Latent profile analysis of depression among home-based old adults patients with stroke in China

Latent profile analysis of depression among home-based old adults patients with stroke in China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Latent profile analysis of depression among home-based old adults patients with stroke in China Tingting Lu, Yingchun Liu, Lihua Shi, Jianfang Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6320611/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Stroke is one of the chronic diseases that endanger the life and health of the old adults, which not only causes physical discomfort but also brings psychological burden to them (such as: anxiety, depression, etc.).In-depth analysis of the types of depression that may occur in old adults patients with stroke can provide a theoretical basis for formulating interventions to accurately reduce the depression level of old adults patients with stroke.In this study, the results of the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS) were used as a data source, and latent profile analysis(LPA) was used to distinguish the types of depression in home-based stroke patients.The profile analysis divided the depression levels of 881 elderly stroke patients into low-level (13.0%), medium-level (52.0%), and high-level (35.0%).Multiple logistic regression showed that living in cities, hypertension and anxiety predicted high levels of depression in the comparison between the low-level and the high-level.The results of the study divided the depression level of home-based stroke patients into three potential profiles, which provided a theoretical basis for clinical nursing staff to formulate more detailed intervention plans. stroke home-based old adults depression latent profile analysis related factor Figures Figure 1 Figure 2 Introduction Population aging is sweeping the globe, changing the demographic structure of today's world.By the beginning of 2021, people aged 60 years or older have been account for 13.5 percent of the world's population(Cacchione,2022).As one of the most populous countries in the world, China's aging trend is also developing, by 2050, China's population aged 60 and above will account for 23.19% of the world's elderly population, entering the deep aging stage(Fang et al.,2015).An aging population has led to an increase in diseases associated with it.Some diseases seriously threaten the life and health of patients because of their hidden, refractory and lifelong nature.With the change of medical model, the old adults have changed from the original pursuit of physical health to the pursuit of physical and mental health(Blanchet et al.,2018).Relevant studies have found that mental health has become an important factor affecting the life satisfaction of the old adults(Puvill et al.,2016).Therefore, timely attention to the mental health of the old adults is of great significance to promote the recovery of diseases and improve the quality of life in old age. As a mental health problem, depression is considered to be the second most common mental disorder among the old adults, with a high incidence in the old adults, and the prevalence has reached 1%-5%(Fond et al.,2023;Snowdon & Lane,2001;Lee et al.,2023).Previous studies have shown that social isolation and functional disability associated with aging may be pathogenic factors for geriatric depression(Oh et al.,2023).In a study on depression in the old adults, it was found that the occurrence of the disease can reduce the health status of patients, quality of life and even increase mortality(Vander Weele et al.,2009).However, in older adults with chronic health problems, suffering from depression was both common and complex(Mitchell & Harvey,2014),Because chronic disease or other social factors can make depression difficult to diagnose and treat, early attention and attention should be paid to depressive symptoms in older patients. Stroke was an acute cerebrovascular disease, which was a common and frequent disease in neurology, with high incidence, high disability rate, high recurrence rate and high mortality rate(Kim et al.,2023).According to the results of the 2019 Global Burden of Disease Study, from 2019 to 2022, there were 940,000 new stroke cases and 760,000 stroke deaths in China(Wang et al.,2022).Stroke not only causes disability in patients, but also brings serious psychological burden to patients(Xia et al.,2019).Post-stroke depression was one of the common complications in patients, which can make the cognitive deficits of patients more obvious, reduce the quality of life, and may make patients appear suicidal(Medeiros et al.,2020;Almeida,2023).With the emergence of emerging medical models, home rehabilitation has been popularized in the treatment of stroke, which means that after the patient's condition is stable, he goes home for follow-up physical and language exercises.However, deviating from the normal medical environment can make depression difficult to detect in patients. Therefore, the depression of home-based stroke patients needs to be screened in time. Previous studies on post-stroke depression mainly focused on the risk factors of depression in patients, the harm of depression to patients, and the treatment of patients after depression(Sun et al.,2023;Guo et al.,2022;Yang et al.,2022).These studies focused on this group of old people with post-stroke depression, and did not analyze the heterogeneity of depression in this group.This study intended to use potential profile analysis(LPA), focusing on individuals, to explore the hidden information within individuals.Latent profile analysis (LPA) mainly identifies heterogeneous subgroups based on continuous indicators representing different dimensions, and its goal is to identify homogeneous subgroups of individuals and distinguish them from other subgroups(Wang et al.,2022;Berlin et al.,2014).Post-stroke depression has been highlighted in previous studies, but different categories of the phenomenon have not been dissected in the old population.Therefore, based on the results of potent profile analysis, early identification of different types of depression in old adults patients with stroke and their related influencing factors is of great significance for the formulation of precise intervention programs. Materials and methods Sample This study used the data of the Chinese Longitudinal Healthy Longevity Survey (CLHLS)released by the Center for Healthy Aging and Development of Peking University. The study covered 23 provinces, municipalities and autonomous regions in China, and respondents were aged 65 years and older.The contents of the investigation mainly include the basic status of the old adults and their families, cognitive function, personality and psychological characteristics, daily activity ability, depression, dementia and so on. All older adults who participated in the study gave informed consent, and the reliability of the data was unanimously recognized by experts.The database is free and open by Peking University ( http://opendata.pku.edu.cn/ ), and we obtained permission to download it according to the application process.According to the purpose of the study, we formulated the inclusion criteria as follows: ( 1 ) Age ≥ 65 years. ( 2 ) Patients with clinically diagnosed stroke. ( 3 ) The patients were in the state of home care. Exclusion criteria: ( 1 )Incomplete completion of the depression scale.( 2 )Incomplete information.Finally, a total of 866 home-based patients with stroke were included in this study(Fig. 1 ). Measurement General information questionnaire A total of 15 socio-demographic and disease-related data were included in the study. Mainly included: gender, age, place of residence, years of education, self-rated quality of life, self-rated health status, whether smoking, drinking, exercise, economic status, marital status, whether hearing impairment, whether taking drugs, whether suffering from high blood pressure, whether dyslipidemia. Depression In the CLHLS, the Short Version of the Center for Epidemiological Studies Depression Scale (CES-D-10) was used to assess the level of depression in study subjects.The scale consists of ten items, using Likert 5 grade scoring method, scoring methods were: always = 1, often = 2, sometimes = 3, rarely = 4, never = 5.Item 5, item 7 and item 10 represent the positive mental state of the patient, and the scoring method is opposite to other items.The higher the score, the higher the level of depression.Previous studies have shown that this scale has good reliability and validity(Park& Yu,2021). In this study, the reliability of this scale was 0.829. Anxiety In the CLHLS, the Generalized Anxiety Disorder Scale (GAD-7) was used to assess how often older adults had experienced anxiety in the last two weeks(Dhira et al.,2021).The scale mainly consisted of 7 items, each item was scored from 0 to 3 points, 0 represented no anxiety in the past two weeks, 1 represented a few days to feel anxious, 2 represented more than half of the time feel anxious, 3 represented almost every day feel anxious, the scale score range was 0 to 28 points, the higher the score, the higher the anxiety level.This scale has been widely used in the old adults in China and has good reliability and validity(Yue et al.,2022). The reliability of this scale in this study was 0.926. Instrumental activities of daily living (IADL) In CHILS, the instrumental Activities of daily Living Scale(IADL) was adapted from the activities of daily Living scale(Lawton & Brody,1969).The scale mainly included 8 items, respectively from 8 aspects of the assessment of the tester's daily life ability. The scale was scored on a scale of 8 to 24, with 1 indicating that it can be completed, 2 indicating that it is somewhat difficult to complete, and 3 indicating that it cannot be completed.The higher the score, the worse the ability to perform daily activities.The scale has good reliability and validity(Carmona-Torres et al.,2019), and the reliability in this study was 0.952. Statistical analysis In this study, SPSS25.0 software was used for univariate,multivariate logistic regression and nonparametric tests. Mplus8.3 software was used to identify potential categories of depression. P < 0.05 indicated that the difference was statistically significant. Descriptive analysis In the descriptive analysis, the data conforming to the normal distribution were tested by parameters, and the results were measured as mean ± standard deviation (M ± SD). Non-parametric test was applied to measurement data with non-normal distribution,and the results were expressed by median (quartile)[M (IQR)].Counting data was expressed in frequency and percentage terms. Latent profile analysis (LPA) Mplus8.3 was used to profile 10 items of the CES-D-10. In this study, the number of potential profiles was increased from 1 to 5 respectively, the fit degree of the five models was tested, and the optimal model was finally selected.The fitting indexes mainly included Akaike information criterion (AIC), Bayesian information criterion (BIC), adjusted BIC(aBIC), entropy index, Lo-Mendell-Rub test (LMR) and Bootstrap Likelihood ratio test (BLRT).When AIC, BIC and aBIC values decrease with the increase of the number of categories, it indicated that the model fits better. The entropy index indicates the accuracy of classification. When the value is ≥ 0.8, the accuracy is > 90%. When the p value corresponding to LMR and BLRT is < 0.05, k is more appropriate than K-1 category. Single factor and multi-factor analysis Differences in sociodemography, anxiety, and IADL among different subgroups were analyzed using χ2 test or Fisher exact probability method and KruskalWallis H test.Variables with statistically significant differences in the results of univariate analysis were included in multiple logistic regression, P < 0.05 was considered statistically significant. Results General demographic characteristics A total of 881 stroke patients aged 65 years and older were included in this study, including 456 males (51.8%) and 425 females (48.2%). In addition, 62.8% of the patients had high blood pressure and 77.2% of the patients were currently taking medication.The remaining general information Demographic information is shown in Table 1 . Potential categories of depression in home-based elderly stroke patients A total of 5 fitting models were explored in this study (Table 2 ).It can be seen from Table 2 that with the increasing number of categories, the values of AIC,BIC and aBIC are continuously decreasing, which indicated that the models of these five categories are qualified.Although the entropy index of 5-profile model is higher than that of the 3- and 4-profile model, the P-value of LMRT of 5-profile model is 0.4063 (P > 0.05), so 5-profile model is excluded.In addition, the entropy index of 4-profile model is higher than that of the 3-profile model, but a part of the population in 4-profile model is relatively small, and the entropy coefficient of 3-profile model is close to that of the 4-profile model, so we finally choose 3-profile model as the optimal model. In order to verify the reliability of LPA results, we calculated the attribution probability of the 3-profile model (Table 3 ). The results showed that the attribution probability of the three potential categories ranged from 0.922 to 0.941, all of which were greater than 90%. This showed that the results of the 3-profile model were reliable. Table 3 Attribution probabilities for each latent profile of subjects Class Profile 1 Profile 2 Profile 3 Profile 1 0.936 0.064 0.000 Profile 2 0.015 0.922 0.063 Profile 3 0.000 0.059 0.941 Naming of latent profile According to the results of profile analysis, it is drawn as Fig. 2 .The values in Fig. 2 were derived from the mean of 10 items in each of the three categories.115 (13.0%) of home-based old adults patients with stroke had lower levels of depression than other categories, which were classified as "low level".308 (35.0%) of elderly home based stroke patients had higher levels of depression than the other categories, which were classified as "high level".The remaining 458 (52.0%) had depression levels somewhere between these two, which named "moderate." Univariate analysis of potential profiles of depression Chi-square test and Kruskal-Wallis H test were used to compare the effects of different socio-demographic data, anxiety and daily activity ability on different potential categories of stroke patients.The results showed that gender, Residential area, education level, self-rated quality of life, self-rated health status, exercise, economic status, taking medication, hypertension, dyslipidemia, anxiety, and daily activity ability had statistical significance (P < 0.05).The specific results are shown in Table 1 . Multinomial logistic regression of depression profiles The meaningful variables in univariate analysis were included as independent variables in multiple logistic regression analysis,three categories of depression were used as dependent variables to compare the differences among independent variables.The assignment of the independent variables are shown in Table 4 . Table 4 Case of variable assignment Variable Assignment mode Gender Male = 1;Female = 2 Age 65–75 = 1;76–85 = 2;86–95 = 3; ≥96 = 4 Residential area City = 1;Town = 2;Rural Education level None = 1;1–10 = 2;≥11 = 3 Marital status Married = 1;Divorced = 2;Widowed = 3 Quality of life Good = 1;Moderate = 2;Poor = 3 Health status Good = 1;Moderate = 2;Poor = 3 Economic income Good = 1;Moderate = 2;Poor = 3 Drink Yes = 1;No = 2 Smoke Yes = 1;No = 2 Exercise Yes = 1;No = 2 Hearing difficulty Yes = 1;No = 2 Take medicine Yes = 1;No = 2 Hypertension Yes = 1;No = 2 Dyslipidemia Yes = 1;No = 2 Anxiety Measured value IADL Measured value The results of multiple logistic regression analysis showed that compared low-level depression with moderate-level depression, male, better quality of life, better health, and exercise predicted low-level depression, while those with hypertension and anxiety predicted middle-level depression.Comparing low to high levels of depression, being male, having a better quality of life, being in better health, exercising predicted low levels of depression, and being anxious, living in an urban area, and having high blood pressure predicted high levels of depression.Comparing high levels of depression with moderate levels of depression, anxiety predicted high levels of depression. The result is shown in the Table 5 . Table 5 Multinomial logistic regression of depression profiles. Variables Low(ref) VS Moderate OR(95%CI) P Low(ref) VS High OR(95%CI) P Moderate(ref) VS High OR(95%CI) P Gender Male 0.570(0.329–0.988) 0.045 0.539(0.296–3.040) 0.044 0.946(0.691–1.296) 0.729 Residential area City Town 1.999(0.970–4.118) 0.060 0.903(0.394–2.072) 0.810 2.327(1.069–5.066) 0.033 0.753(0.312–1.891) 0.528 1.165(0.792–1.712) 0.438 0.833(0.562–1.236) 0.365 Education level None 1–10 0.569(0.269–1.323) 0.204 0.588(0.278−1,243) 0.164 0.615(0.252–1.503) 0.286 0.779(0.331–1.833) 0.567 1.031(0.579–1.780) 0.913 1.325(0.769–2.284) 0.310 Quality of life Good Moderate 0.084(0.026–0.265) <0.001 0.198(0.066–0.591) 0.004 9.503(2.241–40.291) 0.002 1.920(0.471–7.826) 0.363 1.257(0.327–4.833) 0.739 2.634(0.684–10.15) 0.159 Health status Good Moderate 0.251(0.098–0.639) 0.004 0.379(0.206–0.297) 0.002 0.186(0.069–0.504) 0.001 0.401(0.199–0.808) 0.011 0.743(0.469–1.178) 0.206 1.059(0.687–1.634) 0.795 Exercise Yes 0.399(0.194–0.819) 0.012 0.287(0.134–0.615) 0.001 0.720(0.514–1.010) 0.057 Economic income Good Moderate 0.862(0.302–2.457) 0.781 0.797(0.360–1.767) 0.577 0.980(0.986−0.313) 0.980 1.070(0.433–2.646) 0.884 1.143(0.602–2.173) 0.683 1.342(0.750–2.402) 0.322 Take medicine Yes 0.988(0.480–2.034) 0.975 1.132(0.526–2.435) 0.752 1.145(0.801–1.636) 0.457 Hypertension Yes 2.507(1.322–4.753) 0.005 2.462(1.237–4.898) 0.010 0.982(0.712–1.354) 0.912 Dyslipidemia Yes 1.273(0.643–2.518) 0.488 1.264(0.589–2.713) 0.548 0.993(0.636–1.550) 0.975 Anxiety 1.959(1.710–2.245) <0.001 0.712(0.651–0.779) <0.001 0.717(0.642−0.800) <0.001 IADL 1.004(0.953–1.058) 0.872 0.990(0.944–1.038) 0.679 1.006(0.978–1.034) 0.687 Discussion LPA was to classify individuals into different categories according to their response patterns on the observed indicators, and then analyze the differences between different groups.According to the depression scores of home-based old adults patients with stroke, this study divided them into three potential categories: "low-level", "medium-level" and "high-level".It suggested that there was some variation in the level of depression in home-based old adults patients with stroke. The 13.0% of stroke patients were classified as a low-level group, which had a lower level of depression.But in the low depression group, some items scored higher.. For example,item 6,“Are you feel nervous or scared?” It may be related to physical movement disorders, language disorders and other serious complications caused by stroke, and long-term physical discomfort will make patients suffer psychological burdens, resulting in anxiety and fear.be weakened(Yu et al.,2021).In addition, item 9, “Do you feel unable to continue your life?”also scored high.It may be related to the physiological discomfort and economic burden brought by stroke disease, which may cause the quality of life of patients to decline(Ramos-Lima et al.,2018). It made patients lose confidence in their future life.Although depression levels in this group of elderly stroke patients were low, in order to prevent their depression from worsening, their problems cannot be ignored in nursing interventions. The 52.0% of stroke patients were classified as a medium-level group,which is the highest percentage of the three profiles.In this group of patients with moderate levels of depression, item 9, “Do you feel unable to continue your life?” had the highest average score.It showed that patients' daily life was significantly affected, and the impact was more pronounced than in the low and high level groups.Compared with the depressed patients in the low level group and the high level group, the patients in this group are in between, which may become low-level because of better intervention, or may become high-level because of disease, environment and other factors aggravate their depression degree.Therefore, patients with moderate levels of depression need to be effectively identified and early intervention to promote their mental health. The 35.0% of stroke patients were classified as a high-level of depression,indicating a more severe level of depression in this group.It may be related to hemiplegia, aphasia, and the long recovery process experienced by patients after stroke(Cross et al.,2023).long-term disease treatment and medication can cause a certain financial burden to patients. It will cause certain psychological pressure to the patient, resulting in anxiety and depression(GBD 2019 Diseases and Injuries Collaborators,2019).In addition, individual personality traits and family support are also factors that affect the occurrence of depression in stroke patients(Huang et al.,2017).As the group with the most severe depression level, reducing the depression level of this category of patients was of great significance for the rehabilitation and recovery of elderly stroke patients. Therefore, it is necessary to timely publicize the related knowledge of stroke and depression to such patients, improve their confidence in disease treatment and self-rehabilitation ability, and promote their mental health. In this study, gender, residential area, self-rated quality of life, self-rated health status, exercise, hypertension and anxiety were identified as factors influencing depression in home-based old adults patients with stroke.When comparing low-level depression with medium-level depression, male were more inclined to the low-level depression group.It may be related to the physiological factors of women, and changes in hormone levels caused by increasing age are more likely to affect women(Sassarini et al.,2016).Women tend to be more sensitive to exposure to psychological stress events, and when they have a stroke, they were less able to withstand it than men(Huang et al.,2019).In addition, the proportion of older women who were widowed was higher than that of men, which mean that they suffer from psychological stress and loneliness more than men, and will naturally be more prone to depression. In this study, the depression level of old adults patients with stroke living in city was higher than that living in rural. It is inconsistent with previous research.Previous studies have found that the prevalence of depression in rural elderly was higher than that in urban elderly(Liu et al.,2021).However, other studies have found that the incidence of depression in rural people aged 75 and above was relatively low, while the incidence of depression in urban low-income elderly people was relatively high(Xu et al.,2014).Therefore, the higher level of depression in the urban elderly in this study may be related to their lower income level and higher age. The depression level of home-based old adults patients with stroke better self-rated life satisfaction was lower.Previous studies have also found that depression in stroke patients was independently associated with life satisfaction(Oosterveer et al.,2017).After stroke, the physical and social functions of patients were weakened, which made their satisfaction with quality of life decline, and then produce depression(Chan et al.,2021).When patients were able to recover their functions, their anxiety and depression will also be reduced.Therefore, in the process of nursing stroke patients, we should put the physical rehabilitation of patients in the first place, and carry out psychological counseling to reduce the incidence of post-stroke depression. Self-rated health status was a patient's self-assessment of their own health status.Some studies have highlighted self-rated health as an important predictor of depression in patients(Yun & Bae,2020;Woo & Bae,2021).Home-based stroke patients required subsequent rehabilitation therapy to facilitate recovery, so their assessment of their own health reflects the recovery of their physical symptoms and negative emotions.When the patient's self-rated health was satisfied, it not only indicated that the physical function was recovering well, but also indicated that the psychological function was also being repaired, so the depression level will decline.Therefore, the dual role of physical health and mental health should not be ignored when paying attention to the self-rated health of old adults patients with stroke . Exercise has been shown to be effective in reducing symptoms in people with depression, and it is an important way to treat mental illness and physical health problems(Schuch & Stubbs,2019;Knapen et al.,2015).In this study, patients who exercised had significantly lower levels of depression.The results also highlight the important role that exercise plays in reducing the onset of disease.Therefore, community staff should regularly conduct lectures on exercise knowledge, set up places for different exercise items according to personal preferences, and encourage stroke patients to take the initiative to participate in exercise. Hypertension was not only a risk factor for stroke, but also leaded to depression(Rantanen et al.,2018).In this study, hypertension predicted moderate to high levels of depression. Hypertension and depression symptoms can form a toxic combination that can even lead to all-cause mortality(Rantanen et al.,2018).Therefore, it is of great significance to pay attention to the depressive symptoms of old adults patients with stroke with hypertension to reduce the mortality.Health care providers should treat hypertension as a separate risk factor, providing health education and helping patients improve their self-management skills. It is not only necessary to reduce the physical burden brought by the disease, but also to pay attention to the emotional changes of patients and reduce the probability of depression. In this study, anxiety significantly distinguished the levels of depression in 3 categories.Due to the sudden and difficult nature of stroke, patients not only suffer from changes in physical symptoms after the lesion, but also undergo long-term rehabilitation training, which will produce a major breakthrough in the psychological defense line of patients, resulting in anxiety in patients.Related studies have found that anxiety can predict depressive symptoms and depressive disorders(Hou et al.,2023).It indicated that reducing the anxiety level of patients in time can effectively control the occurrence of depression.Therefore, nursing staff should detect the anxiety symptoms of patients as early as possible and intervene in time. Limitations There were some flaws in this study.Firstly,this study screened the CLHLS database and may have missed some factors affecting depression in home-based old adults patients with stroke .Secondly,this study was a cross-sectional study, the results cannot explain the causal relationship between the independent variable and the dependent variable.Thirdly,the data results of this study were self-reported, which may lead to measurement bias. Conclusions This study divided the level of depression in home-based elderly stroke patients into three subgroups.There were statistically significant differences in gender,residential area, self-rated quality of life, self-rated health status, exercise, economic status, hypertension and anxiety among different categories of home-based elderly stroke patients.The results provide a basis for the formulation of intervention measures to reduce the level of depression in elderly stroke patients.In clinical work,medical workers should identify patients with low and moderate levels of depression as early as possible and intervene in time to avoid deterioration of the condition.The patients with high level of depression are taken as the key intervention objects to reduce the level of depression and ensure the life safety of patients.In addition, health care workers should develop personalized and targeted interventions based on different types of depression. Declarations Funding This study was supported by2024 Suzhou Municipal Hospital Nursing Research Project(slyyhl202408). Acknowledgments The authors express their gratitude to all contributors for their diligent work on this study and all participants who voluntarily participated in the research. Authors ’ Contributions All authors made important contributions to this study. TL and LY designed and wrote the article.LY revised and improved the manuscript.ZJ and SL participated in the collection and analysis of data. All authors contributed to the article and approved the submitted version. Conflict of interest There is no conflict of interest in this study. Clinical trial number: Not applicable. Consent for publication Not applicable. Availability of Data and Materials The data that support the findings of this study are openly available in the Chinese Longitudinal Healthy Longevity Survey (CLHLS) at http://opendata.pku.edu.cn/. References Cacchione PZ. 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Medeiros GC, Roy D, Kontos N, Beach SR. Post-stroke depression: A 2020 updated review. Gen Hosp Psychiatry 2020 Sep-Oct;66:70–80. 10.1016/j.genhosppsych.2020.06.011 . Epub 2020 Jun 27. PMID: 32717644. Almeida OP. Stroke, depression, and self-harm in later life. Curr Opin Psychiatry. 2023;36(5):371–5. https://doi.org/10.1097/YCO.0000000000000882 . Sun S, Li Z, Xiao Q, Tan S, Hu B, Jin H. An updated review on prediction and preventive treatment of post-stroke depression. Expert Rev Neurother. 2023;23(8):721–39. https://doi.org/10.1080/14737175.2023.2234081 . Guo J, Wang J, Sun W, Liu X. The advances of post-stroke depression: 2021 update. J Neurol. 2022;269(3):1236–49. https://doi.org/10.1007/s00415-021-10597-4 . Yang NN, Lin LL, Li YJ, Li HP, Cao Y, Tan CX, Hao XW, Ma SM, Wang L, Liu CZ. (2022). Potential Mechanisms and Clinical Effectiveness of Acupuncture in Depression.Current neuropharmacology, 20(4), 738–50. https://doi.org/10.2174/1570159X19666210609162809 Wang Y, Kim E, Yi Z. Robustness of Latent Profile Analysis to Measurement Noninvariance Between Profiles. Educ Psychol Meas. 2022;82(1):5–28. https://doi.org/10.1177/0013164421997896 . Berlin KS, Williams NA, Parra GR. An introduction to latent variable mixture modeling (part 1): overview and cross-sectional latent class and latent profile analyses. J Pediatr Psychol. 2014;39(2):174–87. https://doi.org/10.1093/jpepsy/jst084 . Park SH, Yu HY. How useful is the center for epidemiologic studies depression scale in screening for depression in adults? An updated systematic review and meta-analysis. Psychiatry Res. 2021;302:114037. https://doi.org/10.1016/j.psychres.2021.114037 . Dhira TA, Rahman MA, Sarker AR, Mehareen J. Validity and reliability of the Generalized Anxiety Disorder-7 (GAD-7) among university students of Bangladesh. PLoS ONE. 2021;16(12):e0261590. https://doi.org/10.1371/journal.pone.0261590 . Yue Z, Liang H, Gao X, Qin X, Li H, Xiang N, Liu E. The association between falls and anxiety among elderly Chinese individuals: The mediating roles of functional ability and social participation. J Affect Disord. 2022;301:300–6. https://doi.org/10.1016/j.jad.2022.01.070 . Lawton MP, Brody EM. Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist. 1969;9(3):179–86. Carmona-Torres JM, Rodríguez-Borrego MA, Laredo-Aguilera JA, López-Soto PJ, Santacruz-Salas E, Cobo-Cuenca AI. Disability for basic and instrumental activities of daily living in older individuals. PLoS ONE. 2019;14(7):e0220157. https://doi.org/10.1371/journal.pone.0220157 . Yu M, Wang L, Wang H, Wu H. The effect of early systematic rehabilitation nursing on the quality of life and limb function in elderly patients with stroke sequelae. Am J translational Res. 2021;13(8):9639–46. Ramos-Lima MJM, Brasileiro IC, Lima TL, Braga-Neto P. Quality of life after stroke: impact of clinical and sociodemographic factors. Clin (Sao Paulo Brazil). 2018;73:e418. https://doi.org/10.6061/clinics/2017/e418 . Cross JG, May BR, Mai PQM, Anderson E, Welsh C, Chandran S, Chorath KT, Herr S, Gonzalez D. A systematic review and evaluation of post-stroke depression clinical practice guidelines. J stroke Cerebrovasc diseases: official J Natl Stroke Association. 2023;32(9):107292. https://doi.org/10.1016/j.jstrokecerebrovasdis.2023.107292 . GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a. Lancet (London England). 2020;396(10258):1204–22. https://doi.org/10.1016/S0140-6736(20)30925-9 . systematic analysis for the Global Burden of Disease Study 2019. Huang HC, Huang YC, Lin MF, Hou WH, Shyu ML, Chiu HY, Chang HJ. Effects of Home-Based Supportive Care on Improvements in Physical Function and Depressive Symptoms in Patients With Stroke: A Meta-Analysis. Arch Phys Med Rehabil. 2017;98(8):1666–e16771. https://doi.org/10.1016/j.apmr.2017.03.014 . Sassarini DJ. Depression in midlife women. Maturitas. 2016;94:149–54. https://doi.org/10.1016/j.maturitas.2016.09.004 . Huang LJ, Du MMRN, Liu WTMMRN, Guo YCMMRN, Zhang LNPDLPN, Qin JJBSRN, BS MM, RN, Liu K, MM. Loneliness, Stress, and Depressive Symptoms Among the Chinese Rural Empty Nest Elderly: A Moderated Mediation Analysis. Issues Ment Health Nurs. 2019;40(1):73–8. https://doi.org/10.1080/01612840.2018.1437856 . Liu H, Fan X, Luo H, Zhou Z, Shen C, Hu N, Zhai X. Comparison of Depressive Symptoms and Its Influencing Factors among the Elderly in Urban and Rural Areas: Evidence from the China Health and Retirement Longitudinal Study (CHARLS). Int J Environ Res Public Health. 2021;18(8):3886. https://doi.org/10.3390/ijerph18083886 . Xu Y, Yang J, Gao J, Zhou Z, Zhang T, Ren J, Li Y, Qian Y, Lai S, Chen G. Decomposing socioeconomic inequalities in depressive symptoms among the elderly in China. BMC Public Health. 2016;16(1):1214. https://doi.org/10.1186/s12889-016-3876-1 . Oosterveer DM, Mishre RR, van Oort A, Bodde K, Aerden LA. Depression is an independent determinant of life satisfaction early after stroke. J Rehabil Med. 2017;49(3):223–7. https://doi.org/10.2340/16501977-2199 . Chan SH, Pan Y, Xu Y, Yeung KC. Life satisfaction of 511 elderly Chinese stroke survivors: moderating roles of social functioning and depression in a quality of life model. Clin Rehabil. 2021;35(2):302–13. https://doi.org/10.1177/0269215520956908 . Yun HS, Bae SM. Influence of Health Status, Cognitive Function, and Social Capital on Depressive Symptoms in Korean Older Adults. J PsychoSoc Nurs Ment Health Serv. 2020;58(10):24–31. https://doi.org/10.3928/02793695-20200817-01 . Woo JH, Bae SM. Influence of Health-Related Status and Social Activities on Depressive Symptoms in Korean Older Adults Who Live Alone. J PsychoSoc Nurs Ment Health Serv. 2021;59(2):25–30. https://doi.org/10.3928/02793695-20201203-01 . Schuch FB, Stubbs B. The Role of Exercise in Preventing and Treating Depression. Curr Sports Med Rep. 2019;18(8):299–304. https://doi.org/10.1249/JSR.0000000000000620 . Knapen J, Vancampfort D, Moriën Y, Marchal Y. Exercise therapy improves both mental and physical health in patients with major depression. Disabil Rehabil. 2015;37(16):1490–5. https://doi.org/10.3109/09638288.2014.972579 . Rantanen AT, Korkeila JJA, Löyttyniemi ES, Saxén UKM, Korhonen PE. Awareness of hypertension and depressive symptoms: a cross-sectional study in a primary care population. Scand J Prim Health Care. 2018;36(3):323–8. https://doi.org/10.1080/02813432.2018.1499588 . Hou B, Zhang H. Latent profile analysis of depression among older adults living alone in China. J Affect Disord. 2023;325:378–85. https://doi.org/10.1016/j.jad.2022.12.154 . Tables Tables 1 and 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1and2.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 25 Apr, 2025 Editor assigned by journal 22 Apr, 2025 Editor invited by journal 03 Apr, 2025 Submission checks completed at journal 02 Apr, 2025 First submitted to journal 02 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6320611","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":448141966,"identity":"a1084d99-85c1-4759-b602-1b308c30d847","order_by":0,"name":"Tingting Lu","email":"","orcid":"","institution":"Suzhou Hospital Affiliated to Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Lu","suffix":""},{"id":448141967,"identity":"8509fed4-0b24-41e2-8dab-853db317d1ec","order_by":1,"name":"Yingchun Liu","email":"","orcid":"","institution":"Suzhou Hospital Affiliated to Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yingchun","middleName":"","lastName":"Liu","suffix":""},{"id":448141968,"identity":"588b3d43-5258-44b3-b6ca-6835827f92f1","order_by":2,"name":"Lihua Shi","email":"","orcid":"","institution":"Suzhou Hospital Affiliated to Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lihua","middleName":"","lastName":"Shi","suffix":""},{"id":448141969,"identity":"0005e778-01e3-42b9-8f47-50279adb6121","order_by":3,"name":"Jianfang Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYBAC+xkgkkdOjp+9+cCBDz+I0MII0WJsLNlzLPHgzB6itTAYJ264kWN8mIONCC3M0s3PHn6RMUicOSPnw2EGHgZ5frED+LWwyRwzN5bhMTDu53m74XCBBYPhzNkJ+LXwSCSYSUvw/JGd2Z674fAMHoYEg9sEtEhIpH8DajFg3HAg58FhHjYitBhI5JhJfuAxUNxwIoeBaC1l0gxAvwAD2QAYyBKE/WI/I32b5M8eA1BUPv7w4YeNPL80AS0gwMyLiEEJwspBgPEHMelkFIyCUTAKRi4AAKDPRi+dojA9AAAAAElFTkSuQmCC","orcid":"","institution":"Suzhou Hospital Affiliated to Nanjing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Jianfang","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-03-27 12:38:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6320611/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6320611/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82123800,"identity":"cebbc576-6377-4321-854a-bb3e6b42c67e","added_by":"auto","created_at":"2025-05-07 03:35:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":17876,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of the selection of sample.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6320611/v1/5ae0fa0c6395790c3b6adef1.png"},{"id":82125071,"identity":"08ad4376-48f5-4676-b9f0-5e6c47ea5876","added_by":"auto","created_at":"2025-05-07 03:43:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":46681,"visible":true,"origin":"","legend":"\u003cp\u003eLatent profile model of depression in home-based old adults patients with stroke\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6320611/v1/f0814c070888aa4c403d22ba.png"},{"id":82126041,"identity":"5a97aec3-186e-45fc-af70-e59e33f21323","added_by":"auto","created_at":"2025-05-07 03:59:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":916226,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6320611/v1/aa512467-7dcb-4c73-bde9-99fe2f3b524b.pdf"},{"id":82125072,"identity":"6cf2d636-dd48-48cb-a832-cef77573b944","added_by":"auto","created_at":"2025-05-07 03:43:48","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21929,"visible":true,"origin":"","legend":"","description":"","filename":"Table1and2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6320611/v1/cb5e26718c981452cccf5748.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Latent profile analysis of depression among home-based old adults patients with stroke in China","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePopulation aging is sweeping the globe, changing the demographic structure of today's world.By the beginning of 2021, people aged 60 years or older have been account for 13.5 percent of the world's population(Cacchione,2022).As one of the most populous countries in the world, China's aging trend is also developing, by 2050, China's population aged 60 and above will account for 23.19% of the world's elderly population, entering the deep aging stage(Fang et al.,2015).An aging population has led to an increase in diseases associated with it.Some diseases seriously threaten the life and health of patients because of their hidden, refractory and lifelong nature.With the change of medical model, the old adults have changed from the original pursuit of physical health to the pursuit of physical and mental health(Blanchet et al.,2018).Relevant studies have found that mental health has become an important factor affecting the life satisfaction of the old adults(Puvill et al.,2016).Therefore, timely attention to the mental health of the old adults is of great significance to promote the recovery of diseases and improve the quality of life in old age.\u003c/p\u003e \u003cp\u003eAs a mental health problem, depression is considered to be the second most common mental disorder among the old adults, with a high incidence in the old adults, and the prevalence has reached 1%-5%(Fond et al.,2023;Snowdon \u0026amp; Lane,2001;Lee et al.,2023).Previous studies have shown that social isolation and functional disability associated with aging may be pathogenic factors for geriatric depression(Oh et al.,2023).In a study on depression in the old adults, it was found that the occurrence of the disease can reduce the health status of patients, quality of life and even increase mortality(Vander Weele et al.,2009).However, in older adults with chronic health problems, suffering from depression was both common and complex(Mitchell \u0026amp; Harvey,2014),Because chronic disease or other social factors can make depression difficult to diagnose and treat, early attention and attention should be paid to depressive symptoms in older patients.\u003c/p\u003e \u003cp\u003eStroke was an acute cerebrovascular disease, which was a common and frequent disease in neurology, with high incidence, high disability rate, high recurrence rate and high mortality rate(Kim et al.,2023).According to the results of the 2019 Global Burden of Disease Study, from 2019 to 2022, there were 940,000 new stroke cases and 760,000 stroke deaths in China(Wang et al.,2022).Stroke not only causes disability in patients, but also brings serious psychological burden to patients(Xia et al.,2019).Post-stroke depression was one of the common complications in patients, which can make the cognitive deficits of patients more obvious, reduce the quality of life, and may make patients appear suicidal(Medeiros et al.,2020;Almeida,2023).With the emergence of emerging medical models, home rehabilitation has been popularized in the treatment of stroke, which means that after the patient's condition is stable, he goes home for follow-up physical and language exercises.However, deviating from the normal medical environment can make depression difficult to detect in patients. Therefore, the depression of home-based stroke patients needs to be screened in time.\u003c/p\u003e \u003cp\u003ePrevious studies on post-stroke depression mainly focused on the risk factors of depression in patients, the harm of depression to patients, and the treatment of patients after depression(Sun et al.,2023;Guo et al.,2022;Yang et al.,2022).These studies focused on this group of old people with post-stroke depression, and did not analyze the heterogeneity of depression in this group.This study intended to use potential profile analysis(LPA), focusing on individuals, to explore the hidden information within individuals.Latent profile analysis (LPA) mainly identifies heterogeneous subgroups based on continuous indicators representing different dimensions, and its goal is to identify homogeneous subgroups of individuals and distinguish them from other subgroups(Wang et al.,2022;Berlin et al.,2014).Post-stroke depression has been highlighted in previous studies, but different categories of the phenomenon have not been dissected in the old population.Therefore, based on the results of potent profile analysis, early identification of different types of depression in old adults patients with stroke and their related influencing factors is of great significance for the formulation of precise intervention programs.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample\u003c/h2\u003e \u003cp\u003eThis study used the data of the Chinese Longitudinal Healthy Longevity Survey (CLHLS)released by the Center for Healthy Aging and Development of Peking University. The study covered 23 provinces, municipalities and autonomous regions in China, and respondents were aged 65 years and older.The contents of the investigation mainly include the basic status of the old adults and their families, cognitive function, personality and psychological characteristics, daily activity ability, depression, dementia and so on. All older adults who participated in the study gave informed consent, and the reliability of the data was unanimously recognized by experts.The database is free and open by Peking University (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://opendata.pku.edu.cn/\u003c/span\u003e\u003cspan address=\"http://opendata.pku.edu.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and we obtained permission to download it according to the application process.According to the purpose of the study, we formulated the inclusion criteria as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Age\u0026thinsp;\u0026ge;\u0026thinsp;65 years. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Patients with clinically diagnosed stroke. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) The patients were in the state of home care. Exclusion criteria: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e)Incomplete completion of the depression scale.(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)Incomplete information.Finally, a total of 866 home-based patients with stroke were included in this study(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasurement\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGeneral information questionnaire\u003c/h2\u003e \u003cp\u003eA total of 15 socio-demographic and disease-related data were included in the study. Mainly included: gender, age, place of residence, years of education, self-rated quality of life, self-rated health status, whether smoking, drinking, exercise, economic status, marital status, whether hearing impairment, whether taking drugs, whether suffering from high blood pressure, whether dyslipidemia.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDepression\u003c/h3\u003e\n\u003cp\u003eIn the CLHLS, the Short Version of the Center for Epidemiological Studies Depression Scale (CES-D-10) was used to assess the level of depression in study subjects.The scale consists of ten items, using Likert 5 grade scoring method, scoring methods were: always\u0026thinsp;=\u0026thinsp;1, often\u0026thinsp;=\u0026thinsp;2, sometimes\u0026thinsp;=\u0026thinsp;3, rarely\u0026thinsp;=\u0026thinsp;4, never\u0026thinsp;=\u0026thinsp;5.Item 5, item 7 and item 10 represent the positive mental state of the patient, and the scoring method is opposite to other items.The higher the score, the higher the level of depression.Previous studies have shown that this scale has good reliability and validity(Park\u0026amp; Yu,2021). In this study, the reliability of this scale was 0.829.\u003c/p\u003e\n\u003ch3\u003eAnxiety\u003c/h3\u003e\n\u003cp\u003eIn the CLHLS, the Generalized Anxiety Disorder Scale (GAD-7) was used to assess how often older adults had experienced anxiety in the last two weeks(Dhira et al.,2021).The scale mainly consisted of 7 items, each item was scored from 0 to 3 points, 0 represented no anxiety in the past two weeks, 1 represented a few days to feel anxious, 2 represented more than half of the time feel anxious, 3 represented almost every day feel anxious, the scale score range was 0 to 28 points, the higher the score, the higher the anxiety level.This scale has been widely used in the old adults in China and has good reliability and validity(Yue et al.,2022). The reliability of this scale in this study was 0.926.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eInstrumental activities of daily living (IADL)\u003c/h2\u003e \u003cp\u003eIn CHILS, the instrumental Activities of daily Living Scale(IADL) was adapted from the activities of daily Living scale(Lawton \u0026amp; Brody,1969).The scale mainly included 8 items, respectively from 8 aspects of the assessment of the tester's daily life ability. The scale was scored on a scale of 8 to 24, with 1 indicating that it can be completed, 2 indicating that it is somewhat difficult to complete, and 3 indicating that it cannot be completed.The higher the score, the worse the ability to perform daily activities.The scale has good reliability and validity(Carmona-Torres et al.,2019), and the reliability in this study was 0.952.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eIn this study, SPSS25.0 software was used for univariate,multivariate logistic regression and nonparametric tests. Mplus8.3 software was used to identify potential categories of depression. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated that the difference was statistically significant.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDescriptive analysis\u003c/h3\u003e\n\u003cp\u003eIn the descriptive analysis, the data conforming to the normal distribution were tested by parameters, and the results were measured as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD). Non-parametric test was applied to measurement data with non-normal distribution,and the results were expressed by median (quartile)[M (IQR)].Counting data was expressed in frequency and percentage terms.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eLatent profile analysis (LPA)\u003c/h2\u003e \u003cp\u003eMplus8.3 was used to profile 10 items of the CES-D-10. In this study, the number of potential profiles was increased from 1 to 5 respectively, the fit degree of the five models was tested, and the optimal model was finally selected.The fitting indexes mainly included Akaike information criterion (AIC), Bayesian information criterion (BIC), adjusted BIC(aBIC), entropy index, Lo-Mendell-Rub test (LMR) and Bootstrap Likelihood ratio test (BLRT).When AIC, BIC and aBIC values decrease with the increase of the number of categories, it indicated that the model fits better. The entropy index indicates the accuracy of classification. When the value is \u0026ge;\u0026thinsp;0.8, the accuracy is \u0026gt;\u0026thinsp;90%. When the p value corresponding to LMR and BLRT is \u0026lt;\u0026thinsp;0.05, k is more appropriate than K-1 category.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSingle factor and multi-factor analysis\u003c/h2\u003e \u003cp\u003eDifferences in sociodemography, anxiety, and IADL among different subgroups were analyzed using χ2 test or Fisher exact probability method and KruskalWallis H test.Variables with statistically significant differences in the results of univariate analysis were included in multiple logistic regression, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGeneral demographic characteristics\u003c/h2\u003e \u003cp\u003eA total of 881 stroke patients aged 65 years and older were included in this study, including 456 males (51.8%) and 425 females (48.2%). In addition, 62.8% of the patients had high blood pressure and 77.2% of the patients were currently taking medication.The remaining general information Demographic information is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePotential categories of depression in home-based elderly stroke patients\u003c/h2\u003e \u003cp\u003eA total of 5 fitting models were explored in this study (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).It can be seen from Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e that with the increasing number of categories, the values of AIC,BIC and aBIC are continuously decreasing, which indicated that the models of these five categories are qualified.Although the entropy index of 5-profile model is higher than that of the 3- and 4-profile model, the P-value of LMRT of 5-profile model is 0.4063 (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), so 5-profile model is excluded.In addition, the entropy index of 4-profile model is higher than that of the 3-profile model, but a part of the population in 4-profile model is relatively small, and the entropy coefficient of 3-profile model is close to that of the 4-profile model, so we finally choose 3-profile model as the optimal model.\u003c/p\u003e \u003cp\u003eIn order to verify the reliability of LPA results, we calculated the attribution probability of the 3-profile model (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The results showed that the attribution probability of the three potential categories ranged from 0.922 to 0.941, all of which were greater than 90%. This showed that the results of the 3-profile model were reliable.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAttribution probabilities for each latent profile of subjects\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProfile 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProfile 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProfile 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfile 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfile 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProfile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eNaming of latent profile\u003c/h2\u003e \u003cp\u003eAccording to the results of profile analysis, it is drawn as Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.The values in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e were derived from the mean of 10 items in each of the three categories.115 (13.0%) of home-based old adults patients with stroke had lower levels of depression than other categories, which were classified as \"low level\".308 (35.0%) of elderly home based stroke patients had higher levels of depression than the other categories, which were classified as \"high level\".The remaining 458 (52.0%) had depression levels somewhere between these two, which named \"moderate.\"\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate analysis of potential profiles of depression\u003c/h2\u003e \u003cp\u003eChi-square test and Kruskal-Wallis H test were used to compare the effects of different socio-demographic data, anxiety and daily activity ability on different potential categories of stroke patients.The results showed that gender, Residential area, education level, self-rated quality of life, self-rated health status, exercise, economic status, taking medication, hypertension, dyslipidemia, anxiety, and daily activity ability had statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).The specific results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eMultinomial logistic regression of depression profiles\u003c/h2\u003e \u003cp\u003eThe meaningful variables in univariate analysis were included as independent variables in multiple logistic regression analysis,three categories of depression were used as dependent variables to compare the differences among independent variables.The assignment of the independent variables are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCase of variable assignment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssignment mode\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u0026thinsp;=\u0026thinsp;1;Female\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65\u0026ndash;75\u0026thinsp;=\u0026thinsp;1;76\u0026ndash;85\u0026thinsp;=\u0026thinsp;2;86\u0026ndash;95\u0026thinsp;=\u0026thinsp;3; \u0026ge;96\u0026thinsp;=\u0026thinsp;4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidential area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCity\u0026thinsp;=\u0026thinsp;1;Town\u0026thinsp;=\u0026thinsp;2;Rural\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone\u0026thinsp;=\u0026thinsp;1;1\u0026ndash;10\u0026thinsp;=\u0026thinsp;2;\u0026ge;11\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u0026thinsp;=\u0026thinsp;1;Divorced\u0026thinsp;=\u0026thinsp;2;Widowed\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuality of life\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGood\u0026thinsp;=\u0026thinsp;1;Moderate\u0026thinsp;=\u0026thinsp;2;Poor\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGood\u0026thinsp;=\u0026thinsp;1;Moderate\u0026thinsp;=\u0026thinsp;2;Poor\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGood\u0026thinsp;=\u0026thinsp;1;Moderate\u0026thinsp;=\u0026thinsp;2;Poor\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrink\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1;No\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1;No\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1;No\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHearing difficulty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1;No\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTake medicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1;No\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1;No\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u0026thinsp;=\u0026thinsp;1;No\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeasured value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIADL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeasured value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results of multiple logistic regression analysis showed that compared low-level depression with moderate-level depression, male, better quality of life, better health, and exercise predicted low-level depression, while those with hypertension and anxiety predicted middle-level depression.Comparing low to high levels of depression, being male, having a better quality of life, being in better health, exercising predicted low levels of depression, and being anxious, living in an urban area, and having high blood pressure predicted high levels of depression.Comparing high levels of depression with moderate levels of depression, anxiety predicted high levels of depression. The result is shown in the Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultinomial logistic regression of depression profiles.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow(ref) VS Moderate\u003c/p\u003e \u003cp\u003eOR(95%CI) P\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow(ref) VS High\u003c/p\u003e \u003cp\u003eOR(95%CI) P\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate(ref) VS High\u003c/p\u003e \u003cp\u003eOR(95%CI) P\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.570(0.329\u0026ndash;0.988) 0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.539(0.296\u0026ndash;3.040) 0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.946(0.691\u0026ndash;1.296) 0.729\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidential area\u003c/p\u003e \u003cp\u003eCity\u003c/p\u003e \u003cp\u003eTown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.999(0.970\u0026ndash;4.118) 0.060\u003c/p\u003e \u003cp\u003e0.903(0.394\u0026ndash;2.072) 0.810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.327(1.069\u0026ndash;5.066) 0.033\u003c/p\u003e \u003cp\u003e0.753(0.312\u0026ndash;1.891) 0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.165(0.792\u0026ndash;1.712) 0.438\u003c/p\u003e \u003cp\u003e0.833(0.562\u0026ndash;1.236) 0.365\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003cp\u003eNone\u003c/p\u003e \u003cp\u003e1\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.569(0.269\u0026ndash;1.323) 0.204\u003c/p\u003e \u003cp\u003e0.588(0.278\u0026minus;1,243) 0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.615(0.252\u0026ndash;1.503) 0.286\u003c/p\u003e \u003cp\u003e0.779(0.331\u0026ndash;1.833) 0.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.031(0.579\u0026ndash;1.780) 0.913\u003c/p\u003e \u003cp\u003e1.325(0.769\u0026ndash;2.284) 0.310\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuality of life\u003c/p\u003e \u003cp\u003eGood\u003c/p\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.084(0.026\u0026ndash;0.265) \u0026lt;0.001\u003c/p\u003e \u003cp\u003e0.198(0.066\u0026ndash;0.591) 0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.503(2.241\u0026ndash;40.291) 0.002\u003c/p\u003e \u003cp\u003e1.920(0.471\u0026ndash;7.826) 0.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.257(0.327\u0026ndash;4.833) 0.739\u003c/p\u003e \u003cp\u003e2.634(0.684\u0026ndash;10.15) 0.159\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth status\u003c/p\u003e \u003cp\u003eGood\u003c/p\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.251(0.098\u0026ndash;0.639) 0.004\u003c/p\u003e \u003cp\u003e0.379(0.206\u0026ndash;0.297) 0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.186(0.069\u0026ndash;0.504) 0.001\u003c/p\u003e \u003cp\u003e0.401(0.199\u0026ndash;0.808) 0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.743(0.469\u0026ndash;1.178) 0.206\u003c/p\u003e \u003cp\u003e1.059(0.687\u0026ndash;1.634) 0.795\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExercise\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.399(0.194\u0026ndash;0.819) 0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.287(0.134\u0026ndash;0.615) 0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.720(0.514\u0026ndash;1.010) 0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic income\u003c/p\u003e \u003cp\u003eGood\u003c/p\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.862(0.302\u0026ndash;2.457) 0.781\u003c/p\u003e \u003cp\u003e0.797(0.360\u0026ndash;1.767) 0.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.980(0.986\u0026minus;0.313) 0.980\u003c/p\u003e \u003cp\u003e1.070(0.433\u0026ndash;2.646) 0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.143(0.602\u0026ndash;2.173) 0.683\u003c/p\u003e \u003cp\u003e1.342(0.750\u0026ndash;2.402) 0.322\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTake medicine\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.988(0.480\u0026ndash;2.034) 0.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.132(0.526\u0026ndash;2.435) 0.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.145(0.801\u0026ndash;1.636) 0.457\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.507(1.322\u0026ndash;4.753) 0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.462(1.237\u0026ndash;4.898) 0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.982(0.712\u0026ndash;1.354) 0.912\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia\u003c/p\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.273(0.643\u0026ndash;2.518) 0.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.264(0.589\u0026ndash;2.713) 0.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.993(0.636\u0026ndash;1.550) 0.975\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.959(1.710\u0026ndash;2.245) \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.712(0.651\u0026ndash;0.779) \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.717(0.642\u0026minus;0.800) \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIADL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.004(0.953\u0026ndash;1.058) 0.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.990(0.944\u0026ndash;1.038) 0.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.006(0.978\u0026ndash;1.034) 0.687\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eLPA was to classify individuals into different categories according to their response patterns on the observed indicators, and then analyze the differences between different groups.According to the depression scores of home-based old adults patients with stroke, this study divided them into three potential categories: \"low-level\", \"medium-level\" and \"high-level\".It suggested that there was some variation in the level of depression in home-based old adults patients with stroke.\u003c/p\u003e \u003cp\u003eThe 13.0% of stroke patients were classified as a low-level group, which had a lower level of depression.But in the low depression group, some items scored higher.. For example,item 6,\u0026ldquo;Are you feel nervous or scared?\u0026rdquo; It may be related to physical movement disorders, language disorders and other serious complications caused by stroke, and long-term physical discomfort will make patients suffer psychological burdens, resulting in anxiety and fear.be weakened(Yu et al.,2021).In addition, item 9, \u0026ldquo;Do you feel unable to continue your life?\u0026rdquo;also scored high.It may be related to the physiological discomfort and economic burden brought by stroke disease, which may cause the quality of life of patients to decline(Ramos-Lima et al.,2018). It made patients lose confidence in their future life.Although depression levels in this group of elderly stroke patients were low, in order to prevent their depression from worsening, their problems cannot be ignored in nursing interventions.\u003c/p\u003e \u003cp\u003eThe 52.0% of stroke patients were classified as a medium-level group,which is the highest percentage of the three profiles.In this group of patients with moderate levels of depression, item 9, \u0026ldquo;Do you feel unable to continue your life?\u0026rdquo; had the highest average score.It showed that patients' daily life was significantly affected, and the impact was more pronounced than in the low and high level groups.Compared with the depressed patients in the low level group and the high level group, the patients in this group are in between, which may become low-level because of better intervention, or may become high-level because of disease, environment and other factors aggravate their depression degree.Therefore, patients with moderate levels of depression need to be effectively identified and early intervention to promote their mental health.\u003c/p\u003e \u003cp\u003eThe 35.0% of stroke patients were classified as a high-level of depression,indicating a more severe level of depression in this group.It may be related to hemiplegia, aphasia, and the long recovery process experienced by patients after stroke(Cross et al.,2023).long-term disease treatment and medication can cause a certain financial burden to patients. It will cause certain psychological pressure to the patient, resulting in anxiety and depression(GBD 2019 Diseases and Injuries Collaborators,2019).In addition, individual personality traits and family support are also factors that affect the occurrence of depression in stroke patients(Huang et al.,2017).As the group with the most severe depression level, reducing the depression level of this category of patients was of great significance for the rehabilitation and recovery of elderly stroke patients. Therefore, it is necessary to timely publicize the related knowledge of stroke and depression to such patients, improve their confidence in disease treatment and self-rehabilitation ability, and promote their mental health.\u003c/p\u003e \u003cp\u003eIn this study, gender, residential area, self-rated quality of life, self-rated health status, exercise, hypertension and anxiety were identified as factors influencing depression in home-based old adults patients with stroke.When comparing low-level depression with medium-level depression, male were more inclined to the low-level depression group.It may be related to the physiological factors of women, and changes in hormone levels caused by increasing age are more likely to affect women(Sassarini et al.,2016).Women tend to be more sensitive to exposure to psychological stress events, and when they have a stroke, they were less able to withstand it than men(Huang et al.,2019).In addition, the proportion of older women who were widowed was higher than that of men, which mean that they suffer from psychological stress and loneliness more than men, and will naturally be more prone to depression.\u003c/p\u003e \u003cp\u003eIn this study, the depression level of old adults patients with stroke living in city was higher than that living in rural. It is inconsistent with previous research.Previous studies have found that the prevalence of depression in rural elderly was higher than that in urban elderly(Liu et al.,2021).However, other studies have found that the incidence of depression in rural people aged 75 and above was relatively low, while the incidence of depression in urban low-income elderly people was relatively high(Xu et al.,2014).Therefore, the higher level of depression in the urban elderly in this study may be related to their lower income level and higher age.\u003c/p\u003e \u003cp\u003eThe depression level of home-based old adults patients with stroke better self-rated life satisfaction was lower.Previous studies have also found that depression in stroke patients was independently associated with life satisfaction(Oosterveer et al.,2017).After stroke, the physical and social functions of patients were weakened, which made their satisfaction with quality of life decline, and then produce depression(Chan et al.,2021).When patients were able to recover their functions, their anxiety and depression will also be reduced.Therefore, in the process of nursing stroke patients, we should put the physical rehabilitation of patients in the first place, and carry out psychological counseling to reduce the incidence of post-stroke depression.\u003c/p\u003e \u003cp\u003eSelf-rated health status was a patient's self-assessment of their own health status.Some studies have highlighted self-rated health as an important predictor of depression in patients(Yun \u0026amp; Bae,2020;Woo \u0026amp; Bae,2021).Home-based stroke patients required subsequent rehabilitation therapy to facilitate recovery, so their assessment of their own health reflects the recovery of their physical symptoms and negative emotions.When the patient's self-rated health was satisfied, it not only indicated that the physical function was recovering well, but also indicated that the psychological function was also being repaired, so the depression level will decline.Therefore, the dual role of physical health and mental health should not be ignored when paying attention to the self-rated health of old adults patients with stroke .\u003c/p\u003e \u003cp\u003eExercise has been shown to be effective in reducing symptoms in people with depression, and it is an important way to treat mental illness and physical health problems(Schuch \u0026amp; Stubbs,2019;Knapen et al.,2015).In this study, patients who exercised had significantly lower levels of depression.The results also highlight the important role that exercise plays in reducing the onset of disease.Therefore, community staff should regularly conduct lectures on exercise knowledge, set up places for different exercise items according to personal preferences, and encourage stroke patients to take the initiative to participate in exercise.\u003c/p\u003e \u003cp\u003eHypertension was not only a risk factor for stroke, but also leaded to depression(Rantanen et al.,2018).In this study, hypertension predicted moderate to high levels of depression. Hypertension and depression symptoms can form a toxic combination that can even lead to all-cause mortality(Rantanen et al.,2018).Therefore, it is of great significance to pay attention to the depressive symptoms of old adults patients with stroke with hypertension to reduce the mortality.Health care providers should treat hypertension as a separate risk factor, providing health education and helping patients improve their self-management skills. It is not only necessary to reduce the physical burden brought by the disease, but also to pay attention to the emotional changes of patients and reduce the probability of depression.\u003c/p\u003e \u003cp\u003eIn this study, anxiety significantly distinguished the levels of depression in 3 categories.Due to the sudden and difficult nature of stroke, patients not only suffer from changes in physical symptoms after the lesion, but also undergo long-term rehabilitation training, which will produce a major breakthrough in the psychological defense line of patients, resulting in anxiety in patients.Related studies have found that anxiety can predict depressive symptoms and depressive disorders(Hou et al.,2023).It indicated that reducing the anxiety level of patients in time can effectively control the occurrence of depression.Therefore, nursing staff should detect the anxiety symptoms of patients as early as possible and intervene in time.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThere were some flaws in this study.Firstly,this study screened the CLHLS database and may have missed some factors affecting depression in home-based old adults patients with stroke .Secondly,this study was a cross-sectional study, the results cannot explain the causal relationship between the independent variable and the dependent variable.Thirdly,the data results of this study were self-reported, which may lead to measurement bias.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study divided the level of depression in home-based elderly stroke patients into three subgroups.There were statistically significant differences in gender,residential area, self-rated quality of life, self-rated health status, exercise, economic status, hypertension and anxiety among different categories of home-based elderly stroke patients.The results provide a basis for the formulation of intervention measures to reduce the level of depression in elderly stroke patients.In clinical work,medical workers should identify patients with low and moderate levels of depression as early as possible and intervene in time to avoid deterioration of the condition.The patients with high level of depression are taken as the key intervention objects to reduce the level of depression and ensure the life safety of patients.In addition, health care workers should develop personalized and targeted interventions based on different types of depression.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by2024 Suzhou Municipal Hospital Nursing Research Project(slyyhl202408).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express their gratitude to all contributors for their diligent work on this study and all participants who voluntarily participated in the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003cstrong\u003e\u0026rsquo;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors made important contributions to this study. TL and LY designed and wrote the article.LY revised and improved the manuscript.ZJ and SL participated in the collection and analysis of data. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no conflict of interest in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are openly available in \u0026nbsp;the Chinese Longitudinal Healthy Longevity Survey (CLHLS) at http://opendata.pku.edu.cn/.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCacchione PZ. 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J Affect Disord. 2023;325:378\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jad.2022.12.154\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2022.12.154\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 and 2 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"stroke, home-based, old adults, depression, latent profile analysis, related factor","lastPublishedDoi":"10.21203/rs.3.rs-6320611/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6320611/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eStroke is one of the chronic diseases that endanger the life and health of the old adults, which not only causes physical discomfort but also brings psychological burden to them (such as: anxiety, depression, etc.).In-depth analysis of the types of depression that may occur in old adults patients with stroke can provide a theoretical basis for formulating interventions to accurately reduce the depression level of old adults patients with stroke.In this study, the results of the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS) were used as a data source, and latent profile analysis(LPA) was used to distinguish the types of depression in home-based stroke patients.The profile analysis divided the depression levels of 881 elderly stroke patients into low-level (13.0%), medium-level (52.0%), and high-level (35.0%).Multiple logistic regression showed that living in cities, hypertension and anxiety predicted high levels of depression in the comparison between the low-level and the high-level.The results of the study divided the depression level of home-based stroke patients into three potential profiles, which provided a theoretical basis for clinical nursing staff to formulate more detailed intervention plans.\u003c/p\u003e","manuscriptTitle":"Latent profile analysis of depression among home-based old adults patients with stroke in China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 03:35:44","doi":"10.21203/rs.3.rs-6320611/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-04-25T17:20:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-22T14:56:48+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-03T05:18:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-02T10:54:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Geriatrics","date":"2025-04-02T10:53:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-geriatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bgtc","sideBox":"Learn more about [BMC Geriatrics](http://bmcgeriatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bgtc/default.aspx","title":"BMC Geriatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"af8693ba-6744-4190-b758-7dda2f57b226","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-05-07T03:35:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 03:35:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6320611","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6320611","identity":"rs-6320611","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}