The Role of Physical Vulnerability in the Association Between Social Activities and Cognitive Performance in Rural Older Adults: A Longitudinal Study

Abstract

The aim of this study was to examine the association of social activities with cognitive performance in older adults in the southern area of Brazil, considering the important moderating role of physical vulnerability. A prospective population-based study was conducted in the rural area of Rio Grande, Rio Grande do Sul, Brazil. Self-reported social activities were collected at baseline. Cognitive performance and physical vulnerability were measured in the second wave of data collection. The association of social activities with cognitive performance was determined using robust generalized linear models. In adjusted analysis, the social activities were positively associated with cognitive performance in physically vulnerable older adults. However, this association was not found in those who were nonvulnerable. Our findings may contribute to future investigations of possible explanatory avenues for the association between social activities and cognitive performance as well as the development of interventions aimed at improving cognitive skills.

Keywords

cognition social activities rural longitudinal older adults

Introduction

The older adult population is rapidly growing throughout the world. With the growing life expectancy of human beings, it is estimated that this specific population (over 60 years old) will reach 2 billion individuals by 2050 (Cohen, 2003). In Brazil, the proportion of people aged 65 years and over is projected to increase from 14.3% in 2020 to 32.2% by 2060 (Travassos et al., 2020).

The increasing number of people in this age group has imposed a huge health impact on society. Advanced age usually is accompanied by a greater decline in health (Zhu et al., 1998). The central nervous system is the major area that is affected by aging, resulting in changes in cognitive performance (Murman, 2015). Cognitive performance refers to a general measure of mental processes that are crucial for the conduct of the activities of daily living, such as perception, memory, learning, attention, decision making, and language abilities (Kiely, 2014).

In parallel, aging implies an increased risk for physical vulnerability which increases the chances of a person becoming ill and favors the occurrence of adverse clinical outcomes, impaired well-being, hospitalizations, institutionalization, and death (McGee et al., 2008). Physical vulnerability refers to people's susceptibility to health problems, and also an increased risk of functional decline or death over a 2-year period. This condition can be evaluated through a specific questionnaire, called the Vulnerable Elders Survey-13 (VES-13) (Saliba et al., 2001). The physical vulnerability experienced by older adults will most likely lead to an increased caretaker dependency, essential to perform the activities of daily life.

Although observational studies have demonstrated poor cognitive performance in physically vulnerable older adults compared to a nonvulnerable group (Ávila-Funes et al., 2009; Jürschik et al., 2012), there is a lack of studies investigating the role of physical vulnerability in the association between social factors, such as social activities, and cognitive performance.

Several longitudinal studies have suggested that engagement in social activities is associated with less cognitive decline over time (Barnes et al., 2004; Bourassa et al., 2017; Hwang et al., 2018; James et al., 2011; Kim et al., 2017). In addition, previous research has shown that greater participation in social activities can reduce susceptibility to problems and damage to health in older people by providing intellectual and emotional stimulation (James et al., 2011). Therefore, it is likely that the association between social activities and cognitive performance might be moderated by physical vulnerability.

While older adults in rural areas report having larger social networks than older adults in urban areas, they also report higher levels of loneliness, indicating structural barriers to social connection (Henning-Smith et al., 2019). Residents of rural areas have multiple barriers regarding social connection, including transportation challenges, rough terrain not suitable for locomotion or conducive to social interaction, more limited economic resources, and less access to broadband Internet and cell phone signals (Henning-Smith et al., 2019). In addition, more restricted access to health care among older people living in rural areas can increase physical vulnerability due to age (Travassos & Viacava, 2007).

Given that the physical vulnerability and cognitive decline are consequences of aging, combined with a significant gap in the literature on the role of physical vulnerability in the association between social activities and cognitive performance in rural older adults, this study aimed to evaluate whether physical vulnerability influences the association between social activities and cognitive performance in the rural older adults.

Methods

Participants and Procedures

This is a prospective, population-based, home-based cohort study entitled “EpiRural: a cohort of older adults from the rural area of Rio Grande, RS.” The first wave of inquiries (baseline) happened in 2017 and the second wave in 2018/2019. Of the 1029 older adults included in the baseline, 863 were followed up, resulting in a loss rate of 16.1%. The analytical sample comprised 715 (69.5% of baseline) participants who had full data. There were no sociodemographic differences between the first and second wave.

The EpiRural study was carried out in the rural area of Rio Grande, State of Rio Grande do Sul, located in the southern region of Brazil. This city's estimated population by the Brazilian Institute of Geography and Statistics, for 2010, was 210,000 inhabitants, of whom 1,080 belonged to the age group of 60 years or more and lived in rural areas (IBGE, 2011).

The studied population consisted of older adults (60 years old or more) living in the rural area of Rio Grande in 2017. The baseline sample was based on the 2010 Demographic Census (IBGE, 2011). A selection process was used to select 80% of the households in rural areas. A number between one and five was drawn, and that number corresponded to a household to be skipped in a sequence of houses in a neighborhood, resulting in a said percentage. This procedure ensured that four out of five households were included in the sample. All the older adults of the picked households were invited to participate in the study.

The institutionalized older adults (long-term institutions, hospitals, and penitentiaries) were not included in the study, as well as the ones unable to follow the interview due to physical or mental incapacity. In addition, participants who had a neurological condition were excluded from the analysis (stroke, N = 49; Parkinson's disease, N = 5).

The two waves of the EpiRural study were approved by the Health Research Ethics Committee (CEPAS) of FURG (protocol nos. 51/2017 and 154/2018). Participation was voluntary and involved informed consent.

Measures

Participation in social activities was assessed at baseline with the sum of five verbal questions posed by trained interviewers. Participants were asked about participation in the following four activities in the previous 30 days of the interview: “Did you participate in religious activity? This includes activities such as a church, hoodoo, temple, etc.”; “Did you attend a family party?”; “Did you attend a community party?”; “Did you take a trip?” They were also asked, “In the past seven days, did you spend time with friends or family?” The answer alternatives to all of these questions were Yes or No. Participation in social activities was included as a continuous variable (range from 0 = no activities to 5 = five activities).

Cognitive performance and physical vulnerability were measured in the second wave of data collection using the Mini-Mental State Examination (MMSE) and the Brazilian version of the VES-13 (Maia et al., 2012), respectively. MMSE includes 11 items that give a total score ranging from 0 to 30 points, with the higher scores representing better cognitive function (Folstein et al., 1975). The MMSE has been translated and validated in Brazilian Portuguese by Bertolucci et al. (1994). The VES-13 is made up of 13 items covering age (one question), self-rated health (one question), physical capacity (six questions), and functional capacity (five questions) and takes an average of five minutes to complete. The total score ranges from 0 to 10 points. The vulnerability cutoff is a score ≥3 (Saliba et al., 2001).

Other measures collected in the study were sociodemographic, health status, and lifestyle factors. The following measures were collected at baseline: sex; age (60–69 years, 70–79 years, and 80 years or more); schooling (0, 1–4, 5–8, and 9 or more); family income in minimum wages (<1, 1–1.9, 2–2.9, and 3 or more); marital status (with or without a companion); smoking (never smoked, has smoked but quit, or smokes); alcohol use in the last week (yes or no), and depression (yes or no). The presence or absence of depression was identified through major depressive episode screening using the Patient Health Questionnaire-9 instrument, which assesses the presence of depressive symptoms in the last two weeks, based on the Diagnostic and Statistical Manual of Mental Disorders Fifth edition. The recommended cutoff point is ≥9, which has good psychometric properties, with sensitivity between 77% and 98% and specificity of 75% to 80% (Santos et al., 2013).

Statistical Analysis

Descriptive statistics were used to summarize the sample characteristics (absolute and relative frequency). We used the analysis of variance (ANOVA) test to compare averages between groups. The interaction of physical vulnerability with sex regarding cognitive impairment was tested; however, there was no statistical significance.

Linear regression was used in unadjusted and adjusted analyses of the association between physical vulnerability and cognitive performance. The normality of residuals in linear regression models was graphically confirmed. For adjusted analyses, in the first model, the sex, age, family income, schooling, marital status, smoking, and alcohol use were used as covariates. A second model of regression analysis was used, including social activities in the first model. Social activities × physical vulnerability terms were added in the subsequent model to test whether social activities modified associations between social activities and cognitive performance. If the interaction term was statistically significant, we plotted the moderating variable (physical vulnerability) and tested the slope of the social activities to identify the association driving the interaction.

The information was collected on tablets through the RedCap® program (Research Electronic Data Capture) (Harris et al., 2009). Data conferencing and uploads to the server were performed daily, ensuring the quality and safety of this process. The database was exported from RedCap to the STATA 14.0 statistical package, which also performed data analysis. The level of statistical significance was set at 5%.

Results

A total of 715 participants were included in this study (Table 1). The sample consisted mostly of male individuals (54.6%), married or with a partner (62.6%), and with 1 to 5 years of schooling (67.1%). Approximately half of the individuals were aged between 60 and 69 years (46.1%) and 43.1% had an income between 2 and <3 minimum wages. One-tenth of the sample were smokers (11.9%), about 20.1% consumed alcoholic beverages in the previous week, and 6.9% screened positive for depression. The prevalence of physical vulnerability was 38.6% (95% CI: 35.1%, 42.2%). The average MMSE test score was 21.9 points (95% CI: 21.6, 22.2).

Table 1.

Sample Characteristics According to Cognitive Performance (MMSE score). EpiRural, Rio Grande, RS, Brazil, 2017 and 2018–2019 (N = 715).

			MMSE score
	N	%	Mean (95% CI)	P-value^*
Sex				0.009
Male	390	54.6	22.6 (22.2–23.0)
Female	325	45.4	21.5 (20.8–22.2)
Marital status				0.001
Single/widowed	267	37.4	20.7 (19.8–21.6)
Married or with a partner	448	62.6	22.7 (22.2–23.2)
Schooling (full years)				<0.001
0	129	18.0	18.2 (17.5–18.9)
1–4	480	67.1	21.6 (21.1–22.0)
5–8	88	12.3	24.7 (23.6–25.9)
≥ 9	19	2.6	26.5 (25.8–27.1)
Age (years)				<0.001
60–69	330	46.1	23.1 (22.6–23.5)
70–79	270	37.7	21.9 (21.0–22.7)
≥ 80	116	16.2	19.8 (19.1–20.6)
Family income in MWs				<0.001
<1	51	7.2	19.9 (18.6–21.1)
1–2	211	29.5	21.5 (20.9–22.2)
2.1–3	308	43.1	22.0 (21.3–22.8)
>3	144	20.2	23.9 (23.2–24.5)
Smoking				0.097
Never smoked	365	51.1	21.7 (21.3–22.2)
Former smoker	265	37.0	22.5 (21.6–23.4)
Smokes	85	11.9	22.5 (21.5–23.4)
Declared alcohol consumption (last week)				0.010
No			21.8 (21.3–22.2)
Yes	144	20.1	23.4 (22.7–24.1)
Depression^a				0.041
No			22.3 (21.8–22.7)
Yes	49	6.9	20.6 (19.2–22.0)

MMSE: Mini-Mental State Examination; CI: confidence interval; MW: minimum wage; ANOVA: analysis of variance.

Patient Health Questionnaire-9 score ≥9 points.

*ANOVA test.

The crude and adjusted analyses of the associations of social activities and physical vulnerability with cognitive performance are shown in Table 2. After adjustment, the social activities were positively associated with MMSE score (Model 1: b = − 1.62; 95% CI −2.48, −0.76; Model 2: b = − 1.51; 95% CI −2.37, −0.65), and this association varied by physical vulnerability (beta estimates for interaction = 0.77; 95% CI: 0.01, 1.54; Model 3; Table 2). In physically vulnerable older adults, for every one-unit increase in social activities, the average MMSE score increased by 0.96 points in adjusted analysis (95% CI: 0.45, 1.48; Table 3). However, this association was not found in those who were nonvulnerable. Furthermore, among those who reported no social activities, physically vulnerable older adults scored on average 3.11 points lower on the MMSE score than nonvulnerable older adults (95% CI: −4.85, −1.38; Figure 1); this difference disappeared among those who had at least three social activities.

Figure 1.

Adjusted margins plots of social activities × physical vulnerability interaction effects on Mini-Mental State Examination (MMSE) score, from Table 2. EpiRural, Rio Grande, RS, Brazil, 2017 and 2018–2019 (N = 715). Shading depicts 95% confidence intervals.

Table 2.

Association of Social Activities and Physical Vulnerability with Cognitive Performance. Sample of Elderly Residents from the Rural Area. EpiRural, Rio Grande, RS, Brazil, 2017 and 2018–2019 (N = 715).

	MMSE score
	β (95% CI)	p-value
Unadjusted model
Social activities	0.98 (0.62. 1.34)	<0.001^a
Physical vulnerability	−2.94 (−3.77, −2.11)	<0.001^b
Adjusted models
Model 1
Social activities	0.55 (0.21, 0.89)	0.002^a
Model 2
Social activities	0.48 (0.11, 0.84)	0.010^a
Physical vulnerability	−1.51 (−2.37, −0.65)	<0.001^b
Model 3
Social activities	0.24 (−0.19, 0.67)	0.265^a
Physical vulnerability	−3.07 (−4.85, −1.30)	0.001^b
Social activities × physical vulnerability	0.77 (0.01, 1.54)	0.048^b

MMSE: Mini-Mental State Examination.

Linear trend test.

Heterogeneity test; covariates: sex, age, family income, schooling, marital status, smoking, alcohol use, and depression.

Model 1 = adjustment for covariates.

Model 2 = Physical vulnerability and covariates as simultaneous regressors.

Model 3 = interaction term added in Model 2.

Table 3.

Association Between Social Activities and Mini-Mental State Examination (MMSE) Score According to Physical Vulnerability. Sample of Elderly Residents from the Rural Area. EpiRural, Rio Grande, RS, Brazil, 2017 and 2018–2019 (N = 715).

	Nonvulnerable (n = 439)				Vulnerable (n = 276)
	Crude analysis		Adjusted analysis^a		Crude analysis		Adjusted analysis^a
	β (95% CI)	p-value^*	β (95% CI)	p-value^*	β (95% CI)	p-value^*	β (95% CI)	p-value^*
Social activities	0.58 (0.11, 1.05)	0.017	0.21 (−0.25, 0.68)	0.371	1.30 (0.74, 1.86)	<0.001	0.96 (0.45, 1.48)	<0.001

*Linear trend test.

Confounding variables: sex, age, family income, schooling, marital status, smoking, alcohol use, and depression.

Discussion

To our knowledge, this is the first longitudinal study to investigate the association between social activities and cognitive performance in rural older adults considering the moderating role of physical vulnerability in this relationship. Our results indicated that social activities were positively associated with cognitive performance in physically vulnerable older adults. However, this association was not observed in nonvulnerable older adults.

This research provides novel findings regarding factors that moderate the relationship between social activities and cognitive performance among older adults. Several longitudinal studies that did not include physical vulnerability in their analyses found that engagement in social activities is associated with less cognitive decline over time (Bourassa et al., 2017; Glei et al., 2005; Hwang et al., 2018; Kim et al., 2017; Lee & Kim, 2016), although this has not always been found (Marioni et al., 2015). In our study, social activities were positively associated with cognitive performance. However, by including the interaction term in the regression model, this association varied by the physical vulnerability.

We found that among vulnerable older adults, those who reported more social activities showed better cognitive performance. Studies on the association between social activities and cognitive performance in vulnerable older adults were not found in the literature. It is possible that, in physically vulnerable older adults, social activities can provide enjoyment, improve one's mood, present a context for older adults to share rewarding moments with others, and allow adults to invest time and energy in their interests (Berkman et al., 2000). In addition, cognition might be hampered by negative effects, while positive attitudes and beliefs can be positively linked to late-life cognition (Hertzog et al., 2008).

In our study, social activities were not associated with cognitive performance in nonvulnerable older adults. Although some longitudinal studies associate participation in social activities for healthy older adults (no cardiovascular disease, or other significant medical illness, psychiatric or neurological problems), such as visiting church and neighborhood associations, with a better score in MMSE (Andrew & Rockwood, 2010; Lee et al., 2009; Marioni et al., 2014), other studies did not find this association (Aartsen et al., 2002; Iwasa et al., 2012).

Evidence suggests that the mechanism of association between social activities and cognitive performance could be through physical activity (Hertzog et al., 2008). In their meta-analysis, Sofi et al. (2011) demonstrated that individuals who engaged in low-to-moderate levels of physical activity at baseline had a 35% reduced risk of developing cognitive decline at follow-up interviews, compared with those who did not engage in physical activities (Sofi et al., 2011). It is probable that nonvulnerable older adults engage in more physical activity than vulnerable older adults. Thus, when we stratified the analysis for physical vulnerability groups, we may have indirectly adjusted for physical activity.

Our findings contribute to clinical practice by improving understanding of the physical vulnerability differences in the association of social activities with cognitive performance in older adults. In addition, the cognitive performance differences between vulnerable and nonvulnerable participants disappeared among those who had at least three social activities.

Our study had several strengths. It is based on prospective data, collected with methodological rigor, conducted through a household survey, and with a low percentage of missing data and refusals when compared to other surveys. In addition, despite several publications about cognitive performance in Brazil, few were found to address the issue in rural regions. Regarding the complexity of the phenomenon studied, the present study used a validated instrument for the Brazilian population, which has been used in other countries (Arevalo-Rodriguez et al., 2015; Bertolucci et al., 1994), and our analyses were controlled for a wide variety of variables, such as family income and depression, which can influence both social activities and cognition. In both vulnerable and nonvulnerable older adults, the linear model for social activities exposure had more than 80% power to detect a difference in the unadjusted and adjusted models.

It is also important to consider that the data collected imposes some limitations on the analyses. The biggest limitation is the lack of baseline cognitive performance data. Furthermore, we used a limited measure of social activities that only had five indicators. It is likely that social engagement entails a broader spectrum of activities. It is also pointed out that social activities may be subject to some recall bias as they were self-reported. Finally, it is important to note that the MMSE was developed as a screening tool for cognitive impairment and is not a substitute for assessment based on a clinical interview conducted by psychologists and medical doctors. Thus, regardless of how the instrument is used, the result should be interpreted with caution.

This longitudinal study provides new insights into the relationship between social activities and cognitive performance in rural older adults considering the important moderating role of physical vulnerability. The results showed a positive association between social activities and cognitive performance only in vulnerable older adults, suggesting that the effects of these activities differ according to physical capacity. Our findings may contribute to future investigations of possible explanatory avenues for these associations as well as the development of interventions aimed at improving the performance of cognition.

Footnotes

Authors Contribution

All authors contributed to the study design, data analysis, data interpretation, and drafting and revision of the manuscript and gave final approval of the version to be submitted for publication. The corresponding author had full access to all the data in the study and had final responsibility for the decision to submit it for publication.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work received support from the National Council for Scientific and Technological Development (CNPQ) through a research productivity scholarship, as well as from the Coordination for the Improvement of Higher Level Personnel (CAPES)/Ministry of Education and Pastoral da Criança.

ORCID iD

Pedro San Martin Soares

Author Biographies

Pedro San Martin Soares is a postdoctoral researcher at the Postgraduate Program in Epidemiology at Universidade Federal de Pelotas (UFPel). He received a PhD in Epidemiology from the Universidade Federal de Pelotas (UFPel). His research is currently focused on the effects that environmental factors have on cognitive skills.

Tatiane Nogueira Gonzalez is a PhD student at the Postgraduate Program in Health Sciences at Universidade Federal de Rio Grande (FURG). Her research is currently focused on the health of rural older adults.

Cristina dos Santos Paludo is a PhD student at the Postgraduate Program in Health Sciences at Universidade Federal de Rio Grande (FURG). Her research is currently focused on the health of rural older adults.

Rodrigo Dalke Meucci is an associate professor at the Postgraduate Program in Public Health at Universidade Federal do Rio Grande (FURG). He received a PhD in Epidemiology from the Universidade Federal de Pelotas (UFPel). His research focuses on the health of rural older adults.

References

Aartsen

M. J.

Smits

C. H.

Van Tilburg

Knipscheer

K. C.

Deeg

D. J.

(2002). Activity in older adults: Cause or consequence of cognitive functioning? A longitudinal study on everyday activities and cognitive performance in older adults. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences, 57(2), P153–P162. https://doi.org/10.1093/geronb/57.2.p153.

Andrew

M. K.

Rockwood

(2010). Social vulnerability predicts cognitive decline in a prospective cohort of older Canadians. Alzheimer’s & Dementia, 6(4), 319–325.e311. https://doi.org/10.1016/j.jalz.2009.11.001.

Arevalo-Rodriguez

Smailagic

I Figuls

M. R.

Ciapponi

Sanchez-Perez

Giannakou

Cullum

(2015). Mini-mental state examination (MMSE) for the detection of Alzheimer’s disease and other dementias in people with mild cognitive impairment (MCI). Cochrane Database of Systematic Reviews, 1(3), 1–64. https://doi.org/10.1002/14651858.CD010783.pub2.

Ávila-Funes

J. A.

Amieva

Barberger-Gateau

Le Goff

Raoux

Ritchie

Gutiérrez-Robledo

L. M.

(2009). Cognitive impairment improves the predictive validity of the phenotype of frailty for adverse health outcomes: The three-city study. Journal of the American Geriatrics Society, 57(3), 453–461. https://doi.org/10.1111/j.1532-5415.2008.02136.x.

Barnes

L. L.

De Leon

C. M.

Wilson

R. S.

Bienias

J. L.

Evans

D. A.

(2004). Social resources and cognitive decline in a population of older African Americans and whites. Neurology, 63(12), 2322–2326. https://doi.org/10.1212/01.wnl.0000147473.04043.b3.

Berkman

L. F.

Glass

Brissette

Seeman

T. E.

(2000). From social integration to health: Durkheim in the new millennium. Social Science & Medicine, 51(6), 843–857. https://doi.org/10.1016/s0277-9536(00)00065-4.

Bertolucci

P. H. F.

Mathias

Brucki

Carrilho

Okamoto

Nitrini

(1994). Proposta de padronização do Mini-Exame do Estado Mental (MEEM): Estudo piloto cooperativo (FMUSP/EPM). Arquivos de Neuro-Psiquiatria, 52(1), 225–240. https://doi.org/10.1590/S0004-282X2003000500014.

Bourassa

K. J.

Memel

Woolverton

Sbarra

D. A.

(2017). Social participation predicts cognitive functioning in aging adults over time: Comparisons with physical health, depression, and physical activity. Aging & Mental Health, 21(2), 133–146. https://doi.org/10.1080/13607863.2015.1081152.

Cohen

J. E.

(2003). Human population: The next half century. Science (New York, N.Y.), 302(5648), 1172–1175. https://doi.org/10.1126/science.1088665.

10.

Folstein

M. F.

Folstein

S. E.

McHugh

P. R.

(1975). “Mini-mental state”: A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189–198. https://doi.org/10.1016/0022-3956(75)90026-6.

11.

Glei

D. A.

Landau

D. A.

Goldman

Chuang

Y.-L.

Rodríguez

Weinstein

(2005). Participating in social activities helps preserve cognitive function: An analysis of a longitudinal, population-based study of the elderly. International Journal of Epidemiology, 34(4), 864–871. https://doi.org/10.1093/ije/dyi049.

12.

Harris

P. A.

Taylor

Thielke

Payne

Gonzalez

Conde

J. G.

(2009). Research electronic data capture (REDCap)-a metadata-driven methodology and workflow process for providing translational research informatics support. Journal of Biomedical Informatics, 42(2), 377–381.

13.

Henning-Smith

Moscovice

Kozhimannil

(2019). Differences in social isolation and its relationship to health by rurality. The Journal of Rural Health, 35(4), 540–549. https://doi.org/10.1111/jrh.12344.

14.

Hertzog

Kramer

A. F.

Wilson

R. S.

Lindenberger

(2008). Enrichment effects on adult cognitive development: Can the functional capacity of older adults be preserved and enhanced? Psychological Science in the Public Interest, 9(1), 1–65. https://doi.org/10.1111/j.1539-6053.2009.01034.x.

15.

Hwang

Park

Kim

(2018). Effects of participation in social activities on cognitive function among middle-aged and older adults in Korea. International Journal of Environmental Research and Public Health, 15(10), 2315. https://doi.org/10.3390/ijerph15102315.

16.

IBGE (2011). Censo Demográfico 2010, Vol. 2019. Instituto Brasileiro de Geografia e Estatística (IBGE) .

17.

Iwasa

Yoshida

Kai

Suzuki

Kim

Yoshida

(2012). Leisure activities and cognitive function in elderly community-dwelling individuals in Japan: A 5-year prospective cohort study. Journal of Psychosomatic Research, 72(2), 159–164. https://doi.org/10.1016/j.jpsychores.2011.10.002.

18.

James

B. D.

Wilson

R. S.

Barnes

L. L.

Bennett

D. A.

(2011). Late-life social activity and cognitive decline in old age. Journal of the International Neuropsychological Society, 17(6), 998–1005. https://doi.org/10.1017/S1355617711000531.

19.

Jürschik

Nunin

Botigué

Escobar

M. A.

Lavedán

Viladrosa

(2012). Prevalence of frailty and factors associated with frailty in the elderly population of Lleida, Spain: The FRALLE survey. Archives of Gerontology and Geriatrics, 55(3), 625–631. https://doi.org/10.1016/j.archger.2012.07.002.

20.

Kiely

K. M.

(2014). Encyclopedia of quality of life and well-being research (1st ed., Vol. 1, pp. 974–978). Netherlands: Springer.

21.

Kim

Arai

Kim

(2017). Social activities are associated with cognitive decline in older Koreans. Geriatrics & Gerontology International, 17(8), 1191–1196. https://doi.org/10.1111/ggi.12861.

22.

Lee

S. H.

Kim

Y. B.

(2016). Which type of social activities may reduce cognitive decline in the elderly?: A longitudinal population-based study. BMC geriatrics, 16(1), 1–9. https://doi.org/10.1186/s12877-016-0343-x.

23.

Lee

Kim

Back

J. H.

(2009). The influence of multiple lifestyle behaviors on cognitive function in older persons living in the community. Preventive Medicine, 48(1), 86–90. https://doi.org/10.1016/j.ypmed.2008.10.021.

24.

Maia

Duarte

Secoli

Santos

Lebrão

(2012). Adaptação transcultural do vulnerable elders survey-13 (VES-13): Contribuindo para a identificação de idosos vulneráveis. Revista da Escola de Enfermagem da USP, 46(SPE), 116–122. https://doi.org/10.1590/S0080-62342012000700017.

25.

Marioni

R. E.

Proust-Lima

Amieva

Brayne

Matthews

F. E.

Dartigues

J.-F.

Jacqmin-Gadda

(2014). Cognitive lifestyle jointly predicts longitudinal cognitive decline and mortality risk. European Journal of Epidemiology, 29(3), 211–219. https://doi.org/10.1007/s10654-014-9881-8.

26.

Marioni

R. E.

Proust-Lima

Amieva

Brayne

Matthews

F. E.

Dartigues

J.-F.

Jacqmin-Gadda

(2015). Social activity, cognitive decline and dementia risk: A 20-year prospective cohort study. BMC Public Health, 15(1), 1–8. https://doi.org/10.1186/s12889-015-2426-6.

27.

McGee

H. M.

O'Hanlon

Barker

Hickey

Montgomery

Conroy

O'Neill

(2008). Vulnerable older people in the community: Relationship between the Vulnerable Elders Survey and health service use. Journal of the American Geriatrics Society, 56(1), 8–15. https://doi.org/10.1111/j.1532-5415.2007.01540.x.

28.

Murman

D. L.

(2015). The impact of age on cognition. Seminars in Hearing, 36(3), 111–121. https://dx-doi-org.web.bisu.edu.cn/10.1055/s-0035-1555115.

29.

Saliba

Elliott

Rubenstein

L. Z.

Solomon

D. H.

Young

R. T.

Kamberg

C. J.

Sloss

E. M.

(2001). The vulnerable elders survey: A tool for identifying vulnerable older people in the community. Journal of the American Geriatrics Society, 49(12), 1691–1699. https://doi.org/10.1046/j.1532-5415.2001.49281.x.

30.

Santos

I. S.

Tavares

B. F.

Munhoz

T. N.

Almeida

L. S. P. d.

Silva

N. T. B. d.

Tams

B. D.

Matijasevich

(2013). Sensitivity and specificity of the patient health questionnaire-9 (PHQ-9) among adults from the general population. Cadernos de saude publica, 29(8), 1533–1543. https://doi.org/10.1590/0102-311x00144612.

31.

Sofi

Valecchi

Bacci

Abbate

Gensini

G. F.

Casini

Macchi

(2011). Physical activity and risk of cognitive decline: A meta-analysis of prospective studies. Journal of Internal Medicine, 269(1), 107–117. https://doi.org/10.1111/j.1365-2796.2010.02281.x.

32.

Travassos

Viacava

(2007). Acesso e uso de serviços de saúde em idosos residentes em áreas rurais, Brasil, 1998 e 2003. Cadernos de Saude Publica, 23(10), 2490–2502. https://doi.org/10.1590/S0102-311X2007001000023.

33.

Travassos

G. F.

Coelho

A. B.

Arends-Kuenning

M. P.

(2020). The elderly in Brazil: Demographic transition, profile, and socioeconomic condition. Revista brasileira de estudos de população, 37. https://doi.org/10.20947/S0102-3098a0129.

34.

Zhu

Fratiglioni

Guo

Aguero-Torres

Winblad

Viitanen

(1998). Association of stroke with dementia, cognitive impairment, and functional disability in the very old: A population-based study. Stroke, 29(10), 2094–2099. https://doi.org/10.1161/01.str.29.10.2094.