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Article

How Do Chinese Migrant Workers Avoid Leisure-Time Physical Inactivity?

by
Zhanqiang Zhu
1,2,*,
Jiaying Fu
1 and
Suhong Zhou
1,2
1
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
2
Guangdong Provincial Engineering Research Center for Public Security and Disaster, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(10), 4700; https://doi.org/10.3390/su17104700
Submission received: 3 December 2024 / Revised: 14 January 2025 / Accepted: 21 February 2025 / Published: 20 May 2025
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Abstract

Migrant workers, vital for urban sustainable development, often exhibit leisure-time physical inactivity (LTPI). Few studies have examined LTPI and its constraints among migrant workers. This study aimed to identify the determinants of LTPI and its constraints among migrant workers. Guangzhou was used as a case study through a questionnaire survey of 26 communities (n = 1024). Logistic regression assessed the impact of household registration on LTPI and its interaction effects. The determinants of LTPI among migrant workers were compared with those of the other groups. The study found a link between LTPI and the living environment among residents and migrant workers. Household registration influences LTPI through interactions with factors such as the number of sports facilities and community greetings. The main barriers to leisure-time physical activity among migrant workers were insufficient education, social capital, and green open spaces. This study discusses the underlying mechanisms and proposes measures to address LTPI among migrant workers.

1. Introduction

Migrant workers (also called floating people) in China generally refer to migrants who work in cities without a locally registered household [1,2,3] or to workers who are registered in rural areas but do not have non-rural jobs in towns [4,5,6,7]. Other scholars have narrowly defined migrant workers as people registered in rural areas, but working in a city outside their hometown [8,9]. Migrant workers are critical contributors to urban development, but they are marginal people in urban China [10], as in other countries. The number of migrant workers in China has increased tremendously [11], reaching 375.81 million in 2020 [12]. However, the literature has noted that migrant workers experience health constraints, notably in their integration into urban life [13], especially in accessing urban facilities [14,15]. Migrant workers are now deemed crucial for building inclusive urban societies in China [16]. City governments in inflow areas are obligated to cover the cost of migrant workers entering the city, referred to as the cost of urbanization [17]. Measures to encourage the population urbanization of migrant workers have been taken, and outcomes such as equitable education and a fair residential guaranteed policy are aimed at or even achieved [16]. As Zhu et al. optimally anticipated [18], with the continuous promotion of a new type of urbanization strategy and the gradual improvement of modern urban governance capacity, the balanced and harmonious coexistence of residents in cities and migrant workers will ultimately be achieved. Nevertheless, the mechanisms underlying how and why constraints on daily living among migrant workers, such as leisure-time physical inactivity, have been overlooked.
Leisure-time physical inactivity (LTPI) denotes the absence of engagement in physical activities during leisure, including but not limited to sports, gardening, walking, or cycling [19,20]. The LTPI provides a thread to explore the determinants of individual healthy behaviors and eliminate underlying constraints [21]. Self-reporting generally assesses it as not engaging in regular physical activity during leisure time [21]. Other forms of physical activity, such as transportation or occupational physical activity, are obligatory daily activities, leisure-time physical activity features optionality, relaxation, and benefits to physical and mental health [22]. In contrast, LTPI is associated with individual health risks such as chronic diseases and mortality. Thus, health constraints derived from LTPI have gained increasing attention, especially for marginal groups [23,24]. LTPI is commonly found among migrant workers and does harm their health in urban China [25], as stated in the international literature [26]. Migrant workers appear to have weaker constitutions [27]. They are more exposed to epidemic diseases, occupational health risks, and mental health diseases during their early migration and then to chronic diseases, such as coronary heart disease, stroke, and type 2 diabetes as their income increases [28,29]. However, the underlying constraints in this group were ignored.
In summary, it is essential to understand whether migrant workers experience constraints on LTPI and facilitate their escape. This study aims to identify the determinants that influence LTPI and the corresponding interaction effects among migrant workers. The following three scientific questions were addressed: (a) Does household registration disparity with LTPI exist among residents? (b) What is the interaction effect of household registration on LTPI? (c) Do the determinants of LTPI among migrant workers differ from those of other household registration groups?
This study is structured as follows: Section 1 outlines the introduction. Section 2 presents a literature review to support the study design to investigate LTPI among migrant workers. Section 3 presents a case study and methodology. Section 4 presents the results of the study. Finally, the conclusions and findings are discussed, offering guidance for improving LTPI among migrant workers.

2. Literature Review

2.1. What Are the Determinants of LTPI?

Leisure-time physical (in)activity differs between individuals and social groups. Factors affecting demographic and socio-economic inequalities of LTPI, such as race/ethnicity, gender [30], age [31], marital status [32], income [33], education [20], occupational characteristics [21], and household preference [34], were observed to vary among residents.
The social and built environment surrounding a residence plays a vital role in influencing LPTI among residents and creating an inclusive society [16]. A positive neighborhood environment motivates people to live an active life, whereas a hostile climate encourages a sedentary lifestyle and adversely damages health [35].
A good social environment increases residents’ willingness to participate in physical activities [36]. Social support from households and residential environments is essential. Farrell et al. argued that household preferences (i.e., those of relatives or family members) play an indispensable role in the decision to engage in sports [37]. Residential modes (such as shantytowns or superior living environments) and social features of the neighborhood, such as esthetic convenience and companions, also matter [38]. Moreover, in distinct environments such as urban and rural areas [39], the factors affecting the intensity of leisure-time physical activity also vary significantly.
Therefore, a well-built environment is essential. Su et al. found that perceived urban built environment attributes significantly correlate with leisure-time physical activity [40]. Greater access to physical activity resources and street connectivity increases an individual’s likelihood of being physically active. Environmental factors, such as transportation, green open spaces, and sports facilities, are crucial for encouraging outdoor recreation [41,42,43]. Green open spaces may positively affect leisure-time physical activity [44]. For instance, parks in the vicinity of the neighborhood and green spaces within communities contribute to the outdoor recreation of residents. In summary, as Crespo et al. stated [45], social and built environments such as safety, social support, acculturation, and environmental barriers should be considered to explain LTPI over the sole of demographic and socio-economic characteristics such as race, ethnicity, and social class. Although social and built environments in China have rarely been documented with LTPI, the venue and equipment surrounding individuals are considered core factors in leisure-time physical activity [35].

2.2. What Constraints Might LTPI Impose on Migrant Workers?

In general, systemic investigations into the determinants of migrant workers’ LTPI are lacking. As the multidimensional framework of LTPI discussed in Section 2.1, demographic characteristics and socio-economic status, as well as the social and built environment in the neighborhood, are regarded as determinants of migrant workers as urban residents. Demographic characteristics, such as sex, age, race, and family structure, are rarely documented in Chinese cases. Nor are the factors related to social and built environments. Nevertheless, socio-economic status has been investigated relatively intensively.
Regarding socio-economic status, the nature of an occupation plays a role in influencing the motivation and mood of migrant workers to participate in leisure-time physical activities. According to the literature, migrant workers usually work in urban China’s informal or densely populated sectors such as architecture, catering, and crafts [25]. Additionally, they spend more time on occupational physical activity than urban citizens [27,46]. Migrant workers usually perform extra work during leisure time rather than participate in leisure sports or with the overlap of work and leisure time and space. Regarding the standard eight hours of working per day, migrant workers spent 11–12 h or more on occupational physical activity [25]. Although the occupational physical activities of most migrant workers require a higher level of physical exertion, a surprising result was that the vital capacity/weight and grip strength of the non-overtime group were all significantly better than those of the overtime group (over eight hours of work per day) [27]. Among the migrant workers, only 7.4% of males and 4.4% of females were documented to participate in leisure-time physical activity. While some migrant workers engage in leisure-time physical activity characterized by less intensity, easy access, and low expense, such as walking, jogging, playing ball sports, doing exercises, etc. [27,43], most migrant workers live sedentary and physically inactive lifestyles, exemplified by watching TV, playing pokers, etc., in their leisure time [22].
Cultural differences and migration-related experiences also affect motivation to participate in leisure sports [22]. As migrant workers typically have a low level of education, they face three low-level amenities for leisure-time physical activity: low participation in sports, low demand for exercise, and a low provision of facilities from the neighborhood [28]. Referring to Chinese migration to the US, Yu et al. argued that [47]. Westerners value outdoor recreation as essential, team working, and regular activities, while Chinese migrants view outdoor recreation as non-essential, individual, and consumptive activities. Most migrant workers in China do not even know about outdoor recreation [25]. In addition, migrant workers’ rights to participate in leisure-time physical activity, such as education to participate in sports, have not yet been guaranteed in China [28]. This could be indicated by firms that hire migrant workers who are unwilling to host events to encourage outdoor recreation [27].
Fortunately, acculturation into cities observed among Chinese migrants to the USA, appears to play a crucial role in encouraging them to engage in outdoor recreation, even though Chinese migrant workers in domestic cities have rarely been documented [47]. With the increase in familiarity, migrant workers have been documented to have succeeded in gradually integrating into urban neighborhoods [48]. Owing to social support from local social networks through resident ties, migrant workers are likely to engage in more leisure-time physical activity. Based on this, it is suggested that LTPI among migrant workers be investigated through the citizenization stages of their migration, such as the initial, developmental, and completed stages [28]. New-generation migrants value leisure-time physical activity more than earlier generations [46].
Above all, household registration regulations (the Hukou system) are recognized as a comprehensive factor that may impact the LTPI of migrant workers in a chaotic mechanism. Household registration measures the likelihood of hindering motivation to undertake leisure-time physical activity. As an institutional arrangement, it generally hampers the occupation, socio-economic status, and residential disparity of migrant workers and their integration into the urban environment, resulting in LTPI. First, as proposed by Wang et al. [49], institutionally, the Hukou system creates a gap in work and life opportunities, such as welfare, between migrant workers and residents [50,51,52]. City governments provide affordable housing to low-to-moderate-income individuals, including migrant workers, but it is potentially helpful for migrants to integrate into urban areas [53]. Second, the living environment of migrant workers may differ from that of other residents depending on the wages they receive [54]. In this context, migrant workers mostly encounter discrimination from neighbors in the early stages of migration [55,56,57]. Third, from the household registration perspective, Chen et al. revealed seven dimensions of factors influencing LTPI among Chinese migrant workers [27]. They are listed in descending order of contribution as follows: (a) social and environmental factors; (b) occupational and life features; (c) sports attitude and ability; (d) cultural and lifestyle factors; (e) psychological and survival stress; (f) economic conditions; and (g) physical health factors. However, this complex process requires further verification.
In summary, although health issues among migrant workers have received attention [27], LTPI among migrant workers and the underlying determinants have been barely documented. In particular, the determinants of how and why social and built environments affect LTPI have been overlooked. Given the urgent issues migrant workers face in LTPI, understanding how an urban neighborhood environment might cause migrant workers’ LTPI is essential for them to live in a fair and friendly urban neighborhood.

3. Case Study and Methodology

3.1. Theoretical Framework

This study aimed to identify the determinants and potential constraints experienced by migrant workers in the context of LTPI. Initially, we divided the respondents based on their registered permanent residence into four groups: migrant workers (with a rural hukou outside Guangzhou in this case), migrant urban residents (with an urban hukou outside Guangzhou), local rural residents (with a rural hukou in Guangzhou), and local urban residents (with an urban hukou in Guangzhou).
Based on the theoretical basis of the relationship between LTPI and residents’ demographic characteristics and socio-economic features in Section 2, this study investigates the associations between LTPI and social and built environments. We then assumed that four dimensions of factors, namely demographic characteristics, socio-economic status, green open spaces in the communities, and neighborhood social network, impact the LTPI of the respondents (Table 1). The green open spaces herein refer to sports facilities, in which we include the distribution of nearby green space resources and other facilities for outdoor recreation in a neighborhood. Among the multiple factors related to LTPI, household registration is a comprehensive indicator distinguishing migrant workers from different groups. However, this generally impedes migrant workers’ socio-economic status, occupation, residential disparities, and integration into the urban environment, leading to LTPI. We assume that it impacts migrant workers on LTPI through the interaction effect, and the corresponding determinants of migrant workers vary from the other groups. In this context, we propose the following assumptions based on the above-mentioned scientific questions:
Hypothesis 1 (H1).
Household registration is one factor that influences LTPI among residents.
Hypothesis 2 (H2).
Household registration has an interaction effect on LTPI among residents.
The theoretical framework shows the logic flow of H1 and H2 (Figure 1). To further understand the mechanism by which LTPI differs from the other groups, we propose the third assumption, H3.
Hypothesis 3 (H3).
LTPI among migrant workers is related to living environments that differ from other groups.
Three steps were followed to validate the assumptions. A binary logistic regression model was initially employed to validate the association between household registration and LTPI. Second, an interactive analysis was used to evaluate the impact of household registration on LTPI. Thereafter, we further explored the differences in the determinants of LTPI among the four groups to identify the potential constraints and mechanisms within the registration system. In this step, four logistic regression models were developed to analyze the four groups of samples separated by household registration.

3.2. Study Area and Data

Guangzhou is a megacity in southern China, with a population of 18 million. Its urbanization rate has reached 86.46%, and the built-up area has reached 1249 square kilometers. The State Council awarded it the National Central City, ranking 15th globally. It is a leading city in China in terms of housing market reform, attracting numerous migrant workers [58]. As reported in the seventh national census by the Statistical Bureau of Guangzhou, the number of migrants reached 9.37 million by 2020 [12].
The data used in this study were mainly derived from a survey of questionnaires, points of interest (POIs), and other geospatial data. Questionnaires were collected in the spring of 2016 from 26 communities within six administrative districts located mainly in the central area of the city (Figure 2). This survey covered the main modes of urban settlement in urban China: commercial housing, work unit housing, affordable housing, old streets and alleys, and urban villages. Commercial housing is privately owned and developed for sale or rent. Work unit housing is provided by employers to their employees, usually at a lower cost. Affordable housing is designed for low-income to moderate-income households, often with some form of government subsidy. Old streets and alleys are historic areas with traditional architecture and a unique sense of place. Urban villages are informal housing areas developed on the periphery of cities, often with poor living conditions. The surveyed sites included fourteen commercial housing districts, six work unit housing areas, three affordable housing areas, two old streets and alley areas, and one urban village. These residential communities have well-defined boundaries and shared public facilities and services. At the same time, most have property management teams (except for old streets, alleys, and urban villages). Referring to the relevant literature [9,59], the respondents were residents of selected communities aged between 18 and 60 years, and students were excluded. The questionnaire covered respondents’ demographic characteristics, socio-economic status, social network, and willingness to participate in leisure-time physical activity. The data were collected through random encounters with the respondents in the community, and surveys were conducted with their consent. In total, 1029 questionnaires were completed. Of the 1029 questionnaires retrieved, 1024 were deemed to be valid. SPSS 24 was used for analysis. Spatial analysis was conducted on the ArcGIS 10.5.
In addition, high-resolution remote sensing data were used to create the base map, and a buffer analysis was performed to determine the range of the neighborhoods. Because most of the daily activities of Chinese residents are concentrated within a one-kilometer radius of their homes [59], a neighborhood was defined as the area within a one-kilometer radius of each surveyed census community boundary [60]. The buffer zone was defined as the area located within a one-kilometer radius of a community, and this was used to establish each boundary around the 26 typical communities. Spatial features were interpreted to obtain information about the built environment in the neighborhood. POI data were used to verify information about parks, squares, and sports facilities in the buffer zones.

3.3. Variables and Models

The dependent variable, i.e., “LTPI”, was a binary variable, and “LTPI = 1” indicates that the respondents did not partake in any outdoor or indoor physical exercise during their leisure time, whereas “LTPI = 0” suggests that the respondents did partake in outdoor or indoor physical exercise during their leisure time. Four dimensions of independent factors (Table 1), namely demographic characteristics, socio-economic status, green open space, and social networks, were employed in this study. All variables passed the collinearity test to ensure no collinearity between variables. As mentioned in the theoretical framework, the same approach was adopted for the binary logistic regression models presented below. As a multiple-factor statistical method [61], binary logistic regression models are suitable for classifying the dependent variables.
Sex and age were included to describe respondents’ demographic characteristics. Either age or sex depicts the intensity and type of physical activity. Variables such as household registration, marital status, education, employment, and income were included to describe socio-economic status. Among these variables, household registration is a comprehensive indicator for determining the level of institutional, cultural, and mental impact that different groups of individuals may receive from this system. Marital status indicates the probability of having a family with children, which typically determines whether the respondent participates in sports activities and what kind of sports they might engage in. Education relates to the awareness of health benefits and motivation to engage in outdoor recreation. Employment depicts potential leisure time that could be invested in outdoor recreation. Income refers to financial barriers or the ability to access gymnasiums or sports clubs. Although the relationship between income and leisure-time physical activity remains unclear for migrant workers, income was expected to be a critical indicator for migrant workers in describing their socio-economic status.
In neighborhoods, green open spaces are characterized by high-quality landscaping and are easily accessible. As such, green open spaces immediately attract residents who wish to enjoy themselves and participate in outdoor physical activity. This study included accessible sports facilities within green open spaces in communities. Five variables were included to describe the quantity and accessibility of neighborhood green open spaces. Distance to the nearest park/square (Dis.) refers to the accessibility of comprehensive and high-quality green open spaces. The total green coverage rate (TGR) and green coverage rate within the community (GRW) refer to areas that were accessible to residents, which may have influenced the extent to which residents were interested in improving their fitness and partaking in physical activities. Amenities within and outside communities play a role in outdoor physical activity. Therefore, the number of sports facilities in the community (SFI) and the number of sports facilities outside the community (SFO) were taken as independent variables.
Individual social networks affect residents’ mental states, thus affecting their choice of physical activity. Although little is known about the association between these factors and migrant workers, two variables, the number of relatives and friends in the same community (RFI) and the number of people greeted in the community (PGI), were selected to describe the social environment in the community. These variables reflect the relationships and level of social support available to residents within the neighborhood.

4. Results

4.1. Descriptive Analysis

LTPI was observed among a minority of respondents (n = 236, 23%), while most respondents were physically active in their leisure time (n = 788, 77%) (Table 2). The sample (Table 3) included a slightly higher number of males (50.3%). Respondents were relatively young (61.5%), mainly married (78.6%), in secondary education (66.9%), in full-time work (83.5%), and had a medium monthly income (CNY 3000–4999; 38.7%). Accessibility to the park/square for residents was good; 94% of the residents could access the nearest park/square within a radius of less than one kilometer from their home. The number of sports facilities within and outside the community was 2.91 and 16.82, respectively, which indicated good amenities for leisure-time physical activity. The survey of residents’ social networks showed that a few relatives and friends lived in the community (89.6% had fewer than 10) and residents greeted only a few others in the same community (74.6% greeted fewer than 20 people).
According to household registration types (Table 2), the migrant worker rate accounted for 9.4% (n = 96) of the population, which was less than that of local urban residents (n = 765, 74.7%) and migrant urban residents (n = 132, 12.9%) but more than that of local rural residents (n = 31, 3%). A significant issue was found in LTPI among migrant workers. The proportion of LTPI among migrant workers was 45.8%, far higher than that of the other categories. For example, the prevalence of LTPI among local urban, local rural, and migrant urban residents was 20.8%, 19.4%, and 20.5%, respectively. SPSS version 24.0 was used to conduct a non-parametric rank-sum test to analyze the differences in LTPI among household registration types. The results of the Kruskal–Wallis test showed significant differences in LTPI among respondents with different household registration types (p ≤ 0.05), indicating that the impact of household registration on LTPI was statistically significant. Therefore, a more detailed effect analysis using the binary logistic regression model below is necessary.

4.2. Does Household Registration Disparity with LTPI Exist Among Residents?

This study first developed a binary logistic regression model to explore the factors influencing LTPI among all the respondents. All 14 independent variables were inputted to run the model using a stepwise strategy. The entry criterion for the variables was p < 0.05, and the exclusion criterion was p > 0.10. Each independent variable with a disordered classification (i.e., X1 Gender, X3 Household registration, X4 Marital status, and X6 Employment) refers to their first as the reference group.
Household registration was among the five factors influencing LTPI among all respondents (Table 4). Compared to local urban residents, migrant workers were significantly more likely to have a high level of LTPI. Specifically, they were 2.44 times more likely to be inactive in their leisure time than local urban residents. However, the two household registration types (local rural and migrant urban residents) did not significantly impact residents’ LTPI. As for the other factors, when age, education, the number of sports facilities in the community, or the number of people greeted in the community increased by one unit, the probability of “LTPI = 1” decreased by 0.51, 0.63, 0.91, and 0.72 times, respectively. In other words, the following categories of residents were more likely to be physically inactive during their leisure time: migrant workers, young residents, less educated residents, residents who lived in a community with insufficient sports facilities, and residents who rarely met people in the community.

4.3. What Is the Interaction Effect of Household Registration on LTPI?

Since household registration was identified as a significant factor influencing LTPI, this study subsequently assumed that it plays a role in LTPI through its interaction with other factors. Referring to [62], we employed a new binary logistic regression model and added new interaction variables by multiplying “X3 household registration” with the other significant variables. Thus, it was possible to calculate the factors that significantly affected residents’ LTPI with household registration as an interacting variable.
Table 5 shows that household registration, age, education, household registration*SPI, and household registration*PGI significantly influenced LTPI. In the new model, the original significant variables (“SPI” and “PGI”) were no longer critical, while the interactive variables (“household registration *SPI” and “household registration*PGI”) became significant. This indicates that the effects of SPI and PGI on LTPI can be explained by their interaction with household registration. In contrast to the previous two regression models, we determined the interaction effect of household registration on LTPI. Household registration significantly influenced LTPI among residents, which was moderated by SPI and PGI (Figure 3).
The data showed that migrant workers were still the most inactive group, as this group was 25.91 times more likely to be inactive during leisure time than local urban residents. However, better sports facilities or meeting people in the community can mitigate the constraints migrant workers experience with LTPI. When SPI and PGI increased by one unit, the probability of “LTPI = 1” decreased to 0.77 and 0.43 times, respectively, for migrant workers. These results indicate that social companionship support mitigates problems regarding the mental health of migrant workers [63]. They also suggest that migrant workers are sensitive to encouragement to engage in outdoor recreation through a better built environment and social support.

4.4. Do the Determinants of LTPI Among Migrant Workers Differ?

To further explore the underlying mechanism, we conducted a subsequent analysis of four groups derived from their household registration: “local urban residents”, “local rural residents”, “migrant urban residents”, and “migrant workers”. This study used binary logistic regression models with a stepwise strategy to evaluate each of these groups, and all the variables listed in Table 1 were included. We then identified the factors associated with LTPI for each group and analyzed the differences between migrant workers and other resident groups.
Although all four household registrations were affected by demographic characteristics, it is evident that migrant workers were in a more severe situation, resulting in a high level of LTPI. This mechanism was completely different from those of the other groups (Table 6). LTPI among local urban residents was affected by age and TGR, which indicated that in these instances, demographic characteristics and green open spaces played a role in LTPI. Among the four groups, only marital status affected LTPI among local urban residents, indicating that their neighborhood living environment did not differ from their LTPI. Migrant urban residents were affected by demographic characteristics (gender) and social networks (RFI and PGI), which indicated that social capital was a key factor influencing LTPI among this group. It is worth noting that the migrant workers were constrained not only in terms of their demographic characteristics and socio-economic status (e.g., age and education) and social network (RFI), but they also lacked adequate green open spaces (Dis. and SPI). As a marginal group in cities, migrant workers still suffer from social status disadvantages.

5. Conclusions and Discussion

5.1. Conclusions

This study develops a theoretical framework to identify the determinants and constraints of LTPI among migrant workers. Several binary logistic regression analyses were carried out progressively, and the main findings were as follows.
(1)
Although inclusive cities have been emphasized and progress has been achieved, household registration was still identified as influencing LTPI among residents. It affected LTPI through interaction with living environmental factors, such as the number of sports facilities and the number of people greeted in the community.
(2)
Migrant workers were more likely to have significantly higher LTPI levels than other groups. However, this constraint can be mitigated by increasing the number of sports facilities and the number of people they can greet in the community.
(3)
The main obstacles preventing migrant workers from engaging in leisure-time physical activity were a lack of education, social capital, and neighborhood green open spaces.

5.2. Discussion

The findings of this study revealed significant constraints regarding LTPI among migrant workers. This study verified social stratification by household registration on LTPI among residents and the interaction effects of household registration on the living environment, including sports facilities and social networks. Based on these findings, we obtained the following outcomes:
(1)
The impact of household registration on migrant workers remains significant
In the context of large-scale rural-to-urban migration dating back 30 years to the 1990s, China’s population urbanization, which has been ongoing for many years, emphasizes the continuous promotion of migrant workers’ localization. Thus, the new generation is physically active and integrates well into urban areas [46]. However, this study shows that the impact of household registration on migrant workers remains significant.
Digging into further mechanisms, migrant workers may be limited in their ability to settle in physically and socially inferior environments due to financial and cultural obstacles, which lead to LTPI during early migration. However, with increased acculturation in cities, migrant workers and their descendants are likely to adapt to better social and built environments via residential ties, enabling them to engage in leisure-time physical activity in some way. Nevertheless, it is still difficult to determine whether they are more likely to be deprived than other populations. The physical activities of migrant workers are restricted by their demographic and socio-economic status and their built and social environment, resulting in inactive consciousness, motivation, willingness, and therefore, LTPI behavior. In this study, the results indicate that age reveals that migrant workers may have an elderly population who care for children and thus obtain longer outdoor recreation. Education may indicate the inactivity of migrant workers due to insufficient awareness of physical exercise. Additionally, green open spaces (Dis. and SPI) and social support (RFI) in the surrounding neighborhood significantly encouraged physical activity compared to other groups.
(2)
Measures to help migrant workers to be leisure-time physically active
To help migrant workers avoid LTPI, it is essential to encourage the social integration of migrants by optimizing the social and built environment of the neighborhood. The ‘Action Plan for Health Education and Promotion among Floating Population (2016–2020)’ issued by the National Health and Family Planning Commission aims to improve the health literacy and level of migrant workers, ensuring equal access to essential public health services [64]. Enforcing recreational education will enhance their awareness and motivate them to engage in outdoor recreation. It is also necessary to increase the provision of high-quality sports facilities and create a healthy social environment for migrant workers, which would be beneficial for bridging the gap between this group and other residents in terms of LTPI. Institutional measures to enhance sports public services [65] have been implemented in recent years and should continue to be further developed. The sports program is also deemed a tool to promote the acculturation of migrant workers in urban areas [65,66]. The government is encouraged to optimize green open spaces to enhance the green environment in migrant workers’ areas.

5.3. Prospects and Limitations

This study contributes to the existing understanding of the mechanisms underlying LTPI among migrant workers. Although the sample size of 96 migrant workers in this study was relatively small, which may affect the representativeness and generalizability of the data, the LTPI area among migrant workers is worthy of further research. Future research could improve the independent variables to increase the current small R-squared value of the specific models employed in this study. Variables showing a sense of safety, the motivation of migrants to engage in sports, household preference, acculturation stage, etc., are expected to be included to enhance our present models. This can also be achieved by improving the quality assessment of green open spaces. Likewise, further studies could examine whether residents are more inclined to use fitness resources within the community than outside the community to partake in leisure-time physical activities.

Author Contributions

Conceptualization, Z.Z. and S.Z.; methodology, J.F.; software, J.F.; validation, Z.Z., and S.Z.; formal analysis, J.F.; investigation, J.F.; resources, Z.Z.; data curation, J.F.; writing—original draft preparation, J.F.; writing—review and editing, Z.Z.; visualization, J.F.; supervision, Z.Z.; project administration, Z.Z.; funding acquisition, S.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China, grant number 42271234.

Institutional Review Board Statement

Biomedical Research Ethics Review Committee, School of Public Health, Sun Yat-sen University, China.

Informed Consent Statement

No. 094 (2022) from BRREC, SYSU.

Data Availability Statement

The data are unavailable due to privacy.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 17 04700 g001
Figure 1. Theoretical framework of this study.
Figure 1. Theoretical framework of this study.
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Figure 2. Spatial layout of the selected communities.
Figure 2. Spatial layout of the selected communities.
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Figure 3. The interaction effects of factors influencing LTPI among residents.
Figure 3. The interaction effects of factors influencing LTPI among residents.
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Table 1. Description of variables in the logistic regression models.
Table 1. Description of variables in the logistic regression models.
FeatureVariableDescriptionAssignment
Demographic
characteristics		X1 GenderGenderMale = 1; Female = 2
X2 AgeIn accordance with regulations issued by both China and the World Health Organization, a new age bracket is proposed. The age brackets include adolescents aged 18–28, mature youth aged 29–44, and middle-aged persons aged 45–59. Aged means elderly persons aged 60 years and above. Adolescent youth = 1; Mature youth = 2; Middle-aged persons = 3; Aged = 4
Socioeconomic statusX3 Household registrationHousehold registration categoryLocal urban residents = 1; Local rural residents = 2; migrant urban residents = 3; migrant workers = 4
X4 Marital statusCurrent marital statusUnmarried = 1; Married = 2
X5 Education Lower education refers to those who were awarded no degree or who had a primary school or junior high school education; secondary education refers to those who attended senior high school, technical secondary school and junior college; higher education includes undergraduates and graduates.Lower education level = 1;
secondary education level = 2;
higher education level = 3
X6 EmploymentThree employed states were specified as follows: Full-time, temporary, and unemployed.Full-time = 1; Temporary work = 2; Unemployed = 3
X7 IncomeAccording to the CGMBS *, the mean monthly income of individuals was divided into four levels: Low income (less than 3000 RMB); medium income (3000–4999 RMB); medium and high income (5000–6999 RMB); high income (≥7000 RMB).Low income = 1;
Medium income = 2;
Medium and high income = 3;
High income = 4
Green open space in the communityX8 Dis.The distance between the residential community and the nearest park or square outside the community (unit: m)Continuous variable
X9 GRWThe proportion of green area in the community zoneContinuous variable
X10 TGRThe proportion of green area within the community and within a one-kilometer radius outside the community boundary.Continuous variable
X11 SFIThe number of sport facilities in the community.Continuous variable
X12 SFOThe number of sport facilities within a one-kilometer radius outside the community boundary. Continuous variable
Social network X13 RFIThe number of relatives and friends who live in the same community (including relatives or friends who live together).Less than 5 = 1; 6–10 = 2; 11–20 = 3; 21–30 = 4; More than 30 = 5
X14 PGIThe number of residents (adults) who greet each other when meeting in the community, except their relativesLess than 10 = 1; 10–20 = 2; 21–30 = 3; 31–50 = 4; More than 50 = 5
Note: Green open space variables were based on the GIS, the POI, and a geospatial database; other variables were obtained from the questionnaire survey. * CGMBS denotes the Classification of the Guangzhou Municipal Bureau of Statistics.
Table 2. The mean value of variables of different household registration groups.
Table 2. The mean value of variables of different household registration groups.
Local Urban
Residents		Local Rural ResidentsMigrant Urban ResidentsMigrant WorkersSum
Y LTPI06062510552788
115962744236
Sum76531132961024
Table 3. Average value of indicators by household registration.
Table 3. Average value of indicators by household registration.
Local Urban
Residents		Local Rural ResidentsMigrant Urban
Residents		Migrant Workers
X1 Gender1.4531.3331.7041.386
X2 Age2.1451.6672.0371.568
X4 Marital status1.6981.3331.7041.432
X5 Education 2.1131.8332.0371.750
X6 Employment1.2141.0001.1111.159
X7 Income2.6732.3332.8522.727
X8 Dis.614.767807.000628.593527.795
X9 GRW2820.3080.2720.260
X10 TGR0.1620.2050.1730.190
X11 SFI2.9182.3333.0742.568
X12 SFO16.89917.50014.96316.364
X13 RFI1.5092.8331.5561.159
X14 PGI1.9563.0001.6301.386
Table 4. Binary logistic regression of factors influencing LTPI for all respondents.
Table 4. Binary logistic regression of factors influencing LTPI for all respondents.
VariableBWalsSig.Exp(B)
X2 Age−0.68042.1370.000 ***0.507
X3 Household registration (local urban residents)-14.0810.003 ***--
   Local rural residents−0.1020.0450.8320.903
   Migrant urban residents−0.0260.0120.9130.974
   Migrant workers0.89313.3530.000 ***2.444
X5 Education−0.4677.8620.005 ***0.627
X11 SPI−0.0904.8360.028 ***0.913
X14 PGI−0.32313.8060.000 **0.724
Constant2.08614.3520.000 ***8.056
Percentage correct: 79.9%LR chi2: 94.144 ***
Nagelkerke R square: 0.133
Note: *** and **, indicate significance at 1% and 5% confidence levels, respectively.
Table 5. The factors influencing LTPI with household registration interacting effect.
Table 5. The factors influencing LTPI with household registration interacting effect.
VariableBWalesSig.Exp(B)
X2 Age−0.65538.050.000 ***0.519
X3 Household registration--32.0340.000 ***--
   Local rural residents0.2770.0230.8791.319
   Migrant urban residents 2.0969.0570.003 ***8.132
   Migrant workers3.25523.4390.000 ***25.913
X5 Education−0.4477.1670.007 ***0.640
X11 SPI * X3 Household registration--9.8170.020 **--
   NPI by local rural residents−0.4652.4560.1170.628
   NPI by migrant urban residents−0.2183.1570.0760.804
   NPI by migrant workers−0.2614.1860.041 **0.770
X14 PGI * X3 Household registration--20.5940.000 **--
   PGI by Local rural residents0.2900.2900.5901.336
   PGI by migrant urban residents−0.758.6810.003 **0.472
   PGI by migrant workers−0.84411.6120.001 ***0.430
Constant1.0954.5740.032 **2.990
Percentage correct: 80.1%LR chi2: 112.126 ***
Nagelkerke R square: 0.157
Note: ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.
Table 6. Factors influencing LTPI among different household registration groups.
Table 6. Factors influencing LTPI among different household registration groups.
Local Urban Residents Local Rural ResidentsMigrant Urban Residents Migrant Workers
VariableBVariableBVariableBVariableB
X2 Age−0.391 ***X4 Marriage
(Married)		−2.303 ***X1 Gender
(Female)		1.164 **X2 Age−1.698 ***
X10 TGR−2.654 ** X13 RFI1.304 **X5 Education −2.509 ***
X14 PGI−1.569 ***X8 Dis.−0.003 **
Constant−1.151 *X11 SPI−0.383 **
X13 RFI−1.298 ***
Constant13.143 ***
Sample Size: 765Sample Size: 31Sample size: 132Sample Size: 96
Percentage Correct: 79.2%Percentage Correct: 77.4%Percentage Correct: 80.3%Percentage Correct: 83.3%
LR chi2: 301.297 ***LR chi2: 17.094 ***LR chi2: 20.842 ***LR chi2: 64.438 ***
Nagelkerke R Square: 0.434Nagelkerke R Square: 0.565Nagelkerke R Square: 0.229Nagelkerke R Square: 0.653
Note: ***, **, and * indicate significance at 1%, 5%, and 10% confidence levels, respectively.
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Zhu, Z.; Fu, J.; Zhou, S. How Do Chinese Migrant Workers Avoid Leisure-Time Physical Inactivity? Sustainability 2025, 17, 4700. https://doi.org/10.3390/su17104700

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Zhu Z, Fu J, Zhou S. How Do Chinese Migrant Workers Avoid Leisure-Time Physical Inactivity? Sustainability. 2025; 17(10):4700. https://doi.org/10.3390/su17104700

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Zhu, Zhanqiang, Jiaying Fu, and Suhong Zhou. 2025. "How Do Chinese Migrant Workers Avoid Leisure-Time Physical Inactivity?" Sustainability 17, no. 10: 4700. https://doi.org/10.3390/su17104700

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Zhu, Z., Fu, J., & Zhou, S. (2025). How Do Chinese Migrant Workers Avoid Leisure-Time Physical Inactivity? Sustainability, 17(10), 4700. https://doi.org/10.3390/su17104700

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