Skip to main content

Determinants and outcomes of health-promoting lifestyle among people with schizophrenia

Abstract

Background

Healthy lifestyle is an important protective factor of developing cardiovascular disease in people with schizophrenia. However, little is known about the determinants of lifestyle and its contribution to metabolic syndrome. This study aimed to explore the influencing factors of health-promoting lifestyle (HPL) and its association with metabolic syndrome among people with schizophrenia.

Methods

A cross-sectional study was conducted in twenty-two primary health centers of Guangzhou, China between December 2022 and April 2023. A total of 538 patients with schizophrenia were recruited through convenience sampling. Self-administered scales, questionnaires, and clinical data were collected. Scales and questionnaires included social-demographic information, Health-Promoting Lifestyles Profile (HPLP-C), UCLA Loneliness Scale (ULS), and International Physical Activity Questionnaire-Short Form (IPAQ-SF). Cluster analyses were used to divide participants into two groups based on the distribution characteristics of HPLP-C scores. Logistic regression models were used to identify factors associated with HPL and the association between HPL and metabolic syndrome.

Results

There were 271 participants in the high HPL group and 267 participants in the low HPL group. Logistic regression analysis revealed that loneliness posed a risk factor for high HPL, while high education and moderate-vigorous physical activity served as protective factors for high HPL. Low HPL was a risk factor for the prevalence of metabolic syndrome.

Conclusions

Promotion of high education literacy and a physically active lifestyle should be priority targets in the health management of schizophrenia. Primary healthcare providers can play a pivotal role in assisting patients to mitigate metabolic syndrome by reinforcing healthy lifestyle strategies.

Peer Review reports

Introduction

Schizophrenia is a severe mental disorder that affects approximately 1% of the world’s population [1]. Persons with schizophrenia are much more likely to have a shorter life expectancy than the general population [2]. Previous studies have indicated a strong association between cardiovascular diseases (CVDs) and premature death of patients with schizophrenia [3]. Among these conditions, metabolic syndrome (MetS), characterized by central obesity, high blood pressure, hyperglycemia, and dyslipidemia, emerges as a worthy-considering physical illness concern for people with schizophrenia [4, 5]. Numerous studies have demonstrated a 2- to 3-fold higher prevalence of MetS in people with schizophrenia compared to the general people [6]. The elevated prevalence of MetS has been recognized as the main risk factor for CVDs [7].

Unhealthy lifestyles are the primary cause of MetS. When compared with general population, patients with schizophrenia tend to have poorer dietary, lower physical activity levels and higher usage of tobacco and alcohol [8]. Factors contributing to unhealthy lifestyles may be attributed to the nature of the disease itself, treatment modalities, and social environment. The side effects of antipsychotic drugs, coupled with symptoms like amotivation, apathy, and cognitive deficits, pose challenges for patients in adopting healthy lifestyles [9]. In addition, patients who have lower education levels and socioeconomic status are prone to have unhealthy lifestyles [10]. Notably, patients with schizophrenia often experience high level of stigma and discrimination, exacerbating their social isolation [11] and thereby impeding the development of healthy habits. Several studies have demonstrated that modifiable factors, including poor nutrition, low physical activity level, and high body mass index (BMI) [12, 13] are important determinants of CVDs, besides some static factors, such as genetic vulnerability [14]. A longitudinal study conducted in the United States population found participants who had lower-risk lifestyles, such as non-smoking, restriction of alcohol consumption, keeping a healthy weight, positive physical activity and a balanced diet, tended to live longer [15]. Adopting a healthy lifestyle may alleviate the elevated rates of morbidity and mortality in patients with schizophrenia.

A health-promoting lifestyle (HPL) is a positive way of life that contributes to the development of a high health status. Walker and his colleagues have provided a comprehensive perspective on HPL [16], describing it as a multidimensional model of perceptions and actions, including health responsibility, physical activity, nutrition, interpersonal relations, spiritual growth, and stress management. Researchers have evaluated the level of HPL and its influencing factors in many different populations, such as older adults [17], university students [18], and postmenopausal women [19]. Some research have been conducted in people with physical diseases, such as cardiovascular and cerebrovascular diseases [20]. Risk factors for HPL in physical diseases typically include poor economic income, poor family support, physical inactivity, low education level, poor marital status, and loneliness [20,21,22]. However, there have been limited studies evaluating the level of HPL in people with schizophrenia [23], and exploration of its influencing factors is yet to be undertaken.

Studies on the prevalence of MetS in patients with schizophrenia are replete, while there is a scarcity of studies on examining the risk factors associated with MetS, particularly focusing on HPL. Previous studies have commonly focused on how poor dietary and negative physical activity contribute to MetS. However, the broader scope of HPL may be overlooked, which extends beyond the two factors. One study showed that MetS was related to HPL scores among people with chronic schizophrenia in inpatients settings [23]. To date, there is a dearth of literature examining HPL and the relationship to MetS in primary health settings.

Given the heightened risk of MetS and the critical role of healthy lifestyle, we explored the influencing factors of HPL in people with schizophrenia living in communities using a cross-sectional study design. We aimed to identify the factors associated with HPL in patients with schizophrenia and to explore the relationship between HPL and MetS.

Methods

Study design and participants sampling

We adopted a cross-sectional study design and applied convenience sampling to recruit patients with schizophrenia from twenty-two primary health centers in three districts in Guangzhou, China between December 2022 and April 2023. In accordance with China’s national basic public health service in China, patients with severe mental illness can take annual free physical examination at primary health centers. In this study, primary healthcare professionals, responsible for the mental health of these patients contacted and encouraged them to undergo the physical examination. Participants were recruited based on predefined inclusion and exclusion criteria. Upon completing the survey, participants were rewarded with complementary gifts, such as towel, umbrella and toothbrush, valued at forty yuan.

Inclusion criteria encompassed individuals who: (1) met diagnostic criteria for schizophrenia based on the 10th edition of the International Classification of Diseases (ICD-10); (2) were aged 18 to 65 years; (3) had a course of illness lasting one year or longer and were currently in a chronic state; (4) underwent physical examinations in primary health centers; (5) lived in communities for at least 6 months; (6) comprehended the study contents and provided the written informed consent. Exclusion criteria involved individuals with: (1) hearing and visual disturbances; (2) serious physical disease, such as cardiovascular disease; (3) pregnancy or lactation.

The MetS prevalence, the primary outcome of this study, was used to calculate the sample size required for the study. Specifically, the sample size was calculated by the formula for cross-sectional study: n = Z1−α/22*p(1-p) /d2. We set the p as 24.5% based on a previous meta-analysis [24], and estimated a sample size of 284 people would be needed, with Z = 1.96 at a confidence interval of 95% and allowable error d as 5%. Then we assumed a 60% participation rate in physical examination and a 10% data missing rate. Consequently, the sample size was expanded to 526. We also calculated a minimal sample size for the HPL, the other primary outcome of this study. We ensured a robust sample size of 526 participants, as recommended by Bujang et al. [25]. The guideline suggests a minimum sample size of 500 for observational studies employing logistic regression. Consequently, we assumed that about 526 people will be recruited.

Ethical approval was obtained from the ethics committee of the Affiliated Brain Hospital of Guangzhou Medical University, and all study participants provided written informed consent prior to the survey. A total of 594 patients were initially recruited, with 56 excluded due to missing data, resulting in a final sample of 538 included for statistical analysis.

Data collection and measures

Patients who agreed to participant were invited to complete a battery of paper-based questionnaires. The questionnaires were coded and verified by the research group. Clinical data including blood pressure, glucose, and lipid metabolism indicators of all participants were tested in primary health centers by primary healthcare professionals. The information was stored in Guangzhou Mental Health System and one researcher exported these data for the study.

We used a self-administered questionnaire to obtain social-demographic data including age, sex, marriage status, employment status, education level, tobacco and alcohol consumption, illness duration, weight, height, waist circumference, and use of antipsychotics. Education level was categorized with primary education and below (i.e., primary school and below), secondary education (i.e., junior high school, senior middle school, and vocational school) and higher education (i.e., bachelor and above). Antipsychotics was categorized with first generation antipsychotics only (FGA), second generation antipsychotics only (SGA), both FGA and SGA, and non-antipsychotics treatment.

The Chinese version of health-promoting lifestyles profile (HPLP-C)

The 42-item HPLP-C is a self-rating instrument that is used to identify participants’ health-promoting lifestyles and has been widely used in China [26, 27]. Items are measured using a four-point Likert scale (1 = never, 2 = sometimes, 3 = often and 4 = routinely). The total score ranges from 42 to 168, with higher scores indicating healthier lifestyle choices. In this research, the Cronbach’s alpha of the scale was 0.970, indicating good reliability and validity.

UCLA loneliness scale (ULS)

The 20-item ULS is a self-rating scale, which is used to measure the level of loneliness [28]. Each item is measured using a four-point Likert scale ranging from 0 (never) to 4 (always). Nine items of this scale are negatively worded, and their scores are reverse coded. The total score ranges from 0 to 80. A higher ULS score indicates a higher level of loneliness. In this research, the Cronbach’s alpha of the scale was 0.783.

International physical activity questionnaire-short form (IPAQ-SF)

The IPAQ-SF is a self-administered questionnaire, which is used to measure individuals’ physical activity (PA) during the last seven days [29]. This questionnaire includes low-intensity activities, moderate-intensity activities, and vigorous-intensity activities. Participants were required to report the frequency and duration that they engaged in each intensity activity. The total PA per week for each participant was calculated and then divided into low, moderate, and vigorous three levels following the IPAQ methodology as referenced in previous researches [30, 31].

Diagnostic criteria for metabolic syndrome (MetS)

According to the diagnostic criteria set by the Chinese Diabetes Society [32], MetS is diagnosed when an individual meets at least three out of five of the following conditions: (a) abdominal obesity: waist circumference (WC) ≥ 90 cm in male and ≥ 80 cm in female; (b) fasting blood glucose (FBG) ≥ 5.6 mmol/L or a previous diagnosis and treatment for diabetes; (c) hypertension: systolic blood pressure (SBP) ≥ 130 mmHg or diastolic blood pressure (DBP) ≥ 85 mmHg, or the use of specific medicine for hypertension; (d) fasting triglyceride (TG) ≥ 1.70 mmol/L or the use of specific medicine for lipid abnormalities; and (e) fasting high-density lipoprotein cholesterol (HDL-C) < 1.03 mmol/L in males and < 1.29 mmol/L in females, or the use of specific medicine for lipid abnormalities.

Statistical analysis

All statistical analyses were conducted using SPSS 25.0. Incomplete data were not included in the final data analysis. Mean and standard deviation (SD) were used to describe continuous variables. Numbers and proportions were used to describe categorical variables. A cluster analysis was conducted across the HPLP-C total score using the K-means algorithm. The number of clusters was defined as two and the cluster membership was saved as the grouping variable. The t-test and χ2 test were performed for group comparisons of continuous variables and categorical variables, respectively. We used logistic regression model to explore risk factors (i.e., illness duration, loneliness level, PA level, education level, occupation status, and the use of antipsychotics) associated with HPL. We defined these risk factors as independent variables, while the group of HPL was set as the dependent variable. We also used logistic regression model to explore the relationship between HPL and MetS. In this analysis, risk factors (i.e., HPL, Age, gender, occupation, BMI, loneliness level, PA level and the use of antipsychotics) were defined as independent variables, while MetS was set as the dependent variable. All variables with p < 0.05 in the univariate analyses were included in the logistic regression models. The results of logistic regression were presented as odds ratios (OR) and 95% confidence intervals (95% CI). A two-sided p < 0.05 was considered statistically significant.

Results

Social-demographic and clinical characteristics of participants

A total of 538 patients with schizophrenia (253 male and 285 female) were enrolled in this study. The average age of the participants was 44.70 (SD = 11.45), with a mean illness duration of 16.91 (SD = 10.46), a mean BMI of 24.90 (SD = 5.97) and a mean ULS score of 47.86 (SD = 7.93). The majority of participants were unemployed, using SGA, reported no tobacco and alcohol consumption, and exhibited low PA level. Detailed results of the social-demographic and clinical characteristics were presented in Table 1.

Table 1 Social-demographic and clinical characteristics of participants

Cluster analysis of HPL

The total HPLP-C score for the 538 participants was 90.51 (SD = 25.62). Participants were divided into two groups using the K-means algorithm. There were 271 participants in the high HPL group with an HPLP-C score of 111.44 (SD = 15.81). There were 267 participants in the low HPL group with an HPLP-C score of 69.27 (SD = 13.11).

Comparisons between high HPL group and low HPL group

Participants in the high HPL group were more likely to be occupied than those in the low HPL group (29.9% vs. 17.2%, p = 0.001). The three education levels between the two groups were significantly different (p < 0.001). Participants in the high HPL group had a shorter illness duration than those in the low HPL group (p = 0.042). Antipsychotics treatment between the two groups were also significantly different (p = 0.003). The proportion of high blood pressure, hypertriglyceridemia and high fasting glucose in the high HPL group were all significantly lower than those in the low HPL group (all p < 0.05). In addition, PA levels between the two groups were also significantly different (p < 0.001). The high HPL group had a lower level of loneliness when compared with the low HPL group (p < 0.001). (see Table 2).

Table 2 Differences of social-demographic and clinical characteristics of participants between high HPL group and low HPL group

Influencing factors of HPL

The results showed that loneliness (OR = 0.91, 95%CI: 0.89–0.94, p < 0.001) was a risk factor for participants to have a high level of HPL. In addition, participants with moderate PA (OR = 2.57, 95%CI: 1.64–4.03, p < 0.001) and vigorous PA (OR = 4.77, 95%CI: 2.83–8.02, p < 0.001) were protective factors to have a high level of HPL. Participants with secondary education level (OR = 2.15, 95%CI: 1.34–3.43, p = 0.001) and higher education level (OR = 2.40, 95%CI: 1.47–3.92, p < 0.001) were also protective factors to have a high level of HPL. (See Table 3)

Table 3 Logistic regression analyses for variables associated with HPL

Association between HPL and MetS

There were 200 participants in MetS group and 338 participants in non-MetS group. The univariate analysis between MetS group and non-MetS group was shown as table S1. The results of the multivariate logistic regression showed that participants with low HPL level (OR = 2.38, 95%CI: 1.59–3.55, p < 0.001) was a risk factor for the prevalence of MetS. (see Table 4)

Table 4 Logistic regression analyses of variables associated with MetS

Discussion

To our knowledge, this is the first study to investigate the influencing factors of HPL among people with schizophrenia and its association with MetS. This study found that loneliness was a risk factor for patients to have a high level of HPL, while engaging in moderate-vigorous physical activity and attaining higher education levels were identified as protective factors. We also found that a low level of HPL was negatively associated with the prevalence of MetS. The findings have important public and clinical implications, in particular for primary healthcare providers.

Our study showed that the loneliness score was high among both of the HPL group. This finding was in line with other studies, which demonstrated that patients with schizophrenia experienced high level of loneliness [1, 11]. Our results also showed loneliness had an adverse impact on patients to achieve a high level of HPL. This finding was similar to a Swiss national health survey [33], which found that lonely participants were prone to unhealthy lifestyle behaviors, such as smoking, being less physically active, and less fruit and vegetable consumption when compared with participants who never felt lonely. A national wide survey among Chinese adults also showed a negative associations between lifestyle scores and loneliness [34]. The reason why lonely individuals are easier to have a poor healthy lifestyle may be related with their poor self-motivation, low spiritual growth, insufficient social support and increased social isolation [35,36,37]. Previous studies reported that patients with schizophrenia engage less in and make limited use of community resources and had a poorer social relationship [38]. These findings suggest that more attention should be paid on lonely schizophrenia patients, and health-promoting lifestyle interventions in the future should not only focus on reducing negative health behaviors but also consider their impact on alleviating loneliness levels.

The benefits of PA for both physical health and mental health have been well documented [39]. Our finding of the positive association between PA and HPL was in line with the study of Fischer Aggarwal et al. [40], which found that PA could enhance social support, promote a healthy lifestyle, and reduce the risk of CVD. A systematic review and a meta-analysis focused on exploring the associations of sedentary behavior with disease mortality and physical activity level demonstrated a clear dose-response association between sitting time and CVD mortality in inactive population, while moderate intensity physical activity seemed to eliminate the increased risk of death caused by high sitting time [41, 42]. These studies indicated that moderate PA could help people reduce the unhealthy behavior, especially sedentary behavior, which were consistent with our findings. An explanation of this effect is that PA may have positive effects on emotional regulation, stress adaptation, confidence improvement and stay vigorous. However, over half people with schizophrenia in our study failed to meet recommended PA guidelines, such as accruing 150 min of moderate-vigorous PA per week. Our finding was consistent with Seet V et al. [43], who conducted a cross-sectional study in 380 psychiatric patients in Singapore and found a high prevalence of inadequate physical activity (43.2%). This finding indicated a large gap of PA in patients with schizophrenia. Previous studies also showed the effectiveness of PA on health management. An 18-month prospective study involving people with bipolar disorder indicated that physically active patients had lower levels of anxiety and less insomnia [44]. All these findings strongly support the notion that promotion of moderate-vigorous physical activity is an important health promotion task in patients with schizophrenia.

Education is an important social determinant of health that impact health behaviors. Our findings align with previous research that reported a positive association between higher education levels and healthier lifestyle behaviors. A population-based survey conducted in Girona showed that individuals with a lower education level had more lifestyle-related factors of CVD when compared to those with a higher education level [45]. A survey conducted in 52,029 rural Japanese individuals indicated that individuals with higher education level had a lower BMI in women, and more exercise in individuals younger than 70 years [46]. Previous studies have also showed positive effects of education intervention on improving healthy lifestyle. In a study of Du L and Hu J [47], a 5-week health education intervention among 35 Chinese elder without Alzheimer’s disease (AD) led to an increased AD-related knowledge and improved HPL. A randomized controlled study conducted in the perinatology clinic showed that women with gestational diabetes mellitus who received the health-promoting lifestyle education program had a greater improvement of the healthy lifestyle behaviors and quality of life [48]. People with higher levels of education exhibit a greater likelihood of seeking, understanding, and assessing health-related information, making them more prone to actively engage in health-promoting actions [49]. Therefore, it is important for primary healthcare providers to offer education to people with schizophrenia to improve their health-related knowledge, promote healthy lifestyles, and to prevent or ameliorate MetS.

In our study, we observed that the overall prevalence of MetS among schizophrenia patients was 37.17%, consistent with the pooled MetS prevalence reported in a meta-analysis [50]. The findings highlight elevated rates of MetS in individuals with schizophrenia. We also found a negative association between HPL and MetS. This finding was in line with other studies. Garralda-Del-Villar M et al. [51] conducted a 6-year cohort study on people without MetS to explore the relationship between health lifestyle and MetS incidence. Their findings revealed that higher adherence to a healthy lifestyle corresponded to a lower risk of developing MetS. Similarly, Park YS et al. [52] investigated lifestyle factors among 6995 South Korean adults and found that an increased number of lifestyle risk factors was associated with a higher risk of MetS. One study with 225 Latinos illustrated the HPLP sub-score of physical activity contributed to the risk for MetS [53]. Another study with 1128 individuals with MetS also showed physical activity was the strongest predictor of health-promoting behaviors to improve the lifestyle of patients with MetS [54]. Hence, it is clear that physical activity is an important content of healthy lifestyle and should be given a greater emphasis and be implemented in planning and interventions to reduce the risk of MetS.

Limitations

This study has several limitations that can guide future research in this domain. First, our study employed a non-probability sampling approach due to the specific characteristics of the target population and resource limitations. However, the sampling method may introduce potential selection biases and limit the generalization of our findings although we take multiple strategies to mitigate potential biases, such as established clear inclusion and exclusion criteria to define the target criteria, recruited participants from multiple centers, and provided detailed participants characteristics. External validity of our results across different populations and settings will provide valuable insights into the generalizability of findings. Second, the cross-sectional design makes it impossible to evaluate the causality between health promotion lifestyle and factors including loneliness, education literacy and physical activity. Third, our study used ICD-10 for diagnosing schizophrenia, which may reflect the temporal constraints existing at the initiation of data collection, predating the availability of ICD-11 and DSM-5â„¢. Future research should consider adopting the latest diagnostic criteria for improved alignment with current standards. Forth, this study has no healthy control group, it is difficult to understand the health status gap between patients with schizophrenia and general population.

Conclusions

Our findings suggest that HPL may be a potentially straightforward tool for reducing MetS for patients with schizophrenia. Understanding the influencing factors associated with HPL will provide direction for determining effective strategies to promote healthy lifestyle behaviors and enhance physical health for this population. The results of our findings indicate that health education related to HPL could be offered in primary health centers to help patients improve their health literacy. Primary healthcare professionals are encouraged to devote more attention to promote HPL in patients with schizophrenia and to motivate them to adopt more positive lifestyle choices, such as engaging in moderate-vigorous physical activities.

Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to confidentiality in the informed consent. However, the datasets are available from the corresponding author upon reasonable request.

Abbreviations

MetS:

Metabolic syndrome

BMI:

Body mass index

FGA:

First generation antipsychotics

SGA:

Second generation antipsychotics

HDL-C:

High-density lipoprotein cholesterol

HPL:

Health-promoting lifestyle

PA:

Physical activity

ULS:

UCLA Loneliness Scale

References

  1. Shioda A, Tadaka E, Okochi A. Loneliness and related factors among people with schizophrenia in Japan: a cross-sectional study. J Psychiatr Ment Health Nurs. 2016;23(6–7):399–408.

    Article  CAS  PubMed  Google Scholar 

  2. Ganesh S, Ashok AH, Kumar CN, Thirthalli J. Prevalence and determinants of metabolic syndrome in patients with schizophrenia: a systematic review and meta-analysis of Indian studies. Asian J Psychiatr. 2016;22:86–92.

    Article  PubMed  Google Scholar 

  3. Liu J, Fu L. Metabolic syndrome in patients with schizophrenia: why should we care. Med (Baltim). 2022;101(32):e29775.

    Article  Google Scholar 

  4. Penninx B, Lange SMM. Metabolic syndrome in psychiatric patients: overview, mechanisms, and implications. Dialogues Clin Neurosci. 2018;20(1):63–73.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Moreira FP, Jansen K, Cardoso TA, Mondin TC, Magalhaes PV, Kapczinski F, et al. Metabolic syndrome and psychiatric disorders: a population-based study. Braz J Psychiatry. 2019;41(1):38–43.

    Article  PubMed  Google Scholar 

  6. Zhang P, Huang J, Gou M, Zhou Y, Tong J, Fan F, et al. Kynurenine metabolism and metabolic syndrome in patients with schizophrenia. J Psychiatr Res. 2021;139:54–61.

    Article  PubMed  Google Scholar 

  7. Naderyan Fe’li S, Yassini Ardekani SM, Fallahzadeh H, Dehghani A. Metabolic syndrome and 10-year risk of cardiovascular events among schizophrenia inpatients treated with antipsychotics. Med J Islam Repub Iran. 2019;33:97.

    PubMed  PubMed Central  Google Scholar 

  8. Enez Darcin A, Yalcin Cavus S, Dilbaz N, Kaya H, Dogan E. Metabolic syndrome in drug-naive and drug-free patients with schizophrenia and in their siblings. Schizophr Res. 2015;166(1–3):201–6.

    Article  PubMed  Google Scholar 

  9. Fervaha G, Takeuchi H, Lee J, Foussias G, Fletcher PJ, Agid O, et al. Antipsychotics and amotivation. Neuropsychopharmacology. 2015;40(6):1539–48.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Cai J, Wei Z, Chen M, He L, Wang H, Li M, et al. Socioeconomic status, individual behaviors and risk for mental disorders: a mendelian randomization study. Eur Psychiatry. 2022;65(1):e28.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Suman A, Nehra R, Sahoo S, Grover S. Prevalence of loneliness and its correlates among patients with schizophrenia. Int J Soc Psychiatry. 2023;69(4):906–15.

    Article  PubMed  Google Scholar 

  12. Zhao S, Xia H, Mu J, Wang L, Zhu L, Wang A, et al. 10-year CVD risk in Han Chinese mainland patients with schizophrenia. Psychiatry Res. 2018;264:322–6.

    Article  PubMed  Google Scholar 

  13. Jakobsen AS, Speyer H, Norgaard HCB, Karlsen M, Hjorthoj C, Krogh J, et al. Dietary patterns and physical activity in people with schizophrenia and increased waist circumference. Schizophr Res. 2018;199:109–15.

    Article  PubMed  Google Scholar 

  14. Emul M, Kalelioglu T. Etiology of cardiovascular disease in patients with schizophrenia: current perspectives. Neuropsychiatr Dis Treat. 2015;11:2493–503.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Li Y, Pan A, Wang DD, Liu X, Dhana K, Franco OH, et al. Impact of healthy lifestyle factors on life expectancies in the US Population. Circulation. 2018;138(4):345–55.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Walker SN, Sechrist KR, Pender NJ. The Health-promoting Lifestyle Profile: development and psychometric characteristics. Nurs Res. 1987;36(2):76–81.

    Article  CAS  PubMed  Google Scholar 

  17. Zheng X, Xue Y, Dong F, Shi L, Xiao S, Zhang J, et al. The association between health-promoting-lifestyles, and socioeconomic, family relationships, social support, health-related quality of life among older adults in China: a cross sectional study. Health Qual Life Outcomes. 2022;20(1):64.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Alothman SA, Al Baiz AA, Alzaben AS, Khan R, Alamri AF, Omer AB. Factors Associated with Lifestyle behaviors among University Students-A cross-sectional study. Healthc (Basel). 2024; 12(2).

  19. Abdelaziz EM, Elsharkawy NB, Mohamed SM. Health promoting lifestyle behaviors and Sleep Quality among Saudi Postmenopausal women. Front Public Health. 2022;10:859819.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Li J, Song J, Zhu XL, Chen MF, Huang XF. Analysis of status quo and influencing factors for health-promoting lifestyle in the rural populace with high risk of cardiovascular and cerebrovascular diseases. BMC Cardiovasc Disord. 2023;23(1):118.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Jia L, Du Y, Chu L, Zhang Z, Li F, Lyu D, et al. Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: a cross-sectional study. Lancet Public Health. 2020;5(12):e661–e71.

    Article  PubMed  Google Scholar 

  22. Schrempft S, Jackowska M, Hamer M, Steptoe A. Associations between social isolation, loneliness, and objective physical activity in older men and women. BMC Public Health. 2019;19(1):74.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Yang CY, Lo SC, Peng YC. Prevalence and predictors of metabolic syndrome in people with Schizophrenia in Inpatient Rehabilitation wards. Biol Res Nurs. 2016;18(5):558–66.

    Article  PubMed  Google Scholar 

  24. Li R, Li W, Lun Z, Zhang H, Sun Z, Kanu JS, et al. Prevalence of metabolic syndrome in Mainland China: a meta-analysis of published studies. BMC Public Health. 2016;16:296.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Bujang MA, Sa’at N, Sidik T, Joo LC. Sample size guidelines for logistic regression from Observational studies with large Population: emphasis on the Accuracy between statistics and parameters based on Real Life Clinical Data. Malays J Med Sci. 2018;25(4):122–30.

    PubMed  PubMed Central  Google Scholar 

  26. Huang YH, Qiu QR. The reliability and validity evaluation of health promoting lifestyle. Kaohsiung J Med Sci. 1996;12:529–37.

    CAS  PubMed  Google Scholar 

  27. Zhang C, Zheng X, Zhu R, Hou L, Yang XY, Lu J, et al. The effectiveness of the SMG model for health-promoting lifestyles among empty nesters: a community intervention trial. Health Qual Life Outcomes. 2019;17(1):168.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Wang Y, Li S, Zou X, Xu L, Ni Y. Cross-cultural adaptation and validation of the Chinese version of the loneliness scale for older adults. Geriatr Nurs. 2022;48:190–6.

    Article  PubMed  Google Scholar 

  29. Macfarlane DJ, Lee CC, Ho EY, Chan KL, Chan DT. Reliability and validity of the Chinese version of IPAQ (short, last 7 days). J Sci Med Sport. 2007;10(1):45–51.

    Article  PubMed  Google Scholar 

  30. Zhai X, Wu N, Koriyama S, Wang C, Shi M, Huang T, et al. Mediating effect of perceived stress on the Association between Physical Activity and Sleep Quality among Chinese College Students. Int J Environ Res Public Health. 2021;18(1):289.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Dabrowska-Galas M, Dabrowska J, Ptaszkowski K, Plinta R. High physical activity level may reduce menopausal symptoms. Med (Kaunas). 2019;55(8):466.

    Google Scholar 

  32. Bressington DTMJ, Cheung EF, Petch J, Clark AB, Gray R. The prevalence of metabolic syndrome amongst patients with severe mental illness in the community in Hong Kong-a cross sectional study. BMC Psychiatry. 2013;13:87.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Richard A, Rohrmann S, Vandeleur CL, Schmid M, Barth J, Eichholzer M. Loneliness is adversely associated with physical and mental health and lifestyle factors: results from a Swiss national survey. PLoS ONE. 2017;12(7):e0181442.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Wang X, Wu Y, Shi X, Chen Y, Xu Y, Xu H, et al. Associations of lifestyle with mental health and well-being in Chinese adults: a nationwide study. Front Nutr. 2023;10:1198796.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Zhang Y, Kuang J, Xin Z, Fang J, Song R, Yang Y, et al. Loneliness, social isolation, depression and anxiety among the elderly in Shanghai: findings from a longitudinal study. Arch Gerontol Geriatr. 2023;110:104980.

    Article  PubMed  Google Scholar 

  36. Harorani M, Jadidi A, Zand S, Khoshkhoutabar T, Rafiei F, Beheshti SZ. Spiritual care in hospitalized patients in Iran: an Action Research Study. J Relig Health. 2022;61(5):3822–39.

    Article  PubMed  Google Scholar 

  37. Jadidi A, Ameri F. Social support and meaning of life in women with breast Cancer. Ethiop J Health Sci. 2022;32(4):709–14.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Eglit GML, Palmer BW, Martin AS, Tu X, Jeste DV. Loneliness in schizophrenia: construct clarification, measurement, and clinical relevance. PLoS ONE. 2018;13(3):e0194021.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Farris SG, Abrantes AM. Mental health benefits from lifestyle physical activity interventions: a systematic review. Bull Menninger Clin. 2020;84(4):337–72.

    Article  PubMed  Google Scholar 

  40. Fischer Aggarwal BA, Liao M, Mosca L. Physical activity as a potential mechanism through which social support may reduce cardiovascular disease risk. J Cardiovasc Nurs. 2008;23(2):90–6.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Ekelund U, Brown WJ, Steene-Johannessen J, Fagerland MW, Owen N, Powell KE, et al. Do the associations of sedentary behaviour with cardiovascular disease mortality and cancer mortality differ by physical activity level? A systematic review and harmonised meta-analysis of data from 850 060 participants. Br J Sports Med. 2019;53(14):886–94.

    Article  PubMed  Google Scholar 

  42. Ekelund U, Steene-Johannessen J, Brown WJ, Fagerland MW, Owen N, Powell KE, et al. Does physical activity attenuate, or even eliminate, the detrimental association of sitting time with mortality? A harmonised meta-analysis of data from more than 1 million men and women. Lancet. 2016;388(10051):1302–10.

    Article  PubMed  Google Scholar 

  43. Seet V, Abdin E, Asharani PV, Lee YY, Roystonn K, Wang P, et al. Physical activity, sedentary behaviour and smoking status among psychiatric patients in Singapore - a cross-sectional study. BMC Psychiatry. 2021;21(1):110.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Melo MCA, Garcia RF, de Araujo CFC, Rangel DM, de Bruin PFC, de Bruin VMS. Physical activity as prognostic factor for bipolar disorder: an 18-month prospective study. J Affect Disord. 2019;251:100–6.

    Article  PubMed  Google Scholar 

  45. Redondo A, Benach J, Subirana I, Martinez JM, Munoz MA, Masia R, et al. Trends in the prevalence, awareness, treatment, and control of cardiovascular risk factors across educational level in the 1995–2005 period. Ann Epidemiol. 2011;21(8):555–63.

    Article  PubMed  Google Scholar 

  46. Anzai Y, Ohkubo T, Nishino Y, Tsuji I, Hisamichi S. Relationship between Health Practices and Education Level in the Rural Japanese Population. J Epidemiol. 2000;10(3):149–56.

    Article  CAS  PubMed  Google Scholar 

  47. Du L, Hu J. The effects of health education on knowledge about Alzheimer’s disease and health-promoting behaviours of older Chinese adults in a nursing home: a pilot study. Int J Nurs Pract. 2016;22(1):31–42.

    Article  MathSciNet  PubMed  Google Scholar 

  48. Ural A, Kizilkaya Beji N. The effect of health-promoting lifestyle education program provided to women with gestational diabetes mellitus on maternal and neonatal health: a randomized controlled trial. Psychol Health Med. 2021;26(6):657–70.

    Article  PubMed  Google Scholar 

  49. Uemura KYM, Okamoto H. The effectiveness of an active learning program in promoting a healthy lifestyle among older adults with low health literacy: a Randomized Controlled Trial. Gerontology. 2021;67(1):25–35.

    Article  PubMed  Google Scholar 

  50. Mitchell AJ, Vancampfort D, Sweers K, van Winkel R, Yu W, De Hert M. Prevalence of metabolic syndrome and metabolic abnormalities in schizophrenia and related disorders–a systematic review and meta-analysis. Schizophr Bull. 2013;39(2):306–18.

    Article  PubMed  Google Scholar 

  51. Garralda-Del-Villar M, Carlos-Chilleron S, Diaz-Gutierrez J, Ruiz-Canela M, Gea A, Martinez-Gonzalez MA, et al. Healthy lifestyle and incidence of metabolic syndrome in the SUN Cohort. Nutrients. 2018;11(1):65.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Park YS, Kang SH, Jang SI, Park EC. Association between lifestyle factors and the risk of metabolic syndrome in the South Korea. Sci Rep. 2022;12(1):13356.

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  53. Sutherland LL, Simonson S, Weiler DM, Reis J, Channel A. The relationship of metabolic syndrome and health-promoting lifestyle profiles of latinos in the Northwest. Hisp Health Care Int. 2014;12(3):130–7.

    Article  PubMed  Google Scholar 

  54. Mohammadi M, Ramezankhani A, Mohammadi S, Zahed S, Khabiri F, Khodakarim S, et al. The predictors of metabolic syndrome based on Walker Health-promoting lifestyle in Iran 2016. Diabetes Metab Syndr. 2017;11(Suppl 2): S745–S9.

    Article  Google Scholar 

Download references

Acknowledgements

We thank all participants who agreed to take part in the study, we also thank all the primary healthcare staff who made great efforts for physical examination, support participant recruitment, and data collection.

Funding

This study was funded by Science and Technology Program of Guangzhou (number, 20221A010028; 2023A04J1869; 2023A03J0854), Guangzhou Municipal Key Discipline in Medicine (2021–2023), Guangzhou High-level Clinical Key Specialty and Guangzhou Research-oriented Hospital.

Author information

Authors and Affiliations

Authors

Contributions

Yu Fan: study design, data analysis, and writing the manuscript. Liang Zhou and Shaoling Zhong: revising the manuscript and providing funding for the study. Xiyuan Chen and Jinghua Su: institutional coordination and data collection. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Liang Zhou or Shaoling Zhong.

Ethics declarations

Ethics approval and consent to participate

Ethical approval was obtained from the ethics committee of the Affiliated Brain Hospital of Guangzhou Medical University (approval number: 2022089). All study participants provided written informed consent before participation and participated in the study voluntarily.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

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 http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) 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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fan, Y., Zhou, L., Chen, X. et al. Determinants and outcomes of health-promoting lifestyle among people with schizophrenia. BMC Psychiatry 24, 177 (2024). https://doi.org/10.1186/s12888-024-05625-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12888-024-05625-2

Keywords