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. Author manuscript; available in PMC: 2022 Jan 1.
Published in final edited form as: Transp Res Part F Traffic Psychol Behav. 2020 Dec 18;76:193–201. doi: 10.1016/j.trf.2020.11.007

Planning for Driving Retirement: The Effect of Driving Perceptions, Driving Events, and Assessment of Driving Alternatives

Jonathon M Vivoda a,*, Jiawei Cao a, Athena Koumoutzis a, Annie C Harmon b, Ganesh M Babulal c
PMCID: PMC7945980  NIHMSID: NIHMS1649710  PMID: 33716551

Abstract

Most older adults will eventually stop driving, but few engage in planning for driving retirement. This study assessed whether driving stress, enjoyment, confidence, concerning driving events, and assessment of driving alternatives influence planning. Demographic factors were also included. Data were collected via a mailed transportation survey, with a final sample of 551 older adults who currently drive. Linear regression analyses revealed that more driving retirement planning was associated with greater driving stress, less driving confidence, and a more positive view of driving alternatives. Driving enjoyment and recent concerning driving events were not significantly related. Among the control variables, race and income were significantly related to planning, suggesting that lower income and identifying as Black race were associated with more planning. Gender only approached significance, suggesting that females may plan more than males. Overall, these findings suggest that more driving retirement planning is warranted. Some of the groups known to be at increased risk for driving reduction and cessation plan more for that eventuality than their counterparts. Implications of the study and suggestions for future research are discussed.

Keywords: driving reduction, driving cessation, transportation, driving stress, driving confidence

1. Introduction

As the United States (U.S.) population continues to age, more attention is needed on older adults’ transportation issues. In contrast to other age groups in the U.S. (which have shown a decrease in licensure over time), the number and proportion of older drivers has increased over recent decades (Insurance Institute for Highway Safety, 2020; Sivak & Schoettle, 2012). In 2017, there were nearly 44 million drivers age 65 years or older (U.S. Department of Transportation, 2018), and estimates suggest that this group will comprise about 25% of all drivers by 2030 (Highway Loss Data Institute, 2012).

Driving oneself is the preferred mode of transportation among older adults and is positively associated with quality of life (Dickerson et al., 2013; Eby et al., 2019). Despite a preference for self-mobility, most older adults will give up driving at some point in their lifetime (Babulal et al., 2019; Foley et al., 2002). Prior research suggests that people can expect to spend the final 6–10 years of their life unable to drive themselves (Babulal et al., 2019; Foley et al., 2002), reliant on family and friends, or the available public and private alternatives, to meet their transportation needs. This reality highlights the importance of planning for when that eventuality approaches, an important but understudied activity.

The decision to reduce or stop driving results from a variety of factors. Some of the most common are age-related physical health and cognitive declines, which typically result in the avoidance of specific driving situations (Agramunt et al., 2016; Dickerson et al., 2019; Meng & Siren, 2015). Many older adults attribute driving reduction and cessation specifically to vision-related declines (Agramunt et al., 2016; Blane, 2016) or to situational reasons (e.g., bad weather, lifestyle changes; Meng & Siren, 2015; Molnar et al., 2013). Aside from decisional factors, previous research has found associations between driving reduction/cessation and older age, female gender, non-married/partnered relationship status, less accumulated wealth, and non-White race (Babulal et al., 2018; Dickerson et al., 2019; Dugan & Lee, 2013; Vivoda et al., 2017, 2020). However, the extent to which these factors are related to planning for driving retirement has not yet been determined.

Engagement in driving reduction and cessation may reduce traffic safety concerns, but it can also result in numerous negative outcomes for older adults, suggesting that planning for driving retirement is a vitally important activity. Driving reduction and cessation have been linked to declines in physical and mental health (Chihuri et al., 2016; Dickerson et al., 2019), including increased depression, social withdrawal, cognitive decline, admission into a long term care facility, and even mortality (Choi et al., 2014; Dickerson et al., 2019; Freeman et al., 2006; Marottoli et al., 1997). However, there is encouraging evidence that driving retirement planning can reduce some of these potential negative outcomes (Liddle et al., 2014; Sanford et al., 2020; Windsor & Anstey, 2006), and even improve quality of life post-driving cessation (Musselwhite & Shergold, 2012).

Although most older adults report having the intention to plan for driving cessation, few have taken active steps to prepare (e.g., determining their own needs and preferences, communicating expectations with family or others involved, exploring how to access and navigate available options; Harmon et al., 2018; King et al., 2011). In fact, almost 60% of middle-aged and older adults report no transportation planning, and among those who had engaged in planning, 75–80% identified minimal preparation (Harmon et al., 2018).

The limited nature of practical alternative transportation options and lack of planning for a non-driving future suggests that many older adults are not adequately prepared for driving reduction and cessation (Harmon et al., 2018). Given that many of the aforementioned 44 million U.S. older drivers will eventually transition into nondriving, transportation planning is a crucial public health issue. Tools such as the CDC’s empirically-based MyMobility Plan are available to help determine potential transportation options and facilitate communication (Centers for Disease Control and Prevention, 2020), but such tools are only beneficial if people choose to use them.

Finding ways to encourage driving retirement planning requires a better understanding of what factors encourage or inhibit preparation for future transportation changes. Unfortunately, existing research in this area is limited, typically using smaller samples and qualitative methods to explore individual and family experiences related to driving reduction/cessation and planning (Connell et al., 2012; King et al., 2011). The few quantitative studies in this area have focused primarily on describing the frequency of planning behaviors and beliefs about its usefulness (e.g., Harmon et al., 2018). The purpose of this quantitative study was to expand on that previous research by examining the association between planning for driving retirement with a broad range of psychological and practical driving factors, including driving stress, driving enjoyment, driving confidence, concerning driving events, and assessment of driving alternatives. The following hypotheses were tested in the study:

  1. More perceived driving stress, concerning driving events, and a more positive assessment of alternatives to driving will be related to more planning for driving retirement.

  2. More perceived driving enjoyment and confidence will be related to less planning for driving retirement.

2. Methods

2.1. Data source

Prior to data collection, the University of Michigan (U-M) Health Sciences and Behavioral Sciences Institutional Review Board reviewed and granted approval to the project (HUM00097845). Data were collected from a transportation survey in 2015 in the southeastern Michigan area. A total of 1322 people age between 55 and 84 were mailed the full survey. Respondents were recruited from two registries of older adults who had previously indicated a willingness to participate in research. One comes from the Healthier Black Elders Center, a division of the Urban African American Aging Research (MCUAAR) center, a collaboration between Wayne State University (WSU), Michigan State University, and the University of Michigan (U-M); the other registry is housed at the Claude D. Pepper Older American Independence Center at U-M.

Excluding 35 invalid responses, 872 out of 1287 responses were received back from survey takers, resulting in a 67.8% overall response rate. A more detailed description of data collection can be found in Harmon et al. (2018). Because the focus of this study was to understand how current older drivers plan for their possible non-driving future, only those who currently drive and were at least age 65 were included in this study, resulting in a sample size of 560.

2.2. Measures

2.2.1. Outcome variable

Future transportation planning was a composite variable created by summing three separate measures. The first item asked: How much have you planned for your possible future transportation needs? This includes how you may need to change or adapt how you get around outside your home and new needs for transportation that you may have in the future. The second item asked: Regardless of how much transportation planning you have or haven’t done, how much planning about your transportation do you intend to do in the future? The final item asked: How much have you planned for a time in the future when you may no longer be driving? Each of these items included a five-point response scale with only none/not at all and a lot as anchor labels for the ends of the scale. The final scale had a possible range of 3 to 15 where a higher value represented more planning.

2.2.2. Predictors of interest

Driving stress was measured by asking: How stressful is driving for you currently? The five-point response scale was anchored by not at all stressful and very stressful. This variable was reverse-coded such that 1 represented very stressful and 5 represented not at all stressful to be consistent with other variables where a lower score represented a worse outcome. Driving enjoyment was measured by asking: Whether or not driving is stressful to you, how enjoyable is it for you currently? Driving confidence was measured by asking: How confident are you in your current driving skills and abilities? Five-point scales were used for both of these anchored by not at all [confident] and very [confident].

For concerning driving events, respondents were asked whether they experienced any events (e.g., car accidents or collision, near miss, someone they know stopping driving, finding unexplained dents or dings in vehicle) in the past year that made them consider changing their driving. Respondents checked all events that applied (11 total), and a sum was created with a possible range from 0 to 11.

Respondents were also asked about how well eight different driving alternatives could meet their future transportation needs if they were no longer driving themselves. Alternatives were rides with other drivers, buses, mass transport (light rail, trains, etc.), taxis/cabs, specialized transport, walking, e-hail (e.g., Uber), and other. Each item included a five-point scale anchored by not well at all and very well; these were summed with a possible range from 8–40, such that a higher value represented more a positive view of driving alternatives.

Two similar sets of questions assessed comfort with driving alternatives and likelihood of using driving alternatives. The same modes were assessed; each used a five-point scale with anchor labels; and each was summed to create a composite variable assessing comfort and likelihood of using driving alternatives. However, these two measures were not retained in the final regression model due to large p-values.

2.2.3. Control variables

Demographic variables such as age, gender, race, education, relationship status, and income were controlled for in the analyses. Age and gender were assessed with these open-ended questions: What is your current age? and What is your gender? Respondents could select all race options that applied to them, including White/Caucasian, Black/African American, and Other. Education was measured by asking: What is the highest grade of school or year of college you completed? There were seven original categories that were recoded to high school degree or less, some college, college degree, and some graduate school or higher, to eliminate categories with few respondents. Respondents were also asked about their current relationship status, and they could answer single (never married), married/domestic partnership, divorced/separated, or widowed. To reduce low cell counts, eight original yearly household income categories were combined into these four final groups: less than $25,000, $25,000-$49,999, $50,000-$99,999, and $100,000 and above. Finally, area type was assessed by asking: How would you describe the area where you live? with choices of urban (city), rural, and suburban. Among the control variables, relationship status, education, and area type were not retained in the final regression model due to large p-values.

2.3. Data management and analysis

SAS v9.4 (SAS Institute Inc., 2016) was used to manage and analyze the data. Descriptive statistics were calculated for all variables to check for problems and out-of-range errors, and to evaluate variable distributions. Bivariate analyses were conducted between each predictor and the outcome to assess unadjusted relationships and facilitate regression model building. Multiple linear regression techniques were used to assess the relationships between the predictors and driving retirement planning (outcome variable). Finally, given the well-established differences in driving reduction/cessation by gender, the potential moderating effect of gender on the relationships between planning and each of the other predictors of interest was assessed. During the model building process, regression diagnostics and assumptions were evaluated, including residual plot examination, multicollinearity, outliers, leverage, and influence.

3. Results

3.1. Participants

Among the 560 older adults who were current drivers, nine participants were identified as influential outliers (using Cook’s distance) and excluded after assessing regression model diagnostics, resulting in a final analytic sample of 551. Demographic information is shown in Table 1. The average age was 74.3 years, with a range from 65 to 92. About 83% of the participants were women, and a majority (75%) identified as Black. The sample was highly educated, with about 17% reporting high school education or less, about one-third reporting some college, 15% with a college degree, and the remaining one-third with at least some graduate education. Regarding relationship status, a small number of respondents (6.7%) were never married, while 34%, 32%, and 28% were married/partnered, divorced/separated, and widowed, respectively. Almost one-third of respondents reported a yearly household income less than $25,000, with the largest group between $25,000-$49,000 (39%), about 22% between $50,000-$99,999, and 7.5% over $100,000.

Table 1.

Descriptive statistics for sample demographics (n=551)

Characteristic Mean (SD) or % (n)
Age 74.34 (5.57)
Gender
 Male 16.89% (90)
 Female 83.11% (443)
Race
 White 20.18% (111)
 Black 75.45% (415)
 Other 4.36% (24)
Education
 High school degree or less 17.06% (94)
 Some college 33.21% (183)
 College degree 15.06% (83)
 Some graduate school or higher 34.66% (191)
Relationship Status
 Single/Never married 6.67% (36)
 Married/Domestic partnership 34.07% (184)
 Divorced/Separated 31.67% (171)
 Widowed 27.59% (149)
Income
 <$25,000 31.56% (160)
 $25,000–$49,999 39.25% (199)
 $50,000–$99,999 21.70% (110)
 ≥$100,000 7.50% (38)
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Note: SD=standard deviation; %=percentage; n=number

3.2. Descriptive and Bivariate Results

The average score for driving retirement planning was 6.56 (SD=2.60; recall that the possible range was 3–15 with a higher score representing more planning). Drivers in our sample reported very low driving stress (M=4.10, SD=1.03), medium-to-high driving enjoyment (M=3.87, SD=1.13), and were confident drivers on average (M=4.58, SD=0.68). Few reported having experienced a concerning driving event during the past year with most indicating either zero or one (M=0.47, SD=1.11). In terms of whether driving alternatives could meet their post-driving needs, the mean score was 16.60 (SD=5.79; possible range from 8–40).

Bivariate analyses between the outcome and the control variables revealed that the correlation between planning and age was not significant, but an independent samples t-test showed that women planned significantly more than men (t(531)=2.91, p<0.01). ANOVA results showed significant relationships between planning with race (p=0.01) and income (p<0.05). Respondents who identified as Black planned more (M=6.72) than White respondents (M=5.91), and those with a lower income (< $25,000) planned more (M=7.15) than participants with an income between $25,000-$49,999 (M=6.43). None of the other income groups differed significantly from one another, and education and marital status were not significantly associated with planning in the bivariate context.

Associations between planning and the predictors of interest also revealed several statistically significant relationships. Weak negative correlations were observed between planning with driving stress (r=−0.15, p<0.001) and driving confidence (r=−0.15, p<0.001), while a weak positive correlation was observed between planning and concerning driving events (r=0.12, p<0.01). Each of the variables representing driving alternatives were also significantly related to planning in the bivariate context (meeting future needs: r=0.19, p<0.001, comfort: r=0.09, p<0.05, and likelihood: r=0.12, p<0.01). Planning was not statistically significantly related to driving enjoyment.

3.3. Multiple Regression Results

Results from the final multiple linear regression model are shown in Table 2. Early models included all predictors of interest and control variables. Several variables had very large p-values and were not retained in the final model, as noted earlier, while some non-significant variables were retained if they were part of one of the hypotheses (e.g., enjoyment) or may be of general interest to readers (e.g., age). Race, income, driving stress, driving confidence, and assessment of how well driving alternatives could meet one’s future transport needs were all independently associated with future transportation planning.

Table 2.

Regression results for factors associated with driving retirement planning

Variable β t-value p-value
Age −0.01 −0.35 n.s.
Gender (ref=female)
 Male −0.58 −1.80 0.073
Race (ref=Black)
 White −0.72 −2.30 0.022
 Other −0.25 −0.41 n.s.
Income (ref<$25,000)
 $25,000–$49,999 −0.70 −2.49 0.013
 $50,000–$99,999 −0.65 −1.94 0.053
 ≥$100,000 −0.76 −1.60 n.s.
Driving stress (higher=less) −0.33 −2.43 0.015
Driving enjoyment 0.16 1.33 n.s.
Driving confidence (higher=more) −0.45 −2.34 0.020
Concerning driving events 0.13 1.26 n.s.
Driving alternatives 0.09 4.40 <0.001
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Note: n.s.=not significant; model R2=0.13

Age was not significantly associated with planning for driving retirement. Although gender only approached significance, women may engage in more transportation planning than men (β=−0.58 for men, p=0.073). Older drivers who identified as Black reported significantly more planning than White respondents (β=−0.72 for White, p=0.022). Participants with incomes between $25,000-$49,000 planned significantly less than those with incomes below $25,000 (β=−0.70, p=0.013), echoing the bivariate results. Participants with incomes between $50,000-$99,999 also planned less than those with incomes below $25,000, but that result only approached significance (β=−0.65, p=0.053).

Regression results for predictors of interest suggested that older drivers’ transportation planning was 0.33 lower for every one-point increase in perceived driving stress (reversed five-point scale where a higher value represented less driving stress), after controlling for other variables in the model. In other words, more driving stress was associated with increased planning. For each one-point increase in driving confidence, planning for driving retirement decreased by 0.45 points (p=0.020). Each one-unit increase in the scale that assessed how well other transport modes could meet one’s post-driving needs was associated with an increase of 0.09 in driving retirement planning (p<0.001). Recall that this scale had a possible range of 32 points, and a higher value represented a more positive view of alternative transportation modes. This factor also explained more of the variance in the outcome than any of the other factors. Level of driving enjoyment and the number of concerning driving events experienced during the past year were not significant. The moderating effect of gender on the relationship between planning and each of the predictors of interest was also assessed, but no statistically significant interactions were observed.

4. Discussion

The overall goal of this study was to examine the association between planning for driving retirement with a broad range of emotional and practical driving factors. We hypothesized that more perceived driving stress, concerning driving events, and openness to driving alternatives would be associated with more planning (H1), and that greater perceived driving enjoyment and confidence would be related to less planning (H2). Both hypotheses were partially supported, with significant relationships observed between planning for driving retirement and driving stress, confidence, and driving alternatives, all in the expected directions.

As predicted, more driving stress was related to greater planning. This relationship is reasonable, in that those who find driving stressful may have begun to recognize the likelihood of their impending driving retirement. Previous qualitative research supports this idea, and suggests that drivers who are generally stressed/anxious drivers avoid difficult driving situations and may even give up driving (Gwyther & Holland, 2012). Others have found that reporting driving stress in specific situations (e.g., driving at night, in inclement weather, or when fatigued) is related to driving cessation (Hakamies-Blomqvist & Wahlström, 1998; Hassan et al., 2017; Meng & Siren, 2015), and have also noted that women often report greater levels of driving stress (Meng & Siren, 2015). Interestingly, the relationship with gender does not seem to extend into planning behavior. Gender only approached significance in the current study, but did not significantly moderate the relationship between driving stress and planning.

Previous research has illustrated the importance of driving confidence in decisions about driving reduction and cessation (Hassan et al., 2017; Oxley et al., 2010). The current study found that lower driving confidence was associated with more planning, which is a logical extension of that previous work. Those who remain confident in their driving ability may be unlikely to recognize driving reduction and cessation as inevitabilities or may see them as distal events. Driving reduction/cessation as a response to a decline in driving ability is likely to be mediated by a loss of driving confidence (Hassan et al., 2017; Rudman et al., 2006). As driving difficulties grow and confidence declines, planning for driving retirement may become more likely.

As expected, respondents who viewed driving alternatives more positively were more likely to have planned for driving retirement. However, the current study design could not assess causality or the direction of causality. For example, those who feel more open about how well driving alternatives can meet their needs may view driving reduction and cessation (and planning for these) as less stressful, because they believe they will be able to maintain lifelong mobility. Alternatively, those who have engaged in the planning process may learn more about different modes of transport, which could engender a more positive viewpoint on driving alternatives. More research is needed to clarify causality and the causal direction.

Driving enjoyment and the number of concerning driving events experienced during the past year were not significantly related to planning. Given that driving enjoyment is a positive reaction, and the vast majority of driving is for a specific utilitarian purpose (McGuckin & Fucci, 2018), enjoyment may not be a strong enough factor to prompt a change in planning. It was surprising that recent concerning driving events was not a significant predictor of planning in the multivariable context (it was significant in bivariate analyses). To explore this relationship further, we fit new models that excluded driving confidence and enjoyment to ensure that these were not masking the effect of concerning events. This factor remained non-significant in the new models, and may be due to low variability in this variable within the current sample (M=0.47 on a 0–11 scale).

Age was not significantly related to planning in either the bivariate or regression analyses. Women planned significantly more than men, but this relationship only approached significance once other factors were accounted for. There is some evidence that gender could serve as a proxy for factors like driving confidence/comfort in older driver research (Molnar et al., 2018), which could explain the difference between unadjusted and adjusted findings. Although gender is a well-established factor that affects driving reduction and cessation (Dickerson et al., 2019; Vivoda et al., 2020), more research is needed to understand whether it affects planning.

Respondents who identified as Black planned for driving retirement more than White respondents. Previous studies have found that identification in a racial-ethnic minority group is associated with a greater likelihood of driving reduction and cessation (Choi et al., 2012; Dugan & Lee, 2013; Vivoda et al., 2020). The current results suggest that this group, in preparation for driving reduction and cessation, may engage in driving retirement planning to help mitigate mobility loss.

Participants in the lowest income group planned more than those in the middle groups. Socioeconomic status has also been linked in previous research to a greater risk of driving reduction and cessation (Dit Asse et al., 2014; Dugan & Lee, 2013; Vivoda et al., 2020), so more planning within this group is justified. Given differences in the transportation environment between urban, suburban, and rural environments, and the connection between these factors and driving reduction/cessation (Vivoda et al., 2017), it was surprising that this factor was not significant in the final regression model. It is possible that area type became conflated with race and/or income in the current sample. As noted earlier, respondents were drawn from two registries, one located in an urban area and one suburban. Respondent race and income tended to diverge by area type, driven by the differences in the two registry locations.

4.1. Implications

Results from this study suggest that more planning for driving retirement is warranted. Findings also indicate that those most at risk for driving reduction and cessation plan more than their counterparts (which is a positive finding), but planning remains at a relatively low level. The average planning score was 6.56 on the scale that combined three questions (roughly equivalent to 2.19 on the original five-point scales – below the midpoint), and one item focused on intentions only.

Planning for other aging issues is common, however, and may offer insights into how driving retirement planning can be increased. Americans routinely plan for job retirement, and facilitators come from a variety of sources. Much of that planning is institutionalized within the workplace; individuals also make personal decisions to enroll and contribute to retirement plans; families play an important role; and there are societal expectations about job retirement (Adams & Beehr, 2003; Helman et al., 2015; Matthews & Fisher, 2013). Influences from a variety of levels may be one reason why job retirement planning is common. To increase planning for driving retirement, a similar approach – with discussion, interventions, and influences from multiple levels – may be the most effective strategy. For example, communication and planning for driving reduction and cessation can first occur within the older adult’s family. Adult children often express concerns that older family members may not voluntarily stop driving and take a passive approach to the issue (e.g., refusing to ride with the individual), rather than an active role by initiating planning conversations (Connell et al., 2012). Discussions about planning for driving retirement can also be initiated by medical professionals, who are in the best position to understand someone’s physical and mental well-being (Eby et al., 2019). Physicians report that they are not confident making decisions about fitness to drive (Eby et al., 2019), but initiating the possibility to plan for a non-driving future should be less threatening and a good first step.

Information and initiation of planning discussions could also take place at state licensing agencies with provision of informational materials. Although not sufficient for behavior change, education is a necessary component (Arlinghaus & Johnston, 2017). Finally, planning for driving reduction and cessation would also likely increase with expectational shifts regarding this behavior at the community and societal levels, including planning resources or incentives.

4.2. Future research

With the sample design used in the current study, it was difficult to separate the effects of race, income, and area type, as aspects of these factors may have been conflated. Future research could explore how differences in these factors affect driving retirement planning, especially how availability of driving alternatives in the transportation environment may play a role. The relationship between planning for driving retirement and additional factors not measured in the current study could also be assessed. For example, concerns about driving safety, level of driving comfort, or the effect of different types of stressful events on driving retirement planning would make for interesting future research questions. This study also found that respondents who identified as Black and those with the lowest incomes engaged in more planning behaviors. Future qualitative research could explore the underlying reasons for some of those observed differences.

Future research could also clarify whether mediators (or moderators) play a role in planning for driving reduction/cessation. The current study found that driving stress and confidence were related to planning for driving retirement, but the exact nature of the relationship pathways is unclear. For example, psychological factors like driving stress and confidence may lead to driving reduction, which could trigger planning behavior, and then eventual cessation.

4.3. Limitations and strengths

There were some limitations to this study that must be acknowledged. Given the methodological design, it was not possible to determine causality, only associations. Likewise, the direction of causality could not be established. The fact that the data were collected in only one region of a Midwest state limits generalizability, and the self-reported nature of this study could have skewed results toward social desirability if respondents consider planning as prudent. Participants were drawn from two research registries, and those who agree to be included in a registry may be different than the general public. They may be more likely than others to plan, for example, but that would result in overestimates of planning, which were fairly low in the current study.

The demographics of the sample could be considered both a limitation and a strength. Over 80% of the respondents were women, and about 75% identified their race as Black/African American. Those factors are limitations in terms of generalizability, but strengths in that both groups are underrepresented in most research, but particularly in transportation research (Babulal et al., 2018). These demographic groups are vitally important to study in aging and transportation research. As age increases, women comprise a larger proportion of the population (US Census Bureau, 2020), and projections suggest by 2044 current racial minorities will comprise more than 50% of the U.S. population (Colby & Ortman, 2015). In addition, the higher level of planning observed among Black respondents suggests that overall transportation planning may be even lower in a sample that more closely matched the U.S. population.

Finally, most studies have only focused on anticipated use of alternative transportation options (see e.g., Vivoda et al., 2018), expectations regarding driving reduction and cessation (Babulal et al., 2019), and planning behaviors and beliefs (Harmon et al., 2018). The current study compliments previous research by exploring the factors that predict driving retirement planning among older adults.

4.4. Conclusion

Although most older adults will eventually reduce and stop driving, few adequately plan for these eventualities. A positive finding of this study, however, is that many of the groups that are known to be at increased risk for driving reduction and cessation (e.g., racial minorities, low SES, higher driving stress, lower driving confidence) seem to plan more than their counterparts. Finally, negative psychological factors (e.g., driving stress, lack of driving confidence) may play a bigger role in driving retirement planning than positive factors (e.g., driving enjoyment).

Highlights:

  • More driving retirement planning was related to higher driving stress

  • More driving retirement planning was related to less driving confidence

  • A more positive view of driving alternatives predicted increased planning

  • Lower income and Black (compared to White) race were related to more planning

  • Driving enjoyment and concerning driving events were not related to planning

Funding:

University of Michigan Research Incentive funds (of Dr. Brian Zikmund-Fisher) covered participant incentives for the project. Ganesh M. Babulal was supported by National Institutes of Health/National Institute on Aging (R01AG067428, R01AG056466, R03AG055482).

Footnotes

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