Introduction

Physical fitness (PF), also referred to as physical conditioning, is the individual’s ability to perform daily activities, engage in recreational activities and respond effectively to emergencies1. Physical fitness is widely recognized as a key indicator of overall health status2. It is commonly categorized into two domains: skill-related components, including agility, balance, coordination, power, speed, and reaction time; and health-related components, such as cardiorespiratory fitness (CRF), musculoskeletal fitness (MSF), and body composition. Together, these components reflect the integrity of multiple physiological systems, such as the musculoskeletal, cardiovascular, hematological, neurocognitive, and endocrine-metabolic systems3,4. As a non-invasive and practical assessment tool, physical fitness testing offers valuable insight into systemic health and has been increasingly applied in both clinical practice and population-level public health surveillance5. The period of childhood, particularly between the ages of 9 and 12, is a critical phase for rapid physical development and the formation of behavioral habits. During this time, children experience accelerated physiological growth and increased physical activity. However, they are also more susceptible to the influence of unhealthy lifestyle habits. Therefore, this period represents a critical window for health interventions and early prevention6,7.

In recent years, with the rapid socio-economic development, ongoing changes in lifestyle, and environmental factors, the issue of children’s physical fitness has become increasingly prominent worldwide, emerging as a major global public health concern8. Epidemiological data show that between 1980 and 2013, the overweight/obesity rate among children and adolescents in developed countries increased from 16.9 to 23.8%, while in developing countries it rose from 8.1 to 12.9%, with this trend continuing9. Notably, cardiorespiratory endurance, a key indicator of physical fitness, has declined at an annual rate of 0.36% since 1958, with a more pronounced decrease in developed countries10. The global concern over children’s physical fitness is becoming more pronounced, particularly in China, where physical fitness faces significant challenges. Although the overall physical fitness level in China has improved since 2005, regional disparities remain substantial: the physical fitness qualification rate in eastern provinces exceeds that of western provinces by 12.8%, largely due to differences in sports infrastructure and school physical education curricula11. Meanwhile, the overweight/obesity rate among Chinese children has tripled since 1995, reaching 19.4% in 2014, with urban male children being particularly affected12. These issues related to physical fitness are significantly associated with the development of various chronic non-communicable diseases, including cardiovascular diseases13type 2 diabetes14,15and certain malignancies16. Notably, physical fitness problems may also have a significant negative impact on children’s mental health17social adaptability18and academic performance. These effects manifest through decreased self-esteem, increased emotional distress, and limitations in children’s ability to participate in peer and school activities, leading to increased sedentary behavior, further declining physical fitness, and creating a negative feedback loop19,20.

In response to the severe challenges posed by children’s physical fitness, governments and international organizations have implemented various intervention measures. The Global Strategy for Women’s, Children’s, and Adolescents’ Health (2016–2030) has recognized children and adolescents as central to achieving the Sustainable Development Goals21. In China, the government has introduced initiatives such as The Healthy China Action (2019–2030)22 and the Dual Reduction Policy23which aim to improve children’s physical fitness by creating a favorable environment for physical activity. However, due to the multifaceted nature of children’s physical fitness, which involves individual, familial, educational, and societal dimensions, effectively addressing these issues requires interdisciplinary integration and interdepartmental collaboration. This complexity constitutes both the primary focus and challenge of current research. Existing empirical studies have identified key factors influencing children’s physical fitness, covering individual behaviors (such as exercise habits24,25sedentary behavior26and malnutrition27,28 and environmental factors (such as academic pressure29,30 and the allocation of sports resources31. However, most studies have concentrated on the individual level and lack systematic theoretical support and multidimensional integration.

This study introduces the Ecological Model of Health Behavior as a scientific framework to explore the mechanisms influencing children’s physical fitness32,33. This model was developed based on Bronfenbrenner’s Social Ecological Systems Theory, which emphasizes that an individual’s development and behaviors are shaped by dynamic interactions within multiple nested environmental systems34,35. Building on this theoretical foundation, McLeroy et al. (1988) further refined the framework to address health-related behaviors, highlighting five levels of influence: individual, interpersonal (e.g., family and peers), organizational (e.g., schools), community, and policy environments36. Using a multidimensional framework, this cross-sectional study examines how interconnected factors across the first four ecological levels (individual, interpersonal, organizational, and community) collectively shape children’s physical fitness outcomes (Fig. 1).

Importantly, this study aligns with the United Nations Sustainable Development Goals (SDGs), specifically Goal 3: Ensure healthy lives and promote well-being for all at all ages, and more precisely, Target 3.4, which aims to reduce premature mortality from non-communicable diseases (NCDs) through prevention and the promotion of mental and physical well-being37. Given that physical fitness in childhood serves as a crucial foundation for long-term health, strengthening fitness-related monitoring and interventions among school-aged children represents a strategic entry point for advancing SDG 3.438.

By applying the socioecological framework to physical fitness monitoring among children aged 9–12 in Shandong Province, this study generates region-specific empirical evidence on the determinants of physical fitness. Our findings inform multi-level public health strategies that address both behavioral and structural factors-such as enhancing school physical activity environments, supporting family-based health interventions, and promoting health education policies. Ultimately, these efforts contribute to the development of a health-literate generation and support sustainable development through child-centered, evidence-based interventions.

Fig. 1
figure 1

Conceptual framework based on the ecological model.

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Materials and methods

Study design and participants

This study adhered strictly to the recommendations of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines39employing a stratified random cluster sampling method to systematically conduct the survey. To comprehensively assess the physical fitness status of children aged 9 to 12 years in Shandong Province, the research team selected 47 primary schools across six cities—Jinan, Qingdao, Linyi, Dongying, Heze, and Yantai—during the period from April to May 2024. These cities were strategically chosen to represent the eastern, western, southern, northern, and central regions of Shandong Province, ensuring geographic diversity and regional representativeness of the sample. The geographic distribution of sampled schools and participating students across the six study cities is detailed (Table 1) to ensure methodological transparency. This table provides detailed information on the number of valid participants from each city, thus improving clarity regarding the sampling coverage and representativeness across the province. This sampling design ensured that the sample adequately reflected the demographic characteristics of children in Shandong Province.

Table 1 Sampling distribution and physical fitness test results of children aged 9–12 years in different cities.
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The study employed a two-stage sampling method: In the first stage, stratified sampling was conducted based on educational levels to ensure balanced representation across grades (Grades 3–6). In the second stage, cluster sampling was implemented, with natural classes within each grade defined as sampling units. Random sampling was then used to select classes. This multi-stage sampling strategy not only ensured the accurate representation of each grade but also guaranteed that the sample included students from varying academic levels and socio-cultural backgrounds. As a result, the approach effectively minimized selection bias and created a diverse yet methodologically rigorous research sample.

A total of 29,856 eligible students were included in the study. Exclusion criteria included individuals with severe organ diseases, significant physical disabilities or deformities, and those experiencing acute symptoms such as colds or fever. All included students completed both the physical fitness tests and the questionnaire surveys, achieving a 100% participation rate, with an effective participation rate of 97.37%. The gender distribution of the sample was balanced, with 15,109 males (50.61%) and 14,747 females (49.39%).

Physical fitness assessment and questionnaire survey

Physical fitness assessments were conducted in accordance with “National student physical health standard (Revised 2014)”, issued by the Ministry of Education of the People’s Republic of China40. These standards serve as the official national guideline for evaluating the physical fitness of students across different school levels, providing a systematic, comprehensive, and objective assessment framework41. The testing protocol includes core physical indicators such as body composition, cardiopulmonary endurance, muscular strength, flexibility, and speed, which together reflect the overall physical fitness status of school-aged children. To ensure consistency and accuracy, all physical fitness tests were administered using standardized instruments across all testing sites. Height and weight were measured using calibrated ultrasonic height-weight measuring devices (HW-900Y, Zhengzhou Hengwei Medical Instruments Co., Ltd.), and vital capacity was assessed using portable electronic spirometers (FGC-A+, Beijing Jafron Biomedical Co., Ltd.). Sit-and-reach flexibility was tested using standardized measurement boxes. Pull-up performance (males) was assessed by counting the number of valid repetitions, with each repetition requiring an overhand grip and the chin to pass above the bar; no time limit was imposed. For female participants, sit-up performance was evaluated over a timed 1-min interval, with repetitions manually counted by trained evaluators and the timing monitored using digital stopwatches accurate to ± 0.01s to ensure strict adherence to the time limit. Standing long jump distances were recorded using non-slip metric mats, and 50-m sprint times were measured with electronic timing gates. The 1-min rope skipping test was counted manually by trained assessors following uniform timing and counting protocols. Shuttle run (50 m×8) performance was recorded using fixed track markings and stopwatches.

All measurement devices were calibrated before each testing session in accordance with the manufacturers’ guidelines, and periodic checks were performed throughout the data collection process to maintain measurement accuracy. To ensure the reliability and comparability of results across regions, all assessors received standardized pre-survey training, including theoretical instruction and hands-on demonstrations, focusing on equipment operation, testing procedures, and error reduction. A detailed standard operating manual was provided to all personnel, and regular supervision and quality control checks were conducted by senior researchers throughout the data collection period.

All personnel involved in testing and survey administration received standardized, comprehensive training to ensure proficiency in the physical fitness testing procedures and field epidemiological survey methods. Before the formal data collection began, the research team recruited qualified personnel and conducted a unified training program focusing on physical fitness test standards and protocols. After the training, all testers were required to pass a rigorous skill assessment to ensure consistency and reliability across different regions and personnel.

In addition, to evaluate inter-rater reliability and ensure consistency across different testing teams, a subset of participants (approximately 10% of the total sample) was independently assessed by multiple trained evaluators using the same protocols. Intraclass correlation coefficients (ICCs) were calculated for key continuous physical fitness indicators (e.g., height, weight, vital capacity, sit-and-reach, sprint time), with all ICC values exceeding 0.87, indicating good to excellent reliability. These methodological safeguards were implemented to minimize measurement bias and ensure uniformity of assessment results across diverse geographic regions and testing personnel. The assessments followed a standardized sequence: height (cm) and weight (kg) were measured first, followed by the evaluation of vital capacity (ml). Subsequent tests included sit-and-reach flexibility (cm), pull-ups for males (number of repetitions), 1-min sit-ups for females (number of repetitions), standing long jump (cm), and a 50-m sprint (seconds). The final tests consisted of 1-min rope skipping (number of repetitions) and the 50-m × 8 shuttle run (seconds) for participants in Grades 5–6. A total score of ≥ 60 was set as the benchmark for passing, representing the threshold for physical fitness test qualification. The total score was calculated as a weighted sum of individual test scores, with different weights assigned to each parameter. The weight distribution for physical fitness parameters is as follows: Body Mass Index (BMI), calculated as weight (kg) divided by height (m) squared, accounted for 15%, vital capacity for 15%, the 50-m sprint for Grades 3–4 for 20%, sit-and-reach flexibility for 20%, 1-min rope skipping for 20%, and 1-min sit-ups for 10%. For Grades 5–6, the 50-m sprint accounted for 20%, sit-and-reach flexibility for 10%, 1-min rope skipping for 10%, 1-min sit-ups for 20%, and the 50-m × 8 shuttle run for 20%.

The questionnaire was administered anonymously, ensuring strict confidentiality. The survey collected data on the following variables: Demographic characteristics: gender, age, residential status, annual family income (in yuan)42and parental education levels (father’s and mother’s education). Family environment: frequency of secondhand smoke exposure, whether parents engage in physical activity, and whether parents support their children’s participation in physical activity. Physical activity (both school and non-school): frequency of physical activity per week, duration of each exercise session (in hours), and exercise intensity. Lifestyle factors: daily sleep duration (hours), daily screen time (hours), and daily homework time (hours). Dietary behaviors: frequency of breakfast consumption per week, frequency of meat intake per week, frequency of vegetable intake per week, frequency of fruit intake per week, frequency of egg consumption per week, frequency of milk intake per week, and frequency of fast-food consumption per week (see Supplementary Tables 2 and Supplementary Table 3).

Prior to the commencement of the formal survey, the researchers provided detailed explanations of the study’s objectives, significance, and participation process to the students and their legal guardians. Following the completion of the physical fitness tests, questionnaires were distributed to the students. Given the participants’ cognitive levels, the questionnaire was completed with the assistance of their parents. Upon collection, all completed questionnaires were systematically organized and data were entered into a computer for analysis using a dual-entry method, with two independent data entries to ensure accuracy.

The reliability and validity of the questionnaire were assessed prior to the formal survey implementation. To evaluate test–retest reliability, the questionnaire was administered twice to a subsample of participants with a 2-week interval. The results indicated no significant differences between the two administrations, demonstrating satisfactory temporal stability.

Content validity was established through a structured expert review process. A panel of five experts was invited, including two specialists in public health, two in physical education, and one in child epidemiology. Experts were selected based on their academic qualifications (all held doctoral degrees) and extensive experience in child health and physical fitness research. Each expert independently assessed the relevance, clarity, and comprehensiveness of each item using a 5-point Likert scale (1 = not relevant, 5 = highly relevant). Based on their evaluations, the Item-level Content Validity Index (I-CVI) was calculated, and only items with I-CVI ≥ 0.80 were retained. The Scale-level CVI (S-CVI) was also computed, demonstrating acceptable overall content validity. Discrepancies were resolved through consensus discussions, and minor modifications were made to improve item clarity and cultural appropriateness for the target population.

In accordance with the STROBE guidelines, data completeness was rigorously examined before statistical analysis39. Questionnaires with more than 10% missing responses were excluded from the final dataset. For entries with minor missing values, multiple imputation using the Markov Chain Monte Carlo (MCMC) method was applied to minimize potential bias and maximize data utility. To further ensure that missingness did not introduce systematic bias, we conducted an independent samples t-test (for age) and a chi-square test (for gender) comparing respondents with missing data to those with complete data. No statistically significant differences were observed (p > 0.05), suggesting that the missing data were missing at random and that the sample remained representative.

This study was approved by the Ethics Committee of Shandong Institute of Petroleum and Chemical Technology (Approval No.: KY-2024-021). All research procedures were conducted in strict accordance with relevant guidelines, regulations, and ethical standards. Prior to the implementation of the study, written informed consent was obtained from the legal guardians of all participants, who also signed the informed consent forms.

Statistical analysis

A database was established using Epi Data 3.1 software, and data entry was verified through dual independent entry. Statistical analyses were performed using SPSS version 27.0. Continuous variables are presented as mean ± standard deviation (\(\overline{X}\)± S), while categorical variables are described using frequency (percentage) [n (%)] for descriptive statistics. Prior to analysis, data were cleaned to identify and exclude incomplete or implausible entries. Cases with missing key variables were excluded via listwise deletion, as the proportion of missing data was less than 5%. Categorical variables were coded based on standardized criteria. For instance, physical fitness was categorized as “qualified” (≥ 60 points) and “unqualified” (< 60 points), following national evaluation standards. The prevalence of physical fitness was calculated as the proportion of students whose total score met or exceeded the 60-point threshold.

The factors influencing physical fitness and associated analyses were conducted using chi-square (χ2) tests and multivariable unconditional logistic regression analysis, with a significance level set at α = 0.05. For univariate analyses, logistic regression was used, with results expressed as odds ratios (OR) accompanied by 95% confidence intervals (CI) to provide an accurate estimate of effect sizes. To assess the goodness-of-fit and calibration of the final logistic regression model, the Hosmer–Lemeshow test was employed, ensuring the robustness of the analytical framework43.

Results

The age distribution was as follows: 6,795 students aged 9 years (22.76%), 7,048 students aged 10 years (23.61%), 7,601 students aged 11 years (25.46%), and 8,412 students aged 12 years (28.17%). The mean age of the participants was 10.59 ± 1.123 years.

To address the study’s objective of identifying key determinants of physical fitness among children, this section presents both univariate and multivariate analyses of sociodemographic characteristics, behavioral patterns, and lifestyle factors. The goal is to reveal statistically significant variables associated with physical fitness test qualification, thereby providing evidence to inform the design of targeted health interventions and public health strategies.

Univariate analysis of sociodemographic and behavioral factors associated with physical fitness qualification rates in children

Based on the 2024 physical fitness data for 9–12-year-old children in Shandong Province (Table 2), the overall qualification rate for physical fitness was 90.16%. Notably, gender, age, and residential status were found to significantly influence the physical fitness qualification rate (P < 0.05), whereas annual family income, father’s education level, and mother’s education level had no significant impact on the qualification rate (P > 0.05). Specifically, the qualification rate for female children (90.89%) was slightly higher than that for males (89.45%). The qualification rate increased progressively with age, with 12-year-old children having the highest qualification rate (91.11%). Additionally, children from rural areas had a significantly higher qualification rate (91.34%) compared to their urban counterparts (89.09%).

Table 2 Qualification rates of physical fitness tests among children aged 9–12 years in Shandong province: demographic characteristics analysis (2024 Survey).
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Univariate analysis of factors associated with physical fitness qualification rates in children

The results of the univariate analysis (Table 3) revealed that several factors were significantly associated with the physical fitness qualification rate in children aged 9 to 12 years. These factors included frequency of secondhand smoke exposure, parental interest in physical activity, parental support for children’s participation in physical activity, frequency of weekly physical activity, duration of each exercise session, exercise intensity, daily sleep duration, daily screen time, daily homework time, frequency of weekly breakfast consumption, and frequency of consumption of fried foods and high-calorie fast food (P < 0.05).

Table 3 Univariable analysis of physical fitness test qualification rates among children aged 9–12 years in Shandong province: 2024 cross-sectional survey.
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Univariate analysis revealed that several behavioral and lifestyle factors were significantly associated with children’s physical fitness qualification rates. Children never exposed to secondhand smoke exposure had a higher qualification rate (90.49%) compared to those exposed occasionally (90.00%) or daily (88.73%) (χ2 = 10.835, P = 0.004). Parental factors also played a significant role: children whose parents both enjoyed physical activity had the highest qualification rate (91.63%) compared to those with inactive parents (88.01%) (χ2 = 76.163, P < 0.001), and those receiving parental support had a notably higher qualification rate (91.23%) than those without support (75.59%) (χ2 = 526.366, P < 0.001).

Physical activity characteristics were influential as well. Children engaging in activity 3–5 times per week had a qualification rate of 90.64%, which increased to 93.63% for those with over five sessions weekly (χ2 = 130.696, P < 0.001). Longer exercise duration (> 1 h/session: 90.87% vs. <0.5 h: 89.15%) (χ2 = 10.318, P = 0.006) and moderate intensity (90.35%) (χ2 = 7.487, P = 0.024) were associated with better outcomes.

Sleep and sedentary behaviors were also significantly related to fitness status. Children sleeping 6–8 h/day had the highest qualification rate (91.36%), whereas those sleeping < 6 h had the lowest (85.75%) (χ2 = 100.106, P < 0.001). Excessive screen time (> 3 h/day) corresponded to the lowest qualification rate (85.36%) compared to < 1 h/day (90.82%) (χ2 = 64.642, P < 0.001), and longer daily homework time (> 3 h/day) was linked to a lower qualification rate (88.55%) (χ2 = 23.468, P < 0.001).

In terms of dietary habits, children who consumed breakfast daily had a higher qualification rate (90.49%) than those who skipped breakfast (88.06%) (χ2 = 15.616, P = 0.001). Similarly, frequent consumption of fried and high-calorie fast food (> 3 times/week: 88.58%) was associated with poorer fitness outcomes (χ2 = 27.387, P < 0.001).

Multivariable logistic regression analysis of physical fitness test qualification rates in children

The results of the multivariable logistic regression analysis (Table 4) indicated that several factors were significantly associated with the risk of physical fitness non-compliance among children, after adjusting for gender, age, and residential status.

Table 4 Multivariable analysis of physical fitness test qualification rates among children aged 9–12 years in Shandong province: results from the 2024 cross-sectional study.
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Daily exposure to secondhand smoke exposure significantly increased the risk of non-compliance (OR = 1.212, 95% CI:1.078–1.362, P = 0.001). In contrast, parental involvement in physical activity served as a protective factor: children whose parents participated in physical activity had a lower risk of non-compliance (OR = 0.668, 95% CI: 0.601–0.743, P < 0.001), as did those receiving parental support for physical activity (OR = 0.297, 95% CI: 0.266–0.332, P < 0.001). Higher frequency of weekly physical activity was associated with a significantly reduced risk, with OR = 0.680 (95% CI: 0.623–0.743, P < 0.001) for 3–5 times/week and OR = 0.447 (95% CI: 0.382–0.524, P < 0.001) for more than 5 times/week, respectively. Similarly, longer exercise duration was protective, with OR = 0.875 (95% CI: 0.797–0.960, P = 0.005) for 0.5–1 h/session and OR = 0.829 (95% CI: 0.717–0.959, P = 0.012) for more than 1 h/session. Moderate exercise intensity also significantly reduced the risk compared to low-intensity activity (OR = 0.872, 95% CI: 0.763–0.997, P = 0.045). Adequate sleep duration was strongly associated with better fitness outcomes, with children sleeping 6–8 h/day (OR = 0.569, 95% CI: 0.499–0.649, P < 0.001) or more than 8 h/day (OR = 0.760, 95% CI: 0.662–0.873, P < 0.001) showing significantly lower risk of non-compliance. In contrast, prolonged screen exposure increased the risk, with OR = 1.142 (95% CI: 1.044–1.249, P = 0.004) for 1–3 h/day and OR = 1.712 (95% CI: 1.496–1.958, P < 0.001) for more than 3 h/day. Spending more than 3 h/day on homework also increased the likelihood of non-compliance (OR = 1.294, 95% CI: 1.158–1.447, P < 0.001). Children who consumed breakfast daily (OR = 0.765, 95% CI: 0.626–0.936, P = 0.009) or 3–6 times/week (OR = 0.796, 95% CI: 0.641–0.990, P = 0.040) had lower risks of non-compliance. Conversely, frequent consumption of fried or high-calorie fast foods significantly increased the risk, with OR = 1.227 (95% CI: 1.120–1.344, P < 0.001) for 2–3 times/week and OR = 1.268 (95% CI: 1.116–1.441, P < 0.001) for more than 3 times/week.

Discussion

This study provides a comprehensive evaluation of the physical fitness status among children aged 9–12 years in Shandong Province, China. It systematically examines the influence of multiple behavioral and environmental factors—including secondhand smoke exposure, family support for physical activity, exercise behaviors, sleep duration, sedentary behaviors (related to academic load and screen time), and dietary habits—on children’s compliance with physical fitness standards. The findings indicate that physical fitness in children is not determined by a single behavioral or background variable but is instead the result of complex, multilayered, and interacting influences. This aligns with the Ecological Model of Health Behavior, which emphasizes that individual health behaviors are shaped by interconnected factors within the family, school, and community environments. These results not only provide empirical evidence for understanding the determinants of physical fitness in children but also provide a robust foundation for designing targeted, multi-level, and evidence-based interventions.

The present study found that the physical fitness test qualification rate was significantly higher among rural children compared to their urban counterparts. This urban–rural disparity may stem from structural differences in lifestyle44available physical activity spaces45and levels of social support46. Urban children are often subject to greater academic pressure47have easier access to electronic devices, and exhibit higher levels of sedentary behavior due to restricted space for outdoor activities48. In contrast, rural children typically benefit from more opportunities for outdoor play, a more natural daily rhythm, and greater spontaneous engagement in physical activity49,50. This trend is consistent with previous research linking urbanization to declining physical fitness levels in children. The findings underscore the need for urban planning and educational systems to prioritize the creation of supportive environments that facilitate and encourage physical activity among children.

At the family level, parental physical activity and support, together with exposure to secondhand smoke, have been identified as significant factors influencing children’s compliance with physical fitness standards. Parents who engage in regular exercise serve as positive role models, promoting healthy lifestyle behaviors in their children through both observation and shared participation, while also providing crucial emotional support that encourages sustained physical activity51,52. Conversely, exposure to secondhand smoke within the household has been shown to adversely affect children’s respiratory and cardiovascular health, impair lung function, and increase the risk of chronic conditions, all of which can undermine physical fitness and overall well-being53,54. This harmful environmental exposure may also limit children’s capacity or motivation to engage in physical activity55,56. These contrasting influences at the family level align with life course theory, which highlights the profound impact of early life experiences on shaping long-term health trajectories57,58. Therefore, strengthening family-based interventions—including parent education programs, parent–child co-activity initiatives, and implementation of household smoke-free policies—may be essential for promoting children’s physical fitness and establishing enduring healthy habits.

In terms of physical activity behaviors, appropriate frequency, intensity, and duration of exercise were found to be closely associated with children’s physical fitness. The study indicates that moderate-intensity activities—such as brisk walking, jump rope, and cycling—are particularly effective in enhancing physical fitness among children. In contrast, high-intensity exercise may lead to physical fatigue or psychological stress due to excessive load59,60,61. These findings support the recommendations of the World Health Organization Guidelines on Physical Activity and Sedentary Behaviour for Children and Adolescents, which advocate for regular, age-appropriate, and moderate-intensity physical activity62. The results also provide practical guidance for the design and implementation of physical education curricula and community-based intervention programs aimed at improving child health outcomes63.

At the individual behavioral level, sleep was confirmed in this study as a critical determinant of children’s physical fitness. Adequate sleep contributes not only to hormonal regulation and physical recovery but also to cognitive functioning, attention levels, and athletic performance64,65,66. Conversely, prolonged screen time and increased academic burden were identified as primary contributors to insufficient sleep, which in turn may lead to reduced physical activity and declining physical fitness67,68. In the current societal context, the widespread emphasis on academic achievement and increasing digital immersion warrant heightened awareness and coordinated efforts from parents, schools, and broader society to address these challenges.

With regard to dietary behaviors, regular breakfast consumption was positively associated with higher physical fitness test qualification, whereas frequent intake of high-calorie fast food exhibited a negative impact. Breakfast provides essential energy and nutrients necessary to support children’s learning and physical activity throughout the day69. In contrast, fast foods are typically high in fat, sugar, and sodium, and are closely linked to overweight, metabolic dysfunction, and reduced physical performance70,71. These findings underscore the importance of integrating nutritional education alongside physical activity interventions to effectively promote children’s overall health and physical fitness.

It is noteworthy that, beyond the individual and family-level factors discussed above, broader socio-environmental variables may also exert significant influence on children’s physical fitness. This study did not include factors such as access to community sports resources, the quality of school-based physical education programs, mental health status, peer relationships, and family socioeconomic status. Future research should adopt multilevel modeling approaches to explore the social determinants of physical fitness in children, thereby enabling a more comprehensive theoretical framework and informing more effective, context-sensitive intervention strategies.

This study has several limitations that should be acknowledged. First, its cross-sectional design precludes causal inference between influencing factors and physical fitness outcomes. Second, lifestyle-related data were collected via self-reported questionnaires, which may be subject to recall bias and social desirability effects. Third, the study sample was drawn from a single province in eastern China, which may limit the generalizability of the findings to other regions or populations. Despite these limitations, the study provides regionally representative data with practical relevance and offers valuable evidence to support the development of culturally appropriate and age-specific policies and interventions aimed at improving children’s physical fitness.

Conclusion

This study provides a comprehensive assessment of the physical fitness status of children aged 9–12 in Shandong Province and highlights key behavioral and environmental factors associated with physical performance. The findings indicate that second-hand smoke exposure, parental support for physical activity, exercise habits, screen time, sleep, and dietary behaviors are significantly correlated with physical fitness levels. Families, educational institutions, and the broader society must work in coordination, adopting a comprehensive approach to enhance children’ physical fitness trajectories, ensuring harmonious and holistic development. Future studies should employ longitudinal designs to clarify causal relationships and provide stronger guidance for evidence-based policy formulation.