Abstract
Objective
Healthy aging is considered an effective way to deal with the challenges of aging. Intrinsic capacity (IC, A composite indicator of physical and mental ability) and functional ability (FA, including the inherent capabilities of the individual, the environment in which the individual lives, and the interaction of people with their environment.) are two key elements of this concept. However, the structure, intensity, and mediation analysis of longitudinal changes in IC and FA have not been specifically studied.
Methods
A theoretical model was constructed by analyzing the concept of “health aging”. A national database (China Health and Retirement Longitudinal Study, CHARLS) was used as the data source for the study. A range of physical and mental measures were employed to construct intrinsic abilities using factor analysis, and a measure of disability was used as a proxy for FA. Descriptive and regression analyses were also conducted to preliminarily assess the distribution of the variables on the IC). This study used cross-lagged models to examine structural and effect differences between IC and FA in the longitudinal study, along with mediation analysis to analyze the mechanism of the two.
Results
IC shows a normal distribution and is positively correlated with education and income. In the structural analysis, IC and FA indicate significant cross-lagged effects in the longitudinal direction. The effect of IC on FA is also greater than that of FA on IC; such an effect also tends to intensify over time. Furthermore, multimorbidity mediates the effect of IC on FA, but the mediating effect is not very large. Finally, social participation did not significantly mediate the effect of FA on IC.
Conclusion
By analyzing the structure, strength, and interaction path between IC and FA, research has found that diseases are just a process of FA development. However, the source variable - patient’s IC - has received less attention. This suggests that policymakers should not only focus on identifying a disease, but also on assessing, maintaining, and providing intervention before the disease. The authorities should also promote the implementation of people-centered rather than disease-centered measures in the health delivery system to achieve value transformation.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12877-025-06493-5.
Keywords: Value shift, Cross-lagged model, Healthy aging, Longitudinal study
Introduction
Healthy aging has been recognized as a core concept in the implementation of public policies targeting the older population. It is defined by the World Health Organization (WHO) as “the process of developing and maintaining healthy functional ability (FA) in older persons” [1]. Rather than focusing on disease, this definition directs more attention to the functioning of older adults as a dynamic process throughout the life course [2, 3]. Based on the shift in perspective, more studies have begun to focus on the development of older adults’ abilities and the relationship between different abilities [4, 5]. For example, visual impairment in older adults may potentially affect social participation, and mental cognition may also affect older adults’ daily activities.
The interaction between intrinsic capacity (IC) and environmental characteristics determines functional abilities (Fig. 1). IC is defined as “the combination of all physical and mental abilities that an individual can utilize at any one time” [6]. In practice, it consists of five interrelated areas: cognitive (e.g. memory, numeracy, etc.), psychological (e.g., mood, mental health, etc.), sensory (both visual and auditory), vitality (e.g., nutritional status), and movement(e.g., walking speed, grip strength, etc.) [7–9]. IC represents the biological reserve of older adults and has a significant impact on their functional abilities. Meanwhile, the environment also plays an important role, and supportive environments often help people build and maintain their abilities [10, 11].
Fig. 1.
Relationship between IC and FA based on healthy aging ((Source: World report on ageing and health; IC: Intrinsic capacity; FA: Functional ability)
Many studies have focused on the impact of IC on adverse health outcomes in older adults. One study constructed the IC index and reported that is more predictive of instrumental daily living ability than multimorbidity [12]. Meanwhile, some studies analyzed the trajectory of IC and found that categories with sustained growth exhibited higher quality of life [13]. Lin et al. pointed out that the decrease in IC is closely related to the decrease in biomarkers in the body, while Chen found that the deterioration of IC significantly predicted functional decline [8, 11]. In their study, they pointed out that IC is related to self-care ability and social support [14].
Meanwhile, fewer studies have focused on the potential bidirectional effect (the interaction between two variables) of functional ability (FA) on IC. The social determinants for health model hold that the health level affected by gender, heredity, and other factors is also affected by multiple factors, such as individual occupation, social facilities, and economic development [15, 16]. Hence, some studies have pointed out that environmental factors and individuals’ FA (e.g., disability) could also adversely affect their cognitive, emotional, and other abilities, which are also important components of IC [17–19]. Based on these theories and studies, the present study assumes a possible feedback effect on IC in older adults’ FA.
In addition, IC and FA may display different characteristics over time. Previous longitudinal studies have focused on the longitudinal trajectory of IC or one of these abilities and analyzed the influencing factors. For example, Liu analyzed the longitudinal trajectory of IC and pointed out its close link to weakness (physical frailty) [20]. Okonkwo found a significant decline in cognitive function in patients with cardiovascular disease, while Yan concluded that disability has different effects in the urban and rural depression trajectories [21, 22]. Nevertheless, the interaction between IC and FA has received less attention in terms of longitudinal analyses performed in previous studies. According to the definition of healthy aging, IC is the functional basis of FA, which in turn positively affects IC. Therefore, this study intends to investigate the longitudinal relationship between the two. Therefore, the following hypotheses are proposed in this study:
Hypothesis 1: Respondents with higher intrinsic capacity in the current period were able to observe higher functional ability in the later stages.
Hypothesis 2: Respondents with higher functional ability in the current period were able to observe higher intrinsic capacity in the later stages.
Furthermore, the effect strength between IC and FA may also vary longitudinally. IC and FA represent different aspects of individual health, and the life course theory holds that individual health is affected by personal historical events, combinations of different events, and a wide range of environmental factors [23, 24]. While acknowledging the impact of environmental factors on health, life course theory also emphasizes the importance of personal abilities [25, 26]. Therefore, the IC that represents the biological reserve of older adults may be the basis of FA, and the more environmentally sensitive FA may affect the whole population rather than the specific individual. Hence, the effect of FA on IC may be smaller than the effect of IC on FA. Based on this assumption, the following hypothesis is proposed in the study of effect strength:
Hypothesis 3: The longitudinal effect of intrinsic capacity on functional ability will be greater than the effect of functional ability on intrinsic capacity.
Finally, further mechanism analysis is necessary on the larger interaction path between IC and FA. In the above analysis, the hypothesis of the study preliminarily compares the effect size between IC and FA. However, in terms of the mechanism of effect, the relevant studies mainly discussed the potential causes of the reported outcomes, but there was a lack of further quantitative analysis [27, 28]. According to the viewpoint of the bio-psychosocial medical model, attention should be paid not only to the disease itself, but also to the broad range of psychological and social factors that contribute to its occurrence and progression [29–31]. Multimorbidity has been applied in most studies as an indication of a patient’s disease status [32, 33]. Hence, multimorbidity may have a mediating role in the effect of IC on FA. In addition, the maintenance of functioning often relies on a supportive environment and social participation (SP) [34], and greater participation in social activities tends to promote individual capacity development. Some studies have reported that participating in more social activities is linked to better mental health [35, 36]. In comparison, patients with less SP tend to experience pain, fatigue, anxiety and depression [37]. Hence, the following hypotheses related to such a mechanism are presented as follows:
Hypothesis 4: Intrinsic capacity will affect the functional ability observed later through multimorbidity.
Hypothesis 5: Functional ability will affect the intrinsic capacity observed later through social participation.
The research framework based on the abovementioned research hypotheses is shown in Fig. 2.
Fig. 2.
Framework diagram of the study (IC: Intrinsic capacity; FA: Functional ability; SP: Social participation)
The study provides new insights into and a more comprehensive understanding of the complex relationship between IC and FA as recently proposed by the WHO [6]. This study deeply analyzes the longitudinal structure between IC and FA, guided by the WHO theory of healthy aging. In particular, in understanding the cross-lagged relationship between IC and FA in more detail and its pathways, this study provides a new practical idea and intervention method for healthy aging. This work also has some reference for dealing with the increasingly serious phenomenon of global aging.
Methods
Data source
The data used in this study come from the China Health and Retirement Longitudinal Study (CHARLS), a nationwide longitudinal survey of the population aged 45 years and above in China [38]. CHARLS is an ongoing large-scale interdisciplinary survey project organized by the National Development Research Institute of Peking University, jointly implemented by the China Social Sciences Survey Center of Peking University and by the Youth League Committee of Peking University. The surveys are conducted every two or three years. Four waves of investigation were already conducted (2011, 2013, 2015, and 2018). Due to the lack of specific indicators for evaluating IC in the fourth wave, this study chose the first three waves of data for analysis.
The CHARLS samples were analyzed at the county and rural household levels in four stages. Specifically, in the county and rural samplings, CHARLS uses probability proportional to scale sampling (PPS). A total of 150 counties (districts) were randomly selected nationwide using the population of each district and county in 2009 as reference, along with the regional, urban–rural, and GDP as stratified indicators. Based on the permanent population of each village or community in 2009, three villages (communities) were randomly selected from 150 counties (districts). Finally, 450 villages (communities) were obtained. Next, we randomly selected 80 households from the information list of all households in each village (community).
A total of 13,565 people participated in the third phase of the survey, including 5072 people aged 60 above. Since the core variable of the study is IC, the constituent indicators of its various dimensions are crucial to the results of the study. Therefore, the study removed samples with missing specific indicators of IC (a total of 429 people). Therefore, a total of 4643 samples were included in the study for analysis.
Key variables
Intrinsic capacity (IC)
In this work, IC is referred to as the biological surplus within an individual. Most of the relevant IC studies have chosen to measure the biological indicators of physical health, as well as the cognitive, emotional, and other mental health indicators to comprehensively reflect the level of IC [9, 39]. The biomarkers of the simple physical examination performed in the first three waves of the CHARLS survey (walking speed, grip strength, balance, sit-stand tests, etc.) and the physical and mental health variables included in the questionnaire (emotion, memory, sleep, vision, hearing, etc.) were used in the present study to fully reflect the IC of the older population. The attachment appendix1 shows the overall framework of IC construction. Appendix 2 displays the specific variables included in this study that are available in the questionnaire. Appendix3,4 and 5 show the factor analysis process for constructing the IC.
Functional ability (FA)
The WHO defines the FA of older adults as their ability to do things they consider valuable. Previous studies generally concluded that disability is an outcome indicator of intrinsic ability loss. The definitions of the two indicators of activities of daily living (ADL) and instrumental activities of daily living (IADL) measure individuals’ ability to independently satisfy their individual functions and use tools to meet them, reflecting to some extent the performance of their functions in their environments [40, 41]. Therefore, the sum of ADL and IADL scores is applied to comprehensively reflect a patient’s FA.
Potential confounding variables
Drawing on research on the influencing factors of IC components, gender, education, income, age, and marital status are closely related to cognitive, memory, emotional, and other forms of IC and disability. Therefore, we included age and income (logarithmically) in the study as continuous variables and controlled for gender, education, marital status, and place of residence as categorical variables.
Statistical method
Construction of IC
We used exploratory factor analysis (EFA) to analyze the component dimensions of IC, calculating a factor score that represents the IC level of each respondent based on the weights and coefficients of the variance of IC interpreted in each dimension. The variable composition of IC and the calculation method of factor analysis are explained in detail in the attachment section.
IC vs. FA
First, we used descriptive analysis to characterize the distribution difference of IC in each control variable and correlation analysis to describe the relationship between IC and FA. Second, we used OLS to analyze the relationship between IC and FA after controlling for some confounding variables. The results were more intuitively displayed in the form of forest maps.
Longitudinal study and mechanism analysis
Based on the above hypotheses, a cross-lagged model was used to analyze the longitudinal relationship between IC and FA. Furthermore, we employed the coefficient difference test to compare the longitudinal strength of IC and FA. Finally, we used longitudinal mediation analysis to explore the mechanism between IC and FA.
Software and statistical standard design
Stata was used to perform EFA and calculate factor scores to derive the IC of each respondent; it was also applied to perform descriptive statistical and correlation analyses. In addition, we used Mplus7.0 to run cross-lagged models and longitudinal mediating effect models. The statistical standard was set at P < 0.05.
Ethical approval
Ethical approval for data collection in CHARLS was obtained from the Biomedical Ethics Review Committee of Peking under the Biomedical Ethics Review Committee of Peking University (IRB00001052–11015). All interviewees provided written informed consent before their recruitment to this study.
Results
Descriptive statistical analysis
The study removed samples with missing specific indicators of IC (n = 429) and filled in basic information (age, education, etc.) based on longitudinal three wave information., Then a total of 4643 respondents were included in this study. Appendix1 in the supplement details the various variables included in the studies to synthesize IC and their implications. Figures 3 and 4, 5 s in Appendix 3 show the distribution of characteristic roots for the common factors of factor analysis. According to the general criteria for selecting common factors (eigenvalue greater than 1), four common factors were selected in each of the three waves of investigation. Appendix 4 further describes the factor loads of each variable on the four common factors (using orthogonal rotation). Appendix 5 describes the weights of the common factors calculated based on the proportion of variance that each common factor can explain. Based on the predicted four common factor values and weights, the IC of each respondent was calculated.
Figure 3 presents the distributions of IC throughout the three waves of the survey, with larger values as continuous data indicating better IC of older adults. Figure 3 also shows that the IC of older adults in the three waves of surveys, which we included in the study, basically present normal distributions, although the number of samples greater than 0 gradually decreases in the three waves of surveys.
Fig. 3.
IC distribution based on factor analysis (IC: Intrinsic capacity; The number following the variable represents the number of waves observed)
Figure 4 shows the IC and FA correlation analysis results indicating a significant negative correlation between IC and FA in the three waves of investigation. In other words, respondents with higher IC had better physical functions. However, it should be noted that this result is only a preliminary exploration of the relationship between IC and FA without considering other confounding factors. Tables 5s, 6s and 7s in Appendix 6 describe the correlation of IC with other variables.
Fig. 4.
Correlation diagram of IC and FA (IC: Intrinsic capacity; FA: Functional ability; The number following the variable represents the number of waves observed)
In Table 1, IC is treated as a four-categorical variable in the analysis of its distribution over sample features. Except for income in the first wave, IC shows significant distribution differences across different characteristics of the respondents. In particular, the respondents with higher IC had a higher proportion of no multimorbidity, were male, partnered, and living in urban areas. In addition, IC was negatively associated with the age and income of the respondents.
Table 1.
Descriptive statistics of IC and other variables
| Wave1 | Wave2 | Wave3 | ||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Variables | IC (Quartile) | p | IC (Quartile) | p | IC (Quartile) | p | ||||||||||
| Multimorbidity | 1 | 2 | 3 | 4 | <0.01 | 1 | 2 | 3 | 4 | <0.01 | 1 | 2 | 3 | 4 | <0.01 | |
| No |
555 (47.8%) |
610 (52.54%) |
667 (57.45%) |
745 (64.22%) |
473 (40.74) |
556 (47.89) |
613 (52.8) |
697 (60.09) |
299 (25.75) |
391 (33.71) |
464 (39.97) |
551 (47.5) |
||||
| Yes |
606 (52.2%) |
551 (47.46%) |
494 (42.55%) |
415 (35.78%) |
688 (59.26) |
605 (52.11) |
548 (47.2) |
463 (39.91) |
862 (74.25) |
769 (66.29) |
697 (60.03) |
609 (52.5) |
||||
| Age | <0.01 | <0.01 | <0.01 | |||||||||||||
|
69.64 (0.183) |
68.07 (0.170) |
67.19 (0.157) |
66.65 (0.154) |
71.08 (0.179) |
69.90 (0.166) |
69.09 (0.152) |
69.49 (0.174) |
73.1 (0.177) |
71.68 (0.164) |
71.07 (0.156) |
71.61 (0.175) |
|||||
| Gender | <0.01 | <0.01 | <0.01 | |||||||||||||
| Male |
310 (26.7) |
518 (44.62) |
690 (59.43) |
837 (72.16) |
349 (30.06) |
520 (44.79) |
706 (60.81) |
780 (67.24) |
324 (27.91) |
534 (46.03) |
680 (58.57) |
816 (70.34) |
||||
| Female |
851 (73.3) |
643 (55.38) |
471 (40.57) |
323 (27.84) |
812 (69.94) |
641 (55.21) |
455 (39.19) |
380 (32.76) |
837 (72.09) |
626 (53.97) |
481 (41.43) |
344 (29.66) |
||||
| Education | <0.01 | <0.01 | <0.01 | |||||||||||||
| Illiteracy |
913 (78.64) |
708 (60.98) |
573 (49.35) |
453 (39.05) |
892 (76.83) |
721 (62.1) |
555 (47.8) |
479 (41.29) |
899 (77.43) |
729 (62.84) |
553 (47.63) |
465 (40.09) |
||||
| Elementary school |
176 (15.16) |
318 (27.39) |
361 (31.09) |
336 (28.97) |
192 (16.54) |
301 (25.93) |
358 (30.84) |
340 (29.31) |
196 (16.88) |
283 (24.4) |
364 (31.35) |
348 (30) |
||||
| Middle school |
63 (5.43) |
108 (9.3) |
170 (14.64) |
268 (23.10) |
71 (6.12) |
116 (9.99) |
178 (15.33) |
244 (21.03) |
57 (4.91) |
122 (10.52) |
186 (16.02) |
244 (21.03) |
||||
| Senior High school or above |
9 (0.78) |
27 (2.33) |
57 (4.91) |
103 (8.88) |
6 (0.52) |
23 (1.98) |
70 (6.03) |
97 (8.36) |
9 (0.78) |
26 (2.24) |
58 (5) |
103 (8.88) |
||||
| Marital status | <0.01 | <0.01 | <0.01 | |||||||||||||
| Yes |
814 (70.11) |
931 (80.19) |
964 (83.03) |
975 (84.05) |
806 (69.42) |
864 (74.42) |
955 (82.26) |
948 (81.72) |
718 (61.84) |
869 (74.91) |
911 (78.47) |
914 (78.79) |
||||
| No |
347 (29.89) |
230 (19.81) |
197 (16.97) |
185 (15.95) |
355 (30.58) |
297 (25.58) |
206 (17.74) |
212 (18.28) |
443 (38.16) |
291 (25.09) |
250 (21.53) |
246 (21.21) |
||||
| Location | <0.01 | <0.01 | <0.01 | |||||||||||||
| Rural |
317 (27.3) |
393 (33.85) |
417 (35.92) |
482 (41.55) |
297 (25.58) |
364 (31.35) |
437 (37.64) |
511 (44.05) |
296 (25.5) |
355 (30.6) |
441 (37.98) |
517 (44.57) |
||||
| Urban |
844 (72.7) |
768 (66.15) |
744 (64.08) |
678 (58.45) |
864 (74.42) |
797 (68.65) |
724 (62.36) |
649 (55.95) |
865 (74.5) |
805 (69.4) |
720 (62.02) |
643 (55.43) |
||||
| Income | 0.058 | <0.01 | 0.001 | |||||||||||||
|
3.605 (0.010) |
3.641 (0.010) |
3.676 (0.010) |
3.715 (0.011) |
3.815 (0.010) |
3.850 (0.009) |
3.863 (0.009) |
3.906 (0.009) |
3.939 (0.010) |
3.935 (0.011) |
3.979 (0.009) |
4.013 (0.010) |
|||||
IC Intrinsic capacity
Figures5a–c show the multivariate regression results of IC and the respondent characteristics, revealing some findings that univariate analysis could not visually demonstrate. First, the analysis showed that older respondents with higher levels of education had higher levels of IC and that such an effect increased with increasing differences in respondents’ levels of education. Second, the effect of income on IC is mainly reflected in the medium and above level. Finally, marital status had no significant association with IC after controlling for confounding variables.
Fig. 5.
a Regression analysis of IC and other variables in wave1 (IC: Intrinsic capacity; The number following the variable represents the number of waves observed). b Regression analysis of IC and other variables in wave2 (IC: Intrinsic capacity; The number following the variable represents the number of waves observed). c Regression analysis of IC and other variables in wave3 (IC: Intrinsic capacity; The number following the variable represents the number of waves observed)
Table 8s in Appendix 7 shows the fit indicators for the model. It is suggested that the cross-lagged model has a good fit. Figure 6 shows the results of the cross-lagged model between IC and FA, revealing a significant longitudinal cross-lagged relationship between IC and FA. The IC of the first wave of the survey significantly and negatively predicted the FA of the second wave. Similarly, the FA of the first wave of the survey significantly and negatively predicted the IC of the second wave. The same results were also found in the second and third waves of the survey. Thus, it more clearly reflects the longitudinal evolution structure of different abilities of healthy aging in a quantitative way. This supports hypothesis 1 and 2 of the study. IC is an indicator that comprehensively reflects the abilities of the older adults, so although the coefficient is small, its role could not be ignored. It should be noted that the effect of baseline on the second wave is smaller than the effect of the second wave on the third wave. This may be because the study population are older adults, and some undiscovered molecular mechanisms have a greater impact on the function of older adults over time.
Fig. 6.
Cross-lagged model of IC and FA (IC: Intrinsic capacity; FA: Functional ability; The number following the variable represents the number of waves observed)
Table 2 shows the results of the hypothesis testing analysis of the effect comparison on the cross-lagged model. The results indicated that the effect of IC on FA was significantly greater than that of FA on IC. In terms of the longitudinal change of the variable itself, the effect of IC in the second to third wave was significantly greater than that in the first to second wave. Similarly, FA showed the same trend in vertical changes. This supports hypothesis 3 of the study.
Table 2.
Comparison of the effect strength of IC and FA
| Effect difference | Coefficient (standard error) | P |
|---|---|---|
| (IC1→FA2)- (FA1→IC2) | −0.433(0.061) | <0.001 |
| (IC2→FA3)- (FA2→IC3) | −0.569(0.078) | <0.001 |
| (IC1→IC2)- (IC2→IC3) | −0.073(0.021) | <0.001 |
| (FA1→FA2)- (FA2→FA3) | −0.298(0.052) | <0.001 |
The number following the variable represents the number of waves observed
IC Intrinsic capacity, FA Functional ability
The mechanism between variables is further discussed. Table 8s in Appendix 6 shows the fit indicators for the mediation model. It is suggested that the model with multimorbidity as the mediating variable has a good fit. Figure 7 shows the results of the analysis in terms of the mechanism between variables, mainly indicating that multimorbidity significantly mediates the effects of IC to FA in the longitudinal direction. A longitudinal mediating effect model was constructed using baseline IC, third-wave FA, and second-wave multimorbidity. The results showed that multimorbidity significantly mediated the effects between IC and FA. Nevertheless, the mediating effect of multimorbidity accounted for only about 9% based on the proportion of effects shown in Table 3. IC is an overall indicator of a comprehensive and integrated response to the biological reserve of a person, and the effect on FA may also be influenced by the natural aging of a person. Whereas the current progress of related molecular mechanism studies needs to be comprehensive and in-depth, this may be a possible explanation for our results regarding the smaller mediating effect of multimorbidity. Thus, the reasons for this result and the public measures that must be taken deserve further discussion. This result supports hypothesis 4 of the study.
Fig. 7.
Mediating analysis of multimorbidity on IC to FA effects (IC: Intrinsic capacity; FA: Functional ability; The number following the variable represents the number of waves observed)
Table 3.
Mediating test of multimorbidity on IC to FA effect
| Pathway | Coefficient (standard error) | P |
|---|---|---|
| Total | −0.98(0.073) | <0.001 |
| Direct | −0.888(0.073) | <0.001 |
| Indirect | −0.092(0.012) | <0.001 |
IC Intrinsic capacity, FA Functional ability
Figure 8 shows the mediating effect of FA on IC. Table 8s in Appendix 7 shows the fit indicators of the SP mediation model. It is suggested that the model fitting of SP as a mediator variable is poor. The results showed that no significant mediating effect of social participation was found among the effects of FA on IC (Table 4). This confirms that hypothesis 5 of the study is not valid.
Fig. 8.
Mediating analysis of SP on FA to IC effects (IC: Intrinsic capacity; FA: Functional ability; SP: Social participation; The number following the variable represents the number of waves observed)
Table 4.
Mediating test of SP on IC to FA effect
| Pathway | Coefficient (standard error) | P |
|---|---|---|
| Total | −0.056(0.005) | <0.001 |
| Direct | −0.055(0.073) | <0.001 |
| Indirect | <0.001 | 0.482 |
IC Intrinsic capacity, FA Functional ability, SP Social participation
Discussion
Upon analyzing the longitudinal relationship between IC and FA based on the concept of healthy aging, four novel findings are obtained. First, we found that the relationship between IC and FA is a dynamic process of mutual influence. Second, IC is a stronger predictor of FA. It was found that the impact of the second wave IC on the third wave FA was greater than the impact of the second wave IC on the first wave FA. Finally, the mediating effect of multimorbidity was found on the critical path. These findings may be related to the characteristics of changes in intrinsic abilities over time and the health management practices of older adults.
The fact that IC may not be linear over time may explain the gradual strengthening of the longitudinal effect. Numerous previous studies have revealed the longitudinal evolution of physical and mental ability in older adults, highlighting the fact that abilities of older adults gradually decline over time. However, it is not just a simple linear relationship [42]. Over time, many of the abilities of older adults will decline more rapidly, and the impact of such a decline will gradually increase [43–45]. This may explain the phenomenon indicating that the effect of IC on itself and on FA are gradually strengthened with time. This finding also indicates the urgent need to establish and implementing an IC identification, monitoring, and differentiated service system to better cope with the challenge of aging.
The mediating effect of coexistence of multiple diseases suggests that while focusing on “disease screening”, more attention should be paid to the assessment of the IC of the older adults. Although the concept of healthy aging proposed by WHO attaches great importance to the functional development of the older adults, policy makers often start from diseases in practice. For example, current public health services pay more attention to hypertension and diabetes of the older adults, and physical examination also pays more attention to disease indicators [46]. However, this study found that diseases are only one stage in the process of disability evolution, and more attention should be paid to the comprehensive abilities of older adults with physical and mental abilities as the core (such as IC). This discovery enlightens us that there is an urgent need to design and implement scientifically effective capacity assessment, intervention, and maintenance systems to address the challenges of aging and the coexistence of multiple diseases, in order to achieve a “forward shift” of disease and disability.
As a concrete example, the health management of diabetes in older adults is, in most cases, more concerned with the diagnosis of diabetes and the post-diagnosis health management program. However, under a people-centered orientation, the focus should be on controlling the risk of diabetes in the entire population (covering lifestyle habits, genetic factors, and living conditions, etc.). In the population that already has diabetes, attention should be paid not only to disease management, but also to the combined effects of diabetes on the health capacity of older adults (e.g., on mobility, vision, hearing, etc.), and intervention programs should be formulated so as to improve quality of life. The lessons learned from related health management could also be generalized to other chronic diseases.
The study also found that the mediating effect of disease was relatively low, which may be due to the deficiencies in the screening, diagnosis, and treatment of chronic diseases in the Chinese health system. According to a national survey, the overall prevalence of diabetes is 10.9%, but more than 60% of cases go undiagnosed [47]. Furthermore, only 37% of patients are aware of their diagnosis, and only 32% receive treatment. Of those with high blood pressure, fewer than one in three are treated and fewer than one in 12 are able to control their blood pressure levels [48]. Based on inadequate chronic disease management, the respondents with multimorbidity in the current study may be considered patients with more severe diseases, and a large number of them may not be aware of their diagnoses at all. Thus, the mediating effect may be underestimated. At the same time, this finding also reveals the urgent need to create a people-centered ability assessment system.
In summary, China’s health policy now needs to shift its value towards the older adults: it is urgent to establish and improve a service supply system based on capacity maintenance and combined with disease diagnosis and treatment, rather than making disease screening and management the sole goal of the health system. This requires top-level design at the government level, strengthening the functional integration of multiple stakeholders such as public health officials, family doctor teams, specialist doctors, and family members. Only through collaborative efforts can the WHO’s advocated goal of healthy aging be achieved (Fig. 9).
Fig. 9.
Policy implications of the study
Limitations
Firstly, the data included in the study only includes three waves of data from the CHARLS survey. Therefore, the longitudinal observation time is not particularly sufficient. Therefore, in the future, when more wave data is available, more comprehensive and long-term research can be further explored. In addition, the indicators used in the study include self-reported data from some respondents, such as mental health, sleep, and disability. Although this may underestimate the results of the study, it also indicates that the results are robust.
Conclusions
This study mainly found an unbalanced cross lag model, with IC having a stronger impact on FA, and verified the mediating effect of multiple incidence rate. The results of this study provide new insights into the evolutionary mechanisms of physical function in the older adults, namely that diseases are the pathways through which their physical functions change. In the future, while paying attention to diseases in the older adults, we should also focus on maintaining their internal abilities.
Supplementary Information
Acknowledgements
The authors thank the Open Database Platform of Peking University for providing data support for this study. Thanks also to the interviewers, respondents and organizers who participated in the CHARLS survey.
Code availability
The code used and/or analysed during the current study are available from the corresponding author on reasonable request.
Abbreviations
- CHARLS
China health and retirement longitudinal study
- IC
Intrinsic capacity
- FA
Functional ability
- SP
Social participation
- WHO
The world health organization
- PPS
Probability proportional to scale sampling
- ADL
Activities of daily living
- IADL
Instrumental activities of daily living
- EFA
Exploratory factor analysis
Authors’ contributions
CY and HS conceived the overall framework of the study and provided guidance for the quantitative analysis. YS, RC, XS and RL worked together to write the paper.
Funding
A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.
Data availability
Our dataset is available on http://charls.pku.edu.cn/zh-CN/page/data/harmonized charls.
Declarations
Ethics approval and consent to participate
Ethical approval for data collection in CHARLS was obtained from the Biomedical Ethics Review Committee of Peking University (IRB00001052–11015).
All interviewees gave written informed consent before recruitment to the study.
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.
Yiran Shen, Ruoyun Cao, Xue Sang and Ruyue Lin contributed equally to this work.
Contributor Information
Hongpeng Sun, Email: hpsun@suda.edu.cn.
Chaoyang Yan, Email: cyyan1212@suda.edu.cn.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Our dataset is available on http://charls.pku.edu.cn/zh-CN/page/data/harmonized charls.