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Encouragement to be More Physically Active or to Lessen Sedentary Behavior; Are These Two as the Same?


1 Faculty of Nursing and Midwifery, Qazvin University of Medical Sciences, Qazvin, IR Iran
2 Department of Health Education, Faculty of Medicine, Tarbiat Modares University, Tehran, IR Iran
*Corresponding author: Shamsaddin Niknami, Department of Health Education, Faculty of Medicine, Tarbiat Modares University, Tehran, IR Iran. Tel: +98-2182883549, E-mail: Niknamis@modares.ac.ir.
Biotechnology and Health Sciences. 2015 May; 2(2): e26201 , DOI: 10.17795/bhs-26201
Article Type: Research Article; Received: Feb 1, 2015; Accepted: Apr 15, 2015; epub: May 23, 2015; ppub: May 2015

Abstract


Background: There are a considerable number of published studies that strongly support the benefits of physical activity in children, and to achieve such benefits, guidelines recommend children to participate in at least 60 minutes of physical activity every day. There is a vicious cycle between low physical activity and increased body mass index.

Objectives: The aim of this study was to assess daily physical activity and sedentary behavior in overweight and obese children.

Patients and Methods: In this descriptive analytical study, 300 overweight and obese children were recruited to assess their daily physical activity, energy expenditure and screen times with standard techniques. Distribution of these variables was examined according to their demographic characteristics. Using Pearson’s correlation test and linear regression the predictor effect between these variables was discovered.

Results: There was a significant difference between physical activity and screen time according to Relative Body Mass Index (RBMI), gender and grade (P ≤ 0.001). Pearson’s correlation test discovered a positive association between Weekly Screen Time (WST) and Daily Physical Activity (DPA) (r ≈ 0.37, P ≤ 0.001). In the regression model, children’s screen time explained only about 13% of change in body mass index.

Conclusions: Overweight and obese children may consume more energy according to the international recommendations. In parallel with this, they experience more sedentary behavior, as well. In this situation, planning to decrease their sedentary behavior is at greater priority.

Keywords: Physical Activity; Child; Obesity; Students

1. Background


There are a considerable number of published studies that strongly support the benefits of physical activity in children, including: obesity and overweight prevention, healthy bones and muscles, chronic disease prevention, self-esteem improvement, and anxiety and stress reduction (1-5). Recent studies indicated that physical activity can improve academic performance and increase school achievements, and students may be more efficient learners after physical activity performance (6-10). To achieve such benefits, guidelines recommend children to participate in at least 60 minutes of physical activity every day (11).

Despite the abundance of data supporting the benefits of physical activity enhancement in children, some researchers discovered that children in many countries, especially in developing and underdeveloped nations, have not met the recommended level of physical activity (1, 2, 4, 10, 12).

Lack of adequate physical activity in children may lead to bad consequences during both childhood and adulthood. Increasing Body Mass Index (BMI) is one the obvious consequences of low physical activity. Initiation of a healthy life style in children is more convenient than change of an unhealthy life style in adults, thus health education and behavior change intervention in children are suitable for researchers. The main theme in such researches was how to increase physical activity in children to result more energy expenditure.

Interventions to increase energy consumptions in children with Relative Body Mass Index (RBMI) above the 85th percentile are in limited consequences; overweight and obese children tend to restrict their physical activity, although some of them like to overestimate the amount of their physical activity (13-16). As shown in Figure 1, in a vicious cycle, low physical activity results increased BMI, and increased BMI results in low physical activity. In this study we proposed that overweight and obese children do more physical activity according to the international Physical Activity (PA) recommendations, and spent more time watching TV or playing computer games.

Figure 1.
Vicious Cycle Between Physical Activity and Body Mass Index

2. Objectives


The primary aim of this study was to assess daily physical activity and sedentary behavior in overweight and obese children. Next, the study aimed to (a) examine correlations between children’s screen time (ST) and physical activity, (b) construct a regression model for RBMI and ST in children and (c) compare the children’s ST and PA according to standard recommendations.

3. Patients and Methods


3.1. Sampling and Study Design

This descriptive-analytical study was part of a study in Qazvin university of medical sciences. The six main constructs required to develop an educational intervention were assessed by the entire study; we selected 50 subjects per construct.

First, using the cluster sampling method, seventy elementary schools were selected from two districts of the Qazvin province, and next 300 overweight and obese students of 5th and 6th grade were recruited in the study. The Human Research Ethics Committee of the Qazvin University approved the study, and written informed parental consents and children assents were obtained prior to their recruitment in the study.

3.2. Measurements
3.2.1. Demographic and Relative Body Mass Index Measures

Socio-demographic characteristics of the sample, such as: age, gender, grade and RBMI were gathered by a questionnaire. Relative body mass index of schoolchildren were collected from school health profiles. Based on this profile, students with RBMI of over the 85th percentile were selected and then their RBMI were calculated again with a standard method (17).

3.2.2. Physical Activity in Children

Physical activity of children was measured using the previous day physical activity recall (PDPAR) tool. This tool is a standard instrument with 30-minute time blocks, which can assess previous physical activity of children. Some general activities are listed on the form, and the participants enter their main activity during each time period. To help participants select the correct level of intensity, the instrument provides pictorial representations of the four levels of relative intensity. Prior to the application of the PDPAR tool, we educated students on how to mark each block based on their main activities. Finally, based on the PDPAR scoring protocol, the energy consumption for physical activity was calculated according to Metabolic Equivalence (MET) (18).

3.2.3. Screen Time in Children

Screen time explains any time spent on sedentary behaviors such as watching TV, video games and computer games. Screen time in children was assessed based on two methods; self-report and parent-report, with a standard questionnaire. In the self-report method, children estimated their weekly hours and minutes spent on sedentary behaviors. At the same time, in the parent-report, the researcher asked the mothers to estimate their children’s screen time.

3.3. Analysis

After data coding, all data were entered in the SPSS software for analysis. The significance level was set on two-tailed with P ≤ 0.05. Demographic data of children were analyzed using descriptive analysis methods. Differences between physical activity level according to gender, grade and RBMI were analyzed using the chi-square test. Also, distribution of children’s screen time by gender, grade and RBMI was analyzed with the chi-square test. Pearson’s correlation test was used to assess the correlation between the two methods of screen time measurement; self-report and mother’s report. Finally, by the linear regression model, children’s RBMI was regressed to their screen time, to discover the predictor impact of children’s screen time on their weight status.

4. Results


Children’s characteristics are presented in Table 1. They were in the range of 10.2 to 12.3 years old and 55% of them were obese (n = 165) with almost equal distributions in gender and grade variables. Measures of Daily Physical Activity (DPA) and Weekly Screen Time (WST) in children according to their gender, grade and RBMI are shown in Table 2. There were significant differences between DPA according to RBMI (P ≤ 0.001); overweight children had lower DPA than obese cases (4.6 ± 1.1 ver. 5.1 ± 0.9). Also, the t-test revealed a significant difference between mean DPA based on the grade variable, children of the 6th grade had lower DPA than children of the 5th grade (3.5 ± 0.8 ver. 4.1 ± 0.9) (P ≤ 0.001). Gender-based distribution of DPA indicated that boys were more physically active than girls (5.53 ± 0.64 ver. 4.2 ± 0.9) (P ≤ 0.001).

Distribution of WST in children revealed that obese children spent more time watching a screen (computer or TV) than overweight cases (23.0 ± 6.9 ver. 17.5 ± 7.2), and there was a significant difference between the two groups (P ≤ 0.001). Children at the 5th grade spent more time watching a screen (13.9 ± 2.5 ver. 12.5 ± 2.1), also boys had more WST (25.3 ± 5.6) than girls (15.8 ± 6.0), and the t-test showed that this difference was significant (P ≤ 0.001).Children’s RBMI was regressed to WST, and the model was significant (F = 45, P = 0.001, R2 = 13). In the model, WST explained about only 13% of RBMI.

Pictorial exhibition of correlation between WST and DPA are shown in Figure 2. Pearson’s correlation test discovered a positive association between WST and DPA (r ≈ 0.37, P ≤ 0.001).

Table 1.
Demographic Characteristics of Children
Table 2.
Distribution of Physical Activity and Screen Time Based on Gender, Grade and Relative Body Mass Index a
Figure 2.
Correlation Between Daily Physical Activity and Weekly Screen Time

5. Discussion


The primary aim of this study was to investigate the prevalence of PA and ST in children according to their gender, grade and RBMI. Secondary aims were to (a) examine the correlation between children’s screen time and physical activity, (b) construct a regression model for RBMI and ST in children and (c) compare the children’s ST and PA according to standard recommendations.

Our study revealed that obese children, regardless of their gender, had higher DPA than overweight children. Children’s PA was measured by PDPAR and it is possible that obese children overestimated their daily physical activity. Although most studies have demonstrated that increasing RBMI is accompanied by decreasing DPA in children (17, 19-23), yet similar findings have been shown in the study of Manley et al. (17). We suppose that such children like to do an overestimation of their DPA. This is due to our measurement technique, PDPAR, a self-report recall tool for assessment of DPA in children (24, 25). Although, its validity and reliability have been qualified with a pedometer and an accelerometer (18), yet in most studies researchers tend to assess children’s PA with PDPAR at the same time as a pedometer or other validated measure (26-29).

Another finding of this study showed a gender-based difference in DPA; boys had more DPA than girls. We almost found no study in contrast with this finding. Although the recommended level of PA for boys is more than girls (28, 30-33), some social inequalities and social barriers may be the common cause for such differences (34).

Based on our findings, children of the 6th grade had less DPA than those of the 5th grade. The associations between physical activity and cognitive performance have been assessed in many studies (35-38). Although, in most cases a positive association was found, yet a few studies consider only a specific type of physical activity such as rhythmic ones, and believe that only these lead to cognitive ability enhancement (10). In our study’s context, children at the 6th grade were forced to read more to pass exams for sharp-witted governmental schools. We think that this may be the main cause for the decreasing PA at the 6th grade. This finding is supported by some other studies (39).

Pearson’s correlation test showed that there was a positive association between DPA and ST; this means that children with high DPA in spite of their high RBMI had more ST. As a result, in addition to the positive association between RBMI and DPA, our study revealed that the association between DPA and ST was positive, as well. This means that children with higher DPA spend more time watching TV or playing games. Regarding physical activity guidelines, children in our study were more physically active than the recommended level, and simultaneously spend time more screen time in spite of their ascending trend in RBMI.

Based on the findings, children in our study were more physically active than the recommended level, and obese children had more PA than overweight children and along with these results, they experienced more ST. Thus, in such situations, to decline RBMI decreasing ST may be more effective than DPA enhancement.

Acknowledgments

The authors sincerely thank all those who participated in this study.

Footnotes

Authors’ Contributions: Kazem Hosseinzadeh: study design and data collection. Shamsaddin Niknami: analysis of data and SPSS software application. Alireza Hidarnia: Academic writing and submission.

References


  • 1. Bustamante EE, Wilbur JE, Marquez DX, Fogg L, Staffileno BA, Manning A. Physical activity characteristics and depressive symptoms in older Latino adults. Mental Health and Physical Activity. 2013;6(2):69-77.
  • 2. Carver A, Timperio A, Hesketh K, Crawford D. Are children and adolescents less active if parents restrict their physical activity and active transport due to perceived risk? Soc Sci Med. 2010;70(11):1799-805. [DOI] [PubMed]
  • 3. Cheng LA, Mendonca G, Farias Junior JC. Physical activity in adolescents: analysis of the social influence of parents and friends. J Pediatr (Rio J). 2014;90(1):35-41. [DOI] [PubMed]
  • 4. Cherry BJ, Zettel-Watson L, Chang JC, Shimizu R, Rutledge DN, Jones CJ. Positive associations between physical and cognitive performance measures in fibromyalgia. Arch Phys Med Rehabil. 2012;93(1):62-71. [DOI] [PubMed]
  • 5. Corsino L, Lin PH, Batch BC, Intille S, Grambow SC, Bosworth HB, et al. Recruiting young adults into a weight loss trial: report of protocol development and recruitment results. Contemp Clin Trials. 2013;35(2):1-7. [DOI] [PubMed]
  • 6. Pirrie AM, Lodewyk KR. Investigating links between moderate-to-vigorous physical activity and cognitive performance in elementary school students. . Mental Health and Physical Activity. 2012;5(1):93-8.
  • 7. Ruiz JR, Ortega FB, Castillo R, Martin-Matillas M, Kwak L, Vicente-Rodriguez G, et al. Physical activity, fitness, weight status, and cognitive performance in adolescents. J Pediatr. 2010;157(6):917-922 e1-5. [DOI] [PubMed]
  • 8. van der Niet AG, Smith J, Scherder EJ, Oosterlaan J, Hartman E, Visscher C. Associations between daily physical activity and executive functioning in primary school-aged children. J Sci Med Sport. 2014. [DOI] [PubMed]
  • 9. Otabe Y, Hattori S, Yamatsu K. Sedentary behavior and academic performance in Japanese junior high school students. Science & Sports. 2014;29:S19.
  • 10. Carlier M, Delevoye-Turrell Y, Dione M. Cognitive Benefits of Physical Activity Increased when Producing Rhythmic Actions. Procedia - Social and Behavioral Sciences. 2014;126:235-6.
  • 11. Rasberry CN, Lee S, Robin L, Laris BA, Russell LA, Coyle KK, et al. The association between school-based physical activity, including physical education, and academic performance: a systematic review of the literature. Preventive medicine. 2011;52:S10-20.
  • 12. Chang RW, Semanik PA, Lee J, Feinglass J, Ehrlich-Jones L, Dunlop DD. Improving physical activity in arthritis clinical trial (IMPAACT): Study design, rationale, recruitment, and baseline data. Contemporary clinical trials. 2014;39(2):224-35.
  • 13. de Vries E, Soerjomataram I, Lemmens VE, Coebergh JW, Barendregt JJ, Oenema A, et al. Lifestyle changes and reduction of colon cancer incidence in Europe: A scenario study of physical activity promotion and weight reduction. Eur J Cancer. 2010;46(14):2605-16. [DOI] [PubMed]
  • 14. Jerome GJ, McAuley E. Physical activity levels among adult computer users. Computers in Human Behavior. 2011;27(3):1207-10.
  • 15. Moher D, Liberati A, Tetzlaff J, Altman DG, Prisma Group . Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Int J Surg. 2010;8(5):336-41. [DOI] [PubMed]
  • 16. Murphy MH, Donnelly P, Shibli S, Foster C, Nevill AM. Physical activity, walking and leanness: an analysis of the Northern Ireland Sport and Physical Activity Survey (SAPAS). Prev Med. 2012;54(2):140-4. [DOI] [PubMed]
  • 17. Manley D, Cowan P, Graff C, Perlow M, Rice P, Richey P, et al. Self-efficacy, physical activity, and aerobic fitness in middle school children: Examination of a pedometer intervention program. Journal of pediatric nursing. 2014;29(3):228-37.
  • 18. Trost SG, Ward DS, McGraw B, Pate. R. R. . Validity of the Previous Day Physical Activity Recall (PDPAR) in Fifth-Grade Children. Pediatric Exercise Sci. 1999;11(4):341-8.
  • 19. Bezold CP, Konty KJ, Day SE, Berger M, Harr L, Larkin M, et al. The effects of changes in physical fitness on academic performance among New York City youth. J Adolesc Health. 2014;55(6):774-81. [DOI] [PubMed]
  • 20. Bond DS, Thomas JG, Unick JL, Raynor HA, Vithiananthan S, Wing RR. Self-reported and objectively measured sedentary behavior in bariatric surgery candidates. Surg Obes Relat Dis. 2013;9(1):123-8. [DOI] [PubMed]
  • 21. Cardon G, De Craemer M, De Bourdeaudhuij I, Verloigne M. More physical activity and less sitting in children: Why and how? Science & Sports. 2014;29:S3-5.
  • 22. Contento IR, Koch PA, Lee H, Calabrese-Barton A. Adolescents Demonstrate Improvement in Obesity Risk Behaviors after Completion of Choice, Control & Change, a Curriculum Addressing Personal Agency and Autonomous Motivation. the American Dietetic Association. 2010;110(12):1830-9.
  • 23. Dewar DL, Morgan PJ, Plotnikoff RC, Okely AD, Batterham M, Lubans DR. Exploring changes in physical activity, sedentary behaviors and hypothesized mediators in the NEAT girls group randomized controlled trial. J Sci Med Sport. 2014;17(1):39-46. [DOI] [PubMed]
  • 24. Hyde AL, Elavsky S, Doerksen SE, Conroy DE. Habit strength moderates the strength of within-person relations between weekly self-reported and objectively-assessed physical activity. Psychology of Sport and Exercise. 2012;13(5):558-61.
  • 25. Van Hoye A, Nicaise V, Sarrazin P. Self-reported and objective physical activity measurement by active youth. Science & Sports. 2014;29(2):78-87.
  • 26. Alarie N, Kent L. Physical Activity Assessment and Impact. In: Watson RR, editor(s). Diet and Exercise in Cystic Fibrosis. Boston: Academic Press. Boston: Academic Press; 2015. p. 299-306. [DOI]
  • 27. O'Reilly GA, Spruijt-Metz D. Current mHealth technologies for physical activity assessment and promotion. Am J Prev Med. 2013;45(4):501-7. [DOI] [PubMed]
  • 28. Painter P, Clark L, Olausson J. Physical function and physical activity assessment and promotion in the hemodialysis clinic: a qualitative study. Am J Kidney Dis. 2014;64(3):425-33. [DOI] [PubMed]
  • 29. Panza GA, Weinstock J, Ash GI, Pescatello LS. Psychometric Evaluation of the Timeline Followback for Exercise among College Students. Psychol Sport Exerc. 2012;13(6):779-88. [DOI] [PubMed]
  • 30. Pinto BM, Goldstein MG, Papandonatos GD, Farrell N, Tilkemeier P, Marcus BH, et al. Maintenance of exercise after phase II cardiac rehabilitation: a randomized controlled trial. Am J Prev Med. 2011;41(3):274-83. [DOI] [PubMed]
  • 31. Pringle A, Zwolinsky S, McKenna J, Daly-Smith A, Robertson S, White A. Effect of a national programme of men's health delivered in English Premier League football clubs. Public Health. 2013;127(1):18-26. [DOI] [PubMed]
  • 32. Song M, Carroll DD, Fulton JE. Meeting the 2008 physical activity guidelines for Americans among U.S. youth. Am J Prev Med. 2013;44(3):216-22. [DOI] [PubMed]
  • 33. Sun K, Song J, Manheim LM, Chang RW, Kwoh KC, Semanik PA, et al. Relationship of meeting physical activity guidelines with quality-adjusted life-years. Semin Arthritis Rheum. 2014;44(3):264-70. [DOI] [PubMed]
  • 34. Salmon J, Veitch J, Abbott G, Chin AM, Brug JJ, teVelde SJ, et al. Are associations between the perceived home and neighbourhood environment and children's physical activity and sedentary behaviour moderated by urban/rural location? Health Place. 2013;24:44-53. [DOI] [PubMed]
  • 35. Donnelly JE, Lambourne K. Classroom-based physical activity, cognition, and academic achievement. Prev Med. 2011;52:S36-42.
  • 36. Esteban-Cornejo I, Tejero-Gonzalez CM, Sallis JF, Veiga OL. Physical activity and cognition in adolescents: A systematic review. J Sci Med Sport. 2014. [DOI] [PubMed]
  • 37. Everson-Hock ES, Taylor AH, Ussher M. Readiness to use physical activity as a smoking cessation aid: A multiple behaviour change application of the Transtheoretical Model among quitters attending Stop Smoking Clinics. Patient education and counseling. 2010;79(2):156-9.
  • 38. Sandroff BM, Dlugonski D, Pilutti LA, Pula JH, Benedict RH, Motl RW. Physical activity is associated with cognitive processing speed in persons with multiple sclerosis. Mult Scler Relat Disord. 2014;3(1):123-8. [DOI] [PubMed]
  • 39. Hosseinzadeh K, Niknami S, Hidarnia A. Prevalence of Daily Physical Activity and Obesity Among Students Grade 5th and 6th. Biotech Health Sci. 2014;1(2):ee22084

Table 1.

Demographic Characteristics of Children

VariablesValuesRange
Children (n = 300)10.2 - 12.3 y
Age, y a11.2 ± 1.1
Gender b
Male145 (48.5)
Female155 (51.5)
School Grade b
5th 152 (50.5)
6th 148 (49.5)
RBMI c
85 - 95 percentile135 (45)
≥ 95 percentile 165 (55)
a Values are presented as Mean ± SD.
b Values are presented as No (%).
c RBMI; Relative Body Mass Index.

Table 2.

Distribution of Physical Activity and Screen Time Based on Gender, Grade and Relative Body Mass Index a

VariablesDPAWSTWSTt-test
Gender0.000
Male5.53 ± 0.6425.3 ± 5.623.2 ± 4.4
Female4.2 ± 0.915.8 ± 6.015.2 ± 6.3
Grade0.000
5th4.1 ± 0.913.9 ± 2.514.8 ± 2.3
6th3.5 ± 0.812.5 ± 2.112.2 ± 2.3
RBMI0.000
Overweight4.6 ± 1.117.5 ± 7.216.8 ± 6.9
Obese5.1 ± 0.923.0 ± 6.921.1 ± 5.8
a DPA: Daily Physical Activity. WST: Weekly Screen Time (self-report). WST: Weekly Screen Time (parent-report). DPA was calculated based on daily energy consumption (MET). WST was calculated based on total hours spent per week.

Figure 1.

Vicious Cycle Between Physical Activity and Body Mass Index

Figure 2.

Correlation Between Daily Physical Activity and Weekly Screen Time