
==== Front
J Funct Morphol Kinesiol
J Funct Morphol Kinesiol
jfmk
Journal of Functional Morphology and Kinesiology
2411-5142
MDPI

10.3390/jfmk9030150
jfmk-09-00150
Article
Subjective Cognitive Impairment and Physical Activity: Investigating Risk Factors and Correlations among Older Adults in Spain
https://orcid.org/0000-0001-7977-6837
Franco-García Juan Manuel Conceptualization Software Investigation Writing – original draft Writing – review & editing 1
https://orcid.org/0000-0002-5740-305X
Denche-Zamorano Ángel Conceptualization Methodology Software Formal analysis Resources Data curation Writing – original draft Supervision 2
https://orcid.org/0000-0002-6377-9950
Carlos-Vivas Jorge Validation Formal analysis Writing – review & editing Project administration 3
https://orcid.org/0000-0002-7239-960X
Castillo-Paredes Antonio Validation Visualization Funding acquisition 4*
https://orcid.org/0000-0002-3375-092X
Mendoza-Holgado Cristina Conceptualization Validation Writing – original draft Writing – review & editing Visualization 5
https://orcid.org/0000-0002-4054-9132
Pérez-Gómez Jorge Methodology Writing – original draft Writing – review & editing Supervision Project administration 1
Riebe Deborah Academic Editor
1 Health Economy Motricity and Education (HEME) Research Group, Faculty of Sport Sciences, University of Extremadura, 10003 Cáceres, Spain; jmfrancog@unex.es (J.M.F.-G.); jorgepg100@gmail.com (J.P.-G.)
2 Promoting a Healthy Society Research Group (PHeSO), Faculty of Sport Sciences, University of Extremadura, 10003 Cáceres, Spain; denchezamorano@unex.es
3 Physical Activity for Education, Performance and Health (PAEPH) Research Group, Faculty of Sport Sciences, University of Extremadura, 10003 Cáceres, Spain; jorgecv@unex.es
4 Grupo AFySE, Investigación en Actividad Física y Salud Escolar, Escuela de Pedagogía en Educación Física, Facultad de Educación, Universidad de Las Américas, Santiago 8370040, Chile
5 Social Impact and Innovation in Health (InHEALTH), Faculty of nursing and Occupational Therapy, University of Extremadura, 10003 Cáceres, Spain; cristinamh@unex.es
* Correspondence: acastillop85@gmail.com
28 8 2024
9 2024
9 3 15027 6 2024
26 8 2024
27 8 2024
© 2024 by the authors.
2024
https://creativecommons.org/licenses/by/4.0/ Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Subjective cognitive impairment in older persons has a substantial influence on their quality of life and can progress to serious illnesses such as dementia. Physical activity level can help prevent cognitive decline and improve cognitive performance. The aim of this study was to investigate the association between frequency of physical activity and subjective cognitive impairment in Spanish adults aged 65 and over, and to identify different risk factors. Using data from the EHSS20 survey, the study focused on 7082 participants who provided information on cognitive impairment and physical activity. Key predictor variables included age, gender, BMI, marital status, and education level. A significant relationship was found between BMI category and gender, with 66.5% of the population being overweight or obese. Men were more likely to be overweight than women. Socio-demographic factors such as educational level, marital status, and physical activity frequency showed dependent associations with sex. Women had a higher prevalence of subjective cognitive impairment than men. A strong association was found between frequency of physical activity and subjective cognitive impairment, with inactive older people having the highest prevalence of subjective cognitive impairment. Older women who engage in little physical exercise and have less education are at risk for subjective cognitive impairment. Furthermore, for both men and women, being overweight was associated with a more reduced risk than obesity. Significant relationships were also discovered between subjective cognitive impairment, frequency of physical exercise, gender, BMI, and degree of education. In conclusion, older, sedentary women with high BMI and less education are more likely to experience subjective cognitive impairment.

body composition
dual task
executive function
exercise
memory
Open Access Program of Universidad de Las AméricasThe APC was funded by the Open Access Program of Universidad de Las Américas.
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pmc1. Introduction

Subjective cognitive impairment in older people has received much attention because of its impact on quality of life [1]. As the world’s population ages, understanding the impact of subjective cognitive limitations becomes increasingly important to promote well-being and address age-related problems [2]. Subjective cognitive impairment is defined as self-perceived impairments in memory and other cognitive abilities and is thought to be an early indicator of a more serious cognitive impairment, such as moderate cognitive impairment and dementia [3]. Due to their subjective characteristics, diagnostic methods are not entirely clear-cut, and a personalized diagnostic process is recommended to identify or exclude medical conditions [4]. Some of the most commonly used diagnostic strategies are clinical judgement, interviews, psychometric tests such as the Mini-Mental State Examination (MMSE) or the Montreal Cognitive Assessment (MoCA), neuroimaging (such as magnetic resonance imaging and positron emission tomography), and biomarkers (such as amyloid and tau protein levels and olfactory identification) [4,5].

Research has shown that cognitive impairment has important implications for health promotion, [6,7] so understanding cognitive impairment, its incidence, and impact may be fundamental to developing effective interventions to reduce prevalence [8]. In Spain, 4624 individuals in five communities were studied; the adjusted prevalence for the population studied was 18.5%, with women having significantly higher adjusted rates than men, and the prevalence increased exponentially with age, reaching 45.3% in those aged over 85 years [9].

From the perspective of physical and cognitive function, physical activity level (PAL) appears to be an emerging factor that may act as a protective strategy against cognitive impairment [10,11]. Moderate to vigorous physical activity (PA) has been shown to improve cognitive function [12]. Thus, promoting an active lifestyle may help older people maintain their cognitive abilities [13]. However, the frequency of physical activity (PAF) affects the overall effectiveness of PAL [14,15]. Scientific evidence suggests that regular PA is associated with a variety of health benefits, including the prevention and treatment of non-communicable diseases such as heart disease, hypertension, stroke, diabetes, and some malignancies [16]. In particular, PAF has a significant impact on cognitive function, suggesting that regular PA is associated with improved cognitive performance and overall brain health [17,18]. Various types of PA, such as strength training or aerobics (playing tennis, swimming, walking, hiking, or dancing) as well as their intensity and frequency, have an essential role in promoting active ageing, protecting psychophysical well-being, and sustaining cognitive functioning in older persons [19,20,21,22]. In terms of frequency and intensity, compared to sedentary adults, performing PA once a week, one to three times a week, or more, with intensities ranging from low to high, was associated with better cognitive test scores [23]. In regards to time, those who did at least 3 h of PA each week scored considerably higher on the Montreal Cognitive Assessment (MoCA) than those who did less [24]. However, the World Health Organization’s physical activity guidelines recommend for older adults that a minimum of 150–300 min of moderate-intensity physical activity or 75–150 min of vigorous-intensity physical activity weekly (or an equivalent combination of both) are sufficient for health benefits such as improvements in cognitive health [25]. Thus, persistent PA may be required to preserve cognitive function and promote overall well-being in older persons [26,27].

The association between several risk factors and cognitive impairment in older people has also been investigated [28,29]. One of these factors is body mass index (BMI), overweight, and obesity, which have been reported to be more associated with a lower risk of cognitive decline than normal weight in middle-aged and older people [29,30]. However, the role of exercise in this association is critical, as vigorous PA was found to be a mediator, accounting for approximately 5.94% of the association between obesity and cognitive impairment [31]. These findings illustrate the complex relationship between BMI, physical activity, and cognitive health.

Another risk factor is that educational attainment is strongly associated with cognitive impairment in older people, with higher levels of education corresponding to a lower likelihood of dementia and mild cognitive impairment [32,33]. This protective effect may be due to the cognitive reserve, defined as individual differences in the ability to cope with pathological changes in the brain [34] through formal education, as well as healthy lifestyles and access to medical care, which are often associated with higher levels of education [35].

A study conducted in Spain to investigate the prevalence of cognitive impairment in an ageing older population and its association with social factors found a prevalence of 22.2% when distributed by age or level of education, with women having a higher likelihood of cognitive impairment than men [36].

Marital status is also an important factor to consider when determining cognitive impairment [37,38]. The impact of marital status on cognitive functioning in older people has been highlighted by the finding that marital status significantly predicts cognitive impairment [39,40]. Married older people had better cognitive functioning than their single, divorced, or bereaved counterparts [41,42,43]. These findings highlight the need to understand the complexity of social relationships. In Spain, both the incidence of cognitive impairment and marital status are strongly associated with older people’s quality of life and mental health [9,44]. Understanding how marital status affects cognitive function in this population is crucial for the development of effective therapies.

In Spain, the European Health Survey (EHSS20) is one of the valid data sources that can be used to study the association between physical activity frequency and subjective cognitive impairment as well as to identify possible risk factors [45].

Based on the above, it seems crucial to study the associations between subjective cognitive impairment and PAF in the Spanish elderly population, as well as to study risk factors such as age, sex, BMI, and level of education and their association with cognitive impairment, as this may be crucial to better understand the elements influencing mental health and to implement effective intervention strategies to maintain optimal cognitive function. Therefore, the aim of this study was to investigate the association between PAF and cognitive impairment (memory or concentration problems) in people aged 65 years and older in Spain through the EHSS20. We also sought to identify risk factors such as age, sex, physical inactivity, BMI, and educational level within these groups that could explain the prevalence of cognitive deficits.

2. Materials and Methods

Data from EHSS20 [45] were used to conduct a cross-sectional descriptive study based on responses. This survey carried out by the National Statistics Institute (INE) and coordinated by the European Statistical Office (Eurostat) aims to investigate the health status of the resident population in Spain, as well as other health markers and socio-demographic factors. The study used a stratified random sampling approach in three stages. First, municipalities were divided into strata and randomly selected. Households in these strata were then randomly selected. Finally, an adult was randomly selected from the selected households. The survey methodology includes all methodological parameters and survey specific information (EHSS20) [45]: data storage and processing, sample calculation, treatment of missing data, how the interviews were conducted, among others.

In accordance with Regulation 2016/679 of the European Parliament and of the Council of the European Union of 27 April 2016 on the protection of individuals with regard to the processing of personal data and on the free movement of personal data, and derogating from Directive 95/46/EC, these data are public and anonymous, and are therefore considered non-confidential data, and data protection principles were not required. No approval from an approved ethics committee was required.

2.1. Participants

People who volunteered to participate in the survey were questioned in person by INE-trained staff. It was carried out from July 2019 to July 2020 (due to the COVID-19 pandemic, personal surveys have been conducted by telephone since 17 March 2020). All data and questionnaire responses can be downloaded for free from the INE website. On the website, the data are presented in different formats: .R, .sav, .csv, .sas, and .dta. Therefore, the data can be processed with different statistical programs. For this research, the data were extracted in .sav format (SPSS Statistics Data Document). The EHSS20 had a final sample size of 22,072. All participants were individuals aged 15 and up who lived in family homes in Spain. To get to the final sample of this study, the following inclusion criteria were used: (1) be an advanced adult (aged 65–94); (2) provide data on cognitive impairment (based on response to item Q.38.a: Do you have difficulty to remember or to concentrate?); and (3) provide data on PAF (based on response to item Q.112: Which of these scenarios best characterizes the frequency with which you engage in physical activity in your spare time?). After applying these criteria, 14,990 people were not included (14,905 under 65, 78 over 95, and 7 who did not provide PAF data (they presented “Don’t Know/don’t answer” as a response to item Q.112)), resulting in a final sample of 7082 people. Figure 1 depicts the flow diagram along with the sample’s selection criteria.

2.2. Variables Extracted from the Survey

2.2.1. Outcome Variables

Subjective Cognitive Impairment Levels. This variable was extracted from responses to the Q.38.a variable: Do you have difficulty to remember or to concentrate? There are 4 possible answers: (1) No, no difficulty (“None”); (2) Yes, some difficulty (“Some”); (3) Yes, many difficulties (“A lot”); and (4) I can’t do it at all (“Absolutely”), or Don’t Know/don’t answer (DK/DA). Therefore, this variable grouped participants according to these levels of subjective cognitive limitations: “None”, “Some”, “A lot”, and “Absolutely”.

Subjective Cognitive Impairment. This dichotomous variable was created from the responses on the subjective cognitive impairment levels variable (item Q.38.a) and grouped participants into two groups: with subjective cognitive impairment (Yes) and without subjective cognitive impairment (No). For this purpose, the results were grouped into 2 categories: (1) No: those participants who answered (No, no difficulty); (2) Yes: those participants who answered (Yes, some difficulty or yes, many difficulties or can’t do it at all).

2.2.2. Independent, Predictor, and Covariate Variables

Participants who did not provide information on any of the following criteria were excluded from analyses using the variable “no data” but were included in all other analyses.

Age: This continuous variable was obtained from the survey variable “AGEa”.

Sex: This was obtained from the survey variable “SEXOa”, which had two possible responses: men or women.

Physical Activity Frequency (PAF): This is taken from item Q.112. The question was: “Which of these scenarios best characterizes the frequency with which you engage in physical activity in your spare time?” with four possible answers. For this investigation, the groups were called: (1) Never: those individuals who said (I do not exercise); (2) Occasional: individuals who responded (I do occasional physical activity or sport); (3) Frequently: participants who responded (I do physical activity several times a month); and (4) Very frequently: participants who responded (I do physical or sport training several times a week). Or DK/DA.

Body Mass Index (BMI) Group: This was based on the survey variable “BMIa”. Participants were divided into groups based on their BMI (weight in kg divided by height in meters squared). The following four groups were formed: underweight (BMI < 18.5), normal (BMI ≥ 18.5 and <25), overweight (BMI ≥ 25 and <30), and obesity (BMI ≥ 30). A total of 655 participants did not submit data on this variable.

Civil status: This was based on the answers participants provided to item Q.4b: What is your legal marital status? There were five possible answers: (1) Single; (2) Married; (3) Widowed; (4) Legally separated; and (5) Divorced; or (DK/DA). Sixteen participants did not submit data on this variable.

Study level: This information was taken from the “STUDY” (EHSS 2020). These factors represented the greatest level of study attained by the subjects. For this investigation, individuals were divided into five groups: (1) Primary Studies (participants with completed or incomplete primary education); (2) Secondary Studies (participants with compulsory secondary education with or without a diploma); (3) Bachelor’s Degree (participants with Bachelor’s Degree Studies); (4) Vocational Training (participants with Vocational Education and Training at an intermediate or higher level or equivalent); and (5) University.

2.3. Statistical Analysis

The Kolmogorov–Smirnov test was used to determine the normality of the data for the continuous variable (age). Descriptive analysis was used to characterize the sample using the following variables: age (median and IQR, a continuous variable), BMI group, Study Level, Civil Status, PAF, Subjective Cognitive Impairment, and Subjective Cognitive Impairment Levels (absolute and relative frequencies, categorical variables). The Mann–Whitney U test was used to examine possible age differences between participants by gender. The Chi-square test was used to examine potential dependent relationships between sex and all categorical variables. Cramer’s V and Phi coefficients were calculated where appropriate to determine the strength of these associations. The post hoc paired z-test for independent proportions was used to investigate potential sex differences in the proportions of categorical variables.

The Chi-square test was used to examine the dependent associations between PAF and the variables Subjective Cognitive Impairment and Subjective Cognitive Impairment Levels. Cramer’s V coefficient was used to determine the strength of the associations. To investigate potential differences in the proportions of subjective cognitive impairment and subjective cognitive impairment levels as a function of PAF, the post hoc paired z-test for independent proportions was used.

Multiple binary logistic regression was used to examine the risks of having subjective cognitive impairment, with subjective cognitive impairment as the dependent variable and the remaining study factors (Age, Sex, BMI group, Civil Status, Study Level, and PAF) as independent variables. A significance level greater than 0.95 was used for all analyses. All analyses were performed using the statistical program IBM SPSS Statistical version 25.3.

3. Results

The statistics on the participants’ ages were not regularly distributed (p < 0.001). The sample median age was 75 (12) years, with no sex differences (p = 0.129). BMI category and sex showed a dependent connection (X2 = 85.2, df = 3, p < 0.001, V = 0.115). In total, 66.5% of the population was considered overweight or obese. Men were more likely to be overweight than women (52.3% vs. 41.5%, p < 0.001). Dependence correlations were established between sex and various socio-demographic factors such as Study Level (X2 = 107.4, df = 4, p < 0.001, V = 0.123), Civil Status (X2 = 851.7, df = 4, p < 0.001, V = 0.347), and PAF (X2 = 97.9, df = 3, p < 0.001, V = 0.118). Subjective memory impairments were shown to be associated with sex (X2 = 73.6, df = 3, p < 0.001, V = 0.102) and prevalence (X2 = 71.3, df = 1, p < 0.001, φ = 0.100). Women exhibited a greater prevalence of subjective cognitive impairment compared to men (31.2% vs. 22.1%, p < 0.001) (Table 1).

A dependency association was discovered between PAF and subjective cognitive impairment (X2 = 345.9, df = 3, p < 0.001, V = 0.221) (Supplementary Materials Table S1). The inactive elderly had the highest prevalence of subjective cognitive impairment (37.9%), with significant differences from the other categories (Supplementary Materials Table S1). Figure 2 depicts the prevalence of subjective cognitive impairment as a function of PAF.

PAF and subjective cognitive impairment levels showed a dependent connection (X2 = 437.3, df = 9, p < 0.001, V = 0.143). In all the subjective cognitive impairment levels, the inactive groups had a higher prevalence, with the lowest prevalence seen in the groups that exercised more regularly, with significant differences between these group (Table 2).

In analyzing risk factors for subjective cognitive impairment, women had a higher risk than men (OR: 1.25, CI95%: 1.11–1.42, p < 0.001), as did older age (OR: 1.08, CI95%: 1.07–1.09, p < 0.001). In contrast, those with a greater PAF and educational level were at a decreased risk (Table 3). The overweight group had the lowest risk of subjective cognitive impairment (OR: 0.84, CI95%: 0.72–0.98, p < 0.001) compared to the obese group, whereas there were no significant differences in risk among the other groups. Female, elderly, inactive, with a low educational level, and obesity are the characteristics associated with the highest chance of suffering from subjective cognitive impairment. The model accounted for 17% of the variance (Nagelkerke’s R2).

4. Discussion

This study investigated the relationships between subjective cognitive impairment (difficulty remembering or concentrating) and PAF in people aged 65 years old and over in Spain. The risks factors associated with subjective cognitive impairment (sex, BMI, level of education, marital status) were also examined. The risk profile of subjective cognitive impairment was also determined. The main results showed dependent correlations between subjective cognitive impairment and PAF. Significant relationships were also found between these limitations and risk factors such as sex, BMI, and level of study. It was also found that being an older, sedentary woman with a low level of education and a BMI above 30 put you at the highest risk of developing subjective cognitive impairment.

PA is therefore considered one of the most important modifying and preventive agents, as previous research has shown that modifiable risk factors are determinants of the onset of cognitive decline, including subjective memory complaints, mild cognitive impairment, or dementia [46]. There is evidence of a strong relationship between subjective cognitive impairment and PAF [23,47,48]. We found that older people with a higher PAF had a significant reduction in subjective cognitive impairment over time, compared to those who were less physically active [49]. Other findings consistent with ours suggest that regular PA may have a protective effect against perceived cognitive impairment [50]. Furthermore, subjective memory complaints are a crucial stage in the development of preventive therapeutic strategies to prevent the further development of a pathological clinical state [51]. Despite the data presented above, it may be important to conduct additional research on the relationship between PAF and perceived cognitive impairment, as it is more complex than previously thought and may be mediated by other factors such as general health status and psychosocial factors [52].

BMI, as an indicator of overall health [53], can be significantly influenced by regular physical activity, which not only helps to maintain a healthy BMI by burning calories but also improves body composition and regulates metabolism. This suggests that physical activity is essential for managing and preventing health problems associated with high BMI [54]. Longitudinal studies have shown that those who exercise regularly are less likely to develop obesity [55,56,57]. In addition, a high BMI, particularly in obese people, has been associated with an increased likelihood of subjective cognitive impairment [58,59]. This association has been attributed to a variety of causes, including the detrimental effects of increased body fat on vascular and metabolic health [29,60]. Obesity is associated with chronic inflammation, insulin resistance, and endothelial dysfunction, all of which can lead to cognitive impairment [61]. Regular PA has been associated with significant improvements in memory and other cognitive abilities in older people, particularly those at higher risk of cognitive decline due to high BMI [62,63]. Scientific research suggests that promoting active lifestyles may be an effective strategy for improving metabolic and cognitive health, particularly in at-risk populations [64]. In addition to being active and healthy, non-modifiable characteristics such as age, sex, and education have been found to explain some of the differences in cognition in older adults [65].

Educational attainment as a non-modifiable aspect, together with Stern’s contributions on cognitive reserve, has found potential applications in the field of healthy ageing [66]. Thus, aspects such as educational attainment and other lifestyle-related aspects have been studied extensively. These studies have shown that a higher level of education increases the set of skills and personal characteristics that enable the person to cope better with brain damage and cognitive decline, both in healthy ageing and in the onset of cognitive disorders such as mild cognitive impairment [67]. It is therefore necessary to consider cognitive reserve as a protective factor in cognitive ageing. In the analysis of our results, the data are consistent with this trend and show an association between academic level and cognitive complaints. However, other studies have found that the correlation between educational and global cognitive change is unrelated [68]. One hypothesis that could address this ambiguity is the difficulty in finding measurable and objective tools to assess the amount of cognitive reserve and the appropriate way to apply these indicators in research [69].

With regard to sex, our findings are similar to those of previous research, particularly in pathological conditions, where greater cognitive impairment is observed in women in comparison to men [70]. However, it could be argued that there are several factors conditioning this phenomenon. Firstly, age is the main non-modifiable risk factor for the occurrence of cognitive impairment, so it should be noted that women have a longer life expectancy, which may determine a higher prevalence in the female gender [71]. However, in our data analysis, the median age of the women is higher than that of men, but this difference is not statistically significant, so this hypothesis is not consistent with our results. Second, as other studies have shown, subjective memory complaints such as those reflected in our research, can also be attributed to depressive states and psychiatric comorbidities [51,72]. This observation is noteworthy because previous studies have found a positive association between various factors associated with geriatric depression, such as female gender or low daily physical activity [73]. PA has also shown anti-depressant benefits and may be useful for public health interventions [74].

Socio-cultural factors may also play a role in the development and maintenance of cognitive impairment in older people [75]. Historically, women have had less access to culture and formal education in recent decades, and this is reflected in our results. In our sample, we observed that women outnumber men only at the primary level of education; the higher the level of education, the lower the presence of women, and the median is lower than that of men. This difference is significant at the secondary education, high school, and university levels. As discussed earlier, an individual’s educational background is a component of cognitive reserve. Therefore, individuals with a better cognitive reserve are likely to be better able to cope with the changes brought about by cognitive ageing [76].

Based on our expertise, the robustness of our findings and the representativeness of the sample considered indicate considerable potential. However, the study had some limitations. Due to the cross-sectional nature of study, causal relationships between PAF and cognitive impairment could not be established. Participants rated their cognitive impairment and frequency of physical activity in the EHSS using self-report questionnaires. In the case of cognitive performance, there may be a bias in people who have cognitive impairment but experience anosognosia in the early stages and are unable to perceive the impairment. Therefore, this survey should include tests that assess participants’ cognitive abilities as well as objective data on people’s PAF. It would be useful to have both objective and subjective data to characterize the variables in this group. In addition, on the 17 March 2020, the confinement in Spain was declared. From this date onwards, interviews were conducted by telephone rather than in person, which may have influenced the results. The limitations of this study provide an opportunity for future research. It is also important to note that although BMI is widely used to assess weight status, there are many factors that need to be considered [77]. BMI does not distinguish between people with low skeletal muscle mass, high fat weight, sarcopenic obesity, and normally healthy people with higher skeletal muscle mass and lower fat weight, nor does it determine how fat mass is distributed; it may also vary by gender, race, ethnicity, or age [77]. Therefore, body composition data would need to be complemented by other measures such as waist-to-hip ratio [78,79].

Based on the results of this study, and taking into account the limitations mentioned above, the availability of these data could help promote community health strategies and implement prevention programs aimed at reducing physical inactivity and inactive behaviors to improve the cognitive health of the elderly Spanish population. The researchers believe that maintaining an adequate level of physical activity at this stage of life may protect against age-related cognitive impairment. In addition, the factors that influence subjective cognitive impairment and decline in older people need to be identified and taken into account in order to improve approaches to preventing cognitive decline and dementia.

Future studies should further investigate the relationship between PAF and subjective cognitive impairment, considering the complexity of the relationship and the likelihood that it is mediated by other factors, such as general health status and psychosocial variables. In addition, longitudinal research would be beneficial to establish causal relationships and use objective instruments to assess both cognitive function and physical activity. Additional measures of body composition, such as waist-to-hip ratio, need to be included to complement BMI assessments and provide a more complete understanding of the impact of obesity and other factors on cognitive health. Future studies should also look at gender differences and the role of cognitive reserve in preventing cognitive decline, considering the impact of socio-cultural and educational factors on these dynamics.

5. Conclusions

We conclude that there are significant associations between subjective cognitive impairment and frequency of physical activity in Spanish adults aged 65 and over. Thus, the frequency of physical activity was identified as a potential risk factor for subjective cognitive impairment, together with sex, BMI, and educational level. The lowest risk of self-reported cognitive impairment was found in older people with a frequency of several times per month. People who exercised several times a month had less than a third of the risk reported by inactive people, while those who exercised several times a week had half the risk. In addition, it was revealed that being an older, inactive woman, and having a BMI greater than 30 and a low level of education were the highest risk profiles for subjective cognitive impairment in Spain.

Acknowledgments

The authors J.M.F.-G. (FPU20/04143) and Á.D.-Z. (FPU20/04201) acknowledge the grant from the Spanish Ministry of Education, Culture and Sport. Grants J.M.F-G. (FPU20/04143) and Á.D.-Z. (FPU20/04201) were funded by MCIN/AEI/10.13039/501100011033 and, as appropriate, by “European Social Found Investing in your future” or by “European Union NextGenetionEU/PRTR”.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/jfmk9030150/s1. Table S1. Subjective Cognitive Impairment according to Physical Activity Frequency.

Author Contributions

Conceptualization, J.M.F.-G., C.M.-H. and Á.D.-Z.; methodology, Á.D.-Z. and J.P.-G.; software, J.M.F.-G. and Á.D.-Z.; validation, A.C.-P., C.M.-H. and J.C.-V.; formal analysis, Á.D.-Z. and J.C.-V.; investigation, J.M.F.-G.; resources, Á.D.-Z.; data curation, Á.D.-Z. and J.C.-V.; writing—original draft preparation, J.M.F.-G., C.M.-H., A.D.-Z. and J.P.-G.; writing—review and editing, J.M.F.-G., J.C.-V., C.M.-H. and J.P.-G.; visualization, A.C.-P. and C.M.-H.; supervision, J.P.-G. and A.D.-Z.; project administration, J.C.-V. and J.P.-G.; funding acquisition, A.C.-P. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Before the surveys began, the Spanish National Statistics Institute told the participants that they had been chosen at random to participate. Furthermore, they were informed about the survey’s characteristics and the anonymous treatment of the data, and they were asked if they agreed to participate voluntarily and anonymously.

Data Availability Statement

Data will be made available upon reasonable request by the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Figure 1 Flow diagram of the study sample’s eligibility criteria.

Figure 2 Prevalence of subjective cognitive impairment according to PAF.

jfmk-09-00150-t001_Table 1 Table 1 Descriptive analysis.

Variables	Total = 7082	Men = 2994	Women = 4088	X2	df	p	V	
Median	IQR	Median	IQR	Median	IQR	
Age (Years)	75	(12)	74	(11)	76	(13)	n.a.	n.a.	0.129	n.a.	
BMI Group	n	%	n	%	n	%	X2	df	p *	V	
Underweight	77	1.2	21	0.7	56	1.6 **	85.2	3	<0.001	0.115	
Normal	2073	32.3	774	27.5	1299	36.0 ***	
Overweight	2968	46.3	1471	52.3	1497	41.5 ***	
Obesity	1299	20.2	547	19.4	752	20.9	
Study Level											
Primary	4250	60.0	1628	54.4	2622	64.1 ***	107.4	4	<0.001	0.123	
Secondary	1221	17.2	510	17.0	711	17.4	
Bachelor’s Degree	550	7.8	280	9.4	270	6.6 ***	
Vocational training	359	5.1	191	6.4	168	4.1 ***	
University	702	9.9	385	12.9	317	7.8 ***	
Civil Status											
Single	612	8.7	322	10.8	290	7.1 ***	851.7	4	<0.001	0.347	
Married	3584	50.7	2006	67.1	1578	38.7 ***	
Widowed	2486	35.2	478	16.0	2008	49.3 ***	
Legally separated	165	2.3	88	2.9	77	1.9 **	
Divorced	219	3.1	95	3.2	124	3.0	
PAF											
Inactive	3222	45.5	1159	38.7	2603	50.5 ***	97.9	3	<0.001	0.118	
Occasional	2903	41.0	1396	46.6	1507	36.9 ***	
Various/Month	405	5.7	183	6.1	222	5.4	
Various/Week	552	7.8	256	8.6	296	7.2 *	
Subjective Cognitive Impairment Levels											
No	5146	72.7	2332	77.9	2814	68.8 ***	73.6	3	<0.001	0.102	
Yes, something	1483	20.9	521	17.4	962	23.5 ***	
Yes, a lot	345	4.9	108	3.6	237	5.8 ***	
Yes, absolutely	108	1.5	33	1.1	75	1.8 *	
Subjective Cognitive Impairment							X2	df	p	φ	
No	5146	72.7	2332	77.9	2814	68.8 ***	71.3	1	<0.001	0.100	
Yes	1936	27.3	662	22.1	1274	31.2 ***	
df (degree freedom); IQR (Interquartile range); n (participants); n.a. (not applicable); % (Percentage); p (p-value from Mann–Whitney U test); p * (p-value from Chi-square test); * (significant differences in proportions between gender with p < 0.05 (** p < 0.01; *** p < 0.001) from post hoc pairwise z-test for independent proportions); X2 (Chi-square statistic); V (Cramer’s V coefficients); φ (Phi coefficients).

jfmk-09-00150-t002_Table 2 Table 2 Subjective cognitive impairment levels according to PAF.

Variables	Physical Activity Frequency	X2	df	p	V	
Subjective Cognitive Impairment Levels	Never (A)	Occasionally (B)	Frequently (C)	Very Frequently (D)	
n	%	n	%	n	%	n	%					
None	2002	61.1%	2318	79.8%	356	87.9%	470	85.1%	437.3	9	<0.001	0.143	
Some	848	26.3%	563	17.9%	45	11.1%	71	12.9%	
A lot	275	8.5%	56	1.9%	4	1.0%	10	1.8%	
Absolutely	9	3.0%	10	0.3%	0	0.0%	1	0.2%	
Proportions’ differences post hoc	
None				A (p < 0.001) ***	A (p < 0.001) ***
B (p = 0.001) **	A (p < 0.001) ***
(p = 0.023) *				
Some		B (p < 0.001) ***
C (p < 0.001) ***
D (p < 0.001) ***	C (p = 0.004) **
D (p < 0.025) *								
A lot		B (p < 0.001) ***
C (p < 0.001) ***
D (p < 0.001) ***										
Absolutely		B (p < 0.001) ***
D (p = 0.001) ***										
df (degree freedom); p (p-value from pairwise z-test for independent proportions); * (p < 0.05); ** (p < 0.01); *** (p < 0.001); X2 (Chi-Square); V (V‘s Cramer coefficients).

jfmk-09-00150-t003_Table 3 Table 3 Logistic binary regression model for memory problems risk factor.

Model for Subjective Cognitive Impairment	
	β	Adjusted OR	95% C.I.	p-Value	
Age	0.073	1.08	1.07	1.09	<0.001 ***	
Sex						
Men		Reference				
Women	0.224	1.25	1.11	1.42	<0.001 ***	
PAF						
Never		Reference			<0.001 ***	
Occasional	−0.519	0.60	0.52	0.68	<0.001 ***	
Various/Month	−1.152	0.32	0.22	0.45	<0.001 ***	
Various/Week	−0.652	0.52	0.40	0.68	<0.001 ***	
Study Level						
Primary		Reference			<0.001 ***	
Secondary	−0.381	0.68	0.57	0.81	<0.001 ***	
Bachelor’s Degree	−0.771	0.46	0.35	0.61	<0.001 ***	
Vocational Training	−0.289	0.75	0.55	1.01	0.060	
University	−0.786	0.46	0.36	0.58	<0.001 ***	
BMI Group						
Obesity		Reference			0.029 *	
Underweight	−0.003	1.00	0.59	1.68	0.991	
Normal	0.018	1.02	0.86	1.21	0.833	
Overweight	−0.175	0.84	0.72	0.98	0.030 *	
Constant	−6.261	0.00			<0.001 ***	
β (Beta); CI (Confidence Interval).; OR (Odds ratio); * (p-value < 0.05); *** (p-value < 0.001).

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References

1. He C. Kong X. Li J. Wang X. Chen X. Wang Y. Zhao Q. Tao Q. Predictors for Quality of Life in Older Adults: Network Analysis on Cognitive and Neuropsychiatric Symptoms BMC Geriatr. 2023 23 850 10.1186/s12877-023-04462-4 38093173
2. Shimada H. Makizako H. Lee S. Doi T. Lee S. Tsutsumimoto K. Harada K. Hotta R. Bae S. Nakakubo S. Impact of Cognitive Frailty on Daily Activities in Older Persons J. Nutr. Health Aging 2016 20 729 735 10.1007/s12603-016-0685-2 27499306
3. Stewart R. Subjective Cognitive Impairment Curr. Opin. Psychiatry 2012 25 445 10.1097/YCO.0b013e3283586fd8 23037961
4. Jessen F. Amariglio R.E. Buckley R.F. van der Flier W.M. Han Y. Molinuevo J.L. Rabin L. Rentz D.M. Rodriguez-Gomez O. Saykin A.J. The Characterisation of Subjective Cognitive Decline Lancet Neurol. 2020 19 271 278 10.1016/S1474-4422(19)30368-0 31958406
5. Jobin B. Zahal R. Bussières E.-L. Frasnelli J. Boller B. Olfactory Identification in Subjective Cognitive Decline: A Meta-Analysis J. Alzheimer’s Dis. 2021 79 1497 1507 10.3233/JAD-201022 33459721
6. Sáez de Asteasu M.L. Martínez-Velilla N. Zambom-Ferraresi F. Casas-Herrero Á. Izquierdo M. Role of Physical Exercise on Cognitive Function in Healthy Older Adults: A Systematic Review of Randomized Clinical Trials Ageing Res. Rev. 2017 37 117 134 10.1016/j.arr.2017.05.007 28587957
7. Jenkins A. Tree J.J. Thornton I.M. Tales A. Subjective Cognitive Impairment in 55-65-Year-Old Adults Is Associated with Negative Affective Symptoms, Neuroticism, and Poor Quality of Life J. Alzheimer’s Dis. 2019 67 1367 1378 10.3233/JAD-180810 30689577
8. Song R. Fan X. Seo J. Physical and Cognitive Function to Explain the Quality of Life among Older Adults with Cognitive Impairment: Exploring Cognitive Function as a Mediator BMC Psychol. 2023 11 51 10.1186/s40359-023-01087-5 36814329
9. Vega Alonso T. Miralles Espí M. Mangas Reina J.M. Castrillejo Pérez D. Rivas Pérez A.I. Gil Costa M. López Maside A. Arrieta Antón E. Lozano Alonso J.E. Fragua Gil M. Prevalence of Cognitive Impairment in Spain: The Gómez de Caso Study in Health Sentinel Networks Neurologia 2018 33 491 498 10.1016/j.nrl.2016.10.002 27939116
10. Song D. Yu D.S.F. Li P.W.C. Lei Y. The Effectiveness of Physical Exercise on Cognitive and Psychological Outcomes in Individuals with Mild Cognitive Impairment: A Systematic Review and Meta-Analysis Int. J. Nurs. Stud. 2018 79 155 164 10.1016/j.ijnurstu.2018.01.002 29334638
11. Erickson K.I. Hillman C. Stillman C.M. Ballard R.M. Bloodgood B. Conroy D.E. Macko R. Marquez D.X. Petruzzello S.J. Powell K.E. Physical Activity, Cognition, and Brain Outcomes: A Review of the 2018 Physical Activity Guidelines Med. Sci. Sports Exerc. 2019 51 1242 10.1249/MSS.0000000000001936 31095081
12. Pitrou I. Vasiliadis H.-M. Hudon C. Body Mass Index and Cognitive Decline among Community-Living Older Adults: The Modifying Effect of Physical Activity Eur. Rev. Aging Phys. Act. 2022 19 3 10.1186/s11556-022-00284-2 35033022
13. Küster O.C. Fissler P. Laptinskaya D. Thurm F. Scharpf A. Woll A. Kolassa S. Kramer A.F. Elbert T. von Arnim C.A.F. Cognitive Change Is More Positively Associated with an Active Lifestyle than with Training Interventions in Older Adults at Risk of Dementia: A Controlled Interventional Clinical Trial BMC Psychiatry 2016 16 315 10.1186/s12888-016-1018-z 27608620
14. Lustyk M.K.B. Widman L. Paschane A.A.E. Olson K.C. Physical Activity and Quality of Life: Assessing the Influence of Activity Frequency, Intensity, Volume, and Motives Behav. Med. 2004 30 124 132 10.3200/BMED.30.3.124-132 15816315
15. Musich S. Wang S.S. Hawkins K. Greame C. The Frequency and Health Benefits of Physical Activity for Older Adults Popul. Health Manag. 2017 20 199 207 10.1089/pop.2016.0071 27623484
16. Liu H. Liu S. Wang K. Zhang T. Yin L. Liang J. Yang Y. Luo J. Time-Dependent Effects of Physical Activity on Cardiovascular Risk Factors in Adults: A Systematic Review Int. J. Environ. Res. Public Health 2022 19 14194 10.3390/ijerph192114194 36361072
17. Fox K.R. The Influence of Physical Activity on Mental Well-Being Public Health Nutr. 1999 2 411 418 10.1017/S1368980099000567 10610081
18. Koščak Tivadar B. Physical Activity Improves Cognition: Possible Explanations Biogerontology 2017 18 477 483 10.1007/s10522-017-9708-6 28492999
19. Gheysen F. Poppe L. DeSmet A. Swinnen S. Cardon G. De Bourdeaudhuij I. Chastin S. Fias W. Physical Activity to Improve Cognition in Older Adults: Can Physical Activity Programs Enriched with Cognitive Challenges Enhance the Effects? A Systematic Review and Meta-Analysis Int. J. Behav. Nutr. Phys. Act. 2018 15 63 10.1186/s12966-018-0697-x 29973193
20. Klimova B. Dostalova R. The Impact of Physical Activities on Cognitive Performance among Healthy Older Individuals Brain Sci. 2020 10 377 10.3390/brainsci10060377 32560126
21. Ingold M. Tulliani N. Chan C.C.H. Liu K.P.Y. Cognitive Function of Older Adults Engaging in Physical Activity BMC Geriatr. 2020 20 229 10.1186/s12877-020-01620-w 32616014
22. D’Aurizio G. Festucci F. Di Pompeo I. Tempesta D. Curcio G. Effects of Physical Activity on Cognitive Functioning: The Role of Cognitive Reserve and Active Aging Brain Sci. 2023 13 1581 10.3390/brainsci13111581 38002541
23. de Souto Barreto P. Delrieu J. Andrieu S. Vellas B. Rolland Y. Physical Activity and Cognitive Function in Middle-Aged and Older Adults: An Analysis of 104,909 People From 20 Countries Mayo Clin. Proc. 2016 91 1515 1524 10.1016/j.mayocp.2016.06.032 27720454
24. Alonzo M. Ringman J.M. Schneider L.S.S. Braskie M.N. Toga A.W. Zlokovic B.V. Chui H.C. Joe E.B. Lower Physical Activity Is Associated With Objective But Not Subjective Cognitive Impairment In Older Adults Without Dementia Alzheimer’s Dement. 2023 19 e077408 10.1002/alz.077408
25. Yu D.J. Yu A.P. Bernal J.D.K. Fong D.Y. Chan D.K.C. Cheng C.P. Siu P.M. Effects of Exercise Intensity and Frequency on Improving Cognitive Performance in Middle-Aged and Older Adults with Mild Cognitive Impairment: A Pilot Randomized Controlled Trial on the Minimum Physical Activity Recommendation from WHO Front. Physiol. 2022 13 1021428 10.3389/fphys.2022.1021428 36200056
26. Angevaren M. Aufdemkampe G. Verhaar H.J.J. Aleman A. Vanhees L. Physical Activity and Enhanced Fitness to Improve Cognitive Function in Older People without Known Cognitive Impairment Cochrane Database Syst. Rev. 2008 3 CD005381 10.1002/14651858.CD005381.pub3
27. Falck R.S. Davis J.C. Best J.R. Crockett R.A. Liu-Ambrose T. Impact of Exercise Training on Physical and Cognitive Function among Older Adults: A Systematic Review and Meta-Analysis Neurobiol. Aging 2019 79 119 130 10.1016/j.neurobiolaging.2019.03.007 31051329
28. Hudon C. Escudier F. De Roy J. Croteau J. Cross N. Dang-Vu T.T. Zomahoun H.T.V. Grenier S. Gagnon J.-F. Parent A. Behavioral and Psychological Symptoms That Predict Cognitive Decline or Impairment in Cognitively Normal Middle-Aged or Older Adults: A Meta-Analysis Neuropsychol. Rev. 2020 30 558 579 10.1007/s11065-020-09437-5 32394109
29. Qu Y. Hu H.-Y. Ou Y.-N. Shen X.-N. Xu W. Wang Z.-T. Dong Q. Tan L. Yu J.-T. Association of Body Mass Index with Risk of Cognitive Impairment and Dementia: A Systematic Review and Meta-Analysis of Prospective Studies Neurosci. Biobehav. Rev. 2020 115 189 198 10.1016/j.neubiorev.2020.05.012 32479774
30. Xu X. Xu Y. Shi R. Association between Obesity, Physical Activity, and Cognitive Decline in Chinese Middle and Old-Aged Adults: A Mediation Analysis BMC Geriatr. 2024 24 54 10.1186/s12877-024-04664-4 38212676
31. De Sousa R.A.L. Santos L.G. Lopes P.M. Cavalcante B.R.R. Improta-Caria A.C. Cassilhas R.C. Physical Exercise Consequences on Memory in Obesity: A Systematic Review Obes. Rev. 2021 22 e13298 10.1111/obr.13298 34105227
32. Meng X. D’Arcy C. Education and Dementia in the Context of the Cognitive Reserve Hypothesis: A Systematic Review with Meta-Analyses and Qualitative Analyses PLoS ONE 2012 7 e38268 10.1371/journal.pone.0038268 22675535
33. Xu W. Tan L. Wang H.-F. Tan M.-S. Tan L. Li J.-Q. Zhao Q.-F. Yu J.-T. Education and Risk of Dementia: Dose-Response Meta-Analysis of Prospective Cohort Studies Mol. Neurobiol. 2016 53 3113 3123 10.1007/s12035-015-9211-5 25983035
34. Stern Y. Cognitive Reserve: Implications for Assessment and Intervention Folia Phoniatr. Logop. 2013 65 49 54 10.1159/000353443 23941972
35. Cadar D. Stephan B.C.M. Jagger C. Sharma N. Dufouil C. den Elzen W.P.J. Gussekloo J. Aartsen M. Huisman M. Deeg D. Is Education a Demographic Dividend? The Role of Cognitive Reserve in Dementia-Related Cognitive Decline: A Comparison of Six Longitudinal Studies of Ageing Lancet 2015 386 S25 10.1016/S0140-6736(15)00863-6
36. Millán-Calenti J.C. Tubío J. Pita-Fernández S. González-Abraldes I. Lorenzo T. Maseda A. Prevalence of Cognitive Impairment: Effects of Level of Education, Age, Sex and Associated Factors Dement. Geriatr. Cogn. Disord. 2009 28 455 460 10.1159/000257086 19907183
37. Tower R.B. Kasl S.V. Moritz D.J. The Influence of Spouse Cognitive Impairment on Respondents’ Depressive Symptoms: The Moderating Role of Marital Closeness J. Gerontol. Ser. B 1997 52B S270 S278 10.1093/geronb/52B.5.S270 9310099
38. Liu H. Zhang Y. Burgard S.A. Needham B.L. Marital Status and Cognitive Impairment in the United States: Evidence from the National Health and Aging Trends Study Ann. Epidemiol. 2019 38 28 34.e2 10.1016/j.annepidem.2019.08.007 31591027
39. Lawton M.P. Moss M. Kleban M.H. Marital Status, Living Arrangements, and the Well-Being of Older People Res. Aging 1984 6 323 345 10.1177/0164027584006003002 6544987
40. Liu H. Zhang Z. Zhang Y. A National Longitudinal Study of Marital Quality and Cognitive Decline among Older Men and Women Soc. Sci. Med. 2021 282 114151 10.1016/j.socscimed.2021.114151 34174580
41. Mazzuco S. Meggiolaro S. Ongaro F. Toffolutti V. Living Arrangement and Cognitive Decline among Older People in Europe Ageing Soc. 2017 37 1111 1133 10.1017/S0144686X16000374
42. Lam J. Bardo A.R. Yamashita T. LONELINESS, MARITAL STATUS, AND COGNITIVE IMPAIRMENT AMONG OLDER AMERICANS Innov. Aging 2019 3 S378 10.1093/geroni/igz038.1387
43. Zhang D. Zheng W. Li K. The Relationship between Marital Status and Cognitive Impairment in Chinese Older Adults: The Multiple Mediating Effects of Social Support and Depression BMC Geriatr. 2024 24 367 10.1186/s12877-024-04975-6 38658842
44. Silberman-Beltramella M. Ayala A. Rodríguez-Blázquez C. Forjaz M.J. Social Relations and Health in Older People in Spain Using SHARE Survey Data BMC Geriatr. 2022 22 276 10.1186/s12877-022-02975-y 35369862
45. Ministerio de Sanidad—Sanidad En Datos—Encuesta Europea de Salud En España 2020 Available online: https://www.sanidad.gob.es/estadEstudios/estadisticas/EncuestaEuropea/Enc_Eur_Salud_en_Esp_2020.htm (accessed on 6 June 2024)
46. Ngandu T. Lehtisalo J. Solomon A. Levälahti E. Ahtiluoto S. Antikainen R. Bäckman L. Hänninen T. Jula A. Laatikainen T. A 2 Year Multidomain Intervention of Diet, Exercise, Cognitive Training, and Vascular Risk Monitoring versus Control to Prevent Cognitive Decline in at-Risk Elderly People (FINGER): A Randomised Controlled Trial Lancet 2015 385 2255 2263 10.1016/S0140-6736(15)60461-5 25771249
47. Nemoto Y. Sato S. Takahashi M. Takeda N. Matsushita M. Kitabatake Y. Maruo K. Arao T. The Association of Single and Combined Factors of Sedentary Behavior and Physical Activity with Subjective Cognitive Complaints among Community-Dwelling Older Adults: Cross-Sectional Study PLoS ONE 2018 13 e0195384 10.1371/journal.pone.0195384 29659622
48. Omura J.D. Brown D.R. McGuire L.C. Taylor C.A. Fulton J.E. Carlson S.A. Cross-Sectional Association between Physical Activity Level and Subjective Cognitive Decline among US Adults Aged ≥45 Years, 2015 Prev. Med. 2020 141 106279 10.1016/j.ypmed.2020.106279 33035548
49. Busse A.L. Gil G. Santarém J.M. Jacob Filho W. Physical Activity and Cognition in the Elderly: A Review Dement. Neuropsychol. 2009 3 204 208 10.1590/S1980-57642009DN30300005 29213629
50. McEwen S.C. Siddarth P. Abedelsater B. Kim Y. Mui W. Wu P. Emerson N.D. Lee J. Greenberg S. Shelton T. Simultaneous Aerobic Exercise and Memory Training Program in Older Adults with Subjective Memory Impairments J. Alzheimer’s Dis. 2018 62 795 806 10.3233/JAD-170846 29480182
51. Garcia-Ptacek S. Eriksdotter M. Jelic V. Porta-Etessam J. Kåreholt I. Manzano Palomo S. Subjective Cognitive Impairment: Towards Early Identification of Alzheimer Disease Neurologia 2016 31 562 571 10.1016/j.nrl.2013.02.007 23601758
52. Willroth E. The Role of Psychosocial and Lifestyle Factors in Promoting Cognitive Health Innov. Aging 2023 7 393 394 10.1093/geroni/igad104.1302
53. Volkow N.D. Wang G.-J. Telang F. Fowler J.S. Goldstein R.Z. Alia-Klein N. Logan J. Wong C. Thanos P.K. Ma Y. Inverse Association Between BMI and Prefrontal Metabolic Activity in Healthy Adults Obesity 2009 17 60 65 10.1038/oby.2008.469 18948965
54. Kyle U.G. Genton L. Gremion G. Slosman D.O. Pichard C. Aging, Physical Activity and Height-Normalized Body Composition Parameters Clin. Nutr. 2004 23 79 88 10.1016/S0261-5614(03)00092-X 14757396
55. Slentz C.A. Houmard J.A. Kraus W.E. Exercise, Abdominal Obesity, Skeletal Muscle, and Metabolic Risk: Evidence for a Dose Response Obesity (Silver Spring) 2009 17 S27 S33 10.1038/oby.2009.385 19927142
56. Petridou A. Siopi A. Mougios V. Exercise in the Management of Obesity Metabolism 2019 92 163 169 10.1016/j.metabol.2018.10.009 30385379
57. Atakan M.M. Koşar Ş.N. Güzel Y. Tin H.T. Yan X. The Role of Exercise, Diet, and Cytokines in Preventing Obesity and Improving Adipose Tissue Nutrients 2021 13 1459 10.3390/nu13051459 33922998
58. Raji C.A. Ho A.J. Parikshak N.N. Becker J.T. Lopez O.L. Kuller L.H. Hua X. Leow A.D. Toga A.W. Thompson P.M. Brain Structure and Obesity Hum. Brain Mapp. 2010 31 353 364 10.1002/hbm.20870 19662657
59. Feinkohl I. Lachmann G. Brockhaus W.-R. Borchers F. Piper S.K. Ottens T.H. Nathoe H.M. Sauer A.-M. Dieleman J.M. Radtke F.M. Association of Obesity, Diabetes and Hypertension with Cognitive Impairment in Older Age CLEP 2018 10 853 862 10.2147/CLEP.S164793
60. Kim W. Jang H. Kim Y.T. Cho J. Sohn J. Seo G. Lee J. Yang S.H. Lee S.-K. Noh Y. The Effect of Body Fatness on Regional Brain Imaging Markers and Cognitive Function in Healthy Elderly Mediated by Impaired Glucose Metabolism J. Psychiatr. Res. 2021 140 488 495 10.1016/j.jpsychires.2021.06.011 34153903
61. Buie J.J. Watson L.S. Smith C.J. Sims-Robinson C. Obesity-Related Cognitive Impairment: The Role of Endothelial Dysfunction Neurobiol. Dis. 2019 132 104580 10.1016/j.nbd.2019.104580 31454547
62. Cisek-Woźniak A. Mruczyk K. Wójciak R.W. The Association between Physical Activity and Selected Parameters of Psychological Status and Dementia in Older Women Int. J. Environ. Res. Public Health 2021 18 7549 10.3390/ijerph18147549 34299996
63. Xiong J. Ye M. Wang L. Zheng G. Effects of Physical Exercise on Executive Function in Cognitively Healthy Older Adults: A Systematic Review and Meta-Analysis of Randomized Controlled Trials: Physical Exercise for Executive Function Int. J. Nurs. Stud. 2021 114 103810 10.1016/j.ijnurstu.2020.103810 33248291
64. Hendrickx H. McEwen B.S. van der Ouderaa F. Metabolism, Mood and Cognition in Aging: The Importance of Lifestyle and Dietary Intervention Neurobiol. Aging 2005 26 (Suppl. S1) 1 5 10.1016/j.neurobiolaging.2005.10.005
65. Liu T. Luo H. Tang J.Y. Wong G.H. Does Lifestyle Matter? Individual Lifestyle Factors and Their Additive Effects Associated with Cognitive Function in Older Men and Women Aging Ment. Health 2020 24 405 412 10.1080/13607863.2018.1539833 30520690
66. Stern Y. Cognitive Reserve Neuropsychologia 2009 47 2015 2028 10.1016/j.neuropsychologia.2009.03.004 19467352
67. Kaur A. Sonal A. Ghosh T. Ahamed F. Cognitive Reserve and Other Determinants of Cognitive Function in Older Adults: Insights from a Community-Based Cross-Sectional Study J. Fam. Med. Prim. Care 2023 12 1957 1964 10.4103/jfmpc.jfmpc_2458_22
68. Wilson R.S. Yu L. Lamar M. Schneider J.A. Boyle P.A. Bennett D.A. Education and Cognitive Reserve in Old Age Neurology 2019 92 e1041 e1050 10.1212/WNL.0000000000007036 30728309
69. Stern Y. Arenaza-Urquijo E.M. Bartrés-Faz D. Belleville S. Cantilon M. Chetelat G. Ewers M. Franzmeier N. Kempermann G. Kremen W.S. Whitepaper: Defining and Investigating Cognitive Reserve, Brain Reserve, and Brain Maintenance Alzheimer’s Dement. 2020 16 1305 1311 10.1016/j.jalz.2018.07.219 30222945
70. 2020 Alzheimer’s Disease Facts and Figures Alzheimer’s Dement. 2020 16 391 460 10.1002/alz.12068 32157811
71. Hebert L.E. Weuve J. Scherr P.A. Evans D.A. Alzheimer Disease in the United States (2010–2050) Estimated Using the 2010 Census Neurology 2013 80 1778 1783 10.1212/WNL.0b013e31828726f5 23390181
72. Reid L.M. Maclullich A.M.J. Subjective Memory Complaints and Cognitive Impairment in Older People Dement. Geriatr. Cogn. Disord. 2006 22 471 485 10.1159/000096295 17047326
73. Zenebe Y. Akele B. W/Selassie M. Necho M. Prevalence and Determinants of Depression among Old Age: A Systematic Review and Meta-Analysis Ann. General Psychiatry 2021 20 55 10.1186/s12991-021-00375-x 34922595
74. Laird E. Rasmussen C.L. Kenny R.A. Herring M.P. Physical Activity Dose and Depression in a Cohort of Older Adults in The Irish Longitudinal Study on Ageing JAMA Netw. Open 2023 6 e2322489 10.1001/jamanetworkopen.2023.22489 37428505
75. Kim S. Kim M.J. Kim S. Kang H.S. Lim S.W. Myung W. Lee Y. Hong C.H. Choi S.H. Na D.L. Gender Differences in Risk Factors for Transition from Mild Cognitive Impairment to Alzheimer’s Disease: A CREDOS Study Compr. Psychiatry 2015 62 114 122 10.1016/j.comppsych.2015.07.002 26343475
76. Proust-Lima C. Amieva H. Letenneur L. Orgogozo J.-M. Jacqmin-Gadda H. Dartigues J.-F. Gender and Education Impact on Brain Aging: A General Cognitive Factor Approach Psychol. Aging 2008 23 608 620 10.1037/a0012838 18808250
77. Buss J. Limitations of Body Mass Index to Assess Body Fat Workplace Health Saf. 2014 62 264 10.1177/216507991406200608 24971823
78. Rankinen T. Kim S.-Y. Pérusse L. Després J.-P. Bouchard C. The Prediction of Abdominal Visceral Fat Level from Body Composition and Anthropometry: ROC Analysis Int. J. Obes. 1999 23 801 809 10.1038/sj.ijo.0800929
79. Corrêa M.M. Thumé E. De Oliveira E.R.A. Tomasi E. Performance of the Waist-to-Height Ratio in Identifying Obesity and Predicting Non-Communicable Diseases in the Elderly Population: A Systematic Literature Review Arch. Gerontol. Geriatr. 2016 65 174 182 10.1016/j.archger.2016.03.021 27061665
