Abstract
Background
Medical students face elevated health risks due to academic stress and a sedentary lifestyle. Given the geographical and ethnic diversity of Southwest China, it is particularly important to understand the factors associated with physical fitness and bone health in this population. However, evidence regarding the physical fitness and bone health of medical students in this region remains limited.
Methods
This cross-sectional study included 334 medical students (99 males and 235 females; aged 17–24 years) from Southwest China, comprising both Han and ethnic minority students. Body mass index (BMI) was calculated, a composite physical fitness index (PFI) was derived from six standardized fitness tests, and calcaneal bone mineral density (BMD) was assessed using quantitative ultrasound. Gender-stratified analyses, partial correlations adjusted for age and BMI, and multiple regression models were performed.
Results
Male students showed higher prevalences of overweight/obesity (18.18% vs. 5.96%) and osteopenia (6.06% vs. 2.98%) than females. A nonlinear association was observed between BMI and physical fitness, with normal-weight students demonstrating the highest PFI. No significant differences were observed between Han and ethnic minority students in physical fitness or bone health indicators. Muscle strength emerged as the strongest predictor of BMD, with bent-leg sit-ups predicting BMD in females (B = 0.290, p = 0.020) and pull-ups predicting BMD in males (B = 0.543, p = 0.014).
Conclusions
Physical fitness and bone health differed according to sex and BMI status among medical students in Southwest China. Muscle strength was identified as the most important modifiable factor associated with BMD. These findings support the development of sex-specific and culturally appropriate health promotion strategies to improve musculoskeletal health among ethnically diverse university populations.
Keywords: Medical students, Physical fitness, Bone mineral density, Muscle strength, Body mass index, Health promotion, Ethnicity
Background
The global insufficiency of physical activity represents a significant public health challenge, particularly among adolescents and young adults. In China, over 80% of students fail to meet recommended daily physical activity levels [1, 2], contributing to increased risks of obesity, metabolic disorders, and poor mental health [3, 4]. Medical students are especially vulnerable due to prolonged academic stress, sedentary behaviors, and limited time for recreational physical activity [5–10]. These factors may compromise both physical fitness and bone health, a critical concern given that early adulthood is a pivotal period for peak bone mass accumulation [11–13]. Health disparities may be further influenced by ethnic background, socioeconomic conditions, and culturally shaped lifestyle factors [14–16]. Southwest China is characterized by substantial ethnic diversity, providing a valuable setting for investigating physical fitness and bone health among medical students from different ethnic backgrounds. Although previous studies have examined physical fitness or bone mineral density (BMD) in general student populations, few have focused on medical students in ethnically diverse regions of China, and none have comprehensively assessed the interrelationships among body mass index (BMI), physical fitness, and BMD within this population. This study aimed to address this gap by investigating physical fitness and bone health among medical students in Southwest China. By examining sex-specific patterns, potential ethnic differences, and key modifiable factors, this study seeks to provide evidence for targeted health promotion strategies in ethnically diverse populations.
Objective
This study aimed to examine physical fitness and bone health among medical students in Southwest China, with particular attention to sex-specific differences, potential ethnic variations, and key modifiable factors. The study addressed the following research questions:
What are the patterns of physical fitness and BMD among medical students in Southwest China, and do significant gender differences exist?
To what extent does BMI correlate with physical fitness, and is this relationship linear or nonlinear?
Which components of physical fitness most strongly predict bone health in male and female students?
Methods
Study design and population
This cross-sectional study was conducted at Youjiang Medical University for Nationalities (Baise, Guangxi) from September to December 2024. Cluster sampling recruited 334 undergraduate medical students aged 17–24 years. Inclusion criteria: ability to complete fitness assessments and written informed consent. Exclusion criteria: severe cardiopulmonary disease, exercise contraindications, or fracture within 3 months. All tests are performed by trained personnel, and key indicators are measured 3 times to take the maximum value to reduce errors. The final sample comprised 99 males and 235 females. Ethnic minority students accounted for 65.57% of the cohort, including 56.59% Zhuang, 5.69% Yao, 2.10% Mulao, 0.90% Dong, and 0.30% Miao students, while 34.43% were Han students. This study was conducted in accordance with the Declaration of Helsinki. The study was approved by the Ethics Committee of Youjiang Medical University for Nationalities (approval number: 2024070102). All participants provided informed consent.
Anthropometric measurements and BMI grouping
Height (m) and weight (kg) were measured without shoes using a height and weight tester (LK-T1016, Lingkang, China). BMI was calculated using the formula BMI = weight/height2 (kg/m2). BMI was categorized per the Chinese adult BMI classification criteria (WS/T 428–2013): underweight (< 18.5), normal weight (18.5–23.9), overweight (24.0-27.9), and obese (≥ 28.0). (National Health and Family Planning Commission of the People’s Republic of China, 2013)
Physical fitness test
The physical fitness test follows the National Student Physical Fitness Standards protocol and includes vital capacity, 50-meter sprint, sit-ups, standing long jump, 800/1000-meter run, pull-ups, and curl-ups [17, 18]. For males, the six indicators included vital capacity, 50-meter sprint, sit-and-reach, 1000-meter run, standing long jump, and pull-ups; for females, pull-ups were replaced with bent-leg sit-ups. According to the National Student Physical Health Standard (Revised 2014), pull-ups are used to assess upper-body muscular endurance in male students, whereas bent-leg sit-ups are used to assess core muscular endurance in female students. Therefore, these sex-specific tests were applied in accordance with the national protocol rather than for direct comparison between sexes.
Vital capacity
A spirometer (LK-T2016, Lingkang, China) was used to measure vital capacity. Students place their mouth into the mouthpiece, stand in front of the instrument, and hold the handle correctly. Then, they press a button, take a deep breath, and exhale completely. The instrument automatically calculates the maximum spirometry volume. This is measured three times, and the maximum value (in mL) is recorded. The vital capacity-to-weight ratio is calculated by dividing vital capacity by body weight.
50-meter sprint
Use a 50-meter running tester (LK-T6026, Lingkang, China) on a flat track to measure time twice and record the best time (in seconds).
Sit-and-reach test
This study used a sit-and-reach test (LK-T5016, Lingkang, China) to assess low back flexibility. Each participant sat barefoot on the test apparatus with their knees straight. The maximum reach (in cm) with both arms extended was recorded three times. The best result was recorded.
800/1000-m run
This study was conducted using a middle- and long-distance running tester (LK-T3016, Lingkang, China). Each student stood at the starting line and was asked to run either 800–1000 m as quickly as possible, using a digital stopwatch to record their time (in seconds). All girls participated in the 800-meter run, while boys participated in the 1000-meter run.
Standing long jump
This study was conducted using a standing long jump tester (LK-T6016, Lingkang, China). Measure the distance from the take offline to the point where your heels land (in centimeters) three times and record the best result [19].
Pull-ups
Pull-ups are used to assess upper limb muscle strength. This study used a pull-up tester (LK-T5056, Lingkang, China) for testing. The test is scored based on the number of pull-ups completed. The subject jumps up and pulls against a horizontal bar with both hands. After standing still, the subject simultaneously pulls against the bar with both arms. All male participants followed standard technique.
Bent-leg sit-up
This study used the sit-up tester for testing (LK-T5026, Lingkang, China). Each participant was instructed to lie on a mat with their knees bent 90 degrees, raise their upper body, and touch their elbows to their knees. The number of sit-ups completed in one minute was recorded. All female participants followed standard technique.
Physical Fitness Index (PFI)
Based on the results of all tests, a sex-specific composite Physical Fitness Index (PFI) was generated and defined as the sum of the standardized values (Z scores) of all six fitness tests. To account for sex-related differences in physical performance, each indicator was standardized separately for males and females using the mean (µ) and standard deviation (σ) derived from the corresponding sex within the study population. Specifically, each indicator was converted into a sex-specific Z score according to the formula Z = (X − µ) / σ. The PFI was calculated as the sum of the six Z scores. For better comparability, the Z scores for the 50-meter sprint and the 800/1000-meter run were reverse-coded so that higher values consistently indicated better physical performance.
Bone density test
Calcaneal bone mineral density (BMD) was assessed using quantitative ultrasound because of its good reproducibility and suitability for large-scale population screening. Measurements were performed using an ultrasound bone densitometer (OSTEOKJ3000; Nanjing Kejin Industrial Co., Ltd., Nanjing, China), which was calibrated daily according to the manufacturer’s instructions. The device generated T-scores, Z-scores, and strength index (SI) values. According to the World Health Organization (WHO) criteria, participants were classified as having normal bone mass (T-score ≥ − 1.0), osteopenia (− 2.5 < T-score < − 1.0), or osteoporosis (T-score ≤ − 2.5). Because part of the study population was younger than 20 years, age-matched Z-scores were additionally considered in accordance with the recommendations of the International Society for Clinical Densitometry (ISCD), and participants with Z-scores below − 2.0 were classified as having low bone density for age [20, 21]. The strength index (SI), an ultrasound-derived parameter reflecting overall bone quality, was further used for exploratory analyses [22]. Based on the built-in reference database of the ultrasound device, participants were categorized into low, normal, and good bone status groups. All measurements were performed by the same operator under standardized environmental conditions (25 ± 1 °C), and the day-to-day coefficient of variation of the instrument was less than 3%.
Data analysis
Data were analyzed using SPSS version 27.0 (IBM Corp., Armonk, NY, USA). Descriptive statistics were expressed as mean ± standard deviation (SD) for continuous variables and frequencies for categorical variables. Normality was assessed using the Shapiro–Wilk test. Comparisons between two groups were performed using the independent-samples t test, whereas differences among multiple groups were analyzed using one-way analysis of variance (ANOVA). When significant overall differences were detected, Tukey’s post hoc multiple comparison test was applied to determine pairwise differences between groups. Categorical variables were compared using the chi-square test. Partial correlation analyses adjusted for age and BMI were used to examine associations between variables, and multiple linear regression analysis was performed to identify predictors of BMD. Statistical significance was defined as a two-sided P value < 0.05.
Results
Basic characteristics of participants
Participants had a mean age of 18.97 ± 1.08 years, BMI 20.41 ± 2.85 kg/m², PFI 0.00 ± 2.84, and BMD Z-score 1.52 ± 1.48. Males had higher overweight/obesity rates (18.18% vs. 5.96%) and osteopenia prevalence (6.06% vs. 2.98%) than females (Table 1). No osteoporosis cases were identified.
Table 1.
Basic characteristics of the study participants (n = 334)
| Variables | Total(n = 334) | Female(n = 235) | Male(n = 99) |
|---|---|---|---|
| Age (years) | |||
| Mean ± SD | 18.97 ± 1.08 | 18.81 ± 1.03 | 19.36 ± 1.12 |
| Range | 17–24 | 17–21 | 17–24 |
| ≤ 18 |
107 (32.04%) |
84 (35.74%) |
23 (23.23%) |
| 20 |
95 (28.44%) |
57 (24.26%) |
38 (38.38%) |
| 21 |
14 (4.19%) |
6 (2.55%) |
8 (8.08%) |
| ≥ 22 |
2 (0.60%) |
0 (0.00%) |
2 (2.02%) |
| Ethnicity | |||
| Zhuang |
189 (56.59%) |
134 (57.02%) |
55 (55.56%) |
| Han |
115 (34.43%) |
79 (33.62%) |
36 (33.36%) |
| Yao |
19 (5.69%) |
15 (6.38%) |
4 (4.04%) |
| Mulao |
7 (2.1%) |
4 (1.7%) |
3 (3.03%) |
| Others* |
4 (1.2%) |
3 (1.28%) |
1 (1.01%) |
| Height (cm) | |||
| Mean ± SD | 161.03 ± 7.64 | 157.53 ± 4.50 | 169.32 ± 7.15 |
| Range | 142.0-190.0 | 142.0-169.0 | 150.0-190.0 |
| Weight (kg) | |||
| Mean ± SD | 53.14 ± 9.90 | 49.79 ± 6.89 | 61.09 ± 11.36 |
| Range | 38.6-108.7 | 38.6–92.1 | 41.5-108.7 |
| Body mass index (kg/m2) | |||
| Mean ± SD | 20.41 ± 2.85 | 20.04 ± 2.46 | 21.27 ± 3.49 |
| Range | 15.34–38.06 | 15.43–35.10 | 15.34–38.06 |
| Underweight |
79 (23.65%) |
13 (5.53%) |
9 (9.09%) |
| Normal |
227 (67.96%) |
208 (88.51%) |
72 (72.73%) |
| Overweight |
22 (6.59%) |
12 (5.11%) |
14 (14.14%) |
| Obesity |
6 (1.80%) |
2 (0.85%) |
4 (4.04%) |
| Physical fitness index (PFI) | |||
| Mean ± SD | 0.00 ± 2.84 | 0.00 ± 3.74 | 0.00 ± 3.91 |
| Range | -8.13-9.23 | -9.56-10.27 | -9.85-10.09 |
| Bone mineral density (T-score) | |||
| Mean ± SD | 1.52 ± 1.48 | 1.66 ± 1.45 | 1.18 ± 1.51 |
| Range | -1.9-6.2 | -1.9-6.2 | -1.9-5.9 |
| Normal ( > − 1) |
321 (96.10%) |
228 (97.02%) |
93 (93.94%) |
| Osteopenia (− 1–2.4) |
13 (3.89%) |
7 (2.98%) |
6 (6.06%) |
| Osteoporosis ( ≤ − 2.5) |
0 (0.00%) |
0 (0.00%) |
0 (0.00%) |
| Bone mineral density (Z-score) | |||
| Mean ± SD | 1.52 ± 1.48 | 1.66 ± 1.45 | 1.19 ± 1.51 |
| Range | -1.9-6.2 | -1.9-6.2 | -1.9-6.0 |
| Bone mineral density (Strength Index) | |||
| Mean ± SD | 111.15 ± 16.32 | 108.27 ± 14.52 | 117.98 ± 18.30 |
| Range | 72–175 | 72–153 | 81–175 |
* Others include Dong (n = 3) and Miao (n = 1)
Comparison of physical fitness and bone health between Han and Ethnic minority students
A comparison was made between Han and ethnic minority students in terms of physical fitness and skeletal health indicators. No significant differences were observed between the two groups in BMI, PFI, T-score, Z-score, strength index, overweight/obesity prevalence, or the prevalence of low bone density for age (all P > 0.05) (Table 2).
Table 2.
Comparison of physical fitness and bone health between Han and Ethnic minority students
| Variable | Han (n = 115) | Ethnic Minority (n = 219) | P |
|---|---|---|---|
| BMI (kg/m²) | 20.44 ± 3.13 | 20.39 ± 2.71 | 0.877 |
| PFI | 0.22 ± 3.67 | −0.12 ± 3.77 | 0.427 |
| T-score | 1.69 ± 1.54 | 1.44 ± 1.45 | 0.144 |
| Z-score | 1.70 ± 1.54 | 1.44 ± 1.45 | 0.144 |
| Strength Index | 113.51 ± 17.52 | 109.98 ± 15.58 | 0.07 |
| Overweight/Obesity, n (%) | 10 (8.7%) | 18 (8.2%) | 0.59 |
| Low bone density for age (Z-score < − 2.0), n (%) | 5 (4.3%) | 11 (5.0%) | 0.57 |
Analysis of health-related physical fitness scores by BMI
Overweight/obese students performed better in vital capacity and 50-meter sprint but worse in flexibility (sit-and-reach), lower limb power (standing long jump), and endurance (800/1000-m run) compared to normal-weight peers (Tables 3 and 4). Normal-weight students had higher PFI than underweight or overweight/obese groups, indicating a nonlinear U-shaped relationship. BMI-fitness correlations were stronger in males, particularly for pull-ups (upper-body strength) (p < 0.001).
Table 3.
Differences in health-related physical fitness among female groups stratified by BMI category [mean (SD)] (n = 235)
| Items | Underweight (1) | Normal (2) | Overweight (3) | Obesity (4) | F | P | Post Hoc Multiple Comparison | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1–2 | 1–3 | 1–4 | 2–3 | 2–4 | 3–4 | |||||||
| Vital capacity | -0.88 (0.61) | 0.01 (0.99) | 0.58 (0.97) | 1.00 (0.63) | 5.737 | < 0.001 | 0.008 | 0.001 | 0.055 | 0.199 | 0.480 | 0.942 |
| Vital capacity weight index | 0.31 (0.62) | 0.01 (1.00) | -0.18 (0.97) | -2.33 (0.07) | 4.371 | 0.005 | 0.715 | 0.598 | 0.003 | 0.911 | 0.005 | 0.022 |
| 50-m sprint | -0.16 (0.83) | 0.04 (0.99) | -0.15 (1.18) | -1.94 (0.00) | 2.871 | 0.037 | 0.898 | 1.000 | 0.086 | 0.918 | 0.027 | 0.086 |
| Sit and reach | -0.39 (1.14) | 0.04 (0.98) | -0.07 (0.96) | -1.40 (1.99) | 1.781 | 0.152 | 0.416 | 0.851 | 0.546 | 0.979 | 0.177 | 0.304 |
| Standing long jump | 0.14 (0.55) | 0.02 (1.01) | -0.17 (1.16) | -1.52 (0.23) | 2.148 | 0.095 | 0.975 | 0.867 | 0.128 | 0.920 | 0.133 | 0.287 |
| 800-m run | -0.14 (0.79) | -0.00 (1.02) | 0.06 (0.90) | -1.02 (0.05) | 0.797 | 0.497 | 0.956 | 0.997 | 0.422 | 0.996 | 0.483 | 0.491 |
| Bent-leg sit-up | -0.18 (0.71) | 0.04 (1.02) | -0.35 (0.84) | -1.01 (1.17) | 1.460 | 0.226 | 0.859 | 0.974 | 0.693 | 0.541 | 0.446 | 0.822 |
| PFI | -0.15 (2.26) | 0.15 (3.69) | -0.87 (4.46) | -9.22 (0.47) | 4.561 | 0.004 | 0.971 | 0.958 | < 0.001 | 0.865 | 0.002 | < 0.001 |
BMI=Body mass index; PFI=physical fitness index
Table 4.
Differences in health-related physical fitness among male groups stratified by BMI category [mean (SD)] (n = 99)
| Items | Underweight (1) | Normal (2) | Overweight (3) | Obesity (4) | F | P | Post Hoc Multiple Comparison | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1–2 | 1–3 | 1–4 | 2–3 | 2–4 | 3–4 | |||||||
| Vital capacity | -0.85 (0.63) | 0.03 (0.98) | 0.38 (1.07) | 0.12 (0.94) | 3.080 | 0.031 | 0.058 | 0.019 | 0.351 | 0.585 | 0.998 | 0.961 |
| Vital capacity weight index | 0.58 (0.68) | 0.18 (0.86) | -0.75 (0.89) | -1.96 (0.95) | 13.149 | < 0.001 | 0.557 | 0.002 | < 0.001 | 0.002 | < 0.001 | 0.068 |
| 50-m sprint | -0.33 (0.82) | 0.15 (0.91) | -0.09 (1.14) | -1.68 (0.90) | 5.300 | 0.002 | 0.471 | 0.931 | 0.086 | 0.817 | 0.001 | 0.019 |
| Sit and reach | -0.40 (1.17) | 0.03 (0.97) | 0.00 (1.07) | 0.42 (0.89) | 4.630 | 0.005 | 0.618 | 0.781 | 0.523 | 1.000 | 0.871 | 0.882 |
| Standing long jump | -0.05 (0.65) | 0.15 (0.97) | -0.32 (0.97) | -1.53 (1.05) | 0.738 | 0.532 | 0.933 | 0.909 | 0.051 | 0.332 | 0.004 | 0.115 |
| 1000-m run | -0.16 (0.85) | 0.10 (0.95) | -0.07 (1.15) | -1.22 (1.11) | 2.433 | 0.070 | 0.872 | 0.996 | 0.280 | 0.930 | 0.049 | 0.171 |
| Pull-up | -0.33 (0.73) | 0.21 (0.97) | -0.46 (0.93) | -1.45 (0.12) | 6.036 | < 0.001 | 0.359 | 0.986 | 0.192 | 0.068 | 0.004 | 0.246 |
| PFI | -0.69 (2.88) | 0.83 (3.55) | -1.70 (3.96) | -7.42 (2.65) | 8.391 | < 0.001 | 0.617 | 0.910 | 0.011 | 0.076 | < 0.001 | 0.026 |
BMI=Body mass index; PFI=physical fitness index
Analysis of health-related physical fitness scores by BMD
As shown in Tables 5 and 6, the pattern of differences in health-related physical fitness after stratification by BMD status differed from that observed when stratified by BMI. Among females, those with normal and good BMD performed significantly better than those with low BMD in the 50-meter run, sit-and-reach, standing long jump, sit-up, and PFI (p < 0.05). In males, however, no statistically significant differences were observed between BMD groups in any of the physical fitness measures.
Table 5.
Differences in health-related physical fitness among female groups stratified by BMD category [mean (SD)] (n = 235)
| Items | Low (1) | Normal (2) | Good (3) | F | P | Post Hoc Multiple Comparison | ||
|---|---|---|---|---|---|---|---|---|
| 1–2 | 1–3 | 2–3 | ||||||
| BMI | 18.69 (1.76) | 20.52 (2.49) | 20.09 (2.48) | 4.345 | 0.014 | 0.011 | 0.030 | 0.549 |
| Vital capacity | -0.18 (0.85) | -0.10 (0.97) | 0.05 (1.03) | 0.766 | 0.466 | 0.953 | 0.582 | 0.651 |
| Vital capacity weight index | 0.08 (0.65) | -0.20 (0.90) | 0.04 (1.06) | 1.134 | 0.323 | 0.392 | 0.995 | 0.326 |
| 50-m sprint | -0.22 (0.83) | -0.36 (0.86) | 0.13 (1.03) | 5.052 | 0.007 | 0.851 | 0.257 | 0.009 |
| Sit and reach | 0.04 (1.16) | -0.38 (0.93) | 0.10 (0.98) | 4.696 | 0.010 | 0.982 | 0.169 | 0.019 |
| Standing long jump | -0.28 (0.74) | -0.33 (0.85) | 0.12 (1.04) | 4.058 | 0.019 | 0.244 | 0.964 | 0.013 |
| 800-m run | -0.15 (0.89) | -0.02 (1.54) | 0.03 (0.82) | 0.313 | 0.731 | 0.868 | 0.717 | 0.962 |
| Bent-leg sit-up | -0.36 (0.80) | -0.35 (0.79) | 0.14 (1.04) | 6.0980 | 0.003 | 1.000 | 0.038 | 0.003 |
| PFI | -0.90 (3.04) | -1.64 (3.47) | 0.56 (3.77) | 7.122 | < 0.001 | 0.718 | 0.187 | 0.001 |
BMD=Bone Mineral Density; BMI=Body mass index; PFI=physical fitness index
Table 6.
Differences in health-related physical fitness among male groups stratified by BMD category [mean (SD)] (n = 99)
| Items | Low (1) | Normal (2) | Good (3) | F | P | Post Hoc Multiple Comparison | ||
|---|---|---|---|---|---|---|---|---|
| 1–2 | 1–3 | 2–3 | ||||||
| BMI | 21.35 (2.98) | 21.71 (4.14) | 21.19 (3.44) | 0.124 | 0.884 | 0.979 | 0.995 | 0.874 |
| Vital capacity | -0.17 (0.70) | -0.20 (0.71) | 0.04 (1.06) | 0.392 | 0.677 | 0.998 | 0.893 | 0.705 |
| Vital capacity weight index | -0.50 (0.86) | -0.43 (0.77) | 0.10 (1.02) | 2.250 | 0.111 | 0.989 | 0.391 | 0.183 |
| 50-m sprint | -0.51 (1.18) | -0.24 (0.84) | 0.07 (1.01) | 1.238 | 0.295 | 0.867 | 0.420 | 0.548 |
| Sit and reach | -0.49 (1.27) | -0.07 (0.56) | 0.04 (1.04) | 0.054 | 0.948 | 0.708 | 0.481 | 0.920 |
| Standing long jump | -0.12 (1.11) | -0.04 (0.74) | 0.01 (1.04) | 0.712 | 0.493 | 0.987 | 0.955 | 0.983 |
| 1000-m run | -0.55 (0.13) | -0.03 (0.83) | 0.04 (1.05) | 0.823 | 0.442 | 0.593 | 0.412 | 0.967 |
| Pull-up | -0.62 (0.82) | -0.31 (0.81) | 0.09 (1.02) | 1.972 | 0.145 | 0.825 | 0.268 | 0.364 |
| PFI | -2.79 (3.74) | -1.13 (3.06) | 0.35 (3.98) | 2.201 | 0.116 | 0.693 | 0.186 | 0.407 |
BMD=Bone Mineral Density; BMI=Body mass index; PFI=physical fitness index
Correlations between BMD and anthropometric measurements and physical fitness adjusted for age and BMI
Partial correlations (adjusted for age and BMI) showed female BMD (T-score, Z-score, strength index) correlated positively with vital capacity-to-weight index, 50-meter sprint, sit-and-reach, sit-ups, and PFI (p < 0.05) (Table 7). Male BMD correlated with pull-ups and PFI (p < 0.01) (Table 8).
Table 7.
Associations between T-score, Z-score, and strength index and anthropometry and physical health in females, adjusted for age and BMI (n = 235)
| Height | Weight | Vital capacity | Vital capacity weight index | 50-m sprint | Sit and reach | Standing long jump | 800-m run | Bent-leg sit-up | PFI | |
|---|---|---|---|---|---|---|---|---|---|---|
| T-score | ||||||||||
| r | -0.020 | -0.007 | 0.122 | 0.139 | 0.235 | 0.212 | 0.060 | 0.076 | 0.258 | 0.267 |
| p | 0.765 | 0.911 | 0.063 | 0.034 | < 0.001 | 0.001 | 0.365 | 0.248 | < 0.001 | < 0.001 |
| Z-score | ||||||||||
| r | -0.017 | -0.005 | 0.123 | 0.139 | 0.236 | 0.215 | 0.059 | 0.077 | 0.259 | 0.268 |
| p | 0.796 | 0.945 | 0.061 | 0.034 | < 0.001 | 0.001 | 0.370 | 0.239 | < 0.001 | < 0.001 |
| Strength Index | ||||||||||
| r | -0.032 | -0.020 | 0.122 | 0.145 | 0.230 | 0.212 | 0.072 | 0.066 | 0.260 | 0.268 |
| p | 0.629 | 0.758 | 0.063 | 0.027 | < 0.001 | 0.001 | 0.276 | 0.312 | < 0.001 | < 0.001 |
PFI=physical fitness index
Table 8.
Associations between T-score, Z-score, and Strength Index and Anthropometric and Physical Health in Males, Adjusted for Age and BMI (n = 99)
| Height | Weight | Vital capacity | Vital capacity weight index | 50-m sprint | Sit and reach | Standing long jump | 1000-m run | Pull-up | PFI | |
|---|---|---|---|---|---|---|---|---|---|---|
| T-score | ||||||||||
| r | -0.12 | -0.093 | 0.037 | 0.159 | 0.194 | 0.157 | 0.186 | 0.026 | 0.373 | 0.276 |
| p | 0.243 | 0.365 | 0.722 | 0.119 | 0.056 | 0.124 | 0.068 | 0.803 | < 0.001 | 0.006 |
| Z-score | ||||||||||
| r | -0.122 | -0.096 | 0.038 | 0.161 | 0.193 | 0.16 | 0.193 | 0.029 | 0.379 | 0.281 |
| p | 0.232 | 0.351 | 0.712 | 0.115 | 0.058 | 0.118 | 0.059 | 0.779 | < 0.001 | 0.005 |
| Strength Index | ||||||||||
| r | -0.106 | -0.082 | 0.043 | 0.162 | 0.197 | 0.169 | 0.203 | 0.049 | 0.378 | 0.292 |
| p | 0.300 | 0.422 | 0.674 | 0.114 | 0.054 | 0.099 | 0.046 | 0.637 | < 0.001 | 0.004 |
PFI=physical fitness index
Predictors of BMD
The results of stepwise regression analysis between the T-score, Z-score, and Strength Index and anthropometric and physical fitness variables for the female group are shown in Table 9 Bent-leg sit-up (B = 0.290, p = 0.020) was the main component predicting the T-score. The average explanatory power of the T-score constructed from the vital capacity weight index, 50-m sprint, sit and reach, bent-leg sit-up, and PFI was 7.2% (R²). Bent-leg sit-up (B = 0.292, p = 0.019) was the main component predicting the Z-score. The average explanatory power of the Z-score constructed from the vital capacity weight index, 50-m sprint, sit and reach, bent-leg sit-up, and PFI was 7.2% (R²). Bent-leg sit-up (B = 2.929, p = 0.019) was the main component predicting the Strength Index. The average explanatory power of the Strength Index constructed by vital capacity weight index, 50-m sprint, sit and reach, bent-leg sit-up, and PFI was 7.2% (R2).
Table 9.
Associations between T-score, Z-score, and strength index and anthropometry and physical health in female students, adjusted for age and BMI (n = 235)
| B | SE | β | t | p Value | R 2 | |
|---|---|---|---|---|---|---|
| T-score | ||||||
| (Constant) | 1.660 | 0.092 | 18.044 | < 0.001 | 0.072 | |
| Vital capacity weight index | -0.008 | 0.123 | -0.005 | -0.062 | 0.951 | |
| 50-m sprint | 0.124 | 0.156 | 0.086 | 0.798 | 0.425 | |
| Sit and reach | 0.088 | 0.157 | 0.061 | 0.564 | 0.573 | |
| Bent-leg sit-up | 0.290 | 0.124 | 0.200 | 2.345 | 0.020 | |
| PFI | -0.007 | 0.066 | -0.019 | -0.109 | 0.913 | |
| Z-score | ||||||
| (Constant) | 1.664 | 0.092 | 18.068 | < 0.001 | 0.072 | |
| Vital capacity weight index | -0.008 | 0.123 | -0.006 | -0.067 | 0.947 | |
| 50-m sprint | 0.123 | 0.156 | 0.085 | 0.792 | 0.429 | |
| Sit and reach | 0.093 | 0.157 | 0.064 | 0.590 | 0.556 | |
| Bent-leg sit-up | 0.292 | 0.124 | 0.201 | 2.355 | 0.019 | |
| PFI | -0.007 | 0.066 | -0.019 | -0.111 | 0.912 | |
| Strength Index | ||||||
| (Constant) | 108.268 | 0.922 | 117.408 | < 0.001 | 0.072 | |
| Vital capacity weight index | 0.016 | 1.232 | 0.001 | 0.013 | 0.989 | |
| 50-m sprint | 1.064 | 1.559 | 0.073 | 0.683 | 0.495 | |
| Sit and reach | 0.932 | 1.570 | 0.064 | 0.594 | 0.553 | |
| Bent-leg sit-up | 2.929 | 1.241 | 0.202 | 2.361 | 0.019 | |
| PFI | -0.051 | 0.663 | -0.013 | -0.076 | 0.939 | |
PFI=physical fitness index
Table 10 shows the results of stepwise regression analyses of the T-score, Z-score, and Strength Index with anthropometric and physical fitness variables for the malen group. Pull-up (B = 0.543, p = 0.014) was the primary component predicting the T-score. The average explanatory power of the T-score constructed from pull-up and PFI was 11.5% (R²). Pull-up (B = 0.553, p = 0.012) was the primary component predicting the Z-score. The average explanatory power of the Z-score constructed from pull-up and PFI was 11.8% (R²). Pull-up (B = 6.456, p = 0.025) was the primary component predicting the Strength Index. The average explanatory power of the Strength Index constructed from standing long jump, pull-up, and PFI was 12.2% (R²).
Table 10.
Associations between T-score, Z-score, and strength index and anthropometric and physical health in males, adjusted for age and BMI (n = 99)
| B | SE | β | t | p Value | R 2 | |
|---|---|---|---|---|---|---|
| T-score | ||||||
| (Constant) | 1.184 | 0.145 | 8.188 | < 0.001 | 0.115 | |
| Pull-up | 0.543 | 0.217 | 0.359 | 2.507 | 0.014 | |
| PFI | -0.011 | 0.055 | -0.028 | -0.195 | 0.846 | |
| Z-score | ||||||
| (Constant) | 1.193 | 0.144 | 8.277 | < 0.001 | 0.118 | |
| Pull-up | 0.553 | 0.216 | 0.366 | 2.559 | 0.012 | |
| PFI | -0.012 | 0.055 | -0.030 | -0.209 | 0.835 | |
| Strength Index | ||||||
| (Constant) | 117.980 | 1.75 | 67.405 | < 0.001 | 0.122 | |
| Standing long jump | 0.220 | 2.949 | 0.012 | 0.074 | 0.941 | |
| Pull-up | 6.456 | 2.830 | 0.353 | 2.282 | 0.025 | |
| PFI | -0.054 | 1.027 | -0.011 | -0.052 | 0.958 | |
PFI=physical fitness index
Discussion
This study examined the relationship between body mass index (BMI), physical fitness, and bone health among medical students in southwest China. The results showed that physical fitness and bone health varied by gender and BMI status. No significant differences were observed between Han Chinese and ethnic minority students in terms of BMI, physical fitness, or bone health indicators. These findings suggest that, in this study population, the influence of ethnicity on the aforementioned outcomes was relatively limited, while gender and BMI appear to be more important determinants.
Our study found that overweight/obese students performed better in indicators such as vital capacity and 50-meter sprint but performed significantly worse in flexibility (sit-and-reach), lower limb explosive power (standing long jump) and endurance (800-meter/1000-m run). This is highly consistent with the research results of Chen et al. and Mak et al. on Chinese college students [23, 24]. BMI and physical fitness have a nonlinear U-shaped relationship, and the comprehensive PFI of the normal weight group is significantly better than that of the underweight or overweight/obese group. This “double-edged sword” effect can be explained by the balance between mechanical load and metabolic efficiency: weight gain can increase the basic load of muscle work (such as the respiratory muscle strength on which lung capacity depends), but excess body fat will increase exercise resistance and reduce flexibility and endurance [25–27].
From the perspective of gender differences, the correlation between BMI and physical fitness is significantly stronger for boys than for girls, which is consistent with the results of many existing studies [28]. The negative impact of overweight/obesity on pull-ups (upper body strength) is particularly pronounced in men, likely because muscle mass increases faster with BMI in men than in women, but body fat accumulation has a more pronounced offsetting effect on absolute strength [29]. There was no significant difference in PFI between the underweight and normal weight groups in females, suggesting that female body fat has a “buffering effect” on physical fitness; the distribution pattern dominated by subcutaneous fat has less interference with metabolism and exercise efficiency [30].
The most important finding of this study is that muscle strength is the strongest predictor of bone mineral density. The strong positive correlations between the PFI and its components (such as sit-ups and pull-ups) and BMD emphasize the importance of overall physical fitness for bone growth. The gender-specific association between muscle strength and BMD may be related to differences in mechanical load distribution: bent-leg sit-ups (core strength) increase pelvic and lumbar bone stress in females, while pull-ups (upper-body strength) enhance upper limb and axial bone stimulation in males, both of which promote osteoblast activity [31]. Guimarães et al. [32]also found a positive correlation between multi-joint muscle strength and bone mineral content (BMC) in young men, further supporting the mechanism by which strength training is a protective factor for bone health. Mechanical load generated by muscle contraction is a powerful osteogenic stimulus [33]. Furthermore, our study found a positive correlation between bone density and flexibility (sit-and-reach) in females, echoing the findings of Moradell et al. [34] in older men, suggesting that increased trunk mobility may indirectly promote bone metabolism by reducing the risk of sports injuries. In contrast, the correlation between the standing long jump (lower limb explosive power) and bone density in males was weaker, likely because lower limb strength is more strongly influenced by body weight [31]. This correlation was weakened after adjusting for BMI, consistent with the conclusion of Tachiki et al. [35] that “the relationship between upper limb strength and bone density is more stable.” However, in our study the R2 of the regression model was 7.2% (females) and 11.5% (males), indicating that other unmeasured factors (such as dietary calcium intake and vitamin D status) may also contribute to BMD variation.
The positive correlation between BMI and calcaneal BMD in this study is consistent with the Mendelian randomization study results of Song et al. [36], which showed that BMI causally promotes BMD at weight-bearing sites such as the calcaneus and lumbar spine, possibly through a dual mechanism of increased mechanical load and estrogen secretion from adipose tissue [37]. Although previous studies have reported a positive association between BMI and calcaneal BMD, no significant relationship between BMI and BMD was observed in the present study. This may be related to sample characteristics: the subjects in this study were 17–24 years old, before peak femoral neck BMD [38], whereas the sample of Song et al. was an adult population with greater bone metabolic stability. In addition, our study used calcaneal BMD (peripheral bone), while Song et al.‘s study may have used axial bone BMD (such as lumbar spine). The difference in bone location may also lead to different association results.
From a health promotion perspective, the present findings have several important implications for medical universities in Southwest China, where students are drawn from diverse ethnic and cultural backgrounds. Firstly, the higher prevalence of being overweight/obese and having osteopenia observed among male students suggests that gender-specific health promotion strategies may be more effective than uniform interventions. Secondly, the observed non-linear relationship between body mass index and overall physical fitness highlights the importance of maintaining a healthy weight range rather than focusing solely on weight loss or gain. Thirdly, identifying muscle strength as a key modifiable factor associated with bone mineral density highlights the potential value of strength-oriented physical activity interventions, particularly core strength training for female students and upper-body resistance training for male students. Together, these findings provide empirical evidence in support of the development of strength-focused, tailored health promotion programs in medical universities, which could contribute to improved musculoskeletal health among ethnic minority medical students.
This study has several limitations. First, the cross-sectional design cannot establish causality. Longitudinal studies are needed to confirm whether improved physical fitness leads to increased bone mineral density. Second, although we adjusted for age and BMI, other potential confounders, such as dietary calcium intake, vitamin D status, hormone levels, precise amount of physical activity, and lifestyle habits (such as smoking and alcohol consumption), were not accounted for, which may have influenced the results. In particular, the lack of detailed dietary and biochemical assessments, including calcium intake and vitamin D status, should be considered when interpreting the findings. Third, the sample size of male participants was relatively small (n = 99), with only 6 cases of osteopenia (6.06%); this limited sample size may reduce statistical power. In future studies, we will extend the monitoring time span and increase the sample size. Fourth, this study used calcaneal BMD measured by ultrasound to assess bone health; however, the calcaneus is a peripheral bone, and its BMD has limited correlation with axial bones (such as the lumbar spine and femoral neck), which are core sites for evaluating peak bone mass and osteoporosis risk. In addition, dual-energy X-ray absorptiometry (DXA), the gold standard for BMD assessment, was not available in this study. Thus, calcaneal BMD cannot fully reflect axial BMD, and in future studies we will further verify this by combining it with lumbar spine/femoral neck BMD.
This study underscores the critical role of muscle strength as the primary predictor of bone health among medical students in this region, with notable gender-specific mechanisms—core strength in females and upper-body strength in males. These findings support the integration of targeted strength training into health promotion programs within medical universities. Implementing such strategies can help mitigate bone health disparities and foster long-term well-being in this future healthcare workforce.
Conclusion
This study revealed significant sex- and BMI-related differences in physical fitness and bone health among medical students in Southwest China, with males showing higher rates of overweight/obesity and osteopenia compared to females. A nonlinear relationship was observed between BMI and physical fitness, with normal-weight students demonstrating superior overall fitness. Importantly, muscle strength emerged as the strongest modifiable predictor of bone mineral density, with gender-specific patterns—core strength in females and upper-body strength in males. These findings highlight the importance of incorporating strength-based, gender-sensitive physical activity strategies into university health promotion programs. Such strategies may help mitigate long-term health risks, reduce disparities, and support the well-being of future healthcare professionals from ethnic minority backgrounds.
Acknowledgements
The authors would like to thank all the participants who participated in the research and the staff from the School of Basic Medical Sciences of Youjiang Medical University for Nationalities who assisted with the physical fitness test in the study.
Abbreviations
- BMD
Bone mineral density
- BMI
Body mass index
- BW
Body weight
- PFI
Physical fitness index
- SD
Standard deviation
- SPSS
Statistical package for the social sciences
- WHO
World health organization
- VIF
Variance inflation factor
Author contributions
Conceptualization, Yongliang Wang and Yuanji Teng; methodology, Yongliang Wang, Fangyi Shu and Yanfu Yuan; validation, Yongliang Wang, Jianxin Xue and Jin Zhang; formal analysis, Yongliang Wang; investigation, Fangyi Shu, Yanfu Yuan, Jianxin Xue and Jin Zhang; resources, Yuanji Teng; data curation, Yongliang Wang; writing—original draft preparation, Yongliang Wang; writing—review and editing, Yuanji Teng; visualization, Yongliang Wang; supervision, Yuanji Teng; project administration, Yuanji Teng. All authors have read and agreed to the published version of the manuscript.
Funding
The study was supported by a grant from the 2024 Annual Education Supervision Special Project of the “14th Five-Year Plan” for Guangxi Educational Science (Guangxi Education Science [2024] No.22), titled “Innovative Practice Research on the Integration Mode of In-class and Out-of-class Physical Education Teaching in Medical Colleges under the Background of New Medical Science” (Project No.: 2024ZJY958).
Data availability
The raw data supporting the conclusions of this article will be made available by the authors on request.
Declarations
Institutional review board statement
The study was approved by the Ethics Committee of Youjiang Medical University for Nationalities (protocol code: 2024070102 and date of approval: 1 July 2024).
Informed consent
Written informed consent was obtained from all participants involved in the study.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Data Availability Statement
The raw data supporting the conclusions of this article will be made available by the authors on request.