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. Author manuscript; available in PMC: 2024 Mar 1.
Published in final edited form as: J Aging Health. 2022 Aug 23;35(3-4):221–229. doi: 10.1177/08982643221122639

Decision making and blood sugar indicators in older African American adults

Veronica Eloesa 1, Melissa Lamar 1,2, Lei Yu 1,3, David A Bennett 1,3, Lisa L Barnes 1,2,3, Patricia A Boyle 1,2
PMCID: PMC10266504  NIHMSID: NIHMS1862990  PMID: 35997533

Abstract

Objectives:

Decision making is a modifiable behavior associated with health outcomes. We investigated the association of decision making with blood sugar indicators in older community-dwelling African American adults.

Methods:

Participants were 328 older African American adults from community-based studies (mean age=78). Decision making was assessed using a performance-based measure (range:0–12). Blood sugar indicators were non-fasting hemoglobin A1c and blood glucose. Using regression, we assessed the relationship between decision making and each blood sugar indicator, controlling for demographics. We additionally examined if an association varied by known diabetes diagnosis.

Results:

Lower decision making was associated with higher HbA1c (b: −0.05, p-value: 0.03), but not blood glucose. In an interaction analysis the association of lower decision making with higher levels of HbA1c was present only among individuals with known diabetes (b (with diabetes): −0.13, p-value: <0.01)

Discussion:

Decision making may contribute to glycemic control in African American older adults with diabetes.

Keywords: Decision Making, Diabetes Management, African Americans

Background

Diabetes affects more than 35 million Americans, an excess of 10% of the population (Centers for Disease Control and Prevention, 2020). Complications for those with diabetes can include retinopathy and blindness, kidney disease, cardiovascular disease including stroke and peripheral artery disease (PAD), which can result in lower limb amputation, and premature mortality (Papatheodorou et al., 2018). However, diabetes and related comorbidities and premature mortality are not equally distributed among the population. African American adults have higher rates of diagnosed diabetes than any other racial/ethnic group (Centers for Disease Control and Prevention, 2020) and they are more likely to have undiagnosed diabetes, defined as high hemoglobin A1c without a diabetes diagnosis (Cheng et al., 2019). Further, they are more likely to have diabetes complications than non-Hispanic Whites including nearly 50% higher odds of diabetic retinopathy, and about 30% higher odds of diabetic nephropathy, and almost twice the odds of end stage renal disease (Harris et al., 1998; Young et al., 2003).

Decision making, a complex behavior that involves evaluation, comparison, and selection of an optimal choice among competing alternatives, may contribute to successful diabetes management. Decision making requires the interaction of many abilities (e.g. cognitive) and resources (e.g. financial) and is shaped by both contextual (e.g. financial and health literacy, experiential) and psychosocial (e.g. personality, psychological) factors (Boyle et al., 2022; Stewart et al., 2018). Aging related changes in these abilities, resources, and contexts accompanied by deteriorating brain health make it more likely that older adults experience lower levels of decision making, exemplified by a vulnerability to decisional errors on performance based measures, relative to their younger peers (Agarwal et al., 2009; Tymula et al., 2013). While sound decision making is important for well-being at all stages in life, it is particularly vital later in life, when older adults are faced with higher burden of disease and accompanying complex medical and financial decisions. In previous work, we have shown that decision making, particularly in the domains of healthcare and finance, is associated with a range of health outcomes among older adults, including mortality, incident Alzheimer’s disease (AD) and mild cognitive impairment (MCI), brain health, and blood pressure (Boyle et al., 2013; Gamble et al., 2015; Han et al., 2018; Korniotis & Kumar, 2011; Lamar et al., 2020; Lusardi & Mitchelli, 2007; Stewart et al., 2019). As older adults face many important and complex financial and healthcare choices, often while managing chronic illnesses and a fixed income, the importance of decision making is paramount. Decision making could impact health outcomes via a variety of pathways such as medication adherence, scheduling and attending doctors’ visits, and management of specialists and insurance. Degraded decision making could also be a harbinger of adverse health outcomes, indicating a need for extra scaffolding and support for vulnerable older adults. Notably, however, and despite considerable interest in decision making among older adults and substantial variation in access by race to resources that influence decision making ability (e.g. financial, health care, and educational quality), most studies linking decision making with health outcomes have been conducted in predominantly White samples.

Given growing recognition that decision making is critical for maintaining health (Lamar et al., 2020; Stewart et al., 2019), it is essential to study the impact of decision making on health outcomes in populations who carry a high burden of disease and face unique social and structural conditions that affect disease management. African American individuals have faced historical exclusion and marginalization from health systems leading to limited experiences and exposure to the institutions and providers that populate them (Feagin & Bennefield, 2014; Nardone et al., 2020). Experiences of institutionalized racism and marginalization lead to lower levels of trust, making engagement in physician prescribed care regimens less likely (Musa et al., 2009). Taken along with myriad other social determinants of health, these upstream circumstances have been shown to impact diabetes rates and control (Hill-Briggs et al., 2021). In the same way, they could also impact decision making by limiting exposure, knowledge, and resources.

Maintenance of blood sugar levels generally and as a part of a comprehensive diabetes management plan is a complex and demanding process; one which requires a variety of healthcare and financial decisions such as selecting and utilizing insurance, handling medication and cost, and navigating health systems. In this study, we examined the relationship between decision making and diabetes control, a chronic condition of major public health importance. Specifically, we examined the relationship between decision making and two objectively measured blood sugar indicators in older African American adults from Rush Alzheimer’s Disease Center cohort studies. Blood sugar indicators included hemoglobin A1c, a long-term measure of glycemic stability, and blood glucose, a short-term marker of blood sugar control. Based on previous studies in mainly older White populations, we hypothesized that lower decision making would be associated with higher levels of these indicators. In subsequent analyses we included an interaction term for known diabetes with decision making to see if the relationship between decision making and blood sugar indicators varied by known diabetes status.

Methods

Study Participants

Participants were older African American adults from three cohort studies: the Minority Aging Research Study (MARS), the Rush Memory and Aging Project (MAP), and the Rush African American Clinical Core (Barnes et al., 2012; Bennett et al., 2018; Schneider et al., 2009). All studies were approved by the Institutional Review Board of Rush University Medical Center and all participants gave written informed consent to participate in the studies. All participants reside in the Chicago metropolitan area and surrounding suburbs. Participants were recruited from a variety of community-based settings including churches, subsidized senior housing, social service agencies, and African American clubs, fraternities and sororities. All three studies are conducted by the same research team at the Rush Alzheimer’s Disease Center and have largely overlapping evaluations, facilitating the merging of data for combined analyses. Participants in the studies undergo yearly uniform structured clinical evaluations at their place of residence. Evaluations include a structured interview to ascertain demographics and a variety of lifestyle and experiential items, a neurological examination, a blood draw, and a battery of cognitive tests. All studies are ongoing.

A decision making sub-study was incorporated into MAP in 2010, in the Clinical Core in 2015, and in MARS in 2017. Across the three studies, there were 1,251 African American participants. Upon initiation of the decision making study, 56 had died, 38 had withdrawn from their parent study, and 55 were ineligible to participate due to medical conditions, having moved out of the geographical area, or having been diagnosed with dementia. Of 1,102 eligible participants, 401 have completed the decision making sub-study to date (most of the remaining have yet to be approached due to the relatively recent addition of the decision making studies in the Clinical Core and MARS), and 328 had information on all variables. This analysis focuses on these 328 participants.

Measures

Decision making was measured using a 12-item measure that was designed for use among older adults. The measure is performance-based and each item has an objectively correct or incorrect answer; higher scores on the scale indicate better (more accurate) decision making than lower scores. An individual would be considered to have suboptimal performance if they answered very few questions correctly, represented by a low score on the decision making measure. The measure involves a series of tables that were specifically designed to resemble materials used in real world financial and health care decision settings (Finucane & Gullion, 2010). Respondents are asked to make choices about mutual funds and healthcare plans, respectively. Questions assess aspects of financial and health decision making of which comprehension and integration of information vary in difficulty. There are both literal questions, where a participant is asked to identify specific information, and inferential questions, where a correct response requires integration of multiple pieces of information. Total score is calculated by summing the number of items answered correctly and ranges from 0–12, with higher scores indicating better decision making. Prior to use, the measure was extensively piloted in a sample of older adults in order to ensure appropriate levels of comprehension. In prior research, the measure has been shown to have high inter-rater reliability and short-term temporal stability (Finucane et al., 2005; Finucane & Gullion, 2010). Our team has previously reported that this measure is associated with a range health outcomes including cognition, dementia, blood pressure, and mortality among older adults (Boyle et al., 2013; Boyle, Yu, Buchman, et al., 2012; Boyle, Yu, Wilson, et al., 2012; Lamar et al., 2020).

Blood sugar indicators were measured using blood samples collected by phlebotomists or trained nurses and specimens were analyzed at Quest Diagnostics. Indicators used in this study were hemoglobin A1c (HbA1c) and blood glucose, both of which represent objective assessments of blood sugar control. Hemoglobin A1c represents a long-term (i.e. 3-month) indicator of glycemic stability, whereas blood glucose represents a short-term indicator of blood sugar control. HbA1c is expressed as a percentage of hemoglobin and blood glucose (mg/dL) was measured as part of a basic metabolic panel. Blood sugar indicators measured concurrent with the decision making battery were used in analyses.

Covariates in this study included demographics, known diabetes status, hypertensive status, depressive symptoms, and cognitive function. Demographic variables include age, sex, and level of education (number of years completed). Known diabetes was self-report and/or based on prescription medications provided to the study team. Hypertension was assessed via medication usage and/or the average of three blood pressure readings: two seated and one standing (Lamar et al., 2020). Prescription drugs for diabetes and hypertension were classified using Medi-Span Drug Data Base System (Medi-Span., 1995).

A modified 10-item version of the Center for Epidemiologic Studies of Depression (CES-D) scale was used to assess depressive symptomology (Kohout et al., 1993). The score ranged from 0–10, with higher scores indicating more depressive symptomology.

Finally, cognitive function was assessed using a battery of 19 performance-based cognitive measures that assess five cognitive domains: episodic memory, semantic memory, working memory, perceptual speed, and visuospatial ability. A global composite score is created by converting the raw score on each test Z-scores using the baseline mean and SD of the full cohort then averaging the scores. Detailed information on the complete battery has been published elsewhere (Bennett et al., 2018).

Statistical Analyses

We first examined bivariate associations between decision making and the two blood sugar indicators, as well as key participant characteristics including demographics (age, sex, education) and other covariates using correlations or independent sample t-tests as appropriate. We then used a series of linear regression models to assess the relationship between decision making, HbA1c, and blood glucose. We first controlled for demographics only, with age and education mean centered. We then added covariates including known diabetes status, hypertensive status, and depressive symptomology. In a secondary analysis, we included the interaction of known diabetes status with decision making to determine whether the observed associations between decision making and blood sugar indicators varied by known diabetes status. As a sensitivity analysis to ensure that findings were not driven by low cognitive function, we excluded individuals scoring at the bottom 10% of our global composite of cognition. For estimates of association we present beta coefficients with 95% confidence intervals and p-values. A one-unit change on the score on the decision making scale corresponds to the estimated change in the modeled blood sugar indicator. All analyses were programmed using SAS/STAT software, Version 9.4 of the SAS System for Linux (SAS Institute, Cary, NC).

Results

Participant characteristics and bivariate correlations

On average, study participants were just under 78 years old and had completed 15 years of education. Most of the participants were female (81%). Just under 20% of participants had a known diagnosis of diabetes. Almost all the participants (94%) had hypertension. The median CES-D score was 1.0 (IQR: 2.0, range 0–8). Demographic and additional study population descriptions can be found in Table 1.

Table 1:

Participant Characteristics, Diabetes Indicators, and Decision Making in analytic sample (N=328)

Mean (sd) or % (N) Range
Participant Characteristics
Age (years) 77.4 (6.6) 62.5 – 93.4
Education – years 15.2 (3.1) 8 – 30
Female 81.1% (266)
Diabetes diagnosis 18.6% (61)
Hypertensive status 83.6% (307)
Depressive Symptomology Median: 1.0 (IQR: 2.0) 0 – 8
Diabetes Indicators
Hemoglobin A1c (%) 6.1 (0.8) 4.5 – 10.2
Blood glucose (mg/dL) 96.7 (29.5) 53 – 263
Decision Making 7.0 (2.4) 0 – 12
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Notes: SD: Standard Deviation; IQR: Interquartile range, mg/dL: milligrams per decilitre

Hemoglobin A1c and blood glucose were moderately and positively correlated with each other (r=0.59, p<0.01). Bivariate correlations between participant characteristics and blood sugar indicators showed that lower decision making and global cognition were correlated with higher levels of HbA1c (r=−0.13, p=0.02 and r=−0.12, p=0.03, respectively). More depressive symptomology was also correlated with higher levels of HbA1c (r=0.11, p=0.04). Neither age nor education were correlated with HbA1c. Independent sample t-tests indicated that HbA1c did not vary by sex or hypertensive status. Both blood sugar indicators differed by known diabetes status; those with a known diabetes diagnosis had higher levels of HbA1c (7.16±1.06) and blood glucose (122.10±38.11) than those without a diagnosis (5.79±0.48, p<0.01 and 90.49±23.13, p<0.01, for HbA1c and blood glucose, respectively). Blood glucose level was not correlated with any other participant characteristics (Table 2).

Table 2:

Correlation of Participant Characteristics and Diabetes Indicators (N=328)

Hemoglobin A1c (%) Glucose (mg/dL)
Age (years) 0.01 (0.86) 0.08 (0.13)
Education (years) −0.05 (0.35) −0.04 (0.50)
Depressive Symptomology 0.11 (0.04) 0.02 (0.69)
Decision Making −0.13 (0.02) −0.07 (0.21)
Global Cognition −0.12 (0.03) −0.08 (0.14)
Glucose (mg/dL) 0.59 (<0.01)
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Note: Values are Pearson correlation coefficient (p-value)

Association of decision making with blood sugar indicators

In a regression model adjusted for demographics, lower decision making scores were associated with higher HbA1c levels (b = −0.05, se = 0.02, p = 0.03). Decision making was not associated with blood glucose levels, however. The association between decision making and HbA1c persisted after further controlling for hypertension, known diabetes status, and depressive symptoms (b = −0.04, se = 0.02, p = 0.03). In a sensitivity analysis which excluded individuals in the bottom 10% of cognitive function, the association persisted (b = −0.03, se = 0.02, p =0.04).

A secondary analysis to assess whether the association of decision making with HbA1c varied by known diabetes status revealed that the association between decision making and HbA1c was only observed among individuals with a known diabetes diagnosis (b for interaction = −0.13, se = 0.04, p <0.01). All models for HbA1c are shown in Table 3. The differing relationship between decision making and HbA1c by known diabetes diagnosis is illustrated in Figure 1. This figure shows that for those with known diabetes, lower decision making scores are related to higher levels of HbA1C whereas among those with no known diabetes diagnosis there is no relationship between HbA1c level and decision making. To contextualize this, for someone with known diabetes and in the 90th percentile of decision making, their predicted HbA1c value is 6.68 whereas someone in the 10th percentile of decision making has a predicted value of 7.54. There was no association between decision making and blood glucose levels, regardless of known diabetes status (Table 4).

Table 3:

Associations of Decision Making with Hemoglobin A1C (N=328)

HbA1C
Core Fully Adjusted Interaction
Covariate b (95% CI) p-value b (95% CI) p-value b (95% CI) p-value
Age −0.003 (−0.02, 0.01) 0.62 −0.002 (−0.01, 0.01) 0.77 −0.001 (−0.01, 0.01) 0.84
Sex 0.03 (−0.19, 0.26) 0.77 0.10 (−0.08, 0.28) 0.28 0.10 (−0.07, 0.28) 0.25
Education 0.00 (−0.03, 0.03) 0.98 0.003 (−0.02, 0.03) 0.78 0.01 (−0.02, 0.03) 0.67
Decision Making −0.05 (−0.09, −0.004) 0.03 −0.04 (−0.07, 0.003) 0.03 −0.01 (−0.05, 0.02) 0.44
Diabetes Status 1.39 (1.20, 1.57) <.001 1.36 (1.17, 1.54) <0.01
Depressive Symptomology −0.01 (−0.05, 0.03) 0.72 −0.004 (−0.05, 0.04) 0.84
Hypertensive Status 0.02 (−0.30, 0.33) 0.92 0.02 (−0.29, 0.32) 0.92
Diabetes Status x
Decision Making 		−0.13 (−0.20, −0.05) <0.01
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Note: b: Beta value, CI: Confidence Interval

Figure 1: Relationship between Decision Making and Hemoglobin A1c by Diabetes Diagnosis in analytic sample (n=328).

Figure 1:

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Table 4:

Associations of Decision Making with Blood Glucose (N=328)

Blood Glucose
Core Fully Adjusted Interaction
Covariate b (95% CI) p-value b (95% CI) p-value b (95% CI) p-value
Age 0.33 (−0.18, 0.85) 0.21 0.38 (−0.10, 0.87) 0.12 0.38 (−0.10, 0.86) 0.12
Sex 1.25 (−6.96, 9.46) 0.76 1.42 (−6.31, 9.15) 0.72 1.39 (.6.36, 9.13) 0.72
Education −0.27 (−1.39, 0.85) 0.63 −0.31 −1.36, 0.74) 0.56 −0.33 (−1.38, 0.73) 0.54
Decision Making −0.48 (−2.01, 1.05) 0.54 −0.10 (−1.52, 1.31) 0.89 −0.28 (−1.82, 1.25) 0.72
Diabetes Status 32.62 (24.82, 40.43) <0.01 32.86 (25.01, 40.71) <0.01
Depressive Symptomology −1.09 (−2.89, 0.72) 0.24 −1.11 (−2.92, 0.69) 0.23
Hypertensive Status −4.13 (−17.50, 9.25) 0.54 −4.13 (−17.51, 9.26) 0.54
Diabetes Status x
Decision Making 		1.00 (−2.21, 4.20) 0.54
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Note: b: Beta value, CI: Confidence Interval

Discussion

We examined the association of decision making with objective blood sugar indicators among more than 300 older community-dwelling African American adults. We found that lower levels of decision making were associated with higher levels of HbA1c, particularly among individuals with a known diabetes diagnosis. The association of decision making with HbA1c persisted even after controlling for hypertensive status and depressive symptomology, as well as in sensitivity analyses that excluded participants with low cognitive function. There was no association between decision making and blood glucose levels, however. Whereas blood glucose levels represent a snapshot of blood sugar control, higher levels of HbA1c are indicative of glycemic instability over longer periods of time and controlled levels of HbA1c are a good indicator of successful diabetes management in individuals with a diabetes diagnosis. Individuals with diabetes face complex health and lifestyle decisions daily, and these findings suggest that interventions to improve decision making may facilitate better health outcomes particularly among individuals managing a diabetes diagnosis.

This is one of very few studies examining the relationship between individual decision making and objectively measured health indicators specifically among older African American adults. Lower decision making and related constructs have been associated with a range of important adverse health outcomes including cognitive decline, incident dementia, mortality and elevated blood pressure, but this work is largely limited to White samples (Boyle et al., 2013; Boyle, Yu, Buchman, et al., 2012; Lamar et al., 2020; Stewart et al., 2018). Although there is a literature on health literacy and diabetes management among African American individuals (e.g. Lamar et al., 2019; Rodriguez, 2013), to the best of our knowledge, the extent to which individual decision making plays a role in blood sugar control and diabetes management among African American adults has not been explored. While there is a literature on shared decision making (i.e. patient/provider communication on complex decision making about medication selection, support system engagement, lifestyle modifications, and resource identification in the management of complex diseases) (Peek et al., 2009), our work is distinct because the measure of decision making used in this study focused on individual decision making ability as opposed to mutual engagement in decision making processes. Our work addressing the lack of available data from African American individuals is an important contribution. African Americans are at particularly high risk for diabetes, as well as several other chronic conditions so investigations into correlates of health management specific to African Americans is of broad public health importance (Gillespie, 2016; Karter et al., 2013)

Our finding that lower decision making is associated with lower glycemic control as represented by higher levels of HbA1c among older African American adults with known diabetes has important implications for interventions. Clinical practice guidelines for diabetes management encompass not only glycemic control via consistent medication use but also via change in lifestyle habits (Brown et al., 2016). Although older African American adults are less likely to engage in important behavioral lifestyle risk factors that are associated with diabetes, like drinking and smoking, other circumstances linked to social inequities and structural discrimination commonly faced by this population (could be important targets for ecological interventions For example, Bancks et al. found that individual and neighborhood level social determinants of health contributed to disparities in diabetes above and beyond biological risk factors (Bancks et al., 2017). Neighborhood factors that are more limited in segregated areas, such as access to physical activity spaces and healthy food resources, have been shown to be related to lower incidence of type 2 diabetes (Auchincloss et al., 2009). Further, discriminatory practices in health care services have been associated with worse care and more complications for African Americans with diabetes. This association does not work through worse self-care behaviors, however, providing strong evidence that structural factors contribute to poorer health outcomes among African Americans (Peek et al., 2011)

Acknowledging the complexities patients face, at the patient level, individualized care plans using a patient-centered approach are recommended by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD) (Inzucchi et al., 2015). This approach requires open communication with medical providers when making complex decisions about diabetes management including medication selection, support system engagement, resource identification, and lifestyle modifications. Notably, however, there are structural barriers to successful implementation of shared decision making-based approaches in particular for African American individuals who have experienced a legacy of racism leading to mistrust in medical institutions, and still experience biases from physicians that further complicate such interventions (Peek et al., 2010; Serrano et al., 2016).

Previous research has suggested that universal and consistent health care coverage can play a role in improving glycemic control for African American adults with diabetes, but such systemic changes do not eliminate health disparities (Brown et al., 2021; McWilliams et al., 2009). Both population wide and individually tailored interventions are necessary to address this complex health condition. Interventions to increase glycemic control among diabetics are important not only for diabetes management itself, but also for management of diabetes as a risk factor for other conditions of older age. Individuals with diabetes are more likely to be diagnosed with Alzheimer’s disease, and this risk is higher among older African American adults with diabetes compared to other racial/ethnic groups (Mayeda et al., 2014). The present results suggest that interventions to improve decision making may lead to better health outcomes among African American adults with known diabetes. Further, we have shown among older Whites that decision making is linked to a wide range of other adverse health outcomes including vascular health conditions and incident Alzheimer’s Disease (Lamar et al., 2020; Stewart et al., 2019).

This study has several strengths including objective measurement of blood sugar indicators and use of a previously validated measure of decision making. Blood measured levels of hemoglobin A1c and blood glucose are both important indicators of glycemic control for older adults and use of these objective blood-based measures allows for a more precise and reliable evaluation of disease management than use of self-report alone. Our decision making measure was specifically designed for use in older populations and has been previously validated and shown to be associated with multiple health outcomes in older adults (Boyle et al., 2022; Finucane & Gullion, 2010). We adjusted for potential confounders including hypertension, assessed both via blood pressure and use of anti-hypertensive medication, depressive symptomology, and, importantly, known diabetes status. We also conducted sensitivity analyses to ensure that our results were not driven by individuals with low levels of cognitive function. Additionally, by examining the relationship between decision making and blood sugar indicators in an all African American cohort, we were able to assess variability in the relationship within this group and show that decision making is especially important among those with a known diabetes diagnosis. However, our study also has limitations. First, while the individuals in this study are community-dwelling and so likely more representative than studies that recruit from healthcare clinics, participants are highly educated and urban dwelling and may lack generalizability to the broader US older African American population. Second, our analysis is cross-sectional in nature, so we cannot infer causation. While we think that level of decision making could be a driver of difficulties with long term glycemic control via challenges related to medication management, cost-related nonadherence, and certain lifestyle factors (especially those systemically-related), it is also possible that the relationship runs in the reverse. We will address these issues and examine these associations longitudinally as sufficient data accrue. Finally, our decision making measure is limited to 12 items and not specific to diabetes management or blood sugar control. Our choice of measures was guided by an interest in measuring decision making in a manner that would allow us to study multiple health and related outcomes (i.e., not just one disease/condition). It is noteworthy, however, that performance on this measure has been linked to a number of health outcomes for older adults, providing evidence of external validity. Additionally, while our decision making measure assesses an individual’s decision making ability, it does not directly assess the many structural barriers to access and exposure to financial and health care institutions. Such barriers certainly influence decision making ability more strongly among subgroups of the population who face these barriers, including African Americans, and studies of the role of such barriers are greatly needed. However, this also highlights the need to for research examining within group heterogeneity (as opposed to solely racial differences) to understand contexts for targeted interventions.

Our results suggest that supportive environments for decision making could be particularly important among older African American adults who have chronic conditions, such as diabetes. Taken in context with our prior work, results suggest that improvements in decision making may confer a variety of health benefits across different populations. Additional research is needed to determine the range of health outcomes that may benefit from decision making interventions in older African American adults.

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