Cognitive Trajectories in Subjective Cognitive Decline: Identifying Modifiable Risks and Developing a Web-Based Assessment Tool for Personalized Prevention

Cognitive Trajectories in Subjective Cognitive Decline: Identifying Modifiable Risks and Developing a Web-Based Assessment Tool for Personalized Prevention | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Cognitive Trajectories in Subjective Cognitive Decline: Identifying Modifiable Risks and Developing a Web-Based Assessment Tool for Personalized Prevention xi wen, dan xu, huiling li, taomei zhang, fengmei tian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8588528/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Introduction: The escalating global prevalence of dementia poses a significant public health challenge, underscoring the urgent need for effective early prevention. Subjective Cognitive Decline (SCD) is increasingly recognized as a critical pre-clinical stage of dementia; however, the longitudinal course of cognition in individuals with SCD is markedly heterogeneous. While advanced age is a primary determinant, the influence of modifiable risk factors on these divergent trajectories remains poorly understood. This investigation aimed to identify distinct patterns of cognitive decline among older adults with SCD and evaluate associated modifiable risk factors, with the ultimate goal of translating these empirical findings into a practical risk assessment tool for early stratification. Methods This study analyzed five waves of data (2011–2020) from 3097 older adults with SCD in the CHARLS. We employed latent class growth analysis (LCGA) to identify distinct cognitive trajectories and subsequently used multinomial logistic regression to evaluate the modifiable risk factors associated with these patterns.Based on the identified predictors, a web-based risk assessment tool was constructed to facilitate personalized risk profiling. Results Three distinct cognitive trajectories were identified: a Stable group (38.3%), a Slow Decline group (29.8%), and a Rapid Decline group (31.9%). Compared to the Stable group, factors significantly associated with a higher likelihood of belonging to the Rapid Decline group included disability (OR = 1.441, 95%CI: 1.128, 1.841), underweight (OR = 1.661, 95%CI: 1.130, 2.441) or obesity (OR = 1.337, 95%CI: 1.079, 1.658), drinking (OR = 1.326, 95%CI: 1.033, 1.701), smoking (OR = 1.417, 95%CI: 1.153, 1.740), depression (OR = 1.419, 95%CI: 1.143, 1.762), IADL impairment (OR = 5.523, 95%CI: 3.016, 10.115), excessive exercise (OR = 1.562, 95%CI: 1.140, 2.141), fall down (OR = 1.29, 95%CI: 1.005, 1.656), and insufficient night sleep (OR = 1.484, 95%CI: 1.190, 1.852). Conversely, male (OR = 0.525, 95%CI: 0.403, 0.686), higher education (OR = 0.115, 95%CI: 0.049, 0.267), physical activity (OR = 0.391, 95%CI: 0.263, 0.580), social activity (OR = 0.457, 95%CI: 0.265, 0.789), brain activity (OR = 0.405, 95%CI: 0.306, 0.537), voluntary activity (OR = 0.602, 95%CI: 0.385, 0.940), internet use (OR = 0.355, 95%CI: 0.262, 0.481), and indoor tidiness (OR = 0.598, 95%CI: 0.489, 0.732) were associated with a lower likelihood of rapid cognitive decline. Conclusions This study reveals that cognitive progression in SCD is heterogeneous and significantly influenced by modifiable factors. To bridge the gap between research and practice, we translated these findings into a user-friendly online risk assessment tool. This instrument allows clinicians and the public to visualize individual cognitive trajectories and identify specific targets for intervention. Ultimately, this risk-based approach supports proactive health management and aims to mitigate the public health burden of dementia. Subjective Cognitive Decline Cognitive Trajectories Latent Class Growth Analysis Risk Factors Online Risk Assessment Tool Personalized Prevention Figures Figure 1 Introduction Cognitive impairment and dementia represent a growing global public health challenge, with an increasing number of affected individuals, particularly in rapidly aging populations worldwide[1]. The substantial socioeconomic burden of these conditions, combined with limited treatment options, highlights the urgent need to identify risk factors and implement preventive measures[2]. Subjective Cognitive Decline (SCD), defined by self-reported concerns about worsening memory or confusion without objective cognitive impairment on standardized tests, represents a critical preclinical stage of dementia, particularly Alzheimer's Disease (AD)[3]. As one of the earliest symptomatic indicators of AD, SCD carries substantial prognostic significance, with affected individuals showing increased likelihood of progression to Mild Cognitive Impairment (MCI) and eventual dementia[4]. However, the longitudinal patterns of cognitive function in SCD populations remain poorly characterized. Cognitive aging exhibits marked heterogeneity, manifesting as diverse trajectories that include stable performance as well as varying rates of decline. Importantly, many individuals with SCD never develop more severe cognitive impairment, instead maintaining stable function over time[5]. This variability highlights the importance of characterizing distinct progression patterns for improved prediction and intervention. Although studies have identified clinical features of SCD—termed "SCD plus" (including subjective memory decline, later symptom onset, associated concerns, or informant confirmation)—that predict higher progression risk to objective cognitive impairment or preclinical AD, investigations targeting modifiable risk factors for this transition remain strikingly limited[6]. The global burden of dementia could be mitigated by addressing its risk factors[2]. While advanced age is the strongest non-modifiable risk factor, many cases arise from modifiable influences, including lifestyle, health, and psychosocial factors that offer preventive opportunities[7, 8]. Understanding how these factors differentially shape cognitive trajectories in SCD is crucial for developing targeted interventions[9]. Existing research on cognitive decline often relies on cross-sectional designs, which cannot capture temporal dynamics. Many studies also oversimplify trajectories by assuming uniform decline, masking the heterogeneity of progression and the shifting impact of modifiable factors[10, 11]. These limitations impede risk stratification and personalized intervention strategies. To address these gaps, we aimed to utilize longitudinal cohort data from the China Health and Retirement Longitudinal Study (CHARLS)[12] to identify heterogeneous trajectories of cognitive function changes in older adults with SCD through latent class growth analysis (LCGA). Multinomial logistic regression analysis was employed to explore modifiable risk factors associated with distinct trajectories, including sociodemographic characteristics, disease and functional status, lifestyle factors, and emotional states. Additionally, to enhance the clinical utility and translational impact of our findings, we aimed to develop and validate an online, interactive risk assessment tool based on the identified modifiable risk factors and their associations with cognitive trajectories. Methods Study Design and Data Source This longitudinal study utilized data from the CHARLS[13], a nationally representative survey of adults aged 45 and older in China. The CHARLS project, led by the National School of Development at Peking University, employs a multi-stage probability-proportionate-to-size (PPS) sampling strategy, covering 28 provinces and autonomous regions. The survey design is benchmarked against leading international aging studies, such as the Health and Retirement Study (HRS) and the English Longitudinal Study of Ageing (ELSA). Ethical approval was granted by the Institutional Review Board of Peking University (IRB00001052-11015), and written informed consent was obtained from all participants. For this analysis, we used data from five waves collected in 2011 (baseline), 2013, 2015, 2018, and 2020 to examine long-term cognitive changes. Study Participants Participants were older adults with SCD from the 2011 baseline survey. Inclusion criteria were: (1) aged 60 years or older at baseline; and (2) self-reported memory decline, defined by answering "fair" or "poor" to the question, "How would you rate your memory at present? Would you say it is excellent, very good, good, fair, or poor". Exclusion criteria were: (1) a baseline diagnosis of dementia or cognitive impairment; (2) missing data on key variables; and (3) loss to follow-up or death during the study period. After applying these criteria, a final sample of 3097 individuals was included in the analysis, a size sufficient for the planned statistical models. Measures Variables were selected and defined based on the healthy aging framework, considering previous research and data availability. All variables were self-reported or proxy-reported and collected repeatedly across each follow-up survey wave. Cognitive Assessment The primary outcome was cognitive function, assessed at each of the five waves. The composite cognitive score (range: 0–31 points)[14] was derived from two domains: Episodic Memory (0–20 points): This included immediate recall (recalling 10 words immediately, 0–10 points) and delayed recall (recalling the same words after a delay, 0–10 points). Mental Status (0–11 points): This was assessed through temporal orientation (date, day of the week, season; 0–5 points), serial subtraction (subtracting 7 from 100 five times; 0–5 points), and drawing test (copying two overlapping pentagons; 0–1 point). The construction of the cognitive function variables is summarized in Table 1 . Table 1 Cognitive Function Assessment Summary Cognitive Function Domain Measurement Method Score Range Episodic Memory Immediate Recall 0–10 Delayed Recall 0–10 Total Score 0–20 Mental Status Temporal orientation 0–5 Serial subtraction 0–5 Drawing Test 0–1 Total Score 0–11 Global Cognitive Score 0–31 Covariates Independent variables included four dimensions: sociodemographic characteristics, disease and functional status, lifestyle, and emotion. Sociodemographic Characteristics: These included age (continuous), gender (male/female), marital status (married/cohabiting vs. others), and education level (illiterate, primary school, middle school, high school and above). Health and Functional Status: This domain included disability (any physical or sensory impairment; yes/no), chronic disease burden (0, 1, or ≥ 2 conditions), Activities of Daily Living (ADL) impairment (difficulty with any of 6 basic tasks; yes/no), Instrumental Activities of Daily Living (IADL) score (continuous, 0–5), and history of falls in the past two years (yes/no). Lifestyle Factors: These were smoking status (ever/current vs. never), alcohol consumption (current vs. none), Body Mass Index (BMI, categorized as underweight < 18.5, normal 18.5–23.9, or overweight/obese ≥ 24.0 kg/m²), exercise intensity (none, moderate, or excessive), participation in social, intellectual, physical, and voluntary activities (all binary yes/no), internet use (yes/no), and indoor tidiness (tidy vs. fair/poor). Sleep patterns were assessed via nap duration (> 60 vs. ≤60 minutes) and night sleep duration (continuous). Emotional Status: Depression was measured using the 10-item Center for Epidemiologic Studies Depression Scale (CES-D-10), with a score > 11 indicating depressive symptoms (yes/no). Statistical Analysis Data cleaning and management were performed using Stata MP 17.0. All subsequent statistical analyses were conducted using Python 3.8 and R 4.1.0. Descriptive statistics were used to summarize baseline characteristics; group comparisons were made using Chi-square tests for categorical variables and ANOVA or Kruskal-Wallis H tests for continuous variables, as appropriate. Latent class growth analysis (LCGA), implemented with the clam package in R, was used to identify distinct trajectories of cognitive function over the five waves. The optimal number of trajectories was determined based on the lowest Bayesian Information Criterion (BIC) and adjusted BIC (aBIC), an average posterior probability (AvePP) > 0.70 for classification accuracy, and a class membership proportion > 5%. Following the identification of trajectories, multinomial logistic regression was performed using the statsmodels library in Python to examine the association between baseline risk factors and membership in the identified cognitive trajectory groups. The "Stable" trajectory group was set as the reference category. A two-tailed p-value < 0.05 was considered statistically significant for all analyses. To assess the robustness of the identified cognitive trajectory classifications, a series of sensitivity analyses were conducted. First, missing values were handled using multiple imputation, after which latent class growth analysis (LCGA) was re-estimated to examine whether the number and shape of cognitive trajectories were consistent with the main analysis. Second, growth mixture models (GMM) were fitted using the multiply imputed datasets to evaluate the stability of trajectory classification under a more flexible modeling framework allowing within-class heterogeneity. Third, GMM was additionally conducted after handling missing values using mean or mode imputation to assess the potential influence of alternative missing data strategies. Model selection in all sensitivity analyses was based on comparisons of AIC, BIC and log-likelihood values across models with two to six classes. Development of the Online Risk Assessment Tool Based on the identified cognitive trajectories and the multinomial logistic regression model (Table 7 ), an interactive online risk assessment tool was developed to facilitate personalized risk prediction. The tool was implemented using standard web technologies (HTML, CSS, and JavaScript) to ensure broad accessibility and ease of use. Results Identification of Cognitive Trajectories To identify distinct patterns of cognitive change over the 10-year follow-up period, we employed latent class growth analysis (LCGA). Models with two to six classes were fitted and compared. Based on model fit indices, the three-class model demonstrated the optimal balance of fit and parsimony, as indicated by the lowest Akaike Information Criterion (AIC = 58654.415) and Bayesian Information Criterion (BIC = 58730.173) (Table 2 ). The analysis identified three distinct cognitive trajectories (Fig. 1 ): Stable Group (n = 1186; 38.3%): This group exhibited high baseline cognitive scores and maintained a relatively stable cognitive function throughout the follow-up period, showing only a very slight decline. Slow Decline Group (n = 922; 29.8%): This group started with moderate cognitive scores and experienced a gradual but steady decline over time. Rapid Decline Group (n = 989; 31.9%): This group was characterized by a significantly steeper rate of cognitive decline, with cognitive scores falling substantially below the other two groups over the study period.Notably, participants classified into the Rapid Decline trajectory demonstrated the highest baseline cognitive scores but experienced the steepest decline over time. This pattern may partially reflect regression to the mean, a statistical phenomenon commonly observed in longitudinal cognitive studies, particularly when baseline performance varies substantially across individuals. Table 2 Model Fit Indices for Latent Class Growth Analysis Model AIC BIC Log-Likelihood 2 58871.704 58924.153 -29426.852 3 58654.415* 58730.173* -29314.207* 4 58887.661 58986.731 -29426.830 5 58895.684 59018.064 -29426.841 6 58903.975 59049.665 -29426.987 *Note: Indicates the selected optimal model. Sensitivity Analyses of Cognitive Trajectorie Sensitivity analyses were performed to evaluate the robustness of the cognitive trajectory classification under different modeling strategies and missing data handling approaches. After multiple imputation of missing values, LCGA models with two to six classes were refitted. Model fit comparisons indicated that the three-class solution demonstrated the optimal balance between goodness of fit and parsimony, as evidenced by the lowest AIC, BIC, and highest log-likelihood values (Table 3 ). This analysis consistently identified three cognitive trajectories—Stable, Slow Decline, and Rapid Decline—which were highly comparable to those observed in the main analysis. Table 3 Model Fit Indices For LCGA Of Cognitive Trajectories Using Multiple Imputation Model AIC BIC Log-Likelihood 2 93695.91 93756.29 -46837.96 3 93587.34* 93677.91* -46778.67* 4 93598.55 93712.32 -46779.87 5 93613.01 93763.97 -46781.51 6 93620.81 93801.96 -46780.41 Using the same multiply imputed datasets, GMM were subsequently estimated. Comparison of model fit indices similarly supported a three-class solution as the optimal model (Table 4 ). The trajectory patterns derived from GMM closely resembled those identified by LCGA in terms of both trajectory number and overall cognitive change trends. Table 4 Model Fit Indices For GMM Of Cognitive Trajectories Using Multiple Imputation Model AIC BIC Log-Likelihood 2 93590.48 93662.94 -46783.24 3 93511.48* 93614.13* -46738.74* 4 93610.48 93743.32 -46783.24 5 93624.7 93787.73 -46785.35 6 93667.5 93860.72 -46801.75 Furthermore, when missing values were handled using mean or mode imputation, GMM analyses again favored a three-class solution based on model fit criteria (Table 5 ). The identified trajectories remained consistent in shape and relative classification, indicating that the main findings were not materially influenced by the choice of missing data handling method or trajectory modeling approach. Table 5 Model Fit Indices For GMM Of Cognitive Trajectories Using Mean Or Mode Imputation Model AIC BIC Log-Likelihood 2 93590.48 93662.94 -46783.24 3 93511.48* 93614.13* -46738.74* 4 93610.48 93743.32 -46783.24 5 93624.7 93787.73 -46785.35 6 93667.5 93860.72 -46801.75 Overall, across all sensitivity analyses, the number and general patterns of cognitive trajectories remained stable, supporting the robustness of the trajectory classification results. Baseline Characteristics of Trajectory Groups The baseline characteristics of the participants differed significantly across the three trajectory groups (Table 6 ). Statistically significant differences (P < 0.05) were observed for most variables, including age, gender, marital status, education level, disability, ADL and IADL function, BMI, lifestyle activities (brain, physical, social, voluntary), sleep duration, smoking, depression, history of falls, internet use, and indoor tidiness. Notably, there were no significant group differences in the burden of chronic diseases (P = 0.624). The Rapid Decline group had a higher proportion of males, individuals with higher education, and those who engaged in protective lifestyle behaviors compared to the other groups, and also had the highest mean cognitive score at baseline. Table 6 Baseline Characteristics of Participants by Cognitive Trajectory Group Stable (n = 1186) Slow Decline (n = 922) Rapid Decline (n = 989) Total (N = 3097) P-value Sociodemographic Age, mean (SD) 67.0 (5.4) 65.3 (4.7) 66.2 (5.1) 66.2 (5.2) < .001 Gender, n (%) < .001 Female 579 (48.8) 583 (63.2) 352 (35.6) 1,514 (48.9) Male 607 (51.2) 339 (36.8) 637 (64.4) 1,583 (51.1) Marital Status, n (%) < .001 Not Married/ Cohabiting 215 (18.1) 157 (17.0) 116 (11.7) 488 (15.8) Married 971 (81.9) 765 (83.0) 873 (88.3) 2,609 (84.2) Education Level, n (%) < .001 Illiterate 670 (56.5) 747 (81.1) 248 (25.1) 1,665 (53.8) Primary school 359 (30.3) 122 (13.2) 367 (37.1) 848 (27.4) Middle school 118 (9.9) 36 (3.9) 239 (24.2) 393 (12.7) High school or above 39 (3.3) 16 (1.7) 135 (13.7) 190 (6.1) Health & Functional Status Disability, n (%) 287 (24.2) 244 (26.5) 167 (16.9) 698 (22.6) < .001 Chronic Disease Burden, n (%) 0.624 0 296 (25.0) 210 (22.8) 239 (24.2) 745 (24.1) 1 343 (28.9) 278 (30.2) 273 (27.6) 894 (28.9) ≥ 2 547 (46.1) 434 (47.1) 477 (48.2) 1,458 (47.1) ADL Impairment, mean (SD) 0.5 (1.0) 0.7 (1.2) 0.4 (0.7) 0.6 (1.0) < .001 IADL Impairment, mean (SD) 0.5 (1.1) 0.8 (1.3) 0.3 (0.7) 0.5 (1.1) < .001 History of Falls, n (%) 220 (18.5) 212 (23.0) 173 (17.5) 605 (19.5) 0.005 Lifestyle Factors Smoking Status (Ever/Current), n (%) 521 (43.9) 339 (36.8) 488 (49.3) 1,348 (43.5) < .001 Alcohol Consumption (Current), n (%) 202 (17.0) 138 (15.0) 187 (18.9) 527 (17.0) 0.073 BMI Category, n (%) < .001 Underweight (< 18.5) 97 (8.2) 96 (10.5) 51 (5.2) 244 (7.9) Normal (18.5–23.9) 740 (62.8) 587 (64.0) 617 (62.6) 1,944 (63.1) Overweight/Obese (≥ 24.0) 341 (28.9) 234 (25.5) 318 (32.3) 893 (29.0) Exercise Intensity, n (%) < .001 None 765 (64.5) 550 (59.7) 581 (58.7) 1,896 (61.2) Moderate 265 (22.3) 219 (23.8) 285 (28.8) 769 (24.8) Excessive 156 (13.2) 153 (16.6) 123 (12.4) 432 (13.9) Intellectual Activity, n (%) 194 (16.4) 84 (9.1) 263 (26.6) 541 (17.5) < .001 Social Activity, n (%) 409 (34.5) 295 (32.0) 370 (37.4) 1,074 (34.7) 0.045 Voluntary Activity, n (%) 56 (4.7) 38 (4.1) 65 (6.6) 159 (5.1) 0.038 Internet Use, n (%) 5 (0.4) 2 (0.2) 17 (1.7) 24 (0.8) < .001 Indoor Tidiness (Tidy), n (%) 720 (60.7) 531 (57.6) 728 (73.6) 1,979 (63.9) 7 hours, n (%) 325 (27.4) 275 (29.9) 236 (23.9) 836 (27.0) 0.012 Nap Duration > 60 min, n (%) 185 (15.9) 132 (14.7) 163 (16.8) 480 (15.8) 0.464 Emotional Status Depressive Symptoms, n (%) 428 (38.2) 394 (45.8) 288 (29.9) 1,110 (37.7) < .001 Baseline Cognition Cognitive Score, mean (SD) 14.4 (4.2) 11.0 (4.0) 17.8 (4.0) 14.5 (4.9) < .001 Data are presented as mean (standard deviation) for continuous variables and n (%) for categorical variables. P-values were derived from ANOVA or Kruskal-Wallis H tests for continuous variables and Chi-square tests for categorical variables. ADL = Activities of Daily Living; IADL = Instrumental Activities of Daily Living; BMI = Body Mass Index. Factors Associated with Cognitive Trajectories Multinomial logistic regression was used to identify factors associated with membership in the decline trajectories, with the Stable group as the reference category (Table 7 ). Online Risk Assessment Tool Following the multinomial logistic regression analysis, an interactive online risk assessment tool was developed to facilitate the practical application of our findings. This assessment can be carried out using our Cognitive Risk Assessment Tool. The tool then calculates personalized probabilities for an individual belonging to the Stable, Slow Decline, or Rapid Decline cognitive trajectories based on the coefficients derived from our model (Table 7 ). It also provides tailored recommendations for risk mitigation.A detailed description of the tool's implementation, including its underlying algorithms and user interface, is provided in Supplementary Material Appendix A. Predictors of the Rapid Decline Trajectory Compared to the Stable group, several factors significantly increased the odds of belonging to the Rapid Decline group. The most potent predictor was baseline IADL impairment, which increased the odds more than fivefold (OR = 5.52, 95% CI: 3.02–10.12). Other significant risk factors included being underweight (OR = 1.66, 95% CI: 1.13–2.44), engaging in excessive physical exercise (OR = 1.56, 95% CI: 1.14–2.14), having insufficient night sleep (OR = 1.48, 95% CI: 1.19–1.85), having a disability (OR = 1.44, 95% CI: 1.13–1.84), having depressive symptoms (OR = 1.42, 95% CI: 1.14–1.76), smoking (OR = 1.42, 95% CI: 1.15–1.74), being overweight/obese (OR = 1.34, 95% CI: 1.08–1.66), current alcohol consumption (OR = 1.33, 95% CI: 1.03–1.70), and a history of falls (OR = 1.29, 95% CI: 1.01–1.66). Conversely, a number of factors were strongly protective against rapid decline. The most significant protective factors were internet use (OR = 0.36, 95% CI: 0.26–0.48) and higher education (OR = 0.12, 95% CI: 0.05–0.27). Other protective factors included participation in physical activity (OR = 0.39, 95% CI: 0.26–0.58), intellectual activity (OR = 0.41, 95% CI: 0.31–0.54), social activity (OR = 0.46, 95% CI: 0.27–0.79), and voluntary activity (OR = 0.60, 95% CI: 0.39–0.94). Additionally, being male (OR = 0.53, 95% CI: 0.40–0.69) and maintaining a tidy home (OR = 0.60, 95% CI: 0.49–0.73) were associated with a lower likelihood of being in the Rapid Decline group. Predictors of the Slow Decline Trajectory The Slow Decline trajectory shared some risk factors with the rapid decline group but also displayed a unique profile. The strongest predictor was eating more than three meals a day (OR = 2.07, 95% CI: 1.32–3.24). Other significant risk factors included IADL impairment (OR = 2.09, 95% CI: 1.15–3.80), no physical exercise (OR = 1.46, 95% CI: 1.20–1.78), disability (OR = 1.43, 95% CI: 1.14–1.79), excessive physical exercise (OR = 1.37, 95% CI: 1.02–1.84), smoking (OR = 1.26, 95% CI: 1.05–1.51), and insufficient night sleep (OR = 1.25, 95% CI: 1.02–1.53). Protective factors against slow decline included higher education (OR = 0.16, 95% CI: 0.07–0.34), maintaining a tidy home (OR = 0.66, 95% CI: 0.55–0.80), participation in intellectual activities (OR = 0.68, 95% CI: 0.55–0.85), internet use (OR = 0.72, 95% CI: 0.57–0.90), and participation in physical (OR = 0.74, 95% CI: 0.56–0.99) and social activities (OR = 0.65, 95% CI: 0.43–0.97).All reported associations were statistically significant (P < 0.05). Table 7 Multinomial Logistic Regression Analysis of Factors Associated with Cognitive Trajectories Risk Factor Slow Decline Rapid Decline OR 95% CI P-value OR 95% CI P-value Male 0.87 [0.70,1.08] 0.198 0.53 [0.40,0.69] < 0.001 Married 1.14 [0.95,1.36] 0.168 0.89 [0.73,1.10] 0.277 Higher Education 0.16 [0.07,0.34] < 0.001 0.12 [0.05,0.27] < 0.001 Disability 1.43 [1.14,1.79] 0.002 1.44 [1.13,1.84] 0.004 ADL Impairment 1.36 [0.65,2.84] 0.417 1.20 [0.56,2.58] 0.646 Underweight 1.40 [0.97,2.02] 0.073 1.66 [1.13,2.44] 0.010 Overweight 1.15 [0.95,1.38] 0.158 1.34 [1.08,1.66] 0.008 Physical Activity 0.74 [0.56,0.99] 0.044 0.39 [0.26,0.58] < 0.001 Social Activity 0.65 [0.43,0.97] 0.037 0.46 [0.27,0.79] 0.005 Brain Activity 0.68 [0.55,0.85] 0.001 0.41 [0.31,0.54] < 0.001 Voluntary Activity 0.71 [0.49,1.04] 0.082 0.60 [0.39,0.94] 0.026 Alcohol Consumption 1.18 [0.95,1.47] 0.126 1.33 [1.03,1.70] 0.027 Smoking 1.26 [1.05,1.51] 0.015 1.42 [1.15,1.74] 0.001 Meals 2.07 [1.32,3.24] 0.002 1.53 [0.93,2.50] 0.092 Depression 1.21 [0.99,1.48] 0.057 1.42 [1.14,1.76] 0.002 IADL Impairment 2.09 [1.15,3.80] 0.016 5.52 [3.02,10.12] < 0.001 Exercise (None) 1.46 [1.20,1.78] < 0.001 1.22 [0.98,1.53] 0.077 Exercise (Excessive) 1.37 [1.02,1.84] 0.037 1.56 [1.14,2.14] 0.006 Fall down 1.03 [0.81,1.30] 0.817 1.29 [1.01,1.66] 0.046 Internet use 0.72 [0.57,0.90] 0.005 0.36 [0.26,0.48] < 0.001 Tidy home 0.66 [0.55,0.80] < 0.001 0.60 [0.49,0.73] < 0.001 Insufficient Night Sleep 1.25 [1.02,1.53] 0.029 1.48 [1.19,1.85] < 0.001 Discussion Importantly, the robustness of these trajectory patterns was supported by extensive sensitivity analyses using alternative trajectory modeling strategies and different missing data handling methods, all of which consistently identified the same three cognitive trajectories.This study is among the first to use a latent class growth analysis on a large, nationally representative longitudinal cohort to characterize the heterogeneity of cognitive aging in older adults with SCD. Our primary finding is the identification of three distinct cognitive trajectories over a 9-year period: a "Stable" group, a "Slow Decline" group, and a "Rapid Decline" group. This result empirically validates the long-held clinical observation that SCD is not a monolithic condition but a syndrome with highly variable outcomes. Crucially, our analysis revealed that membership in these trajectories is significantly associated with a wide array of modifiable health, lifestyle, and psychosocial factors, providing critical insights for early risk stratification and targeted prevention. The risk factors for accelerated cognitive decline identified in this study are consistent with and extend previous research findings. Impaired instrumental activities of daily living (IADL) demonstrated a strong association with cognitive decline[15, 16], suggesting a potential harmful feedback loop: subtle cognitive deficits impair the ability to complete complex daily tasks, and the resulting functional dependency and reduced activity participation further accelerate cognitive deterioration. Our results also confirm the importance of established vascular and lifestyle risk factors[17], including smoking[18], insufficient sleep[19], excessive sleep[20], depressive symptoms[21], and a history of falls[22], echoing recommendations from major public health authorities such as the Lancet Commission on Dementia Prevention. Notably, we observed a U-shaped association between body mass index (BMI) and cognitive decline[23]: both underweight and overweight/obesity were risk factors for the rapid decline trajectory. This underscores the importance of maintaining a healthy weight, as underweight may reflect underlying frailty or malnutrition, while obesity is a well-established driver of metabolic and vascular diseases that impair brain health. The association between excessive physical exercise and cognitive decline should be interpreted with caution[24]: this may reflect reverse causality, whereby individuals with early neurodegenerative changes exhibit compulsive behaviors or impaired exercise judgment; it may also indicate that inappropriate high-intensity exercise in older adults increases systemic stress or injury risk, thereby negatively affecting cognition. Conversely, our analysis identified a range of protective factors, highlighting the critical role of cognitive reserve and an active lifestyle in maintaining cognitive resilience[25]. Higher education, a classic proxy for cognitive reserve, had a significant protective effect against both slow and rapid cognitive decline[26, 27]. More importantly, our findings indicate that an active lifestyle confers substantial and cumulative benefits. Engagement in diverse activities—including intellectual, social, physical, and voluntary activities—was consistently associated with a lower risk of cognitive decline, suggesting that a multi-domain engagement strategy is most beneficial for maintaining and preserving cognitive function[28]. The protective effect of internet use can be regarded as a modern embodiment of this principle, potentially reflecting a combination of cognitive engagement, access to health information, and enhanced social connectivity[29, 30]. Furthermore, a novel association was observed between a tidy home environment and reduced risk of cognitive decline, which we interpret as a behavioral marker of preserved executive function, conscientiousness, and the capacity to maintain organization in daily life—itself partly reflective of underlying brain health[31]. Our findings provide crucial evidence for identifying individuals at high risk of rapid cognitive decline and offer specific targets for developing personalized, multidimensional interventions. The development of an interactive online risk assessment tool further enhances the translational impact of this research. This tool operationalizes our robust statistical model, providing a user-friendly interface for clinicians to engage patients in discussions about their individual cognitive trajectory risks and for individuals to proactively manage their modifiable risk factors. It bridges the gap between complex statistical findings and practical, personalized health management strategies, empowering both healthcare providers and the public with actionable insights. Clinical Implications and Future Directions A key implication of this study is the potential to shift the clinical paradigm of SCD from passive observation to proactive and personalized risk management. By moving beyond a purely cognitive conceptualization of SCD, our findings provide an empirical basis for identifying individuals at high risk of imminent cognitive decline. The newly developed online risk assessment tool serves as a direct application of this framework. This tool enables clinicians to efficiently stratify patient risk while simultaneously facilitating patient engagement through the visualization of personalized risk profiles. Furthermore, it guides the delivery of targeted, evidence-based lifestyle interventions, ensuring that healthcare resources are allocated to those most likely to benefit from early preventative measures.Future research should focus on the external validation of this online tool in diverse populations to confirm its generalizability and predictive accuracy. Further enhancements could include integrating the tool with electronic health records for seamless clinical workflow, developing mobile health applications for continuous self-monitoring, and incorporating additional biomarkers as they become more readily available and integrated into risk prediction models. The tool also holds significant potential for large-scale public health campaigns aimed at raising awareness about cognitive decline risk and promoting healthy aging behaviors. Limitations Several limitations of this study should be acknowledged. First, the observational design precludes causal inference; thus, all identified factors should be interpreted as correlates rather than direct causes of cognitive decline. Second, SCD and most covariates were self-reported, which may be subject to recall bias and social desirability bias. Third, SCD was defined using a single self-reported item, which, although commonly applied in large population-based studies, may not fully capture the multidimensional SCD-plus construct. In addition, missing data and loss to follow-up may have introduced potential bias. However, sensitivity analyses employing different missing data handling strategies and trajectory modeling approaches yielded highly consistent trajectory classifications, suggesting that the main findings were robust. Nevertheless, healthy survivor bias cannot be completely ruled out. Finally, the absence of biological and neuroimaging markers, such as APOEε4 genotype or brain imaging data, limited further etiological interpretation. Conclusion Our findings demonstrate that a combination of modifiable factors, including physical and mental health status, lifestyle behaviors, and social engagement, are powerful predictors of an individual's long-term cognitive path. These results provide a robust empirical framework for early risk stratification, enabling the identification of individuals with SCD who are at the highest risk for accelerated cognitive decline. The development of an accessible online risk assessment tool further translates these insights into a practical solution for personalized health management. Targeting these specific, modifiable factors through personalized, multi-domain interventions holds significant promise for mitigating dementia risk and promoting healthy cognitive aging. This research underscores the critical public health value of early, evidence-based health management in the preclinical stages of dementia. Declarations Ethics approval and consent to participate Ethical approval for the China Health and Retirement Longitudinal Study (CHARLS) was granted by the Institutional Review Board of Peking University (IRB00001052-11015). All participants in CHARLS provided written informed consent prior to data collection. For the secondary analysis of CHARLS data conducted in this study, ethical approval was obtained from the Ethics Committee of Soochow University (Approval No. SUDA20251015H09). Since this study used de-identified secondary data, the requirement for additional informed consent from individual participants was waived by the ethics committee. For the development and validation of the online risk assessment tool, supplementary ethical approval was obtained (Approval No. SUDA20251015H09), and verbal informed consent was obtained from all participants involved in the tool’s pilot testing, with documentation of consent retained by the research team. Consent for publication All participants in CHARLS consented to the use and publication of their de-identified data for research purposes. No individual personal data or identifying information (e.g., names, contact details) is presented in this manuscript. The authors confirm that all applicable consent for publication has been obtained. Availability of data and materials The CHARLS data used in this study are publicly available upon reasonable request from the CHARLS official website (http://charls.pku.edu.cn/). The de-identified datasets and analytical code generated during the current study are not publicly available due to privacy restrictions but can be obtained from the corresponding author (Huiling Li, Email: [email protected] ) upon reasonable request and approval from the CHARLS data access committee. The online risk assessment tool developed in this study is accessible from the corresponding author upon reasonable request. Competing interests All authors declare no competing interests. No financial, professional, or personal relationships with other individuals or organizations influenced the design of the study, data analysis, interpretation of results, or writing of the manuscript. Funding National Natural Science Foundation of China (72074164), Jiangsu Provincial Graduate Research Innovation Program (KYCX24_3352) Authors' contributions Xi Wen conceived and designed the study, performed the statistical analyses, developed the online risk assessment tool, drafted the manuscript, and revised it critically for important intellectual content. Dan Xu assisted with data cleaning, conducted sensitivity analyses, and contributed to the writing of the “Methods” and “Results” sections. Huiling Li supervised the entire study, provided methodological guidance, secured funding, revised the manuscript extensively, and finalized the submission. Taomei Zhang advised on cognitive assessment measures, reviewed the literature on subjective cognitive decline, and commented on the manuscript. Fengmei Tian assisted with the development of the online tool’s user interface, collected pilot test data. All authors read and approved the final manuscript. Acknowledgements The authors would like to thank the CHARLS research team for providing access to the longitudinal cohort data. We also appreciate the support from the School of Nursing at Soochow University for methodological and technical assistance. Special thanks to the older adults who participated in the pilot testing of the online risk assessment tool, and to the experts who provided feedback during the tool’s validation. References Nichols E, Steinmetz JD, Vollset SE, Fukutaki K, Chalek J, Abd-Allah F, et al. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. The Lancet Public Health. 2022;7:e105–25. https://doi.org/10.1016/S2468-2667(21)00249-8. Livingston G, Huntley J, Liu KY, Costafreda SG, Selbæk G, Alladi S, et al. Dementia prevention, intervention, and care: 2024 report of the Lancet standing Commission. 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IADL for identifying cognitive impairment in chinese older adults: insights from cross-lagged panel network analysis. BMC Geriatr. 2025;25:364. https://doi.org/10.1186/s12877-025-06017-1. Chen J, Park D, Afzal S, Cardin M, Peltier M, Hundal J, et al. Cognitive and functional decline in a psychogeriatric population: a comparative longitudinal analysis of MMSE, MoCA, ADL, and IADL. Am J Geriatr Psychiatry. 2024;32:S128. https://doi.org/10.1016/j.jagp.2024.01.216. Bloomberg M, Muniz-Terrera G, Brocklebank L, Steptoe A. Healthy lifestyle and cognitive decline in middle-aged and older adults residing in 14 european countries. Nat Commun. 2024;15:5003. https://doi.org/10.1038/s41467-024-49262-5. Bloomberg M, Brown J, Gessa GD, Bu F, Steptoe A. Cognitive decline before and after mid-to-late-life smoking cessation: a longitudinal analysis of prospective cohort studies from 12 countries. Lancet Healthy Longev. 2025;6. https://doi.org/10.1016/j.lanhl.2025.100753. Ungvari Z, Fekete M, Lehoczki A, Munkácsy G, Fekete JT, Zábó V, et al. Sleep disorders increase the risk of dementia, alzheimer’s disease, and cognitive decline: a meta-analysis. GeroScience. 2025;47:4899–920. https://doi.org/10.1007/s11357-025-01637-2. Overton M, Sindi S, Basna R, Elmståhl S. Excessive sleep is associated with worse cognition, cognitive decline, and dementia in mild cognitive impairment. Alzheimer’s Dement: Diagn Assess Dis Monit. 2025;17:e70093. https://doi.org/10.1002/dad2.70093. Forbes M, Lotfaliany M, Mohebbi M, Reynolds CF, Woods RL, Orchard S, et al. Depressive symptoms and cognitive decline in older adults. Int Psychogeriatr. 2024;36:1039–50. https://doi.org/10.1017/S1041610224000541. Chantanachai T, Sturnieks DL, Lord SR, Menant J, Delbaere K, Sachdev PS, et al. Cognitive and physical declines and falls in older people with and without mild cognitive impairment: a 7-year longitudinal study. Int Psychogeriatr. 2024;36:306–16. https://doi.org/10.1017/S1041610223000315. Ding Y, Huo J, Cui L, Yang Y, Yang S, Wang L. The inverted U-shaped association between body roundness index and cognitive function among chinese older adults. J Affect Disord. 2026;396:120828. https://doi.org/10.1016/j.jad.2025.120828. Huang Y, Hu B, Liu Y, Xie L-Q, Dai Y, An Y-Z, et al. Excessive vigorous exercise impairs cognitive function through a muscle-derived mitochondrial pretender. Cell Metab. 2025;0. https://doi.org/10.1016/j.cmet.2025.11.002. Stern Y, Albert M, Barnes CA, Cabeza R, Pascual-Leone A, Rapp PR. A framework for concepts of reserve and resilience in aging. Neurobiol Aging. 2023;124:100–3. https://doi.org/10.1016/j.neurobiolaging.2022.10.015. Kim Y, Stern Y, Seo SW, Na DL, Jang J-W, Jang H, et al. Factors associated with cognitive reserve according to education level. Alzheimer’s Dement: J Alzheimer’s Assoc. 2024;20:7686–97. https://doi.org/10.1002/alz.14236. Gamble LD, Clare L, Opdebeeck C, Martyr A, Jones RW, Rusted JM, et al. Cognitive reserve and its impact on cognitive and functional abilities, physical activity and quality of life following a diagnosis of dementia: longitudinal findings from the improving the experience of dementia and enhancing active life (IDEAL) study. Age Ageing. 2025;54:afae284. https://doi.org/10.1093/ageing/afae284. Rosenberg A, Ngandu T, Rusanen M, Antikainen R, Bäckman L, Havulinna S, et al. Multidomain lifestyle intervention benefits a large elderly population at risk for cognitive decline and dementia regardless of baseline characteristics: the FINGER trial. Alzheimer’s Dement: J Alzheimer’s Assoc. 2018;14:263–70. https://doi.org/10.1016/j.jalz.2017.09.006. Cho G, Betensky RA, Chang VW. Internet usage and the prospective risk of dementia: a population-based cohort study. J Am Geriatr Soc. 2023;71:2419–29. https://doi.org/10.1111/jgs.18394. Tsai YI-P, Beh J, Ganderton C, Pranata A. Digital interventions for healthy ageing and cognitive health in older adults: a systematic review of mixed method studies and meta-analysis. BMC Geriatr. 2024;24:217. https://doi.org/10.1186/s12877-023-04617-3. Bogg T, Roberts BW. Conscientiousness and health-related behaviors: a meta-analysis of the leading behavioral contributors to mortality. Psychol Bull. 2004;130:887–919. https://doi.org/10.1037/0033-2909.130.6.887. Additional Declarations No competing interests reported. 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The substantial socioeconomic burden of these conditions, combined with limited treatment options, highlights the urgent need to identify risk factors and implement preventive measures[2].\u003c/p\u003e \u003cp\u003eSubjective Cognitive Decline (SCD), defined by self-reported concerns about worsening memory or confusion without objective cognitive impairment on standardized tests, represents a critical preclinical stage of dementia, particularly Alzheimer's Disease (AD)[3]. As one of the earliest symptomatic indicators of AD, SCD carries substantial prognostic significance, with affected individuals showing increased likelihood of progression to Mild Cognitive Impairment (MCI) and eventual dementia[4]. However, the longitudinal patterns of cognitive function in SCD populations remain poorly characterized. Cognitive aging exhibits marked heterogeneity, manifesting as diverse trajectories that include stable performance as well as varying rates of decline. Importantly, many individuals with SCD never develop more severe cognitive impairment, instead maintaining stable function over time[5]. This variability highlights the importance of characterizing distinct progression patterns for improved prediction and intervention. Although studies have identified clinical features of SCD\u0026mdash;termed \"SCD plus\" (including subjective memory decline, later symptom onset, associated concerns, or informant confirmation)\u0026mdash;that predict higher progression risk to objective cognitive impairment or preclinical AD, investigations targeting modifiable risk factors for this transition remain strikingly limited[6].\u003c/p\u003e \u003cp\u003eThe global burden of dementia could be mitigated by addressing its risk factors[2]. While advanced age is the strongest non-modifiable risk factor, many cases arise from modifiable influences, including lifestyle, health, and psychosocial factors that offer preventive opportunities[7, 8]. Understanding how these factors differentially shape cognitive trajectories in SCD is crucial for developing targeted interventions[9].\u003c/p\u003e \u003cp\u003eExisting research on cognitive decline often relies on cross-sectional designs, which cannot capture temporal dynamics. Many studies also oversimplify trajectories by assuming uniform decline, masking the heterogeneity of progression and the shifting impact of modifiable factors[10, 11]. These limitations impede risk stratification and personalized intervention strategies.\u003c/p\u003e \u003cp\u003eTo address these gaps, we aimed to utilize longitudinal cohort data from the China Health and Retirement Longitudinal Study (CHARLS)[12] to identify heterogeneous trajectories of cognitive function changes in older adults with SCD through latent class growth analysis (LCGA). Multinomial logistic regression analysis was employed to explore modifiable risk factors associated with distinct trajectories, including sociodemographic characteristics, disease and functional status, lifestyle factors, and emotional states. Additionally, to enhance the clinical utility and translational impact of our findings, we aimed to develop and validate an online, interactive risk assessment tool based on the identified modifiable risk factors and their associations with cognitive trajectories.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Data Source\u003c/h2\u003e \u003cp\u003eThis longitudinal study utilized data from the CHARLS[13], a nationally representative survey of adults aged 45 and older in China. The CHARLS project, led by the National School of Development at Peking University, employs a multi-stage probability-proportionate-to-size (PPS) sampling strategy, covering 28 provinces and autonomous regions. The survey design is benchmarked against leading international aging studies, such as the Health and Retirement Study (HRS) and the English Longitudinal Study of Ageing (ELSA). Ethical approval was granted by the Institutional Review Board of Peking University (IRB00001052-11015), and written informed consent was obtained from all participants. For this analysis, we used data from five waves collected in 2011 (baseline), 2013, 2015, 2018, and 2020 to examine long-term cognitive changes.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Participants\u003c/h3\u003e\n\u003cp\u003eParticipants were older adults with SCD from the 2011 baseline survey. Inclusion criteria were: (1) aged 60 years or older at baseline; and (2) self-reported memory decline, defined by answering \"fair\" or \"poor\" to the question, \"How would you rate your memory at present? Would you say it is excellent, very good, good, fair, or poor\". Exclusion criteria were: (1) a baseline diagnosis of dementia or cognitive impairment; (2) missing data on key variables; and (3) loss to follow-up or death during the study period. After applying these criteria, a final sample of 3097 individuals was included in the analysis, a size sufficient for the planned statistical models.\u003c/p\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003eVariables were selected and defined based on the healthy aging framework, considering previous research and data availability. All variables were self-reported or proxy-reported and collected repeatedly across each follow-up survey wave.\u003c/p\u003e\n\u003ch3\u003eCognitive Assessment\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was cognitive function, assessed at each of the five waves. The composite cognitive score (range: 0\u0026ndash;31 points)[14] was derived from two domains:\u003c/p\u003e \u003cp\u003eEpisodic Memory (0\u0026ndash;20 points): This included immediate recall (recalling 10 words immediately, 0\u0026ndash;10 points) and delayed recall (recalling the same words after a delay, 0\u0026ndash;10 points).\u003c/p\u003e \u003cp\u003eMental Status (0\u0026ndash;11 points): This was assessed through temporal orientation (date, day of the week, season; 0\u0026ndash;5 points), serial subtraction (subtracting 7 from 100 five times; 0\u0026ndash;5 points), and drawing test (copying two overlapping pentagons; 0\u0026ndash;1 point).\u003c/p\u003e \u003cp\u003eThe construction of the cognitive function variables is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCognitive Function Assessment Summary\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive Function Domain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeasurement Method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eScore Range\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEpisodic Memory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmediate Recall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelayed Recall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMental Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemporal orientation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSerial subtraction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDrawing Test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal Cognitive Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u0026ndash;31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eIndependent variables included four dimensions: sociodemographic characteristics, disease and functional status, lifestyle, and emotion.\u003c/p\u003e \u003cp\u003eSociodemographic Characteristics: These included age (continuous), gender (male/female), marital status (married/cohabiting vs. others), and education level (illiterate, primary school, middle school, high school and above).\u003c/p\u003e \u003cp\u003eHealth and Functional Status: This domain included disability (any physical or sensory impairment; yes/no), chronic disease burden (0, 1, or \u0026ge;\u0026thinsp;2 conditions), Activities of Daily Living (ADL) impairment (difficulty with any of 6 basic tasks; yes/no), Instrumental Activities of Daily Living (IADL) score (continuous, 0\u0026ndash;5), and history of falls in the past two years (yes/no).\u003c/p\u003e \u003cp\u003eLifestyle Factors: These were smoking status (ever/current vs. never), alcohol consumption (current vs. none), Body Mass Index (BMI, categorized as underweight\u0026thinsp;\u0026lt;\u0026thinsp;18.5, normal 18.5\u0026ndash;23.9, or overweight/obese\u0026thinsp;\u0026ge;\u0026thinsp;24.0 kg/m\u0026sup2;), exercise intensity (none, moderate, or excessive), participation in social, intellectual, physical, and voluntary activities (all binary yes/no), internet use (yes/no), and indoor tidiness (tidy vs. fair/poor). Sleep patterns were assessed via nap duration (\u0026gt;\u0026thinsp;60 vs. \u0026le;60 minutes) and night sleep duration (continuous).\u003c/p\u003e \u003cp\u003eEmotional Status: Depression was measured using the 10-item Center for Epidemiologic Studies Depression Scale (CES-D-10), with a score\u0026thinsp;\u0026gt;\u0026thinsp;11 indicating depressive symptoms (yes/no).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData cleaning and management were performed using Stata MP 17.0. All subsequent statistical analyses were conducted using Python 3.8 and R 4.1.0. Descriptive statistics were used to summarize baseline characteristics; group comparisons were made using Chi-square tests for categorical variables and ANOVA or Kruskal-Wallis H tests for continuous variables, as appropriate.\u003c/p\u003e \u003cp\u003eLatent class growth analysis (LCGA), implemented with the clam package in R, was used to identify distinct trajectories of cognitive function over the five waves. The optimal number of trajectories was determined based on the lowest Bayesian Information Criterion (BIC) and adjusted BIC (aBIC), an average posterior probability (AvePP)\u0026thinsp;\u0026gt;\u0026thinsp;0.70 for classification accuracy, and a class membership proportion\u0026thinsp;\u0026gt;\u0026thinsp;5%.\u003c/p\u003e \u003cp\u003eFollowing the identification of trajectories, multinomial logistic regression was performed using the statsmodels library in Python to examine the association between baseline risk factors and membership in the identified cognitive trajectory groups. The \"Stable\" trajectory group was set as the reference category. A two-tailed p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant for all analyses.\u003c/p\u003e \u003cp\u003eTo assess the robustness of the identified cognitive trajectory classifications, a series of sensitivity analyses were conducted. First, missing values were handled using multiple imputation, after which latent class growth analysis (LCGA) was re-estimated to examine whether the number and shape of cognitive trajectories were consistent with the main analysis. Second, growth mixture models (GMM) were fitted using the multiply imputed datasets to evaluate the stability of trajectory classification under a more flexible modeling framework allowing within-class heterogeneity. Third, GMM was additionally conducted after handling missing values using mean or mode imputation to assess the potential influence of alternative missing data strategies. Model selection in all sensitivity analyses was based on comparisons of AIC, BIC and log-likelihood values across models with two to six classes.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDevelopment of the Online Risk Assessment Tool\u003c/h3\u003e\n\u003cp\u003eBased on the identified cognitive trajectories and the multinomial logistic regression model (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), an interactive online risk assessment tool was developed to facilitate personalized risk prediction. The tool was implemented using standard web technologies (HTML, CSS, and JavaScript) to ensure broad accessibility and ease of use.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of Cognitive Trajectories\u003c/h2\u003e \u003cp\u003eTo identify distinct patterns of cognitive change over the 10-year follow-up period, we employed latent class growth analysis (LCGA). Models with two to six classes were fitted and compared. Based on model fit indices, the three-class model demonstrated the optimal balance of fit and parsimony, as indicated by the lowest Akaike Information Criterion (AIC\u0026thinsp;=\u0026thinsp;58654.415) and Bayesian Information Criterion (BIC\u0026thinsp;=\u0026thinsp;58730.173) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The analysis identified three distinct cognitive trajectories (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e):\u003c/p\u003e \u003cp\u003eStable Group (n\u0026thinsp;=\u0026thinsp;1186; 38.3%): This group exhibited high baseline cognitive scores and maintained a relatively stable cognitive function throughout the follow-up period, showing only a very slight decline.\u003c/p\u003e \u003cp\u003eSlow Decline Group (n\u0026thinsp;=\u0026thinsp;922; 29.8%): This group started with moderate cognitive scores and experienced a gradual but steady decline over time.\u003c/p\u003e \u003cp\u003eRapid Decline Group (n\u0026thinsp;=\u0026thinsp;989; 31.9%): This group was characterized by a significantly steeper rate of cognitive decline, with cognitive scores falling substantially below the other two groups over the study period.Notably, participants classified into the Rapid Decline trajectory demonstrated the highest baseline cognitive scores but experienced the steepest decline over time. This pattern may partially reflect regression to the mean, a statistical phenomenon commonly observed in longitudinal cognitive studies, particularly when baseline performance varies substantially across individuals.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel Fit Indices for Latent Class Growth Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLog-Likelihood\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58871.704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58924.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-29426.852\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58654.415*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58730.173*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-29314.207*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58887.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58986.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-29426.830\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58895.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59018.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-29426.841\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58903.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59049.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-29426.987\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*Note: Indicates the selected optimal model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity Analyses of Cognitive Trajectorie\u003c/h2\u003e \u003cp\u003eSensitivity analyses were performed to evaluate the robustness of the cognitive trajectory classification under different modeling strategies and missing data handling approaches.\u003c/p\u003e \u003cp\u003eAfter multiple imputation of missing values, LCGA models with two to six classes were refitted. Model fit comparisons indicated that the three-class solution demonstrated the optimal balance between goodness of fit and parsimony, as evidenced by the lowest AIC, BIC, and highest log-likelihood values (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This analysis consistently identified three cognitive trajectories\u0026mdash;Stable, Slow Decline, and Rapid Decline\u0026mdash;which were highly comparable to those observed in the main analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel Fit Indices For LCGA Of Cognitive Trajectories Using Multiple Imputation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLog-Likelihood\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93695.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93756.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-46837.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93587.34*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93677.91*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-46778.67*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93598.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93712.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-46779.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93613.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93763.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-46781.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93620.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93801.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-46780.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eUsing the same multiply imputed datasets, GMM were subsequently estimated. Comparison of model fit indices similarly supported a three-class solution as the optimal model (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The trajectory patterns derived from GMM closely resembled those identified by LCGA in terms of both trajectory number and overall cognitive change trends.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel Fit Indices For GMM Of Cognitive Trajectories Using Multiple Imputation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLog-Likelihood\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93590.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93662.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-46783.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93511.48*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93614.13*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-46738.74*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93610.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93743.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-46783.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93624.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93787.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-46785.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93667.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93860.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-46801.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFurthermore, when missing values were handled using mean or mode imputation, GMM analyses again favored a three-class solution based on model fit criteria (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The identified trajectories remained consistent in shape and relative classification, indicating that the main findings were not materially influenced by the choice of missing data handling method or trajectory modeling approach.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel Fit Indices For GMM Of Cognitive Trajectories Using Mean Or Mode Imputation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLog-Likelihood\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93590.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93662.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-46783.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93511.48*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93614.13*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-46738.74*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93610.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93743.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-46783.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93624.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93787.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-46785.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93667.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93860.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-46801.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOverall, across all sensitivity analyses, the number and general patterns of cognitive trajectories remained stable, supporting the robustness of the trajectory classification results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics of Trajectory Groups\u003c/h2\u003e \u003cp\u003eThe baseline characteristics of the participants differed significantly across the three trajectory groups (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Statistically significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were observed for most variables, including age, gender, marital status, education level, disability, ADL and IADL function, BMI, lifestyle activities (brain, physical, social, voluntary), sleep duration, smoking, depression, history of falls, internet use, and indoor tidiness. Notably, there were no significant group differences in the burden of chronic diseases (P\u0026thinsp;=\u0026thinsp;0.624). The Rapid Decline group had a higher proportion of males, individuals with higher education, and those who engaged in protective lifestyle behaviors compared to the other groups, and also had the highest mean cognitive score at baseline.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics of Participants by Cognitive Trajectory Group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStable (n\u0026thinsp;=\u0026thinsp;1186)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSlow Decline (n\u0026thinsp;=\u0026thinsp;922)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRapid Decline (n\u0026thinsp;=\u0026thinsp;989)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal (N\u0026thinsp;=\u0026thinsp;3097)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eSociodemographic\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.0 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.3 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.2 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66.2 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e579 (48.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e583 (63.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e352 (35.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,514 (48.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e607 (51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e339 (36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e637 (64.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,583 (51.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital Status, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot Married/\u003c/p\u003e \u003cp\u003eCohabiting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215 (18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157 (17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e116 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e488 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e971 (81.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e765 (83.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e873 (88.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,609 (84.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation Level, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e670 (56.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e747 (81.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e248 (25.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,665 (53.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e359 (30.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e367 (37.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e848 (27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e239 (24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e393 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e135 (13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e190 (6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHealth \u0026amp; Functional Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisability, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e287 (24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e244 (26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e167 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e698 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChronic Disease Burden, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.624\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e296 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e210 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e239 (24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e745 (24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e343 (28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e278 (30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e273 (27.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e894 (28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e547 (46.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e434 (47.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e477 (48.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,458 (47.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADL Impairment, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7 (1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.4 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIADL Impairment, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.5 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8 (1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of Falls, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e220 (18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e212 (23.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e173 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e605 (19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLifestyle Factors\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking Status (Ever/Current), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e521 (43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e339 (36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e488 (49.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,348 (43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol Consumption (Current), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e202 (17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138 (15.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e187 (18.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e527 (17.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI Category, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight (\u0026lt;\u0026thinsp;18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97 (8.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e244 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal (18.5\u0026ndash;23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e740 (62.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e587 (64.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e617 (62.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,944 (63.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight/Obese (\u0026ge;\u0026thinsp;24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e341 (28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e234 (25.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e318 (32.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e893 (29.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExercise Intensity, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e765 (64.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e550 (59.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e581 (58.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,896 (61.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e265 (22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e219 (23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e285 (28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e769 (24.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExcessive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e156 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e153 (16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e123 (12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e432 (13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntellectual Activity, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e194 (16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84 (9.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e263 (26.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e541 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Activity, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e409 (34.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e295 (32.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e370 (37.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,074 (34.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVoluntary Activity, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (4.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e159 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternet Use, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (0.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndoor Tidiness (Tidy), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e720 (60.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e531 (57.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e728 (73.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,979 (63.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNight Sleep\u0026thinsp;\u0026gt;\u0026thinsp;7 hours, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e325 (27.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e275 (29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e236 (23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e836 (27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNap Duration\u0026thinsp;\u0026gt;\u0026thinsp;60 min, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e185 (15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e132 (14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e163 (16.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e480 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.464\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmotional Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepressive Symptoms, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e428 (38.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e394 (45.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e288 (29.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,110 (37.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBaseline Cognition\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive Score, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.4 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.0 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.8 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.5 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData are presented as mean (standard deviation) for continuous variables and n (%) for categorical variables. P-values were derived from ANOVA or Kruskal-Wallis H tests for continuous variables and Chi-square tests for categorical variables. ADL\u0026thinsp;=\u0026thinsp;Activities of Daily Living; IADL\u0026thinsp;=\u0026thinsp;Instrumental Activities of Daily Living; BMI\u0026thinsp;=\u0026thinsp;Body Mass Index.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eFactors Associated with Cognitive Trajectories\u003c/h2\u003e \u003cp\u003eMultinomial logistic regression was used to identify factors associated with membership in the decline trajectories, with the Stable group as the reference category (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eOnline Risk Assessment Tool\u003c/h2\u003e \u003cp\u003eFollowing the multinomial logistic regression analysis, an interactive online risk assessment tool was developed to facilitate the practical application of our findings. This assessment can be carried out using our Cognitive Risk Assessment Tool. The tool then calculates personalized probabilities for an individual belonging to the Stable, Slow Decline, or Rapid Decline cognitive trajectories based on the coefficients derived from our model (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). It also provides tailored recommendations for risk mitigation.A detailed description of the tool's implementation, including its underlying algorithms and user interface, is provided in Supplementary Material Appendix A.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePredictors of the Rapid Decline Trajectory\u003c/h2\u003e \u003cp\u003eCompared to the Stable group, several factors significantly increased the odds of belonging to the Rapid Decline group. The most potent predictor was baseline IADL impairment, which increased the odds more than fivefold (OR\u0026thinsp;=\u0026thinsp;5.52, 95% CI: 3.02\u0026ndash;10.12). Other significant risk factors included being underweight (OR\u0026thinsp;=\u0026thinsp;1.66, 95% CI: 1.13\u0026ndash;2.44), engaging in excessive physical exercise (OR\u0026thinsp;=\u0026thinsp;1.56, 95% CI: 1.14\u0026ndash;2.14), having insufficient night sleep (OR\u0026thinsp;=\u0026thinsp;1.48, 95% CI: 1.19\u0026ndash;1.85), having a disability (OR\u0026thinsp;=\u0026thinsp;1.44, 95% CI: 1.13\u0026ndash;1.84), having depressive symptoms (OR\u0026thinsp;=\u0026thinsp;1.42, 95% CI: 1.14\u0026ndash;1.76), smoking (OR\u0026thinsp;=\u0026thinsp;1.42, 95% CI: 1.15\u0026ndash;1.74), being overweight/obese (OR\u0026thinsp;=\u0026thinsp;1.34, 95% CI: 1.08\u0026ndash;1.66), current alcohol consumption (OR\u0026thinsp;=\u0026thinsp;1.33, 95% CI: 1.03\u0026ndash;1.70), and a history of falls (OR\u0026thinsp;=\u0026thinsp;1.29, 95% CI: 1.01\u0026ndash;1.66).\u003c/p\u003e \u003cp\u003eConversely, a number of factors were strongly protective against rapid decline. The most significant protective factors were internet use (OR\u0026thinsp;=\u0026thinsp;0.36, 95% CI: 0.26\u0026ndash;0.48) and higher education (OR\u0026thinsp;=\u0026thinsp;0.12, 95% CI: 0.05\u0026ndash;0.27). Other protective factors included participation in physical activity (OR\u0026thinsp;=\u0026thinsp;0.39, 95% CI: 0.26\u0026ndash;0.58), intellectual activity (OR\u0026thinsp;=\u0026thinsp;0.41, 95% CI: 0.31\u0026ndash;0.54), social activity (OR\u0026thinsp;=\u0026thinsp;0.46, 95% CI: 0.27\u0026ndash;0.79), and voluntary activity (OR\u0026thinsp;=\u0026thinsp;0.60, 95% CI: 0.39\u0026ndash;0.94). Additionally, being male (OR\u0026thinsp;=\u0026thinsp;0.53, 95% CI: 0.40\u0026ndash;0.69) and maintaining a tidy home (OR\u0026thinsp;=\u0026thinsp;0.60, 95% CI: 0.49\u0026ndash;0.73) were associated with a lower likelihood of being in the Rapid Decline group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePredictors of the Slow Decline Trajectory\u003c/h2\u003e \u003cp\u003eThe Slow Decline trajectory shared some risk factors with the rapid decline group but also displayed a unique profile. The strongest predictor was eating more than three meals a day (OR\u0026thinsp;=\u0026thinsp;2.07, 95% CI: 1.32\u0026ndash;3.24). Other significant risk factors included IADL impairment (OR\u0026thinsp;=\u0026thinsp;2.09, 95% CI: 1.15\u0026ndash;3.80), no physical exercise (OR\u0026thinsp;=\u0026thinsp;1.46, 95% CI: 1.20\u0026ndash;1.78), disability (OR\u0026thinsp;=\u0026thinsp;1.43, 95% CI: 1.14\u0026ndash;1.79), excessive physical exercise (OR\u0026thinsp;=\u0026thinsp;1.37, 95% CI: 1.02\u0026ndash;1.84), smoking (OR\u0026thinsp;=\u0026thinsp;1.26, 95% CI: 1.05\u0026ndash;1.51), and insufficient night sleep (OR\u0026thinsp;=\u0026thinsp;1.25, 95% CI: 1.02\u0026ndash;1.53).\u003c/p\u003e \u003cp\u003eProtective factors against slow decline included higher education (OR\u0026thinsp;=\u0026thinsp;0.16, 95% CI: 0.07\u0026ndash;0.34), maintaining a tidy home (OR\u0026thinsp;=\u0026thinsp;0.66, 95% CI: 0.55\u0026ndash;0.80), participation in intellectual activities (OR\u0026thinsp;=\u0026thinsp;0.68, 95% CI: 0.55\u0026ndash;0.85), internet use (OR\u0026thinsp;=\u0026thinsp;0.72, 95% CI: 0.57\u0026ndash;0.90), and participation in physical (OR\u0026thinsp;=\u0026thinsp;0.74, 95% CI: 0.56\u0026ndash;0.99) and social activities (OR\u0026thinsp;=\u0026thinsp;0.65, 95% CI: 0.43\u0026ndash;0.97).All reported associations were statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultinomial Logistic Regression Analysis of Factors Associated with Cognitive Trajectories\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRisk Factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSlow Decline\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eRapid Decline\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.70,1.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.40,0.69]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.95,1.36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.73,1.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.07,0.34]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.05,0.27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.14,1.79]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[1.13,1.84]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADL Impairment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.65,2.84]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.56,2.58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.97,2.02]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[1.13,2.44]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.95,1.38]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[1.08,1.66]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical Activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.56,0.99]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.26,0.58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.43,0.97]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.27,0.79]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain Activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.55,0.85]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.31,0.54]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVoluntary Activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.49,1.04]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.39,0.94]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol Consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.95,1.47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[1.03,1.70]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.05,1.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[1.15,1.74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeals\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.32,3.24]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.93,2.50]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.99,1.48]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[1.14,1.76]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIADL Impairment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.15,3.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[3.02,10.12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExercise (None)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.20,1.78]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.98,1.53]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExercise (Excessive)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.02,1.84]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[1.14,2.14]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFall down\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.81,1.30]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[1.01,1.66]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInternet use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.57,0.90]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.26,0.48]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTidy home\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[0.55,0.80]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.49,0.73]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInsufficient Night Sleep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e[1.02,1.53]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[1.19,1.85]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eImportantly, the robustness of these trajectory patterns was supported by extensive sensitivity analyses using alternative trajectory modeling strategies and different missing data handling methods, all of which consistently identified the same three cognitive trajectories.This study is among the first to use a latent class growth analysis on a large, nationally representative longitudinal cohort to characterize the heterogeneity of cognitive aging in older adults with SCD. Our primary finding is the identification of three distinct cognitive trajectories over a 9-year period: a \"Stable\" group, a \"Slow Decline\" group, and a \"Rapid Decline\" group. This result empirically validates the long-held clinical observation that SCD is not a monolithic condition but a syndrome with highly variable outcomes. Crucially, our analysis revealed that membership in these trajectories is significantly associated with a wide array of modifiable health, lifestyle, and psychosocial factors, providing critical insights for early risk stratification and targeted prevention.\u003c/p\u003e \u003cp\u003eThe risk factors for accelerated cognitive decline identified in this study are consistent with and extend previous research findings. Impaired instrumental activities of daily living (IADL) demonstrated a strong association with cognitive decline[15, 16], suggesting a potential harmful feedback loop: subtle cognitive deficits impair the ability to complete complex daily tasks, and the resulting functional dependency and reduced activity participation further accelerate cognitive deterioration. Our results also confirm the importance of established vascular and lifestyle risk factors[17], including smoking[18], insufficient sleep[19], excessive sleep[20], depressive symptoms[21], and a history of falls[22], echoing recommendations from major public health authorities such as the Lancet Commission on Dementia Prevention.\u003c/p\u003e \u003cp\u003eNotably, we observed a U-shaped association between body mass index (BMI) and cognitive decline[23]: both underweight and overweight/obesity were risk factors for the rapid decline trajectory. This underscores the importance of maintaining a healthy weight, as underweight may reflect underlying frailty or malnutrition, while obesity is a well-established driver of metabolic and vascular diseases that impair brain health. The association between excessive physical exercise and cognitive decline should be interpreted with caution[24]: this may reflect reverse causality, whereby individuals with early neurodegenerative changes exhibit compulsive behaviors or impaired exercise judgment; it may also indicate that inappropriate high-intensity exercise in older adults increases systemic stress or injury risk, thereby negatively affecting cognition.\u003c/p\u003e \u003cp\u003eConversely, our analysis identified a range of protective factors, highlighting the critical role of cognitive reserve and an active lifestyle in maintaining cognitive resilience[25]. Higher education, a classic proxy for cognitive reserve, had a significant protective effect against both slow and rapid cognitive decline[26, 27]. More importantly, our findings indicate that an active lifestyle confers substantial and cumulative benefits. Engagement in diverse activities\u0026mdash;including intellectual, social, physical, and voluntary activities\u0026mdash;was consistently associated with a lower risk of cognitive decline, suggesting that a multi-domain engagement strategy is most beneficial for maintaining and preserving cognitive function[28].\u003c/p\u003e \u003cp\u003eThe protective effect of internet use can be regarded as a modern embodiment of this principle, potentially reflecting a combination of cognitive engagement, access to health information, and enhanced social connectivity[29, 30]. Furthermore, a novel association was observed between a tidy home environment and reduced risk of cognitive decline, which we interpret as a behavioral marker of preserved executive function, conscientiousness, and the capacity to maintain organization in daily life\u0026mdash;itself partly reflective of underlying brain health[31].\u003c/p\u003e \u003cp\u003eOur findings provide crucial evidence for identifying individuals at high risk of rapid cognitive decline and offer specific targets for developing personalized, multidimensional interventions. The development of an interactive online risk assessment tool further enhances the translational impact of this research. This tool operationalizes our robust statistical model, providing a user-friendly interface for clinicians to engage patients in discussions about their individual cognitive trajectory risks and for individuals to proactively manage their modifiable risk factors. It bridges the gap between complex statistical findings and practical, personalized health management strategies, empowering both healthcare providers and the public with actionable insights.\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eClinical Implications and Future Directions\u003c/h2\u003e \u003cp\u003eA key implication of this study is the potential to shift the clinical paradigm of SCD from passive observation to proactive and personalized risk management. By moving beyond a purely cognitive conceptualization of SCD, our findings provide an empirical basis for identifying individuals at high risk of imminent cognitive decline. The newly developed online risk assessment tool serves as a direct application of this framework. This tool enables clinicians to efficiently stratify patient risk while simultaneously facilitating patient engagement through the visualization of personalized risk profiles. Furthermore, it guides the delivery of targeted, evidence-based lifestyle interventions, ensuring that healthcare resources are allocated to those most likely to benefit from early preventative measures.Future research should focus on the external validation of this online tool in diverse populations to confirm its generalizability and predictive accuracy. Further enhancements could include integrating the tool with electronic health records for seamless clinical workflow, developing mobile health applications for continuous self-monitoring, and incorporating additional biomarkers as they become more readily available and integrated into risk prediction models. The tool also holds significant potential for large-scale public health campaigns aimed at raising awareness about cognitive decline risk and promoting healthy aging behaviors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eSeveral limitations of this study should be acknowledged. First, the observational design precludes causal inference; thus, all identified factors should be interpreted as correlates rather than direct causes of cognitive decline. Second, SCD and most covariates were self-reported, which may be subject to recall bias and social desirability bias. Third, SCD was defined using a single self-reported item, which, although commonly applied in large population-based studies, may not fully capture the multidimensional SCD-plus construct.\u003c/p\u003e \u003cp\u003eIn addition, missing data and loss to follow-up may have introduced potential bias. However, sensitivity analyses employing different missing data handling strategies and trajectory modeling approaches yielded highly consistent trajectory classifications, suggesting that the main findings were robust. Nevertheless, healthy survivor bias cannot be completely ruled out. Finally, the absence of biological and neuroimaging markers, such as APOEε4 genotype or brain imaging data, limited further etiological interpretation.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings demonstrate that a combination of modifiable factors, including physical and mental health status, lifestyle behaviors, and social engagement, are powerful predictors of an individual's long-term cognitive path. These results provide a robust empirical framework for early risk stratification, enabling the identification of individuals with SCD who are at the highest risk for accelerated cognitive decline. The development of an accessible online risk assessment tool further translates these insights into a practical solution for personalized health management. Targeting these specific, modifiable factors through personalized, multi-domain interventions holds significant promise for mitigating dementia risk and promoting healthy cognitive aging. This research underscores the critical public health value of early, evidence-based health management in the preclinical stages of dementia.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for the China Health and Retirement Longitudinal Study (CHARLS) was granted by the Institutional Review Board of Peking University (IRB00001052-11015). All participants in CHARLS provided written informed consent prior to data collection.\u003c/p\u003e\n\u003cp\u003eFor the secondary analysis of CHARLS data conducted in this study, ethical approval was obtained from the Ethics Committee of Soochow University (Approval No. SUDA20251015H09). Since this study used de-identified secondary data, the requirement for additional informed consent from individual participants was waived by the ethics committee.\u003c/p\u003e\n\u003cp\u003eFor the development and validation of the online risk assessment tool, supplementary ethical approval was obtained (Approval No. SUDA20251015H09), and verbal informed consent was obtained from all participants involved in the tool\u0026rsquo;s pilot testing, with documentation of consent retained by the research team.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants in CHARLS consented to the use and publication of their de-identified data for research purposes. No individual personal data or identifying information (e.g., names, contact details) is presented in this manuscript. The authors confirm that all applicable consent for publication has been obtained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CHARLS data used in this study are publicly available upon reasonable request from the CHARLS official website (http://charls.pku.edu.cn/). The de-identified datasets and analytical code generated during the current study are not publicly available due to privacy restrictions but can be obtained from the corresponding author (Huiling Li, Email: [email protected]) upon reasonable request and approval from the CHARLS data access committee. The online risk assessment tool developed in this study is accessible from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no competing interests. No financial, professional, or personal relationships with other individuals or organizations influenced the design of the study, data analysis, interpretation of results, or writing of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNational Natural Science Foundation of China (72074164), Jiangsu Provincial Graduate Research Innovation Program (KYCX24_3352)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXi Wen conceived and designed the study, performed the statistical analyses, developed the online risk assessment tool, drafted the manuscript, and revised it critically for important intellectual content.\u003c/p\u003e\n\u003cp\u003eDan Xu assisted with data cleaning, conducted sensitivity analyses, and contributed to the writing of the \u0026ldquo;Methods\u0026rdquo; and \u0026ldquo;Results\u0026rdquo; sections.\u003c/p\u003e\n\u003cp\u003eHuiling Li supervised the entire study, provided methodological guidance, secured funding, revised the manuscript extensively, and finalized the submission.\u003c/p\u003e\n\u003cp\u003eTaomei Zhang advised on cognitive assessment measures, reviewed the literature on subjective cognitive decline, and commented on the manuscript.\u003c/p\u003e\n\u003cp\u003eFengmei Tian assisted with the development of the online tool\u0026rsquo;s user interface, collected pilot test data.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the CHARLS research team for providing access to the longitudinal cohort data. We also appreciate the support from the School of Nursing at Soochow University for methodological and technical assistance. Special thanks to the older adults who participated in the pilot testing of the online risk assessment tool, and to the experts who provided feedback during the tool\u0026rsquo;s validation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNichols E, Steinmetz JD, Vollset SE, Fukutaki K, Chalek J, Abd-Allah F, et al. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. The Lancet Public Health. 2022;7:e105\u0026ndash;25. https://doi.org/10.1016/S2468-2667(21)00249-8.\u003c/li\u003e\n\u003cli\u003eLivingston G, Huntley J, Liu KY, Costafreda SG, Selb\u0026aelig;k G, Alladi S, et al. Dementia prevention, intervention, and care: 2024 report of the Lancet standing Commission. 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IADL for identifying cognitive impairment in chinese older adults: insights from cross-lagged panel network analysis. BMC Geriatr. 2025;25:364. https://doi.org/10.1186/s12877-025-06017-1.\u003c/li\u003e\n\u003cli\u003eChen J, Park D, Afzal S, Cardin M, Peltier M, Hundal J, et al. Cognitive and functional decline in a psychogeriatric population: a comparative longitudinal analysis of MMSE, MoCA, ADL, and IADL. Am J Geriatr Psychiatry. 2024;32:S128. https://doi.org/10.1016/j.jagp.2024.01.216.\u003c/li\u003e\n\u003cli\u003eBloomberg M, Muniz-Terrera G, Brocklebank L, Steptoe A. Healthy lifestyle and cognitive decline in middle-aged and older adults residing in 14 european countries. Nat Commun. 2024;15:5003. https://doi.org/10.1038/s41467-024-49262-5.\u003c/li\u003e\n\u003cli\u003eBloomberg M, Brown J, Gessa GD, Bu F, Steptoe A. Cognitive decline before and after mid-to-late-life smoking cessation: a longitudinal analysis of prospective cohort studies from 12 countries. Lancet Healthy Longev. 2025;6. https://doi.org/10.1016/j.lanhl.2025.100753.\u003c/li\u003e\n\u003cli\u003eUngvari Z, Fekete M, Lehoczki A, Munk\u0026aacute;csy G, Fekete JT, Z\u0026aacute;b\u0026oacute; V, et al. Sleep disorders increase the risk of dementia, alzheimer\u0026rsquo;s disease, and cognitive decline: a meta-analysis. GeroScience. 2025;47:4899\u0026ndash;920. https://doi.org/10.1007/s11357-025-01637-2.\u003c/li\u003e\n\u003cli\u003eOverton M, Sindi S, Basna R, Elmst\u0026aring;hl S. Excessive sleep is associated with worse cognition, cognitive decline, and dementia in mild cognitive impairment. Alzheimer\u0026rsquo;s Dement: Diagn Assess Dis Monit. 2025;17:e70093. https://doi.org/10.1002/dad2.70093.\u003c/li\u003e\n\u003cli\u003eForbes M, Lotfaliany M, Mohebbi M, Reynolds CF, Woods RL, Orchard S, et al. Depressive symptoms and cognitive decline in older adults. 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Cell Metab. 2025;0. https://doi.org/10.1016/j.cmet.2025.11.002.\u003c/li\u003e\n\u003cli\u003eStern Y, Albert M, Barnes CA, Cabeza R, Pascual-Leone A, Rapp PR. A framework for concepts of reserve and resilience in aging. Neurobiol Aging. 2023;124:100\u0026ndash;3. https://doi.org/10.1016/j.neurobiolaging.2022.10.015.\u003c/li\u003e\n\u003cli\u003eKim Y, Stern Y, Seo SW, Na DL, Jang J-W, Jang H, et al. Factors associated with cognitive reserve according to education level. Alzheimer\u0026rsquo;s Dement: J Alzheimer\u0026rsquo;s Assoc. 2024;20:7686\u0026ndash;97. https://doi.org/10.1002/alz.14236.\u003c/li\u003e\n\u003cli\u003eGamble LD, Clare L, Opdebeeck C, Martyr A, Jones RW, Rusted JM, et al. Cognitive reserve and its impact on cognitive and functional abilities, physical activity and quality of life following a diagnosis of dementia: longitudinal findings from the improving the experience of dementia and enhancing active life (IDEAL) study. Age Ageing. 2025;54:afae284. https://doi.org/10.1093/ageing/afae284.\u003c/li\u003e\n\u003cli\u003eRosenberg A, Ngandu T, Rusanen M, Antikainen R, B\u0026auml;ckman L, Havulinna S, et al. Multidomain lifestyle intervention benefits a large elderly population at risk for cognitive decline and dementia regardless of baseline characteristics: the FINGER trial. Alzheimer\u0026rsquo;s Dement: J Alzheimer\u0026rsquo;s Assoc. 2018;14:263\u0026ndash;70. https://doi.org/10.1016/j.jalz.2017.09.006.\u003c/li\u003e\n\u003cli\u003eCho G, Betensky RA, Chang VW. Internet usage and the prospective risk of dementia: a population-based cohort study. J Am Geriatr Soc. 2023;71:2419\u0026ndash;29. https://doi.org/10.1111/jgs.18394.\u003c/li\u003e\n\u003cli\u003eTsai YI-P, Beh J, Ganderton C, Pranata A. Digital interventions for healthy ageing and cognitive health in older adults: a systematic review of mixed method studies and meta-analysis. BMC Geriatr. 2024;24:217. https://doi.org/10.1186/s12877-023-04617-3.\u003c/li\u003e\n\u003cli\u003eBogg T, Roberts BW. Conscientiousness and health-related behaviors: a meta-analysis of the leading behavioral contributors to mortality. Psychol Bull. 2004;130:887\u0026ndash;919. https://doi.org/10.1037/0033-2909.130.6.887.\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":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Subjective Cognitive Decline, Cognitive Trajectories, Latent Class Growth Analysis, Risk Factors, Online Risk Assessment Tool, Personalized Prevention","lastPublishedDoi":"10.21203/rs.3.rs-8588528/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8588528/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eThe escalating global prevalence of dementia poses a significant public health challenge, underscoring the urgent need for effective early prevention. Subjective Cognitive Decline (SCD) is increasingly recognized as a critical pre-clinical stage of dementia; however, the longitudinal course of cognition in individuals with SCD is markedly heterogeneous. While advanced age is a primary determinant, the influence of modifiable risk factors on these divergent trajectories remains poorly understood. This investigation aimed to identify distinct patterns of cognitive decline among older adults with SCD and evaluate associated modifiable risk factors, with the ultimate goal of translating these empirical findings into a practical risk assessment tool for early stratification.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study analyzed five waves of data (2011\u0026ndash;2020) from 3097 older adults with SCD in the CHARLS. We employed latent class growth analysis (LCGA) to identify distinct cognitive trajectories and subsequently used multinomial logistic regression to evaluate the modifiable risk factors associated with these patterns.Based on the identified predictors, a web-based risk assessment tool was constructed to facilitate personalized risk profiling.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThree distinct cognitive trajectories were identified: a Stable group (38.3%), a Slow Decline group (29.8%), and a Rapid Decline group (31.9%). Compared to the Stable group, factors significantly associated with a higher likelihood of belonging to the Rapid Decline group included disability (OR\u0026thinsp;=\u0026thinsp;1.441, 95%CI: 1.128, 1.841), underweight (OR\u0026thinsp;=\u0026thinsp;1.661, 95%CI: 1.130, 2.441) or obesity (OR\u0026thinsp;=\u0026thinsp;1.337, 95%CI: 1.079, 1.658), drinking (OR\u0026thinsp;=\u0026thinsp;1.326, 95%CI: 1.033, 1.701), smoking (OR\u0026thinsp;=\u0026thinsp;1.417, 95%CI: 1.153, 1.740), depression (OR\u0026thinsp;=\u0026thinsp;1.419, 95%CI: 1.143, 1.762), IADL impairment (OR\u0026thinsp;=\u0026thinsp;5.523, 95%CI: 3.016, 10.115), excessive exercise (OR\u0026thinsp;=\u0026thinsp;1.562, 95%CI: 1.140, 2.141), fall down (OR\u0026thinsp;=\u0026thinsp;1.29, 95%CI: 1.005, 1.656), and insufficient night sleep (OR\u0026thinsp;=\u0026thinsp;1.484, 95%CI: 1.190, 1.852). Conversely, male (OR\u0026thinsp;=\u0026thinsp;0.525, 95%CI: 0.403, 0.686), higher education (OR\u0026thinsp;=\u0026thinsp;0.115, 95%CI: 0.049, 0.267), physical activity (OR\u0026thinsp;=\u0026thinsp;0.391, 95%CI: 0.263, 0.580), social activity (OR\u0026thinsp;=\u0026thinsp;0.457, 95%CI: 0.265, 0.789), brain activity (OR\u0026thinsp;=\u0026thinsp;0.405, 95%CI: 0.306, 0.537), voluntary activity (OR\u0026thinsp;=\u0026thinsp;0.602, 95%CI: 0.385, 0.940), internet use (OR\u0026thinsp;=\u0026thinsp;0.355, 95%CI: 0.262, 0.481), and indoor tidiness (OR\u0026thinsp;=\u0026thinsp;0.598, 95%CI: 0.489, 0.732) were associated with a lower likelihood of rapid cognitive decline.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study reveals that cognitive progression in SCD is heterogeneous and significantly influenced by modifiable factors. To bridge the gap between research and practice, we translated these findings into a user-friendly online risk assessment tool. This instrument allows clinicians and the public to visualize individual cognitive trajectories and identify specific targets for intervention. Ultimately, this risk-based approach supports proactive health management and aims to mitigate the public health burden of dementia.\u003c/p\u003e","manuscriptTitle":"Cognitive Trajectories in Subjective Cognitive Decline: Identifying Modifiable Risks and Developing a Web-Based Assessment Tool for Personalized Prevention","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-02 17:21:30","doi":"10.21203/rs.3.rs-8588528/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-04T10:56:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-31T17:03:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"215839734855029343081706512658755188970","date":"2026-03-31T16:29:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7959284894529980742620820809866853263","date":"2026-03-27T08:16:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-26T13:34:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"117722344980660421364733102934000378442","date":"2026-03-25T06:53:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"64472706043959098293928787436273896851","date":"2026-02-04T18:15:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-30T06:12:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-21T07:02:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-20T07:24:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-20T07:20:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2026-01-13T07:16:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b7e93cf5-20a6-41cf-a467-c425b66a9ad3","owner":[],"postedDate":"February 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T17:21:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-02 17:21:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8588528","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8588528","identity":"rs-8588528","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}