Abstract
Background
The impairment of intrinsic capacity (IC) among older adults is a significant global health challenge, impacting their well-being and quality of life. Despite global efforts to promote healthy aging, research on the prevalence of IC impairment in the Chinese population remains limited. This study aims to examine the prevalence of IC impairment among Chinese older adults and to explore the associations between sociodemographic, health status, and lifestyle factors with IC impairment.
Methods
This study utilized data from the China Health and Aging Tracking Survey (CHARLS) 2020. IC was assessed across five domains in line with WHO guidelines: cognition, locomotion, vitality, sensory, and psychological. Associations between IC impairment and sociodemographic, health status, and lifestyle factors were analyzed using chi-square tests. Binary logistic regression was employed to identify factors associated with IC impairment.
Results
Among 6,941 respondents aged 60 years and above, 83.8% reported IC impairment. Cognitive impairment was predominant (71.8%), followed by psychological (40.4%), locomotion (19.8%), vitality (17.2%), and sensory impairments (8.9%). Binary logistic regression revealed that older age (OR = 2.875, 95% CI: 1.662–4.974), unmarried/divorced/widowed status (OR = 1.494, 95% CI: 1.210–1.846), rural residence (OR = 1.827, 95% CI: 1.551–2.153), presence of multiple chronic diseases (OR = 1.412, 95% CI: 1.120–1.779) and regular physical pain (OR = 1.776, 95% CI: 1.364–2.312) were associated with increased odds of IC impairment. Conversely, higher education (OR = 0.485, 95% CI: 0.306–0.771), pension insurance (OR = 0.717, 95% CI: 0.565–0.910), good self-rated health (OR = 0.448, 95% CI: 0.348–0.577), positive life expectancy (OR = 0.588, 95% CI: 0.486–0.710), social participation (OR = 0.711, 95% CI: 0.618–0.818), adequate sleep (OR = 0.835, 95% CI: 0.722–0.967), and internet access (OR = 0.580, 95% CI: 0.495–0.680)were linked to lower odds of IC impairment.
Conclusions
The study reveals a considerable prevalence of IC impairment among older adults in China, with cognitive impairment being particularly prominent. Our study identifies key factors associated with IC impairment, including sociodemographic, health status, and lifestyle factors. These findings emphasize the need for targeted public health strategies that address these modifiable factors to strengthen IC and improve the quality of aging.
Keywords: Intrinsic capacity, Older adults, China
Background
In 2015, the World Health Organization (WHO) introduced a paradigm shift in assessing the health of older adults through its “Global Report on Aging and Health”. This seminal report redefined healthy aging by emphasizing overall functional capacity over mere disease presence, with intrinsic capacity (IC) identified as a pivotal factor. IC encompasses multidimensional aspects such as cognition, locomotion, vitality, sensory functions (including hearing and vision), and psychological [1, 2]. To further operationalize this concept, WHO’s Clinical Consortium for Healthy Ageing in 2017 detailed IC as crucial for maintaining or enhancing functional performance, underpinning the framework for Integrated Care for Older People (ICOPE) guidelines introduced in 2019 [3].
The concept of IC has since become integral to characterizing older adults’ health globally, with its impairment linked to various adverse outcomes [4–9]. Studies worldwide have revealed significant proportions of older adults experiencing impaired IC: 66.3% in the UK [10], 54.9% in Japan [11], 87.8% in Mexico [12], and 72.7% in China [13]. Factors associated with IC impairment include advanced age [4, 9, 13–16], female gender [4, 9, 12, 15–17], lower education levels [9, 15–19], physical disabilities [14, 20], multiple chronic conditions [10, 18, 21–23], lower socioeconomic status [9, 10, 24–26], and insufficient physical activity [14, 16, 27, 28]. Conversely, favorable early-life environments [29], healthy diets [29–31], and robust social participation [17, 28] are associated with better IC maintenance. Domestic studies in China consistently report significant impairments in IC among older adults [32–34]. These impairments are associated with several key factors, including advanced age [35, 36], female gender [34, 37], lower education levels [32, 36–38], reduced grip strength [32], the presence of multiple chronic diseases [36, 37], and a history of falls [32, 39].
Despite the emphasis on sociodemographic factors in existing research, there is a gap in the systematic analysis of individual health and lifestyle factors associated with IC. Large-scale studies examining these dimensions are still relatively scarce, which limits a deeper exploration of their relationship with IC. Our research aims to fill this gap by providing a more nuanced perspective on these associations, thereby contributing to a broader understanding of IC. These findings may also offer valuable insights for other similar low-to-middle-income country settings, informing the development of more tailored and effective interventions for older adults.
This study specifically seeks to assess the prevalence of IC impairment among older adults in China and explore the relationships between sociodemographic characteristics, health status, and lifestyle factors with IC impairment. The results are intended to serve as a foundational reference for implementing the Integrated Care for Older People (ICOPE) initiative in China while providing insights that could be adapted to meet the needs of aging populations in diverse contexts.
Methods
Data
The data utilized in this study were sourced from the China Health and Aging Tracking Survey (CHARLS) 2020, a large-scale data collection project spearheaded by Peking University’s National Development Research Institute and implemented by Peking University’s China Social Science Research Centre [40]. The CHARLS 2020 sample encompasses 150 randomly selected counties and districts, 450 villages and residences, over 10,000 households, and approximately 18,000 individuals aged 45 years and older. This survey covers both urban and rural areas across China, making it highly representative of the country’s middle-aged and elderly populations. For this study, we extracted data from the “Basic Personal Information” and “Health Status and Functioning” modules of the CHARLS 2020 database, resulting in 81 variables and 19,368 observations. After excluding cases with missing key variable data and individuals younger than 60 years, we obtained a final dataset comprising 6,941 valid samples.
Measures
Outcome variable
Intrinsic capacity (IC) is assessed across five domains: locomotion, cognition, vitality, sensory (hearing and vision), and psychological. Locomotion function was evaluated using 12 questions from the physical dysfunction section of the CHARLS 2020 questionnaire, which covers both activities of daily living (ADLs) and instrumental activities of daily living (IADLs) [41, 42]. Locomotion impairment was defined as any response indicating “Difficult and needs help” or “Unable to complete.”
Cognitive function was evaluated using the Mini-Mental State Examination (MMSE) [43], with scores categorized based on educational attainment: ≤17 for illiterate individuals, ≤ 20 for primary school, ≤ 22 for middle school, and ≤ 23 for those with higher education [44].
According to the WHO Ageing and Health Unit and the Frailty on Ageing Research Group, vitality includes energy, metabolism, neuromuscular function, and immune response [45]. A recent scoping review further emphasized the multidimensional nature of vitality and the absence of a universally accepted measurement approach, noting that recent studies have operationalized this domain through assessments of neuromuscular function, metabolic activity, and immune-stress responses [46]. In this study, we employed the International Physical Activity Questionnaire (IPAQ) to assess energy expenditure related to physical activity, measured in metabolic equivalents (METs) [47]. The IPAQ uses Physical Activity Volume (PAV) to capture an individual’s total energy expenditure from various intensities of physical activities. The PAV is calculated using the formula: PAV = (weekly duration of vigorous activity × 8.0) + (weekly duration of moderate activity × 4.0) + (weekly duration of light activity × 3.3) [48, 49]. Here, the coefficients (8.0, 4.0, and 3.3) correspond to the MET values assigned to each intensity level, with vigorous activity representing a high energy demand at 8 METs, moderate activity at 4 METs, and light activity at 3.3 METs. A total weekly energy expenditure of less than 600 METs is indicative of insufficient vitality [44, 50, 51].
Sensory function was assessed based on interviewer-recorded observations from CHARLS 2020. Respondents were classified as having sensory impairments if they were observed to have ‘poor eyesight,’ ‘poor hearing without a hearing aid,’ or were ‘wearing a hearing aid.’ We acknowledge that these subjective observations may not capture the full spectrum of functional impairments.
Psychological function was evaluated using the Center for Epidemiologic Studies Depression Scale (CES-D), a widely used tool for assessing depression and an independent diagnostic indicator of depression [52]. The CES-D, validated for the Chinese elderly population [53], scores depression severity from 0 to 3 per question. A total CES-D score of ≥ 10 is considered indicative of psychological impairment [54].
An impairment in any of the five domains of IC was considered as IC impairment.
Covariates
The analysis of factors associated with IC among older adults included the following covariates: Sociodemographic Variables included sex, age (categorized as 60–69, 70–79, and ≥ 80), marital status (married vs. never married/divorced/widowed), residence (city/town center, urban-rural fringe/zhenxiang area, village), educational level (illiterate, primary school, middle school, college degree or above), health insurance status (yes or no), and pension insurance status (yes or no).
Self-rated health was evaluated using the question, “How would you rate your overall health? Very good, good, average, poor, or very poor?” with responses grouped into “good” (very good, good) or “poor” (poor, very poor). The number of chronic diseases was determined by asking, “Has a doctor ever told you that you have any chronic diseases?” and categorized as “0,” “1,” or “2 or more.” The history of falls was recorded based on whether respondents reported having ever fallen. Pain frequency was categorized from responses to “How often do you experience discomfort due to pain?” with “not at all” coded as “None,” “a little” and “somewhat” as “a little,” and “quite a bit” and “very much” as “quite a bit.” Accidental injuries were identified using a yes/no question regarding significant injuries that required treatment. Monthly outpatient service use was captured by asking whether the respondent had visited a medical institution or received home medical services in the past month, while annual inpatient service use was determined by whether they had been hospitalized in the past year. Subjective life expectancy was gauged by asking, “How likely is it that you will live to a certain age?” with responses categorized as “unlikely” (almost impossible, unlikely) or “possible” (possible, likely, almost certain).
Lifestyle variables were similarly assessed through specific inquiries. Smoking status was determined using the question, “Do you still smoke or have you quit?” with respondents categorized as “yes” for current smokers and “no” for those who had quit or never smoked. Alcohol consumption was assessed by asking, “In the past year, have you consumed any alcohol, including beer, wine, rice wine, or spirits? How often?” with any drinking coded as “yes,” and no alcohol consumption coded as “no.” Social participation was evaluated by asking whether participants had engaged in any social activities in the past month; any affirmative response or self-reported activity was coded as “yes,” while no participation was coded as “no.” Sleep duration was recorded based on the question, “Over the past month, how many hours did you sleep each night on average?” and categorized as “<7 hours,” “7–9 hours,” or “≥9 hours.” Internet access was assessed by asking, “In the past month, did you use the internet, including for chatting, reading news, watching videos, playing games, or financial management?” and responses were classified as “yes” or “no.”
Statistical analysis
Statistical analysis was performed using SPSS 26.0 software. Frequencies and percentages were reported for categorical variables to summarize the demographic, health-related, and lifestyle profiles of the sample.
Frequency analysis was conducted to determine the prevalence of IC impairment among older adults. The chi-square test was used to explore differences in IC impairment across various sociodemographic characteristics, health statuses, and lifestyle factors, with a significance level of α = 0.05.
Univariate analyses identified variables significantly associated with IC impairment(p < 0.05), which were then entered a binary logistic regression model using a stepwise selection method. In this analysis, we focused on exploring the potential associations between various exposures and IC impairment, rather than examining the relative contributions of different factors. The logistic regression results were presented as adjusted odds ratios (OR) with 95% confidence intervals (CI).
Regression diagnostics were performed to ensure model reliability. Multicollinearity was assessed using variance inflation factors (VIF), all of which were below 2, indicating no significant collinearity among predictors. The Hosmer-Lemeshow test, with a p-value of 0.531, demonstrated a good fit of the logistic regression model, suggesting that the model’s predicted probabilities aligned well with the actual outcomes.
Results
IC and its domains
Tables 1, 2, 3 and 4 summarizes the impairment across the five domains of IC among older adults. Overall, the cognitive domain (71.8%) was the most severely impaired among Chinese older adults, followed by impairments in the psychological (40.4%), locomotion (19.8%), and vitality (17.2%) domains. The sensory domain (8.9%) showed relatively better performance, with only 3% of participants experiencing difficulty seeing clearly, and 6.6% having hearing difficulties.
Table 1.
Proportion of impairments in IC domains
| Domain | Function impaired | Function not impaired |
|---|---|---|
| Cognitive | 4983(71.8%) | 1958(28.2%) |
| Locomotion | 1376(19.8%) | 5565(80.2%) |
| Vitality | 1192(17.2%) | 5749(82.8%) |
| Sensory | 615(8.9%) | 6326(91.1%) |
| Hearing | 206(3.0%) | 6735(97.0%) |
| Vision | 455(6.6%) | 6486(93.4%) |
| Psychological | 2805(40.4%) | 4136(59.6%) |
| 0 domain | 1126(16.2%) | —— |
| 1 domain | 2412(34.8%) | —— |
| 2 domains | 2083(30.0%) | —— |
| 3 domains | 942(13.6%) | —— |
| 4 domains | 323(4.7%) | —— |
| 5 domains | 55(0.8%) | —— |
Table 2.
Sociodemographic characteristics and incidence of IC impairment among older adults
| Variable | Total | Impaired IC | Not Impaired IC |  |
P value |
|---|---|---|---|---|---|
| Sex | 27.490 | < 0.001 | |||
| Male | 3650(52.6%) | 2977(81.6%) | 673(18.4%) | ||
| Female | 3291(47.4%) | 2838(86.2%) | 454(13.8%) | ||
| Age | 67.497 | < 0.001 | |||
| 60–69 | 4641(66.9%) | 3782(81.5%) | 859(18.5%) | ||
| 70–79 | 1975(28.5%) | 1723(87.2%) | 252(12.8%) | ||
| ≥ 80 | 325(4.7%) | 310(95.4%) | 15(4.6%) | ||
| Marital status | 53.251 | < 0.001 | |||
| Married | 5615(80.9%) | 4616(82.2%) | 999(17.8%) | ||
| Unmarried/divorced/widowed | 1326(19.1%) | 1199(90.4%) | 127(9.6%) | ||
| Residence address | 214.657 | < 0.001 | |||
| City center or town center | 1745(25.1%) | 1275(73.1%) | 470(26.9%) | ||
| Combination zone between urban and rural areas | 722(10.4%) | 592(82.0%) | 130(18.0%) | ||
| Village | 4474(64.5%) | 3948(88.2%) | 526(11.8%) | ||
| Educational status | 218.278 | < 0.001 | |||
| Illiterate | 1709(24.6%) | 1586(92.8%) | 123(7.2%) | ||
| Primary school | 3092(44.5%) | 2611(84.4%) | 481(15.6%) | ||
| Middle school | 2008(28.9%) | 1531(76.2%) | 477(23.8%) | ||
| College degree or above | 132(1.9%) | 87(65.9%) | 45(34.1%) | ||
| Medical insurance | 14.979 | < 0.001 | |||
| Yes | 6645(95.7%) | 5543(83.4%) | 1102(16.6%) | ||
| No | 296(4.3%) | 272(91.9%) | 24(8.1%) | ||
| Pension insurance | 16.406 | < 0.001 | |||
| Yes | 6113(88.1%) | 5081(83.1%) | 1032(16.9%) | ||
| No | 828(11.9%) | 734(88.6%) | 94(11.4%) |
Table 3.
Health status, lifestyle and incidence of IC impairment among older adults
| Variable | Total | Impaired IC | Not Impaired IC |
 2
|
P value |
|---|---|---|---|---|---|
| Health status | |||||
| Self-rated health | 202.180 | < 0.001 | |||
| Poor | 1942(28.0%) | 1813(93.4%) | 129(6.6%) | ||
| Average | 3469(50.0%) | 2831(81.6%) | 638(18.4%) | ||
| Good | 1530(22.0%) | 1171(76.5%) | 359(23.5%) | ||
| Chronic diseases | 35.028 | < 0.001 | |||
| none | 4130(59.5%) | 3388(82.0%) | 742(18.0%) | ||
| 1 | 1741(25.1%) | 1470(84.4%) | 271(15.6%) | ||
| ≥ 2 | 1070(15.4%) | 957(89.4%) | 113(10.6%) | ||
| Fallen down | 20.809 | < 0.001 | |||
| Yes | 1271(18.3%) | 1119(88.0%) | 152(12.0%) | ||
| No | 5670(81.7%) | 4696(82.8%) | 974(17.2%) | ||
| Body pain | 137.111 | < 0.001 | |||
| None | 2798(40.3%) | 2240(80.1%) | 558(19.9%) | ||
| A little | 2708(39.0%) | 2230(82.3%) | 478(17.7%) | ||
| Quite a bit | 1435(20.7%) | 1345(93.7%) | 90(6.3%) | ||
| Accidental injuries | 2.348 | 0.125 | |||
| Yes | 189(2.7%) | 166(87.8%) | 23(12.2%) | ||
| No | 6752(97.3%) | 5649(83.7%) | 1103(16.3%) | ||
| Out-patient Service | 16.013 | < 0.001 | |||
| Yes | 1495(21.5%) | 1303(87.2%) | 192(12.8%) | ||
| No | 5446(78.5%) | 4512(82.8%) | 934(17.2%) | ||
| In-patient Service | 28.168 | < 0.001 | |||
| Yes | 1621(23.4%) | 1427(88.0%) | 194(12.0%) | ||
| No | 5320(76.6%) | 4388(82.5%) | 932(17.5%) | ||
| Life Expectancy | 207.441 | < 0.001 | |||
| possible | 4576(65.9%) | 3624(79.2%) | 952(20.8%) | ||
| Impossible | 2365(34.1%) | 2191(92.6%) | 174(7.4%) | ||
| Lifestyle | |||||
| Cigarette smoking | 2.562 | 0.109 | |||
| Yes | 1875(27.0%) | 1549(82.6%) | 326(17.4%) | ||
| No | 5066(73.0%) | 4266(84.2%) | 800(15.8%) | ||
| Alcohol consumption | 45.200 | < 0.001 | |||
| Yes | 2490(35.9%) | 1987(79.8%) | 503(20.2%) | ||
| No | 4451(64.1%) | 3828(86.0%) | 623(14.0%) | ||
| Social participation | 71.151 | < 0.001 | |||
| Yes | 3320(47.8%) | 2652(79.9%) | 668(20.1%) | ||
| No | 3621(52.2%) | 3163(87.4%) | 458(12.6%) | ||
| Sleep duration | 31.382 | < 0.001 | |||
| < 7 h | 4258(61.3%) | 3616(84.9%) | 642(15.1%) | ||
| ≥ 7 h, <9 h | 2140(30.8%) | 1719(80.3%) | 421(19.7%) | ||
| ≥ 9 h | 543(7.8%) | 480(88.4%) | 63(11.6%) | ||
| Internet access | 298.105 | < 0.001 | |||
| Yes | 1857(26.8%) | 1321(71.1%) | 536(28.9%) | ||
| No | 5084(73.2%) | 4494(88.4%) | 590(11.6%) | ||
Table 4.
Binary regression analysis of IC impairment among older adults
| Variable | Category | B | SE | Wald | P value | OR | 95%CI |
|---|---|---|---|---|---|---|---|
| Sociodemographic characteristics | |||||||
| Sex | 0.027 | 0.083 | 0.107 | 0.743 | 1.028 | 0.873 ~ 1.210 | |
| Age | 70–79 | 0.127 | 0.086 | 2.182 | 0.140 | 1.135 | 0.959 ~ 1.343 |
| (Compared with ‘60–69’) | ≥ 80 | 1.056 | 0.280 | 14.252 | < 0.001 | 2.875 | 1.662 ~ 4.974 |
| Marital status | 0.402 | 0.108 | 13.888 | < 0.001 | 1.494 | 1.210 ~ 1.846 | |
|
Residence address (Compared with ‘City center or town center’) |
Combination zone between urban and rural areas | 0.321 | 0.120 | 7.190 | 0.007 | 1.378 | 1.090 ~ 1.742 |
| Village | 0.603 | 0.084 | 51.881 | < 0.001 | 1.827 | 1.551 ~ 2.153 | |
| Education status | Primary school | −0.569 | 0.114 | 24.792 | < 0.001 | 0.566 | 0.453 ~ 0.708 |
| (Compared with ‘Illiterate’) | Middle school | −0.568 | 0.125 | 20.675 | < 0.001 | 0.567 | 0.444 ~ 0.724 |
| College degree or above | −0.723 | 0.236 | 9.382 | 0.002 | 0.485 | 0.306 ~ 0.771 | |
| Medical insurance | −0.343 | 0.227 | 2.283 | 0.131 | 0.710 | 0.455 ~ 1.107 | |
| Pension insurance | −0.332 | 0.121 | 7.490 | 0.006 | 0.717 | 0.565 ~ 0.910 | |
| Health status | |||||||
| Self-rated health | Average | −0.616 | 0.113 | 29.750 | < 0.001 | 0.540 | 0.433 ~ 0.674 |
| (Compared with ‘Poor’) | Good | −0.803 | 0.129 | 38.757 | < 0.001 | 0.448 | 0.348 ~ 0.577 |
| Chronic diseases | 1 | 0.107 | 0.084 | 1.636 | 0.201 | 1.113 | 0.945 ~ 1.311 |
| (Compared with ‘0’) | ≥ 2 | 0.345 | 0.118 | 8.510 | 0.004 | 1.412 | 1.120 ~ 1.779 |
| Fallen down | 0.017 | 0.102 | 0.026 | 0.871 | 1.017 | 0.833 ~ 1.241 | |
| Body pain | A little | −0.039 | 0.077 | 0.256 | 0.613 | 0.962 | 0.826 ~ 1.119 |
| (Compared with ‘None’) | Quite a bit | 0.574 | 0.135 | 18.157 | < 0.001 | 1.776 | 1.364 ~ 2.312 |
| Life expectancy | Possible | −0.532 | 0.096 | 30.473 | < 0.001 | 0.588 | 0.486 ~ 0.710 |
| Out-patient services | 0.069 | 0.095 | 0.522 | 0.470 | 1.071 | 0.889 ~ 1.290 | |
| In-patient services | 0.058 | 0.095 | 0.375 | 0.540 | 1.060 | 0.880 ~ 1.276 | |
| Lifestyle | |||||||
| Alcohol consumption | −0.073 | 0.078 | 0.878 | 0.349 | 0.930 | 0.799 ~ 1.083 | |
| Social participation | −0.341 | 0.072 | 22.624 | < 0.001 | 0.711 | 0.618 ~ 0.818 | |
| Sleep duration | ≥ 7 h, <9 h | −0.180 | 0.075 | 5.785 | 0.016 | 0.835 | 0.722 ~ 0.967 |
| (Compared with ‘<7 h’) | ≥ 9 h | −0.090 | 0.149 | 0.363 | 0.547 | 1.094 | 0.817 ~ 1.465 |
| Internet access | −0.544 | 0.081 | 45.365 | < 0.001 | 0.580 | 0.495 ~ 0.680 | |
Among the 6941 older respondents, 5815 (83.8%) had at least one domain of functional impairment. Additionally, 2083 (30.0%) had impairments in two domains, and 1320 (19.1%) reported impairments in three or more domains (Table 1).
Sociodemographic characteristics and incidence of IC impairment among older adults
Univariate analysis found significant associations between IC impairment and several sociodemographic factors. Higher rates of IC impairment were observed among females (86.2% vs. 81.6%), those aged 80 years and older (95.4% vs. 83.2%), unmarried or divorced/widowed individuals (90.4% vs. 82.2%), rural residents (88.2% vs. 73.1%), those with no formal education (92.8% vs. 65.9%), and those without health insurance (91.9% vs. 83.4%) or pension insurance (88.6% vs. 83.1%).
Health status, lifestyle factors and incidence of IC impairment among older adults
Univariate analyses identified several significant health-related factors associated with increased IC impairment in older adults (p < 0.05). Poor self-rated health (93.4% vs. 76.5%), multiple chronic diseases (89.4% vs. 82.0%), recent falls (88.0% vs. 82.8%), frequent physical pain (93.7% vs. 80.1%), recent outpatient visits (87.2% vs. 82.8%), hospitalizations in the past year (88.0% vs. 82.5%), and pessimistic subjective life expectancy (92.6% vs. 79.2%) were all associated with higher IC impairment. No significant difference was found for those reporting accidental injuries (p > 0.05).
In terms of lifestyle factors, univariate analysis revealed that alcohol consumption (79.8% vs. 86.0%), lack of social participation (87.4% vs. 79.9%), sleeping nine hours or more per day (88.4% vs. 80.3%), and non-Internet use (88.4% vs. 71.1%) were significantly associated with higher IC impairment. No significant difference was found between smokers and non-smokers (p > 0.05).
Binary regression analysis of IC impairment among older adults
The binary logistic regression analysis revealed that age, marital status, residence address, education level, pension insurance, self-rated health, the presence of multiple chronic conditions, frequency of perceived physical pain, subjective life expectancy, social participation, sleep duration, and internet access were significantly associated with the impairment of IC in older adults (p < 0.05). Specifically, Older adults aged 80 years and above exhibited a significantly higher likelihood of experiencing impaired IC compared to those aged 60–69 years (OR = 2.875, 95% CI: 1.662–4.974). Similarly, individuals who were unmarried, divorced, or widowed showed a greater propensity for impaired IC compared to their married counterparts (OR = 1.494, 95% CI: 1.210–1.846). Living in rural areas was associated with greater odds of IC impairment (95% CI: 1.551–2.153) compared to urban or town settings. Higher educational attainment (college degree or above) appeared to be associated with reduced odds of IC impairment (OR = 0.485, 95% CI: 0.306–0.771). Moreover, enrollment in pension insurance was linked to lower odds of IC impairment (OR = 0.717, 95% CI: 0.565–0.910), while reporting good self-rated health was associated with decreased odds of IC impairment (OR = 0.448, 95% CI: 0.348–0.577). The presence of chronic diseases was related to increased odds of IC impairment (OR = 1.412, 95% CI: 1.120–1.779), as was experiencing regular physical pain (OR = 1.776, 95% CI: 1.364–2.312). A positive subjective life expectancy was associated with lower odds of IC impairment (OR = 0.588, 95% CI: 0.486–0.710). Additionally, engaging in social participation (OR = 0.711, 95% CI: 0.618–0.818), having adequate sleep duration (OR = 0.835, 95% CI: 0.722–0.967), and internet access (OR = 0.580, 95% CI: 0.495–0.680) were associated with lower odds of IC impairment.
Discussion
This study aims to assess the prevalence of IC impairment among Chinese older adults and examines the associations between IC impairment and various sociodemographic, health, and lifestyle factors. Analyzing a sample of 6,941 older adults in China, we found a significant prevalence of IC impairment, with 83.8% of participants exhibiting deficits across multiple domains. Among the five intrinsic capacity domains, cognitive function showed the highest prevalence of impairment in our study (71.8%), followed by psychological (40.4%), locomotion (19.8%), vitality (17.2%), and sensory function (8.9%). The predominance of cognitive impairment is broadly consistent with the growing body of evidence on age-related cognitive decline. Potential explanations include the cumulative effects of chronic diseases, lower health literacy among older cohorts, and limited access to cognitive screening and intervention services in community settings [55].
Evidence from a recent systematic review and meta-analysis [56] suggests that locomotion impairment may be more prevalent than cognitive decline, reflecting variability in IC domain patterns across populations. Such differences may arise from variations in demographic characteristics, cultural and environmental contexts, or the assessment tools employed. In our study, the relatively lower prevalence of locomotion impairment may be partly attributable to the use of ADL/IADL-based indicators, which primarily capture functional disability and may underestimate more subtle declines in physical performance.
Similarly, although our operationalization of locomotion and vitality is supported by previous studies, these measures are not the most widely adopted in the literature, and there is currently no standardized method for assessing IC domains [46]. This lack of consensus limits cross-study comparability and highlights the need for future research to develop and validate standardized assessment tools to enhance reliability, consistency, and interpretability.
Age emerged as a significant factor associated with IC impairment, particularly among those aged 80 and above. Consistent with prior studies [6, 11–13, 57, 58], advanced age is closely linked to physiological impairment, the accumulation of chronic diseases, and an increased risk of neurodegenerative conditions, all contributing to greater IC impairment.
Significant disparities in IC were observed between urban and rural older adults, echoing findings in existing literature [10, 58]. According to the Matthew Effect, initial resource disparities can intensify over time, potentially contributing to widening health inequities as individuals age [59]. Urban residents generally benefit from better access to healthcare, higher income, and greater educational opportunities, all factors correlated with stronger IC. In contrast, rural residents frequently face greater challenges due to limited resources, which may be associated with less favorable health outcomes as they age.
Marital status exhibited a significant association with IC impairment, aligning with existing literature [11, 13, 58] Marriage provides a protective effect against adverse health outcomes by promoting healthier behaviors and fostering supportive social networks [60]. In the context of China, the impact of marital status on the physical and mental health of older adults is well-documented, with strong familial and social connections playing a critical role in maintaining their well-being [61].
In line with relative research [23–26, 35], our study establishes a robust association between educational attainment and IC. Higher education levels are correlated with superior IC performance, attributable to enhanced comprehension and assimilation of health-related information. According to Health Behavior Theory, individuals with advanced educational backgrounds are more inclined to engage in health-promoting behaviors [62], which might positively influence their IC in later years. Nonetheless, while this association is robust, the precise mechanisms through which education affects IC are complex and warrant further exploration.
In our study, participation in pension schemes was associated with lower IC impairment rates. Evidence from China indicates that pension recipients use their income to enhance nutritional intake, access better healthcare, increase informal caregiving, and engage in recreational activities. These enhancements may, in turn, contribute to improved health outcomes for older adults [63]. The pension income thus appears to play a significant role in supporting the overall health status of older individuals by facilitating various forms of health-promoting behaviors and resources.
Both self-rated health and subjective life expectancy were identified as significant correlates of IC impairment. These measures reflect individuals’ perceptions of their overall health and their expectations for future well-being [64]. Previous studies have established that self-rated health and subjective life expectancy are reliable indicators of mortality, highlighting their utility in assessing various health outcomes. Their significant association with IC impairment suggests that these perceptions could provide valuable insights into the state of IC [65].
Aligned with previous research [12, 19–26, 35], our study highlights the strong association between chronic disease comorbidities, frequent body pain, and IC impairment in older adults. Individuals with multiple chronic conditions or ongoing pain experience significantly greater IC impairment across various domains. This compounded health burden likely accelerates physical and cognitive decline, leading to more pronounced functional impairments than healthier counterparts [66–68]. The interaction between chronic diseases and ongoing pain may create a feedback loop, further eroding the capacity to maintain health and well-being in later life.
Our findings reveal a significant positive correlation between social participation and IC, echoing the conclusions of prior studies [9, 10, 27, 28], Active involvement in social activities appears to be associated with enhanced psychological well-being and improved activities of daily living (ADL) [69], factors that might potentially bolster IC in older adults.
Optimal sleep duration was associated with a reduced risk of IC impairment. Individuals who achieved a balanced sleep pattern exhibited fewer IC impairments. Research indicates that both excessive and insufficient sleep can disturb circadian rhythms, a disruption that has been linked to diminished cognitive function in older adults [70, 71].
Moreover, our study identified that older adults who regularly engaged with the internet exhibited enhanced IC, potentially attributable to their proactive engagement in health information-seeking activities [72]. The existing body of research supports this finding, indicating that internet usage is positively correlated with cognitive function—one of the core dimensions of IC—which in turn is associated with a reduced risk of IC impairment [73].
Strengths and limitations
This study leverages several strengths that enhance its contribution to understanding intrinsic capacity among elderly Chinese individuals. Firstly, the data sources utilized in this study are robust, drawn from national databases renowned for their comprehensive coverage and reliability. These sources offer a dependable portrayal of the IC among elderly Chinese individuals, thereby ensuring that the study captures a diverse demographic spectrum. Furthermore, this research contributes to existing literature not only through its examination of IC but also by integrating a diverse array of variables. In this regard, the study provides complementary insights into the complex factors associated with IC impairment in older adults. Ultimately, this study offers valuable insights that may contribute to guiding efforts aimed at enhancing the Integrated Care for Older People (ICOPE) initiative in China [74].
However, the study also has several limitations that warrant acknowledgment. Firstly, the exclusion of samples with missing variables may introduce bias into the results. Secondly, the use of dichotomous categories for certain variables may restrict the granularity of statistical analysis. Furthermore, the cross-sectional design of this study limits the ability to establish causal relationships between the variables examined. Thirdly, sensory impairments were assessed based on interviewer observations rather than objective clinical tests. While practical for large-scale surveys, this approach may reduce measurement precision. Additionally, the specific demographic and geographic characteristics of the sample may affect the generalizability of the findings.
Conclusion
The study reveals a considerable prevalence of IC impairment among older adults in China, with cognitive impairment being particularly prominent. Our study identifies key factors associated with IC impairment, including sociodemographic, health status, and lifestyle factors. These findings emphasize the need for targeted public health strategies that address these modifiable factors to strengthen IC and improve the quality of aging in this population.
Acknowledgements
The authors would like to thank the China Health and Retirement Longitudinal Study (CHARLS) research and field team and every respondent in the study for their contributions.
Clinical trial number
This study is a secondary analysis of publicly available data from CHARLS. The data used in this study were de-identified and made available for public use by The Institutional Review Board at Peking University. Therefore, this study does not constitute a clinical trial and does not require a clinical trial registration number.
Authors’ contributions
GZ conceived the study; RW collected and organized relevant literature; YF wrote the main manuscript text and performed data analysis; YQ guided data analysis; GZ, CZ, and YQ critically reviewed the paper. All authors reviewed and approved the final draft.
Funding
This study was funded by the Open Bidding for Selecting the Best Candidates from Guangzhou University of Chinese Medicine (Grant No. N/A, https://www.gzucm.edu.cn/) and the Guangdong Provincial Philosophy and Social Science Planning Project (Grant No. GD23XSH19).
Data availability
The data that support the findings of this study are openly available on CHARLS project website at https://charls.pku.edu.cn/
Declarations
Ethics approval and consent to participate
The Institutional Review Board at Peking University granted ethical approval for all the CHARLS waves. The IRB approval number for the main household survey, including anthropometrics, is IRB00001052-11015. During the fieldwork, each respondent who agreed to participate in the survey was asked to sign two copies of the informed consent. Participants gave informed consent to participate in the study before taking part.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Guanyang Zou, Email: zgy1021@hotmail.com.
Caixia Zhang, Email: rainbowzcx@gzucm.edu.cn.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data that support the findings of this study are openly available on CHARLS project website at https://charls.pku.edu.cn/