Value shift in response to aging: A longitudinal study based on healthy aging

doi:10.21203/rs.3.rs-4120114/v1

Objective

Healthy aging is considered an effective way to deal with the challenges of aging. Intrinsic capacity(IC) and functional ability (FA) are the key elements of this concept. However, the relationship between the two has not been explored in detail.

Methods

A theoretical model was constructed by analyzing the concept of “health aging quantification.” 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 manner, along with mediation analysis to analyze the mechanism of the two.

Results

IC showed normal distribution and an increasing trend in 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

The study provides novel insights into the evolution of disability, in which the discovery of the disease is only a process of the outcome of disability. 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.

Value shift

Cross-Lagged Model

Healthy aging

longitudinal study

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 the elderly population 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 elderly people’s abilities and the relationship between different abilities [4, 5].

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, psychological, sensory (both visual and auditory), vitality, and movement [7–9]. IC represents the biological reserve of the elderly 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].

Figure 1 Relationship between IC and FA based on healthy aging

Many studies have focused on the impact of IC on adverse health outcomes in the elderly population. One study constructed the IC index and reported that is more predictive of instrumental daily living ability than multimorbidity [12]. Yu et al. (2021a) evaluated the intrinsic abilities of the respondents and found that cognitive ability, limited mobility, visual impairment, and depressive symptoms significantly affected poor quality of life. Meanwhile, [13] analyzed the trajectory of the intrinsic abilities of IC and found that categories with sustained growth exhibited higher quality of life. Lin et al. [8] pointed out that the decrease in IC is closely related to the decrease in biomarkers in the body, while Chen et al. [11] found that the deterioration of IC significantly predicted functional decline. In their study, [14] pointed out that IC is related to self-care ability and social support.

Meanwhile, fewer studies have focused on the potential feedback effects of functional ability (FA) on IC. The social determinant for health model [15, 16] holds 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. Hence, some studies [17–19] 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. Based on these theories and studies, the present study assumes a possible feedback effect on IC in elderly 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 [20] analyzed the longitudinal trajectory of IC and pointed out its close link to weakness. Okonkwo et al. [21] found a significant decline in cognitive function in patients with CVD, while Yan et al. [22] concluded that disability has different effects in the urban and rural depression trajectories. Nevertheless, the interaction between IC and FA has received less attention in terms of longitudinal analyses performed in previous studies. Therefore, the following hypotheses are proposed in this study:

Hypothesis 1

IC may affect the FA observed later.

Hypothesis 2

FA may affect the IC observed later.

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 [23, 24] holds that individual health is affected by personal historical events, combinations of different events, and a wide range of environmental factors. 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 residue of the elderly 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 IC on FA may be greater than the effect of FA on IC.

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 [27, 28] mainly discussed the potential causes of the reported outcomes, but there was a lack of further quantitative analysis. According to the viewpoint of the bio-psychosocial medical model [29–31], 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. 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

IC may affect the FA observed later through multimorbidity.

Hypothesis 5

FA may affect the IC observed later through SP.

The research framework based on the abovementioned research hypotheses is shown in Fig. 2.

Figure 2 Framework diagram of the study

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. 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.

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. 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).

Key variables

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, 38]. 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.

FAThe WHO defines the FA of elderly people 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 [39, 40]. Therefore, the sum of ADL and IADL scores is applied to comprehensively reflect a patient’s FA.

Potential confounding variablesDrawing 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.

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.

Descriptive statistical analysis

After processing missing values with multiple interpolation, a total of 4643 respondents who participated in three waves of surveys were included in this study. Appendix1 in the supplement details the various variables included in the studies to synthesize IC and their implications. Table 1s - 3s in Appendix 2 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 3 further describes the factor loads of each variable on the four common factors (using orthogonal rotation). Appendix 4 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 the elderly population. Figure 3 also shows that the IC of the elderly population 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

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 - 7s in Appendix 5 describe the correlation of IC with other variables.

Fig. 4 Correlation diagram of IC and FA

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

Figures 5a–5c 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. 5a Regression analysis of IC and other variables in wave1

Fig. 5b Regression analysis of IC and other variables in wave2

Fig. 5c Regression analysis of IC and other variables in wave3

Table 8s in Appendix 6 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 manner.

Figure 6 Cross-lagged model of IC and FA

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.

Table 2 Comparison of the effect strength of IC and FA

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. Thus, the reasons for this result and the public measures that must be taken deserve further discussion.

Fig. 7 Mediating analysis of multimorbidity on IC to FA effects

Table 3 Mediating test of multimorbidity on IC to FA effect

Figure 8 shows the mediating effect of FA on IC. Table 8s in Appendix 6 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(Table4).

Figure 8 Mediating analysis of SP on FA to IC effects

Table 4 Mediating test of SP on IC to FA effect

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, the effect of this interaction is not balanced, and the effect from IC to FA is stronger. Third, the effects of this disequilibrium intensify over time. Finally, the mediating effect of multimorbidity was found on the critical path. These findings may be related to the fragmentation of the current health service delivery system, the health management of the elderly population, and the changing characteristics of IC over time.

The fragmented service system for the health of the elderly population may be a potential factor in the IC–FA interaction. IC, which represents the biological remnants in the elderly population, is the basis for its function, and previous studies have provided much evidence supporting this notion. Moreover, we found that FA has a significant negative effect on IC, which may attributed to the lack of integrated services, such as rehabilitation and daily life care, for disabled people within China’s health service system [41]. At present, the physical function management of the elderly population, especially the disabled population, mainly relies on relatives to provide informal life care [42], because rehabilitation and professional life care services are not widely available. In addition, social support systems for physical functioning remain inadequate [43]. These adverse factors may have exacerbated the decline in biological residues in the elderly’s bodies.

The vertically unbalanced effects between IC and FA may be due to the lack of a biomarker-based screening and identification system prior to the onset of disease in the health management of the elderly population. At present, China is facing the increasingly serious challenge of an aging society. The response in the health sector is mainly through the implementation of free programs in the field of public health, such as health management of the elderly, especially in relation to hypertension and diabetes. In the medical field, geriatrics and a number of specialist clinics work together to deal with the diseases of the elderly population [44]. However, IC screening and identification for the elderly population has not been implemented on a large scale in either public health or medical services. This deficiency in service content has led to a loss of control of IC management in the elderly population, which in turn, amplified the significant predictive power of IC for subsequent FA.

In addition, 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 the elderly population, highlighting the fact that abilities of the elderly gradually decline over time. However, it is not just a simple linear relationship [45]. Over time, many of the abilities of the elderly population will decline more rapidly, and the impact of such a decline will gradually increase [46, 47]. 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.

Furthermore, the mediating effect of multimorbidity suggests that the service concept should be people-centered rather than disease-centered when dealing with the challenge of aging health. Under the concept of healthy ageing proposed by the WHO, the functional capacity of the elderly has been given significant attention. However, when policymakers pay great attention to the function of the elderly, they often start with diseases in practice [48]. For example, the abovementioned public health services pay more attention to hypertension and diabetes in the elderly population and focus more on disease indicators in physical examination. However, the present study found that disease is only one stage in the evolution of disability, and that older adults’ comprehensive abilities centered on physical and mental abilities (e.g., IC) are of greater concern. This finding suggests that it is urgent to devise and implement a scientific and effective capacity assessment, intervention, and maintenance system to cope with the challenges of aging and multimorbidity, thereby realizing the “threshold advance” of disease and disability.

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 [49]. 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 [50]. 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 a value shift for the elderly. First, the long-term care system for disabled elderly people must be improved as soon as possible. At present, some cities have implemented long-term care insurance to solve the financing problem. However, professional, high-quality life care and medical services remain relatively scarce. Furthermore, for non-disabled elderly people, instead of taking disease screening and management as the sole goal of the health system, there is an urgent need to establish and improve a service supply system based on ability maintenance and combined with disease diagnosis and treatment. This requires top-level design at the government level to strengthen the functional integration of various stakeholders, including public health officials, family doctor teams, specialists, and family members. Such combined efforts can help achieve the goal of healthy aging advocated by the WHO(Figure 9).

Fig.9 Policy implications of the study

Limitations

First, the data included in the study only included data from three waves of the CHARLS surveys. Therefore, the longitudinal time of observation is not particularly sufficient. For this reason, more comprehensive and long-term studies can be further explored in the future when data from more waves become available.

Second, the measures used in the study included self-reported data (e.g., mental health, sleep, and disability) from some respondents. While this may underestimate the results of the study, it also suggests that the results are robust.

Finally, the study’s sample did not include older adults with poor health (e.g., hospitalized patients), which may underestimate the IC, FA, and model results. This suggests that the results of the study may also be appropriate for older people who have poorer health.

The cross-lagged model of IC and FA was constructed and validated under the concept of healthy aging of the WHO, and the mechanism of action was examined. An unbalanced cross-lagged model with a stronger effect of IC on FA was found, and the mediating effect of multimorbidity was verified. The findings of this study provide novel insights into the evolutionary mechanisms of physical function in older adults. It also suggests that health policy for the elderly urgently must shift from a disease-centered to a people-centered paradigm in order to promote the prevention and treatment of disability as well as to realize policy value transformation.

Author contributions

CY conceived the overall framework of the study and provided guidance for the quantitative analysis. YS, XC, XS and RC worked together to write the paper.

Data availability

Our dataset is available on http://charls.pku.edu.cn/zh-CN/page/data/harmonized charls.

Code availability

The code used and/or analysed during the current study are available from the corresponding author on reasonable request.

Consent to participate

All interviewees gave written informed consent before recruitment to the study.

Ethical approval

Ethical approval for data collection in CHARLS was obtained from the Biomedical Ethics Review Committee of Peking University (IRB00001052–11015).

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.

Conflict of interest

All authors declare that there is no conflict of interest.

Rudnicka E, Napierała P, Podfigurna A, Męczekalski B, Smolarczyk R, Grymowicz M. The World Health Organization (WHO) approach to healthy ageing. Maturitas. 2020;139:6–11. http://doi.org/https://doi.org/10.1016/j.maturitas.2020.05.018.
Davis A, McMahon CM, Pichora-Fuller KM, Russ S, Lin F, Olusanya BO, et al. Aging and Hearing Health: The Life-course Approach. Gerontologist. 2016;56(Suppl2):S256–67. http://doi.org/10.1093/geront/gnw033.
Hanson MA, Cooper C, Aihie Sayer A, Eendebak RJ, Clough GF, Beard JR. Developmental aspects of a life course approach to healthy ageing. J Physiol. 2016;594(8):2147–60. http://doi.org/https://doi.org/10.1113/JP270579.
Chhetri JK, Xue QL, Ma L, Chan P, Varadhan R. Intrinsic Capacity as a Determinant of Physical Resilience in Older Adults. J Nutr Health Aging. 2021;25(8):1006–11. http://doi.org/10.1007/s12603-021-1629-z.
Scheve A, Bandawe C, Kohler H-P, Kohler IV. Mental health and life-course shocks in a low-income country: Evidence from Malawi. SSM - Popul Health. 2022;19:101098. http://doi.org/https://doi.org/10.1016/j.ssmph.2022.101098.
Beard JR, Officer A, de Carvalho IA, Sadana R, Pot AM, Michel JP, et al. The World report on ageing and health: a policy framework for healthy ageing. Lancet. 2016;387(10033):2145–54. http://doi.org/10.1016/S0140-6736(15)00516-4.
Tavassoli N, de Souto Barreto P, Berbon C, Mathieu C, de Kerimel J, Lafont C, et al. Implementation of the WHO integrated care for older people (ICOPE) programme in clinical practice: a prospective study. lancet Healthy Longev. 2022;3(6):e394–404. http://doi.org/10.1016/s2666-7568(22)00097-6.
Lin S, Wang F, Zheng J, Yuan Y, Huang F, Zhu P. Intrinsic Capacity Declines with Elevated Homocysteine in Community-Dwelling Chinese Older Adults. Clin Interv Aging. 2022;17:1057–68. http://doi.org/10.2147/CIA.S370930.
Cesari M, Araujo de Carvalho I, Amuthavalli Thiyagarajan J, Cooper C, Martin FC, Reginster J-Y, et al. Evidence for the Domains Supporting the Construct of Intrinsic Capacity. Journals Gerontology: Ser A. 2018;73(12):1653–60. http://doi.org/10.1093/gerona/gly011.
Yu J, Si H, Qiao X, Jin Y, Ji L, Liu Q, et al. Predictive value of intrinsic capacity on adverse outcomes among community-dwelling older adults. Geriatr Nurs. 2021;42(6):1257–63. http://doi.org/10.1016/j.gerinurse.2021.08.010.
Chen JJ, Liu LF, Chang SM. Approaching person-centered long-term care: The trajectories of intrinsic capacity and functional decline in Taiwan. Geriatr Gerontol Int. 2022;22(7):516–22. http://doi.org/10.1111/ggi.14391.
Yu R, Amuthavalli Thiyagarajan J, Leung J, Lu Z, Kwok T, Woo J. Validation of the Construct of Intrinsic Capacity in a Longitudinal Chinese Cohort. J Nutr Health Aging. 2021;25(6):808–15. http://doi.org/10.1007/s12603-021-1637-z.
Salinas-Rodriguez A, Gonzalez-Bautista E, Rivera-Almaraz A, Manrique-Espinoza B. Longitudinal trajectories of intrinsic capacity and their association with quality of life and disability. Maturitas. 2022;161:49–54. http://doi.org/10.1016/j.maturitas.2022.02.005.
Leung AYM, Su JJ, Lee ESH, Fung JTS, Molassiotis A. Intrinsic capacity of older people in the community using WHO Integrated Care for Older People (ICOPE) framework: a cross-sectional study. BMC Geriatr. 2022;22(1):304. http://doi.org/10.1186/s12877-022-02980-1.
Javed Z, Haisum Maqsood M, Yahya T, Amin Z, Acquah I, Valero-Elizondo J, et al. Race, Racism, and Cardiovascular Health: Applying a Social Determinants of Health Framework to Racial/Ethnic Disparities in Cardiovascular Disease. Circulation: Cardiovasc Qual Outcomes. 2022;15(1):e007917. http://doi.org/10.1161/CIRCOUTCOMES.121.007917.
Cotton NK, Shim RS. Social Determinants of Health, Structural Racism, and the Impact on Child and Adolescent Mental Health. J Am Acad Child Adolesc Psychiatry. 2022;61(11):1385–9. http://doi.org/https://doi.org/10.1016/j.jaac.2022.04.020.
Xiang X, An R, Kang S-w, Stagg BC, Ehrlich JR. Disability type, depression, and antidepressants use among older adults in the United States. Aging Ment Health. 2020;24(1):27–34. http://doi.org/10.1080/13607863.2018.1509298.
Tian G, Li R, Cui Y, Zhou T, Shi Y, Yang W, et al. Association between disability, social support and depressive symptoms in Chinese older adults: A national study. Front Public Health. 2022;10:980465. http://doi.org/10.3389/fpubh.2022.980465.
Barboza JJ, Soriano-Moreno AN, Copez-Lonzoy A, Pacheco-Mendoza J, Toro-Huamanchumo CJ. Disability and severe depression among Peruvian older adults: analysis of the Peru Demographic and Family Health Survey, ENDES 2017. BMC Psychiatry. 2020;20(1):253. http://doi.org/10.1186/s12888-020-02664-3.
Liu S, Kang L, Liu X, Zhao S, Wang X, Li J, et al. Trajectory and Correlation of Intrinsic Capacity and Frailty in a Beijing Elderly Community. Front Med (Lausanne). 2021. 8:751586.http://doi.org/10.3389/fmed.2021.751586.
Okonkwo OC, Cohen RA, Gunstad J, Tremont G, Alosco ML, Poppas A. Longitudinal Trajectories of Cognitive Decline among Older Adults with Cardiovascular Disease. Cerebrovasc Dis. 2010;30(4):362–73. http://doi.org/10.1159/000319564.
Yan C, Liao H, Ma Y, Xiang Q, Wang J. Association among multimorbidity, physical disability and depression trajectories: a study of urban-rural differences in China. Qual Life Res. 2021;30(8):2149–60. http://doi.org/10.1007/s11136-021-02807-3.
Worabo HJ. A Life Course Theory Approach to Understanding Eritrean Refugees' Perceptions of Preventive Health Care in the United States. Issues Ment Health Nurs. 2017;38(4):310–6. http://doi.org/10.1080/01612840.2017.1289286.
Cleveland LM, McGlothen-Bell K, Scott LA, Recto P. A life-course theory exploration of opioid-related maternal mortality in the United States. Addiction. 2020;115(11):2079–88. http://doi.org/https://doi.org/10.1111/add.15054.
Eisenberg Y, Vanderbom KA, Vasudevan V. Does the built environment moderate the relationship between having a disability and lower levels of physical activity? A systematic review. Prev Med. 2017;95s:S75. s84.http://doi.org/10.1016/j.ypmed.2016.07.019.
Friis K, Ekholm O, Hundrup YA. The relationship between lifestyle, working environment, socio-demographic factors and expulsion from the labour market due to disability pension among nurses. Scand J Caring Sci. 2008;22(2):241–8. http://doi.org/https://doi.org/10.1111/j.1471-6712.2007.00521.x.
Schweininger S, Forbes D, Creamer M, McFarlane AC, Silove D, Bryant RA, THE TEMPORAL RELATIONSHIP BETWEEN MENTAL HEALTH AND DISABILITY AFTER INJURY, et al. Depress Anxiety. 2015;32(1):64–71. http://doi.org/https://doi.org/10.1002/da.22288.
Lauer EA, Lauer E. Assessing the association between mental health and disability indicators among adults living in the United States. Disabil Health J. 2019;12(1):98–105. http://doi.org/https://doi.org/10.1016/j.dhjo.2018.08.006.
Hong YT, Lin Y-a, Pan YX, Lin JL, Lin XJ, Zhang J, et al. Understanding factors influencing demoralization among cancer patients based on the bio-psycho-social model: A systematic review. Psycho-oncology. 2022;31(12):2036–49. http://doi.org/https://doi.org/10.1002/pon.6023.
Delshad V, Stueck M, Ebadi A, Bidzan M, Khankeh H. Bio-psycho-social health assessment in prehospital emergency technicians: A systematic review. J Educ health promotion. 2022;11:41. http://doi.org/10.4103/jehp.jehp_498_21.
Rassoulian A, Löffler-Stastka H. The Search for Meaning in Medicine When Confronted with Life-Threatening Illness - How to Include a Bio-Psycho-Social Model into Health-Care. Psychiatria Danubina. 2022;34(3):398–406. http://doi.org/10.24869/psyd.2022.398.
Skou ST, Mair FS, Fortin M, Guthrie B, Nunes BP, Miranda JJ, et al. Multimorbidity Nat reviews Disease primers. 2022;8(1):48. http://doi.org/10.1038/s41572-022-00376-4.
Onder G, Vetrano DL, Palmer K, Trevisan C, Amato L, Berti F, et al. Italian guidelines on management of persons with multimorbidity and polypharmacy. Aging Clin Exp Res. 2022;34(5):989–96. http://doi.org/10.1007/s40520-022-02094-z.
Wang R, Chen Z, Zhou Y, Shen L, Zhang Z, Wu X. Melancholy or mahjong? Diversity, frequency, type, and rural-urban divide of social participation and depression in middle- and old-aged Chinese: A fixed-effects analysis. Social science & medicine (1982). 2019;238:112518.http://doi.org/10.1016/j.socscimed.2019.112518.
Chu-Man L, Chang M-Y, Chu M-C. Effects of mahjong on the cognitive function of middle-aged and older people. Int J Geriatr Psychiatry. 2015;30(9):995–7. http://doi.org/https://doi.org/10.1002/gps.4307.
Teh JKL, Tey NP. Effects of selected leisure activities on preventing loneliness among older Chinese. SSM - Popul Health. 2019. 9:100479.http://doi.org/https://doi.org/10.1016/j.ssmph.2019.100479.
Benka J, Nagyova I, Rosenberger J, Macejova Z, Lazurova I, van der Klink JLL, et al. Social participation in early and established rheumatoid arthritis patients. Disabil Rehabil. 2016;38(12):1172–9. http://doi.org/10.3109/09638288.2015.1076071.
Hoogendijk EO, Dent E, Koivunen K. Intrinsic capacity: an under-researched concept in geriatrics. Age Ageing. 2023;52(10):afad183. .http://doi.org/10.1093/ageing/afad183.
Li X, Zheng T, Guan Y, Li H, Zhu K, Shen L, et al. ADL recovery trajectory after discharge and its predictors among baseline-independent older inpatients. BMC Geriatr. 2020;20(1):86. http://doi.org/10.1186/s12877-020-1481-8.
Juniper AR, Connor LT, Self-Perceived. ADL/IADL Function is Influenced by Residual Neurological Impairment, Aphasia, and Anxiety. Can J Occup therapy Revue canadienne d'ergotherapie. 2022;89(3):307–14. http://doi.org/10.1177/00084174221098876.
Li X, Li C, Huang Y. Spatial-temporal analysis of urban-rural differences in the development of elderly care institutions in China. Front Public Health. 2022;10:1086388. http://doi.org/10.3389/fpubh.2022.1086388.
Chen L, Zhang X, Xu X. Health Insurance and Long-Term Care Services for the Disabled Elderly in China: Based on CHARLS Data. Risk Manage Healthc policy. 2020;13:155–62. http://doi.org/10.2147/rmhp.S233949.
Yan C, Liao H, Ma Y, Wang J. Association amongst social support inside or outside the family and depression symptoms: longitudinal study of urban-rural differences in China. Qual Life Res. 2022;31(6):1677–87. http://doi.org/10.1007/s11136-022-03086-2.
Krings MF, van Wijngaarden JDH, Yuan S, Huijsman R. China's Elder Care Policies 1994–2020: A Narrative Document Analysis. Int J Environ Res Public Health. 2022;19(10). http://doi.org/10.3390/ijerph19106141.
Li X, Cao X, Zhang J, Fu J, Mohedaner M, Danzengzhuoga, et al. Accelerated aging mediates the associations of unhealthy lifestyles with cardiovascular disease, cancer, and mortality. J Am Geriatr Soc. 2023. https://doi.org/10.1111/jgs.18611. n/a(n/a).http://doi.org/.
Pronk M, Deeg DJH, Festen JM, Twisk JW, Smits C, Comijs HC et al. Decline in Older Persons’ Ability to Recognize Speech in Noise: The Influence of Demographic, Health-Related, Environmental, and Cognitive Factors. Ear Hear. 2013;34(6).
Sierra F, Kohanski R. Geroscience and the trans-NIH Geroscience Interest Group. GSIG GeroScience. 2017;39(1):1–5. http://doi.org/10.1007/s11357-016-9954-6.
Fang EF, Xie C, Schenkel JA, Wu C, Long Q, Cui H et al. A research agenda for ageing in China in the 21st century (2nd edition): Focusing on basic and translational research, long-term care, policy and social networks. Ageing research reviews. 2020;64:101174.http://doi.org/10.1016/j.arr.2020.101174.
Wang L, Gao P, Zhang M, Huang Z, Zhang D, Deng Q, et al. Prevalence and Ethnic Pattern of Diabetes and Prediabetes in China in 2013. JAMA. 2017;317(24):2515–23. http://doi.org/10.1001/jama.2017.7596.
Lu J, Lu Y, Wang X, Li X, Linderman GC, Wu C, et al. Prevalence, awareness, treatment, and control of hypertension in China: data from 1·7 million adults in a population-based screening study (China PEACE Million Persons Project). Lancet. 2017;390(10112):2549–58. http://doi.org/10.1016/s0140-6736(17)32478-9.

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)
Mstat					＜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)

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

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

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

No competing interests reported.

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Value shift in response to aging: A longitudinal study based on healthy aging

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