physical education of students impact factor

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• Published: 17 December 2020

Physical education class participation is associated with physical activity among adolescents in 65 countries

• Riaz Uddin 1 , 2 , 3 ,
• Jo Salmon 1 ,
• Sheikh Mohammed Shariful Islam 1 , 3 &
• Asaduzzaman Khan 2 , 3  

Scientific Reports volume  10 , Article number:  22128 ( 2020 ) Cite this article

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• Health services
• Paediatric research
• Public health

In this study we examined the associations of physical education class participation with physical activity among adolescents. We analysed the Global School-based Student Health Survey data from 65 countries (N = 206,417; 11–17 years; 49% girls) collected between 2007 and 2016. We defined sufficient physical activity as achieving physical activities ≥ 60 min/day, and grouped physical education classes as ‘0 day/week’, ‘1–2 days/week’, and ‘ ≥ 3 days/week’ participation. We used multivariable logistic regression to obtain country-level estimates, and meta-analysis to obtain pooled estimates. Compared to those who did not take any physical education classes, those who took classes ≥ 3 days/week had double the odds of being sufficiently active (OR 2.05, 95% CI 1.84–2.28) with no apparent gender/age group differences. The association estimates decreased with higher levels of country’s income with OR 2.37 (1.51–3.73) for low-income and OR 1.85 (1.52–2.37) for high-income countries. Adolescents who participated in physical education classes 1–2 days/week had 26% higher odds of being sufficiently active with relatively higher odds for boys (30%) than girls (15%). Attending physical education classes was positively associated with physical activity among adolescents regardless of sex or age group. Quality physical education should be encouraged to promote physical activity of children and adolescents.

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Introduction.

Physical activity is essential for health and wellbeing of children and adolescents 1 . Physical activity improves musculoskeletal, cardiac, metabolic, psychosocial, and cognitive health, and enhances cardiorespiratory and muscular fitness of children and adolescents 1 , 2 , 3 , 4 . Regular participation also decreases adiposity in those who are overweight 3 . For optimal health benefits, the current international guidelines (i.e., the World Health Organization [WHO]) recommends that those aged 5–17-years accumulate at least 60 min of moderate-to-vigorous physical activity daily 5 . Globally, four out of five (81%) adolescents aged 11–17 years do not meet this recommendation and are insufficiently active 6 . Such inactive behaviours during adolescence have both current and future ramifications on health and wellbeing as behaviours such as physical activity established during adolescence can carry over to adulthood 7 , 8 . Therefore, pragmatic strategies to promote physical activity during adolescence around the globe are of critical importance 9 .

Adolescent physical activity occurs in different settings and domains including at home, in the community, for transportation, and at school. Opportunities for physical activity at school include during recess and lunch breaks, school sport and physical education lessons. Physical education classes may provide resources and opportunities for students to accumulate the daily physical activity level and can contribute to daily energy expenditure 10 , 11 . Recent meta-analyses found that 41% of secondary school 12 and 45% of elementary school 13 physical education lessons comprised moderate-to-vigorous physical activity. In many countries, physical education provides children and adolescents the understanding and motivation for an active lifestyle and also creates an environment to acquire knowledge and skills for physical activity throughout life 14 , 15 . In addition, adolescents who may have limited access to space and equipment outside of school can benefit from attending physical education classes at school 10 , 11 . School-based physical education, therefore, can be an accessible source of physical activity for many adolescents and can help develop an active healthy lifestyle 16 . In addition to the number of physical education classes, access to high-quality physical education experience (e.g., teacher behaviours, learning outcomes), which forms the foundation for lifelong engagement in physical activity, is also important for children and adolescents 17 , 18 , 19 .

Available evidence suggest that participation in physical education classes are positively associated with higher levels of physical activity 20 , 21 , 22 , 23 , 24 . However, the evidence is mostly based on single-country studies from high-income countries with limited multi-country study and lack of representation of low- and lower-middle-income countries 25 . A recent multi-country study reported country- and regional-level differences in physical education class participation, which was also differed by sex, age, and country-income classification 26 . In addition, delivery, content and quality of physical education also vary within and between countries 27 , 28 . It is often provided infrequently in schools across countries, and therefore the potential impact on total moderate-to-vigorous physical activity among boys and girls may be limited 29 . In order to obtain a comprehensive global perspective on the relationship between physical education and physical activity, large multi-country studies with representative samples are essential. Given the context and the opportunities that exist in schools for physical activity promotion, in this study, we aimed to examine whether participation in physical education classes (i.e., number of physical education class attendance) is associated with sufficient level of physical activity among adolescents (overall, and by sex and age-group) from 65 countries around the globe. We hypothesised that higher number of physical education class participation would be positively associated with sufficient level of physical activity among adolescents.

Data source

Data for this study were from the Global School-based Student Health Survey (GSHS), a population-based survey of school-going children and adolescents around the world 30 . In all participating countries, the GSHS uses the same standardised sampling technique and study methodology. All participants completed a standardised self-administered anonymous questionnaire, which included, but was not limited to, questions on demographics (e.g., age, sex), participation in physical education classes and physical activity. GSHS adopted questionnaire items, including items to measure physical activity and physical education from the Youth Risk Behavior Survey of American Adolescents. Countries, where GSHS were implemented, were encouraged to use culturally appropriate examples, words, and phrases to ensure sociocultural adaptability of the items. Furthermore, using a rigorous translation and back-translation process with the assistance of WHO and US CDC, countries were allowed to translate the questionnaire into their local language 31 .

As of 8 December 2019, 98 countries/territories around the globe had at least one GSHS dataset publicly available with the surveys being conducted between 2007 and 2016. For countries with more than one GSHS dataset, we used the most recent one available. Of the 98 countries, 84 countries had data on PA, while 67 countries had data on physical education. Two countries (Niue and Tokelau) were excluded from the analyses due to their small sample size (n < 140). The analytical sample consists of 206,417 adolescents aged 11 or younger to 17 years from 65 countries. Only a small proportion of students (1.05%) were in the age group “11 years old or younger”, and for modelling purposes, they were considered as 11 years old for this analysis, as it was not possible to determine what proportion of 1.05% students were younger than 11 years old. All countries provided nationally representative samples.

The GSHS received ethics approval from the Ministry of Education or a relevant Institutional Ethics Review Committee, or both in each of the participating countries. Only those adolescents and their parents who provided written or verbal consent participated. As the current study used retrospective, de-identified, publicly available data, ethics approval was not required for this secondary analysis. Detailed methods of the GSHS have been described on both the US CDC and the WHO websites 30 , 32 .

Outcome measure—physical activity participation

Physical activity was assessed with one item: ‘During the past 7 days, on how many days were you physically active for a total of at least 60 min per day?’ The response options were 0–7 days. Consistent with the WHO recommendations 5 , we defined participants as ‘sufficiently active’ who did ≥ 60 min/day of physical activity on seven days of the week.

Study factor—physical education participation

Physical education class attendance was assessed with one item: ‘During this school year, on how many days did you go to physical education (PE) class each week?’ The responses were classified into three groups: ‘0 day/week’, ‘1–2 days/week’, and ‘≥ 3 days/week’ as used elsewhere 25 , 33 .

Adolescents self-reported age, sex, and daily hours of sitting (when not in school or doing homework) in the survey. Food insecurity was assessed by asking: ‘During the past 30 days, how often did you go hungry because there was not enough food in your home?’ with response options being never, rarely, sometimes, most of the time, and always. As the GSHS did not include any direct measure of socioeconomic status, this variable was used as a proxy measure of socioeconomic status 34 , 35 . Self-reported height and weight were used to compute body mass index (BMI), which was categorised as underweight (BMI < −2SD), overweight (BMI >  + 1SD), and obese (BMI >  + 2SD), relative to median BMI, by age and sex based on the WHO Child Growth Standards 36 .

Statistical analyses

Of the 65 countries with data on physical activity and physical education, nine countries were from Africa, 20 from the Americas, 15 from Eastern Mediterranean, five from South East Asia, and 16 from the Western Pacific region. Using the World Bank country classification, collected at the time of the survey for the respective countries, seven countries were classified as low‐income, 21 lower‐middle‐income, 18 upper‐middle‐income, and 18 high-income. Income classification information was not available for Cook Island. The prevalence estimates of physical activity and physical education were obtained by using a Stata command ‘svyset’ to take into account sampling weights and the clustered sampling design of the surveys.

In examining the country-level association of physical education with physical activity, a set of covariates was considered including age, sex, weight status (i.e., BMI), food insecurity, and sitting time. Sitting time was considered as an adjusting factor given its demonstrated association with physical activity in adolescents 37 . Given the binary nature of physical activity outcome, logistic regression analysis with robust standard errors was used to examine the association at the country level, by taking into account the sampling weight that was applied to each participant record to adjust for non-response and the varying probability of selection. This GSHS weighting factor was applied in an identical way to estimate the association in each participating country. Within the GSHS protocol, weighting accounted for the probability of selection of schools and classrooms, non-responding schools and students, and distribution of the population by sex and grade.

Random effects meta-analysis was used to generate pooled estimates of the association between physical education and physical activity for the overall sample, by country income category (e.g., low-income, lower-middle income, upper-middle income, and high-income), and by WHO region, stratified by sex and age groups (11–14 years vs 15–17 years). Two age groups (11–14 years [early adolescence] and 15–17 years [middle adolescence]) 38 were considered to stratify the analysis in order to examine whether the association estimates vary across phases of adolescence. This analysis used DerSimonian and Laird method 39 with the estimate of heterogeneity being taken from the Mantel–Haenszel model. As the GSHS were conducted across different cultural settings in 65 countries around the world over a long period of time (2007–2016), it was reasonable to assume that the association estimates across countries were likely to vary from survey to survey, which supports the use of random effects meta-analysis that can adjust heterogeneity among studies 40 . The percentage of variability in estimates across studies that is attributable to between study heterogeneity (I 2 ) in our analysis ranges from 54.3 to 80.2%, which suggests a strong presence of heterogeneity in the association estimates, and further supports the use of random effects meta-analysis. All adjusted estimates of the association parameters are presented in the form of odds ratio (OR) and 95% confidence interval (CI). All analyses were conducted by StataSE V14.0.

Ethics approval and consent to participate

The GSHS received ethics approval from both a national government administration and an institutional review board or ethics committee. Only adolescents and their parents who provided written/verbal consent participated. As the current study used retrospective publicly available data, we did not require ethics approval from any Institutional Ethics Review Committee for this secondary analysis.

The mean age of the participating adolescents (n = 206,417) was 14.35 (SD = 1.45) years, 54.4% aged 11–14 years, and 49.2% were girls. The prevalence of sufficient physical activity was 15.0%, with boys having higher prevalence (18.3%) than girls (11.5%). Over half (56.5%) of adolescents participated in physical education classes 1–2 days/week (boys 54.7%; girls 58.3%) and about a quarter (24.2%) participated in physical education classes ≥ 3 days/week (boys 26.8%; girls 21.6%). As shown in Fig.  1 , the overall percentage of adolescents being sufficiently active was greater for those who attended more physical education classes in both sexes.

Proportion of adolescents sufficiently physically active by participation in physical education classes, Global School-based Student Health Survey, 2007–2016.

Estimates of associations of physical education class participation with sufficient physical activity by country are shown in Table 1 . The country-level analysis shows that 50 out of 65 participating countries (77%) demonstrated significant and positive associations between attending physical education classes ≥ 3 days/week and being sufficiently active with 33 countries (51%) revealing at least double the odds (OR ≥ 2.0) of meeting physical activity guidelines. For example, Bolivian adolescents who attended physical education classes ≥ 3 days/week had threefold odds of reporting sufficient physical activity compared with their counterparts who attended no physical education class (OR 3.00, 95% CI 1.93–4.67). In examining the association between attending physical education classes 1–2 days/week and being sufficiently active, 20 countries (31%) demonstrated significant positive associations. For example, Thai adolescents who attended physical education classes 1–2 days/week had double the odds to reporting sufficient physical activity compared with their counterparts who attended no physical education class (OR 2.11, 95% CI 1.39–3.19). As shown in Table 1 , attending physical education classes ≥ 3 days/week was positively and strongly associated with physical activity in all WHO regions with South East Asia region showing the strongest association (OR 2.89, 2.11–3.97), followed by Africa (OR 2.45, 1.72–3.48) and Western Pacific region (OR 2.40, 1.92–3.00). The analysis also showed evidence of positive and moderate association between attending physical education classes 1–2 days/week and being sufficiently active in all WHO regions with the pooled association estimates ranging from OR 1.19 (1.01–1.41) in the Americas region to OR 1.86 (1.03–3.36) in South East Asia.

Overall, adolescents who took physical education classes ≥ 3 days/week, compared to those who did not take any physical education classes, had double the odds of being sufficiently active (OR 2.05, 95% CI 1.84–2.28) with no apparent gender (OR 2.09, 1.88–2.33 for boys; and OR 1.95, 1.69–2.25 for girls) or age (OR 2.19, 1.93–2.48 for 11–14-year-old; and OR 2.03, 1.80–2.28 for 15–17-year-old adolescents) differences (Table 2 ). Adolescents who participated in physical education classes 1–2 days/week had 26% higher odds of being sufficiently active (OR 1.26, 1.15–1.37) with relatively higher odds for boys (OR 1.30, 1.17–1.46) than girls (OR 1.15, 1.03–1.29) and younger adolescents aged 11–14 years (OR 1.28, 1.16–1.42) that older adolescents aged 15–17 years (OR 1.19, 1.08–1.32).

The odds of attending physical education classes ≥ 3 days/week and being sufficiently active were lower in country with higher income (Table 2 ). In low-income countries, adolescents who participated in physical education classes ≥ 3 days/week had 137% higher odds of being sufficiently active (OR 2.37, 1.51–3.73) with comparable odds for boys (OR 2.51, 1.70–3.70) and girls (OR 2.36, 1.31–4.26) and slightly higher odds for younger (OR 2.94, 1.92–4.51) than older adolescents (OR 2.32, 1.36–3.96). In high-income countries, the odds of being sufficiently active was 85% higher for adolescents who attended physical education classes ≥ 3 days/week (OR 1.85; 1.52–2.25) with no apparent gender (boys OR 1.89, 1.50–2.37; girls OR 1.69, 1.36–2.10) or age (younger OR 1.83, 1.47–2.28; older OR 1.80 (1.48–2.19) differences. In lower-middle income countries, adolescents who attended physical education classes 1–2 days/week had 39% higher odds of being sufficiently active (OR 1.39, 1.19–1.62) compared to their counterparts who did not take any physical education classes, with relatively higher odds for boys (OR 1.46, 1.21–1.76) than girls (OR 1.30, 1.03–1.65), and similar odds for younger (OR 1.36, 1.09–1.68) and older adolescents (OR 1.33, 1.16–1.51).

Boys of South East Asian region who participated in physical education classes ≥ 3 days/week had the highest odds of being sufficiently active (OR 3.29, 1.97–5.47), followed by the boys of Africa region (OR 2.41, 1.74–3.33) (Supplementary Table S1 ). Girls of Western Pacific and Africa region who participated in physical education classes ≥ 3 days/week had the highest odds of being sufficiently active (OR 2.68, 1.89–3.77, and OR 2.63, 1.63–4.26, respectively). Even by attending physical education classes 1–2 days/week, boys of the Americas region and girls of Africa region can increase their odds, though not considerably, of being sufficiently active (OR 1.29, 1.06–1.58, and OR 1.41, 1.15–1.73, respectively).

Both younger and older adolescents in all WHO regions demonstrated positive association between ≥ 3 days/week physical education class attendance and meeting the physical activity recommendations (Supplementary Table S1 ). Younger adolescents in South East Asia (OR 3.03, 2.42–3.79) and Africa (OR 2.95, 2.07–4.20), and older adolescents in South East Asia (OR 3.24, 1.57–6.67) who participated in physical education classes ≥ 3 days/week had over three times higher odds of being sufficiently active. There were moderate positive associations between physical education class attendance for 1–2 days/week and meeting the physical activity recommendations for younger adolescents in Africa (OR 1.38, 1.03–1.84), the Americas (OR 1.29, 1.07–1.56), and Eastern Mediterranean regions (OR 1.24, 1.06–1.44), and for older adolescents in Africa (OR 1.24, 1.03–1.48), Eastern Mediterranean (OR 1.26, 1.07–1.49), and Western Pacific region (OR 1.19, 1.01–1.41).

To our knowledge, this is the most extensive global study to assess the association of physical education class attendance with physical activity of adolescents, based on nationally representative samples from 65 countries around the globe. The key finding of our study is that adolescents, irrespective of sex or age, who had a higher frequency (≥ 3 days/week) of physical education class attendance had significantly higher odds of meeting the WHO’s physical activity recommendations. The estimates of association between the frequency of attending physical education and meeting physical activity recommendations were lower among countries with higher income. We observed some regional differences with South East Asia having the highest associations and the Americas having the lowest. Our findings suggest that adolescents, especially girls and those aged 15–17 years, are mostly benefited from a higher frequency (i.e., ≥ 3 days/week) of physical education participation. Our study also found some benefits of less frequent participation in physical education classes (1–2 days/week) in meeting the physical activity guidelines, which is encouraging. About one-third of the countries demonstrated positive association between less frequent participation in physical education classes and meeting the physical activity recommendations, and such association was prominent in boys and younger adolescents in all but low-income countries. Our study thus argues that even less frequent participation in physical education classes can bring some benefits for some adolescents.

Our finding that a higher frequency of physical education class attendance was positively associated with meeting the physical activity recommendations is consistent with other studies in children and adolescents 20 , 21 , 24 , 25 . It has been argued that participation in physical education classes acts as a positive reinforcement to “keep young people going” by being more physically active with less time in sedentary behaviour throughout the day 25 . Physical education classes provide children with an opportunity to familiarise themselves with different types of physical activity, motivates them to be active within the school environment, and potentially also encourages more out-of-school physical activity 41 . Physical activity during physical education classes may reduce fatigue and improve mood by changing neurophysiological stimulation and the brain’s information processing function (i.e., cerebral cortex), which may improve children’s preparedness to move more throughout the day 25 . While the frequency of physical education class is important, it is also critical that children have access to quality physical education 18 , 19 . Previously, researchers have suggested that in spite of the traditional class-based and sports-centred physical education curriculum, physical education ought to be a health-centred dynamic learning experience for children 19 , 42 . Quality physical education is important for age-appropriate cognitive learning and to acquire fitness, develop motor skills and psychosocial and emotional skills, which can help children to lead an active lifestyle, inside and outside of the school environment, throughout their life course 18 , 19 , 42 . Given the role of physical education for active and healthy lifestyle, different stakeholders, including United Nations agencies (i.e., UNESCO) 19 , European Commission 17 , have recommended to ensure quality physical education for children and adolescents, and called for political commitments and actions from Governments and supports from the international communities.

In our study, adolescents boys and girls in low-income countries with ≥ 3 days/week physical education class attendance had the highest odds of meeting the physical activity recommendations, and the associations became smaller (yet significant) with a higher country income classification for both sexes. A previous 12-country study 25 reported similar findings for boys, but not for girls. Unlike our study that is based on self-reported data, the earlier study used a device-based physical activity measure and included Australia and other high-income countries of Europe and North America. In addition to high-income countries, our study included adolescents from low- and lower-middle-income countries. It is possible that for many children, regardless of sex or country income, schools provide the most pragmatic and readily accessible opportunities for various physical activity, while out-of-school physical activity options, logistics, and environments might be variable 10 , 11 . The environments, in general, may be more supportive of out of school physical activity for children in high-income countries than their counterparts in low-income countries; however, high-income countries may have other challenges including gender and socioeconomic disparities in physical activity. For example, children from high-poverty neighbourhood may have fewer opportunities for out of school physical activity in many high-income countries 43 , 44 . Appreciating the heterogeneity in resources for physical education within- and across countries, all governments should consider schools as the primary focus to promote an active and healthy lifestyle among children and adolescents, which is likely to be a cost-effective and opportunistic initiative to get them moving. Our findings also show that physical education is potentially more important in South East Asia than the Americas in promoting physical activity. In addition to environmental support, such variations could be a sign of the quality of the respective physical education programs, including time allocated for physical education across the countries. There is a large heterogeneity in weekly time allocated for physical education in countries around the globe. For example, weekly time for physical education of secondary school students in Bangladesh (180 min) is reportedly higher than in Peru (90 min) 28 . Research is needed to understand whether physical education classes are designed to facilitate physical activity and/or how much time students actually spend in physical activity during physical education classes. It is also important to understand how physical education lessons can help the students to develop skills so that they can be more active both inside and outside of school. This information can help in designing a physical education curriculum with balanced components of physical activity and physical education lessons on other health and wellbeing so that the students can develop a healthy lifestyle. Opportunities for quality physical education should be equitable and inclusive, and available for all children regardless their gender, disability status, socio-economic position, and cultural or religious backgrounds, and the delivery of physical education should be ensured for marginalised and vulnerable groups 19 .

The strengths of our study are the inclusion of a large number of countries around the globe, representing different world regions and income groups. All countries included in our study provided nationally representative data. We used the GSHS sample weighting to account for distribution of the population by age and sex in countries for whose data were analysed. Any potential skewness, by sex or age, in the observed data is unlikely to impact the weighted analysis results. All countries where GSHS was implemented, used a standardised data collection procedure. In all countries, a standardised questionnaire with the same survey items to assess physical activity and physical education class attendance was used, which facilitated our regional comparisons. We adjusted our estimates for several potential covariates to avoid possible confounding effects of these factors.

The findings of our study should be interpreted in light of its limitations. Data for our study were collected using self-reported questionnaire; these data are vulnerable to social desirability and recall bias. Unavailability of GSHS data from European and North American countries, some of the Latin/Central American and Asia and Pacific countries, limits the generalisability of the findings only to the GSHS participating countries. Although a standardised questionnaire was used in all participating countries, there is a lack of information on the reliability and validity of GSHS measures across different countries or cultures. Physical education classes can have different meanings and can constitute different components, including a knowledge-based curriculum component (i.e., lessons and discussions) and/or skill-based physical activity session, in different settings. We did not have any information on components of physical education classes across the participating countries. The cross‐sectional design of the study limits our ability to make any causal inferences from the association estimates. Some adolescents in our study may have had difficulties with understanding the questionnaire because of poor reading skills. In this study, we used data collected between 2007 and 2016, which may have biased the results because of the period effect.

Conclusions

Our study suggests a positive association between regular participation in physical education classes and meeting the physical activity guidelines among children and adolescents around the globe regardless of sex or age group. The odds were lower in high- than low-income countries. The benefits of regular participation in physical education classes to enhance physical activity are universal across all WHO regions, with the highest being observed among adolescents from South East Asian countries. Even less frequent participation in physical education classes (i.e., 1–2 days a week) was related to higher odds of being sufficiently active in all but low-income countries, especially in boys. Thus, the findings support the importance of physical education for ensuring sufficient physical activity among school-going children and adolescents around the globe. Countries must not miss the opportunity to ensure schools deliver a daily or at least 3 days per week of well-designed physical education classes, which can play a vital role in creating active nations around the world.

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Acknowledgements

The authors would like to thank the US Centers for Disease Control and Prevention and the World Health Organization for making the Global School-based Student Health Survey (GSHS) data publicly available for analysis. The authors thank the GSHS country coordinators and other staff members. R.U. is supported by Alfred Deakin Postdoctoral Research Fellowship. J.S. is supported by a National Health and Medical Research Council Leadership Level 2 Fellowship (APP 1176885). S.M.S.I. is supported by the Institute for Physical Activity and Nutrition, Deakin University and a post doctorate fellowship from the National Heart Foundation of Australia (Award #102112).

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Uddin, R., Salmon, J., Islam, S.M.S. et al. Physical education class participation is associated with physical activity among adolescents in 65 countries. Sci Rep 10 , 22128 (2020). https://doi.org/10.1038/s41598-020-79100-9

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Physical Education Schools

What is the impact of physical education on students’ well-being and academic success?

Decreasing time for quality phys-ed to allow more instructional time for core curricular subjects – including math, science, social studies and English – is counterproductive, given its positive benefits on health outcomes and school achievement.

by: Lee Schaefer , Derek Wasyliw

date: June 25, 2018

Download and print the Fact Sheet (232.30 kB / pdf)

Research confirms that healthier students make better learners. The term quality physical education is used to describe programs that are catered to a student’s age, skill level, culture and unique needs. They include 90 minutes of physical activity per week, fostering students’ well-being and improving their academic success. However, instructional time for quality phys-ed programs around the world are being decreased to prioritize other subject areas (especially math, science, social studies and English) in hopes to achieve higher academic achievement. However, several studies have identified a significant relationship between physical activity and academic achievement. Research also demonstrates that phys-ed does not have negative impacts on student success and that it offers the following physical, social, emotional and cognitive benefits:

Quality phys-ed helps students understand how exercise helps them to develop a healthy lifestyle, gain a variety of skills that help them to participate in a variety of physical activities and enjoy an active lifestyle.

Quality phys-ed provides students with the opportunity to socialize with others and learn different skills such as communication, tolerance, trust, empathy and respect for others. They also learn positive team skills including cooperation, leadership, cohesion and responsibility. Students who play sports or participate in other physical activities experience a variety of emotions and learn how to better cope in stressful, challenging or painful situations.

Quality phys-ed can be associated with improved mental health, since increased activity provides psychological benefits including reduced stress, anxiety and depression. It also helps students develop strategies to manage their emotions and increases their self-esteem.

Research tends to show that increased blood flow produced by physical activity may stimulate the brain and boost mental performance. Avoiding inactivity may also increase energy and concentration in the classroom.

Therefore, decreasing time for quality phys-ed to allow more instructional time for core curricular subjects – including math, science, social studies and English – is counterproductive, given its positive benefits on health outcomes and school achievement.

Additional Information Resources

PHE Canada (2018). Quality daily physical education . Retrieved from https://phecanada.ca/activate/qdpe

  Ontario Ministry of Education. (2005).  Healthy schools daily physical activity in schools grades 1 ‐ 3. Retrieved from http://www.edu.gov.on.ca/eng/teachers/dpa1-3.pdf

Ardoy, D. N., Fernández‐Rodríguez, J. M., Jiménez‐Pavón, D., Castillo, R., Ruiz, J. R., & Ortega, F. B. (2014). A Physical Education trial improves adolescents’ cognitive performance and academic achievement: The EDUFIT study.  Scandinavian journal of medicine & science in sports ,  24 (1).

Bailey, R., Armour, K., Kirk, D., Jess, M., Pickup, I., Sandford, R., & Education, B. P. (2009). The educational benefits claimed for physical education and school sport: An academic review.  Research papers in education ,  24 (1), 1-27.

Beane, J.A. (1990). Affect in the curriculum: Toward democracy, dignity, and diversity . Columbia: Teachers College Press.

Bedard, C., Bremer, E., Campbell, W., & Cairney, J. (2017). Evaluation of a direct-instruction intervention to improve movement and pre-literacy skills among young children: A within-subject repeated measures design.  Frontiers in pediatrics ,  5 , 298.

  Hellison, D.R., N. Cutforth, J. Kallusky, T. Martinek, M. Parker, and J. Stiel. (2000). Youth development and physical activity: Linking universities and communities. Champaign, IL: Human Kinetics.

  Ho, F. K. W., Louie, L. H. T., Wong, W. H. S., Chan, K. L., Tiwari, A., Chow, C. B., & Cheung, Y. F. (2017). A sports-based youth development program, teen mental health, and physical fitness: An RCT.  Pediatrics , e20171543.

Keeley, T. J., & Fox, K. R. (2009). The impact of physical activity and fitness on academic achievement and cognitive performance in children.  International Review of Sport and Exercise Psychology ,  2 (2), 198-214.

Kohl III, H. W., & Cook, H. D. (Eds.). (2013).  Educating the student body: Taking physical activity and physical education to school . National Academies Press.

Rasberry, C. N., Lee, S. M., Robin, L., Laris, B. A., Russell, L. A., Coyle, K. K., & Nihiser, A. J. (2011). The association between school-based physical activity, including physical education, and academic performance: a systematic review of the literature.  Preventive medicine ,  52 , S10-S20.  

Sallis, J. F., McKenzie, T. L., Kolody, B., Lewis, M., Marshall, S., & Rosengard, P. (1999). Effects of health-related physical education on academic achievement: Project SPARK.  Research quarterly for exercise and sport ,  70 (2), 127-134.

Strong WB, Malina RM, Blimkie CJ, Daniels SR, Dishman RK, Gutin B, Hergenroeder AC, Must A, Nixon PA, Pivarnik JM, Rowland T, Trost S, & Trudeau F (2005). Evidence based physical activity for school-age youth.  Journal of Pediatrics . 146(6):732–737.

Trudeau, F., & Shephard, R. J. (2008). Physical education, school physical activity, school sports and academic performance.  International Journal of Behavioral Nutrition and Physical Activity ,  5 (1), 10.

Beane, J. A. (1990). Affect in the curriculum: Toward democracy, dignity, and diversity . Columbia University, New York, NY: Teachers College Press.

Meet the Expert(s)

Lee schaefer.

Assistant Professor in the Kinesiology and Physical Education Department at McGill University

Lee Schaefer is an Assistant Professor in the Kinesiology and Physical Education Department at McGill University. His work is generally focused on teacher education and teacher knowle...

Derek Wasyliw

Master’s student in the Kinesiology and Physical Education Graduate Program at McGill University

Derek Wasyliw is a second-year Master’s student in the Kinesiology and Physical Education Graduate Program at McGill University. He is the proud recipient of the 2017-2018 SSHRC Jo...

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Quality research, reports, and professional learning opportunities.

• Open access
• Published: 02 April 2024

The effect of the Sport Education Model in physical education on student learning attitude: a systematic review

• Junlong Zhang 1 ,
• Wensheng Xiao 2 ,
• Kim Geok Soh 1 ,
• Gege Yao 3 ,
• Mohd Ashraff Bin Mohd Anuar 4 ,
• Xiaorong Bai 2 &
• Lixia Bao 1  

BMC Public Health volume  24 , Article number:  949 ( 2024 ) Cite this article

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Metrics details

Evidence indicates that the Sport Education Model (SEM) has demonstrated effectiveness in enhancing students' athletic capabilities and fostering their enthusiasm for sports. Nevertheless, there remains a dearth of comprehensive reviews examining the impact of the SEM on students' attitudes toward physical education learning.

The purpose of this review is to elucidate the influence of the SEM on students' attitudes toward physical education learning.

Employing the preferred reporting items of the Systematic Review and Meta-analysis (PRISMA) statement guidelines, a systematic search of PubMed, SCOPUS, EBSCOhost (SPORTDiscus and CINAHL Plus), and Web of Science databases was conducted in mid-January 2023. A set of keywords associated with the SEM, attitudes toward physical education learning, and students were employed to identify relevant studies. Out of 477 studies, only 13 articles fulfilled all the eligibility criteria and were consequently incorporated into this systematic review. The validated checklist of Downs and Black (1998) was employed for the assessment, and the included studies achieved quality scores ranging from 11 to 13. The ROBINS-I tool was utilized to evaluate the risk of bias in the literature, whereby only one paper exhibited a moderate risk of bias, while the remainder were deemed to have a high risk.

The findings unveiled significant disparities in cognitive aspects ( n  = 8) and affective components ( n  = 12) between the SEM intervention and the Traditional Teaching (TT) comparison. Existing evidence suggests that the majority of scholars concur that the SEM yields significantly superior effects in terms of students' affective and cognitive aspects compared to the TT.

Conclusions

Nonetheless, several issues persist, including a lack of data regarding junior high school students and gender differences, insufficient frequency of weekly interventions, inadequate control of inter-group atmosphere disparities resulting from the same teaching setting, lack of reasonable testing, model fidelity check and consideration for regulating variables, of course, learning content, and unsuitable tools for measuring learning attitudes. In contrast, the SEM proves more effective than the TT in enhancing students' attitudes toward physical learning.

Systematic review registration

( https://inplasy.com/ ) (INPLASY2022100040).

Peer Review reports

Introduction

In recent years, the "student-centered" teaching model, as a more effective alternative to the traditional "teacher-centered" teaching model, has gained increasing attention and recognition from education scholars and departments worldwide [ 1 , 2 ]. Metzler [ 3 ] identified a series of "student-centered" teaching models based on constructivism and social learning theories, each developed for specific course objectives [ 4 , 5 ]. Furthermore, it is widely acknowledged that instructional models are in a constant state of development, involving the generation, testing, refinement, and further testing processes under different educational objectives. These instructional models are designed to enable students to acquire a depth and breadth of knowledge in physical education [ 6 ]. In this regard, a series of instructional models have been identified as effective means to achieve specific objectives. Consequently, numerous studies have established that placing students at the center of the instructional process is the most effective approach [ 7 ], allowing for the assessment of the impact of these models on students' learning in physical education. For instance, Cooperative Learning (CL), rooted in the idea of learning together with others, through others, and for others [ 8 ], aims to promote five essential elements [ 9 ]: interpersonal skills, processing, positive interdependence, promoting interaction, and individual responsibility. The underlying concept of Teaching Game for Understanding (TGFU) involves shifting the focus from technical aspects of gameplay to the context (tactical considerations) through modification of representation and exaggeration [ 4 , 10 ]. Emphasizing placing learners in game situations where tactics, decision-making, and problem-solving are non-negotiable features, despite incorporating skill practice to correct habits or reinforce skills [ 11 ], TGFU is structured around six steps: game, game appreciation, tactical awareness, decision-making, skill execution, and performance. Teaching for Personal and Social Responsibility (TPSR), designed by Hellison [ 12 ], aims to cultivate personal and social responsibility in young people through sports activities, defining four major themes: integration, transfer, empowerment, and teacher-student relationships. It revolves around five responsibility goals: respecting the rights and feelings of others, effort (self-motivation), self-direction, caring (helping), and transferring beyond the "gym" [ 13 ]. The SEM comprises six key structural features: season, affiliation, formal competition, culminating events, record-keeping, and festivity. SEM seeks to provide students with authentic, educationally meaningful sporting experiences within the school sports context, aiming to achieve the goal of developing capable, cultured, and enthusiastic individuals [ 14 ]. This suggests a subtle intersection between SEM's developmental goals and enhancing students' learning attitudes (cognitive and emotional), laying the foundation for the selection of teaching model types in this study.

In previous SEM-centered reviews, the focus primarily centered on the model's positive impact on students' personal and social skills [ 15 , 16 ], motor and cognitive development [ 16 ], motivation [ 17 , 18 ], basic needs [ 18 ], prosocial attitudes [ 18 ], and learning outcomes [ 19 ], and it is concluded that the implementation of SEM has a positive effect on improving students' performance in these aspects. While these reviews contribute valuable insights, they exhibit certain limitations, such as a lack of comprehensive exploration of the model's impact on the cognitive and emotional dimensions in the context of school-based physical education. Therefore, our study attempts to bridge this gap by delving into the nuanced intersection between SEM and students' learning attitudes, aiming to provide a more comprehensive understanding of its impact on educational environments.

In the field of education, a focus on practical application and scholarly discourse is crucial and commendable [ 20 , 21 ]. From a practical perspective, research should offer valuable resources for curriculum designers, educators, and policymakers [ 22 , 23 , 24 , 25 ]. In theoretical terms, the contribution of research lies in addressing gaps in the literature by elucidating dimensions within physical education that remain insufficiently explored [ 26 ]. Our study is dedicated to significantly impacting physical education teaching through the practical application and scholarly discourse surrounding SEM. By revealing the subtle interactions between SEM and attitudes, we aim to provide valuable curriculum implementation recommendations for designers, practitioners, and policymakers, filling the gaps in how SEM shapes learning attitudes in educational environments.

In the realm of attitude research, scholars have traditionally classified attitude components into three types: single-component, two-component, and three-component. Advocates of the single-component view contend that attitudes are confined to the emotional dimension. For example, Fazio and Zanna [ 27 ] define attitude as "an evaluative feeling caused by a given object" (p. 162). Two-component researchers posit that attitudes comprise cognition and emotion, with the affective component measuring emotional attraction or feelings toward the object, and the cognitive component representing beliefs about the object's characteristics [ 28 , 29 ]. Bagozzi and Burnkrant [ 30 ] compared the effectiveness of one-component and two-component attitude models, concluding that incorporating both cognitive and emotional dimensions enhances attitude effectiveness. On the contrary, proponents of the three-component perspective argue that attitudes encompass cognition, emotion, and behavior, suggesting that cognitive and emotional responses to an object influence behavior. However, the three-component view has faced skepticism, with some researchers finding that attitude measurement explains only about 10% of behavior variance. Studies reporting higher correlations often focus on attitudes and behavioral intent rather than explicit behavior itself [ 31 , 32 , 33 ]. Our research places a deliberate emphasis on investigating the intersection between the SEM and attitudes to address a noticeable gap in the existing scholarly landscape. While none of the reviewed literature approached the subject from an attitude theory perspective, we prioritize this theoretical framework, acknowledging that attitudes significantly influence student learning [ 16 , 34 ]. Consequently, the exploration of the interplay between SEM and attitudes is considered indispensable for attaining a thorough comprehension of SEM's potential impact in educational contexts. By integrating attitude theory into this inquiry, there is an aspiration to unveil nuanced insights into the cognitive and emotional dimensions influenced by SEM, thereby enriching the understanding of the model's pedagogical implications.

The chosen systematic review approach in this study aims to enhance the reader's understanding of the research methodology, thereby strengthening the overall scientific rigor of the study [ 35 ].

Protocol and registration

This review adheres to the guidelines set forth by the Preferred Reporting Project for Systematic Review and Meta-Analysis (PRISMA). The review has been registered on the International Registry Platform for Systematic Review and Meta-Analysis Programmes (INPLASY) under the registration number INPLASY2022100040. More information about the review can be found at the following link: https://inplasy.com/ .

Search strategy

In October 2004, Siedentop initiated SEM workshops, attracting widespread attention from scholars both domestically and internationally, marking the beginning of SEM practices [ 36 , 37 ]. Subsequently, in many advanced countries such as the United States, New Zealand, Australia, and the United Kingdom, SE has become a mainstream approach in physical education instruction [ 38 ]. Therefore, the retrieval period for this review is set from October 2004 to December 2023, encompassing relevant articles published during this timeframe. A systematic search of four electronic databases was conducted for relevant articles: SCOPUS, PubMed, EBSCOhost (SPORT Discus and CINAHL Plus), and Web of Science. The search aimed to identify studies on the effects of SEM on attitudes toward physical education learning. We employed advanced search methods and added the following search terms: ("Sport Education Model" OR "Sport Education" OR "Sport season") AND ("learning attitude" OR "sports attitude" OR "cognitive" OR "cognition" OR "usefulness" OR "importance" OR "perceptions" OR "affective" OR "emotional" OR "enjoyment" OR "happiness" OR "well-being" OR "Blessedness" OR "subjective well-being") AND ("student" OR "pupil" OR "scholastic" OR "adolescent" OR "teenager"). The search expressions were combined using logical operators. We also sought assistance from librarians in the field to ensure comprehensive results. Furthermore, we manually examined the reference lists of the included studies to identify additional relevant literature and validate the effectiveness of our search strategy.

Eligibility criteria

We employed the Picos framework, encompassing Population, Intervention, Comparison, Outcomes, and Study Design, as the inclusion criteria for this systematic review (Table  1 ). Furthermore, the selected literature adhered to the following additional criteria: (i) it comprised full English texts published in peer-reviewed journals; (ii) the interventions were conducted within the context of physical education, with a comprehensive description of the intervention process and content; (iii) the effects of the SEM and TT on students' learning attitudes (cognitive and emotional) were compared on at least one dimension; (iv) quasi-experimental designs employing objective tests and measurements, along with studies presenting evaluation results, were considered. Exclusion criteria encompassed studies that combined physical education models with other teaching methods or models (hybrid or invasive). Initially, the search strategy was guided by a librarian, and duplications were eliminated by importing the retrieved literature into Mendeley reference management software. Subsequently, decisions regarding literature exclusion and retention were made through the screening of titles and abstracts. Ultimately, articles deemed highly relevant were read in full. The primary outcome aimed to assess attitudes (cognitive and affective) toward physical learning based on the SEM.

The search strategy was guided by a librarian, and the obtained literature was imported into Mendeley reference management software for duplicate removal. Decisions regarding literature inclusion and exclusion were made based on the screening of titles and abstracts. Articles that were deemed highly relevant were read in their entirety. The primary focus of this review was to assess attitudes (cognitive and affective) toward physical learning, specifically based on the SEM. The designation "not relevant" is employed to characterize articles subjected to thorough scrutiny, which fail to make substantive contributions to the fundamental focus of our research. More precisely, those articles deemed irrelevant were those that omitted consideration of the pivotal variables under examination, namely, cognitive and emotional dimensions. Furthermore, they were not situated within the milieu of a scholastic educational framework for physical education (SEM). This methodological approach has been instituted to uphold the establishment of a centralized and cohesive dataset requisite for subsequent analytical procedures [ 39 ] (See Fig.  1 ).

PRISMA summary of the study selection process

Study selection

Prior to conducting the search, consultation with an experienced librarian was sought to develop an effective retrieval strategy. Following this, two independent reviewers conducted the literature search. All retrieved studies were imported into Mendeley literature management software to identify and eliminate duplicates. Initially, the literature was screened based on the titles by two independent evaluators, who excluded irrelevant studies. Subsequently, the abstracts of the initially selected literature were reviewed against pre-established inclusion criteria to determine their eligibility for inclusion in the study. Finally, the full text of the included literature was reviewed by two authors, who extracted relevant information. In the case of any disagreements, a third author (K.G.S.) was involved in the review process.

Data extraction and quality assessment

The data extraction process involved collecting the following information: (1) author and year of publication; (2) research design, including the type of experiment or teaching project; (3) population details, such as student category, total number of students, age range, and gender distribution, as well as group size; (4) intervention characteristics, including the total number of interventions, weekly frequency of interventions, duration of each intervention, and consistency of intervention location; (5) a comparison group, typically involving the TT and country information; (6) results, which encompassed the measurement tools used, specific indicators measured, and the research findings. The collected data were independently summarized and reviewed by two authors, with the involvement of a third author to resolve any discrepancies or disagreements.

The methodological quality of the selected articles in this systematic review was assessed using the validated checklist developed by Downs and Black [ 40 ]. The checklist consisted of 27 items, which were categorized into three domains: reporting (items 1–10), validity (external validity: items 11–13; internal validity: items 14–26), and statistical power (item 27). Each item was scored, resulting in a total score ranging from 0 to 27, with higher scores indicating higher methodological quality.

In this review, the cross-sectional and longitudinal surveys were scored in detail using the Downs and Black checklist to evaluate the strengths and weaknesses of each study [ 40 ]. The scoring process involved two primary assessors independently assessing the selected studies. In case of any ambiguity or disagreement, a resolution was reached through reconciliation. If disagreements persisted, the assessment was conducted by one of the co-authors until a consensus was reached.

The classification criteria for the scores were as follows: studies with a score below 11 were considered to have low methodological quality, scores ranging from 11 to 19 indicated medium quality, and scores higher than 20 indicated high methodological quality [ 41 ]. Upon assessment, it was found that all selected articles in this review fell within the medium-quality range (see Table  2 ).

The studies risk of bias

The Risk of Bias in Non-randomized Studies-of Interventions (ROBINS-I) tool encompasses seven evaluation areas, which are further divided into three distinct stages: pre-intervention, intervention, and post-intervention. The pre-intervention stage includes two evaluation areas: confounding bias and selection bias of participants. The intervention stage focuses on the evaluation of bias in the classification of interventions. The post-intervention stage comprises four evaluation areas: bias due to deviations from intended interventions, bias due to missing data, bias in the measurement of outcomes, and bias in the selection of reported results. Each evaluation area is composed of multiple signaling questions, amounting to a total of 34 signaling questions.

Methodical quality

The articles underwent assessment using the validated checklist developed by Downs and Black (1998): 11–13 (mean = 12.38; median = 12; mode = 12 & 13). All the articles demonstrated a medium level of quality, indicating their suitability for inclusion in this review. Furthermore, it suggests the potential for higher-quality articles in future studies. Among the thirteen included articles, five were published within the last three years, constituting one-third of the included literature. This observation highlights the ongoing research interest and significance of the SEM in the investigation of various teaching models. In terms of the Hypothesis/aim/objective, participant characteristics, interventions, main findings, data variability, probability values, statistical tests, detailed intervention descriptions, reliable outcome measures, participant source ( n  = 12), participant grouping ( n  = 11), and random allocation ( n  = 3) were adequately addressed. However, aspects such as reporting measurement outcomes in the introduction or methods section, confounder distribution, adverse events following the intervention, characterization of lost-to-follow-up patients, data analysis, blinding of participants and assessors, adjustment for confounding, and identification of chance results with a probability less than 5% ( n  = 0) were not thoroughly addressed. Although the implementation of blind subjects, therapists, and assessors in teaching experiments poses challenges, future research should strive for higher quality and stronger levels of evidence [ 23 ].

After a detailed reading of the literature that meets the inclusion criteria of this review and the extraction and sorting of important information, it is presented in Table  3 .

The bias risk assessment results are summarized in Table  4 , which includes information such as author/date, field of study, study type, risk assessment tool, and overall rating. The main sources of bias identified were confounding factors and outcomes measurement. The evaluation revealed that only two experimental studies in the Confounders field had a moderate risk of bias, while the rest had a high risk of bias. All included literature demonstrated low risk in terms of subject selection, classification of recommended interventions, and deviation from established interventions. Furthermore, one-third of the literature showed low-risk missing data [ 23 , 42 , 50 , 51 ], while other studies did not provide relevant information. Lastly, nearly a third of the literature showed missing data for low-risk.

Overview of sports and experiment design

All thirteen papers included in this review utilized a pre-posttest design. The sports covered in these studies encompassed basketball, volleyball, soccer, ultimate Frisbee, table tennis, hockey, Polskie ringo, ball games, and body movements. Some studies examined two exercise programs [ 23 , 43 ], while the majority of research focused on basketball [ 44 , 52 , 53 ]. The participants in the course experiments were primarily college and high school students, with a limited number of studies investigating primary and junior high school students. The distribution of participants included college students (3), high school students (8), primary school students (1), and junior high school students (1). The sample sizes in these studies ranged from 40 to 508. Since the selected studies were teaching experiments, most of them involved mixed-sex classes, with four studies not specifying the gender of the students. Only one study established three experimental classes and two control classes [ 50 ], while the remaining studies had one experimental class and one control class. The number of interventions ranged from 8 to 25, with each intervention lasting between 45 and 90 min.

The majority of studies in the selected literature directly applied the SEM as the intervention. Five of the studies incorporated constructivism theory [ 48 ], self-determination theory [ 23 , 44 , 47 ], and ARCS learning motivation theory [ 52 ]. None of the literature investigated from the perspective of attitude theory. Furthermore, none of the selected studies mentioned the teaching standards or syllabus used to design the course content, nor did they provide explanations for the rationale behind the experimental teaching content. The number of interventions in the trials ranged from 8 to 25, with up to half of the studies using fewer than 18 interventions [ 42 , 47 , 48 , 49 , 50 , 52 , 53 ], the recommended class hours for large unit teaching are not met [ 54 ]. The duration of each intervention was most commonly reported as 45 or 60 min [ 42 , 43 , 44 , 47 , 49 , 50 , 51 , 52 , 53 ]. The frequency of weekly interventions varied from 1 to 5, but the majority of studies implemented interventions once a week [ 23 , 42 , 43 , 46 , 47 , 48 , 49 ]. The intervention frequency was generally low, and there was a scarcity of studies with higher intervention frequency. With the exception of one article that conducted the intervention in two schools without providing an explanation [ 50 ], the remaining studies were conducted within the same school.

The control classes in the selected literature implemented similar TT and forms, despite variations in naming used by scholars from different countries or even within the same country. The TT employed in the control classes were mainly Direct Instruction in Australia [ 43 , 46 , 47 , 51 , 52 ], Morocco [ 50 ], and Spain [ 42 , 43 , 44 ], In China, the traditional teaching models were referred to as TT [ 48 , 52 ] and Latent Growth Model [ 49 ]; Traditional Style in the United States and England [ 42 ], American Skill-drill-game [ 44 , 45 ], and multiactivity model [ 23 ].

Measuring instruments and main outcomes

The findings of this investigation were classified based on the impact of the SEM on various aspects of students' attitudes toward physical education: cognitive and affective domains. Through the segregation of subjects and constituents from prior research, the favorable and unfavorable indicators of affective and cognitive dimensions were predominantly derived from the existing body of literature.

The effect of SEM on student cognitive

In this literature review, it was evident that all the included studies reached a unanimous conclusion that the overall effectiveness of the SEM surpassed that of the TT. Among these studies, eight of them specifically evaluated students' cognitive performance [ 23 , 42 , 43 , 45 , 48 , 50 , 52 ]. Various assessment instruments were employed, such as the Intrinsic Motivation Inventory (IMI) [ 42 , 43 , 45 ], the Amotivation subscale of the Academic Motivation Scale (AMS) [ 23 ], the attitude questionnaire [ 48 ], the Spanish version of the Sport Satisfaction Instrument (SVSSI) [ 50 ], the ARCS Learning Motivation Scale, the Physical Education Affection Scale (PEAS) [ 52 ], and the ALT-PE data were collected using momentary time sampling for each team by trained coders [ 53 ].

The study participants encompassed junior high school students [ 43 ], high school students [ 23 , 42 , 45 , 48 , 50 ] and College students [ 52 , 53 ]. Most of these investigations revealed that following the intervention of the physical education course, the cognitive abilities of students in the intervention group exhibited significant improvement, surpassing those of the control group instructed through the TT. Conversely, no significant changes were observed within the control group before and after the experiment [ 23 , 42 , 48 , 50 ]. Nevertheless, one study reported a significant decrease in cognitive abilities among students in the control group before and after the experiment [ 54 ], the other two studies showed that both the experimental and control groups showed significant improvements, but the experimental group showed significantly greater improvements [ 52 , 53 ].

The effect of SEM on student's affective

In this comprehensive review, all the included studies examined students' affective aspects. The assessment instruments employed were as follows: Intrinsic Motivation Inventory (IMI) [ 42 , 43 , 44 , 45 , 47 ], Amotivation subscale of the Academic Motivation Scale (AMS) [ 23 ], Intention to be Physically Active Scale (IPAS) [ 46 ], the attitude questionnaire [ 48 ], Physical activity enjoyment scale (PACES) [ 49 ], the Spanish version of the Sport Satisfaction Instrument (SVSSI) [ 50 ], Positive and Negative Affect Scale (PANASN) [ 51 ] and the Physical Education Affection Scale (PEAS) [ 52 ].

The study participants encompassed primary school students [ 51 ], Junior high school students [ 43 ], high school [ 23 , 42 , 44 , 45 , 46 , 47 , 48 , 50 , 51 ] and College students [ 49 , 52 ]. Out of the 12 studies, four reported positive and/or negative interests or enjoyment among students. Among these, two studies indicated that the experimental group students exhibited significantly higher positive affect than the control group students [ 47 , 51 ]. However, the measurement results varied within the control group. One study reported no significant improvement [ 47 ], while another study showed significant improvement, but the effect was significantly greater in the experimental group compared to the control group [ 51 ]. Furthermore, one study demonstrated no significant difference between the two groups as the test indicators did not exhibit significant changes before and after the experiment [ 46 ].

Regarding the investigation of negative affect, three studies reported that the experimental group students exhibited significantly lower negative affect compared to the control group [ 47 , 51 ], with a significant decrease in negative affect observed in the experimental group while no significant change was noted in the control group. Additionally, one study showed no significant difference and no significant improvement in the test results between the two groups before and after the experiment [ 46 ].

Among the remaining eight studies, it was not specified whether the investigation focused on positive or negative effects. Among them, two studies solely compared the improvement effects between the experimental and control groups without conducting intra-group comparisons before and after the experiment, and the results revealed that the experimental group exhibited significantly better outcomes than the control group [ 45 , 49 ]; the remaining six studies conducted comparisons not only between groups before and after the experiment but also within each group. Five studies demonstrated a significant increase in the affected index of the experimental group, while the control group exhibited no significant change [ 23 , 42 , 44 , 48 , 52 ], and one study revealed that the experimental group displayed a significant improvement, while the control group experienced a significant decline [ 43 ].

This paper presents a comprehensive review of the effects of the SEM on students' attitudes towards physical education. Its aim is to distinguish this study from other published research on the application of the SEM interventions among students. The findings indicate that the SE model has the potential to enhance students' attitudes toward physical education in terms of cognition and affect. However, certain factors such as the lack of data on junior high school students and gender differences, the frequency and duration of intervention per week, the variation in the learning environment across groups taught in the same setting, the rationale behind the course content, and the selection of tools for measuring learning attitudes may influence the experimental outcomes. Nonetheless, considering the positive results observed in these studies, is SEM an effective way to interfere with students' attitudes toward physical education learning? In conjunction with the information presented in the " Results " section, this review offers a detailed analysis of the impact of various dimensions of student attitudes toward physical education learning.

As anticipated, eleven out of the thirteen studies included in this review focused on ball games, which aligns with the competitive nature of these sports [ 55 ]. This choice is well-suited to the seasonal characteristics of the Sports Education Model (SEM) [ 56 , 57 ]. When considering gender comparisons, incorporating gender research can enhance the reliability of experimental findings [ 58 , 59 ]. However, in all the studies included, the majority of researchers only used mixed experimental and control groups, without comparing gender distinctions. If significant differences exist in the effect of SEM on the learning attitudes of students of different genders, it would significantly impact the accuracy of the experimental results.

Regarding the frequency, number, and duration of each intervention, some scholars have suggested that these factors may have different effects on the experimental outcomes [ 60 ], However, among the thirteen studies reviewed, the largest number of interventions was only 25 [ 23 ], and most studies had fewer than 20 interventions. Most studies had fewer than 18 interventions. This deviates from the use of large unit teaching advocated by some scholars to enhance students' systematic cognition and learning experience of a sports event [ 54 , 61 ]. In the reform of the school curriculum, the State Council of China issued the Curriculum Standards for Physical Education and Health for Compulsory Education (2022 edition) for students, which also clearly mentioned that the length of class hours for large units should not be less than 18 lessons.

In terms of the rationality of classroom teaching form and content, Hastie et al. [ 62 ] developed an Instructional Checklist to evaluate the effectiveness of the SEM and TT. However, only four of the included studies addressed this aspect [ 46 , 47 , 50 ]. Regarding the selection of measurement tools, none of the studies examined students' learning attitudes using scales developed based on attitude theory. According to the two-component proponents of attitude, attitude theory defines attitude as the affective and cognitive (positive or negative) evaluation of individuals toward the object of attitude [ 28 , 29 , 30 , 63 ]. Failing to assess student attitudes using survey instruments developed based on the structural composition of attitudes is problematic, as these instruments may not accurately measure attitudes [ 64 ]. The critical concern regarding the assessment of student attitudes using survey instruments developed based on the structural composition of attitudes requires a more thorough explanation. This is particularly important because relying on instruments that do not align with the multi-dimensional nature of attitudes, encompassing affective, cognitive, and conative components, may lead to inaccurate measurements [ 64 ]. To elaborate further, historical quantitative investigations in physical education pedagogy often utilized instruments such as Kenyon's [ 65 ] or Simon and Smoll's [ 66 ], which might not capture the complete construct of attitude. For instance, Kenyon's instrument conceptualizes physical activity rather than attitude as a multidimensional construct, while Simon and Smoll's instrument, developed for adults, may not be entirely valid for children. This unidimensional perspective on attitude, focusing solely on the affective dimension, is problematic, as it overlooks the multi-component nature of attitude, as acknowledged in studies by Gonzàles [ 67 ], Mohsin [ 68 ], and Oppenheim [ 69 ]. Therefore, future research endeavors should delve into the intricacies of attitude assessment tools, considering the developmental differences and the multidimensional nature of attitudes to ensure comprehensive and accurate measurement in the context of physical education pedagogy.

The existing literature provides sufficient evidence to support the significant superiority of physical education courses over TT in enhancing students' cognition of physical education learning. The cognitive dimension refers to individuals' evaluation of concepts and beliefs related to specific people, things, and objects, forming a multi-perspective system [ 32 , 49 ]. The development of ideas and beliefs relies on a solid foundation of knowledge about people and things. Students' cognition of physical education learning serves as a prerequisite for fostering positive attitudes toward physical education [ 70 ]. However, among the eight studies included in this review that examined the cognitive components of attitudes, seven studies concluded that SEM and TT had a more significant impact on improving students' perception of attitudes toward physical education learning [ 23 , 42 , 43 , 45 , 48 , 50 , 53 ]. Most of these studies indicated that students' perception of physical education learning did not change significantly under TT. Only one study found that both SEM and TT showed significant improvements before and after the experiment, with no significant difference in the degree of improvement between them [ 52 ]. However, it is noteworthy that the study by Chu et al. [ 49 ] lacked a thorough examination of the model fidelity for both the SEM and TT. The absence of a robust fidelity check raises concerns about the reliability and validity of the observed improvements reported in both SEM and TT groups before and after the experiment. Without ensuring that the implemented instructional models were faithfully executed as intended, it becomes challenging to attribute the observed improvements solely to the effectiveness of the instructional methods. Consequently, the study reports significant improvements in both SEM and TT without a discernible difference in the degree of improvement between them. This underscores the importance of conducting comprehensive model fidelity checks to enhance the credibility and interpretability of research findings, particularly when comparing the effectiveness of different instructional models in educational settings. Although most studies support the significant superiority of the SEM in enhancing students' perception of physical education learning compared to traditional instruction, it is important to note that five out of seven studies were conducted with high school students, limiting the generalizability of the findings to broader populations. This represents a crucial gap in the existing literature regarding learning cognition in physical education. Furthermore, despite having mixed-gender classes, the studies did not include a comparative analysis of students from different genders. Therefore, it is necessary to conduct additional comparative studies on the SEM and TT, encompassing various learning stages and considering the cognition of physical education learning among students of different genders, to enrich the breadth of results.

The majority of sports scholars hold the view that the SEM is superior to the TT in fostering students' emotional experiences in sports learning. The affective dimension pertains to the emotions and emotional experiences of individuals based on cognitive factors related to specific people, things, or objects, such as interest or enjoyment [ 32 , 49 ]. By comparing SEM and TT, eleven out of the thirteen studies analyzing improvements in student physical education learning confirmed that SEM significantly outperformed TT in enhancing student interest or enjoyment [ 23 , 42 , 43 , 44 , 45 , 47 , 48 , 49 , 50 , 51 , 52 ]. Only one study found that both SEM and TT did not lead to significant improvements in student interest or enjoyment, as there were no significant changes in test results before and after the learning social work experiment in both groups [ 46 ]. Notably, three of the studies involved opposite outcomes of positive and negative effects [ 46 , 47 , 51 ], and one study exclusively reported negative affect [ 50 ]. These divergent results underscore the complexity of the relationship between instructional models and students' attitudes towards physical education. Future research endeavors should delve deeper into the factors contributing to such variations, exploring potential moderating variables, instructional nuances, or contextual influences that may elucidate the observed disparities. These findings not only deserve attention for their immediate implications but also emphasize the need for nuanced investigations that can inform the refinement and optimization of instructional approaches in the field of physical education.

Moreover, among the four studies involving 20 or more interventions, three studies conducted within-group comparisons of SEM and TT before and after the experiment [ 23 , 43 , 45 ], and the frequency of weekly interventions varied. One study with a low intervention frequency found a significant decrease in emotional aspects among students in the TT group before and after the experiment [ 43 ]. However, two studies with high intervention frequency found no significant changes in the emotional aspects of students in the TT group before and after the experiment [ 23 , 44 ]. These results contradict Chen's argument (2019) that prolonged treatment may lead to adverse emotions such as anxiety and depression. However, these limited findings do not provide strong evidence and require further validation in future studies with larger sample sizes.

Limitations

In summary, this review presents substantial evidence supporting the superiority of the SEM over TT in enhancing students' attitudes toward physical education learning. However, there are several limitations to consider. Firstly, none of the included studies reported gender differences, which limits the richness and specificity of the research findings. Gender differences, if present, could potentially impact the accuracy of the overall results. Secondly, the studies did not address the influence of class size on teaching experiment outcomes. Determining the optimal number of students per group and the ideal number of groups is an important consideration for achieving optimal teaching effects. Inappropriate, insufficient, or excessive sample sizes can affect the quality and accuracy of experiments [ 71 ]. Thirdly, most studies did not account for the experimental environment or control participants' physical activities outside the experimental setting, which may influence students' attitudes toward physical education learning. Additionally, the studies generally did not consider the impact of factors such as climate and time on students' attitudes during the teaching experiments. Lastly, none of the studies included in this review conducted any short-term or long-term follow-up of students after the trial, making it challenging to determine the long-term effects of SEM on students' attitudes toward physical education learning.

The systematic review conducted provides compelling evidence supporting the positive impact of the SEM on students' attitudes toward physical education learning. However, it is important to note that most of the literature included in this review focused on high school and college students, while there were fewer findings for other school age groups. Urgently needed are comprehensive research initiatives that prioritize investigating the impact of the SEM on attitudes towards physical education learning across diverse age groups, including primary and middle school students. This will contribute to a more inclusive understanding of SEM's effectiveness, ensuring that its benefits are explored and validated across various educational stages, thus providing a solid foundation for evidence-based instructional practices in physical education. Additionally, although SEM is an established teaching model, recent research has shown an increase in its popularity in physical education, with five out of the thirteen studies published in the last three years. Nevertheless, it is crucial to approach the results with caution due to the limitations identified in this study.

To further deepen our understanding of the effectiveness of SEM in improving students' attitudes toward physical education learning, it is imperative to address the issue of model fidelity checks for both SEM and TT. The study highlighted the absence of a thorough examination of the model fidelity in certain investigations, which raises concerns about the reliability and validity of the observed improvements reported in both SEM and TT groups before and after the experiment. Future research should prioritize rigorous fidelity checks to enhance the credibility and interpretability of research findings when comparing the effectiveness of different instructional models.

Moreover, the identified divergent outcomes in some studies, including those with opposite positive and negative effects, as well as studies reporting exclusively negative affect, underscore the complexity of the relationship between instructional models and students' attitudes towards physical education. Therefore, future investigations should explore potential moderating variables, instructional nuances, or contextual influences contributing to such variations. This comprehensive approach will not only help refine our understanding of SEM's impact on attitudes but also aid in the selection of teaching models that align with the demands of contemporary times.

To optimize the study of SEM's influence on students' physical education learning attitudes, it is recommended to increase the number and frequency of interventions appropriately. Additionally, future research endeavors should consider demographic factors such as the gender and age of the students, contributing to a more nuanced understanding of SEM's impact across different populations. This continued exploration will not only verify the advantages of SEM in promoting students' physical education learning but also enrich the research outcomes concerning the influence of SEM on students' attitudes, addressing the identified gaps and fostering advancements in physical education pedagogy.

Availability of data and materials

The data set supporting the conclusions of this article is included within the article.

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Zhang, J., Xiao, W., Soh, K.G. et al. The effect of the Sport Education Model in physical education on student learning attitude: a systematic review. BMC Public Health 24 , 949 (2024). https://doi.org/10.1186/s12889-024-18243-0

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Educating the Student Body: Taking Physical Activity and Physical Education to School (2013)

Chapter: 4 physical activity, fitness, and physical education: effects on academic performance.

Physical Activity, Fitness, and Physical Education: Effects on Academic Performance

Key Messages

•  Evidence suggests that increasing physical activity and physical fitness may improve academic performance and that time in the school day dedicated to recess, physical education class, and physical activity in the classroom may also facilitate academic performance.

•  Available evidence suggests that mathematics and reading are the academic topics that are most influenced by physical activity. These topics depend on efficient and effective executive function, which has been linked to physical activity and physical fitness.

•  Executive function and brain health underlie academic performance. Basic cognitive functions related to attention and memory facilitate learning, and these functions are enhanced by physical activity and higher aerobic fitness.

•  Single sessions of and long-term participation in physical activity improve cognitive performance and brain health. Children who participate in vigorous- or moderate-intensity physical activity benefit the most.

•  Given the importance of time on task to learning, students should be provided with frequent physical activity breaks that are developmentally appropriate.

•  Although presently understudied, physically active lessons offered in the classroom may increase time on task and attention to task in the classroom setting.

A lthough academic performance stems from a complex interaction between intellect and contextual variables, health is a vital moderating factor in a child’s ability to learn. The idea that healthy children learn better is empirically supported and well accepted (Basch, 2010), and multiple studies have confirmed that health benefits are associated with physical activity, including cardiovascular and muscular fitness, bone health, psychosocial outcomes, and cognitive and brain health (Strong et al., 2005; see Chapter 3 ). The relationship of physical activity and physical fitness to cognitive and brain health and to academic performance is the subject of this chapter.

Given that the brain is responsible for both mental processes and physical actions of the human body, brain health is important across the life span. In adults, brain health, representing absence of disease and optimal structure and function, is measured in terms of quality of life and effective functioning in activities of daily living. In children, brain health can be measured in terms of successful development of attention, on-task behavior, memory, and academic performance in an educational setting. This chapter reviews the findings of recent research regarding the contribution of engagement in physical activity and the attainment of a health-enhancing level of physical fitness to cognitive and brain health in children. Correlational research examining the relationship among academic performance, physical fitness, and physical activity also is described. Because research in older adults has served as a model for understanding the effects of physical activity and fitness on the developing brain during childhood, the adult research is briefly discussed. The short- and long-term cognitive benefits of both a single session of and regular participation in physical activity are summarized.

Before outlining the health benefits of physical activity and fitness, it is important to note that many factors influence academic performance. Among these are socioeconomic status (Sirin, 2005), parental involvement

(Fan and Chen, 2001), and a host of other demographic factors. A valuable predictor of student academic performance is a parent having clear expectations for the child’s academic success. Attendance is another factor confirmed as having a significant impact on academic performance (Stanca, 2006; Baxter et al., 2011). Because children must be present to learn the desired content, attendance should be measured in considering factors related to academic performance.

PHYSICAL FITNESS AND PHYSICAL ACTIVITY: RELATION TO ACADEMIC PERFORMANCE

State-mandated academic achievement testing has had the unintended consequence of reducing opportunities for children to be physically active during the school day and beyond. In addition to a general shifting of time in school away from physical education to allow for more time on academic subjects, some children are withheld from physical education classes or recess to participate in remedial or enriched learning experiences designed to increase academic performance (Pellegrini and Bohn, 2005; see Chapter 5 ). Yet little evidence supports the notion that more time allocated to subject matter will translate into better test scores. Indeed, 11 of 14 correlational studies of physical activity during the school day demonstrate a positive relationship to academic performance (Rasberry et al., 2011). Overall, a rapidly growing body of work suggests that time spent engaged in physical activity is related not only to a healthier body but also to a healthier mind (Hillman et al., 2008).

Children respond faster and with greater accuracy to a variety of cognitive tasks after participating in a session of physical activity (Tomporowski, 2003; Budde et al., 2008; Hillman et al., 2009; Pesce et al., 2009; Ellemberg and St-Louis-Deschênes, 2010). A single bout of moderate-intensity physical activity has been found to increase neural and behavioral concomitants associated with the allocation of attention to a specific cognitive task (Hillman et al., 2009; Pontifex et al., 2012). And when children who participated in 30 minutes of aerobic physical activity were compared with children who watched television for the same amount of time, the former children cognitively outperformed the latter (Ellemberg and St-Louis-Desêhenes, 2010). Visual task switching data among 69 overweight and inactive children did not show differences between cognitive performance after treadmill walking and sitting (Tomporowski et al., 2008b).

When physical activity is used as a break from academic learning time, postengagement effects include better attention (Grieco et al., 2009; Bartholomew and Jowers, 2011), increased on-task behaviors (Mahar et al., 2006), and improved academic performance (Donnelly and Lambourne, 2011). Comparisons between 1st-grade students housed in a classroom

with stand-sit desks where the child could stand at his/her discretion and in classrooms containing traditional furniture showed that the former children were highly likely to stand, thus expending significantly more energy than those who were seated (Benden et al., 2011). More important, teachers can offer physical activity breaks as part of a supplemental curriculum or simply as a way to reset student attention during a lesson (Kibbe et al., 2011; see Chapter 6 ) and when provided with minimal training can efficaciously produce vigorous or moderate energy expenditure in students (Stewart et al., 2004). Further, after-school physical activity programs have demonstrated the ability to improve cardiovascular endurance, and this increase in aerobic fitness has been shown to mediate improvements in academic performance (Fredericks et al., 2006), as well as the allocation of neural resources underlying performance on a working memory task (Kamijo et al., 2011).

Over the past three decades, several reviews and meta-analyses have described the relationship among physical fitness, physical activity, and cognition (broadly defined as all mental processes). The majority of these reviews have focused on the relationship between academic performance and physical fitness—a physiological trait commonly defined in terms of cardiorespiratory capacity (e.g., maximal oxygen consumption; see Chapter 3 ). More recently, reviews have attempted to describe the effects of an acute or single bout of physical activity, as a behavior, on academic performance. These reviews have focused on brain health in older adults (Colcombe and Kramer, 2003), as well as the effects of acute physical activity on cognition in adults (Tomporowski, 2003). Some have considered age as part of the analysis (Etnier et al., 1997, 2006). Reviews focusing on research conducted in children (Sibley and Etnier, 2003) have examined the relationship among physical activity, participation in sports, and academic performance (Trudeau and Shephard, 2008, 2010; Singh et al., 2012); physical activity and mental and cognitive health (Biddle and Asare, 2011); and physical activity, nutrition, and academic performance (Burkhalter and Hillman, 2011). The findings of most of these reviews align with the conclusions presented in a meta-analytic review conducted by Fedewa and Ahn (2011). The studies reviewed by Fedewa and Ahn include experimental/quasi-experimental as well as cross-sectional and correlational designs, with the experimental designs yielding the highest effect sizes. The strongest relationships were found between aerobic fitness and achievement in mathematics, followed by IQ and reading performance. The range of cognitive performance measures, participant characteristics, and types of research design all mediated the relationship among physical activity, fitness, and academic performance. With regard to physical activity interventions, which were carried out both within and beyond the school day, those involving small groups of peers (around 10 youth of a similar age) were associated with the greatest gains in academic performance.

The number of peer-reviewed publications on this topic is growing exponentially. Further evidence of the growth of this line of inquiry is its increased global presence. Positive relationships among physical activity, physical fitness, and academic performance have been found among students from the Netherlands (Singh et al., 2012) and Taiwan (Chih and Chen, 2011). Broadly speaking, however, many of these studies show small to moderate effects and suffer from poor research designs (Biddle and Asare, 2011; Singh et al., 2012).

Basch (2010) conducted a comprehensive review of how children’s health and health disparities influence academic performance and learning. The author’s report draws on empirical evidence suggesting that education reform will be ineffective unless children’s health is made a priority. Basch concludes that schools may be the only place where health inequities can be addressed and that, if children’s basic health needs are not met, they will struggle to learn regardless of the effectiveness of the instructional materials used. More recently, Efrat (2011) conducted a review of physical activity, fitness, and academic performance to examine the achievement gap. He discovered that only seven studies had included socioeconomic status as a variable, despite its known relationship to education (Sirin, 2005).

Physical Fitness as a Learning Outcome of Physical Education and Its Relation to Academic Performance

Achieving and maintaining a healthy level of aerobic fitness, as defined using criterion-referenced standards from the National Health and Nutrition Examination Survey (NHANES; Welk et al., 2011), is a desired learning outcome of physical education programming. Regular participation in physical activity also is a national learning standard for physical education, a standard intended to facilitate the establishment of habitual and meaningful engagement in physical activity (NASPE, 2004). Yet although physical fitness and participation in physical activity are established as learning outcomes in all 50 states, there is little evidence to suggest that children actually achieve and maintain these standards (see Chapter 2 ).

Statewide and national datasets containing data on youth physical fitness and academic performance have increased access to student-level data on this subject (Grissom, 2005; Cottrell et al., 2007; Carlson et al., 2008; Chomitz et al., 2008; Wittberg et al., 2010; Van Dusen et al., 2011). Early research in South Australia focused on quantifying the benefits of physical activity and physical education during the school day; the benefits noted included increased physical fitness, decreased body fat, and reduced risk for cardiovascular disease (Dwyer et al., 1979, 1983). Even today, Dwyer and colleagues are among the few scholars who regularly include in their research measures of physical activity intensity in the school environment,

which is believed to be a key reason why they are able to report differentiated effects of different intensities. A longitudinal study in Trois-Rivières, Québec, Canada, tracked how the academic performance of children from grades 1 through 6 was related to student health, motor skills, and time spent in physical education. The researchers concluded that additional time dedicated to physical education did not inhibit academic performance (Shephard et al., 1984; Shephard, 1986; Trudeau and Shephard, 2008).

Longitudinal follow-up investigating the long-term benefits of enhanced physical education experiences is encouraging but largely inconclusive. In a study examining the effects of daily physical education during elementary school on physical activity during adulthood, 720 men and women completed the Québec Health Survey (Trudeau et al., 1999). Findings suggest that physical education was associated with physical activity in later life for females but not males (Trudeau et al., 1999); most of the associations were significant but weak (Trudeau et al., 2004). Adult body mass index (BMI) at age 34 was related to childhood BMI at ages 10-12 in females but not males (Trudeau et al., 2001). Longitudinal studies such as those conducted in Sweden and Finland also suggest that physical education experiences may be related to adult engagement in physical activity (Glenmark, 1994; Telama et al., 1997). From an academic performance perspective, longitudinal data on men who enlisted for military service imply that cardiovascular fitness at age 18 predicted cognitive performance in later life (Aberg et al., 2009), thereby supporting the idea of offering physical education and physical activity opportunities well into emerging adulthood through secondary and postsecondary education.

Castelli and colleagues (2007) investigated younger children (in 3rd and 5th grades) and the differential contributions of the various subcomponents of the Fitnessgram ® . Specifically, they examined the individual contributions of aerobic capacity, muscle strength, muscle flexibility, and body composition to performance in mathematics and reading on the Illinois Standardized Achievement Test among a sample of 259 children. Their findings corroborate those of the California Department of Education (Grissom, 2005), indicating a general relationship between fitness and achievement test performance. When the individual components of the Fitnessgram were decomposed, the researchers determined that only aerobic capacity was related to test performance. Muscle strength and flexibility showed no relationship, while an inverse association of BMI with test performance was observed, such that higher BMI was associated with lower test performance. Although Baxter and colleagues (2011) confirmed the importance of attending school in relation to academic performance through the use of 4th-grade student recall, correlations with BMI were not significant.

State-mandated implementation of the coordinated school health model requires all schools in Texas to conduct annual fitness testing

using the Fitnessgram among students in grades 3-12. In a special issue of Research Quarterly for Exercise and Sport (2010), multiple articles describe the current state of physical fitness among children in Texas; confirm the associations among school performance levels, academic achievement, and physical fitness (Welk et al., 2010; Zhu et al., 2010); and demonstrate the ability of qualified physical education teachers to administer physical fitness tests (Zhu et al., 2010). Also using data from Texas schools, Van Dusen and colleagues (2011) found that cardiovascular fitness had the strongest association with academic performance, particularly in mathematics over reading. Unlike previous research, which demonstrated a steady decline in fitness by developmental stage (Duncan et al., 2007), this study found that cardiovascular fitness did decrease but not significantly (Van Dusen et al., 2011). Aerobic fitness, then, may be important to academic performance, as there may be a dose-response relationship (Van Dusen et al., 2011).

Using a large sample of students in grades 4-8, Chomitz and colleagues (2008) found that the likelihood of passing both mathematics and English achievement tests increased with the number of fitness tests passed during physical education class, and the odds of passing the mathematics achievement tests were inversely related to higher body weight. Similar to the findings of Castelli and colleagues (2007), socioeconomic status and demographic factors explained little of the relationship between aerobic fitness and academic performance; however, socioeconomic status may be an explanatory variable for students of low fitness (London and Castrechini, 2011).

In sum, numerous cross-sectional and correlational studies demonstrate small-to-moderate positive or null associations between physical fitness (Grissom, 2005; Cottrell et al., 2007; Edwards et al., 2009; Eveland-Sayers et al., 2009; Cooper et al., 2010; Welk et al., 2010; Wittberg et al., 2010; Zhu et al., 2010; Van Dusen et al., 2011), particularly aerobic fitness, and academic performance (Castelli et al, 2007; Chomitz et al., 2008; Roberts et al., 2010; Welk et al., 2010; Chih and Chen, 2011; London and Castrechini, 2011; Van Dusen et al., 2011). Moreover, the findings may support a dose-response association, suggesting that the more components of physical fitness (e.g., cardiovascular endurance, strength, muscle endurance) considered acceptable for the specific age and gender that are present, the greater the likelihood of successful academic performance. From a public health and policy standpoint, the conclusions these findings support are limited by few causal inferences, a lack of data confirmation, and inadequate reliability because the data were often collected by nonresearchers or through self-report methods. It may also be noted that this research includes no known longitudinal studies and few randomized controlled trials (examples are included later in this chapter in the discussion of the developing brain).

Physical Activity, Physical Education, and Academic Performance

In contrast with the correlational data presented above for physical fitness, more information is needed on the direct effects of participation in physical activity programming and physical education classes on academic performance.

In a meta-analysis, Sibley and Etnier (2003) found a positive relationship between physical activity and cognition in school-age youth (aged 4-18), suggesting that physical activity, as well as physical fitness, may be related to cognitive outcomes during development. Participation in physical activity was related to cognitive performance in eight measurement categories (perceptual skills, IQ, achievement, verbal tests, mathematics tests, memory, developmental level/academic readiness, and “other”), with results indicating a beneficial relationship of physical activity to all cognitive outcomes except memory (Sibley and Etnier, 2003). Since that meta-analysis, however, several papers have reported robust relationships between aerobic fitness and different aspects of memory in children (e.g., Chaddock et al., 2010a, 2011; Kamijo et al., 2011; Monti et al., 2012). Regardless, the comprehensive review of Sibley and Etnier (2003) was important because it helped bring attention to an emerging literature suggesting that physical activity may benefit cognitive development even as it also demonstrated the need for further study to better understand the multifaceted relationship between physical activity and cognitive and brain health.

The regular engagement in physical activity achieved during physical education programming can also be related to academic performance, especially when the class is taught by a physical education teacher. The Sports, Play, and Active Recreation for Kids (SPARK) study examined the effects of a 2-year health-related physical education program on academic performance in children (Sallis et al., 1999). In an experimental design, seven elementary schools were randomly assigned to one of three conditions: (1) a specialist condition in which certified physical education teachers delivered the SPARK curriculum, (2) a trained-teacher condition in which classroom teachers implemented the curriculum, and (3) a control condition in which classroom teachers implemented the local physical education curriculum. No significant differences by condition were found for mathematics testing; however, reading scores were significantly higher in the specialist condition relative to the control condition (Sallis et al., 1999), while language scores were significantly lower in the specialist condition than in the other two conditions. The authors conclude that spending time in physical education with a specialist did not have a negative effect on academic performance. Shortcomings of this research include the amount of data loss from pre- to posttest, the use of results of 2nd-grade testing that exceeded the national

average in performance as baseline data, and the use of norm-referenced rather than criterion-based testing.

In seminal research conducted by Gabbard and Barton (1979), six different conditions of physical activity (no activity; 20, 30, 40, and 50 minutes; and posttest no activity) were completed by 106 2nd graders during physical education. Each physical activity session was followed by 5 minutes of rest and the completion of 36 math problems. The authors found a potential threshold effect whereby only the 50-minute condition improved mathematical performance, with no differences by gender.

A longitudinal study of the kindergarten class of 1998-1999, using data from the Early Childhood Longitudinal Study, investigated the association between enrollment in physical education and academic achievement (Carlson et al., 2008). Higher amounts of physical education were correlated with better academic performance in mathematics among females, but this finding did not hold true for males.

Ahamed and colleagues (2007) found in a cluster randomized trial that, after 16 months of a classroom-based physical activity intervention, there was no significant difference between the treatment and control groups in performance on the standardized Cognitive Abilities Test, Third Edition (CAT-3). Others have found, however, that coordinative exercise (Budde et al., 2008) or bouts of vigorous physical activity during free time (Coe et al., 2006) contribute to higher levels of academic performance. Specifically, Coe and colleagues examined the association of enrollment in physical education and self-reported vigorous- or moderate-intensity physical activity outside school with performance in core academic courses and on the Terra Nova Standardized Achievement Test among more than 200 6th-grade students. Their findings indicate that academic performance was unaffected by enrollment in physical education classes, which were found to average only 19 minutes of vigorous- or moderate-intensity physical activity. When time spent engaged in vigorous- or moderate-intensity physical activity outside of school was considered, however, a significant positive relation to academic performance emerged, with more time engaged in vigorous- or moderate-intensity physical activity being related to better grades but not test scores (Coe et al., 2006).

Studies of participation in sports and academic achievement have found positive associations (Mechanic and Hansell, 1987; Dexter, 1999; Crosnoe, 2002; Eitle and Eitle, 2002; Stephens and Schaben, 2002; Eitle, 2005; Miller et al., 2005; Fox et al., 2010; Ruiz et al., 2010); higher grade point averages (GPAs) in season than out of season (Silliker and Quirk, 1997); a negative association between cheerleading and science performance (Hanson and Kraus, 1998); and weak and negative associations between the amount of time spent participating in sports and performance in English-language class among 13-, 14-, and 16-year-old students (Daley and Ryan, 2000).

Other studies, however, have found no association between participation in sports and academic performance (Fisher et al., 1996). The findings of these studies need to be interpreted with caution as many of their designs failed to account for the level of participation by individuals in the sport (e.g., amount of playing time, type and intensity of physical activity engagement by sport). Further, it is unclear whether policies required students to have higher GPAs to be eligible for participation. Offering sports opportunities is well justified regardless of the cognitive benefits, however, given that adolescents may be less likely to engage in risky behaviors when involved in sports or other extracurricular activities (Page et al., 1998; Elder et al., 2000; Taliaferro et al., 2010), that participation in sports increases physical fitness, and that affiliation with sports enhances school connectedness.

Although a consensus on the relationship of physical activity to academic achievement has not been reached, the vast majority of available evidence suggests the relationship is either positive or neutral. The meta-analytic review by Fedewa and Ahn (2011) suggests that interventions entailing aerobic physical activity have the greatest impact on academic performance; however, all types of physical activity, except those involving flexibility alone, contribute to enhanced academic performance, as do interventions that use small groups (about 10 students) rather than individuals or large groups. Regardless of the strength of the findings, the literature indicates that time spent engaged in physical activity is beneficial to children because it has not been found to detract from academic performance, and in fact can improve overall health and function (Sallis et al., 1999; Hillman et al., 2008; Tomporowski et al., 2008a; Trudeau and Shephard, 2008; Rasberry et al., 2011).

Single Bouts of Physical Activity

Beyond formal physical education, evidence suggests that multi-component approaches are a viable means of providing physical activity opportunities for children across the school curriculum (see also Chapter 6 ). Although health-related fitness lessons taught by certified physical education teachers result in greater student fitness gains relative to such lessons taught by other teachers (Sallis et al., 1999), non-physical education teachers are capable of providing opportunities to be physically active within the classroom (Kibbe et al., 2011). Single sessions or bouts of physical activity have independent merit, offering immediate benefits that can enhance the learning experience. Studies have found that single bouts of physical activity result in improved attention (Hillman et al., 2003, 2009; Pontifex et al., 2012), better working memory (Pontifex et al., 2009), and increased academic learning time and reduced off-task behaviors (Mahar et al., 2006; Bartholomew and Jowers, 2011). Yet single bouts

of physical activity have differential effects, as very vigorous exercise has been associated with cognitive fatigue and even cognitive decline in adults (Tomporowski, 2003). As seen in Figure 4-1 , high levels of effort, arousal, or activation can influence perception, decision making, response preparation, and actual response. For discussion of the underlying constructs and differential effects of single bouts of physical activity on cognitive performance, see Tomporowski (2003).

For children, classrooms are busy places where they must distinguish relevant information from distractions that emerge from many different sources occurring simultaneously. A student must listen to the teacher, adhere to classroom procedures, focus on a specific task, hold and retain information, and make connections between novel information and previous experiences. Hillman and colleagues (2009) demonstrated that a single bout of moderate-intensity walking (60 percent of maximum heart rate) resulted in significant improvements in performance on a task requiring attentional inhibition (e.g., the ability to focus on a single task). These findings were accompanied by changes in neuroelectric measures underlying the allocation of attention (see Figure 4-2 ) and significant improvements on the reading subtest of the Wide Range Achievement Test. No such effects were observed following a similar duration of quiet rest. These findings were later replicated and extended to demonstrate benefits for both mathematics and reading performance in healthy children and those diagnosed with attention deficit hyperactivity disorder (Pontifex et al., 2013). Further replications of these findings demonstrated that a single bout of moderate-intensity exercise using a treadmill improved performance on a task of attention and inhibition, but similar benefits were not derived from moderate-intensity

FIGURE 4-1 Information processing: Diagram of a simplified version of Sanders’s (1983) cognitive-energetic model of human information processing (adapted from Jones and Hardy, 1989). SOURCE: Tomporowski, 2003. Reprinted with permission.

FIGURE 4-2 Effects of a single session of exercise in preadolescent children. SOURCE: Hillman et al., 2009. Reprinted with permission.

exercise that involved exergaming (O’Leary et al., 2011). It was also found that such benefits were derived following cessation of, but not during, the bout of exercise (Drollette et al., 2012). The applications of such empirical findings within the school setting remain unclear.

A randomized controlled trial entitled Physical Activity Across the Curriculum (PAAC) used cluster randomization among 24 schools to examine the effects of physically active classroom lessons on BMI and academic achievement (Donnelly et al., 2009). The academically oriented physical activities were intended to be of vigorous or moderate intensity (3-6 metabolic equivalents [METs]) and to last approximately 10 minutes and were specifically designed to supplement content in mathematics, language arts, geography, history, spelling, science, and health. The study followed 665 boys and 677 girls for 3 years as they rose from 2nd or 3rd to 4th or 5th grades. Changes in academic achievement, fitness, and blood screening were considered secondary outcomes. During a 3-year period, students who engaged in physically active lessons, on average, improved their academic achievement by 6 percent, while the control groups exhibited a 1 percent decrease. In students who experienced at least 75 minutes of PAAC lessons per week, BMI remained stable (see Figure 4-3 ).

It is important to note that cognitive tasks completed before, during, and after physical activity show varying effects, but the effects were always positive compared with sedentary behavior. In a study carried out by Drollette and colleagues (2012), 36 preadolescent children completed

FIGURE 4-3 Change in academic scores from baseline after physically active classroom lessons in elementary schools in northeast Kansas (2003-2006). NOTE: All differences between the Physical Activity Across the Curriculum (PAAC) group ( N = 117) and control group ( N = 86) were significant ( p <.01). SOURCE: Donnelly et al., 2009. Reprinted with permission.

two cognitive tasks—a flanker task to assess attention and inhibition and a spatial nback task to assess working memory—before, during, and after seated rest and treadmill walking conditions. The children sat or walked on different days for an average of 19 minutes. The results suggest that the physical activity enhanced cognitive performance for the attention task but not for the task requiring working memory. Accordingly, although more research is needed, the authors suggest that the acute effects of exercise may be selective to certain cognitive processes (i.e., attentional inhibition) while unrelated to others (e.g., working memory). Indeed, data collected using a task-switching paradigm (i.e., a task designed to assess multitasking and requiring the scheduling of attention to multiple aspects of the environment) among 69 overweight and inactive children did not show differences in cognitive performance following acute bouts of treadmill walking or sitting (Tomporowski et al., 2008b). Thus, findings to date indicate a robust relationship of acute exercise to transient improvements in attention but appear inconsistent for other aspects of cognition.

Academic Learning Time and On- and Off-Task Behaviors

Excessive time on task, inattention to task, off-task behavior, and delinquency are important considerations in the learning environment

given the importance of academic learning time to academic performance. These behaviors are observable and of concern to teachers as they detract from the learning environment. Systematic observation by trained observers may yield important insight regarding the effects of short physical activity breaks on these behaviors. Indeed, systematic observations of student behavior have been used as an alternative means of measuring academic performance (Mahar et al., 2006; Grieco et al., 2009).

After the development of classroom-based physical activities, called Energizers, teachers were trained in how to implement such activities in their lessons at least twice per week (Mahar et al., 2006). Measurements of baseline physical activity and on-task behaviors were collected in two 3rd-grade and two 4th-grade classes, using pedometers and direct observation. The intervention included 243 students, while 108 served as controls by not engaging in the activities. A subgroup of 62 3rd and 4th graders was observed for on-task behavior in the classroom following the physical activity. Children who participated in Energizers took more steps during the school day than those who did not; they also increased their on-task behaviors by more than 20 percent over baseline measures.

A systematic review of a similar in-class, academically oriented, physical activity plan—Take 10!—was conducted to identify the effects of its implementation after it had been in use for 10 years (Kibbe et al., 2011). The findings suggest that children who experienced Take 10! in the classroom engaged in moderate to vigorous physical activity (6.16 to 6.42 METs) and had lower BMIs than those who did not. Further, children in the Take 10! classrooms had better fluid intelligence (Reed et al., 2010) and higher academic achievement scores (Donnelly et al., 2009).

Some have expressed concern that introducing physical activity into the classroom setting may be distracting to students. Yet in one study it was sedentary students who demonstrated a decrease in time on task, while active students returned to the same level of on-task behavior after an active learning task (Grieco et al., 2009). Among the 97 3rd-grade students in this study, a small but nonsignificant increase in on-task behaviors was seen immediately following these active lessons. Additionally, these improvements were not mediated by BMI.

In sum, although presently understudied, physically active lessons may increase time on task and attention to task in the classroom setting. Given the complexity of the typical classroom, the strategy of including content-specific lessons that incorporate physical activity may be justified.

It is recommended that every child have 20 minutes of recess each day and that this time be outdoors whenever possible, in a safe activity (NASPE,

2006). Consistent engagement in recess can help students refine social skills, learn social mediation skills surrounding fair play, obtain additional minutes of vigorous- or moderate-intensity physical activity that contribute toward the recommend 60 minutes or more per day, and have an opportunity to express their imagination through free play (Pellegrini and Bohn, 2005; see also Chapter 6 ). When children participate in recess before lunch, additional benefits accrue, such as less food waste, increased incidence of appropriate behavior in the cafeteria during lunch, and greater student readiness to learn upon returning to the classroom after lunch (Getlinger et al., 1996; Wechsler et al., 2001).

To examine the effects of engagement in physical activity during recess on classroom behavior, Barros and colleagues (2009) examined data from the Early Childhood Longitudinal Study on 10,000 8- to 9-year-old children. Teachers provided the number of minutes of recess as well as a ranking of classroom behavior (ranging from “misbehaves frequently” to “behaves exceptionally well”). Results indicate that children who had at least 15 minutes of recess were more likely to exhibit appropriate behavior in the classroom (Barros et al., 2009). In another study, 43 4th-grade students were randomly assigned to 1 or no days of recess to examine the effects on classroom behavior (Jarrett et al., 1998). The researchers concluded that on-task behavior was better among the children who had recess. A moderate effect size (= 0.51) was observed. In a series of studies examining kindergartners’ attention to task following a 20-minute recess, increased time on task was observed during learning centers and story reading (Pellegrini et al., 1995). Despite these positive findings centered on improved attention, it is important to note that few of these studies actually measured the intensity of the physical activity during recess.

From a slightly different perspective, survey data from 547 Virginia elementary school principals suggest that time dedicated to student participation in physical education, art, and music did not negatively influence academic performance (Wilkins et al., 2003). Thus, the strategy of reducing time spent in physical education to increase academic performance may not have the desired effect. The evidence on in-school physical activity supports the provision of physical activity breaks during the school day as a way to increase fluid intelligence, time on task, and attention. However, it remains unclear what portion of these effects can be attributed to a break from academic time and what portion is a direct result of the specific demands/characteristics of the physical activity.

THE DEVELOPING bRAIN, PHYSICAL ACTIVITY, AND BRAIN HEALTH

The study of brain health has grown beyond simply measuring behavioral outcomes such as task performance and reaction time (e.g., cognitive

processing speed). New technology has emerged that has allowed scientists to understand the impact of lifestyle factors on the brain from the body systems level down to the molecular level. A greater understanding of the cognitive components that subserve academic performance and may be amenable to intervention has thereby been gained. Research conducted in both laboratory and field settings has helped define this line of inquiry and identify some preliminary underlying mechanisms.

The Evidence Base on the Relationship of Physical Activity to Brain Health and Cognition in Older Adults

Despite the current focus on the relationship of physical activity to cognitive development, the evidence base is larger on the association of physical activity with brain health and cognition during aging. Much can be learned about how physical activity affects childhood cognition and scholastic achievement through this work. Despite earlier investigations into the relationship of physical activity to cognitive aging (see Etnier et al., 1997, for a review), the field was shaped by the findings of Kramer and colleagues (1999), who examined the effects of aerobic fitness training on older adults using a randomized controlled design. Specifically, 124 older adults aged 60 and 75 were randomly assigned to a 6-month intervention of either walking (i.e., aerobic training) or flexibility (i.e., nonaerobic) training. The walking group but not the flexibility group showed improved cognitive performance, measured as a shorter response time to the presented stimulus. Results from a series of tasks that tapped different aspects of cognitive control indicated that engagement in physical activity is a beneficial means of combating cognitive aging (Kramer et al., 1999).

Cognitive control, or executive control, is involved in the selection, scheduling, and coordination of computational processes underlying perception, memory, and goal-directed action. These processes allow for the optimization of behavioral interactions within the environment through flexible modulation of the ability to control attention (MacDonald et al., 2000; Botvinick et al., 2001). Core cognitive processes that make up cognitive control or executive control include inhibition, working memory, and cognitive flexibility (Diamond, 2006), processes mediated by networks that involve the prefrontal cortex. Inhibition (or inhibitory control) refers to the ability to override a strong internal or external pull so as to act appropriately within the demands imposed by the environment (Davidson et al., 2006). For example, one exerts inhibitory control when one stops speaking when the teacher begins lecturing. Working memory refers to the ability to represent information mentally, manipulate stored information, and act on the information (Davidson et al., 2006). In solving a difficult mathematical problem, for example, one must often remember the remainder. Finally,

cognitive flexibility refers to the ability to switch perspectives, focus attention, and adapt behavior quickly and flexibly for the purposes of goal-directed action (Blair et al., 2005; Davidson et al., 2006; Diamond, 2006). For example, one must shift attention from the teacher who is teaching a lesson to one’s notes to write down information for later study.

Based on their earlier findings on changes in cognitive control induced by aerobic training, Colcombe and Kramer (2003) conducted a meta-analysis to examine the relationship between aerobic training and cognition in older adults aged 55-80 using data from 18 randomized controlled exercise interventions. Their findings suggest that aerobic training is associated with general cognitive benefits that are selectively and disproportionately greater for tasks or task components requiring greater amounts of cognitive control. A second and more recent meta-analysis (Smith et al., 2010) corroborates the findings of Colcombe and Kramer, indicating that aerobic exercise is related to attention, processing speed, memory, and cognitive control; however, it should be noted that smaller effect sizes were observed, likely a result of the studies included in the respective meta-analyses. In older adults, then, aerobic training selectively improves cognition.

Hillman and colleagues (2006) examined the relationship between physical activity and inhibition (one aspect of cognitive control) using a computer-based stimulus-response protocol in 241 individuals aged 15-71. Their results indicate that greater amounts of physical activity are related to decreased response speed across task conditions requiring variable amounts of inhibition, suggesting a generalized relationship between physical activity and response speed. In addition, the authors found physical activity to be related to better accuracy across conditions in older adults, while no such relationship was observed for younger adults. Of interest, this relationship was disproportionately larger for the condition requiring greater amounts of inhibition in the older adults, suggesting that physical activity has both a general and selective association with task performance (Hillman et al., 2006).

With advances in neuroimaging techniques, understanding of the effects of physical activity and aerobic fitness on brain structure and function has advanced rapidly over the past decade. In particular, a series of studies (Colcombe et al., 2003, 2004, 2006; Kramer and Erickson, 2007; Hillman et al., 2008) of older individuals has been conducted to elucidate the relation of aerobic fitness to the brain and cognition. Normal aging results in the loss of brain tissue (Colcombe et al., 2003), with markedly larger loss evidenced in the frontal, temporal, and parietal regions (Raz, 2000). Thus cognitive functions subserved by these brain regions (such as those involved in cognitive control and aspects of memory) are expected to decay more dramatically than other aspects of cognition.

Colcombe and colleagues (2003) investigated the relationship of aerobic fitness to gray and white matter tissue loss using magnetic resonance

imaging (MRI) in 55 healthy older adults aged 55-79. They observed robust age-related decreases in tissue density in the frontal, temporal, and parietal regions using voxel-based morphometry, a technique used to assess brain volume. Reductions in the amount of tissue loss in these regions were observed as a function of fitness. Given that the brain structures most affected by aging also demonstrated the greatest fitness-related sparing, these initial findings provide a biological basis for fitness-related benefits to brain health during aging.

In a second study, Colcombe and colleagues (2006) examined the effects of aerobic fitness training on brain structure using a randomized controlled design with 59 sedentary healthy adults aged 60-79. The treatment group received a 6-month aerobic exercise (i.e., walking) intervention, while the control group received a stretching and toning intervention that did not include aerobic exercise. Results indicated that gray and white matter brain volume increased for those who received the aerobic fitness training intervention. No such results were observed for those assigned to the stretching and toning group. Specifically, those assigned to the aerobic training intervention demonstrated increased gray matter in the frontal lobes, including the dorsal anterior cingulate cortex, the supplementary motor area, the middle frontal gyrus, the dorsolateral region of the right inferior frontal gyrus, and the left superior temporal lobe. White matter volume changes also were evidenced following the aerobic fitness intervention, with increases in white matter tracts being observed within the anterior third of the corpus callosum. These brain regions are important for cognition, as they have been implicated in the cognitive control of attention and memory processes. These findings suggest that aerobic training not only spares age-related loss of brain structures but also may in fact enhance the structural health of specific brain regions.

In addition to the structural changes noted above, research has investigated the relationship between aerobic fitness and changes in brain function. That is, aerobic fitness training has also been observed to induce changes in patterns of functional activation. Functional MRI (fMRI) measures, which make it possible to image activity in the brain while an individual is performing a cognitive task, have revealed that aerobic training induces changes in patterns of functional activation. This approach involves inferring changes in neuronal activity from alteration in blood flow or metabolic activity in the brain. In a seminal paper, Colcombe and colleagues (2004) examined the relationship of aerobic fitness to brain function and cognition across two studies with older adults. In the first study, 41 older adult participants (mean age ~66) were divided into higher- and lower-fit groups based on their performance on a maximal exercise test. In the second study, 29 participants (aged 58-77) were recruited and randomly assigned to either a fitness training (i.e., walking) or control (i.e., stretching and toning)

intervention. In both studies, participants were given a task requiring variable amounts of attention and inhibition. Results indicated that fitness (study 1) and fitness training (study 2) were related to greater activation in the middle frontal gyrus and superior parietal cortex; these regions of the brain are involved in attentional control and inhibitory functioning, processes entailed in the regulation of attention and action. These changes in neural activation were related to significant improvements in performance on the cognitive control task of attention and inhibition.

Taken together, the findings across studies suggest that an increase in aerobic fitness, derived from physical activity, is related to improvements in the integrity of brain structure and function and may underlie improvements in cognition across tasks requiring cognitive control. Although developmental differences exist, the general paradigm of this research can be applied to early stages of the life span, and some early attempts to do so have been made, as described below. Given the focus of this chapter on childhood cognition, it should be noted that this section has provided only a brief and arguably narrow look at the research on physical activity and cognitive aging. Considerable work has detailed the relationship of physical activity to other aspects of adult cognition using behavioral and neuroimaging tools (e.g., Boecker, 2011). The interested reader is referred to a number of review papers and meta-analyses describing the relationship of physical activity to various aspects of cognitive and brain health (Etnier et al., 1997; Colcombe and Kramer, 2003; Tomporowski, 2003; Thomas et al., 2012).

Child Development, Brain Structure, and Function

Certain aspects of development have been linked with experience, indicating an intricate interplay between genetic programming and environmental influences. Gray matter, and the organization of synaptic connections in particular, appears to be at least partially dependent on experience (NRC/IOM, 2000; Taylor, 2006), with the brain exhibiting a remarkable ability to reorganize itself in response to input from sensory systems, other cortical systems, or insult (Huttenlocher and Dabholkar, 1997). During typical development, experience shapes the pruning process through the strengthening of neural networks that support relevant thoughts and actions and the elimination of unnecessary or redundant connections. Accordingly, the brain responds to experience in an adaptive or “plastic” manner, resulting in the efficient and effective adoption of thoughts, skills, and actions relevant to one’s interactions within one’s environmental surroundings. Examples of neural plasticity in response to unique environmental interaction have been demonstrated in human neuroimaging studies of participation in music (Elbert et al., 1995; Chan et al., 1998; Münte et al., 2001) and sports (Hatfield and Hillman, 2001; Aglioti et al., 2008), thus supporting

the educational practice of providing music education and opportunities for physical activity to children.

Effects of Regular Engagement in Physical Activity and Physical Fitness on Brain Structure

Recent advances in neuroimaging techniques have rapidly advanced understanding of the role physical activity and aerobic fitness may have in brain structure. In children a growing body of correlational research suggests differential brain structure related to aerobic fitness. Chaddock and colleagues (2010a,b) showed a relationship among aerobic fitness, brain volume, and aspects of cognition and memory. Specifically, Chaddock and colleagues (2010a) assigned 9- to 10-year-old preadolescent children to lower- and higher-fitness groups as a function of their scores on a maximal oxygen uptake (VO 2 max) test, which is considered the gold-standard measure of aerobic fitness. They observed larger bilateral hippocampal volume in higher-fit children using MRI, as well as better performance on a task of relational memory. It is important to note that relational memory has been shown to be mediated by the hippocampus (Cohen and Eichenbaum, 1993; Cohen et al., 1999). Further, no differences emerged for a task condition requiring item memory, which is supported by structures outside the hippocampus, suggesting selectivity among the aspects of memory that benefit from higher amounts of fitness. Lastly, hippocampal volume was positively related to performance on the relational memory task but not the item memory task, and bilateral hippocampal volume was observed to mediate the relationship between fitness and relational memory (Chaddock et al., 2010a). Such findings are consistent with behavioral measures of relational memory in children (Chaddock et al., 2011) and neuroimaging findings in older adults (Erickson et al., 2009, 2011) and support the robust nonhuman animal literature demonstrating the effects of exercise on cell proliferation (Van Praag et al., 1999) and survival (Neeper et al., 1995) in the hippocampus.

In a second investigation (Chaddock et al., 2010b), higher- and lower-fit children (aged 9-10) underwent an MRI to determine whether structural differences might be found that relate to performance on a cognitive control task that taps attention and inhibition. The authors observed differential findings in the basal ganglia, a subcortical structure involved in the interplay of cognition and willed action. Specifically, higher-fit children exhibited greater volume in the dorsal striatum (i.e., caudate nucleus, putamen, globus pallidus) relative to lower-fit children, while no differences were observed in the ventral striatum. Such findings are not surprising given the role of the dorsal striatum in cognitive control and response resolution (Casey et al., 2008; Aron et al., 2009), as well as the growing body

of research in children and adults indicating that higher levels of fitness are associated with better control of attention, memory, and cognition (Colcombe and Kramer, 2003; Hillman et al., 2008; Chang and Etnier, 2009). Chaddock and colleagues (2010b) further observed that higher-fit children exhibited increased inhibitory control and response resolution and that higher basal ganglia volume was related to better task performance. These findings indicate that the dorsal striatum is involved in these aspects of higher-order cognition and that fitness may influence cognitive control during preadolescent development. It should be noted that both studies described above were correlational in nature, leaving open the possibility that other factors related to fitness and/or the maturation of subcortical structures may account for the observed group differences.

Effects of Regular Engagement in Physical Activity and Physical Fitness on Brain Function

Other research has attempted to characterize fitness-related differences in brain function using fMRI and event-related brain potentials (ERPs), which are neuroelectric indices of functional brain activation in the electro-encephalographic time series. To date, few randomized controlled interventions have been conducted. Notably, Davis and colleagues (2011) conducted one such intervention lasting approximately 14 weeks that randomized 20 sedentary overweight preadolescent children into an after-school physical activity intervention or a nonactivity control group. The fMRI data collected during an antisaccade task, which requires inhibitory control, indicated increased bilateral activation of the prefrontal cortex and decreased bilateral activation of the posterior parietal cortex following the physical activity intervention relative to the control group. Such findings illustrate some of the neural substrates influenced by participation in physical activity. Two additional correlational studies (Voss et al., 2011; Chaddock et al., 2012) compared higher- and lower-fit preadolescent children and found differential brain activation and superior task performance as a function of fitness. That is, Chaddock and colleagues (2012) observed increased activation in prefrontal and parietal brain regions during early task blocks and decreased activation during later task blocks in higher-fit relative to lower-fit children. Given that higher-fit children outperformed lower-fit children on the aspects of the task requiring the greatest amount of cognitive control, the authors reason that the higher-fit children were more capable of adapting neural activity to meet the demands imposed by tasks that tapped higher-order cognitive processes such as inhibition and goal maintenance. Voss and colleagues (2011) used a similar task to vary cognitive control requirements and found that higher-fit children outperformed their lower-fit counterparts and that such differences became more pronounced dur-

ing task conditions requiring the upregulation of control. Further, several differences emerged across various brain regions that together make up the network associated with cognitive control. Collectively, these differences suggest that higher-fit children are more efficient in the allocation of resources in support of cognitive control operations.

Other imaging research has examined the neuroelectric system (i.e., ERPs) to investigate which cognitive processes occurring between stimulus engagement and response execution are influenced by fitness. Several studies (Hillman et al., 2005, 2009; Pontifex et al., 2011) have examined the P3 component of the stimulus-locked ERP and demonstrated that higher-fit children have larger-amplitude and shorter-latency ERPs relative to their lower-fit peers. Classical theory suggests that P3 relates to neuronal activity associated with revision of the mental representation of the previous event within the stimulus environment (Donchin, 1981). P3 amplitude reflects the allocation of attentional resources when working memory is updated (Donchin and Coles, 1988) such that P3 is sensitive to the amount of attentional resources allocated to a stimulus (Polich, 1997; Polich and Heine, 2007). P3 latency generally is considered to represent stimulus evaluation and classification speed (Kutas et al., 1977; Duncan-Johnson, 1981) and thus may be considered a measure of stimulus detection and evaluation time (Magliero et al., 1984; Ila and Polich, 1999). Therefore the above findings suggest that higher-fit children allocate greater attentional resources and have faster cognitive processing speed relative to lower-fit children (Hillman et al., 2005, 2009), with additional research suggesting that higher-fit children also exhibit greater flexibility in the allocation of attentional resources, as indexed by greater modulation of P3 amplitude across tasks that vary in the amount of cognitive control required (Pontifex et al., 2011). Given that higher-fit children also demonstrate better performance on cognitive control tasks, the P3 component appears to reflect the effectiveness of a subset of cognitive systems that support willed action (Hillman et al., 2009; Pontifex et al., 2011).

Two ERP studies (Hillman et al., 2009; Pontifex et al., 2011) have focused on aspects of cognition involved in action monitoring. That is, the error-related negativity (ERN) component was investigated in higher- and lower-fit children to determine whether differences in evaluation and regulation of cognitive control operations were influenced by fitness level. The ERN component is observed in response-locked ERP averages. It is often elicited by errors of commission during task performance and is believed to represent either the detection of errors during task performance (Gehring et al., 1993; Holroyd and Coles, 2002) or more generally the detection of response conflict (Botvinick et al., 2001; Yeung et al., 2004), which may be engendered by errors in response production. Several studies have reported that higher-fit children exhibit smaller ERN amplitude during rapid-

response tasks (i.e., instructions emphasizing speed of responding; Hillman et al., 2009) and more flexibility in the allocation of these resources during tasks entailing variable cognitive control demands, as evidenced by changes in ERN amplitude for higher-fit children and no modulation of ERN in lower-fit children (Pontifex et al., 2011). Collectively, this pattern of results suggests that children with lower levels of fitness allocate fewer attentional resources during stimulus engagement (P3 amplitude) and exhibit slower cognitive processing speed (P3 latency) but increased activation of neural resources involved in the monitoring of their actions (ERN amplitude). Alternatively, higher-fit children allocate greater resources to environmental stimuli and demonstrate less reliance on action monitoring (increasing resource allocation only to meet the demands of the task). Under more demanding task conditions, the strategy of lower-fit children appears to fail since they perform more poorly under conditions requiring the upregulation of cognitive control.

Finally, only one randomized controlled trial published to date has used ERPs to assess neurocognitive function in children. Kamijo and colleagues (2011) studied performance on a working memory task before and after a 9-month physical activity intervention compared with a wait-list control group. They observed better performance following the physical activity intervention during task conditions that required the upregulation of working memory relative to the task condition requiring lesser amounts of working memory. Further, increased activation of the contingent negative variation (CNV), an ERP component reflecting cognitive and motor preparation, was observed at posttest over frontal scalp sites in the physical activity intervention group. No differences in performance or brain activation were noted for the wait-list control group. These findings suggest an increase in cognitive preparation processes in support of a more effective working memory network resulting from prolonged participation in physical activity. For children in a school setting, regular participation in physical activity as part of an after-school program is particularly beneficial for tasks that require the use of working memory.

Adiposity and Risk for Metabolic Syndrome as It Relates to Cognitive Health

A related and emerging literature that has recently been popularized investigates the relationship of adiposity to cognitive and brain health and academic performance. Several reports (Datar et al., 2004; Datar and Sturm, 2006; Judge and Jahns, 2007; Gable et al., 2012) on this relationship are based on large-scale datasets derived from the Early Child Longitudinal Study. Further, nonhuman animal research has been used to elucidate the relationships between health indices and cognitive and brain health (see

Figure 4-4 for an overview of these relationships). Collectively, these studies observed poorer future academic performance among children who entered school overweight or moved from a healthy weight to overweight during the course of development. Corroborating evidence for a negative relationship between adiposity and academic performance may be found in smaller but more tightly controlled studies. As noted above, Castelli and colleagues (2007) observed poorer performance on the mathematics and reading portions of the Illinois Standardized Achievement Test in 3rd- and 5th-grade students as a function of higher BMI, and Donnelly and colleagues (2009) used a cluster randomized trial to demonstrate that physical activity in the classroom decreased BMI and improved academic achievement among pre-adolescent children.

Recently published reports describe the relationship between adiposity and cognitive and brain health to advance understanding of the basic cognitive processes and neural substrates that may underlie the adiposity-achievement relationship. Bolstered by findings in adult populations (e.g., Debette et al., 2010; Raji et al., 2010; Carnell et al., 2011), researchers have begun to publish data on preadolescent populations indicating differences

FIGURE 4-4 Relationships between health indices and cognitive and brain health. NOTE: AD = Alzheimer’s disease; PD = Parkinson’s disease. SOURCE: Cotman et al., 2007. Reprinted with permission.

in brain function and cognitive performance related to adiposity (however, see Gunstad et al., 2008, for an instance in which adiposity was unrelated to cognitive outcomes). Specifically, Kamijo and colleagues (2012a) examined the relationship of weight status to cognitive control and academic achievement in 126 children aged 7-9. The children completed a battery of cognitive control tasks, and their body composition was assessed using dual X-ray absorptiometry (DXA). The authors found that higher BMI and greater amounts of fat mass (particularly in the midsection) were related to poorer performance on cognitive control tasks involving inhibition, as well as lower academic achievement. In follow-up studies, Kamijo and colleagues (2012b) investigated whether neural markers of the relationship between adiposity and cognition may be found through examination of ERP data. These studies compared healthy-weight and obese children and found a differential distribution of the P3 potential (i.e., less frontally distributed) and larger N2 amplitude, as well as smaller ERN magnitude, in obese children during task conditions that required greater amounts of inhibitory control (Kamijo et al., 2012c). Taken together, the above results suggest that obesity is associated with less effective neural processes during stimulus capture and response execution. As a result, obese children perform tasks more slowly (Kamijo et al., 2012a) and are less accurate (Kamijo et al., 2012b,c) in response to tasks requiring variable amounts of cognitive control. Although these data are correlational, they provide a basis for further study using other neuroimaging tools (e.g., MRI, fMRI), as well as a rationale for the design and implementation of randomized controlled studies that would allow for causal interpretation of the relationship of adiposity to cognitive and brain health. The next decade should provide a great deal of information on this relationship.

LIMITATIONS

Despite the promising findings described in this chapter, it should be noted that the study of the relationship of childhood physical activity, aerobic fitness, and adiposity to cognitive and brain health and academic performance is in its early stages. Accordingly, most studies have used designs that afford correlation rather than causation. To date, in fact, only two randomized controlled trials (Davis et al., 2011; Kamijo et al., 2011) on this relationship have been published. However, several others are currently ongoing, and it was necessary to provide evidence through correlational studies before investing the effort, time, and funding required for more demanding causal studies. Given that the evidence base in this area has grown exponentially in the past 10 years through correlational studies and that causal evidence has accumulated through adult and nonhuman animal

studies, the next step will be to increase the amount of causal evidence available on school-age children.

Accomplishing this will require further consideration of demographic factors that may moderate the physical activity–cognition relationship. For instance, socioeconomic status has a unique relationship with physical activity (Estabrooks et al., 2003) and cognitive control (Mezzacappa, 2004). Although many studies have attempted to control for socioeconomic status (see Hillman et al., 2009; Kamijo et al., 2011, 2012a,b,c; Pontifex et al., 2011), further inquiry into its relationship with physical activity, adiposity, and cognition is warranted to determine whether it may serve as a potential mediator or moderator for the observed relationships. A second demographic factor that warrants further consideration is gender. Most authors have failed to describe gender differences when reporting on the physical activity–cognition literature. However, studies of adiposity and cognition have suggested that such a relationship may exist (see Datar and Sturm, 2006). Additionally, further consideration of age is warranted. Most studies have examined a relatively narrow age range, consisting of a few years. Such an approach often is necessary because of maturation and the need to develop comprehensive assessment tools that suit the various stages of development. However, this approach has yielded little understanding of how the physical activity–cognition relationship may change throughout the course of maturation.

Finally, although a number of studies have described the relationship of physical activity, fitness, and adiposity to standardized measures of academic performance, few attempts have been made to observe the relationship within the context of the educational environment. Standardized tests, although necessary to gauge knowledge, may not be the most sensitive measures for (the process of) learning. Future research will need to do a better job of translating promising laboratory findings to the real world to determine the value of this relationship in ecologically valid settings.

From an authentic and practical to a mechanistic perspective, physically active and aerobically fit children consistently outperform their inactive and unfit peers academically on both a short- and a long-term basis. Time spent engaged in physical activity is related not only to a healthier body but also to enriched cognitive development and lifelong brain health. Collectively, the findings across the body of literature in this area suggest that increases in aerobic fitness, derived from physical activity, are related to improvements in the integrity of brain structure and function that underlie academic performance. The strongest relationships have been found between aerobic fitness and performance in mathematics, reading, and English. For children

in a school setting, regular participation in physical activity is particularly beneficial with respect to tasks that require working memory and problem solving. These findings are corroborated by the results of both authentic correlational studies and experimental randomized controlled trials. Overall, the benefits of additional time dedicated to physical education and other physical activity opportunities before, during, and after school outweigh the benefits of exclusive utilization of school time for academic learning, as physical activity opportunities offered across the curriculum do not inhibit academic performance.

Both habitual and single bouts of physical activity contribute to enhanced academic performance. Findings indicate a robust relationship of acute exercise to increased attention, with evidence emerging for a relationship between participation in physical activity and disciplinary behaviors, time on task, and academic performance. Specifically, higher-fit children allocate greater resources to a given task and demonstrate less reliance on environmental cues or teacher prompting.

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Tomporowski, P. D., C. L. Davis, P. H. Miller, and J. A. Naglieri. 2008a. Exercise and children’s intelligence, cognition, and academic achievement. Educational Psychology Review 20(2):111-131.

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Physical inactivity is a key determinant of health across the lifespan. A lack of activity increases the risk of heart disease, colon and breast cancer, diabetes mellitus, hypertension, osteoporosis, anxiety and depression and others diseases. Emerging literature has suggested that in terms of mortality, the global population health burden of physical inactivity approaches that of cigarette smoking. The prevalence and substantial disease risk associated with physical inactivity has been described as a pandemic.

The prevalence, health impact, and evidence of changeability all have resulted in calls for action to increase physical activity across the lifespan. In response to the need to find ways to make physical activity a health priority for youth, the Institute of Medicine's Committee on Physical Activity and Physical Education in the School Environment was formed. Its purpose was to review the current status of physical activity and physical education in the school environment, including before, during, and after school, and examine the influences of physical activity and physical education on the short and long term physical, cognitive and brain, and psychosocial health and development of children and adolescents.

Educating the Student Body makes recommendations about approaches for strengthening and improving programs and policies for physical activity and physical education in the school environment. This report lays out a set of guiding principles to guide its work on these tasks. These included: recognizing the benefits of instilling life-long physical activity habits in children; the value of using systems thinking in improving physical activity and physical education in the school environment; the recognition of current disparities in opportunities and the need to achieve equity in physical activity and physical education; the importance of considering all types of school environments; the need to take into consideration the diversity of students as recommendations are developed.

This report will be of interest to local and national policymakers, school officials, teachers, and the education community, researchers, professional organizations, and parents interested in physical activity, physical education, and health for school-aged children and adolescents.

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Physical education for healthier, happier, longer and more productive living

The time children and adults all over the world spend engaging in physical activity is decreasing with dire consequences on their health, life expectancy, and ability to perform in the classroom, in society and at work.

In a new publication, Quality Physical Education, Guidelines for Policy Makers , UNESCO urges governments and educational planners to reverse this trend, described by the World Health Organization (WHO) as a pandemic that contributes to the death of 3.2 million people every year, more than twice as many as die of AIDS.

The Guidelines will be released on the occasion of a meeting of UNESCO’s Intergovernmental Committee for Physical Education and Sport (CIGEPS) in Lausanne, Switzerland, (28-30 January).*

UNESCO calls on governments to reverse the decline in physical education (PE) investment that has been observed in recent years in many parts of the world, including some of the wealthiest countries. According to European sources, for example, funding and time allocation for PE in schools has been declining progressively over more than half of the continent, and conditions are not better in North America.

The new publication on PE, produced in partnership with several international and intergovernmental organizations**, advocates quality physical education and training for PE teachers. It highlights the benefits of investing in PE versus the cost of not investing (cf self-explanatory infographics ).

“The stakes are high,” says UNESCO Director-General Irina Bokova. “Public investment in physical education is far outweighed by high dividends in health savings and educational objectives. Participation in quality physical education has been shown to instil a positive attitude towards physical activity, to decrease the chances of young people engaging in risky behaviour and to impact positively on academic performance, while providing a platform for wider social inclusion.”

The Guidelines seek to address seven areas of particular concern identified last year in UNESCO’s global review of the state of physical education , namely: 1. Persistent gaps between PE policy and implementation; 2. Continuing deficiencies in curriculum time allocation; 3. Relevance and quality of the PE curriculum; 4. Quality of initial teacher training programmes; 5. Inadequacies in the quality and maintenance of facilities; 6. Continued barriers to equal provision and access for all; 7. Inadequate school-community coordination.

The recommendations to policy-makers and education stake-holders are matched by case studies about programmes, often led by community-based nongovernmental organizations. Success stories in Africa, North and Latin America, Asia and Europe illustrate what can be achieved by quality physical education: young people learn how to plan and monitor progress in reaching a goal they set themselves, with a direct impact on their self-confidence, social skills and ability to perform in the classroom.

While schools alone cannot provide the full daily hour of physical activity recommended for all young people, a well-planned policy should promote PE synergies between formal education and the community. Experiences such as Magic Bus (India) which uses physical activity to help bring school drop outs back to the classroom highlight the potential of such school-leisure coordination.

The publication promotes the concept of “physical literacy,” defined by Canada’s Passport for Life organization of physical and health educators as the ability to move “with competence and confidence in a wide variety of physical activities in multiple environments that benefit the healthy development of the whole person. Competent movers tend to be more successful academically and socially. They understand how to be active for life and are able to transfer competence from one area to another. Physically literate individuals have the skills and confidence to move any way they want. They can show their skills and confidence in lots of different physical activities and environments; and use their skills and confidence to be active and healthy.”

For society to reap the benefit of quality physical education, the guidelines argue, planners must ensure that it is made available as readily to girls as it is to boys, to young people in school and to those who are not.

The Guidelines were produced at the request of UNESCO’s Intergovernmental Committee for Physical Education and Sport (CIGEPS) and participants at the Fifth International Conference of Ministers and Senior Officials Responsible for Physical Education and Sport (Berlin 2013). UNESCO and project partners will proceed to work with a number of countries that will engage in a process of policy revision in this area, as part of UNESCO’s work to support national efforts to adapt their educational systems to today’s needs (see Quality physical education contributes to 21st century education ).

Media contact: Roni Amelan, UNESCO Press Service, r.amelan(at)unesco.org , +33 (0)1 45 68 16 50

Photos are available here: http://www.unesco.org/new/en/media-services/multimedia/photos/photo-gallery-quality-physical-education/

* More about the CIGEPS meeting

** The European Commission, the International Council of Sport Science and Physical Education (ICSSPE), the International Olympic Committee (IOC), UNDP, UNICEF, UNOSDP and WHO.

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The physical school environment

The brief explores how physical education facilities – that is, land, buildings, and furniture for education – can affect learning processes and what measures can be undertaken to create the optimal conditions for learners and teachers’ achievement and well-being.

Target 4.a of the Sustainable Development Goal 4 on inclusive and equitable quality education calls for the international community to ‘build and upgrade education facilities that are child, disability and gender sensitive and provide safe, non-violent, inclusive and effective learning environments for all’.  Proposed indicators include water, sanitation, and hygiene (WASH), electricity, and ICT access, as well as access to adapted infrastructure and materials for learners with disabilities (UNESCO, 2016) . Spaces that are safe and healthy have been found to positively affect pupil’s academic outcomes (Barrett et al., 2019) . Given the sizeable budget allocated to physical facilities, around 10–25 per cent of educational expenditures, it is crucial that funds are well spent and that school locations and facilities support access to education and an environment conducive to learning (Beynon, 1997) . The COVID-19 pandemic showed that poor-quality infrastructure (e.g. lack of ventilation) can exacerbate COVID-19 transmission in a school setting (USAID, 2020) . School infrastructure is, therefore, high on the agenda as governments prepare for school reopening. 

What we know

School location.

Schools are an integral part of a community and are best located close to the learners they serve. The need to travel long distances to school can have a negative impact on enrolment and retention (especially among girls and disabled children), as well as attendance and performance. For example, studies have found that students living less than 1 km from their school perform statistically better than those who walk further (Theunynck, 2009) .

School size

Evidence on the impact of school size on learning is mixed. Studies from the USA suggest that smaller schools may contribute to better student outcomes as learners, teachers, and parents see themselves as part of a community (Barrett et al., 2019) , while in India small schools with fewer facilities and a lack of specialist teachers may be resulting in lower outcomes (Rolleston and Moore, 2018) . Data from Senegal showed that school size had no effect on student performance in the early grades, but that attending a large school had adverse effects on student performance by the fourth grade. This may be due to the fact that fourth graders have spent more time in the education system whereas, at the start of the learning process, schools have not yet left their mark on younger learners, whose learning is shaped more by family environment (Koussihouede, 2020) . Barrett et al. (2019) also point to the drawbacks of large schools, citing higher transportation costs, higher administrative overheads, lower graduation rates, higher absenteeism, higher rates of vandalism, and lower teacher satisfaction.

School premises

An ‘inviting physical environment that ensures the safety and health of learners’ helps to enhance the quality of learning (UIS, 2012: 38) . Learning assessment data from Latin America shows a clear relationship between school infrastructure and learning even after controlling the socioeconomic level of the families. The two categories that are most clearly associated with learning outcomes are pedagogical and academic spaces, and connection to services (electricity, telephone, and Internet) (UNESCO Santiago Office and IDB, 2017) .

The quality of infrastructure affects enrolment and completion rates, and it is an important aspect in parents’ satisfaction with and perception of school quality (Gershberg, 2014) . There is evidence to suggest that school construction projects can help raise motivation among students and teachers and improve parental engagement, which subsequently leads to improved academic achievement (Neilson and Zimmerman, 2011) . Well-designed schools can increase the productivity of school staff and cut financial waste on unnecessary services and maintenance (RIBA, 2016) .

Although the literature does not show a strong relationship between students’ exam results and their satisfaction with the condition of school facilities, some studies have demonstrated convincing links between student outcomes and specific aspects of classroom infrastructure in OECD countries (Barrett et al., 2019) . Table 1 summarizes the evidence gathered from the literature.

a. Classroom features that are strongly related to their use. Source: Barrett et al., 2019: 28.

Outside of the classroom, learners need access to adequate outdoor space for organized physical education and sports, as well as for play during break times (UIS, 2012) . Physical activity and recreation have a significant impact on child development and the physical and mental health of learners (Barrett et al., 2019) .

WASH facilities

Schools are one of the most successful and cost-effective resources for targeting children and communities with key health and hygiene interventions (WHO, 2004) . Basic services such as water, sanitation, waste disposal, electricity, and communications also help ensure that children and teachers attend school and remain healthy there (Barrett et al., 2019) . Inadequate WASH facilities affect boys and girls in different ways, and this may contribute to unequal learning opportunities. Specifically, lack of sanitary facilities may mean that female students but also female teachers are absent from school during menstruation (WHO, 2009; Gershberg, 2014) .

Equity and inclusion

Equity issues are an important factor in the relationship among school location, facilities, premises, and student outcomes. The following findings emerged from the literature:

• Schools located in areas with good quality-of-life factors (e.g. higher pay, educated population) may attract and retain more qualified and motivated teachers (Gagnon, 2015) .
• In general, older school buildings and those in poor condition are located in the poorest areas (Barrett et al., 2019) .
• Overcrowded school buildings and heat exposure have been found to have a negative impact on student performance, especially for students from minority and financially disadvantaged backgrounds (Earthman, 2002; Park et al., 2020) .
• The effects of small schools (better attendance, higher graduation rates, greater engagement in extracurricular activities) may be more beneficial to children from disadvantaged backgrounds (Barrett et al., 2019) .
• Accessibility to school (transportation, well-maintained pavements) and in school (wide doors and corridors, adjusted toilets) is still a challenge worldwide (Education International Research, 2018) . The ‘physical “place” of the classroom’ can be improved to support learners with disabilities through the provision of clearly written texts, facilitating the use of assistive devices and adaptive learning resources. Space can be re-organized by moving learners with visual and physical disabilities to the front of the classroom so they can see and hear the teacher (Miles, Westbrook, and Croft, 2018: 79) . Flexible, age-appropriate learning spaces have the potential to improve academic outcomes for all learners (Barrett et al., 2019) .

Condition of school infrastructure

In Africa, the rapid expansion in access to education has surpassed the growth trend in primary school classrooms, often resulting in overcrowded classrooms (Theunynck, 2009) . This is further aggravated by the general poor quality, durability, and functionality of the existing infrastructure that requires renovation. However, poor learning conditions do not only affect developing countries. Environmental conditions in elementary schools (in terms of thermal and air quality) are often inadequate in developed countries (Wargocki and Wyon, 2013 in Barrett et al., 2019) .

Lack of facilities

Many schools lack the basic services necessary to guarantee a safe and healthy environment for learning. In Africa and Latin America, a high proportion of students attend schools with inadequate facilities (e.g. no potable water, lack of working sanitary facilities, broken or missing school furniture) (Theunynck, 2009; UIS, 2012b; UNESCO Santiago Office and IDB, 2017) . For instance, in Latin America, only one-quarter of third graders attend schools that met the infrastructure sufficiency criteria of the study (UNESCO Santiago Office and IDB, 2017) .

Inadequate resource allocation

Beyond budget constraints, ‘the problems of poor infrastructure are often exacerbated by an inefficient and inequitable distribution of construction resource’ (Theunynck, 2009: 10) . In some countries, overcrowded classrooms coexist with underutilized spaces (Theunynck, 2009; Barrett et al., 2019) . This can be partly explained by the high centralization of infrastructure planning, which cannot always accurately assess the actual needs at the local level (Theunynck, 2009; Gershberg, 2014) .

Policy and planning

School design.

In areas that are prone to natural disasters, schools should develop disaster preparedness plans that are reviewed on a regular basis (UIS, 2012a) . Other physical factors to consider for creating an optimal teaching and learning environment include: learner-to-classroom ratios, appropriate furniture for learner comfort, noise levels, heating and ventilation, sex-segregated toilets or latrines, and adequate lighting (Beynon, 1997; Neilson and Zimmerman, 2011; UIS, 2012a; UNESCO et al., 2020) . The COVID-19 pandemic has emphasized the need for adequate WASH facilities and classroom arrangements that facilitate social distancing (UNESCO et al., 2020; Furlani and Tibério Cardoso, 2021) .

It is also important to go beyond health and safety minimum standards and create spaces conducive to participatory learning methodologies. Although its impact on learning remains to be documented, school design can help build a connection between schools and the wider community if conceived in accordance with local climatic and cultural environments (UIS, 2012a; Barrett et al., 2019) .

Infrastructure planning

In areas facing budget constraints, cost-effectiveness analysis can help guide decisions regarding the construction or renovation of classrooms depending on their impact on the quality of learning (Jenkins and Zeinali, 2015) . Steps to create quality learning environments include an audit of the current state of affairs in schools, the development of a plan with specific baseline standards and indicators for reaching them, and a cost evaluation (Theunynck, 2009; UIS, 2012a) . Using up-to-date information on the condition of school infrastructure and adopting a participatory approach (involving parents and communities) are critical elements for successful infrastructure planning (UIS, 2012a; UNESCO Santiago Office and IDB, 2017) .

Access to school places

Maintaining reasonable travel distances implies increasing the number of schools and reducing their size rather than fewer and larger schools (Theunynck, 2009; Barrett et al., 2019) . This means that smaller schools should be locally distributed according to the density of demand (Barrett et al., 2019) .

Improving equity in access

Simple solutions exist when working towards better inclusion of children with physical disabilities, provided that they are incorporated in the school design and planning process (Theunynck, 2009) . These include building ramps, widening door openings, minimizing stairs, attention to topography, etc.

Maintenance of buildings

Consistently maintaining and bringing the necessary improvements to the existing infrastructure can result in a good-quality educational environment in buildings of any age. Additionally, attention to infrastructure sustainability can help accommodate future demographic or pedagogic changes (Barrett et al., 2019) . In this regard, Gershberg (2014) suggests that in developing countries, the decentralization of the education infrastructure can ensure more efficient maintenance.

Plans and policies

• Lao PDR: School construction guidelines (2009)
• Togo: Stratégie nationale du MEPSA en matière de constructions scolaires du primaire (2009)
• Beynon, J. 1997. Physical Facilities for Education: What Planners Need to Know.   Paris: IIEP-UNESCO.
• UNICEF. 2009. Child-Friendly Schools Manual.  New York: UNICEF.
• UNESCO; UNICEF; World Bank; WFP. 2020. Framework for Reopening Schools.
• WHO. 2009.   Water, Sanitation and Hygiene Standards for Schools in Low-cost Settings. Geneva: WHO.

Barrett, P.; Treves, A.; Shmis, T.; Ambasz, D.; Ustinova, M. 2019. The Impact of School Infrastructure on Learning: A Synthesis of the Evidence. Washington, DC: World Bank.

Beynon, J. 1997. Physical Facilities for Education: What Planners Need to Know. Fundamentals of Educational Planning 57. Paris: IIEP-UNESCO.

Earthman, G.I. 2002. ‘School facility conditions and student academic achievement’. Williams Watch Series: Investigating the Claims of Williams v. State of California. Los Angeles: UCLA’s Institute for Democracy, Education, and Access.

Education International Research. 2018. Are We There Yet? Education Unions Assess the Bumpy Road to Inclusive Education. Brussels: Education International.

Furlani, S.; Tibério Cardoso, G. 2021. ‘Rethinking post-Covid-19 school design in Brazil: Adaptation strategies for public schools PEE-12 FNDE’. In: Strategic Design Research Journal , April 2021.

Gagnon, D.J. 2015. ‘School location and teacher supply: Understanding the distribution of teacher effects’. In: Current Issues in Education, 18(3) : 15.

Gershberg, A.I. 2014. ‘Educational infrastructure, school construction & decentralization in developing countries: Key issues for an understudied area’. Working paper 14–12. Atlanta: International Center for Public Policy.

Jenkins, G.P.; Zeinali, A. 2015. ‘Cost-effective infrastructure choices in education: Location, build or repair’. In: South African Journal of Economic and Management Sciences, 18(1): 70–83.

Koussihouede, O. 2020. ‘School size and student performance’. IIEP Learning Portal, 9 September 2020.

Miles, S.; Westbrook, J.; Croft, A. 2018. ‘Inclusions and exclusions in rural Tanzanian primary schools: Material barriers, teacher agency and disability equality’. In: Social Inclusion, 6(1): 73–81.

Neilson, C.; Zimmerman, S. 2011. ‘The effect of school construction on test scores, school enrollment, and home prices’. IZA DP No. 6106. Discussion Paper Series. Bonn: Institute for the Study of Labor (IZA).

Park, R.J.; Goodman, J.; Hurwitz, M.; Smith, J. 2020. ‘Heat and learning’ . In: American Economic Journal: Economic Policy, 12(2) : 306–39.

RIBA (Royal Institute of British Architects). 2016. Better Spaces for Learning. London: RIBA.

Rolleston, C.; Moore, R. 2018. Young Lives School Survey, 2016–17: Value-added analysis in India.  Oxford: Young Lives.

Theunynck, S. 2009. School Construction Strategies for Universal Primary Education in Africa: Should Communities Be Empowered to Build Their Schools? Washington, DC: The World Bank.

UIS (UNESCO Institute for Statistics). 2012a. A place to learn: Lessons from research on learning environments. Technical paper 9. Montreal: UIS.

UIS (UNESCO Institute for Statistics). 2012b. School and teaching resources in Sub-Saharan Africa: Analysis of the 2011 UIS Regional Data Collection on Education. UIS Information Bulletin 9. Montreal: UIS.

UNESCO. 2016. Education 2030: Incheon Declaration and Framework for Action for the Implementation of Sustainable Development Goal 4: Ensure Inclusive and Equitable Quality Education and Promote Lifelong Learning .   Paris: UNESCO.

UNESCO Santiago Office; IDB (Inter-American Development Bank). 2017. Sufficiency, Equity and Effectiveness of School Infrastructure in Latin America According to TERCE . Santiago: UNESCO Office Santiago and Regional Bureau for Education in Latin America and the Caribbean; IDB.

UNESCO; UNICEF; World Bank; World Food Programme. 2020. Framework for reopening schools.  Paris : UNESCO

USAID (United States Agency for International Development). 2020. COVID-19 and Education: Initial Insights for Preparedness, Planning and Response. Washington, DC: USAID.

WHO (World Health Organization). 2004. The physical school environment: An essential component of a health-promoting school. Information series on school health document. Geneva: WHO.

WHO (World Health Organization). 2009. Water, Sanitation and Hygiene Standards for Schools in Low-Cost Settings.  Geneva: WHO.

Related information

• Why education infrastructure matters for learning
• Child-friendly school (CFS)
• Learning environment
• School facilities

Physical Education

Physical education is the foundation of a Comprehensive School Physical Activity Program. 1, 2 It is an academic subject characterized by a planned, sequential K–12 curriculum (course of study) that is based on the national standards for physical education. 2–4 Physical education provides cognitive content and instruction designed to develop motor skills, knowledge, and behaviors for physical activity and physical fitness. 2–4 Supporting schools to establish physical education daily can provide students with the ability and confidence to be physically active for a lifetime. 2–4

There are many benefits of physical education in schools. When students get physical education, they can 5-7 :

• Increase their level of physical activity.
• Improve their grades and standardized test scores.
• Stay on-task in the classroom.

Increased time spent in physical education does not negatively affect students’ academic achievement.

Strengthen Physical Education in Schools [PDF – 437 KB] —This data brief defines physical education, provides a snapshot of current physical education practices in the United States, and highlights ways to improve physical education through national guidance and practical strategies and resources. This was developed by Springboard to Active Schools in collaboration with CDC.

Secular Changes in Physical Education Attendance Among U.S. High School Students, YRBS 1991–2013

The Secular Changes in Physical Education Attendance Among U.S. High School Students report [PDF – 3 MB] explains the secular changes (long-term trends) in physical education attendance among US high school students over the past two decades. Between 1991 and 2013, US high school students’ participation in school-based physical education classes remained stable, but at a level much lower than the national recommendation of daily physical education. In order to maximize the benefits of physical education, the adoption of policies and programs aimed at increasing participation in physical education among all US students should be prioritized. Download the report for detailed, nationwide findings.

Physical Education Analysis Tool (PECAT)

The  Physical Education Curriculum Analysis Tool (PECAT) [PDF – 6 MB] is a self-assessment and planning guide developed by CDC. It is designed to help school districts and schools conduct clear, complete, and consistent analyses of physical education curricula, based upon national physical education standards.

Visit our PECAT page  to learn more about how schools can use this tool.

• CDC Monitoring Student Fitness Levels1 [PDF – 1.64 MB]
• CDC Ideas for Parents: Physical Education [PDF – 2 MB]
• SHAPE America: The Essential Components of Physical Education (2015) [PDF – 391 KB]
• SHAPE America: Appropriate Instructional Practice Guidelines for Elementary, Middle School, and High School Physical Education [PDF – 675 KB]
• SHAPE America: National Standards and Grade-Level Outcomes for K–12 Physical Education 2014
• SHAPE America: National Standards for K–12 Physical Education (2013)
• SHAPE America Resources
• Youth Compendium of Physical Activities for Physical Education Teachers (2018) [PDF – 145 KB]
• Social Emotional Learning Policies and Physical Education
• Centers for Disease Control and Prevention. A Guide for Developing Comprehensive School Physical Activity Programs . Atlanta, GA: Centers for Disease Control and Prevention, US Department of Health and Human Services; 2013.
• Centers for Disease Control and Prevention. School health guidelines to promote healthy eating and physical activity. MMWR . 2011;60(RR05):1–76.
• Institute of Medicine. Educating the Student Body: Taking Physical Activity and Physical Education to School . Washington, DC: The National Academies Press; 2013. Retrieved from  http://books.nap.edu/openbook.php?record_id=18314&page=R1 .
• SHAPE America. T he Essential Components of Physical Education . Reston, VA: SHAPE America; 2015. Retrieved from   http://www.shapeamerica.org/upload/TheEssentialComponentsOfPhysicalEducation.pdf  [PDF – 392 KB].
• Centers for Disease Control and Prevention. The Association Between School-Based Physical Activity, Including Physical Education, and Academic Performance . Atlanta, GA; Centers for Disease Control and Prevention, US Department of Health and Human Services; 2010.
• Centers for Disease Control and Prevention. Health and Academic Achievement. Atlanta: US Department of Health and Human Services; 2014.
• Michael SL, Merlo C, Basch C, et al. Critical connections: health and academics . Journal of School Health . 2015;85(11):740–758.

Healthy Youth

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ORIGINAL RESEARCH article

The influencing factors of individual interest in physical education based on decision tree model: a cross-sectional study.

• 1 School of Physical Education, Changchun Normal University, Changchun, China
• 2 School of Physical Education, Northeast Normal University, Changchun, Jilin Province, China

To identify the key influencing factors and analyze the internal relationship among the factors of individual interest in PE, we conducted a cross-sectional survey of a large sample of Chinese young students based on the decision tree model. A total of 3,640 young students ( M age  = 14.16; 7–18 years; SD = 2.66, 47% boys) were investigated by using six questionnaires, including individual interest in physical PE, self-efficacy, achievement goals, expectancy value in PE, PE knowledge and skills and PE learning environment. Results showed there were a total of seven variables entered into the decision tree model, which was 3 layers high, including 38 nodes. The root node was expectancy value which was divided by sports knowledge and skills and self-efficacy. The third layer included mastery-approach goal, family sports environment, performance-avoidance goal and gender. The results depict that expectancy value of PE was the most important influencing factors of adolescent students’ individual interest in PE in this study, and the other important factors were sports knowledge and skills, self-efficacy, mastery-approach goal, family sports environment, performance-avoidance goal, and gender, respectively. The implications for PE are: (1) Improve the status of the PE curriculum and enhance students’ recognition of the value of PE; (2) Strengthen the teaching of knowledge and skills to avoid low-level repetitive teaching; (3) Enhance success experience and foster sports self-efficacy; and (4) Establish reasonable sports goals to foster individual interest in sports learning.

Introduction

To actively engage with and persist on a learning task, students need to be sufficiently motivated ( Rotgans and Schmidt, 2017 ). Renninger and Hidi (2016) regarded interest as a powerful motivator variable that directs students’ attention to specific objects and stimuli and guides their engagement towards specific activities. In educational research, researchers conceptualized interest as situational and individual ( Chen and Darst, 2002 ). Situational interest is a relatively transient reaction to highly stimulating factors in the immediate environment, whereas individual interest is a relatively long-term preference for a particular subject or activity ( Palmer et al., 2017 ).

In physical education (PE), systematic research on interest has mainly investigated SI and evidence has been accumulated regarding its sources, motivational function, and relationship with learning ( Chen and Wang, 2017 ). Researchers concluded that individual interest has an important effect on performance and cognitive functioning, as students who are interested in a domain or task have been shown to pay more attention, persist for longer periods of time, and acquire more and qualitatively different knowledge than individuals without such an interest ( Hidi, 1990 ; Palmer et al., 2017 ). Despite the role of individual interest has been general accepted, it has been subjected to limited empirical testing in educational settings ( Chen and Wang, 2017 ).

Given that the most recent global estimates show that more than three-quarters (81%) of adolescents do not meet the recommendations for aerobic exercise, as outlined in the 2010 Global Recommendations on Physical Activity for Health ( Bull et al., 2020 ). More than about 60% of children and adolescents do not meet the recommended amount ( Zhu, 2021 ). In the face of the problem of declining physical health and insufficient participation in physical activity among teenagers around the world, it is particularly important to deeply explore the influencing factors of individual interest. However, the influencing factors of individual interest in PE have rarely been studied. Consequently, it is not clear that to what extent each influencing factor will facilitate or hinder the development of individual interest in PE. And there is a lack of targeted strategies on how to improve students’ individual interest in PE in different situations, which makes it difficult to explain and improve the reality of low individual interest in PE among young people in China.

Research showed that the decision tree model analysis method could not only obtain a more intuitive relationship diagram between various influencing factors, but also identify the most critical influencing factors of individual interest in PE, construct a clearer classification standard, and dig deeper into the role of each factor ( Henrard et al., 2015 ; Zhao et al., 2020 ). This method has an in-depth theoretical basis and more targeted practical guidance significance for cultivating individual interest in youth sports participation. Therefore, in view of the practical background of low individual interest in youth sports and lack of targeted promotion strategies, this study adopted decision tree model analysis methods to analyze 3,640 people based on comprehensive consideration of ethical issues (voluntary rights, the right to know, and privacy protection, etc.). A survey of young students aged 7–18 was carried out to reveal the key factors influencing young students’ individual interest in PE and the relationship among the factors, and to further explore the implications of the research results on the promotion of individual interest in young students, aiming to provide a useful reference for improving youth sports learning individual interest.

Social cognitive theory and individual interest

Bandura (1986) first proposed the social cognitive theory, then the theory was widely applied and carried out a large number of empirical studies. Social cognitive theory is widely used by researchers to analyze the influencing factors of individual behavior ( Sumak et al., 2011 ; Zhou et al., 2020 ). This theory analyzes the influencing factors of individual behavior in detail and holds that the generation or change of individual behavior is not only affected by external environmental factors, but also influenced by their own internal psychological factors ( Bandura, 1986 ; Li and Hua, 2022 ). The social cognitive theory explains human behavior using a three-way model in which environment, personal factors, and behavior interact continuously ( Shamizadeh et al., 2019 ; Sebastian et al., 2021 ), and emphasize the role of self-efficacy, expectancy-value, achievement goals, knowledge and other factors.

The relationship between environment factors and interest

Traditional behavioral theory points out that individual behavior depends entirely on external environmental stimuli, despite being greatly questioned and criticized, the role of the environment cannot be ignored. From the perspective of space, environmental factors include three aspects: family environment, school environment, and social environment. Results showed that parents impacted the trajectory of participants’ athletic careers and their general approach toward sport ( Erickson et al., 2017 ). The local environment can affect an individual’s interest, and the space available for sports, the distance to facilities, and quality of the equipment all naturally impact willingness to participate ( Gomes et al., 2016 ). Participants who lived in rural settings were less interested in recreational sports than their urban counterparts ( Chen et al., 2017 ). Existing evidence suggests that the influence of environmental factors on interest is mediated or modulated by other variables, such as self-efficacy ( Halim et al., 2021 ), and the action effect of environmental factors still needs further research.

The relationship between expectancy-value and interest

Motivated behavior is characterized by voluntary choices, persistent effort, and achievement, which are directly associated with students’ expectancy for success and perceived value in specific activities ( Chen et al., 2008 ). The expectancy-value theory argued that students’ expectancy-value motivation directly predicts their achievement and behavior choices, and that student achievement over time predicts their behavior choices ( Eccles et al., 1983 ; Eccles and Wigfield, 1995 ). Expectancy belief and task values have been identified as predictors for both physical activity participation intention ( Xiang et al., 2003 ) and successful performances in physical education ( Gao et al., 2009 ). Findings in other areas have shown that task value ( Bai et al., 2020 ) and utility value ( Hulleman et al., 2010 ; Akcaoglu et al., 2018 ) and interest are closely related.

The relationship between self-efficacy and interest

Self-efficacy is a positively focused ability belief that describes a person’s perception of his ability to successfully complete a specific task ( Bandura, 1977 ). It was found to be as important as value in educational settings and was an important predictor of achievement ( Fryer and Ainley, 2019 ; Nuutila et al., 2021 ). While the majority of self-efficacy research focused on task-level outcomes, Bandura (2011) has clarified that self-efficacy are also related to long-term pursuits such as skill development have developed over time and are not limited to individual events. Increasing empirical evidence supports the important role of self-efficacy in benefits, with long-standing theories suggesting that the two are interconnected over time ( Fryer and Ainley, 2019 ; Nuutila et al., 2020 ).

The relationship between achievement goals and individual interest

Researchers have identified two types of achievement goals that students adopt: mastery and performance goals ( Nicholls, 1984 ; Dweck, 1986 ). Further studies subdivided these achievement goals into approach and avoidance components, presented four categories: mastery-approach goal, performance-approach goal, mastery-avoidance goal, and performance-avoidance goal ( Elliot and McGregor, 2001 ). Numerous studies found a positive correlation between mastery-approach goal and individual interest, but the relationship between performance-approach goals and individual interest is still unclear ( Hulleman et al., 2010 ; Linnenbrink-Garcia et al., 2013 ). Roure et al. (2021) found that the positive correlations between both mastery-approach and performance-approach and individual interest, and confirmed the key role played by students’ mastery-approach goal when considering its relationship with students’ individual interest ( Roure and Lentillon-Kaestner, 2021 ). The meta-analysis results show that, relative to performance-approach and performance-avoidance goals and no-goals, induced mastery-approach goals enhanced performance ( Huang, 2011 , 2012 ), but not motivation ( Noordzij et al., 2021 ). Overall, more research is needed to clearly understand the relationship between students’ achievement goals and their individual interest.

The relationship between knowledge, skills and interest

Reviews have consistently pointed that prior knowledge is one of the most important individual difference brought to the learning experience ( Lin and Chai, 2019 ; Fryer et al., 2021 ). Prior knowledge can account for 30–60% of the variance in future learning ( Tobias, 1994 ). Knowledge refers to one’s understanding of a given domain in either a declarative (factual) or procedural (skillful execution) form ( Alexander et al., 1991 ). A majority of studies showed that the relationship between interest and knowledge may be two-way, students with high individual interest in a field are likely to continue to acquire additional knowledge in that field as they are naturally drawn to the subject and are willing to spend more time and effort to learn more about the subject ( Tobias, 1994 ). And in return, increased knowledge is likely to strengthen the interest, because the expanded knowledge affords the individual to extend the knowledge base on which interest is developed and sustained. Prior knowledge determines interest in learning in physical education ( Zhang et al., 2016 ), interest is a by-product of knowledge ( Rotgans and Schmidt, 2017 ).

The present study

Based on previous research, the purpose of this study was to explore the influencing factors of individual interest in PE from three aspects: demographic factors, environmental factors, and individual factors. Variables investigated include gender, school location, sports environment, expectancy value, sports knowledge and skills, self-efficacy, and achievement goals. As Henrard et al. (2015) argued, the decision tree model was an important classification technique in data mining, and optimal segmentation for multiple types of variables was an important function of this method. Therefore, this study chose the decision tree model as the main method to analyze the importance and internal relationship of each influencing factor. These analyses have theoretical implications for how individual interest develops across the PE learning process, and they are of practical concern to educators seeking to enhance students’ individual interest and sports participation independently.

Materials and methods

Participants.

The present study sample consisted of 3,640 students ( M age  = 14.16; 7–18 years; SD = 2.66, 47% boys) from 110 PE classes, taken from 11 cities located in the Northeast, East, Central, and West regions of China. Students were in grades 1–12. Class sizes ranged from 20 to 65 students per class. Permission to conduct the study was granted by the ethical board of the host university, and agreement was also obtained from the principals of the participating schools.

Individual interest

The Chinese Individual Interest Scale in PE ( Lin, 2019 ) was used to measure students’ individual interest. As Rotgans (2015) argued, the instrument of individual interest should measure at least the following three key components of the definition: (a) willingness to reengage with specific content, (b) positive emotions, and (c) increased value for the topic. Take willingness to participate (e.g., ‘I often take part in sports activities in my spare time’), emotional experience (e.g., ‘Participating in sports activities brings me a lot of fun’) and value embodiment (e.g., ‘I want to work in sports or sports-related industries in the future’) as three dimensions to compile the questionnaire of individual interest in PE. Each of these three dimensions consists of three items. These nine items were randomly arranged and each was rated on a five-point Likert scale, ranging from 1 = ‘strongly disagree’ to 5 = ‘strongly agree’. Lin (2019) established the construct validity of the Chinese Individual Interest Scale in PE using exploratory and confirmatory factor analyses ( χ 2 / df  = normed fit index (NFI) = 0.97, comparative fit index (CFI) = 0.99, Tacker-Lewis index (TLI) = 0.98, incremental fit index (IFI) = 0.99, and root mean squared error of approximation (RMSEA) = 0.045). The internal consistency (Cronbach’s alpha) and test–retest reliability factor for willingness to participate (0.81, 0.87), emotional experience (0.86, 0.84), value embodiment (0.73, 0.82) and for the total scale (0.90, 0.85) among the grade 1–12 school students.

Environment factors for PE

Investigate the sports learning environment from three aspects: school sports environment (including school sports facilities, equipment, PE teachers, sports activities and sports curriculum development, etc.; e.g., ‘How is your school’s sports facilities?’), family sports environment (including family sports equipment, parents’ support, family sports atmosphere, etc.; e.g., What is the atmosphere of your family sports activities?’) and social sports environment (including social sports venues, social sports activities and clubs, etc.; e.g., How about the surrounding sports clubs and activity centers?’). The questionnaire consists of 16 randomly arranged items, and each was rated on a five-point Likert scale, ranging from 1 = ‘very bad’ to 5 = ‘very good’. The construct validity of the questionnaire was established by means of exploratory and confirmatory factor analysis ( Byrne, 2001 ), χ 2 / df  = 1.592, NFI = 0.94, CFI = 0.98, TLI = 0.97, ILI = 0.98, RMSEA = 0.048. The internal consistency (Cronbach’s alpha) and test–retest reliability factor for school sports environment (0.90, 0.92), family sports environment (0.86, 0.91), social sports environment (0.83, 0.88) and for the total scale (0.93, 0.90).

Expectancy-value

Students’ expectancy beliefs and task values were measured using a modified Chinese Expectancy-Value Questionnaire for PE ( Eccles and Wigfield, 1995 ; Chai and Lin, 2019 ). The questionnaire is a 5-point Likert scale of 11 items. Five items were designed to measure expectancy beliefs and six items to measure attainment (importance), intrinsic (interest), and utility (usefulness) values. In completing the questionnaire, students were asked to respond to the items by indicating their preference on the five-point scale attached to each item. For example, in responding to the item “How important do you think PE is for you?” the student can choose a number between 1 and 5, with 5 indicating “very important” and 1 indicating “not important.” The descriptors “very important” and “not important” are printed explicitly on the EVQ to avoid confusion ( Zhu et al., 2012 ). Chai and Lin (2019) confirmed its construct validity by means of confirmatory factor analysis and found that the measurement model of Chinese EVQ was well preserved with χ 2 / df  = 2.73, NFI = 0.99, CFI = 0.99, TLI = 0.99, ILI = 0.99, RMSEA = 0.020. The internal consistency (Cronbach’s alpha) and test–retest reliability factor for expectancy beliefs (0.89, 0.88), attainment values (0.78, 0.89), intrinsic values (0.84, 0.91), utility values (0.84, 0.85) and for the total scale (0.80, 0.87).

Self-efficacy

The Generalized Self-Efficacy Scale (GSES; Schwarzer and Jerusalem, 1995 ) was used to measure students’ self-efficacy. The questionnaire consists of 10 randomly arranged items, and each was rated on a five-point Likert scale, ranging from 1 = ‘strongly disagree’ to 5 = ‘strongly agree’. The internal consistency (Cronbach’s alpha) and test–retest reliability factors in this investigation were 0.86 and 0.89.

Achievement goals

The 2 × 2 Achievement Goals Questionnaire (AFQ-PE) compiled by Guan (2004) was used to measure students’ achievement goals. The scale includes four dimensions: master-approach goal, master-avoidance goal, performance-approach goal, and performance-avoidance goal. Each of these four dimensions consists of three items. These 12 items were randomly arranged and each was rated on a five-point Likert scale, ranging from 1 = ‘strongly disagree’ to 5 = ‘strongly agree’. The internal consistency (Cronbach’s alpha) and test–retest reliability factors in this investigation were 0.89 and 0.88.

Sports knowledge and skills

Use a self-reporting questionnaire to evaluate students’ sports knowledge and skills. The questionnaire consists of six randomly arranged items, and each was rated on a five-point Likert scale, ranging from 1 = ‘strongly disagree’ to 5 = ‘strongly agree’. The items are as follows: (1) ‘I know more about sports knowledge than most of my classmates’; (2) ‘I am familiar with many sports’; (3) ‘I am familiar with many sports’; (4) ‘I have many sports skills better than most of my classmates’; (5) ‘At least one sports skill I master better than most of my classmates’; (6) ‘I have many sports skills better than most of my classmates’. The internal consistency (Cronbach’s alpha) and test–retest reliability factors in this investigation were 0.89 and 0.91.

Data came from a cross-sectional study investigating 7–18 year-old teenage students’ individual interest in PE. Assessments were completed over two-month periods in spring 2019 and fall 2020. All questionnaires will be distributed, filled out, and collected by 11 graduate students who have undergone strict training immediately after the PE class. In order to ensure that all the students fully understand the meaning of the questions and options, the graduate students read the questions aloud to the first and second grade students in elementary school, making corresponding explanations. Then ask students to fill out the questionnaire and raise their hands whenever they encounter problems during the filling process. All in all, the testing of each child took about 20 min.

Statistical analyses

The data was analyzed using SPSS for Windows Version 22.0. Because all of the data in this study were gathered via questionnaires and all items were completed by young students, there may be common method bias in the research supporting this thesis ( Gorrell et al., 2011 ; Mackenzie and Podsakoff, 2012 ). First, the Harman single factor test method was used to conduct common method bias. The specific method was to perform Principal Component Analysis (PCA) on all questionnaires and scale items. The results showed that there are 15 factors with a characteristic value greater than 1, and the variance explained by the first factor is 25.30%, which is less than the critical standard of 40% ( Cao and Chi, 2016 ). The results showed that there was no serious common method bias problem in this study.

Subsequently, create a decision tree model. According to the characteristics of the large sample, multiple indicators, continuous variables, and categorical variables in this study were compared to the accuracy of each model, finally determining the optimized CHAID model for decision tree analysis ( Henrard et al., 2015 ). Among all the variables, gender, grade, and school location were category variables. The two grades of the gender variable “male” and “female” were marked as 1 and 2 respectively, the 12 grades of grade variable “1 ~ 12” were marked as “1 ~ 12” respectively, and the variables of the city and village where the school is located were marked as 1 and 2 respectively; other variables are continuous variables, and the best cut-off point is identified and split by the decision tree model. The model parameters were set as follows: the maximum depth of the decision tree is 5, the minimum number of cases of influencing factor nodes is 200, the minimum number of cases of sub-nodes is 100, the minimum change value of the Gini coefficient is 0.0001, and the recognition accuracy rate of the 10-level cross-validation model is adopted ( Cao and Chi, 2016 ).

The rules for ranking the importance of various factors affecting individual interest in PE are: (1) sort according to the position of the node where the variable is located, the closer the variable is to the root node, the greater the impact on the target variable; (2) At the same level of branches, we compared the value of p and Chi-square of each variable. The smaller the value of p , the greater the impact on the target variable. If the value of p is equal, compare the chi-square value; (3) At non-terminal nodes, if the sample size of the variable is less than 10, the variable is not regarded as an important one.

Descriptive statistics

Table 1 shows the descriptive statistics as well as the correlation matrix between the measures of the study for the whole sample across different grade students. The results show that the correlation among each variable and between each variable and individual interest have reached a significant level ( p < 0.05).

Table 1 . Means (and SD), and intercorrelations between environment factors, expectancy-value, self-efficacy, sports knowledge and skills achievement goals and individual interest.

Construction of decision tree model

The decision tree model of the influencing factors on individual interest in PE created by this research has 3 layers and 38 leaf nodes (see Figure 1 ). The results showed that a total of seven variables entered the model, in order of importance. They are: (1) expectancy-value; (2) sports knowledge and skill mastery; (3) self-efficacy; (4) mastery-approach goal; (5) family sports environment; (6) performance-avoidance goal and (7) gender.

Figure 1 . The decision tree model of the seven influencing factors, including expectancy-value, sports knowledge and skill mastery, self-efficacy, mastery-approach goal, family sports environment, performance–avoidance goal, and gender of individual interest. The asterisk indicates a statistically non-significant level of alpha. 05.

At the first layer of the decision tree structure, students’ individual interest in PE was divided into 8 nodes according to “expectancy-value,” and the difference between each node reached a significant level ( F  = 393.07; p  < 0.05). The higher the students’ expectancy-value, the greater their individual interest in PE. Students whose expectancy-value score ≥ 4.45 had the highest individual interest, and students whose score ≤ 2.55 had the lowest individual interest.

At the second layer of the decision tree structure, 8 nodes of students’ expectancy-value in PE were divided into 20 nodes according to “sports knowledge and skills” and “self-efficacy” (see Figure 1 ). Students whose expectancy-value scores were ≥ 4.45 and were between 3.36 and 3.55 were divided into 3 (self-efficacy scores: < 3.57, 3.57–4.5, > 4.5; F  = 72.43, p  < 0.05; the node no longer grows) and 2 (self-efficacy scores: ≤ 3.10, > 3.10; F  = 58.98, p  < 0.05) nodes, respectively, according to their “self-efficacy.” The higher the self-efficacy, the higher the expectancy-value score. In the self-efficacy score ≤ 3.10 group, there are gender differences in the self-efficacy scores of students, and boys’ self-efficacy scores are higher than those of girls.

Students whose expectancy-value scores in the other six ranges were divided into 2 (sports knowledge and skills scores: ≤ 2.00, > 2.00, F  = 71.86, p  < 0.05), 2 (sports knowledge and skills scores: ≤ 2.83, > 2.83, F  = 82.04, p  < 0.05), 4 (sports knowledge and skills: ≤ 2.50, 2.50–2.83, 2.83–3.33, > 3.33, F  = 64.79, p  < 0.05), 2 (sports knowledge and skills: ≤ 2.83, > 2.83, F  = 53.65, p  < 0.05), 3 (sports knowledge and skills: ≤ 3.33, 3.33–4.00, > 4.00, F  = 111.07, p  < 0.05) and 2 (sports knowledge and skills: ≤ 3.67, > 3.67, F  = 91.11, p  < 0.05) nodes, respectively, according to their “sports knowledge and skills,” the higher the sports knowledge and skills score of students, the higher the expectancy-value score. At the last layer of the decision tree structure, the sports knowledge and skills were divided into 8 nodes: (1) the sports knowledge and skills scores > 2.00 group were divided into 2 nodes (≤ 3.10, > 3.10, F  = 17.59, p  < 0.05) according to their family sports environment; (2) the scores between 2.83 and 3.33 group were divided into 2 nodes (≤ 3.47, > 3.47, F  = 14.20, p  < 0.05) according to their family sports environment; (3) the scores >2.83 were divided into 2 nodes (≤ 3.55, > 3.55, F  = 14.76, p  < 0.05) according to their performance-avoidance goal; (4) the scores ≤ 3.33 group were divided into 2 nodes (≤ 3.33, > 3.33, F  = 36.27, p  < 0.05) according to their mastery-approach goal. In each group, students’ sports knowledge and skill scores increase with the increase of branch indicators.

Decision tree model evaluation

The accuracy recognition result of the 10-layer cross-validation model shows that the accuracy of the decision tree model of the factors affecting individual interest in PE of primary and middle school students constructed in this research was 90.88% (see Table 2 ).

Table 2 . Recognition accuracy rate of the model of factors affecting individual interest in PE of primary and middle school students.

The purpose of the present study was to identify the key influencing factors of individual interest in PE among primary and middle school students in China. To rank the influencing factors according to their importance accurately, we selected a total of 13 variables as the influencing factors of individual interest in PE for decision tree analysis, including gender, grade, school location, school sports environment, family sports environment, social sports environment, expectancy-value, self-efficacy, sports knowledge and skills, master-approaching goal, master-avoidance goal, performance-approach goal, and performance-avoidance goal, and conducted a large sample of 3,640 students selected from 11 cities. The selected questionnaires and scales have been tested for reliability and validity and could be used as measurement tools for this study. There was no common method bias among all the questionnaires and scales.

The decision tree adopts a top-down recursive approach to compare and evaluate the attribute values of nodes within the decision tree and determine the branch down from the node based on the different attribute values ( Zhang et al., 2020 ). The decision tree algorithm has been widely used in different fields since its introduction ( Tao et al., 2016 ). Not only that, the decision tree analysis could also identify the key influencing factors of individual interest in PE of primary and middle school students in China, and make up for the shortcomings in the current research on many influencing factors of sports learning interests. Decision tree algorithm models include CHAID, C5.0, QUEST, and C&R. Combining the characteristics of the large sample, multiple indicators, and the simultaneous existence of continuous variables and categorical variables in this study, the accuracy of related models is compared, and the optimized CHAID model is selected ( Zhao et al., 2020 ). The results show that the constructed decision tree model of the factors affecting individual interest in PE of primary and middle school students was 3 layers high, divided into 38 leaf nodes, and the decision tree model was lush and leafy. In addition, the accuracy of the model was as high as 90.88%, which is satisfactory for the needs of this research.

There was a total of seven variables entered into the decision tree model in this study. In order of importance, they were: expectancy-value, sports knowledge and skills, self-efficacy, mastery-approach goal, family sports environment, performance-avoidance goal, and gender. Among them, except for the two variables of family sports environment and gender, the other variables are all individual factors, which is consistent with previous research conclusions ( Chai and Lin, 2019 ). This result is in line with the ternary interactive determinism of social cognitive theory ( Bandura, 1977 ), which argues that individual factors (expectancy value, self-efficacy, knowledge, and goals are important individual factors), environmental factors, and behavioral factors are dynamic interactions ( Bandura, 1989 , 2001 ; Chiu et al., 2007 ; Jeng et al., 2022 ).

Previous studies have suggested that expectancy beliefs and perceived task values, a source of situational interest, were positively related to after-school physical activity ( Chen et al., 2014 ). In this study, expectancy value was located at the root node of the individual interest decision tree model, indicating that it was the most important factor affecting individual interest. Eccles et al. (1983) argues that students’ learning interest stems from their expectancy beliefs and the value of the task, collectively referred to as “expectancy value.” Expectancy beliefs are students’ perceptions of the possibility of success in the upcoming learning task, and task value, including achievement value, intrinsic value, utility value, and cost, is the student’s perception of the value of the learning task. Previous studies have suggested that expectancy is positively associated with interest ( Xu et al., 2020 ), and task expectancy motivation could predict students’ future interest in math at the individual and class level ( Ruiz-Alfonso et al., 2021 ). This also appeared to be the case in the present study. Not only that, this research further proved that expectancy value was the most important influencing factor of individual interest in PE among all the factors of social cognition theory investigated in this study. It is not difficult to find that the current reality of the implementation of the physical education curriculum in primary and secondary schools in China makes it difficult to improve the life expectancy value of students: (1) Poor attendance rate of PE courses, according to the survey conducted by the State Sports General Administration (2014) , 53.9% of the fourth graders have less than three sessions of PE per week, the serious over-standards of Chinese, mathematics, physics, and other courses were in sharp contrast with this; (2) Poor PE teachers’ team. The number of full-time PE teachers is seriously insufficient, and part-time PE teachers account for a large proportion. And these teachers mostly adopt the “shepherd type,” which makes it difficult to satisfy the students’ interest in classroom sports ( Mao et al., 2019 ); (3) Playground and ground equipment need to be further improved. All kinds of phenomena reveal that the attention paid to the PE curriculum of primary and middle school students in China is not up to standard, and still needs to be improved. Therefore, we appeal to improving students’ expectancy value of PE by enhancing the attention of PE curriculum, teachers’ literature, and teaching environment, so as to improve students’ individual interest.

The relationship between knowledge and interest has always received widespread attention. Almost all researchers take interest as an independent variable and individual interest as a dependent variable, believing that interest is the reason for acquiring knowledge ( Schraw and Lehman, 2001 , 2009 ; Tomlinson et al., 2003 ). Rotgans and Schmidt (2017) examined the causal relationship between students’ individual interest and knowledge acquisition using cross-lagged panel analysis; results showed that individual interest was not the cause but the consequence of the process of learning: individual interest as an affective by-product of learning. In this study, there were 6 groups of students’ expectancy values classified according to their sports knowledge and skills. According to the findings, sports knowledge and skills were the second most important influencing factor of individual PE interests. At present, the phenomenon of low-level repetitive teaching in the PE curriculum is more common in China. After years of study, students still cannot master one or two sports skills proficiently, let alone form a stable individual interest ( Mao et al., 2019 ). Therefore, we argue that while using novel teaching activities to stimulate students’ situational interest, we should also teach students some sports knowledge and skills to cultivate their individual interest, which is obvious, but often overlooked.

At the second level of the decision tree structure, there were 2 groups of students’ expectancy values classified according to their self-efficacy. The results showed that students’ self-efficacy was the third important influencing factor of individual interest in PE. The results of previous studies show that individual interest and self-efficacy are positively correlated ( Armstrong et al., 2009 ). This growing body of empirical evidence supporting the important role of self-efficacy within an interest is buttressed by long-standing theory suggesting that the two are reciprocally linked over time ( Fryer et al., 2016 , 2019; Nuutila et al., 2020 , 2021 ). Fryer et al. (2021) used the potential curve to analyze the role of self-efficacy between knowledge development and individual interest, which lends further support to the critical role played by self-efficacy beliefs within the development not only of knowledge but also of individual interest as a learning outcome. The role of self-efficacy in determining individual interest has been confirmed by a large number of research results. The emotional experience of sports participation, especially a successful experience, is helpful to the establishment of self-efficacy. Therefore, we appeal to strive to enable each student to obtain successful experiences in the process of sports participation and cultivate their sports confidence so as to obtain a long-term and stable individual interest in PE.

There are five branches in the third layer of the decision tree model, and master-approaching goal is the most important variable in this layer, followed by the family sports environment, performance-avoidance goal, and gender. The research of Harackiewicz et al. (2000 , 2008) showed that achievement goals can predict students’ interest and academic achievement in the short term or long term. Among them, mastering goals can effectively predict students’ interest, but there was no predictive effect on academic achievement; on the contrary, achievement goals can effectively predict students’ academic performance, but they cannot predict their learning interest. This research examines the influence of achievement goals on individual interest from four aspects: performance-approach goal, performance-avoidance goal, master-approach goal, and master-avoidance goal. The results showed that master-approach goal and performance-avoidance goal could predict students’ individual interest in PE, and the effort of the master-approach goal was better than the performance-avoidance goal.

In addition, family sports environment and gender have also entered the decision tree model of students’ individual interest in PE, but school sports environment, social sports environment, grade, and school location did not correspondingly. The results of this study confirmed the important role of the family sports environment in the development of students’ individual interest in PE once again. Knight et al. (2016) identified a number of individual and environmental influences on parental involvement in youth sports, and the results showed that parents were involved as supporters, coaches and managers, and providers of opportunities. Parents’ past experiences in sports and as a sport parent, their beliefs, goals, and values, the youth sport context, their concerns regarding others, and their own behavior can affect youth sports. Erickson et al. (2017) used a qualitative methodology to explore the role of significant others in this domain, and the results showed that the parent-athlete relationship influenced athletes’ lives in and beyond sport and could shape athletes’ attitudes, experiences, and behaviors toward doping. Parents are the most important part of the family sports environment for primary and middle school students, so we call on all children’s parents to pay attention to their attitudes towards sports activities and establish a positive family sports environment for their children. At the same time, we have also discovered the weak role of gender in the decision tree model of individual interest influencing factors. This is consistent with the results of previous research and is a current development trend of Chinese students’ individual interest in PE ( Lin, 2019 ).

Conclusions, limitations and future directions

The current study investigates the factors affecting individual interest of primary and secondary school students based on social cognitive theory and ranks multiple influencing factors in order of importance using decision tree model analysis. It has been demonstrated that the most important factor influencing individual interest in PE is expectancy value, which is followed by sports knowledge and skills, self-efficacy, mastery-approach goal, family sports environment, performance-avoidance goal, and gender. The implications for PE are: (1) improve the status of the PE curriculum and enhance students’ recognition of the value of PE; (2) strengthen the teaching of knowledge and skills to avoid low-level repetitive teaching; (3) improve success experience and cultivate sports self-efficacy; and (4) set reasonable sports goals to cultivate individual interest in sports learning.

This study adopted a large sample method to collect a total of 3,640 primary school students nationwide, but for China, with a population of 1.3 billion, the sample size was still slightly insufficient. In addition, this study did not analyze the differences in factors affecting students’ individual interests in PE according to different grades or stages of learning. Future work might further expand the sample size to make the sampling more representative, and it might also analyze the differences in students’ individual interests in stages.

The second limitation lies in the lack of data; data consisted solely of self-reported measures, and all questionnaires and scales were filled out by student groups. However, we focused on students’ subjective motivational perceptions of individual interest, and self-report was the rule rather than the exception. Nonetheless, we recognize the methodological problems that are likely to occur when relying exclusively on self-reported measures ( Knogler et al., 2015 ). Self-reported data potentially suffers from inaccuracy, especially at earlier stages of interest development, when people may lack meta-cognitive awareness of their interest ( Renninger and Su, 2012 ). Therefore, we encourage future work to use multiple sources of information, and to further determine the importance of influencing factors on individual interest in PE.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving human participants were reviewed and approved by Changchun Normal University work place. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.

Author contributions

JL: conceptualization, methodology, software, investigation, formal analysis, and writing—original draft. SZ: data curation and writing—original draft. All authors contributed to the article and approved the submitted version.

This article is supported by the Humanities and Social Science Fund Project of the Ministry of Education of China (22YJC89057), the Humanities and Social Sciences Project Fund of the Jilin Province (2022C106 and 2021C100), the Humanities and Social Sciences Project Fund of the Jilin Provincial Department of Education (JJKH20210908SK and JJKH20180050SK), and the Humanities and Social Sciences Fund of Changchun Normal University ([2020]003).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: adolescents, individual interest, influencing factors, decision tree, physical education

Citation: Lin JB and Zhu SS (2022) The influencing factors of individual interest in physical education based on decision tree model: A cross-sectional study. Front. Psychol . 13:1015441. doi: 10.3389/fpsyg.2022.1015441

Received: 09 August 2022; Accepted: 20 September 2022; Published: 10 October 2022.

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Copyright © 2022 Lin and Zhu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Shan Shan Zhu, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Physical Fitness Linked to Better Mental Health in Young People

A new study bolsters existing research suggesting that exercise can protect against anxiety, depression and attention challenges.

By Matt Richtel

Physical fitness among children and adolescents may protect against developing depressive symptoms, anxiety and attention deficit hyperactivity disorder, according to a study published on Monday in JAMA Pediatrics.

The study also found that better performance in cardiovascular activities, strength and muscular endurance were each associated with greater protection against such mental health conditions. The researchers deemed this linkage “dose-dependent,” suggesting that a child or adolescent who is more fit may be accordingly less likely to experience the onset of a mental health disorder.

These findings come amid a surge of mental health diagnoses among children and adolescents, in the United States and abroad, that have prompted efforts to understand and curb the problem.

The new study, conducted by researchers in Taiwan, compared data from two large data sets: the Taiwan National Student Fitness Tests, which measures student fitness performance in schools, and the National Insurance Research Databases, which records medical claims, diagnoses prescriptions and other medical information. The researchers did not have access to the students’ names but were able to use the anonymized data to compare the students’ physical fitness and mental health results.

The risk of mental health disorder was weighted against three metrics for physical fitness: cardio fitness, as measured by a student’s time in an 800-meter run; muscle endurance, indicated by the number of situps performed; and muscle power, measured by the standing broad jump.

Improved performance in each activity was linked with a lower risk of mental health disorder. For instance, a 30-second decrease in 800-meter time was associated, in girls, with a lower risk of anxiety, depression and A.D.H.D. In boys, it was associated with lower anxiety and risk of the disorder.

An increase of five situps per minute was associated with lower anxiety and risk of the disorder in boys, and with decreased risk of depression and anxiety in girls.

“These findings suggest the potential of cardiorespiratory and muscular fitness as protective factors in mitigating the onset of mental health disorders among children and adolescents,” the researchers wrote in the journal article.

Physical and mental health were already assumed to be linked , they added, but previous research had relied largely on questionnaires and self-reports, whereas the new study drew from independent assessments and objective standards.

The Big Picture

The surgeon general, Dr. Vivek H. Murthy, has called mental health “the defining public health crisis of our time,” and he has made adolescent mental health central to his mission. In 2021 he issued a rare public advisory on the topic. Statistics at the time revealed alarming trends: From 2001 to 2019, the suicide rate for Americans ages 10 to 19 rose 40 percent, and emergency visits related to self-harm rose 88 percent.

Some policymakers and researchers have blamed the sharp increase on the heavy use of social media, but research has been limited and the findings sometimes contradictory. Other experts theorize that heavy screen use has affected adolescent mental health by displacing sleep, exercise and in-person activity, all of which are considered vital to healthy development. The new study appeared to support the link between physical fitness and mental health.

“The finding underscores the need for further research into targeted physical fitness programs,” its authors concluded. Such programs, they added, “hold significant potential as primary preventative interventions against mental disorders in children and adolescents.”

Matt Richtel is a health and science reporter for The Times, based in Boulder, Colo. More about Matt Richtel

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Royal, Wippman Named Presidents-in-Residence

In its first year as a fully endowed program, the Judith Block McLaughlin Presidents-in-Residence program welcomes two new members.

Um... Where Is Everybody?

Families may be the key to ending chronic absenteeism, a pandemic-era problem that has only gotten worse

Phase Two: The Reach

Reach Every Reader on its impact and the project’s next phase

Faculty in the Media

With deep knowledge of the education field, HGSE faculty members influence current conversations in the media, giving educators and students a much-needed voice for positive change.

"Every child has the right to read well. Every child has the right to access their full potential. This society is driven by perfectionism and has been very narrow-minded when it comes to children who learn differently, including learning disabilities."

Department of Physics

Driving national excellence in physics education and teaching

One might call it the Indiana Jones Syndrome.

Henry Jones Jr. may have been a brilliant archaeologist, but confronted by a corridor of confused undergraduates, he fled out his office window.

Being a genius in a particular field doesn't necessarily give professors the skills to effectively pass along their knowledge to students. Indiana Jones, of course, is a fictional cinematic hero.

David A. Craig, the associate department head of the Department of Physics at Oregon State University, nonetheless noticed a similar dilemma confronts physics professors in the real world. They study to become scientists, to conduct research and understand the mysteries of the universe.

Understanding the mysteries of college freshmen, however, is an entirely different science.

"Fundamentally, a Ph.D. is a certification that you're ready to do independent research," said Craig, a faculty member at Oregon State for the past six years. "It's a research-focused credential. Many (but certainly not all) students make some money as graduate students by doing some teaching, but it's only relatively recently that those students have received any training at all in pedagogy, in the theory of teaching and learning and how to be an effective teacher in the classroom."

Craig and other leaders of the American Physical Society, the predominant professional society for physicists in the United States, decided to help teach the teachers. As a member of the society's education committee, Craig was one of the driving forces behind the creation of the Departmental Action Leadership Institute and is currently its co-leader.

Among other things, the Institute helps faculty members acquire the teaching skills that may have eluded them while they were grappling with the finer points of quantum cosmology.

"If you want to achieve effective, sustainable change that will persist over time and be embraced by the entire community, you need to have that change informed by a lot of different voices."

"All of that experience up until that point has been principally focused on research," Craig said. "And what's the first thing that happens? They're plopped into a classroom and told 'Go forth, teach, be wonderful' with little to no training or support."

The good news is that learning how to teach isn't like splitting the atom. "Most academics are smart people," he said. "They can learn if they're receptive and have the proper training, but they're typically just chucked in without that training."

University physics departments sign up for the Institute and create a "departmental action team" — groups of five people assigned to lead changes in their department. Two members of each team are designated "change leaders" who participate directly in Institute activities and lead their action teams.

Each Institute session includes representatives from three to five participating departments who commit to working with two facilitators for a minimum of a year. The curriculum trains the leaders to better understand the situations they face and takes steps for sustainable change.

Following an initial in-person workshop, the Institute consists of approximately 30 hours of video conferences throughout the academic year. So far, five sessions have been held. A sixth is pending funding from the National Science Foundation.

Craig said the Institute does more than help individual faculty members improve their teaching skills. It also helps faculty members handle the demands of leadership. Many academic departments at the universities rotate department head responsibilities among their faculty. Leading a department can be like teaching a class.

It requires skills people may not have developed.

"You're put into a leadership position — often managing budgets, thinking about people, hiring adjuncts," Craig said. "Once again, you're just expected to figure it out. There's no training, no experience. How do you run an effective meeting? How do you deal with free speech issues in your department? How do you deal with mental health issues?"

The Institute covers such basic issues as how to run a meeting. However, Craig said, running meetings is more important and complicated than some people think. "If you want to achieve effective, sustainable change that will persist over time and be embraced by the entire community, you need to have that change informed by a lot of different voices," he said.

That requires effective meetings.

"What we do is train groups of leaders in physics programs to solve those problems themselves in a meaningful and sustainable way."

"How do you have a conversation where everyone contributes instead of the loudest, most vocal, the strongest personality dominating the conversation?" Craig added. "You spend any time in faculty meetings, that happens all the time."

However, he said, the Institute is not there to identify specific problems and prescribe specific solutions. "It's called a leadership institute for a reason," said Craig.

Individual problems depend on the individual context, he added. "Departments have to figure out those things for themselves. What we do is train groups of leaders in physics programs to solve those problems themselves in a meaningful and sustainable way."

Participants take the lead in what issues they feel need to be addressed in their departments. Occasionally, Craig said, they must be reminded that the scientific method applies beyond the confines of physics. "It's funny we have to say this to scientists, but they don't always transfer what they know from science to leadership," he said.

"People often land on what they think the solutions are without really properly understanding the problems they actually have and making their decisions based on actual data and concrete information," he added.

On the other hand, Craig said, physicists have traditionally been pioneers in education. Using scientific methods, they developed discipline-based education and studied how students learn, how to engage with students, and how to support their learning process.

Much of what they have learned has been set down by the American Physical Society in an online resource called the Effective Practices for Physics Programs (or EP3) Guide. The Guide provides basic information and advice for physics departments on best practices and serves as foundation for the Leadership Institute.

It was created by a task force including members of the American Physical Society and American Association of Physics Teachers. Craig co-chaired the task force. Members included Carl Edwin Wieman. The Corvallis native won the Nobel Prize for his work in condensed matter physics in 2001.

Wieman is also the founder and chairman of PhET Interactive Simulations, a web-based directive of University of Colorado Boulder which provides simulations to improve the way that physics, chemistry, biology, earth science and math are taught and learned.

"The Guide lives on the web," Craig said. "It was designed to be something that isn't static. It will be continually reviewed and kept up to date. As new research comes out, it's updated with the new research. It's maintained as a living force in the community by the American Physical Society."

"It was an initiative born out of the idea that we really need to provide national-scale support for physics departments to use the Guide and improve themselves generally."

Much of the information in the Guide traces its provenance to the 2010 SPIN-UP Report by the American Physical Society.

After the Cold War, enrollments in physics programs dropped off rather dramatically. Society members wanted to investigate why. "That led to some early initiatives," Craig said. "One of them was the SPIN-UP Report, which ended up being a landmark investigation into the characteristics of thriving physics programs. The lessons that were learned from that effort ended up making a big difference across the landscape."

The report concluded multiple complex factors were behind the drop in enrollment. One of them was the end of the Cold War and the corresponding drop in defense spending. Other factors included the rise of internet businesses and changes in high school curricula that resulted in a mismatch between science faculty and student expectations.

Although the Guide was a significant achievement, Craig said its effectiveness as an ongoing tool was limited. "Reports can have a big impact, but that impact decays rapidly with time," he said.

"It's great to have all this information out there," he added. "People can read it. They can talk about it in their departments. However, we also need to actively support how people can process information in the Guide and use it to improve their own departments."

Thus the creation of the Departmental Action Leadership Institute.

"It was an initiative born out of the idea that we really need to provide national-scale support for physics departments to use the Guide and improve themselves generally," Craig said.

The Guide and the Institute all stem from basic educational principles, he added.

"Passively listening works for some small few of us, often those of us who end up in academic positions, but it's not the way most people learn most effectively," he said. "Most people learn most effectively when they're actively engaged with the material they're learning — when they have to think about it, work with others and talk about it instead of sitting and listening to someone else talk about it."

Ultimately, said Craig, the Institute hopes to create a vast network of physicists who can all help one another. In that regard, he added, it has already been successful.

"The training has impacted hundreds and hundreds of physicists."

Read more stories about: faculty and staff , physics , student success , teaching

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Appointments at Mayo Clinic

• Weight loss

Exercise for weight loss: Calories burned in 1 hour

Being active can help you lose weight and keep it off. Find out how much you need.

Being active is vital to losing weight and keeping it off. When active, the body uses more energy in the form of calories. And burning more calories than you take in leads to weight loss.

To lose weight, most people need to cut the number of calories they eat and move more. This is according to the 2020-2025 Dietary Guidelines for Americans. Most often, that means cutting daily calories by 500 to 750 to lose 1 1/2 pounds (0.7 kilograms) a week.

Other factors might be involved in losing weight. Because of changes to the body over time, you might need to cut calories more as you age to keep losing weight or to stay at the same weight.

Diet or exercise: Does one matter more?

Both are important. Diet affects weight loss more than physical activity does. Physical activity, including exercise, has a stronger effect in keeping weight from coming back after weight loss.

Losing weight with diet alone and without physical activity can make people weaker. This is because of age-related losses in bone density and muscle mass. Adding resistance training and aerobic exercise to a weight-loss program helps prevent the loss of bone and muscle.

These are the exercise guidelines for most healthy adults from the U.S. Department of Health and Human Services:

Aerobic activity. Get at least 150 minutes of moderate aerobic activity a week. Or get 75 minutes of vigorous aerobic activity a week. You also can get an equal mix of the two types.

Aim to exercise most days of the week. For even more health benefits, strive for 300 minutes a week or more of moderate aerobic activity or 150 minutes of vigorous activity. Exercising this much may help with weight loss or keeping off lost weight. But even small amounts of physical activity can be helpful. Being active for short periods of time during the day can add up and give you great health benefits.

• Strength training. Do strength training exercises for all major muscle groups at least two times a week. One set of each exercise is enough for health and fitness benefits. Use a weight or resistance level heavy enough to tire your muscles after about 12 to 15 repetitions.

Moderate aerobic exercise includes activities such as brisk walking, biking, swimming and mowing the lawn.

Vigorous aerobic exercise includes activities such as running, swimming hard laps, heavy yardwork and aerobic dancing.

Strength training can include use of weights or weight machines, your own body weight, resistance tubing, or activities such as rock climbing.

How much am I burning?

This list shows about how many calories are burned while doing certain exercises for one hour. This is based on a person who weighs 160 pounds (73 kilograms). The calories you burn depend on the exercise you do, how hard you do it, how much you weigh and other factors.

Based on Ainsworth BE, et al. 2011 compendium of physical activities: A second update of codes and MET values. Medicine & Science in Sports & Exercise. 2011;43:1575.

Remember, to lose weight or to keep weight from creeping up on you as you age, you need to eat less and move more. Moving more means adding more physical activity into your life.

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• Physical Activity Guidelines for Americans. 2nd ed. U.S. Department of Health and Human Services. https://health.gov/paguidelines/second-edition. Accessed March 13, 2024.
• Physical activity for a healthy weight. Centers for Disease Control and Prevention. https://www.cdc.gov/healthyweight/physical_activity/index.html. Accessed March 13, 2024.
• Ainsworth BE, et al. 2011 compendium of physical activities: A second update of codes and MET values. Medicine & Science in Sports & Exercise. 2011;43:1575.
• 2020-2025 Dietary Guidelines for Americans. U.S. Department of Health and Human Services and U.S. Department of Agriculture. https://www.dietaryguidelines.gov. Accessed March 13, 2024.
• Perreault L, et al. Obesity in adults: Role of physical activity and exercise. https://www.uptodate.com/contents/search. Accessed March 13, 2024.
• AskMayoExpert. Physical activity (adult). Mayo Clinic; 2022.

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