (1455–5529)
0a 0a
3320 (840–4275) 2940 (1455–3945)
1287 (0–1551) 792 (0–1683)
636 (0–1984) 0 (0–594)
1413 (0–2214) 792 (0–891)
555 (0–3420) 2940 (1455–3585)
19-year-olds
Median Domain-Specific MET-minutes per week (Interquartile Deviation) 16-year-olds
1260 (760–1380) 1760 (592–2520)
15-year-olds
Table 3 Physical Activity Level Grouped by Age and Gender
28
Walking
Activity specific
2 36
Transport related
Total (%)
Work related
Domain specific
Characteristic
Table 2 Percentage Contribution to Total Physical Activity Score by Gender
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.666 .294
.060 .258
320 (0–1290) 0 (0–480) 3780 (2769–5544) 3138 (2061–4788)
.496 .717
.311 .347
.497 .070
.824 .493
.241 .414
P
3102 (1890–3780) 2060 (1095–3052)
247 (41–1138) 668 (198–1707)
697 (0–1792) 305 (0–1188)
1743 (1080–2160) 891 (294–2160)
675 (581–1620) 1020 (600–2040)
20-year-olds
Table 4 Linear Regression Coefficients for MET-Minutes per Week of Total Physical Activity Unadjusted
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Characteristic Sociodemographic factors Age of student 15–17 years 18–20 years Sex Female Male Place of residence Rural Urban Family type Nuclear Joint/Extended Father’s education Illiterate Literate Mother’s education Illiterate Literate Father’s occupation Business Paid employment Agriculture Mother’s occupation Paid employment Agriculture Housewife Economic status Low Medium High Environmental factors Type of school Government Private Distance to school ≤ 30 min > 30 min Mode of transport to school Walking/cycling Motorcycle/bus/rickshaw Playground in school No Yes Provision of extracurricular activities No Yes
Adjusted
b
95% CI
p
b
95% CI
p
388
Reference –181 to 956
.181
145
Reference –453 to 743
.633
810
Reference 315 to 1305
.001
545
Reference 10 to 1080
.046
–186
Reference –693 to 320
.47
–187
Reference –697 to 322
.470
80
Reference –453 to 613
.767
15
Reference –523 to 554
.955
137.1
Reference –416 to 690
.626
400
Reference –217 to 1018
.203
–425
Reference –922 to 72
.093
–257
Reference –842 to 328
.388
–464 544
Reference –960 to 31 –12 to 1099
.067 .055
–410 –178
Reference –1025 to 204 –977 to 621
.190 .662
420 –268
Reference –126 to 967 –772 to 236
.131 .297
40 –9
Reference –688 to 768 –616 to 596
.913 .974
128 –875
Reference –399 to 656 –1396 to –354
.633 .001
–226 –812
Reference –891 to 439 –1526 to –97
.505 .026
–1231
Reference –1725 to –737
< .001
–1158
Reference –1642 to –674
< .001
344
Reference –306 to 994
.299
863
Reference 233 to 1494
.007
–1807
Reference –2495 to –1118
< .001
–1758
Reference –2450 to –1066
< .001
359
Reference –140 to 857
.158
469
Reference –11 to 948
.055
170
Reference –441 to 780
.585
151
Reference –429 to 730
.609 (continued)
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174 Paudel, Subedi, and Mehata
Table 4 (continued) Unadjusted
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Characteristic Neighborhood walkability No Yes Playground/parks near home No Yes Perceived social-support-related factors Perceived family support Yes No Perceived peer support Yes No
Adjusted
b
95% CI
p
b
95% CI
p
–449
Reference –1312 to 413
.307
–535
Reference –1374 to 303
.210
230
Reference –269 to 728
.365
404
Reference –77 to 886
.100
–279
Reference –1164 to 607
.536
–453
Reference –1349 to 443
.321
764
Reference 11 to 1516
.047
832
Reference 67 to 1598
.033
Note. Adjusted for age, sex, family type, place of residence, economic status, father’s education an occupation, mother’s education and occupation, distance to school, mode of transport to school, playground in school, extracurricular activities at school, neighborhood walkability, playground/parks near home, perceived family support, perceived peer support. CI, confidence interval.
Regarding peer support, a negative association was found between support from friends and levels of engagement in PA, but an increase in PA with increased support from family and friends has been shown by other studies.4,34–38 Peer support is more important for leisure-time activities than for transport-related or domestic activities. In the context of Nepal, engagement in leisure-time PA is very low, as shown by the current study as well as other studies.20 Consequently, having peer support still might not have been associated with increased PA. Additional research is required to further explore this finding. We found that students in government schools were more likely to be engaged in PAs than students studying in private schools. This could possibly be because government schools in Nepal have comparatively more space, and almost all of them have large playgrounds. Furthermore, it was observed during field work for the current study that many private schools were run in small areas containing only school buildings, with very limited open space. Another possible reason could be more study pressure on students studying in private schools, resulting in less leisure time for games and sports. A systematic review conducted on environmental correlates of PA has also shown that the likelihood of PA is higher in public schools than private schools.9 Likewise, students who walked or cycled to school were more likely to have high PA levels compared with those who used a motorcycle, bus, or rickshaw; cycling and walking contributed a large share of total METs gained by the students, and more than 85% of the study participants cycled or walked to school every day. Significant association between age and PA was not found in the study. Age was also not significant in the study conducted in Iran30 but findings from a study conducted in Poland showed PA decreases among girls and PA increases among boys with age.29 A systematic review conducted on predicting factors of PA also found age and sex to be the most consistent demographic correlates of PA behavior in adolescents.35 No significant association existed between PA levels and neighborhood walkability and the presence of parks or playgrounds near home, although significant association has been shown by other studies conducted among different age groups.9,13,34,39,40
Strengths and Limitations Random selection of schools and sections in schools is one of the strongest aspects of the study. Furthermore, using a valid questionnaire, we have examined various sociodemographic, environmental, and perceived-social-support-related factors that have been found to be associated with PA among high school students. In addition, the use of IPAQ-LF has given a detailed breakdown of total PA scores across different domains and activity types. The findings of the study will add to the existing knowledge about PA levels and age and gender difference and correlates in Nepal and other developing countries with similar context. Sufficient attention has been paid to minimize recall bias and overreporting in the study. Despite this, the use of self-administered questionnaire and the use of adult version of IPAQ instead of the adolescent version remain a potential limitation. The cross-sectional nature of the study allows the findings to be used to examine associations but limits drawing inferences about causation. Because this study used the adult version of IPAQ, activities carried during physical education classes and recess have been included in the leisure-time-activity domain; this would have been included separately as school-related PA if the adolescent version had been used. This might have caused overreporting of the leisure time domain, although no significant effects are expected in the total PA score. Future studies using the adolescent version can shed further light on this.
Conclusion The study found a high prevalence of PA among high school students. Meanwhile, the prevalence of inactivity, which was 5%, is also quite high considering the setting in which the study was conducted. Total PA score was largely contributed by routine work rather than by recreation-related activities. In addition, engagement of females in such activities was much lower than in males. Further research needs to be carried out incorporating all determinates of PA as identified in the ecological model, and there is also a need to address the determinants of inactivity.
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Physical Activity Level Among Students in Nepal 175
Acknowledgments We thank Ritu Prasad Gartoulla, Amod Kumar Poudyal, and Khadga Bahadur Shrestha for their critical comments while we carried out the research.
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