Association between executive function and physical performance in older Korean adults: Findings from the Korean Longitudinal Study on Health and Aging (KLoSHA)

Archives of Gerontology and Geriatrics 52 (2011) e156–e161

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Association between executive function and physical performance in older Korean adults: Findings from the Korean Longitudinal Study on Health and Aging (KLoSHA) Yoonseok Huh a, Eun Joo Yang b, Seung Ah Lee b, Jae-Young Lim b, Ki Woong Kim a, Nam-Jong Paik b,* a Department of Neurospsychiatry, Seoul National University College of Medicine, Seoul National University Bundang Hospital, 300 Gumi-dong, Bundang-gu, Seongnam-si, Gyeonggi-do 463-707, South Korea b Department of Rehabilitation Medicine, Seoul National University College of Medicine, Seoul National University Bundang Hospital, 300 Gumi-dong, Bundang-gu, Seongnam-si, Gyeonggi-do 463-707, South Korea

A R T I C L E I N F O

A B S T R A C T

Article history: Received 10 July 2010 Received in revised form 13 October 2010 Accepted 16 October 2010 Available online 13 November 2010

Reduced executive function and physical performance are common age-related conditions. This study evaluated the associations between executive function and physical performance in a representative sample of older adults. Cross-sectional data were analyzed from a population-based sample of 629 men and women aged 65 or older and living in one typical city in Korea. Specific aspects of executive function were assessed using the trail making test, digit span test, and lexical fluency test to measure set shifting, working memory and cognitive flexibility functions. Physical performance was measured using performance-oriented mobility assessment (POMA) scores and isokinetic muscle strength. Subjects’ selfefficacy was also assessed using the activities-specific balance confidence (ABC) scale. Results of the lexical fluency test were associated with POMA scores and muscle strength, independent of age, gender, education, comorbidity, physical activity status, depression, and global cognition, suggesting that reduced cognitive flexibility is associated with reduced physical performance and muscle strength. Selfefficacy was also independently associated with physical performance and muscle strength. Clinicians need to consider the association between executive function and physical performance when working to improve physical functioning in an aged population. ß 2010 Elsevier Ireland Ltd. All rights reserved.

Keywords: Executive function of aged Muscle strength in elderly Physical performance of elderly

1. Introduction Impairments in physical performance and muscle strength are common age-related phenomena (Warren et al., 1989; Bassett and Folstein, 1991; Black and Rush, 2002; Kuo et al., 2007) and are related to such factors as age, gender, physical and psychological characteristics, disease, and living environment. In particular, it has been reported that poor cognition is associated with reduced physical function (Guralnik et al., 1994; Carlson et al., 1999; Black and Rush, 2002; Raji et al., 2002; Ble et al., 2005; Malmstrom et al., 2005; Rosano et al., 2005; Fitzpatrick et al., 2008), and vice versa (Wang et al., 2006). Some studies have suggested that cognitive decline affects gait and the risk of falls (Seeman et al., 1994; Tabbarah et al., 2002; Sheridan et al., 2003), and Auyeung et al. (2008) suggested that impaired physical performance was directly related to poor cognition, independently of comorbidities, inactivity, and muscle mass.

* Corresponding author. Tel.: +82 31 787 7731; fax: +82 31 712 3913. E-mail address: [email protected] (N.-J. Paik). 0167-4943/$ – see front matter ß 2010 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.archger.2010.10.018

Reduced executive function is prevalent even among healthy, community-dwelling elderly persons with intact global cognitive function (West, 1996; Grigsby et al., 1998; Royall et al., 2004; Elderkin-Thompson et al., 2008), and recent research has suggested that executive function is related to physical performance (Whitman et al., 2001; Neville and Bavelier, 2002; Coppin et al., 2006; Holtzer et al., 2006; Nieto et al., 2008). Neuroimaging studies have shown that decreased mobility and physical performance in elderly people are related to periventricular white matter abnormalities (Cummings, 1993; Benson et al., 2002; Boyle et al., 2004; Farias et al., 2004), which are also associated with reduced executive function (Thal et al., 2004). To explain the mechanisms underlying the association between executive function and muscle strength, some researchers have proposed that executive dysfunction may interfere with engagement in physical activities and can produce substantial decreases in muscle strength and muscle mass (Skelton et al., 1995; Grigsby et al., 1998). Executive function may also play a significant role in older adults’ ability to accurately perceive their actual physical capacity (Liu-Ambrose et al., 2008). This perceived self-efficacy is known to

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be associated with performance regarding balance and gait (LiuAmbrose et al., 2006; Ko et al., 2009). In addition, perceived selfefficacy is known to predict activity better than actual physical ability (Bandura, 1977). A better understanding of these associations could enhance future interventions aimed at improving physical performance and muscle strength in community-dwelling older adults. Therefore, we examined the associations between executive processes and self-efficacy with performance of balance and mobility and muscle strength after accounting for age, gender, education, comorbidity, current physical activity level, and global cognition in community-dwelling elderly Koreans.

2. Subjects and methods 2.1. Study population The analysis in this study was performed using data obtained during the Korean Longitudinal Study on Health and Aging (KLoSHA) (Park et al., 2007), a population-based study conducted on residents aged 65 years in Seongnam, South Korea, from September 2005 to August 2006. The total population of Seongnam was 931,019 in 2005, and 61,730 (6.6%) of the population was aged 65 years. A simple random sample (n = 1118) was drawn from a roster of 61,730 persons aged 65 years, resident in Seongnam on August 1, 2005. Subjects were invited to participate in the study by letter and telephone. The mean age of the sample was 72.2  6.6 years (range, 64–99), and 405 (36.2%) of the subjects were male. Of the 1118 subjects, 714 agreed to participate and completed the present study (response rate = 63.9%). The mean age of the subjects was 71.9  5.7 years and 42.2% were male. Those aged 85 represented only a small proportion and had a poor response rate; thus, all residents aged 85 in the city of Seongnam (n = 3166) were also invited by letter and telephone. Finally, 272 subjects agreed to participate in KLoSHA. Accordingly, at baseline, the study cohort consisted of 992 subjects. Subjects with a history of stroke or who had suffered a transient ischemic attack (n = 115), Parkinson’s disease (n = 82), dementia (n = 91), and those who did not respond to any of the survey items (n = 55) or neuropsychological tests (n = 77) were excluded from the study. Finally, 629 subjects were included in the analysis. All subjects were fully informed regarding study participation and written informed consent was obtained from all subjects or their legal guardians. All assessments were performed at our hospital in Seongnam. The study protocol was approved by the Institutional Review Board of our hospital. 2.2. Assessment of executive function Comprehensive neuropsychological assessments, including the Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) neuropsychological assessment battery (CERAD-K-N) (Lee et al., 2002), Trail Making Test (TMT), Digit Span Test (DST) (Wechsler, 1987), and Lexical Fluency Test (LFT) (Ruff et al., 1996) were administered by neuropsychologists. We used the TMT to assess set shifting. Part A assesses psychomotor speed and requires the participant to draw lines that connect encircled numbers sequentially. Part B consists of encircled numbers and letters. To index set shifting, we calculated the difference between Part B and Part A completion times (TMT). Smaller differences in scores indicated better set shifting. We used the DST-backward (DST-B) to assess working memory. For this standardized test of working memory, participants heard a sequence of numbers and were asked to recall it in reverse order. Possible scores range from 0 to 9, with higher scores indicating

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better working memory. The average reliability coefficient for this test is 0.88. The LFT was performed to assess cognitive flexibility (Ruff et al., 1996). This test requires participants to use their language ability and assesses sensitivity to cognitive and motor speed; it also involves executive functions such as mental productivity, cognitive flexibility, and the ability to switch responses according to the context of a situation (Perret, 1974; Troyer et al., 1997; Abwender et al., 2001). LFT scores are measured by calculating the total number of acceptable words produced for all three letters. 2.3. Assessment of subjects’ self efficacy The ABC scale (Powell and Myers, 1995; Myers et al., 1998) was designed to evaluate confidence in activities related to balance. This instrument contains 16 items, each representing a range of indoor and outdoor activities that require varying degrees of balance function. Respondents rate each item using a continuous scale from 0% (no confidence) to 100% (complete confidence); this yields a total score out of 100%. The ABC scale has been shown to have good psychometric properties, with high internal consistency, test–retest reliability, and convergent validity with the physical activities subscales (Powell and Myers, 1995). 2.4. Assessment of physical performance Physical performance was assessed using the POMA (Tinetti, 1986). POMA is an instrument that evaluates balance, gait, and mobility in the elderly (Powell and Myers, 1995), and is widely used to assess mobility and fall risk (Tinetti, 1986). The total POMA scale consists of a balance and gait scale, and qualitatively measures balance stability in relation to daily activities and several locomotion patterns. 2.5. Measurement of muscle strength Isokinetic knee extensor muscle strength (at an angular velocity of 608/s) was measured using an isokinetic device (Biodex Medical Systems, Inc., Shirley, NY) (Drouin et al., 2004). Subjects were asked to perform two sets of five repetitions, with a 30-s rest between sets, by exerting maximum pressure on the isokinetic device through the entire range of movement. The concentric peak torque values (Nm) obtained from five torque-angle curves of each set were used to evaluate the extension muscle strengths of knee joints by averaging them, and these were subsequently normalized with respect to body weight (Nm/kg). We used muscle strength at peak torque per kg body weight (Nm/kg) of the right knee extensor muscles. Of the 629 subjects, 458 were tested for the isokinetic muscle strength (Table 1). 2.6. Covariates Age, gender, activity level, and educational level were assessed by three research nurses using standardized questionnaires and by interview. Activity levels were estimated by summing times spent walking, gardening, woodworking, lifting, or shoveling over a typical 24-h period. Global cognition was assessed using the validated mini-mental state examination (MMSE) of the Korean version of the CERAD-K assessment packet (Lee et al., 2002). The MMSE consists of 30 questions that assess orientation, attention, immediate- and short-term recall, language function, and the ability to follow simple verbal and written commands. Scores range from 0 to 30 (worst to best). Severity of depression was evaluated using the Korean version of the geriatric depression scale (GDS). Comorbidity index was determined by summation of self-reported illnesses, including

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Table 1 Characteristics of study participants, n, or mean  S.D. Variables

Men

Number Age, years Education, years 0 0–6 >7 Smoking status Never Current Former Physical activity score Prevalent chronic conditions Hypertension Heart Diabetes mellitus Cancer Arthritis Fracture Lung problem Comorbidity index MMSE score GDS BMI (kg/m2) Physical performance POMA Muscle strength (Nm/kg) n= Executive function DST-B LFT DTMT ABC Note: DTMT = (TMT Part B) * p < 0.05. ** p < 0.001.

Women

329 75.7  8.6

300 73.1  7.0**

10 87 232

60** 118 122

Table 2 Pearson correlation coefficient matrix between the variables studied. Total 74.5  7.9 70 195 354

92 67 170 17.3  7.1

281** 7 12 16.6  5.3

373 74 182 17.0  6.3

122 47 48 27 73 49 28 1.2  0.9 25.7  2.7 9.5  6.8 23.6  3.2

144* 51 45 27 152** 64* 26 1.7  1.1** 23.7  3.6** 11.7  6.7** 24.4  3.2**

266 09 93 54 225 113 54 1.44  1.07 24.7  3.3 10.5  6.8 24.0  3.1

Variable Age, years Education Physical activity scale MMSE GDS Comorbidity index Executive functions TMT DST-B LFT ABC scale *

26.0  3.7 119.7  37.8 254

25.0  4.4* 87.8  30.8** 204

25.6  4.0 105.6  38.3 458

3.9  1.2 23.6  10.2 133.8  67.4 85.6  16.2

3.3  1.1** 19.5  9.7* 143.8  63.8 75.5  19.7**

3.6  1.2 21.6  10.2 138.2  66.0 79.5  20.0

(TMT Part A).

hypertension, heart disease, diabetes, cancer, arthritis, fracture, and respiratory disease. 2.7. Statistical analyses Age-adjusted proportions and means were calculated for all sociodemographic, clinical, and functional characteristics. The associations between the physical performance and muscle strength-dependent and -independent variables of interest (age, education, comorbidity, global cognition, physical activity status, executive function) were determined using Pearson’s correlation coefficient. Alpha was set at p < 0.05. Multiple linear regression models were developed to examine the associations of executive function and fall-related self-efficacy with physical performance and muscle strength. Age, gender, education, comorbidity index, MMSE, GDS, and physical activity status were statistically controlled by entering them into the regression model. These variables were determined from the results of Pearson’s correlation coefficient analyses, and were based on biological relevance. The three executive functions were then entered into the regression model. The ABC scale was entered last into each model. Statistical analysis was conducted using the SPSS software (ver.17.0) and p < 0.05 was taken to indicate statistical significance. 3. Results The demographic characteristics of the 629 subjects included in the study population are shown in Table 1. Overall, the participants had a mean age of 74.5  7.9 years (S.D., range = 65–95). Men had lower comorbidity index, incidence of arthritis and fractures, GDS,

**

POMA

Muscle strength **

0.417 0.058 0.139** 0.281** 0.164** 0.110**

0.374** 0.301** 0.155** 0.351** 0.198* 0.229**

0.147** 0.212** 0.267** 0.634**

0.109* 0.316** 0.355** 0.478**

p < 0.05. p < 0.001.

and higher MMSE and ABC scale than women. Men had better physical performance than women, as measured by POMA and muscle strength. In addition, men had better executive function, such as working memory and cognitive flexibility measured by DST-B and LFT, except set shifting measured by TMT. Table 2 shows the correlation coefficients between the variables. Age, the physical activity scale, MMSE, GDS, and comorbidity index were significantly associated with POMA and muscle strength. TMT, DST-B, and LFT were also significantly associated with POMA and muscle strength. LFT was significantly associated with ABC scale by Pearson correlation analysis (r = 0.358; p < 0.001, data not shown in Table 2). However, when age, gender, education, smoking, comorbidity index, physical activity status, GDS, and MMSE were introduced as covariates, only LFT remained significantly associated with POMA and muscle strength (Table 3; Model 1), whereas DST-B and TMT were not. Age, gender, education, smoking, comorbidity, physical activity scale, GDS, and MMSE accounted for 40.8% and 24.2% of the variance in POMA and muscle strength, respectively. Adding the score for the executive function of cognitive flexibility to the model resulted in changes in R2 of 2.3% and 1.5%, respectively, and significantly improved the regression model. As LFT is correlated with ABC score, it is appropriate to control for self-efficacy. When ABC score was introduced as a covariate, cognitive flexibility was still significantly related to muscle strength, but not with POMA (Table 3; Model 2). Performance on LFT was significantly associated with muscle strength in the final model, and adding the ABC scale to the model resulted in a change in R2 of 1.4%. The total variance accounted for by the final model was 44.3%. After adding the score for the ABC scale to the model, LFT was not significantly associated with POMA (Model 2). Adding the ABC scale to the model resulted in changes in R2 of 20.6%, 21.9%, and 22%, even after adjusting for covariates and the three executive functions, respectively. 4. Discussion The results of this study indicated that cognitive flexibility was significantly related to physical performance and muscle strength, independent of age, gender, education, comorbidity, physical activity, depression, and general cognition in a Korean populationbased cohort. However, other aspects of executive function, working memory and set shifting, were not significantly associated with physical performance or muscle strength after adjusting for other covariates. Our findings also indicated that self-efficacy is independently associated with muscle strength. Muscle strength shows decremental changes with age, and impairment of muscle strength is associated with decreased walking speed, mobility, functional dependence (Rantanen et al.,

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Table 3 Multiple linear regression model summary for POMA and muscle strength. POMA Parameter Cognitive flexibility (LFT) Model 1 Intercept LFT Covariates Model R2 = 0.257 Model 2 Intercept LFT ABC scale Covariates Model R2 = 0.454 Working memory (DST-B) Model 1 Intercept DST-B Covariates Model R2 = 0.244 Model 2 Intercept DST-B ABC scale Covariates Model R2 = 0.453 Set shifting (TMT) Model 1 Intercept TMT Covariates Model R2 = 0.243 Model 2 Intercept TMT ABC scale Covariates Model R2 = 0.452

Estim.  S.E.M

37.701  2.87 0.055**  0.017

Muscle strength 2

Partial R

0.015 0.242

Estim.  S.E.M

282.303  27.427 0.695**  0.171

Partial R2

0.023 0.408

Model R2 = 0.43 21.365  2.39 0.024  0.014 0.109**  0.008

0.017 0.206 0.231

235.836  31.224 0.593**  0.173 0.344**  0.107

0.023 0.014 0.406

Model R2 = 0.443

37.365  2.507 0.161  0.144

0.002 0.242

283.081  27.897 2.265  1.528

0.003 0.408

Model R2 = 0.411

0.153  0.121 0.111**  0.008

0.002 0.219 0.231

227.256  31.487 2.056  1.51 0.109**  0.106

0.003 0.021 0.406

Model R2 = 0.429

0.278  0.077 37.631  2.539 0.002  0.002

0.001 0.242

289.354  28.428 0.03  0.026

0.002 0.408

2

Model R = 0.41 21.2  2.401 0.002  0.002 0.111**  0.008

0.001 0.220 0.231

232.154  31.972 0.026  0.025 0.414**  0.106

0.001 0.021 0.406

Model R2 = 0.428

Notes: Covariates were age, gender, education, smoking, comorbidity index, physical activity score, GDS, MMSE. ** p < 0.001.

2002), falls (Lord et al., 1994), and, ultimately, disability (Evans and Campbell, 1993; Visser et al., 2002). We found that poor cognitive function coexisted with low muscle strength, consistent with previous observations (Auyeung et al., 2008). These latter authors reported that impaired muscle strength was directly related to poor cognition, independent of comorbidities, inactivity, and muscle mass. However, they assessed cognitive function not by domain-specific cognition, but by the global cognitive score of the community screening instrument of dementia. Executive function has specific aspects, such as response inhibition, set shifting, working memory, and cognitive productivity and flexibility (Stokholm et al., 2005). In the present study, cognitive flexibility seemed to be associated with muscle strength, whereas set shifting and working memory were not, after adjusting for age, gender, education, and comorbidities. Our findings also suggest that cognitive flexibility is significantly associated with muscle strength, independently of the number of comorbidities, age, gender, and educational level in a population with multiple chronic diseases. A previous cross-sectional study of community-dwelling older adults (Van Iersel et al., 2008) found that performance on the TMT was not associated with usual gait speed. Liu-Ambrose et al. (2010) demonstrated that only inhibition (Stroop test), but not set shifting (TMT) or working memory (DST-B), was related to gait speed. In contrast to our results, Scherder et al. (2010) suggested that quadriceps strength was associated with working memory, as determined by DST-B. However, they had a small sample of healthy older women and did not include a measure of cognitive flexibility.

Physical activity also has been shown to be associated with executive function, in particular, cognitive flexibility (Scherder et al., 2005; Masley et al., 2009). Previous studies have demonstrated that exercise enhances plasticity of the vascular system and the growth of new neurons (Churchill et al., 2002) and it has been suggested that frontal lobe activities related to cognitive flexibility improved with fitness (Barnes et al., 2003; Colcombe et al., 2003). In patients with Alzheimer’s disease, reduced physical activity with impaired executive cognitive function accounted for weaker muscle strength, due to deconditioning (Wang et al., 2004; White et al., 2004). Furthermore, executive dysfunction may interfere with engagement in exercise and physical activities that could produce substantial increases in muscle strength and muscle mass (Skelton et al., 1995; Grigsby et al., 1998). With regard to the suggestion that reduced muscle strength with impaired executive function was due to deconditioning, we attempted to eliminate the confounding effect of physical activity by adjusting for physical activity scores in logistic regression analysis. Executive function was not simply associated with muscle strength by decreased physical activity because controlling for physical activity score did not affect the results in our study. Although previous reports suggested that muscle strengthening was related to self-efficacy (Tsutsumi et al., 1997; Katula et al., 2008), they did not consider executive functioning, which enables older adults to perceive their actual physical capacity. We found that poor executive function was directly related to impaired muscle strength, independent of self-efficacy.

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Executive function may be related to muscle strength by an enhancing biological mechanism. Cassilhas et al. (2007) found a significant increase in insulin-like growth factor (IGF-1) serum concentration, which has been identified as a hormonal factor preventing brain tissue loss, and increasing the levels of key neurochemicals that improve plasticity and neuronal survival in a group that performed resistance training for 24 weeks compared with controls (Cotman and Berchtold, 2002; Adlard et al., 2005; Vaynman et al., 2006). Our finding that executive function was significantly associated with physical performance is consistent with previous reports (Grigsby et al., 1998; Carlson et al., 1999; Cahn-Weiner et al., 2000; Guo et al., 2001; Malouin et al., 2003). Physical performance, including gait and balance, may require higher-order executive function control processes (Whitman et al., 2001; Nieto et al., 2008) and the integrity of executive function is essential for the fine tuning of previously learned automatic locomotion tasks (Neville and Bavelier, 2002). Recent research suggested that executive function mediated the stability and velocity of gait in older adults when interference was introduced during walking (Coppin et al., 2006; Holtzer et al., 2006). In our previous study (Ko et al., 2009), self-efficacy was shown to be closely related to physical performance. As physical performance in the elderly is complex and multifactorial, it is necessary to evaluate the relationship of executive function and self-efficacy together with physical performance. We found that executive function was associated with physical performance even after adjusting for covariates such as gender, age, educational level, physical activity, and comorbidity, but this relationship was not independent of self-efficacy. This suggests that executive function may be associated with physical performance, mediated by selfefficacy. In an exploratory factor analysis (Lord et al., 2010), selfefficacy was reported to contribute more to the variance than executive function in older adults, and was shown to be more relevant than executive functions to physical performance. Therefore, clinicians need a broader approach to consider specific executive functions and self-efficacy together in rehabilitation strategies for improving physical performance in communityswelling older adults. Some limitations of the study should be considered. First, the cross-sectional design prevents reaching a conclusion on any causal relationship between cognitive function and physical performance. That means cognition and physical performance may have a bidirectional cause-effect relationship or a shared etiology. Longitudinal follow-up study or interventional study will be required to further elucidate this relationship. Second, our study was limited because subjects were recruited by ‘‘walk-in’’ voluntary participation. Thus, our subjects were more likely to be physically able and health-conscious than the general elderly population, which may affect the generalizability of our findings. 5. Conclusion Our findings suggest that reduced cognitive flexibility is related to decreased physical performance and weaker muscle strength, even after adjusting for important covariates. Self-efficacy was shown to be independently related to physical performance and muscle strength. Assessment of domain specific executive functions and self-efficacy in older adults may be useful in predicting the decline in physical function and in planning preventive and therapeutic strategies. Conflict of interest statement None.

Acknowledgements Y. Huh and E.J. Yang contributed equally to this study. This study was supported by an independent research grant (IRG) from Pfizer Global Pharmaceuticals (Grant No. 06-05-039), a grant from the Developing Seongnam Health Promotion Program for the Elderly from Seongnam City Government in Korea (Grant No. 80020050211), and a grant from Korea Healthcare Technology R&D project, Ministry for Health, Welfare and Family Affairs, Republic of Korea (No. A070001).

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