Bidirectional Associations between Daily Activity Limitations and Depressive Symptoms among Middle-aged and Older Adults with Multimorbidity in China: A Cross-Lagged Panel Network Analysis

Bidirectional Associations between Daily Activity Limitations and Depressive Symptoms among Middle-aged and Older Adults with Multimorbidity in China: A Cross-Lagged Panel Network Analysis | 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 Article Bidirectional Associations between Daily Activity Limitations and Depressive Symptoms among Middle-aged and Older Adults with Multimorbidity in China: A Cross-Lagged Panel Network Analysis Jiahui Ding, Xiaojin Hu, Meiling Sun, Shanshan Ge This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9176515/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 This study was based on the 2018 and 2020 rounds of the China Health and Retirement Longitudinal Study (CHARLS). A total of 5,850 middle-aged and elderly people aged 50 years and above with two or more chronic diseases were included. A cross-lagged panel network model was used to analyse the longitudinal predictive relationship between activities of daily living (ADL) and depressive symptoms (DS), stratified by sex. The results revealed that ADL functional limitations were used mainly as predictors, whereas depressive symptoms were mostly in the affected position. Among them, "Housework" (A7) and "Money management" (A12) had strong outwards predictive effects, especially with depression symptoms such as "Effortlessness" (D6) and "Inertia" (D7). A gender comparison revealed that basic ADL nodes such as "Dressing" (A1) and "Getting up" (A4) were more predictive in the female network, whereas "Housework" (A7) was the core predictive node in the male network. The network edge weights and node centrality are estimated with acceptable accuracy and stability. This study highlights that impaired functioning in complex instrumental daily activities (e.g., housework, money management) may be a prodromal indicator of depressive symptoms in middle-aged and older adults with multimorbidity. Gender heterogeneity exists in the association path between functional status and depressive symptoms, suggesting that targeted strategies should be implemented according to gender characteristics in interventions. Early monitoring of functional status is helpful for identifying and preventing mental health risks. Health sciences/Diseases Health sciences/Health care Biological sciences/Psychology Social science/Psychology Health sciences/Risk factors Activities of daily living Depressive symptoms Cross-lagged panel network Longitudinal analysis Middle-aged and elderly Chinese individuals Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Chronic diseases are major contributors to the increasing global burden of disease and inequalities in health outcomes 1,2 . Globally, approximately one in three people with chronic diseases suffer from two or more such conditions simultaneously, a phenomenon known as multimorbidity 3 , and the risk of developing multimorbidity increases significantly with age 4 . With the general increase in life expectancy and the combined effects of multiple factors, such as high body mass index, unhealthy lifestyles, and socioeconomic changes, multimorbidity has become a major public health issue in the context of global aging 5,6 . Relevant studies indicate that multimorbidity is particularly common among populations with limited health resources 7 . Multimorbidity is not merely the accumulation and prevalence of physical illnesses; its onset and progression are influenced by a combination of factors, including an individual’s functional status, lifestyle, and social environment 8,9 . Against this backdrp, a systematic examination of the functional status and mental health outcomes of older adults with multimorbidity is highly important for deepening our understanding of the mechanisms underlying the health impacts in this population 10 . In older adults with multimorbidity, impaired functional status and mental health problems are often intertwined 11 . Activities of daily living (ADL) are a core indicator for measuring the functional status of elderly individuals 12 , and their decline not only reflects a decline in physical function but is also considered an important risk signal for the deterioration of mental health in elderly individuals 13 . Previous studies have generally suggested that ADL functional limitations can increase the risk of depressive symptoms by restricting individual activity ability, reducing social participation, and weakening the social support network 14 . In cross-sectional and longitudinal studies in different countries and cultural backgrounds, the prevalence of depression in older adults with ADL limitations is significantly greater than that in those with normal function 15–18 . In the Chinese population, this association has been consistently supported by multiple studies 19,20 . Sex differences exist in the patterns of multimorbidity 21,22 , characteristics of impaired functional status 23,24 , and occurrence and manifestation of depressive symptoms 25 . Men and women exhibit distinct characteristics in terms of disease spectrum composition, types of functional limitations, and psychological coping strategies; this sex heterogeneity may further influence the pathways linking functional status and depressive symptoms. However, existing studies predominantly analyse overall samples and rarely systematically explore these associations from a gender-specific perspective, which may obscure the differential roles of key functional limitation items and depressive symptom nodes across genders. Furthermore, previous studies 26 have largely relied on overall scores or correlation coefficients for ADLs and depressive symptoms as the primary basis for their conclusions, with few studies conducting in-depth analyses of the structural associations between specific items of functional impairment and specific depressive symptoms. Moreover, the study populations have generally not focused on middle-aged and older adults with multiple comorbidities. Although some studies have begun to employ network analysis methods to characterize the complex relationships between functional and psychological symptoms 27,28 , most of these studies employ cross-sectional designs, which are insufficient to reveal the dynamic interactions between functional limitations and depressive symptoms over time or their potential directionality. In summary, it is necessary to adopt a longitudinal perspective and employ more refined analytical methods to systematically examine the bidirectional predictive relationship between ADLs and depressive symptoms in older adults with multimorbidity in the future. The cross-lagged panel network (CLPN) is a longitudinal analytical method that integrates network modelling with cross-lagged panel models. It can depict the predictive relationships between different variables at adjacent time points while controlling for autoregressive effects, thereby enhancing the inferential power regarding the directional associations between symptoms 29 . This method aids in identifying functional or psychological symptom nodes that occupy key positions in temporal evolution, thereby providing a basis for identifying precise intervention targets 30 . Therefore, this study uses data from the 2018 and 2020 waves of the China Health and Retirement Longitudinal Study (CHARLS) to examine middle-aged and older adults with multimorbidity. A cross-lagged panel network model was constructed to systematically examine the stability and dynamic characteristics of the association structure between various ADL functional items and depressive symptoms and to further compare differences in network structure from a sex-specific perspective. This study was performed to provide empirical evidence for elucidating the mechanisms of psychosomatic comorbidity in this population and to formulate intervention strategies. Methods Study design and participants This study utilized data from the fourth and fifth waves (2018 and 2020) of the China Health and Retirement Longitudinal Study (CHARLS), which is available at https://charls.charlsdata.com/pages/Data/2018-charls-wave4/zh-cn.htm and https://charls.charlsdata.com/pages/Data/2020-charls-wave5/zh-cn.html. Conducted by the National School of Development at Peking University, CHARLS is a nationally representative survey designed to provide high-quality microdata on China’s middle-aged and elderly populations. It serves as a vital resource for research on population aging and its socioeconomic impacts. Since its launch in 2011, CHARLS has employed a multistage stratified random sampling method covering 150 counties and 450 villages across 28 provinces (regions and municipalities). Subsequent surveys are conducted every 2–3 years, with data released to the public one year after each survey is conducted. This study focused on individuals aged ≥ 50 years with at least two chronic conditions from the CHARLS 2018 and 2020 datasets to investigate their mental health status. Among them, 19,816 older adults participated in the 2018 CHARLS. To ensure the accuracy and reliability of the data, we applied the following inclusion criteria: (1) key demographic variables (such as sex, age, educational attainment, marital status, smoking, alcohol consumption, and number of chronic conditions); (2) completion of all items on the Center for Epidemiological Studies Depression Scale (CESD-10) and the Activities of Daily Living Scale; (3) having at least two chronic conditions; (4) being aged 50 years or over; and (5) participation in both the fourth and fifth waves of the survey. Following rigorous screening and data cleaning, 13,966 records were excluded due to missing data or failure to meet the criteria, leaving a final sample of 5,850 participants. Figure 1 shows the sample selection process (Figure 1). In utilizing the CHARLS data, we strictly adhered to the principles of data privacy and complied with all regulations governing the use of the CHARLS database. Prior to downloading and analysing the data, we obtained formal approval from the database administrator. This study complied with the principles of the Declaration of Helsinki, and the CHARLS study was approved by the Biomedical Ethics Committee of Peking University (IRB0000 052-11015). Measurement of depressive symptoms In the CHARLS project, depression was assessed via a shortened version of the CESD-10, comprising 10 items: feeling down, feeling lonely, being troubled by things, feeling afraid, difficulty concentrating, struggling, inability to get started, sleep quality, hope for the future, and feeling happy, with ‘hope for the future’ and ‘feeling happy’ reverse-scored. A 4-point Likert scale ranging from 10 to 40 was used, with higher scores indicating greater severity of depression. In this study, the scale demonstrated adequate reliability and validity, with a Cronbach’s alpha coefficient of 0.811. Assessment of ADL Disability In the CHARLS project, activities of daily living (ADL) are measured on the basis of basic activities of daily living (BADL) and instrumental activities of daily living (IADL). The assessment of ADLs comprises two components: BADLs and instrumental ADLs (IADLs). Six BADL scales and six IADL scales were used to assess ADLs. The BADL scale comprises six items: dressing, bathing, eating, getting in and out of bed, using the toilet, and controlling urination or defecation. The IADL scale comprises six items: housework, cooking, shopping, making telephone calls, taking medication, and managing finances. The responses to each item are coded as follows: ‘Can perform independently’ is coded as 0. ‘Some difficulty or worse’ was coded as 1. In this study, the Cronbach’s alpha coefficients for BADLs and IADLs were 0.749 and 0.755, respectively. Data analysis In this study, we first summarized the demographic characteristics of the participants and calculated the scores on the relevant scales via SPSS 27.0. We subsequently used R 4.5.1 software to estimate the CLPN for older Chinese adults, women, and men. By combining cross-lagged panel models and network models, the CLPN aims to explore the temporal associations between symptoms 29 . Notably, the CLPN method allows for the modelling of directed networks across two time points. The construction of the CLPN model involves several key steps: (a) estimating autoregressive and cross-lagged coefficients via regularized regression, (b) determining the unexpected and expected influences (EI) for each node, and (c) assessing the accuracy and stability of the network. In the initial stage, the CLPN was estimated via a series of regularized regression models to calculate the autoregressive and cross-lagged effects at two time points 31 . The autoregressive effects captured the coefficients of the 2018 symptoms, predicting their own values in 2020 while controlling for all other symptoms and covariates in 2018. After accounting for all other symptoms and covariates in 2018, the cross-lagged effects involved the coefficients of the 2018 symptoms predicting different symptoms in 2020. The estimation process uses maximum likelihood with Lasso regularization applied to the regression coefficients 31 . This method effectively reduces overfitting and eliminates trivial paths by shrinking small regression paths to zero, thereby enhancing the generalizability of the results 32 . The glmnet package 31 was used to compute the regularized regression, and the qgraph package 33 was used for network visualization. Following the estimation, two centrality indices were calculated for each node: the out-EI and in-EI of the cross-lagged effects. The cross-lagged out-EI indicates the extent to which the 2018 node predicts the 2020 node, whereas the in-EI indicates the extent to which the 2020 node is predicted by the 2018 node, excluding autoregressive paths. The accuracy and stability of the network were subsequently assessed via two bootstrapping methods supported by the bootnet package 33 . The accuracy of the edge weights was determined by calculating 95% confidence intervals (CIs) around each edge weight via 1,000 iterations of nonparametric bootstrapping. To evaluate the stability of the network, the case‒drop bootstrapping method was employed to calculate the coefficient of stability (CS), thereby estimating the stability of the order centrality measures. A CS coefficient greater than 0.25 is considered acceptable, whereas a value above 0.50 is deemed excellent 33 . Furthermore, statistical significance tests were conducted to examine the differences between edge weights and node centrality 33 . Following the CLPN group comparison guidelines 29 , the following steps were undertaken: (a) estimating the correlation between the edge list and the proportion of edges with the same direction (positive, negative, or neutral), providing a global measure of symptom connectivity; and (b) estimating the correlation between centrality indices across networks, as well as the consistency between the most central symptoms and the strongest edges, providing specific insights into central symptoms and their associations. Results Sociodemographic characteristics of the participants This study included 5,850 older adults aged between 50 and 91 years (mean age 62.88 ± 7.95) with at least two chronic conditions. Of these, 51.9% were women, and 48.1% were men. Table 1 summarizes the participants’ demographic characteristics. As shown in Table 2, between the two surveys, there was a discernible trend in both the level of disability in activities of daily living (ADL) and in depressive symptoms among older adults in China. In the overall sample, the proportion of patients with ADL item impairment ranged from 2.1% to 15.9% in 2018, increasing to 3.2%–19.5% by 2020. Among these, toileting (A5) and housing (A7) consistently had the highest impairment rates, whereas eating (A3) had the lowest impairment rate but also showed an increasing trend. Analysis by gender revealed that women had higher impairment rates than men across all the ADL/IADL items in both surveys did, with a more pronounced increase over time. In 2020, women had the highest impairment rates for “Toileting" (A5) (23.9%) and "Housework" (A7) (20.7%), whereas men had relatively lower impairment rates for these corresponding items. In terms of depressive symptoms, the mean scores for all items in the overall sample ranged from 0.45--1.34 in 2018 and from 0.49--3.29 in 2020; specifically, the mean scores for “Hopelessness” (D9) and “Unhappiness” (D10) were higher in 2020 than in 2018. The results by gender revealed that women’s levels of depressive symptoms were consistently greater than those of men. The mean scores for women across all items were 0.59–1.51 in 2018 and 0.64–1.52 in 2020, with relatively higher scores for “Restlessness” (D8), “Effortfulness” (D6) and “Hopelessness” (D9). Compared with those in 2018, men in 2020 presented lower overall levels of depressive symptoms, although scores for some symptoms increased slightly. Table 1. Baseline characteristics of the study population, stratified by sex Total(%)/Mean(SD) All Female Male N N=5850 N=3038 N=2812 Age 62.88(7.95) 62.43(7.87) 63.37(8.00) Educational level Below primary school 2290（39.1） 1562(51.4) 728(25.9) Primary school 1389（23.7） 625(20.6) 764(27.2) Middle school 1379（23.6） 563(18.5) 816(29.0) High school and above 792（13.5） 288(9.5) 504(17.9) Marital status Married 4788（81.8） 2347(77.3) 2441(86.8) other 1062（18.2） 691(22.7) 371(13.2) Number of Chronic Conditions 2~4 4518（77.2） 2298(75.6) 2220(78.9) 5~7 1187（20.3） 661(21.8) 466(16.6) 8~13 145(2.5) 79(2.6) 66(2.3) Alcohol Drinking Status Yes 1915（32.7） 412（13.6） 1503(53.4) No 3935（67.3） 2626（86.4） 1309(46.6) Smoking Status Yes 1442（24.6) 162（5.3） 1280(45.5) No 4408 (75.4) 2876（94.7） 1532(54.5) Table 2. Activities of daily living (ADL) disability and depressive symptoms of the study population, stratified by sex and year ADL disability Nodes All (2018) All (2020) Female (2018) Female (2020) Male (2018) Male (2020) Total(%) Dressing A1 398（6.8） 526(9.0) 232（7.6） 307（10.1） 166（5.9） 219（7.8） Bathing A2 492（8.4） 561(9.6) 307（10.1） 338（11.1） 185（6.6） 223（7.9） Eating A3 124（2.1） 187(3.2) 73（2.4） 95（3.1） 51（1.8） 92（3.3） Getting up A4 434(7.4) 505(8.6) 303（10.0） 351（11.6） 131（4.7） 154（5.5） Toileting A5 789(13.5) 1141(19.5) 527（17.3） 725（23.9） 262（9.3） 416（14.8） Elimination control A6 287(4.9) 347(5.9) 149（4.9） 178（5.9） 138（4.9） 169（6.0） Housework A7 932(15.9) 1022(17.5) 599（19.7） 629（20.7） 333（11.8） 393（14.0） Cooking A8 616(10.5) 655(11.2) 368（12.1） 413（13.6） 248（8.8） 242（8.6） Shopping A9 475(8.1) 454(7.8) 325（10.7） 303（10.0） 150（5.3） 151（5.4） Phone use A10 531(9.1) 391(6.7) 363（11.9） 246（8.1） 168（6.0） 145（5.2） Medication A11 295(5.0) 353(6.0) 216（7.1） 262（8.6） 79（2.8） 91（3.2） Money management A12 670(11.5) 578(9.9) 461（15.2） 377（12.4） 209（7.4） 201（7.1） Depressive Symptoms Mean(SD) Depressiveness D1 1.03(1.105) 1.04(1.116) 1.19（1.146） 1.21（1.145） 0.85（1.031） 0.86(1.053) Loneliness D2 0.66(1.052) 0.69(1.063) 0.76（1.121） 0.79（1.117） 0.55（0.961） 0.57(0.99) Bortheredness D3 1.04(1.132) 1.11(1.143） 1.21（1.169） 1.28（1.175） 0.85（1.059） 0.92(1.076) Fearfulness D4 0.45(0.885) 0.49（0.929） 0.59（0.988） 0.64（1.032） 0.30（0.729） 0.33(0.771) Cognitive trouble D5 1.03(1.122) 1.12（1.149） 1.18（1.158） 1.28（1.172） 0.87（1.059） 0.95(1.099) Effortfulness D6 1.12(1.182) 1.14（1.203） 1.25（1.211） 1.26（1.217） 0.97（1.132) 1.01(1.175) Intertia D7 0.5(0.94) 0.56（0.997） 0.59（1.01） 0.67（1.064） 0.39(0.846) 0.45(0.907) Restlessness D8 1.29(1.231) 1.29（1.228） 1.51（1.225） 1.52（1.222） 1.06(1.193) 1.03(1.184) Hopelessness D9 1.34(1.255) 2.29（1.250） 1.35（1.234） 1.4（1.24） 1.33(1.277) 1.36(1.260) Unhappiness D10 1.1(1.181) 3.29（1.173） 1.17（1.195） 1.17（1.175） 1.03(1.162) 1.05(1.167) The ADL-Depression Symptom Network among the Elderly Population in China The correlation matrix for the older adult population in China is presented in Supplementary Table S1. The cross-lagged panel network analysis (CLPN) is visualized as a directed network in Figure 1. The arrows in the figure represent cross-lagged associations between ADLs and depressive symptoms; the model simultaneously controlled for the main effects of each variable in 2018 and the influence of covariates. The autoregressive cross-lagged network diagram incorporating all the variables is shown in Supplementary Figure S1. Among all cross-lagged pathways, the three associations with the largest effect sizes were "Housework" (A7) → “Effortfulness” (D6) (β=0.204), “Cooking” (A8) → “Housework” (A7) (β = 0.136), and “Elimination control” (A6) → “Depressiveness” (D1) (β = 0.130). Furthermore, “Money management” (A12) exhibited relatively concentrated cross-lagged associations with multiple depressive symptoms, including “Money management” (A12) → “Intertia” (D7) (β = 0.122), “Money management” (A12) → “Hopelessness” (D9) (β = 0.121), “Money management” (A12) → “Cognitive trouble” (D5) (β = 0.119), and “Money management” (A12) → “Depressiveness” (D1) (β = 0.119). Further results of the cross-lagged relationships are presented in Supplementary Table S2. In terms of node centrality, “Money management” (A12) had the highest out-expected influence (Out-EI) (1.033), followed by "Housework" (A7) (Out-EI = 0.924), indicating that these ADL nodes play a strong predictive role in the network. Conversely, regarding In-Expected Influence (In-EI), “Effortfulness” (D6) had the highest In-EI (1.023), followed by “Intertia” (D7) (In-EI = 0.902), suggesting that these depression symptom nodes are more susceptible to the influence of the past states of other variables within the network. The standardized estimates of node centrality are shown in Figure 2. The detailed results for unstandardized coefficients are listed in Supplementary Table S3, and the distribution of differences in node centrality is illustrated in Supplementary Figures S2--S3. ADL‒Depression Symptom Networks among Middle-Aged and Older Chinese Women and Men The correlation matrices for middle-aged and older Chinese women and men are presented in Supplementary Tables S4 and S5. Figure 3 illustrates the cross-lagged networks of ADLs and depression symptoms in middle-aged and older Chinese women and men. The similarity analysis of network edge weights revealed a significant yet limited correlation between the edge weights of the male and female networks (r = 0.30, 95% CI: 0.21–0.38, p < 0.001), suggesting a degree of consistency in the overall cross-lagged association structure between the two sexes, although differences remained in the specific path strengths and connection patterns. In the female network, “Getting up" (A4) presented a high degree of outwards predictive power, with significant cross-lagged associations with multiple depression symptom nodes. Furthermore, a strong bidirectional predictive relationship existed between “cooking" (A8) and "dressing" (A1), and both formed dense cross-lagged connections with other ADL nodes, constituting a highly interconnected ADL subnet. Other cross-lagged associations in the female network are shown in Supplementary Table S6 (Figure 3). In contrast, in the male network, "Housework" (A7) emerged as the primary predictive node. Other cross-lagged associations in the male network are shown in Supplementary Table S7 (Figure 3). Cross-lagged network diagrams incorporating autoregressive models for all variables in the networks of older Chinese women and men are shown in Supplementary Figure S4. With respect to node centrality, the out-EI of the male and female networks showed a moderate positive correlation (r =0.43, 95% CI: 0.01--0.72, p = 0.048), indicating that some nodes possessed similar predictive importance in both networks, although the nodes playing the primary predictive role were not entirely consistent. Specifically, in the female network, "Dressing" (A1) had the highest out-EI (0.906), whereas in the male network, “Housework” (A7) had the highest out-EI (1.352). In contrast, the In-EIs of the male and female networks exhibited a high degree of consistency (r=0.76, 95% CI: 0.50–0.90; p < 0.001). Overall, the in-EI was primarily concentrated in nodes related to depressive symptoms, indicating that depressive symptoms were more likely to act as influenced nodes in both networks. Specifically, in the female network, "Intertia" (D7) had the highest In-EI (1.040), whereas in the male network, "Effortfulness" (D6) had the highest In-EI (1.158); both were significantly higher than other nodes in their respective networks. Detailed information on other nodes for females and males is provided in Supplementary Tables S8 and S9 (Figure 4). Accuracy and Stability of Network Parameters The accuracy of the edge weights was tested via the bootstrap method. As shown in Supplementary Figure S6, the 95% confidence intervals for each edge weight were generally narrow, indicating that the edge weight estimates of the network model were highly accurate. The stability of the centrality indices (In-EI and Out-EI) was assessed via the case‒drop bootstrap method. As shown in Supplementary Figure S7, as the sample size decreases, the average correlation coefficient between the ranking of centrality indices and the original sample ranking gradually decreases. The correlation stability (CS) coefficients were 0.750 (out-EI) and 0.672 (in-EI), indicating that the order of node centrality exhibited a high degree of stability. To assess the stability of the network estimates by gender, this study conducted nonparametric bootstrap accuracy analyses of edge weights (1,000 iterations each) on the female (N = 3,038) and male (N = 2,812) subsamples. As shown in Supplementary Figure S8 (women) and S9 (men), the edge weight estimates for both sex-specific networks exhibited moderate precision. Specifically, the 95% confidence intervals for most edge weights were wide and overlapped extensively, indicating numerous associations with high estimation uncertainty within the networks. However, for several edges with the strongest predicted effects in each network, the confidence intervals were relatively narrow and did not include zero, suggesting that the estimates for these core associations were relatively stable and more reliable. To assess the stability of node centrality metrics by gender, this study employed the case‒drop bootstrap method to analyse subsamples of women (N = 3038) and men (N = 2812). As shown in Supplementary Figure S10 (women) and S11 (men), there were differences in the correlation stability (CS) coefficients of the node centrality rankings. The out-EI order exhibited moderate stability: the CS coefficient was 0.672 for the female subsample and 0.595 for the male subsample, both exceeding the recommended threshold of 0.50, indicating that the out-EI node ordering possessed acceptable robustness across gender groups. In contrast, the order of In-EI is less stable: the CS coefficient for both the female and male subsamples is 0.283, which is higher than the minimum acceptable standard of 0.25. Centrality-based tests of differential analysis revealed that, in the networks of older women and men, the symptoms exhibiting the strongest out-EI/in-EI values were statistically more pronounced than most other symptoms in the networks, as shown in Supplementary Figures S12–S13 (women) and S14–S15 (men). This further demonstrates that the results of the centrality analysis are robust and generalizable. Discussion On the basis of longitudinal data from a cohort of middle-aged and older adults with multimorbidity in China, this study employed a cross-lagged panel network model to systematically elucidate the time series interactions between impaired activities of daily living (ADL) function and depressive symptoms. The findings indicate that, after controlling for autoregressive effects, ADL functional items primarily act as outwards-predicting nodes within the network, whereas depressive symptoms are predominantly in a position of being influenced. These findings suggest that, in the context of multimorbidity, functional impairment may be a significant precursor to the onset and exacerbation of depressive symptoms. Furthermore, although the overall network structure was consistent across sexes, significant sex differences were observed at the level of key predictive nodes, suggesting that the functional-emotional association pathways exhibit a degree of sex heterogeneity in populations with multimorbidity. Among the specific functional items, A7 “Housework” exhibited high predictive power and centrality within the network; impairment in this function significantly predicted core depressive symptoms such as D6 "Effortfulness". This finding is consistent with the results of previous studies 27,34 . Chen 35 et al . found through latent profile analysis that, across different subgroups of ADL impairment, the degree of impairment in IADL was generally higher than that in BADL, and this was particularly pronounced in the high-impairment group. Compared with the basic, largely autonomous activities covered by BADLs, IADL—particularly household chores—often involve multistep operations, situational judgement, and sustained physical and cognitive effort; consequently, they are more susceptible to changes in health status and external environmental factors 36–38 . In the context of multimorbidity, the cumulative burden of disease combined with a decline in functional compensatory capacity means that such complex activities often become the first functional domains to be impaired 39–43 . The findings of this study further indicate that such functional limitations precede the onset of depressive symptoms in a time series, suggesting a potential precursor role in the functional emotional change pathway. Notably, the relationship between functional limitations and depressive symptoms was not unidirectional. Previous studies have shown that depressive states can impair older adults’ ability to perform household activities by affecting their attention, memory, and executive function 44 . Moreover, depression-related sleep disturbances, reduced energy levels, and increased anxiety may further limit physical endurance and task persistence 45,46 . However, in conjunction with the findings of this study, which are based on a longitudinal cross-lagged network model, the evidence tends to support the view that, in the context of multimorbidity, impairment in complex daily functioning occurs early in the functional–emotional change pathway. In addition to domestic activities, A12 “money management” also played a prominent and pivotal role in this study, exhibiting the highest out-degree and forming relatively concentrated predictive pathways with multiple depressive symptoms. As a highly complex instrumental activity of daily living, A12 “money management” relies on higher-order cognitive abilities such as memory, attention, and executive function and reflects an individual’s overall capacity to maintain long-term self-management and independence in daily life 47 . Among middle-aged and older adults with multiple comorbidities, issues such as treatment costs, long-term medication management, and daily living expenses are often more complex 48 . When financial management abilities are impaired, individuals are more likely to perceive increased economic uncertainty and a diminished sense of control over their lives, thereby amplifying negative emotional experiences through multiple pathways and driving the development of depressive symptoms. Previous research provides important theoretical explanations for this. Among older adults with cognitive frailty and Alzheimer's disease, a close bidirectional association between financial management ability and depression has been established. Its impairment may serve as an early manifestation of cognitive decline or significantly increase the risk of depression by undermining self-efficacy and decision-making autonomy 49 . Furthermore, some studies suggest that greater financial management competence can, to a certain extent, buffer the adverse effects of loneliness on depressive symptoms, demonstrating its potential psychological protective value 50 . Taken together, the pivotal role of financial management within the ADL depression symptom network revealed in this study suggests that it may serve as a key entry point for the early identification of depression risk and comprehensive intervention. This study constructed and compared cross-lagged networks of ADLs and depressive symptoms in women and men. The results indicate that while the networks of both genders exhibit a degree of consistency in their overall structure, there are marked differences in key predictive nodes and path strengths, suggesting that the interaction between functional limitations and psychological symptoms presents distinct structural characteristics across genders. Previous research has highlighted differences between men and women in terms of functional performance and the trajectory of BADL and IADL 51 . In general, men maintain a relatively high level of functional ability in BADLs, whereas women tend to demonstrate greater participation and autonomy in IADL, particularly in activities related to housework; this disparity may be linked to long-established patterns of gender-based division of labour 52,53 . Furthermore, research indicates that women are more prone to a decline in BADLs during advanced age or when experiencing cognitive impairment, suggesting that their baseline self-care abilities are more sensitive to changes in overall health status 54 . The cross-lagged network analysis in this study has, to some extent, broadened the interpretative scope of these findings. Among older women with multiple comorbidities, nodes related to BADLs—such as A4 “Getting up” and A1 “Dressing”—exhibited a stronger outwards predictive effect and were linked to depressive symptoms via multiple cross-lagged pathways, suggesting that impaired basic self-care abilities may have a more widespread impact on their mental health. In contrast, within the male network, "Housework”—an activity closely associated with IADL—emerged as the most predictive node. This may reflect men’s greater reliance on external support and the family environment in such activities, with limitations in this area being more likely to trigger negative emotional responses. Although some studies have not reported significant sex differences in the overall level of functional dependence 55 , this study indicates that sex differences are more evident in the pathways and mechanisms through which functional impairment influences depressive symptoms than in the degree of functional impairment itself. Limitations First, although the use of a longitudinal cross-lagged network model helps reveal the time series associations between variables, it cannot entirely rule out the influence of potential unmeasured confounding factors on the results; therefore, caution is needed when drawing causal inferences. Second, both ADL and depressive symptoms were based on self-report scales, which may be subject to recall bias or social desirability bias; in particular, subjective errors may arise in the assessment of complex functions (such as money management). Furthermore, as the study sample was drawn from a population of middle-aged and older adults with multimorbidity in China, the generalizability of the findings to populations with different cultural backgrounds or disease profiles requires further validation. This study did not distinguish between the types or severity of chronic diseases; future research could incorporate disease characteristics, biological markers, and intervention trials to further explore the mechanisms of the functional-emotional pathway and its potential for intervention. Conclusions On the basis of a longitudinal cross-lagged network model, this study systematically elucidated the time series dynamic relationship between impaired ADL function and depressive symptoms in older adults with multimorbidity. The results indicate that complex instrumental activities of daily living (such as Housework and Money management) occupy a central position in the network; impairment in these functions precedes the onset of depressive symptoms in the time series, suggesting that such functional impairments may serve as early predictors of changes in mental health. Gender-stratified analysis further revealed that impairment in basic activities of daily living (BADL) has a more widespread impact on mental health in women, whereas limitations in instrumental activities of daily living (IADL) (such as Housework) are more likely to trigger negative emotional responses in men, suggesting significant gender heterogeneity in the functional-emotional pathway. This finding suggests that in health management and intervention practices for individuals with multiple comorbidities, early functional monitoring and risk assessment should be prioritized. Strategies should be tailored to gender-specific characteristics while multidisciplinary resources, including nursing, rehabilitation, and psychological and social support, should be integrated to achieve synergistic interventions that maintain functional capacity and promote mental health. Abbreviations CHARLS Chinese Health and Retirement Longitudinal Study DS Depressive symptoms ADL Activities of daily living CLPN Cross-lagged panel network Declarations Data availability The datasets used and/or analyzed during the current study are publicly available from the China Health and Retirement Longitudinal Study (CHARLS) repository. The specific datasets include: China Health and Retirement Longitudinal Study (CHARLS), Wave 4 (2018): https://charls.charlsdata.com/pages/Data/2018-charls-wave4/zh-cn.html China Health and Retirement Longitudinal Study (CHARLS), Wave 5 (2020): https://charls.charlsdata.com/pages/Data/2020-charls-wave5/zh-cn.html The data are publicly available to researchers upon registration via the official CHARLS website. Author contributions Jiahui Ding is the first author and made significant contributions to the experimental design, data processing, and analysis, and participated in the drafting of the manuscript. Xiaojin Hu and Meiling Sun contributed to data analysis and reviewed and revised the initial draft. Shanshan Ge, as the corresponding author, led the overall research direction, supervised the entire study process, and approved the final manuscript. All authors have read and agreed to the published version of the manuscript. Funding This research project is funded under the 2025 Shanxi Province Major Special Research Programme on Public Administration (Grant No. SXSGGGLYB2517). Competing interests The authors declare that they have no competing interests. References Kehoe, B. et al. The effect of participating in MedEx Wellness, a community-based chronic disease exercise rehabilitation programme, on physical, clinical and psychological health: A study protocol for a cohort trial. Contemp. Clin. Trials Commun. 19 , 100591 (2020). Boyd, C. M. & Fortin, M. Future of Multimorbidity Research: How Should Understanding of Multimorbidity Inform Health System Design? 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Cross-cultural adaptation and psychometric evaluation of the Sinhala version of Lawton Instrumental Activities of Daily Living Scale. PloS One 13 , e0199820 (2018). Chen, H.-L. et al. Multimorbidity patterns and the association with health status of the oldest-old in long-term care facilities in China: a two-step analysis. BMC Geriatr. 23 , 851 (2023). Morris, J. E. et al. Treatment burden for patients with multimorbidity: cross-sectional study with exploration of a single-item measure. Br. J. Gen. Pract. J. R. Coll. Gen. Pract. 71 , e381–e390 (2021). Wang, X.-X. et al. Multimorbidity associated with functional independence among community-dwelling older people: a cross-sectional study in Southern China. Health Qual. Life Outcomes 15 , 73 (2017). Aubert, C. E., Kabeto, M., Kumar, N. & Wei, M. Y. Multimorbidity and long-term disability and physical functioning decline in middle-aged and older Americans: an observational study. BMC Geriatr. 22 , 910 (2022). Chen, Y., Ji, H., Shen, Y. & Liu, D. Chronic disease and multimorbidity in the Chinese older adults’ population and their impact on daily living ability: a cross-sectional study of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). Arch. Public Health Arch. Belg. Sante Publique 82 , 17 (2024). Liu, X. et al. Association between physical activity and mild cognitive impairment in community-dwelling older adults: Depression as a mediator. Front. Aging Neurosci. 14 , 964886 (2022). Read, J. R., Sharpe, L., Modini, M. & Dear, B. F. Multimorbidity and depression: A systematic review and meta-analysis. J. Affect. Disord. 221 , 36–46 (2017). Zhou, K. et al. Latent profile analysis of the symptoms of depression and activities of daily living impairment among older adults. Rehabil. Psychol. 69 , 45–54 (2024). Tabira, T. et al. Age-Related Changes in Instrumental and Basic Activities of Daily Living Impairment in Older Adults with Very Mild Alzheimer’s Disease. Dement. Geriatr. Cogn. Disord. Extra 10 , 27–37 (2020). Larkin, J., Foley, L., Smith, S. M., Harrington, P. & Clyne, B. The experience of financial burden for people with multimorbidity: A systematic review of qualitative research. Health Expect. Int. J. Public Particip. Health Care Health Policy 24 , 282–295 (2021). Untu, I. et al. Neurobiological and therapeutic landmarks of depression associated with Alzheimer’s disease dementia. Front. Aging Neurosci. 17 , 1584607 (2025). Zhao, Y. et al. Moderating effect of instrumental activities of daily living on the relationship between loneliness and depression in people with cognitive frailty. BMC Geriatr. 25 , 121 (2025). Nh, J. et al. Development of a clinical prediction model for the onset of functional decline in people aged 65-75 years: pooled analysis of four European cohort studies. BMC Geriatr. 19 , (2019). Tomás, C., Zunzunegui, M. V., Moreno, L. A. & Germán, C. Dependencia evitable para las actividades de la vida diaria: una perspectiva de género. Rev. Esp. Geriatría Gerontol. 38 , 327–333 (2003). Ryu, H. J. & Moon, Y. Gender Differences in Items of the Instrumental Activities of Daily Living in Mild Cognitive Impairment and Alzheimer’s Disease Dementia. Dement. Neurocognitive Disord. 23 , 107–114 (2024). Calatayud, E. et al. Functional Differences Found in the Elderly Living in the Community. Sustainability 13 , 5945 (2021). Nakagawa, H. B., Ferraresi, J. R., Prata, M. G. & Scheicher, M. E. Postural balance and functional independence of elderly people according to gender and age: cross-sectional study. Sao Paulo Med. J. Rev. Paul. Med. 135 , 260–265 (2017). Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 03 Apr, 2026 Editor assigned by journal 02 Apr, 2026 Editor invited by journal 30 Mar, 2026 Submission checks completed at journal 27 Mar, 2026 First submitted to journal 27 Mar, 2026 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9176515","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":617171829,"identity":"181d757d-5a21-4cd0-82ed-41d26bb61634","order_by":0,"name":"Jiahui Ding","email":"","orcid":"","institution":"Nursing College of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jiahui","middleName":"","lastName":"Ding","suffix":""},{"id":617171830,"identity":"ca275421-d01e-4ada-a98b-d68541801cf8","order_by":1,"name":"Xiaojin Hu","email":"","orcid":"","institution":"First Hospital of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaojin","middleName":"","lastName":"Hu","suffix":""},{"id":617171831,"identity":"64640bb4-1e7c-4574-aba7-9047d8cdd37c","order_by":2,"name":"Meiling Sun","email":"","orcid":"","institution":"Nursing College of Shanxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Meiling","middleName":"","lastName":"Sun","suffix":""},{"id":617171832,"identity":"f99b4793-3c97-4cf5-8c98-ba164afe42eb","order_by":3,"name":"Shanshan Ge","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYBACefb2gw8+VNTw2B9vIFKLYc+ZZMMZZ47JMZw5QKw1NxLMpHnbmI2BDCJ1MPYcSDbgYWNLbJz5eOMNhhqbaIJa2NkbDz6Q4JFJbJZOK7ZgOJaW20CULQYSbIlt0jlmEowNhwlrAflFIsGAObFH8gwpWg4kMBtLSPAQqQUcyA0HjskZ8AD9kkCMX0BR+fjvvxoeA/bDG298qLEhwmFIwEAigRTlEC2k6hgFo2AUjIKRAQBEEUHKpEhxvAAAAABJRU5ErkJggg==","orcid":"","institution":"First Hospital of Shanxi Medical University","correspondingAuthor":true,"prefix":"","firstName":"Shanshan","middleName":"","lastName":"Ge","suffix":""}],"badges":[],"createdAt":"2026-03-20 08:08:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9176515/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9176515/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106542149,"identity":"160a85b1-cafe-4d22-b802-761bbbd662d0","added_by":"auto","created_at":"2026-04-09 16:21:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":41910,"visible":true,"origin":"","legend":"\u003cp\u003ePanel network of the cross-lagged relationship between ADL and depression symptoms. Note: N = 5,850. Blue arrows indicate positive predictions, whereas red arrows indicate negative predictions; the thickness of the arrows reflects the strength of these effects, with thicker arrows indicating a stronger relationship. Covariates (i.e., sex, age, marital status, smoking, alcohol consumption, and education) and autoregressive edges have been omitted from the figure to facilitate visual interpretation.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9176515/v1/b5e8b725bbad3faf1f5e0c4a.png"},{"id":106726053,"identity":"77676496-895e-4b4f-bf5b-b09484cb6ef0","added_by":"auto","created_at":"2026-04-12 18:35:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":102009,"visible":true,"origin":"","legend":"\u003cp\u003eStandardized centrality estimates for the ADL depression symptom network. Note: N = 5,850. Higher values indicate greater centrality of the node.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9176515/v1/18cf4f4b252e7e8d08576274.png"},{"id":106542146,"identity":"0eb71c93-2d1d-4a56-8262-9e99ed27b486","added_by":"auto","created_at":"2026-04-09 16:21:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":259464,"visible":true,"origin":"","legend":"\u003cp\u003eCross-lagged panel network of ADL and depressive symptoms in older women (left) and men (right). Note: Blue arrows indicate positive predictions, whereas red arrows indicate negative predictions; the thickness of the arrows indicates the strength of these effects, with thicker arrows denoting stronger relationships. Covariates (i.e., age, marital status, smoking, alcohol consumption, and education) and autoregressive edges have been omitted from the figure to facilitate visual interpretation.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9176515/v1/56ea5e16c62888bd08201287.png"},{"id":106542148,"identity":"1446cba3-8117-43b3-905e-b3a8d143f05e","added_by":"auto","created_at":"2026-04-09 16:21:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":180117,"visible":true,"origin":"","legend":"\u003cp\u003eStandardized centrality estimates for the ADL depression symptom network in elderly women (left) and men (right). Note: Higher values indicate a stronger centrality.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9176515/v1/9e4f04ca72f66f5312b2c844.png"},{"id":106960172,"identity":"940c7cd1-f4e5-40aa-9e00-664bfa7686e4","added_by":"auto","created_at":"2026-04-15 09:19:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1426301,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9176515/v1/92b02e02-9ee1-4a7d-8437-86aa63f55e2e.pdf"},{"id":106542145,"identity":"2ee5f79f-eff4-45a0-aa8e-ebe96880fca1","added_by":"auto","created_at":"2026-04-09 16:21:05","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":4485163,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-9176515/v1/398bf327c156775b800a9245.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bidirectional Associations between Daily Activity Limitations and Depressive Symptoms among Middle-aged and Older Adults with Multimorbidity in China: A Cross-Lagged Panel Network Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic diseases are major contributors to the increasing global burden of disease and inequalities in health outcomes\u0026nbsp;\u003csup\u003e1,2\u003c/sup\u003e. Globally, approximately one in three people with chronic diseases suffer from two or more such conditions simultaneously, a phenomenon known as multimorbidity\u003csup\u003e3\u003c/sup\u003e, and the risk of developing multimorbidity\u0026nbsp;increases\u0026nbsp;significantly with age\u0026nbsp;\u003csup\u003e4\u003c/sup\u003e. With the general increase in life expectancy and the combined effects of multiple factors, such as high body mass index, unhealthy lifestyles, and socioeconomic changes, multimorbidity has become a major public health issue in the context of global\u0026nbsp;aging\u003csup\u003e5,6\u003c/sup\u003e. Relevant studies indicate that multimorbidity is particularly common among populations with limited health resources\u003csup\u003e7\u003c/sup\u003e. Multimorbidity is not merely the\u0026nbsp;accumulation\u0026nbsp;and prevalence of physical illnesses; its onset and progression are influenced by a combination of factors, including an individual’s functional status, lifestyle, and social environment\u003csup\u003e8,9\u003c/sup\u003e.\u0026nbsp;Against this backdrp, a systematic examination of the functional status and mental health outcomes of older adults with multimorbidity is\u0026nbsp;highly important\u0026nbsp;for deepening our understanding of the mechanisms underlying the health impacts in this population\u003csup\u003e10\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn older adults with multimorbidity, impaired functional status and mental health problems are often intertwined\u003csup\u003e11\u003c/sup\u003e. Activities of daily living (ADL) are a core indicator for measuring the functional status of elderly individuals\u003csup\u003e12\u003c/sup\u003e, and their decline not only reflects\u0026nbsp;a\u0026nbsp;decline in physical function but is also considered an important risk signal for the deterioration of mental health in elderly\u0026nbsp;individuals\u003csup\u003e13\u003c/sup\u003e. Previous studies have generally\u0026nbsp;suggested\u0026nbsp;that ADL\u0026nbsp;functional\u0026nbsp;limitations can increase the risk of depressive symptoms by restricting individual activity ability, reducing social participation, and weakening the social support network\u003csup\u003e14\u003c/sup\u003e. In cross-sectional and longitudinal studies in different countries and cultural backgrounds, the prevalence of depression in older adults with ADL limitations is significantly\u0026nbsp;greater\u0026nbsp;than that in those with normal function\u003csup\u003e15–18\u003c/sup\u003e. In the\u0026nbsp;Chinese\u0026nbsp;population, this association has been consistently supported by multiple studies\u003csup\u003e19,20\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eSex differences exist in the patterns of multimorbidity\u003csup\u003e21,22\u003c/sup\u003e, characteristics of impaired functional status\u003csup\u003e23,24\u003c/sup\u003e, and occurrence and manifestation of depressive symptoms\u003csup\u003e25\u003c/sup\u003e. Men and women exhibit distinct characteristics in terms of disease spectrum composition, types of functional limitations, and psychological coping strategies; this sex heterogeneity may further influence the pathways linking functional status and depressive symptoms. However, existing studies predominantly analyse overall samples and rarely systematically explore these associations from a gender-specific perspective, which may obscure the differential roles of key functional limitation items and depressive symptom nodes across genders.\u003c/p\u003e\n\u003cp\u003eFurthermore, previous studies\u003csup\u003e26\u003c/sup\u003ehave largely relied\u0026nbsp;on overall scores or correlation coefficients for ADLs and depressive symptoms as the primary basis for their conclusions, with few studies conducting in-depth analyses of the structural associations between specific items of functional impairment and specific depressive symptoms. Moreover, the study populations have generally not focused on middle-aged and older adults with multiple comorbidities. Although some studies have begun to employ network analysis methods to characterize the complex relationships between functional and psychological symptoms\u003csup\u003e27,28\u003c/sup\u003e, most of these studies employ cross-sectional designs, which are insufficient to reveal the dynamic interactions between functional limitations and depressive symptoms over time or their potential directionality.\u003c/p\u003e\n\u003cp\u003eIn summary, it is necessary to adopt a longitudinal perspective and employ more refined analytical methods to systematically examine the bidirectional predictive relationship between ADLs and depressive symptoms in older adults with multimorbidity in the future. The cross-lagged panel network (CLPN) is a longitudinal analytical method that integrates network modelling with cross-lagged panel models. It can depict the predictive relationships between different variables at adjacent time points while controlling for autoregressive effects, thereby enhancing the inferential power regarding the directional associations between symptoms\u003csup\u003e29\u003c/sup\u003e. This method aids in identifying functional or psychological symptom nodes that occupy key positions in temporal evolution, thereby providing a basis for identifying precise intervention targets\u003csup\u003e30\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTherefore, this study uses data from the 2018 and 2020 waves of the China Health and Retirement Longitudinal Study (CHARLS) to examine middle-aged and older adults with multimorbidity. A cross-lagged panel network model was constructed to systematically examine the stability and dynamic characteristics of the association structure between various ADL functional items and depressive symptoms and to further compare differences in network structure from a sex-specific perspective. This study was performed to provide empirical evidence for elucidating the mechanisms of psychosomatic comorbidity in this population and to formulate intervention strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design and participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized data from the fourth and fifth waves (2018 and 2020) of the China Health and Retirement Longitudinal Study (CHARLS), which is available at https://charls.charlsdata.com/pages/Data/2018-charls-wave4/zh-cn.htm and https://charls.charlsdata.com/pages/Data/2020-charls-wave5/zh-cn.html. Conducted by the National School of Development at Peking University, CHARLS is a nationally representative survey designed to provide high-quality microdata on China\u0026rsquo;s middle-aged and elderly populations. It serves as a vital resource for research on population aging and its socioeconomic impacts. Since its launch in 2011, CHARLS has employed a multistage stratified random sampling method covering 150 counties and 450 villages across 28 provinces (regions and municipalities). Subsequent surveys are conducted every 2\u0026ndash;3 years, with data released to the public one year after each survey is conducted. This study focused on individuals aged \u0026ge; 50 years with at least two chronic conditions from the CHARLS 2018 and 2020 datasets to investigate their mental health status. Among them, 19,816 older adults participated in the 2018 CHARLS. To ensure the accuracy and reliability of the data, we applied the following inclusion criteria: (1) key demographic variables (such as sex, age, educational attainment, marital status, smoking, alcohol consumption, and number of chronic conditions); (2) completion of all items on the Center for Epidemiological Studies Depression Scale (CESD-10) and the Activities of Daily Living Scale; (3) having at least two chronic conditions; (4) being aged 50 years or over; and (5) participation in both the fourth and fifth waves of the survey. Following rigorous screening and data cleaning, 13,966 records were excluded due to missing data or failure to meet the criteria, leaving a final sample of 5,850 participants. Figure 1 shows the sample selection process (Figure 1).\u003c/p\u003e\u003cp\u003eIn utilizing the CHARLS data, we strictly adhered to the principles of data privacy and complied with all regulations governing the use of the CHARLS database. Prior to downloading and analysing the data, we obtained formal approval from the database administrator. This study complied with the principles of the Declaration of Helsinki, and the CHARLS study was approved by the Biomedical Ethics Committee of Peking University (IRB0000 052-11015).\u003c/p\u003e\n\u003cp\u003eMeasurement of depressive symptoms\u003c/p\u003e\n\u003cp\u003eIn the CHARLS project, depression was assessed via a shortened version of the CESD-10, comprising 10 items: feeling down, feeling lonely, being troubled by things, feeling afraid, difficulty concentrating, struggling, inability to get started, sleep quality, hope for the future, and feeling happy, with\u0026nbsp;‘hope for the future’\u0026nbsp;and\u0026nbsp;‘feeling happy’\u0026nbsp;reverse-scored. A 4-point Likert scale ranging from 10 to 40\u0026nbsp;was used, with higher scores indicating greater severity of depression. In this study, the scale demonstrated adequate reliability and validity, with a Cronbach’s alpha coefficient of 0.811.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of ADL\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDisability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the CHARLS project, activities of daily living (ADL) are measured on the basis of basic activities of daily living (BADL) and instrumental activities of daily living (IADL). The assessment of ADLs comprises two components: BADLs and instrumental ADLs (IADLs). Six BADL scales and six IADL scales were used to assess ADLs. The BADL scale comprises six items: dressing, bathing, eating, getting in and out of bed, using the toilet, and controlling urination or defecation. The IADL scale comprises six items: housework, cooking, shopping, making telephone calls, taking medication, and managing finances. The responses to each item are coded as follows: ‘Can perform independently’ is coded as 0. ‘Some difficulty or worse’ was coded as 1. In this study, the Cronbach’s alpha coefficients for BADLs and IADLs were 0.749 and 0.755, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we first summarized the demographic characteristics of the participants and calculated the scores on the relevant scales via SPSS 27.0. We subsequently used R 4.5.1 software to estimate the CLPN for older Chinese adults, women, and men. By combining cross-lagged panel models and network models, the CLPN aims to explore the temporal associations between symptoms\u003csup\u003e29\u003c/sup\u003e. Notably, the CLPN method allows for the\u0026nbsp;modelling\u0026nbsp;of directed networks across two time points. The construction of the CLPN model involves several key steps: (a) estimating autoregressive and cross-lagged coefficients\u0026nbsp;via\u0026nbsp;regularized regression, (b) determining the unexpected and expected influences (EI) for each node, and (c) assessing the accuracy and stability of the network.\u003c/p\u003e\n\u003cp\u003eIn the initial stage, the CLPN was estimated via a series of regularized regression models to calculate the autoregressive and cross-lagged effects at two time points\u003csup\u003e31\u003c/sup\u003e. The autoregressive effects captured the coefficients of the 2018 symptoms, predicting their own values in 2020 while controlling for all other symptoms and covariates in 2018. After accounting for all other symptoms and covariates in 2018, the cross-lagged effects involved the coefficients of the 2018 symptoms predicting different symptoms in 2020. The estimation process uses maximum likelihood with Lasso regularization applied to the regression coefficients\u003csup\u003e31\u003c/sup\u003e. This method effectively reduces overfitting and eliminates trivial paths by shrinking small regression paths to zero, thereby enhancing the generalizability of the results\u003csup\u003e32\u003c/sup\u003e. The glmnet package\u003csup\u003e31\u003c/sup\u003e was used to compute the regularized regression, and the qgraph package\u003csup\u003e33\u003c/sup\u003ewas used for network visualization. Following the estimation, two centrality indices were calculated for each node: the out-EI and in-EI of the cross-lagged effects. The cross-lagged out-EI indicates the extent to which the 2018 node predicts the 2020 node, whereas the in-EI indicates the extent to which the 2020 node is predicted by the 2018 node, excluding autoregressive paths.\u003c/p\u003e\n\u003cp\u003eThe accuracy and stability of the network were subsequently assessed via two bootstrapping methods supported by the bootnet package\u003csup\u003e33\u003c/sup\u003e. The accuracy of the edge weights was determined by calculating 95% confidence intervals (CIs) around each edge weight via 1,000 iterations of\u0026nbsp;nonparametric\u0026nbsp;bootstrapping. To evaluate the stability of the network, the\u0026nbsp;case‒drop\u0026nbsp;bootstrapping method was employed to calculate the\u0026nbsp;coefficient of stability\u0026nbsp;(CS), thereby estimating the stability of the order centrality measures. A CS coefficient greater than 0.25 is considered acceptable, whereas a value above 0.50 is deemed excellent\u003csup\u003e33\u003c/sup\u003e. Furthermore, statistical significance tests were conducted to examine the differences between edge weights and node centrality\u003csup\u003e33\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFollowing the CLPN group comparison guidelines\u003csup\u003e29\u003c/sup\u003e, the following steps were undertaken: (a) estimating the correlation between the edge list and the proportion of edges with the same direction (positive, negative, or neutral), providing a global measure of symptom connectivity; and (b) estimating the correlation between centrality indices across networks, as well as the consistency between the most central symptoms and the strongest edges, providing specific insights into central symptoms and their associations.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSociodemographic characteristics of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ethe\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eparticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study included 5,850 older adults aged between 50 and 91 years (mean age 62.88 ± 7.95) with at least two chronic conditions. Of these, 51.9% were women, and 48.1% were men. Table 1 summarizes the participants’ demographic characteristics. As shown in Table 2, between the two surveys, there was a discernible trend in both the level of disability in activities of daily living (ADL) and in depressive symptoms among older adults in China. In the overall sample, the proportion of patients with ADL item impairment ranged from 2.1% to 15.9% in 2018, increasing to 3.2%–19.5% by 2020. Among these, toileting (A5) and housing (A7) consistently had the highest impairment rates, whereas eating (A3) had the lowest impairment rate but also showed an increasing trend. Analysis by gender revealed that women had higher impairment rates than men across all the ADL/IADL items in both surveys did, with a more pronounced increase over time. In 2020, women had the highest impairment rates for “Toileting\" (A5) (23.9%) and \"Housework\" (A7) (20.7%), whereas men had relatively lower impairment rates for these corresponding items. In terms of depressive symptoms, the mean scores for all items in the overall sample ranged from 0.45--1.34 in 2018 and from 0.49--3.29 in 2020; specifically, the mean scores for “Hopelessness” (D9) and “Unhappiness” (D10) were higher in 2020 than in 2018. The results by gender revealed that women’s levels of depressive symptoms were consistently greater than those of men. The mean scores for women across all items were 0.59–1.51 in 2018 and 0.64–1.52 in 2020, with relatively higher scores for “Restlessness” (D8), “Effortfulness” (D6) and “Hopelessness” (D9). Compared with those in 2018, men in 2020 presented lower overall levels of depressive symptoms, although scores for some symptoms increased slightly.\u003c/p\u003e\n\u003cp\u003eTable 1. Baseline characteristics of the study population, stratified by sex\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"535\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTotal(%)/Mean(SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAll\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN=5850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN=3038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eN=2812\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e62.88(7.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e62.43(7.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e63.37(8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEducational level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBelow primary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2290（39.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1562(51.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e728(25.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePrimary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1389（23.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e625(20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e764(27.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMiddle school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1379（23.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e563(18.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e816(29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh school and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e792（13.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e288(9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e504(17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4788（81.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2347(77.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2441(86.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eother\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1062（18.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e691(22.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e371(13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of Chronic Conditions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e2~4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4518（77.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2298(75.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2220(78.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e5~7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1187（20.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e661(21.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e466(16.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e8~13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e145(2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e79(2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e66(2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol Drinking Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1915（32.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e412（13.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1503(53.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3935（67.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2626（86.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1309(46.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1442（24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e162（5.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1280(45.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4408 (75.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2876（94.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1532(54.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2. Activities of daily living (ADL) disability and depressive symptoms of the study population, stratified by sex and year\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"733\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eADL disability\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eNodes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAll\u003cbr\u003e\u0026nbsp;(2018)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAll\u003cbr\u003e\u0026nbsp;(2020)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003cbr\u003e\u0026nbsp;(2018)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003cbr\u003e\u0026nbsp;(2020)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003cbr\u003e\u0026nbsp;(2018)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003cbr\u003e\u0026nbsp;(2020)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDressing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e398（6.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e526(9.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e232（7.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e307（10.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e166（5.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e219（7.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBathing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e492（8.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e561(9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e307（10.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e338（11.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e185（6.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e223（7.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEating\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e124（2.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e187(3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e73（2.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e95（3.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51（1.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e92（3.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGetting up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e434(7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e505(8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e303（10.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e351（11.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e131（4.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e154（5.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eToileting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e789(13.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1141(19.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e527（17.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e725（23.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e262（9.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e416（14.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eElimination control\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e287(4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e347(5.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e149（4.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e178（5.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e138（4.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e169（6.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHousework\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e932(15.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1022(17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e599（19.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e629（20.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e333（11.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e393（14.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCooking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e616(10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e655(11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e368（12.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e413（13.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e248（8.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e242（8.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eShopping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e475(8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e454(7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e325（10.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e303（10.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e150（5.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e151（5.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePhone use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e531(9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e391(6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e363（11.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e246（8.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e168（6.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e145（5.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMedication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e295(5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e353(6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e216（7.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e262（8.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e79（2.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e91（3.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMoney management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eA12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e670(11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e578(9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e461（15.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e377（12.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e209（7.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e201（7.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eDepressive Symptoms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean(SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDepressiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.03(1.105)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.04(1.116)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.19（1.146）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.21（1.145）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.85（1.031）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.86(1.053)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLoneliness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.66(1.052)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.69(1.063)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.76（1.121）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.79（1.117）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.55（0.961）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.57(0.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBortheredness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.04(1.132)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.11(1.143）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.21（1.169）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.28（1.175）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.85（1.059）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.92(1.076)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFearfulness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.45(0.885)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.49（0.929）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.59（0.988）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.64（1.032）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.30（0.729）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.33(0.771)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCognitive trouble\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eD5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.03(1.122)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.12（1.149）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.18（1.158）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.28（1.172）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.87（1.059）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.95(1.099)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEffortfulness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eD6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.12(1.182)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.14（1.203）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.25（1.211）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.26（1.217）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.97（1.132)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.01(1.175)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIntertia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eD7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.5(0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.56（0.997）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.59（1.01）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.67（1.064）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.39(0.846)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.45(0.907)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRestlessness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eD8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.29(1.231)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.29（1.228）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.51（1.225）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.52（1.222）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.06(1.193)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.03(1.184)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHopelessness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eD9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.34(1.255)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.29（1.250）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.35（1.234）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.4（1.24）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.33(1.277)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.36(1.260)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUnhappiness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eD10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.1(1.181)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.29（1.173）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.17（1.195）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.17（1.175）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.03(1.162)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.05(1.167)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eThe ADL-Depression Symptom Network\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eamong\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;the Elderly Population in China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; The correlation matrix for the older adult population in China is presented in Supplementary Table S1. The cross-lagged panel network analysis (CLPN) is visualized as a directed network in Figure 1. The arrows in the figure represent cross-lagged associations between ADLs and depressive symptoms; the model simultaneously controlled for the main effects of each variable in 2018 and the influence of covariates. The autoregressive cross-lagged network diagram incorporating all the variables is shown in Supplementary Figure S1. Among all cross-lagged pathways, the three associations with the largest effect sizes were \"Housework\"\u0026nbsp;(A7) → “Effortfulness” (D6) (β=0.204), “Cooking” (A8) → “Housework” (A7) (β = 0.136), and “Elimination control” (A6) → “Depressiveness” (D1) (β = 0.130). Furthermore, “Money management” (A12) exhibited relatively concentrated cross-lagged associations with multiple depressive symptoms, including “Money management” (A12) → “Intertia” (D7) (β = 0.122), “Money management” (A12) → “Hopelessness” (D9) (β = 0.121), “Money management” (A12) → “Cognitive trouble” (D5) (β = 0.119), and “Money management” (A12) → “Depressiveness” (D1) (β = 0.119). Further results of the cross-lagged relationships are presented in Supplementary Table S2.\u003c/p\u003e\n\u003cp\u003eIn terms of node centrality, “Money management” (A12) had the highest out-expected influence (Out-EI) (1.033), followed by \"Housework\" (A7) (Out-EI = 0.924), indicating that these ADL nodes play a strong predictive role in the network. Conversely, regarding In-Expected Influence (In-EI), “Effortfulness” (D6) had the highest In-EI (1.023), followed by “Intertia” (D7) (In-EI = 0.902), suggesting that these depression symptom nodes are more susceptible to the influence of the past states of other variables within the network. The standardized estimates of node centrality are shown in Figure 2. The detailed results for unstandardized coefficients are listed in Supplementary Table S3, and the distribution of differences in node centrality is illustrated in Supplementary Figures S2--S3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eADL‒Depression\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Symptom Networks\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eamong\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Middle-Aged and Older Chinese Women and Men\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe correlation matrices for middle-aged and older Chinese women and men are presented in Supplementary Tables S4 and S5. Figure 3 illustrates the cross-lagged networks of ADLs and depression symptoms in middle-aged and older Chinese women and men. The similarity analysis of network edge weights revealed a significant yet limited correlation between the edge weights of the male and female networks (r = 0.30, 95% CI: 0.21–0.38, p \u0026lt; 0.001), suggesting a degree of consistency in the overall cross-lagged association structure between the two sexes, although differences remained in the specific path strengths and connection patterns. In the female network, “Getting up\"\u0026nbsp;(A4)\u0026nbsp;presented\u0026nbsp;a high degree of outwards predictive power, with significant cross-lagged associations with multiple depression symptom nodes. Furthermore, a strong bidirectional predictive relationship existed between\u0026nbsp;“cooking\" (A8) and \"dressing\" (A1), and both formed dense cross-lagged connections with other ADL nodes, constituting a highly interconnected ADL subnet. Other cross-lagged associations in the female network are shown in Supplementary Table S6 (Figure 3). In contrast, in the male network,\u0026nbsp;\"Housework\"\u0026nbsp;(A7) emerged as the primary predictive node. Other cross-lagged associations in the male network are shown in Supplementary Table S7 (Figure 3). Cross-lagged network diagrams incorporating autoregressive models for all variables in the networks of older Chinese women and men are shown in Supplementary Figure S4.\u003c/p\u003e\n\u003cp\u003eWith respect to node centrality, the out-EI of the male and female networks showed a moderate positive correlation (r =0.43, 95% CI: 0.01--0.72, p = 0.048), indicating that some nodes possessed similar predictive importance in both networks, although the nodes playing the primary predictive role were not entirely consistent. Specifically, in the female network, \"Dressing\" (A1) had the highest out-EI (0.906), whereas in the male network, “Housework” (A7) had the highest out-EI (1.352). In contrast, the In-EIs of the male and female networks exhibited a high degree of consistency (r=0.76, 95% CI: 0.50–0.90; p \u0026lt; 0.001). Overall, the in-EI was primarily concentrated in nodes related to depressive symptoms, indicating that depressive symptoms were more likely to act as influenced nodes in both networks. Specifically, in the female network, \"Intertia\" (D7) had the highest In-EI (1.040), whereas in the male network, \"Effortfulness\" (D6) had the highest In-EI (1.158); both were significantly higher than other nodes in their respective networks. Detailed information on other nodes for females and males is provided in Supplementary Tables S8 and S9 (Figure 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAccuracy and Stability of Network Parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe accuracy of the edge weights was tested via the bootstrap method. As shown in Supplementary Figure S6, the 95% confidence intervals for each edge weight were generally narrow, indicating that the edge weight estimates of the network model were highly accurate. The stability of the centrality indices (In-EI and Out-EI) was assessed via the case‒drop bootstrap method. As shown in Supplementary Figure S7, as the sample size decreases, the average correlation coefficient between the ranking of centrality indices and the original sample ranking gradually decreases. The correlation stability (CS) coefficients were 0.750 (out-EI) and 0.672 (in-EI), indicating that the order of node centrality exhibited a high degree of stability.\u003c/p\u003e\n\u003cp\u003eTo assess the stability of the network estimates by gender, this study conducted nonparametric bootstrap accuracy analyses of edge weights (1,000 iterations each) on the female (N = 3,038) and male (N = 2,812) subsamples. As shown in Supplementary Figure S8 (women) and S9 (men), the edge weight estimates for both sex-specific networks exhibited moderate precision. Specifically, the 95% confidence intervals for most edge weights were wide and overlapped extensively, indicating numerous associations with high estimation uncertainty within the networks. However, for several edges with the strongest predicted effects in each network, the confidence intervals were relatively narrow and did not include zero, suggesting that the estimates for these core associations were relatively stable and more reliable.\u003c/p\u003e\n\u003cp\u003eTo assess the stability of node centrality metrics by gender, this study employed the case‒drop bootstrap method to analyse subsamples of women (N = 3038) and men (N = 2812). As shown in Supplementary Figure S10 (women) and S11 (men), there were differences in the correlation stability (CS) coefficients of the node centrality rankings. The out-EI order exhibited moderate stability: the CS coefficient was 0.672 for the female subsample and 0.595 for the male subsample, both exceeding the recommended threshold of 0.50, indicating that the out-EI node ordering possessed acceptable robustness across gender groups. In contrast, the order of In-EI is less stable: the CS coefficient for both the female and male subsamples is 0.283, which is higher than the minimum acceptable standard of 0.25.\u003c/p\u003e\n\u003cp\u003eCentrality-based tests of differential analysis revealed that, in the networks of older women and men, the symptoms exhibiting the strongest out-EI/in-EI values were statistically more pronounced than most other symptoms in the networks, as shown in Supplementary Figures S12–S13 (women) and S14–S15 (men). This further demonstrates that the results of the centrality analysis are robust and generalizable.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOn the basis of longitudinal data from a cohort of middle-aged and older adults with multimorbidity in China, this study employed a cross-lagged panel network model to systematically elucidate the time series interactions between impaired activities of daily living (ADL) function and depressive symptoms. The findings indicate that, after controlling for autoregressive effects, ADL functional items primarily act as outwards-predicting nodes within the network, whereas depressive symptoms are predominantly in a position of being influenced. These findings suggest that, in the context of multimorbidity, functional impairment may be a significant precursor to the onset and exacerbation of depressive symptoms. Furthermore, although the overall network structure was consistent across sexes, significant sex differences were observed at the level of key predictive nodes, suggesting that the functional-emotional association pathways exhibit a degree of sex heterogeneity in populations with multimorbidity.\u003c/p\u003e\n\u003cp\u003eAmong the specific functional items, A7 “Housework”\u0026nbsp;exhibited high predictive power and centrality within the network; impairment in this function significantly predicted core depressive symptoms such as D6 \"Effortfulness\".\u0026nbsp;This finding is consistent with the results of previous studies\u0026nbsp;\u003csup\u003e27,34\u003c/sup\u003e. Chen\u003csup\u003e35\u003c/sup\u003e \u003cem\u003eet al\u003c/em\u003e. found through latent profile analysis that, across different subgroups of ADL impairment, the degree of impairment in IADL was generally higher than that in BADL, and this was particularly pronounced in the high-impairment group. Compared with the basic, largely autonomous activities covered by BADLs, IADL—particularly household chores—often involve\u0026nbsp;multistep\u0026nbsp;operations, situational judgement, and sustained physical and cognitive effort; consequently, they are more susceptible to changes in health status and external environmental factors\u003csup\u003e36–38\u003c/sup\u003e. In the context of multimorbidity, the cumulative burden of disease combined with a decline in functional compensatory capacity means that such complex activities often become the first functional domains to be impaired\u003csup\u003e39–43\u003c/sup\u003e. The findings of this study further indicate that such functional limitations precede the onset of depressive symptoms in a time series, suggesting a potential precursor role in the functional emotional change pathway.\u003c/p\u003e\n\u003cp\u003eNotably, the relationship between functional limitations and depressive symptoms was not unidirectional. Previous studies have shown that depressive states can impair older adults’\u0026nbsp;ability to perform household activities by affecting their attention, memory, and executive function\u003csup\u003e44\u003c/sup\u003e.\u0026nbsp;Moreover, depression-related sleep disturbances, reduced energy levels, and increased anxiety may further limit physical endurance and task persistence\u003csup\u003e45,46\u003c/sup\u003e. However, in conjunction with the findings of this study, which are\u0026nbsp;based on a longitudinal cross-lagged network model, the evidence tends to support the view that, in the context of multimorbidity, impairment in complex daily functioning occurs early in the\u0026nbsp;functional–emotional\u0026nbsp;change pathway.\u003c/p\u003e\n\u003cp\u003eIn addition to domestic activities, A12\u0026nbsp;“money management” also played a prominent and pivotal role in this study, exhibiting the highest out-degree and forming relatively concentrated predictive pathways with multiple depressive symptoms. As a highly complex instrumental activity of daily living, A12 “money management” relies on higher-order cognitive abilities such as memory, attention, and executive function and reflects an individual’s overall capacity to maintain long-term self-management and independence in daily life\u003csup\u003e47\u003c/sup\u003e. Among middle-aged and older adults with multiple comorbidities, issues such as treatment costs, long-term medication management, and daily living expenses are often more complex\u003csup\u003e48\u003c/sup\u003e. When financial management abilities are impaired, individuals are more likely to perceive increased economic uncertainty and a diminished sense of control over their lives, thereby amplifying negative emotional experiences through multiple pathways and driving the development of depressive symptoms. Previous research provides important theoretical explanations for this. Among older adults with cognitive frailty and Alzheimer's disease, a close bidirectional association between financial management ability and depression has been established. Its impairment may serve as an early manifestation of cognitive decline or significantly increase the risk of depression by undermining self-efficacy and decision-making autonomy\u003csup\u003e49\u003c/sup\u003e. Furthermore, some studies suggest that\u0026nbsp;greater\u0026nbsp;financial management competence can, to a certain extent, buffer the adverse effects of loneliness on depressive symptoms, demonstrating its potential psychological protective value\u003csup\u003e50\u003c/sup\u003e. Taken together, the pivotal role of financial management within the ADL depression symptom network revealed in this study suggests that it may serve as a key entry point for the early identification of depression risk and comprehensive intervention.\u003c/p\u003e\n\u003cp\u003eThis study constructed and compared cross-lagged networks of ADLs and depressive symptoms in women and men. The results indicate that while the networks of both genders exhibit a degree of consistency in their overall structure, there are marked differences in key predictive nodes and path strengths, suggesting that the interaction between functional limitations and psychological symptoms presents distinct structural characteristics across genders. Previous research has highlighted differences between men and women in terms of functional performance and the trajectory of BADL and IADL\u003csup\u003e51\u003c/sup\u003e.\u0026nbsp;In general, men maintain a relatively\u0026nbsp;high\u0026nbsp;level of functional ability in\u0026nbsp;BADLs, whereas\u0026nbsp;women tend to demonstrate greater participation and autonomy in IADL, particularly in activities related to housework; this disparity may be linked to long-established patterns of gender-based division of labour\u003csup\u003e52,53\u003c/sup\u003e. Furthermore, research indicates that women are more prone to a decline in\u0026nbsp;BADLs\u0026nbsp;during advanced age or when experiencing cognitive impairment, suggesting that their baseline self-care abilities are more sensitive to changes in overall health status\u003csup\u003e54\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe cross-lagged network analysis in this study has, to some extent, broadened the interpretative scope of these findings. Among older women with multiple comorbidities, nodes related to BADLs—such as A4 “Getting up”\u0026nbsp;and\u0026nbsp;A1 “Dressing”—exhibited\u0026nbsp;a stronger outwards predictive effect and were linked to depressive symptoms via multiple cross-lagged pathways, suggesting that impaired basic self-care abilities may have a more widespread impact on their mental health. In contrast, within the male network, \"Housework”—an activity closely associated with IADL—emerged as the most predictive node. This may reflect men’s greater reliance on external support and\u0026nbsp;the\u0026nbsp;family environment in such activities, with limitations in this area being more likely to trigger negative emotional responses. Although some studies have not\u0026nbsp;reported\u0026nbsp;significant sex differences in the overall level of functional dependence\u003csup\u003e55\u003c/sup\u003e, this study indicates that sex differences are more evident in the pathways and mechanisms through which functional impairment influences depressive symptoms than in the degree of functional impairment itself.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, although the use of a longitudinal cross-lagged network model helps reveal the time series associations between variables, it cannot entirely rule out the influence of potential unmeasured confounding factors on the results; therefore, caution is needed when drawing causal inferences. Second, both ADL and depressive symptoms were based on self-report scales, which may be subject to recall bias or social desirability bias; in particular, subjective errors may arise in the assessment of complex functions (such as money management). Furthermore, as the study sample was drawn from a population of middle-aged and older adults with multimorbidity in China, the generalizability of the findings to populations with different cultural backgrounds or disease profiles requires further validation. This study did not distinguish between the types or severity of chronic diseases; future research could incorporate disease characteristics, biological markers, and intervention trials to further explore the mechanisms of the functional-emotional pathway and its potential for intervention.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOn the basis of a longitudinal cross-lagged network model, this study systematically elucidated the time series dynamic relationship between impaired ADL function and depressive symptoms in older adults with multimorbidity. The results indicate that complex instrumental activities of daily living (such as Housework and Money management) occupy a central position in the network; impairment in these functions precedes the onset of depressive symptoms in the time series, suggesting that such functional impairments may serve as early predictors of changes in mental health. Gender-stratified analysis further revealed that impairment in basic activities of daily living (BADL) has a more widespread impact on mental health in women, whereas limitations in instrumental activities of daily living (IADL) (such as Housework) are more likely to trigger negative emotional responses in men, suggesting significant gender heterogeneity in the functional-emotional pathway. This finding suggests that in health management and intervention practices for individuals with multiple comorbidities, early functional monitoring and risk assessment should be prioritized. Strategies should be tailored to gender-specific characteristics while multidisciplinary resources, including nursing, rehabilitation, and psychological and social support, should be integrated to achieve synergistic interventions that maintain functional capacity and promote mental health.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCHARLS \u0026nbsp; Chinese Health and Retirement Longitudinal Study\u003c/p\u003e\n\u003cp\u003eDS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Depressive symptoms\u003c/p\u003e\n\u003cp\u003eADL \u0026nbsp; \u0026nbsp; \u0026nbsp; Activities of daily living\u003c/p\u003e\n\u003cp\u003eCLPN \u0026nbsp; \u0026nbsp; \u0026nbsp;Cross-lagged panel network\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are publicly available from the China Health and Retirement Longitudinal Study (CHARLS) repository.\u003c/p\u003e\n\u003cp\u003eThe specific datasets include:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eChina Health and Retirement Longitudinal Study (CHARLS), Wave 4 (2018):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ehttps://charls.charlsdata.com/pages/Data/2018-charls-wave4/zh-cn.html\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eChina Health and Retirement Longitudinal Study (CHARLS), Wave 5 (2020):\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ehttps://charls.charlsdata.com/pages/Data/2020-charls-wave5/zh-cn.html\u003c/p\u003e\n\u003cp\u003eThe data are publicly available to researchers upon registration via the official CHARLS website.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJiahui Ding is the first author and made significant contributions to the experimental design, data processing, and analysis, and participated in the drafting of the manuscript. Xiaojin Hu and Meiling Sun contributed to data analysis and reviewed and revised the initial draft. Shanshan Ge, as the corresponding author, led the overall research direction, supervised the entire study process, and approved the final manuscript. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research project is funded under the 2025 Shanxi Province Major Special Research Programme on Public Administration (Grant No. SXSGGGLYB2517).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKehoe, B. \u003cem\u003eet al.\u003c/em\u003e The effect of participating in MedEx Wellness, a community-based chronic disease exercise rehabilitation programme, on physical, clinical and psychological health: A study protocol for a cohort trial. \u003cem\u003eContemp. Clin. Trials Commun.\u003c/em\u003e \u003cstrong\u003e19\u003c/strong\u003e, 100591 (2020).\u003c/li\u003e\n\u003cli\u003eBoyd, C. 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Med.\u003c/em\u003e\u003cstrong\u003e135\u003c/strong\u003e, 260\u0026ndash;265 (2017).\u003c/li\u003e\n\u003c/ol\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Activities of daily living, Depressive symptoms, Cross-lagged panel network, Longitudinal analysis, Middle-aged and elderly Chinese individuals","lastPublishedDoi":"10.21203/rs.3.rs-9176515/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9176515/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study was based on the 2018 and 2020 rounds of the China Health and Retirement Longitudinal Study (CHARLS). A total of 5,850 middle-aged and elderly people aged 50 years and above with two or more chronic diseases were included. A cross-lagged panel network model was used to analyse the longitudinal predictive relationship between activities of daily living (ADL) and depressive symptoms (DS), stratified by sex. The results revealed that ADL functional limitations were used mainly as predictors, whereas depressive symptoms were mostly in the affected position. Among them, \"Housework\" (A7) and \"Money management\" (A12) had strong outwards predictive effects, especially with depression symptoms such as \"Effortlessness\" (D6) and \"Inertia\" (D7). A gender comparison revealed that basic ADL nodes such as \"Dressing\" (A1) and \"Getting up\" (A4) were more predictive in the female network, whereas \"Housework\" (A7) was the core predictive node in the male network. The network edge weights and node centrality are estimated with acceptable accuracy and stability. This study highlights that impaired functioning in complex instrumental daily activities (e.g., housework, money management) may be a prodromal indicator of depressive symptoms in middle-aged and older adults with multimorbidity. Gender heterogeneity exists in the association path between functional status and depressive symptoms, suggesting that targeted strategies should be implemented according to gender characteristics in interventions. Early monitoring of functional status is helpful for identifying and preventing mental health risks.","manuscriptTitle":"Bidirectional Associations between Daily Activity Limitations and Depressive Symptoms among Middle-aged and Older Adults with Multimorbidity in China: A Cross-Lagged Panel Network Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 16:21:01","doi":"10.21203/rs.3.rs-9176515/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-03T13:08:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-02T06:21:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-30T10:51:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-27T07:53:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-27T07:42:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"090fd239-648d-438b-9283-4f6c447a1a5c","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65681830,"name":"Health sciences/Diseases"},{"id":65681831,"name":"Health sciences/Health care"},{"id":65681832,"name":"Biological sciences/Psychology"},{"id":65681833,"name":"Social science/Psychology"},{"id":65681834,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-04-09T16:21:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 16:21:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9176515","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9176515","identity":"rs-9176515","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}