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Phys Act Nutr > Volume 28(4); 2024 > Article
Kang and Lee: Impact of physical fitness and lifelong education on mild cognitive impairment in older adults

Abstract

[Purpose]

The aim of this study was to explore the relationships among physical fitness, formal education levels, social educational experiences, and mild cognitive impairment in older adults.

[Methods]

Using the Korean version of the mini-mental state examination, senior fitness tests, and questionnaires on social educational experiences and physical activity, the study assessed data on 148 Korean participants 65 years and above. Multiple linear regression analysis was conducted to examine the relationships among the factors.

[Results]

The average age of the participants was 79.54 ± 0.55 years. Educational level showed a significant negative relationship with cognitive function scores, explaining 17.7% of the variance (R² = 0.177, p < 0.001). Current social educational experiences were significantly associated with a lower prevalence of mild cognitive impairment. Those without current social educational experiences were 1.946 times more likely to have MCI (p < 0.05) than those with such experiences. Physical fitness components, such as right upper body strength (OR = 1.171, 95% CI: 1.001-1.370), agility (OR = 1.246, 95% CI: 0.961-1.616), and cardiorespiratory fitness (OR = 0.975, 95% CI: 0.950-0.999), were also significantly associated with cognitive function. Additionally, older adult men had a higher likelihood of MCI than older adult women did (OR = 0.276, 95% CI: 0.097-0.782).

[Conclusion]

The findings highlight the importance of education, ongoing social educational experiences, and physical fitness in maintaining cognitive health in older adults. Thus, promoting lifelong education and physical fitness programs may help reduce the risk of mild cognitive impairment in older adults.

INTRODUCTION

The aging process leads to changes in physical abilities, increased susceptibility to infections, cognitive decline, and other factors that negatively affect the quality of life. Several risk factors may influence susceptibilities: sociodemographic factors, such as education, income level, and occupation [1,2]; socio-environmental factors, such as social and educational experiences or social interactions [3]; and lifestyle factors, such as exercise, drinking, and smoking [4]. Adequate physical activity and/or exercise has been shown to improve frailty and cognitive function; thus, both overall physical activity and structured exercise can play important roles as preventive strategies for many health problems [5], including reduced cognitive functioning [6,7] and dementia [5]. Physical fitness is an important component in the health management of older adults as higher levels of cardiorespiratory fitness [8,9] and muscular strength [10] are associated with enhanced cognitive function in this population. These effects result from improvements in arterial stiffness, and enhancement of neurotransmitter activity, brain-derived neurotrophic factor, and nerve growth factor.
Therefore, improving physical fitness levels through appropriate physical activity is an effective health management strategy for older adults. Although physical activity and fitness in older adults are crucial, there are also perspectives that acknowledge that engagement in physical fitness earlier in life can influence cognitive abilities later. This is because cognitive abilities exhibit different patterns depending on age.
Working memory and information processing speeds tend to slow down throughout life, whereas semantic memory and knowledge generally become weaker [11]. However, positive changes in cognitive functioning resulting from participation in physical fitness intervention programs have been observed for up to 5 years following the conclusion of the program [12]. Therefore, in addition to measuring the changes in older adults, it is important to investigate physical activity levels among those in their middle years to analyze their impact on cognitive function as people age. Although some studies have shown no significant relationship between physical activity and cognitive function [13,14], evaluating fitness factors along with physical activity might help assess the impact of physiological changes from physical activities on cognitive function, allowing for a more precise interpretation of the relationship.
Higher education levels are also recognized as playing a protective role against reduced cognitive functioning in older adults. Educational level refers to the total number of years of formal institutional education, with many studies using this value to assess its impact. However, studies, such as the SHARE study [15] and the Healthy Brain Project [16], have reported that late-life educational experiences through informal learning programs after the age of 50 also positively influence cognitive function in older adults. These social educational experiences primarily refer to community-based programs offered to middle-aged and older adults. The programs include classes that do not confer degrees but cover a wide range of subjects, such as culture, the arts, health, exercise, religion, and the humanities. Research suggests that as middle-aged or older adults, cognitive retention and improvement can be achieved through formal education and community education programs. Understanding which period of education within the entire lifecycle is related to cognitive function in older adults is complex. However, considering previous research results indicate that longer periods of education have more favorable effects on cognitive functions [17] and that social educational experiences during old age have greater impacts on individuals with less formal education [18-20], educational experiences during middle and old age should be evaluated regarding their impacts on cognitive function in older adults.
Social activities in old age are known to have protective cognitive function [21]. Most studies revealing this relationship have focused on social activities during old age, but some scholars argue that the relationship between social environments and cognitive function develops throughout an individual’s life [22] and that cognitive function is plastic, changing over one’s life span [23].
Considering this, the investigation of social activities before old age is important for understanding cognitive function in old age. Although some studies have revealed a positive relationship between social activities and cognitive function, others have reported no impact on cognitive impairment after participation in social activities or differences in the impact on cognitive function depending on the type of social activity and the participant’s sex [25]. Therefore, additional study is required to confirm this relationship. This study investigated demographic characteristics, social activity factors in middle and old age, and lifestyle behavior factors to understand their relation to cognitive function in older adults. The results of this study provide basic data for managing health and preventing cognitive impairment in older adults.

METHODS

Participants

The participants in this study were men and women aged 65 years and above who were recruited through community-based senior centers in Korea. Individuals who could not walk independently, had cardiovascular or neurological diseases, or had issues with hearing, vision, or language abilities that prevented them from appropriately responding to the examiner’s questions were excluded. A total of 150 participants were surveyed, and data from 148 participants were used for the final analysis, after excluding two participants with unfinished measurements and survey responses. All participants signed a consent form before participation and the ethics committee of Sangmyung University approved the study.

Survey process

To reduce errors among the examiners involved in all measurements and survey processes, sufficient training was provided beforehand. The same examiner conducted all tests to minimize intra-examiner errors. The examiners verbally interviewed the participants one-on-one and recorded the responses to ensure that the participants understood the questionnaire items and answered accurately. The number of participants to be recruited was determined using G*Power 3.1.9.6 software with an effect size adjusted for a cross-sectional design.

Participant Characteristics

Participants were asked to record the following information: birth date, currently diagnosed diseases, presence of cardiometabolic diseases as potential factors affecting cognitive function [26,27], number of regular medications, number of cohabiting family members, current employment status (including part-time), religion, and subjective economic status [15].

Educational Attainment, Social Education, and Social Activities

Education level was measured by the total number of years of schooling at a formal education institution. Social educational experiences were measured by self-reported participation in cultural, artistic, or educational classes, such as cultural history, painting, computer education, singing, dance, exercise, Bible study, and Buddhism classes. These classes had to be led by professional instructors, and participants had to be involved for 6 months or more before the interview, either as an older adult or before the age of 65 years. Classes after age 65 were considered current social educational experiences, whereas those before were categorized as earlier experiences.
In both cases, participation for more than 6 months was required to be included as a measure. Current social activities were captured in the interviews through the self-reporting of the participants regarding their participation in regular alumni meetings, friend gatherings, neighborhood gatherings, and religious group meetings in the past 6 months. When participation was reported, the number of hours per week and weeks of participation were also recorded [18].

Fitness Tests

The senior fitness tests were conducted to evaluate the physical fitness levels of the participants. Five fitness tests were conducted as described below.
2 Min Walk Test (2MWT): The 2 min walk test evaluates cardiorespiratory endurance. The length of each participant’s hips and knees were measured, and half of this length was marked on the wall. Participants were instructed to walk in place and raise their knees to the marked height. The examiner did not give commands to ensure participants could walk at their own pace. They were instructed to slow down if they had difficulty. The total number of steps taken in 2 min was measured, and both feet were counted.
Arm Curl Test: The arm curl test evaluates upper-body strength. Participants sat in a chair with a backrest and performed bicep curls using a 2 kg dumbbell for women and a 4 kg dumbbell for men. The total number of arm curls performed in 30 s, with the elbow moving through the full range of motion, was recorded.
Chair Sit-to-Stand Test: The chair sit-to-stand test evaluates lower body strength. The participants placed a chair against a wall, stood up with their knees fully extended, and sat down with their hips in complete contact with the chair. The total number of times that the participants stood up and sat down for 30 s was measured.
3 m Up-and-Go Test: The 3 m up-and-go test was conducted to evaluate dynamic balance. A cone was placed 3 m away from the chair. The participants started seated, stood up at a signal, walked around the cone, and returned to sit back down as quickly as possible without running. The time required to complete the task was measured.
Back-Scratch Test: The back-scratch test evaluates upper-body flexibility. The distance between the middle fingers of both hands behind the back was measured. If the fingers did not touch, the distance was recorded as negative; if they overlapped, the distance was recorded as positive.

Physical Activity Level

The Global Physical Activity Questionnaire (GPAQ) captures physical activity levels. The GPAQ includes items on activity at work, travel to and from places, and recreational activities, and determines the frequency and duration per session, and the time spent sitting or reclining each day. Total physical activity level (min/week) is the sum of the time spent on work, travel, and recreational activities, whereas sedentary behavior (min/week) is the sum of the time spent sitting or reclining. Participants were asked to recall their activities over the past 7 days for all categories above.

Alcohol Dependence

Alcohol dependence was evaluated using a questionnaire [28] consisting of 10 items. Each item was measured on a five-point scale from zero to four. The total score ranged from 0 to 40, with higher scores indicating higher alcohol dependence.

Nicotine Dependence

Nicotine dependence was evaluated using the Fagerstrom Test for Nicotine Dependence (FTND) questionnaire [29]. The questionnaire consists of six items, each rated from zero to three points. The total score ranges from 0 to 10, with higher scores indicating higher nicotine dependence.

Geriatric Depression

Geriatric depression was evaluated using the Geriatric Depression Scale Short Form [30]. This scale consists of 15 items, with “yes” or “no” responses. “Yes” responses are scored as zero points, and “no” responses are scored as one point. The total score is the sum of all item scores, with higher total scores indicating higher levels of depression.

Cognitive Function

Cognitive function was evaluated using the Korean version of the Mini-Mental State Examination (K-MMSE). This test consists of 27 items, and the total score is the sum of the scores for each item.

Statistical Analysis

SPSS PC+ for Windows (version 28.0) was used for data analysis. Participant characteristics were analyzed in terms of means and standard deviations based on the descriptive statistics. Simple regression analysis was used to examine the relationship between cognitive function and years of education. Chi-square tests were used to calculate the relationship between cognitive function and social educational experiences before and after age 65. The relationships between cognitive function and social educational experiences before and after 65 were analyzed using a two-way ANOVA. Each confounding variable, such as income, sex, and comorbidity, was treated as a covariate to remove the main effects, ensuring that the primary results were not distorted. The cut-off point for mild cognitive impairment was defined as a K-MMSE score of ≤26 [31]. Adjustment variables included alcohol consumption, smoking, depression, disease, medication, employment status, income, and religion. The relationships between cognitive function and education before and after age 65 were also examined using logistic regression analysis. As stated above, physical activity was divided into three levels and these were used to analyze the relationship between physical activity and cognitive function. Multiple linear regression analysis calculated the 42 relationships among the independent variables—the tertiles (three levels: Q1 for physical activity ≤ 630 min/week, Q2 for physical activity 613-1132 min/week, and Q3 for ≥ 1133 min/week), total exercise amount, height, weight, body mass index (BMI), and fitness test results—and the dependent variables, namely, cognitive function scores. The relationships between cognitive function and various predictors, including physical fitness components and demographic factors, were explored using binary logistic regression analysis. The significance level was set at p < 0.05.

RESULTS

Participant Characteristics

The general characteristics of the participants are listed in Table 1. The total number of participants was 148, with an overall average age of 79.54 ± 0.55 years. Height, weight, body mass index (BMI), senior fitness test results, GPAQ, and K-MMSE results are presented as the means and standard deviations for men and women. Alcohol dependence, nicotine dependence, diagnosed diseases, medications, cohabiting family members, education, employment, economic status, religion, social educational experiences before 65, current social educational experiences after 65, and depression are presented as overall percentages. Participants who scored 26 or below on the K-MMSE were classified as having mild cognitive impairment, whereas those scoring 27 or higher were classified as without impairment (normal).

Relationship Between Education and K-MMSE Scores

There was a significant negative relationship between years of education and cognitive function scores, accounting for 17.7% (R² = 0.177) of the variance (Table 2).

Relationship Between Current Social Educational Experiences and K-MMSE Scores

When the K-MMSE scores were categorized as mild cognitive impairment (26 or below) or without impairment (27 or above), participants without current social educational experiences had a significantly higher prevalence of mild cognitive impairment (odds ratio: 1.946; 95% CI: 1.002-3.780) compared with those with current social educational experiences.

Relationship Between Physical Activity, Agility, Cardiorespiratory Fitness, Upper Body Strength, and Mild Cognitive Impairment

When analyzing the impact of physical activity levels divided into tertiles (Q1: ≤630, Q2:631-1132, Q3: ≥1133 min/week) on mild cognitive impairment, no significant correlation was found between the participant’s physical activity level and mild cognitive impairment. However, significant correlations were found with agility, cardiorespiratory fitness, and upper body strength based on the fitness tests (Table 3). Better agility and cardiorespiratory fitness were significantly associated with higher K-MMSE scores. Higher right upper body strength (OR = 1.171, 95% CI: 1.001-1.370), better agility (OR = 1.246, 95% CI: 0.961-1.616), and better cardiorespiratory fitness (OR = 0.975, 95% CI: 0.950-0.999) were associated with a lower likelihood of mild cognitive impairment. Additionally, men had a higher likelihood of mild cognitive impairment than women (OR = 0.276, 95% CI: 0.097-0.782). These findings suggest that physical fitness components, such as muscle strength, agility, and cardiorespiratory fitness, are crucial in older adults for maintaining their cognitive function.

DISCUSSION

The purpose of this study was to analyze the impact of formal education, social educational experiences, physical activity, and physical fitness on cognitive function in older adults. A central finding was that higher years of formal education (higher attainment) were associated with better K-MMSE scores. Additionally, current engagement in social educational experiences was significantly linked to a lower prevalence of mild cognitive impairment, with individuals who were not participating in such programs 1.946 times more likely to develop mild cognitive impairment. Although there was no significant relationship between physical activity level and mild cognitive impairment, there were significant associations with cardiorespiratory fitness, strength, and agility. Better agility and cardiorespiratory fitness were significantly associated with higher K-MMSE scores.
The main finding that cardiorespiratory endurance, strength, and agility from fitness factors were significantly related to mild cognitive impairment aligns with previous research, demonstrating that improved fitness positively impacts one’s abilities, such as cognitive flexibility and memory [32]. Previous studies have also shown that improved fitness through physical activity intervention can contribute to better cognitive function [32-35], also supporting our results. Cardiorespiratory endurance is a strong fitness factor influencing cognitive function [36,37], and muscular fitness also relates to brain health [38,39]. This confirms that fitness improvement can influence mild cognitive impairment and suggests the importance of maintaining physical health for cognitive function in old age.
However, no significant relationship was noted between physical activity level and mild cognitive impairment. Other studies have reported that even low levels of physical activity were related to cognitive function, such as 15 min/d or 90 min/week being effective in preventing dementia [40,41], and household chores or leisure walking was associated with a lower risk of cognitive impairment in older women [42]. This inconsistency with prior results may be because one’s cognitive function is related to one’s fitness level. Improved fitness or participation in physical activity can lead to increased brain blood flow, the promotion of nerve growth factors [43], and changes in hippocampal size [44]. These changes require regular and sustained physical activity at a certain intensity [41].
Therefore, the relationship between physical activity level and fitness may not always be consistent. In the current study, physical activity was divided into tertiles (Q1, Q2, and Q3) to analyze its relationship with mild cognitive impairment. It remains uncertain if even relatively high physical intensity levels of exercise directly benefit the brain.
Nevertheless, significant associations were noted among higher education, social educational experiences in old age, and better cognitive function, confirming that education positively influences cognitive function. This finding aligns with previous research suggesting that formal and informal education can increase cognitive reserves and delay a decrease in cognitive functioning [45,46]. These extant studies have proposed that educational programs can promote neural plasticity, strengthen neural connections, and positively impact cognitive functioning. The significant relationship between education level and cognitive function emphasizes the importance of educational experiences and suggests the need to encourage lifelong learning in older adults.
Incorporating physical activity and leisure activities within the context of social education revealed that social education in old age was significantly associated with mild cognitive impairment, but not that before old age. This result contrasts with previous findings indicating that regular participation in physical activity during mid-life (ages 46-65) has significant effects on cognitive function in older adults [7,8,47]. One reason for the contrasting interpretation is that the participants in this study were limited to older adult adults in a specific area, with only 35.1% having participated in late-life education (or physical education) before age 65. Of all the participants, 64.9% had no such experience. Because the participants were recruited from the same residential area, their economic factors and social backgrounds were likely similar, potentially leading to comparable social educational experiences before 65. The high proportion of participants with nine years of formal education (71.6%) and the low proportion with university education or higher (10.2%) indicated a skewed distribution, which might explain the lack of association between social educational experiences before old age and mild cognitive impairment. Thus, the relationship identified between the benefits of formal education and cognitive function suggests that more diverse samples and conditions should be studied [48].
The finding that current social educational experiences positively impact cognitive function aligns with an earlier study [49], and with research that has found that social networks in older adults could delay the onset of dementia [50]. Social activities have also been reported to play a crucial role in preventing dementia [51]. Given that lower education levels are associated with cognitive impairment or dementia risk [52], this study suggests that social educational experiences in older adults could contribute to the cognitive reserve and reduce the risk of cognitive impairment, even in those with less formal education. This underscores the importance of promoting lifelong education in older adults as a potential strategy to prevent cognitive impairment and dementia.
However, several limitations of our study warrant comment. First, our analysis was based on data collected at a specific point in time, making it difficult to observe longitudinal changes and explain the causality between factors influencing cognitive function. Second, physical condition before old age was included in social educational experiences before 65 years.
Considering the historical and social context of Korea, defining physical activity as experiences through social education may limit the clarity of the relationship between the variables. Third, the sample size was small and the participants were limited to a specific region, introducing selection bias and limiting the generalizability of the results. The survey design also limited the inability to interpret whether participants engaged in regular and sustained physical activity at a certain level weekly, as only the last 7 d were recorded. Therefore, it is difficult to conclude that there is no relationship between physical activity and cognitive function based on the results of this study alone. Additionally, we were unable to investigate the effects of mental health history, genetic predisposition to dementia, and diet on cognitive function.
Despite these limitations, this study contributes to the literature by including events in middle-age when investigating factors affecting cognitive function in older adults. Additionally, by using the GPAQ to reflect daily lifestyles and ensuring participants understood the survey questions through one-on-one verbal interviews, the accuracy of the results increased. Various confounding factors affecting cognitive function were also considered in the analysis. Future studies should include participants from diverse regions to eliminate potential effects caused by homogeneity. Additionally, conducting a longitudinal study with a larger sample size will facilitate the analysis of causation, causal relationships among study factors, and the influence of these factors over time. Such analyses will enable a more detailed interpretation of cognitive decline.
This study confirmed that formal education, participation in social educational experiences during old age, and fitness levels play essential roles in cognitive function. These findings highlight the importance of maintaining or improving fitness levels in older adults, such as cardiorespiratory endurance, strength, and agility, as well as increasing participation in social educational experiences, to reduce the risk of mild cognitive impairment. Future research should include more diverse demographic factors, larger sample sizes, and longitudinal study to generalize the findings.

Acknowledgments

This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2023S1A5A2A01076375).

Table 1.
Participant characteristics.
Total (N=148) Female (N=113, 76.4%) Male (N=35, 23.6%)
Age (years) 79.54 ± 0.55 79.90 ± 6.94 78.37 ± 5.996
Height (cm) 153.19 ± 0.64 151.88±6.41 157.43 ± 9.95
Weight (kg) 57.32 ± 0.79 56.92 ± 9.05 58.60 ± 11.35
Body mass index (kg/m2) 24.31 ± 0.31 24.53 ± 3.80 23.58 ± 3.56
Upper limb muscle strength left (kg) 19.08 ± 0.51 18.58 ± 6.15 20.69 ± 6.03
Upper limb muscle strength right (kg) 18.59 ± 0.55 18.12 ± 6.65 20.09 ± 6.47
Lower limb muscle strength (kg) 13.72 ± 0.43 13.72 ± 5.54 13.71 ± 4.23
Flexibility left (cm) -21.78 ± 1.16 -20.39 ± 13.89 -26.29 ± 13.28
Flexibility right (cm) -21.55 ± 1.09 -20.07 ± 12.57 -26.25 ± 14.22
Agility (second) 10.67 ± 0.38 11.01 ± 5.036 9.55 ± 2.85
2-minute walk (meter) 71.94 ± 2.06 70.72 ± 26.28 75.86 ± 20.15
GPAQ-physical activity (minute/week) 983.45 ± 726.91 931.42 ± 725.85 1151.40 ± 714.84
GPAQ -sedentary (minute/week) 383.11 ± 236.23 403.02 ± 255.46 318.86 ± 144.08
K-MMSE (score) 24.80 ± 3.75 24.34 ± 3.91 26.29 ± 2.72
Table 2.
Relationship between formal education and K-MMSE scores.
Model Summary Value Significance (p)
R 0.421
R Squared 0.177
Adjusted R Squared 0.171
Standard Error 0.441
F 31.428 <0.001
t -5.606 <0.001
Table 3.
Relationship between physical activity, agility, cardiorespiratory fitness, upper body strength, and mild cognitive impairment.
Model 1
Model 2
B OR 95% CI for OR B OR 95% CI for OR
Constant -1.136 0.321 -2.520 0.080
Upper limb muscle strength left -0.120 0.887 (0.751, 1.047) -0.089 0.915 (0.772, 1.085)
Upper limb muscle strength right -0.165 1.180 (1.009, 1.379) * 0.158 1.171 (1.001, 1.370) *
Lower limb muscle strength -0.026 0.974 (0.873, 1.087) -0.055 0.947 (0.844, 1.061)
Flexibility left -0.014 0.986 (0.946, 1.027) -0.023 0.977 (0.935, 1.021)
Flexibility right -0.008 1.008 (0.963, 1.055) 0.000 1.000 (0.954, 1.048)
Agility -0.306 1.359 (1.074, 1.719) * 0.220 1.246 (0.961, 1.616) #
2-minute walk -0.024 1.359 (0.953, 0.999) * -0.026 0.975 (0.950, 0.999) *
Age 0.035 1.036 (0.958, 1.120)
Sex (Female=reference) -1.288 0.276 (0.097, 0.782) *
Body mass index -0.021 0.979 (0.863, 1.111)
Hosmer and Lemeshow test 2.323 4.549
Sig. 0.969 0.805
Nagelkerke R2 0.337 0.389

Note: Dependent variable: cognitive impairment (coded as 1), health (coded as 0). OR: odds ratio.

a Hosmer-Lemeshow statistics indicate a poor fit if the significance value is less than 0.05.

* p < 0.05,

# p < 0.10.

Model 1 analyzes the relationship between physical fitness factors (upper body strength, agility, cardiorespiratory endurance) and cognitive impairment, including only fitness variables as independent variables. Model 2 includes both physical fitness factors and demographic variables (sex, age, BMI) as independent variables to analyze their relationship with cognitive impairment.

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