Abstract
A substantial body of literature indicates that cognitive dysfunction adversely affects activities of daily living in patients with acute ischemic stroke (AIS) and is associated with an accelerated onset of frailty. This study aims to verify the association between cognitive dysfunction and frailty in patients with AIS and to explore the serial mediating role of fall risk and independent living skills in this relationship. Participants were consecutively enrolled from August 2023 to December 2024 among hospitalized patients diagnosed with AIS in the neurology department of a tertiary care hospital, using convenience sampling. Data were collected using standardized assessment instruments, including the General Information Questionnaire, the Montreal Cognitive Assessment-Basic Scale, the Morse Fall Scale, the Barthel Index Scale, and the Edmonton Frail Scale. Mediation analyses were conducted using IBM SPSS 29.0 and the PROCESS macro (version 4.1, Model 6). Cognitive dysfunction exhibited significant negative correlations with fall risk (r = −0.249, P < .001) and frailty (r = −0.376, P < .001) and a significant positive correlation with independent living skills (r = 0.315, P < .001). Fall risk was significantly positively correlated with frailty (r = 0.391, P < .001) and significantly negatively correlated with independent living skills (r = −0.448, P < .001). Independent living skills were also significantly negatively correlated with frailty (r = −0.478, P < .001). Mediation analysis revealed that cognitive dysfunction had a direct effect on frailty (−0.032), accounting for 61.54% of the total effect (−0.052); fall risk and independent living skills exerted a partial mediating role in this relationship, with a total indirect effect of −0.020, contributing to 38.46% of the total effect. The association between cognitive dysfunction and frailty in patients with AIS is mediated by fall risk and independent living skills. Clinically, interventions targeting cognitive dysfunction may reduce fall risk and improve independent living skills, thereby mitigating frailty symptoms.
Keywords: acute ischemic stroke, chain mediation analysis, cognitive dysfunction, fall risk, frailty, independent living skills
1. Introduction
Frailty is defined as a decline in an individual’s resilience across multiple domains, including physical, psychological, and social aspects, thereby reducing their capacity to withstand and recover from stressors.[1] A systematic review and dose-response meta-analysis included 16 studies involving a total of 55,897 patients, revealing that the prevalence of frailty among stroke survivors ranged from 11.2 to 75.3%, indicating a severe prevalence of frailty among this population, which warrants serious attention.[2] Stroke, the second leading cause of global mortality, affected approximately 101 million individuals in 2019.[3] Its high incidence, disability rate, and mortality impose a substantial long-term risk of functional decline among patients. Among all cerebrovascular events, ischemic stroke accounts for 70 to 80%.[4] In the acute ischemic stroke (AIS) population, frailty is not only frequently associated with infectious complications and long-term care requirements but also commonly coexists with cognitive dysfunction.[5–7] Patients with impaired cognitive function are more likely to experience activity limitations and a decline in their ability to perform activities of daily living, thereby increasing fall risk and accelerating the progression of frailty. However, existing research predominantly focuses on the direct effects of single factors and lacks a systematic examination of the sequential pathway from cognitive dysfunction to fall risk, independent living skills, and frailty. Therefore, this study employs chain mediation analysis to elucidate the mediating role of fall risk and independent living skills in the relationship between cognitive dysfunction and frailty, thereby providing a theoretical foundation for comprehensive assessment and targeted intervention in patients with AIS.
Cognitive dysfunction refers to a clinical syndrome characterized by memory loss, visuospatial impairment, and other symptoms that appear after a stroke and persist for up to 6 months. The American Academy of Neurology has classified cognitive dysfunction as a class A evidence risk factor for falls in stroke patients.[8] Previous studies have shown that the prevalence of cognitive dysfunction among survivors of AIS is 45.38%.[9] At the same time, a longitudinal survey focusing on Chinese AIS patients found that after adjusting for covariates, Barthel Index (BI) Scale scores remained lower in individuals with cognitive dysfunction than in those with normal cognition throughout the follow-up period.[10] Moreover, cognitive dysfunction is an independent risk factor for frailty in patients with AIS.[11] Frailty is independently associated with reduced penumbra and poorer neurological recovery in hyperAIS.[12] Research has confirmed that neurological deficit constitutes an independent risk factor for cognitive dysfunction in patients with AIS. It may therefore be inferred that favorable neurological recovery exerts a protective effect against cognitive dysfunction.[13]
Fall risk refers to the likelihood of an individual experiencing an accidental fall in a specific environment. It is a quantitative or qualitative description of potential fall risk based on the combined effects of internal physiological and disease factors and external environmental factors. Previous studies have shown that the incidence of falls among hospitalized patients with acute stroke is 3.3%, and among those who do fall, patients with AIS account for as much as 80.33%.[14] Previous studies have also shown that fall risk is one of the key factors influencing the occurrence of frailty in patients with acute stroke.[15] Cognitive dysfunction significantly associated with fall risk includes lack of illness awareness, impulsive behavior, inattention, and persistent movement.[16] Such cognitive dysfunction can lead to changes in gait and stride length in stroke patients, thereby increasing their risk of falls and fall-related injuries.[17] Research indicates that the incidence of falls and related injuries may further increase, attributed to the persistent rise in the prevalence of comorbidities, polypharmacy, and frailty within this population.[18] From this, it follows that in patients with AIS, cognitive dysfunction mediates frailty by increasing fall risk through gait and stride abnormalities.
Independent living skills refer to an individual’s ability to independently perform basic activities and tasks necessary for daily life without the need for continuous assistance from others or with only minimal assistance. Studies have shown that more than 2-thirds of stroke patients suffer from working memory, executive function, and episodic memory impairments.[17] Such cognitive dysfunctions can significantly increase patients’ dependence on daily life and reduce their independent living skills. A study further corroborates this conclusion: cognitive learning potential measured by the dynamic Wisconsin Card Sorting Test failed to add predictive value to rehabilitation outcomes in elderly stroke patients; independent living skills at admission remained the sole significant predictor of rehabilitation outcomes in this population.[19] Although frailty and functional limitations often coexist, evidence-based research shows that activities of daily living assessed using tools such as the BI Scale are not only important indicators of frailty but also independent factors influencing its development.[20,21]
Previous studies have shown that approximately 14 to 39% of stroke survivors experience 1 or more falls during their recovery process.[22] Such falls not only often lead to physical complications such as fractures, soft tissue injuries, and head injuries, but may also further trigger psychological consequences, such as reduced physical activity and increased social isolation.[23] Additionally, recurrent falls or fear of falling can undermine patients’ self-efficacy and confidence in daily activities, thereby accelerating functional decline.[24] A study revealed that 58.7% of patients with AIS experienced sleep disorders within 30 days of onset. The impact of cognitive function on sleep quality was mediated through anxiety and depression.[25] Repeated falls and the fear of falling can elevate patients’ psychological stress levels, thereby worsening their sleep quality. Chronic sleep deprivation, in turn, can further trigger or exacerbate anxiety and depressive symptoms, ultimately leading to increased frailty. From this, it follows that high fall risk not only directly impairs patients’ ability to live independently but also has adverse effects on their mental health. Cross-sectional study results also show that cognitive dysfunction in stroke patients is closely related to multiple daily functional activities, including upper limb dressing, bladder management, bed-to-chair transfers, toilet transfers, and walking.[26]
Existing studies have confirmed that multiple factors influence the prognosis of stroke patients; however, the specific associations between these 4 variables and AIS patients have not yet been established. Although previous studies have provided valuable insights, there remains a significant research gap regarding the association between changes in cognitive dysfunction and frailty, particularly among patients with AIS. Furthermore, the potential role of fall risk and independent living skills in this association has not been clarified. Therefore, this cross-sectional study aims to address this gap by analyzing the associations among cognitive dysfunction, fall risk, independent living skills, and frailty in patients with AIS, and to examine whether fall risk and independent living skills partially mediate the relationship between cognitive dysfunction and frailty.
Therefore, clarifying the relationship between cognitive dysfunction and frailty and identifying the intervenable mediating pathways involved is of great significance for achieving early risk identification and targeted interventions. This study constructed a theoretical hypothesis model (Fig. 1) and proposes the following research hypotheses: fall risk plays a mediating role in the relationship between cognitive dysfunction and frailty, independent living skills play a mediating role in the relationship between cognitive dysfunction and frailty, cognitive dysfunction can indirectly predict frailty level through the chain mediating effect of fall risk and independent living skills.
Figure 1.
Hypothesized framework of the chain mediation model. This figure illustrates the hypothesized relationships among cognitive dysfunction, fall risk, independent living skills, and frailty. The arrows indicate the hypothesized directional relationships between the variables.
2. Methods
2.1. Participants
This study employed convenience sampling to enroll hospitalized patients diagnosed with AIS from the neurology department of a tertiary care hospital between August 2023 and December 2024. Inclusion criteria included: meeting the diagnostic criteria for ischemic stroke as specified in the 2018 Guidelines for the Early Management of Patients with AIS[27]; patients being conscious and able to cooperate in completing relevant questionnaire surveys; and patients who were informed about the study content and signed an informed consent form. Exclusion criteria included: individuals with a history of depression or other mental health disorders; patients with severe liver, kidney, respiratory, or digestive system diseases; patients who had undergone major surgical procedures within the 6 months prior to enrollment; and patients with incomplete clinical data. Sample Size Calculation: The primary outcome measure of this study is frailty. A study involving 90,118 stroke patients reported a frailty prevalence (P) of 23%.[28] The allowable error (δ) was 0.05, and the significance level (α) was set at 0.05 (2-sided), with a corresponding value of 1.96. Calculation formula: n = tα2 P (1−P)/ δ2. Substituting into the formula: 1.962 × 0.23 × (1−0.23)/ 0.052 ≈ 272. Taking into account a 10% loss rate, a total of 303 people needed to be surveyed. This study included a total of 336 valid respondents, with an effective response rate of 90.11%.
2.2. Data collection
Prior to the commencement of data collection, all investigators underwent standardized training. The investigators were neurology nurses with specialized training in neuropsychological assessment delivered by board-certified psychiatrists, and all held the National Psychological Counselor Qualification (Level 3). A standardized questionnaire administration protocol (including a consistent set of scripted instructions) was implemented to ensure procedural uniformity across all patient interviews. Demographic characteristics and clinical parameters were extracted from the electronic medical record system. Prior to questionnaire administration, the study objectives and rationale were comprehensively explained to all participants. Written informed consent was obtained following ethical disclosure, and each participant signed the approved informed consent form. Questionnaires were collected immediately upon completion. Data entry was performed independently by 2 trained research assistants. A separate pair of investigators then conducted dual verification and cross-checking of all entered data. In cases of discrepancy, the original questionnaire was consulted to resolve inconsistencies and ensure data accuracy and completeness.
2.3. Ethical consideration
All participants provided written informed consent prior to study enrollment. The study was conducted in strict accordance with the principles of confidentiality and anonymity. This study received ethical approval from the Institutional Review Board of the affiliated hospital (Approval No.: 2023-02-C029) and was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki.
2.4. Outcome measures
2.4.1. General information questionnaire
Age, gender, educational level, type D personality, number of comorbid chronic diseases.
2.4.2. Montreal Cognitive Assessment-Basic (MoCA-B) Scale
The MoCA-B Scale was developed by Dr Nasreddine team.[29] Cognitive assessment of participants was conducted using the MoCA-B within 72 hours of hospital admission, covering executive function, immediate memory, fluency, orientation, calculation, abstraction, delayed memory, visual perception, naming, and attention. The total score ranges from 0 to 30, with higher scores indicating better cognitive function and less severe cognitive dysfunction. The Cronbach α coefficient of the scale was 0.858.
2.4.3. Morse Fall Scale
The Morse Fall Scale was developed by American scholar Morse in 1989 to predict the probability of an individual falling and has since been widely adopted in multiple countries and healthcare institutions.[30] The scale consists of 6 scoring items, including history of previous falls, presence of multiple clinical diagnoses, use of assistive devices while walking, status of intravenous infusion or medication therapy, gait characteristics, and mental status, with a maximum total score of 125 points. The Pearson correlation coefficients between different dimensions range from 0.72 to 0.93, indicating good internal consistency and stability.
2.4.4. BI Scale
The BI Scale was developed by Mahony and Barthel,[31] and this index is used to assess patients’ ability to perform activities of daily living, including bowel control, bladder control, personal hygiene, eating, bathing, dressing, toileting, transferring, walking, and stair climbing, comprising a total of 10 items. Each item is scored based on the degree of assistance required by the patient, with scores ranging from 15, 10, 5, to 0 points, resulting in a total score ranging from 0 to 100 points. This study used the Chinese version developed by Hou Dz,[32] with a Cronbach α coefficient of 0.916. Higher scores indicate better ability to perform activities of daily living.
2.4.5. Frail Scale
The Frail scale, developed by an expert team from the International Working Group on Nutrition, Health, and Aging in 2008,[33] is used to assess frailty. It includes 5 criteria: fatigue; resistance to exertion, such as difficulty climbing 1 flight of stairs; reduced mobility, inability to walk 1 block; ≥ 5 coexisting diseases; weight loss, > 5.0% weight decrease within 1 year. Each criterion met scores 1 point; unmet criteria score 0 points. Total score ranges from 0 to 5 points: 1 and 2 points indicate pre-frailty, and 3 to 5 points indicate frailty. The scale’s Cronbach α is 0.826.
2.5. Statistical analysis
Data analysis for this study was performed using IBM SPSS 29.0 software. Firstly, categorical data are presented as percentages, and group comparisons were performed using the chi-square test. Continuous data that met the assumption of normality are reported as mean ± standard deviation and were analyzed using the t-test. Then, the potential multicollinearity among the variables was assessed using the variance inflation factor. Harman single-factor test was employed to assess potential common method bias. Finally, the relationships between variables were examined using Pearson correlation analysis. The mediating effect was tested using the PROCESS v4.1 macro. The significance of the mediating effect was assessed using a Bootstrap estimation with 5000 resamples; the effect was deemed significant if the 95% Bootstrap confidence interval did not include zero. In addition, this study included age, gender, and educational level as covariates in the regression analysis and the chain mediation analysis.
All the assumptions underlying this analysis, including the normal distribution of residuals, linearity, homoscedasticity, and multicollinearity (variance inflation factor < 10), have been tested and found to be satisfied. The significance level for all statistical tests in this study was set at P < .05.
3. Results
3.1. Univariate analysis of frailty in patients with AIS
A total of 336 participants were included in this study and were classified into the non-frail group (n = 223) and the frail group (n = 113). The mean age of the frail group (67.7 ± 9.1 years) was higher than that of the non-frail group (61.9 ± 9.6 years). With respect to gender distribution, the proportion of males was higher in the non-frail group (68.6%) than in the frail group (56.6%), whereas the proportion of females was lower in the non-frail group (31.4%) compared to the frail group (43.4%). The results of the univariate analysis indicated that age, gender, and educational level showed statistically significant differences between the non-frail and frail groups (P < .05), whereas type D personality and the number of comorbid chronic diseases did not differ significantly (P > .05). Additional demographic characteristics are summarized in Table 1.
Table 1.
Comparison of sociodemographic characteristics between the frail group and the non-frail group (n = 336).
| Variables | Category | Total (n = 336) | Non-frail (n = 223) | Frail (n = 113) | χ2 /t | P value |
|---|---|---|---|---|---|---|
| Age | 63.9 ± 9.8 | 61.9 ± 9.6 | 67.7 ± 9.1 | −5.261 | < .001 | |
| Gender | 4.700 | .030 | ||||
| Male | 217 (64.6) | 153 (68.6) | 64 (56.6) | |||
| Female | 119 (35.4) | 70 (31.4) | 49 (43.4) | |||
| Educational level | 9.712 | .021 | ||||
| Primary School or Below | 156 (46.4) | 94 (42.2) | 62 (54.9) | |||
| Junior High School | 117 (34.8) | 87 (39.0) | 30 (26.5) | |||
| Senior High School or Technical Secondary School | 44 (13.1) | 26 (11.7) | 18 (15.9) | |||
| Junior College or Above | 19 (5.7) | 16 (7.2) | 3 (2.7) | |||
| Type D personality | 2.881 | .090 | ||||
| No | 220 (65.5) | 153 (68.6) | 67 (59.3) | |||
| Yes | 116 (34.5) | 70 (31.4) | 46 (40.7) | |||
| Number of comorbid chronic diseases | 2.738 | .434 | ||||
| 0 | 105 (31.2) | 71 (31.8) | 34 (30.1) | |||
| 1 | 131 (39.0) | 88 (39.5) | 43 (38.1) | |||
| 2 | 85 (25.3) | 57 (25.6) | 28 (24.8) | |||
| 3 | 15 (4.5) | 7 (3.1) | 8 (7.1) |
n = number of participants.
Bold = Statistically significant differences.
3.2. Common method bias
The results indicate that 6 factors had eigenvalues > 1, with the first common factor explaining 23.890% of the total variance, which did not reach the critical threshold of 40%. Therefore, there is no significant common method bias present in this study. The results of the linear regression show that the variance inflation factor values range from 1.019 to 1.586, indicating that there is no significant multicollinearity among the variables.
3.3. Correlations among cognitive dysfunction, full risk, independent living skills, and frailty
Table 2 presents the correlation coefficients and significance levels for the variables under study. In patients with AIS, independent living skills are significantly associated with multiple indicators: they exhibited a statistically significant positive correlation with cognitive dysfunction (r = 0.315, P < .001) and statistically significant negative correlations with fall risk (r = −0.448, P < .001) and frailty (r = −0.478, P < .001). Additionally, fall risk is not only associated with independent living skills as described above but also significantly positively correlated with frailty (r = 0.391, P < .001) and significantly negatively correlated with cognitive dysfunction (r = −0.249, P < .001). Cognitive dysfunction, in addition to being associated with independent living skills and fall risk, was also significantly negatively correlated with frailty (r = −0.376, P < .001).
Table 2.
Correlation coefficients of study variables (n = 336).
| Variables | Cognitive dysfunction | Fall risk | Independent living skills | Frailty |
|---|---|---|---|---|
| Cognitive dysfunction | 1 | |||
| Fall risk | −0.249 *** | 1 | ||
| Independent living skills | 0.315 *** | −0.448 *** | 1 | |
| Frailty | −0.376 *** | 0.391 *** | −0.478 *** | 1 |
n = number of participants.
P < .001; Bold = Statistically significant differences.
3.4. Chain mediation of fall risk and independent living skills in the relationship between cognitive dysfunction and frailty
The model using the PROCESS v4.1 macro program tested the mediating role of fall risk and independent living skills between cognitive dysfunction and frailty. The results of the regression analysis showed that, without including the mediating variables, cognitive dysfunction negatively predicted frailty. After including the mediating variables (Table 3), the positive predictive effect of cognitive dysfunction on independent living skills (β = 0.416, P < .01) was significant, while the negative predictive effects on frailty (β = −0.032, P < .001) and fall risk (β = −0.472, P < .001) were also significant. Fall risk had a significant negative predictive effect on independent living skills (β = −0.417, P < .001). Fall risk had a significant positive predictive effect on frailty (β = 0.013, P < .01). The predictive paths of each variable are shown in Figure 2. The results of the mediation effect analysis are shown in Table 4: the direct effect value of cognitive dysfunction on frailty is −0.032, accounting for 61.54% of the total effect (−0.052). The total indirect effect value of cognitive dysfunction on frailty, mediated through fall risk and independent living skills, was −0.020, accounting for 19.23% of the total effect (−0.052). Specifically, the mediation effect consists of a single mediation path for fall risk, a single mediation path for independent living skills, and a chain mediation path for fall risk and independent living skills. The indirect effect value of the single mediation path of fall risk is −0.006; the indirect effect value of the single mediation path of independent living skills is −0.009; the indirect effect value of the chained mediating path between fall risk and independent living skills is −0.005.
Table 3.
The chained mediation effect of fall risk and independent living skills.
| Regression equation | Overall fit indices | Regression coefficients | ||||
|---|---|---|---|---|---|---|
| Outcome variable | Predictor Variable | r | R 2 | F | β | t |
| Fall risk | Cognitive dysfunction | 0.310 | 0.096 | 8.793 *** | −0.472 | −3.35 *** |
| Age | 0.284 | 3.127** | ||||
| Gender | −1.474 | −0.816 | ||||
| Educational level | 1.564 | 1.589 | ||||
| Independent living skills | Cognitive dysfunction | 0.501 | 0.251 | 22.146 *** | 0.416 | 3.056** |
| Fall risk | −0.417 | −7.990 *** | ||||
| Age | 0.002 | 0.021 | ||||
| Gender | −2.382 | −1.387 | ||||
| Educational level | 0.477 | 0.509 | ||||
| Frailty | Cognitive dysfunction | 0.569 | 0.324 | 26.231 *** | −0.032 | −3.395 *** |
| Fall risk | 0.013 | 3.334** | ||||
| Independent living skills | −0.023 | −6.117 *** | ||||
| Age | 0.012 | 1.967 | ||||
| Gender | 0.062 | 0.530 | ||||
| Educational level | 0.066 | 1.490 | ||||
P < .01.
P < .001; Bold = Statistically significant differences.
Figure 2.
The chain mediation model of fall risk and independent living skills in the relationship between cognitive dysfunction and frailty. The arrows indicate the direction of the regression paths between variables. The values displayed next to the arrows are the regression coefficients (β). The statistical significance of each path is indicated by asterisks (*P < .05, **P < .01, ***P < .001).
Table 4.
Mediation analysis of fall risk and independent living skills.
| Type of Effect | Effect Size | Boot SE | 95% CI | Ratio of effect |
|---|---|---|---|---|
| Total effect | −0.052 | 0.010 | −0.071 ~ −0.032 | 100.00% |
| Direct effect | −0.032 | 0.009 | −0.050 ~ −0.013 | 61.54% |
| Indirect effect | −0.020 | 0.005 | −0.030 ~ −0.010 | 38.46% |
| Cognitive dysfunction → Fall risk → Frailty | −0.006 | 0.003 | −0.011 ~ −0.002 | 11.54% |
| Cognitive dysfunction → Independent living skills → Frailty | −0.009 | 0.003 | −0.016 ~ −0.003 | 17.31% |
| Cognitive dysfunction → Fall risk → Independent living skills → Frailty | −0.005 | 0.002 | −0.008 ~ −0.002 | 9.62% |
CI = confidence interval, SE = standard error.
4. Discussion
This study investigates the impact of cognitive dysfunction on frailty in patients with AIS, with a focus on the mediating roles of fall risk and independent living skills. The results indicate that cognitive dysfunction significantly predicts frailty in patients with AIS, thereby validating hypothesis 1. Research has found that impairments and declines in executive function are associated with the risk of frailty episodes.[34] And among survivors with comparable levels of frailty, frail patients with cognitive dysfunction have a higher risk of death than frail patients without cognitive dysfunction.[35] Possible reasons are as follows. First, patients with AIS complicated by cognitive dysfunction experience reduced ability in activities of daily living, diminished social interaction, and greater psychological stress due to cerebrovascular events and their complications. Second, frailty inherently manifests as multisystem functional decline, weakening cellular repair and the body’s ability to maintain homeostasis,[36] thereby delaying recovery from neurological damage. At the same time, another study has also demonstrated that frailty often coexists with traditional cardiovascular risk factors, and in such cases, it has a significant impact on disease progression and treatment outcomes.[37] Third, although social support networks play a vital role in stroke rehabilitation, patients’ frailty condition may limit their access to and utilization of such support, resulting in insufficient rehabilitation resources and ultimately affecting rehabilitation outcomes. It follows that early frailty intervention represents a potential strategy for preventing cognitive dysfunction and neuropsychiatric disorders.[38] Furthermore, a history of falls and frailty exhibit synergistic effects, further exacerbating cognitive dysfunction in patients.[39] Therefore, cognitive dysfunction and frailty exhibit a mutually reinforcing relationship.
Fall risk mediates the relationship between cognitive dysfunction and frailty in patients with AIS, thereby validating hypothesis 2. Cognitive dysfunction is closely linked to fall risk in patients with AIS. A possible explanation is that, from a physiological perspective, visual-spatial perception deficits and underestimation of fall risk are significant contributing factors to falls among individuals with cognitive dysfunction.[40] In addition, attention distribution disorders and decreased cognitive-motor dual-task abilities, such as unsteady gait, slow reactions, and excessive risk-taking behavior, further increase fall risk.[41] Yet from a psychological perspective, increased fall risk not only exacerbates patients’ psychological distress, such as social embarrassment and humiliation,[42] but also reduces their participation in daily activities and triggers anxiety or depression.[43] Research also indicates that depression mediates the relationship between psychological distress and frailty, while the coexistence of psychological distress and frailty further diminishes treatment adherence, hinders recovery, and impedes the restoration of activities of daily living.[44] Frailty and depression are therefore both associated with a range of adverse clinical outcomes, such as reduced quality of life, increased healthcare costs, accelerated cognitive decline, disability, and premature mortality. These outcomes ultimately become significant factors accelerating patients’ physical decline.[45,46] Another study indicates that compared to individuals without fall risk, patients at risk of falling typically exhibit poorer health status, reduced physical activity, and higher rates of living alone; moreover, the majority of patients experience falls within 10 years after stroke, with their occurrence closely associated with multiple indicators of frailty.[47] Therefore, AIS patients with a high fall risk after stroke, which is often accompanied by severe cognitive dysfunction, increase the risk of frailty in patients from physiological and psychological perspectives.
Independent living skills also mediate the relationship between cognitive dysfunction and frailty in patients with AIS, thereby validating hypothesis 3. Research shows that activities of daily living can significantly affect loneliness and sleep quality through intermediary pathways.[48] Feelings of loneliness and sleep quality are major factors contributing to frailty in patients, suggesting that the ability to live independently may influence their frailty status. Consistent with the findings of Dakang Ji, activities of daily living can predict the onset of frailty,[49] and studies have confirmed that regular exercise can prevent frailty.[50] The potential mechanisms underlying this mediating pathway may include that reduced physical activity and increased functional dependence may be associated with elevated inflammatory levels and muscle dysfunction.[51] In addition, changes in central nervous system function may also be associated with concurrent declines in cognitive and physical function.[52] These factors may be indirectly related to the observed mediating effect among cognitive dysfunction, independent living skills, and frailty. As the present study did not directly assess the relevant physiological mechanisms, the above explanations should be interpreted as speculative. Further validation through longitudinal studies incorporating biological markers is still needed in the future. Through systematic rehabilitation and proactive guidance, we should facilitate their early recovery of basic daily living functions and encourage appropriate physical activities. Moreover, severe cognitive dysfunction significantly impairs their ability to live independently. This functional decline further exacerbates individuals’ physical and mental health issues, intensifying their state of frailty.
The pathway from fall risk to independent living skills plays a crucial mediating role in the impact of cognitive dysfunction on frailty in patients with AIS, thereby confirming hypothesis 4: that fall risk negatively predicts independent living skills. Research shows that fear of falling not only affects patients’ mobility, self-care abilities, and upper limb function, but also has a negative impact on their mental health.[53] For patients with AIS, a high fall risk reduces their ability to perform independent living skills, which in turn leads to a decline in quality of life and ultimately exacerbates frailty.
This study constructs a chain mediation model where cognitive dysfunction serves as the independent variable, frailty as the dependent variable, and fall risk and independent living skills as mediating variables. It further elucidates the relationships among cognitive dysfunction, frailty, fall risk, and independent living skills in patients with AIS. Cognitive dysfunction can reduce social and outdoor interactions, increase anxiety and depression in patients, and decrease the frequency with which they accept new stimuli, thereby delaying the recovery process. This process not only damages patients’ ability to live independently and their quality of life, but also further exacerbates their frailty. Therefore, it is recommended that medical staff and family members develop diversified training programs, ranging from simple to complex memory training and executive function training, tailored to the degree of cognitive dysfunction in patients with AIS. Such programs can not only improve patients’ cognitive function and reduce the risk of frailty, but also enhance their confidence in combating the disease. At the same time, during daily rehabilitation education, emphasis should be placed on the dual value of cognitive dysfunction intervention in promoting cognitive recovery and alleviating frailty, thereby enhancing the awareness of patients and their families. In addition, cognitive training can improve intracranial neural plasticity and alleviate neurological damage, thereby improving problems such as poor concentration and decreased executive function. At the same time, it can promote dopamine secretion to alleviate patients’ anxiety and depression, ultimately delaying the onset and progression of frailty.
4.1. Limitations
This study also has several limitations that should be noted. First, this study primarily utilized questionnaires as the measurement instrument and employed convenience sampling. These methodological choices may introduce measurement bias due to subjective cognitive biases on the part of respondents, potentially compromising the objectivity of the research findings. Second, the current sample size is relatively small, and it is recommended that future studies expand the sample size to enhance the representativeness of the conclusions. Third, due to the economic development level and racial distribution characteristics of the study area, the generalizability of the study results may be limited. Fourth, because this study employed a cross-sectional design, it was not possible to establish a causal relationship between cognitive dysfunction, fall risk, independent living skills, and frailty. Future research could adopt a longitudinal cohort study design to conduct long-term follow-up observations of the developmental trajectories of cognitive dysfunction, fall risk, independent living skills, and frailty in patients with AIS. This research approach would help to more thoroughly elucidate the causal relationships among the variables and provide a more compelling theoretical basis for developing targeted interventions. Finally, this study used the Frail Scale to assess frailty in patients with AIS. Although this scale is simple and practical in clinical settings, it contains relatively few items. Compared with other multidimensional frailty assessment tools, it may have certain limitations in terms of sensitivity and dimensional coverage, and thus may not fully reflect all characteristics of frailty. Future studies could expand the sampling coverage to include more geographically diverse sample data, thereby enhancing the applicability of the study conclusions across different populations.
5. Conclusion
The results of this study indicate that there is an association between cognitive dysfunction, fall risk, independent living skills, and frailty. Furthermore, the fall risk and independent living skills mediate a chain of effects between cognitive dysfunction and frailty in patients with AIS. At the same time, early intervention is crucial for patients with AIS who have cognitive dysfunction, particularly those at high fall risk or with limited independent living skills. It is recommended that clinical and home care providers pay close attention to patients’ frailty and promptly initiate rehabilitation interventions, as this can help improve patients’ overall functional status.
Acknowledgments
The authors gratefully acknowledge all participants for their time, cooperation, and contribution to this study.
Author contributions
Conceptualization: Junxiao Yang, Shusen Liu, Yan He, Yuhui Zhang.
Data curation: Junxiao Yang, Yuting Niu, Jingya Tai, Zhiling Zhao, Wenling Cui, Yundi Yin.
Investigation: Junxiao Yang, Shusen Liu, Yuhui Zhang, Yuting Niu, Jingya Tai, Zhiling Zhao, Wenling Cui, Yundi Yin.
Methodology: Junxiao Yang, Jingya Tai, Kunling Li.
Software: Junxiao Yang, Shusen Liu, Jingya Tai, Haiya Sun.
Supervision: Junxiao Yang, Hao Sun, Haiya Sun.
Validation: Junxiao Yang, Yan He, Haiya Sun.
Writing – original draft: Junxiao Yang.
Writing – review & editing: Junxiao Yang, Yan He, Hao Sun, Haiya Sun.
Abbreviations:
- AIS
- acute ischemic stroke
- BI
- Barthel Index
- MoCA-B
- Montreal Cognitive Assessment-Basic
- VIF
- variance inflation factor
This research was funded by Key Research Projects in Nursing Science Approved by the Chinese Medical Association Press for 2022–2023 (CMAPH-NRP 2022006).
The authors have no conflicts of interest to disclose.
The datasets generated during and/or analyzed during the current study are not publicly available, but are available from the corresponding author on reasonable request.
How to cite this article: Yang J, Liu S, He Y, Zhang Y, Niu Y, Sun H, Tai J, Zhao Z, Cui W, Yin Y, Li K, Sun H. Chain mediation analysis of fall risk and independent living skills in patients with acute ischemic stroke between cognitive dysfunction and frailty. Medicine 2026;105:23(e49177).
Contributor Information
Junxiao Yang, Email: 2388720694@qq.com.
Shusen Liu, Email: lsshrbcu@163.com.
Yan He, Email: heyan3922@163.com.
Yuhui Zhang, Email: 2952990953@qq.com.
Yuting Niu, Email: 2453043416@qq.com.
Hao Sun, Email: 13721564399@163.com.
Jingya Tai, Email: 736458151@qq.com.
Zhiling Zhao, Email: 346165512@qq.com.
Wenling Cui, Email: cwl125521@qq.com.
Yundi Yin, Email: 378712450@qq.com.
Kunling Li, Email: lkl767805@163.com.
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