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. Author manuscript; available in PMC: 2023 Mar 4.
Published in final edited form as: Nurs Res. 2022 Mar 4;71(4):295–302. doi: 10.1097/NNR.0000000000000589

Relationship of Cognitive and Social Engagement to Health and Psychological Outcomes in Community-Dwelling Older Adults

Daniel Liebzeit 1,2,3, Wan-chin Kuo 4, Beverly Carlson 5, Kimberly Mueller 6,7, Rebecca L Koscik 8, Marianne Smith 9, Sterling Johnson 10,11, Lisa Bratzke 12
PMCID: PMC9245122  NIHMSID: NIHMS1782164  PMID: 35759719

Abstract

Background:

Cognitive and social engagement is an important yet underdocumented aspect of older adult engagement and function.

Objective:

The purpose of this study is to examine relationships between cognitive and social engagement and health and psychological outcomes in a cohort of community-dwelling older adults aged approximately 55–70 years.

Methods:

Analysis of data from the Wisconsin Registry for Alzheimer’s Prevention (WRAP), a multi-wave cohort study with 1,582 participants, using a 1:1 prospective case-control design to examine whether lower cognitive and social engagement at Visit 4 (baseline) is associated with worse health and psychological outcomes at Visit 5 (2 years after Visit 4). WRAP participants were included in the present study if they had complete data on cognitive and social engagement and self-rated health at both visits.

Results:

After matching potential covariates using propensity scores, participants with low cognitive and social engagement (cases) at baseline continued to have significantly lower cognitive and social engagement than the controls (participants with high cognitive and social engagement at baseline) at Visit 5, and they had lower self-rated health and higher surgery rate. Depressive symptoms, cognitive status, and hospitalization at Visit 5 did not significantly differ between cases and controls.

Discussion:

This study provides evidence supporting cognitive and social engagement as an important marker of early decline in activity engagement that may indicate a potential later decline in functional, psychological, and health outcomes.

Keywords: activity, depressive symptoms, function, self-rated health, surgery engagement

Cognitive and social engagement is an important yet underdocumented aspect of older adult engagement and function (Dotson et al., 2008; Rovner et al., 2016; Schinka et al., 2005, 2010). Cognitive engagement includes cognitively stimulating activities, such as board games, crossword puzzles, reading, and writing. Social engagement includes activities with others, such as talking on the phone with friends, visiting friends, and going to social clubs (Schinka et al., 2005, 2010). Together, cognitive and social engagement has been linked to multiple domains of cognition and is a promising indicator of decline in function with aging, cognitive decline, and progression into dementia (Dotson et al., 2008; Rovner et al., 2016; Schinka et al., 2005, 2010). Yet, functional assessment in older adults is often limited to basic activities of daily living (BADLs; self-care activities, such as bathing, dressing, eating, toileting) and instrumental activities of daily living (IADLs; independent-living activities, such as medication and financial management, housework, and food preparation). Recent evidence supports cognitive and social engagement as more pertinent forms of functional assessment among older adults (Liebzeit et al., 2020), and its inclusion is essential in considering function as a multidimensional concept (including independence, role responsibilities, and activity engagement; Liebzeit et al., 2018, 2020; Wang, 2004). Cognitive and social engagement may be significant as a potential marker in younger groups of older adults (55–70 years of age). This large portion of the aging population may experience undocumented and unmitigated decline in cognitive and social engagement indicative of potential later decline in function and cognition.

Prior cross-sectional research on cognitive and social engagement has revealed potential relationships with psychological (depression symptoms, cognitive status) and functional outcomes (e.g., IADL; Dotson et al., 2008; Rovner et al., 2016). Prior research indicates relationships exist between leisure and social activity and cognitive status (Bourassa et al., 2017; Gow et al., 2012; Verghese et al., 2006). Potential mechanisms include higher brain reserve and β-amyloid deposition associated with more complex mental activity (Landau et al., 2012; Valenzuela & Sachdev, 2006). Presence of depressive symptoms has also been associated with cognitive status (Geda et al., 2008), and activity engagement may contribute to feelings of depression and vice versa (Chiao et al., 2011; Glass et al., 2006). Despite this evidence, the long-term effect of cognitive and social engagement on psychological outcomes (depressive symptoms, cognitive status) in older adults remains unclear. Longitudinal research is critical to understanding how engagement trajectories and psychological outcomes may relate to aging.

The underlying mechanisms that explain the relationship between cognitive and social engagement and physical health are poorly understood, partly due to the lack of longitudinal evidence. Although existing evidence shows that participation in certain leisure and social activities (e.g., participation in social groups, sports, cooking, musical, technology use) is associated with better self-rated health (Nilsson et al., 2017; Tomioka et al., 2017, 2019), these findings were based on cross-sectional studies. To date, there is a dearth of evidence demonstrating the long-term effect of social and cognitive engagement on other health outcomes, such as hospitalization and surgery. Multiple hypotheses have been proposed to explain the relationships between cognitive and social engagement and health. It is possible that those who are healthier may be more mobile, have better cognitive status, and are thereby able to engage in activities (social and leisure) more frequently (Bourassa et al., 2017; Gow et al., 2012; Liebzeit et al., 2020; Verghese et al., 2006). It is also possible that those with higher activity engagement feel better about their health, thereby increasing self-rated health or other correlates of health outcomes, such as depressive symptoms (Ho et al., 2014; Meltzer et al., 2012; Strine et al., 2009). Despite supporting evidence, it remains unclear whether cognitive and social engagement affects a range of subjective and objective health outcomes (self-rated health, hospitalization, surgery).

We remain limited in our understanding of relationships between cognitive and social engagement and health and psychological outcomes in older adults, especially important younger segments of the population who may experience undocumented and unmitigated decline in cognitive and social engagement not routinely recognized by providers or researchers. The purpose of this study is to examine both cross-sectional and longitudinal relationships between cognitive and social engagement and health and psychological outcomes in a cohort of community-dwelling older adults aged approximately 55–70 years. This area of study can provide an essential foundation for future research on mechanisms to support health and psychological outcomes in aging individuals and markers of early decline in activity engagement indicative of potential later decline in function and cognition.

Methods

Study Design

This study analyzes data from the Wisconsin Registry for Alzheimer’s Prevention (WRAP R01 AG027161), a multi-wave cohort study with 1,582 participants beginning in 2001 (Johnson et al., 2018). The primary aim of the WRAP cohort study is to examine the biological, health, and lifestyles factors that confer risk or resilience to Alzheimer’s Disease (AD) and AD dementia in participants with and without parental history of Alzheimer’s dementia (Johnson et al., 2018).

For the present study, we used a 1:1 prospective case-control design to examine whether lower cognitive and social engagement at Visit 4 (baseline) is associated with worse psychological (depressive symptoms, cognitive status) and health outcomes (self-rated health, hospitalization, or surgery) at Visit 5 (2 years later; Figure 1). The WRAP participants were included in the present study if they had complete data on cognitive and social engagement as well as self-rated health at Visits 4 and 5 (N = 403). We used participants from these visits because data on cognitive and social engagement were the most complete across these two waves, and most participants were approximately 55–70 years of age. This analysis of existing data was reviewed and approved by the [anonymized] institutional review board.

Figure 1.

Figure 1.

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Conceptual model of relationships between cognitive and social engagement and health and psychological outcomes

Case Identification and Exposure Measure

Low Cognitive and Social Engagement

Cases were defined as those with low cognitive and social engagement based on Florida Cognitive Activities Scale (FCAS). The FCAS is a 25-item measure of cognitive and social activities, asking respondents to rate their activity participation from 0 (used to do activity but not in the past year) to 4 (every day; Schinka et al., 2005, 2010). Activities include board games, crossword puzzles, watching TV, listening to the radio, gardening, reading the newspaper, reading books, writing letters, talking on the phone or visiting friends, artwork or craftwork, home repairs, preparing meals (new and familiar), leading discussions, taking courses, managing investments and other financial work, walking or driving to new places, going to social clubs, attending religious activities or services, and shopping (Schinka et al., 2005, 2010). Scores range from 0 to 100, with a higher score indicating more participation in activities. Internal consistency (α = .65), validity, and sensitivity of the FCAS have been established in both cognitively stable and declining older adults (Schinka et al., 2005, 2010). We used the median score as the cutoff point in differentiating cases from controls. Participants whose FCAS < 40 at Visit 4 were identified as cases (n = 187), while participants whose FCAS ≥ 40 at Visit 4 were identified as controls (n = 216). After 1:1 propensity score matching, 124 cases and 124 controls were included in the longitudinal analysis.

Outcome Measures

Depressive Symptoms

The Center for Epidemiologic Studies Depression Scale (CES-D) score measured depressive symptoms at Visits 4 and 5. The CES-D is a 20 item self-report tool with established validity in community-dwelling older adults (Radloff, 1977). The tool asks how often respondents have experienced symptoms associated with depression over the past week, such as restless sleep, feeling sad, lonely, or lacking enjoyment in life (Radloff, 1977). Items are rated from 0 to 3 (rarely to most of the time), with a total possible score from 0 to 60 and higher scores indicating greater depression (Radloff, 1977). Those with a score of 16 or greater are considered at risk for clinical depression (Radloff, 1977).

Cognitive Status

Cognitive status at Visits 4 and 5 was determined through a multidisciplinary consensus conference review of available objective and subjective measures. Participant status at each visit was categorized into five groups: cognitively unimpaired and stable, cognitively unimpaired and declining, mild cognitive impairment (MCI), impaired but not MCI (e.g., due to long-standing issue), or dementia. A detailed description of the process used to designate cognitive status is described elsewhere (Koscik et al., 2016). Because we were interested in looking at differences between cognitively unimpaired and stable participants versus those impaired and/or declining, we dichotomized this variable into either unimpaired and stable or any other category listed above.

Self-Rated Health

Self-rated health was measured by a single item asking participants to rate their general health as poor (1), fair (2), good (3), very good (4), or excellent (5), at Visits 4 and 5. As a measure of overall health status, Self-rated health has been used extensively in health outcomes research and is included in both the 36-Item Short Form Health Survey, Version 2 (SF-36v2®; RAND Corporation, n.d.) and Patient-Reported Outcomes Measurement Information System (PROMIS®; HealthMeasures, n.d.).

Hospitalization or Surgery

Incident hospitalizations and surgeries were measured by participant report of any hospital admission lasting at least 2 days or any surgeries (a) in the 2 years leading up to Visit 4, and (b) in the 2 years between Visits 4 and 5. Data were coded as 1 for any incidence and 0 for no incidences.

Covariates

As shown in Figure 1, we hypothesized that demographics (e.g., age, sex, race, years of education, and income), number of comorbid conditions, caregiver reported IADL function, and blood-derived ApoE4 genotype might confound the association between cognitive and social engagement and health and psychological outcomes. These covariates were selected based on evidence supporting their association with outcomes examined in this study (Dotson et al., 2008; Rovner et al., 2016). Participants reported family income classification from 1 (less than $10,000 annually) to 8 ($150,000 or more annually). Comorbidity was quantified based on total number of incidents and ongoing chronic conditions reported in WRAP Visits 2 through 4. Participants were given a list of over 40 common conditions and asked if they had ever been diagnosed or told by a health care provider that they had the condition. Function in performing IADLs was measured by caregiver report, using the Lawton and Brody IADL tool that measures ability to use a telephone, shop, prepare food, perform housekeeping and laundry tasks, use transportation, and responsibly manage medications and money (Lawton & Brody, 1969). The tool has established convergent validity (r = .36 – .77), construct validity (coefficient of reproducibility = .93 – .96), and interrater reliability (r = .85) in community-dwelling and community-destined older adults (Lawton & Brody, 1969). ApoE4 genotyping was conducted from collected venous blood samples and summarized based on participants having no E4 allele, one E4 allele, or two E4 alleles.

Data Analysis

Analyses were performed using SAS® software, version 9.4 (SAS Institute Inc., 2016). Continuous variables were summarized using means and standard deviations, and categorical variables were summarized using frequencies and percentages. To examine the cross-sectional association among exposure, outcome, and confounding variables, we summarized and compared the baseline demographic characteristics and health and psychological outcomes between low cognitive and social engagement (cases) and high cognitive and social engagement (controls) using independent sample t-tests for continuous variables and chi-square or Fisher’s exact test for categorical variables. The Mann-Whitney U test (nonparametric) was used for continuous variables that were not normally distributed and ordinal variables.

To alleviate the selection bias and pre-exposure confounding effects in the longitudinal analysis, we matched the cases and controls using 1:1 propensity score based on significant demographic and health outcome variables identified in the cross-sectional analysis and prior literature, including age, sex, years of education, income, comorbidity, and self-rated health. Nearest-neighbor greedy matching without replacement was employed using a caliper of 0.3 on the propensity score scale (DuGoff et al., 2014). Outcome variables from Visit 5 were compared between case and control group (determined from cognitive and social engagement score at Visit 4) using independent sample t-tests for continuous variables and chi-square or Fisher’s exact test for categorical variables. Again, the Mann-Whitney U test (nonparametric) was used for continuous variables that were not normally distributed and ordinal variables. For all statistical tests, p-values less than 0.05 were considered significant.

Results

Cross-Sectional Associations at Baseline

Demographic Variables

Case and control groups were similar in age and race distribution, ApoE4, IADL function, and number of comorbidities (Table 1). Participants with low cognitive and social engagement (cases) were more likely to be female (p < .0001), have fewer years of education (p < .0041), and have lower income classification (p = .0005), compared to participants with high cognitive and social engagement (controls).

Table 1.

Baseline Differences in Demographic Variables Between Cases (Low Cognitive and Social Engagement) and Controls (High Cognitive and Social Engagement)

Variable Low Cognitive and Social Engagement (Cases n = 187) High Cognitive and Social Engagement (Controls n = 216) p-value
Age (mean [SD]) 62.90 (6.27)
Range: 51–75		 63.58 (6.34)
Range: 45–76		 .2858
Sex: Male (n [%]) 116 (62.0%) 179 (82.9%) < .0001
Race: White (n [%]) 186 (99.47%) 214 (99.07%) .6487
Years of education (mean [SD]) 15.98 (3.10)
Range: 12–29		 16.81 (2.69)
Range: 12–27		 < .0041
Income class (n [%]) .0005
1 (1–4) 44 (23.91%) 24 (11.37%)
2 (5–6) 95 (51.63%) 105 (49.76%)
3 (7–8) 45 (24.46%) 82 (38.86%)
Comorbidity (mean [SD]) 3.97 (2.76)
Range: 0–11		 4.21 (2.62)
Range: 0–13		 .3968
IADL (mean [SD]) 15.85 (0.61)
Range: 11–16		 15.85 (0.55)
Range: 10–16		 .6397
ApoE4 (n [%]) .8758
 No E4 allele 121 (64.71%) 135 (62.50%)
 One E4 allele 61 (32.62%) 74 (34.26%)
 Two E4 alleles 5 (2.67%) 7 (3.24%)
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Note. Income classes: 1–4 = $10,000–49,999, 5–6 = $50,000–99,999, 7–8 = $100,000 or more. IADL = Instrumental Activities of Daily Living. Independent sample t-tests were used for continuous variables (age, years of education, comorbidity). Chi-square or Fisher’s exact test was used for categorical variables (sex, race, income classification, ApoE4). The Mann-Whitney U test (non-parametric) was used for continuous variables that were not normally distributed (IADL). For all statistical tests, p-values less than .05 were considered significant.

Outcome Variables

As shown in Table 2, the cross-sectional analysis indicated that participants with low cognitive and social engagement (cases) had lower self-rated health (p = .0208). Depressive symptoms, cognitive status, surgery, and hospitalization at baseline did not significantly differ between low cognitive and social engagement groups (cases) and high cognitive and social engagement groups (controls).

Table 2.

Cross-Sectional Analysis of Outcome Variables in Cases (Low Cognitive and Social Engagement) and Controls (High Cognitive and Social Engagement; visit 4)

Variable Low Cognitive and Social Engagement (Cases n = 187) High Cognitive and Social Engagement (Controls n = 216) p-values
FCAS (mean [SD]) 32.49 (5.34)
Range: 12–39		 47.36 (5.92)
Range: 40–70		 < .0001
CES-D (mean [SD]) 6.91 (7.04)
Range: 0–38		 5.44 (5.47)
Range: 0–35		 .0660
Unimpaired, stable cognitive status (n [%]) 149 (80.54%) 181 (84.19%) .3387
Self-rated health (n [%]) .0208
1 (poor) 1 (.53%) 0 (0%)
2 (fair) 7 (3.74%) 9 (4.17%)
3 (good) 84 (44.92%) 71 (32.87%)
4 (very good) 72 (38.50%) 100 (46.30%)
5 (excellent) 23 (12.30%) 36 (16.67%)
Surgery (n [%]) 71 (37.97%) 74 (34.26%) .4392
Hospitalized (n [%]) 21 (11.23%) 13 (6.02%) .0605
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Note. FCAS = Florida Cognitive Activities Scale. CES-D = Center for Epidemiologic Studies Depression Scale. Participants were grouped into cases and controls using FCAS score. Independent sample t-tests were used for continuous variables (FCAS). Chi-square or Fisher’s exact test was used for categorical variables (cognitive status, self-rated health, surgery, hospitalized). The Mann-Whitney U test (non-parametric) was used for continuous variables that were not normally distributed (CES-D) and ordinal variables (self-rated health). For all statistical tests, p-values less than .05 were considered significant.

Longitudinal Associations

To adjust for the baseline confounding effect (Visit 4) and selection bias on demographic data, the case and control groups were matched on age, sex, years of education, income, comorbidity, and self-rated health. As shown in Figure 2, the standardized mean differences between cases and controls for each covariate after propensity score matching were less than 0.1, suggesting a satisfactory covariate balance between cases and controls. After matching the potential covariates using propensity scores, participants with low cognitive and social engagement at Visit 4 (baseline) continued to have significantly lower cognitive and social engagement at Visit 5 (mean FCAS score = 35.25 vs. 44.13; Table 3). However, the range indicates some crossover of the median cut-point in each group, suggesting that certain participants had changed their cognitive and social engagement over the 2 years (between Visit 4 and Visit 5). Participants with lower cognitive and social engagement at baseline had lower self-rated health (p = .0380) and higher surgery rate (p = .0354) at visit 5. Yet, depressive symptoms, cognitive status, and hospitalization at Visit 5 did not significantly differ between low cognitive and social engagement groups (cases) and high cognitive and social engagement groups (controls).

Figure 2.

Figure 2.

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Standardized mean differences between cases and controls for each covariate before and after propensity score matching (red dots versus blue dots).

Table 3.

Longitudinal Analysis of Outcome Variables in Cases (Low Cognitive and Social Engagement) and Controls (High Cognitive and Social Engagement; visit 5)

Variable Low Cognitive and Social Engagement
(Cases n = 124)		 High Cognitive and Social Engagement
(Controls n = 124)		 p-values
FCAS (mean [SD]) 35.25 (7.27)
Range: 13–55		 44.13 (8.22)
Range: 17–64		 < .0001
CES-D (mean [SD]) 6.41 (6.77)
Range: 0–27		 4.58 (4.74)
Range: 0–21		 .0691
Unimpaired, stable cognitive status (n [%]) 102 (86.44%) 106 (89.83%) .4207
Self-rated health (n [%]) .0380
1 (poor) 1 (.81%) 0 (0%)
2 (fair) 4 (3.23%) 6 (4.84%)
3 (good) 59 (47.58%) 43 (34.68%)
4 (very good) 50 (40.32%) 55 (44.35%)
5 (excellent) 10 (8.06%) 20 (16.13%)
Surgery (n [%]) 54 (43.55%) 38 (30.65%) .0354
Hospitalized (n [%]) 16 (13.01%) 14 (11.67%) .7506
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Note. FCAS = Florida Cognitive Activities Scale. CES-D = Center for Epidemiologic Studies Depression Scale. Independent sample t-tests were used for continuous variables (FCAS). Chi-square or Fisher’s exact test was used for categorical variables (cognitive status, surgery, hospitalized). The Mann-Whitney U test (non-parametric) was used for continuous variables that were not normally distributed (CES-D) and ordinal variables (self-rated health). For all statistical tests, p-values less than .05 were considered significant.

Discussion

This study provides evidence supporting cognitive and social engagement as an important marker of early decline in activity engagement that may indicate a potential later decline in functional, psychological, and health outcomes. Participants in our study demonstrated a wide range in cognitive and social engagement, despite their relative social and demographic homogeneity. This supports the hypothesis that cognitive and social engagement can be a sensitive marker of differences in level of function and engagement in a younger group of older adults (approximately 55–70 years of age). There was also some crossover in how individuals scored on cognitive and social engagement 2 years after baseline assessment, indicating that the instrument is sensitive to changes in engagement over short periods that may often go undetected. Given these findings and the homogeneity of the sample, composed mainly of well-educated, White/Caucasian adults 55–70 years of age, it is crucial to examine other trends in cognitive and social engagement in more diverse samples. It is essential to consider how engagement in activities may differ based on age, race/ethnicity, culture, and socioeconomic status (Dotson et al., 2008; Rovner et al., 2016).

Findings of this study support the hypothesis that cognitive and social engagement may also relate to older adults’ health outcomes (Figure 1). At baseline, cognitive and social engagement was associated with self-rated health; this relationship persisted in longitudinal analysis. Specifically, we found that cognitive and social engagement at baseline was significantly associated with self-rated health approximately 2 years later, after adjusting for self-rated health and demographic and clinical characteristics at baseline. These findings could indicate that cognitive and social engagement may affect older adults’ perceptions of their health. A possible explanation may be that older adults with increased cognitive and social engagement feel more active and/or less socially isolated. Evidence supports relationships between social disconnectedness, lack of social support, or isolation and self-rated health (Cornwell & Waite, 2009; White et al., 2009). It is also possible that their health and/or perceptions of their health may affect their engagement. These findings are supported by prior research indicating increased participation in certain leisure and social activities are associated with better self-rated health (Nilsson et al., 2017; Tomioka et al., 2017, 2019). Further study is important to elucidate the relationship between cognitive and social engagement and older adults’ perceptions of their health and consideration of other important factors related to health and engagement, such as mobility (Liebzeit et al., 2020; Musich et al., 2018; Rosso et al., 2013).

Cognitive and social engagement was also significantly associated with rate of surgery in this sample, which provides further evidence supporting a relationship between cognitive and social engagement and health. This finding is consistent with prior research indicating that older adults’ health events (including illnesses, hospitalizations, or procedures) may affect their engagement in meaningful activities for older adults (Liebzeit et al., 2020). It is possible that declines in function and health that lead to need for surgery could explain simultaneous changes in cognitive and social engagement leading up to surgery. Interestingly, hospitalization was not significantly related to cognitive and social engagement, but further study may be necessary with larger, more diverse samples. It is possible that a more diverse sample in terms of age, race/ethnicity, and health and socioeconomic status would have more variability in the outcomes under study and elucidate potential relationships between cognitive and social engagement and health outcomes (Assari et al., 2020; Dotson et al., 2008; Rovner et al., 2016). As discussed above, cognitive and social engagement was significantly related to self-rated health in this sample, and self-rated health is considered an indicator of morbidity and mortality (Feenstra et al., 2020; Gold et al., 1996; Halford et al., 2012; Mossey & Shapiro, 1982) and is associated with increased health care utilization (especially hospitalization; Assari et al., 2020; DeSalvo et al., 2009; Halford et al., 2012; Kennedy et al., 2001). Therefore, further study on relationships between cognitive and social engagement and objective health outcomes is crucial.

Further study is also important to continue to examine longitudinal relationships between cognitive and social engagement and psychological outcomes, such as presence of depressive symptoms and cognitive status. Previous findings support a cross-sectional association between cognitive and social engagement and depressive symptoms (Dotson et al., 2008; Rovner et al., 2016) and cognitive status (Dotson et al., 2008; Rovner et al., 2016; Schinka et al., 2005, 2010). Unlike these studies, our sample found no relationship between cognitive and social engagement and depressive symptoms nor cognitive status. Relationships between cognitive and social engagement and depressive symptoms did approach significance in our analysis (p = .0691), indicating that our sample’s homogeneity and size contributed to insignificant findings for psychological outcomes. Further research is essential to examine longitudinal relationships with these outcomes to further our understanding of mechanisms to support health and psychological outcomes in aging individuals and markers of early decline.

Study Limitations

The sample for this study was relatively homogenous, primarily composed of well-educated, White/Caucasian adults 55–70 years of age. The homogeneous sample limits the generalizability of our results in other age groups or adults in other racial and ethnic backgrounds; therefore, further research is necessary to examine cognitive and social engagement and its relationships with health and psychological outcomes in other populations, such as a broader age group including older adults 70+ years older or other high-risk groups. However, the sample for our study did, on average, experience approximately four comorbidities, so their experiences may be similar to other older and multimorbid groups.

Implications

This study provides evidence supporting cognitive and social engagement as an important marker of early decline in activity engagement that may indicate potential later decline in functional, psychological, and health outcomes. Findings of this study support the hypothesis of relationships between cognitive and social engagement and health outcomes, including self-rated health and rate of surgery, in a younger group of older adults (approximately 55–70 years of age). This study provides an important foundation for future research on mechanisms to support health and psychological outcomes in aging individuals and markers of early decline in activity engagement indicative of potential later decline in function and cognition. Implications for practice include considering activity engagement as an essential indicator of possible current or later decline in functional, psychological, and health outcomes.

Conclusion

This study provides evidence supporting cognitive and social engagement as an important marker of early decline in activity engagement that may indicate potential later decline in functional, psychological, and health outcomes. It provides an important foundation for future research on mechanisms to support health and psychological outcomes in aging individuals and markers of early decline in activity engagement indicative of potential later decline in function and cognition.

Acknowledgement

This work was supported by the Clinical Translational Science Award (CTSA) program, through the National Institutes of Health National Center for Advancing Translational Sciences (NCATS)[grant number UL1TR00427], the National Institutes of Health Wisconsin Registry for Alzheimer’s Prevention (WRAP) R01 [grant number AG027161] as the source of the cohort, and an OAA Advanced Fellowship in Geriatrics (William S. Middleton Memorial Veterans Hospital, Madison, WI). The content is solely the authors’ responsibility and does not necessarily represent the official views of the National Institutes of Health, the U.S. Department of Veterans Affairs, or the United States Government.

Footnotes

The authors have no conflicts of interest to report.

Ethical conduct of research: This analysis of existing data was reviewed and approved by the University of Wisconsin-Madison Institutional review board.

Clinical Trial Registration: N/A

The University of Wisconsin–Madison Institutional Review Board reviewed this analysis of existing data.

Contributor Information

Daniel Liebzeit, The University of Iowa College of Nursing, Iowa City, IA; University of Wisconsin–Madison School of Nursing, Madison, WI; Geriatric Research, Education and Clinical Center (11G), William S. Middleton Memorial Veterans Hospital, Madison, WI.

Wan-chin Kuo, University of Wisconsin–Madison School of Nursing, Madison, WI.

Beverly Carlson, San Diego State University School of Nursing, San Diego, CA.

Kimberly Mueller, University of Wisconsin–Madison Communication Sciences and Disorders, Madison, WI; Division of Geriatrics, School of Medicine and Public Health, University of Wisconsin – Madison, Madison, WI.

Rebecca L. Koscik, Wisconsin Alzheimer’s Institute and Wisconsin ADRC, Madison, WI.

Marianne Smith, The University of Iowa College of Nursing, Iowa City, IA.

Sterling Johnson, Professor, Division of Geriatrics, School of Medicine and Public Health, University of Wisconsin – Madison, Madison, WI; Associate Director, Wisconsin Alzheimer’s Institute and Wisconsin ADRC, Madison, WI.

Lisa Bratzke, University of Wisconsin–Madison School of Nursing, Madison, WI.

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