Short-Term Impact of Social Distancing Measures During the COVID-19 Pandemic on Cognitive Function and Health Perception of Brazilian Older Adults: A Pre-Post Study

Abstract

Social distancing, a critical measure to manage COVID-19 transmission, is consistently associated with social isolation, a major health issue. Social isolation negatively impacts mental and physical health, particularly among older adults. A pre-post comparison study examined changes in cognitive function and perceived health among 36 community-dwelling Brazilian older adults, assessed pre and post social distancing measures enacted due to COVID-19. A significant increase in cognitive function was found 1 month into social distancing (M = 16.3, p = .002, power = 0.88), with declining scores for vitality (M = −29.3, p < .001, power = 0.99) and mental health (M = −38.1, p < .001, power = 0.99), particularly among participants who lived alone (t = −3.8, p = .001). Older adults exhibit rapid changes in perceived health when excluded from participation in social activities. Health care professionals should consider holistic approaches when addressing the impacts of social isolation on this population.

Keywords

cognition self-rated health technology participation social isolation

Introduction

COVID-19 is a highly infectious disease leading to high rates of morbidity and mortality, particularly among older adults due to their increased risk of developing severe health complications (Applegate & Ouslander, 2020; Shahid et al., 2020; Zhou et al., 2020). One of the main measures to limit infection spread is social distancing (also known as physical distancing; Saez et al., 2020), which disrupts social interactions, activities, and participation. Due to their increased risk of complications, older adults have been advised to follow strict social distancing measures by staying at home and avoiding contact with others. For many, these measures have led to increased social isolation and feelings of loneliness, risk factors already typically associated with older adults’ life situations (Yu et al., 2020). Social isolation has been well documented as a threat to older adults’ quality of life and physical and mental health (Leigh-Hunt et al., 2017). From cognitive decline to psychological distress (Griffin et al., 2020), the absence of social interactions is acknowledged as a primary risk factor for the development of severe mental health conditions (Calati et al., 2019).

Research has demonstrated the negative association between older adults’ health and social isolation and the impact of isolation on the development of depressive symptoms, psychological distress, and cognitive deficits (see Griffin et al., 2020; Lara et al., 2019; Shankar et al., 2013; Yu et al., 2020). These studies have consistently demonstrated that restricted social activities are a significant barrier to older adults’ well-being, despite geographic, economic, or cultural variations (Morcillo-Cebolla et al., 2014; Wang et al., 2020). Limited social interactions were also found to be associated with increased stress (Holt-Lunstad et al., 2015), low sleep quality (Cho et al., 2019), substance abuse, and increased risk for cardiovascular conditions (from hypertension to post-myocardial infarction mortality; Xia & Li, 2018).

In addition to the detrimental role of social isolation in the onset of psychological conditions and chronic diseases, review studies observed a consistent, adverse social isolation effect on how older adults perceived their health and vulnerability. Self-perceived health is a critical concept for understanding a person’s overall health (Morcillo-Cebolla et al., 2014). Perceived health is a robust predictor for mortality among the general population and bears strong associations with the occurrence of chronic conditions, access to health care services, and overall quality of life among older adults (Christian et al., 2011). Perceived health is commonly assessed via standardized questionnaires where participants rate their overall health based on ranked scales (Thong et al., 2008). Although there is no agreement on the exact mechanism underlying the association of social isolation with poor health, a preeminent hypothesis considers the adoption of harmful behaviors as a form of emotional relief (Leigh-Hunt et al., 2017), increased by the lack of exposure to healthy behaviors due to limited social interactions (Lauder et al., 2006).

The COVID-19 pandemic has led to mandatory social distancing, preventing people, particularly older adults, from engaging in social situations, often resulting in extreme social isolation (Meng et al., 2020; Storopoli et al., 2020). Although the impact of isolation on older adults’ health is well-described, the sudden change in people’s habits, routines, and participation due to social distancing requirements are new elements that have not been addressed before. Besides the increased risk of severe complications caused by COVID-19, older adults could be at higher risk of experiencing loneliness and isolation for not having the necessary resources to engage in social activities through virtual, contactless methods (Applegate & Ouslander, 2020).

Study Objective

Currently, research investigating the impact of social isolation due to COVID-19 consists mostly of cross-sectional studies (e.g., Ahmed et al., 2020; Brooks et al., 2020; Qiu et al., 2020), lacking comparisons within the same cohort. Given the challenges and potential long-term complications caused by isolation for older adults’ mental health, we aimed to evaluate changes in cognitive function and perceived health among community-dwelling Brazilian older adults before and during social distancing measures enacted due to the COVID-19 pandemic.

Method

Study Design and Context

A pre-post study design was used to assess older adults’ cognitive function and perceived health at two phases: before and during social distancing required due to the COVID-19 pandemic. The data for this study were collected as part of the translation, adaptation, and validation process of the Activity Card Sort (ACS) for Brazilian Portuguese (ACS-Brazil; see Bernardo et al., 2020). The ACS is a measure of participation, assessing respondents’ engagement in activities within four domains: instrumental, social, and low and high-demand leisure activities (Baum & Edwards, 2001). The ACS-Brazil consists of 83 cards with images of older adults performing several activities in each domain (Orellano-Colón et al., 2014) and is measured by comparing the percentage of current activities to a previous point in time. The ACS-Brazil pre-post study findings will be published elsewhere (Pontes et al., n.d.).

As part of the ACS-Brazil validation, our initial protocol described a two-phase design, where Brazilian older adults, 60 years of age and older, would be assessed at baseline and at least 30 days after the initial assessment. The first data collection phase occurred between December 2019 and February 2020, with the second phase initially scheduled to begin in late April 2020. However, by March 17, 2020, before the second phase of data collection, Brazil’s Ministry of Health had regulated stay-at-home measures, placing the study’s participants on approximately 1 month of social distancing by late April 2020. The original ethics protocol was amended to allow for online data collection and the questionnaire’s required adaptations. After ethics approval, Phase 2 data were collected between April 18 and April 27, 2020.

Participants and Recruitment

For Phase 1, research staff recruited older adults in-person from the waiting rooms of university-run health promotion programs, outpatient clinic, primary health care services, and community service projects. Participants were included if they were 60 years or older, community-dwelling, and able to communicate in Portuguese. Due to the validation and cultural adaptation process, participants with visual and cognitive impairments were excluded. Three graduate students, supervised by a senior researcher, discussed the study’s objectives with potential participants. Those participants who consented to participate chose either to answer the questionnaires onsite or to provide their contact information to schedule a session on a later date in their home. Participants were also encouraged to invite other older adults they knew to participate in the study. Sixty-four older adults participated in the data collection for the first phase.

As only emergency health care centers remained open after social distancing measures, participants from Phase 1 for whom we had contact information (n = 36) were invited to participate in Phase 2 by the same student who conducted their Phase 1 data collection. All participants who were contacted from Phase 1 provided consent to participate in Phase 2. Thirty-six older adults, ages 60 to 80 years, completed both data collection phases and were included in the study.

Procedure

Phase 1: Before social distancing measures

All potential participants received a letter of information onsite and were asked to provide written informed consent before enrolling in the study. Consenting participants completed a demographics questionnaire and three standardized assessments in one session, all administered in-person by one of three trained graduate students under close supervision of a senior researcher.

Phase 2: During social distancing measures

Following initial agreement, participants were sent a link through e-mail or WhatsApp to the online questionnaire, which comprised internet-based replications of the demographics form and the three assessments used during Phase 1. Participants were asked to indicate informed consent to participate before completing the online questionnaire. Those participants with incomplete questionnaires after 3 days were sent a reminder. Due to the unique situation, data were collected in 10 days from between 32 and 41 days (M = 34.2, SD = 2.64) following social distancing measures (March 17, 2020). On average, the mean time between the first and second assessments was approximately 2 months.

Instruments

Demographic questionnaire

Data on age, sex, years of education, marital status, and household composition were obtained through a structured demographic questionnaire.

Mini-Mental State Examination: Brief (MMSE-Brief)

The brief version of the Mini-Mental State Examination (2nd edition) was used to measure cognitive function of the older adult participants. The MMSE-Brief was validated for the Brazilian population and showed good psychometric properties when compared to the original data (Spedo et al., 2018). The brief version of the MMSE investigates four categories (registration, orientation to time, orientation to place, and recall tasks) with a possible total score of 16 points. Final scores are converted into a 0% to 100% scale, with higher marks indicating better cognitive function. Final score and interpretation vary according to age and years of education (Spedo et al., 2018).

Short-form health survey (SF-36)

The SF-36 questionnaire was used to evaluate changes in perceived health observed by participants after the enactment of social distancing measures. This self-administered tool, comprised 36 questions (Laguardia et al., 2011), considers participants’ health status as an individualized process based on life experience and knowledge of disease causes and consequence (Krokavcova et al., 2009). The SF-36 has eight subscales measuring the respondents’ perception of their physical functioning, role physical, bodily pain, general health, vitality, social functioning, role emotional, and mental health, which are represented as the weighted sums of the questions in their section (Ware et al., 1997).

Each domain is converted into a 0 to 100 scale on the assumption that each question carries equal weight, with higher numbers indicating positive results. The SF-36 was validated for the Brazilian population, and psychometric properties of the Brazilian version of the SF-36 (v.2) questionnaire meet the standards established by the International Quality of Life project (Laguardia et al., 2011).

Data Analysis

Descriptive statistics (mean, SD, range) were used to analyze demographic data. As the data indicated a normal distribution through the Shapiro–Wilk test, the differences in cognitive function and health perception were evaluated using a paired-samples T-test. Independent sample t-tests were conducted comparing the MMSE-Brief and SF-36 scores at each phase to assess the impact of age, sex, years of education, marital status, and household composition. Further comparisons among years of education and the independent variables were performed through a one-way ANOVA. Correlations among age, years of education, number of children, cognitive function, and health perception were assessed using Pearson correlation. Post hoc power analyses were calculated using G-Power (version 3.1), and all other statistical tests were performed with SPSS version 26.0. As all questionnaires were filled in either by the student researchers in-person onsite or were required on the online questionnaire, no missing data were recorded for the 36 participants who completed both data collection phases.

Results

Participant Demographics

Thirty-six older adults completed both phases of this study. The participants’ mean age was 67.3 years, ranging from 60 to 80 years, with slightly more women (n = 21) than men. Most participants lived with their spouses, had at least one child, and resided in the same city as their adult-children. Participants had completed an average of 11 years of formal study, with most of them graduating from at least high school (see Table 1).

Table 1.

Participants’ Demographics.

Variable	Mean (SD; range)	n (%)
Age	67.3 (5.5)	—
Years of study	11.6 (3.6; 6–23)	—
Marital Status
Married		23 (63.9)
Widowed		3 (8.3)
Divorced		3 (8.3)
Single		7 (19,5)
Level of education
Middle school		10 (27.8)
High school		16 (44.4)
College/university		10 (27.8)
Household
Lives alone		10 (27.8)
Lives with spouse		23 (63.9)
Lives with adult children		3 (8.3)
Number of children	1.97 (1.13; 0–4)	—
Number of people living in the same house	2.25 (1.05; 1–4)	—

Before and During Social Distancing

Overall results

Overall results showed a significant increase in cognitive function among participants during isolation, t(35) = −3.264, p = .002, with an average increase of 16 points on MMSE score. Despite improved cognitive function, significant decreases in mental health, t(35) = 12.9, p < .001, and vitality, t(35) = 12.08, p < .001, were also observed among all participants, with a mean decline in SF-36 scores of 38.1 points for mental health and 29.3 for vitality. Despite the restrictions imposed by social distancing measures, we did not observe significant changes on SF-36’s social functioning domain (see Table 2).

Table 2.

Comparison of Cognitive Function and Perceived Health Before and During Social Distancing.

Variable	Mean difference (SD)	CI lower	CI Upper	p	Power
MMSE	−16.33 (5.01)	−26.49	−6.17	.002	0.88
SF–36: Vitality	29.3 (14.5)	24.3	34.2	<.001	0.99
SF–36: Physical functioning	1.94 (13.6)	−2.68	6.57	.4	0.13
SF–36: Bodily pain	−5.33 (21.4)	−12.6	1.93	.145	0.30
SF–36: General health	−0.91 (12.5)	−5.15	3.31	.663	0.07
SF–36: Role–physical	−12.5 (42.1)	−26.7	1.72	.83	0.41
SF–36: Role–emotional	−8.33 (31.2)	−18.9	2.23	.119	0.34
SF–36: Social functioning	5.55 (25.6)	4.26	−3.1	.202	0.24
SF–36: Mental health	38.11 (17.2)	32.1	44.1	<.001	0.99

Note. CI = confidence interval; MMSE = Mini-Mental State Exam; SF-36 = Short Form Health Survey; SD = standard deviation.

Sex

A statistically significant difference was observed in MMSE scores between men (32.53, SD = 22.5) and women (53.71, SD = 27.6) before social distancing measures, t(34) = 2.45, p = .02, statistical power = 0.68. However, this difference was not observed in Phase 2 with the MMSE. Both groups presented an overall increase in MMSE scores (men: 61.23, SD = 26.6; women: 61.2, SD = 29.4), with no statistical difference between them. No differences in the SF-36 scores were observed when comparing participants by sex.

Level of education

When comparing the influence of education on the outcomes, the results indicated significant changes in general health, F(2.33) = 4.122, p = .025, Eta Square = 0.28, physical functioning, F(2.33) = 6.49, p = .004, Eta Square = 0.16, and vitality, F(2.33) = 4.727, p = .016, Eta Square = 0.22, from before to during social distancing measures. Post hoc analysis showed improved scores on vitality and general health between participants who had completed middle school to those with higher educational levels.

Household composition

Living with at least one family member in the same house positively affected the MMSE score, indicating a significant mediation of the negative impacts of social distancing. Older adults who lived alone had a small, but non-significant increase in their MMSE scores (46.4, SD = 26.1) when compared to their previous assessment (50.3, SD = 36.3). In contrast, participants who reported living with at least one family member had a significant, t(25) = −3.8, p = .001, statistical power = 0.55, positive change in their cognitive function (65.4, SD = 22.6) when compared to their status prior to social distancing measures (44.3, SD = 28.2).

In addition to changes in cognitive function, we observed significant changes in the participants’ perceived health on the vitality and mental health domains of the SF-36. Although people living alone were similar at baseline to those living with others, we observed a steep decrease in the scores for participants living alone, particularly with the mental health domain, t(9) = 10.78, p < .001, from a mean score of 85.2 points to a mean of 37.2 points during social distancing. Living with more people also appears to improve perceived health status during quarantine; we observed a strong, negative correlation between the number of family members living in the same house and participants’ health perception (r = −0.543, p = .001). Despite the changes in cognitive function, general health, vitality, and mental health, no significant difference was observed between household composition and social functioning (see Figure 1). No significant differences were observed when comparing participants’ age, marital status, and number of children.

Figure 1.

Differences in the MMSE, SF-36 scores before and after the enactment of social isolation measures.

Discussion

This study aimed to evaluate changes in cognitive function and perceived health among community-dwelling Brazilian older adults before and during social distancing measures enacted due to the COVID-19 pandemic. Our results indicated an increase in participants’ cognitive function; however, perceived health demonstrated a steep decline, notably in the domains of vitality and mental health.

The current study found that participants experienced a significant increase in cognitive function from before to approximately 1 month after the social distancing regulations were put into place. Changes in cognitive function during aging are well-documented; however, research has demonstrated a negative impact of social isolation (i.e., decreased social participation) on cognitive function among older adults (Griffin et al., 2020; Shankar et al., 2013; Yu et al., 2020). Our findings on cognitive function, in significant contrast to previous research, may be a result of the immediate and necessary social distancing measures put into place because of the pandemic. Unlike the gradual social isolation experiences that were typically studied within the cognition and aging research, the unique situation of the pandemic required significant and immediate life and routine changes, requiring participants to restructure their daily activities and occupations (Brooks et al., 2020). As Phase 2 occurred approximately 1 month into the distancing measures, participants may have still been cognitively engaged in restructuring their lives. Stressful situations can both positively and negatively influence cognitive function even in the short term (Feeney et al., 2018), and events with a greater significant impact on the lives of older adults can be associated with better cognition (Rosnick et al., 2007). Studies comparing responses from stressful situations between young and older adults observed a higher prevalence of increased stress symptoms among younger participants (Wang et al., 2020). Brooks et al. (2020) hypothesized that quick and increased access to information (and misinformation) through social media or electronic means by younger adults may be contributing to their higher stress levels. In addition, although the average age of retirement in Brazil is approximately 60 years, many older adults continue to work (Cockell, 2014), at times in demanding jobs, due to limited public social security income. The onset of the social distancing measures resulted in work furloughs for many older adults. Although the participants may be experiencing financial stress as a result, the absence of work activities could explain a decrease in daily stress, positively impacting their cognitive function in the short term.

Another explanation for the noted increase in cognitive function is practice effect; although the time between assessments was approximately 2 months, participants may have learned through their previous completion of the assessments. Long-term follow-up studies with healthy older adults have reported a slight but significant improvement in MMSE scores, particularly between baseline and the first re-assessment (see Galasko et al.,1993; Jacqmin-Gadda et al., 1997), with a steady decline on subsequent re-assessments. This small but consistent impact of practice effect on cognitive performance has been observed with both long and short intervals between each assessment (Duff et al., 2008) and is not limited to the MMSE, affecting several other neuropsychological tests (Krenk et al., 2012) regardless of participant’s sex, age, or educational level (Calamia et al., 2012). Although practice effect interferes with the evaluation of cognitive function, as an expected occurrence (Calamia et al., 2012), the absence of a practice effect—rather than occurrence—would be an important clinical indicator of diminished cognitive function (Oltra-Cucarella et al., 2018).

The results also suggest an essential interaction among educational level and cognitive function. Although increased years of education are considered a protective factor for cognitive decline during aging (Griffin et al., 2020), our results indicated that this factor had limited impact during the initial period of social distancing. Prior to the restrictions, the MMSE scores were strongly correlated with years of education; however, no differences in cognitive function were observed among participants with different education levels while they were restricted to their houses.

As cognitive function is determined by the interactions of multiple factors (Duff et al., 2008), it is not feasible to directly correlate cognitive decline with social isolation. In a recent review, Evans et al. (2019) document inconsistencies in studies suggesting the direct association between social isolation and cognitive decline. Given the substantial variations in the definition of social isolation, and the use of different instruments to evaluate specific cognitive functions, the authors suggest caution to assume the existence of such a direct relationship between social isolation and decreased cognitive function (Evans et al., 2019).

This study also found a concerning decline in perceived health, particularly in participants’ vitality and mental health domains. These significant changes are not unusual for people living in socially isolating situations during pandemics. Studies examining the psychological repercussions of social isolation during the MERS and SARS epidemics showed increased levels of anxiety, depression, and anger among people forced to live in social isolation (Jeong et al., 2016), with similar results appearing in the current COVID-19 pandemic (Meng et al., 2020). A study with 1,074 Chinese participants observed increased cases of severe anxiety and depression and hazardous alcohol consumption levels, particularly among people living in areas with higher COVID-19 infection rates (Ahmed et al., 2020). The lack of change in the social functioning domain score was unexpected as we would have assumed it to be impacted by the social distancing measures, and one of the mechanisms by which we expected changes in mental health. It is possible that the SF-36 questions did not target social functioning relevant to COVID-19. The item wording, “During the past 4 weeks, to what extent has your physical health or emotional problems interfered with your normal social activities with family, friends, neighbors, or groups,” asks if health affected neighbor engagement. With COVID-19, the interference with social activities was related to public policy, not health.

Participants with fewer years of education were observed to have better general health, physical function, and vitality on the SF-36. Similar differences in health perception among people with different education levels were also noted on a recent national survey with Chinese people affected by the COVID-19 pandemic (Qiu et al., 2020). Although our results did not show significant changes on participants’ health perception, we noted a significant decrease among participants with higher levels of education, which may be due to higher awareness of their health condition and vulnerability (Morcillo-Cebolla et al., 2014). The relationship between health perception and education level has also been observed in a previous study that identified years of formal education as a strong determinant of perceived health (Kurspahić-Mujčić & Mujčić, 2019). This finding may be particularly relevant during a pandemic; individuals with better access to education may explore and understand more about why COVID-19 is more severe than other types of respiratory infections (Qiu et al., 2020).

Among the factors influencing participants’ perceived health, household’s composition appears to significantly impact the domains of mental health and vitality, suggesting that living with at least one other person reduces the impact of isolation on perceived health. The interaction with people from the same household could provide cognitive stimulation through complex communication and shared experiences (Bennett et al., 2006), improving participants’ perceived health. Social support within a household could be a protective factor for mental health during a pandemic, despite the potential challenges of managing the spread of COVID-19 within multigenerational households. Taylor et al. (2008) also found an association between having an extended household and positive emotional adjustments; however, a recent rapid review found limited evidence supporting the influence of demographic characteristics on psychological aspects during quarantine (Brooks et al., 2020).

Study Limitations and Future Recommendations

Participant attrition from Phase 1 to Phase 2 due to lack of contact information resulted in a limited sample size, restricting the examination of demographic characteristics as predictors. The second phase of our study occurred after participants had been following social distancing measures for approximately 30 days, which may have impacted the results. Longitudinal studies with increased time between assessments are recommended to examine the long-term association of social distancing regulations and cognitive function.

It is important to note that the Brazilian government’s response to the COVID-19 pandemic was precarious at the time of phase two data collection, lacking more organized, evidence-based actions observed in other countries. This precarity and the diverse “essential” needs of our older adult participants made it impossible to measure participant adherence to social distancing and stay-at-home regulations. An extensive survey conducted with 7,554 Brazilians by Storopoli et al. (2020) showed that respondents had the least confidence in their government with hospitals, health care workers, and media scoring significantly higher for their ability to deal with the COVID-19 pandemic. Despite being highly recommended, social distancing measures have not been strictly enforced by the Brazilian government, nor are they significantly accepted and followed by the Brazilian population (Storopoli et al., 2020).

Implications for Practice

Older adults are faced with not only an increased mortality risk but also an increased risk of social isolation and loneliness, due to the COVID-19 social distancing measures currently in effect within many countries. Although technologies are increasingly being used for remote access to social and group activities, older adults, especially those living in low- and middle-income countries, are particularly vulnerable to ruptures in social and support networks, due to digital exclusion. Such limited access represents a damaging repercussion to their participation in meaningful activities, consequently decreasing health perception and quality of life. As such, health care professionals need to advocate, locally and globally, for improved internet and technology access and the development of educational initiatives and digital resources to facilitate the engagement of older adults in meaningful activities and promote social connections and participation (Andonian, 2018).

Conclusion

Our results demonstrated an increase in cognitive function in the short term with marked decline on participants’ vitality and mental health. As these outcomes have been found to affect morbidity and mortality rates, in addition to health perception and quality of life, actions to mitigate the impact of social isolation on the mental health of older adults should be considered by health care professionals during these challenging times. Further research should examine the effects of extended social distancing measures on cognitive function and perceived health during COVID, as well as the post-COVID long-term effects of social distancing measures on older adults.

Footnotes

Author Contributions

P.H.T.Q.A. —Data curation; Formal analysis; Methodology; Writing—original draft; Writing—review & editing.

L.D.B.—Conceptualization; Data curation; Funding acquisition; Investigation; Project administration; Methodology; Writin g—review & editing. T.B.P.- Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Writing—original draft; Writing—review & editing. J.A.D. —Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Writing—review & editing. T.M.S.D. —Data curation; Formal analysis; R.G.F. —Data curation; Formal analysis; K.I.d.S. —Data curation; Formal analysis; J.C.M. —Conceptualization; Formal analysis; Methodology.

Declaration of Conflicting Interest

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: The authors, their immediate families, and any research foundations with which they are affiliated have not received any financial payments or other benefits from any commercial entity related to the subject of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The present study received financial support from the National Council for Scientific and Technological Development (Conselho Nacional de Desenvolvimento Científico e Tecnológico—CNPq), through the Scientific Initiation program, and from the Canadian Institutes of Health Research (FRN: PJT-153261)

ORCID iDs

Pedro H. T. Q. Almeida

Klysna I. Souza

Research Ethics

Project approved by the Instituto Federal do Rio de Janeiro Ethics board, protocol number 3.984.

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