Associations between intrinsic capacity, subjectively perceived environmental support and functional status among rural disabled older adults in China: guided by the healthy ageing framework

Abstract

Background

Global population ageing poses significant challenges to economic development and long-term care systems, making the health and well-being of older adults an urgent social issue.

Objective

To comprehensively investigate the intrinsic capacity, subjectively perceived environmental support, and functional status of rural disabled older adults, clarify the available resources, health conditions, and care needs of this population, and explore factors associated with the interaction between intrinsic capacity and environmental support in relation to functional status.

Methods

A questionnaire survey was conducted among rural disabled older adults in natural villages in Henan Province using the WHO Quality of Life Scale (domains of environment and social relationships), Intrinsic Capacity Assessment Tool, Physical Self-Maintenance Scale, and the Instrumental Activities of Daily Living scale.

Results

Among 343 rural disabled older adults, the mean intrinsic capacity score was 5.68 ± 1.62. Domain-specific scores were as follows: mobility (4.51 ± 2.72), vitality (22.27 ± 4.02), cognition (17.31 ± 7.09), psychological status (6.68 ± 5.69), and sensory ability (1.22 ± 0.52). The highest impairment rates were observed in mobility (99.1%) and cognition (91.0%). The mean item score of the environment domain was 3.44 ± 0.50, and that of the social relationship domain was 3.59 ± 0.50, both at a moderate level; the highest scores were found in physical environment (3.87 ± 0.45) and healthcare and social security services (3.82 ± 0.44). The mean functional status score was 29.57 ± 11.67. Multiple linear regression analysis showed that age, education (junior high school, high school and above), living arrangement (living with children only, living with spouse only, living with spouse and children, living in institution), type of medication taken, monthly income(500~), duration of disability, cause of disability(aging), grip strength level, frailty status, and intrinsic capacity level (declined intrinsic capacity, high and stable intrinsic capacity)were significantly associated with functional status among rural disabled older adults (all P < 0.05). The path analysis results showed that subjectively perceived environmental support was positively associated with intrinsic capacity (β = 0.406, P < 0.001), while intrinsic capacity (β = -0.580, P < 0.001) and subjectively perceived environmental support (β = -0.414, P < 0.001) were negatively associated with functional status.

Conclusion

Rural disabled older adults present with significant impairments in intrinsic capacity and functional status. Subsequent intervention studies may focus on optimizing subjectively perceived environmental support and improving intrinsic capacity, ultimately aiming to prevent or delay disability and promote healthy aging in this population.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12889-026-28445-3.

Background

In October 2015, the World Health Organization proposed a new conceptual framework for “healthy ageing” in the World Report on Ageing and Health. Healthy ageing was defined as the process of developing and maintaining the functional ability that enables well-being in older age. Functional ability refers to the health-related attributes that enable individuals to live and act according to their own values and preferences, and is determined by an individual’s intrinsic capacity, relevant environmental characteristics, and the interactions between the two [1]. The WHO emphasized in its Decade of Healthy Ageing (2020–2030) initiative the importance of “enhancing and maintaining the intrinsic capacity of older people and extending their period of good health” as a key future goal [2]. Intrinsic capacity refers to the composite of all the physical and mental capacities that an individual can draw on at any point in life. It includes five key domains: mobility, vitality, cognition, psychology, and sensory ability. Intrinsic capacity has demonstrated strong predictive value for health outcomes and adverse events in older adults. Yang et al. [3] identified four main areas of adverse outcomes associated with declining intrinsic capacity: (1) Physiological outcomes, including frailty, pneumonia, memory impairment, polypharmacy, urinary incontinence, and poor or fair self-rated health; (2) Clinical outcomes, including Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL) disability, mortality, falls, loss of autonomy, and event dependence; (3) Healthcare utilization, including hospitalization, hospital costs, total pharmacy and medical expenses, and emergency department visits; (4) Other outcomes, such as reduced quality of life.

Environment refers to all external factors that constitute the context in which individuals live, encompassing multiple levels from the micro level, such as family and community, to the macro level of broader society. The development of age-friendly environments is central to external environmental improvement. Previous studies have found that external factors such as older adults’ social relationships, residential environment, social security, and safety protection play a key role in improving intrinsic capacity [4], among which the supportive role of age-friendly external environments has become increasingly prominent [5]. For example, Zhou et al. [6] found that older adults with comorbid diabetes living in more age-friendly communities had significantly better intrinsic capacity. Moreover, neighborhood green spaces, accessible public transportation, and recreational facilities were found to help slow the decline of intrinsic capacity [7]. Sánchez-Sánchez et al. [8] indicated that neighborhood satisfaction, access to facilities, and interpersonal trust can foster close neighborly relationships, reduce loneliness, and promote intrinsic capacity in older adults. Huang et al. [9] reviewed that intrinsic capacity is shaped by multiple social determinants, including socioeconomic status, lifestyle, psychosocial factors, physical environment, and healthcare systems. Approximately 25% of the variability in intrinsic capacity is attributed to genetic factors, while 75% results from accumulated behavioral patterns and risk exposures over the life course [10]. Therefore, the external environment plays a vital role in enhancing intrinsic capacity.

Intrinsic capacity is an internal predictor of physical function decline. Its deterioration can lead to negative outcomes such as falls, disability, and repeated hospitalizations, severely impairing the quality of life of older adults [11]. Research by Philipe de Souto et al. [12] revealed that individuals with impaired intrinsic capacity are more likely to experience frailty and limitations in both ADL and IADL. Moreover, the greater the number of affected intrinsic capacity domains, the higher the risk of functional decline. In summary, older adults’ intrinsic capacity is closely associated with the external environment and functional status. Improving intrinsic capacity and optimizing the external environment are therefore important for enhancing functional status. However, the interrelationships among intrinsic capacity, the external environment, and functional status, as well as the direct effect of the external environment on functional status, have not yet been fully explored among rural disabled older adults. Therefore, this study investigated intrinsic capacity, the external environment, and functional status among rural disabled older adults, and analyzed their associated factors to inform the development of personalized smart integrated care plans. These efforts may help prevent or reverse declines in intrinsic capacity and functional status among rural disabled older adults and promote healthy ageing.

Methods

A cross-sectional survey was used to collect data from disabled older adults in rural China.

Study participants

From February to May 2023, convenience sampling was used to select administrative villages in Qi County, Hebi City, Henan Province, as the study sites. Qi County is a typical agricultural county in central China, and the demographic characteristics, health status, and older adult care patterns of its rural older population are largely consistent with the overall characteristics of rural areas in Henan Province. Therefore, the findings may provide reference for similar regions. In addition, the research team had previously established long-term and stable collaborations with local health administration departments and administrative villages, which helped ensure the survey response rate and data quality.

Inclusion criteria were: Aged 60 years or older; Holding rural household registration, having resided locally for at least one year, and having engaged in agricultural or related work; Assessed as at least mildly disabled using the Physical Self-Maintenance Scale (PSMS); Informed consent and voluntary participation. Exclusion criteria included severe mental disorders that prevented cooperation. The study followed the ethical principles of informed consent, confidentiality, and non-maleficence. According to the empirical rule of 5–10 times the number of variables, with an expected 10–20% attrition rate, and 40 variables preliminarily identified, the minimum sample size required was calculated to be between 222 and 500 cases.

Measurements

Sociodemographic and health-related assessment indicators

A questionnaire was developed based on relevant literature and the characteristics of rural disabled older adults, covering two categories of contents: (1) Sociodemographic characteristics: gender, age, ethnicity, religious belief, education level, marital status, number of children, living arrangement, source of income, monthly personal income, polypharmacy, monthly medical expenses, type of medical insurance, cause of disability, duration of disability, and degree of disability; (2) Health-related assessment indicators: frailty status assessed by the FRAIL Scale, comorbidities evaluated by the age-adjusted Charlson Comorbidity Index (CCI), grip strength measured by an EH106 electronic dynamometer, and Body Mass Index (BMI) calculated from height and body weight.

WHO quality of life scale (Environment + Social Relationships)

Subjectively perceived environmental support was assessed using the environment and social relationships domains of the WHO Quality of Life Scale, localized and validated by Fang Jiqian et al. [13]. This tool includes 40 items across 10 aspects: social security, housing conditions, income sources, access to medical and social services, access to information, recreational activities, environmental quality, transportation, personal relationships, and social support. A five-point Likert scale was used, yielding a total score range of 40–200, with higher scores indicating a more favorable environment. The instrument demonstrated good reliability, with Cronbach’s α coefficients ranging from 0.637 to 0.854.

Intrinsic capacity assessment scale

This study adopted the five-domain assessment of intrinsic capacity in older adults developed by Susana et al. [14]. The specific assessment instruments and coding criteria are detailed in (Table 1). With reference to the internationally recognized cutoff values for the assessment tools of each dimension, the raw scores of the five dimensions were classified into corresponding capacity levels and assigned ordinal values, which were then summed to balance the evaluation weight of each dimension. The scores of the five dimensions were accumulated, with higher scores indicating more stable intrinsic capacity. A total score of 0–4 indicated significant impairment in intrinsic capacity, 5–8 indicated a decline in intrinsic capacity, and 9–10 indicated high and stable intrinsic capacity.

Table 1.

Details of intrinsic capacity assessment instruments

Domain	Assessment instrument	Raw score range	Interpretation	Domain points
Mobility	Short Physical Performance Battery	0–2	Severe mobility impairment	0
		3–9	Mild-to-moderate mobility impairment	1
		10–12	Normal mobility	2
Vitality	Full Mini Nutritional Assessment	<17	Malnutrition	0
		17–23.5	At risk of malnutrition	1
		24–30	Normal nutritional status	2
Cognition	Mini-Mental State Examination (MMSE)	0–9	Moderate to severe cognitive impairment	0
		10–25	Mild to moderate cognitive impairment	1
		26–30	Normal cognitive function	2
Psychology	Cornell Scale for Depression in Dementia (CSDD)	>18	Definite major depressive episode	0
		11–17	Probable major depressive episode	1
		0–10	Normal psychological status	2
Sensory	Self-reported visual/hearing impairment	0	Severe visual/hearing impairment	0
		1	Mild to moderate visual/hearing impairment	1
		2	Normal visual/hearing function	2

Functional status assessment scale

The PSMS and IADL Scale were used to assess the activities of daily living of rural disabled older adults, which reflects their functional status [15]. The PSMS consists of 6 items: toileting, feeding, dressing, grooming, ambulation, and bathing. The IADL Scale includes 8 items: telephone use, shopping, food preparation, housekeeping, laundry, outdoor mobility, medication management, and financial management, resulting in a total of 14 items. Each item is rated on a 4-point scale (1 = completely independent, 2 = some difficulty, 3 = needs assistance, 4 = unable to perform). The total scale score ranges from 14 to 56, with higher scores indicating greater limitations in activities of daily living and more severe functional impairment. The scale has good psychometric properties, with a split-half reliability of 0.901, Cronbach’s α coefficient of 0.966, and cumulative variance contribution rate of 85.549% for structural validity in this study.

Data collection

Data were collected via on-site paper questionnaires by four postgraduate students who received unified training and inter-rater reliability testing prior to the survey. After obtaining support and informed consent from township health centers, the investigators were guided by the public health directors of the township health centers to rural communities, and conducted household surveys with the assistance of village doctors for all disabled older adults who met the inclusion criteria. After introducing the research purpose and methods to the rural disabled older adults, the questionnaires were distributed on site. The distributors uniformly read the questionnaire items to the disabled older adults in a neutral attitude and assisted them in checking. Among them, when using the CSDD to assess the depressive state of the disabled older adults, interviews were conducted with primary caregivers to supplement and verify the older adults’ depression-related performances such as mood, behavior, and sleep in the past two weeks, and the scale scores were completed comprehensively. The questionnaires were collected immediately after completion, and all questionnaires were anonymous. After a three-month survey, a total of 350 questionnaires were collected. Questionnaires with missing values were directly excluded, resulting in 343 valid questionnaires, with an effective response rate of 98%.

Statistical analysis

SPSS 25.0 software was used for data entry and statistical analysis. P–P and Q–Q plots confirmed that the scores of intrinsic capacity, subjectively perceived environmental support, and functional status were approximately normally distributed. Categorical data are presented as frequencies (percentages), and continuous variables as means ± standard deviations. t-tests, one-way ANOVA, nonparametric tests, and multiple linear regression were used to explore factors associated with functional status. Ordinal categorical variables were dummy‑coded prior to the multiple linear regression analysis. Bootstrap resampling with 1,000 replications was performed to verify the robustness of the regression results from the forced entry method. Based on the World Health Organization’s healthy ageing framework, AMOS 24.0 was used to construct a structural equation model (SEM) to fit the association paths among subjectively perceived environmental support, intrinsic capacity, and functional status in rural disabled older adults. After model fit testing, the paths were cautiously optimized according to modification indices and theoretical plausibility, and the model was used to examine the robustness of the healthy ageing framework. The criteria for acceptable model fit were set as follows: normed chi-square (CMIN/DF) < 5, Root Mean Square Error of Approximation (RMSEA) < 0.05, Goodness of Fit Index (GFI) > 0.90, Comparative Fit Index (CFI) > 0.90, Incremental Fit Index (IFI) > 0.90, and Tucker-Lewis Index (TLI) > 0.90. Two-tailed tests were performed for all path coefficients, with a P value < 0.05 considered statistically significant.

Results

Demographic characteristics

All 343 rural disabled older adults were Han Chinese, with a mean age of 74.97 ± 7.83 (60–95) years, mean disability duration of 7.84 ± 8.04 (0.1–60) years, mean grip strength of 12.38 ± 6.62 (0–36.1) kg, and a mean BMI of 23.19 ± 3.49 (14.5–34.2) kg/m². 179(52.2%) participants had mild disability, 90 (26.2%) had moderate disability, and 74 (21.6%) had severe disability. In terms of economic sources, 3.5% of disabled older adults had retirement wages, 8.7% were supported by their children or relatives, 82.3% received pensions and other subsidies provided by the government, and 5.5% earned income through part-time work or farming. The age-corrected CCI ranged from 2 to 10 (4.78 ± 1.79). The disease distribution was as follows: cerebrovascular disease or transient ischemic attack in 136 (39.7%), simple diabetes in 69 (20.1%), hemiplegia in 67 (19.5%), myocardial infarction in 54 (15.7%), congestive heart failure in 37 (10.8%), chronic obstructive pulmonary disease in 35 (10.2%), peptic ulcer in 30 (8.7%), mild liver disease in 20 (5.8%), dementia in 19 (5.5%), localized solid tumor in 17 (5.0%), moderate to severe liver disease in 5 (1.5%), peripheral vascular disease in 3 (0.9%), and solid tumor metastasis in 3 (0.9%). None had connective tissue disease, moderate to severe kidney disease, leukemia, lymphoma, or AIDS.Of the 341 screened, 201 (58.6%) were frail and 140 (40.8%) were pre-frail, with 316 (95.0%) reporting increased resistance, 277 (80.8%) reduced mobility, 235 (68.5%) fatigue, 21 (6.1%) with ≥ 5 diseases, and 32 (9.3%) with weight loss. Other general characteristics are shown in (Table 8).

Table 8.

Multiple linear regression analysis of functional status in rural disabled older adults (N = 343)

Variable	Partial regression coefficient B	Standard error	standardized regression coefficient β	t	P	Tolerance	VIF
Constant	54.297	6.659	——	8.154	< 0.001	——	——
Age	0.339	0.081	0.228	4.183	< 0.001	0.453	2.208
Education: Junior high school	-2.901	1.250	-0.101	-2.321	0.021	0.703	1.422
Education: High school and above	8.015	3.068	0.116	2.612	0.009	0.684	1.463
Living with children only	3.998	1.825	0.119	2.191	0.029	0.454	2.201
Living with spouse only	8.327	2.548	0.347	3.268	0.001	0.119	8.391
Living with spouse and children	6.117	2.608	0.236	2.345	0.020	0.132	7.576
Living in institution	12.114	2.150	0.265	5.634	< 0.001	0.605	1.652
Type of medication taken	-1.320	0.331	-0.189	-3.993	< 0.001	0.596	1.677
Monthly income:500~	-6.257	2.216	-0.113	-2.823	0.005	0.834	1.200
Duration of disability	-0.205	0.064	-0.142	-3.220	0.001	0.693	1.442
Cause of disability: Aging	-7.283	1.330	-0.268	-5.476	< 0.001	0.561	1.782
Grip strength level	-0.199	0.082	-0.113	-2.413	0.016	0.615	1.625
Frailty status	-6.307	1.141	-0.267	-5.528	< 0.001	0.577	1.734
Declined intrinsic capacity	-9.737	1.324	-0.352	-7.356	< 0.001	0.586	1.706
Highly preserved intrinsic capacity	-17.899	4.025	-0.217	-4.447	< 0.001	0.562	1.778

Intrinsic capacity scores of rural disabled older adults

Total intrinsic capacity scores ranged from 1 to 10 (mean: 5.68 ± 1.62). Detailed dimension scores are presented in Table 2: (1) Mobility: Scores ranged from 0 to 11 (mean ± SD: 4.51 ± 2.72). Ninety-one participants(26.5%) had severe mobility impairment, 249(72.6%) had mild-to-moderate mobility impairment, and only 3 (0.9%) were classified as having normal mobility. The prevalence of mobility impairment was 99.1%. (2) Vitality: Nutritional scores ranged from 10.5 to 28 (mean: 22.27 ± 4.02). Malnutrition was present in 42 (12.2%), 148 (43.1%) were at risk of malnutrition, and 153 (44.6%) had normal nutrition. Vitality impairment rate was 55.4%. (3) Cognition: Scores ranged from 2 to 30 (mean: 17.31 ± 7.09). Fifty-one (14.9%) had moderate-to-severe cognitive impairment, 261 (76.1%) had mild-to-moderate impairment, and 31 (9.0%) had normal cognition. Cognitive decline rate was 91.0%. (4) Psychology: Depression scores ranged from 0 to 31 (mean: 6.68 ± 5.69). Nineteen (5.5%) had definite major depression, 57 (16.6%) had possible major depression, and 267 (77.8%) had normal psychological status. (5) Sensory ability: Vision: 84 (24.5%) had normal vision, 219 (63.8%) had mild/moderate impairment, and 40 (11.7%) had severe impairment or blindness. Hearing: 154 (44.9%) had normal hearing, 144 (42.0%) had mild/moderate impairment, and 45 (13.1%) had severe impairment or deafness. Combined: 64 (18.7%) had severe vision/hearing impairment, 232 (67.6%) had mild/moderate impairment, and 47 (13.7%) were unimpaired. The overall rate of sensory impairment was 86.3%. Further details on intrinsic capacity distribution are provided in (Table 3).

Table 2.

Intrinsic capacity domains scores of rural disabled older adults (N = 343)

Intrinsic Capacity Domain	Min	Max	Mean Score (x̄±SD)
Mobility	0	11	4.51 ± 2.72
Balance ability	0	4	2.80 ± 1.65
Walking speed test	0	3	1.35 ± 0.93
Chair stand test	0	4	0.35 ± 0.84
Vitality	10.5	28	22.27 ± 4.02
Cognition	2	30	17.31 ± 7.09
Orientation	0	10	7.02 ± 2.57
Memory	0	3	2.42 ± 0.97
Attention and Computational Ability	0	5	1.41 ± 1.82
Recall Ability	0	3	2.01 ± 1.19
Language Competence	0	9	4.45 ± 2.24
Psychology	0	31	6.68 ± 5.69
Emotional reactivity	0	8	1.57 ± 1.88
Behavioral disturbance	0	6	1.72 ± 1.64
Physical signs	0	5	0.80 ± 1.13
Cyclic functions	0	8	1.96 ± 2.20
Ideational disturbance	0	5	0.63 ± 1.04
Sensory ability	0	2	1.22 ± 0.52
Vision	0	1	0.56 ± 0.29
Hearing	0	1	0.66 ± 0.35

Table 3.

Distribution of intrinsic capacity level among rural disabled older adults (N = 343)

Intrinsic Capacity Level	Points	N	%
Significantly impaired	0–4	72	21.0%
Declining	5–8	264	77.0%
Highly preserved	9–10	7	2.0%

Subjectively perceived environmental support scores of rural disabled older adults

The total score for the environmental domain among rural disabled older adults was (110.23 ± 15.88), with an average item score of (3.44 ± 0.50), indicating a moderate level of environmental support. The total score for the social relationships domain was (28.73 ± 4.01), with an average item score of (3.59 ± 0.50), also reflecting a moderate level. Detailed scores for each subdomain are presented in (Table 4).

Table 4.

Domain scores of environmental and social relationship factors among rural disabled older adults (N = 343)

Domain	Total Score(‾x ± S)	Average Item Score(‾x ± S)
Opportunities for new information	11.04 ± 4.36	2.76 ± 1.09
Financial resources	12.30 ± 4.14	3.08 ± 1.03
Transportation	13.06 ± 3.93	3.27 ± 0.98
Recreation/leisure activities	13.39 ± 3.90	3.35 ± 0.98
Personal relationships	13.60 ± 2.30	3.40 ± 0.58
Home environment	14.59 ± 2.41	3.65 ± 0.60
Physical safety and security	15.07 ± 2.17	3.77 ± 0.54
Social support	15.13 ± 2.02	3.78 ± 0.50
Healthcare and social care access	15.29 ± 1.77	3.82 ± 0.44
Physical environment	15.48 ± 1.80	3.87 ± 0.45

Functional status scores of rural disabled older adults.

The functional status scores of rural disabled older adults ranged from 15 to 55 (29.57 ± 11.67). The physical self-maintenance scores ranged from 7 to 23 (10.86 ± 4.74), and the IADL scores ranged from 8 to 32 (18.71 ± 7.60). Specific functional scores and impairment rates are detailed in (Table 5).

Table 5.

Functional status scores of rural disabled older adults (N = 343)

Item	Min	Max	Mean Score(‾x ± S)	Impairment Rate (%)
Ambulation	2	4	2.49 ± 0.68	100
Managing finances	1	4	3.22 ± 1.00	94.2
Housekeeping	1	4	2.45 ± 1.06	81.3
Community mobility	1	5	2.59 ± 1.23	69.7
Laundry	1	4	2.33 ± 1.25	64.4
Phone use	1	4	2.29 ± 1.30	59.5
Bathing	1	4	2.18 ± 1.14	58.6
Shopping	1	4	2.13 ± 1.26	54.2
Meal preparation	1	4	2.13 ± 1.35	46.6
Grooming	1	4	1.61 ± 0.89	37.6
Toileting	1	4	1.63 ± 1.00	33.5
Dressing	1	4	1.55 ± 0.92	30.0
Medication management	1	4	1.56 ± 1.09	23.0
Feeding	1	4	1.39 ± 0.78	23.0

Comparison of functional status scores of rural disabled older adults.

Rural disabled older adults showed statistically significant differences in functional status scores across different categories of “age, education, marital status, number of children, living arrangement, monthly income, type of medication taken, form of medical insurance, duration of disability, cause of disability, grip strength level, BMI grade, frailty status, and intrinsic capacity grade” (P < 0.05). Details are shown in (Table 6).

Table 6.

Univariate analysis of functional status in rural disabled older adults (N = 343)

Item	Category	N(%)	Mean Score(‾x ± S)	Statistic	P-value
Gender	Male	137(39.9)	30.15 ± 11.86	-0.099b	0.922
	Female	206(60.1)	29.18 ± 11.56
Age (years)	60 ~	40(11.7)	33.28 ± 13.93	33.092c	< 0.001
	65 ~	135(39.4)	25.82 ± 10.11
	75~	132(38.5)	30.22 ± 11.57
	85~	36(10.5)	37.11 ± 9.60
Religious beliefs	Not	279(81.3)	29.90 ± 12.10	0.059 b	0.953
	Yes	64(18.7)	28.13 ± 9.54
Education	Primary school and below	261(76.1)	30.95 ± 11.42	10.389a	< 0.001
	Junior high school	72(21.0)	24.17 ± 10.89
	High school and above	10(2.9)	32.50 ± 13.16
Marital status	Married	235(68.5)	30.29 ± 12.15	-2.053 b	0.040
	Others (Divorced/Widowed)	108(31.5)	27.79 ± 10.39
Number of children	1	14(4.1)	28.57 ± 6.85	13.821c	0.001
	2	123(35.9)	32.68 ± 12.29
	3~	206(60.1)	27.78 ± 11.19
Child structure	Only sons	101(29.4)	31.56 ± 10.88	2.169a	0.116
	Only daughters	19(5.5)	27.84 ± 11.99
	Both sons and daughters	223(65.0)	28.81 ± 11.92
Living arrangement	Alone	44(12.8)	21.27 ± 6.04	40.237c	< 0.001
	With children only	48(14.0)	28.98 ± 9.80
	With spouse only	130(37.9)	31.35 ± 12.79
	With spouse and children	97(28.3)	29.13 ± 11.53
	Institution	24(7.0)	38.08 ± 7.81
Monthly income (RMB)	< 500	313(91.3)	29.98 ± 11.52	7.403c	0.025
	500~	16(4.7)	21.31 ± 4.22
	1000~	14(4.1)	29.79 ± 16.99
Type of medication taken	0	20(5.8)	24.60 ± 6.84	16.223c	0.006
	1	67(19.5)	31.00 ± 12.12
	2	38(11.1)	26.11 ± 14.08
	3	50(14.6)	32.44 ± 12.80
	4	65(19.0)	27.94 ± 8.15
	5	103(30.0)	30.51 ± 11.97
Medical expenses/month	< 100	137(39.9)	30.82 ± 12.21	2.838c	0.242
	100 ~	179(52.2)	28.98 ± 11.72
	500~	27(7.9)	27.11 ± 7.39
Form of medical insurance	Medical insurance for urban workers	2(0.6)	32.00 ± 4.24	9.547c	0.008
	Medical insurance for urban and rural residents	325(94.8)	29.95 ± 11.74
	Self-pay	16(4.7)	21.44 ± 7.32
Duration of disability (years)	< 1	46(13.4)	30.57 ± 11.42	13.108c	0.011
	1 ~	90(26.2)	30.03 ± 12.58
	5 ~	110(32.1)	28.80 ± 10.62
	10 ~	20(5.8)	38.60 ± 13.67
	15~	77(22.4)	27.18 ± 10.64
Cause of disability	Disease	240(70.0)	31.00 ± 11.99	15.191c	0.001
	Accident	20(5.8)	30.75 ± 14.60
	Aging	83(24.2)	25.13 ± 8.58
Grip strength level (kg)	< 4.9	37(10.8)	35.65 ± 13.40	46.562c	< 0.001
	4.9 ~	84(24.5)	32.99 ± 12.00
	8.6 ~	18(5.2)	28.89 ± 9.70
	10.1 ~	127(37.0)	29.39 ± 10.83
	17.4~	77(22.4)	23.38 ± 9.22
BMI grade	< 18.5	20(5.8)	36.85 ± 6.38	14.730c	0.002
	18.5 ~	206(60.1)	29.70 ± 11.36
	24 ~	85(24.8)	28.02 ± 11.99
	28~	32(9.3)	28.25 ± 12.85
Frailty status	Frailty	201(58.6)	33.45 ± 11.86	28.552c	< 0.001
	Pre-frailty or Robust	142(41.4)	24.07 ± 8.88
Intrinsic capacity grade	Severely impaired	72(21.0)	41.38 ± 10.56	84.274c	< 0.001
	Declining	264(77.0)	26.68 ± 9.81
	Highly preserved	7(2.0)	17.14 ± 1.46

Statistical notations: a = F-value (ANOVA), b = Z-value (Mann-Whitney U), c = H-value (Kruskal-Wallis)

Multiple linear regression analysis of factors influencing functional status in rural disabled older adults

Taking the functional status score of rural disabled older adults as the dependent variable, statistically significant variables from univariate analysis were recoded (Table 7) and included in multiple linear regression. Ten variables entered the final model: age, education (junior high school, high school and above), living arrangement (living with children only, living with spouse only, living with spouse and children, living in institution), type of medication taken, monthly income(500~), duration of disability, cause of disability(aging), grip strength level, frailty status, and intrinsic capacity level (declined intrinsic capacity, high and stable intrinsic capacity)(F = 19.347, R = 0.756, R²=0.571, adjusted R²=0.541, DW = 2.044, VIF < 10, P < 0.05). Furthermore, all 95% confidence intervals of the regression coefficients did not include 0, indicating stable regression coefficients (as detailed in Table 8).

Table 7.

Assignment of independent variables

Argument	Assignment method
Age	Input as raw values
Education	The dummy variable was set with “Primary school and below” as the control group: Primary school and below (Z1 = 0, Z2 = 0), Junior high school (Z1 = 1, Z2 = 0), High school and above (Z1 = 0, Z2 = 1)
Marital status	1 = Married, 2 = Other (divorced/widowed)
Number of children	1 = 1、2 = 2、3 = ≥ 3
Living arrangement	The dummy variable was set with “Living alone” as the control group: Alone (Z1 = 0, Z2 = 0, Z3 = 0, Z4 = 0), With children only (Z1 = 1, Z2 = 0, Z3 = 0, Z4 = 0), With spouse only (Z1 = 0, Z2 = 1, Z3 = 0, Z4 = 0), With spouse and children (Z1 = 0, Z2 = 0, Z3 = 1, Z4 = 0), Institution (Z1 = 0, Z2 = 0, Z3 = 0, Z4 = 1)
Monthly income (RMB)	The dummy variable was set with “<500” as the control group: < 500 (Z1 = 0, Z2 = 0), 500~ (Z1 = 1, Z2 = 0), 1000~ (Z1 = 0, Z2 = 1)
Type of medication taken	0 = 0、1 = 1、2 = 2、3 = 3、4 = 4、5 = 5
Form of medical insurance	The dummy variable was set with “Self-pay” as the control group: Self-pay (Z1 = 0, Z2 = 0), Medical insurance for urban workers (Z1 = 1, Z2 = 0), Medical insurance for urban and rural residents (Z1 = 0, Z2 = 1)
Duration of disability	Input as raw values
Cause of disability	The dummy variable was set with “Disease” as the control group: Disease (Z1 = 0, Z2 = 0), Accident (Z1 = 1, Z2 = 0), Aging (Z1 = 0, Z2 = 1)
Grip strength level (kg)	Input as raw values
BMI grade	Input as raw values
Frailty status	1 = Frailty、2 = Pre-frailty or Robust
Intrinsic capacity grade	The dummy variable was set with “Severely impaired” as the control group: Severely impaired (Z1 = 0, Z2 = 0), Declining (Z1 = 1, Z2 = 0), Highly preserved (Z1 = 0, Z2 = 1)

Analysis of the association path model between intrinsic capacity, subjectively perceived environmental support and functional status among rural disabled older adults.

The dimension scores of subjectively perceived environmental support, intrinsic capacity and functional status of rural disabled older adults were included in the structural equation model to construct an association path model consistent with the connotation of the WHO Healthy Ageing framework, and the model was optimized and adjusted according to the modification indices. The final model fit indices were as follows: CMIN/DF = 3.645, RMSEA = 0.039, IFI = 0.921, TLI = 0.902, CFI = 0.917, and GFI = 0.925, indicating good model fit. Based on the standardized results, subjectively perceived environmental support was positively associated with intrinsic capacity (β = 0.406, P < 0.001), while intrinsic capacity was negatively associated with functional status (β=-0.580, P < 0.001). Subjectively perceived environmental support was also negatively associated with functional status (β=-0.414, P < 0.001). Subjectively perceived environmental support was indirectly associated with functional status through intrinsic capacity, with a standardized indirect effect of -0.236 and a standardized total effect of -0.649, suggesting that intrinsic capacity played a partial mediating role between subjectively perceived environmental support and functional status. The dimension weight results showed that, within intrinsic capacity, cognition (β = 0.828) and locomotion (β = 0.601) made relatively large contributions. Within subjectively perceived environmental support, leisure and recreational activities (β = 0.770), housing environment (β = 0.726), transportation conditions (β = 0.713), access to new information (β = 0.712), and personal conditions (β = 0.705) made relatively large contributions. All P values were less than 0.05, and the association paths are shown in (Fig. 1).

Fig. 1.

Association path model of intrinsic capacity, subjectively perceived environmental support and functional status among rural disabled older adults

Discussion

The motor ability and cognitive ability of rural disabled older adults are severely impaired

This study found that intrinsic capacity was substantially impaired among rural disabled older adults, with 21.0% of participants having severely impaired intrinsic capacity and 77.0% having declining intrinsic capacity. These proportions were higher than those reported in previous studies [16–18], which may be related to the fact that all participants in this study had varying degrees of disability in daily self-care. This is consistent with the WHO healthy ageing framework, which indicates that a gradual decline in intrinsic capacity in older adults may lead to severe disability. In addition, path analysis showed that intrinsic capacity was negatively associated with functional status (β= -0.580, P < 0.001), suggesting that better intrinsic capacity was associated with less severe functional limitation. This finding is consistent with the results of Leung et al. [19]. Intrinsic capacity has been shown to predict functional decline in both IADL and ADL [20]. Yaxuan et al. [21] also reported that declining intrinsic capacity was an independent risk factor for disability within one year among community-dwelling older adults. These findings suggest that intrinsic capacity assessment should be used as an early warning indicator for identifying the risk of functional decline among rural older adults, and that exercise training and cognitive interventions should be provided as early as possible to delay or reverse functional decline.

Among the five dimensions of intrinsic capacity, locomotion and cognitive function had relatively high contribution weights. The prevalence of mobility impairment was 99.1%, and the chair stand test had the lowest score (0.35 ± 0.84), indicating weak lower-limb muscle strength among rural disabled older adults. This may be related to the fact that all surveyed rural disabled older adults had physical mobility limitations, and that physical functions gradually decline with age, accompanied by an increasing prevalence of sarcopenia caused by ageing. Sales et al. [22] found a significant association between declining intrinsic capacity and sarcopenia. However, the limited availability of rehabilitation services, assistive devices, and regular exercise programs in rural areas may also constrain the improvement of older adults’ locomotion. These findings suggest that routine exercise-based rehabilitation training should be prioritized in rural areas. Low-intensity exercise programs, such as resistance-band training, seated Baduanjin, and adapted yoga, may be implemented, with a particular focus on foot muscle strengthening and gait/muscle training to improve locomotor function among disabled older adults [23].

The prevalence of cognitive impairment was also high in this study, reaching 91.0%, with relatively low scores in attention and calculation (1.41 ± 1.82) and recall (2.01 ± 1.19). This may be associated with the high prevalence of cerebrovascular disease or transient ischemic attack among participants, which may have caused a certain degree of brain function impairment. In addition, insufficient brain rehabilitation resources in rural areas, delayed disease detection, and delayed intervention may further aggravate cognitive decline. Mobility limitations among disabled older adults may also narrow their living space and restrict social participation, thereby reducing cognitive stimulation. Decreased acetylcholine activity in the brain, together with negative emotions and psychological stress, may also contribute to cognitive decline [24]. These findings suggest that cognitive stimulation programs can be embedded into rural community care services, with particular attention to training in attention, calculation, and recall, combined with psychological counseling and family interaction support, to reduce the adverse effects of restricted living space and insufficient social participation on cognitive function.

Subjectively perceived environmental support and social relationships are relatively positive among rural disabled older adults

The domain scores of subjectively perceived environmental support and social relationships among rural disabled older adults were at a moderate level, with relatively higher scores in medical services and social security (3.82 ± 0.44) and environmental conditions (3.87 ± 0.45). This may be related to the continuous improvement of rural primary healthcare services and relevant policies in China in recent years, including basic public health services, family doctor contract services, medical insurance, long-term care insurance, and pension subsidies. These measures may have improved rural disabled older adults’ subjective perceptions of medical services and social security to some extent. Aishu et al. [25] emphasized that access to medical services significantly affects health outcomes among rural older adults. This suggests that rural telemedicine and mobile medical services should be actively promoted, and that digital technologies should be used to improve access to high-quality medical resources. In addition, rural areas generally have lower population density, larger living spaces, and better air quality, which may contribute to greater living comfort. Yashi [26] reported that perceived convenience, village environment, and housing conditions among rural older adults had positive effects on mental health, especially in terms of functionality and accessibility. This suggests that the government can carry out age-friendly renovations in infrastructure, living environments, social participation, and cultural inclusiveness [27], while actively developing rural pastoral care for older adults.

Rural disabled older adults had relatively low scores in financial resources (3.08 ± 1.03) and access to new information (2.76 ± 1.09), which may be related to their lack of stable financial resources and heavy long-term care burden [28]. Most participants relied mainly on government subsidies or support from children and relatives for daily living, while also facing considerable medical expenditure pressure. In addition, rural disabled older adults had poor digital literacy and faced a prominent digital divide. Most of them used mobile phones only for making and receiving calls, lacked channels for obtaining new information, and had limited ability to actively search for information. Moreover, restricted living space and reduced intergenerational communication may further contribute to information isolation. Meng [29] found that village doctors were the most trusted source of information in rural areas. This suggests that village doctors can serve as key health information disseminators by providing age-friendly health education through telephone follow-up, village committee broadcasts, rural television programs, printed health materials, and simplified digital platforms. Digital literacy training can also be carried out at rural senior learning sites to help disabled older adults and their caregivers obtain information on medical care, rehabilitation, subsidy applications, and long-term care services.

The structural equation model showed that subjectively perceived environmental support was positively associated with intrinsic capacity (β = 0.406, P < 0.001), while intrinsic capacity (β = -0.580, P < 0.001) and subjectively perceived environmental support (β = -0.414, P < 0.001) were negatively associated with functional status. Intrinsic capacity played a partial mediating role between subjectively perceived environmental support and functional status. This finding supports the WHO healthy ageing framework, indicating that environmental support may influence functional status through both direct and indirect pathways. The direct association may reflect the roles of convenient transportation, age-friendly housing, social support, and access to medical services in reducing limitations in daily functioning. The indirect association suggests that supportive environments may help maintain locomotion, cognitive function, psychological well-being, and social participation, thereby delaying the decline in intrinsic capacity. Yu et al. [30] further confirmed that both subjective and objective social support can buffer the relationship between intrinsic capacity and adverse health outcomes. Participation in leisure activities has also been shown to help slow the decline in psychological and other dimensions of intrinsic capacity among older adults [31]. These findings suggest that care strategies for rural disabled older adults should shift from single disease management to an integrated “environment–capacity–function” intervention approach. Within subjectively perceived environmental support, leisure and recreational activities (β = 0.770), housing environment (β = 0.726), transportation conditions (β = 0.713), access to new information (β = 0.712), and personal conditions (β = 0.705) made relatively large contributions. This indicates that priority can be given to organizing leisure activities, improving housing safety, enhancing transportation convenience, increasing access to health information, and supporting family caregiving conditions, so as to strengthen perceived environmental support and maintain functional ability among rural disabled older adults.

Functional status of rural disabled older adults is influenced by multiple factors.

The mean functional status score of rural disabled older adults was 29.57 ± 11.67, with relatively high impairment rates in physical mobility (100%) and financial management (94.2%). This is consistent with the heavy burden of locomotion and cognitive function impairment observed in this study. Physical mobility limitations may affect both basic activities of daily living and instrumental activities of daily living, while cognitive difficulties, especially declines in attention, calculation, and recall, may impair complex daily functions such as financial management, telephone use, and medication management. The impairment rates for eating and medication use were relatively low, both at 23.0%, suggesting that this population still retained basic self-care ability. These findings indicate that functional maintenance interventions should prioritize physical mobility and complex instrumental activities of daily living. Daily living task training, simulated shopping and financial management training, simplified medication lists, and smart home medication boxes may be used to enhance older adults’ proactive health management ability.

Socio-demographic factors

The results of multiple regression analysis showed that age and educational level were associated factors of functional status among rural disabled older adults (P < 0.05), which was consistent with the findings of Hua et al. [32]. Older age was associated with poorer functional status, consistent with the findings of Ghimire et al. [33] on functional status among rural older adults. This may be related to age-related declines in muscle strength, memory, balance, and daily living ability, as well as the accumulated burden of chronic diseases [34]. Fen et al. [35] found that instrumental activities of daily living declined rapidly among men over 70 years of age, and age contributed to gender differences in functional status among rural older adults to some extent. Rural disabled older adults with junior high school education had better functional status than those with primary school education, which may be because higher educational attainment is associated with stronger health awareness, better access to health information, and greater self-management ability. However, the functional status of participants with high school education or above was poorer than that of those with primary school education, which was inconsistent with the findings of Kaikai [36]. Given the small sample size of this subgroup (n = 10, 2.9%), the estimate may be unstable and affected by sampling bias. Therefore, this finding needs to be further verified in studies with larger samples and more balanced educational distributions.

Participants with a monthly income of more than 500 RMB had better functional status than those with lower monthly income. Higher income may improve access to standardized treatment for chronic diseases, rehabilitation support, assistive devices, and home care services. In addition, Qing et al. [37] found that older adults with higher socioeconomic status had better intrinsic capacity, which was accompanied by improved functional status. In contrast, economic hardship may weaken rural disabled older adults’ ability to seek timely medical care and maintain long-term rehabilitation. This suggests that medical assistance, rehabilitation subsidies, assistive device adaptation, and long-term care service support should be strengthened for rural disabled older adults with financial difficulties, so as to reduce the constraints of economic burden on functional maintenance and rehabilitation service utilization.

Participants taking a greater variety of medications had better functional status, which was inconsistent with conventional expectations. This may be related to reverse causality in the cross-sectional design. Older adults with relatively better functional status may be more able to actively seek medical care, attend regular follow-up visits, obtain prescriptions, and adhere to medication regimens, whereas bedridden or severely disabled older adults may take fewer medications despite greater health needs because of reduced access to healthcare. Compared with those living alone, participants living with their children, spouses, or in nursing institutions had poorer functional status. This may reflect a selection effect related to care needs: older adults with more severe functional limitations are more likely to live with family members or receive institutional care, whereas those living alone may retain relatively better independent living ability. However, Lan et al. [38] reported that children’s caregiving support had a negative effect on daily activity ability among rural older adults, while Vo et al. [39] found that living alone increased the risk of adverse health outcomes. These findings suggest that actual care needs should be identified based on functional assessment. For older adults living alone, attention should be paid to potential safety risks and risks of sudden illness. For those living with family members or receiving institutional care, caregiver training, stress support, and rehabilitation guidance should be strengthened to avoid further deterioration of residual function due to care substitution.

Individual physical health factors

Duration of disability, grip strength level, and frailty status were significantly associated with functional status among rural disabled older adults (P < 0.05). Notably, a longer duration of disability was associated with better functional status. This may be because older adults with a shorter duration of disability may still be in an unstable phase after disease onset or functional impairment, whereas those with long-term disability may have gradually developed adaptive coping strategies, accumulated rehabilitation experience, and received relatively stable family caregiving support. In addition, individuals with severe functional decline may be less likely to remain in the community or participate in the survey for a long period; therefore, the possibility of survivor bias cannot be excluded.

Stronger grip strength was associated with better functional status (P < 0.05), which was consistent with the findings of Hao et al. [40]. Grip strength is a commonly used indicator for assessing upper-limb muscle function in older adults and has been shown to effectively predict intrinsic capacity, functional status, and mortality risk in this population [41, 42]. However, the mean grip strength of rural disabled older adults in this study was only 12.38 ± 6.62 kg (range: 0–36.1 kg), which fell within the frailty cutoff range for grip strength in older men (8.6–17.4 kg) and women (4.9–10.1 kg) [41]. This indicates that grip strength was generally low among rural disabled older adults, suggesting that low-cost upper-limb strength training, such as grip ball exercises, resistance-band training, object-lifting exercises, and seated upper-limb training, can be implemented to improve their functional status.

Regression analysis showed that older adults with relatively robust physical status had poorer functional status (P < 0.05), which was contrary to the findings of Liu et al. [43]. This result may be related to the very small sample size of the robust subgroup (n = 2, 0.6%) and the remaining heterogeneity after combining frailty status subgroups. In contrast, one-way ANOVA showed that frail disabled older adults had poorer functional status than those with pre-frailty or robust status, which was consistent with the findings of Jingyi et al. [44]. Frailty can increase vulnerability, reduce independence and autonomy, narrow social networks, and exert multiple adverse effects on daily living and psychological status in older adults. This suggests that frailty can be regarded as a key intervention target for slowing functional decline. Routine frailty screening should be conducted in primary healthcare management, and stratified intervention plans should be developed according to frailty severity. For older adults with poor mobility, gentle exercise programs such as seated Baduanjin, adapted yoga, and bedside training may be used to gradually improve physical frailty and maintain functional status.

Conclusions

Rural disabled older adults report relatively good perceived environmental support and social relationships, but their intrinsic capacity declines significantly, with motor and cognitive abilities being particularly impaired. The functional status of disabled older adults is poor, especially in terms of physical mobility and financial management, which show high rates of impairment. Functional status is influenced by multiple factors including age, duration of disability, grip strength, and frailty status. Perceived environmental support positively affects the intrinsic capacity of disabled older adults, thereby promoting functional performance. Future efforts may consider using smart integrated care models to optimize medical services, social security, leisure and entertainment, and information access, as well as developing multicomponent exercise programs and cognitive training to promote healthy aging among rural disabled older adults.

Nevertheless, this study employed cross-sectional survey data to construct the correlational path model, which can only reflect the associations among variables at the time of investigation, without clarifying their temporal order and causal relationships. Moreover, residual confounding factors may still exist, so caution is required when interpreting the results. Meanwhile, there is content overlap between the physical activity dimension of intrinsic capacity assessment and the ADL/IADL scales for functional status evaluation, which may artificially inflate the strength of associations between variables. And the sensory capacity indicators in this study were assessed using older adults’ self-reported visual and auditory acuity, without support from objective measurement data, which introduces self-report bias and is prone to information bias. Additionally, this study adopted convenience sampling to recruit rural disabled older adults from a single region, resulting in limited sample representativeness, which restricts the generalizability of the study findings to some extent. In view of the above limitations, future research can carry out multi-center, large-sample longitudinal follow-up studies to clarify the causal relationships among variables and identify the dynamic change trajectories and early warning factors of intrinsic capacity in rural disabled older adults. Furthermore, multi-source data and objective assessment tools can be applied to further control methodological bias, improve the generalizability and reliability of research conclusions, and provide personalized and digital-intelligent integrated care services for rural disabled older adults.

Abbreviations

WHO

World Health Organization

ADL

Activities of Daily Living

IADL

Instrumental Activities of Daily Living

CCI

Charlson Comorbidity Index

BMI

Body Mass Index

MMSE

Mini-Mental State Examination

CSDD

Cornell Scale for Depression in Dementia

PSSM

Physical Self-Maintenance Scale

SEM

Structural Equation Model

CMIN/DF

Normed Chi-square

RMSEA

Root Mean Square Error of Approximation

GFI

Goodness of Fit Index

CFI

Comparative Fit Index

IFI

Incremental Fit Index

TLI

Tucker-Lewis Index

Authors’ contributions

Study design: YTT, YZData collection: YTT, MYZ, PYYData analysis: YTT, SYYStudy supervision: YZManuscript writing: YTTCritical revisions for important intellectual content: YZ, LML.

Funding

This work was supported by the National Natural Science Foundation of China (71874162).

Data availability

The datasets used and analysed during the current study are available from the first author on reasonable request.

Declarations

Ethics approval and consent to participate

This study complies with the Declaration of Helsinki and has been reviewed by the Ethics Review Committee for Life Sciences of Zhengzhou University (Ethics Number: ZZUIRB2021-155), and adheres to the ethical principles of informed consent, confidentiality, and non-maleficence. All participants signed the informed consent form prior to the survey.

Consent for publication

An unauthorized version of the Chinese MMSE was used by the study team without permission, however this 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 or language, or by any means without written permission of PAR(www.parinc.com).

Competing interests

The authors declare no competing interests.