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. Author manuscript; available in PMC: 2024 Apr 1.
Published in final edited form as: J Appl Gerontol. 2023 Dec 12;43(4):454–464. doi: 10.1177/07334648231211742

Relationship of Decisional Conflict About Driving Habits Between Older Adult Drivers and Their Family Members and Close Friends

Nicole R Fowler 1,2,3, Rachel L Johnson 4, Ryan Peterson 4, Matthew W Schroeder 5, Faris Omeragic 6, Carolyn DiGuiseppi 7, S Duke Han 8, Linda Hill 9, Marian E Betz 6,10
PMCID: PMC10922263  NIHMSID: NIHMS1938727  PMID: 38087851

Abstract

This study examines the relationship of decisional conflict about driving habits between older adult drivers (≥70 years old) and their family members and close friends. This secondary analysis utilizes data originating from a multi-site randomized controlled trial assessing the effect of a driving decision aid (DDA) intervention. Decisional conflict about stopping or changing driving habits for drivers was measured with the Decisional Conflict Scale (DCS). Dyadic associations between drivers’ and study partners’ (SPs’) DCS scores were analyzed using an actor-partner interdependence model. Among 228 driver-SP dyads, Dyadic DCS was correlated at baseline (r = .18, p < .01), and pre-intervention DCS was associated with post-intervention DCS (p < .001 for SPs [β = .73] and drivers [β = .73]). Drivers’ baseline DCS and SPs’ post-intervention DCS were slighly correlated (β = .10; p = .036). Higher decisional conflict about driving among older drivers is frequently shared by their SPs. Shared decisional conflict may persist beyond intervening to support decision-making about driving cessation.

Keywords: decision making, driving, older adults, family

Introduction

There are 48 million drivers aged ≥65 years in the United States (Center for Disease Control, 2022). The number of older adult drivers is projected to increase to one in five drivers by 2030 (Lyman et al., 2002). Compared to younger drivers, older drivers experience additional risks that come with aging, such as greater functional and cognitive impairments. (Cicchino & McCartt, 2015; Dobbs et al., 2005; Insurance Institute for Highway Safety (IIHS)-Highway Loss Data Institute (HLDI), 2022). Further, crash fatality incidence increases significantly in older adults aged ≥75 years old (Cicchino, 2015). Given that it has been estimated that people live 7–10 years beyond the time that they stop driving, more conversations about changing driving habits and driving cessation are occurring and will continue (Foley et al., 2002).

The decision for older adults to alter driving habits or to stop driving altogether is complex. It involves health and psychological outcomes, balancing safety with independence, and frequently includes the involvement of family members (Chihuri et al., 2016; Foley et al., 2002; Harrison & Ragland, 2003). For example, driving cessation is associated with less access to health care, increased social isolation, depression, and higher mortality risk (Edwards et al., 2009; Fonda et al., 2001; Marottoli et al., 1993, 2000; Ragland et al., 2005). Driving is often viewed as an extension of independence, making the decision to stop driving emotional (Goins et al., 2015), with concerns about placing burden on family members and friends (King et al., 2011). Family members and close friends play an integral role in the decision-making process for the older adult driver’s decision, though these conversations can be difficult to have (Feng & Meuleners, 2020; Kostyniuk & Shope, 2003; Puvanachandra et al., 2008) and are not part of routine primary care for older adults. Therefore, there is a critical need for interventions that support driving decisions and preferences among older adult drivers and their social network of family members and friends.

Data for this analysis uses data from the Advancing Understanding of Transportation Options (AUTO) study, a multi-site, two-armed randomized controlled trial testing the efficacy of the driving decision aid ((Healthwise, 2016) for older adult drivers and their study partner, which included family members and friends (Betz et al., 2021, 2022). We found that the decision aid decreased decisional conflict and increased driving knowledge in older adult drivers (Betz et al., 2022). Prior analyses of the impact of the decision aid did not evaluate its influence on the study partner’s decisional conflict or how their decisional conflict was associated with the driver’s. The objective of this secondary analysis is to analyze the concordance of decisional conflict among older adult drivers and their study partners when prompted with the decision of the older adult driver to stop or continue driving. An actor-partner interdependence model (APIM) analysis was used to understand the interconnected pathways in which one dyad member’s emotions, cognition, and behaviors about driving influence the emotions, cognition, and behaviors of the other dyad member (Cook & Kenny, 2005; Kelley et al., 2003; Kelley & Thibaut, 1978).

Method

Study Design and Participants

Data for this secondary analysis were from the AUTO trial collected from older adult drivers and their study partners. A complete description of the study protocol and methods has been previously published (Betz et al., 2021). Eligible drivers were adults aged ≥70 years without severe cognitive impairment (5-minute Montreal Cognitive Assessment (MoCA) ≥21). Eligible study partners were family members or close friends aged ≥18 with no severe cognitive impairment (5-minute MoCA ≥21) that drivers identified as someone who may be involved in their driving decisionmaking. In order to target drivers likely to consider driving cessation, drivers were also required to have ≥1 pre-existing medical diagnosis associated with reduced driving ability and increased driving risk (Betz et al., 2021). This list of comorbidities consisted of medical conditions indicated by previous literature and expert clinical opinions to be associated with possible driving impairment and possible with driving cessation (e.g., cognitive impairment and macular degeneration) (Falkenstein et al., 2020; Nguyen et al., 2023; Snellgrove, 2005). Diagnoses of the drivers were confirmed by electronic medical records and verbal confirmation at the time of eligibility screening. Drivers had to speak English, have access to a telephone for follow-up interviews, have a valid driver’s license from their state of residence, drive ≥1 time per week, and report no major changes in health, vision, or hearing since their last license renewal and that the Department of Motor Vehicles in their state would have no major concern about their driving. All patients were recruited from lists of active primary care practices affiliated with universities in San Diego, California (University of California San Diego; UCSD), Denver, Colorado (Colorado University; CU), or Indianapolis, Indiana (Indiana University; IU). Additional inclusion criterion for study partners were that they spoke English and had access to a telephone for follow-up interviews. Drivers and study partners were excluded if they were previously or actively enrolled in another LongROAD study (Li et al., 2017), were in legal custody or institutionalized, or had severe cognitive impairment (5-minute MoCA <21). Not all drivers (N = 301) in the parent trial were required to have a study partner enroll, so this APIM analysis included only dyads of drivers with an enrolled study partner (N = 228). Enrollment took place between December 2019 and June 2021. Informed consent was obtained from all participants at the baseline study encounter. This study was approved by the institutional review boards of University of California San Diego, University of Colorado, and Indiana University. The AUTO trial is registered on cliniclialtrials.gov (Identifier NCT04141891).

Measures

Decisional Conflict.

Driver and study partner decisional conflict was measured using the Decisional Conflict Scale (DCS) (O’Connor, 1995). The DCS is a 16-item scale assessing the decision quality, internal conflict, and values concordance towards a medical decision (O’Connor, 1993; O’Connor, 1995). For the AUTO study, we modified the DCS to assess the decision quality of study partner’s decision whether the driver should stop or continue driving (Betz et al., 2021, 2022). The DCS has a test-retest correlation of .81, with internal consistency ranging between .78 and .92 (O’Connor, 1995).

Cognitive Status.

Driver cognitive status was measured using the five-minute MoCA (Wong et al., 2015) and the published cut score of ≤21 was used to indicate possible cognitive impairment. The five-minute MoCA is both a valid and reliable measure of cognitive function, as it is correlated with the MoCA (r = .87, p < .001) and has high test-retest reliability.

Decision Making.

Everyday decision-making in older adults was measured using the Short Portable Assessment of Capacity for Everyday Decision-making (SPACED) scale (Lai & Karlawish, 2007). The SPACED scale is a semi-structured interview assessing four domains of everyday decision-making: understanding, appreciation, comparative reasoning, and consequential reasoning. The SPACED scale has good reliability with Cronbach’s alpha coefficients exceeding .84 (Lai et al., 2008). The four subsections each rated as “0 = inadequate,”“1 = marginal,” and “2 = adequate.” Total scores are summed and can range from 0 to 8.

Sociodemographic Characteristics.

We collected both driver and study partner’s sociodemographic information including age, gender, race, ethnicity, self-reported income, highest grade completed, marital status, current living arrangements, and residence accommodations (e.g., living alone, with a spouse/partner). We also collected the study partners’ relationship to the driver. In addition, we collected information about study partner dependence on the driver for any transportation needs of their own and if they depend on the driver for errands that require a car. Driver and study partner neighborhood socioeconomic disadvantage was calculated using the area deprivation index (ADI) (Kind & Buckingham, 2018; Singh, 2003; Singh & Siahpush, 2002). The ADI is a compounded score of 17 measures of income, employment, education, and housing information collected from 2009 to 2013 data from a national study, and calculated for each neighborhood in the United States (United States Census Bureau, 2022). Total scores range between 1 and 100, with higher scores indicating greater socioeconomic disadvantage.

Analysis.

Driver and study partner demographics and outcomes at each timepoint were summarized with frequencies and percentages, or means and standard deviations, as appropriate. Concordance was measured between driver and study partner outcomes at each time point and by intervention group with Lin’s concordance correlation coefficients for continuous variables and Cohen’s kappa statistics for binary variables. To model the relationship between driver and study partner DCS before and after the DDA or control arm, an APIM (Cook & Kenny, 2005) was fit using a structural equation modeling approach with the lavaan package in R (Rosseel, 2012). Covariances were estimated between driver and study partner DCS at each time point, and the model was estimated with full information maximum likelihood to allow for participants with missing DCS data at one time point to still be included in the analysis. While this analysis uses pre- and post-intervention data from a randomized controlled trial, the intervention was not a focus of this secondary analysis. Therefore, a multiple groups model was considered that simultaneously fit the model within the control and intervention arm to determine if relationships differed by study arm; however, based on Akaike information criterion (AIC) and a likelihood ratio test, this model did not fit the data as well as the model ignoring study arm and is therefore not presented. Co-variates were not included in the model as there were no demographic or dyad characteristic differences found between control and intervention randomization groups. In exploratory analyses, two multiple groups models were considered with two groups: (1) dyads where study partners depend on the driver for at least some transportation versus dyads where study partner’s do not depend on the driver for any transportation and (2) study partners who were spouses of the driver versus study partners who were not spouses (adult children, sibling, friend, and other family member). The relationship of study partner to driver was collapsed due to some small categories (e.g., 3.5% of study partners were siblings); additional combinations of this variable were tested and spouse versus non-spouse was the best fitting model by AIC. All statistical analyses were conducted with R version 4.2.0. (R Core Team, 2022).

Results

Sociodemographic Characteristics

Driver and study partner sociodemographic characteristics are summarized in Table 1. Drivers and study partners were recruited in approximately equal numbers across the three study sites. Drivers were on average 77.1 years old (standard deviation [SD] 5.1 years), 50.0% female, 94.7% white, 98.7% non-Hispanic, and 66.1% married/ partnered. Most lived with a spouse/partner (62.8%) in a private home or apartment (92.1%) and in areas with relatively low deprivation (ADI mean 25.0, SD 23.3). Study partners were 66.1 years old on average (SD 13.9), 65.8% female, 92.1% white, 95.6% non-Hispanic, and 76.3% married/partnered. Study partners were most commonly the spouse/partner to the driver (54.6%), followed by their adult child (21.1%), friend (14.1%), or sibling or other family member (10.1%). Most study partners communicated with drivers every day (72.8%) and saw the driver in-person every day (61.4%). Over half had discussed driving safety with the driver (53.1%) and many depended on the driver for at least some transportation (39.5%).

Table 1.

Driver and study partner sociodemographic characteristics.

Demographics Drivers (N = 228) Study partners (N = 228)
Site, n (%)
 Site 1 – Blinded for Review 74 (32.5%) 74 (32.5%)
 Site 2 – Blinded for Review 73 (32.0%) 73 (32.0%)
 Site 3 – Blinded for Review 81 (35.5%) 81 (35.5%)
Age (years), mean (SD) 77.1 (5.1) 66.1 (13.9)
Female, n (%) 114 (50.0%) 150 (65.8%)
Race, n (%)
White 215 (94.7%) 209 (92.1%)
Black or African American 6 (2.6%) 8 (3.5%)
Asian 2 (0.9%) 3 (1.3%)
American Indian or Alaska Native/Native Hawaiian or Other Pacific Islander 0 (0.0%) 2 (0.9%)
Other 4 (1.8%) 5 (2.2%)
Ethnicity, n (%)
Hispanic 3 (1.3%) 10 (4.4%)
Non-Hispanic 222 (98.7%) 217 (95.6%)
Highest grade completed, n (%)
Less than high school (HS) or HS graduate 19 (8.3%) 13 (5.7%)
Some college or vocational/technical school 50 (21.9%) 42 (18.4%)
College graduate 57 (25.0%) 78 (34.2%)
Post-graduate degree (masters, doctorate) 102 (44.7%) 92 (40.4%)
Employment status, n (%)
Employed full time 14 (6.1%) 51 (22.5%)
Employed part time 22 (9.6%) 22 (9.7%)
No paid employment 5 (2.2%) 10 (4.4%)
Retired or unable to work because of physical disability 187 (82.0%) 140 (61.7%)
Other 0 (0.0%) 4 (1.8%)
Current marital status, n (%)
Married/partnered 150 (66.1%) 174 (76.3%)
Widowed/divorced/never married 77 (33.9%) 54 (23.7%)
Currently living situation a, n (%)
Alone 67 (29.6%) 42 (18.7%)
With a spouse or partner 142 (62.8%) 165 (73.3%)
With a friend/children/other family member(s) 22 (9.7%) 39 (17.3%)
Residence, n (%)
Private home or apartment 210 (92.1%) 218 (96.0%)
Assisted living/retirement community/senior community 18 (7.9%) 9 (4.0%)
Area deprivation index (ADI), mean (SD) 25.0 (23.3) 25.5 (23.8)
Relationship to Driver, n (%)
Spouse -- 124 (54.6%)
Adult child -- 48 (21.1%)
Sibling/Other family member -- 23 (10.1%)
Friend -- 32 (14.1%)
How often SP communicates with driver, n (%)
Less than once a month/2–3 times a month -- 11 (4.8%)
Once a week -- 9 (3.9%)
Multiple times a week -- 42 (18.4%)
Every day -- 166 (72.8%)
How often SP sees driver in person, n (%)
Less than once a month -- 18 (7.9%)
2–3 times a month -- 28 (12.3%)
Once a week -- 21 (9.2%)
Multiple times a week -- 21 (9.2%)
Every day -- 140 (61.4%)
Has discussed driving safety with driver, n (%) -- 121 (53.1%)
Depends on driver for own transportation, n (%) -- 75 (32.9%)
Depends on driver for children’s transportation, n (%) -- 20 (8.8%)
Depends on driver for errands that require a car, n (%) -- 50 (21.9%)
Does not depend on driver for transportation needs, n (%) -- 138 (60.5%)
Able to provide rides for driver, n (%) -- 205 (89.9%)
If able, willing to provide rides for driver -- 205 (100.0%)
Short Portable Assessment of Capacity for Everyday Decision-making (SPACED) score, mean (SD) 7.7 (0.8) --
5-minute Montreal Cognitive Assessment (MoCA) score, mean (SD) 25.6 (2.2) 26.1 (2.2)
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Note. Missingness was <1.5% in all variables and therefore missing data is not shown in the table.

a

Participants may select >1 option, so percentages do not sum to 100%.

Concordance Between Driver and Study Partner

Driver and study partner outcomes are summarized in Table 2. Differences in means between drivers and study partners were tested and concordances between dyad responses are also presented. Overall, decisional conflict was low at each time point with 65.0% of drivers and 58.2% of study partners with a DCS score <25 before the intervention and 71.7% of drivers and 63.3% of study partners with DCS <25 post-intervention. Similarly, most drivers and study partners reported they are strongly leaning towards the driver continuing to drive, although this percentage was lower in study partners than in drivers before (64.5% in study partners, 80.7% in drivers) and after the intervention (55.5% in study partners, 77.1% in drivers). Concordance was positive but low for most outcomes before and after the intervention, suggesting that drivers and study partners only slightly or weakly agree on the level of decisional conflict and whether the driver should continue driving.

Table 2.

Scale variables measured on both driver and study partner at pre- and post-intervention are summarized. Differences between driver and study partner responses are tested with paired t-tests for continuous variables and McNemar’s tests for binary variables. Concordance (95% CI) is presented with Lin’s concordance correlation coefficient to measure agreement in continuous variables and Cohen’s kappa statistic for binary variables; it is not presented for variables with >2 levels.

N (%) or mean (SD) Driver (N = 228) Study partner (N = 228) p value Concordance measure (95% CI)
Pre-intervention
Decisional conflict scale (DCS) score a 18.5 (12.3) 20.5 (16.8) 0.087 0.17 (0.04, 0.29)
DCS < 25 143 (65.0%) 131 (58.2%) 0.122 0.09 (−0.04, 0.22)
 DCS subscale: informed 18.3 (15.7) 21.2 (20.4) 0.088 0.02 (−0.10, 0.15)
 DCS subscale: values clarity 19.0 (15.9) 18.8 (19.5) 0.926 0.10 (−0.03, 0.23)
 DCS subscale: support 16.8 (14.9) 17.5 (16.9) 0.643 0.08 (−0.05, 0.21)
 DCS subscale: uncertainty 23.5 (19.7) 24.7 (23.1) 0.530 0.19 (0.06, 0.31)
 DCS subscale: effective decision 15.9 (13.2) 20.0 (18.2) 0.003 0.14 (0.02, 0.26)
When you think about [the driver] driving, which way are you leaning? 6.6 (0.9) 6.3 (1.3) <0.001 0.19 (0.07, 0.30)
 1 (Leaning Toward Stopping Driving) 0 (0.0%) 3 (1.3%)
 2 2 (0.9%) 2 (0.9%)
 3 1 (0.4%) 7 (3.1%)
 4 (Undecided) 9 (3.9%) 10 (4.4%)
 5 9 (3.9%) 18 (7.9%)
 6 23 (10.1%) 41 (18.0%)
 7 (Leaning Toward Continuing Driving) 184 (80.7%) 147 (64.5%)
Post-intervention
Decisional conflict scale (DCS) score a 14.2 (13.4) 16.7 (14.9) 0.024 0.22 (0.09, 0.34)
DCS < 25 162 (71.7%) 143 (63.3%) 0.050 0.05 (−0.08, 0.18)
 DCS subscale: informed 11.7 (15.0) 14.3 (15.7) 0.051 0.14 (0.02, 0.27)
 DCS subscale: values clarity 13.1 (15.1) 14.5 (16.0) 0.278 0.18 (0.06, 0.31)
 DCS subscale: support 12.9 (15.6) 15.1 (16.2) 0.131 0.11 (−0.02, 0.24)
 DCS subscale: uncertainty 18.6 (18.7) 21.8 (21.6) 0.061 0.22 (0.09, 0.33)
 DCS subscale: effective decision 14.2 (14.3) 17.4 (16.2) 0.013 0.22 (0.09, 0.33)
When you think about [the driver] driving, which way are you leaning? b 6.7 (0.7) 6.2 (1.2) <0.001 0.15 (0.05, 0.25)
 1 (Leaning Toward Stopping Driving) 0 (0.0%) 2 (0.9%)
 2 0 (0.0%) 2 (0.9%)
 3 1 (0.4%) 8 (3.5%)
 4 (Undecided) 5 (2.2%) 10 (4.4%)
 5 6 (2.6%) 22 (9.7%)
 6 40 (17.6%) 57 (25.1%)
 7 (Leaning Toward Continuing Driving) 175 (77.1%) 126 (55.5%)
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Note.

a

DCS scores could be calculated at pre-intervention for N = 220 drivers and N = 225 study partners and for N = 226 drivers and N = 226 study partners post-intervention. Total DCS scores could not be calculated for those missing any number of components of the 16 DCS questions.

b

The denominator for both drivers and study partners was N = 227 when asked which way they were leaning about driving post-intervention.

Actor-Partner Interdependence Model

The results of the APIM model with driver and study partner DCS pre- and post-intervention are summarized in Figure 1. Since a multiple groups model that accounted for randomization group (control vs. intervention) did not improve model fit over a model without randomization group, we fit the model in which all participants are included, and randomization group is not accounted for. Before the intervention, driver and study partner DCS were significantly correlated and had a positive relationship. In drivers, pre- and post-intervention DCS were strongly positively associated (driver “actor” effect), such that every one-point increase in pre-intervention DCS was associated with a post-intervention DCS that was .73 points higher on average (95% CI: .62, .84; p < .001). This relationship between pre- and post-intervention DCS was nearly identical in study partners (the study partner “actor” effect) (B = .73; 95% CI: .67, .80; p < .001).

Figure 1.

Figure 1.

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Actor-Partner Interdependence Model results with driver and study partner decision conflict pre- and post-decision aid intervention. (a) Spouses and (b) non-spouses.

The drivers’ (partner) effect on study partners is defined as the effect of the drivers’ pre-intervention DCS on the study partner’s post-intervention DCS. For every one-point higher the drivers’ DCS pre-intervention, study partner’s average DCS post-intervention was .10 points higher on average (95% CI: .01, .18; p = .036). This effect was significant and positive, but much smaller than the actor effect within either group, suggesting that higher levels of pre-intervention driver decisional conflict were associated with only slightly higher levels of study partner conflict after viewing the study materials.

The study partners’ (partner) effect on drivers is defined as the effect of the study partners’ pre-intervention DCS on the drivers’ post-intervention DCS. Study partners’ pre-intervention DCS was not significantly associated with drivers’ post-intervention DCS (B = .04; 95% CI: .04, .12; p = .354), suggesting no partner effect of study partner decisional conflict on drivers’ decisional conflict in this sample.

In our first exploratory model, the APIM was simultaneously fit in study partners who did and did not depend on the driver for transportation (results not shown). While inclusion of this variable did not improve fit over our primary model in Figure 1, we wanted to examine whether the magnitude of actor and partner effects would differ between these two groups. Actor effects within drivers and study partners were similar within both groups, and the partner effect of study partners on drivers was small and nonsignificant for both groups. While the partner effect of drivers on study partners in the group where they rely on drivers was larger than the partner effect in those who do not depend on drivers for any transportation, the effect was not significant in either group.

In another exploratory model, the APIM was simultaneously fit in comparing study partners/drivers who were spouses to those who were not spouses (adult child, sibling, friend, other family member; or “non-spouses”); this model fit significantly better than our primary model (chi-square difference test: p < .001) and had a lower AIC. We found similar actor effects in drivers and study partners in both spouses and non-spouses, as well as similar partner effects of study partners on drivers. The primary differences between the two were: (i) a significant correlation between pre-intervention DCS scores in drivers and study partners in spouses (r = .18, p = .002) but not in non-spouses (r = .11, p = .294), and (ii) a significant partner effect of driver’s pre-intervention DCS on study partner’s post-intervention DCS in spouses (B = .14, p = .022) but not in non-spouses (B = .04, p = .541). This suggests that the significant partner effect in our primary model (Figure 1) was driven by the relationship between drivers and study partners who were spouses, rather than across all drivers and study partners.

Discussion

Shared decision-making for older adult driving cessation is difficult. Our analyses attempt to describe the complexity of this decision by looking at the concordance of decisional conflict between older adults and their study partners (family members and close friends) about driving and if a driving decision aid (DDA) intervention impacts this concordance. Overall, concordance on dyadic decisional conflict was positively associated, but low both at pre-intervention and remained low after viewing the decision aid or control materials. At baseline, we found an association between driver and study partner decisional conflict before viewing the DDA or control arm materials. In addition, we found strong actor effects between pre- and post-intervention driver and study partner decisional conflict meaning that whatever decisional conflict each member of dyad felt before the DDA was consistent with their decisional conflict after reviewing the DDA. Regarding partner effects, we found that driver pre-intervention decisional conflict was significantly related to the study partners’ post-intervention decisional conflict, meaning that higher driver decisional conflict before the intervention was related to slightly higher study partner decisional conflict after the intervention. This supports previous models of decision-making about driving in that the appreciation of this decision impacts the family (Rudman et al., 2006). However, this was not the case for a study partner (partner) effect with driver post-intervention DCS. Furthermore, our exploratory analysis indicated that study partners’ dependence on the driver for transportation did not differ from our original APIM models results—showing that study partner dependence for transportation does not significantly change the interpretation of our analysis. However, the relationship between study partners and drivers does affect our interpretation of the original APIM model results: study partners and drivers who were spouses had results that were more highly correlated at baseline and had significant partner effects from drivers’ pre-intervention DCS scores to study partner’s post-intervention DCS scores, suggesting that there is a more highly connected relationship surrounding driving conflict between spouses than non-spouses in these dyads.

Though older adult driving cessation is an emotional decision (Goins et al., 2015), a large proportion of drivers and study partners in our study at pre- and post-intervention reported a lower scores on the DCS (<25). Indicating lower decisional conflict when making the decision whether the driver should stop or continue driving compared to other shared decisions such as advanced care-planning and end-oflife decisions (Garvelink et al., 2019). While this low decisional conflict was surprising, prior qualitative studies examining the perspectives of adult children on their older adult parent’s potential driving cessation provided some insights for this (Connor et al., 2021). The researchers found that a third of family members of drivers aged ≥75 years old discussed driving cessation with the driver, though only 15% mentioned that the driver had thought about it and had intentions on managing the transition towards driving cessation. Further, prior research also found that older drivers are likely to oversell their driving abilities (Freund et al., 2005; Marottoli & Richardson, 1998; Windsor et al., 2008; Wood et al., 2013). Likewise, older drivers in our sample favored continuing driving (pre- and post-intervention) so their low decisional conflict may be explained by their favorable bias toward their driving abilities or anticipation of cessation (Oxley et al., 2009). As such, this could explain why study partners exhibited a slightly lower score for than the drivers regarding the driver continuing driving. Our sense is that this result is driven from study partners being able to consider the drivers’ risks more objectively and that drivers are less likely to consider cessation as they are motivated to maintain the independence and self-sufficiency that driving gives them. Another thing to consider is that research supports that individuals endorsing a lower decisional conflict are more likely to follow through with their current decisions (O’Connor et al., 1998) and when decisions are more immediate, decisional conflict is likely to be higher (Garvelink et al., 2019). This means that drivers and study partners might be more likely to have high decisional conflict about driving decisions and be more comfortable with their decision for the driver to either stop or continue driving if the decision was more immediate or a result of an accident or change in health condition. Therefore, it may be beneficial to follow concordance for decisional conflict regarding older adult driving cessation from a longitudinal perspective. Collecting longitudinal data on driving cessation could provide insight into factors that may influence decisional conflict among older adult drivers and their study partners when considering whether the driver should stop or consider driving.

The most interesting finding from our analysis was the concordance of decisional conflict among the driver and the study partner. More specifically, decisional conflict was associated between the driver and study partner pre-intervention. When using an actor-partner interdependence model to unpack shared decisional conflict pre- and post-intervention, we found some intriguing results. Higher pre-intervention driver decisional conflict was related to slightly higher study partner decisional conflict after reviewing the driving decision aid or control arm materials. Meaning that drivers who had lower quality decisions pre-intervention (higher DCS) had study partners who were more likely to rate their decisions as lower quality post-intervention (higher DCS). In summary, the relationship between decisional conflict among both members of the dyad maintained significance throughout the intervention. These findings indicate that decisional conflict about stopping or continuing driving is likely to persist post-intervention. Therefore, it may be appropriate to promote further intervention modalities for drivers and study partners still experiencing decisional conflict.

When considering preferences for continuing driving, both drivers’ and SPs rate drivers favorably. This finding may not be generalizable to drivers at a higher risk for driving cessation. However, our study does add merit from a clinical standpoint for when clinicians should have driving discussions among older adults at-risk for driving cessation. Targeting drivers earlier when they are healthy could lead to conversations and decisions before there is an event or if the driver’s risk for their safety or the safety of others is too high. This is supported by literature indicating discussions of self-regulatory behaviors for safety around driving (Hassan et al., 2015) the benefits of advanced care planning for driving cessation (Liddle et al., 2013; Musselwhite & Shergold, 2013; Sanford et al., 2020; Scott et al., 2020; Sinnott et al., 2019; Windsor & Anstey, 2006). Older drivers are typically not prepared for driving cessation (Harmon et al., 2018; Kostyniuk & Shope, 2003), and navigating these conversations with family members are often complicated (Feng & Meuleners, 2020; Kostyniuk & Shope, 2003; Puvanachandra et al., 2008). Advanced planning initiatives could better prepare older adult drivers manage the benefits and harms of driving cessation and make these conversations with family members less challenging in the future.

Limitations

While we were able to fit the model in study partners who were spouses/partners compared to those who were not spouses/partners, a limitation with our study is that we had to collapse this variable into two groups because some of these groups were very small. Future research with larger sample sizes will provide better power to analyze these relationships in drivers’ friends, adult children, and other family members. Another limitation with our study is that our sample is mostly white, non-Hispanic, highly educated, English-speaking, and therefore not generalizable to the population. In addition, multiple studies have demonstrated that women and racial/ethnic minorities are more likely to cease driving in later life compared to their male and non-Hispanic white counterparts (Choi et al., 2012; Choi & Mezuk, 2013; Freeman et al., 2006; Mezuk & Rebok, 2008); therefore, studying decisional conflict regarding driving cessation in these populations should be further explored. Likewise, we had a healthy group of older adult drivers. Our sample may not be generalizable to older drivers at-risk for driving cessation.

Conclusion

Higher decisional conflict about driving among older drivers is frequently shared by their study partners. This shared decisional conflict may persist even after intervention to reduce decisional conflict. Better understanding this actor-partner relationship among older drivers and their family and close friends has the potential to improve the decisional conflict of millions of older adults as they consider stopping or continuing to drive.

What this paper adds

  • Concordance on decisional conflict between older drivers and their study partners was positive but low for most outcomes, suggesting that drivers and study partners only slightly agree on whether the older driver should continue driving.

  • Higher levels of driver decisional conflict at the beginning of the study were associated with only slightly higher levels of study partner decisional conflict after viewing the study materials.

  • Though dyad decisional conflict was positively associated, older adult drivers and their study partners’ decisional conflict was low.

Applications of study findings

  • Decisional conflict regarding older adult driving cessation may persist beyond viewing a driving decision aid or control materials—indicating a need for interventions to monitor long-term shared decision-making in older adult driving habits and cessation.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was supported by the National Institute of Aging at the National Institutes of Health (Grant Number R01 AG059613). This project was also supported by NIH/NCATS Colorado CTSA Grant Number UL1 TR002535.

Footnotes

Institutional Review Board

Colorado Multiple Institutional Review Board (COMIRB) Protocol Number: 19–0059.

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.

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