Next Article in Journal
Body Fat Percentage and Normal-Weight Obesity in the Chinese Population: Development of a Simple Evaluation Indicator Using Anthropometric Measurements
Next Article in Special Issue
Effectiveness of the Biography and Life Storybook for Nursing Home Residents: A Quasi-Experimental Study
Previous Article in Journal
After-School Extracurricular Activities Participation and Depressive Symptoms in Chinese Early Adolescents: Moderating Effect of Gender and Family Economic Status
Previous Article in Special Issue
Dietary Patterns among Older People and the Associations with Social Environment and Individual Factors in Taiwan: A Multilevel Analysis
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Social Interaction, Lifestyle, and Depressive Status: Mediators in the Longitudinal Relationship between Cognitive Function and Instrumental Activities of Daily Living Disability among Older Adults

School of Nursing, Peking University, No. 38 Xueyuan Road, Haidian District, Beijing 100191, China
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2022, 19(7), 4235; https://doi.org/10.3390/ijerph19074235
Submission received: 26 February 2022 / Revised: 29 March 2022 / Accepted: 31 March 2022 / Published: 1 April 2022
(This article belongs to the Special Issue Health Promotion for Older Adults)

Abstract

:
(1) Background: Cognitive decline is associated with instrumental activities of daily living (IADL) disability. Intervention targeting the mediators of this association will provide a path to avoid cognition-related IADL disability. (2) Methods: This study used data of wave 2008 (baseline) and wave 2014 of Chinese Longitudinal Healthy Longevity Surveys. Structural equation modeling was conducted to examine the mediating effect of social interaction, lifestyle (fruit and vegetable intake; exercise habits), and depressive status on the association between four baseline cognitive function dimensions (measured by the Chinese version of the Mini-Mental State Examination) and five (2014) IADL dimensions (visiting neighbors, shopping, preparing meals, washing clothes, and taking public transportation). (3) Results: Among 1976 older adults, 29.1% developed IADL disability 6 years later. The cognition–disability association was completely mediated by social interaction (estimate = −0.095, p < 0.001), lifestyle (estimate = −0.086, p < 0.001), and depressive status (estimate = −0.017, p = 0.003). The mediating effects of social interaction (46.3% variances explained) and lifestyle (42.0% variances explained) were both larger than that of depressive status (8.3% variances explained). (4) Conclusions: The development of interventions aimed at improving social interaction, depression, and lifestyle could be of value to prevent cognition-related IADL disability.

1. Introduction

Aging is one of the biggest known risk factors for many human diseases. Although the incidence of disability has decreased due to improvements in living standards and medical care, there is also a contention that the aging of the population and the improvement of survivability will result in frailer older adults surviving with health problems. Thus, the number of disabled older adults may be increasing [1]. Progressive disability has adverse impacts on older adults’ daily lives and is related to long-term economic burden and mortality [2]. Compared with basic activities of daily living (ADLs), IADLs seem to be more complex daily activities requiring a higher level of cognitive function, such as shopping or managing medications. A study suggests that the association between cognitive function and ADLs depends substantially on IADLs [3]. Moreover, hippocampal and cortical gray matter volumes are associated with IADLs, indicating that cognitive decline contributes to the incidence of IADL disability [4], which has been found to predict the onset of dementia in the future [5]. New insights into the relationship linking cognitive function and IADL disability could help reveal new ways to break the vicious circle between cognitive impairments and IADL disability, improve life independence, and increase life expectancy and well-being of older adults.
The Human Capital Model [6] posits that intellectual capital (e.g., executive function, brain structure/function), physical capital (e.g., physical function), social capital (e.g., social network, social support), emotional capital (e.g., depression), financial capital (e.g., income), and individual capital (e.g., participation in sports, activity knowledge and skills) are linked together. Among the factors that can be modified, reduced social interaction has been found to be associated with early signs of cognitive decline [7]. Previous studies showed that individuals with lower cognitive function take part in fewer social activities [8], indicating a possible bidirectional relationship between cognitive function and social interaction. Social interaction is also essential for the prevention of functional disability [9]. Therefore, we hypothesized that social interaction would be a critical mediator in the cognitive function–IADL disability association. In addition, a healthy lifestyle, including regular exercise [10] and a healthy diet [11], is associated with improved cognitive function [12]. Among many kinds of food, fruit and vegetables, with high levels of folate, vitamins, and antioxidants, have been shown to be related to cognitive function [13], and regular consumption of fruits and vegetables contributes to improved physical function [14]. Exercise decreases IADL disability as well [15]. Hence, we hypothesized that lifestyle would also mediate the association between cognitive function and IADL disability. Moreover, previous studies provided evidence that cognitive decline, especially poorer memory at baseline, is associated with worse depressive symptoms at follow-up [16], and older adults with depression are less physically active compared to those without depression [17,18], which increases disuse, limitation in multiple daily functioning domains, and the risk of IADL disability in older adults [19,20]. This might be because depressive symptoms impair fine operational skills, via neuroinflammatory or morphological changes in the brain, involved in executive control and mood processing [20,21]. The studies examining the effect of depressive symptoms on the association between cognitive function and IADL disability showed that depressive symptoms significantly mediated and moderated the association between them [20,21]. However, so far, no study has compared the contribution of depression and other factors in the relationship between cognitive decline and future IADL disability.
Previous studies focused on the effects of social interaction, lifestyle, and depression on cognitive function and/or IADL disability. However, the existing studies have rarely paid attention to multiple mediations of these factors on the association between cognitive function and IADL disability. In terms of finding the most effective intervention target and path to better improve the life independence of older adults, it would be critical to explore and examine whether there are differences in the contribution of the three modifiable factors on the cognition–disability association. The findings may be helpful to develop a coordinated intervention plan for the prevention of cognitive decline-induced IADL disability in older individuals.
Therefore, this study aimed to test the longitudinal relationship between cognitive function and IADL disability in older adults with respect to the three mediators of social interaction, lifestyle, and depression and compare the differences in the mediating effects between these three mediators. We hypothesized that superior cognitive function at baseline was associated with more social interaction, a healthier lifestyle, and a milder depressive status over a 6-year follow-up, resulting in better IADL performance.

2. Materials and Methods

2.1. Study Population

This study drew on data from the 2008 to 2014 Chinese Longitudinal Healthy Longevity Surveys (CLHLS) [22]. The CLHLS was a nationally representative and public dataset of older adults based on randomly selected counties and cities covering 22 out of 31 provinces in mainland China. The CLHLS was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-13074).
In this study, we used data over a 6-year period with 2 waves of assessments to conduct a prospective analysis. Participants were initially recruited in the 2008 survey (n = 16,954). After excluding individuals aged <65 years in the 2008 survey (n = 391), disability of IADL in the 2008 survey (n = 10,418), death, or loss to follow-up in the 2014 survey (n = 2791), missing baseline (2008) cognitive function (n = 1026), endpoint (2014) IADL (n = 171), social interaction (n = 15), lifestyle (n = 47) information, and other covariables (n = 47), a total of 1976 participants were included in this study (Figure 1).

2.2. Measurements

2.2.1. Cognitive Function

Cognitive function in the 2008 survey was assessed by the Chinese version of the Mini-Mental State Examination (MMSE) [22]. The measurement of cognitive function in the questionnaire involved five dimensions: orientation, registration, recall, attention and calculation, and language. Orientation was measured by asking the participants to recall the time of day, month, date, season, place, and food names. Registration was measured by asking the participants to repeat three words just heard, and recall was measured by asking the participants to repeat three words learned earlier. The dimensions of registration and recall were integrated into the dimension of episodic memory in this study [23]. Attention and calculation were measured by asking the participants to subtract 3 from 30 serially five times and draw a figure of overlapping pentagons. Language was measured by asking the participants to name the objects, repeat a sentence, and act according to the instructions. Scores ranged from 0 to 12 for orientation, and from 0 to 6 for the other three dimensions (episodic memory, attention and calculation, language). The total score of global cognitive function ranges from 0 to 30 [23], and a higher score indicates superior cognitive function. The Cronbach’s alpha coefficient of MMSE in this study was 0.761.

2.2.2. Instrumental Activities of Daily Living (IADL) Disability

Information on IADL disability drew on the data in the 2014 survey, including visiting neighbors, shopping, preparing meals, washing clothes, and taking public transportation. The participants were asked whether they could complete these tasks by themselves (1 = Yes, I can do it independently; 2 = Yes, but I need some help; 3 = No, I cannot do it) [24]. The total IADL disability score ranged from 5 to 15. The Cronbach’s alpha coefficient of IADL in this study was 0.906. In binary logistic regression analyses, the participants entered the IADL disability group if they reported needing help or inability to perform the task (i.e., a response of 2 or 3 was associated with needing help; a total score of more than 5 was considered IADL disability), for each one of the 5 IADL items. In SEM analyses, the levels of each IADL item (ranging from 1 to 3) were entered into the model. The higher the score of each item, the severer the disability regarding that IADL item.

2.2.3. Social Interaction

Information on social interaction drew on the data in the 2014 survey and was collected by asking the following question: “Do you now regularly perform the following activities? 1. Play cards and/or mahjong; 2. Organized social activities” [25] (5 = almost every day, 4 = once for a week, 3 = once for a month, 2 = sometimes, 1 = never). The total social interaction score ranged from 2 to 10.

2.2.4. Lifestyle

Information on lifestyle drew on the data in the 2014 survey. Lifestyle was constructed based on three factors: daily intake of fruit, daily intake of vegetables, and regular physical activity performance [26]. Information on fruit and vegetables was collected by asking the intake frequency of fruit and vegetables (1 = almost every day, or quite often, 0 = occasionally, or never). Information on regular physical activity was collected by asking “Do you do exercises regularly at present?” (1 = yes, 0 = no). The score of lifestyle rating ranged from 0 to 3.

2.2.5. Depressive Status

Information on the depressive status drew on the data in the 2014 survey and was collected by asking the participants the following questions: “1. Do you always look on the bright side of things? 2. Do you often feel fearful or anxious? 3. Do you often feel lonely and isolated? 4. Do you feel the older you get, the more useless you are? 5. Are you as happy as when you were younger?” [27] (1 = always, 2 = often, 3 = sometimes, 4 = seldom, 5 = never). Question 1 and 5 were reversely scored. The total depressive status score ranged from 5 to 25. The Cronbach’s alpha coefficient of the depressive status in this study was 0.713.

2.2.6. Covariates

As possible confounders, variables that have been demonstrated to be associated with cognitive function and IADL disability in previous studies [23,28], including sociodemographic factors and health-related variables available in the 2008 survey, were examined as covariates in this study. The sociodemographic variables (Table 1) included age, gender, education, regions of residence, marital status, occupation, household income, and medical costs per year. The health-related variables included alcohol consumption, smoking status, self-reported health status, self-reported quality of life, night sleep duration, self-reported sleep quality, body mass index (BMI), and chronic diseases (including self-report hypertension, diabetes, and heart disease).

2.3. Statistical Analyses

Statistical analyses were performed using SPSS version 18.0 statistical software (IBM Corp., Armonk, NY, USA). We compared the baseline characteristics between participants with and without endpoint (2014) IADL disability using chi-square tests and t-tests. Regression analyses were conducted to test the association among cognitive functions, IADL disability, and the three mediators (social interaction, lifestyle, depressive status) to ensure that these variables were pairwise correlated and that the hypothesis models were statistically separable, which is a prerequisite for mediating analyses with structural equation modeling (SEM).
Covariance-based SEM (CB-SEM) was conducted to explore the mediating role of social interaction, lifestyle, and depressive status in the association between baseline cognitive function and IADL disability over a 6-year follow-up in older adults using AMOS 22.0 (IBM Corp., Armonk, NY, USA). CB-SEM is a multivariate technique to test whether theoretical models are compatible with observed data [29]. In our model, cognitive function was the independent latent variable with four dimensions (orientation, episodic memory, attention/calculation, and language) of MMSE as manifest variables. IADL disability was the dependent endogenous latent variable with five items (visiting neighbors, shopping, preparing meals, washing clothes, and taking public transportation) as its manifest variables. Social interaction (indicated by leisure group activity and organized social activity), lifestyle (indicated by fruit and vegetable intake and exercise habits), and depressive status (manifested by the five indicators including optimistic thinking, fearful/anxious, lonely/isolated, feeling useless, happiness) were three endogenous latent mediators, assessed by each subitem of their measurements. Model fit was assessed using the ratio of χ2 and degree of freedom (CMIN/DF), the goodness-of-fit index (GFI), the adjusted goodness-of-fit index (AGFI), the comparative fit index (CFI), the normed fit index (NFI), and the root-mean-square error of approximation (RMSEA). We aimed at an adequate fit: CMIN/DF < 5.0, GFI ≥ 0.90, AGFI ≥ 0.90, CFI ≥ 0.90, NFI ≥ 0.90, RMSEA ≤ 0.08. A bootstrapping (5000 times) method was used to estimate the significance of the indirect effects of the mediators.

3. Results

Table 1 shows the baseline (wave 2008) characteristics of the study participants in the presence of IADL disability (wave 2014). A total of 1976 older adults were included in the study (Figure 1). Over a 6-year follow-up of 1976 older adults without IADL disability at baseline, 576 (29.1%) developed IADL disability. There were significant differences between the groups with and without IADL disability in age, gender, education, marital status, occupation, drinking and smoking at present (2008), and BMI (all p < 0.01; Table 1). In addition, compared to the participants without IADL disability, those who developed IADL disability were more likely to have less social interaction, a more severe depressive status, a worse lifestyle, and a poorer global and domain-specific cognitive function (all p < 0.05; Table 1).
The results of linear regression analyses (covariates included) showed that global cognitive function was significantly associated with the three candidate mediators of social interaction, depressive status, and lifestyle. The results of logistic regression analyses showed that global cognitive function was significantly associated with IADL disability (p = 0.023). The associations between IADL disability and social interaction (p < 0.001), depressive status (p < 0.001), and lifestyle (p = 0.017) were significant (on-line Supplementary Material).
Structural equation modeling was used in this study to analyze and compare the multiple mediating effects of the three potential mediators (social interaction, lifestyle, and depressive status) on the longitudinal association between cognitive function dimensions and IADL disability by obtaining coefficient estimates that reflected the degree of the relationship between the variables, including the direct effect, indirect effect, and total effect (i.e., the sum of the direct effect and the indirect effect). Before the mediator entered the model, the association between baseline cognitive function and IADL 6 years later was −0.19 (p < 0.001). After the three mediators entered the model, the direct effect of cognitive function on IADL (estimate = −0.007, 95%CI = [−0.124, −0.147], p = 0.885) was not statistically significant (Figure 2), indicating that the association linking cognitive function and IADL disability was completely mediated by social interaction, lifestyle, and depressive status. The fit indices of the SEM model were acceptable (Figure 2).
As shown in Table 2, the total effect of cognitive function on IADL disability was statistically significant, and the indirect effects of cognitive function on IADL disability through social interaction, lifestyle, and depressive status were −0.095 (95% CI = [−0.183, −0.007], p < 0.001), −0.086 (95% CI = [−0.168, −0.004], p < 0.001), and −0.017 (95% CI = [−0.032, −0.003], p = 0.003), respectively. Social interaction explained 46.3% of the variances in the association of cognitive function with IADL disability, lifestyle explained 42.0% of the variances, and depressive status explained 8.3% of the variances. In addition, the difference in the mediating effects between social interaction and depressive status, and that between lifestyle and depressive status were both significant (p < 0.001 and p = 0.013), and the mediating difference between lifestyle and social interaction was not significant. This suggested that social interaction and lifestyle factors (including vegetable and fruit intake and excise habits) could counteract and improve the effect of depression on the cognitive decline-induced operational disability. Furthermore, attention and calculation had the greatest contribution to the salient variable of cognitive function, followed by episodic memory, language, and orientation. Disability in shopping had the greatest contribution to IADL disability, followed by visiting neighbors, preparing meals, washing clothes, and taking public transportation.

4. Discussion

The study examined the association between baseline cognitive dimensions and IADL disability over a 6-year follow-up and the mediating roles of social interaction, depressive status, and lifestyle in this association. The results showed that cognitive function was associated with IADL disability 6 years later among older adults, and the association was completely mediated by social interaction, lifestyle, and depressive status.
In this study, 29.1% of the participants developed IADL disability in 6 years. A prospective study in Italy found that 14.3% of nondisabled participants developed IADL disability over 3 years [30], which is lower than the value found in our study. This might be because the 3-year follow-up period was shorter than that in our study. Kim’s study [31] in Korea also reported a lower incidence (21.6%) of IADL disability over a 10-year follow-up. This might be because, compared to our study, there were younger participants, fewer current smokers, and a higher proportion of good self-reported health status in Kim’s study, elements that have been shown to be significantly associated with improved IADL disability [32].
The finding on the negative association between cognitive function and IADL disability is consistent with those of other longitudinal studies [20,33,34], suggesting that more severe cognitive function was associated with an increased risk of IADL disability over the next few years among older adults without baseline IADL disability. Moreover, a previous study [33] indicated that limitations in executive function and episodic memory were important in predicting a greater risk for future functional decline and might cause difficulties in remembering appointments and shopping items, which contributed the most to the latent variable of IADL disability in our study. Moreover, a 6-year predictor study [35] found that the two behavioral variables of attention and memory were the only independent cognitive predictors of IDAL decline after multivariate analysis. In our study, attention and memory made the largest and second-largest contributions to the latent variables of cognitive function.
The results of this study showed that the cognitive function of older adults positively predicted their social interaction and lifestyle and negatively predicted their depressive status. Meanwhile, social interaction, depressive status, and lifestyle have significant effects on the IADL disability of older adults. This means that cognitive function negatively affected IADL disability by upregulating social interaction and lifestyle, as well as downregulating the depressive status. The endpoint social interaction and lifestyle contributed more indirect effects than the depressive status to the longitudinal association between cognition and IADL disability.
Older adults may receive higher social support and find companions to perform sport activities together through increased social interactions [36]. A review [37] revealed that social support was reliably associated with beneficial effects on the cardiovascular, endocrine, and immune systems. In addition, physical activity has been shown to be related to higher social support and closer friends; thus, those with increased social support may have a greater opportunity to exercise [38]. Moreover, emotional support from others is positively associated with intrinsic motivation for physical activity, suggesting that more emotional support from others will promote pleasure and enjoyment in physical activity, making people feel more motivated to exercise [39]. In this study, lifestyle was constructed based on three factors: daily intake of fruit, daily intake of vegetables, and regular exercise practice; we found positive effects of lifestyle on IADL, which is in line with previous findings that participants with regular exercise habits might be less prone to functional disability than those without the same habits [40], and frequent consumption of fruits and vegetables is beneficial to the physical functions of older adults, including IADL and grip strength [14].
On the other hand, social interaction could also mediate the cognitive function–IADL disability association through psychological access. Frequent social interactions may provide various emotional benefits through enjoyable and meaningful experiences, such as a sense of belonging and companionship [41], therefore improving both physical and mental health [42]. Second, social interactions may involve the performance of social roles, which promotes a sense of meaning and self-efficacy [43]. Low self-efficacy has been found to be predictive of functional decline among older adults [44]. A study [45] performed SEM on IADLs in China and found that social interactions such as hanging out and playing cards were shown to be significantly associated with IADLs.
The mechanism underlying the mediating role of depressive status in the cognitive function–IADL disability association may be linked to a lack of interest in all or most things [46]. Older individuals with more severe depressive symptoms reported more negative social interactions [47] and a lack of sense of belonging [48]. A review [49] including 66 studies showed that low social support, social activity restriction, and tense interpersonal relationships are all related to depression. Therefore, people with a depressive status were potentially less engaged in physical activities, which subsequently increased IADL disability [50]. Another explanation is linked to changes in brain function and structure. It has been reported that depression is related to impaired neuroplasticity of the medial prefrontal cortex and hippocampus; moreover, memory deficits have been hypothesized to be directly associated with neuroplasticity deficits in the hippocampus [51]. Previous studies [52] found that cognitive impairments, especially memory deficits, are evident in patients with depression and may reduce individuals’ capacity to finish IADLs, such as shopping and taking transportation. Second, the “vascular hypothesis” of depression proposes that cerebrovascular disease of the frontal lobe leads to emotional changes [53] and is associated with IADL disability [54]. Depression in older individuals was related to prefrontal subcortical circuit lesions, with the most common neuroimaging abnormalities being increased signal hyperintensity in the deep white matter of the frontal lobe and gray matter of the basal ganglia [55]; additionally, depression itself has been strongly associated with coronary and cerebrovascular events [56]. Hence, the mediating effect of depressive status may be linked to brain function and structural changes that lead to IADL disability.
This study has several limitations. First, the depressive status and IADL disability data were based on self-reported measures, which might have resulted in information bias. Second, the assessments of lifestyle, namely, exercise practice and food intake, lacked the specific intakes and duration. Third, a large proportion of data were excluded due to missing information, which might have caused potential biases in sampling. This study’s strength includes the fact that, with a longitudinal study design, the temporal ordering of the association between cognitive function and incident IADL can be validated [20] from the current study. In addition, the relatively large sample size made the results robust, with sufficient statistical power.
The current findings have important implications for clinical practice and intervention. The results of this study contribute to the current knowledge by providing evidence that improving social interaction, lifestyle, and depressive status may alleviate the negative effects of cognitive decline on IADL disability. Specifically, the current study found that, after adjustment for certain other demographic and health-related factors related to cognition and disability, the three influencing factors had a complete mediating effect on the cognition–IADL association, which explained the behavioral mechanism of cognitive decline-induced instrumental disability to a certain extent. Moreover, the current study found that social interaction and lifestyle contributed equally to the longitudinal association between cognitive decline and IADL disability, and their effects were significantly higher than the mediating effect of depression. Furthermore, we found that recreational social activities and organized social activities contributed the same to the social interaction factor in the model. As regards diet, daily vegetable intake, fruit intake, and exercise habits contributed equally to the lifestyle factor in the model; loneliness and anxiety/fear had the highest contribution to depression in the model. These findings provide data support for the formulation of rehabilitation care programs for instrumental disability caused by cognitive decline, that is, for the accurate design of intervention time, duration, and intensity of each factor in the joint intervention plan according to their contributions. Moreover, the period during which an older adult exhibits mild functional limitation but remains independent in IADLs may provide a critical window of opportunity to intervene. Fortunately, these mediators appear to be modifiable, suggesting opportunities for future interventions.

5. Conclusions

This retrospective longitudinal study showed that cognitive dimensions are associated with IADL disability in older adults, and the effect of cognitive function on IADL disability is completely mediated by social interaction, lifestyle, and depressive status. Further development of coordinated interventions aimed at keeping IADL independence through improving social interaction, depression, and lifestyle could be of value to improve the mental health of older adults and achieve successful aging.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijerph19074235/s1 https://osf.io/ms7rh/?viewonly=0c72f6ba6d9042f5af13091d4508aaf6, Table S1: Linear Regression Models and Logistic Regression Models Addressing the Mediating Effects of Social Interaction, Depression Status, and Lifestyle on the Relationship Between baseline Global Cognitive Function and IADL in 2014.

Author Contributions

Conceptualization, methodology, original draft writing, and review and editing writing, Q.L. and C.W.; data curation analysis, and visualization Q.L.; supervision and funding acquisition, C.W. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Key R&D Program of China [grant number 2020YFC2008803], the General project of National Social Science Foundation of China (grant number 21BGL229), and the Beijing Natural Science Foundation (grand number 7202086).

Institutional Review Board Statement

The CLHLS was conducted in accordance with the Declaration of Helsinki and approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-13074).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The raw data used in this study are freely available from the Chinese Longitudinal Healthy Longevity Survey (CLHLS; https://opendata.pku.edu.cn/dataset.xhtml?persistentId=doi%3A10.18170/DVN/WBO7LK, accessed on 30 June 2020).

Acknowledgments

The authors thank the Center for Healthy Aging and Development Studies at Peking University for providing the CLHLS data. CLHLS was supported by the U.S. National Institutes on Aging, China Natural Science Foundation, China Social Science Foundation, and United Nations Fund for Population Activities.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Zeng, Y.; Feng, Q.; Hesketh, T.; Christensen, K.; Vaupel, J.W. Survival, disabilities in activities of daily living, and physical and cognitive functioning among the oldest-old in China: A cohort study. Lancet 2017, 389, 1619–1629. [Google Scholar] [CrossRef]
  2. Stineman, M.G.; Xie, D.; Pan, Q.; Kurichi, J.E.; Saliba, D.; Streim, J. Activity of daily living staging, chronic health conditions, and perceived lack of home accessibility features for elderly people living in the community. J. Am. Geriatr. Soc. 2011, 59, 454–462. [Google Scholar] [CrossRef] [Green Version]
  3. Rosenthal, E.; Brennan, L.; Xie, S.; Hurtig, H.; Milber, J.; Weintraub, D.; Karlawish, J.; Siderowf, A. Association between cognition and function in patients with Parkinson disease with and without dementia. Mov. Disord. 2010, 25, 1170–1176. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Cahn-Weiner, D.A.; Farias, S.T.; Julian, L.; Harvey, D.J.; Kramer, J.H.; Reed, B.R.; Mungas, D.; Wetzel, M.; Chui, H. Cognitive and neuroimaging predictors of instrumental activities of daily living. J. Int. Neuropsychol. Soc. 2007, 13, 747–757. [Google Scholar] [CrossRef]
  5. Reppermund, S.; Brodaty, H.; Crawford, J.D.; Kochan, N.A.; Draper, B.; Slavin, M.J.; Trollor, J.N.; Sachdev, P.S. Impairment in instrumental activities of daily living with high cognitive demand is an early marker of mild cognitive impairment: The Sydney memory and ageing study. Psychol. Med. 2013, 43, 2437–2445. [Google Scholar] [CrossRef] [Green Version]
  6. Bailey, R.; Hillman, C.; Arent, S.; Petitpas, A. Physical activity as an investment in personal and social change: The Human Capital Model. J. Phys. Act. Health 2012, 9, 1053–1055. [Google Scholar] [CrossRef] [PubMed]
  7. Kotwal, A.A.; Kim, J.; Waite, L.; Dale, W. Social function and cognitive status: Results from a US nationally representative survey of older adults. J. Gen. Intern. Med. 2016, 31, 854–862. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Cohn-Schwartz, E. Pathways from social activities to cognitive functioning: The role of physical activity and mental health. Innov. Aging 2020, 4, igaa015. [Google Scholar] [CrossRef] [PubMed]
  9. Mendes de Leon, C.F.; Glass, T.A.; Berkman, L.F. Social engagement and disability in a community population of older adults: The New Haven EPESE. Am. J. Epidemiol. 2003, 157, 633–642. [Google Scholar] [CrossRef]
  10. Bauman, A.; Merom, D.; Bull, F.C.; Buchner, D.M.; Fiatarone Singh, M.A. Updating the evidence for physical activity: Summative reviews of the epidemiological evidence, prevalence, and interventions to promote “active aging”. Gerontologist 2016, 56 (Suppl. 2), S268–S280. [Google Scholar] [CrossRef] [PubMed]
  11. Aune, D.; Giovannucci, E.; Boffetta, P.; Fadnes, L.T.; Keum, N.; Norat, T.; Greenwood, D.C.; Riboli, E.; Vatten, L.J.; Tonstad, S. Fruit and vegetable intake and the risk of cardiovascular disease, total cancer and all-cause mortality-a systematic review and dose-response meta-analysis of prospective studies. Int. J. Epidemiol. 2017, 46, 1029–1056. [Google Scholar] [CrossRef] [PubMed]
  12. Ngandu, T.; Lehtisalo, J.; Solomon, A.; Levälahti, E.; Ahtiluoto, S.; Antikainen, R.; Bäckman, L.; Hänninen, T.; Jula, A.; Laatikainen, T.; et al. A 2 year multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive decline in at-risk elderly people (FINGER): A randomised controlled trial. Lancet 2015, 385, 2255–2263. [Google Scholar] [CrossRef]
  13. Ye, X.; Bhupathiraju, S.N.; Tucker, K.L. Variety in fruit and vegetable intake and cognitive function in middle-aged and older Puerto Rican adults. Br. J. Nutr. 2013, 109, 503–510. [Google Scholar] [CrossRef] [PubMed]
  14. Gehlich, K.H.; Beller, J.; Lange-Asschenfeldt, B.; Köcher, W.; Meinke, M.C.; Lademann, J. Consumption of fruits and vegetables: Improved physical health, mental health, physical functioning and cognitive health in older adults from 11 European countries. Aging Ment. Health 2020, 24, 634–641. [Google Scholar] [CrossRef] [PubMed]
  15. Dos Santos, V.R.; Gobbo, L.A. Occupational and leisure-time physical activity decreases the odds of disability in older adults: Prospective study. Eur. J. Sport Sci. 2021, 21, 927–934. [Google Scholar] [CrossRef]
  16. Yu, J.; Lim, H.-Y.; Abdullah, F.N.d.o.M.; Chan, H.-M.; Mahendran, R.; Ho, R.; Kua, E.-H.; Power, M.J.; Feng, L. Directional associations between memory impairment and depressive symptoms: Data from a longitudinal sample and meta-analysis. Psychol. Med. 2018, 48, 1664–1672. [Google Scholar] [CrossRef] [PubMed]
  17. Wassink-Vossen, S.; Collard, R.M.; Oude Voshaar, R.C.; Comijs, H.C.; de Vocht, H.M.; Naarding, P. Physical (in)activity and depression in older people. J. Affect. Disord. 2014, 161, 65–72. [Google Scholar] [CrossRef]
  18. Van Gool, C.H.; Kempen, G.I.J.M.; Penninx, B.W.J.H.; Deeg, D.J.H.; Beekman, A.T.F.; van Eijk, J.T.M. Relationship between changes in depressive symptoms and unhealthy lifestyles in late middle aged and older persons: Results from the longitudinal aging study Amsterdam. Age Ageing 2003, 32, 81–87. [Google Scholar] [CrossRef] [Green Version]
  19. Zivin, K.; Llewellyn, D.J.; Lang, I.A.; Vijan, S.; Kabeto, M.U.; Miller, E.M.; Langa, K.M. Depression among older adults in the United States and England. Am. J. Geriatr. Psychiatry 2010, 18, 1036–1044. [Google Scholar] [CrossRef] [Green Version]
  20. Wu, C. The mediating and moderating effects of depressive symptoms on the prospective association between cognitive function and activities of daily living disability in older adults. Arch. Gerontol. Geriatr. 2021, 96, 104480. [Google Scholar] [CrossRef]
  21. Gong, B.; Wu, C. The mediating and moderating effects of depression on the relationship between cognitive function and difficulty in activities of daily living among postmenopausal women. Menopause 2021, 28, 667–677. [Google Scholar] [CrossRef] [PubMed]
  22. Zeng, Y.; Vaupel, J.; Xiao, Z.; Liu, Y.; Zhang, C. Chinese Longitudinal Healthy Longevity Survey (CLHLS), 1998–2014; Inter-university Consortium for Political and Social Research: Ann Arbor, MI, USA, 2017. [Google Scholar] [CrossRef]
  23. Zhang, Q.; Wu, Y.; Han, T.; Liu, E. Changes in cognitive function and risk factors for cognitive impairment of the elderly in China: 2005-2014. Int. J. Environ. Res. Public Health 2019, 16, 2847. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Zhang, Y.; Xiong, Y.; Yu, Q.; Shen, S.; Chen, L.; Lei, X. The activity of daily living (ADL) subgroups and health impairment among Chinese elderly: A latent profile analysis. BMC Geriatr. 2021, 21, 30. [Google Scholar] [CrossRef] [PubMed]
  25. Deng, Q.; Liu, W. Physical exercise, social interaction, access to care, and community service: Mediators in the relationship between socioeconomic status and health among older patients with diabetes. Front Public Health 2020, 8, 589742. [Google Scholar] [CrossRef]
  26. Shi, Z.; Zhang, T.; Byles, J.; Martin, S.; Avery, J.C.; Taylor, A.W. Food habits, lifestyle factors and mortality among oldest old Chinese: The Chinese Longitudinal Healthy Longevity Survey (CLHLS). Nutrients 2015, 7, 7562–7579. [Google Scholar] [CrossRef] [Green Version]
  27. Shen, K.; Zhang, B.; Feng, Q. Association between tea consumption and depressive symptom among Chinese older adults. BMC Geriatr. 2019, 19, 246. [Google Scholar] [CrossRef] [Green Version]
  28. Zhang, H.; Wang, Z.H.; Wang, L.M.; Qi, S.G.; Li, Z.X. Study on activities of daily living disability in community-dwelling older adults in China. Chin. J. Epidemiol. 2019, 40, 266–271. [Google Scholar] [CrossRef]
  29. Riou, J.; Guyon, H.; Falissard, B. An introduction to the partial least squares approach to structural equation modelling: A method for exploratory psychiatric research. Int. J. Methods Psychiatr Res. 2016, 25, 220–231. [Google Scholar] [CrossRef]
  30. Balzi, D.; Lauretani, F.; Barchielli, A.; Ferrucci, L.; Bandinelli, S.; Buiatti, E.; Milaneschi, Y.; Guralnik, J.M. Risk factors for disability in older persons over 3-year follow-up. Age Ageing 2010, 39, 92–98. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  31. Kim, S.; Kim, M.; Han, D. Incidence rates of disability and its associated factors among Korean community-dwelling older adults. Iran. J. Public Health 2020, 49, 1718–1726. [Google Scholar] [CrossRef]
  32. Connolly, D.; Garvey, J.; McKee, G. Factors associated with ADL/IADL disability in community dwelling older adults in the Irish longitudinal study on ageing (TILDA). Disabil. Rehabil. 2017, 39, 809–816. [Google Scholar] [CrossRef]
  33. Lau, K.M.; Parikh, M.; Harvey, D.J.; Huang, C.J.; Farias, S.T. Early cognitively based functional limitations predict loss of independence in instrumental activities of daily living in older adults. J. Int. Neuropsychol. Soc. 2015, 21, 688–698. [Google Scholar] [CrossRef] [Green Version]
  34. Diem, S.J.; Lui, L.-Y.; Langsetmo, L.; Taylor, B.; Cawthon, P.M.; Cauley, J.A.; Ensrud, K.E.; Study of Osteoporotic, F. Effects of mobility and cognition on maintenance of independence and survival among women in late life. J. Gerontol. A Biol. Sci. Med. Sci. 2018, 73, 1251–1257. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Bennett, H.P.; Corbett, A.J.; Gaden, S.; Grayson, D.A.; Kril, J.J.; Broe, G.A. Subcortical vascular disease and functional decline: A 6-year predictor study. J. Am. Geriatr. Soc. 2002, 50, 1969–1977. [Google Scholar] [CrossRef] [PubMed]
  36. Chan, E.; Procter-Gray, E.; Churchill, L.; Cheng, J.; Siden, R.; Aguirre, A.; Li, W. Associations among living alone, social support and social activity in older adults. AIMS Public Health 2020, 7, 521–534. [Google Scholar] [CrossRef] [PubMed]
  37. Uchino, B.N.; Cacioppo, J.T.; Kiecolt-Glaser, J.K. The relationship between social support and physiological processes: A review with emphasis on underlying mechanisms and implications for health. Psychol. Bull. 1996, 119, 488–531. [Google Scholar] [CrossRef] [PubMed]
  38. Lindsay Smith, G.; Banting, L.; Eime, R.; O’Sullivan, G.; van Uffelen, J.G.Z. The association between social support and physical activity in older adults: A systematic review. Int. J. Behav. Nutr. Phys. Act. 2017, 14, 56. [Google Scholar] [CrossRef] [PubMed]
  39. McNeill, L.H.; Wyrwich, K.W.; Brownson, R.C.; Clark, E.M.; Kreuter, M.W. Individual, social environmental, and physical environmental influences on physical activity among black and white adults: A structural equation analysis. Ann. Behav. Med. 2006, 31, 36–44. [Google Scholar] [CrossRef]
  40. Kanamori, S.; Kai, Y.; Kondo, K.; Hirai, H.; Ichida, Y.; Suzuki, K.; Kawachi, I. Participation in sports organizations and the prevention of functional disability in older Japanese: The AGES Cohort Study. PLoS ONE 2012, 7, e51061. [Google Scholar] [CrossRef]
  41. Prieto-Flores, M.-E.; Fernandez-Mayoralas, G.; Forjaz, M.J.; Rojo-Perez, F.; Martinez-Martin, P. Residential satisfaction, sense of belonging and loneliness among older adults living in the community and in care facilities. Health Place 2011, 17, 1183–1190. [Google Scholar] [CrossRef]
  42. Thoits, P.A. Mechanisms linking social ties and support to physical and mental health. J. Health Soc. Behav. 2011, 52, 145–161. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Glass, T.A.; de Leon, C.M.; Marottoli, R.A.; Berkman, L.F. Population based study of social and productive activities as predictors of survival among elderly Americans. BMJ 1999, 319, 478–483. [Google Scholar] [CrossRef] [Green Version]
  44. Mendes de Leon, C.F.; Seeman, T.E.; Baker, D.I.; Richardson, E.D.; Tinetti, M.E. Self-efficacy, physical decline, and change in functioning in community-living elders: A prospective study. J. Gerontol. B Psychol. Sci. Soc. Sci. 1996, 51, S183–S190. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Wang, B.; Wu, Y.; Zhang, T.; Han, J.; Yu, L.; Sun, W. Effect of physical activity on independent living ability among community-dwelling elderly in urban areas of Liaoning Province in China: A population-based study. BMJ Open 2019, 9, e023543. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Rosenström, T.; Jokela, M. Reconsidering the definition of major depression based on collaborative psychiatric epidemiology surveys. J. Affect Disord. 2017, 207, 38–46. [Google Scholar] [CrossRef] [Green Version]
  47. Steger, M.F.; Kashdan, T.B. Depression and everyday social activity, belonging, and well-being. J. Couns. Psychol. 2009, 56, 289–300. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  48. Hagerty, B.M.; Williams, R.A.; Coyne, J.C.; Early, M.R. Sense of belonging and indicators of social and psychological functioning. Arch. Psychiatr. Nurs. 1996, 10, 235–244. [Google Scholar] [CrossRef]
  49. Lenze, E.J.; Rogers, J.C.; Martire, L.M.; Mulsant, B.H.; Rollman, B.L.; Dew, M.A.; Schulz, R.; Reynolds, C.F., 3rd. The association of late-life depression and anxiety with physical disability: A review of the literature and prospectus for future research. Am. J. Geriatr. Psychiatry 2001, 9, 113–135. [Google Scholar] [CrossRef]
  50. Boisgontier, M.P.; Orsholits, D.; von Arx, M.; Sieber, S.; Miller, M.W.; Courvoisier, D.; Iversen, M.D.; Cullati, S.; Cheval, B. Adverse childhood experiences, depressive symptoms, functional dependence, and physical activity: A moderated mediation model. J. Phys. Act. Health 2020, 17, 790–799. [Google Scholar] [CrossRef]
  51. McEwen, B.S.; Sapolsky, R.M. Stress and cognitive function. Curr. Opin. Neurobiol. 1995, 5, 205–216. [Google Scholar] [CrossRef]
  52. Price, R.B.; Duman, R. Neuroplasticity in cognitive and psychological mechanisms of depression: An integrative model. Mol. Psychiatry 2020, 25, 530–543. [Google Scholar] [CrossRef] [PubMed]
  53. Schillerstrom, J.E.; Royall, D.R.; Palmer, R.F. Depression, disability and intermediate pathways: A review of longitudinal studies in elders. J. Geriatr. Psychiatry Neurol. 2008, 21, 183–197. [Google Scholar] [CrossRef] [PubMed]
  54. Jackson, C.A.; Jones, M.; Tooth, L.; Mishra, G.D.; Byles, J.; Dobson, A. Multimorbidity patterns are differentially associated with functional ability and decline in a longitudinal cohort of older women. Age Ageing 2015, 44, 810–816. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  55. de Groot, J.C.; de Leeuw, F.E.; Oudkerk, M.; Hofman, A.; Jolles, J.; Breteler, M.M. Cerebral white matter lesions and depressive symptoms in elderly adults. Arch. Gen. Psychiatry 2000, 57, 1071–1076. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Hare, D.L.; Toukhsati, S.R.; Johansson, P.; Jaarsma, T. Depression and cardiovascular disease: A clinical review. Eur. Heart J. 2013, 35, 1365–1372. [Google Scholar] [CrossRef] [PubMed] [Green Version]
Figure 1. Flowchart of inclusion and exclusion of participants in the study.
Figure 1. Flowchart of inclusion and exclusion of participants in the study.
Ijerph 19 04235 g001
Figure 2. Structural equation modeling of the association between cognitive function and IADL disability. (A) Structural equation modeling of cognitive function and IADL disability. (B) Structural equation modeling of the mediating effects of social interactions, depressive status, and lifestyle on the cognitive function–IADL disability association. Path coefficients are presented with standardized estimates. ** p < 0.01; *** p < 0.001. AGFI = adjusted goodness-of-fit index; CFI = comparative fit index; CMIN/DF = ratio of Chi-square and degree of freedom; GFI = goodness-of-fit index; NFI = normed fit index; RMSEA = root-mean-square error of the approximation; SA, social activity.
Figure 2. Structural equation modeling of the association between cognitive function and IADL disability. (A) Structural equation modeling of cognitive function and IADL disability. (B) Structural equation modeling of the mediating effects of social interactions, depressive status, and lifestyle on the cognitive function–IADL disability association. Path coefficients are presented with standardized estimates. ** p < 0.01; *** p < 0.001. AGFI = adjusted goodness-of-fit index; CFI = comparative fit index; CMIN/DF = ratio of Chi-square and degree of freedom; GFI = goodness-of-fit index; NFI = normed fit index; RMSEA = root-mean-square error of the approximation; SA, social activity.
Ijerph 19 04235 g002
Table 1. Participants’ baseline (2008) characteristics according to IADL disability in 2014, from Chinese Longitudinal Healthy Longevity Surveys (CLHLS).
Table 1. Participants’ baseline (2008) characteristics according to IADL disability in 2014, from Chinese Longitudinal Healthy Longevity Surveys (CLHLS).
CharacteristicTotalWithout IADL DisabilityWith IADL DisabilityStatistics
(n = 1976)(n = 1400)(n = 576)t2p-Value
Sociodemographic Variables
Age, year (SD)74.3 (7.4)72.4 (6.2)79.0 (7.9)−17.896<0.001
Gender, n (%) 20.212<0.001
 Male1225 (62.0)912 (46.2)313 (15.8)
 Female751 (38.2)488 (24.7)263 (13.3)
Education, year (SD)4.0 (4.0)4.5 (4.1)2.9 (3.5)8.630<0.001
Region of residence, n (%) 0.0390.844
 Urban255 (12.9)182 (9.2)73 (3.7)
 Rural1721 (87.1)1218 (61.6)503 (25.5)
Marital status, n (%) 49.416<0.001
 Married and living with a spouse1295 (65.5)985 (49.8)310 (15.7)
 Others681 (34.5)415 (21.0)266 (13.5)
Occupation, n (%) 9.3460.009
 Manual worker1633 (82.6)1136 (57.5)497 (25.2)
 Nonmanual worker295 (14.9)231 (11.7)64 (3.2)
 Others48 (2.4)33 (1.7)15 (0.8)
HI/year, thousand yuan (SD)19.5 (22.2)19.4 (21.7)19.6 (23.5)−0.1690.866
MC/year, thousand yuan (SD)3.4 (13.5)3.4 (13.8)3.2 (12.7)0.3410.733
Social interaction (SD)3.3 (1.9)3.6 (2.0)2.6 (1.3)12.141<0.001
 Play cards and/or mah-jong1.8 (1.4)2.0 (1.5)1.4 (1.0)9.999<0.001
 Organized social activities1.5 (1.1)1.6 (1.2)1.3 (0.8)7.595<0.001
Health-Related Variables
Drinking at present (2008), n (%)545 (27.6)417 (21.1)128 (6.5)11.6880.001
Smoking at present (2008), n (%)572 (28.9)443 (22.4)129 (6.5)16.966<0.001
Exercise at present (2008), n (%)891 (45.1)628 (31.8)263 (13.3)0.1060.745
Self-reported Health, n (%) 0.1710.918
 Good1177 (59.6)838 (42.4)339 (17.2)
 Fair607 (30.7)427 (21.6)180 (9.1)
 Poor192 (9.7)135 (6.8)57 (2.9)
Self-reported quality of life, n (%) 5.5450.063
 Good1200 (60.7)831 (42.1)369 (18.7)
 Fair697 (35.3)506 (25.6)191 (9.7)
 Poor79 (4.0)63 (3.2)16 (0.8)
Night sleep duration, hour (SD)7.6 (1.9)7.5 (1.8)7.7 (1.9)−1.8660.062
Self-reported sleep quality, n (%) 0.6650.717
 Good1389 (70.3)978 (49.5)411 (20.8)
 Fair393 (19.9)285 (14.4)108 (5.5)
 Poor194 (9.8)137 (6.9)57 (2.9)
Body mass index, kg/m2 (SD)21.8 (3.6)21.9 (3.6)21.5 (3.5)2.7060.007
Chronic diseases, n (%)
 Hypertension457 (23.1)308 (15.6)149 (7.5)3.4340.064
 Diabetes66 (3.3)50 (2.5)16 (0.8)0.7960.372
 Heart disease208 (10.5)144 (7.3)64 (3.2)0.2950.587
Global cognitive function (SD)28.4 (2.3)28.6 (2.0)27.8 (2.7)6.553<0.001
 Orientation11.6 (1.0)11.7 (0.9)11.5 (1.1)2.8590.004
 Memory5.5 (1.0)5.6 (0.9)5.4 (1.2)4.288<0.001
 Attention and calculation5.4 (1.0)5.5 (0.9)5.1 (1.2)7.034<0.001
 Language5.9 (0.4)5.9 (0.4)5.9 (0.4)2.3060.021
Depressive status (SD)10.8 (3.4)10.3 (3.3)11.9 (3.5)−9.690<0.001
Lifestyle, score (SD)1.8 (0.8)1.9 (0.8)1.7 (0.8)6.041<0.001
 Fruit, n (%)902 (45.6)655 (33.1)247 (12.5)2.5070.113
 Vegetable, n (%)1838 (93.0)1221 (67.4)507 (25.7)31.229<0.001
 Exercise, n (%)880 (44.5)680 (34.4)200 (10.1)31.688<0.001
Notes: HI, household income; IADL = instrumental activities of daily living; MC, medical costs; SD = standard deviation.
Table 2. Mediators in the longitudinal association between cognitive function and the severity of IADL disability.
Table 2. Mediators in the longitudinal association between cognitive function and the severity of IADL disability.
VariablesStandardized
Estimate
Standard
Errors
LLCIULCIpRatio
Direct effect on IADL
 Cognitive function−0.0070.072−0.1240.1470.885-
Indirect effect
 Social interaction (a1 × b1)−0.0950.045−0.183−0.007<0.00146.34%
 Lifestyle (a2 × b2)−0.0860.042−0.168−0.004<0.00141.95%
 Depressive status (a3 × b3)−0.0170.007−0.032−0.0030.0038.29%
Total effect−0.2050.039−0.282−0.128<0.001-
Difference of indirect effects
 a1 × b1 − 2 × b2−0.0080.055−0.1150.0980.875-
 a1 × b1 − a3 × b3−0.0770.043−0.1620.007<0.001-
 a2 × b2 − a3 ×b3−0.0690.041−0.1490.0110.013-
Notes: IADL = instrumental activities of daily living; LLCI = lower limit of 95% confidence interval; ULCI = upper limit of 95% confidence interval.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Li, Q.; Wu, C. Social Interaction, Lifestyle, and Depressive Status: Mediators in the Longitudinal Relationship between Cognitive Function and Instrumental Activities of Daily Living Disability among Older Adults. Int. J. Environ. Res. Public Health 2022, 19, 4235. https://doi.org/10.3390/ijerph19074235

AMA Style

Li Q, Wu C. Social Interaction, Lifestyle, and Depressive Status: Mediators in the Longitudinal Relationship between Cognitive Function and Instrumental Activities of Daily Living Disability among Older Adults. International Journal of Environmental Research and Public Health. 2022; 19(7):4235. https://doi.org/10.3390/ijerph19074235

Chicago/Turabian Style

Li, Qiuhong, and Chao Wu. 2022. "Social Interaction, Lifestyle, and Depressive Status: Mediators in the Longitudinal Relationship between Cognitive Function and Instrumental Activities of Daily Living Disability among Older Adults" International Journal of Environmental Research and Public Health 19, no. 7: 4235. https://doi.org/10.3390/ijerph19074235

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop