Psychological Well-being Relates to Healthcare and Financial Decision Making in a Study of Predominantly White Older Adults

Abstract

The purpose of this study was to test the hypotheses that psychological well-being is associated with healthcare and financial decision making in older adults and that this association varies by the level of cognitive function. Participants were 1082 older adults (97% non-Latino White; 76% women; mean age = 81.04 years; SD = 7.53) without dementia (median MMSE score = 29.00, IQR = 27.86–30.00). In a regression model adjusted for age, gender, and years of education, higher levels of psychological well-being were associated with better decision making (estimate = 0.39, standard error [SE] = 0.11, p < .001), as was better cognitive function (estimate = 2.37, SE = 0.14, p < .0001). In an additional model, an interaction of psychological well-being and cognitive function was significant (estimate = −0.68, SE = 0.20, p < .001), such that higher levels of psychological well-being were most beneficial for decision making among participants with lower levels of cognitive function. Higher levels of psychological well-being may help sustain decision making among older persons, particularly those with lower levels of cognitive function.

Keywords

older adults cognitive aging psychological well-being decision making

What this paper adds

• Positive psychological resources are associated with numerous favorable functional and health outcomes in aging, yet previous research on decision making, specifically, has largely focused on the role of negative psychological factors.

• Multiple aspects of psychological well-being, a positive psychological construct encompassing six domains including purpose in life, may confer benefit to healthcare and financial decision making in older age.

• These associations persisted after adjusting for depressive symptoms and anxiety, negative psychological attributes, suggesting that the impact of psychological well-being is relatively independent of psychological distress.

Applications of study findings

• Psychological well-being is amenable to intervention, and improvements in well-being may benefit decision making in aging, in addition to promoting positive physical and psychological health outcomes.

• Motivational interviewing and cognitive behavioral therapies focused on psychological well-being may facilitate better decision making and promote other positive health and functional outcomes in aging.

• Evidence-based intervention strategies may strengthen psychological well-being among older adults with diminished cognitive abilities to improve their decision making, in part, through peer support and confidence-building.

Introduction

Healthcare and financial decision making is critically important for maintaining health and independence, especially for older adults (Agarwal et al., 2009; Jacobson et al., 2014; Moye & Marson, 2007). Accumulating evidence suggests that many older adults are vulnerable to poor decision making in these domains (Finucane et al., 2002, 2005; Spreng et al., 2016). For example, in the healthcare domain, many older adults have difficulties understanding eligibility criteria for Medicare Part D, taking part in enrollment processes, and selecting cost-effective prescription drug coverage (Heiss et al., 2013; Rivera-Hernandez et al., 2021; Stults et al., 2018). Older adults also encounter challenges in the financial domain and are vulnerable to fees for late credit card payments, subprime home equity loans and lines of credit, and mismanagement of retirement funds (Agarwal et al., 2009; Howard & Yazdipour, 2014). Overall, poor healthcare and financial decision making is associated with numerous deleterious outcomes for older adults, including susceptibility to financial fraud and scams, medication nonadherence, incident dementia, and early mortality (Burnes et al., 2017; Naci et al., 2014; Stewart et al., 2019).

Considering the many adverse outcomes associated with poor healthcare and financial decision making, it is vital to identify factors that facilitate better decision making among older adults. We conceptualize decision making as a complex ability supported by diverse resources, including many that are uniquely impacted by aging (Amundsen, 2021; Boyle et al., 2022; Han et al., 2016; Spreng et al., 2016; Stewart et al., 2018). For instance, an established, foundational relationship exists between cognition and decision making, and it is widely recognized that aging is associated with deteriorating cognitive resources (Boyle et al., 2013a; Centers for Disease Control and Prevention (CDC), 2015; Han et al., 2016; Stewart et al., 2018). In our conceptual framework, psychological and experiential factors also impact decision making and related behavioral economic outcomes, and we further hypothesize that these factors may be most impactful in the context of diminished cognitive resources (Boyle et al., 2022). Burgeoning evidence suggests that psychological resources contribute to decision making in older age with some operating independently of cognition but many interacting with cognition to affect decision making (Amundsen, 2021; Glover et al., 2021; James et al., 2011; Miyashita et al., 2021; Stewart et al., 2020). For example, we have found that risk aversion is associated with decision making independent of cognition, but loneliness interacts with cognition to affect decision making (Boyle et al., 2012a; Stewart et al., 2020). Notably, in our work, the effects of psychological and experiential factors were most prominent among older adults with lower cognition (Glover et al., 2021; Stewart et al., 2020; Stewart et al., 2022).

Most previous research has focused on negative psychological attributes, and relatively little is known about the associations between positive psychological constructs and decision making (Denburg et al., 2009; Gautam & Mathur, 2018; Kang et al., 2019; Lichtenberg et al., 2016, 2020; Nguyen et al., 2013). One exception is our recent finding that purpose in life—a positive psychological construct that refers to one’s sense of meaning and investment in life experiences—is associated with better decision making among older adults, especially those with poorer cognitive function (Stewart et al., 2022). Purpose in life, however, is only one component of the larger construct of psychological well-being, which refers to one’s broader sense that life is good and marked by active and meaningful engagement and positive interactions with others. Psychological well-being consists of six domains (i.e., autonomy, environmental mastery, personal growth, positive relations with others, purpose in life, and self-acceptance) and is a construct of particular interest due to its robust associations with a range of positive outcomes in aging, including better mental health, longer life expectancies, and disability- and disease-free longevity (Boccardi & Boccardi, 2019; Dodge et al., 2012; Gheno, 2021; Kafka & Kozma, 2002; Ryff, 2014; Ryff & Keyes, 1995; Wilson et al., 2013; Zaninotto & Steptoe, 2019). Thus, it is possible that the benefits of psychological well-being extend into other life domains such as decision making. Furthermore, from an intervention standpoint, it is important to understand whether potential beneficial effects of psychological well-being are mainly due to purpose in life or additional domains of well-being.

Here, we extended our prior work by testing the hypotheses that psychological well-being is associated with healthcare and financial decision making among community-dwelling older adults without dementia and interacts with the level of cognitive function to influence decision making. We also aimed to understand whether the association of psychological well-being with decision making is independent of related but conceptually distinct negative psychological attributes—depressive symptoms and anxiety. We then explored relationships between six domains of psychological well-being and decision making to understand whether the relationship between psychological well-being and decision making is attributed to one or more specific domains. Lastly, we examined whether the interaction between psychological well-being and cognition was general in nature or domain-specific by examining five specific cognitive domains (i.e., episodic memory, visuospatial ability, perceptual speed, semantic memory, and working memory). Study results may provide insight into the role of psychological well-being in decision making among older adults and highlight potential avenues for decision-making interventions.

Methods

Participants

Participants were from the Rush Memory and Aging Project (MAP), an ongoing clinical-pathologic cohort study of aging (Bennett et al., 2018). MAP began enrollment in 1997, with recruitment of nondemented, community-dwelling older adults who are tested within their homes. MAP participation includes a thorough clinical evaluation consisting of medical history, neurological examination, cognitive function testing, risk factor assessment, and organ donation at death. An MAP substudy on decision making began in 2010 and included assessments of healthcare and financial decision making (Boyle et al., 2012a, 2012b). All participants report their race (e.g., White) and ethnicity (i.e., Hispanic: yes or no) based on categories from the 1990 United States Census Bureau (United States Census Bureau Report, 1990) as well as their gender (i.e., male or female), date of birth, years of education, and income. Annual income is measured using the Show-Card Method from the Established Populations for Epidemiologic Studies of the Elderly, in which participants are asked to select 1 ($0.00 to $4999) of 10 ($75,000 and over) levels of total annual family income (Huntley et al., 1993). Both MAP and the substudy on decision making were approved by the Institutional Review Board (Protocol Number 07071201) at Rush University Medical Center. All participants signed an informed consent document for each study. Data can be requested at https://www.radc.rush.edu. Eligibility for these analyses required the absence of dementia at the baseline assessment of the decision-making substudy based on a review of medical history, neurological examination, and cognitive function testing and then classified by a clinician using the criteria of the Joint Working Group of the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association (Bennett et al., 2018). Of 1315 alive MAP participants, 127 had not yet completed the decision-making substudy at the time of the current analyses, 66 were diagnosed with dementia, and 40 had missing data for variables of interest, including decision making. As a result, 1082 participants were included in the current analyses.

Assessment of Healthcare and Financial Decision Making

Healthcare and financial decision making was measured using a validated, performance-based instrument developed for use in older adults and to reflect feasible real-world healthcare and financial decisions that they may face in their daily lives (Finucane et al., 2005; Finucane & Gullion, 2010; Stewart et al., 2018). The measure consisted of 12 items—a 6-item healthcare component and a 6-item financial component. Each component consisted of three simple items and three complex items. Participants viewed tables displaying information about HMO plans or mutual funds and then answered questions requiring comprehension and integration of the tabulated information. For example, one simple healthcare item consisted of a table displaying five characteristics of three HMO plans (i.e., member satisfaction, preventive care strategies, access to specialists, customer service, and premiums) and asked participants the following question: “Look at the information provided and tell me, what percentage of members are very satisfied with physician access?” One complex healthcare item displayed the same five characteristics for six additional HMO plans (i.e., nine plans in total) and asked participants the following question: “You don’t want any HMO that is below average on member satisfaction, customer service, or access to specialists. Finally, you DO want an HMO that is above average on preventive care strategies. Based on the information in the table below, which HMO should you choose?” The total number of items answered correctly was summed for each participant (range = 0–12), with higher scores indicating better decision making. This measure has demonstrated sufficient internal consistency and is associated with other important decision-making resources (i.e., cognitive function, health and financial literacy, and risk aversion) and adverse outcomes in old age (i.e., accelerated cognitive decline, incident Alzheimer’s dementia, and mortality) (Boyle et al., 2013b;Boyle, et al., 2013; Stewart et al., 2018, 2019).

Assessment of Psychological Well-being

Psychological well-being was measured using a modified version of Ryff’s Scales of Psychological Wellbeing (Wilson et al., 2013). The modified scale consists of 18 items, including 3 items each that assess 6 domains of psychological well-being: autonomy, environmental mastery, personal growth, positive relations with others, purpose in life, and self-acceptance. One item from the positive relations with others domain indicated, “People would describe me as a giving person, willing to share my time with others.” An item from the environmental mastery domain stated, “I am good at managing the responsibilities of daily life.” Participants were asked to rate each item according to how the item applied to them using a 7-point Likert-type scale with response options: 1 = strongly agree, 2 = agree, 3 = slightly agree, 4 = neither agree nor disagree, 5 = slightly disagree, 6 = disagree, and 7 = strongly disagree. Positively worded items were reverse coded, and then all three item responses for each domain were averaged to create a domain-specific score (e.g., a score for the domain of personal growth), and all item responses were averaged to create a total psychological well-being score. Higher scores indicated greater psychological well-being.

Assessment of Cognitive Function

Participants completed a battery of 21 performance-based measures of cognitive function (Bennett et al., 2018; Wilson et al., 2002). Two tests, the Mini-Mental State Examination (MMSE) and Complex Ideational Material, were used for descriptive or diagnostic purposes only. The remaining 19 performance-based tests assessed 5 domains of cognitive function: (1) episodic memory (7 tests: Word List Memory, Recall, and Recognition from the CERAD neuropsychological battery; immediate and delayed recall of Logical Memory Story A, and the East Boston Story); (2) visuospatial ability (2 tests: Judgment of Line Orientation and Standard Progressive Matrices); (3) perceptual speed (4 tests: the oral version of the Symbol Digit Modalities Test, Number Comparison, Stroop Color Naming, and Stroop Word Reading); (4) semantic memory (3 tests: Category Fluency, Boston Naming, and the National Adult Reading Test); and (5) working memory (3 tests: Digit Span subtests [forward and backward] of the Wechsler Memory Scale-Revised and Digit Ordering). Participants’ raw scores on each of the 19 performance-based tests were converted to z-scores using the baseline mean and standard deviation of the full MAP cohort (Wilson et al., 2005). All 19 z-scores were averaged to yield a composite score of global cognitive function for each participant. The five specific cognitive domains were calculated by averaging the z-scores from the tests in each cognitive domain.

Other Covariates: Depressive Symptoms and Anxiety

Depressive symptoms were assessed using a modified 10-item version of the Center for Epidemiologic Studies Depression (CES-D) Scale (Kohout et al., 1993). Participants were asked whether or not (0 = no and 1 = yes) they experienced each of 10 symptoms during a majority of the time during the previous week. A sample question included, “I felt that people disliked me.” Positively worded or positive attributes (e.g., “I was happy.”) were reverse coded so that the participant’s response was consistent with being a depressive symptom. All items were then summed for a total number of reported symptoms, with higher scores indicating more depressive symptoms.

Anxiety was measured using a modified 10-item version of the Anxiety Trait Scale from the State-Trait Anxiety Inventory (Wilson et al., 2003). Participants were asked whether or not (0 = no and 1 = yes) they generally experienced 10 affective states (e.g., “I lack self-confidence.”). Positively worded or positive attributes (such as, “I feel pleasant.”) were reverse coded so that the participant’s response was consistent with anxious feelings. Items were then summed for a total number of reported experiences, with higher scores indicating more anxiety.

Analyses

First, we assessed bivariate associations of demographic characteristics (i.e., age, gender, and years of education), psychological well-being, and global cognitive function with decision making using Pearson correlations or t-tests, as appropriate. Second, we examined associations of psychological well-being and global cognitive function with decision making (the continuous outcome variable) in a series of linear regression models. The initial model examined direct associations of both psychological well-being and global cognitive function with decision making after adjusting for age, gender, and years of education. For the core model, we then added an interaction term for psychological well-being with global cognitive function to the initial model to examine whether psychological well-being interacts with global cognitive function to impact decision making. Third, to address potential confounding by depressive symptoms and anxiety, we performed two separate linear regression models with additional terms for each psychological factor and its interaction with cognition. Fourth, to explore the potential role(s) of specific domains of psychological well-being, we repeated the core model but replaced psychological well-being with each of the six domains of well-being in separate linear regression models. Lastly, we repeated the core model but replaced global cognition with five specific cognitive domains in five separate linear regression models to understand possible impacts of five specific cognitive domains. Estimates from linear regression models represent unstandardized regression coefficients.

We also performed secondary analyses to examine the role of income in the relationships between psychological well-being and global cognitive function with decision making. To do so, we re-ran the previous core model to include income as a covariate and an interaction term for income and psychological well-being. All analyses were conducted using SAS software, version 9.4 of the SAS system for Linux.

Results

Participant Characteristics

Participants (N = 1082; 97% non-Latino White; 76% women) had a mean score of 7.96 (SD = 2.65) on the decision-making measure, with women (mean = 7.74, SD = 2.67) scoring lower than men on average (mean = 8.67, SD = 2.44) (t₁₀₈₁ = −5.01, p < .0001). In bivariate analyses, younger age (Pearson’s r = –0.31, p < .0001), more years of education (Pearson’s r = 0.36, p < .0001), higher levels of psychological well-being (Pearson’s r = 0.28, p < .0001), and higher levels of global cognitive function (Pearson’s r = 0.57, p < .0001) were associated with higher scores on the decision-making measure. Additional descriptive information is reported in Table 1.

Table 1.

Participant Characteristics (N = 1082).

Characteristics	Range of Scores	Mean (SD)
Age	58.81–100.78	81.04 (7.53)
Gender, women (%)		823 (76%)
Ethnicity and race, non-Latino White (%)	1039 (97%)
Years of education	5.00–30.00	15.60 (3.11)
Income, median (IQR)	1–10 ($0.00 to $75,000 and over)	8 ($35,00 to $49,999)
MMSE score, median (IQR)	17.00–30.00	29.00 (27.86–30.00)
Healthcare and financial decision making	0.00–12.00	7.96 (2.65)
Psychological well-being	1.00–7.00	5.58 (0.58)
Global cognitive function	−1.59–1.60	0.23 (0.51)
Depressive symptoms, median (IQR)	0.00–10.00	0.00 (0.00–1.00)
Anxiety, median (IQR)	0.00–10.00	1.00 (0.00–2.00)

Associations of Psychological Well-being with Healthcare and Financial Decision Making

Both higher levels of psychological well-being (estimate = 0.39, standard error [SE] = 0.11, t₁₀₇₆ = 3.42, p < 0.001) and higher global cognitive function (estimate = 2.37, SE = 0.14, t₁₀₇₆ = 17.36, p < 0.0001) were associated with better decision making. Furthermore, in a second model that included an interaction term for psychological well-being with global cognitive function, the interaction was significant (estimate = −0.68, SE = 0.20, t₁₀₇₄ = −3.36, p < .001), such that higher levels of well-being were associated with better decision making particularly among participants with lower global cognitive function (see Figure 1).

Potential Confounders: Depressive Symptoms and Anxiety

Depressive symptoms and anxiety were not associated with decision making—neither independently nor in interaction with global cognitive function. Moreover, the inclusion of depressive symptoms and anxiety did not substantially change the association of psychological well-being or the interaction between psychological well-being and global cognitive function with decision making (see Tables 2 and 3).

Table 2.

Associations of Psychological Well-being, Global Cognitive Function, and Depressive Symptoms with Healthcare and Financial Decision Making.

Terms	Estimate	Standard Error	t-Statistic	p-Value
Psychological well-being	0.55	0.13	4.30	<0.0001
Global cognitive function	6.92	1.26	5.48	<0.0001
Depressive symptoms	0.04	0.05	0.80	0.42
Psychological well-being X global cognitive function	−0.80	0.22	−3.63	0.0003
Depressive symptoms X Global cognitive function	−0.12	0.09	−1.34	0.18

^*Based on a multivariable linear regression model controlling for age, gender, and years of education. Significance was tested using a t-statistic with degrees of freedom of 1072.

Table 3.

Associations of Psychological Well-being, Global Cognitive Function, and Anxiety with Healthcare and Financial Decision Making.

Terms	Estimate	Standard Error	t-Statistic	p-Value
Psychological well-being	0.46	0.13	3.55	0.0004
Global cognitive function	2.11	0.18	11.48	<0.0001
Anxiety	0.007	0.04	0.16	0.87
Psychological well-being X global cognitive function	−0.73	0.23	−3.23	0.001
Anxiety X Global cognitive function	−0.11	0.08	−1.31	0.19

*Based on a multivariable linear regression model controlling for age, gender, and years of education. Significance was tested using a t-statistic with degrees of freedom of 1034.

Relation of Six Domains of Psychological Well-being and Healthcare and Financial Decision Making

Significant main effects existed for five of the six domains: autonomy, environmental mastery, personal growth, positive relations with others, and purpose in life. A significant main effect did not exist for the domain of self-acceptance. We also observed significant interactions between global cognitive function and four of the six domains of psychological well-being. Higher levels of autonomy, environmental mastery, personal growth, and purpose in life were associated with better decision making, especially among participants with lower global cognitive function. We did not observe significant interactions between global cognitive function and two domains of psychological well-being—self-acceptance and positive relations others (see Table 4).

Table 4.

Associations of Six Specific Domains of Psychological Well-being and Global Cognitive Function with Healthcare and Financial Decision Making.

Terms	Estimate	Standard Error	t-Statistic	p-Value
Model 1
Self-acceptance	0.12	0.07	1.56	0.12
Global cognitive function	2.35	0.15	15.70	<0.0001
Self-acceptance X Global cognitive function	−0.18	0.14	−1.34	0.18
Model 2
Autonomy	0.23	0.08	3.05	0.002
Global cognitive function	2.25	0.15	15.28	<0.0001
Autonomy X global cognitive function	−0.42	0.14	−3.04	0.002
Model 3
Environmental mastery	0.18	0.08	2.27	0.02
Global cognitive function	2.32	0.14	16.22	<0.0001
Environmental mastery X global cognitive function	−0.28	0.14	−2.04	0.04
Model 4
Personal growth	0.28	0.09	3.14	0.002
Global cognitive function	2.30	0.14	15.90	<0.0001
Personal growth X global cognitive function	−0.29	0.15	−1.96	0.0502
Model 5
Positive relations with others	0.21	0.08	2.72	0.007
Global cognitive function	2.32	0.15	15.97	<0.0001
Positive relations with others X global cognitive function	−0.25	0.13	−1.89	0.0584
Model 6
Purpose in life	0.28	0.07	3.81	0.0002
Global cognitive function	2.38	0.14	17.33	<0.0001
Purpose in life X global cognitive function	−0.39	0.12	−3.14	0.002

*Based on multivariable linear regression models controlling for age, gender, and years of education. Significance was tested using a t-statistic with degrees of freedom of 1072 for each of the six models.

Relationships Between Psychological Well-being and Five Cognitive Domains

We observed significant interactions for the domains of perceptual speed and working memory; specifically, psychological well-being was particularly beneficial for decision making among persons with lower perceptual speed and working memory abilities (see Table 5).

Table 5.

Associations of Psychological Well-being and Five Specific Cognitive Domains with Healthcare and Financial Decision Making.

Terms	Estimate	Standard Error	t-Statistic	p-Value
Model 1
Psychological well-being	0.62	0.13	4.74	<0.0001
Episodic memory	0.87	0.14	6.01	<0.0001
Psychological Well-being X episodic memory	−0.28	0.18	−1.59	0.11
Model 2
Psychological well-being	0.58	0.12	4.87	<0.0001
Visuospatial ability	0.98	0.11	8.79	<0.0001
Psychological well-being X visuospatial ability	−0.20	0.14	−1.39	0.16
Model 3
Psychological well-being	0.36	0.11	3.22	<0.0001
Perceptual speed	1.31	0.11	11.43	0.001
Psychological well-being X perceptual speed	−0.50	0.13	−3.75	0.0002
Model 4
Psychological well-being	0.45	0.12	3.74	0.0002
Semantic memory	1.57	0.14	10.94	<0.0001
Psychological well-being X semantic memory	−0.05	0.18	−0.27	0.79
Model 5
Psychological well-being	0.69	0.12	5.62	<0.0001
Working memory	0.80	0.11	6.99	<0.0001
Psychological well-being X working memory	−0.37	0.15	−2.48	0.01

Secondary Analyses

The direct effect of income was not statistically significant (estimate = 0.06, SE = 0.04, t₁₀₃₄ = 1.52, p = .13). Furthermore, the interaction between income and psychological well-being was not statistically significant (estimate = −0.06, SE = 0.05, t₁₀₃₄ = −1.19, p = .23). However, main effects for both psychological well-being (estimate = 0.44, SE = 0.13, t₁₀₃₄ = 3.41, p = <.001) and global cognitive function (estimate = 1.98, SE = 0.17, t₁₀₃₄ = 11.35, p = <.0001) as well as their interaction (estimate = −0.69, SE = 0.22, t₁₀₃₄ = −3.12, p = .002) remained significant with the inclusion of income and its interaction with psychological well-being in the model.

Discussion

The current study tested the hypotheses that psychological well-being is associated with healthcare and financial decision making among community-dwelling older adults without dementia and that well-being interacts with the level of cognitive function to influence decision making. Psychological well-being was associated with better decision making, with higher levels of well-being most beneficial for decision making among participants with lower levels of global cognitive function. These associations persisted after adjusting for depressive symptoms and anxiety, suggesting that the impact of psychological well-being is relatively independent of these characteristics of psychological distress. Notably, four specific domains of psychological well-being—autonomy, environmental mastery, personal growth, and purpose in life—were related to decision making, particularly for participants with poorer global cognitive function. These results suggest that multiple aspects of psychological well-being may confer benefit to decision making in older age. Furthermore, psychological well-being interacted with two specific cognitive domains, perceptual speed and working memory, such that higher levels of well-being were associated with better decision making among persons with slower perceptual speed and poorer working memory. The current study suggests that a general sense of contentedness, active and meaningful participation in one’s own life, and positive engagement with others facilitate better decision making among older adults, especially those with diminished cognitive function. It is important to identify factors that modify the foundational relationship between cognition and decision making for two key reasons: (1) such factors represent potentially modifiable intervention targets, and (2) this approach allows us to identify groups of vulnerable older adults (e.g., those with diminished cognition). Hence, these results have important implications regarding targets to investigate for more focused interventions to improve decision making among older adults.

Positive psychological resources are associated with numerous favorable functional and health outcomes in aging, yet previous research on decision making, specifically, has largely focused on the role of negative psychological factors (Boccardi & Boccardi, 2019; Dodge et al., 2012; Glover et al., 2021; Gheno, 2021; Kafka & Kozma, 2002; Stewart et al., 2020; Wilson et al., 2013; Zaninotto & Steptoe, 2019). The current study results add to a burgeoning body of research centering on positive psychological attributes. Importantly, these results extend our understanding of the scope of outcomes affected by positive psychological factors and suggest that psychological well-being can provide additional opportunities for people to thrive or flourish in older age, including in the realm of decision making. Thriving reflects more than merely surviving or living in the absence of negative psychological attributes or ailments—rather, thriving denotes active participation and engagement in one’s life and related activities (Diener et al., 2017; Martinson & Berridge, 2015). We postulate that higher levels of psychological well-being are essential to thriving in older age, including better decision making.

The current findings also extend our emerging work demonstrating that positive psychological resources play a critical role in decision making among older adults with diminished cognitive abilities (Glover et al., 2021; Stewart et al., 2020; Stewart et al., 2022). For example, we reported that more frequent engagement in cognitive activity—a positive psychological resource—was associated with better decision making, particularly among older adults with lower levels of cognitive function (Glover et al., 2021). We suspect that positive psychological resources are especially beneficial in the context of relatively lessened cognitive abilities because older adults can leverage such attributes to compensate in the face of lower levels of cognitive function. Taken together, our work suggests that opportunities offered by positive psychological resources can be particularly advantageous to a specific subgroup of older adults—those with lower levels of cognitive function—and that this subgroup may represent a target group for intervention studies.

It is unknown exactly why positive psychological attributes might benefit decision making when cognitive resources are low. Perhaps a greater sense of psychological well-being provides older adults with lower levels of cognitive function, especially slower perceptual speed and poorer working memory, with the energy and motivation needed to seek and effortfully process information to make better decisions (Boyle et al., 2008, 2012c). Another possibility is the presence of the “stability-despite-loss-paradox,” where older adults’ psychological well-being remains stable in aging, even in the context of decreased cognitive function (Braun et al., 2017; Gerstorf et al., 2007). For example, older adults with consistently elevated levels of psychological well-being may feel secure or confident in their ability to go about their daily activities, despite cognitive loss, due to a general sense of contentedness, openness to seeking information, and engagement with others, which allows them to focus more on making sound decisions. Relatedly, older adults with a greater sense of psychological well-being may more readily accrue knowledge through positive and meaningful engagement with their environments and others that, in turn, can be used to make better decisions (Ryff et al., 2016). Lastly, psychological well-being and related constructs may enhance neurologic health either by promoting neural efficiency or through known associations with lower clinical stroke risk and fewer macroscopic lacunar infarcts, and the ability to facilitate a person’s general functionality despite accumulating Alzheimer’s brain pathology (Boyle et al., 2010, 2012c; Kim et al., 2013; Yu et al., 2015). Thus, psychological well-being may strengthen a person’s brain and benefit decision making through better brain health, particularly in the face of lower cognitive functioning.

Current study findings have important implications for interventions and suggest that motivational interviewing and cognitive behavioral therapy focused on well-being may facilitate better decision making and promote other positive health and functional outcomes in aging. Previous evidence has suggested that psychological well-being is amenable to intervention and that improvements in well-being lead to more positive physical and psychological health outcomes (Stochl et al., 2019; Ryff et al., 2016; Thomson et al., 2018; Walshe et al., 2020). Indeed, psychological well-being may be an ideal therapeutic target as it is a modifiable psychological resource, related to positive outcomes in aging, and may enhance motivation in relation to decision making and a host of other healthy behaviors (Ryff et al., 2016). Furthermore, it is possible that evidence-based intervention strategies may strengthen psychological well-being among older adults with diminished cognitive abilities to improve their decision making, in part, through peer support and confidence-building (Stochl et al., 2019; Thomson et al., 2018). Future research may address optimal intervention strategies to bolster psychological well-being.

The current study has limitations and strengths. One limitation is the homogeneity among study participants regarding race and gender as well as the high years of education of participants. Currently, we are collecting data regarding decision making among older adults belonging to racial and ethnic minoritized communities, in addition to older persons with fewer years of education. A second limitation is the cross-sectional nature of the current study. While we hypothesize that psychological well-being is associated with decision making and interacts with the level of cognitive function to influence decision making, this is a cross sectional study and it is possible that directionality occurs in the opposite way, such that decision making impacts psychological well-being. Longitudinal data collection in our cohorts is ongoing; hence, we will be better able to address directionality of the relationships between various psychological constructs, particularly psychological well-being, and decision making as well as how these associations change over time. A third potential limitation is that the findings in the current study may not translate to broader or a different set of healthcare and financial decisions faced by older adults. Conversely, this study has several strengths including a well-characterized group of community-dwelling older adults without dementia and used established measures of psychological well-being, cognitive function, and healthcare and financial decision making. We also adjusted for important covariates including depressive symptoms and anxiety, which increases confidence in our findings. Overall, the current study demonstrates the important role of psychological well-being, a positive psychological resource, in relation to healthcare and financial decision making in old age, particularly in the context of diminished cognitive function.

Footnotes

Acknowledgments

We would like to thank all MAP study and decision-making substudy participants. We would also like to thank all staff at the Rush Alzheimer’s Disease Center.

Author Contributions

CMG, LY, and PAB contributed to the conception and design of the research study; CMG, LY, and PAB drafted the work; CMG, LY, CCS, RSW, DAB, ML, and PAB revised the work critically for important intellectual content; CMG, LY, CCS, RSW, DAB, ML, and PAB provided approval for publication of the content; CMG and PAB agree to be accountable for all aspects of the work.

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by grants from the National Institute on Aging (grant numbers: R01AG034374, R01AG033678, and R01AG060376 to PAB; P30AG072975, P30AG010161, and R01AG17917 to DAB).

Institutional Review Board

The MAP Study and decision-making substudy were approved by the Institutional Review Board (Protocol Number 07071201) at Rush University Medical Center. All participants signed an informed consent document for each study.

ORCID iDs

Crystal M. Glover

Christopher C. Stewart

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