Daily Living,Depressive Symptoms,and Cognition in Older Adults With Chronic Diseases

Abstract

This study examined the associations of activities of daily living (ADL) limitations and depressive symptoms with global and domain-specific cognition among older adults with chronic diseases. Data were drawn from the 2020 wave of the China Health and Retirement Longitudinal Study, including 5,112 participants aged ≥60 years. Functional ability and depressive symptoms were assessed using the ADL scale and CES-D-10, respectively. Cognitive function was assessed using CHARLS cognitive measures, including the Telephone Interview of Cognitive Status-10 and other cognition-related items. Spearman correlation and path analyses were performed using AMOS and Stata. ADL limitations were associated with poorer global and domain-specific cognitive function, and depressive symptoms accounted for part of these associations in the path models. The proportion accounted for by the indirect effect ranged from 17.39% for visuospatial ability to 60.27% for delayed recall. These findings suggest that functional status, depressive symptoms, and cognition are closely interrelated in this population.

Keywords

activities of daily living cognitive function depression older adults chronic diseases

1. Introduction

With accelerating population aging worldwide, cognitive impairment, including dementia, has become a major challenge to healthy aging.¹ According to the World Health Organization, approximately 57 million people worldwide were living with dementia in 2021, with nearly 10 million new cases diagnosed annually.² China faces a similarly substantial burden, with projections suggesting that the number of older adults with cognitive impairment may reach 23.3 million by 2030.^3,4 Among older adults, those with chronic diseases are particularly vulnerable to cognitive decline. Conditions such as hypertension and diabetes have been associated with accelerated cognitive deterioration through pathways involving vascular damage, inflammation, and metabolic dysregulation.⁵ Given the high prevalence of chronic diseases in aging populations, identifying factors associated with cognitive function in this group is essential for informing strategies to maintain cognitive health.

Activities of Daily Living (ADL) reflect an individual’s capacity to perform essential self-care tasks and are widely used indicators of functional status and independence in older adults.⁶ With advancing age, declines in physiological capacity often lead to reduced ADL performance, particularly among those with chronic diseases.^7,8 Functional limitations in ADL are also closely related to psychological well-being. Older adults with impaired functional ability often experience reduced autonomy, increased dependency, and restricted social participation, all of which have been associated with a higher burden of depressive symptoms. Empirical studies consistently report that ADL limitations are associated with increased levels of depression in older populations, especially among those with multimorbidity.^9,10

Depressive symptoms are prevalent among older adults, particularly in those with chronic conditions, and represent an important dimension of mental health in later life.^11,12 A substantial body of literature has demonstrated that depression is associated with cognitive performance and cognitive decline. Older adults with higher levels of depressive symptoms tend to exhibit poorer cognitive function and an elevated risk of dementia.¹³ Although the strength of this association may vary depending on symptom severity, duration, and individual vulnerability, cumulative evidence suggests that depression is consistently linked to cognitive deterioration. This relationship has also been observed in populations with chronic diseases, where greater disease burden is associated with higher levels of depressive symptoms and, in turn, poorer cognitive outcomes.^14-16

Building on these lines of evidence, ADL limitations, depressive symptoms, and cognitive function may be closely interconnected in older adults with chronic diseases. However, prior research has primarily examined these relationships separately and has largely focused on global cognitive outcomes, with less attention to their combined associations and whether these associations vary across specific cognitive domains. Therefore, this study examined the associations of ADL limitations and depressive symptoms with global and domain-specific cognitive function in older Chinese adults with chronic diseases.

2. Methods

2.1. Data and Sample

This study used data from the China Health and Retirement Longitudinal Study (CHARLS), an ongoing, nationally representative cohort established to investigate population aging in China and support interdisciplinary research.¹⁷ CHARLS employs a multistage, probability-proportional-to-size sampling strategy to survey middle-aged and older adults aged 45 years and above from 150 counties/districts across 28 provinces in China. Detailed information on the study design, sampling procedures, questionnaires, and publicly available data can be found on the official CHARLS website and in the cohort profile and user guide (see Supplemental Material S1 for the database source and access link). All survey protocols were approved by the Peking University Biomedical Ethics Review Committee (IRB00001052-11015). Written informed consent was obtained from all participants.

For this cross-sectional analysis, we used data from the fifth regular wave of CHARLS, completed in 2020. We restricted the sample to adults aged 60 years and older with at least one chronic condition. Chronic disease status was defined as having any of 14 physician-diagnosed conditions recorded in CHARLS, including hypertension, dyslipidemia, diabetes or elevated blood glucose, malignant tumor, and other conditions (the full list is available in the CHARLS documentation and questionnaires).¹⁷ We excluded participants who did not have a chronic condition (n = 2722), were younger than 60 years (n = 6185), or had missing data on key study variables (n = 5376). The final analytic sample comprised 5112 participants. The participant selection process is shown in Figure 1.

Figure 1.

Flowchart of participant selection

2.2. Measures

2.2.1. Cognitive function

Cognitive function is multi-dimensional, and we measured it using four components: orientation to time, attention, episodic memory, and visuospatial ability. The measurement methods were consistent with prior publications from the CHARLS.¹⁸ Specifically, orientation to time and attention were assessed using the Telephone Interview of Cognitive Status (TICS-10). Orientation to time was evaluated by asking participants to provide the current date (including the month, day, year, day of the week, and season), and attention measured by serial subtractions of 7 from 100 five times consecutively. Both scores ranged from 0 to 5. Immediate recall was assessed by asking participants to recall ten Chinese nouns immediately after presentation, whereas delayed recall was assessed by asking them to recall the same nouns after a 10-minute delay. Both scores ranged from 0 to 10. Visuospatial ability was assessed by presenting two overlapping pentagons to participants and asking them to draw the image as exactly as possible. A score of 1 was given for a correct drawing and 0 for an incorrect one. Finally, global cognition was reflected by the sum of the scores from the four aforementioned measures, with a possible total score ranging from 0 to 31.

2.2.2. Activities of Daily Living

Activities of daily living (ADL) were assessed using the Katz Index of Independence in Activities of Daily Living to measure basic ADL (BADL)¹⁹ and the Lawton Instrumental Activities of Daily Living Scale to measure instrumental ADL (IADL).²⁰ Both instruments are widely recognized tools for evaluating functional ability among older adults. The assessment encompassed two dimensions: Basic ADL (BADL)—covering fundamental self-care tasks such as eating, bathing, dressing, toileting, and mobility—and Instrumental ADL (IADL)—involving more complex daily tasks such as using the telephone, shopping, preparing meals, managing finances, and housekeeping. In total, the combined instrument consisted of 12 items, each rated on a 4-point Likert scale ranging from “no difficulty” (1) to “cannot complete” (4). Item scores were summed to yield a total ADL score ranging from 12 to 48, with higher scores indicating poorer functional capacity or greater impairment. This composite measure has been extensively validated among Chinese older adults, demonstrating good reliability and construct validity. In the present study, internal consistency for all 12 ADL items combined was acceptable (Cronbach’s α = 0.807), indicating that the items coherently captured the underlying construct of functional ability.

2.2.3. Depression

Depressive symptoms were measured with the 10-item Center for Epidemiologic Studies Depression Scale (CES-D-10) adapted by Andresen et al.²¹ The CES-D-10 captures the frequency of depressive affect, somatic complaints, and positive emotions experienced over the past week. Each item is rated on a 4-point Likert scale from 1 (“rarely or none of the time”) to 4 (“most or all of the time”). Items 5 and 8 are positively worded and reverse-scored, while the remaining eight items are scored in the forward direction. The total score ranges from 10 to 40, with higher scores indicating more severe depressive symptomatology. The CES-D-10 has been previously validated in Chinese older populations and is widely used to assess depressive symptoms among older adults in China.²² In this study, internal consistency was satisfactory (Cronbach’s α = 0.789), indicating adequate reliability.

2.2.4. Sociodemographic and Behavioral Covariates

Covariates were selected a priori based on prior literature and their potential associations with both ADL and cognitive function in older adults.^23-25 Covariates included sociodemographic characteristics (age, sex, marital status, education level, residence, and household income) and behavioral factors (smoking status and alcohol consumption), all of which were obtained from the standardized CHARLS questionnaire. Age was grouped into three categories: 60–69 years, 70–79 years, and ≥80 years. Marital status was classified as with spouse (currently married or living with a partner) or without spouse (including widowed, divorced, or never married). Education level was divided into four categories: illiteracy, primary school, junior high school, and senior high school or above. Residence was classified as urban, suburban, or rural based on the corresponding residence variable in CHARLS.¹⁷ Household income was dichotomized at the sample median of annual household income (26,400 yuan) and categorized as lower (≤26,400 yuan) versus higher (>26,400 yuan) for analysis. Smoking status was classified as current, former, or never smoked, and alcohol consumption was classified as drinking more than once a month, drinking less than once a month, or not drinking in the past year.

2.3. Data Analysis

Continuous variables were summarized as mean ± standard deviation (x̄ ± s), and categorical variables as counts and percentages. Continuous variables were compared using t tests or one-way ANOVA, as appropriate. Spearman’s rank correlation was used to examine pairwise associations among the primary study variables. We employed hierarchical regression analyses in Stata 18.0 to assess the association between ADL and cognitive function. In the first step, demographic variables such as age, gender, marital status, education level, and place of residence were entered as covariates, followed by ADL scores in the subsequent step to evaluate their incremental predictive value. Standardized regression coefficients and corresponding p-values were reported.

To examine the association between ADL impairment, depression, and cognition, path analyses were conducted using AMOS 22.0 and Stata 18.0. In the prespecified path models, ADL impairment was entered as the independent variable, depression as the intermediate variable, and either the global cognitive score or each of the five cognitive domains as the dependent variable in separate models. The path models were adjusted for the same set of covariates to minimize potential confounding. Bootstrap resampling (2000 draws) was applied to obtain bias-corrected confidence intervals for indirect, direct, and total effects. Effects were considered statistically significant when the bias-corrected 95% confidence interval did not include zero, and a two-tailed P < 0.05 was regarded as the threshold for significance. To explore potential bidirectionality, an alternative directional model specifying cognitive function as the independent variable, depression as the intermediate variable, and ADL as the dependent variable was also examined as a sensitivity analysis.

3. Results

3.1. Demographics and Characteristics of the Study Population

A total of 5112 older adults with chronic diseases were included in the study. Among them, 2983 were men, accounting for 58.35% of the participants. More than half of the patients were aged 60–69 years, representing 61.83% of the total sample. Most participants were married (84.47%) and had received at least primary education, with nearly half completing only primary school. Over half of the sample (58.14%) resided in rural areas, reflecting the demographic distribution of older adults in the general population. Except for sex, the total cognitive function scores differed significantly among patients of different age groups, marital status, educational levels, and places of residence (P < 0.05), as shown in Table 1.

Table 1.

Demographics and Characteristics of the Study Population.(n=5112)

Characteristic	Case/n	Proportion/%	Global cognitive score ( $\bar{x} \pm s$ )	t/F	P
Sex				1.20	0.232
Male	2983	58.35	17.37±4.12
Female	2129	41.65	17.23±4.28
Age/years
60-69	3161	61.83	17.64±4.06	33.40	<0.001
70-79	1661	32.49	16.95±4.24
≥80	290	5.67	15.87±4.77
Marital status				5.16	<0.001
With spouse	4318	84.47	17.44±4.16
Without spouse	794	15.53	16.61±4.26
Education level				246.23	<0.001
Illiteracy	545	10.66	14.43±4.21
Primary school	2424	47.42	16.63±4.07
Junior high school	1287	25.18	18.32±3.65
Senior high school	856	16.74	19.58±3.67
Residence				140.22	<0.001
Urban	1569	30.69	18.65±3.92
Suburban	571	11.17	17.69±3.84
Rural	2972	58.14	16.54±4.20
Household income				-8.71	<0.001
Low level	1774	34.70	16.61±4.20
High level	3338	65.30	17.68±4.14
Smoking status				6.12	0.013
Smoker	2818	55.13	17.10±4.11
Not smoker	1942	37.99	17.50±4.05
Alcohol consumption
Drinking	1465	28.66	17.70±4.09	28.14	<0.001
No drinking	527	10.31	17.06±4.23
ADL score	13.16±2.71
Depression score	18.29±6.24

Note. Alcohol consumption was classified according to self-reported alcohol use during the past year, with drinking defined as any alcohol consumption during that period; t-test for sex and household income; one-way ANOVA for age group, marital status, education level, residence, smoking status, and Alcohol consumption.

3.2. Analysis of Common Method Bias

The Harman’s single-factor test was employed to examine the potential common method bias. The percentage of variance explained by the first factor relative to the total variance was used as the criterion. The results indicated that the first factor accounted for 11.11% of the total variance, which was below the 40% threshold, suggesting that no serious common method bias existed in this study. This finding implies that the observed associations among variables were unlikely to be driven by measurement artifacts.

3.3. Correlations Among the Main Study Variables

The results of the Spearman rank correlation analysis showed a positive correlation between ADL and depression (ρ=0.339, P<0.001). Both ADL and depression were negatively correlated with the total cognitive score and each cognitive domain, with correlation coefficients ranging from −0.047 to −0.155 for ADL and from −0.078 to −0.238 for depression. Among the cognitive domains, , delayed recall showed the strongest correlation with immediate recall (ρ=0.512, P<0.001), and global cognitive score was most strongly correlated with delayed recall (ρ=0.829, P<0.001), followed by immediate recall (ρ=0.739, P<0.001), as shown in Table 2.

Table 2.

Correlations Among the Main Study Variables(ρ Value)

	ADL	Depression	Global cognitive score	Orientation	Calculation	Immediate recall	Delayed recall	Visuospatial ability
ADL	1
Depression	0.333***	1
Global cognitive score	-0.135***	-0.238***	1
Orientation	-0.155***	-0.200***	0.523***	1
Calculation	-0.069***	-0.104***	0.456***	0.211***	1
Immediate recall	-0.059***	-0.166***	0.739***	0.209***	0.132***	1
Delayed recall	-0.093***	-0.167***	0.829***	0.226***	0.139***	0.512***	1
Visuospatial ability	-0.047***	-0.078**	0.255***	0.132***	0.099***	0.075***	0.099***	1

***P<0.001, **P<0.01, * P<0.05.

3.4. Hierarchical Regression Analysis

A regression model was constructed with global cognitive function as the dependent variable. In the first step, demographic and other confounding variables were entered into the model to establish the baseline model (Model 1). In the second step, the ADL variable was added to Model 1 to construct Model 2.

The results of Model 1 indicated that older age, rural residence, and no alcohol consumption were negatively associated with global cognitive function (P<0.05), whereas higher educational attainment and non-smoking status were positively associated with global cognitive function (P<0.05). Specifically, participants aged 70–79 years and ≥80 years had lower cognitive scores than those aged 60–69 years, and rural residents had lower cognitive scores than urban residents. Compared with illiterate participants, those with primary school education or above had higher cognitive scores.

In Model 2, ADL remained negatively associated with global cognitive function after adjustment for covariates (β=−0.129, SE=0.027, P<0.001), indicating that greater ADL limitations were associated with lower global cognitive function (Table 3).

Table 3.

Hierarchical Regression Results of ADL on Global Cognitive Function

Variables		Model 1			Model 2
Variables		β	SE	P Value	β	SE	P Value
Sex	Male	ref
Sex	Female	0.443	0.303	0.143	0.586	0.303	0.053
Age/years	60-69	ref
	70-79	-0.388	0.167	0.020	-0.343	0.166	0.039
	≥80	-1.817	0.327	<0.001	-1.713	0.326	<0.001
Marital status	With spouse	ref
Marital status	Without spouse	-0.272	0.239	0.256	-0.257	0.238	0.282
Education level	Illiteracy	ref
	Primary school	2.056	0.324	<0.001	2.038	0.322	<0.001
	Junior high school	3.386	0.339	<0.001	3.352	0.337	<0.001
	Senior high school	4.192	0.362	<0.001	4.166	0.361	<0.001
Residence	Urban	ref
	Suburban	-0.323	0.273	0.237	-0.277	0.272	0.308
	Rural	-1.109	0.188	<0.001	-1.044	0.188	<0.001
Household income	Low level	ref
Household income	High level	0.087	0.165	0.598	0.053	0.164	0.747
Smoking status	Smoker	ref
Smoking status	Not smoker	0.316	0.152	0.037	0.366	0.152	0.016
Alcohol consumption	Drinking	ref
Alcohol consumption	No drinking	-0.534	0.153	<0.001	-0.492	0.153	0.001
ADL					-0.129	0.027	<0.001
F value			32.06			31.60
R²			0.128			0.136

3.5. Path Analysis

The standardized path diagram of the adjusted total cognitive function score and its subdomains is presented in Figure 2. The model fit indices indicated a good overall model fit, as shown in Table 4. The models using each cognitive domain as the dependent variable also demonstrated good fit.

Figure 2.

Standardized path diagrams of the models for global and domain-specific cognitive functions

Table 4.

Model Fit Indices for Different Cognitive Outcome Measures

Cognitive outcome measure	χ²/d	GFI	AGFI	CFI	TLI	SRMR	RMSEA
Global cognitive score	1.96	0.993	0.966	0.954	0.915	0.017	0.060
Orientation	6.18	0.993	0.964	0.948	0.911	0.021	0.062
Calculation	7.71	0.992	0.955	0.907	0.930	0.019	0.073
Immediate recall	6.39	0.992	0.977	0.949	0.969	0.018	0.061
Delayed recall	1.74	0.993	0.976	0.916	0.917	0.035	0.057
Visuospatial ability	5.46	0.996	0.982	0.919	0.958	0.023	0.059

As shown in Table 5, after controlling for sex, age, marital status, education level, residence, household income, smoking status, and alcohol consumption, the mediation analysis showed that total effect of ADL on cognitive function among older adults with chronic diseases was –0.090 (95% CI: –0.117 to –0.059). The direct effect of ADL on cognitive function was –0.042 (95% CI: –0.071 to –0.012), while the indirect effect of ADL on cognitive function through depression was –0.048 (95% CI: –0.059 to –0.038). These findings suggest that poorer physical functioning was associated with lower cognitive function both directly and indirectly through depressive symptoms. Depressive symptoms accounted for part of the indirect association between ADL and cognitive function, representing 53.33% of the overall association and underscoring the potential importance of emotional health in the relationship between physical and cognitive health.

Table 5.

Mediation Results for Global and Domain-Specific Cognition

Cognitive outcome measure	Total effect β (95%CI)	Direct effect β (95%CI)	Indirect effect β (95%CI)	Mediated proportion(%)
Global cognitive score	-0.090(-0.117,-0.059)	-0.042(-0.071,-0.012)	-0.048(-0.059,-0.038)	53.33
Orientation	-0.119(-0.147,-0.089)	-0.088(-0.117,-0.055)	-0.031(-0.041,-0.022)	26.05
Calculation	-0.053(-0.078,-0.019)	-0.030(-0.061,-0.001)	-0.018(-0.029,-0.007)	33.96
Immediate recall	-0.059(-0.088,-0.028)	-0.036(-0.067,-0.004)	-0.023(-0.030,-0.014)	38.98
Delayed recall	-0.073(-0.102,-0.041)	-0.028(-0.060,-0.004)	-0.044(-0.055,-0.034)	60.27
Visuospatial ability	-0.046(-0.073,-0.018)	-0.038(-0.064,-0.009)	-0.008(-0.018,-0.001)	17.39

Bold values indicate statistical significance at P< 0.05.

Similar indirect associations through depression were observed across all cognitive domains. The indirect effects were −0.031 (95% CI: −0.041 to −0.022) for orientation, −0.018 (95% CI: −0.029 to −0.007) for calculation, −0.023 (95% CI: −0.030 to −0.014) for immediate recall, −0.044 (95% CI: −0.055 to −0.034) for delayed recall, and −0.008 (95% CI: −0.015 to −0.001) for visuospatial ability. The mediated proportions ranged from 17.39% for visuospatial ability to 60.27% for delayed recall.

3.6. Additional Path Analyses for BADL and IADL

To clarify the differential implications of BADL and IADL, we performed supplementary analyses using BADL and IADL separately in place of the composite ADL measure, with global cognition as the outcome. As shown in Figure S1 and Tables S1-S2, the overall pattern of indirect associations remained similar, although the strength of the associations varied between BADL and IADL.

3.7. Sensitivity Analyses for Model Direction

In sensitivity analyses, as shown in Figure S2 and Tables S3-S4, the alternative directional model from cognition to ADL through depression also showed significant paths, suggesting that the observed associations may be compatible with bidirectional or mutually reinforcing processes. The significance of the alternative model suggests that the relationships among ADL, depression, and cognition may involve bidirectional or mutually reinforcing processes rather than a strictly one-way mechanism.

4. Discussion

The present study found that Activities of Daily Living (ADL) limitations were associated with poorer global and domain-specific cognitive function among older adults with chronic diseases. Depressive symptoms accounted for part of these associations in the cross-sectional path models, and the proportion of indirect association varied across cognitive domains. The BADL/IADL additional analyses showed broadly consistent patterns. These findings suggest that functional limitations and depressive symptoms are closely intertwined in relation to cognitive performance in this population.

The direct association between ADL limitations and global cognitive function, together with the indirect association through depressive symptoms, may reflect the close interconnection of physical functioning, psychological well-being, and cognitive performance among older adults with chronic diseases. ADL limitations may be accompanied by reduced mobility, lower physical activity, fewer opportunities for social participation, and increased reliance on caregivers.²⁶ These conditions may be linked not only to poorer cognitive performance but also to depressive symptoms through loss of independence and reduced perceived control.^27,28 Depressive symptoms, in turn, may be associated with cognitive performance through lower motivation, attention, processing efficiency, and engagement in daily activities.²⁹ Thus, functional limitations and depressive symptoms may jointly characterize a vulnerable profile associated with poorer cognitive function among older adults with chronic diseases.

In the domain-specific analyses, ADL limitations were associated with lower performance across all cognitive domains, with indirect associations through depressive symptoms also observed. However, the proportion of the indirect association varied substantially across cognitive domains, ranging from 17.39% for visuospatial ability to 60.27% for delayed recall. This domain-specific pattern may reflect differences in the sensitivity of cognitive functions to depressive symptoms. Delayed recall relies on attention, encoding, consolidation, and retrieval processes, and is closely linked to hippocampal and medial temporal lobe memory systems.^30,31 Depressive symptoms have also been associated with poorer memory performance and alterations in hippocampal structure and fronto-limbic regulation.^32,33 These overlapping memory- and mood-related systems may partly explain why the indirect association through depressive symptoms was more pronounced for delayed recall. By contrast, visuospatial ability may depend more on perceptual, spatial, and parietal–occipital processes,³⁴ which may be less directly captured by depressive symptoms in the present model. Nevertheless, differences in these proportions may also reflect measurement error, unequal sensitivity of cognitive tests, or instability of estimates from cross-sectional path models. Clinically, this pattern suggests that poorer memory-related performance in older adults with chronic diseases and ADL limitations may need to be interpreted alongside depressive symptoms, rather than being viewed solely as a marker of cognitive impairment.

The additional analyses separating BADL and IADL showed patterns generally consistent with the combined ADL measure, suggesting that the observed associations were not driven by a single component of daily functioning. This separation is important because BADL and IADL capture different levels of functional impairment. BADL mainly reflects basic self-care capacity and physical dependence, and impairment in BADL may indicate more severe functional decline and greater need for daily care. In contrast, IADL involves more complex activities, such as managing household tasks, communication, transportation, and medication use, which require planning, attention, memory, executive function, and interaction with the environment.³⁵ Therefore, IADL limitations may be more sensitive to early cognitive vulnerability, whereas BADL limitations may reflect more advanced functional impairment.³⁶ The consistent findings across BADL and IADL support the use of the combined ADL measure and suggest that both basic and instrumental functional limitations are relevant to the observed ADL–depressive symptoms–cognitive performance pattern.

Previous evidence on the ADL–depressive symptoms–cognition relationship remains mixed, particularly regarding the direction of these associations. Some studies have examined ADL limitations and cognitive impairment as correlates of depressive symptoms,³⁷ whereas others have positioned cognitive function as an antecedent of depressive symptoms through ADL.³⁸ A recent national study also reported that the indirect association involving ADL was observed only among men, further suggesting that these pathways may vary across populations and model specifications.³⁹ Consistent with this uncertainty, the alternative directional model in the present study also showed significant paths, suggesting that the observed associations may be bidirectional or mutually reinforcing rather than strictly one-way. Longitudinal studies are needed to clarify the temporal ordering among functional limitations, depressive symptoms, and cognitive performance.

5. Strengths and Limitations

This study has several strengths. First, it examined both global and domain-specific cognitive outcomes, providing a more nuanced understanding of the associations among ADL limitations, depressive symptoms, and cognitive performance. Second, the additional analyses separating BADL and IADL, together with the alternative directional model, helped clarify the consistency and interpretive boundaries of the observed associations.

Nevertheless, several limitations should be noted. First, ADL limitations, depressive symptoms, chronic disease status, and health-related behaviors were self-reported, which may have introduced recall and reporting bias. Second, the cross-sectional design precludes establishing temporal ordering among ADL limitations, depressive symptoms, and cognitive function. Thus, the observed indirect association should be interpreted within the cross-sectional path model rather than as evidence of causal mediation. Finally, residual confounding may remain, as cognitive reserve, social support, and other health-related characteristics were not fully captured in the available data.

6. Conclusion

In conclusion, this study indicates that functional limitations, depressive symptoms, and cognitive performance are closely interrelated in older adults with chronic diseases. The findings extend previous work by showing that this interrelationship differs across cognitive domains, with memory-related performance appearing particularly relevant to depressive symptoms. The additional BADL/IADL analyses further suggest that both basic and instrumental functional limitations are involved in this functional–psychological–cognitive profile. These findings support a more integrated interpretation of functional status, depressive symptoms, and cognition in older adults with chronic diseases, while longitudinal studies are needed to clarify temporal ordering and clinical implications.

Supplemental Material

Supplemental Material - Daily Living, Depressive Symptoms, and Cognition in Older Adults With Chronic Diseases

Supplemental Material for Daily Living, Depressive Symptoms, and Cognition in Older Adults With Chronic Diseases by Cui Luo, Xiannan Xian, Jing Yin and Yu Shi in American Journal of Alzheimer’s Disease & Other Dementias®.

Supplemental Material

Supplemental Material - Daily Living, Depressive Symptoms, and Cognition in Older Adults With Chronic Diseases

Footnotes

Acknowledgments

We gratefully acknowledge the dedication of the investigators, staff members, and survey participants of the CHARLS, whose continuous efforts in designing, implementing, and maintaining this national research infrastructure enabled the use of these valuable data.

ORCID iDs

Cui Luo

Xiannan Xian

Ethical Considerations

The studies were approved by the Peking University Biomedical Ethics Review Committee (IRB00001052-11015). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author Contributions

CL led the conceptualization of the study, conducted data curation and formal analysis, drafted the original manuscript, and contributed to reviewing and editing. XN contributed to drafting the original manuscript, conducted formal analysis, and assisted with reviewing and editing. JY contributed to reviewing and editing the manuscript. YS provided critical review and editing of the manuscript. CL and YS jointly supervised the study and served as co-corresponding authors.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Data Availability Statement

Publicly available datasets were analyzed in this study. This data can be found at: .

Supplemental Material

Supplemental material for this article is available online.

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