Combined effects of frailty status and cognitive impairment on health-related quality of life among community dwelling older adults

Combined effects of frailty status and cognitive impairment on health-related quality of life among community dwelling older adults

Archives of Gerontology and Geriatrics 87 (2020) 103999 Contents lists available at ScienceDirect Archives of Gerontology and Geriatrics journal hom...

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Archives of Gerontology and Geriatrics 87 (2020) 103999

Contents lists available at ScienceDirect

Archives of Gerontology and Geriatrics journal homepage: www.elsevier.com/locate/archger

Combined effects of frailty status and cognitive impairment on healthrelated quality of life among community dwelling older adults

T

Chia-Lin Lia,b,*, Hsing-Yi Changc, Fiona F. Stanawayd a

Department of Health Care Management, College of Management, Chang Gung University, 259 Wen-Hwa 1st Road, Kwei-Shan, Tao-Yuan 333, Taiwan Division of Endocrinology and Metabolism, Departments of Internal Medicine, Chang Gung Memorial Hospital, Chang Gung University, Tao-Yuan City, Taiwan c Division of Preventive Medicine and Health Service Research, Institute of Population Health Sciences, National Health Research Institutes, #35, Keyan Road, A3223, Zhunan Town, Maoli 350, Taiwan d Sydney School of Public Health, University of Sydney, NSW 2006, Australia b

A R T I C LE I N FO

A B S T R A C T

Keywords: Frailty Cognitive impairment Health related quality of life Taiwan

Objectives: The aim of the present study was to investigate the combined association of frailty/pre-frailty and cognitive impairment with health related quality of life (HRQOL) among community dwelling older adults. Methods: Data came from a cross-sectional study of community-dwelling older adults aged 65 years or older, who participated in the 2013 National Health Interview Survey in Taiwan. Frailty was determined based on the Fatigue, Resistance, Ambulation, Illness, and Loss of weight (FRAIL) scale proposed by the International Association of Nutrition and Aging. The Mini-Mental State Examination was used to assess cognitive function. HRQOL was measured using the European Quality of Life-5 Dimensions questionnaire (EQ-5D) that assesses three levels of functioning for the dimensions of mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Poisson regression models were performed to estimate prevalence ratios (PR) and 95 % Confidence Intervals (95 % CI) for health problems in all EQ-5D domains. Results: In this study, 11.0 % of participants aged 65 years and older had co-occurring frailty/pre-frailty and cognitive impairment. After adjustment for other factors, compared with participants who were physically robust with normal cognition, participants with co-occurring frailty/pre-frailty and cognitive impairment had PRs of 10.38 (95 % CI 7.56–14.26), 9.66 (95 % CI 6.03–15.48), 9.37 (95 % CI 6.92–12.68), 3.04 (95 % CI 2.53–3.64), and 5.63 (95 % CI 3.83–8.28) for reporting problems with mobility, self-care, usual activities, pain/discomfort, and anxiety/depression, respectively. Conclusions: There is a high prevalence of co-occurrence of frailty/pre-frailty and cognitive impairment in older adults, and this co-occurrence was strongly associated with self-reported health problems across all EQ-5D domains.

1. Introduction Physical frailty and cognitive impairment are two common conditions in the older population that are associated with worse health outcomes. However, there is increasing awareness that the combined presence of these two conditions may have multiplicative effects on the risk of negative health outcomes such as neurocognitive disorders, nursing home admissions, disability, and mortality (Aliberti et al., 2019; Feng, Zin Nyunt, Gao, Feng, Lee et al., 2017; Lee et al., 2018; Liu, Han, Gahbauer, Allore, & Gill, 2018). The co-occurrence of physical frailty and cognitive impairment, in the absence of dementia and other neurodegenerative diseases, has been called “cognitive frailty” by the

International Academy on Nutrition and Aging and the International Association of Gerontology and Geriatrics (Kelaiditi et al., 2013). Cognitive frailty is an important concept as it can enable the identification of older individuals with increased vulnerability who may benefit from interventions aimed at improving physical and cognitive decline (Panza et al., 2015; Ruan et al., 2015). Health related quality of life (HRQOL) is an important health outcome to consider in terms of cognitive frailty, not only because it captures a person’s perception of their health status, but also because it can be used to identify individuals who are at higher risk of adverse outcomes and who therefore require more care (Parlevliet, MacNeilVroomen, Buurman, de Rooij, & Bosmans, 2016). To date there has

⁎ Corresponding author at: Department of Health Care Management, College of Management, Chang Gung University, 259 Wen-Hwa 1st Road, Kwei-Shan, TaoYuan 333, Taiwan. E-mail addresses: [email protected] (C.-L. Li), [email protected] (H.-Y. Chang), fi[email protected] (F.F. Stanaway).

https://doi.org/10.1016/j.archger.2019.103999 Received 9 October 2019; Received in revised form 12 December 2019; Accepted 14 December 2019 Available online 17 December 2019 0167-4943/ © 2019 Elsevier B.V. All rights reserved.

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of weight. Fatigue was assessed by asking participants how much of the time they felt tired in the past 1 week. Responses of “all” or “most of the time” were given a score of 1. Resistance was assessed by asking participants whether they have difficulty climbing ten steps, and ambulation was assessed by asking participants whether they have difficulty walking 100 m (about one block). Responses of “some difficulty”, “much difficulty”, or “unable to carry out” received a score of 1. Illnesses were assessed by asking participants if a medical professional has ever told them that they have any of the following conditions: diabetes, heart disease, hypertension, stroke, asthma, kidney disease, chronic lung disease, cancer, and arthritis. A report of five or more illnesses received a score of 1. Loss of weight in FRAIL is assessed by asking participants if they have lost more than 5 % of their weight in the last 6 months. However, there was no equivalent variable available from the 2013 NHIS database. Thus, in this study, loss of weight was assessed by body mass index (BMI was calculated as weight [kg] divided by height squared [m2]), and participants scored 1 point if their BMI was less than 18.5 kg/m2 (Woo, Leung, & Morley, 2012). FRAIL scores ranged from 0 to 5. Participants with scores ranging from 3 to 5 were defined as frail, 1–2 as pre-frail, and 0 as robust. The Mini-Mental State Examination (MMSE) was used to assess cognitive function after obtaining permission from Psychological Assessment Resources (PAR), Inc. This scale provides a total score ranging from 0 to 30 points, with higher scores representing better cognitive function (Folstein, Folstein, & McHugh, 1975). As previous studies have found that level of education significantly affects the scores obtained in the Chinese version of the MMSE, the cut-point for cognitive impairment was set according to educational level as recommended (Katzman et al., 1988). In this study, the cut-points for cognitive impairment were set at 17 for participants who were illiterate and had no schooling, 20 for those with 1–6 years of education, and 24 for those with 7 or more years of education. HRQOL was measured using the European Quality of Life-5 Dimensions questionnaire (EQ-5D) (Rabin & de Charro, 2001). The EQ5D measures self-assessed health status in the five dimensions of mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Possible responses are no problems, some problems, or extreme problems. In this study, the responses for each dimension were dichotomized as no problems versus some problems or extreme problems to identify the presence of health problems across EQ-5D domains. Basic demographic information such as age, sex, years of education, and marital status were obtained from the questionnaires. Other covariates, including smoking status (current or former smoker; yes/no) and exercise (at least 10 min of leisure time physical activity; yes/no).

been only limited research examining the association between coexisting frailty and cognitive impairment and HRQOL among older adults. Moreover, studies have found conflicting results. Feng et al. analyzed data from community dwelling older adults aged 55 years and over participating in the Singapore Longitudinal Ageing Study (SLAS). They found that frail and pre-frail participants with cognitive impairment were more likely to have poor HRQOL compared to their cognitively normal counterparts (Feng, Zin Nyunt, Gao, Feng, Yap et al., 2017). In contrast, Ament and colleagues conducted a longitudinal study of physically frail community-dwelling adults aged 70 years or older in the Netherlands (N = 475), using the Groningen Frailty Indicator to assess frailty across physical, cognitive, social, and psychological dimensions. During a 1-year follow-up period, they did not find any effect of cognitive decline on poor quality of life in those older adults who were physically frail (Ament, de Vugt, Verhey, & Kempen, 2014). To date no studies in Taiwan have examined the association between coexisting frailty status and cognitive impairment and HRQOL in a population-based national sample of older adults. In Taiwan, the prevalence of frailty, pre-frailty, and cognitive impairment among community dwelling adults aged 65 years and over is 9.8 %, 44.4 %, and 22.2 %, respectively (Lin et al., 2011; Wu, Lan, Chen, Chiu, & Lan, 2011). These findings highlight that frailty, pre-frailty, and cognitive impairment are common, that they are often under-recognized among community dwelling older people, and that a vulnerable subgroup of these older adults will have coexisting frailty and cognitive impairment. Older adults with pre-frailty have different profiles in terms of clinical, functional, and behavioral characteristics to normal healthy aging, and have an increased risk of progressing to frailty (Ruan et al., 2015). There is also increasing research suggesting that implementing interventions in older adults with pre-frailty may achieve better outcomes than intervening at a later stage, and as a result those with prefrailty have been proposed as a target for prevention (Ruan et al., 2015). Therefore, in this study, we have merged the categories of prefrailty and frailty as ‘frailty status’. There were two objectives of this study. First, we aimed to describe the prevalence of coexisting frailty status and cognitive impairment and associated factors from a national sample of older adults 65 years or older who participated in the 2013 National Health Interview Survey in Taiwan. Second, we aimed to investigate the combined effect of frailty status and cognitive impairment on HRQOL among community dwelling older adults. 2. Methods 2.1. Study population

2.3. Statistical analysis This study includes participants from the 2013 cross-sectional National Health Interview Survey (NHIS) in Taiwan. The survey sample was drawn from the National Registry Database through a complex multistage design that has been described in detail previously (Li, Stanaway, Lin, & Chang, 2018; Shih, Chang, Lu, & Hurng, 2014). The original sample comprised 23,296 participants (response rate 75.2 %), including 3203 individuals aged 65 years and older. The survey obtained ethical approval from the Institutional Review Board of the National Health Research Institutes. All study participants provided signed informed consent. Of these potential participants, we excluded 172 persons with a pre-existing dementia or Parkinson's disease diagnosis and 338 persons with incomplete data for the Mini-Mental State Examination. This resulted in 2693 eligible participants for the analysis.

We used Pearson’s Chi-Square test to examine factors associated with the presence of frailty status and/or cognitive impairment. Poisson regression was used to estimate prevalence ratios (PR) (Spiegelman & Hertzmark, 2005; Zhang & Yu, 1998). Poisson regression models were performed to assess the independent associations of frailty status and/ or cognitive impairment with HRQOL across all domains of the EQ-5D after adjustment for possible confounders including age, sex, education, marital status, smoking status, and exercise. All analyses were conducted using SAS statistical software, version 9.4 (SAS Institute, Cary, NC, USA). Pearson’s Chi-Square test was carried out using SAS (SAS Institute, Cary, NC, USA)-callable SUDAAN (RTI, Research Triangle Park, NC, USA) and sampling weights were applied so that the sample would be representative of the whole Taiwanese population.

2.2. Measures 3. Results Frailty and pre-frailty were determined using the FRAIL scale, a widely used scale to assess frailty proposed by the International Academy on Nutrition and Aging (Abellan van Kan, Rolland, Bergman et al., 2008; Abellan van Kan, Rolland, Morley, & Vellas, 2008). FRAIL includes five criteria: fatigue, resistance, ambulation, illnesses, and loss

Table 1 presents the characteristics of older adults by frailty status and/or cognitive impairment. The prevalence of co-occurring frailty status with cognitive impairment among community-dwelling older adults aged 65 years and above was 11.0 %. Participants with co2

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Table 1 Characteristics of participants by the presence of frailty status and/or cognitive impairment. Variables

Frail or Pre-Frail / Cognitive impairment

Age (%) 65–74 75+ Sex (% female) Education (%) 0 years 1–6 years 7+ years Marital status (% married or living with partner) Current or former smoker (% yes) Exercise (% yes) a

No / No (N = 1512)

No / Yes (N = 365)

Yes / No (N = 486)

Yes / Yes (N = 330)

1038(70.1) 474(29.9) 697(46.7)

201(53.5) 164(46.5) 191(55.4)

213(41.9) 273(58.1) 300(61.4)

110(35.0) 220(65.0) 222(70.5)

334(19.6) 738(47.1) 440(33.3) 1084(72.1) 411(28.2) 925(63.2)

99(25.3) 140(38.6) 126(36.1) 223(61.8) 94(23.4) 207(55.3)

202(38.7) 202(36.3) 82(25.0) 269(51.8) 99(18.9) 180(39.1)

142(42.0) 124(33.5) 64(24.5) 160(46.6) 58(17.3) 97(33.8)

P-valuea < 0.0001

< 0.0001 < 0.0001

< 0.0001 0.0021 < 0.0001

Categorical variables were compared using Pearson’s Chi-square test and shown as percentages.

Table 2 Health problems across all EQ-5D domains by the presence of frailty status and/or cognitive impairment.   EQ-5D dimension

Mobility (% yes) Self-care (% yes) Usual activities (% yes) Pain/discomfort (% yes) Anxiety/depression (% yes) a

Frail or Pre-Frail / Cognitive impairment Total (%) (N = 2693)

No / No (N = 1512)

No / Yes (N = 365)

Yes / No (N = 486)

Yes / Yes (N = 330)

P-valuea

381(13.0) 155(5.3) 369(12.8) 634(22.8) 212(7.2)

17(1.7) 1(0.1) 21(1.7) 190(12.6) 36(2.1)

12(6.5) 2(0.5) 12(5.3) 56(16.5) 12(2.3)

177(32.9) 51(9.9) 159(31.6) 222(45.0) 89(19.4)

175(51.1) 101(31.6) 177(52.9) 166(51.2) 75(22.0)

< 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001

Categorical variables were compared using Pearson’s Chi-square test and shown as percentages.

4. Discussion

occurring frailty status and cognitive impairment were more likely to be older, female, have less education, be unmarried, live alone, and report no exercise. Table 2 presents responses to EQ-5D domains by the presence of frailty status and/or cognitive impairment and demonstrates that older adults with co-occurring frailty status and cognitive impairment were more likely to report health problems across all EQ-5D domains. Table 3 presents adjusted prevalence ratios (PR) and 95 % confidence intervals (95 % CI) for health problems in all EQ-5D domains by frailty status and/or cognitive impairment. After adjustment for confounders, no statistically significant differences in any health problems were observed across any EQ-5D domain between the reference group who were physically robust with normal cognition and those with cognitive impairment alone. Compared with the reference group, older adults with frailty status alone were more likely to report health problems in mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Compared with the reference group, older adults with co-occurring frailty status and cognitive impairment were more likely to report health problems in mobility, self-care, usual activities, pain/ discomfort, and anxiety/depression.

We found a high prevalence of co-occurring frailty status and cognitive impairment among older adults in Taiwan. Moreover, this combination was associated with an increased likelihood of self-reporting health problems across several EQ-5D domains. Our study found that 11.0 % of participants aged 65 years and older had co-occurring frailty status and cognitive impairment. The PRs for reporting problems across the EQ-5D domains of mobility, self-care and usual activities were substantially greater when coexisting frailty status and cognitive impairment were considered compared to either factor alone. The PRs for pain/discomfort and anxiety/depression were largely similar when considering frailty status alone or considering frailty status in combination with cognitive impairment. The major strength of our study is that we investigated the prevalence of frailty status and cognitive impairment in a population-based national sample of older adults. We found that 4.1 %, 23.8 %, and 23.6 % of Taiwanese older adults aged 65 years and above were frail, prefrail, or had cognitive impairment, respectively. Moreover, 43.9 % of frail participants and 38.5 % of pre-frail participants had comorbid cognitive impairment. This high prevalence of co-occurring frailty status with cognitive impairment among community-dwelling older

Table 3 Adjusteda Prevalence Ratios (PR) and 95 % Confidence Intervals (95 % CI) for health problems in EQ-5D all domains by the presence of frailty status and/or cognitive impairment.

No / No

Mobility PR (95 % CI) Reference

Self-care PR (95 % CI) Reference

 Usual activities PR (95 % CI) Reference

Pain/Discomfort PR (95 % CI) Reference

Anxiety/depression PR (95 % CI) Reference

No / Yes Yes / No Yes / Yes

0.91 (0.49–1.69) 8.08 (5.91–11.05)*** 10.38 (7.56–14.26)***

0.28 (0.07–1.17) 3.85 (2.33–6.36)*** 9.66 (6.03–15.48)***

0.81 (0.44–1.50) 6.44 (4.76–8.72)*** 9.37 (6.92–12.68)***

1.06 (0.81–1.40) 2.90 (2.46–3.43)*** 3.04 (2.53–3.64)***

1.02 (0.55–1.91) 4.60 (3.19–6.61)*** 5.63 (3.83–8.28)***

Frail or Pre-Frail / Cognitive impairment

*p-value < 0.05, **p-value < 0.01, ***p-value < 0.001. a Adjusted for age, sex, education, marital status, smoking status, and exercise. 3

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2015). These discrepancies may be due to different methods to assess cognitive impairment. The prior study used the Abbreviated Mental Test to classify participants into severely impaired, moderately impaired, and normal cognitive function. In this study, we used MMSE to assess cognitive impairment with a dichotomous variable (yes or no), which may be less sensitive for detecting differences in health problems than a variable with several degrees of impairment. This may also explain our finding that cognitive impairment alone was not associated with reporting health problems across any EQ-5D domains. It is possible that lack of data on the severity of cognitive impairment may have resulted in the failure to reach statistical significance. It is also possible that maintenance of robust physical functioning in individuals with cognitive impairment alone means there is little impact on their quality of life compared to those participants with both frailty status and cognitive impairment. Therefore, our results indicate that screening for physical frailty in those with cognitive impairment may offer additional discriminatory power in identifying older adults with cognitive impairment who are at increased risk of experiencing health problems. This study has several limitations. First, the cross-sectional design of this study prevents us from drawing conclusions about the temporal relationships between frailty, cognitive impairment and HRQOL. Second, the participants included in this study are limited to those who had no diagnosed dementia and Parkinson’s disease, and who were able to complete the assessments for cognitive function. The comparison of the characteristics between respondents who were included (N = 2693) and excluded due to missing data for MMSE (N = 338) suggests that our sample could be biased toward individuals who are younger, have a higher education level, are married or living with a partner, engage in exercise, and are less likely to be frail. Therefore, we have likely underestimated the prevalence of frailty or pre-frailty, and as a result we may also have underestimated the prevalence of co-occurring frailty status and cognitive impairment. The rates of self-reported health problems in EQ-5D domains may also be underestimated. Despite this potential underestimation, we were able to demonstrate statistically significant and strong associations, suggesting that the association between co-occurring frailty status and cognitive impairment and HRQOL is substantial. Our findings have important practical implications for developing intervention strategies to support the care of older adults. We found that co-occurring frailty status and cognitive impairment is common in older adults. It is notable that among our participants, as many as 40.6 % were either frail or pre-frail, had cognitive impairment, or had comorbid frailty status and cognitive impairment. These findings show that a substantial proportion of older people have impaired physical function or cognition and as a result may need additional support to care for their health (Table 2). Our data showed that compared to physically robust and cognitively normal participants, participants with co-occurring frailty status and cognitive impairment were more likely to be older, female, have less education, and report no exercise (Table 1). These attributes are in line with previous findings (Shimada et al., 2018). Healthcare providers should pay careful attention to older patients who have these characteristics, so as to identify those who would benefit most from interventions aimed at preventing physical functional and cognition decline. Our observations highlight the importance of screening for both physical and cognitive deficits to identify more vulnerable groups who experience health problems. Health care strategies for older adults with lower cognition could be aimed at improving physical functioning through early evaluation and management of underlying physical disorders and other chronic conditions. In contrast, strategies for older adults with frailty or pre-frailty could be aimed at the prevention and early detection and control of cognitive decline. Further prospective longitudinal investigations and intervention studies are needed to explore the underlying causes of reduced HRQOL in older adults with cooccurring frailty status and cognitive impairment and whether such

adults is consistent with previous studies. One recent study comprising 4570 community-dwelling older adults aged 65 years and older from Japan used the presence of slow walking speed or muscle weakness to define frailty and used multidimensional cognitive tasks to assess cognitive impairment (Shimada et al., 2018). They found a prevalence of comorbid physical frailty and cognitive impairment of 9.8 %. One population-based cohort study on a sample of 2375 Chinese Singaporeans aged 55 years and above without dementia or degenerative disorders, used the Cardiovascular Health Study frailty criteria and the Chinese version of the Mini-Mental State Examination to assess frailty and cognition and found that 10.7 % of the population were frail or pre-frail and cognitively impaired (Feng, Zin Nyunt, Gao, Feng, Lee et al., 2017). These figures are very similar to our observed prevalence rate of 11.0 % despite the use of different definitions of frailty and/or cognitive impairment. To our knowledge, this is the first study to assess the association between co-occurring frailty status and cognitive impairment with HRQOL in older Taiwanese. After adjustment for other factors, there was no significant association between cognitive impairment alone and reporting health problems across any EQ-5D domain. However, frail or pre-frail participants, with or without cognitive impairment, were significantly more likely to report health problems across all EQ-5D domains. Moreover, the PRs for reporting problems across several EQ-5D domains were greater when coexisting frailty status with cognitive impairment were taken into account. These findings support those of Feng et al. although different measures of HRQOL were used. Feng et al. analyzed data from community dwelling older adults aged 55 and above using the HRQOL Short Form-12 (SF-12) physical component score (PCS) as a quality of life measure (Feng, Zin Nyunt, Gao, Feng, Yap et al., 2017). They found that participants with cognitive impairment alone had no increased risk of being in the lowest quartile of PCS12 scores. However, participants with frailty or pre-frailty, had a significantly increased risk of scores in the lowest quartile of PCS-12, and this risk was further increased with the presence of coexisting cognitive impairment. Our results showing that frail or pre-frail participants were more likely to have poor HRQOL is consistent with evidence from other studies (Crocker et al., 2019; Jürschik et al., 2012). Crocker et al. conducted a systematic review and reported an inverse relationship between frailty and quality of life across a range of constructs including those focused on health, such as limitations in activities, pain, and mental health (Crocker et al., 2019). We also found that the relationship of combined frailty status and cognitive impairment with HRQOL was stronger than that of frailty status alone. This combined effect of frailty and cognitive impairment is supported by past research. Godin et al. conducted secondary analyses using data from the first two waves of the Survey of Health, Ageing and Retirement in Europe (SHARE) and demonstrated that frailty and cognitive impairment influenced each other (Godin, Armstrong, Rockwood, & Andrew, 2017). Moreover, they found that impaired physical health or cognition accumulated and lead to increased frailty and cognitive decline. In the English Longitudinal Study of Ageing, Ding et al. investigated pathways from physical frailty to basic activities of living activity limitations and suggested that indirect effects of physical frailty on activity limitation through cognitive impairment were stronger with older age (Ding, Kuha, & Murphy, 2017). In this study, among participants with frailty or pre-frailty alone, the PRs for reporting problems in the EQ-5D domains of pain/discomfort and anxiety/depression were similar with participants with frailty status in combination with cognitive impairment. This finding indicates that there was little effect of cognitive impairment on reporting problems in pain/discomfort and anxiety/depression. In contrast to our results, Pan et al. analyzed cross-sectional data among older adults aged 60 years and above in China and concluded that older Chinese adults with cognitive dysfunction were more likely to reporting of problem in pain/discomfort and anxiety/depression (Pan et al., 4

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individuals could benefit from interventions to improve their HRQOL.

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5. Conclusions In summary, our results contribute to the literature by providing new data on the association between co-occurring frailty status and cognitive impairment and HRQOL among a population-based sample of community-dwelling older adults in Taiwan. Our results highlight the high prevalence of co-occurring frailty status and cognitive impairment in community-dwelling older adults. Moreover, this combination has a greater impact on self-reported health problems across multiple EQ-5D domains than either frailty status or cognitive impairment alone, after taking into account other risk factors. Declaration of Competing Interest The authors declare no conflicts of interest. Acknowledgement This study was supported (in part) by grants (MOST 108-2410-H182 -010 -MY3) from the Ministry of Science and Technology, Taiwan. References Abellan van Kan, G., Rolland, Y., Bergman, H., Morley, J. E., Kritchevsky, S. B., & Vellas, B. (2008). The I.A.N.A Task Force on frailty assessment of older people in clinical practice. Journal of Nutrition Health & Aging, 12(1), 29–37. https://doi.org/10.1007/ bf02982161. Abellan van Kan, G., Rolland, Y. M., Morley, J. E., & Vellas, B. (2008). Frailty: Toward a clinical definition. Journal of the American Medical Directors Association, 9, 71–72. https://doi.org/10.1016/j.jamda.2007.11.005. Aliberti, M. J. R., Cenzer, I. S., Smith, A. K., Lee, S. J., Yaffe, K., & Covinsky, K. E. (2019). Assessing risk for adverse outcomes in older adults: The need to include both physical frailty and cognition. Journal of The American Geriatrics Society, 67(3), 477–483. https://doi.org/10.1111/jgs.15683. Ament, B. H. L., de Vugt, M. E., Verhey, F. R. J., & Kempen, G. I. J. M. (2014). Are physically frail older persons more at risk of adverse outcomes if they also suffer from cognitive, social, and psychological frailty? European Journal of Ageing, 11(3), 213–219. https://doi.org/10.1007/s10433-014-0308-x. Crocker, T. F., Brown, L., Clegg, A., Farley, K., Franklin, M., Simpkins, S., et al. (2019). Quality of life is substantially worse for community-dwelling older people living with frailty: Systematic review and meta-analysis. Quality of Life Research, 28(8), 2041–2056. https://doi.org/10.1007/s11136-019-02149-1. Ding, Y. Y., Kuha, J., & Murphy, M. (2017). Pathways from physical frailty to activity limitation in older people: Identifying moderators and mediators in the English Longitudinal Study of Ageing. Experimental Gerontology, 98, 169–176. https://doi. org/10.1016/j.exger.2017.08.029. Feng, L., Zin Nyunt, M. S., Gao, Q., Feng, L., Lee, T. S., Tsoi, T., et al. (2017). Physical frailty, cognitive impairment, and the risk of neurocognitive disorder in the Singapore Longitudinal Ageing Studies. The Journals of gerontology. Series A, Biologycal Sciences and Medical Sciences, 72(3), 369–375. https://doi.org/10.1093/ gerona/glw050. Feng, L., Zin Nyunt, M. S., Gao, Q., Feng, L., Yap, K. B., & Ng, T. P. (2017). Cognitive frailty and adverse health outcomes: Findings from the Singapore longitudinal ageing studies (SLAS). Journal of the American Medical Directors Association, 18(3), 252–258. https://doi.org/10.1016/j.jamda.2016.09.015. Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). Mini-mental state. A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189–198. https://doi.org/10.1016/0022-3956(75) 90026-6. Godin, J., Armstrong, J. J., Rockwood, K., & Andrew, M. K. (2017). Dynamics of frailty

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