Activity and social correlates of physical fitness in Lithuanian schoolchildren

doi:10.21203/rs.3.rs-4989953/v1

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Activity and social correlates of physical fitness in Lithuanian schoolchildren

https://doi.org/10.21203/rs.3.rs-4989953/v1

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Background. Physical fitness (PF), an important marker of health and well-being, is influenced by a multitude of factors. This study aimed to quantify the associations of social environment factors with different aspects of PF among schoolchildren.

Methods. Participants of this cross-sectional study were apparently healthy adolescents aged 11–18 years who attended regular secondary schools (n=4925, 51% girls). PF was assessed using the 8-item Eurofit test battery. Social environment (parents’ education, family’s and friends’ support for physical activity (PA)), personal motivation for PA, actual PA level and participation in organised sports status all were derived using questionnaires. Age- and sex- adjusted body mass index (BMI) cut-points were used to classify adolescents as overweight/obese or not.

Results. Up to 45% of the variability in PF levels was explained by utilizing all the factors from questionnaires and adding BMI. Age, sex, and BMI were most strongly associated with PF, while parents’ education, personal motivation for PA, friends’ support for PA, and organised sports participation were additional significant correlates of PF among schoolchildren. However, familial support for PA and current PA level were typically non-significantly associated with PF.

Conclusion. Parents’ education, motivation, friends’ support for PA, and organised sports participation were important social- and activity-related correlates of current levels of PF among adolescents.

Health sciences/Health care/Health services

Health sciences/Health care/Nutrition

Health sciences/Health care/Paediatrics

Health sciences/Health care/Public health

Fitness

Eurofit Test Battery

Physical Activity

Social Environment

MeSH terms include: Humans

Adolescent

cross-sectional studies

motivation

physical fitness

social environment

Appropriate physical fitness (PF) level is one of the main deterrents of many modern diseases ^1–3. Ample of evidence showing that high-calibre athletes have increased life-expectancy ^4–7 and reduced chronic disease burden after sports career discontinuation ^8,9. Additionally, PF is also emerging to be a significant associate of the cognitive performance ^10–14 and mental health ^2,15,16. A recent prospective longitudinal study showed that low levels of PF, and particularly low cardiorespiratory fitness, in adolescence was associated with an increased burden of cardiometabolic conditions in later life ¹⁷. A general decline in PF of young population over recent decades ^18–21 is quite concerning, and a better understanding of the factors associated with PF is an important step in curbing such negative trends.

An important factor linked with good health and physical fitness is undoubtedly levels of PA ^22–26. Importantly, intense organized sports seem to render superior benefits over leisure time physical activity (PA) ^7,27,28. Social factors are among the various environmental factors known to be important for PA across various populations ²⁹. It is possible that the social environment influences PF and sports participation is therefore important but yet under-investigated health-related factor. Many environmental factors are associated with PA levels among adults ²⁹ and adolescents ³⁰, for example, higher levels of leisure time PA are associated with higher socioeconomical status ³¹ and support from family and friends ^32–35, and personal motivation ³⁵. However, we are not aware of any study that has analysed the links between immediate social environment and personal attitudes towards PA with PF. In the current study we tested how various aspects of PF associate with parents’ education, support for PA by parents and friends, as well as actual level of PA of schoolchildren and their sports participation.

The main aim of the current study was to quantify associations between social support for PA, motivation for PA, and reported PA levels with various aspects of PF among adolescents. We have hypothesized that support for PA by (1) family and (2) friends, (3) personal attitude towards PA, (4) reported PA level, and (5) organised sports participation were significantly associated with PF in order of progressive importance, ranked arbitrarily based on their proximity to and putative effect on PF. In addition, we tested how PF is associated with non-modifiable biological factors sex and age, as well as weight status and parents’ education.

2.1. Participants and organisation

Apparently, healthy adolescents aged 11–18 years from Lithuanian secondary schools (n = 4925) participated in PF and social environment data collection during their physical education classes. PF data were collected in 2022 using the Eurofit test battery as described previously ¹⁸. The Eurofit comprises the following fitness tests: flamingo balance (balance), plate tapping (upper-body speed/coordination), sit-and-reach (flexibility), standing broad jump (lower-body power), sit-ups (abdominal/core endurance), bent arm hang (upper-body strength/endurance), 10×5-m shuttle run (agility), and the 20-m shuttle run (cardiorespiratory endurance). In brief, regular secondary schools from across the country were selected from the national registry using nested random sampling. Body mass index (BMI) was calculated after measuring the body mass and height of participants on the day of fitness testing, with international BMI cut points used to classify adolescents as overweight/obese or not ³⁶. In addition, questionnaires were provided and completed during a PE class scheduled 1-week later. Age (in whole years) and sex were self-reported.

Before being recruited for the study, all schoolchildren and their parents/guardians provided the informed consent form. The study was approved by the Lithuanian Sports University’s Committee of Bioethics (permission no. 27720) and complied with the principles outlined in the Declaration of Helsinki. Descriptive sociodemographic characteristics of the participants are presented in Table 1.

Table 1

Descriptive characteristics of participants.
	Total (n = 4925)	Girls (n = 2507; 50.9%)	Boys (n = 2418; 49.1%)	Girls vs. Boys (p-value)
Non-modifiable factors
Age, years	14.1 ± 2.2	14.1 ± 2.2	14.0 ± 2.2	NS
Mother’s Education, % of having higher	59.7	58.6	60.9	NS
Father’s Education, % of having higher	44.6	42.4	46.8	0.002
Family’s Support for PA, scored 1 to 5	2.5 ± 0.8	2.5 ± 0.7	2.6 ± 0.8	NS
Friends‘ Support for PA, scored 1 to 5	2.7 ± 0.8	2.6 ± 0.8	2.8 ± 0.8	< 0.001
Modifiable factors
% overweight/obese (BMI-based cut point)	11.1	9.6	13.2	< 0.001
Motivation for PA, scored 9 to 36	30.7 ± 5.8	30.2 ± 5.8	31.2 ± 5.8	0.019
PA of at least 1 h/d, %	15.4	11.9	19.0	< 0.001
Organised sports participation, %	66.0	60.0	72.3	< 0.001
NS, not significant (p > 0.05); PA, physical activity; BMI, Body Mass Index.

2.2. Questionnaires for correlates of physical fitness

Social support. Social support from family and friends was assessed using a 13-item subscale of Sallis’ Support for Exercise Survey ³⁷. For both support by family and friends, identical questions were used. This scale included items that addressed encouragement to be physically active (e.g., “Gave me rewards for exercising”), involvement in PA (e.g., “Offered to exercise with me”), and facilitation (e.g., “Changed their schedule so we could exercise together”) during the last 3 months. Response options were: (1) never, (2) rarely, (3) a few times, (4) often, or (5) very often. Responses to two negative items (“Complained about the time I spend exercising” and “Criticized me or made fun of me for exercising“) were reversed. A higher total score corresponded with higher perceived social support. In the current study, the internal consistency for family support was α = 0.87 and for friends’ support was α = 0.89, reflecting high reliability.

Motivation for PA. Motivation for PA was assessed using a 9-item scale that captures school students' attitudes toward exercising because of physical health motives (e.g., “I become physically stronger when I do sports”), cognitive health motives (e.g., “Exercise improves thinking”), understanding the importance of PA (e.g., “It is important to exercise”), and satisfaction with PA (e.g., “Exercising is fun”). Each answer had four options: 1) „No“, 2) „Rather No than Yes“, 3) „Rather Yes than No“, and 4) „Yes“. The internal consistency of this scale was Cronbach α = 0.92. The scale was constructed by the study authors and was based on the adjusted Pictorial Motivation Scale in Physical Activity ³⁸. Exploratory factor analysis with Promax rotation and principal Axis Factoring derived one factor (KMO = 0.927; d_f=36; p < 0.001), explaining 57% of the variance. Confirmation factor analysis supported the one-factor model (CFI = 0.971, TLI = 0.956, RMSEA = 0.082 [0.077–0.086], SRMR = 0.029; χ² = 778.6, d_f=24; p < 0.0001). The standardized estimates of item loads were from 0.688 to 0.807.

Parents’ education level was provided by parents themselves when signing the consent form. Often one parent consented and answered on behalf of both parents/stepparents/guardians by providing information about whether their highest level of education (mother and father) was high school, vocational, college, or university. Education was then categorized as higher education (graduation from either college or university) or lower education.

Physical activity was assessed by asking students “On average, how much time per day do you exercise or do sports in such a way that you sweat and breathe faster?“ Response options were: (1) almost none, (2) up to 30 min/day, (3) up to 1 hour/day, or (4) 1 hour or more per day. PA was then categorised as not meeting (answers 1 to 3) or meeting the PA recommendations (answer 4) of the World Health Organization (https://iris.who.int/bitstream/handle/10665/336656/9789240015128-eng.pdf?sequence=1).

Participation in organized sports was assessed by asking students “Do you participate in any sports or other organized PA outside the school?” Answers referred to the frequency of participation per week from “None” to “Every day”. For this study, the item was dichotomized into “I participate” or “I do not participate” in organised sports.

2.3. Grouping of correlates into four models

We have included non-modifiable factors (i.e., independent of the will of the participants) into model 1. These were sex, age, and parents’ education. We also included BMI in model 1 as a known significant correlate of many aspects of PF. Parents’ and friends’ support for PA (model 2), personal (attitude towards PA, model 3) factors, and reported PA level and organised sports participation (model 4) factors were grouped according to their arbitrary potential for the effect on PF (in order from more distal to more proximate).

2.4. Statistics

Analyses were completed using SPSS statistical software (v28, SPSS Inc., Chicago, IL, USA) and Mplus (v8.0, Los Angeles, CA, USA). Descriptive characteristics were reported as frequencies (percentage) for categorical variables and mean ± standard deviation for continuous variables. Differences in means were calculated by Student’s t-test for independent samples and χ² criteria were employed to identify differences between sexes. The internal consistency of scales was measured using Cronbach’s alpha. Hierarchical linear regression analysis using four models was used to assess the associations between the outcomes (physical fitness variables) and the independent variables. The first model included four sociodemographic (age, sex, and parents’ education) and physical (BMI) factors. In the second model, social support from family and friends was added. The third model additionally included a more proximate factor of PF, a personal motivation for PA. The fourth model added the most PF proximate behavioural variables of reported PA level and organised sports participation. The effect of added variables in each model is presented by ΔR² and represents the additive explained variance on the outcome variables. For the sake of conciseness, β coefficients and their p-values of only the final model are presented. Results were considered significant at p < 0.05.

For a Confirmatory Factor Analysis, to confirm structure of the questionnaire, the following goodness of fit indices were employed: 1) the root mean square error of approximation (RMSEA) with its 90% confidence interval, a population-based fit index that is independent of the sample size. Values of 0.01, 0.05, and 0.08 indicate respectively excellent, good, and mediocre fit; 2) the Standardized Root Mean Squared Residual (SRMR) - a direct assessment of how well an a priori model reproduces the sample data. Values < 0.05 were considered a very good fit, while values < 0.08 were interpreted as a good fit; 3) the comparative fit index (CFI); and 4) Tucker–Lewis index (TLI) to compare the fit of a hypothesized model with that of a baseline model (i.e., the model with the worst fit), where values > 0.90 indicate a good model fit, and values > 0.95 indicate a very good model fit ³⁹.

3.1. Descriptive outcomes

Parents reported higher education as their highest level of education in 44.6% and 59.7% of the cases for fathers and mothers, respectively (Table 1). Only 15.4% of adolescents reported meeting the recommended minimal level of at least 60 min of PA per day, with 66.0% participating in organised sport at least once per week. A higher percentage of boys than girls reporting meeting the PA recommendation and participated in organised sport (p < 0.001). Of those participating in organised sport, 21.5% reported meeting the PA recommendation compared to only 3.5% who did not participate in organised sport.

A total of 11.1% of adolescents, and a higher percentage of boys than girls (p < 0.001), were classified as overweight/obese. Motivation for PA and support for PA by friends were higher among boys than girls, while there were no differences between sexes in family support for PA.

3.2. Correlates of physical fitness

Most social and personal factors correlated significantly with several aspects of PF. Age and organised sports participation were two factors significantly associated with all aspects of PF; sex was associated with all aspects of PF except upper-body speed/coordination; and motivation for PA significantly correlated with all aspects of PF except balance (Table 2). BMI was significantly correlated with all aspects of PF except for flexibility and abdominal/core endurance. Support by friends was associated with superior PF, while family support was not associated with any aspect of children’s PF except for a small negative association with Endurance. PA level was not associated with PF except for upper-body strength/endurance. Collectively, social and personal factors were most strongly associated with cardiorespiratory endurance and lower-body power, explaining as much as 39% and 45% of the variation, respectively (Table 2).

Table 2

Predictors of physical fitness among Lithuanian adolescents.
Factor (factor’s group)	Flamingo (Balance)	Plate tapping (Upper-body speed/coordination)	Sit-and-reach (Flexibility)	Broad jump (Lower-body power)	Sit-ups (Abdominal/core endurance)	Hanging (Upper-body strength/endurance)	10x5-m shuttle run (Agility)	20-m shuttle run (Cardiorespiratory endurance)
Model 1
Age	β = 0.133 (p < 0.001)	β = 0.378 (p < 0.001)	β = 0.222 (p < 0.001)	β = 0.462 (p < 0.001)	β = 0.148 (p < 0.001)	β = 0.208 (p < 0.001)	β = 0.133 (p < 0.001)	β = 0.413 (p < 0.001)
Sex (Boys)	β = 0.224 (p < 0.001)	NS	β = − 0.414 (p < 0.001)	β = 0.354 (p < 0.001)	β = 0.275 (p < 0.001)	β = 0.365 (p < 0.001)	β = 0.244 (p < 0.001)	β = 0.270 (p < 0.001)
BMI	β = − 0.147 (p < 0.001)	β = − 0.051 (p = 0.004)	NS	β = − 0.183 (p < 0.001)	NS	β = − 0.235 (p < 0.001)	β = − 0.179 (p < 0.001)	β = − 0.192 (p < 0.001)
Mother‘s Education (higher)	β = -0.108 (p < 0.001)	β = -0.076 (p < 0.001)	NS	β = 0.039 (p = 0.025)	NS	NS	β = 0.083 (p < 0.001)	β = 0.087 (p < 0.001)
Father‘s Education (higher)	β =- 0.045 (p = 0.047)	NS	β = − 0.053 (p = 0.011)	β = 0.047 (p = 0.007)	NS	NS	NS	β = 0.057 (p = 0.002)
Model 2
PA-related Family’s Support	NS	NS	NS	NS	NS	NS	NS	β = − 0.055 (p = 0.003)
PA-related Friends‘ Support	NS	β = 0.043 (p = 0.045)	NS	β = − 0.043 (p = 0.013)	β = 0.086 (p < 0.001)	NS	β = 0.056 (p = 0.007)	NS
Model 3
Motivation for PA	NS	β = 0.041 (p = 0.039)	β = 0.054 (p = 0.005)	β = 0.077 (p < 0.001)	β = 0.054 (p = 0.006)	β = 0.059 (p = 0.003)	β = 0.085 (p < 0.001)	β = 0.102 (p < 0.001)
Model 4
Current PA level	NS	NS	NS	NS	NS	β = 0.038 (p = 0.048)	NS	NS
Organised sports participation	β = 0.070 (p = 0.002)	β = 0.109 (p < 0.001)	β = 0.046 (p = 0.027)	β = 0.144 (p < 0.001)	β = 0.145 (p < 0.001)	β = 0.077 (p < 0.001)	β = 0.143 (p < 0.001)	β = 0.189 (p < 0.001)
Δ R² (1)	0.099 (p < 0.001)	0.151 (p < 0.001)	0.213 (p < 0.001)	0.407 (p < 0.001)	0.152 (p < 0.001)	0.240 (p < 0.001)	0.189 (p < 0.001)	0.329 (p < 0.001)
Δ R² (2)	0.004 (p = 0.003)	0.008 (p < 0.001)	0.004 (p = 0.001)	0.017 (p < 0.001)	0.021 (p < 0.001)	NS	0.015 (p < 0.001)	0.013 (p < 0.001)
Δ R² (3)	0.002 (p = 0.018)	0.003 (p = 0.001)	0.003 (p = 0.001)	0.009 (p < 0.001)	0.006 (p < 0.001)	0.005 (p < 0.001)	0.010 (p < 0.001)	0.016 (p < 0.001)
Δ R² (4)	0.004 (p = 0.004)	0.011 (p < 0.001)	0.002 (p = 0.016)	0.018 (p < 0.001)	0.020 (p < 0.001)	0.008 (p < 0.001)	0.019 (p < 0.001)	0.032 (p < 0.001)
BMI, body mass index; PA, physical activity; NS, not significant (p > 0.05). Note. The direction of the correlations for Flamingo balance, Plate tapping, and 10x5-m shuttle agility run are inverted for a positive β to represent a positive relationship.

Hierarchical linear regression analysis showed that model 1 factors (age, sex, BMI, and parents’ education) explained most of the variation in PF (bottom of Table 2), which differed among various aspects of PF. Some factors were more strongly associated with aspects of PF (e.g., age on lower-body power and cardiorespiratory endurance), others more modestly (e.g., parents’ education on most aspects of PF), and some non-significantly (e.g., sex on upper-body speed/coordination or BMI on flexibility and abdominal/core endurance) (upper part of Table 2). Model 2 factors (PA-related support) explained slightly more variability for some aspects of PF, whilst separate analyses of these two support factors showed it was friends’ support that was more strongly associated with PF than familial support. Motivation for PA (model 3) consistently explained more variance across all aspects of PF. The two factors of model 4 diverged in their prediction power of PA level (dichotomized into meeting the recommendation for moderate-to-vigorous PA volume or failing to meet) having negligible effect by being associated only with hanging performance; meanwhile, organised sports participation had a significant positive effect across all eight aspects of PF.

The results of this study revealed that in addition to well-established factors such as sex, age, and overweight status, social environment factors like parents’ education, friends’ support for PA, personal motivation for PA, and organised sports participation significantly explained the variation in the PF of Lithuanian adolescents.

Factors affecting both PF are complex, but it is clear that volume and type of regular PA are of primary importance to PF status. In its own way, PA is associated with a myriad of factors ²⁹, including the children’s household environment harrin ⁴⁰ and social skills (empathy) ⁴¹. Importantly, among various benefits, regular PA is inversely associated with depressive symptoms in adolescents ⁴². In addition, PA level is negatively associated with overweight/obesity status ^29,43, supporting the negative association in our study between BMI and performance on PF tests where body mass was either transported (i.e. running, jumping) or supported (i.e. hanging). Notably, it was organised sports participation rather than reported leisure time PA that was consistently associated with different aspects of PF. Of relevance, even if we have not analysed unhealthy habits in this study, others have reported that organised sports participation is negatively associated with unfavorable behaviours like smoking, using snus, and alcohol consumption ⁴⁴, which in turn might negatively affect PF.

Social support has been proposed to be more important than the intrinsic desire for PA (exercise intention or ‘subjective norm’) of adults’ actual PA ⁴⁵. In addition, the importance of a close social environment in forming the attitude, norms, and habits towards PA have been observed by others ^46,47. In our adolescent population of 11-to 18-year-olds, both personal motivation and friends’ support for PA were associated with objectively measured PF. Interestingly, compared to girls, boys showed not only better performance on nearly all PF tests, but also had higher friends’ support for PA, personal motivation for PA, and a higher percentage were active and participated in organised sport, despite a higher percentage being classified as overweight/obese. Consistent with previous research ⁴⁸, we found that upper-body speed/coordination was similar for boys and girls but boys outperformed girls on all other PF tests.

Since PF during adolescence prognosticates the risk of major non-transmittable (cardiometabolic) disease burden at least up to the age of 60 years ⁴⁹, a practical consideration is warranted for promoting the development of PF in youth through PA, sports participation, and overall healthy lifestyle to both build the bumper and develop health-promoting habits. Close social environment affects PA level ^32–35 and children’s PF positively associates with PF of their parents’ ⁵⁰. However, our findings on PF status stand for the more important effect of friends’ support while family’s influence on adolescence's actual level of PF seems negligible. The lack of effect of family support for PA on PF observed in our study deserves further investigation of the underlying reasons. It could be speculated that the specific age of our population (teenagers) precludes the acceptance of support for being more active from parents and rather peers are more influential at this stage of life.

Limitations. One of the limitations of the study was self-reported levels of PA and the categorization into two groups (meeting or not meeting the WHO recommended amount of PA per day). Additionally, specifics of the engaged sports (i.e. more technical or physical; endurance or power; etc.) or a degree of involvement were not analysed, as it was not the modes of PA stratified. Moreover, a prolonged lockdown implemented for most of the organized formal PA about a year before the study might well have decreased some aspects of PF ⁵¹, changed some social and personal factors analysed here, and thus putatively the relationships. The timescale of reconditioning after the lockdown was released is however unclear, and with the cross-sectional analysis of the current study, it was not possible to solve this effect. The motivation of the schoolchildren to perform the tests maximally was left not tightly controlled or even estimated due to the large scale of the study. It also needs to be admitted that our model of the analysis (design of the study) did not allow for a clear cause-effect relationship between the factors analysed. Finally, the study was cross-sectional and performed on the participants of quite a homogeneous population, which may limit the generalizability of the findings to other groups (e.g. differing in age and ethnic mix).

Social environmental factors, independent of more stable factors such as sex, age and parent’s education, were significantly associated with all aspects of PF among adolescents. More specifically, motivation for PA, acting more through participation in organised sport, was significantly associated with all aspects of physical fitness, with self-reported leisure-time PA levels mostly non-significantly related to PF. Similarly, friends’ support for PA was more often associated with PF than was familial support for PA.

Competing interests

The authors declare that they have no competing interests.

Funding

The project was financed with a grant from the Lithuanian Research Council (no. S-MIP-22-45).

Author Contribution

BM conceived and designed the study, collected and analyzed the data, and helped to draft the manuscript; TV analyzed the data and drafted the manuscript; AE conceived and designed the study and collected the data. GT analyzed the data and helped to draft the manuscript. All authors have read and approved the final version of the manuscript, and agree with the order of presentation of the authors.

Data Availability

All data generated or analysed during this study are included in this published article.

Milanović, Z. et al. Broad-spectrum physical fitness benefits of recreational football: a systematic review and meta-analysis. Br. J. Sports Med. 53 (15), 926–939. 10.1136/bjsports-2017-097885 (2019).
Poon, E. T. C., Wongpipit, W., Sun, F., Tse, A. C. Y. & Sit, C. H. P. High-intensity interval training in children and adolescents with special educational needs: a systematic review and narrative synthesis. Int. J. Behav. Nutr. Phys. Act. 20 (1), 13. 10.1186/s12966-023-01421-5 (2023).
Oja, P. et al. Health Benefits of Different Sports: a Systematic Review and Meta-Analysis of Longitudinal and Intervention Studies Including 2.6 Million Adult Participants. Sports Med. Open. 10 (1), 46. 10.1186/s40798-024-00692-x (2024).
Sarna, S., Sahi, T., Koskenvuo, M. & Kaprio, J. Increased life expectancy of world class male athletes. Med. Sci. Sports Exerc. 25 (2), 237–244 (1993).
Garatachea, N. et al. Elite athletes live longer than the general population: a meta-analysis. Mayo Clin Proc. ;89(9):1195–1200. doi: (2014). 10.1016/j.mayocp.2014.06.004
Ruiz, J. R., Fiuza-Luces, C., Garatachea, N. & Lucia, A. Reduced mortality in former elite endurance athletes. Int. J. Sports Physiol. Perform. 9 (6), 1046–1049. 10.1123/ijspp.2013-0492 (2014).
Foulkes, S. et al. Outrunning the grim reaper: longevity of the first 200 sub-4 min mile male runners. Br. J. Sports Med. 58 (13), 717–721. 10.1136/bjsports-2024-108386 (2024).
Antero-Jacquemin, J., Pohar-Perme, M., Rey, G., Toussaint, J. F. & Latouche, A. The heart of the matter: years-saved from cardiovascular and cancer deaths in an elite athlete cohort with over a century of follow-up. Eur. J. Epidemiol. 33 (6), 531–543. 10.1007/s10654-018-0401-0 (2018).
Runacres, A., Mackintosh, K. A. & McNarry, M. A. Health Consequences of an Elite Sporting Career: Long-Term Detriment or Long-Term Gain? A Meta-Analysis of 165,000 Former Athletes. Sports Med. 51 (2), 289–301. 10.1007/s40279-020-01379-5 (2021).
Monacis, D. Regional observatory of physical fitness in children and adolescents in southern Italy. Kinesiologia Slovenica (2023).
Venckunas, T. et al. Interval Running Training Improves Cognitive Flexibility and Aerobic Power of Young Healthy Adults. J. Strength. Cond Res. 30 (8), 2114–2121. 10.1519/JSC.0000000000001322 (2016).
Pinto Pereira, S. M., Mitchell, J. J., Blodgett, J. M., Hamer, M. & Norris, T. Is cardiorespiratory fitness associated with cognitive outcomes in mid-adulthood? Findings from the 1958 British birth cohort. Scand. J. Med. Sci. Sports. 33 (12), 2613–2619. 10.1111/sms.14525 (2023).
Jalanko, P. et al. Associations between physical fitness and cerebellar gray matter volume in adolescents. Scand. J. Med. Sci. Sports. 34 (1), e14513. 10.1111/sms.14513 (2024).
Han, Z., Zhang, L., Ma, M. & Keshavarzi, M. Effects of MicroRNAs and Long Non-coding RNAs on Beneficial Action of Exercise on Cognition in Degenerative Diseases: A Review. Mol Neurobiol Published online June. 13 10.1007/s12035-024-04292-4 (2024).
Bäckmand, H., Kaprio, J., Kujala, U. & Sarna, S. Personality and mood of former elite male athletes–a descriptive study. Int. J. Sports Med. 22 (3), 215–221. 10.1055/s-2001-16382 (2001).
Herring, M. P. & Meyer, J. D. Resistance exercise for anxiety and depression: efficacy and plausible mechanisms. Trends Mol. Med. 30 (3), 204–206. 10.1016/j.molmed.2023.11.016 (2024).
Zillikens, M. C. et al. Large meta-analysis of genome-wide association studies identifies five loci for lean body mass. Nat. Commun. 8 (1), 80. 10.1038/s41467-017-00031-7 (2017).
Venckunas, T., Emeljanovas, A., Mieziene, B. & Volbekiene, V. Secular trends in physical fitness and body size in Lithuanian children and adolescents between 1992 and 2012. J. Epidemiol. Community Health. 71 (2), 181–187. 10.1136/jech-2016-207307 (2017).
Tomkinson, G. R. et al. Temporal Trends in the Standing Broad Jump Performance of 10,940,801 Children and Adolescents Between 1960 and 2017. Sports Med. 51 (3), 531–548. 10.1007/s40279-020-01394-6 (2021).
Jaakkola, T., Gråsten, A., Huhtiniemi, M. & Huotari, P. Changes in the continuous leaping performance of Finnish adolescents between 1979 and 2020. J. Sports Sci. 40 (13), 1532–1541. 10.1080/02640414.2022.2091344 (2022).
Dykstra, B. J., Griffith, G. J., Renfrow, M. S., Mahon, A. D. & Harber, M. P. Cardiorespiratory and Muscular Fitness in Children and Adolescents with Obesity. Curr. Cardiol. Rep. 26 (5), 349–357. 10.1007/s11886-024-02036-3 (2024).
Booth, F. W., Roberts, C. K. & Laye, M. J. Lack of exercise is a major cause of chronic diseases. Compr. Physiol. 2 (2), 1143–1211. 10.1002/cphy.c110025 (2012).
Poitras, V. J. et al. Systematic review of the relationships between objectively measured physical activity and health indicators in school-aged children and youth. Appl. Physiol. Nutr. Metab. 41 (6 Suppl 3), S197–239. 10.1139/apnm-2015-0663 (2016).
Fowles, J. R., O’Brien, M. W., Solmundson, K., Oh, P. I. & Shields, C. A. Exercise is Medicine Canada physical activity counselling and exercise prescription training improves counselling, prescription, and referral practices among physicians across Canada. Appl. Physiol. Nutr. Metab. 43 (5), 535–539. 10.1139/apnm-2017-0763 (2018).
Kim, Y. et al. The combination of cardiorespiratory fitness and muscle strength, and mortality risk. Eur. J. Epidemiol. 33 (10), 953–964. 10.1007/s10654-018-0384-x (2018).
Lee, E. Y. et al. Report card grades on physical activity for children and adolescents from 18 Asian countries: Patterns, trends, gaps, and future recommendations. J. Exerc. Sci. Fit. 21 (1), 34–44. 10.1016/j.jesf.2022.10.008 (2023).
Andersen, L. B., Schnohr, P., Schroll, M. & Hein, H. O. All-cause mortality associated with physical activity during leisure time, work, sports, and cycling to work. Arch. Intern. Med. 160 (11), 1621–1628. 10.1001/archinte.160.11.1621 (2000).
Schnohr, P. et al. Various Leisure-Time Physical Activities Associated With Widely Divergent Life Expectancies: The Copenhagen City Heart Study. Mayo Clin Proc. ;93(12):1775–1785. doi: (2018). 10.1016/j.mayocp.2018.06.025
Trost, S. G., Owen, N., Bauman, A. E., Sallis, J. F. & Brown, W. Correlates of adults’ participation in physical activity: review and update. Med. Sci. Sports Exerc. 34 (12), 1996–2001. 10.1097/00005768-200212000-00020 (2002).
Stalsberg, R. & Pedersen, A. V. Effects of socioeconomic status on the physical activity in adolescents: a systematic review of the evidence. Scand. J. Med. Sci. Sports. 20 (3), 368–383. 10.1111/j.1600-0838.2009.01047.x (2010).
Stalsberg, R. & Pedersen, A. V. Are Differences in Physical Activity across Socioeconomic Groups Associated with Choice of Physical Activity Variables to Report? Int. J. Environ. Res. Public. Health. 15 (5), 922. 10.3390/ijerph15050922 (2018).
Leslie, E. et al. Insufficiently active Australian college students: perceived personal, social, and environmental influences. Prev. Med. 28 (1), 20–27. 10.1006/pmed.1998.0375 (1999).
Rech, C. R., Reis, R. S., Hino, A. A. F. & Hallal, P. C. Personal, social and environmental correlates of physical activity in adults from Curitiba, Brazil. Prev. Med. 58, 53–57. 10.1016/j.ypmed.2013.10.023 (2014).
Woods, C. B. et al. Socio-ecological correlates of physical activity in a nationally representative sample of adolescents across Ireland and Northern Ireland. Prev. Med. Rep. 23, 101472. 10.1016/j.pmedr.2021.101472 (2021).
Parker, K., Brown, H. L. & Salmon, J. Are There Common Correlates of Adolescents’ Sport Participation and Screen Time? Res. Q. Exerc. Sport. 94 (2), 374–382. 10.1080/02701367.2021.1998305 (2023).
Cole, T. J., Bellizzi, M. C., Flegal, K. M. & Dietz, W. H. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ. 320 (7244), 1240–1243. 10.1136/bmj.320.7244.1240 (2000).
Sallis, J. F., Grossman, R. M., Pinski, R. B., Patterson, T. L. & Nader, P. R. The development of scales to measure social support for diet and exercise behaviors. Prev. Med. 16 (6), 825–836. 10.1016/0091-7435(87)90022-3 (1987).
Reid, G., Vallerand, R., Poulin, C. & Crocker, P. The development and validation of the pictorial motivation scale in physical activity. Motivation Emot. 33, 161–172. 10.1007/s11031-008-9117-x (2009).
Kline, R. B. Principles and Practice of Structural Equation Modeling.
Harrington, D. M. et al. Household-level correlates of children’s physical activity levels in and across 12 countries. Obes. (Silver Spring). 24 (10), 2150–2157. 10.1002/oby.21618 (2016).
Masini, A., Rossi, C., Dallolio, L. & Mascherini, G. A cross-sectional analysis between physical activity levels, weight status and empathy-related behaviors in a sample of Tuscany primary school children. J. Sports Med. Phys. Fit. 64 (8), 835–840. 10.23736/S0022-4707.24.15915-4 (2024).
Forte, C. et al. Associations between screen-time, physical activity and depressive symptoms differ based on gender and screen-time mode. Eur. Child. Adolesc. Psychiatry. 32 (11), 2313–2322. 10.1007/s00787-022-02080-w (2023).
Martínez-González, M. A., Martínez, J. A., Hu, F. B., Gibney, M. J. & Kearney, J. Physical inactivity, sedentary lifestyle and obesity in the European Union. Int. J. Obes. Relat. Metab. Disord. 23 (11), 1192–1201. 10.1038/sj.ijo.0801049 (1999).
Martinsen, M. & Sundgot-Borgen, J. Adolescent elite athletes’ cigarette smoking, use of snus, and alcohol. Scand. J. Med. Sci. Sports. 24 (2), 439–446. 10.1111/j.1600-0838.2012.01505.x (2014).
Courneya, K., Plotnikoff, R., Hotz, S. & Birkett, N. Social Support and the Theory of Planned Behavior in the Exercise Domain. Am. J. Health Behav. 24, 300–308. 10.5993/AJHB.24.4.6 (2000).
Kerner, M. S. & Grossman, A. H. Attitudinal, social, and practical correlates to fitness behavior: a test of the theory of planned behavior. Percept. Mot Skills. 87 (3 Pt 2), 1139–1154. 10.2466/pms.1998.87.3f.1139 (1998).
Michels, T. C. & Kugler, J. P. Predicting exercise in older Americans: using the theory of planned behavior. Mil Med. 163 (8), 524–529 (1998).
Tomkinson, G. R. et al. European normative values for physical fitness in children and adolescents aged 9–17 years: results from 2 779 165 Eurofit performances representing 30 countries. Br. J. Sports Med. 52 (22), 1445–14563. 10.1136/bjsports-2017-098253 (2018).
Laakso, P. T. T. et al. The association of adolescent fitness with cardiometabolic diseases in late adulthood: A 45-year longitudinal study. Scand. J. Med. Sci. Sports. 34 (1), e14529. 10.1111/sms.14529 (2024).
Colley, R. C. et al. Do fit kids have fit parents? Health Rep. 32 (1), 3–12. 10.25318/82-003-x202100100001-eng (2021).
Giuriato, M. et al. Physical fitness trend after COVID-19 restrictions in Italian school-aged children: a cross-sectional study. J. Sports Med. Phys. Fit. 63 (9), 995–1002. 10.23736/S0022-4707.23.14943-7 (2023).

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Activity and social correlates of physical fitness in Lithuanian schoolchildren

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Abstract

1. Introduction

2. Methods

2.1. Participants and organisation

2.2. Questionnaires for correlates of physical fitness

2.3. Grouping of correlates into four models

2.4. Statistics

3. Results

3.1. Descriptive outcomes

3.2. Correlates of physical fitness

4. Discussion

5. Conclusion

Declarations

Competing interests

Funding

Author Contribution

Data Availability

References

Additional Declarations

Status:

Version 1