|Year : 2017 | Volume
| Issue : 3 | Page : 64-70
Pattern and Determinants of Physical Activity in Rural and Urban Adolescents of North India: A Population Based Study
Rambha Pathak1, Mitasha Singh2, Anmol Goyal3, Rashmi Agarwalla1, RKD Goel4
1 Department of Community Medicine, Hamdard Institute of Medical Sciences and Research, Jamia Hamdard, New Delhi, India
2 Department of Community Medicine, ESIC Medical College and Hospital, Faridabad, Haryana, India
3 Department of Community Medicine, Maharishi Markandeshwar Medical College, Kumarhatti, Solan, Himachal Pradesh, India
4 Department of Community Medicine, Maharishi Markandeshwar Medical College, Mullana, Ambala, Haryana, India
|Date of Web Publication||4-Jul-2017|
Department of Community Medicine, ESIC Medical College and Hospital, Faridabad, Haryana, 121001
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Background: Developing countries are experiencing an epidemic of physical inactivity. However, only a few studies have focused on domains and pattern of physical activity (PA) among adolescents. Objective: This study attempts to find out the region specific environmental and social determinants of PA in 10 to 19-year age group. Materials and Methods: A descriptive cross-sectional survey was conducted in the government and private schools of district Ambala, Haryana, situated in North India over a period of 1 year (2013–2014). A stratified random sampling technique was used for a sample size of 1714 participants. PA was assessed using an interviewer-administered youth PA questionnaire which prompts volunteers to self-report on the mode, frequency, and duration of PA and sedentary activities in different domains, including school time and leisure time over the past 7 days. Results: The average duration of screen time per day was reported significantly higher among female as compared to male adolescents; however, it was more than 2 h among both the genders. The average total moderate-to-vigorous PA (MVPA) duration consistently remained less than 60 min/day and metabolic equivalent of task minutes decreased significantly (P = 0.00) with an increasing age after 12 years. Being overweight and obese had 30% less chance of carrying out MVPA as compared to those with body mass index (BMI) <85th percentile [odds ratio; 95% confidence interval (CI): 0.70; 0.59–0.82]. Conclusion: The current study reported an overall higher duration of sedentary activity as compared to MVPA among adolescents of North India. PA was mostly associated with environmental factors and inactivity was most associated with sociodemographic factors.
Keywords: Adolescents, inactivity, leisure time activity, screen time
|How to cite this article:|
Pathak R, Singh M, Goyal A, Agarwalla R, Goel R. Pattern and Determinants of Physical Activity in Rural and Urban Adolescents of North India: A Population Based Study. Int J Nutr Pharmacol Neurol Dis 2017;7:64-70
|How to cite this URL:|
Pathak R, Singh M, Goyal A, Agarwalla R, Goel R. Pattern and Determinants of Physical Activity in Rural and Urban Adolescents of North India: A Population Based Study. Int J Nutr Pharmacol Neurol Dis [serial online] 2017 [cited 2017 Oct 20];7:64-70. Available from: http://www.ijnpnd.com/text.asp?2017/7/3/64/209423
| Introduction|| |
Global health is being influenced by three trends: population-aging, rapid unplanned urbanization, and globalization, all of which result in unhealthy environments and behaviors. Physical inactivity has been identified as the fourth leading risk factor for global mortality (6% of deaths globally). Physical inactivity is estimated as being the principal cause for approximately 21 to 25% of breast and colon cancer burden, 27% of diabetes, and approximately 30% of ischemic heart disease burden. It is estimated currently that of every 10 deaths, six are attributable to noncommunicable diseases which in turn are associated with physical inactivity.
A dose–response relationship appears to exist showing that the greater doses of physical activity (PA) are associated with improved indicators of cardiorespiratory and metabolic health. In general, it appears that higher volumes or intensities of PA are likely to have greater benefits, but research in this area is still limited.,
Adolescents (10–19 years) constitute about one-fifth of India’s population. Adolescence is a time when risks for some long-term adult health problems may become evident. Thus, this pivotal developmental period offers special opportunities for preventive and health-promoting services, and interventions at this stage can transform the social and economic fortunes of the country.
Very few studies in India have captured the various domains in which adolescents can be active., Owing to the dose–response relationship between PA and health benefits [i.e., the frequency, duration, intensity, type, and total amount of PA needed for health enhancement and prevention of non communicable diseases (NCDs)], it is important to address the knowledge gap on determinants of physical inactivity. Gender, age, socioeconomic status (SES), and parental and peer influences were among the most-researched correlates in the Western world, but there is scarcity of evidence from India. Hence, this study was planned to find out the pattern of PA and inactivity among Indian adolescents. It also attempts to find out the region specific environmental and social determinants of PA in this age group.
| Materials and Methods|| |
A descriptive cross-sectional survey was conducted in the government and private schools of district Ambala, Haryana, situated in North India over a period of 1 year (2013–2014).
The study population included students of 10 to 19 years age group in their fifth to 12th grades of schools located in study area. Literature review reveals that the prevalence of obesity in school going adolescent is in the range of 10 to 30%. The minimal sample size required for this study is 1600, based on the assumption of a 20% overweight/obesity rate, a 95% confidence level, and a 10% relative error margin. Considering a nonresponse rate at 10%, the total sample size was, thus, 1760.
Stratified random sampling technique was used for sample collection. Ambala district has a total number of 224 higher secondary and senior secondary schools (as per the data available with District Education Officer’s office, Ambala). Both public and private schools with coeducation in urban and rural areas were included in this study. As the number of schools in the government and private sectors are in the ratio of 2:1, the numbers of students to be included in the study were 1174 and 586 from government and private schools, respectively. Probability proportionate to size technique was used to select schools from the list of schools obtained.
Permission was obtained from the management of each of the schools before data collection, and the consent procedures and study protocols were approved by the institute’s research and ethics committee.
To ensure equal representation of all the blocks, the number of schools to be included in the study depended upon the total number of schools in that area. From each of the randomly selected schools, one section of one grade (fifth through 12 grade) was selected at random to include the students from 10 to 19 years age group. In parent–teacher meetings, a written consent was obtained from parents of all the participants prior to interview. Guardians of 20 students from schools of rural area and 26 students from schools of urban area did not consent to the participation in the study, hence were excluded. Thus, only 1714 students were included for the study.
PA was assessed using an interviewer-administered youth PA questionnaire (available at: www.mrc-epid.cam.ac.uk) based on the Children’s Leisure Activities Study Survey and prompted volunteers to self-report on the mode, frequency and duration of PA, and sedentary activities in different domains, including school time and leisure time over the past 7 days.
Metabolic equivalent or metabolic equivalent of task (MET) (multiples of basal metabolic rate reflecting intensity of activity) was assigned for each reported activity using the Compendium of Energy Expenditure for Youth, and a published compilation of METs. Sedentary activity was defined, as activity with MET levels below 1.5 and included television or video viewing, computer games, and passive games. Moderate-to-vigorous PA (MVPA) was computed using a MET cutoff value of 3 or above. Based on current recommendations, children were categorized in two groups based on whether they engaged in daily MVPA above or below 60 min. WHO recommends that children and youth aged 5 to 17 years should accumulate at least 60 min of MVPA daily. The duration in minutes of a specific activity was multiplied with specific MET value to obtain a composite measure encompassing duration and intensity (MET-min). A cutoff of exposure to more than 2 h/day of entertainment media or “screen time” (i.e., time sitting in front of a television or computer screen or playing video games) has been taken as a proxy of sedentary activity.
Standard techniques were used for anthropometric measurements. Overweight was classified as BMI ≥85th to <95th percentile for age and sex; obesity is classified as BMI ≥95th percentile for age and sex.
All data were analyzed by using Epi-Info version 7 (CDC, Atlanta, GA). Frequency and duration of PA and sedentary activities in different domains, including school time and leisure time are presented as means. Further the duration of different intensities of physical activities were transformed into categorical variable and presented as percentages. The means were tested using Student’s t test and analysis of variance (ANOVA); percentages were tested using chi-square and standard error of difference of proportions. The factors significant on univariate analysis were included in bivariate logistic regression model with MVPA and sedentary activity as dependent variables. P value less than 5% was considered as significant.
| Results|| |
Out of the 1714 adolescent participants, 825 students (48%) came from the rural area, whereas 889 students (52%) came from the urban area. More than half of the study population comprised males (55%) as compared to 45% females. The proportion of overweight adolescents was 18% and obese was 8%. The proportion of overweight and obesity was higher among urban area (68.6 and 89.6%, respectively) as compared to rural area (31.4 and 10.5%, respectively). Around half of the females of the study population were overweight (50.4%), and obesity was highly prevalent among males (62.1%).
In the present study, only 38.3% of adolescents were found to be spending less than 2 h of screen time per day. Around 40 and 11% of adolescents reported >1680 METs/week and >2520 METs/week, respectively. Out of those, who performed >1680 METs/week 23% were observed to be overweight and obese as compared to 9.8% of those who performed >2520 METs/week. This distribution was statistically significant [Figure 1].
|Figure 1: Proportion of adolescents who exceeded certain cut off values for physical activity|
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Sex-wise distribution of sedentary activities (min/day) among adolescents show that average total sedentary activity duration [mean ± standard deviation (SD); 195 ± 25] and total sedentary activity MET minutes (mean ± SD; 292.5 ± 23) were statistically higher among females than males. Neither males nor females adhered to the >60 min MVPA/day recommendation. Average duration of screen time per day (television and computer viewing) was reported significantly higher among females as compared to male adolescents; however, it was more than 2 h among both the genders [Table 1].
Age-wise distribution of the duration of PA among adolescents shows that total sedentary activity duration was increasing significantly (P = 0.00) across the increasing age groups; from 154 ± 19 to 222.5 ± 30 min/day (10 years through >18 years age). Average screen time increased significantly after 12 years of age and was consistently higher than 2 h/day. Average total MVPA duration consistently remained less than 60 min/day and MET minutes decreased significantly (P = 0.00) with an increasing age after 12 years of age.
On analyzing the pattern of time spent on various types of physical activities, it was observed that, in the study, population the mean time spent in out of school activities and sports in neighborhood was higher as compared to time spent in physical education program in school activities [Table 2].
The sociodemographic other than age and sex and various environmental factors were also studied. The logistic regression analysis shows that being overweight and obese (BMI ≥ 85th percentile) had 30% less chances of carrying out MVPA as compared to those with BMI <85th percentile [odds ratio (OR); 95% CI: 0.70; 0.59–0.82]. The educational status of mother was positively associated with the PA level of the adolescents. Mothers with education up to intermediate (OR; 95% CI: 0.85; 0.64–0.93) or graduation degree (OR; 95% CI: 0.63; 0.46–0.77) had 15 to 37% less chance of their children being involved in sedentary activity, and this association was statistically significant (P < 0.05) [Table 3].
|Table 3: Bivariate logistic regression analysis showing determinants of physical activity|
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Higher socioeconomic status was observed to be significantly positively associated with MVPA among adolescents. In concordance with this finding, there was also significant negative association between upper classes (upper middle and upper class) and physical inactivity among adolescents [Table 3].
Negative parental attitude toward PA was observed to have 32% higher association with inactivity among adolescents (OR; 95% CI: 1.32; 1.21–1.44). Also parents with no PA were positively associated with their adolescent children being sedentary (OR; 95% CI: 1.20; 1.11–1.31). There was 24% higher chance of being involved in sedentary activities among those who had no peer influence of PA (OR; 95% CI: 1.24; 1.13–1.38). Nonavailability of facilities inside school and in neighborhood for playing/cycling/walking had significant negative association with MVPA among adolescents [Table 3].
| Discussion|| |
The current study examined the pattern of duration and intensity of self-reported PA among adolescents of a North Indian state and also explored the determinants of the intensity of PA. It was observed that both duration and intensity of sedentary activity was higher among older children and females. These findings are in agreement with results from various studies among adolescents of South India, Nigeria, other African countries, and even the developed world.,,,, Studies on representative US adolescent sample revealed that PA decreases dramatically across age groups between childhood and adolescence and continues to decrease into adulthood., The mean duration of all forms of sedentary activity in the current study increased significantly across the increasing age; however, computer use duration was observed with maximum increase from 10 to 18 years of age. Sedentary lifestyle with additive effects of increased diet predisposes to obesity and further to noncommunicable diseases.
The increasing sedentary behavior among females in our study could be due to influence of Indian culture, as adolescent girl spends most of her time in domestic and sedentary activities when not in school. Those girls who are not involved in domestic and unstructured activities at home spend their out-of-school time watching television, playing sedentary games, chatting, or using computers. In addition, due to safety reasons girls are not allowed to engage in outdoor activities.
Outdoor games time is also reduced in the higher age group as a result of the increased study hours. One of the main findings of our study is that the MVPA outside school (in neighborhood), both in terms of duration and intensity, was observed to be higher than that inside the school. This was in contrast to the findings from South India, Nigeria, and South Africa.,,, The findings are, however, supported by reports from United States and European adolescents because they spend a larger proportion of their PA time outdoor after school., Rapid urbanization and schools with insufficient infrastructure (such as inadequate space for sports or the lack of physical education instructors) could be responsible for the decrease in physical education programs in school. Hence, the adolescents may prefer activities in their neighborhood.
The study population did not meet the international recommendations of engaging in 60 min of MVPA per day especially after the age of 12 years. Similar findings were reported among Nigerian adolescents and among south Indian cohort the duration per day reduced from 62.3 to 56.0 min from baseline to follow-up (after 3 years)., In India, in addition to poor environment provided by schools in terms of space and facilities for sports, insecurity among parents to send their children out to play, increasing traffic and passive transportation for commuting are some possible reasons of increasing inactivity.
The prevalence of overweight and obesity was found to be 18 and 8% in the present study. However, a similar study from Haryana reported a lower prevalence of 11 and 5.7% of overweight and obesity, respectively. High BMI, denoting overweight and obesity, was positively associated with sedentary activity in our study, and this finding was consistent with other studies.,, The vicious circle of increasing BMI leading to decreased PA, which further leads to obesity, is evident from the results.
Maternal education was inversely associated with a pattern of high inactivity, and this finding was consistent with other studies from India, Africa, and United States.,,, Having a mother with a graduate or professional degree was associated with an adjusted OR of 0.61 for high inactivity among US adolescents and 0.63 in our study. Probably because educated mothers promote physical activities. Self-reported MVPA showed significant but trivial differences between adolescents of European cities whose mother had a low education and those with a high education. Contrasting findings from developed world show that highly educated parents of developed countries encourage their children to study more, which in turn can compensate the higher amount of time spent watching TV and/or PC/video gaming, frequently seen in adolescents with lower educated parents, hence accounting to increased sedentary activities. Observing the increasing trend of inactivity across the world, a similar pattern is predictable in future India too.
Lower middle SES showed no association with MVPA or sedentary activity as compared to lower class in our study. High SES had significant positive association with MVPA consistent with findings from other countries.,,,, It has been hypothesized that the lower and lower middle classes has fewer chances to participate in formal sports/cycling/leisure time activities, but they are more involved in unstructured activities and household chores than their privileged peers. These findings are useful for the development of PA interventions in low-SES adolescent populations.
Van der Horst et al., in their study, confirmed that attitude, self-efficacy, and goal orientation/motivation were positively associated with PA. Current study reported similar findings of positive association of MVPA with parental attitude and self-efficacy.
A review of various studies strongly confirms that previous PA and self-efficacy are consistently positively associated with change in PA in 10 to 13 years age group. Peer influence, parents involved in PA and availability of services which promote PA were also independently associated with MVPA in the current analysis. However, in a review of various studies, behavioral and environmental factors in higher age (14 years and above) showed that higher scores on social support, and self-efficacy measures were consistently associated with smaller decline in PA.
The lesser availability of facilities for physical education at school is a major determinant of physical inactivity for children as a third of the day is spent in school. Although norms for physical education in schools have been described, adherence to these norms is generally low, globally. A review of interventions has indicated that in the short term, the school setting is effective in increasing PA. Physical education programs at school should, therefore, be strengthened to ensure effective participation of all children at adequate levels of PA.
The strength of the present study is the selection of large sample representative of both urban and rural schools of India. It improves generalizability of our results. It focuses on adolescents and their PA level which have documented health benefits, including increased physical fitness (both cardiorespiratory fitness and muscular strength), reduced body fat, favorable cardiovascular and metabolic disease risk profiles, enhanced bone health, and reduced symptoms of depression. Moreover, there are very few studies from India focusing on different domains and intensity of PA using valid instruments.
There are few limitations in the current study. The cross-sectional nature of the study makes it difficult to draw conclusions on temporality of associations and causality. Self-report measure gives important qualitative information, but shortcomings of measurement bias, recall bias, and inaccurate estimates of PA intensities cannot be underestimated.
In conclusion, the current study reported an overall higher duration of sedentary activity as compared to MVPA among adolescents of North India. The adolescents did not meet the criteria of minimum recommendations of 60 min of MVPA. The average screen time, a surrogate for sedentary activities, remained consistently higher across both sexes and increasing age. PA was mostly associated with environmental factors such as facilities inside school and in neighborhood, positive parental attitude, and self-efficacy of adolescents. Inactivity was most associated with sociodemographic factors like age, gender, SES, and maternal education. The findings of this study have pointed toward societal level intervention strategies for increasing PA and decreasing inactivity among Indian adolescents.
The authors would take the privilege to thank the field workers and school teachers for extending their full support in carrying out the survey and anthropometric measurements.
Financial support and sponsorship
The study was funded by Indian Council of Medical Research, New Delhi, India.
Conflict of interest
There are no conflicts of interest.
| References|| |
Janssen I. Physical activity guidelines for children and youth. Appl Physiol Nutr Metab 2007;32:109-21.
Global Health Risks: Mortality and Burden of Disease Attributable to Selected Major Risks. Geneva: World Health Organization; 2009.
The Global Burden of Disease: 2004 Update. Geneva: World Health Organization; 2008.
Janssen I, Leblanc A. Systematic review of the health benefits of physical activity in school-aged children and youth. Int J Behav Nutr Phys Act 2010;7:40.
Physical Activity Guidelines Advisory Committee (PAGAC). Physical Activity Guidelines Advisory Committee Report, 2008. Washington, DC: US Department of Health and Human Services; 2008.
Swaminathan S, Selvam S, Thomas T, Kurpad A, Vaz M. Longitudinal trends in physical activity patterns in selected urban south Indian school children. Indian J Med Res 2011;134:174-80.
] [Full text]
Kuriyan R, Bhat S, Thomas T, Vaz M, Kurpad AV. Television viewing and sleep are associated with overweight among urban and semi-urban South Indian children. Nutr J 2007;6:1-4.
Craggs C, Corder K, van Sluijs EM, Griffin SJ. Determinants of change in physical activity in children and adolescents. A systematic review. Am J Prev Med 2011;40:645-58.
Kalra S, Unnikrishnan AG. Obesity in India: the weight of the nation. J Med Nutr Nutraceuticals 2012;1:37-41
World Health Organization. Preventing chronic diseases: a vital investment. World Global Report. Geneva: World Health Organization; 2005.
Wang G, Dietz WH. Economic burden of obesity in youths aged 6 to 17 years: 1979–1999. Pediatrics 2002;109:E81-1.
Kaur S, Kapil U, Singh P. Pattern of chronic diseases amongst adolescent obese children in developing countries. Curr Sci 2005;88:1052-6.
Oyeyemi AL, Ishaku CM, Oyekola J, Wakawa HD, Lawan A, Yakubu S et al.
Patterns and associated factors of physical activity among adolescents in Nigeria. PLoS One 2016;11:e0150142.
Micklesfield LK, Pedro TM, Kahn K, Kinsman J, Pettifor JM, Tollman S et al.
Physical activity and sedentary behavior among adolescents in rural South Africa: levels, patterns and correlates. BMC Public Health 2014;14:40.
Muthuri SK, Wachira LJM, Onywera VO, Tremblay MS. Correlates of objectively measured overweight/obesity and physical activity in Kenyan school children: results from ISCOLE Kenya. BMC Public Health 2014;14:436.
Olds T, Wake M, Patton G, Ridley K, Waters E, Williams J et al.
How do school-day activity patterns differ with age and gender across adolescence? J Adolesc Health 2009;44:64-72.
Caspersen CJ, Pariera MA, Curran MK. Changes in physical activity patterns in the United States, by sex and cross-sectional age. Med Sci Sports Exerc 2000;32:1601-9.
Troiano RP, Berrigan D, Dodd KW, Mâsse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc 2008;40:181-8.
De Cocker K, Ottevaere C, Sjöström M, Moreno LA, Wärnberg J, Valtueña J et al.
Self-reported physical activity in European adolescents: results from the HELENA (Healthy Lifestyle in Europe by Nutrition in Adolescence) study. Public Health Nutr 2010;14:246-54.
Dunton GF, Whalen CK, Jammer LD, Floro JN. Mapping the social and physical contexts of physical activity across adolescence using ecological momentary assessment. Ann Behav Med 2007;34:144-53.
Rohilla R, Rajput M, Rohilla J, Malik M, Garg D, Verma M. Prevalence and correlates of overweight/obesity among adolescents in an Urban City of North India. J Fam Med Prim Care 2014;3:404-8.
Haerens L, Deforche B, Maes L, Cardon G, De Bourdeaudhuij I. Physical activity and endurance in normal weight versus overweight boys and girls. J Sports Med Phys Fitness 2007;47:344-50.
Steinbeck K. The importance of physical activity in the prevention of overweight and obesity in childhood: a review and an opinion. Obes Rev 2001;2:117-30.
Hallal PC, Andersen LB, Bull FC, Guthold R, Haskell W, Ekelund U, The Lancet Physical Activity Series Working Group. Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet 2012;380:294-305.
Micklesfield LK, Pedro TM, Kahn K, Kinsman J, Pettifor JM, Tollman S et al.
Physical activity and sedentary behavior among adolescents in rural South Africa: levels, patterns and correlates. BMC Public Health 2014;14:40-6.
McVeigh JA, Norris SA, Cameron N, Pettifor JM. Associations between physical activity and bone mass in black and white South African children at age 9 yr. J Appl Physiol 2004;97:1006-12.
Bovet P, Paccaud F, Chiolero A. Socio-economic status and obesity in children in Africa. Obes Rev 2012;13:1080.
Gordon-Larsen P, McMurray RG, Popkin BM. Determinants of adolescents’ physical activity and inactivity patterns. Pediatrics 2000;105:1-8.
Muthuri SK, Wachira LJM, Leblanc AG, Francis CE, Sampson M, Onywera VO et al.
Temporal trends and correlates of physical activity, sedentary behaviour, and physical fitness among school-aged children in Sub-Saharan Africa: a systematic review. Int J Environ Res Public Health 2014;11:3327-59.
Ottevaere C, Huybrechts I, Bbenser J, Bourdeaudhuij ID, Cuenca-Garcia M, Dallongeville J et al.
Clustering patterns of physical activity, sedentary and dietary behavior among European adolescents: the HELENA study. BMC Public Health 2011;11:328.
Van der Horst K, Paw MJ, Twisk JW, Van Mechelen W. A brief review on correlates of physical activity and sedentariness in youth. Med Sci Sports Exerc 2007;39:1241-50.
[Table 1], [Table 2], [Table 3]