Decision Making Among Older Adults: the AUTO Study
Phase: NA
Conditions: Diabetic Retinopathy, Macular Degeneration, Glaucoma, Retinitis Pigmentosa, Vision Disorders, Acute Coronary Syndrome, Implantable Defibrillator User, Congestive Heart Failure, Hypertrophic Obstructive Cardiomyopathy, Orthostatic Hypotension, Syncope, Presyncope, Narcolepsy, Dementia, Multiple Sclerosis, Parkinson Disease, Brain Injuries, Spinal Cord Injuries, Stroke, Vertigo, Dizziness, Seizures, Substance Use, Insulin Dependent Diabetes Mellitus, Arthritis, Foot--Abnormalities, Chronic Obstructive Pulmonary Disease, Obstructive Sleep Apnea, End Stage Renal Disease, Sleep Apnea, Insomnia, Restless Legs Syndrome
Sponsor: University of Colorado, Denver

AUTO Study Protocol: Version Date: 12/22/21 
PI: Marian Betz, MD, MPH 
COMIRB No: 19-0059 
Protocol# 19-0059  
 
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Advancing Understanding of Transportation Options (AUTO) 
 
NCT04141891 
 
12/3/2024 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

AUTO Study Protocol: Version Date: 12/22/21 
PI: Marian Betz, MD, MPH 
COMIRB No: 19-0059 
Protocol# 19-0059  
 
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COMIRB Protocol 
COLORADO MULTIPLE INSTITUTIONAL REVIEW BOARD 
CAMPUS BOX F-490    TELEPHONE:  303-724-1055  FAX: 303-724-0990 
 
Protocol #: 19-0059 
Project Title: Decision Making Among Older Adults: the AUTO Study 
Principal Investigator: Marian Betz, MD, MPH 
Version Date: 12/22/21 
 
Hypotheses and Specific Aims: 
This Stage II randomized, controlled, longitudinal trial seeks to assess the acceptability, 
feasibility, and effects of a driving decision aid use among geriatric patients and providers. This 
multi-site trial will (1) test the driving decision aid (DDA) in improving decision making and 
quality (knowledge, decision conflict, values concordance and behavior intent); and (2) 
determine its effects on specific subpopulations of older drivers (stratified for cognitive function, 
decisional capacity, and attitudinally readiness for a mobility transition). Our overarching 
hypotheses are that the DDA will help older adults make high-quality decisions, which will 
mitigate the negative psychosocial impacts of driving reduction, and that optimal DDA use will 
target certain populations and settings. 
The challenge of how to support older adults making decisions about driving1,2 gains urgency 
with the aging population, the increase in older adults with cognitive impairment,3 and the fact 
that widespread use of driverless cars is likely distant.4 Although informational websites5 and 
self-assessment tools exist, a key knowledge gap remains: how to help older drivers actually 
make decisions about their driving in a way that is individualized and supports autonomy. In 
clinical medicine, decision aids6 are used to increase patient knowledge and decision quality.7 A 
web-accessible driving decision aid (DDA)8 was iteratively developed (NIH Model Stage I)9 to 
meet all international decision aid standards10 and is now ready for efficacy testing (Stage II),9 
but questions remain. How do cognitive or emotional factors influence a decision aid’s utility or 
timing for use? Should it be given to all older drivers at a certain age, or with certain conditions? 
Should there be pre-screening for a driver’s cognitive state or readiness to consider changes? 
When and where should the DDA be used? 
In this 5-year research project, our goals are to test how much the DDA improves outcomes in 
older drivers and to identify who benefits most from the DDA. We seek to facilitate optimal 
decision-making by identifying and leveraging cognitive, emotional and motivational factors. 
Aim 1: In a multi-site, two-armed randomized trial of older drivers (n=300; ≥70 years) and family 
members, to test the effect of a web-based DDA as compared to control (web-based information 
only5 on: 
1a. Immediate decision quality (measured by the Decisional Conflict Scale), 
hypothesizing that more DDA participants will make high-quality decisions; 

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1b. Longitudinal psychosocial outcomes at 12 and 24 months, hypothesizing that DDA 
participants will have reduced prevalence of depressive symptoms and of decision regret 
but maintained life space 
1c. Longitudinal driving behaviors (including reduction or cessation) at 12 and 24 
months, hypothesizing that the DDA- although not intended to direct participants to 
continue or stop driving- will lead to changes. 
Aim 2: Using data from Aim 1, to use stratified analyses to determine the DDA’s effects in 
specific subpopulations, including: 
2a. Older drivers with versus without cognitive impairment, hypothesizing that the DDA 
will improve decision quality more in cognitively intact drivers; 
2b. Older drivers with maintained versus impaired decisional capacity, hypothesizing that 
the DDA will improve decision quality more in drivers with maintained decisional 
capacity; 
2c. Older drivers who are attitudinally more ready versus less ready for a mobility 
transition, hypothesizing that the DDA will improve decision quality more in drivers who 
are ready for transition. 
Aim 3: Through qualitative interviews and surveys with key stakeholders (older drivers, family 
members, healthcare providers, and experts in the field), to identify the desirable settings for 
DDA use, including: 
3a. acceptability and suggestions for modifications or targeted use with subpopulations; 
3b. preferred locations, such as clinical settings, community programs, or at home. 
The proposed study will provide the groundwork for tailoring intervention delivery and preparing 
for its implementation. 
Background and Significance: 
“Driving retirement” is a critical life decision. As of 2015, there were over 25 million drivers aged 
≥70 years, and 70% of adults aged ≥85 years still had a driver’s license.11 Driving is closely 
linked to well-being, and driving retirement can negatively impact independence and mental 
health.12-18 A recognized national goal, therefore, is to help older drivers stay on the road as long 
as it is safe, as maintaining mobility and community involvement promotes health and 
longevity.19-21 Safety remains a consideration since some older drivers are at increased risk of 
crashes20,22,23 and fatal crash rates increase after age 75,24 but generally older drivers pose a 
greater risk to themselves than to the community around them.25,26 Precise estimation of an 
individual driver’s risk remains difficult, as on-road testing is costly and not always available27 
and office-based assessment is often impractical28,29 and not routine.30 Thus, decisions about 
driving retirement typically involve some combination of the older driver, involved family 
members or trusted friends, and healthcare providers, depending on the individual’s 
circumstances and preferences. 
Decision-making is a multi-dimensional process. Decisions are affected by complex interactions 
between myriad internal and external factors, including cognitive ability, decisional capacity, 
emotions, personality, and environmental or pragmatic factors. Older drivers fear losing their 

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independence, being abandoned, or becoming a burden on others, while simultaneously fearing 
causing harm to others on the road,31 and decisions about driving are complicated by financial 
and lifestyle considerations, such as alternative transportation options. The mechanisms of 
decision-making also change over an individual’s life, and the process can be further 
complicated by attitudinal factors (e.g., emotional readiness for a major transition) and cognitive 
function. Understanding how key attitudinal and cognitive factors affect decisions about driving 
gains critical urgency with the aging population in the US who rely heavily on driving 
themselves.  
Cognitive impairment complicates driving decisions. While physical conditions and medications 
can affect driving, Alzheimer’s disease and other forms of progressive cognitive impairment 
have the strongest link to both driving risk and the need for eventual driving cessation.22,32 Lack 
of insight, education, and/or recognition of cognitive impairment by spouses and families are all 
likely contributing factors. Given the estimate of almost 16 million older adults in the US with 
dementia by 2050,33 the decision-making needs around driving for this group—and the 
transportation needs for our society—will be significant. This does not even factor in the large 
number of older adults with other acute or chronic medical conditions that also negatively impact 
cognition and driving, such as stroke, Parkinson’s Disease, and visual impairment. The role of 
family members, trusted friends, and healthcare providers in supporting a driver through driving 
retirement gains additional importance in the context of cognitive impairment or concerns about 
decisional capacity. 
Decision aids- based on evidence and theory- can help with difficult decisions. Decision aids are 
tools that facilitate decision-making and have been developed and used for medical issues from 
acne treatment to weight control,6 but rarely (to date) for decisions about “life issues.” A unifying, 
recommended model for decision aid development is the Ottawa Decision Support Framework 
(Figure 1),34 which draws upon concepts from psychology, decision conflict, social support, and 
self-efficacy. The framework posits that decisional needs (e.g., knowledge, conflict/uncertainty, 
and values) affect decision quality, with the highest quality decision being one that is both 
informed and reflective of the individual’s values. Decision quality, in turn, affects patient actions 
and, ultimately, impacts both health outcomes and patient’s feelings about the decision. The 
overall assertion is that the highest quality decision will have the best outcome for the patient. 
Therefore, decision aids can enhance decision quality by addressing unmet decisional needs; 
specifically, decision aids facilitate complicated decisions by (a) identifying the decision to be 
made, (b) describing risks and benefits of various options, (c) assisting the patient in clarifying 
personal values, and (d) activating the patient for decision-making.35,36 In a Cochrane review of 
115 randomized trials (with 34,444 participants total), decision aids improved multiple decision 
outcomes, including knowledge, satisfaction, decisional conflict and regret, and communication.7 

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Older drivers need a tool to help their decision-making. Various guides and self-assessment 
tools exist to help older drivers and their families think about driving,37-41 but what was missing 
was a tool to walk a driver through the process of deciding whether or not to stop driving. 
Decision aids have been developed for financial, long-term care, and end-of-life issues,6 and a 
small pilot (n=12) found a paper-based driving decision aid (DDA) acceptable and useful,42 
although their tool is specific to individuals with dementia in Australia (which has different laws 
and systems of alternative transportation). Healthwise, a nonprofit organization that provides 
decision support tools and other services to enhance patient-centered decision making, 
released a DDA in 20158 for US 
drivers with or without dementia 
(Table 1). The tool was developed 
according to international decision 
aid standards,10,43 is available 
online, and is easily accessible by 
25% of clinicians in the United 
States. Unclear, however, is its 
effect on decision quality (i.e., 
satisfaction) or longer-term 
psychosocial outcomes, or which 
groups might benefit most from it 
(Figure 1). 
Attitudinal readiness for driving retirement also affects decisions. The Transtheoretical 
Model44,48 has been applied to the stages of transition involved with driving cessation, with the 
 
Figure 1. Theoretical framework of decision aid’s immediate and long-term impact. Adapted from 
Ottawa Decision Support Framework 
 
 
Table 1. Driving Decision Aid content  
Figure 2. Stages of driving retirement, in context of Transtheoretical Model  
Yellow boxes (Contemplation & Preparation) may represent ideal stages for decision aid use 

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idea that drivers progress—albeit at different rates—from pre-contemplation (the older driver 
with no concerns or plans to stop) through maintenance (the former driver who has found 
usable alternative transportation; Figure 2). The Transtheoretical Model posits ambivalence is a 
natural consequence of considering changes to health behavior, with each stage representing 
definable activities along the continuum of readiness for change. The Assessment of Readiness 
for Mobility Transition (ARMT)45,46 is a validated, attitudinal measure developed to help 
providers identify an older adult’s openness to changes and loss of transportation-related 
mobility. Drivers with higher ARMT total scores are less prepared for and more resistant to 
discussing or using transportation alternatives because they believe mobility loss inevitably 
leads to loss of independence and becoming a burden to others. In contrast, drivers with lower 
ARMT total scores are more likely to openly and directly discuss mobility loss without the 
perceived risk to their self-identity. Ideally, the ARMT could guide ongoing, personalized mobility 
planning, as healthcare providers could tailor discussions by identifying an individual’s stage 
along the spectrum.  
 
Older drivers want time to prepare for changes- but discussions and decisions are often 
postponed. The Trans-theoretical Model posits (Figure 2), and prior work confirms, that older 
drivers want time to prepare for driving retirement.31 In a qualitative meta-synthesis of existing 
studies, our team found: driving discussions are emotionally charged; context matters; providers 
are trusted and viewed as authority figures; communication should occur over a period of time 
rather than suddenly; and older adults desire agency in the decision to stop driving.31 
Unfortunately, in the real-world these difficult conversations are often delayed until a crash 
occurs or a new medical or social crisis develops, leaving little time for preparation31 
Engagement with healthcare providers is recommended for decisions about planning for future 
driving retirement,22,31,47 but this has been difficult to operationalize in routine clinical practice 
because of system constraints,28,29 leaving discussions in primary care to when significant 
safety concerns are already present and demand action.28,48 In a review of electronic health 
records (EHR) from 240 patients’ primary care encounters over a one-year period, we found 
documented conversations occurred with only 8-15% of older patients in general internal 
medicine clinics and 28% of those in a geriatric clinic.30 Understanding the barriers and 
facilitators to decision aid use in clinical practice will be key for future research and real-world 
use. Given the estimated 17-year time lag from original research to implementation of evidence-
based interventions into practice,49 it is critical to consider dissemination & implementation (D&I) 
frameworks when developing, testing and refining interventions.  
Study Team and Preliminary Studies: 
Table 2: AUTO study team, by key expertise and key study roles 
      
Key Expertise 
Key Roles 
 
Older 
drivers 
Decision 
making 
Cognitive 
function 
Trial 
design 
Trial 
measures 
Recruit- 
ment 
 
 
 
 
 
 
 
M. Betz, MD, MPH (PI)  - CU 
● 
 
 
● 
● 
● (CU) 
C. DiGuiseppi, MD, PhD – CU 
● 
 
 
● 
● 
 
C. Knoepke, PhD, MSW, LCSW- CU  
 
● 
 
 
● 
 
D. Matlock, MD, MPH – CU 
 
● 
 
● 
 
 
L. Hill, MD, MPH – UCSD 
● 
 
 
 
 
● (UCSD) 
D. Han, PhD – USC 
 
 
● 
 
● 
 
N. Fowler, PhD – IU 
 
● 
● 
 
 
● (IU) 

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Our multi-site, transdisciplinary research team has an established record of success in a broad 
range of topics related to older drivers and decision making. Table 2 represents the study team 
members’ specific key areas of expertise and study responsibilities. 
Decision research: Dr. Betz currently leads a team with Dr. Matlock to refine and test a decision 
aid on firearm storage in times of suicide risk (ED-AID, 17-2299), using a similar evaluation 
framework and measures to those proposed here. The team has completed development of the 
web-based tool and is testing it in a pilot randomized controlled trial (summer, 2018). Dr. 
Matlock, as Director of the UCD Program for Patient Centered Decisions, brings significant 
experience in the development, measurement, and implementation of decision aids 51, 52 and is 
PI on a hybrid effectiveness-implementation randomized trial of a decision aid for implantable 
cardioverter-defibrillators (NIH/NHLBI R01). Dr. Hill has experience in shared decision-making 
research with older drivers (through a program at the UCSD Memory Aging and Resiliency 
Clinic). Dr. Fowler is a decision scientist at the IU Center for Aging Research who focuses on 
older adults with ADRD and caregivers. 
Clinical and epidemiological research with older adults: The team has significant relevant 
experience in trials and longitudinal studies with older adults, though none of their current 
projects overlap with this trial. Drs. Betz, DiGuiseppi, and Hill currently collaborate as Site PIs 
on the LongROAD study, an observational study of a cohort of 2990 healthy older drivers in five 
states that is currently in year 3 of longitudinal follow-up.53 For the LongROAD study, they 
recruited 1200 drivers in Denver and San Diego in only 21 months. Through her NIA Beeson 
K23 project, Dr. Betz recruited 315 older drivers (65% participation) for an on-road test and one-
month follow-up, with 90% overall retention. Dr. Fowler also has experience recruiting and 
following older adults and their family members, including as PI of a large trial (426 dyads; 
NIH/NIA R01) testing a decision aid for caregivers of women with ADRD.54-57 Our study team 
also has experience in examining the emotional aspects of driving and mobility changes, 
including prior collaborations using the ARMT and qualitative studies of the views of older 
drivers and healthcare providers.1, 15, 18, 19, 35 
Research with cognitively-impaired older adults: Dr. Fowler has extensive experience 
conducting quantitative and qualitative research with cognitively impaired older adults and their 
family members.54-60 The focus of her work is on medical decision making for older adults with 
ADRD and early identification of ADRD in primary care. In her role as co-leader of the Outreach 
and Recruitment Core for the NIA funded Indiana Alzheimer Disease Center, she supports IU 
faculty who want to conduct research with persons with ADRD and their family members. Dr. 
Han is a board-certified clinical neuropsychologist who has published extensively on cognitive 
aging, MCI and ADRD61-65 as well as cognitive impairment in the context of decision making in 
old age.66, 67 He is the former recipient of an NIA Beeson K23 award on decision making in old 
age and current PI of an R01 (NIH/NIA) focused on the racial differences in decision making in 
old age.  
Qualitative methods: Dr. Betz has extensive experience in qualitative methods, including 
interviews and focus groups with older adults and healthcare providers.1, 15, 18, 19, 35 Dr. Matlock 
and Dr. Knoepke have also conducted significant qualitative work exploring decisional needs 
and cognitive biases surrounding implantable defibrillators,68-70 left ventricular assist devices,71-76 
and advance care planning/hospice.77-79 
Outcome Measures and Analysis: 

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Key measures in this trial (Table 3, additional details below) are tied directly to our theoretical 
framework (Figure 1) and assess DDA effects (Aim 1) and potentially differential subgroup 
effects (Aim 2).  
Immediate outcomes: Our primary outcome will be the DDA’s effect on decision quality, a 
fundamental element of the Ottawa Decision Support Framework34 as a precursor to behavior 
change (Figure 1). A high quality decision is defined as an informed patient making a decision 
consistent with their values.50,51 
-Decision Conflict: The Decisional Conflict Scale (DCS) measures internal conflict or 
ambivalence about the decision, with higher internal conflict (or ambivalence) indicating the 
decision is less in-line with personal values. The DCS is closely linked to overall decision quality 
and is a recommended primary outcome measure.50-52 The DCS estimates decisional conflict 
through personal perceptions of issues such as uncertainty in choosing options, modifiable 
factors contributing to uncertainty, and effective decision-making (e.g., expressing satisfaction 
with the choice). The DCS is a 16-item scale (with Likert scale response options) that has high 
reliability and test-retest correlation (Cronbach’s alpha coefficients > 0.78).53 In prior work, the 
DCS has been shown to discriminate between known groups who make or delay decisions 
(effect size 0.4-0.8),53 with lower scores indicating low decision conflict (and greater likelihood of 
implementing a decision). Scores <25 (out of 100 total) are associated with implementing 
decisions.53 
-Values Concordance: We will use the “Values Clarity”53 subscale of the DCS to examine 
specifically how much participants feel their decisions are in line with their values. Three DCS 
items are summed and multiplied by 8.33; scores range from 0 (extremely clear) to 100 
(extremely unclear about personal values). 
-Knowledge: Questions will assess concepts about driving presented in the DDA and control 
group website. 
-Self-Efficacy: We will measure decision self-efficacy (one’s self-confidence or belief in one’s 
ability to make decisions) using the Decision Self-Efficacy Scale,54 as decision aids can 
increase self-efficacy. Transformed scores range from 0 (extremely low) to 100 (extremely high 
self-efficacy). 
Longitudinal outcomes: Key longitudinal outcomes, linked to our theoretical framework (Figure 
1), are psychosocial and mobility-related. 
-Depression: We will measure depression using the PROMIS 4-item scale, with higher scores 
indicating higher depression. All PROMIS scores are analyzed as standardized T-scores 
(mean=50, SD=10).55 
-Decision regret: The Ottawa Decision Regret Scale. This validated measure correlates with 
decision satisfaction and conflict, and overall quality of life. Scored from 0-100, high scores 
represent higher regret.56 
-Life space: Life space is a global measure of mobility and community engagement. The Life-
Space Assessment instrument (UAB Study of Aging) is a validated tool assessing recent 
mobility and function.57,58 Composite scores range from 0 (bedbound) to 120 (travel out of town 

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every day without assistance);59 scores of ≤60 are correlated with lower levels of social 
participation and higher mortality.60 
-Driving behaviors: We will assess driving frequency (days per week), avoidance in certain 
situations (e.g., night), driving cessation (none, partial, complete), and self-reported crashes (≥1 
versus none). 
 
 
 

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Table 3. Key study measures. Shown in relation to measurement point (pre- or post- DDA or 
website 
Participant, and analytic plan.  ● = about self, ○ = as surrogate about older driver 
Domain and Key Measures 
Drivers Family/friends 
Analytic Plan 
Enroll F/u Enroll 
F/u 
Pre Post 
Pre Post 
 
Hypoth. 
Scoring 
Immediate 
outcome 
 
 
 
 
 
 
 
 
 
 
Decision Conflict 
Decisional Conflict Scale (DCS) ● 
● 
● 
● 
● 
● 
1⁰ 
Outcome 
1a, 2a, 
2b, 2c 
Score <25 
Knowledge 
Knowledge questionnaire 
● 
● 
● 
 
● 
● 
2⁰ 
Outcome 
1a 
Descriptive 
(cat.) 
Values 
concordance 
DCS Values Clarity subscale 
● 
● 
● 
● 
● 
● 
2⁰ 
Outcome 
1a 
Descriptive 
(cat.) 
Self-efficacy 
Decision Self-Efficacy scale 
● 
● 
● 
 
● 
● 
2⁰ 
Outcome 
1a 
Descriptive 
(contin.) 
Longitudinal 
outcome 
 
 
 
 
 
 
 
 
 
 
Depression 
PROMIS depression 
● 
 
● 
 
 
 
2⁰ 
Outcome 
Covariate 
1b 
1c 
T-score >50 
Descriptive 
(contin.) 
Decision regret 
Ottawa Decision Regret Scale 
 
 
● 
 
 
● 
2⁰ 
Outcome 
1b 
Descriptive 
(contin.) 
Life space 
Life-Space Assessment 
● 
 
● 
 
 
○ 
2⁰ 
Outcome 
1b 
Descriptive 
(contin.) 
Driving behaviors Amount; reduction or cessation 
● 
 
● 
 
 
○ 
2⁰ 
Outcome 
1c 
Descriptive 
(cat.) 
Subgroups 
 
 
 
 
 
 
 
 
 
 
Cognitive 
screening 
5 minute MoCA 
 
 
● 
 
 
● 
Eligibility 
Covariate 
-- 
Score ≥21 
Cognitive function BTACT, OTMT 
● 
 
 
 
 
 
Stratify 
2a 
Normal vs. 
impaired 
Insight 
Beck Cognitive Insight Scale  
● 
 
● 
 
 
 
Stratify 
2a 
R-C index ≥3 
Decisional 
capacity 
SPACED 
● 
 
● 
 
 
 
Stratify 
2b 
# correct 
answers 
Attitudes about 
driving 
ARMT 
● 
 
● 
 
 
 
Stratify 
Covariate 
2c 
1c 
Mean score 
≥3.57 

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Subgroups: Analyses will include stratification by cognitive and attitudinal factors. 
-Cognitive function: At baseline we will administer the Brief Test of Adult Cognition by 
Telephone,61 which assesses episodic memory (word list recall immediate and delayed), 
Category verbal fluency, Backward digit span, 30-Second Counting task, Number series, Stop & 
GoSwitch Task, and Executive Functioning. We will augment the BTACT with the Oral Trail 
Making Test (OTMT).62 Dr. Han will categorize participants’ cognitive function as MCI, dementia 
or non-impaired based on the BTACT Composite and OTMT-B (z-score < -1.5 is impaired). 
[Note: initial study protocol included RBANS and TMT instead of BTACT and OTMT; see 
protocol version 10.14.19 for more details] 
-Insight: The Beck Cognitive Insight Scale63 comprises two sub-scales (self-reflectiveness and 
self-certainty); the composite “R-C index” (self-reflectiveness score minus self-certainty score) 
reflects overall cognitive insight. In one study each sub-scale and the R-C index were lower in 
Domain and Key Measures 
Drivers Family/friends 
Analytic Plan 
Enroll F/u Enroll 
F/u 
Pre Post 
Pre Post 
 
Hypoth. 
Scoring 
Other covariates  
 
 
 
 
 
 
 
 
 
Demographics 
E.g., age, gender, 
race/ethnicity, marital/home 
status, education, income, 
urban/rural 
● 
 
● 
● 
 
●  Covariate 
all 
Descriptive 
(cat.) 
Physical health 
Medical history, Activities of 
Daily Living, PROMIS global 
health 
● 
 
● 
 
 
● 
Covariate 
all 
Descriptive 
(cat.) 
Mental health 
PROMIS Emotional Support, 
Social Isolation; Perceived 
Stress Scale 
● 
 
● 
 
 
 
Covariate 
1b, 1c 
Descriptive 
(cat.) 
Personality 
TIPI 
● 
 
 
 
 
 
Covariate 
1c, 2c 
Descriptive. 
(cat.) 
Driving behaviors Crashes, self-driving 
technologies 
● 
 
● 
 
 
○ 
Covariate 
1c 
Descriptive 
(cat.) 
Driving resources Alternative transportation, 
support  
● 
 
● 
● 
 
● ○ Covariate 
1c 
Descriptive 
(cat.) 
Driving education Use of websites, courses, or 
other material driving or 
cessation 
● 
 
● 
● 
 
●  Covariate 
1c 
Descriptive 
(cat.) 
Family 
questionnaire 
Relationship to driver, co-
reliance 
 
 
 
● 
 
● 
Covariate 
1a 
Descriptive 
(cat.) 
Life Events 
Major Life Events 
 
 
● 
 
 
●○ Covariate 
all 
Descriptive 
(cat.) 
Coronavirus-
related health 
Coronavirus/COVID-19 
Health/Exposure Status 
 
● 
● 
 
● 
● 
 
 
 

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those with probable Alzheimer’s disease compared to control.64 We will use a conservative cut-
off, with <3 categorized as low insight.65 
-Decisional capacity: This construct is related to but distinct from cognitive ability.66 We will use 
the Short Portable ACED (SPACED) to measure understanding, appreciation, comparative 
reasoning, and consequential reasoning. Each of these four criteria are scored a 0 for 
inadequate, 1 for marginal, and 2 for adequate. This produces as total range of 0 to 8. In the 
end, participants can be dichotomized as those with 0s versus others, median split, 8’s versus 
others, etc. Scores can also be used as a continuous outcome measure in regression models.. 
-Attitudes about driving: The ARMT46 measures affective and emotional aspects of present or 
future mobility changes associated with cessation. It has four subscales: anticipatory anxiety, 
perceived burden, avoidance, and adverse situation (i.e., the view that mobility loss is harmful to 
quality of life). Each of the 24 items is rated on a 1-5 Likert scale; high total average scores 
(mean >3.57) indicate low readiness to transition.46 
Other covariates: Additional measures are for eligibility screening, key covariates, and 
exploratory analyses. 
-Cognitive screening: We will use the 5-minute Montreal Cognitive Assessment [MoCA]69, 81 with 
a score <21 being an enrollment exclusion criterion due to the likelihood of poor driving ability 
and impaired decisional capacity. However, even individuals with MoCA scores ≥21 may have 
impaired decision-making capacity. If concerns arise during the consent process that the 
participant does not understand aspects of the study, the participant will not be enrolled in the 
study. 
-Demographics: These will include basic demographic characteristics like age, gender, and 
residence (e.g., private residence vs senior apartment complex, alone vs with others, urban vs 
rural location). 
-Physical and emotional health: These measures will include assistance for Activities of Daily 
Living (ADLs), medical comorbidities, and overall perceived health.53, 106-108 Progressive medical 
conditions associated with reduced driving ability and increase risk of cessation (eligibility 
criterion, above) will be categorized by type (e.g., visual, cognitive or musculoskeletal) or 
particular diagnosis, depending on sample prevalence. 
-Emotional health: We will also collect PROMIS measures of Emotional Support (4-item) and 
Social Isolation (4-item) and the 4-item Perceived Stress Scale.109 
-Personality: As personality may relate to driving (e.g., enjoyment versus anxiety with driving) 
we will use the Ten-Item Personality Inventory (TIPI), a shortened questionnaire for personality 
dimensions.110 
-Driving resources: Resources assessed will include access to and use of alternative 
transportation, interest in self-driving technologies, and emotional, financial or logistical support 
from others. 
-Driving education: To assess possible control group contamination, at baseline and follow-up 
we will assess exposure to websites, videos, courses, or other educational resources about 
driving and driving cessation. 

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-Family member questionnaire: Questions will assess the relationship between the driver and 
family member, including frequency of contact and co-reliance for transportation (e.g., does 
older driver provide rides for family member’s children, is family member available and willing to 
provide rides to older driver). 
-Coronavirus/COVID-19 Health/Exposure Status (informed by the Coronavirus Health Impact 
Survey (CRISIS)): Questions will assess current impact of Coronavirus pandemic on physical 
and mental health, as well as other select domains (e.g., impact on important events, routine in-
person activities, financial and living stability, etc).  
Analysis for Aim 1: Analyses will be performed according to the principle of intention-to-treat, 
including all randomized drivers. Unless otherwise specified, hypothesis tests will be two-sided 
with alpha=.05, with 95% confidence intervals or p values reported. All canalyses will be 
performed using SAS/STAT® software. First, descriptive statistics will be computed for baseline 
patient characteristics, initially testing for differences between control and intervention groups 
(including for key covariates, Table 3.3). We will assess site effects by comparing demographic 
variables across the sites, using ANOVA for continuous measures and chi-square tests of 
proportions for categorical measures. If no statistically significant differences exist among sites, 
we will ignore site and formally test the differences in proportions or means as described below. 
If there are significant differences among sites, then (assuming enough events) we will use 
separate bivariate logistic regression models for each aim described below, with a fixed effect 
for site and with treatment arm as the main predictor. For longitudinal analyses, we will conduct 
logistic regression with unstructured correlation of generalized estimating equations (GEE) to 
account for the repeated observations for each participant. Statistical significance will be 
evaluated at one-sided p<.05 level. Analyses of the DDA’s longitudinal effects may be 
vulnerable to bias, as control arm participants may be exposed to intervention arm messages 
through exposure to available materials or courses related to driving safety or cessation (e.g., 
websites, physician counseling, AARP courses). We will assess exposure to other sources of 
information through structured questions. To mitigate the impact of this potential bias, we will 
adjust measurements of contamination as applicable. In all analyses, validation of distributional 
and parameterization assumptions will be checked and data transformations (e.g. log-
transformations) or alternative methods will be implemented as appropriate. 
Aim 1a. Test the DDA’s effect on immediate decision quality (measured by the Decisional 
Conflict Scale). Hypothesis 1a: More DDA participants will have high-quality (DCS<25) scores, 
compared to control, immediately after the intervention. Our primary outcome measure will be 
the proportion of participants with DCS score <25. We will perform multiple logistic regression to 
test the DDA’s effect on decision quality. We are also interested in jointly modeling the 
proportion of family members with DCS score <25, so a bivariate logistic regression model will 
be used for analysis to allow assessment of marginal effects of the intervention on patients and 
family members separately while jointly accounting for the correlation between participants and 
family members, as well as their concordance/discordance in decision quality. In the small pilot 
trial of Australian drivers with dementia, mean DCS scores decreased from 22.5 to 7.5 (out of 
100) after the DA;42 should we find that DCS score distributions do not allow dichotomization 
based on the cut-point of 25, we will instead compare means between the groups. Missing data 
for the primary measure should be minimal, given that this will be obtained at enrollment. 
Additional analyses will include examining components of decision quality to identify changes in 

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measures like knowledge, values-concordance, or self-efficacy, as they may shed light on the 
mechanisms underlying decision-making about driving. 
Family member data: In addition to the analysis under 1a, we will examine the degree of 
concordance (or discordance) within driver-family dyads on various measures, including current 
versus desired level of family involvement in driving retirement process (part of the driving 
questionnaire). To evaluate agreement we will look at the kappa coefficients at baseline and at 
follow-up points, as well as apply this class of repeated-measure concordance correlation 
coefficients. For longitudinal data, we will examine changes across the other time points (6, 12, 
18, 24 months) using GEE after adjusting covariates. 
Aim 1b. Test the DDA’s longitudinal effect on psychosocial outcomes at 12 and 24 months. 
Hypothesis 1b: Participants in the DDA group will have reduced prevalence of depressive 
symptoms and of decision regret but maintained life space, at 12 and 24 months after 
enrollment, compared to control participants. We will use descriptive statistics, including 
proportions with 95% CIs or medians and interquartile ranges, for comparison between 
treatment groups. We will utilize GEE with a linear link, and a linear mixed model (LMM) with a 
random intercept, fixed effects and a linear link for each outcome. We will adjust for baseline 
characteristics (gender, age, education, etc.), driving behaviors and other variables. We will 
perform sensitivity analyses to examine the missing assumption under maximum likelihood. 
Aim 1c. Test the DDA’s longitudinal effect on driving behaviors at 12 and 24 months. 
Exploratory: The DDA does not provide specific recommendations as to whether the participant 
should stop driving or not. We will compare the proportion of participants who change their 
reported driving behaviors (reduce driving or cease altogether) over the follow-up period to 
examine whether the DDA appears to affect them. We will apply GEE with a logistic link, and a 
generalized LMM (GLMM) with a random intercept and a logistic link for each outcome. Also, we 
will adjust demographic variables and appropriate variables. 
Secondary analyses: Secondary analyses by gender will be conducted, as we hypothesize 
there will be differences in behavior and outcomes based on gender, and the belief that the 
responses to DDA intervention may be heterogeneous between men and women. Aim 1a-1c will 
be considered gender analyses using logistic regression and GLMM. We will also examine the 
degree of concordance (or discordance) within driver-family dyads on various measures, 
including DCS and current versus desired level of family involvement in driving retirement 
process (part of the driving questionnaire). For longitudinal data, we will examine changes 
across time points (follow-up at 6, 12, 18, 24 months) and correlation between older driver and 
family member report. 
Analysis for Aim 2: We will first test for interaction between DDA effect and each subgroup 
stratifying factor. We will then use separate stratified subgroup analyses to examine the effects 
of potential influential cognitive or affective factors on the effect of the DDA (using DCS score, 
the primary outcome from Aim 1a). As in Aim 1, we will first evaluate demographic 
characteristics and site effects; all comparisons within subgroups will be between DDA and 
control groups, using intention-to-treat analysis. The main analysis for each subgroup variable 
will be an unadjusted test of the intervention by subgroup variable interaction in a statistical 
model appropriate for the particular outcome (DCS score); specifically, separate multiple logistic 
models will include intervention group, subgroup, and an intervention-by-subgroup interaction as 

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factors. Potentially confounding variables (e.g., age, gender) will also be examined in separate 
multivariable logistic models for each sub-aims. 
Aim 2a. Compare the effect of the DDA on decision quality in older drivers with versus without 
cognitive impairment. Hypothesis 2a: The impact of the DDA will be greater among those 
without cognitive impairment, as they are able to process the DDA’s explanation of facts and 
options to facilitate a decision. We will examine interaction between cognitive function and the 
DDA’s immediate effect on decision quality (DCS score) by dichotomizing cognitive function into 
impaired (comprising MCI and dementia) non-impaired. For our initial analyses, we will compare 
impaired versus non-impaired and run separate simple logistic regression models for each 
group to determine how cognitive function impacts the relationship between treatment group 
(DDA or control) and decision quality, as measured by DCS scores. For each cognitive function 
subgroup, the outcome variable of interest will be the dichotomized decision quality measure 
(DCS <25 versus DCS ≥25), and the intervention group status will be the independent variable. 
Should we have adequate sample size, we may examine the MCI and dementia subgroups 
independently. 
Aim 2b. Compare the effect of the DDA on decision quality in older drivers with maintained 
versus impaired decisional capacity. Hypothesis 2b: The impact of the DDA will be greater 
among those with retained decisional capacity. Decisional capacity scores (range: 0-8) will be 
examined as a linear variable with controlling for confounding by cognitive function. We will 
examine the distribution of decisional capacity among the study’s 300 participants to define 
subgroups; participants will be divided into 2-3 subgroups based on decisional capacity scores. 
As with cognitive impairment above, a simple logistic regression model will be run for each 
decisional capacity subgroup with dichotomized decision quality measure as the outcome of 
interest and intervention group status as the independent variable.  Additionally, a non-stratified 
analysis can be conducted in which decisional capacity score is included as an additional 
independent variable model. 
Aim 2c. Compare the effect of the DDA on decision quality in older drivers who are more versus 
less attitudinally ready for transitions. Hypothesis 2c: The impact of the DDA will be greater in 
those readier for mobility changes, as defined by lower ARMT scores, as they are “primed” for 
decision-making (Figures 3.1 and 3.2). We will follow methods similar to Aim 2a, using logistic 
regression to examine interaction between ARMT score and the DDA’s effect on DCS scores by 
dichotomizing ARMT into high (mean>3.57) versus low/medium (mean 1-3.56). Depending on 
the distribution of ARMT scores in our sample, we may examine those with low ARMT scores 
(mean<2.3), as this group may be more open to mobility changes.45 
Secondary analyses: Additional analyses will include dichotomizing groups based on insight into 
cognitive function, using the Beck Cognitive Insight Score and following procedures outlined in 
Aim 2a. We will also examine trajectories of attitudinal readiness to stop driving over time; this 
will be the first longitudinal study of ARMT scores, and we suspect scores will ultimately 
decrease (indicating greater readiness for driving retirement) but may first increase (as drivers 
cope with feeling threatened or worried about the transition). Employing an exploratory 
trajectory analysis to the data (rather than a recurrent cross-sectional approach) allows us to 
focus on changes over time for each individual. By following individual trajectories, we are likely 
to observe factors influencing individuals’ decisions rather than have these factors obscured by 
focusing on recurrent cross-sections of the data. Data will be organized as time-ordered, 

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sequential matrices (one matrix per unit of analysis). Descriptive statistics for the ARMT scores 
will be summarized at each time point. In addition, study participants will be grouped into 
subcategories based on the type of trajectory (both shape and scale) being observed. 
Intervention group status and its effect on trajectory will be formally explored. Other variables of 
interest will be assessed in an exploratory manner. Additional secondary analyses will examine 
interactions between personality factors, ARMT, and the DDA. We will also explore the effect of 
gender, recognizing that women are more likely than men to self-regulate driving and to stop 
driving sooner.70,71 
Analysis for Aim 3: In a mixed-method study with key stakeholders (drivers, family members, 
healthcare providers, and experts in the field), we will inform future DDA testing and eventual 
implementation. 
Aim 3a. Explore acceptability of the DDA, including recommended modifications. 
Aim 3b. Explore views on the ideal settings for DDA use (e.g., clinical setting versus community 
program). For these qualitative data, we will use a team-based, mixed inductive-deductive 
approach to review and code transcripts, identify dominant themes, compare themes across 
content areas, and then group these themes or create sub-themes. We will use “member-
checking” to discuss final themes and theme organization with key informants. 
Figure 3. 
Sample Size: Our target 
sample size for Aim 1 (n=300 
older drivers; 150 DDA and 
150 control) was chosen to 
allow detection of a 20-40% 
difference between the DDA 
and control arms (depending 
on underlying proportions for 
each of the treatment arms) 
for the primary Aim 1a 
measure (DCS score <25; 
Figure 3.5) at a power of 90% 
and a 0.05 significance level, 
while allowing for 10% loss to 
follow-up. Our overall sample 
size also allows for stratified 
analyses (Aim 2). Estimates for the effect of the driving DDA on behavior among older drivers 
do not exist, so our assumption of a 20-40% difference is conservative (prior work with other 
DAs has found an effect size of 40-80% on DCS between groups53). For Aim 2, pilot work 
suggests estimates ~30% of participants will have cognitive impairment and 30% will have high 
ARMT scores (as described above). A stratified sample size of 90 should allow us to detect a 
30% difference between the DDA and control groups, at 80% power (see Figure 3). For Aim 3, 
we estimate needing 15-20 interviews per stakeholder group (n=20 older drivers and family 
members/study partners; n=20 healthcare providers; n=20 experts in the field of older adult 
driving safety) to reach the “saturation point” (where additional interviews are unlikely to reveal 
 
 

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new themes) while allowing for adequate group diversity. Please note the older drivers and 
family members/study partners are all Aim 1 participants.  
Description of Population to be Enrolled: 
Eligible drivers: Eligible 
older drivers (n=300) 
must: be aged ≥ 70 years 
without severe cognitive 
impairment (5-minute 
MoCA ≥ 21). 72 Additional 
eligibility criteria are in 
Figure 4. We seek to enroll 
drivers more likely to be 
primed to consider driving 
retirement, so an 
additional eligibility 
criterion will be having at 
least one diagnosis of a 
progressive medical 
condition associated with 
reduced driving ability and 
increased risk of cessation 
(defined by the study 
team; preliminary list 
includes macular 
degeneration, Parkinson’s 
disease, Alzheimer’s 
disease, stroke-related 
paralysis or weakness, 
and syncope). 
Family Members: We also aim to enroll one “family member” (i.e., relative or close friend, but 
will be referenced as “family member” or “study partner”) for each driver (n=up to 300), ideally 
someone who might be involved in decision-making about driving or in providing support for the 
transition to non-driving. Eligible family members/study partners (Figure 4) must not have 
severe cognitive impairment (5-minute MoCA ≥ 21) and can participate via telephone; eligible 
drivers can participate via telephone, as well. Based on prior experience, we expect to recruit at 
least 200 family members, allowing adequate power for planned analyses. To ensure this, we 
will allow ≤100 older adults to participate without family. 
For Aim 3, Key stakeholders for engagement in this exploratory work will include the following. 
We will invite a subsample of older drivers and family members to participate in qualitative 
interviews after their Aim 1 trial participation is complete. A sample of healthcare providers at 
participating sites will be invited to participate. At each site, eligible providers will be physicians, 
physician assistants, nurse practitioners, social workers, and case managers working in the 
clinics from which drivers were recruited. Additionally, we will invite national experts in older 
driver research and policy, drawing upon suggestions and connections from the study team and 

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NIA officials. For example, we will invite leaders from organizations such as the NIA, American 
Geriatrics Society, AARP, and, National Highway Traffic Safety Administration. All Aim 3 
participants will be 18 years of age or older. 
Study Design and Research Method 
We will use a multi-site, two-armed randomized controlled trial of older drivers (n=300; ≥70 
years) from clinical settings, and one family member each (n=up to 300), with longitudinal 
follow-up. Our goals are to test how much the DDA improves outcomes and identify who 
benefits most from the DDA. Our evaluation of the DDA’s efficacy (Aim 1) and its relative effect 
in subgroups (Aim 2) corresponds to Stage II in the NIH Stage Model for Behavioral Intervention 
Development.9 Our findings from Aims 1 & 2 could identify necessary refinements (Stage I) and 
inform future efficacy, effectiveness or implementation trials. 
Sites: For this multi-site trial, we plan to include clinics associated with three sites to provide 
geographic diversity, along with access to populations with racial and ethnic variability. We 
request that COMRIB serve as the single IRB for this research study, with other IRBs ceding 
oversight. 
• 
University of Colorado Health: Participants will be recruited primarily from three 
clinics associated with University of Colorado Health. 
o UCHealth Senior’s Clinic – Aurora, CO 
o UCHealth Internal Medicine Anschutz clinic – Aurora, CO 
o UCHealth Internal Medicine Lowry clinic – Denver, CO 
• 
University of California-San Diego: Participants will be recruited primarily from five 
clinics and four practices associated with University of California – San Diego 
o UCSD The Shiley Eye Clinic – San Diego, CA 
o UCSD Neurological Institute – San Diego, CA 
o UCSD Memory Disorders Clinic – San Diego, CA 
o UCSD Senior Medicine Clinic – San Diego, CA 
o Memory Aging and Resiliency Clinic – San Diego, CA 
o UCSD Internal Medicine Practice – San Diego, CA (four practices) 
• 
Indiana University: Participants will be recruited primarily from 29 primary care 
clinics associated with the Indiana University School of Medicine. 
o Indiana Health – Indianapolis, IN (20 clinics) 
o Eskenazi Health – Indianapolis, IN (9 clinics) 
§ 
Eskenazi Health is not a part of Indiana University and will not be actively 
engaged in research. They will be participating as a recruitment site only. 
Recruitment: We will follow methods used successfully in the LongROAD cohort study.73 
Research assistants (RAs) at each site will identify potentially-eligible older drivers by screening 
the electronic health records of clinic patients to identify patients aged ≥70 years with at least 
one relevant medical diagnosis. 
Table 4. Medical Conditions that May Impact Driving80 
Medical Condition 
Specific Examples 
Diseases/conditions affecting vision 
Diabetic retinopathy 
Macular degeneration 
Glaucoma 

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Retinitis pigmentosa 
Field cuts 
Low visual acuity even after 
correction 
Cardiovascular disease, especially when associated with 
presyncope, syncope, or cognitive deficits 
Unstable coronary syndrome 
Implantable defib. 
Congestive heart failure 
Hypertrophic obstructive 
cardiomyopathy 
Orthostatic hypotension 
Syncope or presyncope 
Neurologic disease 
Narcolepsy 
Dementia 
Multiple sclerosis 
Parkinson disease 
Brain injury 
Spinal cord injury 
Stroke 
Vertigo or dizziness 
Seizure 
Psychiatric disease 
Alcohol or other substance 
abuse 
Metabolic disease 
IDDM 
 
Musculoskeletal disabilities 
Arthritis and foot abnormalities 
 
Respiratory disease 
Chronic obstructive pulmonary 
disease 
Obstructive sleep apnea 
Chronic renal failure 
End Stage Renal Disease 
Hemodialysis 
Insomnia 
Sleep apnea 
Insomnia 
Restless leg syndrome 
 
Potentially eligible drivers will receive an informational opt-out letter or email (as well as a 
recruitment flyer) describing the study goals and objectives, eligibility requirements, procedures, 
and duration. The opportunity to opt out of further contact by either email or telephone will be 
offered, as will information on who to contact to learn more about the study. All persons who do 
not opt out within 2 weeks, or have not already contacted study personnel, will be called by a 
Research Assistant (RA). A maximum of 3 calls per potential participant, at different times and 
on different days, will be implemented. A telephone message will be left after the first call and 
again after the final call, with contact information. Once a potential participant is reached by 
telephone, the RA will screen for eligibility (including consideration of driving retirement and the 
requirement of a score ≥21 on the telephone MoCA 5-minute protocol69), explain the study, and 
request verbal consent. Those who refuse verbal consent to participation will not be contacted 
again. 
It is possible that older drivers may learn about the AUTO study independent of study team 
outreach, i.e., via self-referral (e.g., an AUTO participant may share information about the study 
and study team contact information with a friend or loved one that they think may be interested 
in participating). The study team will consider enrolling self-referred older drivers as a secondary 
recruitment method if rates of recruitment are lower than anticipated.  

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Enrollment, and Consent: Interested and eligible older adults will be scheduled for an initial 
enrollment visit (can take place in person or by phone) and asked to identify a family member or 
close friend (“study partner”) for participation and to provide that person’s contact information. 
We will contact that family member/friend to describe the study and assess eligibility and 
interest. For drivers and study partners who are enrolled in person (vs. by phone), the in-person 
enrollment session will include written informed consent including HIPAA authorization. For 
drivers and study partners who are remotely enrolled by phone, verbal consent will be obtained 
using a postcard consent script. Drivers will be asked to sign a separate form to allow the 
researchers to access their DMV driving records (the study team will access participants’ driving 
and accident records for the past 12 months before enrollment and up to every 12 months after 
enrollment.) We will attempt to schedule the driver and his/her family member for the same 
enrollment appointment, but they can be done separately if necessary or if the family member, 
driver, or both are participating by telephone. The study team has requested a waiver of 
documentation of informed consent for study partners and drivers who elect to participate via 
telephone only (i.e., study partners and drivers who do not have any in person contact with the 
study team). In these cases, great care will be taken to ensure the study partners and drivers 
have a comprehensive understanding of the study and their rights as research participants. The 
study team will also provide these participants with a copy of the “post-card consent” document, 
via mail or email, that includes details about study participation.  Randomization will occur after 
enrollment. Family members will be allocated to the same treatment arm as their older driver 
partner. 
Randomization: At each site, enrolled drivers (with their family members) will be randomly 
assigned in equal numbers to the DDA or control arm (Figure 4). To reduce bias and aim for 
balance among arms, we will randomize patients in blocks with randomly varied block sizes of 4 
and 6.74,75 While RAs delivering the intervention to patients cannot be blinded to treatment, we 
will conceal allocation using a centralized, computer-generated list that RAs will access only 
after enrollment.76 Other study staff, including a different RA (whenever possible) doing follow-
up calls, will be blinded to study arm. 
Baseline Measurements & Intervention: The driver and family member will be randomized to 
the same arm but will complete all study measures and interventions separately from each 
other. For each participant (including those enrolled in person as well as those enrolled remotely 
by phone), the RA will use a study tablet or laptop to administer the introductory questionnaire 
(Figure 4, Table 3). In the intervention arm, participants will then complete the web-based DDA; 
control group participants will be directed to the NIA “Older Drivers” website.5 After completing 
the DDA or website review, all participants in both arms will answer questions about their 
knowledge, values, and plans related to driving cessation (Table 3). For family members, 
questions will generally be about the older driver, though there will be specific questions 
concerning their relationship, frequency of contact, and possible co-dependence for 
transportation. Whenever possible, participants will receive a printed handout of either their 
DDA results or the NIA webpage (for those enrolled in person) or will receive instruction from 
the RA about how to save an electronic copy of the DDA results or the NIA webpage (for those 
enrolled remotely by phone). 
Identifying driving risk: At the request of the DSMB, the study team will use American 
Academy of Neurology (AAN) guidelines to identify drivers who are at moderate risk for being 
unsafe to drive (https://www.aan.com/PressRoom/home/GetDigitalAsset/8471). Per AAN 

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guidelines, “moderate risk” will be a Clinical Dementia Rating (CDR) of 0.5 with “few” to 
“several” risk factors, or a CDR of 1.0 with “no” or “few” risk factors”. AUTO uses the 5-minute 
MoCA, rather than the CDR or MMSE. MoCA scores of 18-25 suggest “mild” impairment while 
10-17 suggest “moderate”. Drivers must score ≥21 on the 5-minute MoCA to be eligible, 
therefore, participants with “moderate” cognitive impairment (as defined by AAN guidelines) are 
not enrolled. 
As recommended by DSMB, we will identify participants with a MoCA score of 21-25 as having 
possible mild cognitive impairment. For those participants, we will review any additional AAN 
driving risk factors (see table below). 
 Drivers with MoCA scores of 21-25 
and ≥3 risk factors will be identified as 
“moderate risk” for the purposes of 
these safety checks. For participants 
identified as having moderate risk, we 
will perform an ad hoc review. We will 
report these findings to the DSMB at 
the regularly scheduled intervals 
unless there is an AE/SAE as 
previously described. 
For participants meeting these 
thresholds, we will recommend a 
driving evaluation and provide the 
participant with information from 
ADED (Association for Driver 
Rehabilitation Specialists) about local 
resources. We will request participants 
share the subsequent driving 
evaluation results, although 
participants may remain in the study if 
they decline either the evaluation or data sharing. Any participant who receives a 
recommendation for a driving evaluation will be noted in the study database so we can later 
perform sensitivity analyses to see if that recommendation (or the evaluation, if obtained) affects 
the observed efficacy of the study intervention (driving decision aid). 
Longitudinal follow-up: Participants will be contacted for telephone follow-up at pre-specified 
intervals (6, 12, 18, and 24 months after initial visit for drivers; 12 and 24 months after initial visit 
for study partners). Participants will be contacted via phone call, text message, or email 
(depending on participants’ preference) to schedule the follow-up interviews. For these follow-up 
interviews, the RA will contact the participant at the participant’s preferred phone number at a 
mutually-convenient time. Trained RAs across sites will follow COMIRB policies regarding 
contacting participants for follow up. Per suggested guidelines, participants will be contacted at 
least three times, but no more than 10 contact attempts will be made. Additionally, staff will 
attempt to reach the participant at different times of the day, leave a brief message on second 
attempt followed by a more detailed message on third (for participants who elect to receive 
reminders by phone call), and will reach out by email and/or text message as needed/as 

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preferred. Follow-up questionnaires will be administered via phone by RA unless the participant 
strongly prefers to complete an in-person interview. During follow up, participants will repeat all 
psychosocial and decisional measures. The study team will access participants’ driving records 
for the past 12 months before enrollment and again up to every 12 months after enrollment. 
Aim 3 Recruitment: For providers, study recruitment will include flyers posted in staff work 
areas and up to 3 emails to eligible providers, asking interested providers to contact study staff 
for further description of the study and scheduling of an interview. For experts, we will send up 
to 3 emails and describe the study in greater detail to interested respondents. For the older 
drivers and family members/study partners, at the end of the 24 month interview we will ask 
participants if they are interested in being contacted with information about the interview 
opportunity. Study staff will follow-up with these interested participants via phone or email, 
depending on the participant’s preference.  
Measures: Measures (Table 3.4) will be assessed through structured questionnaires and semi-
structured qualitative interviews. Areas of focus will include recommended modifications to the 
DDA itself, the preferred setting for use (e.g., primary care clinic, older driver educational 
program, at home), and the recommended target audience (e.g., all drivers certain conditions or 
ages, family members). When possible, we will use existing scales (e.g., Ottawa Acceptability 
Scale) and Consolidated Framework for Implementation Research (CFIR) interview guides. 
Table 3.4: Domains, constructs, and measures 
CFIR Domain 
Selected CFIR Constructs 
Measures 
Drivers 
Family 
Providers 
Experts 
Intervention 
Adaptability; Design Complexity 
Cost 
Recommended edits 
Feasibility of use 
Costs for D&I 
● 
● 
● 
● 
● 
● 
● 
 
 
 
 
● 
Inner Setting 
Site 
Recommended setting for use 
● 
● 
● 
● 
Outer Setting 
Patient needs 
External policies and incentives 
Description; barriers & facilitators 
External strategies affecting use 
● 
● 
● 
● 
● 
● 
Characteristics 
of Individuals 
Beliefs about intervention 
Attitudes (including acceptability) ● 
● 
● 
● 
Process 
Planning; Engaging 
As needed for setting 
 
 
● 
● 
 
Data collection: Structured questionnaires will be administered to trial participants as well as to 
participating providers and experts. We will conduct semi-structured interviews with a 
subsample of trial participants, as well as providers and experts, to provide deeper 
understanding of the perspectives of key stakeholders concerning future D&I of the DDA, and, 
more broadly, decision support tools for older adults. Interviews will follow an interview guide, 
last 30-60 minutes, and be digitally recorded. They will be held via telephone or video 
conference depending on participants’ preference. 
Incentives: As incentive to participate, older drivers will be provided with $50 for the enrollment 
interview and $15 for each of four phone calls; family members or friends will receive $25 for the 
enrollment visit (as it is shorter than the drivers’) and $15 for each of two follow-up calls. For 
Aim 3, participants will receive a $25 gift card for their participation in the qualitative 
interview/survey. Method of compensation will include but not be limited to the following: gift 
card, money order, check. Compensation will be provided after completion of study activities, 
i.e., after the enrollment interview and after each follow-up study interview. Additional activities 

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to maximize longitudinal retention can include: annual birthday phone calls or emails; phone call 
or email reminders prior to appointments; and a study website with site contact information. We 
may also consider other low-cost items like annual holiday greeting cards or study magnets. 
Data Management: The RAs at each site will enter the enrollment and contact information, 
initial question responses, and telephone follow-up responses into a secure research database 
(REDCap, Research Electronic Data Capture) maintained by the Colorado Clinical & 
Translational Sciences Institute. RAs at each site will be able to see only their site’s data, and 
limited study team members (e.g., PI, biostatistician, and analyst) will be able to see data from 
all sites. We will register the trial on clinicaltrials.gov and follow CONSORT guidelines.77,78 For 
Aim 3, questionnaire responses will be entered into a secure REDCap database by the 
participant or RA. All interviews will be digitally recorded and transcribed. Qualitative data will be 
analyzed using Dedoose, an online platform for team-based qualitative analyses with project-
specific data encryption. 
Data Safety Monitoring Plan: 
The Principal Investigator (Dr. Betz) will be responsible for the ongoing oversight, review, and 
reporting of adverse events related to the study. Dr. Betz will personally review each potential 
adverse event to determine the course of action, including reporting to appropriate groups, 
offices, or agencies, or identifying a need for making protocol changes. 
A Data Safety and Monitoring Board (DSMB) will also be created to meet NIH requirements for 
monitoring of clinical trials. Members of the DSMB will be selected based on their expertise in 
biostatistics, gerontology, and clinical trials. They will be selected from institutions and research 
groups to minimize conflict of interests with the study’s investigators. 
In the event that a conflict of interest within the IDMC is identified, it will be disclosed to 
COMIRB. The DSMB will also provide recommendations to the PI on how to resolve the conflict 
of interest, which will then be reported to NIA and COMIRB. If a resolution cannot be found, a 
new member will be appointed to the DSMB. 
The first task of the DSMB will be to approve the DSMB charter, which outlines procedures 
including meeting frequency. The DSMB will meet twice yearly or as-needed, such as in the 
event of a SAE. Any DSMB recommendations to amend the protocol will be reviewed and 
approved by Colorado Multiple Institutional Review Board (COMIRB) before implementation of 
proposed changes. The DSMB will review and evaluate study progress, data quality, risks and 
benefits, and other factors that will influence the overall study outcomes, safety, and feasibility 
during biannual meetings. Data reviewed by the DSMB will identify participants by their study ID 
only. If confidentiality is lost, the appropriate actions will be taken based on institutional policies. 
The DMSB will review data and make recommendations on any concerns or adverse events 
related to the study. These recommendations include altering or stopping with respect to safety 
concern for participants. Although not expected in this study, any serious adverse events will be 
reported to COMIRB, the DSMB, and NIA within 48 hours. 
The DSMB will deliver an annual report to NIA, unless a report is requested more frequently. 
This report will include information on the safety and progress of the study. This report will 
include a summary of any AEs or SAEs and provide recommendations about whether the study 
should be continued, modified, or terminated. 

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Adverse Events and Serious Adverse Events: Adverse and serious adverse events will be 
defined based on guidance from Colorado Multiple Institutional Review Board and the NIH. An 
Adverse Event (AE) is any unfavorable and unintended sign (including abnormal laboratory 
finding), symptom, or disease temporally associated with the use of a medical treatment or 
procedure, regardless of whether it is considered related to the medical treatment or procedure 
(attribution of unrelated, unlikely, possible, probable or definite). A serious adverse event (SAE) 
includes any untoward medical occurrence that at any dose results in death or the immediate 
risk of death, hospitalization or prolonging of an existing hospitalization, persistent or significant 
disability/incapacity or a congenital anomaly/birth defect (NIH guide-6/11/99). 
The DSMB will review any unexpected events that may occur with study participants to 
determine whether the event is considered an adverse event, and if the event is study related. 
Research staff will alert the Site PI and Dr. Betz to any potential AE or SAE, who will assist with 
arranging any needed services for the participant. Within 48 hours of discovery of a SAE, it will 
be reported to COMIRB, and within one week to the DSMB and study’s assigned project officer. 
Within two weeks of an AE being discovered, it will be reported to COMIRB, the project officer, 
and DSMB. In all cases, the PI, under advice of COMIRB, will determine what further, 
assessment, follow-up or action is required for an AE or SAE. A summary of SAEs that occurred 
during the study year will be part of the annual progress report that is submitted to both 
COMIRB and NIA. If it is unclear if an event meets criteria for a SAE by definition, the PI will 
refer it to the DSMB to determine if it meets SAE definition. 
Description, Risks and Justification of Procedures 
Risks to Human Subjects: 
This is a minimal risk study. One risk of this study is loss of privacy due to breach of 
confidentiality. We will make every attempt to keep information private. Screening logs will not 
contain any identifying information. To ensure adequate protection of confidentiality, any age 
above 89 will be treated as an identifier and will be recorded as 89+ in screening logs. Data will 
be stored in a secure, password protected database on the University of Colorado server, (or 
equivalent institutional servers at other sites), with access limited to study personnel. Some 
participants may be uncomfortable discussing driving. They may choose not to answer certain 
questions. The research team will report any activities resulting in participant discomfort or injury 
to the IRB immediately. 
Older adult drivers will be asked questions related to their driving behavior in addition to 
measures related to physical health, cognitive impairment, and decision-making capability. 
Mandatory reporting of potential driver impairment due to medical conditions varies by state. Of 
note, in this study we will not be assessing actual driving function, and no single question or 
cognitive test is accurate in identifying driving risk. Driving cessation itself comes with risks, so 
the study team will not automatically report drivers with particular MoCA scores. However, 
during the informed consent process, we will explain the study protocol related to participants 
who have clear visual, cognitive or functional deficits suggesting a driving safety concern, and 
individuals must agree to the protocol in order to enroll. In identifying potential impairment, 
research staff will draw upon evidence and impressions from responses as well as participant 
observation; examples might include a participant who is unable to remember the question just 
asked, who is disoriented to the purpose of the questions, who mentions a diagnosis of 
cognitive impairment/dementia or new difficulties with memory, who mentions new significant 

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physical limitations (including site, reflexes and strength), or who mentions problems with 
driving. In such cases, the research staff member would document the date and specific 
observations and review the issue with the Site PI. If the concerns are deemed relatively mild, 
the study team will provide the participant with information on community resources for driving 
assessment and rehabilitation and will recommend that they discuss their driving with their 
primary care provider. If the concerns are deemed more serious than “relatively mild,” the study 
team would similarly provide information (including specific concerns) to the participant with the 
written recommendations that they talk with their primary care provider and/or have a driving 
safety evaluation and that they not drive until cleared. The informed consent form gives 
participants the option of allowing the research team to contact a primary care provider and/or 
another designee if the investigators’ concerns about driving risks are more serious than 
“relatively mild.” If consent was given, the study team would reach out to the designated person 
or people to express their concerns about the subjects’ driving safety. Otherwise, additional 
survey responses will not be shared with anyone who is not involved with the study (including 
physicians, family members, or hospital or law enforcement authorities) unless specifically 
requested by the participant, this includes the co-enrolled family member or friend. 
This study will enroll older adults who potentially may experience mild to moderate levels of 
cognitive impairment, creating a potential risk for informed consent for study participation. Level 
of impairment will be measured through administration of the 5-minute MoCA during an initial 
phone screen; the participant must score ≥21 to be eligible to be scheduled for an enrollment 
visit. While completing the consent process during the initial study interview (in person or 
remotely, via phone), staff will ask a series of questions to determine understanding of study 
procedures. In the event there is cognitive concern, the participant will not be allowed to enroll in 
the study. 
At each follow up for all drivers and study partners, a 5-minute MoCA will be repeated to 
determine current cognitive impairment. As all participants have completed informed consent at 
initial enrollment, potential diminished cognitive capacity does not limit the participant’s right to 
continued participation in follow up calls. During consent at enrollment, participants will have 
reviewed study requirements, tasks, duration, and an explanation that all participants have the 
right to continue or withdraw as they wish. Therefore, cognitive impairment will not automatically 
withdraw a participant from the study. Further, this study poses minimal risk to the participant. In 
the event that severe cognitive impairment disrupts the research team’s ability to collect follow 
up data from the participant, the study team may choose to discontinue all or specific portions of 
follow-up procedures. 
Because of the study’s affiliation with clinical sites, there is the potential risk of coercion. We will 
minimize this risk by being very clear that participation is completely voluntary, separate from 
their clinical care, and that a participant can withdraw from the study at any time. Additionally, 
we will make clear that the patient’s clinical care will not be impacted by the decision to 
participate or not to participate. No clinicians or providers from the participating clinics will be 
involved in the consenting of patients. There will be no exchange of payment for study 
participant referrals. 
This study will not involve pregnant women, human fetuses, neonates, children, or prisoners. 
Study participation will not interfere with clinical care. The study informed consent form will 
include a written description of all of these risks, including the potential for eventual driving 

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cessation due to study participation, and all of these risks will be discussed during the informed 
consent process. This study will not enroll older adults with severe cognitive impairment. 
Adequacy of Protection against Risks 
All staff participating in the project will complete compliance and human subjects research 
training. All recruitment materials will be submitted for approval by COMIRB. No one outside of 
the research team will know that a participant was involved in the study unless the participant 
discloses this him/herself. All research records and data will be kept in a locked file in the PI’s 
and Site PIs’ offices and/or in password-protected electronic files; recordings will be destroyed 
seven years after collection to further protect confidentiality. 
An additional risk of this study is loss of privacy due to breach of confidentiality, although proven 
safeguards will be put in place to protect participant confidentiality as much as possible.  Data 
will be entered electronically into REDCap, a COMIRB-preferred method of data storage. 
REDCap is a secure, web application designed to support data capture for research studies, 
providing user-friendly web-based case report forms, real-time data entry validation (e.g. for 
data types and range checks), audit trails and a de-identified data export mechanism to 
common statistical packages (SPSS, SAS, Stata, R/S-Plus). The system was developed by a 
multi-institutional consortium which includes University of Colorado Denver and was initiated at 
Vanderbilt University. The database is hosted at the University of Colorado Denver 
Development and Informatics Service Center, which will be used as a central location for data 
processing and management. 
Potential Benefits and Importance of the Knowledge to be Gained 
All Aim 1 participants will be compensated for the enrollment session and for each telephone 
follow-up call (drivers: $50 for first interview and $15 per follow-up call; family members/friends: 
$25 for first interview and $15 per follow-up call). For Aim 3, participants will receive a $25 gift 
card for their participation in the qualitative interview/survey. The study is not designed to 
improve the health of study participants.  However, some participants may benefit from the 
assessment tests by learning more about their health and functioning. The decision aid may 
help participants make more informed and value-concordant decisions about driving, which 
could enhance their health and mobility over subsequent years. Results of the study will 
contribute to helping older adults maintain safe mobility by extending the time period over which 
they can safely drive and to successfully transition to non-driving when it becomes necessary. 
Participants in the control website group are also receiving information, albeit in an unstructured 
way, and may benefit from that knowledge. The findings will expand the knowledge on factors 
that affect decision making, specifically to the important decision of driving cessation in older 
adults, which offers the potential to benefit the millions of older adult drivers in the US. The 
findings will also contribute to traffic safety and public health, by providing much-needed 
information for advancing the science and practice of safe mobility through a life-course 
perspective. 
Potential Challenges and Strategies 
In terms of feasibility, the study team is uniquely positioned to test the DDA, given the 
investigators’ breadth and depth of complementary areas of expertise, as well as experience 
recruiting older adults for clinical trials and longitudinal studies. Regarding the choice of the 
Healthwise DDA, we recognize that the use of a commercial product (albeit from a nonprofit 

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organization) can, in some cases, complicate full dissemination. However, Healthwise decision 
aids are already widely available. The Healthwise DDA was developed according to and meets 
international standards29 and is web-accessible and ready for efficacy testing, and Healthwise is 
excited for our team’s study. Overall, we feel that the use of the Healthwise DDA for this study 
makes the most sense scientifically and with a view toward future widespread use. Regarding 
the choice of control, the NIA “Older Drivers” website best represents typical care, as it is an 
easily-accessible website that provides basic information about driving risk and driving 
cessation. However, the NIA website does not guide the individual through the decision, unlike 
the DDA being evaluated, making it an appropriate control for this study. We considered other 
websites (e.g., from AARP)111, 112 but these include self-assessment tools, videos, and 
worksheets that make them more intensive than the DDA. Issues related to research with older 
drivers include variation in state laws for reporting of potentially unsafe drivers: among the study 
sites, such reporting is mandatory in California but optional in Colorado and Indiana. We will not 
be assessing actual driving ability. However, should study staff have strong concern that a 
participant may have high risk of crash, we will follow IRB and state regulations concerning 
safety reporting, having explained this during informed consent. 
Another potential challenge is the development of semi-autonomous vehicles. With availability 
estimated to become widespread ≥10 years from now,23 such technology does not negate the 
importance of research related to older driver self-regulation and driving retirement. However, in 
our study questionnaires we will assess use of, and interest in, such technologies.  Finally, we 
anticipate being unable to enroll a family member for every participant, as many older adults are 
socially isolated. But exploration of the role of family members in decisions about driving—
including their participation rate in the trial—is important. Our power calculations are based on 
older driver participation, so family participation should not affect overall DDA efficacy testing. 
However, to allow adequate power for family member analyses, as described above, we will 
ensure that at least 200 participants enrolled have a family member also enrolled. 
Conducting research with cognitively-impaired participants poses certain challenges, but it is 
critically important. Cognitive impairment from Alzheimer’s disease and other processes is 
associated with eventual driving impairment and the need for driving retirement, although the 
diagnosis of dementia alone does not mandate retirement.32 Our team has experience recruiting 
cognitively-impaired older drivers for longitudinal research, and we will follow our standard 
practices to respect participant rights and safety while also being mindful of community safety. 
In the event there is cognitive concern, the participant will not be allowed to enroll in the study.. 
For potentially impaired drivers, we will follow state requirements for reporting to authorities or 
providers, with disclosure of these procedures during the consent process. It is important to note 
that we are not assessing driving function or crash risk, and no screens or cognitive tests 
adequately determine these. We chose measures for cognitive function and decisional capacity 
after careful consideration and discussion. We chose the 5 minute MoCA as the initial screening 
test (and telephone follow-up test) because of its various formats and common use in clinical 
practice, and the SPACED tool for its feasibility in real-world settings. Use of the BTACT and 
OTMT tests will allow categorization of participants into cognitive function subgroups, enabling a 
more refined analysis related to cognitive function. We considered the Assessment of Capacity 
for Everyday Decision-making,79 but it requires identifying driving as its functional problem and 
could bias trial results. 
Conclusions: 

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The proposed line of work described in this protocol combines innovative design, a uniquely-
qualified team, and the potential for eventual widespread dissemination and impact. Our 
innovative application of the decision aid model to older driver decision making offers the 
possibility of facilitating decisions about driving retirement in a patient-centered, acceptable, 
feasible way, and consequentially it has the potential to reduce the negative psychosocial 
outcomes associated with driving retirement. Our study team is uniquely qualified to complete 
the proposed project, given its multidisciplinary expertise in relevant domains and its track 
record of enrollment of older drivers. There is a high potential for wide-spread dissemination in 
the future, given the large number of older drivers, the DDA’s web format, already broad reach 
of Healthwise tools, and our inclusion of a range of stakeholder groups in evaluation. 
Understanding how, with whom, and when to use a driving decision aid has the potential to 
significantly improve the independence, health, and well-being of millions of older adults. 
 
 

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