Associations of pre-stroke function disability and post-stroke cognitive impairment among older adults in China

Abstract

Cognitive impairment frequently occurs following stroke, yet its underlying risk factors remain insufficiently elucidated. This study explored how pre-stroke disability, related factors, and post-stroke cognitive results are connected in elderly Chinese individuals. This study analyzed China Health and Retirement Longitudinal Study (CHARLS) data from 2015 to 2018 to evaluate activities of daily living (ADL) and instrumental activities of daily living (IADL) disabilities using standardized scales. Binary logistic regression analyzed the potential link between pre-stroke ADL disability and subsequent post-stroke cognitive impairment. Subgroup analyses were stratified by age, gender, residence, and education, with sensitivity analyses conducted to test result robustness. Out of 404 participants, 18.1% experienced cognitive decline following a stroke. The logistic regression analysis identified a strong link between functional limitations in ADL or IADL before the stroke and subsequent cognitive impairment (P < 0.001). Those with impaired ADL function had an elevated risk (OR = 2.20; 95% CI: 1.27–3.75), and a similar pattern was observed for IADL limitations (OR = 3.31; 95% CI: 1.94–5.65). These findings were further supported by sensitivity analyses conducted with linear regression models, reinforcing the reliability of the observed associations. This research verified that limitations in ADL and IADL prior to stroke were significantly linked to cognitive decline following stroke. Among older stroke patients in China, both ADL and IADL impairments emerged as relevant predictors of post-stroke cognitive impairment. Evaluating functional status in adults aged 45 and above may aid in the early detection of those at elevated risk for future cognitive deterioration.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-026-39018-0.

Introduction

Stroke remains a major global health concern, particularly among middle-aged and older adults, imposing a substantial burden of disease¹. In China, it ranks among the leading contributors to mortality and disability-adjusted life years (DALYs) lost². Cognitive impairment and dementia frequently occur in stroke survivors, significantly complicating both medical care and daily support. Consequently, comprehensive investigations into the epidemiology and risk factors of cognitive decline after stroke are crucial for developing targeted strategies aimed at prevention and early intervention.

Among the many potential influencing factors, functional ability has received more and more attention³. Functional ability is usually assessed by basic activities of daily living (ADL) and instrumental activities of daily living (IADL). ADLs refer to the essential abilities necessary for an individual to manage self-care without assistance⁴. IADL cover more advanced skills for independent living compared to ADL. Participants’ ADLs were measured across six domains (dressing, bathing, eating, getting out of bed, toileting and continence), whereas instrumental activities of daily living were assessed via five domains (housework, cooking, shopping, medication use and financial management). ADL/IADL disability represents a major public health concern, as loss of independence increases individuals’ vulnerability to their environment and is associated with reduced quality of life^5,6. According to a large number of previous studies, lower ADL scores are associated with a range of adverse outcomes, including depression, grip strength, and memory-related disorders^7–9.

In recent years, numerous studies focusing on middle-aged and older adults have revealed a complex relationship between functional disability, mental health, and cognitive ability. A previous cross-sectional study found that older adults with lower levels of physical activity generally had poorer cognitive function and mental well-being, suggesting that increasing physical activity may help enhance cognitive function and mental health in the elderly¹⁰. A longitudinal study based on national data from China and Europe indicated a significant bidirectional relationship between disabilities in activities of daily living (ADL) and instrumental activities of daily living (IADL) and multimorbidity. ¹¹ Studies have shown that 57% of patients develop cognitive impairment within one year after stroke, with only one-third having purely vascular causes, suggesting that post-stroke cognitive impairment is a complex condition often involving both vascular and degenerative factors¹².In light of this, it becomes particularly important to further explore its underlying mechanisms and to control modifiable risk factors through early prevention strategies. Among the existing studies, the risk of post-stroke cognitive impairment in middle-aged and older adults’ ins insufficiently explored in localized research. These findings highlight the need to explore the longitudinal association between pre-stroke ADL/IADL disability and subsequent cognitive outcomes after stroke.

To address this research gap, the present study leveraged nationally representative data from the China Health and Retirement Longitudinal Study (CHARLS). We first conducted a cross-sectional analysis based on the Wave 3 dataset (2015) to explore the correlation between pre-stroke ADL/IADL limitations and post-stroke cognitive function among Chinese stroke patients aged. Meanwhile, a longitudinal analysis was performed using the Wave 4 dataset (2018) to further verify the association between ADL/IADL limitations and post-stroke cognitive function. This study aims to provide objective scientific evidence for elucidating the pathogenesis of post-stroke cognitive impairment and formulating its early intervention measures as well as prevention and control strategies.

Materials and methods

Study population

We analyzed information from CHARLS, a large-scale, population-based longitudinal study targeting adults in China aged 45 and older¹³. The CHARLS project focuses on analyzing China’s population aging problem and promoting interdisciplinary aging research, with surveys conducted every 2 to 3 years. CHARLS is a biennial or triennial survey using a multi-stage, probability-proportional-to-size (PPS) sampling design, covering 28 provinces, 150 counties, and 450 villages across China. Face-to-face interviews are employed to obtain participant information, including their demographics, social, economic, and health conditions. In addition, physical measurements and blood sample collection are part of CHARLS. Additional information regarding the sampling design and data quality management is available in other documents¹³.CHARLS received ethical clearance from the Institutional Review Board of Peking University (approval number: IRB00001052-11015). Prior to enrollment, all participants provided written informed consent.

Baseline information was gathered during the third wave of the CHARLS survey in 2015 (Wave 3), while follow-up data were derived from the fourth wave conducted in 2018 (Wave 4). Participants responded to structured questionnaires that captured data on sociodemographic variables, lifestyle behaviors, and health conditions.

Based on predefined criteria, 25,182 individuals who did not meet the inclusion requirements were excluded from the analysis: (1) individuals younger than 45 years; (2) missing demographic information; (3) self-reported stroke history at baseline; (4) incomplete ADL or IADL data for both 2015 and 2018;(5) missing cognitive function assessments in 2015 and 2018;(6) presence of cognitive impairment in 2015.Ultimately, 404 participants were included in the final analysis. The process used to select participants for the study is described in detail, as shown at the end in Fig. 1.

Fig. 1.

Flowchart on the sample selection.

Assessment of ADL/IADL disability

Functional disability was evaluated separately during waves 3 (2015) and 4 (2018) using assessments based on ADL and IADL questionnaires.

ADL tasks include basic self-care such as dressing, bathing, transferring between bed and chair, toileting, eating, and controlling bladder and bowel functions¹⁴. In contrast, IADL refers to more complex daily activities like household maintenance, shopping, meal preparation, medication management, and financial handling¹⁵.

Response options included: (i) “No difficulty at all”; (ii) “Some difficulty, but task can be completed independently”; (iii) “Difficulty requiring assistance”; and (iv) “Completely unable to perform the task.” The response items employed in the IADL scale and ADL scale are consistent in content and format. Participants were considered functionally impaired if they reported difficulty or inability in any of the 11 activities; individuals with incomplete data for any item were labeled as missing.

To enhance sensitivity and provide a more comprehensive measure of functional impairment, the ADL and IADL scales were merged into a single composite index, enabling a broader range of disability to be captured. Consistent with prior studies^8,11,16, outcomes were dichotomized: any level of dependence in at least one ADL or IADL task was classified as disability.

Assessment of cognitive function

Cognitive performance was measured based on methods adapted from earlier studies using CHARLS data. The evaluation focused on episodic memory and mental acuity, drawing on a framework comparable to that of the U.S. Health and Retirement Study (HRS)¹⁷, and utilized a modified version of the Telephone Interview for Cognitive Status (TICS) questionnaire.

Episodic memory was evaluated using a 10-word recall test, where participants were first asked to immediately recall words after hearing them read aloud by the interviewer. Following a delay of approximately four minutes, a second recall was conducted. The episodic memory score was calculated as the average of the immediate and delayed recall scores, with possible values ranging from 0 to 10.

Mental acuity was measured through a combination of tasks, including reproducing a geometric figure, identifying the current date, season, and weekday, as well as performing a serial subtraction task (subtracting seven from 100 in five successive steps). The total cognitive function score ranged from 0 to 21, with higher scores reflecting greater cognitive ability. Consistent with existing research, cognitive impairment was defined as scoring one or more standard deviations below the average cognitive performance level¹⁸.

Covariates

The analysis adjusted for various potential confounders, including demographic, socioeconomic, and health-related factors. These included age, gender, body mass index (BMI), yearly household expenditure, educational attainment (categorized as primary education or lower vs. secondary education or higher), type of residence (urban vs. rural), and marital status (living with spouse, married but living apart, single, divorced, or widowed). Lifestyle factors such as current drinking frequency (more than once monthly, less than once monthly, or never) and current smoking status were also included. Additionally, the model controlled for the presence of chronic illnesses, classified as none, one, or more than two conditions. For the evaluation of depression symptoms, the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10) was used, and this scale has a total score of 30.

Statistical analysis

Descriptive analyses were conducted to summarize the dataset, with continuous variables expressed as means and standard deviations (SD), and categorical variables reported as counts and percentages. Participants were stratified by cognitive impairment status in 2018. For categorical data, chi-square tests were used to assess group differences, while continuous variables were analyzed using one-way ANOVA if normally distributed, or the Kruskal–Wallis test when normality assumptions were violated.

To explore the relationship between pre-stroke functional limitations and cognitive decline after stroke, longitudinal data from 2015 to 2018 were analyzed using logistic regression models to explore the relationship between pre-stroke functional limitations and cognitive impairment. Three different models with various combinations of covariates were utilized. Model 1 was unadjusted; Model 2 controlled for age and sex; and Model 3 included additional covariates such as current smoking status, current drinking frequency, number of chronic conditions, cognition in baseline depression symptoms and BMI.

Subgroup analyses were further conducted according to age group (45–59 vs. ≥60 years), sex, educational attainment, and urban versus rural residence. All significance tests were two-sided, and a p-value < 0.05 was considered statistically significant. Data analyses were performed using The R Project for Statistical Computing version 4.4.2 (https://www.r-project.org/), developed by the R Foundation for Statistical Computing in Vienna, Austria.

Results

Baseline characteristics

Overall, a total of 404 participants who were newly diagnosed with stroke during the 2018 wave were included in this study. The average age was 61.73 ± 8.00 years. Of them, 52.72%were male (n = 213),18.1% of the participants had post-stroke cognitive impairment at the time of follow-up. Among those who developed post-stroke cognitive impairment, 38.36% of participants had ADL disability in 2015, and 45.2% had IADL disability in 2015. The average age of the post-stroke cognitive impairment group (65.48 ± 7.11) was higher than that of the cognitive normal group (60.900 ± 7.96). Post-stroke cognitive impairment was also more common in those with baseline cognitive impairment, rural residence, low educational attainment, and low IADL levels. Baseline characteristics are described in greater detail in Table 1.

Table 1.

Baseline characteristics of participants.

Variable	Level	Overall	Cognitive impairment	Normal	p
n		404	73	331
Age		61.728 (7.998)	65.479 (7.114)	60.900 (7.955)	< 0.001
BMI		24.947 (4.836)	24.316 (8.503)	25.096 (3.456)	0.247
Annual Household Expenditure		15115.465 (23612.229)	10264.810 (9121.472)	15953.850 (25207.432)	0.150
Sex (%)	Female	191 (47.28)	36 (49.32)	155 (46.83)	0.798
	Male	213 (52.72)	37 (50.68)	176 (53.17)
Residence (%)	Rural	224 (55.45)	49 (67.12)	175 (52.87)	0.037
	Urban	180 (44.55)	24 (32.88)	156 (47.13)
Marital status (%)	Married and living with a spouse	333 (82.63)	57 (78.08)	276 (83.64)	0.428
	Married but living without a spouse	17 (4.22)	3 (4.11)	14 (4.24)
	Single, divorced, and windowed	53 (13.15)	13 (17.81)	40 (12.12)
Education Status (%)	Elementary school or below	228 (56.44)	62 (84.93)	166 (50.15)	< 0.001
	Middle school or above	176 (43.56)	11 (15.07)	165 (49.85)
Ever/current smoker (%)	No	201 (49.75)	36 (49.32)	165 (49.85)	1.000
	Yes	203 (50.25)	37 (50.68)	166 (50.15)
Drinking Status (%)	Drink but less than once a month	40 (9.93)	7 (9.59)	33 (10.00)	0.991
	Drink more than once a month	95 (23.57)	17 (23.29)	78 (23.64)
	Non-drinker	268 (66.50)	49 (67.12)	219 (66.36)
Number of chronic diseases (%)	≥ 2	302 (74.75)	57 (78.08)	245 (74.02)	0.702
	0	37 (9.16)	5 (6.85)	32 (9.67)
	1	65 (16.09)	11 (15.07)	54 (16.31)
Cognitive in 2015		11.6 (2.8)	8.7 (2.9)	12.1 (2.5)	< 0.001
ADL (%)	No	303 (75.00)	45 (61.64)	258 (77.95)	0.006
	Yes	101 (25.00)	28 (38.36)	73 (22.05)
IADL (%)	No	305 (75.50)	40 (54.79)	265 (80.06)	< 0.001
	Yes	99 (24.50)	33 (45.21)	66 (19.94)
ADL score		0.5 (1.0)	0.7 (1.2)	0.4 (1.0)	0.019
IADL score		0.4(0.9)	0.9(1.3)	0.3(0.7)	< 0.001

* Values are mean (SD) or n (%) as appropriate. All p values are two-tailed. Bold font indicates significant difference between groups.

Association between pre-stroke disability and post-stroke cognitive impairment

To further clarify the association between pre-stroke disability and post-stroke cognitive impairment, we reanalyzed their relevance by controlling for the covariates in the base model (Table 2). In Model 1, without adjusting for any covariates, ADL disability (OR = 2.20, 95% CI: 1.27–3.75) and IADL disability (OR = 3.31, 95% CI: 1.94–5.65) were both significantly associated with post-stroke cognitive impairment. After adjusting for age and gender, ADL disability was significantly associated with post-stroke cognitive impairment (OR = 1.85, 95% CI: 1.05–3.22). Similarly, IADL disability (OR = 2.97, 95% CI: 1.69–5.22) was also significantly associated with post-stroke cognitive impairment. After adjusting for age, gender, the number of chronic diseases, BMI, ever/current smoker, drinking status, depression symptoms and cognition score in baseline as potential confounding variables, While the association between ADL disability and post-stroke cognitive impairment was attenuated after full adjustment, IADL disability remained significantly associated with cognitive outcomes (p < 0.05). For a more comprehensive analysis of pre-stroke function Disability and post-stroke cognitive impairment, we re-analyzed the ADL and IADL scores as continuous variables separately, with the results shown in Table 3; this analysis confirmed the robustness of the association between them.

Table 2.

Associations between pre-stroke ADL/IADL disability for post-stroke cognitive impairment in different models.

	Model 1		Model 2		Model 3
	OR (95% CI)	p-value	OR (95% CI)	p-value	OR (95% CI)	p-value
ADL	2.20 (1.27 ~ 3.75)	0.0041	1.85 (1.05 ~ 3.22)	0.0305	1.90 (0.84 ~ 4.29)	0.119
IADL	3.31 (1.94 ~ 5.65)	< 0.001	2.97 (1.69 ~ 5.22)	< 0.001	2.73 (1.10 ~ 6.81)	0.0297

Model 1: No adjustment.

Model 2: Adjusted for age, gender.

Model 3: Adjusted for age, gender, smoking status, drinking status, number of chronic conditions, BMI, cognition in 2015 + depression symptoms.

Table 3.

Associations between pre-stroke ADL/IADL score for post-stroke cognitive impairment in different models.

	Model 1		Model 2		Model 3
	OR (95% CI)	p-value	OR (95% CI)	p-value	OR (95% CI)	p-value
ADL Score	1.27 (1.03 ~ 1.56)	0.023	1.19 (0.95 ~ 1.47)	0.117	1.32 (0.83 ~ 4.04)	0.0741
IADL Score	1.93 (1.50 ~ 2.51)	< 0.001	1.89 (1.45 ~ 2.50)	< 0.001	1.59 (1.08 ~ 2.39)	0.0204

Model 1: No adjustment.

Model 2: Adjusted for age, gender.

Model 3: Adjusted for age, gender, smoking status, drinking status, number of chronic conditions, BMI, cognition in 2015 + depression symptoms.

Subgroup analysis

Subgroup analyses were conducted based on age, gender, residence, and educational level.

As shown in Fig. 2, ADL disability was significantly associated with an increased risk of cognitive impairment among women (OR = 2.55, 95% CI: 1.19–5.43), whereas the association was not statistically significant among men (OR = 1.87, 95% CI: 0.83–4.02).As depicted in Fig. 3, pre-stroke IADL disability is significantly linked to a heightened risk of cognitive impairment following a stroke in both males and females (Male: OR = 4.05, 95% CI: 1.81–8.97; Female: OR = 2.91, 95% CI: 1.39 − 6.18).

Fig. 2.

Logistic regression analysis of ADLs disability among subgroups.

Fig. 3.

Logistic regression analysis of IADLs disability among subgroups.

Further, participants with pre-stroke ADL disability that were aged ≥ 60 years, had a higher risk of post-stroke cognitive impairment. Furthermore, participants aged over 60 years who had pre-stroke ADL limitation exhibited a significantly higher risk of developing post-stroke cognitive impairment (OR = 2.25, 95% CI: 1.21–4.15). A similar association was observed for those with limitations in IADL (OR = 3.20, 95% CI: 1.73–5.95), indicating that pre-stroke functional decline in older adults is a key predictor of cognitive deterioration following stroke.

Subgroup analysis based on place of residence revealed that rural participants with pre-stroke ADL disability had a significantly elevated risk of post-stroke cognitive impairment (OR = 2.28, 95% CI: 1.15–4.49, p = 0.017), whereas the association in urban residents was not statistically significant (OR = 2.07, 95% CI: 0.81–5.08, p = 0.115). A similar pattern was observed in the IADL model. Rural participants with pre-stroke IADL disability had a markedly increased risk of post-stroke cognitive impairment (OR = 4.17, 95% CI: 2.13–8.21, p < 0.001), whereas the association in urban participants remained non-significant (OR = 2.02, 95% CI: 0.76–5.03, p = 0.142).

In terms of educational level, the association between pre-stroke ADL disability and post-stroke cognitive impairment remained significant among participants with elementary education or below (OR = 1.86, 95% CI: 1.00–3.46, p = 0.049), while no statistically significant association was observed in those with middle school education or above (OR = 2.47, 95% CI: 0.62–8.72, p = 0.169). Similarly, in the IADL model, a significant association was found in participants with elementary education or below (OR = 2.36, 95% CI: 1.29–4.34, p = 0.005).

Sensitivity analysis

In sensitivity analysis, we used linear regression to repeatedly analyze the association between pre-stroke disability and pro-stroke cognitive impairment. Considering the differences in sociodemographic factors, we used multiple imputation by chained equations (MICE) to impute the missing covariates. The multivariate-adjusted association between pre-stroke disability and pro-stroke cognitive impairment persisted significantly compared to the reference group of no depressive symptoms (Additional Table 1).

Discussion

In this study, we observed a significant association between higher levels of pre-stroke ADL/IADL disability and post-stroke cognitive impairment. Patients with pre-stroke ADL/IADL disabilities were at greater risk of developing cognitive impairment after stroke. Subgroup analyses by age, gender, residence, and education level suggested that stroke patients aged over 60, females, those living in rural areas, and those with an education level of elementary school or below were at higher risk of cognitive impairment. Given that IADL tasks involve higher cognitive demands, the observed association may partly arise from the cognitive components inherent in IADL assessment.

In our study, 25% of participants had pre-stroke ADL disability, and 24.5% had pre-stroke IADL disability. This finding is consistent with an observational study involving 3,432 stroke participants, which reported that approximately 45% of the participants had premorbid disability¹⁹.A longitudinal study of stroke patients with pre-existing disabilities revealed that the prevalence of mild pre-stroke disability increased with age, was disproportionately higher among females, and was more common in individuals from lower socioeconomic backgrounds compared to those with higher socioeconomic status²⁰.This is generally consistent with the findings from our subgroup analysis.

Post-stroke cognitive impairment has emerged as a significant and pressing complication in stroke survivors. Up to 72% of patients demonstrate some degree of cognitive impairment following stroke²¹.A study on the stroke population in Ireland projected that by 2025, approximately 49% of stroke survivors will experience cognitive impairment, and 24.6% may progress to dementia²². Similarly, a study conducted in Chongqing, China, reported a post-stroke cognitive impairment prevalence of 37.1%, with stroke-related cognitive impairment at 32.2%, and a 29.6% prevalence following a first-ever stroke²³. A lower baseline functional status has been identified as a potential factor for post-stroke cognitive impairment and dementia²⁴.Among stroke patients, the burden of stroke-related disability may even begin to emerge before the onset of the stroke itself²⁵.The influence of pre-stroke functional status is not confined to short-term neurological recovery but resonates throughout the long-term course of cognitive and physical functioning.

The underlying mechanisms linking pre-stroke disability to post-stroke cognitive impairment are likely multifactorial. Beyond the direct physiological burden, the association may be mediated by inflammation-related pathways. Physical limitations often exacerbate psychological vulnerability, which has been linked to dysregulated immune responses. Recent evidence highlights that inflammation-related psychological vulnerability plays a critical role in neurocognitive outcomes²⁶, suggesting that chronic functional impairment may trigger systemic inflammation and neuroinflammatory pathways, ultimately accelerating cognitive decline following a stroke.

Previous studies have shown that pre-stroke functional status is an important predictor of stroke prognosis. Older adults with pre-stroke mobility limitations and dementia are more likely to experience poor functional outcomes and an increased risk of mortality after stroke²⁷.Pre-stroke physical activity is significantly positively associated with physical quality of life three months after stroke, with a more pronounced effect observed among patients with mild disability²⁸.

Moreover, previous studies have found that lower levels of daily living activities may be associated with a higher likelihood of developing memory-related diseases⁹.Moderate-intensity physical activity helps maintain cognitive function in middle-aged and elderly individuals and may provide long-lasting benefits²⁹. Further studies have demonstrated that pre-stroke functional status also has strong predictive value for discharge destination³⁰.These findings suggest that early assessment and promotion of functional capacity prior to stroke onset may provide valuable opportunities for improving post-stroke outcomes and mitigating cognitive impairment.

The factors outlined in the final model affect post-stroke cognitive outcomes through complex physiological mechanisms that are not fully comprehended, possibly working through multiple pathways. Pre-stroke disability most likely reflects chronic cerebrovascular pathology, particularly cerebral small vessel disease, chronic cerebral hypoperfusion, and white matter lesions that lead to functional decline. These underlying conditions serve as a high-risk foundation for the development of post-stroke cognitive impairment³¹. A study found that among 224 elderly patients with depression, larger volumes of white matter lesions were associated with more severe functional impairments in basic activities of daily living (BADL) and IADL. This association remained significant even after controlling for variables such as age, gender, severity of depression, and physical illnesses³².In the acute phase, pre-stroke disability may prolong the time window for symptom recognition and medical consultation, thereby potentially affecting treatment effectiveness³³.The severity of a patient’s pre-stroke disability may also be a key factor in physicians’ decisions regarding endovascular treatment, and insufficient treatment could further exacerbate post-stroke cognitive decline³⁴.Moreover, follow-up research examining endovascular therapy administered beyond the traditional time window in patients with large vessel occlusion indicated that individuals with pre-existing disabilities exhibited significantly lower rates of functional recovery than their non-disabled counterparts³⁵.Similarly, another study reported increased mortality among stroke patients with prior disabilities who underwent intravenous thrombolysis, in contrast to those without such impairments³⁶.In addition, indicators of physical frailty—such as slower gait speed and diminished handgrip strength—have been strongly linked to both cognitive performance and daily functional ability in stroke survivors³⁷.These findings highlight the necessity of tailoring treatment plans to patients’ baseline functional status and emphasize the role of timely rehabilitation and preventive interventions in alleviating the compounding impact of disability on cognitive outcomes after stroke.

Strengths and limitation

In China, a nationally representative sample was used to investigate the association between pre-stroke disability and post-stroke cognitive impairment, with the aim of gaining a deeper understanding of how pre-stroke functional limitations affect cognitive outcomes among older adults. The results revealed a significant link between pre-stroke disability and subsequent cognitive decline. Whether considered as a risk factor or an early manifestation, monitoring the functional status of older adults prior to stroke onset is crucial for identifying potential post-stroke cognitive impairment. Strengthening comprehensive management and developing preventive strategies for at-risk individuals may help reduce the risk and burden of post-stroke cognitive impairment.

Several limitations of the present study should be considered. First, the study did not control for other potentially important factors, such as social environment, psychological status, and physical health, which may influence the relationship between pre-stroke functional disability and post-stroke cognitive impairment. Given that this study utilized data from a longitudinal cohort, it was not feasible to examine all potential factors influencing functional outcomes after stroke. This may limit the comprehensiveness and interpretability of the findings. Second, Similar to the limitations noted in other related studies, this study relied on self-reported data for functional disability. Particularly in cases where the measurement tool lacks sufficient sensitivity to slight changes in functional capacities, this scenario may affect how stable the associations are. Third, given the exclusion of a substantial number of participants due to missing data, potential selection bias cannot be ruled out. Those included might represent a healthier subset of the population.

Conclusion

Among middle-aged and older adults with stroke in China, pre-stroke ADL and IADL impairments are significantly associated with post-stroke cognitive impairment. Paying early attention to ADL and IADL limitations in older adults may help in the timely and comprehensive identification of individuals at risk for post-stroke cognitive decline.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

X.H. wrote the main manuscript text, Z.M made significant contributions to the revision process and T.X. reviewed and proofread the manuscript. All authors approved the final version of the manuscript.

Data availability

Further information regarding the CHARLS dataset can be accessed at the following link: http://charls.pku.edu.cn/pages/data/111/zhcn.html.The datasets supporting the findings of this study can be obtained from the corresponding author upon reasonable request.

Declarations

Competing interests

The authors declare no competing interests.

Ethics statement

The human studies received approval from the Institutional Review Board of Peking University (IRB00001052-11015). All procedures were conducted in compliance with applicable laws and institutional guidelines. In accordance with national regulations and institutional policies, written informed consent from participants or their legal representatives/next of kin was not required.