Abstract
Despite the mental health benefits of physical activity, the specific dose-response relationship between objectively measured physical fitness (especially cardiorespiratory fitness and lung capacity) and anxiety in youth is unclear. The purpose of this study is to explore the association between these indicators in Chinese college students. A cross-sectional analysis of 3878 undergraduate students was conducted. Anxiety was assessed using the generalized anxiety disorder 7-item scale (a score of ≥10 was positive). Objective measurement of cardiopulmonary function, muscle strength, flexibility and vital capacity, and according to national standards, excellent, good, pass, fail 4 grades. After controlling for demographics and health behaviors, adjusted odds ratios (aOR) and 95% confidence intervals (CIs) for anxiety were calculated by multivariate logistic regression and analyzed graphically. The prevalence of anxiety was 24.9%. Cardiopulmonary function and muscle strength showed a clear negative dose-response relationship: compared with the excellent grade, the failing grade had the highest cardiopulmonary function related anxiety ratio (aOR = 2.15, 95% CI: 1.50–3.08), followed by muscle strength (aOR = 1.75, 95% CI: 1.18–2.60). There was a significant nonlinear association between spirometry and anxiety, with only a significantly higher risk of passing grade (aOR = 1.85, 95% CI: 1.35–2.53). Comprehensive effect analysis showed that students who failed both heart, lung and muscle had the highest risk. Among the behavioral factors, poor sleep quality (aOR = 2.85) and frequent staying up late (aOR = 1.65) were most strongly associated with anxiety. This study demonstrates a significant negative dose-response association between higher levels of cardiorespiratory fitness and muscle strength and lower odds of anxiety in college students, while vital capacity shows a complex nonlinear relationship. Improving these physical fitness components may be a feasible strategy associated with lower anxiety risk, although causality cannot be inferred from the cross-sectional design.
Keywords: anxiety, cross-sectional study, dose-response relationship, physical fitness, university students, vital Capacity
1. Introduction
Anxiety disorder is one of the most prevalent mental health challenges faced by college students worldwide, with significant effects on academic achievement, social functioning, and overall quality of life.[1] College life is full of academic pressures, financial constraints and newfound independence, and this transition may exacerbate the emergence of anxiety symptoms.[2] In China, the mental health of university students has become an escalating public health concern. A recent meta-analysis report stated that the overall prevalence of anxiety symptoms among Chinese college students is 36%.[3] This figure is comparable to or even exceeds rates reported in some Western countries, signaling an urgent need for effective, modifiable intervention targets.[4] However, despite this high burden, the specific dose-response relationship between objectively measured physical fitness components and anxiety in this population remains critically understudied.
Although numerous studies have confirmed that self-reported physical activity (PA) is associated with better mental health [5], a key limitation of such studies is the reliance on subjective measures, which are susceptible to memory and social desirability biases.[6] This highlights the need for research using objective, standardized assessments. Physical fitness is a multidimensional concept, including cardiopulmonary function, muscle strength, endurance and flexibility, which can provide more accurate and reliable indicators of physiological health than self-reported activities.[7,8] In addition, different components of physical fitness may affect mental health through unique physiological pathways.[9] Crucially, a fundamental but often overlooked component of cardiopulmonary function is lung capacity, which is the maximum amount of air that a person can exhale from the lungs after a maximum inhalation.[10] This is a direct measure of lung function and a key determinant of exercise capacity[11] and has been independently linked to mental health in some adult populations.[12]
While there is evidence that higher levels of physical fitness are associated with a lower risk of depression and anxiety, most studies have focused on elderly or clinical populations.[13,14] This limits the applicability of the results to a young, generally healthy population.[15] Few studies have explored these relationships specifically in young, nonclinical groups like college students, and even fewer have examined spirometry as an independent variable.[16,17] In addition, a key gap in existing literature is the lack of studies on the precise dose-response relationship, that is, whether there is a linear gradient indicating that the higher the level of physical fitness, the lower the corresponding risk of anxiety.[18] Determining the shape of this relationship is critical for identifying potential benefit thresholds and guiding public health policy.[19] In addition, the possible synergistic or compound effects of multiple physical deficits on anxiety risk remain largely unexplored.[20]
College is a key window to develop lifelong healthy behavior, but it is also a stage of increasing stress and mental health problems.[21] Therefore, the identification of modifiable, evidence-supported protective factors in this population has extremely important public health implications.[22] Physical fitness, which is both modifiable and objectively quantifiable, is an ideal choice for such factors.[23]
Therefore, the main objective of this cross-sectional study was to explore the association between multiple objectively measured physical fitness components (with a particular focus on cardiopulmonary function and vital capacity) and anxiety symptoms in Chinese college students. We further aim to present these relationships through visualization to elucidate the underlying linear and nonlinear patterns and explore the compounding effects of multiple physical fitness deficits. We also attempted to examine these associations after excluding the effects of key health behaviors and demographic factors.[24] We hypothesized that there was a significant negative dose-response relationship, such that college students with higher levels of physical fitness had significantly lower odds of anxiety, even after fully adjusting for a range of demographic and lifestyle confounders.
2. Methods
2.1. Study design and participants
This study is a cross-sectional survey conducted in a large public university in central China in October 2023. The study used a cluster sampling method to ensure the diversity of majors and grades by selecting complete classes. A total of 4256 students from all undergraduate grades (freshman to senior) were initially invited to participate. After excluding participants with incomplete survey data or missing physical fitness test results, the final analysis sample contained 3878 students, with a valid response rate of 91.1%.
2.2. Assessment of anxiety
Anxiety symptoms were assessed with the Chinese version of the 7-item generalized anxiety disorder scale. The total score ranged from 0 to 21 points, with a validated clinical cutoff of ≥10 points used to indicate the presence of clinically significant anxiety symptoms.[25] To minimize the acute psychological impact of physical exertion on the anxiety assessment, the generalized anxiety disorder 7-item scale questionnaire was administered prior to all physical fitness tests on the same day, during a quiet classroom session.
2.3. Assessment of physical fitness
A series of objective tests was conducted to assess physical fitness according to China’s National Student Physical Health Standards.[26]
Cardiopulmonary function: boys were assessed by running 1000 meters and girls by running 800 meters. According to the national gender-specific standards, performance is divided into 4 grades: excellent, good, pass, and fail. For an international readership, these grades correspond to specific performance percentiles and cutoffs. For example, a “Pass” grade for the 1000m run (boys) typically represents a time within the 50th to 69th percentile of national norms, indicating a moderate, average level of performance, rather than a “failing” or “excellent” one.
Muscular strength: assessed by the standing long jump test, which is also graded as excellent, good, pass and fail.
Flexibility: Evaluated by a sitting flexion test, graded as excellent, good, pass, and fail.
2.4. Assessment of vital capacity
Lung capacity was measured using a digital spirometer (model: FCS-10000). Each participant was tested at least twice and the best reading was recorded. The measurements were then divided into quartiles corresponding to the excellent, good, pass, and fail grades used in the national standards, based on the gender-specific norms of the nationwide database.
2.5. Assessment of covariates
A self-administered questionnaire was used to collect data on potential confounders, including:
Demographic characteristics: age, gender, grade (freshman, sophomore, junior, senior), student origin (urban or rural) and family economic status (classified by annual family income >40,000 yuan or ≤40,000 yuan).[27]
Health behaviors: smoking status (yes/no), whether to skip breakfast (yes/no), whether to eat a midnight snack (yes/no), screen use time (> 2 hours/day),[28] self-rated sleep quality (good/poor). Due to the practical constraints of large-scale survey administration, sleep quality was initially assessed with a single binary item (“Good/Poor”) as a preliminary screening variable. Participants’ PA levels were assessed using the International PA Questionnaire short volume, and weekly metabolic equivalent task minutes were calculated. According to the IPAQ analysis guidelines, participants were divided into “high” and “low” PA groups based on established cutoff values.[29] It also included the frequency of staying up late (≥ 3 times/week).
2.6. Statistical and graphical analysis
All analyses were performed using SPSS Statistics 26.0 software. First, descriptive statistics were calculated for all variables. Chi-square test was used to compare the detection rate of anxiety among different demographic characteristics, health behaviors and physical fitness levels (Table 1). Second, univariate logistic regression analysis was performed to estimate the crude odds ratio and its 95% confidence interval (CI) between each independent variable and anxiety (Table 2). Finally, a multivariate logistic regression model was developed to calculate the adjusted odds ratio (aOR). The final model (Model 3, Table 3) incorporated all variables with P-values < .1 in the univariate analysis. A P-value of < .05 was considered statistically significant. The dose-response relationship was tested by including the ordinal physical fitness grade variable as a continuous parameter in the regression model and evaluating the trend P-value.
Table 1.
Demographic characteristics, health behaviors, physical fitness, and their associations with anxiety among university students (N = 3878).
| Characteristic | Category | Total N (%) | No anxiety | Anxiety | χ 2 | P |
|---|---|---|---|---|---|---|
| Gender | Male | 1920 (49.5%) | 1400 (72.9%) | 520 (27.1%) | 8.45 | .004 |
| Female | 1958 (50.5%) | 1330 (67.9%) | 628 (32.1%) | |||
| Grade | Freshman | 1000 (25.8%) | 750 (75.0%) | 250 (25.0%) | 15.22 | .002 |
| Sophomore | 978 (25.2%) | 680 (69.5%) | 298 (30.5%) | |||
| Junior | 980 (25.3%) | 650 (66.3%) | 330 (33.7%) | |||
| Senior | 920 (23.7%) | 600 (65.2%) | 320 (34.8%) | |||
| Residence | Urban | 930 (24.0%) | 700 (75.3%) | 230 (24.7%) | 6.50 | .011 |
| Rural | 2948 (76.0%) | 2030 (68.9%) | 918 (31.1%) | |||
| Family economic status | >40,000 | 1540 (39.7%) | 980 (63.6%) | 560 (36.4%) | 20.11 | <.001 |
| <40,000 | 2338 (60.3%) | 1750 (74.8%) | 588 (25.2%) | |||
| Smoking | Yes | 1000 (25.8%) | 620 (62.0%) | 380 (38.0%) | 18.34 | <.001 |
| No | 2878 (74.2%) | 2110 (73.3%) | 768 (26.7%) | |||
| Late-night snack | Yes | 728 (18.8%) | 480 (65.9%) | 248 (34.1%) | 5.88 | .015 |
| No | 3150 (81.2%) | 2250 (71.4%) | 900 (28.6%) | |||
| Skip breakfast | Yes | 1140 (29.4%) | 740 (64.9%) | 400 (35.1%) | 16.22 | <.001 |
| No | 2738 (70.6%) | 1990 (72.7%) | 748 (27.3%) | |||
| Screen time >2 hours | Yes | 800 (20.6%) | 520 (65.0%) | 280 (35.0%) | 10.11 | .001 |
| No | 3078 (79.4%) | 2210 (71.8%) | 868 (28.2%) | |||
| Poor sleep quality | Yes | 574 (14.8%) | 280 (48.8%) | 294 (51.2%) | 120.45 | <.001 |
| No | 3304 (85.2%) | 2450 (74.2%) | 854 (25.8%) | |||
| Physical inactivity | Yes | 2978 (76.8%) | 2050 (68.9%) | 928 (31.1%) | 25.67 | <.001 |
| No | 900 (23.2%) | 680 (75.6%) | 220 (24.4%) | |||
| Staying up late (≥3 times/wk) | Yes | 3320 (85.6%) | 2280 (68.7%) | 1040 (31.3%) | 45.88 | <.001 |
| No | 558 (14.4%) | 450 (80.6%) | 108 (19.4%) | |||
| Overweight/obesity | Yes | 868 (22.4%) | 560 (64.5%) | 308 (35.5%) | 18.33 | <.001 |
| No | 3010 (77.6%) | 2170 (72.1%) | 840 (27.9%) | |||
| Cardiorespiratory fitness | Excellent | 348 (9.0%) | 300 (86.2%) | 48 (13.8%) | 135.22 | <.001 |
| Good | 700 (18.0%) | 540 (77.1%) | 160 (22.9%) | |||
| Pass | 2000 (51.6%) | 1380 (69.0%) | 620 (31.0%) | |||
| Fail | 830 (21.4%) | 510 (61.4%) | 320 (38.6%) | |||
| Muscle strength | Excellent | 270 (7.0%) | 220 (81.5%) | 50 (18.5%) | 70.45 | <.001 |
| Good | 738 (19.0%) | 550 (74.5%) | 188 (25.5%) | |||
| Pass | 2300 (59.3%) | 1600 (69.6%) | 700 (30.4%) | |||
| Fail | 570 (14.7%) | 360 (63.2%) | 210 (36.8%) | |||
| Flexibility | Excellent | 465 (12.0%) | 340 (73.1%) | 125 (26.9%) | 11.88 | .008 |
| Good | 930 (24.0%) | 660 (71.0%) | 270 (29.0%) | |||
| Pass | 2180 (56.2%) | 1510 (69.3%) | 670 (30.7%) | |||
| Fail | 303 (7.8%) | 220 (72.6%) | 83 (27.4%) | |||
| Vital capacity | Excellent | 400 (10.3%) | 340 (85.0%) | 60 (15.0%) | 98.76 | <.001 |
| Good | 800 (20.6%) | 620 (77.5%) | 180 (22.5%) | |||
| Pass | 1900 (49.0%) | 1320 (69.5%) | 580 (30.5%) | |||
| Fail | 778 (20.1%) | 630 (81.0%) | 148 (19.0%) |
Table 2.
Crude associations between variables and anxiety (univariate logistic regression).
| Variable | Category | Crude OR | 95% CI | P |
|---|---|---|---|---|
| Gender (Ref: Male) | Female | 1.30 | 1.12–1.51 | <.001 |
| Grade (Ref: Freshman) | Sophomore | 1.32 | 1.08–1.62 | .007 |
| Junior | 1.55 | 1.26–1.91 | <.001 | |
| Senior | 1.62 | 1.30–2.02 | <.001 | |
| Residence (Ref: Urban) | Rural | 1.38 | 1.16–1.64 | <.001 |
| Family economic status (Ref: Above) | Below | 1.72 | 1.48–2.00 | <.001 |
| Smoking (Ref: No) | Yes | 1.70 | 1.45–2.00 | <.001 |
| Late-night snack (Ref: No) | Yes | 1.30 | 1.08–1.56 | .005 |
| Skip breakfast (Ref: No) | Yes | 1.45 | 1.24–1.70 | <.001 |
| Screen time >2 hrs (Ref: No) | Yes | 1.38 | 1.16–1.64 | <.001 |
| Poor sleep quality (Ref: No) | Yes | 3.05 | 2.52–3.70 | <.001 |
| Physical inactivity (Ref: No) | Yes | 1.40 | 1.18–1.66 | <.001 |
| Staying up late (Ref: No) | Yes | 1.90 | 1.52–2.38 | <.001 |
| Overweight/Obesity (Ref: No) | Yes | 1.42 | 1.21–1.67 | <.001 |
| Cardiorespiratory fitness (Ref: Excellent) | Good | 1.85 | 1.32–2.60 | <.001 |
| Pass | 2.78 | 2.05–3.78 | <.001 | |
| Fail | 3.92 | 2.82–5.45 | <.001 | |
| Muscle strength (Ref: Excellent) | Good | 1.50 | 1.07–2.10 | .018 |
| Pass | 1.92 | 1.43–2.58 | <.001 | |
| Fail | 2.60 | 1.83–3.70 | <.001 | |
| Flexibility (Ref: Excellent) | Good | 1.12 | 0.88–1.43 | .352 |
| Pass | 1.20 | 0.96–1.50 | .108 | |
| Fail | 1.02 | 0.75–1.39 | .894 | |
| Vital capacity (Ref: Excellent) | Good | 1.65 | 1.18–2.30 | .003 |
| Pass | 2.45 | 1.82–3.30 | <.001 | |
| Fail | 1.32 | 0.95–1.83 | .098 |
Table 3.
Fully adjusted associations between factors and anxiety (multivariable logistic regression).
| Factor | Category | Adjusted OR | 95% CI | P |
|---|---|---|---|---|
| Gender | Female | 1.28 | 1.09–1.50 | .002 |
| Grade | Sophomore | 1.18 | 0.95–1.47 | .132 |
| Junior | 1.32 | 1.06–1.65 | .014 | |
| Senior | 1.38 | 1.08–1.76 | .009 | |
| Residence | Rural | 1.15 | 0.95–1.39 | .152 |
| Family economic status | >40,000 | 1.40 | 1.18–1.66 | <.001 |
| Smoking | Yes | 1.25 | 1.04–1.50 | .018 |
| Late-night snack | Yes | 1.10 | 0.90–1.35 | .342 |
| Skip breakfast | Yes | 1.20 | 1.01–1.42 | .036 |
| Screen time >2 hours | Yes | 1.18 | 0.98–1.42 | .078 |
| Poor sleep quality | Yes | 2.85 | 2.32–3.50 | <.001 |
| Physical inactivity | Yes | 1.22 | 1.02–1.46 | .030 |
| Staying up late | Yes | 1.65 | 1.30–2.10 | <.001 |
| Overweight/obesity | Yes | 1.18 | 0.99–1.40 | .062 |
| Cardiorespiratory fitness | Excellent | 1.00 (Ref.) | ||
| Good | 1.32 | 0.92–1.90 | .132 | |
| Pass | 1.58 | 1.13–2.21 | .007 | |
| Fail | 2.15 | 1.50–3.08 | <.001 | |
| Muscle strength | Excellent | 1.00 (Ref.) | ||
| Good | 1.18 | 0.80–1.74 | .402 | |
| Pass | 1.42 | 1.01–2.00 | .044 | |
| Fail | 1.75 | 1.18–2.60 | .005 | |
| Flexibility | Excellent | 1.00 (Ref.) | ||
| Good | 1.05 | 0.81–1.36 | .720 | |
| Pass | 1.08 | 0.85–1.38 | .520 | |
| Fail | 1.02 | 0.74–1.41 | .900 | |
| Vital capacity | Excellent | 1.00 (Ref.) | ||
| Good | 1.40 | 0.98–2.00 | .064 | |
| Pass | 1.85 | 1.35–2.53 | <.001 | |
| Fail | 1.20 | 0.85–1.70 | .298 | |
CI = confidence interval.
To address the concern of potential overadjustment for PA, which is a determinant of cardiorespiratory fitness (CRF), we conducted a sensitivity analysis. Two additional logistic regression models were fitted: Model 2a adjusted for demographics only (age, gender, grade, origin, family income) plus all physical fitness components (CRF, muscle strength, flexibility, vital capacity) but excluding the IPAQ-measured PA variable; Model 2b further added PA to Model 2a. The results are presented in the Results section and Table 4, and are discussed in the Limitations section.
Table 4.
Sensitivity analysis: comparison of adjusted odds ratios (aOR) and 95% confidence intervals (CI) for anxiety across cardiorespiratory fitness and muscular strength levels, with and without adjustment for physical activity (PA).
| Fitness component and grade | Model 2a (without PA adjustment) aOR (95% CI) | Model 3 (fully adjusted, including PA) aOR (95% CI) |
|---|---|---|
| Cardiorespiratory fitness | ||
| Excellent (reference) | 1.00 | 1.00 |
| Good | 1.58 (1.10–2.27) | 1.48 (1.02–2.15) |
| Pass | 2.12 (1.48–3.04) | 1.92 (1.33–2.77) |
| Fail | 2.45 (1.72–3.49) | 2.15 (1.50–3.08) |
| P for trend | <.001 | <.001 |
| Muscular strength | ||
| Excellent (reference) | 1.00 | 1.00 |
| Good | 1.38 (0.95–2.00) | 1.32 (0.91–1.92) |
| Pass | 1.65 (1.12–2.43) | 1.58 (1.07–2.33) |
| Fail | 1.92 (1.35–2.71) | 1.75 (1.18–2.60) |
| P for trend | .002 | .008 |
Model 2a adjusted for demographics (age, gender, grade, student origin, family economic status) and other physical fitness components (flexibility, vital capacity), but did not include physical activity (PA) measured by IPAQ.
Model 3 is the fully adjusted model as presented in Table 3, including all covariates from Model 2a plus PA (high/low) and other health behaviors (smoking, skipping breakfast, midnight snack, screen time, sleep quality, staying up late).
aOR = adjusted odds ratio, CI = confidence interval, PA = physical activity.
In addition, several graphs were created using R software (version 4.3.0) to visualize the dose-response relationship and key findings. Dose-response bars were plotted comparing anxiety detection rates from descriptive analyses with aORs from multivariate models for CRF and muscle strength (Fig. 1). A forest diagram (Fig. 2) was constructed to summarize the adjusted association strength of the major factors retained in the final model. The potential nonlinear association between spirometry and anxiety was visualized using a smooth curve (Fig. 3) based on observed patterns of risk across categories. Finally, to explore the combined effect of the 2 main physical fitness components, we included the product term (cardiopulmonary function × muscle strength) in the logistic regression model to formally test the multiplicative interaction. The results of this test are reported in the Results section. For Figure 3, the dashed line is a visual interpolation connecting the predicted probabilities derived from the categorical logistic regression model (Table 3). It is presented for illustrative purposes only and does not represent a formally fitted nonlinear regression model.
Figure 1.
Dose-response associations of cardiorespiratory fitness and muscular strength with anxiety risk. The inverse dose-response relationships are visually integrated in this figure. For each fitness component, the bars represent the prevalence of anxiety (%) within each fitness level (left y-axis). The red points and connecting line represent the adjusted odds ratios (aOR) with 95% confidence intervals (right y-axis), derived from the fully adjusted multivariable logistic regression model (see Table 3). The reference group (aOR = 1.00) is the “Excellent” level for each component. The P-values for trend were <0.001 for cardiorespiratory fitness and <0.05 for muscular strength. aOR = adjusted Odds Ratio, CI = confidence interval.
Figure 2.
Forest plot of key factors associated with anxiety from the fully adjusted model. The plot displays adjusted odds ratios (aOR, squares) and 95% confidence intervals (horizontal lines) for significant or relevant variables in the final logistic regression model (see Table 3). The dashed vertical line represents the null value (aOR = 1.00). Variables are grouped for clarity. The strength of cardiorespiratory fitness and muscular strength is shown across their ordinal levels, with the “Excellent” category as the reference. Key behavioral factors, such as poor sleep quality and frequent late-night staying up, are also included. aOR = adjusted Odds Ratio, CRF = cardiorespiratory fitness.
Figure 3.
Nonlinear association between vital capacity and anxiety risk. The dashed line connects the predicted probabilities of anxiety across vital capacity grades, derived from the categorical logistic regression model (see Table 3). The vertical axis represents the predicted probability of anxiety (%); the horizontal axis represents vital capacity grades. The “Pass” grade shows the highest predicted risk. Data points (red circles) represent the predicted probabilities for each grade.
3. Results
3.1. Sample characteristics and prevalence of anxiety
A total of 3878 college students, 49.5% of whom were male, were included in this cross-sectional analysis. The overall prevalence of anxiety symptoms was 24.9% (n = 968). The demographic characteristics, health behaviors and physical fitness level distribution of the participants are shown in Table 1. There were significant differences in anxiety detection rates between all categories (chi-square P-values < .05). Notably, anxiety detection rates were higher among females, students in higher grades, students with poorer health behaviors (e.g., poor sleep quality, lack of PA), and students with lower levels of physical fitness.
3.2. Crude dose-response associations between physical fitness components and anxiety
Univariate logistic regression analysis showed a significant crude dose-response relationship between lower levels of physical fitness and higher odds of anxiety (Table 2). Specifically, both CRF and muscle strength showed clear hierarchical associations. Compared with the “excellent” grade as a reference, the crude odds ratio of anxiety increased gradually with the decline of physical fitness grade: for cardiopulmonary function, the odds ratio was 1.65 (good), 2.78 (pass) and 3.92 (fail), respectively (trend P < .001); For muscle strength, the ORs were 1.50 (good), 1.92 (pass), and 2.60 (fail) (trend P < .001). Vital capacity showed a significant but nonlinear pattern, with elevated ORs observed for “good” and “pass” ratings (OR = 1.65 and OR = 2.45, respectively, P < .01), while “fail” ratings did not reach statistical significance (OR = 1.32, P = .098).
3.3. Dose-response patterns of CRF and muscular strength
The negative dose-response relationships of CRF and muscle strength with anxiety risk are visually summarized in Figure 1. The figure shows the increasing trend of anxiety detection rate (left vertical axis) and the corresponding aOR (right vertical axis) with the decline of physical fitness level. Notably, the gradient slope of the cardiorespiratory association was steeper than that of muscle strength, with the “fail” grade showing the highest relative risk. Visual trends clearly support a linear dose-response pattern for both components.
3.4. Key factors associated with anxiety: a summary visualization
To concisely present the relative magnitude of the association strength of the key variables in the final fully adjusted model, a forest plot was drawn (Fig. 2). Remarkably, the chart revealed that poor sleep quality was the single factor most strongly associated with anxiety, with an even greater effect than failing CRF. Staying up late frequently also showed considerable strength of association.
3.5. NonLinear association with vital capacity
In contrast to the linear gradients observed for other physical fitness components, the association between lung capacity and anxiety exhibited nonlinear characteristics (Fig. 3). The visualization pattern showed that the predicted probability of anxiety was highest among individuals with a moderate level of lung capacity (“pass” grade). The risk was low at both the extreme high (“excellent”) and extreme low (“failing”) ends of the spirometric distribution. This is consistent with the result that only the “pass” grade in the regression model showed a significant association. This suggests that there may be a complex relationship between the 2 involving behavioral or psychological adjustment factors. Of note, the dashed line in Figure 3 is a visual interpolation of the categorical model results, not a formal nonlinear model fit.
3.6. Combined effects of CRF and muscular strength
The multiplicative interaction between CRF and muscular strength was formally tested by including their product term in the regression model. The interaction term was not statistically significant (P > .05), indicating no significant synergy or antagonism on the multiplicative scale. Therefore, the heatmap in Figure 4 is presented for descriptive purposes only to visualize the combined categorical risk pattern. The heat map showed that the risk of anxiety was lowest when both components were “excellent.” As the level of either component decreases, the risk increases. The highest risk tier (marked in red) consists of students who “fail” in both CRF and muscle strength. Notably, failure in cardiorespiratory function alone was associated with a higher risk than failure in muscle strength alone, confirming the dominant role of cardiorespiratory function suggested by the steeper gradient in Figure 1.
Figure 4.
Combined effects of cardiorespiratory fitness and muscular strength on anxiety risk. The heatmap visualizes the relative anxiety risk associated with joint categories of cardiorespiratory fitness (rows) and muscular strength (columns). The relative risk score in each cell is derived from an analysis of combined effects, with the “Excellent/Excellent” combination serving as the reference (score = 1.0). The color gradient from green to red indicates increasing risk, categorized as Low (<1.5), Moderate (1.5–2.0), High (2.0–2.5), and Very High (>2.5). The cell values represent the relative risk score. The highest risk is observed for students failing in both fitness components.
3.7. Sensitivity analysis for PA adjustment
To assess potential overadjustment bias, we compared models with and without the IPAQ-measured PA variable. In Model 2a (excluding PA), the aOR for CRF “fail” grade was 2.45 (95% CI: 1.72–3.49), which was higher than the aOR of 2.15 (95% CI: 1.50–3.08) in the fully adjusted Model 3 (including PA). Similar attenuation was observed for muscle strength “fail” grade (aOR from 1.92 to 1.75). These results indicate that our primary Model 3 provides a more conservative estimate of the association and that PA may partially mediate the relationship between physical fitness and anxiety. Detailed results are presented in Table 4.
4. Discussion
This study provides strong evidence for an independent dose-response association between objectively measured physical fitness (especially cardiopulmonary function and muscle strength) and anxiety risk in a large sample of Chinese college students. The provided visualizations reinforce these findings by clearly delineating linear gradients, relative effect sizes, nonlinear patterns of spirometry, and composite risk for multiple physical fitness deficits. The most striking finding was a clear graded negative association: as levels of CRF and muscle strength declined from “excellent” to “failing,” the adjusted odds of anxiety showed a significant and gradual increase. This pattern suggests that higher physical fitness is associated with lower odds of anxiety. However, the association with vital capacity is more complex and nonlinear. The following discussion of potential mechanisms is offered as a theoretical framework for interpreting our findings and generating future hypotheses and should not be interpreted as causal evidence derived from the current cross-sectional design.
4.1. Potential mechanisms for CRF and muscular strength
While causal pathways cannot be determined from our data, the observed associations are biologically plausible and may be hypothetically explained by several mechanisms. The robust dose-response relationship for cardiorespiratory function (CRF) could be associated with enhanced cardiovascular and metabolic efficiency, which implies superior cerebral blood flow and oxygen delivery capacity.[30] This might support the structural and functional integrity of brain regions that play a key role in emotion regulation, such as the prefrontal cortex and hippocampus.[31] Regular aerobic exercise to improve CRF can enhance vascular endothelial function and promote angiogenesis in these key brain regions, which could be a basis for improving stress adaptability.[32] Chronic stress and anxiety are associated with hyperactivity of the hypothalamic-pituitary-adrenalaxis and increased cortisol levels.[33] Regular aerobic exercise that enhances CRF is known to improve the negative feedback sensitivity of the hypothalamic-pituitary-adrenal axis, thereby potentially promoting a more adaptive physiological stress response and reducing anxiety susceptibility.[34,35] Exercise upregulation of neurotrophic factors, especially brain-derived neurotrophic factor (BDNF), is required to establish high CRF.[36] BDNF promotes neural plasticity, synaptic health, and neuronal survival, especially in the hippocampus, which is adversely affected by chronic stress.[37] Furthermore, exercise-mediated BDNF upregulation may modulate neurotransmitter systems, such as serotonin and GABA, which are central to emotion regulation.[38] Moreover, aerobic exercise regulates key neurotransmitter systems associated with mood and anxiety disorders, including serotonin, dopamine, and endocannabinoids, which together promote improved mood regulation and reduced anxiety symptoms.[39,40] The significant association with muscle strength suggests that the benefits extend beyond aerobic exercise. Mechanistically, skeletal muscle has now been recognized as an endocrine organ.[41] Resistance training and strength-building activities stimulate the secretion of muscle factors such as irisin.[42] Some muscle factors have anti-inflammatory properties and could counteract the mild systemic inflammation associated with anxiety and depression morbidity mechanisms.[43] Emerging evidence suggests that resistance training can reduce proinflammatory cytokines (e.g., IL-6, TNF-α) while increasing anti-inflammatory muscle factors, thereby creating a systemic environment that is not conducive to neuroinflammation and anxiety.[44] From a psychological perspective, greater muscle strength may enhance self-efficacy, body image, and overall sense of control.[45] This enhanced confidence can serve as a psychological buffer to cope with the academic and social pressures prevalent in the university environment, thereby reducing anxiety.[46] The sense of mastery and control gained from progressive strength training may be particularly helpful in alleviating the feelings of helplessness often associated with anxiety disorders.[47]
4.2. The nuanced association of vital capacity
The finding of a nonlinear relationship (Fig. 3), that only the “pass” level was significantly associated with higher anxiety risk, challenges the simple dose-response assumption. This inverted U-shaped association suggests that the relationship between lung function and mental health may be mediated by behavioral or psychological factors not captured in our model.[48] The nonlinear pattern for vital capacity, where only the “Pass” grade showed a significant association, is intriguing and somewhat counterintuitive. One speculative explanation, which remains untested in our data, is that students with very low vital capacity (“Fail” grade) might represent a subgroup with distinct behavioral or health characteristics (e.g., existing conditions or different patterns of social engagement),[49–51] which could potentially pre-influence their exposure to anxiety-provoking situations. However, this interpretation is purely hypothesis-generating. Alternatively, students in the “Pass” category may be fully exposed to college pressures but lack superior cardiorespiratory resources as physiological buffers.[52,53] The attenuated association in the extreme group may also reflect collinearity with the overall CRF measure (1000/800m run) already included in the model, as vital capacity is a core component of CRF.[54] Given the cross-sectional design, we cannot distinguish between these possibilities, and future longitudinal studies with more detailed physiological and behavioral measures are needed to clarify this nonlinear relationship.
4.3. Implications of combined fitness deficits
The joint effect analysis (Fig. 4) provides practical insights into targeted interventions. College health programs may prioritize students with deficits in both CRF and muscle strength because they represent the highest risk group. The integration of aerobic exercise and resistance training content into campus health promotion programs may be more effective in preventing anxiety than the promotion of either type of exercise alone.[55] In addition, the visualization pattern in Figure 4 is consistent with the steeper gradient of the CRF in Figure 1, suggesting that improving cardiorespiratory function may lead to greater mental health benefits than focusing on strength alone. From a public health perspective, our findings highlight the importance of moving beyond broad PA recommendations to promote targeted CRF and muscle strength as potential strategies for the primary and secondary prevention of anxiety in adolescents. University policies should consider mandating or incentivizing regular physical fitness assessments and providing tailored exercise prescriptions, similar to academic instruction, to support physical and mental health.[56] Future longitudinal and intervention studies are necessary to confirm these causal pathways and clarify the exact mechanisms involved.
4.4. Limitations
Interpretation of these findings must take into account several limitations. First, the cross-sectional design precludes any clear conclusions about causality or directionality, and the well-documented phenomenon of reverse causality, in which anxiety leads to sedentary behavior and decreased physical fitness, remains a plausible alternative explanation; Future studies should employ a longitudinal design with repeated measures to better clarify the temporal order between physical changes and changes in anxiety symptoms.[57] Second, our assessment of sleep quality, a key covariate, was limited to a binary self-report item (“good/poor”) and lacked the reliability and multidimensional granularity of a validated instrument such as the Pittsburgh Sleep Quality Index; Thus, the observed large effect size of poor sleep (aOR = 2.85) may be exaggerated due to residual confounding or measurement error bias, and this strong association is even stronger than main exposure we are interested in, emphasizing that our model captures a wide range of unmodified sleep disturbance signals and should be interpreted with caution; Future studies should employ standardized sleep assessment tools to accurately distinguish the independent effects of sleep and physical health on anxiety. Third, as discussed in Section 3.7, adjusting for PA may represent an overadjustment for the underlying mediator, and our sensitivity analysis suggests that excluding PA from the model results in a slightly stronger association for CRF and muscle strength, indicating that our primary model provides more conservative estimates; the distinction between confounding and mediating should be carefully considered in future longitudinal analyses. Fourth, although we adjusted for various potential confounders, we could not completely exclude residual confounders (e.g., genetic factors, personality traits such as neuroticism, or unmeasured social stressors). Fifth, despite the use of a validated self-rating anxiety scale, reporting bias may still exist, and the inclusion of objective biomarkers of chronic stress (such as hair cortisol concentrations) or the use of structured clinical interviews in future studies could enhance the measurement of outcomes.[58] Finally, the speculative interpretations regarding the nonlinear vital capacity findings are not directly testable with our cross-sectional data and should be interpreted with caution.
5. Conclusion
This study demonstrates that objectively measured physical fitness, particularly CRF and muscle strength, has an independent, negative dose-response association with anxiety risk in Chinese college students. These associations appear visually as clear linear gradients. The research has further revealed a nonlinear pattern for vital capacity and demonstrated that simultaneous deficits in CRF and muscle strength are associated with the greatest risk of anxiety. These findings are biologically plausible, with hypothetical mechanisms involving neuroendocrine regulation, neuroplasticity, and inflammatory pathways. The complex association with vital capacity highlights the need for a detailed understanding of how different components of physical fitness are associated with mental health. Overall, this study highlights the value of objective physical fitness assessment as a tool to identify at-risk students and points out that targeted physical fitness enhancement is associated with lower anxiety risk and represents a promising component of campus mental health promotion strategies. However, causality cannot be inferred from this cross-sectional design, and reverse causation remains a possible explanation.
Acknowledgments
We thank the participants and colleagues for their contributions to this effort.
Author contributions
Conceptualization: Mengchan Gao, Caizhu Gao.
Data curation: Mengchan Gao, Hang Yin, Caizhu Gao.
Investigation: Hang Yin, Caizhu Gao, Huidong Wang.
Software: Hang Yin.
Formal analysis: Caizhu Gao.
Funding acquisition: Caizhu Gao, Huidong Wang.
Methodology: Caizhu Gao, Huidong Wang.
Project administration: Caizhu Gao.
Validation: Huidong Wang.
Visualization: Huidong Wang.
Writing – original draft: Mengchan Gao, Hang Yin, Huidong Wang.
Writing – review & editing: Mengchan Gao, Huidong Wang.
Abbreviations:
- aOR
- adjusted odds ratio
- BDNF
- brain-derived neurotrophic factor
- CI
- confidence interval
- CRF
- cardiorespiratory fitness
- PA
- physical activity
This study was supported by the Education Project of Liaoning Provincial Social Science Planning Fund in 2024 (L24BED001).
This study was approved by the Ethics Committee of the School of PE at Changzhou Vocational Institute of Industry Technology and conducted in accordance with the Helsinki Declaration. All participants have obtained written informed consent to participate in this study.
The authors have no conflicts of interest to declare.
The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
How to cite this article: Gao M, Yin H, Gao C, Wang H. Dose-response associations between physical fitness components and anxiety in Chinese university students: A focus on cardiorespiratory fitness and vital capacity. Medicine 2026;105:29(e49804).
Contributor Information
Mengchan Gao, Email: gaocaizhu_ku_edu@163.com.
Hang Yin, Email: yinhang9227@163.com.
Caizhu Gao, Email: gaocaizhu_ku_edu@163.com.
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