C. Peng1, J.A. Burr1, Y. Yuan2, K.L. Lapane2
1. Department of Gerontology, John W. McCormack Graduate School of Public and Global Studies, University of Massachusetts Boston, 100 Morrissey Boulevard, Boston, MA, USA;
2. Department of Population and Quantitative Health Sciences, University of Massachusetts Chan Medical School, Worcester, MA, USA.
Corresponding Author: Changmin Peng, Department of Gerontology, John W. McCormack Graduate School of Public and Global Studies, University of Massachusetts Boston, 100 Morrissey Boulevard, Boston, MA, 02125–3393,USA, email@example.com
J Frailty Aging 2023;in press
Published online January 18, 2023, http://dx.doi.org/10.14283/jfa.2023.1
BACKGROUND: Physical frailty and cognitive impairment are prevalent globally, particularly in China, which is experiencing an unprecedented aging of its large population.
OBJECTIVES: Examine the association between physical frailty and the level and rate of change of cognitive function, globally and by domain, among community-dwelling Chinese older adults, and quantify the mediation effects from activities of daily living (ADL) limitations and depressive symptoms.
SETTING: China Health and Retirement Longitudinal Study (2011-2018).
PARTICIPANTS: 5,431 eligible adults aged ≥ 60 years with valid information on physical frailty.
MEASUREMENTS: Physical frailty, cognitive function, ADL limitations, and depressive symptoms were respectively assessed by frailty phenotypes, the Telephone Interview for Cognitive Status (episodic memory, executive function, and orientation), performance in six daily tasks, and the eight-item Center for Epidemiological Studies Depression Scale. Latent growth curve models were used to address the objectives.
RESULTS: Compared to adults who were non-frail, those who were pre-frail (β = −0.06) and frail (β = −0.13) reported significantly worse global cognitive function and episodic memory (pre-frail: β = −0.05; frail: β = −0.14), executive function (pre-frail: β = −0.04, frail: β = −0.10), and orientation (pre-frail: β = −0.06; frail: β = −0.07) at baseline; those who were frail were more likely to experience faster decline in global cognitive function (β = 0.12) and episodic memory (β = 0.08). ADL limitations (β = −0.07) and depressive symptoms (β = −0.14) significantly mediated the association between physical frailty and the level of cognitive function, but not its rate of decline.
CONCLUSIONS: Intervention strategies that help maintain cognitive function may benefit from early screening and assessment of physical frailty. For pre-frail and frail older Chinese adults, programs designed to help improve or maintain activities of daily living and reduce number of depressive symptoms may contribute to better cognitive performance.
Key words: Cognitive ability, physical health, emotional well-being.
Estimates show that by 2050 China will have 366 million people aged ≥65 years with 10 million living with Alzheimer’s disease and related dementias (1, 2). Adequate cognitive function is associated with the ability to maintain independence and have a reasonable quality of life, and cognitive decline is associated with increased risk of cognitive impairment and dementia (3). Physical frailty, characterized by reduced physiological reserve and resilience to internal and external stressors, often leaves individuals at increased risks to adverse health outcomes, including hospitalization, disability, depressive disorders, cognitive impairment, and mortality (4–7).
Understanding the relationship between physical frailty and cognitive function could help identify those who are at higher risk for cognitive decline and allow for the development of interventions to prevent and manage cognitive health.
Cross-sectional studies have consistently demonstrated that being physically frail is associated with poorer cognitive performance (8–10). Physically frail older adults tend to participate in fewer social activities and have less contact with others, both of which are important for mental stimulation and brain health. However, findings from longitudinal studies of the relationship between physical frailty and cognitive function are mixed (9). Some studies suggest that older adults with more severe physical frailty were more likely to experience an onset of cognitive decline and had increased risk of cognitive impairment and incident dementia (11–13), while other studies failed to find an association between physical frailty and subsequent cognitive decline (14). In addition, some cross-sectional and a few longitudinal studies found that older adults who were physically pre-frail or frail performed worse across cognitive domains, including episodic memory, executive function, and orientation (8, 11, 15–17). Further, Boyle et al. (11) found that physical frailty was associated with the rate of decline in cognitive domains, but this association was not found in Bunce et al study (15). Thus, more research is needed to explore these relationships longitudinally, especially in the under-studied Chinese context (8, 9).
The mechanisms linking physical frailty and cognitive function and its domains remain elusive (8, 15, 18, 19), but disability and depression are plausible mechanisms in need of investigation. As suggested in previous empirical studies and systematic reviews, physical frailty accelerated with individuals’ functional decline, and compared to non-frail older adults, frail older adults are likely to have more limitations in their activities of daily living (ADL) or develop subsequent disability (6, 20–22). Physical frailty is a robust and independent risk factor for persistence of depression (23, 24), as demonstrated in research that found physical frailty was associated with a higher likelihood of incident depressed mood (25), increased odds of developing depressive symptoms (26), and subsequent greater levels of depression (20, 27). On the other hand, among older adults with ADL limitations (28) and depression (29, 30), associations with worse cognitive function and faster cognitive decline have been reported. It is likely that basic ADL may have a significant cognitive component that is an important predictor of cognitive function and decline (28), while depression may trigger a chain of events leading to cognitive deterioration; it is also possible that these conditions share etiological factors (15, 31). However, limited research has quantified the mediating effects of limitations in ADL and depressive symptoms on the relationship between physical frailty and cognitive function and its domains (15, 32).
To address these research gaps, this study examined the relationship between physical frailty and the level of and rate of change in global cognitive function and individual cognitive domains as well as the mediating effects of ADL limitations and depressive symptoms among Chinese adults aged ≥ 60 years. We hypothesized that older adults who were pre-frail or frail would have lower cognitive function at baseline and a faster rate of cognitive decline than their non-frail counterparts. We also hypothesized that older adults who were pre-frail or frail would have more ADL limitations and more depressive symptoms, which are in turn, associated with lower levels of cognitive function and faster decline in cognitive function and domains. Figure 1 shows the conceptual model guiding this study.
Covariates were controlled for outcome variables and mediators. Paths a and b represent the direct association between physical frailty and intercept and slope of cognitive function or domains, respectively. Paths c x e and c x h represents the indirect association between physical frailty and intercept and slope of cognitive function or domains through activities of daily living limitations. Paths d x f and d x g represents the indirect association between physical frailty and intercept and slope of cognitive function or domains through depressive symptoms.
Data Source and Study Sample
This study used four waves of the China Health and Retirement Longitudinal Study (CHARLS; 2011, 2013, 2015, 2018), a nationally representative survey that collected a wide range of information on demographic, social, and economic characteristics, along with measures of health and health behaviors among community-dwelling Chinese adults aged ≥ 45 years. The CHARLS recruited 17,708 eligible respondents in 2011 to participate in a panel study. Respondents were interviewed every two years. The inclusion criteria for the current study were as follows: (a) baseline survey participation (n = 17,708); (b) aged ≥ 60 years at baseline as information on physical slowness was only collected in this group (n = 7,290); and, (c) provided valid information on at least four components of physical frailty at baseline (n = 5,431; most of the excluded cases were due to survey protocols, as by design a random half sample of the respondents provided physical inactivity information. The study used public data; thus, the study is exempt from approval by an institutional review board.
Cognitive function. Respondents’ cognitive function was measured across each of the four waves using the Chinese version of the Telephone Interview for Cognition Status scale (TICS), including scores for episodic memory, executive function, orientation (33). Episodic memory was a summative score of immediate and delayed recall of ten Chinese nouns (range=0–20). Executive function was assessed with respondents’ performance on the serial sevens subtraction test over five trials (range = 0–5). Orientation was measured by asking respondents to name the date of the interview (month, day, year; range = 0–3), and to identify the day of the week (1 = correct; 0 = incorrect). Scores for all items were summed to represent respondents’ global cognitive function (range = 0–29).
Physical frailty. The most widely used physical frailty phenotype scale was employed to assess frailty (5, 20, 34). This scale included five components: weakness, slowness, shrinking, exhaustion, and inactivity at baseline. We followed the criteria adopted by Wu et al. (34). Weakness was assessed with a maximum handgrip strength for either hand equal to or less than 20% of the weighted population distribution (measured in a standing position), adjusting for sex and body mass index (BMI). Slowness was identified with the amount of time respondents took to walk 2.5 meters compared to the slowest 20% of the weighted population distribution. Shrinking was identified as either having a BMI of ≤18.5 kg/m2 or weight loss of five kilograms or more in the past year. Exhaustion was defined as a score of two or more on two items taken from Center for Epidemiological Studies Depression Scale (CESD; Chinese version), including “could not get going” and “felt everything was an effort.” Inactivity was identified as self-reported inability to walk for at least 10 minutes continuously in a typical week. Respondents without the presence of any components were considered “non-frail,” those with one or two frailty components were defined as “pre-frail,” and those with three or more frailty components were identified as “frail.”
Limitations in Activities of Daily Living. The number of ADL limitations was assessed by whether respondents self-reported they had difficulty in performing each of the six daily tasks at baseline, including dressing, bathing, eating, getting into/out of bed, using the toilet, and controlling urination (range = 0–6).
Depressive symptoms. The 10-item CESD index was used to assess respondents’ baseline depressive symptoms. Respondents were asked to report the frequency in the previous week that they: a) “were bothered by things,” b) “had trouble keeping concentration,” c) “felt depressed,” d) “felt everything was an effort,” e) “felt hopeful about the future,” f) “felt fearful,” g) “had restless sleep,” h) “were happy,” i) “felt lonely,” and j) “could not get going.” Each item was assessed on a four-point scale (0 = rarely or none of the time [<1 day], 1 = some or little of the time [1-2 days], 2 = occasionally or a moderate amount of the time [3-4 days], 3 = most or all of the time [5-7 days]). Items g and d were excluded because these were used to measure exhaustion (see above). Items e and h were reverse-coded to match the direction of the other items; the total score for the eight items was summed to identify a depressive symptoms index (range = 0–24; Cronbach’s alpha = 0.75).
Covariates. We controlled for the following baseline variables: age (in years), gender (1 = female, 0 = male), marital status (1 = married, 0 = other marital status), education (1 = no formal education/illiterate [reference group], 2 = less than elementary school, 3 = elementary school and above), per capita household consumption (log-transformed), worked for pay (1 = yes, 0 = no), and ever smoked (1 = yes, 0 = no). We also controlled for hukou registration status as an indicator of access to social and economic benefits for those with non-agricultural status (1 = agricultural, 0 = non-agricultural). Social participation was assessed by whether respondents participated in any social activities (e.g., interacted with friends, played games (e.g., mahjong, chess), attended sports or social clubs, participated in community organization or charity work, and attended educational course) in the past month (1 = yes, 0 = no).
We conducted descriptive analyses. Also, physical frailty group differences were examined using a chi-square test for categorical variables and ANOVA for continuous variables. Latent growth curve models within a structural equation modeling framework were estimated to examine the associations among physical frailty, ADL limitations, depressive symptoms, and cognitive function, adjusting for all covariates. We first examined the direct association between physical frailty and the level and rate of change of cognitive function. Next, we examined the indirect association of physical frailty on the level and rate of change of cognitive function by adding the number of ADL limitations and depressive symptoms to the model. The indirect associations were estimated by using the MODEL INDIRECT command in Mplus 7.4. Finally, we repeated the steps outlined above to examine whether ADL limitations and depressive symptoms mediated the association between physical frailty and cognitive domains. Tucker–Lewis index (TLI) and comparative fit index (CFI) greater than 0.95, root mean square error of approximation (RMSEA) lower than 0.08, and standardized root mean square residual (SRMR) lower than 0.05 were used to determine the goodness of model fit.
To address the potential bias from missing data, multiple imputation was performed, an approach accounting for uncertainties associated with missing cases and providing unbiased and valid estimates based on information from available data (35). A total of 15 imputed datasets were generated based on demographic characteristics, health behaviors, social engagement, and health conditions to improve the measurement quality of the imputed values. In addition, we performed a sensitivity analysis for the growth curve models using a sample without multiple imputation for missing data to compare results across these two approaches. The results from these two samples were similar.
Study Sample Characteristics
Descriptive characteristics of the study sample are presented in Table 1. For the full sample, 32.5% were physically robust, 60.9% were pre-frail, and 6.7% were frail. The average cognitive function score at baseline was 13.08. On average, respondents reported 0.47 ADL limitations and had a score of 7.30 on the depressive symptoms index. The mean age of the respondents was 67.84 years, and the average per capita household consumption was 6,478 yuan (seven yuan equals approximately one US dollar). Approximately one-half of the sample was female, 76.1% were married, around 41.7% had at least an elementary school education, 79.3% reported rural hukou status, 50.2% were working for pay, 43.1% reported ever smoking tobacco, and 45.3% participated in social activities.
Notes. N = 5,431. ADL = activities of daily living; a. Chi-square test for categorical variables and ANOVA for continuous variables. *p < .05, **p < .01, ***p < .001.
Compared to pre-frail and frail older adults, non-frail older adults reported better cognitive function (F = 39.95, p < .001), episodic memory (F = 41.66, p < .001), executive function (F = 40.31, p < .001), orientation (F = 30.63, p < .001), fewer ADL limitations (F = 131.04, p < .01) and had a lower score on the depressive symptoms index (F = 184.82, p < .001). Frailty group differences across respondents’ demographic characteristics were observed: participants who were frail tended to be older, not married, without formal education, with agricultural hukou status, were not working, and they had lower per capita household consumption than those who were not frail.
Direct and Indirect Associations between Physical Frailty and Cognitive Function
We tested the direct association between physical frailty and cognitive function as shown in Table 2 Model 1. The values of the fit indices showed that the model fit the data well: χ2(29) = 286.14, CFI = 0.98, TLI = 0.96, RMSEA = 0.04, and SRMR = 0.02. Older Chinese adults in the pre-frail group (β = −0.06, p < .001) and in the frail group (β = −0.13, p < .001) had worse cognitive function at baseline compared to the non-frail older adults, adjusting for all covariates. In addition, frail older adults reported faster cognitive decline than non-frail older adults (β = 0.12, p = .001). We did not find a significant statistical difference between pre-frail and non-frail older adults with respect to the rate of cognitive decline (β = −0.01, p = .86).
We further examined the indirect association of physical frailty on cognitive function through limitations in ADL and depressive symptoms as shown in Table 2 Model 2 This model also fit the data well: χ2 = 339.20, CFI = 0.98, TLI = 0.95, RMSEA = 0.04, and SRMR = 0.02. Pre-frail and frail older Chinese adults reported more ADL limitations (pre-frail: β = 0.09, p < .001; frail: β = 0.20, p < .001) and had a higher score on the depressive symptoms index (pre-frail: β = 0.15, p < .001; frail: β = 0.25, p < .001) than non-frail older Chinese adults (not shown in table). In addition, ADL limitations (β = −0.07, p < .001) and the depressive symptoms index (β = −0.14, p < .001) were associated with level of cognitive function; however, these measures were not significantly associated with the rate of change in cognitive function (ADL limitations: β = 0.06, p = .09; depressive symptoms: β = 0.03, p = .46).
Notes. N = 5,431. Standardized coefficients are reported. ADL = activities of daily living; a. Reference group = non-frail. b. Reference group = no formal education. c. Log transformed. *p < .05, **p<0.01, ***p<0.001.
Direct and Indirect Associations between Physical Frailty and Cognitive Domains
We also explored the relationships between physical frailty and three domains of cognition to detect which of these are likely to be associated with physical frailty. As shown Table 3 Model 1, compared to non-frail older adults, pre-frail and frail older adults were more likely to report lower levels of episodic memory (pre-frail: β = −0.05, p = .01; frail: β = −0.14, p < .001), executive function (pre-frail: β = −0.04, p = .01, frail: β = −0.10, p < .001), and orientation (pre-frail: β = −0.06, p < .001; frail: β = −0.07, p<.001). In addition, frail but not pre-frail older adults were more likely to have faster decline in episodic memory than non-frail older adults (pre-frail: β = −0.03, p = .30; frail: β = 0.08, p = .01). We did not find significant differences between non-frail and pre-frail or frail older adults in terms of the rate of change in executive function (pre-frail: β = −0.04, p = .01; frail: β = −0.10, p < .001) and orientation (pre-frail: β = 0.04, p = .45; frail: β = −0.04, p = .50).
Notes. N = 5,431. Standardized coefficients are reported. ADL = activities of daily living; a. Reference group = non-frail. bReference group = no formal education. cLog transformed. Covariates were controlled but not shown in table. All models fit the data well; *p < .05, **p<0.01, ***p<0.001.
In Table 3 Model 2, we examined whether ADL limitations and depressive symptoms mediated the association between physical frailty and cognitive domains. We found that pre-frail and frail older adults were more likely to report more ADL limitations (pre-frail: β = 0.09, p < .001; frail: β = 0.20, p < .001) and depressive symptoms (pre-frail: β = 0.15, p < .001; frail: β = 0.25, p < .001), and these measures were further associated with lower levels of episodic memory (ADL limitations: β = −0.06, p = .00; depressive symptoms: β = −0.14, p < .001), but not the rate of change in episodic memory (ADL limitations: β = 0.03, p = .36; depressive symptoms: β = 0.02, p = .55). Regarding executive function, pre-frailty or frailty was related to ADL limitations and depressive symptoms, however, only depressive symptoms was related to the level of executive function (ADL limitations: β = −0.03, p = .11; depressive symptoms: β = −0.08, p < .001). In terms of orientation, pre-frailty or frailty was associated with more ADL limitations and depressive symptoms, and number of ADL limitations was further associated with the level and rate of change in orientation (orientation level: β = −0.10, p < .001; orientation change: β = 0.15, p = .01), while number of depressive symptoms was only associated with the level of but not the rate of change in orientation (orientation level: β = −0.14, p < .001; orientation change: β = 0.01, p = .84).
Total, Direct, and Indirect Association between Physical Frailty and Cognitive Function and Domains
Results for the total, direct, and indirect associations of physical frailty with cognitive function are presented in Table 4. The total association for pre-frail older Chinese adults and frail older Chinese adults for the level of cognitive function was −0.06 (p < .001) and −0.13 (p < .001), respectively. The mediating effects of ADL limitations and depressive symptoms for the association for those older Chinese adults in the pre-frail and frail groups with the level of cognitive function were −0.03 (p < .001) and −0.05 (p < .001), respectively. The total indirect associations between physical frailty and the rate of change of cognitive function through ADL limitations and depressive symptoms were not statistically significant (pre-frail: β = 0.01, p = .13; frail: β = 0.02, p = .10). The total, direct, and indirect associations of physical frailty with cognitive domains were also summarized in Table 4.
Notes. N = 5,431. Standardized coefficients are reported. ADL = activities of daily living. a. Reference group = non-frail; *p < .05, **p<0.01, ***p<0.001.
Using a national sample of community-dwelling Chinese older adults aged ≥60 years, we found that compared to non-frail older adults, older adults who were pre-frail and frail reported worse baseline cognitive function and frail older adults reported a faster rate of cognitive decline over the eight-year follow-up period. Further, findings from growth curve models indicated that the observed association between physical frailty and the level of cognitive function may in part be attributed to limitations in ADL and depressive symptoms. In terms of physical frailty and several specific cognitive domains, our study results suggested that physical frailty was related to all three cognitive domains at baseline and frail older adults showed significant decline in episodic memory than non-frail older adults. Furthermore, ADL limitations and depressive symptoms mediated the association between physical frailty and the level of episodic memory. ADL limitations mediated the association between physical frailty and the level of and rate of change in orientation, and depressive symptoms mediated the association between physical frailty and the level of executive function and orientation.
Physical Frailty and Cognitive Function and Cognitive Domains
The close relationship between physical frailty and cognitive impairment has been established in numerous studies across the globe (8–11), although underlying mechanisms remain unclear. The two conditions of physical frailty and cognitive function may share the same underlying pathophysiology, such as pathological changes to the brain (12, 19, 36), although biomedical evidence is still needed to confirm the presence of mutual biomarkers or biological pathways. Combined, physical frailty and cognitive function could also represent different aspects of the construct of “cognitive frailty,” defined as the co-occurrence of physical frailty and cognitive impairment in the absence of overt dementia or other neurological conditions (37). Our finding regarding a significant negative association between physical frailty and both global cognitive function and three cognitive domains (i.e., episodic memory, executive function, and orientation) at baseline was consistent with prior literature (8, 15–17, 38), and these findings contributed to the growing body of evidence about the association between physical frailty and cognitive function and cognitive domains in Chinese older adults.
In terms of the rate of decline in cognitive function, we found those who were frail appear to experience more rapid decline over time than those who were not frail in our study, although no significant differences were found between pre-frail and non-frail older adults. These findings were consistent with most prior research, where greater physical frailty was associated with a faster rate of cognitive decline (11–13). However, this was not consistent with the Drame et al. study (14), where physical frailty did not predict the subsequent cognitive decline. The non-significant finding of the Drame et al. study may be attributed to its study population [hospitalized older patients aged 75 years and above] and the instruments used to measure physical frailty [four frailty indexes]. In addition, a recent study conducted by Chu et al. (38) found that physical frailty was associated with baseline cognitive function but not with cognitive decline above and beyond its’ individual criteria. Future research should examine whether a physical frailty phenotype construct makes additional contributions to our understanding of the level of and rate of change in cognitive function, after adjusting for individual components in the Chinese population.
Consistent with finding reported by Boyle et al. (11), we found that, compared to non-frail older adults, frail older adults experienced a faster decline in episodic memory. However, this was not consistent with the Bunce et al. (15) study, where physical frailty was not associated with changes in episodic memory. The contrasting results may be due to differences in the measurement of episodic memory. Our study and Boyle et al. (11) study used immediate and delayed words recall tests to assess respondents’ memory function, while respondents in the Bunce et al. (15) study were asked to perform multiple tasks of word recall, along with name, face, address recall, and figure reproduction. Our study did not support an association between physical frailty and the decline in executive function and orientation. This may be due to that episodic memory is a more sensitive measure for detecting cognitive deterioration than other cognitive domains (39). Given that few longitudinal studies have examined the association between physical frailty and specific cognitive domains, more studies are needed to validate our results (40).
Previous longitudinal studies also examined various forms of incident neurocognitive disorders as the outcome of interest with mixed results. While some studies indicated physical frailty was a significant predictor of incident diagnosis of mild cognitive impairment (11) or dementia (18, 41, 42), others did not find such a positive association (43, 44). Indeed, physical frailty was only positively associated with future onset of mild cognitive impairment or dementia in the co-existence of impairment in cognitive function measured by Mini-Mental State Examination and/or the Isaacs Set Test at baseline (43, 44). In the context of our study, we focused on long-term decline in cognitive function measured by the TICS and did not examine if and to what extent physical frailty would predict incident clinical diagnosis of cognitive impairment, because such information was not available in the longitudinal version of the CHARLS. However, the most recent cross-sectional wave of the CHARLS (2018) included the Harmonized Cognitive and Dementia Assessment in China file for respondents aged 60 years or above, which can be used to ascertain the diagnoses of mild cognitive impairment and dementia upon further data validation, offering new opportunities for future studies to assess how physical frailty is associated with the incidence of these diagnoses.
Mechanisms Linking Physical Frailty with Cognitive Function and Cognitive Domains
Additional epidemiological evidence illustrated several other mechanisms that could explain how physical frailty is linked to cognitive function, such as through chronic inflammation, cardiovascular disease, hypertension, vascular diseases, psychological factors (e.g., mood), and social isolation (4, 9). Our study further contributed to the literature by showing that the association between physical frailty and level of cognitive function could be partly explained (mediated) by limitations in ADL and depressive symptoms. Compared to non-frail older Chinese adults, older Chinese adults who were pre-frail or frail reported more ADL limitations, and in turn, they had lower levels of cognitive function. It is likely physical frailty is associated a higher risk of physical disability and physiological distress due to activity restrictions (6, 8, 20, 24), and these conditions were found to be independently associated with cognitive function (28–30).With regards to depressive symptoms, the finding that depressive symptoms also mediated the association between physical frailty and cognitive performance is consistent with previous research that showed the positive relationship between physical frailty and depression (26, 45, 46), and that depression may serve as a significant factor for predicting cognitive function (47–49). Our study did not find significant mediating effects of ADL limitations and depressive symptoms for the association between physical frailty and rate of cognitive decline.
In terms of the indirect association between physical frailty and specific cognitive domains, we found that pre-frailty and frailty were associated with lower levels of episodic memory through ADL limitations and depressive symptoms pathways. In addition, pre-frailty or frailty was related to the level and rate of change in orientation through ADL limitations, while pre-frailty and frailty contributed to the level of executive function and orientation through depressive symptoms. As suggested by results from previous studies (15, 32), future research investigating the underlying mechanisms for the association between physical frailty and cognitive function and its domains would help improve our understanding of these complex relationships, including focus on working memory, sematic memory, processing speed, visuospatial ability. To the best of our knowledge, this is the first study of its kind to quantify empirically the indirect association through ADL limitations and depressive symptoms between physical frailty and cognitive function and its domains in Chinese older adults. We suggested future research on samples of older adults from other developing countries should examine these possibilities to confirm our results in other contexts.
Implications for Intervention
As physical frailty was significantly associated with cognitive function, timely assessment, and early identification, along with effective intervention, may help older adults at risk (50). We suggest interventions aimed at slowing or even reversing the progression of physical frailty may help older adults to maintain cognitive function. Previous studies found multi-domain interventions, such as exercise and nutrition supplementation, can help improve body balance, muscle capacity and overall physical function and reduce levels of frailty in pre-frail and frail older adults (51, 52). In addition, lifestyle interventions, such as memory and reasoning training, nutrition counseling, and participation in physical activity, can reduce the risk of developing physical frailty (53). Dance interventions also have been shown to effectively reduce the presence and severity of frailty in older adults (54).
Given the mediating roles of limitations in ADL and depressive symptoms in the association between physical frailty and level of cognitive function and cognitive domains, these conditions can serve as additional intervention targets to disrupt the association between physical frailty and cognitive impairment. Interventions designed to improve or maintain older adults’ ability to perform ADL and reduce the number of depressive symptoms may help to slow the onset of cognitive decline. A recent systematic review found that strength and resistance exercise, balance training, and weight management can improve ADL in frail older adults, as these trainings specifically target specific dimensions of physical function (55). Further, studies have reported that peer-based interventions designed to improve self-help skills and peer support, deal with stress and loss, practice goal setting, and build healthy relationships can reduce the number of depressive symptoms among older adults (56–58). This is because health-related beliefs and behaviors are more likely to be influenced and changed by peers with similar backgrounds. Social support from peers can also help older adults cope with adversity and stress and build self-efficacy, thereby reducing psychological stress, including depressive symptoms.
Limitations and Future Directions
Our results should be interpreted in light of a few limitations. CHARLS only collected physical activity information in a random half of respondents. As such, similar to previous research (34), we created our analytic sample of those with valid information on at least four components of physical frailty. However, selection bias from this exclusion may not be ruled out when interpreting the results, as we found those respondents who were not included in the study were more likely to be older, female, non-married, and illiterate than participants included in our analysis. Further, we focused on the association between baseline physical frailty and cognitive decline over the following eight years of observation, and we did not evaluate if changes in physical frailty was associated with cognitive decline over the course of the study. This was mainly due to three reasons: (a) data on weight loss were only collected in the 2011 and 2013 waves of the CHARLS, thus the weight loss phenotype, and therefore, physical frailty would be underestimated in later waves; (b) data on physical activity was only collected in a random half of respondents at each wave, and as such, when linking data across waves, the number of participants with valid measures of physical frailty was substantially reduced; (c) CHARLS 2018 wave did not collect information on physical frailty. However, given the co-occurrence of physical frailty and cognitive impairment (44, 59) and the potential for a bidirectional relationship (60), it would be worthwhile in future research to examine the long-term dynamic trajectory of physical frailty and cognitive function among older Chinese adults with models that consider the significant mediating effect from limitations in ADLs and depressive symptoms.
Using four waves of data from the large national CHARLS survey, findings indicated that in Chinese older adults, being physically frail in later life was associated with a lower level of cognitive function and faster decline in cognitive function and episodic memory. Limitations in ADLs and depressive symptoms were shown to mediate the association physical frailty and cognitive impairment, which not only contribute to our understanding of potential underlying mechanisms between these two prominent conditions, but also serve as modifiable intervention targets to address them in older adults.
Acknowledgments: This analysis uses data from the Harmonized CHARLS dataset and Codebook, Version D as of June 2021, developed by the Gateway to Global Aging Data. The development of the Harmonized CHARLS was funded by the National Institute on Aging (R01 AG030153, RC2 AG036619, R03 AG043052). For more information, please refer to https://g2aging.org/.
Conflict of interest: All authors declare that there is no conflict of interest.
Funding information: Yiyang Yuan was supported by National Institute on Aging (grant number 5F99AG068591).
Ethical standards: This was a secondary data analysis of existing datasets that did not involve human subjects.
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