Abstract
This study investigated the relationship between grip strength and cognitive function in older adults, focusing on the chained mediating effects of life-space mobility and depression. A multisite stratified sampling method was used to select 382 older adults from Guangxi Zhuang Autonomous Region, China. Grip strength was measured using a dynamometer, while life-space mobility, depression status, and cognitive function were assessed via standardized scales. Statistical descriptions were performed using SPSS 27.0, while a chained mediation model was constructed using Model 6 of the PROCESS 4.2 plugin for SPSS 27.0. The mediation effects were tested via Bootstrap sampling (5000 repeated samples). Results revealed that the direct effect of grip strength on cognitive impairment accounted for 56.9% of the total effect. life-space mobility and depression exhibited chain mediating effects between grip strength and cognitive function, accounting for 8.5% of the total effect. The indirect mediating effects of life-space mobility and depression contributed 17.5% and 17.1% to the total effect, respectively. The study demonstrates a significant correlation between grip strength and cognitive function in older adults, with life-space mobility and depression playing significant mediating roles between these two variables.
Keywords: Aged, Grip strength, Cognition, Life-space mobility, Depression
Subject terms: Health care, Medical research
Introduction
Against the backdrop of a continuously aging global population, cognitive dysfunction among the older adults has emerged as a pressing global public health challenge requiring urgent attention. According to the World Health Organization, the number of people living with dementia worldwide is projected to rise to 115 million by 20501. Dementia is characterized by cognitive impairment as its core clinical manifestation, often involving overall decline across multiple cognitive domains such as memory, language, executive function, and social interaction2. Among various subtypes of dementia, Alzheimer’s disease is the most prevalent, accounting for approximately 63% to 70% of all cases3. Notably, in 2021, China’s total number of dementia patients (including Alzheimer’s disease) constituted 29.8% of the global total, with a prevalence rate significantly higher than the world average3. Cognitive function is influenced by multiple factors, including demographic characteristics, comorbid conditions, mental health, and lifestyle choices4,5. Furthermore, cognitive impairment can diminish patients’ quality of life across various dimensions and impose substantial economic and caregiving burdens on families and society.
Grip strength and cognitive function
Grip strength evaluation indicators include absolute grip strength and relative grip strength. The data measured by common grip strength measuring instruments is absolute grip strength, which is usually closely related to the strength of lower arms, legs and core muscles. As a simple, feasible and non-invasive evaluation index, it is more suitable to be included in the health check-ups for the older adults, and its value is also closely related to the health status6. This cross-sectional investigation identified an inverse correlation between grip strength and cognitive dysfunction risk, with the high-grip cohort demonstrating a statistically significant protective advantage over low-grip counterparts7. Cohort studies have demonstrated that the grip strength of older adults individuals with cognitive impairment declines at a more accelerated rate over time. Furthermore, this reduction in grip strength serves as a significant predictor of cognitive decline8. According to a meta-analysis, individuals in the lowest grip strength tertile exhibit a 42% elevated dementia risk and a 22% increased Alzheimer’s disease risk compared to the highest tertile group9. Brain imaging evidence shows that weak grip strength is often accompanied by brain atrophy and decreased white matter fiber density10. Although the association and prediction are not fully clear, many studies have also concluded that grip strength has a predictive effect on cognitive relationship11. Based on this evidence, we hypothesize that there is a significant correlation between grip strength and cognitive function in older adults.
The mediating role of life-space mobility
Life-space mobility reflects the extent and range of movement within an individual’s living environment over a specific time period, serving as a measure of the spatial breadth and frequency of daily activities12. Among older adults, muscle strength is closely linked to physical function and activity capacity, with grip strength being a key indicator of overall muscle mass and function. Declining muscle strength limits daily activity capabilities, including walking ability and the range of community activities13. Otably, higher grip strength levels correlate positively with greater life-space mobility range. Enhanced grip strength is also associated with improved independent living capacity and higher social engagement, factors that collectively promote expanded life-space mobility14. Conversely, reduced life-space mobility correlates with accelerated cognitive decline. Maintaining broad life-space mobility is recognized as a protective factor in slowing cognitive decline among older adults15. Systematic reviews further indicate that individuals with restricted life-space mobility experience accelerated cognitive decline, with moderate correlations observed particularly in executive function, memory, and information processing speed16. Based on this evidence, we hypothesize that the association between grip strength and cognitive function may be mediated by life-space mobility. This mediating pathway exerts positive effects on cognitive function by enhancing environmental stimulation and social engagement. Therefore, we propose that life-space mobility serves as a mediator in the link between grip strength and cognitive function. in older adults.
The mediating role of depression
Multiple studies indicate a significant association between grip strength and depression. TA 10-year cohort study revealed that a 5-kilogram decrease in grip strength corresponds to a 7% increase in depression risk, suggesting that maintaining higher muscle strength plays a positive role in preventing depression17. Surveys conducted among depression patients further found that grip strength possesses predictive value for cognitive impairment18. Both longitudinal and cross-sectional studies support a positive association between stronger grip strength and higher cognitive levels alongside milder depressive symptoms19. Research indicates that grip strength is negatively correlated with the presence of depression in adults. Exercises that increase muscle mass and strength may help prevent depression20. Depression is an important risk factor for cognitive decline. Long-term studies have shown that timely identification and intervention of depressive symptoms may help maintain cognitive abilities21. Furthermore, empirical studies have shown that depression is a key mediator in the relationship between grip strength and cognitive health. Specifically, changes in muscle grip strength affect cognitive outcomes to a certain extent by regulating depressive symptoms22. It is plausible that grip strength could exert a protective effect on cognitive health partly by reducing depressive symptoms, which are themselves a risk factor for cognitive decline. Therefore, we propose that depression serves as a mediator in the link between grip strength and cognitive function in older adults.
The role of life-space mobility and depression
Previous studies have demonstrated that both life-space mobility and depressive symptoms have been shown to influence the relationship between grip strength and cognitive function. However, these two mediators may not operate in isolation. Numerous past studies have demonstrated that both life-space mobility and depressive symptoms respectively have evidence that can affect the relationship between grip strength and cognitive ability. Restricted life-space activities represent the possibility of social isolation and are closely related to depression. People with restricted life-space activities are three times more likely to suffer from depression than those without restricted life-space activities23. In addition, literature review and analysis of a number of relevant studies have found that life-space mobility limitation will affect the occurrence of depression, and depression is also a significant factor for the decline of life-space mobility ability24. Life-space mobility and depression sequentially mediate the association between grip strength and cognitive function, forming a pathway that aligns with the biopsychosocial model: from a biological factor (grip strength), through a social factor (life-space mobility), to a psychological factor (depression), and ultimately to the health outcome (cognition). Based on the above analysis, we propose a chain mediation model in which life-space mobility and depression sequentially mediate the association between grip strength and cognitive function.
This study examines the sequential mediating role of life-space mobility and depressive symptoms in the relationship between grip strength and cognitive decline in older adults. The theoretical foundation is the biopsychosocial model, which emphasizes that health outcomes arise from complex interactions among biological, psychological, and social factors. This framework provides an ideal perspective for investigating cognitive function in older adults. The chained mediation model proposed in this study directly corresponds to the three-dimensional structure of this model: biological factors, represented by grip strength, reflect neuromuscular function and systemic physiological reserves; social factors, measured by life-space mobility, reflect the scope of an individual’s social participation and environmental interaction; psychological factors, manifested by depressive symptoms, involve emotional and mental states. We hypothesize and validate a sequential mediation pathway: grip strength indirectly influences cognitive function via life-space mobility and subsequently through depressive symptoms. This study aims to uncover the underlying mechanisms through which grip strength affects cognitive decline and provide theoretical foundations for early preventive interventions.
Method
Research object
This study is a multicenter cross-sectional survey. A stratified sampling method was employed, with stratification based on economic level, selecting one city from each stratum. In November 2024, Nanning, Yulin, and Baise cities in Guangxi Zhuang Autonomous Region, China, were selected as study sites. Subsequently, three communities, three villages, and two hospitals were randomly selected as survey locations. The target population comprised older adults residents in these areas. Inclusion criteria: age ≥ 60 years; ability to comprehend questionnaire content and provide information; signed informed consent. Exclusion criteria: inability to provide valid information.
Ethics approval and consent to participate
This study followed the principles of the Declaration of Helsinki and relevant Chinese ethical codes, and all procedures were performed in accordance with these guidelines. The study protocol was reviewed and approved by the Ethics Committee of the Second Affiliated Hospital of Guangxi Medical University (approval number: 2023-KY-0905). All participants volunteered to participate in the study. Prior to participation, each individual received detailed instructions regarding the purpose of the study, procedures, potential risks and benefits, confidentiality measures, and their right to withdraw at any time without paying a penalty. Following this explanation, written informed consent was obtained from each participant. To ensure the privacy of the participants, all data collected were anonymized. Access to anonymized data was strictly limited to authorized researchers participating in the study. Through these measures, the rights and welfare of all participants were protected throughout the study.
Measurements
Demographic characteristics
After reviewing relevant literature25,26, the research team collaboratively designed this questionnaire based on the study objectives. The questionnaire includes variables such as age, gender, marital status, educational attainment, place of residence, monthly income, smoking history, and drinking history. Among these variables, subsequent univariate analyses of the outcome variable will identify statistically significant variables to be selected as covariates.
Grip strength
An electronic grip strength program (Xiangshan CAMRY handgrip device, model EH101, Guangdong, China) was used to measure the grip strength of the older adults. The measurement methods strictly followed the standard operating procedures recommended by the Asian Sarcopenia Society27 : (1) Equipment Preparation and Calibration: Calibrate the handgrip dynamometer before the measurement to ensure the accuracy of the measurement results. (2) Subjects stood on both feet with feet hip-width apart and elbows fully extended, and measurements were performed with the dominant hand. If the subject was unable to stand independently, measurements were allowed in a seated position. (3) Measurement process: the subject held the handle of the dynamometer with maximum strength for at least 5 s each time for a total of 2 measurements. Rest for at least 30 s between measurements. If the older adults could not stand independently, seated measurements were allowed. (4) Result recording: The maximum value of the two measurements was taken as the final grip strength value and recorded.
Life-space mobility
The Chinese version of the Life-space Mobility Assessment (LSA-C) was developed by the Center for Aging Research at the University of Alabama at Birmingham28. The Chinese version of the “Living Space Assessment Scale for Older Adults”29 is used to evaluate the extent of living spaces within five areas that older adults have actually visited in their daily lives over the past month. This scale consists of three parts: activity range, frequency and independence (with a total score ranging from 0 to 120 points). In this study, the activity range index was adopted, that is, the area was divided with the bedroom at home as the center. Those who have not left the bedroom will receive 0 points, those who have left the bedroom without leaving home will receive 1 point, those who have left home without leaving the apartment building will receive 2 points, those who have left the apartment building without leaving the community/village will receive 3 points, those who have left the community without leaving the street/town will receive 4 points, and those who have left the street/town will receive 5 points. Among them, a score of 0 to 60 indicates that the living space is at a low level, that is, the living space is first; A score of 61 to 80 indicates a medium level, while a score of 81 to 120 indicates a high level. In this study, the Cronbach’s α coefficient of this scale was 0.862.
Depression
The Patient Health Questionnaire-9(PHQ-9) is a scale developed based on the diagnostic criteria for depressive disorders in the Diagnostic and Statistical Manual of Mental Disorders compiled by the American Psychiatric Association30. It contains 9 items, which are divided into four categories: “None,” “How many days,” “More than Half,” and “Almost every day,” corresponding to 0, 1, 2, and 3 points respectively, with the highest total score being 27 points. In terms of scoring, a score of 5 or above indicates depression. Specifically, scores of 5–9 suggest mild depression, 10–14 indicate moderate depression, 15–19 represent moderately severe depression, and 20–27 point to severe depression. In this study, the Cronbach’s α coefficient of the PHQ-9 scale was 0.847, indicating good reliability.
Cognitive function
Ascertain Dementin 8(AD8), This scale is a simplified Chinese version compiled by the University of Washington31. The scale measures cognitive abilities like memory and orientation by having subjects answer questions. The assessment uses subjects’ “yes” or “no” responses as the basis, with a cutoff of ≥ 2 for cognitive impairment. In this study, the Cronbach’s α coefficient for the scale was 0.822, reflecting acceptable reliability.
Data collection and quality control
To ensure the scientific rigor of the study and the accuracy of the data, all research team members underwent standardized training. The training covered the specific content of the questionnaire survey, survey requirements, potential issues that may arise during the survey process, and corresponding solutions. During the survey, after obtaining informed consent from respondents, team members conducted face-to-face interviews. Any missing data or logical errors were promptly clarified with respondents for supplementation and entered in real-time into the Questionnaire Star system. A total of 395 questionnaires were distributed. After excluding 13 invalid questionnaires that did not meet age requirements, 382 valid questionnaires were ultimately collected, achieving a valid response rate of 96.7%.
Statistical method
After importing the questionnaire star rating system data into Excel, two researchers verified its accuracy. Data processing was completed using SPSS 27.0 statistical software. Normally distributed measurement data were described using mean ± standard deviation; non-normally distributed measurement data were expressed as median (interquartile range). Count data were statistically described using frequency and percentage, with the influence of extreme values reduced by applying a 1% tail truncation method. Pearson correlation analysis was applied to normally distributed variables, while Spearman correlation analysis was used for non-normally distributed variables. Chain mediation analysis was conducted using Model 6 in Process 4.2, an add-on module within SPSS 27.0. Bootstrap resampling was performed 5,000 times within a 95% confidence interval for validation. All measurement items underwent unrotated exploratory factor analysis using Harman’s one-factor test, with a maximum factor loading threshold set below 40.000%. The two-tailed test level was set at α = 0.05. All predictor variables were standardized to mitigate multicollinearity issues. This study simultaneously examined direct and indirect effects through mediation models; however, given the cross-sectional nature of the data, these effects should be interpreted as associations rather than causal relationships.
Results
Common method bias test
The Harman single-factor test was used to conduct non-rotational exploratory factor analysis for all measurement items. The results show that there are 8 components with eigenvalues greater than 1. The principal factor accounted for 23.972% of the total variance, significantly lower than the critical threshold of 40.00%. The absence of a single factor dominating the variance explanation indicates that the variance of the commonly used methods has been effectively controlled. These findings indicate that the common methods in the dataset have relatively small biases.
Descriptive statistics for general information and variables
As shown in Table 1, descriptive statistics of participants’ demographic characteristics indicate that age, marital status, place of residence, and Pension/monthly income are statistically significant factors affecting cognitive function. Therefore, potential confounding factors were selected based on a review of prior literature (age, gender, educational attainment, marital status, place of residence, smoking and drinking history, and monthly income). In univariate analysis, age, marital status, place of residence, and monthly income/pension were significantly associated with cognitive function (p < 0.05); thus, these variables were included as covariates in the mediation model. Other potential factors such as comorbidities and physical activity were considered but excluded due to limited data availability. After normality testing, grip strength, activities of daily living capability, depression severity, and cognitive function scores were found to be non-normally distributed. Therefore, the median and quartile method was used to describe the scores of these variables.
Table 1.
Demographic characteristics(N = 382).
| Variable | (n,%)or [M༈P25,P75༉] | Cognitive function | χ2/Z | P | |
|---|---|---|---|---|---|
| <2points(n=239,%) | ≥ 2points(n=143,%) | ||||
| Age | 72(66,78) | −4.684 | <0.001 | ||
| Sex | 1.871 | 0.171 | |||
| Male | 123(32.3) | 83(34.7) | 40(28.0) | ||
| Female | 259(67.8) | 156(62.3) | 103(72.0) | ||
| Marital status | 13.230 | 0.001 | |||
| Married | 266(69.6) | 181(75.7) | 85(59.4) | ||
| unmarried | 3(0.8) | 1(0.4) | 2(1.4) | ||
| Widowed spouse | 111(29.1) | 55(23.0) | 56(39.2) | ||
| Divorce | 2(0.5) | 2(0.8) | 0(0) | ||
| Place of residence | 12.825 | <0.001 | |||
| Towns and cities | 176(46.1) | 127(53.1) | 49(34.3) | ||
| Rural areas | 206(53.9) | 112(46.9) | 94(65.7) | ||
| Smoking status | 5.665 | 0.059 | |||
| Yes | 40(10.5) | 31(13.0) | 9(6.3) | ||
| No | 330(86.4) | 199(83.3) | 131(91.6) | ||
| Have given up | 12(3.1) | 9(3.8) | 3(2.1) | ||
| Alcohol consumption status | 3.304 | 0.192 | |||
| Yes | 37(9.7) | 28(11.7) | 9(6.3) | ||
| No | 336(88.0) | 206(86.2) | 130(90.9) | ||
| Have given up | 9(2.4) | 5(2.1) | 4(2.8) | ||
| Pension/monthly income(Renminbi) | 24.695 | <0.001 | |||
| No fixed income | 137(35.9) | 76(31.8) | 61(42.7) | ||
| <1000 | 108(28.3) | 57(23.8) | 51(35.7) | ||
| 1000–3000 | 66(17.3) | 46(19.2) | 20(14.0) | ||
| 3001–5000 | 51(13.4) | 41(17.2) | 10(7.0) | ||
| >5000 | 20(5.2) | 19(7.9) | 1(0.7) | ||
M(P25,P75), median and interquartile range.
Analysis of correlation
This study examined the relationships among grip strength (independent variable), cognitive function (dependent variable), and two hypothesized mediating variables: life-space mobility and depressive symptoms. Establishing statistically significant pairwise associations among these constructs is a prerequisite for subsequent mediation analysis. Therefore, Spearman’s rank correlation analysis was first conducted on the following variables: grip strength scores, life-space mobility measurements, depression symptom scores, and cognitive function scores. As shown in Table 2, the results indicate that in the older adults population, higher grip strength is significantly positively correlated with greater life-space mobility, while being significantly negatively correlated with increased depression symptoms and cognitive decline. Life-space mobility scores showed significant negative correlations with both depression severity and cognitive impairment. Conversely, among the study participants, depression severity was significantly positively correlated with cognitive impairment.
Table 2.
Statistical description and correlation analysis results.
| M(P25,P75) | Grip strength | life-space mobility | depression | cognitive function | |
|---|---|---|---|---|---|
| Grip strength | 21.0(15.6,26.9) | 1 | 0.383** | −0.227** | −0.299** |
| life-space mobility | 64.0(48.0,80.0) | 1 | −0.351** | −0.338** | |
| depression | 1.0(0,3.0) | 1 | 0.331** | ||
| cognitive function | 1.0(0,3.0) | 1 |
*P<0.05,**P<0.01.M(P25,P75),median and interquartile range.
Chain mediation effect analysis
After rigorously controlling for potential confounding factors such as age, marital status, place of residence, and pension/monthly income, this study delved into the chain mediation effect with cognitive function as the dependent variable, older adults grip strength as the independent variable, and life-space mobility and depression as mediating variables. Using Model 6 in the SPSS 27.0 program, the chain mediation effect of older adults life-space mobility and depression on grip strength and cognitive function was analyzed. The table displays the regression structure of the multiple mediation model. Findings reveal: Grip strength in older adults positively correlates with life-space mobility but negatively correlates with both depression and cognitive function. Life-space mobility negatively correlates with both depression and cognitive function. Depression positively correlates with cognitive decline in older adults.
The results of the mediating effect analysis are shown in the following Tables 3 and 4. The total effect of grip strength on cognitive function is −0.2471, among which the direct effect is −0.1406, accounting for 56.9% of the total effect, and the 95% confidence interval does not include 0, indicating a significant effect. There are three paths of indirect effects: the mediating effect of life-space mobility, the mediating effect of depression, and the chain-like intermediate effect from life-space mobility to depression. The 95% confidence intervals of these three paths do not include 0, which indicates a significant indirect effect. The mediating effects of life-space mobility and depression accounted for 17.5% and 17.1% respectively. The chain mediating effect from life-space mobility to depression accounted for 8.5% of the total effect.
Table 3.
Chain mediation model.
| Outcome variable | Predictor variable | R2 | F | β | SE | t |
|---|---|---|---|---|---|---|
| Model1 | 0.2619 | 16.5437 | 0.8124 | 0.6816 | 1.1920 | |
| life-space mobility | Grip strength | 0.3290 | 0.0602 | 5.4627** | ||
| Model2 | 0.1714 | 8.5473 | 1.6416 | 0.7245 | 2.2659* | |
| Depression | Grip strength | −0.2192 | 0.0664 | −3.3007** | ||
| Life-space mobility | −0.3299 | 0.0549 | −6.0056** | |||
| Model3 | 0.2503 | 12.3847 | −1.4220 | 0.6948 | −2.0466* | |
| Cognitive function | Grip strength | −0.1406 | 0.0642 | −2.1902* | ||
| Life-space mobility | −0.1318 | 0.0548 | −2.4057* | |||
| Depression | 0.1926 | 0.0494 | 3.8995** |
*P < 0.05,**P < 0.01.
Table 4.
Bootstrap analysis.
| Effect size | Standard error | Boot 95% CI | Proportion(%) | ||
|---|---|---|---|---|---|
| LL | UL | ||||
| Grip strength →Cognitive function | −0.1406 | 0.0642 | −0.2668 | −0.0144 | 56.9 |
| Total mediation effect | −0.1065 | 0.0297 | −0.1781 | −0.0613 | 43.1 |
| Grip strength→ life-space mobility →cognitive function | −0.0434 | 0.0186 | −0.0842 | −0.0106 | 17.5 |
| Grip strength → Depression → Cognitive function | −0.0422 | 0.0189 | −0.0867 | −0.0122 | 17.1 |
| Grip strength → life-space mobility → Depression → Cognitive function | −0.0209 | 0.0086 | −0.0406 | −0.0070 | 8.5 |
| Total effect | −0.2471 | 0.0630 | −0.3710 | −0.1231 | 100 |
The hypothesis proposed in this study was proved by the results, and the mediating path relationship among the variables is shown in Fig. 1.
Fig. 1.
Chain mediation model.
Note: Regarding life-space mobility, depression, grip strength and cognition Function *P < 0.05, **P < 0.01.
Discussion
This study examines the relationship between grip strength levels and cognitive function in Chinese older adults, as well as the important mediating roles of life-space mobility and depression in this process. This study confirms that there is a significant correlation between grip strength and cognitive function in older adults. The innovation of this study’s chain mediation model lies not only in confirming two mediating variables but also in proposing a sequential pathway for the research hypothesis: from biological to social to psychological factors. Grip strength (biological) promotes life-space mobility (social), which in turn buffers depression (psychological), ultimately contributing to the maintenance of cognitive function. By detailing the interrelatedness of these mediators within the grip strength-cognition relationship, the model offers deeper insights than dual mediation. It provides a comprehensive perspective grounded in the biopsychosocial model, potentially suggesting that interventions targeting multiple nodes along this pathway may be more effective for preserving cognitive function in older adults.
Therefore, hypothesis H1 holds true in this study. It is suggested that in older adults care assessment, grip strength test can be used as a simple tool for cognitive risk screening. Meanwhile, some studies have found that reduced muscle mass may intensify cognitive decline due to elevated levels of pro-inflammatory cytokines. Grip strength, as a clinical alternative indicator for evaluating muscle reserve and systemic inflammation, can indirectly reflect such pathophysiological processes11. Furthermore, research32has found that cognitive dysfunction can also affect grip strength levels. A decline in cognitive function (especially executive function) can influence exercise planning and coordination, leading to a reduction in the accuracy of grip strength control. older adults people with cognitive dysfunction may indirectly suffer from disuse atrophy of muscles due to reasons such as decreased muscle mass affecting physical activity, weakened motivation for movement, reduced self-care ability or decreased social activities. Cognitive dysfunction is often accompanied by a decrease in grip strength. Strengthening muscle strength and improving grip strength can enhance physical functions, thereby delaying cognitive dysfunction and even improving cognitive functions33.Previous studies have shown that grip strength not only predicts cognitive function in older adults but also improves it. Therefore, this study examines the relationship between grip strength and cognitive function by treating grip strength as an independent variable.
This study found that life-space mobility significantly mediates the relationship between grip strength and cognitive function in older adults: Older adults with stronger grip strength were positively associated with greater life-space mobility and higher cognitive function scores. The hypothesis H2 is proved. This is roughly the same as previous studies13,14. Some researchers believe that lack of physical activity may occur in the early stage of cognitive impairment. At the same time, muscle mass decreases with time and grip strength also decreases, while increased physical activity may increase brain plasticity and thus improve cognitive function34. Grip strength represents the muscle mass, dynamic balance and functional mobility of the older adults, which directly affect the mobility of their living space14. older adults people with good muscle strength can have better activity level and ability, and their life-space mobility level may also be improved. Life space activity can also reflect the social participation and daily activity ability of the older adults14. Older adults with higher life-space mobility may be exposed to more diverse environments and activities. Such diversified stimulation can improve cognitive function by promoting the increase of hippocampal volume35. Research by other scholars and the mediating effect findings explored in this study indicate that life-space mobility mediates the relationship between grip strength and cognitive function. This may occur because reduced grip strength in older adults could impact their life-space mobility, and diminished life-space mobility may weaken the brain-stimulating effects of environmental diversity, thereby indirectly affecting cognitive function. Concurrently, restricted life-space mobility may also reduce daily activity levels, affecting muscle strength and leading to diminished grip strength indicators.
According to the findings of this study, depression was found to account for 17.1% of the mediating effect observed between grip strength and cognitive function. Lower grip strength levels were associated with higher depression scores and worse cognitive function in older adults, validating the hypothesis H3 of this study. This is consistent with previous studies22. This result may be related to the dual pathways of depression: behavior-wise, depression may reduce exercise compliance and accelerate muscle disuse; Physiologically, depression leads to glucocorticoid-related neurotoxicity and increased pro-inflammatory factors, which may impair muscle metabolism and cognitive function at the same time36]– [37. Higher grip strength reflects better musculoskeletal health and physical functioning, and it increases self-confidence to be able to perform daily activities and work tasks more easily. At the same time, the effects of depression on cognitive function may result from common and specific brain structural changes, which may affect cognitive function through the decline of emotional processing, memory integration and social activity participation38.
From the present study, life-space mobility and depression have a chain mediating effect between grip strength and cognition in older adults. Hypothesis H4 was verified. This pathway, compared with the above two pathways, combines the effects between life-space mobility and depression, and the two are more correlated than the other variables. Decreased life-space mobility is associated with higher depression, which is consistent with previous studies39. The chain mediating effect was 8.5%, and the total mediating effect was 43.1%. Although the size of the chain mediating effect is relatively small, its statistical significance suggests that life-space mobility limitation may exacerbate depression and thus impair cognitive function, while life-space mobility limitation may often lead to limitations in social participation and social activities, which may cause social isolation and accompanied by depressive symptoms40. This aligns with the “bio-psycho-social” medical model, thereby providing a multidimensional perspective for interventions aimed at preventing or improving cognitive function in older adults.
This study, grounded in the significant associations between grip strength, life-space mobility, depressive symptoms, and cognitive function, offers a multidimensional explanation for understanding cognitive health in older adults based on the bio-psycho-social model. At the biological level, grip strength serves as a simple, low-cost screening tool suitable for primary care settings to identify individuals at risk for cognitive decline, with grip strength training as an intervention entry point to improve physical function. At the psychological level, emphasis should be placed on early detection of depression and psychological adjustment. At the social level, communities and policies can encourage older adults to increase resistance training. Actively expanding older adults’ life-space mobility, promoting social participation, and building community support networks are recommended. Environmental support can be enhanced through aging-friendly community initiatives, such as improving pedestrian environments, installing barrier-free facilities, and establishing community activity centers. Although cross-sectional designs limit causal determination, this study suggests that multidimensional bio-psycho-social integration strategies may be more effective than single interventions. Future longitudinal or intervention studies are recommended to validate the efficacy of this comprehensive approach, providing evidence for community and policy innovations supporting cognitive health.
In summary, our study found that grip strength is significantly associated with cognitive function in older adults, with this relationship being independently and sequentially mediated by life-space mobility and depression. This finding provides valuable insights for public health policymakers and primary care practitioners: enhancing physical strength, supporting and encouraging outdoor activities, and promoting mental health can serve as effective strategies for maintaining cognitive health in aging populations. However, further large-scale prospective studies are needed to validate these findings.
This study has several limitations. First, the cross-sectional design cannot establish causal relationships between variables; longitudinal research is needed to determine temporal associations. Second, although methodological and statistical measures were employed to mitigate common method bias, self-reported data remain susceptible to memory bias among older adults participants. Future research should adopt multi-method assessments to enhance measurement validity. Third, participant recruitment was limited to China’s Guangxi Zhuang Autonomous Region, potentially restricting the generalizability of findings due to regional socioeconomic factors. Fourth, while this study incorporated some factors influencing outcomes, it did not fully account for covariates such as chronic diseases and health conditions. Subsequent research should: (a) expand geographic and socioeconomic diversity to enhance external validity; (b) Employ longitudinal designs to examine the sustained impact of grip strength on cognitive function; (c) Develop integrated intervention protocols combining personalized grip training with cognitive training programs; (d) Further refine control for confounding variables related to cognitive function in future studies.
Conclusion
In summary, this study reveals a significant association between grip strength and cognitive function among Chinese older adults. More importantly, it elucidates the underlying mechanisms by identifying life space mobility and depression as independent and sequential mediators in this relationship. These findings align with the biopsychosocial model, emphasizing that cognitive health is influenced by the complex interplay of biological, social, and psychological factors. Practically, the findings suggest that interventions targeting cognitive function in older adults should adopt a multidimensional strategy: enhancing muscle strength through resistance training, encouraging outdoor activities and social engagement to expand activities of daily living, and implementing early screening and intervention for depression. The synergistic combination of these three approaches can form an effective program to promote cognitive health in the elderly population. Future longitudinal studies or intervention trials are needed to validate these pathways and translate research findings into actionable public health measures.
Author contributions
All the authors contributed to the conception and design of the research. Data collection and analysis, the first draft of the manuscript was completed by ZhiXin Li, and on-site research. Data collection and data entry were carried out by Haowei Liu.Jingyun Zeng.Huiming Zhou.Lichong Lai.Haichen Wu.Pengxin Dong.Yidan Chai.Ping Huang. The corresponding author, Huiqiao Huang, provided guidance and supervision for the writing of the paper, and all authors commented on the previous versions of the manuscript. All the authors read and approved the final manuscript.
Funding
This research was funded by the older adults Nutrition Improvement Action Project of the Second Affiliated Hospital of Guangxi Medical University (2025037).
Data availability
If necessary, the data of this study can be obtained from the corresponding author.
Declarations
Ethical approval
This study was approved by the Ethics Committee of the Second Affiliated Hospital of Guangxi Medical University (NO.2023-KY0905), and strictly complied with the Declaration of Helsinki and relevant Chinese ethical regulations. All participants provided written informed consent.
Consent to participate
Informed consent was obtained from all individual participants included in the study.
Consent to publish
Additional informed consent was obtained from all individual participants for whom identifying information is included in this article.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.World Health Organization. A blueprint for dementia research. (2022). https://www.who.int/publications/i/item/9789240058248
- 2.Aarsland, D. Epidemiology and pathophysiology of dementia-related psychosis. J. Clin. Psychiatry81, AD19038BR1C (2020). [DOI] [PubMed]
- 3.Wang, G. et al. China alzheimer report 2024. J. Diagn. Concepts Pract.23, 219–256 (2024). [Google Scholar]
- 4.Xing, X. et al. Multimorbidity, healthy lifestyle, and the risk of cognitive impairment in Chinese older adults: a longitudinal cohort study. BMC Public. Health. 24, 46 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Rivan, N. F. M. et al. Incidence and predictors of cognitive frailty among older adults: A Community-based longitudinal study. Int. J. Environ. Res. Public. Health. 17, 1547 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Bohannon, R. W. G. Strength: An indispensable biomarker for older adults. Clin. Interv Aging. 14, 1681–1691 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Li, Z. et al. Correlation between grip strength and cognitive impairment in older adults of Beijing. Chin. J. Prev. Contr Chron. Dis.31, 444–448 (2023). [Google Scholar]
- 8.Angel, B. et al. Grip strength trajectories and cognition in english and Chilean older adults: A Cross-Cohort study. Pers. Med.12, 1230 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Kunutsor, S. K., Isiozor, N. M., Voutilainen, A. & Laukkanen, J. A. handgrip strength and risk of cognitive outcomes: new prospective study and meta-analysis of 16 observational cohort studies. GeroScience44, 2007–2024 (2022). [DOI] [PMC free article] [PubMed]
- 10.Duchowny, K. A. et al. Associations between grip strength and dementia Risk, Cognition, and neuroimaging outcomes in the UK biobank cohort study. JAMA Netw. Open.5, e2218314 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Cui, M., Zhang, S., Liu, Y., Gang, X. & Wang, G. Grip strength and the risk of cognitive decline and dementia: A systematic review and meta-analysis of longitudinal cohort Studies.Front. Aging Neurosci.13, 625551 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Sheets, K. M. et al. Life-space mobility and cognition in community-dwelling late-life women: A cross-sectional analysis. J. Am. Geriatr. Soc.73, 206–213 (2025). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Dong, X., Mo, Y., Wang, X. & Wang, X. Effects of resistance exercise on muscle mass, muscle strength, and physical performance in older adults at risk of sarcopenia.Chin. Nurs. Manag. 21, 1190–1195 (2021). [Google Scholar]
- 14.Fristedt, S., Kammerlind, A. S. & Fransson, E. I. & Ernsth Bravell, M. Physical functioning associated with life-space mobility in later life among men and women. BMC geriatr. 22, 364 (2022). [DOI] [PMC free article] [PubMed]
- 15.Silberschmidt, S. et al. Life-Space mobility and cognitive decline among Mexican Americans aged 75 years and older. J. Am. Geriatr. Soc.65, 1514–1520 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.De Silva, N. A., Gregory, M. A., Venkateshan, S. S., Verschoor, C. P. & Kuspinar, A. Examining the Association between Life-Space Mobility and Cognitive Function in Older Adults: A Systematic Review. Aging Res2019, 3923574(2019). [DOI] [PMC free article] [PubMed]
- 17.Cabanas-Sánchez, V. et al. Muscle strength and incidence of depression and anxiety: Findings from the UK biobank prospective cohort study. J. Cachexia Sarcopenia Muscle.13, 1983–1994 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Firth, J. et al. Association between muscular strength and cognition in people with major depression or bipolar disorder and healthy Controls. J. JAMA Psychiatry. 75, 740–746 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Jiang, R. et al. Associations between grip strength, brain structure, and mental health in > 40,000 participants from the UK biobank. BMC Med.20, 286 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Qiu, L. et al. Associations of muscle mass and strength with depression among US adults: A cross-sectional NHANES study. J. Affect. Disord363, 373–380 (2024). [DOI] [PubMed] [Google Scholar]
- 21.Loyal, M. S. et al. Longitudinal associations between late-life depression, cerebrovascular disease and cognition. Affect. Disord. 376, 59–67 (2025). [DOI] [PubMed] [Google Scholar]
- 22.Chen, X. et al. Relationships between sarcopenia, depressive symptoms, and mild cognitive impairment in Chinese community-dwelling older adults. J. Affect. Disord.286, 71–77 (2021). [DOI] [PubMed] [Google Scholar]
- 23.Iyer, A. S. et al. Life-Space mobility and clinical outcomes in COPD. J.Int.J. Chron. Obstruct. Pulmon Dis.13, 2731–2738 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Chung, M. H., Leung, S. F. & Välimäki, M. Use of tracking technology to examine life-space mobility among people with depression: A systematic review protocol. BMJ Open.10, e034208 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Fan, P., Li, H., Xu, H. & Rong, C. A chain mediation model reveals the association between depression and cognitive function in the older adults. Sci. Rep.14, 31375 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Kong, X. J. Bidirectional association between cognitive function and depressive symptoms in middle-aged and older adults: mediating effect analysis of physical activity. Master’s Thesis, Qingdao University; (2024). 10.27262/d.cnki.gqdau.2024.000959
- 27.Chen, L. K. et al. Asian working group for sarcopenia: 2019 consensus update on sarcopenia diagnosis and treatment. J. Am. Med. Dir. Assoc.21, 300–307 (2020). [DOI] [PubMed] [Google Scholar]
- 28.Peel, C. et al. Assessing mobility in older adults: the UAB study of aging Life-Space assessment. Phys. Ther.85, 1008–1119 (2005). [PubMed] [Google Scholar]
- 29.Ji, M., Zhou, Y., Liao, J. & Feng, F. Pilot study on the Chinese version of the life space assessment among community-dwelling older adults. Arch. Gerontol. Geriatr.61, 301–306 (2015). [DOI] [PubMed] [Google Scholar]
- 30.Kroenke, K., Spitzer, R. L. & Williams, J. B. The PHQ-9: validity of a brief depression severity measure. J. Gen. Int. Med.16, 606–613 (2001). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Li, T. et al. Reliability and validity of the Chinese version of the AD8. Chin. J. Intern. Med.51, 777–780 (2012). [PubMed] [Google Scholar]
- 32.Liu, S. W. et al. Bridging reduced grip strength and altered executive function: specific brain white matter structural changes in patients with alzheimer’s disease. Clin. Interv Aging. 19, 93–107 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Liu, Y. et al. Relationship between physical performance and mild cognitive impairment in Chinese community-dwelling older adults. Clin. Interv Aging. 16, 119–127 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Erickson, K. I., Weinstein, A. M. & Lopez, O. L. Physical activity, brain plasticity, and alzheimer’s disease. Arch. Med. Res.43, 615–621 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Urban-Wojcik, E. J. et al. Diversity of daily activities is associated with greater hippocampal volume. Cogn. Affect. Behav. Neurosci.22, 75–87 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Oudbier, S. J. et al. Pathophysiological mechanisms explaining the association between low skeletal muscle mass and cognitive function. J. Gerontol. Biol. Sci. Med. Sci.77, 1959–1968 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Herbert, J. & Lucassen, P. J. Depression as a risk factor for alzheimer’s disease: genes, steroids, cytokines and neurogenesis - What do we need to know? Front. Neuroendocrinol41, 153–171 (2016). [DOI] [PubMed] [Google Scholar]
- 38.Zacková, L., Jáni, M., Brázdil, M., Nikolova, Y. S. & Marečková, K. Cognitive impairment and depression: meta-analysis of structural magnetic resonance imaging studies. NeuroImage clin.32, 102830 (2021). [DOI] [PMC free article] [PubMed]
- 39.Polku, H. et al. Life-space mobility and dimensions of depressive symptoms among community-dwelling older adults. Aging. Health19, 781–789 (2015). [DOI] [PubMed] [Google Scholar]
- 40.Tosato, M. et al. Lifestyle changes and psychological well-being in older adults during COVID-19 pandemic. Clin. Geriatr. Med.38, 449–459 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Rolandi, E. et al. Targeting brain health in subjective cognitive decline: Insights from a multidomain randomized controlled trial. Aging Clin. Exp. Res.37, 151 (2025). [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
If necessary, the data of this study can be obtained from the corresponding author.