Ozcandrive cohort study

Safety Science 119 (2019) 219–226

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Changes in driving patterns of older Australians: Findings from the Candrive/Ozcandrive cohort study

T

J.L. Charltona, , S. Koppela, A. D'Eliaa, P. Huaa, R. St. Louisa, P. Darzinsb,c, M. Di Stefanod, M. Odelle, M. Porterf, A. Myersg, H. Tuokkoh, S. Marshalli ⁎

a

Monash University Accident Research Centre, Monash University, Australia Eastern Health, Australia c Eastern Health Clinical School Monash University, Australia d La Trobe University, Australia e Victorian Institute of Forensic Medicine, Australia f Centre on Aging and Faculty of Kinesiology and Recreation Management, University of Manitoba, Winnipeg, Manitoba, Canada g University of Waterloo, Canada h University of Victoria, Canada i Ottawa Hospital Research Institute, Canada b

ABSTRACT

This paper describes changes in driving patterns over a five-year period of drivers aged 75 years and older (Year 1: Male = 68.9%; Age = M = 79.5 years, SD = 3.4 years, Range: 75.0–88.0 years) in the Candrive/Ozcandrive cohort study. Participants completed various functional and health assessments and selfreported driving questionnaires. In-vehicle data-loggers, installed in participants’ own vehicles, also monitored spatio-temporal characteristics of participants’ everyday driving trips. Data for a subset of one hundred and ninety-one Ozcandrive participants from Melbourne, Australia were analysed. Reductions in driving trip distance and frequency were observed over the five years. On average, in Year 1, participants drove 1223 (SD = 502) trips, totalling 8993 (SD = 5169) kilometers annually, decreasing significantly to 1028 trips (SD = 559) trips and 6787 (SD = 4624) kilometers in Year 5. On average, in Year 1, participants’ driving trips were around 7.5 km (SD = 3.2), decreasing in distance significantly to 6.9 km (SD 3.9) in Year 5. Log-normal General Estimating Equation (GEE) modelling was conducted for selected driving exposure measures (annual distance driven, annual trip frequency, trip distance, etc.). Reductions in overall annual distance driven were significantly associated with being female, increasing age, withdrawal from the study for health reasons and lower night-time driving comfort scores (marginally significant). Reductions in annual trip frequency were associated with increasing age and withdrawal from the study for health reasons. Results suggest drivers practiced self-regulation, which may reflect adaption to deterioration in health and functional status.

1. Introduction Older drivers are one of the highest risk groups for crash-related deaths and serious injuries per number of drivers and per distance travelled (Koppel et al., 2011; Langford and Koppel, 2006). This increased risk has commonly been attributed to their frailty and associated injury susceptibility (Augenstein, 2001; Li et al., 2003), and also to age-related declines in cognition, vision and psychomotor abilities and increased medical conditions and medication use (Eby et al., 2008; Molnar et al., 2007). Given that these age-related declines appear to compromise driving safety, older adults have been encouraged to selfregulate their driving by making changes to driving patterns such as driving less frequently or avoiding challenging road environments (Molnar et al., 2015). These self-regulatory practices may enable most older adults to continue driving safely for longer periods and to maintain their community mobility and independence (Gwyther and ⁎

Holland, 2012). Much of the research surrounding self-regulation among older drivers is based on self-reported data. Previous research has revealed that older drivers report that they self-regulate by reducing the frequency of driving and their driving distances or by avoiding challenging road situations such as driving at night, in bad weather or during peak hour (Baldock et al., 2006; Charlton et al., 2006; Molnar et al., 2013). Reported reasons for self-regulation include declines in vision, psychomotor abilities and cognitive functions, as well as effects of chronic medical conditions (Charlton et al., 2006; Vance et al., 2006). However, these self-reported results are susceptible to recall and social desirability bias (Grengs et al., 2008; Sullman and Taylor, 2010). Objective methods of collecting self-regulation data such as naturalistic driving studies (NDS) that examine real-world driving behaviour (Blanchard et al., 2010) may counter this potential bias. In-car recording devices (ICRDs), which provide data on naturalistic driving patterns such as the

Corresponding author. E-mail address: [email protected] (J.L. Charlton).

https://doi.org/10.1016/j.ssci.2018.11.008 Received 5 January 2018; Received in revised form 7 September 2018; Accepted 10 November 2018 Available online 27 November 2018 0925-7535/ © 2018 Elsevier Ltd. All rights reserved.

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time/date of trips, speed and distance travelled, and better assess the type and degree of self-regulatory driving behaviour (Staplin et al., 2008), are used for NDS. Several recent NDS have provided further insights into self-regulatory patterns of older drivers. Blanchard and Myers (2010) conducted one of the first studies of older drivers’ naturalistic driving patterns for a week, and found that lower driving comfort and lower perceived driving abilities were related to reduced driving exposure, including at night and on highways, as well as fewer trips close to home. In a similar study examining older adults’ naturalistic driving patterns for a week, Coxon et al. (2015) found that lower self-reported driving confidence was associated with shorter distances travelled from home and average trip distances, as well as reduced night-driving. To account for climatic influences on driving, an additional NDS examined older adults’ winter driving patterns over two consecutive weeks and revealed that older drivers were more likely to make trips for social or entertainment purposes on days with good weather, and out-of-town trips on days with good road conditions (Myers et al., 2011). It was also noted that compared to male older drivers, female older drivers had significantly lower driving comfort scores, and were less likely to drive, on days with adverse weather or road conditions. Other studies of older drivers with Parkinson’s Disease, Alzheimer’s Disease and early stage dementia have reported several differences in the driving patterns of neurologically impaired populations compared to healthy controls including: fewer trips overall, fewer trips at night and on freeways, shorter distances and more trips close to home (Crizzle and Myers, 2013; Eby and Molnar, 2012; Festa et al., 2013). Collectively, these NDS provide evidence of self-regulatory practices among older drivers, as well as the relationship between driving patterns and older drivers’ demographic and functional performance characteristics. A notable limitation of existing NDS is the lack of longitudinal designs to identify when older drivers change their behaviour. The range of time that driving patterns have been examined naturalistically in previous studies has varied from one to two weeks (Crizzle and Myers, 2013; Festa et al., 2013) to one to two months (Eby and Molnar, 2012), which may not adequately capture the full extent of individuals’ driving practices, particularly if driving is affected by events such as vacations and illnesses (Coxon et al., 2015) or fluctuates seasonally such as in areas with winter road conditions (Sabback and Mann, 2005). Given that they provide more accurate representations of potential changes in driving patterns and/or exposure, longitudinal designs could better enable researchers to investigate whether effective self-regulation reduces crash involvement (Blanchard and Myers, 2010). Likewise, researchers could ascertain the direction of causal relationships such as whether changes to older drivers’ vision or cognitive abilities contribute to modifications to self-regulation or vice versa (Blanchard and Myers, 2010). This is especially important as cross-sectional studies show steeper declines in functional outcomes with increasing age, which contribute to increases in reported self-regulation (Donorfio et al., 2008). Recently, two noteworthy longitudinal older driver NDS have been conducted and have allowed researchers to measure trends in realworld driving patterns of older adults across multiple years. The longest NDS of older drivers to date is Candrive/Ozcandrive, a prospective cohort study of older drivers across sites in Canada, Australia and New Zealand which aims to develop valid assessment tools for clinicians in the identification of older drivers who may be unfit to drive (Marshall et al., 2013). Similar to previous cross-sectional studies, subsets of these data have shown that poor performance on tests of psychomotor speed and executive functioning, as well as lower self-ratings of perceived driving abilities and feelings of driving comfort, were associated with increased self-reported self-regulation such as reduced driving frequency and exposure to difficult driving conditions (Molnar et al., 2014; Rapoport et al., 2013). LongROAD (Longitudinal Research on Aging Drivers) is also a longitudinal prospective cohort study which is monitoring older drivers in the United States with a focus on issues

around driving patterns, self-regulation and crash risk during the ageing process (Molnar et al., 2015). It is expected that this study will produce the largest database on older drivers to date (Li et al., 2017). There is a great need to compare self-reported data with naturalistic driving data. One comparison of older drivers’ self-reported driving patterns with objective data, collected through electronic devices installed in the older driver’s vehicle, revealed that older drivers’ selfreported number of trips and driving distances were inaccurate and that older drivers may not actually self-regulate as they report (Blanchard et al., 2010; Festa et al., 2013). Given that there is expected to be a significant growth of older adults in the driving population (Koppel and Berecki-Gisolf, 2015) and that driving a private vehicle is their preferred mode of transport, there are benefits to long-term monitoring and analysis of their driving patterns and propensity to compensate for age-related declines in performance. Self-monitoring and self-regulation, as needed, not only have the potential to reduce crash-related deaths and serious injuries, but may also inform licensing policies. This may be particularly relevant if older adults who are not fit to drive remove themselves from risk and the older adults who are fit to drive do not have unnecessary restrictions to their licences, and therefore can maintain their mobility and independence (Marottoli and Richardson, 1998; Marshall et al., 2002) This study aimed to describe the real-world driving patterns of a cohort of older drivers and how these change over time using Ozcandrive data. In particular the study also aimed to examine whether changes in objective measures of driving behaviour were associated with functional performance and self-reported driving-related practices. 2. Method 2.1. Candrive/Ozcandrive project The Candrive/Ozcandrive study is a multicentre, prospective cohort study which involves a total of 1230 older drivers from Canada, Australia and New Zealand. In addition to the naturalistic driving data collected, participants completed yearly assessments, which documented demographic and driving-history questions, measures of functional performance, medications and medical conditions, and self-reported information on driving-related comfort, abilities and practices. Full details on sample recruitment, inclusion/exclusion criteria, and annual assessment protocols can be found elsewhere (Marshall et al., 2013). 2.2. Participants The Australian subset of the Candrive/Ozcandrive study is comprised of 257 participants living in the greater Melbourne area in Victoria, Australia. All participants were required to meet the following inclusion criteria: (a) aged 75 or older; (b) held a valid driver’s license; (c) drove at least four times per week; (d) drove a 2003 model vehicle or newer, and (e) did not have an absolute contraindication to driving, as defined by the Austroads Fitness to Drive Guidelines (Austroads, 2013). 2.3. Measures 2.3.1. Naturalistic driving data Monitoring of participants’ driving patterns occurred throughout the study using a custom-designed ICRD (OttoView-CD autonomous data logging device) and software suite that was developed for Candrive/Ozcandrive by Persen Technologies Inc. (Winnipeg, Manitoba). The ICRD was powered through the on-board diagnostic port of the participants’ primary vehicle. The ICRD collected information from the vehicle (e.g., time/date of trip, speed, distance travelled and vehicle parameters) and vehicle location was registered using the Global Positioning System (GPS). Data were saved at a rate of 1 Hz onto 220

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Table 1 Description of naturalistic driving data measures. Driving Behaviour

Outcome Variable

Definition

Driving Distance Driving Frequency Trip Distance Night Time Driving Peak Hour Driving

Total distance Total trips Mean trip distance % Night % Peak hour

Shorter/Longer Trips

% ≤5 km or > 20 km

Kilometres driven per year Number of trips per year Mean distance per trip (ignition on/off, km) Proportion of total annual trips driven at night (i.e., between 1800 and 600 h) Proportion of total annual trips driven during peak traffic hours (i.e., weekday periods between 700 and 930 h or between 1600 and 1800 h) Proportion of trips falling into the following trip length categories: ≤5 km or > 20 km

a Secure Digital (SD) card that was changed approximately every 4 months to ensure adequate storage space. For participants who shared their vehicle with another driver, a radio frequency identifier system (RFID) was attached to the study participants’ car keys. The RFID signals marked the study participants’ driving data, thus allowing other driver data to be disregarded. A log book was also provided for shared vehicles for the purpose of recording details for all non-participant driving trips. Additionally, in the event that participants changed their primary vehicle, every effort was made to transfer the ICRD device into the new vehicles on the same day the vehicles were acquired. The driving behaviours and naturalistic driving variables analysed in this paper are described in Table 1.

abilities (e.g., see road signs at night, make quick driving decisions) on a four-point scale (where 0 = poor, 3 = very good). Total scores can range from 0 to 45, with higher scores indicating more positive perceptions of driving abilities (Blanchard et al., 2010; MacDonald et al., 2008). Situational driving frequency and avoidance: Driving practices were assessed using the Situational Driving Frequency (SDF) and Avoidance (SDA) scales. On the SDF scale, participants are asked how often they drive, on average, in 14 different driving scenarios (e.g., at night, on highways, in rural areas, in heavy traffic or rush hour in town, on trips lasting 2 h each way, etc.) on a five-point scale: never (0), rarely (1 = less than once a month), occasionally (2 = more than once a month but less than weekly), often (3 = one to three days a week) or very often (4 = four to seven days a week). Total scores can range from 0 to 56 with higher scores indicating driving more often in challenging situations. On the SDA scale, older drivers are asked ‘If possible, do you try and avoid any of these driving situations?’, and to ‘Check all that apply” on a list of 192 situations (e.g., night, dawn or dusk, bad weather conditions in general, heavy rain, making left hand turns (acrosstraffic), etc.). The last item, ‘No I don’t try to avoid any of these situations’, is used to ensure that people have considered all the situations. Scores can range from 0 to 19, with higher scores indicating greater avoidance (MacDonald et al., 2008; Myers et al., 2008).

2.3.2. Functional ability measures Five measures of functional ability were selected for analysis from the annual assessment battery, including three measures of cognition, one physical assessment and one vision assessment: Montreal Cognitive Assessment (MoCA), Mini-Mental State Examination (MMSE), Trail Making Test B (Trails B), Rapid Pace Walk (RPW) and the Snellen eye chart. 2.3.2.1. Cognitive assessments. The MoCA (Nasreddine et al., 2005) and MMSE (Folstein et al., 1975) are brief cognitive assessments with total scores ranging from 0 to 30. Scores below 26 (MoCA) and 24 (MMSE) indicate cognitive impairment. Trails B (Moses, 2004) is a timed measure of general cognitive function and executive functioning which involves connecting 25 numbers and letters in alternating order (i.e., 1 to A to 2 to B, etc.). The score is the overall time in seconds required to complete the connections, where a time in excess of 180 s may indicate increased risk of crash (Staplin et al., 2003).

2.3.2.4. Medications and medical conditions. At each annual assessment, participants listed current prescribed and over-the-counter medications. Additionally, participants were asked if they had experienced or been diagnosed with a range of medical conditions. If yes, participants then rated the medical condition on a four-point scale: current mild or past significant problem (1), moderate problem that requires first-line therapy (2), severe problem (3) or extremely severe problem (4). The ‘severity of medical conditions’ was calculated by summing all values between one and four for each medical condition. The ‘total number of current medical conditions’ was calculated by the number of medical conditions that had a severity rating between one and four. The total number of medications (Meds) was selected for this analysis. A change in data collection at Year 5 precluded use of the medical conditions measure in regression modelling. Pearson correlational analyses for Year 1 data showed that the two measures were related; that is, medication use was a reliable proxy for the sum of all medical conditions, r = 0.69, p < 0.001.

2.3.2.2. Physical and vision assessments. RPW is a timed measure of motor speed, balance and coordination (Carr et al., 2010). A time in excess of 10 s may indicate increased crash risk (Staplin et al., 2003). The Snellen eye chart provides a measure of Visual Acuity. Visual Acuity scores obtained from the Snellen eye chart were converted to the logarithm of the minimum angle of resolution (LogMAR) (Holladay, 1997). A LogMAR score of 0.0 is considered normal vision, whereas a score of +0.3 is considered reduced vision and is the Australian legal driving limit (Austroads, 2013). 2.3.2.3. Self-reported driving-related abilities and practices. Five measures of self-reported driving-related abilities and practices were analysed: driving comfort (Day and Night), perceived driving abilities, and situational driving frequency and avoidance. Driving comfort: Driving comfort was measured using two scales that assess comfort of driving in various situations during the day and at night. The 13-item daytime and 16-item night-time Driving Comfort Scales (DCS-D, and DCS-N, respectively) ask participants to rate their comfort while driving in a range of situations. Possible scores range from 0 to 100 percent, with higher scores indicating greater driving comfort (Blanchard et al., 2010; MacDonald et al., 2008). Perceived driving abilities: The 15-item Perceived Driving Abilities (PDA) scale asks participants to rate various aspects of their current

2.4. Data analyses Driving data for 257 participants were cleaned and filtered against trip criteria, yielding a total of 191 participants included in the modelling analyses. Of these, 153 participants had a full five years of driving data while 38 participants dropped out during the five years, including 29 who withdrew for health reasons during the study period (Health Dropout). Table 2 shows the exclusion criteria for driving data for the 66 participants whose data were not included in the current analyses. In addition to these criteria, driving trips were excluded from analysis if: the ICRD data indicated that no RFID fob was detected for that trip, or if trip times overlapped by at least 50% with an entry in the 221

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respectively). Participants’ performance on the functional measures remained relatively stable over the five-year period (see Table 4). For example, participants’ performance on the MoCA, MMSE, Trails B and the Visual Acuity assessment did not change from Year 1 to Year 5. However, participants took significantly more time to complete the RPW task in Year 5 compared to Year 1. As shown in Table 5, participants’ reported driving-related abilities and practices indicated some changes over the five-year period. Participants’ perceived driving abilities and frequency of driving in challenging driving situations significantly decreased across Year 1 to Year 5. On the other hand, participants reported that their comfort during day-time and night-time driving, as well as their tendency to avoid driving situations, had not changed from Year 1 to Year 5. In terms of self-reported Meds, participants reported that they were using significantly more medications in Year 5 (8.18) than in Year 1 (5.34), F(1, 198) = 146.28, p < 0.001. Table 7 summarises the descriptive statistics for participants’ naturalistic driving patterns in Years 1 and 5, recorded by the ICRDs. On average, participants drove significantly fewer kilometers and made fewer trips in Year 5 compared to Year 1. In Year 1, trip distances were around 7.5 km decreasing significantly to just under 7 km per trip in Year 5. Relatively few trips were made at night (between 18:00 and 6:00) with around 9 percent of trips at night-time in Year 1, with a small but significant decrease observed in Year 5. Around two-thirds of trips were ‘short’ distances (5 km or less) and there was a small but significant increase in Year 5. In contrast, participants made relatively few ‘longer’ trips (i.e., > 20 km), with the proportion dropping slightly from Year 1 to 5. This effect just failed to reach statistical significance. Table 8 shows the relative risks (and 95% confidence intervals) for the GEE models examining whether driver characteristics as well as changes in measures of functional performance and self-reported driving-related practices were statistically significantly associated with changes in objective measures of driving behaviour across Years 1–5. The GEE model for Total Distance (km) revealed that reductions in overall distance travelled were significantly associated with being female, increasing age, an increase in Visual Acuity, lower DCS-N scores and Health Dropouts. Compared to females, males were associated with 54.8 percent greater total distance travelled. An increase in age by one year was associated with a 3.5 percent decrease in total distance travelled. An increase in Visual Acuity by one unit is associated with a decrease of 0.6 percent in total distance travelled. An increase in DCS-N by one unit was associated with a 0.2 percent increase in total distance travelled (marginally significant). Compared to Health Dropouts, nonDropouts were associated with 31.5 percent greater total distance travelled. The GEE model for Total Trips revealed that reductions in annual trip frequency were associated with increasing age, poorer Visual Acuity and Health Dropouts. An increase in age by one year was associated with a 1.6 percent decrease in total trips. An increase in Visual Acuity by one unit was also associated with a decrease of 0.5 percent% in total trips (marginally significant). Compared to Health-Dropouts, non-Dropouts were associated with 25.6 percent greater total trips. Shorter average trip distances were associated with being female, lower cognitive scores and lower DCS-N scores. Compared to females, males were associated with 34.6 percent greater mean trip distance. An increase in MMSE by one unit was associated with a 6.9 percent increase in mean trip distance. An increase in DCS-N by one unit (higher comfort level) was associated with 0.3 percent increase in mean trip distance. An increasing proportion of ‘shorter’ distance trips (≤5 km) was significantly associated with increasing age (marginally significant) and non-Dropouts. An increase in age of one year was associated with a 0.8% increase in percentage trips within 5 km (marginally significant). Compared to Health-Dropouts, non-Dropouts were associated with 8.9 percent greater proportion of trips within 5 km.

Table 2 Description of Exclusion criteria. Number of participants excluded

66

Had missing data for the current variables of interest Had any unexplained interruptions in their driving (defined as breaks in driving of one month or greater that did not coincide with interruptions recorded in the participant’s secondary drivers log book) Had data that was affected by RFID fob detection issues (defined as periods of one month or greater during which no RFID fob was detected) Drove a secondary vehicle more than 30% of their total distance driven (calculated based on participant’s annual estimates of primary and secondary vehicle usage) Had entries in their secondary drivers log book that differed significantly from driving data (mismatch between dates/times recorded in log book and ICRD recording on at least 28 days in total) or had recorded secondary driver trip times as ‘unknown’ on at least 28 days in total

secondary driver’s log book. 2.4.1. Analyses of Variance To examine whether there were significant changes in functional abilities, health and driving patterns across the five-year study period, separate repeated measures Analyses of Variance (ANOVA) were conducted (Year 1 vs. Year 5) using SPSS version 23 (IBM Corp, 2015). 2.4.2. Generalised Estimating Equations Log-normal General Estimating Equations (GEEs) were used to examine whether measures of functional performance and self-reported driving-related practices were statistically significantly associated with objective measures of driving behaviour across Years 1–5. Individual models were run for each driving behaviour outcome measure, including: total distance, total trips, mean trip distance, % night, % peak hour, % ≤5 km and % > 20 km. GEEs with an unstructured correlation matrix were used to take into account within-subject correlation in the longitudinal study design. Models were run using SPSS version 23.0 (IBM Corp., 2015). Before inclusion in the GEE models, the functional performance and self-reported driving-related practices measures were checked to see if they were correlated with each other. Inclusion of correlated factors in the models could lead to spurious results, which could not be readily interpreted. Where measures are correlated, it means that they measure the same underlying construct and thus only one of the correlated variables was included in each GEE model. The factors included in each model were Gender, Age, RPW, Trails B, Visual Acuity, MoCA, MMSE, PDA, DCS-N, Meds and Health Dropout. 3. Results Tables 3–6 summarise the descriptive statistics for participants’ demographic and self-reported driving characteristics, performance on functional ability measures, self-reported driving-related abilities and practices and medications/medical conditions. As shown in Table 3, most participants were aged 75–79 years in Year 1 (54.1%) and, by Year 5, the majority were aged 80–84 years (62.1%). In terms of participants’ gender, most participants were male in Year 1 (70.8%), however the proportion of males had decreased to 67.7 percent by Year 5. Most participants reported that they were married in Year 1 (59.9%), however the proportion of participants who reported that they were widowed increased from Year 1 (26.1%) to Year 5 (33.3%). When asked about their frequency of driving, most participants reported in Year 1 that they drove 4–6 times per week (59.1%), however this had decreased to 46.5 percent in Year 5. In Year 1, most participants reported that they drove between 5000 and 10,000 km per year (44.0%). However, the proportion of participants who reported that they drove between 5000 and 10,000 km per year in Year 5 had decreased to 31.3 percent and the proportion of participants who reported that they drove between 3000 and 5000 km per year or 1000 and 3000 km per year had increased (17.2%; 16.2%, 222

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Table 3 Demographic and self-reported driving characteristics. Demographic and Driving Characteristics Number of participants Age group

75–79 years 80–84 years 85–89 years 90–94 years Male Female Single (never married) Married Common-law (Defacto) Divorced/separated Widowed Primary school High school Trade/Technical certificate Diploma Degree Postgraduate Daily 4–6 times per week 2–3 times per week Once a week 0–1000 km 1001–3000 km 3001–5000 km 5001–10,000 km 10,001–15,000 km 15,001–20,000 km 20,001–25,000 km > 25,000 km

Gender Marital status

Highest level of education

Frequency of driving

Estimated kilometres driven in past year

Y1 % (N)

Y2 % (N)

Y3 % (N)

Y4 % (N)

Y5 % (N)

257 54.10 (139) 34.60 (89) 10.90 (28) 0.40 (1) 70.80 (182) 29.20 (75) 6.60 (17) 59.90 (154) 0.80 (2) 6.60 (17) 26.10 (67) 24.10 (62) 10.50 (27) 15.60 (40) 28.40 (73) 15.20 (39) 6.20 (16) 40.90 (105) 59.10 (152) 0.00 0.00 0.00 7.40 (19) 8.20 (21) 44.00 (113) 27.20 (70) 8.60 (22) 2.30 (6) 2.30 (6)

242 42.10 (102) 41.70 (101) 15.70 (38) 0.40 (1) 71.50 (173) 28.50 (69) 6.20 (15) 58.70 (142) 0.80 (2) 7.00 (17) 27.30 (66) 24.00 (58) 9.10 (22) 14.50 (35) 29.30 (71) 16.90 (41) 6.20 (15) 49.60 (120) 45.50 (110) 5.00 (12) 0.00 0.00 2.10 (5) 13.20 (32) 44.20 (107) 26.00 (63) 9.10 (22) 2.90 (7) 2.50 (6)

229 29.30 (67) 51.10 (117) 17.90 (41) 1.70 (4) 69.90 (160) 30.10 (69) 6.60 (15) 55.00 (126) 0.90 (2) 6.60 (15) 31.00 (71) 26.20 (60) 9.20 (21) 12.20 (28) 28.80 (66) 17.00 (39) 6.60 (15) 39.70 (91) 52.00 (119) 8.30 (19) 0.00 1.30 (3) 8.30 (19) 11.80 (27) 42.80 (98) 24.90 (57) 6.10 (14) 3.10 (7) 1.70 (4)

216 19.90 (43) 56.00 (121) 19.40 (42) 4.60 (10) 68.50 (148) 31.50 (68) 6.90 (15) 53.70 (116) 0.90 (2) 6.50 (14) 31.90 (69) 27.30 (59) 6.90 (15) 12.50 (27) 29.60 (64) 16.70 (36) 6.90 (15) 40.70 (88) 49.50 (107) 9.70 (21) 0.00 0.90 (2) 10.20 (22) 13.40 (29) 40.70 (88) 23.10 (50) 6.50 (14) 3.20 (7) 1.90 (4)

198 7.10 (14) 62.10 (123) 23.70 (47) 7.10 (14) 67.70 (134) 32.30 (64) 7.60 (15) 52.00 (103) 1.00 (2) 6.10 (12) 33.30 (66) 27.80 (55) 7.10 (14) 11.60 (23) 29.80 (59) 17.20 (34) 6.60 (13) 44.90 (89) 46.50 (92) 7.60 (15) 1.00 (2) 1.50 (3) 16.20 (32) 17.20 (34) 31.30 (62) 24.20 (48) 6.10 (12) 2.50 (5) 1.00 (2)

A decreasing proportion of ‘longer’ trips (> 20 km) was associated with being female, lower cognitive scores and lower DCS-N scores. . Compared to females, males were associated with 68.8 percent greater percentage trips exceeding 20 km. An increase in MMSE by one unit was associated with an 8.2% increase in percentage trips greater than 20 km. An increase in DCS- N by one unit was associated with a 0.6 percent increase in trips exceeding 20 km. Compared to HealthDropouts, non-Dropouts were associated with 51.9 percent greater percentage of all trips greater than 20 km. Reduction in the proportion of night-time trips was significantly associated with increasing age, better Visual Acuity and lower DCS-N scores. An increase in age of one year was associated with a4.3 percent decrease in percentage night-time trips. An increase in Visual Acuity by one unit was associated with an increase of 1 percent in percentage night trips. An increase in DCS-N by one unit was associated with 0.4 percent increase in percentage night trips.

Reduction in the proportion of peak hour trips was significantly associated with increasing age and better Visual Acuity. An increase in age of one year was associated with a 1.9 percent decrease in percentage peak hour trips. An increase in Visual Acuity by one unit was associated with an increase of 1.2 percent in percentage peak hour trips. 4. Discussion The aims of the current study were two-fold: (1) to describe the realworld driving patterns of the Ozcandrive cohort of older drivers and how these change over a five-year period; and (2) to examine whether changes in objective measures of driving behaviour were associated with functional performance and self-reported driving-related practices. In terms of the first aim, the findings from the current study revealed a pattern of decreased real-world driving by Ozcandrive participants over the five-year period, characterised by a progressive

Table 4 Performance on functional ability measures.

Number of participants MoCA MMSE Trails B Rapid Pace Walk Visual Acuity (LogMAR)

Mean (SD) Range % (N) unimpaired Mean (SD) Range % (N) unimpaired Mean (SD) Range % (N) unimpaired Mean (SD) Range % (N) unimpaired Mean (SD) Range % (N) unimpaired

(≥26) (≥24) (≤180 s) (≤10) (≤+0.30)

Year 1

Year 2

Year 3

Year 4

Year 5

F statistic

p value

257 26.47 (2.32) 19–30 70.00 (180) 28.97 (1.14) 25–30 100.00 (257) 114.81 (50.15) 41–407 92.20 (237) 6.95 (1.39) 4–14 97.30 (250) 0.11 (0.15) −0.20 to 0.90 95.70 (245)

242 26.58 (2.30) 19–30 71.90 (174) 29.04 (1.17) 21–30 99.60 (241) 113.03 (53.11) 43–566 92.10 (222) 7.38 (1.51) 4–16 96.30 (233) 0.08 (0.13) −0.20 to 0.40 99.20 (237)

229 26.83 (2.37) 20–30 69.40 (159) 29.04 (1.15) 22–30 99.60 (228) 109.81 (46.86) 36–408 92.10 (211) 7.56 (1.55) 4–16 97.40 (223) 0.07 (0.14) −0.20 to 0.90 98.70 (224)

216 27.10 (2.44) 18–30 79.60 (172) 28.88 (1.33) 23–30 99.50 (215) 114.97 (61.65) 39–597 88.40 (191) 7.23 (1.58) 3–14 96.30 (208) 0.08 (0.13) −0.20 to 0.50 98.60 (212)

198 26.87 (2.72) 16–30 70.20 (139) 28.91 (1.40) 23–30 99.50 (196) 110.97 (45.97) 45–290 91.90 (181) 7.59 (1.72) 4–18 95.90 (189) 0.12 (0.15) −0.20 to 1.00 95.90 (188)

F(1,198) 0.02

0.89

0.69

0.41

2.47

0.12

60.37

< 0.001

0.34

0.56

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Table 5 Self-reported driving-related abilities and practices. Self-reported driving-related abilities and practices

Y1 Mean (SD) Range

Y2 Mean (SD) Range

Y3 Mean (SD) Range

Y4 Mean (SD) Range

Y5 Mean (SD) Range

F statistic

p value

Number of participants Driving Comfort Scale (DCS) – Day (Max = 100)

257 76.35 (14.59) 17.31–100 68.63 (19.42) 0–100 34.14 (5.98) 14–45 32.89 (6.71) 11–55

242 76.39 (14.90) 23.08–100 69.68 (18.58) 1.56–100 33.50 (6.42) 15–45 32.99 (6.92) 10–50

229 76.94 (15.02) 19.23–100 68.62 (19.74) 0–100 33.44 (6.33) 13–45 32.78 (6.90) 9–52

216 76.61 (14.21) 32.69–100 67.59 (20.40) 0–100 33.21 (6.49) 17–45 32.16 (7.00)

198 76.08 (15.88) 21.15–100 67.67 (19.96) 3.13–100 32.71 (7.00) 12–45 31.42 (6.35) 13–45

F(1,198) 0.03

0.87

0.13

0.72

8.24

0.01

30.36

< 0.001

5.33 (3.80) 1–19

5.39 (3.60) 1–19

5.57 (3.71) 1–19

0.22

0.64

Driving Comfort Scale (DCS) – Night (Max = 100) Perceived Driving Abilities (Max = 45) Situational Driving Frequency (Max = 56) Situational Driving Avoidance (Max = 20)

Number of participants Number of health dropouts Total number of medications currently used Total number of current medical conditions severity-rated 1–4 Severity of medical conditions (sum values rated 1–4)

Y2 Mean (SD) Range

Y3 Mean (SD) Range

Y4 Mean (SD) Range

Y5 Mean (SD) Range

257 9 5.31 (3.31) 0–17 10.57 (4.13) 2–23 14.30 (5.87) 2–32

242 10 6.23 (3.52) 0–20 12.50 (4.34) 2–23 17.13 (6.53) 3–33

229 5 5.92 (3.30) 0–16 13.89 (4.38) 3–27 18.63 (6.56) 4–37

216 10 6.39 (3.19) 0–17 14.59 (4.28) 4–24 19.67 (6.37) 6–37

198 12 8.14 (4.26) 0–23 N/A

4.57 (3.92) 0–16

respect to changes in exposure over the study period, with higher MMSE scores associated with reductions in trip distance only. Additionally, the current findings showed consistent gender effects, with females showing greater reductions in overall driving distances, shorter trip distances and reductions in the proportion of longer distance trips (> 20 km) confirming gender differences reported in previous studies (e.g., Charlton et al., 2006; D’Ambrosio et al., 2008; Kostyniuk and Molnar, 2008; Molnar et al., 2014; Naumann et al., 2011; West et al., 2003). Changes in the circumstances under which to drive or not – particularly reductions in night-time trips – was primarily associated with increasing age and lower DCS-N scores. This may reflect less need as one ages, or alternatively, a desire to drive less in more challenging conditions, as suggested by the lower night-time driving comfort scores, reflecting more appropriate pre-trip decision-making to reduce risk. Findings for night driving were consistent with previous studies. For example, using the DCS in conjunction with objectively derived driving data, Blanchard and Myers (2010) found drivers’ perceived lower comfort to be significantly related to lower driving exposure in general as well as for specific driving situations including less night-time driving. A curious finding was that reductions in proportion of nighttime (and peak hour) trips was associated with better Visual Acuity. It should be noted that this effect was relatively small and notwithstanding small changes in vision evident across the study period, the differences were not significant and all drivers met vision standards for driving. Reduced cognitive function appeared to play a relatively minor role in determining when to drive with no significant influence on nighttime or peak hour driving. Similarly, physical functional abilities did not appear to play a significant role in driving changes. This may reflect the relative good health and functional status of the cohort. However, it should be noted that there was a considerable range of performance on several functional measures; notably, around 30 percent of the sample scored below the impairment criterion (i.e., < 26) on the MoCA across the study period. It may be that these deficits had no clinical or functional relevance and/or perhaps drivers were able to compensate for

Table 6 Medications and medical conditions. Y1 Mean (SD) Range

7–49 4.67 (3.84) 0–18

N/A

reduction in annual distances travelled, fewer driving trips and a higher proportion of ‘shorter’ distance trips. Notably, only 16 percent of driving trips were driven during peak hour traffic and fewer than 10 percent of trips were driven at night at the start of the study and night driving further decreased over the study period. These findings provide robust, objective data over a five-year period lending support to findings of previous studies describing reduced driving based on older drivers’ self-report and short-term NDS (Baldock et al., 2006, Blanchard and Myers, 2010, Charlton et al., 2006, Coxon et al., 2015, Myers et al., 2011) These patterns may reflect adaption to changes in health and physical functional status and/or lifestyle choices (Charlton et al., 2006; Molnar et al., 2014). The study also examined associations between changes in objective measures of driving behaviour and a range of driver characteristics including driver demographics, functional performance, health- and medical-status, and self-reported driving patterns and abilities. In general, reduced driving was associated with increasing age, lower perceived comfort during night-time driving, or withdrawal from the study due to ill health. Cognition played a relatively small role with

Table 7 Group means, standard deviations and range for naturalistic driving variables for Years 1 and 5 and summary of comparison tests of significance.

Number of participants Total distance Total trips Mean trip distance (km) % Night % Peak hour % ≤5 km % > 20 km

Y1 M (SD)

Y5 M (SD)

F statistic F(1,152)

p value

191 8993 (5169) 1223 (502) 7.48 (3.21) 8.62 (6.20) 16.81 (6.09) 63.69 (12.46) 6.51 (5.73)

153 6787 (4624) 1028 (559) 6.87 (3.88) 7.75 (6.51) 16.15 (7.66) 67.12 (13.32) 5.97 (6.65)

63.36 38.93 6.88 8.04 1.51 8.01 3.46

< 0.0001 < 0.0001 0.0096 0.0052 0.222 0.0053 0.065

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Table 8 Relative risks (RR) and 95% confidence intervals (CI) for generalised estimating equation models examining measures of functional performance and self-reported driving-related practices.

Gender (male vs female) Age RPW Trails B Visual Acuity MoCA MMSE PDA DCS-N Meds Health Dropout (non-dropout vs dropout)

Total distance

Total trips

Mean distance

% Night trips

% Peak hour

% ≤5 km

% > 20 km

RR 95% CI 1.548*** 1.33, 1.79 0.965*** 0.94, 0.98 1.007 0.98, 1.03 1.000 0.99, 1.00 0.994ø 0.98, 1.00 1.013 0.99, 1.02 1.004 0.97, 1.03 0.999 0.99, 1.00 1.002ø 1.00, 1.005 1.007 0.99, 1.02 1.315* 1.03, 1.67

RR 95% CI 1.108 0.96, 1.26 0.984* 0.97, 0.99 0.997 0.98, 1.01 0.999 0.99, 1.00 0.995ø 0.99, 1.00 1.008 0.99, 1.02 0.998 0.97, 1.01 0.999 0.99, 1.00 1.000 0.99, 1.00 1.008 0.99, 1.02 1.256* 1.05, 1.50

RR 95% CI 1.346ø 0.98, 1.84 1.007 0.97, 1.04 1.007 0.96, 1.05 1.000 0.99, 1.00 0.998 0.99, 1.00 1.017 0.98, 1.04 1.069** 1.02, 1.11 0.995 0.98, 1.00 1.003ø 1.00, 1.007 1.001 0.98, 1.01 1.022 0.68, 1.52

RR 95% CI 0.829 0.58, 1.16 0.957* 0.92, 0.99 0.999 0.97, 1.02 1.000 0.99, 1.00 1.010** 1.00, 1.01 0.989 0.96, 1.01 1.003 0.95, 1.04 0.992 0.98, 1.00 1.004* 1.00, 1.007 1.001 0.97, 1.02 1.306 0.84, 2.03

RR 95% CI 0.980 0.87, 1.09 0.981** 0.96, 0.99 1.000 0.98, 1.01 1.000 1.00, 1.00 1.012*** 1.00, 1.016 0.997 0.98, 1.01 1.007 0.98, 1.02 1.003 0.99, 1.01 1.000 0.99, 1.00 0.994 0.98, 1.00 1.097 0.96, 1.25

RR 95% CI 0.996 0.93, 1.06 1.008* 1.00, 1.01 0.997 0.98, 1.00 1.000 1.00, 1.00 1.000 0.99, 1.00 0.999 0.99, 1.00 0.996 0.98, 1.00 1.000 0.99, 1.00 0.999 0.99, 1.00 0.996 0.99, 1.00 1.089* 1.00, 1.17

RR 95% CI 1.688* 1.05, 2.71 1.001 0.94, 1.06 0.995 0.91, 1.08 1.000 0.99, 1.00 1.000 0.99, 1.00 1.018 0.97, 1.06 1.082* 1.00, 1.16 0.987 0.96, 1.01 1.006ø 0.99, 1.01 1.006 0.97, 1.03 1.447 0.64, 3.22

*** p < 0.0005. ** p < 0.01. * p < 0.05. ø p < 0.1.

of the results. The current study documents some unique observations for changes in driving patterns over a five-year period for a cohort of healthy and active older Australian drivers. The study also provides important new insights into driver-related health, demographic, psychosocial and functional factors associated with changes in driving patterns. Importantly, the current study provides robust evidence for selfregulatory driving patterns and supports previous self-reported data and recent short-duration NDS (Baldock et al., 2006, Blanchard and Myers, 2010, Charlton et al., 2006, Coxon et al., 2015, Myers et al., 2011). The observed patterns of reduced driving are suggestive of increased levels of self-regulation, reflecting more appropriate pre-trip decision-making to reduce risk. However, while these patterns are indicative of intentions to improve safety, the relationship with risk (crash involvement and infringements) remains to be explored.

these cognitive declines in other ways. Alternatively, it is possible that those with deficits, who arguably should be self-regulating, were not and this may reflect a lack of insight or awareness of deficits. Insight into functional impairments has been flagged as an important pre-requisite for self-regulation (e.g., Ball et al., 1998; Holland and Rabbitt, 1992; Owsley et al., 2003) and may be more important than actual driving ability (Anstey et al., 2005). Yet a third explanation for the absence of significant changes in functional abilities is that those with functional declines were predominantly those who dropped out during the five-year period and thus, the influence of these functional measures was masked by the strong effect of dropout status in the models. Some limitations of the study should be noted. First, the older drivers were a convenience sample of independent, healthy older drivers who made a commitment to participate in a longitudinal study and therefore the results may not be generalisable to all older drivers. Indeed, older drivers’ performance on the functional measures was quite high according to conventional benchmarks for impairment. Further analyses are underway to compare the sample with Australian health population data. Scores on self-reported driving-related measures (comfort, abilities and practices) were also high relative to prior samples of older drivers (Blanchard and Myers, 2010). Nevertheless, functional performance and self-reported driving measures, as well as real-world driving patterns, are expected to decline further as the sample ages and as they may develop age-related medical conditions and/or health problems. Further analyses are planned to track selfregulatory trends as the cohort ages and to explore associations with a broader array of functional, health and psychosocial measures and across an eight year-period (to be completed by mid-2019). It should also be noted that the study inclusion criteria required that participants’ vehicles were available from 2003 or newer (i.e. vehicles were a maximum of seven years old at the commencement of the study). However, recent research based on crash data has demonstrated that Australian older drivers (aged 65+ years) were more likely to be driving medium, small and light passenger vehicles which were aged nine years (Koppel et al., 2018). This may also limit the generalisability

Acknowledgements Ozcandrive (including the Ozcandrive eDOS subproject) is funded by an Australian Research Council Linkage Grant (LP 100100078) to the Monash University in partnership with La Trobe University, VicRoads, Victorian Government Department of Justice and Victoria Police, the Transport Accident Commission, New Zealand Transport Agency, Ottawa Hospital Research Institute and Eastern Health. The Candrive II study was funded by a Team Grant from the Canadian Institutes of Health Research (CIHR) entitled ‘The CIHR Team in Driving in Older Persons (Candrive II) Research Program’ (grant 90429). The authors thank Lorraine Atkinson, Ozcandrive Program Manager, for her role in managing and operationalizing the study for the Australian and New Zealand sites. The authors gratefully acknowledge the invaluable contribution of the Ozcandrive team, including: Amy Allen, Louise Beasley, Russ Boag, Matthew Catchlove, Cara Dawson, Johan Davydov, Lei Gryffydd, Yik-Xiang Hue, Andrew Lyberopoulos, Elizabeth Jacobs, Duncan Joiner, Jason Manakis, Kevin Mascarenhas, Rachel Osborne, Emma Owen, Hiep Pham, Jarrod Verity 225

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and Zefi Vlahodimitrakou. The authors also thank the Ozcandrive cohort study older drivers for their dedication. Without their commitment, this publication would not have been possible.

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