Abstract
Background
Community-dwelling older adults residing in non–age-friendly external environments face a higher risk of restricted life-space mobility. Although improving the external environment may help mitigate this risk, environmental interventions are often complex, challenging to implement, and demonstrate limited effectiveness. Enhancing intrinsic capacity, however, may offer a novel approach to addressing the risk of restricted life-space mobility in older adults.
Objectives
This study aims to examine the relationships among the external environment, intrinsic capacity, and life-space mobility, while also analyzing the mediating role of intrinsic capacity between the external environment and life-space mobility.
Methods
From March to June 2024, a cross-sectional study was conducted using a multistage sampling method involving 808 community-dwelling older adults aged 60 years and above in Yanji, Jilin Province, China. Data were collected using standardized instruments, including a general information questionnaire, the Life-Space Assessment for older adults, the Intrinsic Capacity Assessment Tool, and the External Environment Assessment Tool. All data were analyzed using SPSS 28.0. Pearson correlation analysis was performed to explore the relationships between variables, and mediation analysis was conducted using Model 4 in the PROCESS macro for SPSS.
Results
The incidence of limited life-space mobility was 55.4%, with life-space mobility score of 60.00 ± 30.76, an intrinsic capacity score of 180.10 ± 29.12, and an external environment score of 80.40 ± 17.82. Pearson correlation analysis revealed that life-space mobility was positively correlated with both the external environment and intrinsic capacity, with correlation coefficients of 0.721 and 0.622, respectively (P < 0.01). Additionally, the external environment was positively correlated with intrinsic capacity, yielding a correlation coefficient of 0.699 (P < 0.01). The mediation analysis results indicated a significant total association between the external environment and life-space mobility (B = 0.560, t = 16.875, P < 0.001). The pattern of associations was consistent with partial mediation by intrinsic capacity, with a significant indirect path (Effect = 0.278, 95% CI [0.199, 0.355]), accounting for 22.35% of the total association.
Conclusions
The life-space mobility of older adults living in the community was moderately low. The external environment was associated with life-space mobility both directly and indirectly through intrinsic capacity. To improve the functional ability of community-dwelling older adults, attention should be given to enhancing their life-space mobility, optimizing the external environment, and strengthening intrinsic capacity. These efforts will contribute to achieving the goal of healthy aging.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12877-026-07050-4.
Keywords: Life space, Mobility, Intrinsic capacity, Environment, Older adults, Community health services
Introduction
Activity is a key factor in maintaining physical function and social participation, as well as a core element in preserving independence [1–3]. As individuals age, mobility becomes the foundation of health [4, 5]. It has been identified as a critical predictor of health and well-being in older adults [6–8]. Life space is a multidimensional construct composed of three subspaces: physical, economic, and sociocultural. Life-space mobility is utilized to assess an individual’s activity level, particularly concerning their engagement in practical activities within their living environment over a specific timeframe. The activity zones consist of concentric areas extending outward from the bedroom [9]. Numerous studies have demonstrated that restricted life-space mobility in older adults leads to a reduced quality of life [5, 10], cognitive decline [11], and an increased risk of mortality [12]. In light of the shifting medical focus from treatment to prevention, early intervention for restricted life-space mobility in community-dwelling older adults is particularly crucial for promoting healthy aging [13].
There is a close association between the external environment and life-space mobility among older adults. The external environment encompasses all external factors that constitute an individual’s living context at both micro and macro levels. It represents a person-centered living system integrating multiple dimensions, including physical and social environments [14]. Different dimensions of the external environment influence life-space mobility in diverse ways [15, 16]. As older adults experience physiological decline, their needs and interactions with the environment also change, highlighting the interplay between individual characteristics and the external environment [17]. Consequently, older adults living in less supportive environments are at a higher risk of restricted life-space mobility [3]. Although improving the external environment can help reduce this risk, existing environmental interventions are often complex, difficult to implement, and yield limited effectiveness. Notably, the association between the external environment and life-space mobility may be mediated by other factors.
From the perspective of social-ecological theory [18], the concept of Intrinsic Capacity (IC) offers a crucial framework for understanding this mechanism [19]. IC is regarded as a “microsystem” that reflects an individual’s composite functional ability, encompassing five key domains: cognition, locomotion, psychological well-being, sensory function, and vitality [19, 20]. It serves as the foundation for maintaining and expanding life-space mobility in later life. Social-ecological theory further posits that individuals are influenced by their surrounding environments. Within this framework, the external environment functions as the “mesosystem,” which can significantly impact older adults’ intrinsic capacity. Previous research has demonstrated that a supportive external environment contributes to the maintenance and enhancement of IC, underscoring the importance of environmental friendliness for healthy aging [21, 22]. Therefore, both theoretical and empirical evidence suggest that intrinsic capacity may mediate the relationship between the external environment and life-space mobility—an idea that provides the conceptual foundation for this study.
Existing studies have examined specific components of the external environment—such as social participation or social networks [23, 24], public transportation, and urban facilities [25, 26]—and their relationships with life-space mobility. However, these studies typically focus on individual environmental factors rather than the overall external environment. Research on the subdomains of intrinsic capacity has demonstrated that cognitive, psychological, sensory, vitality, and locomotor capacities are each positively associated with life-space mobility [3, 27]. Nonetheless, empirical evidence linking the overall construct of intrinsic capacity to life-space mobility remains limited. Despite growing research interest, few studies have simultaneously investigated the external environment, intrinsic capacity, and life-space mobility within a unified framework. In particular, the mediating role of intrinsic capacity in the association between the external environment and life-space mobility among community-dwelling older adults has not been sufficiently explored.
In this context, the study aims to examine the relationships among the external environment, intrinsic capacity, and life-space mobility from a socio-ecological theoretical perspective. It further analyzes the mediating role of intrinsic capacity between the external environment and life-space mobility, offering new insights for enhancing life-space mobility in community-dwelling older adults and promoting healthy aging.
Material and methods
Study design
A cross-sectional study was conducted and reported following the Strengthening the Reporting of Observational Studies (STROBE) statement [28]. Please refer to Supplementary File 1.
Setting and sample
From March to June 2024, a multistage sampling method was employed. In the first stage, one subdistrict was selected from each of the eastern, central, and western regions of Yanji City, Jilin Province, based on geographical distribution, resulting in three subdistricts. In the second stage, the 29 communities within these subdistricts served as the sampling frame, from which two communities were randomly selected from each subdistrict, yielding a total of six communities. In the third stage, to ensure the effective recruitment of eligible older adults and the completion of high-quality questionnaire surveys in community settings, participants meeting the inclusion criteria were recruited from these six communities using convenience sampling.
Inclusion criteria
(1) Age ≥ 60 years; (2) Long-term community residence (≥ 6 months); (3) Provision of informed consent and voluntary participation.
Exclusion criteria
(1) Severe communication or cognitive impairment (MMSE score ≤ 9) [29]; (2) Self-reported severe diseases such as advanced cardiac, hepatic, or renal dysfunction, respiratory failure, or malignant tumors, which might prevent completion of the survey; (3) Current participation in other related research projects; (4) Diagnosed history of psychiatric disorders.
Based on preliminary findings from our research team, which indicated a 40% prevalence of life-space restriction among community-dwelling older adults [3], the sample size was calculated using the formula:
. With α = 0.05、E = 0.05, and P = 0.40, the estimated sample size was 369. Accounting for a 10% invalid questionnaire rate, the minimum required sample size was 406. Considering the results of estimation approaches, combined with accessibility of survey sites and available resources, a total of 820 participants were ultimately recruited for this study.
Research instruments
Sociodemographic questionnaire
Based on previous research [3], the researchers developed a comprehensive demographic information questionnaire tailored to the specific circumstances of the participants. The questionnaire included items on gender, age, nationality, personal monthly income, education level, marital status, residence, family structure type, use of auxiliary tools, self-assessed health status, and fear of falling.
Life space assessment(LSA)
The life space assessment scale for older adults, developed by the University of Alabama at Birmingham, was translated into Chinese and applied to the older adult population in China by Ji et al. [30] in 2015. This scale measures spatial mobility in five areas over the past month using the following questions: (1) Apart from your own bedroom, have you been to other rooms in your home? (2) Have you been to places outside your home? For example, the porch, terrace, hallway (in an apartment building), garage, or driveway? (3) Apart from your own yard or apartment building, have you been to nearby places outside your yard or apartment building (but within the community)? (4) Have you been to places outside the community (but within the town)? (5) Have you been to places outside the town? Additionally, participants are asked about the frequency of these events (“How often do you go?“) and independence (“Did you use any assistive tools, devices, or help from others?“). The scores for each dimension are calculated as follows: Life-space level (1–5) × activity frequency (less than once a week = 1, 1–3 times a week = 2, 4–6 times a week = 3, daily = 4) × independence (1 = assistance from others, 1.5 = assistance from tools, 2 = no assistance from others or tools). The total score for life-space is calculated by summing the scores from five dimensions, which range from 0 points (indicating no experience outside the bedroom) to 120 points (indicating independent daily excursions beyond the town). The higher the total score, the greater the level of life-space mobility, where a score < 60 indicates restricted life-space mobility. Given the significant differences in spatial structure, living habits, and economic development between Chinese communities and those in other countries, the terms “geographical area” and “town” in the scale have been substituted with “street” to align with the context of Chinese administrative units. The Chinese version of the Life-Space Assessment (LSA-C) demonstrates good psychometric properties, with a test–retest reliability coefficient of 0.760 [30]. In this study, the Cronbach’s α for the LSA was 0.840, indicating satisfactory reliability and validity.
Intrinsic capacity assessment tool
Based on the Integrated Care for Older People (ICOPE) guidelines on person-centred assessment and pathways in primary care, published by the World Health Organization (WHO) [21], intrinsic capacity was evaluated across five core domains: sensory function, vitality, locomotor capacity, psychological capacity, and cognitive capacity. The total intrinsic capacity score was calculated by summing the domain scores, with higher values indicating better intrinsic capacity. In the present study, the composite tool demonstrated excellent internal consistency, with a Cronbach’s α of 0.907. Details regarding the specific instruments, scoring methods, and reliability coefficients for each domain are provided in Supplementary Table 1.
Cornell medical index(CMI)
The A factor in CMI is used to assess sensory abilities (vision and hearing). It was initially developed by Brodman et al. and later revised and translated by Xu et al. [31] The A factor comprises 9 questions related to vision and hearing, with each question scored as 1 or 0. A higher score indicates better sensory function. The test-retest reliability coefficient for this scale is 0.940. In this study, the Cronbach’s α coefficient for this scale is 0.940.
-
(2)
Mini-mental stateexamination(MMSE)
Mini-Mental State Examination (MMSE), originally developed by Folstein et al., was used to assess cognitive ability in this study. An unauthorized version of the Chinese MMSE was initially used without prior permission [29]; however, this issue has since been resolved with PAR, and publication of the results has been permitted under specific conditions (see the Competing Interests section). The MMSE assesses five domains: orientation, memory, attention, calculation, and language ability, comprising a total of 30 items. Each item is scored as 0 or 1, yielding a total score ranging from 0 to 30, with higher scores indicating better cognitive function. The Cronbach’s alpha coefficient in this study was 0.833.
-
(3)
Barthel index(BI)
The BI is utilized to evaluate activity ability, originally developed by Mahoney and Barthel, and later revised and translated into Chinese by Wang et al. [32] It comprises 10 items: eating, bathing, grooming, dressing, bowel control, bladder control, toileting, transferring between bed and wheelchair, walking on level ground, and climbing stairs. Each item is assigned a score of 0, 5, 10, or 15 points, with a total score ranging from 0 to 100 points; a higher score indicates better activity ability. The Cronbach’s α coefficient for this scale is 0.916. In this study, the Cronbach’s α coefficient for the scale is 0.916.
-
(4)
Mini⁃nutritional assessment short⁃form(MNA-SF)
Developed by Rubenstein et al. [33], this scale is used to assess vitality status. The scale includes 6 items: Body Mass Index (0–3), weight loss in the past three months (0–3), stress situations or acute illness in the past three months (0 or 2 points), activity ability (0 or 2 points), mental illness (0–2 points), and appetite loss, indigestion, or difficulty chewing and swallowing in the past three months (0–2 points). The total score ranges from 0 to 14, with higher scores indicating better vitality status. Its Cronbach’s α coefficient is 0.703. In this study, the Cronbach’s α coefficient for this scale is 0.703.
-
(5)
Positive and negative affect scale(PANAS)
The PPANAS is utilized to evaluate psychological states, developed by Watson et al. and translated into Chinese by Huang et al. [34] The scale comprises 20 items and encompasses two dimensions: positive affect and negative affect. A 5-point Likert scale is employed for scoring, with each item rated from 1 to 5. The score range for each subscale is 10 to 100 points, with higher scores indicating better psychological states. The Cronbach’s α coefficient for the scale is 0.850. In this study, the Cronbach’s α coefficient for the scale is 0.850.
External environment assessment tool
The external environment was assessed using the World Health Organization Quality of Life Scale (WHOQOL-100), translated into Chinese by Fang et al. [35]. Following the WHO’s recommended external environment framework [36], six dimensions were selected: housing environment, transportation conditions, medical services and social security, social safety, personal relationships, and social support, comprising a total of 24 items. These dimensions were chosen because they comprehensively capture three core environmental domains influencing older adults’ quality of life: the physical environment (housing, transportation), the service and policy environment (medical care, social security, safety), and the interpersonal environment (relationships, social support). A five-point Likert scale was used, with total scores ranging from 24 to 120, where higher scores indicated a more favorable external environment. In the original validation study, Cronbach’s α values for these domains ranged from 0.637 to 0.854. In the present study, reliability and confirmatory factor analyses were conducted to verify the measurement properties of this tool. The overall Cronbach’s α was 0.942, and confirmatory factor analysis demonstrated good structural validity, with standardized factor loadings ranging from 0.815 to 0.844, composite reliability of 0.929, and average variance extracted of 0.686, all exceeding recommended thresholds. These findings indicate that the instrument possesses excellent reliability and validity in this study population.
Data collection
The research team comprised five graduate nursing students who received comprehensive training and guidance prior to conducting the investigation, ensuring the study’s impartiality and accuracy. Trained investigators recruited community-dwelling older adults using two methods: door-to-door household surveys and convenience sampling in designated community areas. Paper questionnaires were distributed on-site during these interactions. The investigators followed standardized instructions and provided individualized assistance to help older adult participants complete the questionnaires. Upon completion, the questionnaires were immediately collected and reviewed for any missing or incomplete information, which was promptly addressed. For older adults with limited education or visual impairments, investigators employed a question-and-answer approach to facilitate the completion of the questionnaires. All data were securely stored in a locked cabinet to ensure confidentiality, with access restricted to authorized project team members. All participants who complete the questionnaire received a small gift valued at $1.0. Utilizing a double-entry data input and verification process. Across the six selected communities, approximately 1,250 older adults met the inclusion criteria. Through a combination of home visits and on-site recruitment, 820 individuals were contacted and invited to participate; 18 refused (refusal rate 1.5%). A total of 808 participants completed the questionnaire survey, yielding a valid response rate of 98.5% (see Fig. 1). Due to privacy protection regulations, demographic and health information of nonparticipants could not be systematically obtained. However, field observations and feedback from community staff indicated that nonparticipants were primarily individuals who were away from home, bedridden, or unwilling to participate.
Fig. 1.
Flowchart of participant recruitment and data collection
Data analysis
Data were first manually checked for completeness, then coded and entered into EpiData version 3.1 before being exported to IBM SPSS Statistics version 28.0 (IBM Corp., Armonk, NY, United States) for further analysis. Prior to analysis, the normality of continuous variables was assessed using Q–Q plots, and the results indicated that the data met the assumptions required for parametric testing. Measurement data conforming to a normal distribution are expressed as mean ± standard deviation (
±s)), while categorical data are presented as relative frequencies. Harman’s single-factor test was employed to examine potential common method bias. Multicollinearity was diagnosed using the variance inflation factor (VIF) and tolerance (TOL) values. The total scores for external environment, intrinsic capacity, and life-space mobility all followed normal distributions; therefore, Pearson correlation analysis was conducted to explore the relationships among these three variables. The core research hypothesis posited that the external environment influences life-space mobility indirectly through intrinsic capacity, constituting a single, well-defined mediating pathway. Model 4 of the PROCESS macro (version 4.1) for SPSS, developed by Hayes (http://www.afhayes.com), was specifically designed to test such single-mediator models (X → M → Y) and thus aligns closely with the theoretical framework of this study. The forced entry method was applied to test the mediation effects. Based on the key determinants of life-space mobility highlighted in our previous scoping review [3] and the common practice of including sociodemographic variables as fundamental covariates in health outcome research, the following covariates were controlled in the mediation model: (1) Demographic characteristics-sex, age, and ethnicity, which are primary determinants of health disparities and mobility differences across groups; (2) Socioeconomic status-monthly income and education level, which influence individuals’ access to resources such as healthcare, transportation, and better living environments, potentially affecting external environment, intrinsic capacity, and mobility; (3) Social support and family structure—marital status, living arrangement, and family structure type, which reflect the individual’s social support network and have been shown to influence health behaviors, psychological state, and engagement in outdoor activities; (4) Functional and psychological status-use of assistive devices, self-rated health, and fear of falling, which directly reflect functional status and psychological barriers that affect mobility and activity range. The mediation model was tested on a sample of 808 participants. A two-tailed significance level was set at α = 0.05, and a P-value < 0.05 was considered statistically significant.
Results
Characteristics of the participants
This study included a total of 808 community-dwelling older adults, with a mean age of 72.1 ± 8.0 years (range: 60–94 years). For descriptive purposes, age was categorized into three groups: 60–69 years (46.0%), 70–79 years (32.7%), and ≥ 80 years (21.3%). Detailed sociodemographic information is presented in Table 1.
Table 1.
General information of surveyed patients(n = 808)
| Variable | Number of people (percentage, %) |
|---|---|
| Gender | |
| Male | 467(57.8) |
| Female | 341(42.2) |
| Age group(years) | |
| 60–69 | 372(46.0) |
| 70–79 | 264(32.7) |
| ≥ 80 | 172(21.3) |
| Nation | |
| Han Chinese | 489(60.5) |
| Chinese Korean nationality | 314(38.9) |
| Others | 5(0.6) |
| Personal monthly Income(CNY) | |
| Not | 144(17.8) |
| <1000 | 206(25.5) |
| 1000–3000 | 377(46.7) |
| >3000 | 81(10.0) |
| Education level | |
| Illiteracy | 199(24.6) |
| Primary school | 223(27.6) |
| Junior high school | 268(33.2) |
| Senior high school | 83(10.3) |
| High school and above | 35(4.3) |
| Marriage | |
| Married | 651(80.6) |
| Unmarried/Divorced/Widowed | 157(19.4) |
| Residence | |
| Living alone | 257(31.8) |
| With spouse | 447(55.3) |
| Live with children or other relatives | 104(12.9) |
| Family structure type | |
| Conjugal family/Absolute empty nest b | 124(15.4) |
| Relative empty nest c | 330(40.8) |
| Non-empty nest | 354(43.8) |
| Use auxiliary tools. | |
| Yes | 107(13.2) |
| No | 701(86.8) |
| Self-assessment of health status | |
| Good | 245(30.3) |
| General | 379(46.9) |
| Worse | 184(22.8) |
| Afraid of falling. | |
| Yes | 495(61.3) |
| No | 313(38.7) |
Note: aThe number outside the parentheses represents the number of individuals, while the number inside the parentheses indicates the prevalence rate as a percentage
bRefers to situations where the children of older adults live either abroad or in other cities within the country, resulting in separation that makes regular meetings difficult
cRefers to situations where older parents and their adult children do not live together but reside in the same city or nearby area, maintaining some spatial distance while remaining connected
CNY= Chinese Yuan
Scores of variables scales
Community-dwelling older adults have a mobility score of 60.00 ± 30.76 for their life-space mobility. Among them, 448 individuals have limited life-space mobility, resulting in a limitation rate of 55.4%. The specific scores can be found in Table 2.
Table 2.
Total scores and scores for each dimension of intrinsic capacity, external environment, and life-space mobility (n = 808)
| Variable | Score Range | Score( ±s) |
|---|---|---|
| Life-space mobility | 0 ~ 120 | 60.00 ± 30.76 |
| Outside the bedroom at home | 1 ~ 8 | 6.02 ± 2.92 |
| Outside the bedroom at home | 2 ~ 16 | 11.48 ± 5.62 |
| Within the residential building but outside the community | 3 ~ 24 | 17.54 ± 7.67 |
| Outside the residential area but within this street | 4 ~ 32 | 16.53 ± 12.35 |
| Any other streets in this city | 5 ~ 40 | 8.43 ± 12.77 |
| Intrinsic capacity | 99 ~ 256 | 180.10 ± 29.12 |
| Sensory abilities | 0 ~ 9 | 5.62 ± 2.52 |
| Vibrant state | 0 ~ 14 | 9.98 ± 2.88 |
| Ability to act | 9 ~ 110 | 83.79 ± 21.88 |
| Psychological state | 20 ~ 100 | 55.03 ± 11.74 |
| Cognitive abilities | 0 ~ 30 | 25.70 ± 5.69 |
| External environment | 24 ~ 120 | 80.40 ± 17.82 |
| Traffic conditions | 4 ~ 20 | 11.21 ± 3.35 |
| Housing environment | 4 ~ 20 | 13.70 ± 4.10 |
| Medical services and social Security | 4 ~ 21 | 13.99 ± 3.00 |
| Social security | 4 ~ 20 | 13.06 ± 2.82 |
| Personal relationships | 4 ~ 20 | 13.23 ± 3.19 |
| Social assistance | 4 ~ 20 | 14.22 ± 3.64 |
Common method bias test and collinearity diagnosis
Using Harman’s single-factor analysis method, four factors with eigenvalues greater than 1 were extracted. The first factor accounted for 35.77% of the total variance, which is below the 40% threshold [37], indicating that no significant common method bias was present in this study. Multicollinearity among the independent variables was assessed using the variance inflation factor (VIF) and tolerance (TOL). The VIF values ranged from 1.097 to 3.043 (all < 5), and tolerance values ranged from 0.329 to 0.911 (all > 0.1) [37], suggesting that no multicollinearity existed among the independent variables.
Correlation between variables
The results of the Pearson correlation analysis indicate that life-space mobility is positively correlated with the external environment and intrinsic capacity (P < 0.01). Furthermore, the external environment is positively correlated with intrinsic capacity (P < 0.01), as shown in Table 3.
Table 3.
Correlation analysis of Intrinsiccapacity, life-space mobility and external environment scores(r value)
| Variable | Life-space mobility | External environment | Intrinsic capacity |
|---|---|---|---|
| Life-space mobility | 1 | — | — |
| External environment | 0.721d | 1 | — |
| Intrinsic capacity | 0.622d | 0.699d | 1 |
Note: — indicates repeated data; d indicates P < 0.01
Analysis of the mediating effect
After controlling for covariates such as gender, age, nationality, personal monthly income, education level, marital status, residence, family structure, use of auxiliary tools, self-assessment of health status, and fear of falling, the study examined the mediating effect of intrinsic capacity between the external environment and life-space mobility. The results of the mediation analysis for Model 4 indicate that the positive impact of the external environment on life-space mobility is significant (B = 0.560, t = 16.875, P < 0.001). Additionally, the positive impact of the external environment on intrinsic capacity is significant (B = 0.699, t = 27.779, P < 0.001), and the positive impact of intrinsic capacity on life-space mobility is also significant (B = 0.231, t = 6.947, P < 0.001), as illustrated in Fig. 2. By utilizing PROCESS 4.1 to select Model 4 [24], the bootstrap resampling analysis with 5,000 iterations reveals that in the pathway “external environment → intrinsic capacity → life-space mobility,” the effect value of intrinsic capacity is 0.278 [95% CI (0.199, 0.355)]. The 95% confidence interval does not include 0, indicating that the mediating effect is established [38]. The external environment can not only directly affect life-space mobility but can also indirectly influence it through intrinsic capacity. The total effect is 1.244, the direct effect is 0.966, and the mediating effect is 0.278, with the mediating effect accounting for 22.35% of the total effect, as presented in Table 4.
Fig. 2.
The mediating effect of Intrinsiccapacity on the relationship between external environment and life-space mobility
Table 4.
Mediating effect of intrinsiccapacity between external environment and life-space mobility
| Variable | Effect size | Bootstrap SE | Boot 95%CI | Effect size |
|---|---|---|---|---|
| Overall effect | 1.244 | 0.042 | 1.162–1.327 | 100% |
| Direct effect | 0.966 | 0.057 | 0.854–1.079 | 77.65% |
| Indirect effect | 0.278 | 0.040 | 0.199–0.355 | 22.35% |
Discussion
This study investigated the level of life-space mobility among community-dwelling older adults and examined the associations between the external environment and life-space mobility, as well as the potential mediating role of intrinsic capacity. The findings revealed that the prevalence of restricted life-space mobility was 55.4%. The external environment was significantly associated with life-space mobility both directly and indirectly through intrinsic capacity, with the mediating effect accounting for 22.35% of the total association.
The life-space mobility of community-dwelling older adults was found to be at a moderate to low level. The average score was lower than the mean of 66.80 points reported in a meta-analysis of Chinese regions [39]. The prevalence of restricted life-space mobility (55.4%) was higher than that reported by Ren (42.2%) [40] and Zhao (48.2%) [41]. Two possible explanations emerge from these findings. First, this study was conducted in ethnic minority regions along China’s northeastern border, whereas prior surveys primarily focused on central and eastern areas. These regions differ substantially in transportation infrastructure, healthcare accessibility, living standards, and available social resources. Second, climatic conditions in the study area may also contribute to these differences. When weather conditions exceed the adaptive capacity of older adults, their likelihood of outdoor mobility decreases [42]. The northeastern region is characterized by low temperatures and pronounced seasonal variations [43], which may be associated with reduced life-space mobility. Notably, the scores in this study were lower than the 2015–2019 average (75.23 points) but higher than those from 2020 to 2022 (50.67 points). Overall, the scores remained lower than those reported in studies from North America [44, 45], Europe [10, 46], Japan [25], and other regions, possibly reflecting differences in study populations and contextual characteristics.
The findings also indicate that the intrinsic capacity of community-dwelling older adults requires improvement. The total intrinsic capacity score in our study was consistent with that reported by Weng et al. [47] but higher than that reported by Li et al. [48]. This discrepancy may be attributed to differences in data collection periods—our study was conducted in 2024, whereas Li et al. collected data in 2020, during the COVID-19 pandemic. The pandemic has been associated with declines in vitality, psychological well-being, and mobility among older adults [49], potentially reducing intrinsic capacity. Lower scores in the psychological status, sensory capacity, and vitality domains align with previous findings [48]. Prior studies have shown that 44% of older adults experience declines in one or more dimensions of intrinsic capacity [49]. Although declines in hearing and vision are natural with aging, many older adults perceive these changes as normal and may neglect symptom management [50], which is associated with lower sensory capacity. Additionally, physiological decline, changes in family structure and social roles, and reduced social participation have all been linked to decreased psychological status, sensory capacity, and vitality [5, 51, 52], which may collectively constrain intrinsic capacity and physical function. Therefore, regular assessments of intrinsic capacity and functional status should be implemented. Based on these evaluations, comprehensive interventions—including healthcare services, environmental modifications, and long-term care systems—should be developed to support and maintain intrinsic capacity among older adults.
Heightened attention should be given to the constituent elements of the external environment for elderly residents in the community. This study reveals that external environment scores align with previous research findings [53]. Among various environmental components, transportation infrastructure received particularly low scores, warranting special focus. In developed nations such as the United States, Australia, and the Netherlands, private vehicles constitute the primary mode of transportation for older adults; for instance, 89.3% of American seniors rely on personal vehicles for mobility [54]. In contrast, current transportation patterns among the elderly in China are predominantly characterized by walking (as the primary mode) and public transit (as the secondary mode), constrained by limitations in transportation infrastructure, cultural norms, and economic factors [55]. Only 3% of Chinese seniors currently utilize private vehicles [56]. As physical capabilities decline with age, the environmental requirements of older adults—particularly concerning transportation systems and social infrastructure—undergo corresponding transformations. The mobility opportunities and conditions provided by external environments directly influence the travel behaviors of elderly residents. Therefore, in terms of social relations, communities should adopt a dual approach: on one hand, they should strengthen outreach efforts to foster a supportive atmosphere that encourages elderly participation in cultural activities; on the other hand, they should enhance community environments through micro-environmental design improvements, such as upgrading accessibility facilities and installing clear road traffic signs. This should be complemented by promoting interdisciplinary integration and coordinated development between public health initiatives and healthy city/community programs, emphasizing the impact of physical environments on life-space mobility among community-dwelling older adults. Particular attention should be given to addressing population heterogeneity and environmental disparities within elderly communities.
The results of this study indicate that intrinsic capacity is partially associated with the relationship between the external environment and life-space mobility among community-dwelling older adults, with the mediating association accounting for 43.39% of the total effect. This finding suggests that the external environment may be related to life-space mobility indirectly through intrinsic capacity. The maintenance of physical function and social participation in older adults appears to be shaped by the interaction between their intrinsic capacity and external environment [19]. The interplay between intrinsic capacity and the external environment is crucial for the functioning of older adults, and even small changes in individuals or their surroundings may have notable long-term associations with health outcomes [57]. Consistent with the person–environment fit theory, a higher compatibility between personal characteristics and external environmental conditions tends to enhance individual well-being and positive experiences [58]. Given the age-related decline in intrinsic capacity, age-friendly external environments may play a compensatory role. Such environments can help alleviate functional limitations through adaptive mechanisms that respond to older adults’ specific needs and may partially counterbalance physiological declines [59]. De Andrade et al. [60] highlight that environmental exposures and contextual factors are strongly associated with the accumulation of disease burden across individuals and populations. For older adults, key external environmental factors—such as transportation accessibility, habitability of the physical environment, and availability of healthcare services—are closely linked to their ability to engage in healthier lifestyles, access preventive healthcare, and manage chronic conditions effectively [14, 21, 30]. These external factors may collectively be associated with variations in life-space mobility through long-term, cumulative effects. Changes in the external environment often involve infrastructure construction and policy modifications across multiple levels—including national, societal, community, and family domains—making interventions complex and challenging to implement effectively. Therefore, it may be more feasible for primary healthcare providers to focus on intrinsic capacity as a central target of intervention, using personalized and staged management strategies [5]. The results of this study indicate that intrinsic capacity is partially associated with the relationship between the external environment and life-space mobility among community-dwelling older adults, with the mediating association accounting for 43.39% of the total effect. This finding suggests that the external environment may influence life-space mobility indirectly through intrinsic capacity. The maintenance of physical function and social participation in older adults appears to be shaped by the interaction between their intrinsic capacity and the external environment [19]. The interplay between intrinsic capacity and the external environment is crucial for the functioning of older adults, and even small changes in individuals or their surroundings may have significant long-term associations with health outcomes [57]. Consistent with the person–environment fit theory, greater compatibility between personal characteristics and external environmental conditions tends to enhance individual well-being and positive experiences [58]. Given the age-related decline in intrinsic capacity, age-friendly external environments may play a compensatory role. Such environments can help alleviate functional limitations through adaptive mechanisms that respond to older adults’ specific needs and may partially offset physiological declines [59]. De Andrade et al. [60] highlight that environmental exposures and contextual factors are strongly associated with the accumulation of disease burden across individuals and populations. For older adults, key external environmental factors—such as transportation accessibility, habitability of the physical environment, and availability of healthcare services—are closely linked to their ability to engage in healthier lifestyles, access preventive healthcare, and manage chronic conditions effectively [14, 21, 30]. These external factors may collectively be associated with variations in life-space mobility through long-term, cumulative effects. Changes in the external environment often involve infrastructure development and policy modifications across multiple levels—including national, societal, community, and family domains—making interventions complex and challenging to implement effectively. Therefore, it may be more feasible for primary healthcare providers to focus on intrinsic capacity as a central target of intervention, employing personalized and staged management strategies [5]. Interventions should consider older adults’ external environmental characteristics and contextual conditions to ensure their relevance and adaptability [14]. For older adults in a stable stage, healthcare professionals can emphasize maintaining intrinsic capacity at an optimal level. For those experiencing gradual declines in intrinsic capacity, the management priority lies in identifying potential causes of decline [21] and addressing them to minimize further impairment. For older adults whose intrinsic capacity has progressed to disability, primary care providers should act as coordinators, integrating resources across families, communities, healthcare institutions, and social welfare organizations [61]. Building a comprehensive care system can maximize functional potential, encourage participation in social activities, and ultimately improve life-space mobility and quality of life [1, 2, 62, 63]. At the level of urban planning and community design, health institutions and community administrators should enhance organizational coordination and promote cross-sector collaboration between public health and healthy city/community initiatives. Special attention should be given to the influence of the physical environment on life-space mobility among community-dwelling older adults [16, 19], recognizing the heterogeneity of older populations and environmental diversity to foster mobility-supportive and inclusive community environments.
Limitations
First, this study employed a cross-sectional design. Therefore, causal or temporal relationships could not be established, and reverse causation cannot be ruled out [37]. Second, the measurement of the external environment relied on subjective perceptions, which may conceptually overlap with intrinsic capacity and affect the estimated strength of associations. Although tests for multicollinearity and common method bias indicated model stability, future studies should employ more objective and fine-grained measures to validate these findings. Third, the study sample was obtained using a combination of stratified cluster random sampling and convenience sampling, which may have led to the underrepresentation of older adults with poor health status or mobility limitations. Finally, despite controlling for multiple sociodemographic variables, the possibility of residual confounding. Additionally, the mediation model assumes no unmeasured confounding; therefore, its causal interpretation should be approached with caution. Future research should incorporate objective health records and broader environmental indicators to further evaluate the robustness of the current findings.
Conclusion
This study found that life-space mobility among older individuals in the community is at a moderately low level. The external environment was associated with life-space mobility through both a direct path and an indirect path via intrinsic capacity. This conclusion underscores the importance of enhancing intrinsic capacity and fostering a supportive external environment to maintain the life-space mobility of older individuals. This understanding may provide new perspectives and recommendations for improving life-space mobility among older adults.
Supplementary Information
Acknowledgements
We would like to express our sincere gratitude to all the participants and student surveyors who took part in this study.
Abbreviations
- IC
Intrinsic Capacity
- LSA
Life Space Assessment
- MMSE
Mini-Mental State Examination
- MNA-SF
Mini-Nutritional Assessment Short-Form
- PANAS
Positive and Negative Affect Scale
- SPSS
Statistical Package for the Social Sciences
- STROBE
Strengthening the Reporting of Observational Studies in Epidemiology
Authors’ contributions
G.M.W contributed to conceptualization, methodology, investigation, data curation, formal analysis, and writing - original draft preparation. S.X.M. performed data re-analysis, validation, and methodology refinement, and contributed to writing - review & editing. Z.H.W. was responsible for supervision, project administration, resources, and writing - review & editing. All authors read and approved the final manuscript.
Funding
None to declare.
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
Ethical approval for this study was obtained from the Ethics Review Committee of the Medical School (Clinical Trial Number: 20231008). Data collection was conducted in accordance with the principles of anonymity and informed consent from the participants. Throughout the study, the ethical guidelines outlined in the Declaration of Helsinki were strictly adhered to.
Consent for publication
Not applicable.
Competing interests
The authors declare the following competing interest: An unauthorized version of the Chinese MMSE was used by the study team without permission; however, this issue has now been rectified with PAR. The MMSE is a copyrighted instrument and may not be used or reproduced, in whole or in part, in any form, language, or by any means without the written permission of PAR (https://www.parinc.com). A copy of this article will be sent to PAR to confirm compliance with their requirements. The authors declare that they have no competing interests. Having obtained permission from all the authors, I declare that: An unauthorized version of the Chinese MMSE was used by the study team without permission; however, this issue has now been resolved with PAR. The MMSE is a copyrighted instrument and may not be used or reproduced, in whole or in part, in any form or language, or by any means, without the written permission of PAR.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Supplementary Materials
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.