Skip to main content

Analysis of influence of physical health factors on subjective wellbeing of middle-aged and elderly women in China



Despite a maturing literature on the association between subjective wellbeing (SWB) and mental condition, little is known regarding the happiness–physical health relation in China, among middle-aged and elderly women (MAEW) in particular. This study aimed to understand the effect of physical health on the SWB of MAEW in China.


Data from the 2014 and 2018 China Family Panel Study were used to analyse the SWB of women over the age of 45 years. In addition, descriptive statistics was used to describe the population distribution and panel ordered logit regression for regression analysis.


Majority of the respondents reported satisfactory SWB, and the proportion of the respondents who were very happy and happy was more than 68%. In terms of health factors, self-rated health, 2-week morbidity and BMI were significantly related to the SWB of MAEW (all P-values < 0.05). Physical exercise (P-value < 0.01) was positively associated with SWB, whereas smoking status and drinking status were not related to SWB. In addition, demographic indicators, such as registered residence (P-value < 0.01), income (P-value < 0.01) and social status (P-value < 0.01), significantly affected the SWB of MAEW.


This study showed that MAEW’s physical health could affect their SWB. Increased attention should be paid to the physical health of MAEW to improve their SWB. Policy mechanisms could be designed to motivate MAEW to take the initiative to engage in regular physical activity to improve their SWB. In addition, increased attention be paid to groups with low socioeconomic status and high stress, especially those who are employed, to improve residents’ happiness.

Peer Review reports


Subjective wellbeing (SWB) refers to a person’s self-evaluation of life quality, which is a comprehensive evaluation of emotions, including life satisfaction, happiness, pessimism and optimism [1]. This is a positive psychological state characterised by high level of life satisfaction, high level of positive emotion and low level of negative emotion. Philosophers have debated the nature of a good life for millennia and one conclusion that has emerged from this debate is that a good life is a happy life [2]. The United Nations considers SWB as an important component of life quality in its Human Development Report and suggested that member states include ‘happiness’ in their assessment of their ‘national development index’ [3]. As an indicator of the quality of life and psychological status of residents [4], SWB has also been incorporated into government policies globally and become an important performance indicator of general concern, such as in France [5] and Britain [6].

However, according to the World Happiness Report [7, 8], the happiness ranking of Mainland Chinese is consistently in the middle and lower ranges in the world. From 2013 to 2020, during the golden age of China’s rapid economic growth, the ranking of Mainland Chinese not only did not increase but even declined. Therefore, in China’s unique social environment, the status quo of SWB of some special Groups in China must be given attention. Middle-aged and elderly women (MAEW, aged 45 years and above) in China are a group worthy of study because they are experiencing a different social, work, family and living environment from other groups in China and similar groups in other countries.

Unlike other countries, Chinese women are expected by society to not only be responsible for their families but also perform well in the workplace [9]. They have a higher level of labour force participation than women in other countries. According to the International Labour Organisation, the labour participation rate of Chinese women was approximately 60% in 2020 [10]. However, influenced by traditional Chinese culture and Confucianism, Chinese women’s family responsibilities, such as cooking, doing laundry and caring for their children, persist [9, 11]. As a result, the MAEW in China are experiencing conflict between work and family [12]. If a woman marries at the age of 25, by the time she is 45, she has experienced the dual pressure of work and life for 20 years, and this pressure continues to increase over time. In terms of work, due to the potential conflict between the time devoted to taking care of family and work input, MAEW face greater competitive pressure and even the risk of dismissal and their work pressure is much higher than that of man and young women [13]. As for their family, aging parents and children’s heavy study pressure put forward high requirements for family care from women [14]. With regard to physiology, middle-aged women may be in menopause and their mental and physical conditions may be relatively fragile [15]. In addition to urban middle-aged women, rural middle-aged women do not have professional jobs and their participation in unstable agricultural labour could bring great pressure [16]. Moreover, rural areas are deeply influenced by traditional Chinese culture. Thus, the family status of middle-aged women in rural areas is lower than that of middle-aged women in cities.

The above evidence showed that Chinese MAEW have been under great pressure and time sacrifice for a long time. Under this heavy pressure, MAEW may not have sufficient time for sleep and physical exercise and their lifestyles [17] and health behaviour may change, which could lead to a decline in health and an increase in chronic diseases [18, 19]. Due to the irreversible characteristics of health damage, this health status of MAEW could continue to affect the whole stage of old age. According to a survey of middle-aged and older adults in China, depression and obesity in middle-aged and elderly Chinese women are significantly higher than those in men of the same age group [20]. Furthermore, more than 75.8% of women over the age of 60 years have at least one chronic disease [21]. In China’s social environment, studying the SWB level of MAEW and exploring the effect of health status and health behaviour on SWB are of great significance.

The past years has witnessed an emerging volume of literature exploring SWB. Most were limited to the relationship between SWB and personality [22], mental condition [23], subjective health [24] and socioeconomic status [25]. Research has found that positive SWB is beneficial for a number of life activities [26, 27] and it has a promoting effect on individual health and longevity [28,29,30]. However, research about the effects of physical health on happiness is limited. Therefore, the present study aimed to determine whether this group’s physical health status affects its SWB. Data from the China Family Panel Study (CFPS) were used to investigate the effect of physical health on the SWB of MAEW.


Data sources

The CFPS is a nationwide biennial household survey organised by the Institute of Social Science Survey of Peking University in 2010. The CFPS sample covers 25 provinces/municipalities/autonomous regions in China, with a target sample size of 16,000 households. The population in the survey area accounts for 94.5% of the total Mainland Chinese population and thus is highly representative. The Peking University Biomedical Ethics Review Committee provided ethical approval of the survey (approval number: IRB00001052-14,010). Respondents were given a statement explaining the purpose of the study and all study participants signed a written informed consent prior to being investigated.

Panel data from two waves of CFPS surveys were used (i.e. 2014 and 2018), with total sample sizes of 35,720, and 37,147, respectively. This study mainly examined the influence of physical health factors on the SWB of MAEW. Therefore, cases with missing values in physical health factors, SWB and demographic indicators were deleted. Ultimately, two phase-tracking samples of 4,997 individuals were obtained and a total of 9,994 observations were conducted.

Variables and definitions

Dependent variable

The dependent variable was SWB. In the 2014 and 2018 CFPS surveys, the question to determine SWB is ‘How happy do you feel?’ In the 2014 and 2018 CFPS surveys, the answers range from 0 (lowest score) to 10 (highest score), with 0–6 denoting low SWB and 7–10 denoting high SWB, referring to the classification of the score (0–10) by the residents on the questionnaire [31, 32].

Physical health

The following five indicators were employed to evaluate physical health (health indicators that reflect body condition): self-reported health (SRH), 2-week morbidity (whether the participants felt physical discomfort in the past 2 weeks), BMI, chronic disease (whether the participants experienced a doctor-diagnosed chronic disease in the past 6 months) and hospitalisation (whether the participants were hospitalised due to illness in the past 12 months). In the CFPS questionnaire, the SRH was recorded on a five-point scale as follows: 1 = excellent, 2 = very good, 3 = good, 4 = not good and 5 = poor. Following Chinese standard practices, BMI was classified into four categories in accordance with the guidelines on the prevention and treatment of overweight and obesity amongst Chinese adults: BMI < 18.5 (underweight), 18.5 ≤ BMI < 24 (normal), 24 ≤ BMI < 28 (overweight) and BMI ≥ 28 (obese) [33].

Health behaviour

Three factors that may affect health were selected, namely, physical exercise, smoking status and drinking status. Physical exercise was measured by asking the participants the question ‘How often did you exercise in the past week?’ The responses were classified as ‘never’ (0 times) and ‘sometimes’ (≥ one time). Smoking status was measured by asking the participants whether they smoked in the past month (0 = ‘no’ and 1 = ‘yes’). Drinking status was measured by asking the participants whether they drank alcohol more than three times a week in the past month (0 = ‘no’ and 1 = ‘yes’).

Sociodemographic variables

Other factors that may affect individual SWB were selected, including age, marital status, educational level, income, social status, employment situation and registered residence. Age was classified into three categories as follows: 0 = 45–59 years, 1 = 60–74 years and 2 =  ≥ 75 years. Marital status was coded as 0 = never married, widowed or divorced or 1 = married. Education was classified into the following four levels: 0 = primary school and below, 1 = junior high school, 2 = senior high school and 3 = college or university and above. Employment situation was measured by asking the participants whether they were currently employed (0 = ‘no’ and 1 = ‘yes’). Registered residence was categorised as urban (1) or rural (0) and income and social status were measured by a score from 1 (lowest score) to 5 (highest score).

Statistical analysis

As mentioned, the SWB of MAEW was measured from 0 to 10 as an ordinal categorical variable; thus, using the panel ordered logit regression for empirical analysis was appropriate. The SWB of MAEW was the dependent variable Y, which is an ordered variable with k levels (k = 11 in this paper). In addition, this study tested the robustness of the model by using the fixed effects model.

The panel ordered logit model was established on the basis of a potential variable \(happiness\), which was replaced by \(h\) below. The setting of the panel model for the latent variable \(h\) is generally as follows:

$${h}_{it}={{\beta }^{T}health}_{it}+{\alpha }^{T}{ptiv}_{it}+{\epsilon }_{it},$$

where i (i = 1,2,3···) represents the individual, t is number of years, \(\beta\) and \(\alpha\) are parameters and \({\epsilon }_{it}\) is a random error term subject to logical distribution. In addition, \({health}_{it}\) and \({ptiv}_{it}\) are independent variables, where \({health}_{it}\) represents the health-related indicators for individual i at time t and \({ptiv}_{it}\) represents demographic indicators for individual i at time t.

The relationship between the latent variable \({h}_{it}\) and the ordered variable \({H}_{it}\) is as follows:

$${H}_{it}\left\{\begin{array}{c}0,{h}_{it}\le {c}_{0}\\ 1,{c}_{0}<{h}_{it}\le {c}_{1}\\ 2,{c}_{1}<{h}_{it}\le {c}_{2}\\ 3,{c}_{2}<{h}_{it}\le {c}_{3}\\ 4,{c}_{3}<{h}_{it}\le {c}_{4}\\ 5,{c}_{4}<{h}_{it}\le {c}_{5}\\ 6,{c}_{5}<{h}_{it}\le {c}_{6}\\ 7,{c}_{6}<{h}_{it}\le {c}_{7}\\ 8,{c}_{7}<{h}_{it}\le {c}_{8}\\ 9,{c}_{8}<{h}_{it}\le {c}_{9}\\ 10,{c}_{9}<{h}_{it}\le {c}_{10}\end{array}\right.$$

\({c}_{j}\) is the threshold value, which is the value of the ordered variable \({H}_{it}\).

Further, we can transform this equation into the following equation:

$${H}_{it}=j, {c}_{j-1}<{h}_{it}\le {c}_{j}, j=\mathrm{1,2},\mathrm{3,4},\mathrm{5,6},\mathrm{7,8},\mathrm{9,10}$$

The panel ordered logit model is defined as:

$$p\left({H}_{it}=j\right)=p\left( {c}_{j-1}<{h}_{it}\le {c}_{j}\right)=p\left( {c}_{j-1}-{{\beta }^{T}health}_{it}-{\alpha }^{T}{ptiv}_{it}<{h}_{it}\le {c}_{j}-{{\beta }^{T}health}_{it}-{\alpha }^{T}{ptiv}_{it}\right)=F\left( {c}_{j}-{{\beta }^{T}health}_{it}-{\alpha }^{T}{ptiv}_{it}\right)-F({c}_{j-1}-{{\beta }^{T}health}_{it}-{\alpha }^{T}{ptiv}_{it})$$

where \(F\left(\right)\) is the cumulative distribution function of the logistic distribution.


General characteristics of participants

Table 1 shows the frequency and distribution of the characteristics of the participants of the 2014 and 2018 CFPS surveys. Majority of the respondents reported satisfactory SWB, with more than 65% of the respondents in the two surveys claiming to have high SWB. In terms of health factors, approximately 45% of the respondents believed their health was poor and more than 43% felt physical discomfort within 2 weeks. In addition, more than 60% of the respondents were overweight. In terms of health behavioural factors, the data in 2018 showed that more than half of the respondents performed physical exercise. More than 95% of the respondents said they did not smoke nor drink.

Table 1 Characteristics of the study sample

Panel ordered logit regression analysis of SWB

Table 2 presents the results of the panel ordered logit regression analysis. SRH and BMI were positively related to the SWB of MAEW and 2-week morbidity was negatively correlated with SWB. Moreover, physical exercise was positively associated with SWB, whereas smoking status and drinking status had no statistically significant effect on the SWB of MAEW. In addition, in terms of individual characteristics, the higher the residents’ income and social status, the higher their likelihood of having high SWB. The respondents who were married, unemployed and living in an urban area had higher SWB than those who were unmarried, employed and living in a rural area; the difference was statistically significant. Finally, educational level had no statistically significant effect on the SWB of MAEW.

Table 2 Panel ordered Logit regression analysis of subjective well-being


This study used two wave data of CFPS (a nationally representative sample of older Chinese) to explore the causal effects of different intensities of physical exercise on successful ageing. The panel ordered logit regression was adopted for statistical analysis. This study also controlled for various confounding factors.

The results showed that majority of the respondents reported satisfactory SWB and those who had high SWB was more than 68%. This study also revealed that physical health was related to SWB.

Firstly, the MAEW with low self-rated health and those who felt unwell in the past 2 weeks had low SWB. This finding indicated that physical health could have certain effects on SWB [28]. However, the present study also found no significant correlation between chronic diseases, hospitalisation and SWB. Other studies have found similar results. Some scholars [34] also revealed that when controlling for different measures of subjective health status, the effect size between chronic disease and SWB decreased or was not even significant. This result may indicate that patients with long-term illness have adapted to the related effects of the disease. Thus, their SWB is not affected by chronic diseases. People with high BMI generally tended to have a low level of happiness [35, 36], which contradicted the results of the present study. The reason for this result may be because the subjects of the present study were MAEW in China. As Chinese traditional culture believes that being fat means being healthy, MAEW may believe that the fatter they are, the higher their quality of life is [31].

Secondly, weekly physical exercise had a positive effect on the SWB of MAEW. Studies have found that physical exercise could improve individual health, which in turn, could enhance SWB [37]. Evidence showed that most people with the initiative to exercise have an optimistic attitude and participating in physical exercise could enhance an individual’s sense of pleasure [38], thereby improving SWB. Gremeaux V et al. [39] found that moderate-intensity exercise not only could partially reverse the effect of the ageing process on the physiological function but also likely to yield emotional benefits. Unfortunately, heavy burden from family and work could limit the time and energy of MAEW in China for conducting physical exercise, which may lead to low health status, resulting in low level of SWB. Therefore, the government and health policymakers need to consider measures, such as designing some incentive systems and building some sports venues, to promote physical exercise participation amongst MAEW groups to improve the SWB of these groups.

Thirdly, the MAEW with high income and social status had high SWB, which was consistent with the findings of Anderson C et al. [40] and Duffy RM et al. [41]. However, many researchers believed that after monthly income reaches a certain high level, SWB may decline [42, 43]. This phenomenon was not observed in the present study, thereby indicating that the overall income of MAEW in China remains relatively low.

In addition, this study found that smoking and drinking had no statistically significant effect on the SWB of MAEW, different from the findings of some studies in other countries [44, 45]. The reason for this outcome may be because in the Confucian society of China, a certain level of discrimination and antipathy exists towards women smoking and drinking; thus, women’s drinking and smoking have been viewed as pathological and women’s pleasure from drinking and smoking has been overlooked.


Firstly, the variables were obtained from questionnaire surveys and the indicators were answered subjectively by the respondents. Thus, the data may be affected by the respondents’ recall bias and the bias of the interviewers. Secondly, on the basis of the existing health indicators in the CFPS surveys, the selection of physical health-related variables was limited, indicating that other physical health indicators could not be included. Thirdly, owing to data constraints, some important variables (e.g. environment, children’s situations and family property) that may affect the relationship between physical health and SWB were not included in the analysis. Therefore, in future studies, researchers could reduce the bias of respondents by adding objective indicators and improve the index system in the questionnaire to make the effect of physical health on SWB more scientific.


This study uses data from a large nationwide panel survey (i.e., the CFPS) to analyze the impact of physical health on the SWB of MAEW. We find that physical factors have an impact on SWB, and demographic indicators such as registered residence, income, and social status also have a significant effect on the SWB of MAEW. These findings provide strong policy support for the government to improve the SWB of the public. Based on the results of our study, the government should pay increased attention to the SWB of MAEW, especially those who are currently employed, and promote the improvement of SWB by improving residents’ health, social, and economic status. Moreover, the government should build additional sports facilities and encourage residents to engage in physical exercise, which can improve their health and increase their happiness. In addition, to increase the credibility of the results of our study, we use a fixed effects model to test robustness, and the results are consistent with the results of the panel ordered logit regression. This outcome shows that the research results are reliable.

Availability of data and materials

The datasets generated and/or analysed during the current study are available in the CFPS Public Data repository,



Subjective Wellbeing


Middle-aged And Elderly Women


China Family Panel Study


  1. Diener E, Oishi S, Tay L. Advances in subjective well-being research. Nat Hum Behav. 2018;2(4):253–60.

    Article  PubMed  Google Scholar 

  2. Diener E, Oishi S, Lucas RE. Personality, culture, and subjective well-being: emotional and cognitive evaluations of life. Annu Rev Psychol. 2003;54:403–25.

    Article  PubMed  Google Scholar 

  3. United Nations News: The general assembly adopted a resolution recommending that the concept of "happiness" be included in the assessment of the National Development Index. Accessed 11 May 2021.

  4. Medvedev ON, Landhuis CE. Exploring constructs of well-being, happiness and quality of life. PeerJ. 2018;6:e4903.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Angner E. Subjective well-being. J Soc Econ. 2010;39(3):361–8.

    Article  Google Scholar 

  6. World Health Organization: The happiness effect. Accessed 11 May 2021.

  7. Helliwell JF, Layard R, Sachs J, De Neve J-E, editors. World Happiness Report 2020. New York: Sustainable Development Solutions Network; 2020.

    Google Scholar 

  8. Helliwell JF, Layard R, Sachs J, editors. World Happiness Report 2013. New York: UN Sustainable Development Solutions Network; 2013.

    Google Scholar 

  9. Shui Y, Xu D, Liu Y, Liu S. Work-family balance and the subjective well-being of rural women in Sichuan, China. BMC Womens Health. 2020;20(1):1. Published 2020 Jan 2.

    Article  PubMed  PubMed Central  Google Scholar 

  10. International labour organization: statistics. Accessed 11 May 2021.

  11. Choi J, Chen C. Gender differences in perceived work demands, family demands, and life stress among married Chinese employees. Manag Organ Rev. 2006;2(2):209–29.

    Article  Google Scholar 

  12. Zhou S, Da S, Guo H, Zhang X. Work-family conflict and mental health among female employees: a sequential mediation model via negative affect and perceived stress. Front Psychol. 2018;9:544. Published 2018 Apr 17.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Zeng Y, Brasher MS, Gu D, Vaupel JW. Older parents benefit more in health outcome from daughters 'Than Sons' emotional care in China. J Aging Health. 2016;28(8):1426–47.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Chai H, Fu R, Coyte PC. How do middle-aged Chinese men and women balance caregiving and employment income? Healthcare (Basel). 2021;9(4):415. Published 2021 Apr 3.

    Article  Google Scholar 

  15. Nelson HD. Menopause Lancet. 2008;371(9614):760–70.

    Article  PubMed  Google Scholar 

  16. Yi J, Zhong B, Yao S. Health-related quality of life and influencing factors among rural left-behind wives in Liuyang China. BMC Womens Health. 2014;14:67. Published 2014 May 14.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Hamer M. Psychosocial stress and cardiovascular disease risk: the role of physical activity. Psychosom Med. 2012;74(9):896–903.

    Article  PubMed  Google Scholar 

  18. Siervo M, Wells JC, Cizza G. The contribution of psychosocial stress to the obesity epidemic: an evolutionary approach. Horm Metab Res. 2009;41(4):261–70. [published correction appears in Horm Metab Res. 2009 Apr;41(4):270].

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Rozanski A, Blumenthal JA, Kaplan J. Impact of psychological factors on the pathogenesis of cardiovascular disease and implications for therapy. Circulation. 1999;99(16):2192–217.

    Article  CAS  PubMed  Google Scholar 

  20. Luo H, Li J, Zhang Q, et al. Obesity and the onset of depressive symptoms among middle-aged and older adults in China: evidence from the CHARLS. BMC Public Health. 2018;18(1):909. Published 2018 Jul 24.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Wang LM, Chen ZH, Zhang M, et al. Zhonghua Liu Xing Bing Xue Za Zhi. 2019;40(3):277–83.

    Article  CAS  PubMed  Google Scholar 

  22. Ng W, Russell Kua WS, Kang SH. The relative importance of personality, financial satisfaction, and autonomy for different subjective well-being facets. J Psychol. 2019;153(7):680–700.

    Article  PubMed  Google Scholar 

  23. Becker C, Kirchmaier I, Trautmann ST. Marriage, parenthood and social network: subjective well-being and mental health in old age. PLoS One. 2019;14(7):e0218704. Published 2019 Jul 24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Hajek A, König HH. Flexible goal adjustment moderates the link between self-rated health and subjective well-being. Findings from the general population. Aging Ment Health. 2021;25(7):1345–50.

    Article  PubMed  Google Scholar 

  25. Wheatley D, Buglass SL. Social network engagement and subjective well-being: a life-course perspective. Br J Sociol. 2019;70(5):1971–95.

    Article  PubMed  Google Scholar 

  26. Tenney ER, Poole JM, Diener E. Does positivity enhance work performance?: why, when, and what we don’t know. Res Organ Behav. 2016;36:27–46.

    Article  Google Scholar 

  27. Larkin GL, Fredrickson BL, Waugh WE, et al. What good are positive emotions in crises? a prospective study of resilience and emotions following the terrorist attacks on the United States on September 11th, 2001. J Pers Soc Psychol. 2003;84:365–76.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Zaninotto P, Steptoe A. Association between subjective well-being and living longer without disability or illness. JAMA Netw Open. 2019;2(7):e196870. Published 2019 Jul 3.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Sabatini F. The relationship between happiness and health: evidence from Italy. Soc Sci Med. 2014;114:178–87.

    Article  PubMed  Google Scholar 

  30. Veenhoven R. Healthy happiness: effects of happiness on physical health and the consequences for preventive health care. J Happiness Stud. 2008;9(3):449–69.

    Article  Google Scholar 

  31. Xu W, Sun H, Zhu B, et al. Analysis of factors affecting the high subjective well-being of Chinese residents based on the 2014 China family panel study. Int J Environ Res Public Health. 2019;16(14):2566. Published 2019 Jul 18.

    Article  PubMed Central  Google Scholar 

  32. Fan X, Guo X, Ren Z, et al. The prevalence of depressive symptoms and associated factors in middle-aged and elderly Chinese people. J Affect Disord. 2021;293:222–8.

    Article  PubMed  Google Scholar 

  33. Hou X, Lu J, Weng J, et al. Impact of waist circumference and body mass index on risk of cardiometabolic disorder and cardiovascular disease in Chinese adults: a national diabetes and metabolic disorders survey. PLoS One. 2013;8(3):e57319.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Wang X, Jia X, Zhu M, Chen J. Linking health states to subjective well-being: an empirical study of 5854 rural residents in China. Public Health. 2015;129(6):655–66.

    Article  CAS  PubMed  Google Scholar 

  35. Stutzer A, Meier AN. Limited self-control, obesity, and the loss of happiness. Health Econ. 2016;25(11):1409–24.

    Article  PubMed  Google Scholar 

  36. Forste R, Moore E. Adolescent obesity and life satisfaction: perceptions of self, peers, family, and school. Econ Hum Biol. 2012;10(4):385–94.

    Article  PubMed  Google Scholar 

  37. Kim ES, Kubzansky LD, Soo J, Boehm JK. Maintaining healthy behavior: a prospective study of psychological well-being and physical activity. Ann Behav Med. 2017;51(3):337–47. PMID:27822613; PMCID:PMC5420498.

    Article  PubMed  Google Scholar 

  38. Kushlev K, Drummond DM, Diener E. Subjective well-being and health behaviors in 2.5 million Americans. Appl Psychol Health Well Being. 2020;12(1):166–87.

    Article  PubMed  Google Scholar 

  39. Gremeaux V, Gayda M, Lepers R, Sosner P, Juneau M, Nigam A. Exercise and longevity. Maturitas. 2012;73(4):312–7.

    Article  PubMed  Google Scholar 

  40. Anderson C, Kraus MW, Galinsky AD. The local-ladder effect: social status and subjective well-being. Psychol Sci. 2012;23:764–71.

    Article  PubMed  Google Scholar 

  41. Duffy RM, Mullin K, O’Dwyer S, Wrigley M, Kelly BD. The economic recession and subjective well-being in older adults in the Republic of Ireland. Ir J Psychol Med. 2019;36(2):99–104.

    Article  CAS  PubMed  Google Scholar 

  42. Ea Sterlin RA. Will raising the incomes of all increase the happiness of all? J Econ Behav Organ. 1995;27(1):35–47.

    Article  Google Scholar 

  43. Yu Z, Wang F. Income inequality and happiness: an inverted U-shaped curve. Front Psychol. 2017;24(8):2052. PMID:29225588; PMCID:PMC5705943.

    Article  Google Scholar 

  44. Vaadal K, Dahl SL. Maturing out of ‘binge drinking’: young adult women’s performance of age and gender in times of transition. J Youth Stud. 2021;24(10):1326–39.

    Article  Google Scholar 

  45. Drehmer JE. Sex differences in the association between countries’ smoking prevalence and happiness ratings. Public Health. 2018;160:41–8.

    Article  CAS  PubMed  Google Scholar 

Download references


Not applicable


Not applicable

Author information

Authors and Affiliations



DW, HXG, XX, and KY contributed to the study design. DW conducted the data analysis, with statistical advice and contributions from HXG and DH on the interpretation of results. All authors approved the final version.

Corresponding author

Correspondence to Hongxia Gao.

Ethics declarations

Ethics approval and consent to participate

The Peking University Biomedical Ethics Review Committee provides ethical approval of the survey (approval number: IRB00001052-14010). Respondents are given a statement explaining the purpose of the study, and all study participants had signed written informed consent prior to being investigated. The study protocol is performed in accordance with the relevant guidelines.

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1.


Additional file 2: Table 1.

Fixed effects model analysis of subjective well-being.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, 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 changes were made. 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 The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wang, D., Gao, H., Xu, X. et al. Analysis of influence of physical health factors on subjective wellbeing of middle-aged and elderly women in China. BMC Public Health 22, 1127 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: