==== Front Int J Environ Res Public Health Int J Environ Res Public Health ijerph International Journal of Environmental Research and Public Health 1661-7827 1660-4601 MDPI 33260999 10.3390/ijerph17238821 ijerph-17-08821 Article The Relationship between Physical Exercise and Cognitive Function in Korean Middle Aged and Elderly Adults without Dementia Koh Youngseung 1† Oh Yeonsu 1† Park Haesung 1† https://orcid.org/0000-0002-1199-6822Kim Woorim 2* https://orcid.org/0000-0002-2306-5398Park Eun-Cheol 34* 1 Yonsei University College of Medicine, Seoul 03722, Korea; yskoh361@yonsei.ac.kr (Y.K.); nasaoh@naver.com (Y.O.); comet129@naver.com (H.P.) 2 Division of Cancer Control & Policy, National Cancer Control Institute, National Cancer Center, Gyeonggi-do 10408, Korea 3 Institute of Health Services Research, Yonsei University, Seoul 03722, Korea 4 Department of Preventive Medicine, Yonsei University College of Medicine, Seoul 03722, Korea * Correspondence: wklaura@gmail.com (W.K.); ecpark@yuhs.ac (E.-C.P.); Tel.: +82-31-920-2940 (W.K.); +82-2-2228-1862 (E.-C.P.)† Denotes equal contribution. 27 11 2020 12 2020 17 23 882111 11 2020 26 11 2020 © 2020 by the authors.2020Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).This study investigated the association between physical exercise and cognitive function in Koreans aged 45 years or above without dementia. Data from the 2006 to 2018 Korean Longitudinal Study of Aging (KLoSA) were used. The general characteristics of the study population were investigated using analysis of variance (ANOVA). The association between total exercise time per week and cognitive function, measured based on the Mini-Mental State Examination (MMSE) scores, was investigated using the generalized estimating equation (GEE) model. Subgroup analysis was conducted based on age, educational level, and marital status. A total of 8888 participants were investigated, of which 5173 (58.2%) individuals did not exercise regularly. Among participants who did exercise, 676 (7.6%) individuals were categorized into the Q1, 1157 (13.0%) into the Q2, 908 (10.2%) into the Q3, and 974 (11.0%) into the Q4 group. The mean MMSE score was 26.81 ± 3.17. Compared to the ‘no’ exercise group, better MMSE scores were found in the Q1 (β: 0.3523, p ≤ 0.0001), the Q2 (β: 0.2011, p ≤ 0.0001), the Q3 (β: 0.4075, p ≤ 0.0001), and the Q4 groups (β: 0.3144, p ≤ 0.0001) after adjustment. The magnitude of this association was stronger in participants aged 65 years or above and in single or separated individuals. The findings of this study confirm a positive association between physical exercise and MMSE scores in the middle aged and elderly. physical exerciseexercise durationexercise frequencycognitive functionmiddle agedelderly ==== Body 1. Introduction Dementia is a degenerative disease characterized by noticeable cognitive decline, often diagnosed when normal social and occupation functioning of an individual is compromised due to cognitive impairment [1]. Age is a strong risk factor for dementia because it is a degenerative disease, making dementia an important issue in many East Asian countries that face a rapidly aging population. Issues on dementia are particularly significant in South Korea because it is one of the world’s most rapidly aging countries [2]. The prevalence of dementia shows an increasing trend, with it estimated at 5.0% and the prevalence of mild cognitive impairment at 27.0% [3]. Unsurprisingly, the number of dementia patients in expected to continuously increase in the future, because patient numbers tend to nearly double with each 5.8-year increase in average age [4]. Although the causes of dementia are not fully understood due to its complex and multifactorial nature, features such as eating habits, mental diseases including depression, genetics, and individual lifestyle have been reported as common risk factors [5,6,7,8,9,10,11,12]. Specifically, physical exercise is a well-known and modifiable risk factor for many age-related diseases, including dementia [13]. In fact, the positive association between physical exercise and cognitive function in the middle-aged and elderly has been well established. A previous study presented that aerobic and balance exercise is beneficial in improving cognitive performance in middle-aged adults [14]. Likewise, physical exercise has been related to better cognitive and brain health in the elderly [15]. However, despite such importance, few studies have investigated this relationship in East Asian countries because dementia has only gained significant importance recently compared to many Western countries with an already aged population. Furthermore, the few Korean studies present on this topic were relatively small-scaled or targeted only a specific group of individuals [16,17]. Therefore, the aim of this study was to investigate the association between physical exercise and cognitive status in middle and older aged Korean adults without dementia using the Mini-Mental State Exam (MMSE), a simple scale developed to test cognitive ability. The hypothesis was that individuals who regularly exercise will score better on the MMSE scale than their physically inactive counterparts. Additionally, subgroup analysis was conducted based on age, educational level, and marital status to further examine how these factors can potentially interplay in the main relationship stated above. 2. Materials and Methods 2.1. Study Population This study used data from the 2006 to 2018 Korean Longitudinal Study of Aging (KLoSA). The KLoSA is a biennial panel study conducted by the Korean Employment Information Service. Individuals aged 45 years or above at the 2006 baseline were randomly selected nationwide. Households residing on islands, including Jeju island, were excluded from the sampling frame. Institutionalized individuals were also excluded. The target population was selected using a stratified, multi-stage area probability sampling method to obtain a nationally representative sample of individuals aged 45 years or above. Region was first stratified into urban and rural areas based on the 15 administrative districts of South Korea, which was further categorized into apartment and ordinary housing (houses, townhouses, multifamily houses) areas [18]. Of the 1000 sample districts, each of the 15 administrative districts were allocated with 15 sample districts (225 total), and the remaining 775 sample districts were assigned based on population size [18]. A total of 10,254 individuals were included in the 2006 data [19]. The average follow-up rate was 77.6% (2008: 86.6%; 2010: 81.7%; 2012: 80.1%; 2014: 80.4%; 2016: 79.6%; 2018: 78.8%) [19]. Years of observation were 2006, 2008, 2010, 2012, 2014, 2016, and 2018. Information was collected through face-to-face interviews conducted using the Computer Assisted Personal Interviewing (CAPI) technique. Missing values of the 20 major variables were replaced using the multiple imputation method [20]. The reported proportion of missing data were between 10% to 20% for income or wealth related variables and less than 5% for the other variables in the KLoSA. Details on the study population are shown in Figure 1. Of the 10,254 individuals included in the 2006 data, 489 participants incapable of performing at least one of the basic Activities of Daily Living (ADL) and 819 participants with MMSE scores below 19 were excluded because those with poor basic ADL performance or dementia may be unable to perform regular physical exercise. Additionally, 58 individuals with missing values on depression measured using the Centre for Epidemiological Studies Depression (CES-D) were not included. This led to the final baseline study population of 8888 individuals. 2.2. Outcome Measure The outcome measure of this study was cognitive function, measured using the Korean-Mini-Mental Status Examination (K-MMSE). The MMSE is a known index used to examine and diagnose patients with dementia [21]. The K-MMSE has previously been validated in a comparative study. A K-MMSE score over 24 points implies ‘definite normal’ status, 20–23 points implies ‘cognitive decline’, and below 19 points implies ‘dementia [21]’. 2.3. Physical Exercise Physical exercise was measured based on the following three items: total exercise time per week (minutes per week), duration per exercise (minutes per exercise), and exercise frequency per week (days per week). The KLoSA collects information on exercise frequency and duration, measured based on the recommended level of physical exercise performed regularly [22]. This is a standardized question utilized widely in previous literature to outline self-reported exercise [23]. Total exercise time per week, the main variable of interest, was calculated based on exercise duration and frequency. Individuals exercising regularly were categorized into quartiles. The ranges were set at 120, 240, and 420 min per week. 2.4. Covariates Different demographic, socioeconomic, and health behavior-related covariates were incorporated in this study. The included covariates were sex (male or female), age (45–64 years or 65 years or above), age2, household income (quartiles), educational level (high school or below or college or above), marital status (single or separated, or married and cohabiting with spouse), depressive symptoms (no or yes), chronic diseases (none, one, or two or above), smoking status (no or yes), perceived health status (poor or fair), and region (rural areas, small to medium sized cities, or large cities). Age2 was included as a covariate to account for the strong association between age and the dependent variable [24]. Chronic diseases included diabetes, hypertension, and chronic obstructive pulmonary disease (COPD). Depressive symptoms were measured using the 10-item Centre for Epidemiological Studies Depression (CES-D 10) scale. The CES-D 10 has been widely utilized to screen for depressive symptoms in older adults [25]. 2.5. Statistical Analysis One-way analysis of variance (ANOVA) was used to investigate the general characteristics of the study population. The association between physical exercise and MMSE scores were analyzed using the generalized estimating equation (GEE) model, an extension of the quasi-likelihood approach used to analyze longitudinal correlated data in the form of counts [26]. The GEE model takes into account time variation and the correlations between repeated measurements found in a longitudinal study design [26]. The GEE model was applied because the KLoSA data are longitudinal in design and contain repeated measurements, inferring the need to account for the correlation within subjects. Total exercise time per week was the main interesting variable, although analysis was also conducted for the other two items on physical exercise based on separate models. Model 1 depicts the results of the unadjusted analysis and Model 2 that of the adjusted analysis. Subgroup analysis was conducted based on age, educational level, and marital status. All p-values were two-tailed, and the level of significance was set at 0.05. All analyses were conducted using the SAS version 9.4 (SAS, Cary, NC, USA). 2.6. Ethical Concerns This paper complies with the ethical standards of the relevant national and institutional committees on human experimentation and the Helsinki Declaration of 1975. This study used open, secondary data in which all personal information was anonymized and unidentifiable, therefore attainment of an informed consent was waived. Access to the KLoSA data was acquired after upon its approval by the Institutional Review Board (IRB) of the Korea National Institute for Ethics Policy (P01-201909-22-002). The KLoSA data are also part of the Official Statistics collected and utilized based on the Statistics Act of Korea. 3. Results The characteristics of the study participants at baseline are shown in Table 1. Of the 8888 participants, 5173 (58.2%) individuals did not exercise regularly. Among individuals who did exercise regularly, 676 (7.6%) participants were included into the Q1 group, 1157 (13.0%) into the Q2 group, 908 (10.2%) into the Q3 group, and 974 (11.0%) into the Q4 group. Individuals who did not exercise had the poorest MMSE scores. The mean MMSE scores at baseline were 26.81 ± 3.17, as displayed in Table 2. The results of the GEE analysis on the association between MMSE scores and physical exercise (total exercise time per week, duration per exercise, and exercise frequency per week) are presented in Table 3. Model 1 shows the results of the unadjusted analysis and Model 2 shows that of the adjusted analysis. MMSE scores tended to escalate as total exercise time per week increased in both models. Without adjustment, individuals in the Q1 group (β: 0.5261, p ≤ 0.0001), the Q2 group (β: 0.3244, p ≤ 0.0001), the Q3 group (β: 0.5815, p ≤ 0.0001), and the Q4 group (β: 0.5626, p ≤ 0.0001) showed better MMSE scores than participants who did not exercise. After adjustment, compared to the ‘no’ exercise group, better MMSE scores were found in the Q1 (β: 0.3523, p ≤ 0.0001), Q2 (β: 0.2011, p ≤ 0.0001), Q3 (β: 0.4075, p ≤ 0.0001), and Q4 groups (β: 0.3144, p ≤ 0.0001) groups. Analysis on the relationship between MMSE scores and duration per exercise and exercise per frequency showed similar tendencies, with MMSE scores showing a positive correlation with exercise length and frequency. The results of the subgroup analysis on the association between MMSE scores and physical exercise by age, education level, and marital status are shown in Table 4. The tendencies of the main findings were generally maintained in all groups. However, the magnitude of increase was more profound in older individuals aged 65 years or above than those aged between 45 and 64 years. The degree of association was also stronger in single or separated individuals compared to married participants cohabiting with their spouse. The p-values for interaction showed significance for all three variables, namely age (p ≤ 0.0001), education level (p = 0.0377) and marital status (p = 0.0002). 4. Discussion The results of this study confirm an association between physical exercise and MMSE scores in the Korean middle-aged and elderly population without dementia. Individuals not exercising regularly showed comparatively poorer MMSE scores compared to the ‘yes’ exercise group. The highest level of increases was found in the Q3 exercise group, followed by the Q1, Q4, and Q2 groups. The positive correlations between physical exercise and cognitive function were maintained when examining physical exercise based on duration per workout (minutes per exercise) and frequency (days per week). The presented findings are in accordance with previous studies, which have demonstrated that physical exercise is related to higher cognitive performance in adults [27]. A previous meta-analysis confirmed that physical exercise can reduce risk of cognitive deficits, supporting the results of this study [28]. Previous studies have also revealed the positive influence of exercise duration on cognitive debilitation [29]. Additionally, exercise frequency has also been associated with cognitive functioning, with studies reporting that even regular or light exercise can reduce the risk of developing dementia in the elderly [30]. Considering the high proportion of middle aged and elderly individuals reporting to not exercise regularly in South Korea, the results offer important insights by revealing the potential benefits of physical exercise. In this study, individuals regularly exercising clearly showed better cognitive performance than those not conducting exercise. However at the same time, a dose-response relationship was not found because compared to the ‘no’ exercise group, the Q3 group showed the highest level of increase in MMSE score, followed by the Q1, Q4, and Q2 groups. Many previous studies on this topic have reported a similar inclination. Specifically, despite the establishment of a strong relationship between physical exercise and cognitive status, reports on its dose–response relationship have shown mixed results because they do not show a stepwise increase based on exercise frequency or duration [31,32]. Others suggest various levels of exercise quantity or quality as a threshold level to prevent cognitive decline [33,34]. As such, future targeted studies are needed to verify the presence of a dose–response relationship on this topic. The results of the subgroup analysis show that the relationship between physical exercise and cognitive function is maintained regardless of age, educational level, and marital status. However, a stronger degree of association was found in the elderly aged 65 years or above. The assistive benefits of exercise on cognitive function may have been more profound in older aged adults because physical and cognitive functioning are known to decline with age. Moreover, the relationship found was also more significant in single or separated individuals compared to married individuals cohabiting with their spouse. The results are understandable, considering that social factors, such as cohabitation status, have been previously associated with cognitive decline [35,36]. In fact, cohabitation status has been suggested as a risk factor for cognitive deterioration, with studies reporting that the elderly living alone or with a non-spouse family member may be more vulnerable to physical and cognitive deterioration [37,38,39]. This may result due to the loss of a spouse leading to disruptions in carrying out daily activities or routines [40]. This study has some limitations. Firstly, exercise intensity could not be taken into account due to data limitation. The KLoSA data only provide information on exercise frequency per week and exercise duration per workout. However, the KLoSA basic analysis report states that exercise duration and frequency was measured based on the recommended level of physical exercise individuals responded to participate in the survey [22]. Thus, a certain level of homogeneity was ensured in the data utilized. Secondly, although cognitive status was measured based on the previously validated K-MMSE, assessment made based on its cut-off value does not necessarily imply a clinical diagnosis of dementia. Thirdly, although this study excluded individuals with ADL limitations or dementia because they may have a reduced level of physical competence, the possibility of reverse causality cannot be completely ruled out. Lastly, although this study did adjust for various confounding variables, partial residual confounding may still have been present. However, despite the limitations stated above, this study is unique in that it examined the relationship between physical exercise and cognitive function using a large, nationally representative sample of South Korean middle-aged and elderly adults. The findings offer important insights because they reveal that participating in physical exercise itself can help preserve cognitive functioning. 5. Conclusions The findings of this study confirm a positive association between participating in physical exercise and better MMSE scores in the middle aged and elderly without dementia. This tendency was maintained regardless of age, educational level, and marital status. The degree of this relationship was magnified in individuals aged 65 years or above and in single or separated individuals. The results highlight the importance of promoting physical exercise to prevent cognitive declines in older aged adults, which is important considering the rapidly aging population found in many countries. Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Author Contributions Y.K., Y.O., H.P., W.K. and E.-C.P. designed the study, interpreted the data, wrote the initial manuscript, and reviewed the final paper. W.K. and E.-C.P. are the guarantors of this work and were responsible for the overall direction of the study. All authors have read and agreed to the published version of the manuscript. Funding This research received no external funding. Conflicts of Interest The authors declare no conflict of interest. Figure 1 Study population selection process. ijerph-17-08821-t001_Table 1Table 1 General characteristics of the study population. MMSE Scores N (%) Mean ± SD p-Value Total Exercise Time per Week (min/week) None 5173 (58.2) 26.43 ± 3.38 <0.0001 Q1 676 (7.6) 27.47 ± 2.77 Q2 1157 (13.0) 27.23 ± 2.85 Q3 908 (10.2) 27.52 ± 2.65 Q4 974 (11.0) 27.21 ± 2.75 Duration per exercise (minutes/exercise) None 5173 (58.2) 26.43 ± 3.38 <0.0001 Short (≤59) 1220 (13.7) 26.96 ± 2.98 Moderate (60–119) 1822 (20.5) 27.45 ± 2.70 Long (≥120) 673 (7.6) 27.73 ± 2.44 Exercise frequency per week (days/week) Not frequent (0) 5173 (58.2) 26.43 ± 3.38 <0.0001 Moderate (1–3) 1472 (16.6) 27.68 ± 2.54 Frequent (4–7) 2243 (25.2) 27.11 ± 2.88 Sex Male 4081 (45.9) 27.39 ± 2.79 <0.0001 Female 4807 (54.1) 26.32 ± 3.39 Age 45–64 3196 (36.0) 28.14 ± 2.25 <0.0001 65 or above 2596 (29.2) 27.08 ± 2.86 Household income Low 2245 (25.3) 25.88 ± 3.41 <0.0001 Low-middle 2314 (26.0) 26.40 ± 3.32 Middle-high 2115 (23.8) 27.15 ± 2.90 High 2214 (24.9) 27.85 ± 2.59 Educational level High school or below 7796 (87.7) 26.56 ± 3.23 <0.0001 College or above 1092 (12.3) 28.62 ± 1.91 Marital status Single or separated 1622 (18.2) 25.31 ± 3.67 <0.0001 Married and cohabiting with spouse 7266 (81.8) 27.14 ± 2.95 Depressive symptoms No 6417 (72.2) 27.25 ± 2.94 <0.0001 Yes 2471 (27.8) 25.66 ± 3.44 Chronic diseases None 5999 (67.5) 27.12 ± 3.04 0.8421 1 2285 (25.7) 26.23 ± 3.30 2 or above 604 (6.8) 25.89 ± 3.48 Smoking status No 7079 (79.6) 26.66 ± 3.23 0.9359 Yes 1809 (20.4) 27.40 ± 2.83 Perceived health status Fair 4580 (51.5) 27.72 ± 2.65 <0.0001 Poor 4308 (48.5) 25.84 ± 3.38 Region Rural areas 4044 (45.5) 27.10 ± 3.05 <0.0001 Small to medium sized cities 2941 (33.1) 26.83 ± 3.15 Large cities 1903 (21.4) 26.16 ± 3.36 Total 8888 (100.0) 26.81 ± 3.17 ijerph-17-08821-t002_Table 2Table 2 Mean MMSE scores by survey year. Year Mean ± SD 2006 26.81 ± 3.17 2008 26.61 ± 3.20 2010 26.61 ± 3.28 2012 26.77 ± 3.23 2014 26.61 ± 3.39 2016 26.87 ± 3.31 2018 26.72 ± 3.39 ijerph-17-08821-t003_Table 3Table 3 The association between MMSE scores and physical exercise. Model 1 * Model 2 * β SE p-Value β SE p-Value Total exercise time per week (minutes/week) None Ref Ref Q1 0.5261 0.0446 <0.0001 0.3523 0.0436 <0.0001 Q2 0.3244 0.0448 <0.0001 0.2011 0.0433 <0.0001 Q3 0.5815 0.0415 <0.0001 0.4075 0.0399 <0.0001 Q4 0.5626 0.0473 <0.0001 0.3144 0.0463 <0.0001 Duration per exercise (minutes/exercise) None Ref Ref Short (≤59) 0.2840 0.0426 <0.0001 0.2903 0.0414 <0.0001 Moderate (60–119) 0.5742 0.0349 <0.0001 0.3576 0.0337 <0.0001 Long (≥120) 0.7321 0.052 <0.0001 0.2662 0.0508 <0.0001 Exercise frequency per week (days/week) Not frequent (0) Ref Ref Moderate (1–3) 0.5433 0.0377 <0.0001 0.2713 0.0364 <0.0001 Frequent (4–7) 0.4692 0.0352 <0.0001 0.3552 0.0338 <0.0001 Sex Male Ref Female −0.8513 0.0461 <0.0001 Age 45–64 Ref 65 or above 0.2269 0.0421 <0.0001 Age2 −0.0007 0.0000 <0.0001 Household income Low Ref Low-middle 0.3337 0.0419 <0.0001 Middle-high 0.6016 0.043 <0.0001 High 0.8689 0.0445 <0.0001 Educational level High school or below Ref College or above 0.0257 0.0511 0.6150 Marital status Single or separated Ref Married and cohabiting with spouse 0.3077 0.0571 <0.0001 Depressive symptoms No Ref Yes −0.6813 0.0303 <0.0001 Chronic diseases None Ref 1 −0.0430 0.0414 0.2991 2 or above −0.1125 0.0689 0.1026 Smoking status No Ref Yes −0.0501 0.0482 0.2988 Perceived health status Poor Ref Fair 0.4301 0.0296 <0.0001 Region Rural areas Ref Small to medium sized cities −0.2952 0.0461 <0.0001 Large cities −0.5011 0.0537 <0.0001 * Total exercise time per week, duration per exercise, and exercise frequency per week analyzed in three separate models, adjusted for the same variables; Results of the covariates are from the analysis on total exercise time per week. SE, standard error. ijerph-17-08821-t004_Table 4Table 4 Results of the subgroup analysis. MMSE Scores * β SE p-Value β SE p-Value Age Middle-aged (45–64) Elderly (65+) Total exercise time per week (min/week) None Ref Ref Q1 0.2553 0.0479 <0.0001 0.5403 0.0778 <0.0001 Q2 0.1600 0.0494 0.0012 0.2442 0.0727 0.0008 Q3 0.2115 0.0465 <0.0001 0.6488 0.0674 <0.0001 Q4 0.1023 0.0565 0.0701 0.5267 0.0727 <0.0001 Education level Middle school or below High school or above Total exercise time per week (min/week) None Ref Ref Q1 0.3441 0.0458 <0.0001 0.4271 0.122 0.0005 Q2 0.1781 0.045 <0.0001 0.4512 0.1296 0.0005 Q3 0.4161 0.0414 <0.0001 0.1059 0.1338 0.4287 Q4 0.3221 0.0487 <0.0001 0.1383 0.138 0.3164 Marital status Single or separated Married and cohabiting with spouse Total exercise time per week (min/week) None Ref Ref Q1 0.3593 0.1189 0.0025 0.3460 0.0461 <0.0001 Q2 0.2207 0.1154 0.0559 0.1923 0.0458 <0.0001 Q3 0.4616 0.1203 0.0001 0.3906 0.0414 <0.0001 Q4 0.5174 0.1374 0.0002 0.2765 0.0484 <0.0001 * Adjusted for sex, age, household income, education level, marital status, depressive symptoms, chronic diseases, smoking status, perceived health status, and region. SE, standard error. ==== Refs References 1. Hugo J. Ganguli M. Dementia and cognitive impairment: Epidemiology, diagnosis, and treatment Clin. Geriatr. Med. 2014 30 421 442 10.1016/j.cger.2014.04.001 25037289 2. Kim K.W. Park J.H. Kim M.H. Kim M.D. Kim B.J. Kim S.K. Kim J.L. Moon S.W. Bae J.N. Woo J.I. A nationwide survey on the prevalence of dementia and mild cognitive impairment in South Korea J. Alzheimers Dis. 2011 23 281 291 10.3233/JAD-2010-101221 21178286 3. Han J.W. Kim T.H. Kwak K.P. Kim K. Kim B.J. Kim S.G. Kim J.L. Kim T.H. Moon S.W. Park J.Y. Overview of the Korean Longitudinal Study on Cognitive Aging and Dementia Psychiatry Investig. 2018 15 767 774 10.30773/pi.2018.06.02 30086611 4. Kim Y.J. Han J.W. So Y.S. Seo J.Y. Kim K.Y. Kim K.W. Prevalence and trends of dementia in Korea: A systematic review and meta-analysis J. Korean Med. Sci. 2014 29 903 912 10.3346/jkms.2014.29.7.903 25045221 5. Gray G.E. Nutrition and dementia J. Am. Diet. Assoc. 1989 89 1795 1802 2687349 6. Heston L.L. Dementia associated with Parkinson’s disease: A genetic study J. Neurol. Neurosurg. Psychiatry 1980 43 846 848 10.1136/jnnp.43.9.846 7420108 7. Roman G.C. Vascular dementia prevention: A risk factor analysis Cereb. Dis. 2005 20 Suppl. 2 91 100 10.1159/000089361 8. Jorm A.F. History of depression as a risk factor for dementia: An updated review Aust. N. Z. J. Psychiatry 2001 35 776 781 10.1046/j.1440-1614.2001.00967.x 11990888 9. Pastor P. Moreno F. Clarimon J. Ruiz A. Combarros O. Calero M. Lopez de Munain A. Bullido M.J. de Pancorbo M.M. Carro E. MAPT H1 Haplotype is Associated with Late-Onset Alzheimer’s Disease Risk in APOEvarepsilon4 Noncarriers: Results from the Dementia Genetics Spanish Consortium J. Alzheimers Dis. 2016 49 343 352 10.3233/JAD-150555 26444794 10. Rodriguez F.J. Picken M.M. Lee J.M. Amyloid Deposition in the Central Nervous System Humana Press Tortowa, NJ, USA 2015 11. Saczynski J.S. Pfeifer L.A. Masaki K. Korf E.S. Laurin D. White L. Launer L.J. The effect of social engagement on incident dementia: The Honolulu-Asia Aging Study Am. J. Epidemiol. 2006 163 433 440 10.1093/aje/kwj061 16410348 12. Laurin D. Verreault R. Lindsay J. MacPherson K. Rockwood K. Physical activity and risk of cognitive impairment and dementia in elderly persons Arch. Neurol. 2001 58 498 504 10.1001/archneur.58.3.498 11255456 13. Yu F. Chen Y. Mathiason M.A. Wan Q. Lin F.V. Cognitive and physical factors affecting daily function in Alzheimer’s disease: A cross-sectional analysis Nurs. Health Sci. 2019 21 14 20 10.1111/nhs.12426 29736953 14. Formenti D. Cavaggioni L. Duca M. Trecroci A. Rapelli M. Alberti G. Komar J. Iodice P. Acute Effect of Exercise on Cognitive Performance in Middle-Aged Adults: Aerobic Versus Balance J. Phys. Act. Health 2020 1 8 10.1123/jpah.2020-0005 15. Tyndall A.V. Clark C.M. Anderson T.J. Hogan D.B. Hill M.D. Longman R.S. Poulin M.J. Protective Effects of Exercise on Cognition and Brain Health in Older Adults Exerc. Sport Sci. Rev. 2018 46 215 223 10.1249/JES.0000000000000161 30001269 16. Minn Y.K. Choi S.H. Suh Y.J. Jeong J.H. Kim E.J. Kim J.H. Park K.W. Park M.H. Youn Y.C. Yoon B. Effect of Physical Activity on the Progression of Alzheimer’s Disease: The Clinical Research Center for Dementia of South Korea Study J. Alzheimers Dis. 2018 66 249 261 10.3233/JAD-180333 30282355 17. Park H.K. Jeong J.H. Moon S.Y. Park Y.K. Hong C.H. Na H.R. Song H.S. Lee S.M. Choi M. Park K.W. South Korean Study to Prevent Cognitive Impairment and Protect Brain Health Through Lifestyle Intervention in At-Risk Elderly People: Protocol of a Multicenter, Randomized Controlled Feasibility Trial J. Clin. Neurol. 2020 16 292 303 10.3988/jcn.2020.16.2.292 32319247 18. Jang S.N. Korean Longitudinal Study of Ageing (KLoSA): Overview of research design and contents Encyclopedia of Geropsychology Pachana N. Springer Singapore 2015 10.1007/978-981-287-080-3_55-1 19. Korea Employment Information Service 2020 Korean Longitudinal Study of Ageing User’s Guide Korea Employment Information Service Chungcheongbuk-do, Korea 2020 20. Little R.J.A. Missing data adjustments in large surveys J. Bus. Econ. Stat. 1988 6 287 296 21. Kang I.W. Beom I.G. Cho J.Y. Son H.R. Accuracy of Korean-Mini-Mental Status Examination Based on Seoul Neuro-Psychological Screening Battery II Results Korean J. Fam. Med. 2016 37 177 181 10.4082/kjfm.2016.37.3.177 27274389 22. Shin J.K. Ahn J.K. Kim K.H. Kim E.Y. Kwon Y.S. The 2016 Korean Longitudinal Study of Ageing Basic Analysis Report Korea Employment Information Service Chungbuk, Korea 2017 23. Gerst K. Michaels-Obregon A. Wong R. The impact of physical activity on disability incidence among older adults in Mexico and the United States J. Aging Res. 2011 420714 10p 10.4061/2011/420714 21584238 24. Chen H.Y. Dufresne L. Burr H. Ambikkumar A. Yasui N. Luk K. Ranatunga D.K. Whitmer R.A. Lathrop M. Engert J.C. Association of LPA Variants With Aortic Stenosis: A Large-Scale Study Using Diagnostic and Procedural Codes From Electronic Health Records JAMA Cardiol. 2018 3 18 23 10.1001/jamacardio.2017.4266 29128868 25. Irwin M. Artin K.H. Oxman M.N. Screening for depression in the older adult: Criterion validity of the 10-item Center for Epidemiological Studies Depression Scale (CES-D) Arch. Intern. Med. 1999 159 1701 1704 10.1001/archinte.159.15.1701 10448771 26. Hanley J.A. Negassa A. Edwardes M.D. Forrester J.E. Statistical analysis of correlated data using generalized estimating equations: An orientation Am. J. Epidemiol. 2003 157 364 375 10.1093/aje/kwf215 12578807 27. Lin S. Yang Y. Qi Q. Wei L. Jing N. Jie Z. Xia L. Shifu X. The Beneficial Effect of Physical Exercise on Cognitive Function in a Non-dementia Aging Chinese Population Front. Aging Neurosci. 2019 11 238 10.3389/fnagi.2019.00238 31555125 28. Sofi F. Valecchi D. Bacci D. Abbate R. Gensini G.F. Casini A. Macchi C. Physical activity and risk of cognitive decline: A meta-analysis of prospective studies J. Intern. Med. 2011 269 107 117 10.1111/j.1365-2796.2010.02281.x 20831630 29. Lytle M.E. Vander Bilt J. Pandav R.S. Dodge H.H. Ganguli M. Exercise level and cognitive decline: The MoVIES project Alzheimer Dis. Assoc. Disord. 2004 18 57 64 10.1097/01.wad.0000126614.87955.79 15249848 30. Andel R. Crowe M. Pedersen N.L. Fratiglioni L. Johansson B. Gatz M. Physical exercise at midlife and risk of dementia three decades later: A population-based study of Swedish twins J. Gerontol. A Biol. Sci. Med. Sci. 2008 63 62 66 10.1093/gerona/63.1.62 18245762 31. Ohman H. Savikko N. Strandberg T.E. Kautiainen H. Raivio M.M. Laakkonen M.L. Tilvis R. Pitkala K.H. Effects of Exercise on Cognition: The Finnish Alzheimer Disease Exercise Trial: A Randomized, Controlled Trial J. Am. Geriatr. Soc. 2016 64 731 738 10.1111/jgs.14059 27037872 32. Cass S.P. Alzheimer’s Disease and Exercise: A Literature Review Curr. Sports Med. Rep. 2017 16 19 22 10.1249/JSR.0000000000000332 28067736 33. Brown B.M. Peiffer J.J. Martins R.N. Multiple effects of physical activity on molecular and cognitive signs of brain aging: Can exercise slow neurodegeneration and delay Alzheimer’s disease? Mol. Psychiatry 2013 18 864 874 10.1038/mp.2012.162 23164816 34. Jia R.X. Liang J.H. Xu Y. Wang Y.Q. Effects of physical activity and exercise on the cognitive function of patients with Alzheimer disease: A meta-analysis BMC Geriatr. 2019 19 181 10.1186/s12877-019-1175-2 31266451 35. Roe C.M. Xiong C. Miller J.P. Morris J.C. Education and Alzheimer disease without dementia: Support for the cognitive reserve hypothesis Neurology 2007 68 223 228 10.1212/01.wnl.0000251303.50459.8a 17224578 36. Sharp E.S. Gatz M. Relationship between education and dementia: An updated systematic review Alzheimer Dis. Assoc. Disord. 2011 25 289 304 10.1097/WAD.0b013e318211c83c 21750453 37. Saito T. Murata C. Aida J. Kondo K. Cohort study on living arrangements of older men and women and risk for basic activities of daily living disability: Findings from the AGES project BMC Geriatr. 2017 17 183 10.1186/s12877-017-0580-7 28814289 38. Rist P.M. Liu S.Y. Glymour M.M. Families and Disability Onset: Are Spousal Resources Less Important for Individuals at High Risk of Dementia? Am. J. Geriatr. Psychiatry 2016 24 585 594 10.1016/j.jagp.2016.02.003 27066733 39. Lund R. Nilsson C.J. Avlund K. Can the higher risk of disability onset among older people who live alone be alleviated by strong social relations? A longitudinal study of non-disabled men and women Age Ageing 2010 39 319 326 10.1093/ageing/afq020 20208073 40. Naef R. Ward R. Mahrer-Imhof R. Grande G. Characteristics of the bereavement experience of older persons after spousal loss: An integrative review Int. J. Nurs. Stud. 2013 50 1108 1121 10.1016/j.ijnurstu.2012.11.026 23273923