Abstract
Background
This study aims to analyze the associations of 24-hour physical activity (PA) behaviors, including moderate-to-vigorous intensity physical activity (MVPA), light-intensity physical activity (LPA), sedentary behavior (SB), and sleep, and the effects of their redistribution on body composition markers in college students.
Methods
A cross-sectional survey was conducted among 108 college students (51 males and 57 female) in Hangzhou. ActiGraph GT3X+ accelerometers were used to objectively measure 24 h PA behaviors. The height, weight, waist circumference (WC) and hip circumference of the participants were measured by standard measuring instruments, and body mass index (BMI) and waist-hip ratio (WHR) were calculated. Body fat percentage (BFP), visceral fat area (VFA), fat mass and fat-free mass were measured using the InBody 720. Compositional linear regression was used to analyze the relationship between 24 h physical activity behavior and these parameters.
Results
1) Compositional regression analysis showed that MVPA was negatively correlated with BMI, BFP, WHR, WC, fat mass and VFA after adjusting for age, sex, monthly expenditure, smoke status, and alcohol consumption (p < 0.05). LPA was also negatively correlated with WC and fat mass (p < 0.05), while SB was positively correlated with BFP, WC and fat mass (p < 0.05). 2) Replacing sleep or SB with MVPA for 15 min, BMI, BFP, WHR, WC, fat mass, and VFA were significantly decreased (p < 0.05). When LPA was replaced with MVPA for 15 min, BFP, WHR, WC and fat mass also significantly decreased (p < 0.05). While replacing SB with LPA for 15 min, it results in reductions in BMI, BFP, WHR, WC and fat mass (p < 0.05). However, after replacing MVPA with sleep, SB or LPA for 15 min, BMI, BFP, WHR, WC, fat mass and VFA significantly increased (p < 0.05), and LPA was replaced with SB for 15 min, BMI, BFP, WC and fat mass significantly increased (p < 0.05). 3) Dose-effect analysis showed that the substitution of MVPA and other activitys had asymmetric effects on BMI. MVPA had the best effect on BMI reduction when it replaced SB; With the prolongation of MVPA substitution for other activities, the degree of BMI reduction was gradually decrease. However, with the gradual increase of other activities replacing MVPA, BMI showed a rapid increase.
Conclusions
Our findings indicate that increased MVPA time is associated with lower levels of BMI, BFP, WHR, WC, fat mass and VFA, and increased SB associated with increased BFP, WC and fat mass. Thus, replacing SB with MVPA or LPA may be effective strategies for improving body composition in college students and decrease the risk of obesity for college students.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-026-26931-2.
Keywords: Physical activity, Sedentary behavior, Sleep, Obesity, Compositional data analysis
Introduction
Overweight or obesity has become a global public health problem [1]. In China, according to the “Report on Nutrition and Chronic Diseases of Chinese Residents in 2020”, the overweight and the obesity rate of adults in China is 34.3% and is 16.4% respectively, accounting for more than half of the adult population [2], and it is expected to increase to 70.5%, affecting about 810 million people by 2030 [3]. Obesity is a major risk factor for cardiovascular diseases, diabetes, and certain cancers and other chronic noncommunicable diseases [4]. Physical activity (PA), including moderate-to-vigorous intensity physical activity (MVPA) and light-intensity physical activity (LPA), sedentary behavior (SB), and sleep are important factors affecting adult obesity [5–7]. Previous research has found that these behaviors are independent risk factors for obesity in adults [8–10]. However, these studies ignore the interdependence among the behavioral activities of PA, SB and sleep in a day, and a change in the time of one behavior will inevitably lead to a change in the time of other behaviors. Therefore, it has been suggested that multiple behaviors of individuals should be considered comprehensively to fully explore the effects of multiple behavior changes in individual health outcomes [11].
Mekary RA firstly developed isotemporal substitution model (ISM), which is widely-regarded as the gold standard method for modeling calorie-containing foods in nutritional epidemiology [12–14]. However, the ISM was based on epidemiology research in daily activity without incorporating that daily time (24-h). Chastin et al. proposed the idea of ISM analysis under the framework of component data [15], that is compositional data analysis (CoDA), which allocates time to the combined effects of different behaviors in a circadian or diurnal cycle, and time spent in one behavior necessarily displaces time spent in other behaviors. They introduced CoDA to health promotion research by investigating the combined effects of PA, SB and sleep on obesity and heart metabolic health indicators. At present, some researchers use CoDA to explore the impact of MVPA, LPA, SB and sleep time proportion and isotemporal substitution on obesity risk in Childhood, adolescents and the elderly. Their results showed that the time increase in MVPA reduced the risk of obesity, and the time increase in SB increased the risk of obesity. Moreover, replacing time spent in SB with vigorous PA may be a powerful tool against obesity [16–18]. Based on these results, 24 h activity guideline has been recommended for children, adolescents and older adults [19–21].
There were around 47.63 million college students in China in 2023 [22], with an overweight and obesity prevalence of 20.2% [23]. The college years represent a critical developmental stage, marking the transition from late adolescence to early adulthood, and are key for establishing independent PA habits. Developing a regular PA during this period can mitigate the risks of weight gain, obesity, and cardiovascular diseases [24]. Therefore, there is a need to determine how the change of PA behaviors impact on developing obesity of college students.
However, at present, few scholars have used CoDA to analyze the relationship between 24-h activity and health indicators of college students. This study was conducted on college students in Hangzhou, Zhejiang Province, China. The purpose of this study is to assess: (1) Time distribution of 24-hour movement behaviors among Chinese college students by using accelerometer analysis; (2) The relationship between the time distribution of MVPA, LPA, SB and sleep and the body composition indexes by component data analysis; (3) using the component data, the prediction of the difference in body composition indexes among MVPA, LPA, SB and sleep after 15 min isotemporal substitution; (4) the dose-effect relationship between the duration of MVPA, LPA, SB and sleep and the relevant parameters of body composition after replacement.
Methods
Study participants
This study involved college students aged 20–22 years in Hangzhou, China. Physical activity (MVPA and LPA), SB, sleep and body composition were measured. This study was approved by the Ethics Committee of Hangzhou Normal University (approval number: 2024 − 0801). All participants were informed and agreed to participate in this study voluntarily.
The sample size was based on the study by Chen [25] using G*Power 3.1 software (Heinrich Heine University, Dusseldorf, Dusseldorf, Germany). The Cohen ƒ2 was designated as 0.2 with an alpha of 0.05 (two-tailed) and a desired statistical power (1-β) of 0.9, the a priori power analysis indicated that a minimum sample size of 82 participants was necessary according to the definition of a small effect size [26] with estimated an 20% dropout rate, the adjusted minimum sample size was 103 participants.
After the minimum sample size was determined, a cluster sampling method was used, with colleges as the clustering units. Three colleges were randomly selected from the ten colleges at Hangzhou Normal University, Zhejiang Province. A total of 217 students were invited in this study and 148 students signed the informed consent. The exclusion criteria were a history of heart disease, respiratory disease, liver or kidney disease, or other chronic medical conditions, and people with limited PA and sleep disorders. Actigraph GT3X human movement Energy Consumption monitor (Actigraph, Pensacola, FL, USA, hereinafter referred to as “accelerometer”) was used for measuring the PA, SB and sleep. During the testing period, participants were instructed to wear the accelerometer on their right hip continuously for 7 days, including during sleep and removing it during aquatic activities such as swimming. The duration of PA, SB and sleep were measured for one week and effective screening was conducted according to the PA criteria proposed by Troiano et al. [27]. Participants wore it for at least 16 hours a day and at least 4 days a week (including 3 workdays and 1 weekend), so that the test was considered valid and included in the data analysis [28, 29]. Among 148 college students who participated in the study, the PA test data of 108 college students reached the effective standard, and the effective rate was 72.97%. At the same time, 147 college students were given a questionnaire to inquire about their basic information and sleep duration, and 147 questionnaires were collected at last, with a recovery rate of 100%. Therefore, the final sample size included was 108 college students.
Measures of 24-h PA, SB and sleep behaviors
Accelerometer has been validated as a reliable instrument for the objective measurements of PA levels in adults, effectively differentiating between SB and non-SB [30]. Before the test, the researchers explained the purpose of the study to the participants and the specifications and precautions for wearing the accelerometer, and then they were given accelerometers. Researchers checked on participants’ equipment to ensure data validity every day in the testing period. The data recording of the accelerometers began at 0 o ‘clock on the second day after distribution and was stopped at midnight (24:00) on the eighth day. After the accelerometer was returned, the data was collected and statistically analyzed using ActiLife 6.13.6 software.
The data measurement by accelerometer is recorded at a time interval of 30s. PA was classified into SB (0 ~ 99 CMP), LPA (100 ~ 2019 CMP) and MVPA (≥ 2020 CMP) according to the intensity classification standard suitable for adults proposed by Troiano et al. [27]. Sleep duration was assessed using a self-reported sleep questionnaire [31]. Participants were asked to recall their average daytime and nighttime sleep duration over the past month separately for weekdays and weekends [32]. Total daily sleep duration was calculated as the sum of daytime and nighttime sleep. To account for differences between weekdays and weekends, average daily sleep duration was calculated using a weighted formula: [(weekday daytime sleep duration + weekday nighttime sleep duration) × 5 + (weekend daytime sleep duration + weekend nighttime sleep duration) × 2]/7 [32].
Body composition indexes
Measurements were conducted by trained researchers in strict accordance with “Chinese Students Physical Fitness and Health Investigation and Testing Rules”. The height of participants was measured by the vertical distance from the top of the participant’s head to the ground (unit cm, accurate to 0.1 cm) and the body weight was measured by the body mass of participants only wearing close clothing (unit, kg, accurate to 0.1 kg) by using the weight scale (GMCS-IV, Jianmin, Beijing, China) and body mass index (BMI = kg/m2) was calculated. Hip circumferences and Waist circumferences (WC, cm) were made using a soft ruler to measure the length of a horizontal circle at 0.5 ~ 1.0 cm above the participants’s navel around the abdomen and a horizontal circle around the widest part of the hips, respectively (unit, cm, accurate to 0.1 cm). The Waist-to-hip ratio (WHR) was calculated. Body composition (fat mass, kg; body fat percentage, BFP, %; visceral fat area, VFA, cm²; and fat-free mass, kg) was evaluated utilizing bioelectrical impedance analysis with the InBody 720 (Biospace Co., Ltd., Seoul, Korea) [33]. During the body composition test, participants were asked to wear light clothing, remove any metal objects that come into contact with their bodies, maintain proper posture, and avoid talking or moving. At the time of the test, each participants’ gender, age, whether they smoked, drank alcohol and how much they spent each month were also recorded.
Covariates
The covariates were selected according to Ferrari and Powell’s research [34, 35]. These included age, sex, smoking status, alcohol consumption, and monthly expenditure, all of which were collected through questionnaires.
Statistical analysis
Based on the 24 h guidelines for active component analysis proposed by Chastin et al. [15], Data were analyzed by using R version 4.2.2 (R Development Core Team, Vienna, Austria). We employed the CoDA methodologies established by Dumuid’s [36]. The analysis was conducted using the “Compositions”, “robCompositions”, “deltacomp”, “devtools”, “tidyverse”, and “lmtest” packages. Descriptive statistics, including mean, standard deviation (SD), frequency, and percentage (%) were used to characterize the participants. The central tendency of each activity behavior component was represented by geometric mean (adjusted to collectively sum to 1440 min) and dispersion (a log-ratio variation matrix).
Based on the CoDA framework, the 24-hour activity behavior data (MVPA, LPA, SB, and sleep) of each participant were treated as compositional data. Isometric log-ratio (ilr) transformation was applied to convert the four movement behaviors into three ilr coordinates, which were used as independent variables. Based on a sequential binary partition (SBP) approach, three ilr-transformed variables (zi1, zi2, and zi3) were constructed for each movement behavior to reflect its relative allocation compared with the remaining behaviors. Taking MVPA as an example, the corresponding ilr coordinate system is defined as follows:
, 
, 
.
Compositional multiple linear regression models were then fitted separately for each obesity-related indicator, including BMI, BFP, WC, WHR, fat mass, fat-free mass, and VFA, which served as the dependent variables. The ilr-transformed movement behavior coordinates were included as independent variables. Gender, age, smoking status, drinking status, and monthly expenditure were entered as covariates in all models to control for potential confounding. Model fit was evaluated using the coefficient of determination (R²). Model assumptions were assessed through residual diagnostics.
According to the multiple linear regression models. On the basis of the total time (1440 min) and other activity times kept constant, the 15 min of a certain activity is allocated to another activity to assess the predicted values and 95% confidence intervals (CI) of adiposity measures following the reallocating a fixed duration of time between two activities. Predictions were made for pairwise reallocations ranging from 10 to 60 min (in 10-minute intervals) to analyze the dose-effect relationship, with corresponding estimates were plotted. All statistical tests were two-sided, and a significance level of α = 0.05 was used.
Results
Participant characteristics
Of the 148 participants who met the inclusion criteria, 6 (4.05%) withdrew from the study, 20 (13.51%) failed to adhere to the requirement of wearing the device for a minimum of four days per week, which included three weekdays and one weekend day. 1 (0.68%) lost the device, and 13 (8.78%) did not participate in the body composition tests. Therefore, 108 (72.97%) participants were included in the analyses (Fig. 1), including female 57 (52.78%), male 51 (47.22%), 102 non-smokers (94.44%), 6 smokers (5.55%), 77 drinkers (71.29%) and 31 non-drinkers (28.70%). The monthly consumption was less than 1500 RMB in 16 (14.81%), 1,500-3,000 RMB in 83 (76.85%), and more than 3,000 RMB in 9 (8.33%). All the details on the descriptivec characteristics of the participants are presented in Table 1.
Fig. 1.
Sample screening process
Table 1.
Descriptive characteristics for the included participants (n = 108)
| Variable | Total (n = 108) |
|---|---|
| Age (years), mean (SD) | 20.47 (2.22) |
| Sex, n (%) | |
| Female | 57 (52.78) |
| Male | 51 (47.22) |
| Adiposity indicators, mean (SD) | |
| BMI (Kg/m2) | 22.12 (3.36) |
| Body fat percentage (%) | 23.70 (8.46) |
| Fat-free mass (Kg) | 47.63 (9.55) |
| Fat mass (Kg) | 14.9 (6.62) |
| Waist-circumference (cm) | 76.38 (9.04) |
| WHR | 0.83 (0.05) |
| VFA (cm2) | 49.28 (29.06) |
| Smoking status, n (%) | |
| No | 102 (94.44) |
| Current | 6 (5.55) |
| Alcohol consumption, n (%) | |
| No | 77 (71.29) |
| Weekly | 31 (28.70) |
| Monthly expenditure, n (%) | |
| < 1500 RMB | 16 (14.81) |
| 1,500—3,000 RMB | 83 (76.85) |
| >3,000 RMB | 9 (8.33) |
BMI body mass index, WHR waist-to-hip ratio, VFA visceral fat area
Compositional characteristics of 24-h movement behaviours
MVPA, LPA, SB and sleep of the participants’ average daily were expressed using the geometric means and arithmetical means and the percentage within 24 h. Geometric means of MVPA, LPA, SB and sleep are 61.01 min (4.24%), 129.71 min (9.01%), 790.66 min (51.91%), 458.62 min (31.85%) respectively. Arithmetic mean of those are 65.06 min (4.52%), 133.90 min (9.30%), 785.38 min (54.54%), 455.66 min (31.64%) respectively (Table 2). Table 2 also shows the difference in the description of central tendency between the geometric means and the arithmetic means. Compared with the geometric means, the arithmetic means underestimates the description of SB and sleep to a certain extent, while overestimating the description of MVPA and LPA. Figure 2 showed that the variance-covariance matrix is used to describe the dispersion of among the variables in the component data. The variance matrix analysis reveals that the logarithmic ratio of the pairwise distances between the elements in the 24-hour activity profile is greater than 0, indicating that there is a mutual correlation among the elements. Among them, the logarithmic ratio of the pairwise distances between SB and sleep (ln SB/Sleep = 0.02) is the smallest, indicating that the two activities are most closely related and are the most likely to be converted. The logarithmic ratio of the pairwise distances between SB and MVPA (ln SB/MVPA = 0.19), SB and LPA (ln SB/LPA = 0.13), and MVPA and LPA (ln LPA/MVPA = 0.14) are all relatively high, indicating that a lower association of MVPA with other activity behaviors.
Table 2.
Geometric and arithmetic means for sleep, sedentary behaviour, LPA, and MVPA
| Behaviour | Geometric mean (minutes/day/%) | Arithmetic mean (minutes/day/%) |
|---|---|---|
| MVPA | 61.01 (4.24) | 65.06 (4.52) |
| LPA | 129.71 (9.01) | 133.90 (9.30) |
| SB | 790.66 (54.91) | 785.38 (54.54) |
| Sleep | 458.62 (31.85) | 455.66 (31.64) |
SB Sedentary behaviour, LPA light-intensity physical activity, MVPA moderate-to-vigorous physical activity
Fig. 2.
Pair-wise log-ratio change matrix for sleep, SB, LPA and MVPA time. SB: Sedentary behaviour, LPA: light-intensity physical activity, MVPA: moderate-to-vigorous physical activity
A compositional regression analysis of 24-hour movement behaviors and body composition of college students
After adjustment for covariates, the compositional regression analysis revealed significant correlations between various movement behaviours and adiposity indicators (Table 3). The time proportion of MVPA (B MVPA = -3.369, p = 0.006; B MVPA =-8.530, p < 0.001; B MVPA = -0.054, p < 0.001; B MVPA = -9.491, p < 0.001; B MVPA = -8.071, p < 0.001; B MVPA = -31.569, p < 0.001) was negatively correlated with BMI, BFP, WHR, WC, fat mass and VFA. LPA (B LPA = -7.203, p < 0.05; B LPA= -4.869, p < 0.05) was also negatively correlated with WC, fat mass. That is, with the increase in MVPA time (the relative decrease in time of LPA, SB, and sleep), BMI, BFP, WHR, WC, fat mass, and VFA decreases or as LPA time increases (relative reduction in time of MVPA, SB, and sleep), WC decreases, and fat mass decreases.
Table 3.
Multiple linear regression analyses of the relationship between ilr coordinates and adiposity measures
| ilr Regression models | Model fit | ||||
|---|---|---|---|---|---|
| B | SE | p value | R 2 | p value | |
| BMI | |||||
| ilr Sleep/(SB*LPA*MVPA) | 1.299 | 2.673 | 0.628 | 0.318 | < 0.001 |
| ilr SB/(Sleep*LPA*MVPA) | 4.377 | 2.367 | 0.066 | ||
| ilr LPA /(Sleep*SB*MVPA) | -2.307 | 1.199 | 0.057 | ||
| ilr MVPA /(Sleep*SB*LPA) | -3.369 | 0.996 | 0.006 | ||
| BFP | |||||
| ilr Sleep/(SB*LPA*MVPA) | 0.944 | 5.391 | 0.861 | 0.563 | < 0.001 |
| ilr SB/(Sleep*LPA*MVPA) | 12.010 | 4.755 | 0.013 | ||
| ilr LPA /(Sleep*SB*MVPA) | -4.425 | 2.419 | 0.070 | ||
| ilr MVPA /(Sleep*SB*LPA) | -8.530 | 2.096 | < 0.001 | ||
| WHR | |||||
| ilr Sleep/(SB*LPA*MVPA) | 0.024 | 0.040 | 0.560 | 0.268 | < 0.001 |
| ilr SB/(Sleep*LPA*MVPA) | 0.059 | 0.036 | 0.101 | ||
| ilr LPA /(Sleep*SB*MVPA) | -0.029 | 0.018 | 0.117 | ||
| ilr MVPA /(Sleep*SB*LPA) | -0.054 | 0.015 | < 0.001 | ||
| WC | |||||
| ilr Sleep/(SB*LPA*MVPA) | 2.204 | 6.178 | 0.722 | 0.457 | < 0.001 |
| ilr SB/(Sleep*LPA*MVPA) | 14.490 | 5.449 | 0.009 | ||
| ilr LPA /(Sleep*SB*MVPA) | -7.203 | 2.772 | 0.018 | ||
| ilr MVPA /(Sleep*SB*LPA) | -9.491 | 2.303 | < 0.001 | ||
| Fat mass | |||||
| ilr Sleep/(SB*LPA*MVPA) | 1.248 | 4.840 | 0.797 | 0.378 | < 0.001 |
| ilr SB/(Sleep*LPA*MVPA) | 11.692 | 4.279 | 0.007 | ||
| ilr LPA /(Sleep*SB*MVPA) | -4.869 | 2.171 | 0.027 | ||
| ilr MVPA /(Sleep*SB*LPA) | -8.071 | 1.804 | < 0.001 | ||
| Fat-free mass | |||||
| ilr Sleep/(SB*LPA*MVPA) | -2.907 | 5.204 | 0.578 | 0.654 | < 0.001 |
| ilr SB/(Sleep*LPA*MVPA) | 5.920 | 4.590 | 0.200 | ||
| ilr LPA /(Sleep*SB*MVPA) | -2.397 | 2.335 | 0.307 | ||
| ilr MVPA /(Sleep*SB*LPA) | -0.616 | 1.940 | 0.752 | ||
| VFA | |||||
| ilr Sleep/(SB*LPA*MVPA) | 18.402 | 23.705 | 0.439 | 0.226 | < 0.001 |
| ilr SB/(Sleep*LPA*MVPA) | 26.849 | 20.908 | 0.202 | ||
| ilr LPA /(Sleep*SB*MVPA) | -13.683 | 20.908 | 0.201 | ||
| ilr MVPA /(Sleep*SB*LPA) | -31.569 | 8.836 | < 0.001 | ||
All models adjusted for age, sex, monthly expenditure, smoke status, and alcohol consumption
Significant associations are shown in bold
SB Sedentary behaviour, LPA light-intensity physical activity, MVPA moderate-to-vigorous physical activity, ilr isometric log-ratio, B unstandardized regression coefficient of the first ilr coordinate, referring to the association of time spent in one specific behaviour relative to time spent in other movement components on BMI [e.g., ilr MVPA/(SB*Sleep*LPA) denotes the association of MVPA time relative to SB, Sleep and LPA time on BMI], SE standard error, BMI body mass index, BFP body fat percentage, WHR waist-to-hip ratio, WC waist-circumference, VFA visceral fat area
The estimated differences in body composition outcomes for reallocations of 15 min between time-use behaviors in college students
The effects of MVPA, LPA, SB and sleep on body composition were studied with 15 min as the replacement time. The results (Table 4) showed that reallocating 15 min from sleep to MVPA, BMI (-0.68 kg/m2), BFP (-1.64%), WHR (-0.01), WC (-1.87 cm), fat mass (-1.57 kg), and VFA (-10.36 cm²); Similarly, replacing SB with MVPA, significant changes were showed in BMI (-0.71 kg/m2), BFP (-1.82%), WHR (-0.01), WC (-2.05 cm), fat mass (-1.73 kg), and VFA (-9.86 cm²) (p < 0.05). Replacing LPA with MVPA, BFP (-1.12%), WHR (-0.01), WC (-1.04 cm), fat mass (-1.02 kg) had all significant differences (p < 0.05). Replacing SB with LPA, BMI (-0.29 kg/m2), BFP (-0.64%), WHR (-0.01), WC (-0.92 cm), fat mass (-0.66 kg) showed significant differences (p < 0.05). Replacing sleep with LPA, WC (-0.75 cm) also showed significantly difference (p < 0.05). Furthermore, time reallocation from MVPA to LPA gave rise to significant increase in BMI (0.61 kg/m2), BFP (1.63%), WHR (0.01), WC (1.64 cm), fat mass (1.51 kg), and VFA (0.91 cm²) (p < 0.05). Conversely, BMI (0.86 kg/m2, 0.90 kg/m2), BFP (2.10%, 2.27%), WHR (0.01, 0.01), WC (2.38 cm, 2.56 cm), fat mass (2.01 kg, 2.16 kg), and VFA (3.53 cm², 3.82 cm²) were significantly increased from MVPA to sleep and from MVPA to SB (all p < 0.05). Additionally, significant increase in BMI (0.31 kg/m2), BFP (0.70%), WC (1.00 cm, 2.56 cm) and fat mass (0.71 kg) from LPA to SB (p < 0.05) and BMI (0.61 kg/m2, body fat percentage (1.63%), WHR (0.01), WC (1.64 cm), fat mass (1.51 kg), and VFA (0.91 cm²) from MVPA to LPA (p < 0.05) were observed. Yet, no significant changes in body composition indicators were observed between sleep and SB (all p > 0.05).
Table 4.
Predicted changes (95% CI) for each of outcomes 15-minute isotemporal substitution among physical activity, SB, and sleep
| Remove 15 min |
Add 15 min |
BMI | BFP | WHR | WC | Fat mass | Fat-free mass | VFA |
|---|---|---|---|---|---|---|---|---|
| (kg/m2) | (%) | (cm/cm) | (cm) | (kg) | (kg) | (cm2) | ||
| MVPA | Sleep | 0.86 | 2.10 | 0.01 | 2.38 | 2.01 | 0.07 | 3.53 |
| (0.33, 1.39) * | (1.03, 3.17) * | (0.01, 0.02) * | (1.16, 3.60) * | (1.05, 2.97) * | (-0.96, 1.10) | (3.53, 12.92) * | ||
| MVPA | SB | 0.90 | 2.27 | 0.01 | 2.56 | 2.16 | 0.25 | 3.82 |
| (0.41, 1.38) * | (1.28, 3.26) * | (0.01, 0.02) * | (1.43, 3.68) * | (1.28, 3.05) * | (-0.70, 1.20) | (3.82, 12.48) * | ||
| MVPA | LPA | 0.61 | 1.63 | 0.01 | 1.64 | 1.51 | -0.08 | 0.91 |
| (-0.01, 1.22) | (0.38, 2.88) * | (0.00, 0.02) * | (0.20, 3.07) * | (0.39, 2.64) * | (-1.29, 1.13) | (0.91, 11.92) * | ||
| Sleep | MVPA | -0.68 | -1.64 | -0.01 | -1.87 | -1.57 | -0.03 | -10.36 |
| (-1.11, -0.25) * | (-2.51, -0.77) * | (-0.02, 0.00) * | (-2.87, -0.88) * | (-2.35, -0.79) * | (-0.87, 0.80) | (-10.36, -2.72) * | ||
| Sleep | SB | 0.03 | 0.17 | 0.00 | 0.17 | 0.15 | 0.18 | -2.08 |
| (-0.19, 0.26) | (-0.28, 0.62) | (0.00, 0.00) | (-0.35, 0.69) | (-0.25, 0.56) | (-0.26, 0.62) | (-2.08, 1.89) | ||
| Sleep | LPA | -0.25 | -0.47 | 0.00 | -0.75 | -0.50 | -0.14 | -4.57 |
| (-0.56, 0.05) | (-1.09, 0.15) | (-0.01, 0.00) | (-1.46, -0.03) * | (-1.06, 0.06) | (-0.74, 0.46) | (-4.57, 0.91) | ||
| SB | MVPA | -0.71 | -1.82 | -0.01 | -2.05 | -1.73 | -0.22 | -9.86 |
| (-1.10, -0.33) * | (-2.59, -1.04) * | (-0.02, -0.01) * | (-2.93, -1.16) * | (-2.42, -1.04) * | (-0.96, 0.53) | (-9.86, -3.06) * | ||
| SB | Sleep | -0.04 | -0.17 | 0.00 | -0.18 | -0.16 | -0.18 | -1.89 |
| (-0.26, 0.18) | (-0.62, 0.27) | (0.00, 0.00) | (-0.69, 0.33) | (-0.56, 0.24) | (-0.61, 0.25) | (-1.89, 2.02) | ||
| SB | LPA | -0.29 | -0.64 | -0.01 | -0.92 | -0.66 | -0.33 | -3.83 |
| (-0.52, -0.06) * | (-1.12, -0.17) * | (-0.01, 0.00) * | (-1.47, -0.38) * | (-1.08, -0.23) * | (-0.78, 0.13) | (-3.83, 0.35) | ||
| LPA | MVPA | -0.40 | -1.12 | -0.01 | -1.04 | -1.02 | 0.14 | -9.36 |
| (-0.94, 0.14) | (-2.21, -0.02) * | (-0.02, 0.00) | (-2.29, 0.21) | (-2.00, -0.04) * | (-0.92, 1.19) | (-9.36, 0.26) | ||
| LPA | Sleep | 0.28 | 0.53 | 0.00 | 0.83 | 0.55 | 0.17 | -0.96 |
| (-0.05, 0.61) | (-0.14, 1.19) | (0.00, 0.01) | (0.06, 1.59) * | (-0.04, 1.15) | (-0.47, 0.82) | (-0.96, 4.90) | ||
| LPA | SB | 0.31 | 0.70 | 0.00 | 1.00 | 0.71 | 0.35 | -0.43 |
| (0.06, 0.57) * | (0.17, 1.22) * | (0.00, 0.01) | (0.40, 1.61) * | (0.23, 1.18) * | (-0.16, 0.86) | (-0.43, 4.21) |
All models adjusted for age, sex, monthly expenditure, smoke status, and alcohol consumption
SB Sedentary behaviour, LPA light-intensity physical activity, MVPA moderate-to-vigorous physical activity, BMI body mass index, BFP body fat percentage, WHR waist-to-hip ratio, WC waist-circumference, VFA visceral fat area
* p < 0.05
Dose–response between 24-hour movement behaviors and adiposity indicators
To further explore the dose-response relationship between reciprocal substitution of 24-hour physical activity behaviors and body composition, we selected the substitution effects with significant PA behaviors (including MVPA and sleep, MVPA and SB, MVPA and LPA, sleep and LPA, and LPA and SB) to explore dose-response relationship between the reciprocal substitution of different physical activity behaviors and BMI (Fig. 3). The results showed that: 1) When MVPA and sleep, MVPA and SB, and MVPA and LPA were replaced by each other, the change of BMI showed slowly decrease, but would rapidly increase in the opposite direction. The difference in the dose-response relationship between LPA and sleep, and LPA and SB were not significant. When 60 min of MVPA replaced LPA, SB, and sleep, BMI increased by 11.21, 12.25, and 12.11 kg/m2 respectively. In contrast, when 60 min of PA, SB, and sleep replaced MVPA, BMI decreased by only 0.75, 2.30, and 2.16 kg/m2, respectively.2) As the time for MVPA to substitute sleep, SB, and LPA increased, BMI showed a continuous downward trend, reduction range from big to small MVPA replaces SB, sleep, and LPA in turn. 3) When MVPA substituted LPA, SB, and sleep for 10 min, BMI decreased by 0.37 kg/m2, 0.57 kg/m2, and 0.55 kg/m2, respectively, but in the subsequent 10 ~ 60 min, the decrease in BMI gradually slowed down, with the decrease range of being 0.37 ~ 11.21 kg/m2, 0.57 ~ 12.25 kg/m2, and 0.54 ~ 12.11 kg/m2 respectively. When LPA, SB, and sleep substituted MVPA for 10 min, their BMI increased by 0.28 kg/m2, 0.49 kg/m2, and 0.47 kg/m2, respectively, but in the subsequent 10 ~ 60 min, the increase in BMI gradually increased, with the range of increase being 0.28 ~ 0.76 kg/m2, 0.49 ~ 2.30 kg/m2, and 0.47 ~ 2.16 kg/m2, suggesting that 10 min is the turning point of BMI benefit change when MVPA and other behaviors substitute each other (Fig. 3 and Supplementary 1).
Fig. 3.
Predicted changes (95% CI) in BMI related to pairwise reallocations of time from one activity behavior (rows) to another (columns). [e.g. row three, column one represents the Predicated changes (95% CI) in BMI by reallocating time from Sedentary Behaviour to MVPA, compared with BMI at the mean composition]. BMI body mass index, LPA light-intensity physical activity, MVPA moderate-to-vigorous physical activity
Discussion
In this study, we use the objective measurement method of accelerometer to explore the interaction between 24-h activity behavior from the perspective of time distribution and substitution by component analysis method. In this way, the accuracy of the data is guaranteed. It will provide preliminary evidence on the relationship between 24-hour movement behaviors and body composition indicators among college students. We completed an analysis of daily activity behaviors among college students using compositional means and variance matrix analysis. Our results showed that college students in China spend the majority of their time in SB (54.91%) and sleep (31.85%), with limited LPA (9.01%) and MVPA (4.24%) in 24-h period. MVPA has only 61.01 min/day, consistent with Wang Shuai’s research [37], indicating that it may be one of the main causes of overweight or obesity among Chinese college students. Moreover, the proportion of daily SB time relative to total PA is consistent with Miller JM’s study [38]. Our results of variance matrix showed that the equidistance log-ratio variance among all elements in 24 h PA was greater than 0, indicating that all elements were correlated with each other. Among them, the isometric log-ratio variance of SB and sleep is the smallest, which indicates that these two activities have the highest correlation degree and are the easiest to convert, this may be possibly due to their low energy expenditure [39]. The log-ratio variance of MVPA and other activities is higher, indicating that the correlation degree between MVPA and other activities is low. On the contrary, the transition between SB and MVPA is least likely, which may be a challenge faced by college students transitioning from a sedentary lifestyle to MVPA. This requires a stronger motivation, which may result from a combination of relevant factors such as psychology, environment and resources [40]. However, addressing these challenges is critical to promoting healthier, more active lifestyles among college students.
We also studied the relationship between 24-hour PA behaviour and body composition indexes of Chinese college students through CoDA. Our findings showed that MVPA was negatively correlated with BMI, BFP, WHR, WC, fat mass and VFA. LPA was also negatively correlated with WC and fat mass. However, SB was positively correlated with BFP, WC and fat mass. This is consistent with the results of previous studies using the component analysis method [35, 41, 42], and also consistent with the energy consumption characteristics of each activity element [43], suggesting that to effectively reduce the risk of obesity in college students, it is necessary to increase the MVPA and LPA time, reduce time of SB and sleep.
Our results on the effects of isotemporal substitution of various elements of 24-h activity on the body composition of college students show that MVPA instead of other activities with 15 min replacement significantly improved various obesity indicators, consistent with Meyer on healthy adults aged 20–35 and Gupta on blue-collar workers [44, 45], both of which highlight the benefits for BMI, WC, and BFP, indicating that MVPA may be effectively enhance lipid metabolism, elevate post-exercise energy expenditure, and improve fat oxidation, thereby contributing to a more substantial energy expenditure [46]. Moreover, our study also found that replacing SB with LPA improved all obesity indicators except VFA, which is related with energy expenditure increased. However, after replacing SB with MVPA or LPA of the same time respectively, the biggest difference was found that LPA did not cause a significant reduction in VFA, which may be due to the energy consumption increased with the extension of MVPA and the visceral adipose tissue mobilized earlier than the subcutaneous adipose tissue [47]. Our results indicate that both MVPA and LPA are of great significance for the prevention and control of obesity in China college students. It may be that increasing the time of MVPA has a better effect on reducing the risk of obesity in these students, which again verifies the importance of activity intensity in their health benefits and it may be closely related to the fact that MVPA involves more fast muscle fibers, increased skeletal muscle mass, and promoted energy consumption during and after exercise [48]. It may also be related to the fact that higher intensity PA stimulates the body to secrete more lipolysis hormones such as adrenaline, norepinephrine, growth hormone, etc., which accelerate the mobilization of triglycerides in adipose tissue and are conducive to lipolysis [49–51]. According to the significant effect of MVPA and LPA replacement, students can increase the intensity of activities during leisure time and public physical education classes to realize the conversion between LPA and MVPA. At the same time, the conversion between SB and MVPA can be realized through activities such as walking and climbing stairs in spare time or leisure time.
We also conducted a dose-effect analysis on the 24-h activity factors with significant substitution effect, taking BMI as an example, and found that the mutual substitution effect between MVPA and other behaviors has obvious asymmetry, that is when MVPA replaced SB, sleep, and LPA, BMI predicted values gradually decreased. However, when other behaviors replaced MVPA, there was a rapid increase in BMI predicted values. This result is consistent with Willems’s research [52], the substitution effect may be related to the proportion of time that different physical activities are replaced. In our study, the average duration of MVPA was 61.01 min, and 10-minute substitution accounted for 16.39% of the MVPA time. Such replacement would cause a large substitution effect, indicating that MPVA activity of about 70 min/day, has the highest lipid reduction efficiency. Therefore, it is recommended the optimal recommended amount of college students is 70 min MVPA activity per day. Our results underscore the importance to either maintain or extend the MVPA time proportion spent on for college students. This means that when the proportion of time spent on MVPA decreases, the risk of a significant increase in BMI increases significantly.
Limitation
There are several limitations in the present study. First, the results mainly represent the behaviors of university students in eastern China, and the relatively small sample size may limit the generalizability of the findings. Therefore, the applicability of these results to other regions needs further confirmation. Future research should include larger sample sizes and a broader range of locations to clarify the effects of different activity substitutions on obesity among college students. Second, although a cluster sampling design was employed, the statistical analyses did not explicitly model the nested data structure (students within colleges) using multilevel approaches. As a result, residual clustering effects cannot be fully ruled out. Future studies with a larger number of clusters are warranted to better account for institutional-level clustering. Third, this study employed a cross-sectional design, which only allows for the identification of associations rather than causal relationships. Future research should adopt intervention or cohort study designs to establish causal links between substitution effects and obesity indicators. Finally, although we controlled for gender, age, smoking, alcohol consumption, and monthly expenditure, dietary factors that may influence obesity were not considered. Future studies should include dietary habits to provide a more comprehensive analysis.
Conclusion
First, the time proportions of MVPA and LPA were negatively associated with body composition indicators (except fat-free mass), whereas the time proportions of SB and sleep were significantly positively associated with BFP, WC, and fat mass among the college students studied.
Second, replacing MVPA with LPA, SB, or sleep was associated with changes in BMI. The substitution effects were asymmetrical: reallocating time from MVPA to LPA, SB, or sleep was associated with gradual increases in BMI, whereas reallocating time from LPA, SB, or sleep to MVPA was associated with decreases in BMI. Furthermore, substituting MVPA for SB showed a stronger association with BMI reduction than substituting LPA for sleep. The largest estimated changes were observed for reallocations of 10 min per day.
Third, substituting LPA for SB was also associated with lower BMI, although the effect was weaker than that of MVPA substitutions. Overall, our findings support recommendations to increase MVPA and LPA and reduce SB to improve body composition among college students.
Supplementary Information
Acknowledgements
We extend our gratitude to Professor Xiaofeng Zhu of Jiaxing University for his generous support. We also thank the participants for their contributions to this study, as well as all the authors for their assistance throughout the project.
Abbreviations
- PA
Physical activity
- MVPA
Moderate-to-vigorous intensity physical activity
- LPA
Light-intensity physical activity
- SB
Sedentary behavior
- WC
Waist circumference
- BMI
Body mass index
- WHR
Waist-hip ratio
- BFP
Body fat percentage
- VFA
Visceral fat area
- ISM
Isotemporal substitution model
- CoDA
compositional data analysis
Authors’ contributions
PW conceived of the study, participated in its design, performed the statistical analysis, and coordination and drafted the manuscript; XLC performed the statistical analysis and drafted the manuscript; JLH, CJT and JJ participated in its collected date; CGL and MHZ participated in manuscript preparation. All authors read and approved the final manuscript.
Funding
This study is funded by Zhejiang Province Philosophy and Social Science Planning Project (19NDJC054YB) to Ping Wang and Zhejiang Natural Science Foundation (Exploration Project, LY24H170002) to Ping Wang and Zhejiang Province Education Science Planning Project (General Project, 2024SCG019) to Ping Wang and Zhejiang University Student Science and Technology Innovation Activity Plan (Xinmiao Talents Program) (2024R426B066) to Xiaolong Chen and Hangzhou Philosophy and Social Science Planning Project (Funded by Hangzhou Normal University Sports Events and Health Promotion Research Center, Project number, 2023JD21) to Ping Wang and Wushu and ethnic traditional sports national first-class undergraduate major construction project (4055C5212331202) to Ping Wang.
Data availability
All data and codes used in analysis and projections are available upon request to the corresponding author.
Declarations
Ethics approval and consent to participate
This study was approved by the Ethics Committee of Hangzhou Normal University (approval number: 2024 − 0801). All participants were informed and agreed to participate in this study voluntarily.
Consent for publication
Not applicable.
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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Associated Data
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Supplementary Materials
Data Availability Statement
All data and codes used in analysis and projections are available upon request to the corresponding author.