Stages of change in leisure-domain physical activity behavior and its influencing factors among Chinese college students

Abstract

Objective

To explore the distribution and influencing factors of the change stages of leisure-time physical activity behavior of college students and provide theoretical support for improving the physical and mental health level of college students.

Methods

A stratified, clustered, and staged sampling method was employed to survey and measure the Body morphology of 49,717 college students in China, including sociodemographic information, leisure-time physical activity behavior, health behavior, and health status indicators.

Results

A total of 41,620 valid questionnaires were collected, with 17,212 males (41.4%) and 24,408 females (58.6%). Among the university students, 52.3% are in the pre-contemplation stage of change regarding Leisure-time physical activity behaviour, but only 6.4% are in the maintenance stage. Factors such as gender, year of study, ethnicity, the region of the university, sleep quality, BMI, and anxiety levels significantly influence the stages of change in leisure-time physical activity behaviour among university students.

Conclusion

The intention of college students to engage in regular leisure-time physical activity behavior in China is weak, and numerous factors affect the change stage of leisure physical activity behavior of college students.

Introduction

Physical activity (PA) encompasses all types, intensities, and categories of human activity [1]. The substantial health benefits of PA include reduced overall mortality rates [2, 3], risks of cardiovascular diseases [4], cancer incidence [5], and the occurrence of psychological issues such as depression and anxiety [6, 7]. However, physical inactivity (PIA) is an important global public health issue [8]. According to the World Health Organization (WHO) classification, human PA is divided into the following four categories: occupation domain physical activity, transport domain physical activity, household domain physical activity, and leisure-domain physical activity (LDPA) [3]. Among these, the health promotion benefits of LDPA engagement are the most substantial [9–11]. LDPA refers to activities that are not essential for daily life but are decided upon by individuals, including exercise, competitive sports, walking, dancing, and gardening [3]. From the perspective of PIA intervention, compared to other types of PA, the feasibility of encouraging LDPA is the strongest [3, 12]. Therefore, selecting LDPA for targeted research is grounded in a robust theoretical basis and has practical importance.

Human PA is a complex behavior, and the development of regular PA habits cannot be summarized as an “all or nothing” phenomenon [13, 14]. The stages of individual PA behavior change can be explained by the Transtheoretical Model and Stages of Change (TMSC), also known in some studies as the “Cross-Theoretical Model” or “Stages of Behavior Change Model” [15–17]. James Prochaska and Carlo DiClemente proposed this model in the 1980s. Initially, TMSC was used to explain the process by which smokers quit smoking. The core idea of the model is that behavior change is not an isolated event but a continuous process encompassing a series of events (stages), with stratification and classification as the primary methods of studying these events. TMSC involves the stratification and classification of behavior changes, analyzing at which stage of human behavior change is currently occurring, thereby enabling identification of targeted measures for effective intervention. In the 1990 s, Marcus applied the TMSC to the study the stages in human PA behavior change, which was validated in multiple empirical studies [18].

Building on the TMSC, scholars have developed the concept of Motivational Readiness for Physical Activity (MRPA) [13, 14, 19]. The core idea of MRPA is that the stages of human behavior change involving PA cannot be simply classified as an “all or nothing” phenomenon. Instead, MRPA represents a complex process featuring temporal dimensions of behavioral stage changes. The stages of PA behavior change can be conceptually divided into the following five phases: “Pre-contemplation → Contemplation → Preparation → Action → Maintenance” [20]. Understanding the MRPA concept involves consideration of the nonparticipating population in PA during their leisure time as heterogeneous. This heterogeneity is demonstrated in the different stages of PA behavior change within this population [21, 22].

An important issue that scholars currently face is how to help individuals in different stages of PA behavior change successfully transition to the final “Maintenance” stage [23, 24]. From the perspective of intervening in PIA populations, adopting targeted intervention strategies based on an individual’s stage of PA behaviour change can effectively enhance the intervention’s effectiveness [25, 26]. In this study, the goal is to encourage all university students to maintain regular participation in LDPA over the long term, which serves as the core motivation for this research [14].

Previous studies have shown that scholars in different countries have conducted large-sample surveys to analyse the PA behaviour change stages of their respective populations. For example, scholars in Brazil and Germany have conducted studies on the PA behaviour change stages in their countries [17]. However, research on university students’ LDPA behaviour change stages in China is relatively scarce. Most existing domestic studies focus on university students’ physical exercise behaviour or on PA behaviour change stages in children and adolescents or the elderly, with a lack of large-sample survey data specifically on university students’ LDPA behaviour change stages [23, 24, 27, 28]. Therefore, this study performs a large-sample epidemiological survey on the current status of LDPA behavior change stages among Chinese college students and analyzes the related influencing factors [29–31], adolescents, and the elderly in China [14, 32, 33].

The marginal contributions of this study are as follows: first, a comprehensive survey of the status of LDPA behavior change stages among Chinese college students was conducted to provide supporting data for future interventions targeting PIA among college students. Second, the physical and psychological health behaviors of college students were measured to obtain actual data to help scholars comprehensively understand the health status of college students in China. This research aims to provide a theoretical basis for promoting the physical and mental health of college students through statistical analysis of the data.

Methods

Participants

Data were collected using an epidemiological survey method. A total of 49,717 college students from 106 universities across 31 provinces, autonomous regions, and municipalities in China were surveyed, with 41,620 valid questionnaires returned. A total of 24,408 female students accounted for 58.6% of the respondents. The survey targeted undergraduate students in ordinary higher education institutions in mainland China, referencing the List of Ordinary Higher Education Institutions in China (as of June 15, 2023) published by the Chinese Ministry of Education. This study specifically included undergraduate and junior college students but excluded postgraduate (master’s and doctoral) students. A stratified, clustered, and staged sampling method was used in the selection of survey participants.

Determination of sampling sites

Each province (region, city) was allocated three sampling sites to ensure the representativeness of the subjects. The approach involved selecting an equal number of samples from cities under the jurisdiction of each province and autonomous region as sampling locations (cities). Provincial capital cities were designated as “Category 1” sampling locations (cities), and the other two locations (cities) were selected based on geographical considerations within the province or autonomous region. One city with an average level of socioeconomic development was selected as a “Category 2” sampling location (city), and another city with relatively lower socioeconomic development was selected as a “Category 3” sampling location (city). In the case of municipalities directly under the central government, samples were drawn mainly through random cluster sampling. However, the principle of having three sampling locations was maintained.

Determination of sampling units

The selection of sampling units considered the following three main factors: (1) Higher education institutions, including vocational and technical colleges, should be officially registered with the Ministry of Education. (2) The unit must meet the sampling requirements (e.g., age, number of people, and grade distribution). (3) A person responsible for questionnaire distribution who is willing to participate in the monitoring over the long term must be a designated. (4) The sampled students from the selected higher education institutions have already returned for the autumn semester.

Grouping and sample size.

The sample was divided into two populations based on gender (male and female) and then into eight categories based on grade level. The minimum sample size for each category (e.g., first-year male college students) was 45 individuals. The total sample size for each province (region, municipality) was 1080 individuals, with an anticipated national questionnaire completion (excluding Hong Kong, Macau, and Taiwan regions) of 33,480 individuals. The survey was conducted using the Questionnaire Star software for electronic questionnaires during two weeks of teaching in October 2023 (October 9th to 22nd), collecting a total of 49,717.

Measurement

The survey included questionnaires and Body shape measurements. The average time for subjects to complete the entire set of questionnaires was 5:56 min. The mature scales selected for the measurement indicators have all passed the test of the measurement indexes among the students of colleges and universities in China to ensure the quality of questionnaire analyses. The body shape measurement of college students follows the relevant requirements in the Chinese National Physical Fitness Standard for Students (Revised in 2014). Measurement and analysis indicators are as follows.

Outcome variables

The change stages in LDPA behavior among college students were assessed using the European Health Interview Survey–Physical Activity Questionnaire (EHIS–PAQ) and the Stages of Change for Physical Activity (SCPA) scale [34–38]. These questionnaires have standardized administration procedures and have demonstrated high reliability and validity within the college student population [34]. The items of EHIS–PAQ and SCPA have been published in peer-reviewed papers.

The Chinese versions of EHIS–PAQ and SCPA were translated using the “backtranslation method.” Initially, two master’s students (specializing in exercise psychology within the field of sports science) independently translated the questionnaires into Chinese. A preliminary Chinese draft was formed after a collective discussion. Subsequently, a Chinese sports scholar (PhD Professor) was invited to back translate the content, which was then compared and revised against the original questionnaires to ensure that the translations were close in meaning to the original text while also being appropriate for Chinese culture and language expressions. Finally, an expert in the field of exercise psychology (PhD Professor) was invited to optimize each item of the Chinese versions based on expert advice, followed by further revisions and improvements.

The validity of the EHIS–PAQ was assessed using criterion validity, in which the PA levels of college students during a typical week were measured using the ActiGraph GT3X-BT triaxial accelerometer (Pensacola, FL, USA). Thirty-six college students participated in the test (20 females, accounting for 55.6%), wearing the accelerometer continuously for seven days, except during activities such as bathing, swimming, or sleeping. The device was worn on the upper part of the right iliac crest. The ActiGraph software (Version 6.11.4) was used for data downloading and preliminary analysis after the device testing period (Parameters set as shown in Table 1). Re-measurement was promptly conducted for participants whose measurement data were incomplete or did not meet the requirements.

Table 1.

Physical activity measurement parameter settings list of GT3X-BT

Serial number	Parameter content	Setup
1	Sampling interval	15 s
2	Not worn definition	Choi algorithm (2011)
3	Wearing hours per day	≥ 540 min
4	Different intensity thresholds	Butte (Counts)
	SB	0 to 239
	LPA	240–2119
	MPA	2120–44,449
	VPA	4450 and above

SB sedentary behavior, static behavior, LPA light physical activity MPA  moderate physical activity, VPA vigorous physical activity

The EHIS–PAQ assessment was conducted the day after the accelerometer wear period ended. Participants were categorized into “active” and “inactive” groups based on the test data. The “active” group was defined in accordance with the WHO guidelines on PA and sedentary behavior (i.e., engaging in at least 150 min of moderate-intensity aerobic physical activity per week or 75–150 min of vigorous-intensity aerobic physical activity; or an equivalent combination of moderate- and vigorous-intensity activity) [12]. The correlation between the “active” group identified through instrument-measured PA and the EHIS–PAQ assessment was calculated. The definition of the “active” group based on EHIS–PAQ included transport-related and Leisure-time physical activities. The overall correlation coefficient was 0.602 (P < 0.001), indicating that the EHIS–PAQ demonstrates desirable validity [39].

The validity of SCPA was assessed through criterion validity using the College Student Physical Exercise Behavior Change Questionnaire developed by Yin serving as the criterion tool [40]. This questionnaire has demonstrated high reliability and validity within the Chinese college student population and is equipped with normative data for this group. A total of 156 college students (105 females, accounting for 67.3%) participated in the questionnaire test. The results showed that the overall correlation between SCPA and the College Student Physical Exercise Behavior Change Questionnaire was 0.663 (P < 0.001), indicating that SCPA exhibits desirable validity [39].

The assessment and calculation procedure are as follows: initially, students were classified into “active” and “inactive” groups using EHIS–PAQ. Participants were first briefed on the meaning of LDPA, followed by inquiries regarding the number of days and duration per session of at least 10 min of moderate to vigorous LDPA engagement during their leisure time over the past week [35, 36]. Additionally, participants were asked to report the number of times they undertook muscle-strengthening exercises during the past week. The classification criteria for the “active” group were based on the WHO standards (i.e., engaging in at least 150 min of moderate to vigorous PA per week or at least 75 min of vigorous PA, along with muscle-strengthening exercises at least twice a week during leisure time) [12]. Subsequently, SCPA was used to ask participants three questions regarding their leisure-time physical activities [14, 37, 41].

Question 1 targeted active group students, asking, “How long have you been adhering to LDPA?” with response options “<6 months” and “≥ 6 months.” Participants selecting “<6 months” were categorized into the “Action” stage, while those selecting “≥6 months” were categorized into the “Maintenance” stage. Questions 2 and 3 targeted the inactive group. Question 2 asked, “Do you intend to engage in LDPA?” with response options “Yes” or “No.” Participants selecting “No” were categorized into the “Precontemplation” stage. If “Yes” was chosen, Question 3 followed: “How long have you intended to engage in LDPA?” with response options “>6 months,” “between 1 and 5 months,” and “<1 month.” Selections of “>6 months” were categorized into the “precontemplation” stage, “between 1 and 5 months” into the “Contemplation” stage, and “<1 month” into the “Preparation” stage [41]. Figure 1 shows the algorithm for determining the stages of LDPA behavior change in college students.

Fig. 1.

Algorithm for determining the stage of change in LDPA behavior among college students

Predictive variable

Predictive variables include sociodemographic information, health behavior variables, and health status variables, which are further divided into physical and psychological parts.

Sociodemographic information encompasses gender, grade, ethnicity (Han and ethnic minorities), and school location. Given the vast territory and diverse ethnicities of China, variables such as “ethnicity” and “region” were incorporated into the analysis. The division of school locations into eastern, central, and western regions follows the guidelines set by the Statistical Data Management Center of the National Bureau of Statistics.

Health behavior variables include smoking, drinking behaviors, and sleep. Smoking behavior is categorized into “never smoked,” “occasional smoking,” and “regular smoking,” according to the WHO definitions. Drinking behavior is divided into “never drank,” “drank without getting drunk,” and “drank and got drunk,” based on the number of days of drinking and getting drunk in the past month, referencing the American Youth Risk Behavior Survey. Sleep behavior is assessed using the Pittsburgh Sleep Quality Index (PSQI) for college student sleep behavior measurement [42]. Chinese scholars such as Liu and Lu have tested the reliability and validity of PSQI among college students, confirming its good applicability in this group [43, 44]. The PSQI assesses the quality of sleep over the past month, with a total score ranging from 0 to 21, revealing that a high score indicates poor sleep quality. PSQI consists of 19 self-assessment items and 5 other-assessment items, with the 19th self-assessment item and the 5 other-assessment items not contributing to the scoring. It is primarily divided into seven components: subjective sleep quality, sleep latency, sleep efficiency, sleep disturbances, use of sleep medications, and daytime dysfunction. The total PSQI score ranges from 0 to 21, with higher scores indicating poorer sleep quality (the normative scores for sleep quality of Chinese adults are as follows: 0–5 for good sleep quality, 6–10 for fair sleep quality, 11–15 for average sleep quality, and 16–21 for poor) [45].

Health status variables include physical health (comprising body morphology and eyesight condition) and mental health (encompassing depression detection, anxiety detection, and general self-efficacy), totaling five variables. Body Morphology is represented by BMI, with testing and classification of height and weight of college students following the National College Student Physical Health Standards (2014 Edition). Table 2 details specific BMI categories. Eyesight condition is assessed by inquiring about the use of corrective glasses and pathological poor vision within the past month, classified into “normal vision” and “poor vision” for analysis.

Table 2.

Body mass index (BMI) single item scoring table (Unit: kg/m2)

hierarchy	schoolboy	schoolgirl
normalcy	17.9 to 23.9	17.2 to 23.9
low weight	≤ 17.8	≤ 17.1
overweight (baggage, freight)	24.0–27.9	24.0–27.9
obese	≥ 28.0	≥ 28.0

Depression status is assessed in accordance with the methods of mental health epidemiology in the Mental Health Blue Book: Report on the Development of National Mental Health in China (2019–2020) [46]. The Center for Epidemiological Studies Depression Scale (CES-D) is used for screening [47]. Respondents rate the frequency of symptoms in the past week on a scale of 0 to 3. A score of 10 is the threshold for risk, with 17 indicating a high risk of depression [47]. Categories include “no risk of depression,” “mild risk of depression,” and “severe risk of depression.” He conducted a reliability and validity test of the CES-D using a nationally stratified random sample of 30,801 individuals from the general population [48]. The results displayed that the internal consistency reliability of the CES-D ranged from 0.85 to 0.88, with a test–retest reliability of 0.49 (P < 0.001). The correlation of each item with the total score was more significant than 0.5, indicating that the CES-D has good reliability and validity and can thus be used to assess depression levels across different age groups. In this study, the internal consistency reliability (Cronbach’s alpha) of the CES-D was 0.822 (for positively worded items) and 0.859 (for negatively worded items), and the test–retest reliability coefficient was 0.619.

Anxiety status is measured using the Generalized Anxiety Disorder scale (GAD-7) for anxiety level assessment [49]. The Generalized Anxiety Disorder Scale (GAD-7) consists of 7 items, including “feeling nervous, anxious, or on edge,” “being unable to stop or control worrying,” “worrying excessively about different things,” “having difficulty relaxing,” “becoming restless due to anxiety,” “becoming easily irritated,” and “feeling afraid as though something terrible might happen“ [50]. Participants rate the frequency of anxiety symptoms over the past two weeks on a scale of 0 to 3. The total score is the sum of seven items, ranging from 0 to 21, where 0–4, 5–9, 10–14, and 15–21 indicate no anxiety, mild anxiety, moderate anxiety, and severe anxiety, respectively. Fu et al. utilized the GAD-7 to investigate the mental health of college students in China during the COVID-19 pandemic [46]. The validation of the reliability of GAD-7 revealed that its internal consistency coefficient reached 0.901, indicating that the GAD-7 can generally be used for screening anxiety risks among Chinese college students. In this study, the internal consistency reliability of the GAD-7 was 0.781, and the test–retest reliability coefficient was 0.803.

General self-efficacy is measured for college students using the General Perceived Self-efficacy Scale (GSES) [51]. The General Perceived Self-efficacy Scale (GSES) was proposed by Schwarzer in 1993, and it evaluates the subject’s feelings, thoughts, and actions. Originally consisting of 20 items, the scale was revised in 1997 to include 10 items. Previous research supports the connection between general self-efficacy and changes in PA behavior [52]. The threshold score for GSES is set at 2.5; scores below this threshold indicate low self-efficacy, with “low” and “normal” categories established for detailed analysis [53]. The Chinese version of GSES, which was revised and translated by Wang in 2001, underwent reliability and validity analyses [54]. Results showed that GSES has good reliability, demonstrating an internal consistency coefficient (Cronbach’s alpha) of 0.87, test–retest reliability of r = 0.83 (P < 0.001), and split-half reliability of r = 0.82 (n = 401, P < 0.001).

Statistical analysis

Data processing was conducted using SPSS 25.0 and EXCEL software. (1) Data preprocessing was initially performed, and valid questionnaires were selected in accordance with the criteria for effective data filtering. (2) The Chi-square test was utilized to verify the differences under various MRPA levels among predictive variables, employing Cramer’s V coefficient (V coefficient) for the analysis of effect size in differences. A V coefficient between 0.1 and 0.3 indicates the presence of minor differences, that between 0.3 and 0.5 denotes medium differences, and a V more significant than 0.5 indicates large differences [55]. (3) Multinomial logistic regression analysis was applied to explore the preliminary associations between stages of LDPA behavior changes and various predictive variables.

Results

The valid questionnaires for this research were 41,620, with a valid questionnaire rate of 83.7%, of which 17,212 (41.4%) were male, and the overall response rate met the sampling requirements.

Descriptive analyses.

Table 3 presents the distribution of university students’ stages of change in LDPA behaviour. Overall, most university students in China (89.0%) fall into the “inactive” group. Specifically, 52.3% of students are in the pre-intention stage of LDPA behaviour change (n = 21,777), 12.4% are in the intention stage (n = 5,164), 24.3% are in the preparation stage (n = 10,114), 4.6% are in the action stage (n = 1,900), and only 6.4% are in the maintenance stage (n = 2,665).

Table 3.

List of stages of change in LDPA behavior of college students (N = 41,620)

variant		N	%	Pre-contemplation	Contemplation	Preparation	Action	Maintenance	statistical value
				n = 21,777	n = 5164	n = 10,114	n = 1900	n = 2665
distinguishing between the sexes	schoolboy	17,212	41.4	36.7	11.2	31.6	8.1	12.3	x2 = 4481.570 P < 0.001 V = 0.328
	schoolgirl	24,408	58.6	63.3	13.3	19.1	2.1	2.2
grade
	first year	23,878	57.4	49.9	13.3	25.8	4.8	6.2	x2 = 322.957 P < 0.001 V = 0.188
	second year	10,605	25.5	52.8	12.4	24.5	4.2	6.1
	third grade	5479	13.2	60.8	9.6	18.4	4.1	7.1
	fourth grade*	1658	4.0	56.2	9.0	20.7	4.5	9.5
nation
	Han ethnic group	38,360	92.2	53.1	12.4	24.0	4.4	6.1	x2 = 244.224 P < 0.001 V = 0.159
	national minority	3260	7.8	43.7	13.0	27.5	6.3	9.5
district (not necessarily formal administrative unit)
	eastern part	14,657	35.2	53.5	12.2	24.6	4.4	5.2	x2 = 280.984 P < 0.001 V = 0.166
	western part	10,300	24.7	48.5	12.0	25.5	5.6	8.3
	central section	16,663	40.0	53.6	12.8	23.3	4.0	6.3
cigarette smoking
	Never smoked	36,986	88.9	52.2	12.4	24.3	4.6	6.5	x2 = 4.936 P = 0.764 V = 0.011
	occasional cigarette	3371	8.1	52.6	12.6	24.3	4.6	5.9
	Regular smoking	1263	3.0	53.9	12.8	23.4	3.9	5.9
drinking wine
	Never had a drink	20,337	48.9	52.3	12.4	24.3	4.5	6.4	x2 = 2.288 P = 0.971 V = 0.007
	Drinking without intoxication	20,572	49.4	52.3	12.4	24.4	4.6	6.4
	Drinking had been drunk	711	1.7	53.0	13.5	22.2	4.8	6.5
sleep†
	an excellent	18,192	43.7	52.7	12.5	24.0	4.6	6.2	x2 = 110.823 P < 0.001 V = 0.106
	not bad	15,653	37.6	52.0	12.4	24.6	4.4	6.5
	usual	5579	13.4	51.6	12.1	24.5	4.9	6.9
	poorly	2196	5.3	52.9	12.6	24.1	4.6	5.7
BMI
	low weight	4608	11.1	59.9	12.6	21.7	2.9	3.0	x2 = 237.415 P < 0.001 V = 0.176
	normalcy	24,976	60.0	51.7	12.5	24.4	4.5	6.9
	overweight (baggage, freight)	5563	13.4	48.9	12.5	25.8	5.8	7.0
	obese	6433	15.5	52.5	11.8	24.5	5.1	6.2
vision
	unhealthy	31,599	75.9	52.3	12.2	24.4	4.5	6.5	x2 = 5.975 P = 0.021 V = 0.012
	normalcy	10,021	24.1	52.3	12.9	23.9	4.7	6.1
gloomy
	risk-free	26,782	64.3	52.4	12.3	24.3	4.6	6.3	x2 = 2.471 P = 0.963 V = 0.008
	moderate risk	11,368	27.3	51.9	12.5	24.5	4.6	6.5
	high risk	3470	8.3	52.6	12.7	23.6	4.4	6.6
apprehensive
	hasn’t	24,403	58.6	52.6	12.3	24.3	4.5	6.3	x2 = 12.498 P = 0.407 V = 0.017
	mild (symptoms etc.)	12,421	29.8	51.9	12.5	24.4	4.7	6.6
	moderately	3548	8.5	51.6	13.3	24.1	4.3	6.7
	serious	1248	3.0	54.4	11.1	24.7	4.7	5.0
General self-efficacy
	lower (one’s head)	13,864	33.3	52.1	12.4	24.5	4.6	6.4	x2 = 0.563 P = 0.967 V = 0.004
	normalcy	27,756	66.7	52.4	12.4	24.2	4.5	6.4
(grand) total		41,620	100	52.3	12.4	24.3	4.6	6.4

^*Senior students, including “fifth-year” students in clinical medicine and other disciplines

^†Missing data in this category

V stands for the effect size Cramer’s V coefficient

A difference was observed in the stage of LDPA behavioral change among college students of different genders (V = 0.328), demonstrating the higher activity of male students than female students, with male students (12.3%) revealing a substantially higher percentage in the maintenance stage than female students (2.2%). Differences were also found in the stages of LDPA behavioral change among college students of different grades (V = 0.188), with fourth-year students (9.5%) having the highest percentage in the maintenance stage. A difference was observed in the stage of LDPA behavioral change among college students of different ethnicities (V = 0.159), with ethnic minority college students (9.5%) revealing a higher percentage in the maintenance stage than Han Chinese college students (6.1%). Moreover, a difference was found in the stage of LDPA behavioral change among students in different school locations (V = 0.166); specifically, college students in the western region had the highest percentage of retention stage (8.3%).

College students with inferior sleep quality had the lowest percentage of maintenance stage at 5.7%, while differences were also observed in the stage of LDPA behavioral change among college students with different sleep quality (V = 0.106). A difference was also found in the stage of LDPA behavioral change among college students with different BMI categories (V = 0.176), with overweight college students (7.0%) revealing a substantially higher percentage in the maintenance stage than the other categories. No differences were observed in smoking behavior (P = 0.764), drinking behavior (P = 0.971), visual acuity (P = 0.021, V = 0.012), depression (P = 0.963), anxiety (P = 0.407), and general self-efficacy (P = 0.967).

Regression analysis

Table 4 presents a list of the results of the multivariate regression analyses with reference to former intentional-stage college students. Gender, grade, and ethnicity were included to construct the multifactorial logistic regression equation. Results showed that seven variables, including gender, grade, ethnicity, school location, sleep quality, BMI, and anxiety level, significantly predicted the stage of change in LDPA of college students. Among the sleep variables, the effect of average sleep quality on the stage of LDPA behavioral change among college students was statistically significant (OR = 1.255; 95% CI: 1.005–1.581) relative to poor sleep quality. Among the body shape variables, the predictive effect of normal and overweight BMI was significant (p < 0.05). Among the anxiety level variables, all other levels of anxiety demonstrated significant other predictive effects relative to severe anxiety. The predictive effects of smoking behavior, drinking behavior, visual acuity, depression level, and general self-efficacy were not statistically significant.

Table 4.

Multinomial logistic regression results predicting stages of change in LDPA behavior (N = 41,620)

predictor variable	Contemplation				Preparation				Action				Maintenance
	Wald Significance	OR	Lower 95% CI	95% CI ceiling	Wald Significance	OR	Lower 95% CI	95% CI ceiling	Wald Significance	OR	Lower 95% CI	95% CI ceiling	Wald Significance	OR	Lower 95% CI	95% CI ceiling
Sex_Male	< 0.001	1.438	1.347	1.536	< 0.001	2.925	2.780	3.077	< 0.001	7.011	6.286	7.820	< 0.001	10.252	9.262	11.348
Grade_1	< 0.001	1.643	1.376	1.962	< 0.001	1.352	1.187	1.541	0.518	1.085	0.846	1.392	< 0.001	0.638	0.528	0.769
Grade_2	< 0.001	1.484	1.236	1.783	< 0.001	1.327	1.159	1.519	0.612	1.070	0.825	1.387	0.002	0.727	0.596	0.886
Grade_3	0.948	0.993	0.816	1.209	0.156	0.900	0.778	1.041	0.976	1.004	0.761	1.325	0.036	0.799	0.647	0.986
Ethnicity_Han	< 0.001	0.789	0.703	0.886	< 0.001	0.745	0.680	0.815	< 0.001	0.619	0.527	0.727	< 0.001	0.553	0.481	0.636
Region_East	0.178	0.952	0.887	1.022	< 0.001	1.130	1.068	1.197	< 0.001	1.302	1.159	1.462	0.631	1.026	0.925	1.137
Region_West	0.120	0.938	0.865	1.017	0.002	1.107	1.038	1.180	< 0.001	1.449	1.281	1.639	< 0.001	1.463	1.317	1.624
Smoking_Never smoked	0.981	0.998	0.831	1.198	0.906	1.009	0.870	1.170	0.319	1.172	0.858	1.600	0.636	1.066	0.819	1.387
Smoking_Occasional Smoking	0.897	1.014	0.826	1.244	0.972	1.003	0.850	1.184	0.494	1.128	0.799	1.590	0.789	0.961	0.715	1.291
Drinking_never drank alcohol	0.409	0.904	0.713	1.148	0.669	1.045	0.855	1.277	0.429	0.858	0.587	1.254	0.466	0.882	0.630	1.235
Smoking_Drinking not drunk	0.398	0.904	0.714	1.143	0.536	1.065	0.873	1.298	0.534	0.888	0.611	1.291	0.527	0.898	0.644	1.252
Sleep quality_very good	0.583	1.044	0.894	1.220	0.915	0.993	0.877	1.125	0.796	0.968	0.757	1.238	0.430	1.094	0.875	1.369
Sleep Quality_Okay	0.649	1.036	0.889	1.208	0.510	1.042	0.922	1.178	0.839	0.975	0.765	1.243	0.111	1.196	0.960	1.491
Sleep Quality_General	0.973	0.997	0.847	1.174	0.592	1.036	0.910	1.180	0.538	1.083	0.840	1.398	0.025	1.255	1.005	1.581
BMI_low weight	0.451	1.048	0.928	1.183	0.115	1.082	0.981	1.193	0.254	0.883	0.713	1.093	0.084	0.834	0.678	1.025
BMI_Normal	< 0.001	1.180	1.078	1.290	< 0.001	1.307	1.218	1.402	< 0.001	1.380	1.207	1.576	< 0.001	1.864	1.653	2.102
BMI_Overweight	0.016	1.151	1.026	1.292	< 0.001	1.178	1.077	1.289	0.001	1.307	1.108	1.542	0.001	1.291	1.108	1.505
Visual condition bad	0.083	0.939	0.874	1.008	0.843	1.006	0.950	1.065	0.236	0.934	0.835	1.045	0.486	1.036	0.937	1.146
Depression_No Risk	0.590	0.961	0.833	1.109	0.431	1.048	0.933	1.177	0.733	1.041	0.826	1.312	0.152	0.867	0.713	1.054
Depression_Mild Risk	0.721	0.975	0.848	1.121	0.393	1.051	0.938	1.177	0.705	1.045	0.833	1.309	0.224	0.888	0.733	1.076
Anxiety_No	0.166	1.165	0.938	1.447	0.855	0.985	0.834	1.163	0.946	0.989	0.712	1.373	0.034	1.399	1.025	1.908
Anxiety_mild	0.105	1.193	0.964	1.477	0.860	0.985	0.837	1.161	0.856	1.030	0.746	1.422	0.028	1.410	1.038	1.916
Anxiety_Moderate	0.029	1.268	1.025	1.569	0.983	0.998	0.847	1.177	0.690	0.936	0.676	1.296	0.030	1.402	1.033	1.903
General self-efficacy classification_low	0.938	1.003	0.938	1.071	0.850	0.995	0.944	1.049	0.921	0.995	0.897	1.104	0.519	0.970	0.886	1.063

Discussion

The health concept of “some movement is beneficial, more movement is better, moderate effort is key, and persistence is essential” has been widely accepted, and the positive impact of regular PA on health has been confirmed by numerous studies [1]. Increasing research indicates that different populations undergo various stages in the process of changing their physical activity levels, which provides a basis for developing personalized intervention strategies [56]. Particularly in interventions targeting university students, considering their stage in the PA behaviour change process may enhance the effectiveness of these interventions.

This study adopts a large-sample epidemiological survey approach to explore the stages of behaviour change in LDPA among Chinese university students. Notably, only 6.4% of students are in the maintenance stage of LDPA. Consequently, most students are still in the early stages of behaviour change, specifically in the “preparation” or “pre-intention” stages. This finding highlights the challenges faced by university students in achieving lasting, consistent physical activity habits.

Through the analysis of several potential influencing factors, the study identifies significant associations between gender, academic year, ethnicity, geographic location of the university, sleep quality, BMI, and anxiety levels with the stages of LDPA behaviour change. This suggests that university students’ physical activity behaviours are influenced by multiple factors. Therefore, intervention strategies should take these individual differences into account when being designed, and more targeted intervention plans should be developed. The study concludes that intervention strategies based on the stages of behaviour change can more effectively promote the transition of university students from low-intensity physical activity to maintaining regular physical activity habits in the long term. Future research could further explore how personalized intervention strategies can be applied in different stages of behaviour change to improve university students’ PA levels and overall health. According to existing literature, the proportion of Chinese university students engaging in regular LDPA is notably low. For instance, a recent study examined the MRPA (Moderate to Vigorous Physical Activity) levels among German adults [14]. Like this study, the algorithm used to calculate the stages of LDPA behaviour change in that research was also adapted from the study by Ronda et al. [29]. This study found that 21.3% of German adults were in the “Action” and “Maintenance” stages [29, 57]. Using the same measurement method, a study in Brazil showed that 24% of Brazilian adults were in the “Maintenance” stage of LDPA [29]. A survey in Portugal on adolescent physical activity revealed that 60.1% of adolescents were in the “Maintenance” stage, though it should be noted that this study was focused on all types of physical activity [58]. The analysis suggests that the poor regularity of LDPA among Chinese university students may be related to the global decline in physical activity levels across various populations. Additionally, the Chinese education system tends to place greater emphasis on academic performance, often overlooking the development of physical health and athletic ability. The inadequacy of physical education in schools has led to a lower level of awareness and participation in LDPA among university students, which is one of the key reasons for the low levels of regular LDPA.

Furthermore, an important prerequisite for developing a regular LDPA habit is that university students need to master “one or two sports skills” themselves. However, research on school sports in China indicates that university students still face significant challenges in mastering specialized sports skills. The lack of proficiency in specific sports may be a key factor affecting students’ regular participation in LDPA. Specifically, the absence of sports skills can lead to a lack of confidence or interest during physical activity, which in turn affects their motivation to continue participating and their determination to maintain regular exercise over the long term [13, 29]. Studies show that the mastery of sports skills is closely linked to an individual’s attitude towards and engagement in physical activity. University students who lack these skills are more likely to experience frustration during exercise, leading to reluctance to invest enough time and energy in maintaining regular activity. Therefore, helping university students acquire basic sports skills not only enhances their confidence and willingness to participate but also lays the foundation for cultivating long-term, stable LDPA habits. By strengthening the development of specialized sports skills, students are better able to engage in physical activities, enjoy the benefits of exercise, and establish a sustainable pattern of physical activity.

Gender and academic year are factors influencing the stages of LDPA behaviour change among university students [14, 29, 59]. This is consistent with previous research findings, although there is a difference in the gender aspect. In a study conducted among adults in southern Brazil, it was found that women were in more active stages of behaviour change (i.e., in the action and maintenance stages), which contrasts with the findings of this study. This discrepancy may be attributed to cultural differences. In China, certain cultural beliefs and gender stereotypes may affect women’s enthusiasm for participating in physical activities. Additionally, senior students in university are more likely to engage in regular LDPA, which may be related to their greater maturity, time management skills, and reduced social pressures. Ethnicity also influences the stages of LDPA behavior change among college students, with students from ethnic minorities being more active. This finding could be attributed to cultural traditions and value differences because various ethnicities harbor distinct cultural traditions and values. Some ethnic groups, such as the Mongolian ethnicity, place emphasis on physical health and activity, valuing the importance of health, strength, and flexibility in their cultural traditions and considering PA as a crucial means of maintaining physical and mental health.

Furthermore, the results indicate that students from universities in the western region tend to remain in the maintenance stage of LDPA behavior, possibly due to geographical and climatic conditions. Some universities in the western region, is characterized by beautiful natural landscapes such as mountains and grasslands, offer students additional opportunities for outdoor activities and physical exercise. By contrast, the high degree of urbanization and land-use pressures in some central and eastern regions may limit the construction of various sports facilities, enabling the easy participation of students in the western region in regular LDPA.

Sleep quality is an influential factor in the stages of LDPA behavior change among college students [60]. The importance of sleep for human health is unquestionable. In this study, university students with higher sleep quality exhibited greater levels of activity, which may be closely related to the role of sleep in energy and fatigue management, mental and physical health, and the regulation of the body’s internal clock [57]. However, the results of this study show that sleep quality only significantly predicts the maintenance stage of LDPA behaviour among university students. This finding may be related to the measurement tool used to assess sleep quality. While the PSQI is an effective tool for measuring sleep quality, its normative data for the university student population in China is somewhat outdated. Especially in recent years, with the rise of screen-based behaviours, the PSQI has not been updated to reflect current lifestyles. Therefore, the existing measurement tool may not fully capture the sleep quality status of modern university students, potentially affecting the accuracy of the study’s results.

Body morphology is an influential factor in the stages of LDPA behavior change among college students, revealing that those with normal or overweight BMI are active in LDPA. This phenomenon may be attributed to health awareness, self-esteem, and body image factors [61]. College students with average or overweight body morphology might be highly concerned regarding their health. Those with an average weight might reflect a relatively healthy weight status, while overweight students could be reminded of the need to act to improve their physical condition. This health consciousness might motivate them to increase their LDPA engagement to maintain or improve their health levels. Additionally, an ideal body morphology can introduce high self-esteem and positive body image perceptions to college students, possibly aligning closely with mainstream beauty standards, which positively affect their confidence and active behaviors. Conversely, being overweight might cause dissatisfaction with their appearance, driving the willingness to engage in LDPA.

The level of anxiety is another influential factor in the stages of LDPA behavior change among college students, with those having lower anxiety levels being more active in LDPA. This finding may be associated with mental health status and stress management factors [62]. In terms of mental health, low anxiety levels are generally linked to improved psychological states. College students with low anxiety may have superior emotional regulation abilities, a tendency toward positive self-perception, and positive emotional experiences. Such psychological health can encourage optimistic and confident participation in various LDPA activities.

Regarding stress management, college students with low anxiety levels may possess effective psychological stress management strategies. These students might have coping abilities and problem-solving skills to handle daily life stress and challenges and use LDPA activities to adjust their psychological state and reduce stress. This ability to manage psychological stress can increase their willingness and capacity to engage in LDPA.

Despite its precise sampling and large sample size, this study also has Limitations. First, the measurement of LDPA behavior change stages among college students is based on retrospective self-reported questionnaires, which may introduce recall bias and increase the probability of Type I errors. Second, the proportion of fourth-year students in the sample is less than 5% due to the gradual preparation period during the survey, which could magnify the impact of sampling errors when analyzing the “grade” variable, reducing the external validity of the conclusions. Additionally, some of the data norms used in this study, such as PSQI and GSES, are outdated, potentially increasing systematic errors due to measurement.

Conclusions

Most LDPA behaviors of college students are in the pre-intentional stage. Gender, grade, ethnicity, school location, sleep quality, body shape, and anxiety level are the factors that affect the stage of LDPA change. The intention of college students to engage in regular LDPA is weak. Thus, additional factors affecting the change stage of LDPA among college students and its formation mechanism should be further explored using longitudinal pursuits, double-masked experiments, and other research tools in future studies. In developing intervention programmed for PIA college students, classifying the PA change stage of college students is necessary to target the programmed development of intervention.

Abbreviations

CES-D

Center for Epidemiological Studies Depression Scale

EHIS-PAQ

European Health Interview Survey – Physical Activity Questionnaire

GAD-7

Generalized Anxiety Disorder scale

GSES

General Perceived Self-efficacy Scale

LDPA

Leisure-domain physical activity

MRPA

Motivational Readiness for Physical Activity

PA

Physical activity

PIA

physical inactivity

PSQI

Pittsburgh Sleep Quality Index

SCPA

Stages of Change for Physical Activity

TMSC

Transtheoretical Model and Stages of Change

WHO

World Health Organization

Authors’ contributions

Mrs. S.H.: data collect and manuscript editing Mr. Q.C.: data analysis and original draft writing Mr. F.M.: data analysis and original draft writing Mr. B.L.: data collect Mr. H.L.: data collect Mrs. L.Z.: data collect Mrs. Y.L.: data collect Mr. C.Y.: review and editing.

Funding

This research was supported by the following funds:

1 Preparation for the 15th National Games Sichuan Province key sports (rhythmic gymnastics) science and technology support system establishment and application. (NO. 2023YFS0452).

2 2024 General Project of Philosophy and Social Science Research in Jiangsu Universities, “Research on the Promotion of College Students’ Physical and Mental Health Based on the Improvement of Physical Literacy” (No. 2024SJYB1253)”.

3 Special fund for "Sichuan Provincial Key Laboratory of Sports for Adolescent Mental Health promotion".

These funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Data availability

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

This study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of Nantong University (No 70/2022). Informed consent was obtained from all participants involved in this study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.