Multidimensional stress among rural older adults in China: status, typology, and mental health associations.

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Learn more: PMC Disclaimer | PMC Copyright Notice BMC Geriatr . 2026 Apr 23;26:802. doi: 10.1186/s12877-026-07516-5 Multidimensional stress among rural older adults in China: status, typology, and mental health associations Xiuliang Dai 1 , Boyue Sun 1 , Lijian Wang 1, ✉ Author information Article notes Copyright and License information 1 School of Public Policy and Administration, Xi’an Jiaotong University, No. 28, West Xianning Road, Xi’an, Shaanxi 710049 China ✉ Corresponding author. Received 2025 Dec 31; Accepted 2026 Apr 13; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13238003  PMID: 42021161 Abstract Background With the intensification of population aging in China, research on stress among older adults and its associations with mental health has become increasingly important. While previous studies have examined economic and health stress among older adults, research on stress related to caregiving, family relations, and children’s development remains insufficient. Therefore, this study aimed to classify stress and its factors, and further investigate the relationship between stress and mental health. Methods Based on data of 728 participants from the “Comprehensive Social Survey on the Care Status of Home-based Older Adults” conducted by Xi’an Jiaotong University in 2023, we first performed a descriptive analysis of multidimensional stress among rural older adults in China. Subsequently, Latent Class Analysis was used to categorize the types of stress, multinomial logistic regression was also used to identify factors associated with stress type. Finally, an Analysis of Covariance was used to explore the relationship between stress types and mental health. Results Social adaptation stress remained at a relatively high level among rural older adults in China. Three latent types of stress were identified among older adults: “mild stress” (48.6%), “moderate stress” (40.6%), and “severe stress” (10.8%). Compared to the “Mild Stress Type,” the “Moderate Stress Type” was more likely to be associated with “Income is at an average level” (RRR: 3.254, P = 0.006), “Relatively low income” (RRR: 5.739, P < 0.001), and “Very low income” (RRR: 24.002, P < 0.001). Conversely, the “Mild Stress Type” was more likely to be associated with a “High school/Vocational school” education level (RRR: 0.310, P = 0.008). Compared to the “Severe Stress Type”, the “Mild Stress Type” was more likely to be associated with a “Divorced” marital status (RRR: 0.003, P < 0.001). The relationship between stress and mental health follows a U-shaped pattern. Older adults with the “moderate stress type” exhibit the best mental health, followed by those with the “mild stress type”. Conclusions This study classified stress among older adults into three categories, and identified a U-shaped association between stress and mental health. Our findings may help the Chinese government implement measures that are better suited to older adults in China, particularly in addressing stress and mental health. Keywords: Multidimensional stress, Mental health, Rural older adults in China Background Population aging has become an increasingly serious global issue since the beginning of the twenty-first century. Research indicates that the proportion of the global population aged 65 years and above is projected to increase from 9% in 2024 to 16% in 2050. Consequently, addressing the social problems caused by population aging has become a critical topic in national and social governance [ 1 ]. Compared to other countries, the aging population in China is characterized by its massive scale and rapid growth. By the end of 2023, the population of older adults aged 60 years and above in China had reached approximately 297 million, accounting for 21.1% of the total population; of these, 217 million were aged 65 years and above, accounting for 15.4% of the total population. Thus, China is estimated to become a severely aged society by 2035 [ 2 ]. Unlike the modernization processes in Western developed countries, the economic and social development in China is marked by distinct urban-rural disparities [ 3 ]. Particularly, rural household income levels are significantly lower than those of urban households, while public services in rural areas, such as healthcare and care services, are developing slowly. Therefore, community-level support for older adults is relatively insufficient, which increases the severity of the aging population and the challenges faced in rural areas [ 4 ]. Additionally, the traditional family care function in rural areas, which is influenced by the rural-to-urban migration of the labor force and shrinking family structures, is rapidly weakening, and traditional intergenerational support cannot effectively meet the rapidly growing care needs of older adults [ 5 ]. Moreover, compared with urban residents, rural residents have a relatively weaker awareness of health management and disease prevention and low accessibility to high-quality medical services [ 6 ]. Consequently, the prevalence of chronic diseases among rural older adults is significantly higher than that among urban older adults [ 7 ]. Furthermore, older rural adults in China rely mainly on labor income and financial support from their children. Studies show that approximately 70% of older adults obtain income through labor [ 8 ], while the role of the public basic pension system is limited, with approximately 80% of rural older adults receiving a basic monthly pension of approximately 200 RMB [ 9 ]. Hence, Chinese parents, influenced by traditional Chinese culture and family relations, bear significant responsibility for their adult children’s employment and marriage. Financial support from parents is also crucial for the children’s development. Sex imbalance in the Chinese society also remains prominent; thus, the cost of marriage for rural men is generally high, resulting in considerable economic stress and psychological burden on their parents [ 10 , 11 ]. Therefore, research on these issues is of great value for identifying difficulties in addressing population aging in rural China and improving the well-being of older adults. The Stress and Coping Theory provides an important perspective for the academic community to identify the dilemmas faced by older adults and study the related outcomes of these dilemmas. This theory posits that an individual’s physical and mental health is shaped by the dynamic balance between their capabilities and environmental stresses; when stress exceeds an individual’s coping capacity, physical and mental problems arise [ 12 , 13 ]. Consequently, accurately identifying and assessing the multidimensional stress of older adults may systematically reveal stress in complex environments and help form integrated intervention pathways. In this regard, existing research has extensively discussed dimensions such as economy, health, and social adaptation among older adults [ 14 , 15 ]. Owing to the decline in physical function, health stress among older adults is the most prominent. For instance, chronic diseases, primarily diabetes and hypertension, are significant among older adults worldwide [ 16 – 18 ] and the number and complexity of comorbid conditions increase with age [ 19 – 21 ] However, unlike other countries, older adults in China place greater emphasis on maintaining dignity and avoiding being a burden to others, preferring mutual aid over the unilateral receipt of help [ 22 , 23 ], which poses challenges for their health management and timely medical consultations. Notably, adequate financial support is a basic requirement for meeting the daily needs of older adults, and includes direct financial contributions from the children and indirect social security systems. Accordingly, insufficient financial support significantly affects life satisfaction and reduces utilization of health services [ 24 , 25 ]. However, the basic pension system in China exhibits a clear urban-rural divide, with the economic security level of rural older adults generally being lower t han that of urban older adults [ 26 , 27 ]. Moreover, according to the theory of active aging, social participation is a crucial factor influencing the well-being of older adults [ 28 ]. Thus, retirement and a decline in physiological capabilities often lead to decreased social participation and a reduced sense of social value. Furthermore, digital technology has profoundly affected individual lifestyles and public service delivery modes, with the digital divide causing older adults to face dilemmas in social adaptation [ 29 , 30 ]. The studies reviewed above highlight the range of stress that older adults commonly face. It is worth noting that stress and mental health, while closely related, are conceptually distinct. Stress refers to the physical and psychological responses an individual experiences when confronted with internal or external demands [ 31 , 32 ], whereas mental health refers to one’s overall capacity to sustain emotional well-being, cognitive functioning, and social adaptation [ 33 ]. Chronic or excessive stress can compromise mental health, yet a higher level of mental health also enables individuals to manage stress more effectively. For this reason, when examining mental health outcomes among older adults, it is necessary to treat stress as a key antecedent variable, while remaining attentive to the differences between the two constructs in terms of their definitions and measurement. Mental health problems among older adults are becoming increasingly prevalent worldwide [ 34 ]. According to the World Health Organization, approximately 14.1% of adults aged 70 and above are living with a mental disorder, and depression remains the most common mental health condition in later life [ 35 ]. The number of older adults worldwide plagued by mental health issues, such as depression, is increasing, becoming a public health concern [ 36 ]. Multiple studies have indicated that high stress levels are significantly correlated with mental health disorders in older adults [ 37 , 38 ]. Daily hassles are the most common and direct stress, and long-term exposure to such stress can trigger attention deficits and low mood, potentially leading to severe psychological problems such as major depression [ 39 , 40 ]. The association between economic stress and mental health problems has also been widely confirmed. A Swedish study indicated that young people in poor economic conditions experience more mental distress and anxiety about the future than those in good economic conditions [ 41 ]. Similarly, compared to wealthy older adults, those who subjectively evaluate their economic status as below the local average experience more severe negative emotions due to economic stress and are more likely to feel dissatisfied with their lives [ 42 ]. Additionally, compared to urban areas, the outflow of young and middle-aged laborers from rural China impacts traditional family relations, making family support and stress related to children’s development important factors influencing the mental health of rural older adults. Moreover, due to the relative scarcity of community public services and activities in rural areas, older adults lack sufficient social support because of inadequate social participation, making them more prone to feelings of loneliness and emptiness [ 43 , 44 ], which increases their social adaption stress and exacerbates their sense of social isolation [ 45 ]. Although existing research has concentrated on the relationship between stress and mental health among older adults, significant gaps remain. First, the multidimensional stress experienced by older rural adults in China was not sufficiently comprehensively analyzed. Particularly, existing studies have focused on economic, health, and social adaptation stress but have neglected caregiving stress, family relationship stress, and children’s development stress. In the rural Chinese society, parents bear significant responsibilities regarding their children’s employment and marriage. However, due to the current economic situation and demographic sex structure, employment and marriage have become difficult for rural youth, causing distress to their parents. Given that children are responsible for the financial support and life care of older adults, changes in family structure weaken the family’s caregiving function. If the care needs of older adults are not effectively met, family conflicts may be triggered, intensifying family relationship stress. Second, previous studies did not analyze multidimensional stress types. As previous studies have demonstrated clear associations between different stresses that are collectively influenced by individual and family characteristics, analyzing the stress types experienced by older adults would allow for a better understanding of overall stress levels and group characteristics, thereby identifying key groups requiring policy intervention. Third, although some studies have confirmed the correlation between stress and mental health in older adults, others have pointed out that multidimensional stress harms mental health. Thus, the specific relationship between stress types and mental health requires further investigation. Consequently, we aimed to conduct a descriptive analysis of the multidimensional stress experienced by rural older adults in China to better understand the status of each stress dimension. Methods Data source The data used in this study were derived from the “Comprehensive Social Survey on the Care Status of Home-based Older Adults” conducted in China in 2023. Given the uneven economic and social development across different regions of China and the significant differences in the degree of population aging and development of community care services, we selected the Shandong and Jiangsu provinces in the eastern region, Henan and Hubei provinces in the central region, and Shaanxi and Gansu provinces in the western region as survey locations. In the eastern region, Jiangsu Province has a relatively high level of economic development and a well-established system of elderly care services, making it a solid representative of the more developed eastern provinces. Shandong Province has a large elderly population and a high degree of aging, with pronounced urban-rural disparities, reflecting the typical conditions of eastern provinces where population aging stress and eldercare service provision coexist. In the central region, Henan Province has a large resident population and a substantial elderly population base, characteristic of a major demographic province. Hubei Province represents the central region in terms of overall socioeconomic development and contains a wide variety of urban and rural community types at different levels, which helps capture the diversity of eldercare needs and service resource allocation across the central region. In the western region, Shaanxi Province is marked by the strong radiating influence of its provincial capital and significant regional variation across the Guanzhong, Northern Shaanxi, and Southern Shaanxi areas, reflecting the internal development gradient and the layered nature of eldercare service development in the west. Gansu Province has a comparatively low level of economic development, with a large share of rural and less developed areas, and its conditions for family-based care and community support are more representative of the underdeveloped western provinces. Together, these six provinces span different levels of development, aging, and urban-rural composition across the eastern, central, and western regions, providing a reasonably comprehensive picture of the regional variation in home-based elderly care in China, which are highly representative. Survey participants were older adults in urban and rural communities. The inclusion criteria were: (1) age ≥ 60 years; (2) residence in the community for ≥ 6 months; and (3) ability to communicate normally with interviewers and cognitive capacity to complete the survey. The study employed a combination of stratified and random sampling. Within each province, two districts or counties were randomly selected, and within each district or county, two rural communities were chosen. Fifty older adults were then randomly surveyed in each community. Participants were identified through a list of eligible individuals or an accessible sampling frame within each community, with random selection applied throughout to minimize subjective sampling bias. Prior to the survey, the research purpose, content, and confidentiality principles were explained to the respondents, ensuring that participation was voluntary. The survey team consisted of three teachers and seven graduate students from Xi’an Jiaotong University. All personnel involved had the ability to conduct social surveys and received training before the survey began. This study was approved by the Ethics Review Committee of the Health Science Center of Xi’an Jiaotong University (Approval No.: 2018 − 1200). Before data collection began, interviewers fully informed all participants of the study purpose, response requirements, and confidentiality principles, and proceeded only after obtaining informed consent. For older adults with limited literacy, poor eyesight, or difficulty writing, trained interviewers read each question aloud and recorded the responses on the spot, without prompting or influencing the answers in any way, so as to ensure the accuracy and completeness of the data. Given the thorough prior communication and high level of participant cooperation, and with all questionnaires verified on site, no substantially invalid questionnaires were identified. A total of 807 questionnaires were distributed. After excluding 79 that had too many missing values on key variables, 728 valid questionnaires were retained, yielding an effective response rate of 90.2%. During the data cleaning stage, the research team conducted dual verification and logical checks on all questionnaires, with particular attention to internal consistency across items. For missing values, questionnaires with a high proportion of missing data on core variables that could not be recovered from the original records were excluded. For non-core variables with a small proportion of missing values, median imputation based on comparable cases was applied to avoid introducing notable bias into the overall findings. Outliers were identified by reference to the expected range of each variable and the context of the survey, including ages falling outside a plausible range, contradictory responses within a scale, or strings of identical answers across consecutive items. Each flagged case was then reviewed against the original questionnaire and the entered data, and a judgment was made as to whether the value reflected a data entry error or a genuine extreme response. Confirmed entry errors were corrected, while values that could not be verified and were clearly implausible were treated as missing. After cleaning, the data were of sufficient quality to meet the requirements of the subsequent statistical analyses. The study included 728 older adults, with a mean age of 70.44. The sample characteristics are shown in Table 1 . Table 1. Descriptive statistics of sample Variable Quantity/Mean Percentage/Standard Deviation Variable Setting Age 60–69 354 48.60% 1 = 60–69 70–79 296 40.60% 2 = 70–79 ≥ 80 78 10.80% 3 = ≥ 80 Sex Men 365 50.10% 1 = Men Women 363 49.90% 2 = Women Education Level Primary school and below 457 62.80% 1 = Primary school and below Junior high school 191 26.20% 2 = Junior high school High school/Vocational school 67 9.20% 3 = High school/Vocational school College and above 13 1.80% 4 = College and above Marital Status Never married 18 2.50% 1 = Never married First marriage 590 81.10% 2 = First marriage Remarried 14 1.90% 3 = Remarried Widowed 5 0.70% 4 = Widowed Divorced 101 13.90% 5 = Divorced Income Level Very high income 86 11.80% 1 = Very high income Relatively high income 244 33.50% 2 = Relatively high income Income is at an average level 152 20.90% 3 = Income is at an average level Relatively low income 188 25.80% 4 = Relatively low income Very low income 58 8.10% 5 = Very low income Living Situation Living alone 104 14.30% 1 = Living alone Couple 467 64.10% 2 = Couple Parents/Children 157 21.70% 3 = Parents/Children Multidimensional Stress Health stress 2.89 1.358 1–5 Economic stress 3.28 1.300 1–5 Caregiving stress 3.68 1.241 1–5 Children’s development stress 4.04 1.076 1–5 Family relationship stress 4.32 0.898 1–5 Social adaptation stress 3.39 1.220 1–5 Mental Health Score 41.29 6.104 12–60 Open in a new tab Measures Multidimensional stress in older adults This study focused on six aspects of stress: health, economy, caregiving, children’s development, family relationship, and social adaptation among rural older adults in China. Based on the demographic, economic, and social contexts of rural China, and the Elders Life Stress Inventory (ELSI) developed by Aldwin et al. [ 46 ], we referred to some existing research [ 25 , 26 , 30 ]. Rather than adopting the original ELSI items directly, this study used the ELSI as a theoretical framework for identifying sources of life stress among older adults, and adapted, refined, and supplemented the relevant items to reflect the actual living circumstances of rural older adults in China. We synthesized the stress domains repeatedly highlighted in these studies and translated them into six context-specific items. Each of the six items was rated on a 5-point scale, scores ranging from 1 to 5, with higher scores indicating higher stress levels. Respondents answered the questions based on their own situation. Rather than decomposing the internal structure of each stress dimension in detail, this study aimed to assess the subjective stress that rural older adults in China experience across six core life domains at the level of overall perception. Each dimension was therefore measured with a single self-rated item capturing the general stress level within that domain. This approach improves the feasibility of administration and the ease of comprehension in large-scale community surveys, and is particularly suitable for older adults of advanced age or relatively limited educational background. The six items were “Stress caused to you by physical health/medical treatment,” “Stress caused to you by family poverty (economic),” “Stress caused to you by children’s emotional /employment issues,” “Stress caused to you by family relationship ,” “Stress caused to you by friend/neighbor relations,” and “Do you worry about having no one to take care of you if you become immobile in the future?”, which correspond to the health, economics, children’s development, family relationships, social adaptation, and caregiving stress aspects, respectively. Reliability analysis indicated that the six-item stress scale had good internal consistency (Cronbach’s alpha = 0.812). Mental health Mental health is commonly measured using indicators such as depression, anxiety, perceived stress, and subjective well-being. Currently, international studies on the mental health of older adults are focused on depression. Therefore, we assessed the mental health status of older adults by measuring depression levels using existing depression measurement tools such as the Center for Epidemiologic Studies Depression Scale (CES-D) [ 47 ], Geriatric Depression Scale (GDS) [ 48 , 49 ], and Hamilton Depression Rating Scale (HAMD) [ 50 ]. Particularly, we drew upon the CES-D and combined it with the social context of rural China and the cognitive level of older adults to design a depression scale containing the following 12 items: “I was bothered by things that usually don’t bother me,” “I had trouble keeping my mind on what I was doing,” “I felt depressed,” “I felt that everything I did was an effort,” “I felt fearful,” “I feel like I can’t continue my life,” “I felt lonely,” “Did you feel you had nothing to do all day recently?” and “Did you feel you had no one to talk to recently?”, “I felt hopeful about the future,” “I felt happy,” and “I felt my life was fulfilling.” Each item was rated on a 5-point scale ranging from 1 (rarely or none of the time) to 5 (most or all of the time). The items “I felt hopeful about the future,” “I felt happy,” and “I felt my life was fulfilling” were reverse coded during data analysis. The respondents answered these questions based on their situation in the past week to maximally reflect their true condition. The sum of the scores for the 12 items was considered the mental health score, Higher scores indicate poorer mental health. Reliability analysis showed that the 12-item mental health scale had good internal consistency (Cronbach’s alpha = 0.890). An analysis of the correlations between items showed that the correlation coefficients among the items are above 0.7. Individual characteristics The covariates were set based on two dimensions: demographic characteristics and socioeconomic factors. Demographic characteristics included age, sex, marital status, and household size [ 51 ]. Socioeconomic factors included the individuals’ position in the social structure and the resources they possess; thus, education and income levels were selected as key socioeconomic covariates. These variables were prioritized because they represent relatively stable background characteristics that are associated with stress exposure among older adults and may influence depressive symptoms, but are generally not considered direct outcomes of stress. By contrast, variables such as health status and social participation, while potentially related to both stress and depression, may themselves result from prolonged stress exposure and thus function as potential mediators or proximal outcomes. Including them in the model could introduce overadjustment and obscure the total effect of stress on depressive symptoms. Therefore, this study controlled for background confounders that are relatively distal and temporally prior to stress, in order to improve the robustness and internal validity of the model estimates. Statistical analysis First, descriptive statistics were used to analyze the variables, describing sample characteristics in terms of age, sex, education level, marital status, income level, and living situation, and analyze the multidimensional stress and mental health levels of older adults. Subsequently, LCA was employed to identify stress types among rural older adults. LCA is an exploratory classification method based on latent variable models that classify a sample into several homogeneous latent subgroups based on individual performance on multiple discrete observed indicators [ 52 ]. Models with 1–4 classes were sequentially fitted, and multiple model fit indices were compared to determine the optimal number of classes, including the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and entropy. Generally, smaller AIC and BIC values indicate better model fit, and a ΔBIC > 10 between models suggests that the newly added class significantly improves model quality [ 53 ]. Entropy measures the classification uncertainty, ranging from 0 to 1, with values closer to 1 indicating a more accurate classification. Additionally, the Lo-Mendell-Rubin Likelihood Ratio Test and Bootstrap Likelihood Ratio Test were combined to determine the number of classes. When increasing the number of classes no longer significantly improves the model fit or results in a class proportion that is too small (e.g., < 5%), the optimal classification has been achieved [ 54 ]. Multinomial logistic regression was then used to examine factors associated with stress type. Stata 17 was used for the estimation, with robust standard errors to control for heteroscedasticity. The “Mild Stress Type” served as the reference group to evaluate how explanatory variables influenced the Relative Risk Ratio (RRR) of an individual being classified as “Moderate Stress Type” or “Severe Stress Type.” The first value of each variable was set as the reference level for dummy coding for independent variables as follows: Age reference: “60–69”; Sex reference: “Men”; Education reference: “Primary school and below”; Marital status reference: “Never married”; Income reference: “Very high income”; Living situation reference: “Living alone.” An RRR > 1 with statistical significance indicated that the characteristic significantly increased the probability of being in the “Moderate Stress Type” or “Severe Stress Type” group compared to the reference group, after controlling for other factors. Conversely, an RRR of < 1 indicated a protective effect. Two-tailed tests were used to assess the model (* P < 0.05, ** P < 0.01, *** P < 0.001). Analysis of covariance (ANCOVA) [ 55 ] was then conducted with mental health as the dependent variable to examine whether mental health outcomes differed across stress types among older adults. After model fitting, marginal effect estimation (margins) was used to calculate the adjusted means of the mental health scores for each stress class by controlling for covariates. Post hoc tests (Lincom) were used to compare the mean differences between the groups to clarify pairwise differences between the stress groups. Finally, the model conducted polynomial trend tests to explore the linear and nonlinear effects of stress classes on mental health. Furthermore, the effect size of the group main effect (partial ) was calculated to assess the magnitude of the group main effect, comprehensively revealing the relationship between stress classes and the mental health of older adults. Results Descriptive statistics of variables Descriptive statistics of the 728 valid cases are shown in Table 1 (at the end). The “60–69” and “70–79” age groups were predominant in the cohort, accounting for 48.6% and 40.6% of the population, respectively, while those aged “≥ 80” years accounted for 10.8% of the cohort. The sex distribution was balanced, with 50.1% and 49.9% of them being men and women, respectively. The overall education level was low, with “Primary school and below” and “Junior high school” together accounting for 89.0%; “High school /Vocational school” accounting for 9.2%, and “College and above” account for only 1.8% of the cohort. Marriage status, namely “First marriage”, “Widowed”, “Remarried”, “Never married”, and “Divorced” accounted for 81.1%, 13.9%, 1.9%, 2.5%, 0.7% of the cohort, respectively. Household economic status showed polarization: participants with “Very high income” and “Relatively high income” accounted for 45.3% of the cohort, while those with “Relatively low income” and “Very low income” accounted for 33.9% of the cohort, and “Income is at an average level” accounted for 20.9% of the cohort. The majority of the participants lived with couples (64.1%), followed by those living with parents/children (21.7%) and those living alone (14.3%). Figure 1 shows the multidimensional stress levels of older adults in rural areas. The stress caused by family relationships was the highest (M = 4.32, SD = 0.898), followed by children’s development stress (M = 4.04, SD = 1.076), indicating that family and intergenerational issues are the primary sources of stress for older adults. Notably, the standard deviation for family relationship stress was small, suggesting a high consensus among respondents that “family relationship is a major resource of stress.” Unexpectedly, health stress was not prominent among older adults (M = 2.89, SD = 1.358). Meanwhile, caregiving stress (M = 3.68), social adaptation stress (M = 3.39), and economic stress (M = 3.28) were Moderate Stress Types. The mean total mental health score was 41.29 (out of 60), with a standard deviation of 6.104, indicating that the overall psychological stress level in the surveyed group was generally high. Fig. 1. Open in a new tab Description of multidimensional stress levels among older adults Stress types and associated factors among rural older adults in China Since the six stress indicators were measured on 5-point scales reflecting the intensity of each stress domain, they were treated as approximately continuous in the latent class modeling. models containing 1–4 latent classes were constructed and fitted sequentially using Mplus 8.3 software. The model fit indices for latent classes 1–4 are listed in Table 2 . Among all models, the 3-class solution achieved a good balance between statistical fit and interpretability. Compared to the 2-class model, the 3-class model showed further reductions in AIC, BIC, and adjusted BIC, a slight improvement in entropy (0.868), and statistically significant BLRT and VLMR-LRT results, indicating better model fit and clearer class separation. Although the 4-class model yielded marginally lower information criteria values, the VLMR-LRT was no longer significant, suggesting that adding a fourth class did not meaningfully improve the model. The 4-class solution also offered limited gains in interpretability. Therefore, the 3-class model was selected as the most parsimonious and interpretable solution. Table 2. Fit statistics for latent class models of stress among older adults Model LL AIC BIC ABIC Entropy BLRT P VLMR P Class Probabilities 1-class model –6240.446 12528.891 12639.059 12562.851 – – – 100 2-class model –5639.316 11376.632 11601.556 11445.966 0.863 < 0.001 < 0 0.001 39.55, 60.45 3-class model –5412.728 10973.456 11313.138 11078.165 0.868 < 0.001 < 0.001 39.64, 18.10, 42.26 4-class model –5294.746 10787.493 11241.932 10927.577 0.848 < 0.001 0.614 27.37,15.93,38.96, 17.74 Open in a new tab Subsequently, we conducted an in-depth analysis of the latent class probability plot shown in Fig. 2 to clarify the heterogeneity of stress types and assigned names. The results indicated that different stress classes exhibited distinct differences across the six stress dimensions. Specifically, the first class accounted for approximately 48.6% of the sample. The probability scores across all six stress dimensions were close to the lowest values; thus, this class was defined as the “Mild Stress Type.” Older adults in this category generally experienced relatively low levels of stress regarding their health, finances, family, interpersonal relationships, and future care. The second class accounted for 40.6% of the total sample, with probability scores across items situated at a moderate level; hence, it was defined as the “Moderate Stress Type.” The third class accounted for 10.8% of the sample and exhibited the highest probability scores across all dimensions, particularly for “Social adaptation” and “Family relationship” stresses. Simultaneously, probability scores for “Economic stress” and “ Children’s development stress” remained at high levels. Therefore, this category was defined as the “Severe Stress Type.” Although the three stress classes showed subtle differences on certain dimensions, their overall patterns were consistent with no marked structural distinctions. Accordingly, the three classes were named based on the overall level of stress. Fig. 2. Open in a new tab Latent class probability plot A multinomial logistic regression model was then used to examine factors associated with stress class membership. The three classes identified by LCA (Class 1 = Mild Stress Type, Class 2 = Moderate Stress Type, Class 3 = Severe Stress Type) served as the dependent variable, with the covariates from the LCA included as explanatory variable. Model fit statistics indicated a log pseudo likelihood of -680.04, a pseudo R² of approximately 0.097, and an overall model , suggesting that the model was statistically significant. As shown in Table 3 (at the end), educational level, marital status, and income level were significant predictors of caregiving stress ( P < 0.05). Compared to the “Mild Stress Type,” the “Moderate Stress Type” was more likely to be associated with “Income is at an average level” (RRR: 3.254, P = 0.006), “Relatively low income” (RRR: 5.739, P < 0.001), and “Very low income” (RRR: 24.002, P < 0.001). Conversely, the “Mild Stress Type” was more likely to be associated with a “High school/Vocational school” education level (RRR: 0.310, P = 0.008). Compared to the “Severe Stress Type”, the “Mild Stress Type” was more likely to be associated with a “Divorced” marital status (RRR: 0.003, P < 0.001), while other factors were not significant. Table 3. Relative Risk Ratios (RRR) and statistical significance Variables Mild Stress Type(Reference group) Moderate Stress Type Severe Stress Type RRR Std. Err. z P RRR Std. Err. z P Age (60–69) 70–79 0.627 0.158 -1.850 0.064 0.918 0.225 -0.350 0.727 > 80 0.793 0.343 -0.540 0.592 1.009 0.411 0.020 0.982 Sex (Men) Women 0.907 0.211 -0.420 0.673 0.881 0.197 -0.570 0.571 Education Level (Primary school and below) Junior high school 1.023 0.272 0.090 0.930 1.153 0.302 0.540 0.587 High school/Vocational school 0.310 0.136 -2.660 0.008** 0.851 0.301 -0.460 0.648 College and above 0.675 0.827 -0.320 0.748 4.956 5.689 1.390 0.163 Marital Status (Never married) First marriage 1.623 1.244 0.630 0.528 1.245 0.906 0.300 0.763 Remarried 0.609 0.623 -0.490 0.628 0.337 0.360 -1.020 0.308 Widowed 1.815 1.495 0.720 0.470 0.875 0.700 -0.170 0.867 Divorced 1.266 1.519 0.200 0.844 0.003 0.001 -11.840 < 0.001*** Income Level (Very high income) Relatively high income 1.114 0.444 0.270 0.786 0.669 0.217 -1.240 0.214 Income is at an average level 3.254 1.394 2.750 0.006** 0.761 0.287 -0.720 0.469 Relatively low income 5.739 2.439 4.110 < 0.001*** 0.724 0.281 -0.830 0.404 Very low income 24.002 19.440 3.920 < 0.001*** 2.436 1.981 1.090 0.274 Living Situation (Living alone) Couple 0.908 0.348 -0.250 0.801 0.801 0.286 -0.620 0.534 Parents/Children 0.797 0.323 -0.560 0.575 1.173 0.438 0.430 0.670 Open in a new tab Correlation analysis between stress types and mental health Analysis of covariance (ANCOVA) with robust standard errors was used to compare mental health scores across stress classes. After controlling for covariates, differences in total mental health scores remained statistically significant (Wald main effect test: F(2, 709) = 65.85, P < 0.001), indicating that stress class had a significant effect on mental health. The full model yielded an R² of 0.367, suggesting that stress class and covariates together explained approximately 37% of the variance in total mental health scores. Model comparison further showed that adding stress class to the covariate-only model produced a ΔR² of approximately 0.111, indicating an independent contribution of stress class. The corresponding partial was approximately 0.15, reflecting a medium-to-large effect. Overall, stress class had a statistically significant and practically meaningful impact on mental health among older adults. Results are presented in Table 4 . Table 4. Analysis of variance Stress Types Mental Health Score SE 95% CI Lower Limit 95% CI Upper Limit Mild Stress Type 46.369 0.659 45.075 47.662 Moderate Stress Type 43.116 0.472 42.190 44.042 Severe Stress Type 50.343 0.394 49.571 51.116 Open in a new tab The “Moderate Stress Type” exhibited the lowest mental health score, at approximately 43.1, followed by the “Mild Stress Type” at approximately 46.4, whereas the “Severe Stress Type” had the highest score at approximately 50.3. Psychological stress levels showed a U-shaped trend: severe stress > mild stress > moderate stress. Notably, this U-shaped relationship was not entirely consistent with the intuitive simple linear relationship that “higher stress leads to higher psychological stress,” suggesting the existence of a non-linear underlying mechanism. Subsequently, we conducted post-hoc pairwise comparisons to evaluate the significance of the differences in mental health scores among the stress classes. As shown in Table 5 , the mean differences among the three groups were statistically significant (all p < 0.001), with a clear gradient relationship. The “Severe Stress Type” had the highest level of psychological stress, which was significantly higher than the “Mild Stress Type” (difference Δ = +3.975) and “Moderate Stress Type” (difference Δ = +7.227). Conversely, the mental health score of the “Moderate Stress Type” was significantly lower than that of the “Mild Stress Type” (difference Δ = -3.253), indicating that this group had the best mental health status among the three groups. These differences further support the existence of a “U-shaped” pattern. Table 5. Post-hoc pairwise comparisons Comparison Difference Δ SE t value 95%CI P value Moderate Stress Type vs. Mild Stress Type -3.253 0.820 -3.96 [-4.863, -1.642] < 0.001 Severe Stress Type vs. Mild Stress Type + 3.975 0.744 5.34 [2.514, 5.436] < 0.001 Severe Stress Type vs. Moderate Stress Type + 7.227 0.640 11.30 [5.972, 8.483] < 0.001 Open in a new tab We then conducted linear and nonlinear trend analyses to further identify the relationship between stress and mental health. Polynomial contrast results showed that the linear trend term was significant (F = 28.54, P < 0.001), as was the quadratic trend term (F = 68.20, P < 0.001). This indicated that the relationship between stress types and mental health contained a significant quadratic non-linear component, in addition to the effective linear component. This result suggests that the pattern of the influence of different stress levels on the mental health of older adults includes a significant nonlinear U-shaped effect. Finally, we calculated partial η² effect sizes using two approaches to assess the practical significance of the stress group effect. The effect size estimate based on the robust Wald test was partial , while the estimate based on the analysis of variance (ANOVA) model assuming equal variances was partial . The two results were similar, indicating a strong main group effect. This suggests that stress classes can explain a relatively large proportion of the variance in the mental health of older adults, indicating that the role of stress in mental health of older adults cannot be ignored. Discussion Using data from the 2023 “Comprehensive Social Survey on the Care Status of Home-based Older Adults,” this study investigated multidimensional stress levels and types, and their impact on mental health among rural older adults in China. Unlike previous findings that economic and health stresses are the most severe stress types [ 16 , 17 , 24 , 25 ], our results indicated that family relationship stress and children’s development stress were the two most prominent stress for older rural adults. This reflects the profound changes in intergenerational relationships within rural families during periods of rapid social transition. In general, older adults, who are influenced by traditional “filial piety,” still view family harmony and children’s well-being as the core of later-life quality. However, family miniaturization and labor outflow have weakened traditional family support [ 5 ], creating a gap between expectations and reality in emotional and care support, leading to family relationship stress. Additionally, in the Chinese cultural context, parents feel responsible for their adult children’s marriages and employment [ 10 ]. “Marriage squeezes” and employment difficulties in rural areas transmit economic and psychological burdens to older adult parents [ 11 ]. Moreover, concerns regarding having no one to provide care for in the future has become a key stress dimension, matching the reality of weakened family care functions [ 7 ]. These findings suggests that when addressing issues affecting rural older adults, we must go beyond traditional health and economic perspectives and place stress arising from family and intergenerational interactions at the core. The findings of this study align with the existing research on social adaptation among older adults, indicating that social adaptation stress among rural older adults in China is prominent and cannot be ignored. Descriptive statistics indicated that social adaptation stress remained at a relatively high level. Notably, results of the LCA demonstrated that social adaptation stress exhibited high probability scores in the “Moderate Stress Type” and “Severe Stress Type” groups. Particularly in the “Severe Stress Type,” the severity of this stress was nearly tied with family relationship stress as the highest factor. This suggests that regardless of whether older adults face severe economic or health issues, difficulty adapting to social changes is a common pain point for them. This stress stems from multiple sources such as the outflow of the young and middle-aged labor force, which has led to the “hollowing out” of villages, weakening the traditional neighborhood support networks for older adults; the rapid iteration of digital technology, which has excluded many older adults from modern lifestyles and public services [ 28 , 29 ]; and the loss of social roles and the shrinking of social circles, which have exacerbated their feelings of loneliness and social isolation [ 44 , 46 ]. These findings are consistent with existing research emphasizing that social isolation is a critical risk factor for psychological problems among older adults [ 45 ]. Therefore, support policies for rural older adults must not be limited to the material level and should also focus on their spiritual well-being, helping them rebuild social connections and overcome barriers to social adaptation. Based on LCA, we generally categorized the stress types of rural older adults in China into three classes: “Mild Stress Type,” “Moderate Stress Type,” and “Severe Stress Type,” with the “Mild Stress Type” accounting for the largest proportion at 48.6%. Further investigation of the factors influencing this classification revealed that economic status served as the core indicator distinguishing these three groups. The results of the Multinomial Logistic Regression analysis revealed that compared to older adults with the highest income, older adults with very low income were over 23 times more likely to belong to the “Moderate Stress Type” rather than the “Mild Stress Type” (RRR = 24.002), highlighting the decisive role of the economic foundation. Additionally, older adults in the “Mild Stress Type” possessed relatively better economic conditions and higher education levels, with lower levels across all stress dimensions. As indicated in the previous analysis, having a high school or vocational school education significantly reduced the risk of falling into a higher stress group (RRR = 0.310). In contrast, the “Moderate Stress Type” group faced dual economic and family stresses. While their income levels were similar to those of the “Mild Stress Type,” their stress scores in the dimensions of “family relations” and “children’s development” were significantly higher than those of the “Mild Stress Type,” forming a unique stress pattern characterized by the coexistence of economic, family, and intergenerational stresses. Meanwhile, the “Severe Stress Type” represented a typical group with overlapping multiple disadvantages; they exhibited high scores across all stress dimensions, with economic issues being the most prominent. This aligns with the findings of Zhao et al., who suggested that economic deprivation is a fundamental factor driving older adults into comprehensive predicaments [ 3 ]. Notably, marital dissolution (especially divorce) was a significant risk factor for entering the “Severe Stress Type.” The lack of spousal support makes individuals more susceptible to high-stress states [ 56 ], with particularly pronounced social adaptation stress and family relationship stress. Consequently, this finding clearly demonstrates that the stress experienced by rural older adults is not unidimensional, but the result of multiple intertwined factors. While economic foundation determines the basic stress type, family structure and intergenerational relationships shape the specific morphology of that stress. An unexpected finding was the nonlinear U-shaped relationship between the stress type and mental health. While participants with the “Severe Stress Type” had the worst mental health, those with the “Moderate Stress Type” actually had better mental health than the “Mild Stress Type.” This challenges the traditional cognition that “lower stress is always better.” Severe stress depletes resources and adversely affects health. However, excessively low stress levels (i.e., mild stress) may be associated with role loss and social disengagement. Thus, when older adults feel that they have nothing to do and are not needed, an intense sense of loneliness and loss of value may also harm their mental health. This finding aligns with previous research on social isolation and depression [ 57 – 59 ]. In contrast, the “Moderate Stress Type” had the best psychological state, possibly because moderate family responsibilities maintained their social participation and sense of being needed [ 60 ], thereby transforming stress into motivation to sustain life goals [ 58 , 61 ], achieving psychological adaptation. This finding suggests that support should not just be about stress reduction but should also help them maintain appropriate social participation and meaningful roles. However, this study also has some limitations. First, although the cross-sectional data employed can demonstrate the correlation between stress and mental health, they cannot establish a causal relationship. Therefore, future studies should conduct longitudinal tracking surveys on the same cohort of older adults and assess their situation at multiple time points to observe how stress and psychological conditions change over time, thereby further investigating the causal relationship between the two. Second, the measurement of stress and mental health relied primarily on self-report scales, which may have introduced subjective bias. Consequently, future research could extend beyond questionnaire interviews with older adults to include face-to-face interviews with their family members and community workers or even reference objective data recorded by health monitoring devices, thereby supplementing the findings with information from multiple sources. Finally, the sample covered the Eastern, Central, and Western regions; however, given the vast territory and cultural diversity of China, future studies should aim to improve the representativeness of the sample. Particularly, this should include areas inhabited by ethnic minorities and regions with significant urban-rural development disparities, to test the applicability of the research findings across different regional and cultural contexts, and provide a reference for formulating care policies that are better tailored to local realities. Based on the findings, this study proposes the following recommendations. First, support systems for older adults should shift from singular material security to equal emphasis on material and spiritual support. Given that stress related to family relations, children’s development, and social adaptation constitutes the core distress for rural older adults, policy interventions should not be limited to increasing pensions or improving healthcare. Rather, they should focus on building community-level social support networks. Organizing cultural activities and providing digital skills training can help older adults rebuild their social connections and alleviate feelings of social isolation, thereby indirectly mitigating tense intergenerational relationships. Second, the singular goal of reducing stress should be re-evaluated, and a care philosophy of “Moderate Stress Type” should be advocated. This study found that moderate stress is associated with better mental health, indicating that helping older adults maintain meaningful social roles is crucial. Therefore, governments and communities should create conditions that encourage older adults to participate in activities, such as rural governance, volunteer services, and cultural transmission. This would transform them from care recipients to social contributors, enabling them to realize their self-worth through the process of being needed, thereby converting stress into a positive driving force to promote mental health. Conclusions Based on social survey data of rural older adults in China, this study found that stress related to family relations, children’s development, and social adaptation was relatively prominent. LCA identified three types of stress: “mild (48.6%), “moderate (40.6%), and “severe (10.8%). Income level had the most significant impact on stress type.The study identified a non-linear “U-shaped” association between stress levels and mental health: the “Severe Stress Type” had the worst mental health, while the “Moderate Stress Type” had the best. This indicates that moderate controllable stress plays a positive role in helping older adults maintain social connections, realize self-worth, and promote mental health. Acknowledgements We thank all participants for their support of this study. We also thank Editage for providing English language editing assistance during the preparation of this manuscript. Abbreviations ELSI Elders Life Stress Inventory CES-D Center for Epidemiologic Studies Depression Scale GDS Geriatric Depression Scale HAMD Hamilton Depression Rating Scale LCA Latent Class Analysis AIC Akaike Information Criterion BIC Bayesian Information Criterion RRR Relative Risk Ratio ANCOVA Analysis of Covariance ANOVA Analysis of Variance Authors’ contributions Xiuliang Dai, Boyue Sun and Lijian Wang contributed to the study design and overall concept. Xiuliang Dai conducted the literature review. Boyue Sun performed the data analysis and interpreted the statistical findings, and also prepared the initial draft of the manuscript. Xiuliang Dai and Lijian Wang critically revised the manuscript. All authors reviewed, edited, and approved the final version. Funding This research was funded by Ministry of Education’s Major Project for Research in Philosophy and Social Sciences in China (24JZD039), Postdoctoral Science Foundation of Shaanxi Province (2023BSHEDZZ87) and Shaanxi Provincial Innovation Capability Support Program - General Soft Science Project(2025KG-YBXM-162). Data availability The research team retains ownership of the datasets used for this study, which do not involve any personal privacy information. Dataset access is available through the corresponding author for reasonable requests. Declarations Ethics approval and consent to participate Ethical approval was obtained from the Medical Ethics Committee of the Health Science Center, Xi’an Jiaotong University (No. 2018 − 1200). Participation was voluntary, and electronic informed consent was secured. To protect anonymity and confidentiality, no personally identifiable data were collected. Consent for publication Not applicable. 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