Rest intolerance and associated factors among Chinese nursing students: a cross-sectional network analysis
Abstract Background In today’s fast paced society, the phenomenon of rest intolerance has emerged as a prevalent psychological concern, which has been shown to pose potential risks to the mental and physical health of many people. However, rest intolerance remains underexplored, particularly among n...
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BMC
2025-07-01
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| Series: | BMC Nursing |
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| Online Access: | https://doi.org/10.1186/s12912-025-03472-4 |
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| author | Jiukai Zhao Yibo Wu Jiayi Xu Kunshuo Du Yu Yang Shuang Zang |
| author_facet | Jiukai Zhao Yibo Wu Jiayi Xu Kunshuo Du Yu Yang Shuang Zang |
| author_sort | Jiukai Zhao |
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| description | Abstract Background In today’s fast paced society, the phenomenon of rest intolerance has emerged as a prevalent psychological concern, which has been shown to pose potential risks to the mental and physical health of many people. However, rest intolerance remains underexplored, particularly among nursing students who face high academic pressure. Objective This study aims to investigate rest intolerance and its associated factors among nursing students using the social ecological model, and to identify central indicators for intervention targets that alleviate this issue through network analysis. Methods This research is a nationwide cross-sectional study using network analysis. From June 23 to September 29, 2024, the Rest Intolerance Scale-8 (RIS-8) was administered to evaluate rest intolerance among 1,878 nursing students in China. Data collection was conducted using stratified and quota sampling methods. Based on the social ecological model, factors potentially associated with rest intolerance were identified and subsequently investigated using random forest classification. In addition, to further elucidate factors associated with rest intolerance, univariate and multivariable generalized linear models were conducted. Finally, network analyses were undertaken to evaluate central indicators of rest intolerance and core factors among nursing students. Central indicators were identified based on their expected influence value. A higher expected influence value signifies a greater importance of the nodes in the network. The network’s stability was assessed using a case-dropping procedure. Results The self-reported level of rest intolerance indicated a median score of 24 points. The results of the multivariate generalized linear model analysis demonstrated that nursing students exhibiting higher scores of the study variables are associated with increased feelings of rest intolerance, including higher levels of online social networking addiction (β = 0.32; 95% CI = 0.26 to 0.38), depressive symptoms (β = 0.19; 95% CI = 0.09 to 0.28), perceived stress (β = 0.38; 95% CI = 0.24 to 0.51), and anxiety symptoms (β = 0.45; 95% CI = 0.17 to 0.74). Individuals with higher levels of health literacy (β = -0.17; 95% CI = -0.31 to -0.03) demonstrated a reduced likelihood of experiencing rest intolerance. In addition, the statement “When I am resting or having fun, I feel guilty” from the RIS-8 exhibited the greatest expected influence value within the network. Central indicators of anxiety and depressive symptoms were identified in the network configuration related to rest intolerance and its associated factors. Conclusions Nursing students achieved a score of 60% of the total maximum on the Rest Intolerance Scale. Nursing educators and policymakers should fully consider the identified associated factors of this study when designing nursing education programs and relevant policies. Central indicators can be treated as intervention targets to effectively alleviate nursing students’ feelings of rest intolerance and promote their mental health. Clinical trial number Not applicable. |
| format | Article |
| id | doaj-art-6a4302c32b1048d2a3a1e33e8157c995 |
| institution | Kabale University |
| issn | 1472-6955 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Nursing |
| spelling | doaj-art-6a4302c32b1048d2a3a1e33e8157c9952025-08-20T03:45:23ZengBMCBMC Nursing1472-69552025-07-0124111710.1186/s12912-025-03472-4Rest intolerance and associated factors among Chinese nursing students: a cross-sectional network analysisJiukai Zhao0Yibo Wu1Jiayi Xu2Kunshuo Du3Yu Yang4Shuang Zang5Department of Community Nursing, School of Nursing, China Medical UniversitySchool of Public Health, Peking UniversityDepartment of Community Nursing, School of Nursing, China Medical UniversityDepartment of Community Nursing, School of Nursing, China Medical UniversityDepartment of Vascular Surgery, The First Affiliated Hospital of China Medical UniversityDepartment of Community Nursing, School of Nursing, China Medical UniversityAbstract Background In today’s fast paced society, the phenomenon of rest intolerance has emerged as a prevalent psychological concern, which has been shown to pose potential risks to the mental and physical health of many people. However, rest intolerance remains underexplored, particularly among nursing students who face high academic pressure. Objective This study aims to investigate rest intolerance and its associated factors among nursing students using the social ecological model, and to identify central indicators for intervention targets that alleviate this issue through network analysis. Methods This research is a nationwide cross-sectional study using network analysis. From June 23 to September 29, 2024, the Rest Intolerance Scale-8 (RIS-8) was administered to evaluate rest intolerance among 1,878 nursing students in China. Data collection was conducted using stratified and quota sampling methods. Based on the social ecological model, factors potentially associated with rest intolerance were identified and subsequently investigated using random forest classification. In addition, to further elucidate factors associated with rest intolerance, univariate and multivariable generalized linear models were conducted. Finally, network analyses were undertaken to evaluate central indicators of rest intolerance and core factors among nursing students. Central indicators were identified based on their expected influence value. A higher expected influence value signifies a greater importance of the nodes in the network. The network’s stability was assessed using a case-dropping procedure. Results The self-reported level of rest intolerance indicated a median score of 24 points. The results of the multivariate generalized linear model analysis demonstrated that nursing students exhibiting higher scores of the study variables are associated with increased feelings of rest intolerance, including higher levels of online social networking addiction (β = 0.32; 95% CI = 0.26 to 0.38), depressive symptoms (β = 0.19; 95% CI = 0.09 to 0.28), perceived stress (β = 0.38; 95% CI = 0.24 to 0.51), and anxiety symptoms (β = 0.45; 95% CI = 0.17 to 0.74). Individuals with higher levels of health literacy (β = -0.17; 95% CI = -0.31 to -0.03) demonstrated a reduced likelihood of experiencing rest intolerance. In addition, the statement “When I am resting or having fun, I feel guilty” from the RIS-8 exhibited the greatest expected influence value within the network. Central indicators of anxiety and depressive symptoms were identified in the network configuration related to rest intolerance and its associated factors. Conclusions Nursing students achieved a score of 60% of the total maximum on the Rest Intolerance Scale. Nursing educators and policymakers should fully consider the identified associated factors of this study when designing nursing education programs and relevant policies. Central indicators can be treated as intervention targets to effectively alleviate nursing students’ feelings of rest intolerance and promote their mental health. Clinical trial number Not applicable.https://doi.org/10.1186/s12912-025-03472-4Rest intoleranceNursing studentsCross-sectional network analysisChinese |
| spellingShingle | Jiukai Zhao Yibo Wu Jiayi Xu Kunshuo Du Yu Yang Shuang Zang Rest intolerance and associated factors among Chinese nursing students: a cross-sectional network analysis BMC Nursing Rest intolerance Nursing students Cross-sectional network analysis Chinese |
| title | Rest intolerance and associated factors among Chinese nursing students: a cross-sectional network analysis |
| title_full | Rest intolerance and associated factors among Chinese nursing students: a cross-sectional network analysis |
| title_fullStr | Rest intolerance and associated factors among Chinese nursing students: a cross-sectional network analysis |
| title_full_unstemmed | Rest intolerance and associated factors among Chinese nursing students: a cross-sectional network analysis |
| title_short | Rest intolerance and associated factors among Chinese nursing students: a cross-sectional network analysis |
| title_sort | rest intolerance and associated factors among chinese nursing students a cross sectional network analysis |
| topic | Rest intolerance Nursing students Cross-sectional network analysis Chinese |
| url | https://doi.org/10.1186/s12912-025-03472-4 |
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