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Examination of driving comfort and self-regulatory practices in older adults using in-vehicle devices to assess natural driving patterns
Accident Analysis and Prevention 42 (2010) 1213–1219
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Examination of driving comfort and self-regulatory practices in older adults using in-vehicle devices to assess natural driving patterns Robin A. Blanchard ∗ , Anita M. Myers Department of Health Studies and Gerontology, University of Waterloo, 200 University Ave West, Waterloo, Ontario N2L 3G1, Canada
a r t i c l e
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Article history: Received 2 October 2009 Received in revised form 5 January 2010 Accepted 19 January 2010 Keywords: Driving self-regulation Older drivers Driving reduction Perceptions
a b s t r a c t Several studies have shown that age, gender, visual problems and confidence are associated with selfregulatory practices such as reduced exposure and avoidance of night and highway driving. To date, however, self-regulation has only been examined through self-report. The purpose of this study was to further our understanding of the association between driver characteristics, perceptions and selfregulation by monitoring the patterns of 61 drivers (mean age 80.4 ± 5.5; 59% women) for one week using in-vehicle devices. Usual self-regulatory practices were assessed using the Situational Driving Frequency (SDF) and Avoidance (SDA) Scales, while perceptions were measured using the Driving Comfort and Perceived Driving Abilities Scales. Additional evidence for test–retest reliability was obtained with a separate sample of 39 older drivers. Lower comfort and poorer perceived abilities were significantly related to actual behavior: reduced exposure (km, duration) in general and at night, average and maximum radii from home and driving in challenging situations (such as on highways). Neither sex nor age was associated with any of the driving indicators. While longitudinal studies are required to determine temporality (when drivers change their behavior) and directionality (does lower comfort lead to driving restrictions or vice versa), this is the first study to demonstrate that driver perceptions are associated with actual self-regulatory practices. © 2010 Elsevier Ltd. All rights reserved.
1. Introduction Attempts to regulate older drivers through mandatory renewal requirements and licensing restrictions are controversial and vary widely between countries and across regions. There is no accepted standard (Langford and Koppel, 2006) and limited evidence that policies other than in-person renewal are effective in reducing fatality rates (Grabowski et al., 2004). It has also been argued that many older drivers are capable of regulating their own behavior (e.g., Eberhard, 1996) and there is substantial evidence that driving changes with age. Compared to younger drivers, older adults drive less often, closer to home (e.g., Benekohal et al., 1994; Collia et al., 2003), in the daytime, on weekdays and in familiar areas (e.g., Collia et al., 2003; Keall and Frith, 2004). Older drivers, particularly women, also tend to avoid driving at night, in bad weather, in rush hour, on highways, and making complex manoeuvres such as left-hand turns (Baldock et al., 2006; Benekohal et al., 1994; Charlton et al., 2006; Eberhard, 1996; Hakamies-Blomqvist and Wahlström, 1998). While often assumed that seniors change their behavior to compensate for declining
∗ Corresponding author. Tel.: +1 250 545 0140; fax: +1 519 746 2510. E-mail addresses: [email protected] (R.A. Blanchard), [email protected] (A.M. Myers). 0001-4575/$ – see front matter © 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.aap.2010.01.013
abilities (e.g., Eberhard, 1996), these patterns may simply reflect preferences and lifestyle, such as less need to drive or flexible schedules (Ball et al., 1998; Charlton et al., 2006). Older drivers are overly involved in certain types of crashes, such as at intersections and involving multiple vehicles (e.g., Ryan et al., 1998). Crash risk, however, may be reduced if drivers adopt safer driving practices commensurate with their skills (Eby and Molnar, 2009) and regulate their driving to reduce task demands (Kostyniuk and Molnar, 2008). Raising awareness and promoting appropriate self-regulatory practices through education programs and health care professionals (e.g., eye specialists) may also be cheaper than government intervention (Ball et al., 1998). For instance, Nasvadi (2007) found that three-quarters of participants said they changed their driving practices after attending a mature driver education program. Drivers can self-regulate by reducing exposure (frequency and distance), modifying their driving patterns (such as when and where they drive), avoiding more complex driving situations and ultimately by giving up driving completely (Ball et al., 1998; Charlton et al., 2006; Kostyniuk and Molnar, 2008; Myers et al., 2008). Researchers are attempting to understand the factors that influence the adoption of self-regulatory practices, and ultimately whether such practices reduce crash involvement and severity. While Ball et al. (1998) found that older drivers with visual and attention impairments were more likely to reduce their exposure
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(# days/week) and avoid particular driving situations, Kostyniuk and Molnar (2008) found that sex had a stronger influence on selfregulation than age and physical functioning (self-reported ability to walk 1/4 of a mile and vision). As noted by Kostyniuk and Molnar (2008), however, confidence may explain the gender effect. A growing number of studies are showing that driver perceptions (particularly confidence or comfort) may be a key determinant of self-regulatory practices in older drivers (Baldock et al., 2006; Charlton et al., 2006; MacDonald et al., 2008; Marottoli and Richardson, 1998; Molnar and Eby, 2008; Myers et al., 2008; Rudman et al., 2006). For instance, Charlton et al. (2006) found that confidence was more important than other variables in characterizing self-regulators. MacDonald et al. (2008), meanwhile, showed that perceived comfort and abilities were more strongly related to situational driving frequency and avoidance than objective driving abilities, consistent with Bandura’s (1986) social cognitive theory. Charlton et al. (2006) and Molnar and Eby (2008) found that, with the exception of night driving, older drivers (particularly men) were quite confident and relatively few self-regulated. Both studies used categorical confidence ratings and defined self-regulators as those who avoided one or more difficult driving situations; however, the measures themselves comprised different items. Some studies have used only one item to assess confidence (e.g., Nasvadi, 2007). Findings are difficult to interpret and compare as driver perceptions have not been clearly defined or consistently measured (Myers et al., 2008). Only the Driving Comfort Scales (DCSs) and the Perceived Driving Abilities (PDA) Scales have been systematically developed with older drivers and subjected to rigorous psychometric examination. Other tools to measure confidence have included only one item on night driving (e.g., Marottoli and Richardson, 1998; Molnar and Eby, 2008) or two at most (e.g., Baldock et al., 2006; Charlton et al., 2006). Older drivers were adamant that most driving situations were more challenging at night, leading to separate day (DCS-D) and night (DCS-N) Driving Comfort Scales. The DCS and PDA (current and change) scales have demonstrated hierarchiality, unidimentionality, person and item reliabilities, goodness of fit and interval properties, as reported in Myers et al. (2008) and MacDonald et al. (2008), respectively. The DCSs also have good test–retest reliability (Myers et al., 2008). Two scales to assess self-regulatory behavior – Situational Driving Frequency (SDF) and Situational Driving Avoidance (SDA) – were also developed with older drivers; however, only internal consistency has been reported (MacDonald et al., 2008). Molnar and Eby (2008) noted that some subjects initially described themselves as self-regulators but later reported avoidance, questioning the reliability of these self-reports. While it is important to have reliable measures of perceptions and avoidance, it is vital to have measures of natural driving behavior. Studies to date have relied on self-report measures of driving exposure and patterns, which are subject to recall and social desirability bias (Lajunen and Summala, 2003). Based on objective data, there is evidence that estimated driving distance (km) is inaccurate (Huebner et al., 2006; Blanchard et al., 2009) and that older drivers may not regulate as much as they say they do (Blanchard et al., 2009). This paper reports the findings from two studies. The purpose of the first was to gather further psychometric evidence on the perception and self-regulatory measures by replicating the test–retest reliability of the DCSs with a new sample and examining the stability of PDA, SDF and SDA scores. The purposes of the primary study were to (1) replicate prior findings concerning the association between driver characteristics, perceptions and self-reported regulatory practices; (2) extend this area of investigation to actual driving patterns; and (3) examine the correspondence between
self-reported and objective measures of driving. The third objective is the subject of a separate article (Blanchard et al., 2009). This paper addresses the associations between driver characteristics, perceptions and self-regulatory practices. As licensing requirements may influence self-regulation (Charlton et al., 2006; Rudman et al., 2006), it is important to note that this study took place in Ontario, Canada which has a mandatory in-person Senior Drivers Renewal Program (SDRP) for drivers once they turn 80 and every 2 years thereafter. 2. Methods 2.1. Participants For the psychometric examination, the DCSs, PDA, SDF and SDA scales were completed twice, approximately one week apart (M = 7.6 days, S.D. = 1.6), by 39 older drivers not involved in other studies. This sample ranged in age from 65 to 86 (M = 73.6, S.D. = 5.1), 53.8% were female and 38.5% had some post-secondary education. For the natural driving study, a sample of 61 current drivers with a valid license, living in Southwestern Ontario, mostly in urban/suburban areas (86.9%), were assessed between spring and early fall (described in Blanchard et al., 2009). To obtain drivers aged 80+, we purposefully recruited from the Ministry of Transportation of Ontario’s SDRP. To obtain a sample of drivers under the age of 80 we recruited from senior centres, recreation and apartment complexes in the same region and during the same timeframe. The sample (59% female) ranged in age from 67 to 92 (M = 80.4, S.D. = 5.5) with 57.4% age 80 or over. About half (56%) had completed some post-secondary education, but only 8.2% were still working (mostly part-time). Most rated their health as excellent or good (93.4%) and said they could walk a quarter of a mile (88.5%). Diagnosed health conditions most often reported were arthritis, rheumatism or osteoporosis (60.7%), followed by cardiac problems (54%) and vision disorders (36.1%). Almost half the sample had undergone cataract surgery (48.3%); however, no one rated their eyesight as “worse than most their age”. On average, the sample had 57.7 years (S.D. = 10.6) of driving experience. The most common driving problems reported over the past year were near misses (23%); only 3 (4.9%) were involved in a crash. The majority felt driving was extremely or very important (90.1%), 85.2% were the primary driver in their household, 31.3% said others relied on them to drive and only 16.4% had seriously thought about driving reduction (16.4%) or cessation (9.8%). 2.2. Measures 2.2.1. Self-reported driving habits, barriers and restrictions A questionnaire was used to obtain information on usual driving patterns (e.g., times of day, roadways) and preferences for getting around. Similar to Baldock et al. (2006), respondents were also asked to rate (on a 4-point Likert scale from “not at all” to “very much so”) the extent to which 11 factors presented a barrier or challenge to reducing when and where they drove. Total scores could range from 0 to 21, with higher values indicating more perceived challenges. The 14-item SDF and 20-item SDA scales were used to assess self-reported driving restrictions. The SDF assesses how often people drive in challenging situations, while the SDA asks people to check particular situations they try to avoid, if possible. Scores on the SDF can range from 0 to 56, while scores on the SDA can range from 0 to 20, with higher scores indicating greater frequency and greater avoidance of challenging driving situations, respectively (MacDonald et al., 2008).
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2.2.2. Perceived driving comfort and abilities Perceived driving comfort was assessed using the 13-item day (DCS-D) and 16-item night (DCS-N) Driving Comfort Scales. When rating their level of comfort on the 5-point scale (0%, 25%, 50%, 75%, 100%), respondents are asked to consider confidence in their own abilities and driving skills, as well as the situation itself and to assume normal traffic flow unless otherwise noted. Scores on the DCS-D and DCS-N scales can range from 0% to 100%, with higher scores indicating higher levels of comfort (Myers et al., 2008). Perceived driving abilities were assessed using the 15-item PDA scale (4-point rating scale from poor to very good) and the 15-item PDA change scale comparing one’s abilities to 10 years ago (4-point rating from “a lot worse” to “better”). Higher scores (range 0–45) indicate more positive perceptions and fewer declines, respectively (MacDonald et al., 2008). 2.2.3. Objective driving data As described in Blanchard et al. (2009), natural driving patterns were assessed using two recording devices, a CarChip E/X® (Davis Instruments, Hayward, CA) and an Otto Driving Mate® (Otto; Persen Technologies Inc., Winnipeg, MB). The CarChip plugs into the on-board diagnostic port, while the Otto (a small GPS device) is mounted on the dashboard. While both devices collect similar timestamped information, ‘cold starts’ at the beginning of trips (Duncan et al., 2009) can result in lost or missing GPS data, especially for short trips (Porter and Ash, 2008). Thus, the Otto data was used only to examine roadways and radius (i.e., distance from home). Restricted radius may be an important indicator of self-regulation that has not been previously examined. The CarChip, which has shown minimal error in distance recording (Huebner et al., 2006), was used for all other driving data. 2.3. Procedures During the first visit, the researcher obtained consent, asked participants to complete the background questionnaire and explained the trip logs (used to record who drove, number of passengers and weather conditions). The CarChip and Otto were then installed in the person’s vehicle and a set of trip logs placed in the car. Subjects were asked to drive as usual over the subsequent week. The researcher then met with participants again to pick up the devices and trip logs. Subjects were asked to complete, in order: the DCSs, SDF, SDA and PDA scales and the driving habits questionnaire, followed by an interview. 2.4. Analysis For the psychometric examination, internal consistency was calculated using Cronbach’s alpha, while test–retest reliability was examined using the intraclass correlation coefficient, model 2, 1 (ICC2,1 ) (Shrout and Fleiss, 1979). For the driving study, total scores were calculated for the perception (DCS and PDA) and self-regulation (SDF and SDA) scales, following the developers’ instructions (Myers et al., 2008; MacDonald et al., 2008). Data from the CarChips (retrieved for 58 subjects) and Otto (retrieved for 55 subjects) were downloaded and cleaned (i.e., removing trips with 0.0 km and by drivers not in the study) prior to analysis. To determine complete trips, segments were linked by cross-referencing CarChip data with logs and/or Otto data. Archives were consulted for daily times of sunrise/sunset and weather conditions (Blanchard et al., 2009). Using the GPS data and digital maps, roadways driven were examined, including freeways (multi-lane, divided with speed limits usually greater than 90 km/h) and highways (two-lane with speed limits 70 km/h greater). The average and maximum radii each
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person drove from his/her home were also calculated for each trip. A composite Frequency Index of actual driving in challenging situations was calculated based on 11 of the 14 situations depicted in the SDF scale, as described in Blanchard et al. (2009). Descriptive statistics were used to depict sample characteristics, perception scores, as well as self-reported and actual driving patterns. Comparative statistics (parametric and non-parametric, as appropriate) were used to examine associations between driver perceptions with self-reported and actual driving patterns. Gender and age group (<80 versus 80+) differences were examined throughout. 3. Results 3.1. Further psychometric support The DCS-D and DCS-N scales showed good test–retest reliability over the week (ICC2,1 = .89 and .92, respectively), while the PDA scales showed moderate test–retest reliability (ICC2,1 = .65 and .66). Internal consistency was better for the current (˛ = .92) than for the change PDA scale (˛ = .77). Both the SDF and SDA scales showed high internal consistency (˛ = .92 and .87) and good test–retest reliability (ICC = .89 and .86), respectively. 3.2. Driver perceptions Table 1 presents the scores on the driver perception measures. All scores were normally distributed except for the PDA change score and DCS-N item #1. This item (driving at night in good weather and traffic conditions) was examined separately as it may be indicative of progressive declines in comfort level (Myers et al., 2008). Overall the sample had a moderate level of driving comfort, given the theoretical mean of 50%. While correlated (r = .82, p < .001), DCS-N scores were significantly lower than DCS-D scores (paired-t(60) = 7.46, p < .001). Perceived driving abilities were generally good (theoretical mean 22.5) and not seen as changing much over the past 10 years. Perceived abilities were positively correlated with comfort level (DCS-D, r = .41, p < .001 and DCS-N, r = .45, p < .001), while PDA change scores were inversely related (DCS-D: = −.25, p < .01; DCS-N: = −.37, p < .01). Maintaining one’s current lifestyle was viewed as the primary barrier to driving reduction (rated “very much” by 63%), followed by location of shops and services (59%), difficulty with public transit (47%), not wanting to bother others (42%), availability of others (24%), others relying on them (24%) and physical difficulty getting places (22%). Men had higher comfort for both day and night driving (t(59) = 2.44, p = .001 and t(59) = 3.46, p = .001, respectively), including DCS-N item #1 (z = −2.59, p = .01). Although not shown in Table 1, those living in rural areas also had higher DCS-N (M = 72.1, S.D. = 24.3 versus M = 51.7, S.D. = 24.0, t(59) = 2.22, p = .05) and perceived barrier scores (M = 14.6, S.D. = 3.2 versus M = 10.5, S.D. = 6.1, t(57) = 2.93, p = .009). Subjects diagnosed with cataracts, glaucoma or macular degeneration had significantly lower comfort at night (t(59) = 2.50, p = .02), poorer perceived abilities now (t(59) = 2.19, p = .03) and compared to 10 years ago (z = −1.96, p = .05). Those taking prescription medications rated their abilities lower than those who were not (M = 31.9, S.D. = 6.2 versus M = 37.8, S.D. = 4.7; t(57) = 2.86, p = .02). Individuals whose physician had asked them about driving had lower scores on the DCS-N (t(57) = 2.15, p = .05), DCS-D (t(57) = 2.03, p = .06) and PDA (t(57) = 3.51, p = .002) scales, while those who had talked with an eye care professional had lower scores on the DCS-D (t(58) = 2.30, p = .03), DCS-N (t(58) = 3.18, p = .003) scales and DCS-N item #1 (z = −2.86, p = .004). Subjects who had thought about driving reduction also had significantly
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Table 1 Perception scores by sex and age group. Total sample (n = 61)
Sex
Age group
Men (n = 25)
Women (n = 36)
<80 (n = 26)
80+ (n = 35)
DCS-Da
68.9 (15.2) 30.8–100
74.2 (14.4) 44.2–98.0
65.2 (14.7) 30.8–100
68.9 (16.2) 44.2–100
68.8 (14.6) 30.8–98.1
DCS-Na
54.3 (24.8) 6.3–100
63.4 (25.3) 12.5–100
48.0 (22.7) 6.3–100
54.1 (26.6) 10.9–100
54.5 (23.9) 6.3–100
DCS-N #1b
83.6 (19.8) 25–100
90.0 (19.1) 25–100
79.2 (19.4) 50–100
87.5 (19.0) 50–100
80.7 (20.2) 25–100
PDA (current)
32.5 (6.3) 15–45
33.4 (6.3) 21–45
31.9 (6.3) 15–44
32.9 (7.4) 15–45
32.3 (5.3) 21–43
PDA change
19.1 (5.9) 2–44
19.0 (7.9) 2–44
19.1 (4.0) 15–31
19.4 (5.7) 2–31
18.9 (6.0) 7–44
Barriers
11.0 (6.0) 0–21
11.3 (6.6) 0–21
10.8 (5.6) 0–21
10.9 (6.6) 0–21
11.1 (5.7) 0–21
Note: Values denote mean (S.D.) and range. DCS-D, DSC-N: day and night Driving Comfort Scales, respectively; DCS-N #1: item 1 on DCS-N (night in good weather and traffic conditions); PDA: perceived driving abilities; barriers: barriers to driving reduction or cessation score. a Significant sex difference (p < .001). b Significant sex difference (p < .01).
lower night comfort (t(59) = 2.09, p = .05) and higher perceived barriers scores (t(57) = 2.10, p = .05). Other characteristics (e.g., age group, primary versus non-primary driver, education, self-rated health, number of health problems) were not significantly related to DCS, PDA or perceived barriers scores. 3.3. Self-reported driving patterns Compared to 10 years ago, 54.1% said that they drove less now, 29.5% about the same and 13.6% more. Reported frequency of driving in challenging situations was moderate (M = 30.2, S.D. = 9.0 out of 56) and significantly higher for men (M = 33.9, S.D. = 8.6 versus M = 27.8, S.D. = 8.6; t(58) = 2.69, p = .01). Avoidance of challenging situations was relatively low (M = 9.2, S.D. = 4.8 out of 20). While women had higher SDA scores (M = 9.9, S.D. = 4.6 versus M = 8.3, S.D. = 5.0), the difference was not significant. No age group differences emerged. 3.4. Actual driving patterns Table 2 shows the driving patterns of the sample over the week. There were no significant differences in exposure by month of
participation, age group or sex, although total distance by gender approached significance (t(56) = 1.80, p = .08). A total of 137.5 trips (33% of all trips) involved at least one passenger (up to four), averaging 2.37 trips (S.D. = 2.53) over the week. Most trip segments were on city streets (88%), followed by residential streets (50%). Fewer were on rural roads (13%), highways (7%) or freeways (5%). There were no significant differences in driving exposure (i.e., distance, duration or number of trips) by day of the week or weekend versus weekday. However, the sample drove significantly less at night (dusk to dawn) compared to the morning (dawn to 11:59 am; paired-t(57) = 5.82, p < .001) and afternoon (12:00 pm to 4:59 pm; paired-t(57) = 7.38, p < .001). People drove significantly more during off-peak hours (9 am to 4 pm, 7 pm to 6 am) than peak hours (6 am to 9 am and 4 pm to 7 pm), paired-t(57) = 6.88, p < .001. Only 16 people (56% men; 8 in each age group) drove at night over the week, anywhere from 1 to 5 days and from 2.7 to 129.4 km (average 25 km). Five people started their trip at night. Men drove more km (t(14) = 2.24, p = .05), trip segments (t(14) = 2.50, p = .03) and for longer durations (t(14) = 2.91, p = .02) at night than women. There was no significant difference in km at night by study month.
Table 2 One-week driving behavior by sex and age group. Total sample
Sex
Age group
Men
Women
<80
80+
Days
5.2 (1.9) 1–7
5.5 (1.9) 1–7
5.0 (1.8) 1–7
5.1 (1.9) 1–7
5.2 (1.9) 1–7
Trips
7.1 (3.9) 0.5–17
7.8 (4.3) 0.5–17
6.6 (3.5) 1–14
6.7 (5.3) 1–14
7.4 (4.2) 0.5–17
Stops
14.8 (10.1) 1–44
16.2 (11.6) 2–44
13.8 (8.9) 1–36
13.9 (10.1) 1–42
15.6 (10.1) 1–44
Duration (h:min)
4:07 (3:06) 0:07–13:20
4:46 (3:38) 0:22–13:20
3:39 (2:37) 0:07–10:10
3:43 (2:48) 0:07–10:10
4:25 (3:19) 0:19–13:20
Distance (km)
164.1 (158.4) 4.2–633.3
210.8 (190.7) 8.5–633.3
131.1 (123.6) 4.2–548.0
149.0 (149.2) 4.2–548.0
176.4 (166.8) 6.8–633.3
Average radius
7.6 (7.6) 1.0–45.1
9.6 (9.9) 1.0–45.1
6.2 (5.4) 1.8–23.1
7.6 (7.1) 2.0–26.2
7.4 (7.9) 1.0–45.1
Maximum radius
21.3 (27.4) 1.8–113.7
27.5 (32.6) 1.8–113.7
17.1 (22.9) 2.5–107.5
23.4 (31.9) 3.2–113.7
18.8 (23.6) 1.8–93.7
Note: Values denote mean (S.D.) and range.
R.A. Blanchard, A.M. Myers / Accident Analysis and Prevention 42 (2010) 1213–1219 Table 4 Associations between perceptions and indicators of actual driving behavior.
Table 3 Associations between perceptions and self-reported restrictions. DCS-D
DCS-N
DCS-N #1
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PDA
PDA change
SDA score SDF score
−.48a .45a
−.59a .58a
−.31b .54a
−.51a .51a
.21 −.13
Types of roads Rural t-value Freeways t-value Highways t-value
1.33 2.19b 3.13c
2.66c 2.85c 3.83a
1.74 2.65c 1.89
.33 1.18 1.57
.60 1.35 2.34b
Time of day At night t-value
3.38c
5.18a
2.77c
2.09b
.63
Note: DCS-D: day Driving Comfort Scale; DCS-N: night Driving Comfort Scale; DCSN #1: item 1 on DCS-N; PDA: perceived driving abilities; SDA: Situational Driving Avoidance scale; SDF: Situational Driving Frequency scale. Values presented are Pearson’s r for SDA and SDF, except for DCS-N #1 PDA change (Spearman’s ). tTests were used for types of roads and time of day, except for DCS-N #1 and PDA change (Mann–Whitney U-coefficients). a p < .001. b p < .05. c p < .01.
Those who drove at night generally drove greater distances (t(56) = 1.96, p = .06), longer durations (t(56) = 2.20, p = .04) and made more stops (t(56) = 2.14, p = .04) overall, compared to those who did not drive at night. “Night” drivers also scored significantly higher on the SDF scale (t(56) = 2.03, p = .05) and significantly lower on the SDA scale (t(56) = 3.38, p = .002) compared with “non-night” drivers. A total of 34 trips (by 25 individuals) occurred in the rain. Distance driven in the rain averaged 17.9 km (S.D. = 14.5), with men driving significantly more than women (t(23) = 3.53, p = .004). Weather conditions were also examined for the days people did not drive (106 instances); most (81%) were on days with favorable conditions (i.e., no rain or fog).
3.5. Associations between perceptions and patterns 3.5.1. Self-reported driving patterns As shown in Table 3, DCS and PDA scores were significantly related to SDF and SDA scores, in the expected direction. With respect to usual patterns (on the habits questionnaire), DCS-D and DCS-N scores were significantly higher for those who reportedly drove on freeways, highways and at night, compared to those who did not. Only DCS-N scores discriminated between people who did and did not typically drive on rural roads (higher comfort, more driving on such roads). Those who typically drive at night also had higher perceived driving abilities, while those who typically drive on highways perceived less change in their abilities.
Distance (km) Duration Radius (avg) Radius (max) # Trips # Stops # Days Night (km) Night (duration) Frequency Index
DCS-D
DCS-N
.24 .16 .25 .23 .05 .18 −.01 .29c .29c .15
a
.43 .39b .39b .30c .21 .34b .14 .35b .40b .40b
DCS-N #1 b
.36 .35b .37b .28c .26c .35b .24 .25c .23 .40b
PDA b
.36 .38b .22 .35b .34b .37b .29c .15 .13 .39b
Barriers .31c .30c .19 .26 .32c .30c .31c .30c .27c .34b
Note: DCS-D, DSC-N: day and night Driving Comfort Scales, respectively; DCS-N #1: item 1 on DCS-N (night in good weather and traffic conditions); PDA: Perceived Driving Abilities Scale; barriers: barriers to driving reduction or cessation score. All values are Pearson’s r, with the exception of DCS-N#1 (Spearman’s ). a p < .001. b p < .01. c p < .05.
3.5.2. Actual driving patterns As shown in Table 4, DCS-N, PDA and perceived barriers scores were significantly related to most driving indicators, including the Frequency Index. Those who drove closer to home had significantly lower night driving comfort levels (overall and in good weather) and perceived abilities (maximum radius only); daytime comfort level bordered on significance (p = .07). Lower DCS-D scores were significantly related to more trips as a passenger (r = −.23, p = .05). Those who drove at night had both significantly higher DCS-D (75.6 versus 66.5, t(59) = 2.17, p = .04) and DCS-N (70.9 versus 48.4, t(59) = 3.71, p < .001) scores. None of the indicators were significantly associated with PDA change scores. Finally, people were categorized by whether they scored above or below the midpoint (50%) on the DCS-D and DCS-N scales, respectively. As shown in Table 5, those who scored below the midpoint drove significantly less (km, duration, number of trips), closer to home and less often in challenging situations. The only non-significant variable was stops. 4. Discussion Prior findings that older drivers are generally confident, consider their abilities to be as good or better than others and that relatively few regulate their driving (e.g., Charlton et al., 2006; Marottoli and Richardson, 1998; Molnar and Eby, 2008), may be largely a function of how these constructs have been measured. Determining the extent to which older drivers self-regulate, as well as factors associated with the adoption of self-regulatory practices, require reliable and valid measures.
Table 5 Driving exposure and patterns in persons with low versus high daytime and nighttime comfort levels. DCS-D score
Distance (km) Duration (h:min) Radius (average) Radius (maximum) # Trips # Stops # Days Night (km) Night (duration) Frequency Index
t (p)
≤50%, n = 7
>50%, n = 51
70.7 (35.7) 02:36 (01:01) 3.9 (1.9) 8.5 (8.0) 5.6 (1.5) 8.6 (2.9) 5 (1.3) .62 (1.6) 00:02 (00:04) 4.8 (1.3)
176.9 (164.5) 04:17 (03:11) 7.9 (7.8) 22.1 (28.2) 7.3 (4.2) 15.8 (10.4) 5.2 (1.9) 7.9 (22.2) 00:09 (00:20) 7.5 (4.2)
3.98 (<.001) 2.84 (.008) 2.96 (.005) 2.65 (.01) 2.09 (.05) 3.95 (<.001) .25 (.81) 2.30 (.03) 2.55 (.01) 3.22 (.005)
Note: DCS-D, DSC-N: day and night Driving Comfort Scales, respectively. Values presented as M (S.D.).
DCS-N score
t (p)
≤50%, n = 26
>50%, n = 32
95.3 (113.7) 2:54 (2:36) 4.3 (2.6) 11.5 (17.0) 6.4 (3.4) 11.7 (8.8) 5 (1.8) .69 (2.9) 00:01 (0:05) 5.5 (3.2)
220.0 (168.8) 5:02 (3:06) 10.1 (9.1) 28.4 (31.5) 7.7 (4.4) 17.5 (10.5) 5.3 (2.0) 11.7 (26.8) 00:14 (00:23) 8.6 (4.1)
3.35 (.001) 2.86 (.006) 3.41 (.002) 2.57 (.01) 1.31 (.20) 2.27 (.03) .69 (.49) 2.35 (.03) 3.05 (.004) 3.15 (.003)
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Psychometric examination with a new sample of 39 older drivers provided further evidence that both driving comfort scales have good test–retest reliability (Myers et al., 2008) and new evidence concerning the reliability of the PDA, SDF and SDA scales. Although the PDA scales have good structural properties (MacDonald et al., 2008), they showed only moderate test–retest reliability. Internal consistency was better for the current than the change scale, as might be expected. Perceived driving ability has often been measured using only one item (e.g., Marottoli and Richardson, 1998). Drivers may be more likely to acknowledge areas of weakness on multi-item measures and ratings of specific (e.g., seeing signs at night) versus general abilities (e.g., overall vision) are more likely to correspond with objective measures such as contrast sensitivity (MacDonald et al., 2008). Consistent with previous studies, DCS-N scores were significantly lower than DCS-D scores, while both correlated with PDA scores in the expected direction (Myers et al., 2008; MacDonald et al., 2008). Significant relationships between driver perceptions (comfort and abilities) and usual self-regulatory practices (situational frequency and avoidance) were also in keeping with prior results (MacDonald et al., 2008). Rudman et al.’s (2006) model postulates that driving comfort, influenced by a multitude of intrapersonal, interpersonal and environmental factors, may be a key determinant of self-regulation. Furthermore, the decision to stop driving may be based on reaching a personally unacceptable level of comfort. Until now, however, investigation has been limited to self-reported driving practices. Lower night comfort scores (overall and in good conditions) were significantly related to reduced exposure (km and duration), average and maximum radius from home, night driving and driving in other challenging situations (Frequency Index). Comparatively, DCS-D scores were significantly related to only two indictors (night km and duration). However, those who scored below the midpoint on either the DCS-N or DSC-D scale were significantly more likely to restrict their driving. Comfort level at night may progressively decline to the point where people are uncomfortable even in good weather and traffic conditions (item 1 on the DCS-N scale) before daytime driving comfort is appreciably affected (Myers et al., 2008). While only 12% of the sample scored at or below the midpoint (≤50) on the DCS-D, 44.9% scored below the midpoint on the DCS-N scale. The sample had notably higher comfort scores for day versus night driving, as was expected. There is ample evidence that night driving is particularly problematic for older drivers. For instance, over half of Charlton et al.’s (2006) sample who reportedly avoided night driving specifically attributed this to vision-related issues, especially glare from lights. Other studies have also shown that vision disorders and impairments are more strongly related to self-regulation than cognitive or motor impairments (e.g., Ball et al., 2006; Molnar and Eby, 2008). Present participants diagnosed with vision disorders had significantly lower comfort at night, as well as poorer perceptions of driving abilities. Men had higher comfort scores which have been previously found in some studies (e.g., Myers et al., 2008), but not others (e.g., MacDonald et al., 2008). As suggested by Kostyniuk and Molnar (2008), confidence may explain the gender effect that has emerged in several studies on self-reported regulatory practices. In fact Charlton et al. (2006) found that inclusion of a confidence variable significantly reduced the contribution of other factors (gender, age, vision problems) in avoidance ratings. Unfortunately, our sample was too small to examine the relative contribution of driver characteristics and perception scores (as well as possible interactions), as predictors of actual driving patterns via regression modelling.
Generally people who volunteer for driving studies do not have significant cognitive, motor or attention deficits (Molnar and Eby, 2008) and may be more confident than older drivers in general. Our sample was older than those in many previous studies (over half were 80 or over). However, this 80+ cohort may not be representative since all had successfully completed provincial renewal requirement. Otherwise they would not have qualified for the present study (i.e., their license would have been suspended or revoked). Charlton et al. (2006) speculated that regular license retesting procedures may foster self-checking and, if so, one would expect to find more self-regulatory behavior among older drivers in jurisdictions with age-based requirements. However it must be kept in mind that not everyone holding a valid license actually drives and some might let their license lapse rather than undergo assessment (Langford and Koppel, 2006). In-person renewal may serve as a deterrent for the oldest group (85+) who doubt their capabilities (Grabowski et al., 2004). What is most unique about Ontario’s biannual SDRP for drivers 80+ is mandatory attendance at a group education session. The session reviews rules of the road, effects of medical conditions and medications, frequent types of collisions and strategies for avoiding or dealing with these situations. Driving education may positively or negatively affect driving confidence (Nasvadi, 2007; Owsley et al., 2004). Drivers with discrepancies between perceived and actual abilities (indicating lack of awareness) were found to be more confident and less likely to reportedly self-regulate MacDonald et al. (2008). Older drivers who overestimate their capabilities and have high levels of confidence may be more likely to exceed their limitations and pose a safety risk to themselves and others (Marottoli and Richardson, 1998; MacDonald et al., 2008). Older drivers are concerned about being reported to authorities and losing their licenses. Presenting a favorable image is particularly likely when people are asked about driving problems (Lajunen and Summala, 2003) or whether their driving practices changed post-intervention (e.g., Nasvadi, 2007). The use of invehicle devices is far less intrusive than using video cameras or in-car observers (Lajunen and Summala, 2003) and is more likely to capture natural driving behavior than road testing which typically takes place in daylight and good conditions (Molnar and Eby, 2008). In the follow-up interviews, our sample reported that the vehicle devices did not affect their driving; half the sample (55.7%) also said that their driving patterns over the study week were fairly typical. Various reasons (e.g., visitors, personal or family illness, special events) were given for driving more (9.8%) or less (34.4%) than usual. Circumstances (e.g., appointments, commitments) may largely dictate when and where people drive, although they may prefer not to drive at night, on highways, in rush hour or in bad weather (Blanchard et al., 2009). Perceived challenges or barriers to driving reduction were significantly associated with all objective indicators, except for radius from home. By the same token, a snapshot of driving over one week may not fully capture usual practices or seasonal fluctuations (Blanchard et al., 2009). Over half our sample said that they drove less now compared to 10 years ago, however, it is unknown what precipitated this change. People may simply prefer not to drive or have less need to drive as they age. Thus far, all studies on self-regulatory practices, including the present, have been cross-sectional. Only longitudinal studies can confirm the extent to which older adults actually change their behavior as they age and whether various types of adaptations reduce crash involvement. Such a study is currently underway in Canada following 1000 drivers aged 70+ for 5 years using sophisticated in-vehicle devices to monitor natural driving, reliable tools to assess driver perceptions and ministry records to examine crashes.
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5. Conclusions This is the first study to demonstrate that driver perceptions are significantly related to actual driving patterns using in-vehicle devices. Although driving was only monitored for one week with a small sample, the findings extend our knowledge of self-regulatory practices among older drivers and provide direction for future research. These findings need to be replicated with larger samples and using multivariate analyses. Other aspects of driving behavior, such as keeping up with the flow of traffic and following distance, also need to be examined. Also, prospective studies are required to determine temporality and directionality. While lower comfort and poorer perceived abilities (possibly mediated by noticeable declines in vision and other factors) may lead to self-imposed driving restrictions, it is equally plausible (and consistent with Bandura’s theory) that restricted driving may lead to reduced comfort and potentially reduced driving skills due to lack of practice. Acknowledgements The authors wish to thank Alex Crizzle for assistance collecting the test–retest data, as well as Dr. Michelle Porter for loaning us equipment and feedback on earlier drafts. References Baldock, M.R.J., Mathias, J.L., McLean, A.J., Berndt, A., 2006. Self-regulation and its relationship to driving ability among older adults. Accid. Anal. Prev. 38, 1038–1045. Ball, K., Owsley, C., Stalvey, B., Roeneker, D., Sloane, M., Graves, M., 1998. Driving avoidance and functional impairment in older drivers. Accid. Anal. Prev. 30, 313–322. Ball, K.K., Roenker, D.L., Wadley, V.G., Edwards, J.D., Roth, D.L., McGwin Jr., G., et al., 2006. Can high-risk older drivers be identified through performance-based measurements in a Department of Motor Vehicles setting? J. Am. Geriatr. Soc. 54, 77–84. Bandura, A., 1986. Social Foundations of Thought and Action: A Social Cognitive Theory. Prentice-Hall, Englewood Cliffs, NJ. Benekohal, R.F., Michaels, R.M., Shim, E., Resende, P.T., 1994. Effects of aging on older drivers’ travel characteristics. Transp. Res. Rec. 1438, 91–98. Blanchard, R.A., Myers, A.M., Porter, M.M., 2009. Correspondence between selfreported and objective measures of driving exposure and patterns in older drivers. Accid. Anal. Prev. doi:10.1016/j.aap.2009.09.018.
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Charlton, J.L., Oxley, J., Fildes, B., Oxley, P., Newstead, S., Koppel, S., O’Hare, M., 2006. Characteristics of older drivers who adopt self-regulatory driving behaviours. Transp. Res. Rec. F 9, 363–373. Collia, D.V., Sharp, J., Giesbrecht, L., 2003. The 2001 national household travel survey: a look into the travel patterns of older Americans. J. Safety Res. 34, 461–470. Duncan, M.J., Badland, H.M., Mummery, W.K., 2009. Applying GPS to enhance understanding of transport-related physical activity. J. Sci. Med. Sport 12, 549–556. Eberhard, J., 1996. Safe mobility of senior citizens. J. Int. Assoc. Traffic Safety Serv. 20, 29–37. Eby, D.W., Molnar, L.J., 2009. Older adult safety and mobility: issues and research needs. Public Works Manage. Policy 13, 288–300. Grabowski, D.C., Campbell, C.M., Morrisey, M.A., 2004. Elderly licensure laws and motor vehicle fatalities. J. Am. Med. Assoc. 291, 2840–2846. Hakamies-Blomqvist, L., Wahlström, B., 1998. Why do older drivers give up driving? Accid. Anal. Prev. 30, 305–312. Huebner, K., Porter, M.M., Marshall, S.C., 2006. Validation of an electronic device for measuring driving exposure. Traffic Inj. Prev. 7, 76–80. Keall, M.D., Frith, W.J., 2004. Older driver crash rates in relation to type and quantity of travel. Traffic Inj. Prev. 5, 26–36. Kostyniuk, L.P., Molnar, L.J., 2008. Self-regulatory driving practices among older adults: health, age and sex effects. Accid. Anal. Prev. 40, 1576–1580. Lajunen, T., Summala, H., 2003. Can we trust self-reports of driving? Effects of impression management on driver behaviour questionnaire responses. Transp. Res. F 6, 97–107. Langford, J., Koppel, S., 2006. The case for and against mandatory age-based assessment of older drivers. Transp. Res. Part F: Traffic Psychol. Behav. 9, 353–362. MacDonald, L.M., Myers, A.M., Blanchard, R.A., 2008. Correspondence among older drivers’ perceptions, abilities, and behaviors. Top. Geriatr. Rehabil. 24, 239–252. Marottoli, R.A., Richardson, E.D., 1998. Confidence in, and self-rating of driving ability among older drivers. Accid. Anal. Prev. 30, 331–336. Molnar, L.J., Eby, D.W., 2008. The relationship between self-regulation and drivingrelated abilities in older drivers: an exploratory study. Traffic Inj. Prev. 9, 314–319. Myers, A.M., Paradis, J., Blanchard, R., 2008. Conceptualizing and measuring driving confidence in older adults. Arch. Phys. Med. Rehabil. 89, 630–640. Nasvadi, G.E., 2007. Changes in self-reported driving behaviour following attendance at a mature driver education program. Transp. Res. Rec. F 10, 358–369. Owsley, C., McGwin, G., Phillips, J.M., McNeal, S.F., Stalvey, B.T., 2004. Impact of an education program on the safety of high-risk visually impaired older drivers. Am. J. Prev. Med. 26, 222–229. Porter, M.,M., Ash, M.H., 2008. The effect of a new road safety device with auditory alerts on older drivers. In: Proceedings of the 18th Canadian Multidisciplinary Road Safety Conference, Whistler, British Columbia, pp. 1–13. Rudman, D., Friedland, J., Chipman, M., Sciortino, P., 2006. Holding on and letting go: the perspectives of pre-seniors and seniors on driving self-regulation in later life. Can. J. Aging 25, 65–76. Ryan, G.A., Legge, M., Rosman, D., 1998. Age related changes in drivers’ crash risk and crash type. Accid. Anal. Prev. 30, 379–387. Shrout, P.E., Fleiss, J.L., 1979. Intraclass correlations: uses in assessing rater reliability. Psychol. Bull. 2, 420–428.
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Accident Analysis and Prevention journal homepage: www.elsevier.com/locate/aap
Examination of driving comfort and self-regulatory practices in older adults using in-vehicle devices to assess natural driving patterns Robin A. Blanchard ∗ , Anita M. Myers Department of Health Studies and Gerontology, University of Waterloo, 200 University Ave West, Waterloo, Ontario N2L 3G1, Canada
a r t i c l e
i n f o
Article history: Received 2 October 2009 Received in revised form 5 January 2010 Accepted 19 January 2010 Keywords: Driving self-regulation Older drivers Driving reduction Perceptions
a b s t r a c t Several studies have shown that age, gender, visual problems and confidence are associated with selfregulatory practices such as reduced exposure and avoidance of night and highway driving. To date, however, self-regulation has only been examined through self-report. The purpose of this study was to further our understanding of the association between driver characteristics, perceptions and selfregulation by monitoring the patterns of 61 drivers (mean age 80.4 ± 5.5; 59% women) for one week using in-vehicle devices. Usual self-regulatory practices were assessed using the Situational Driving Frequency (SDF) and Avoidance (SDA) Scales, while perceptions were measured using the Driving Comfort and Perceived Driving Abilities Scales. Additional evidence for test–retest reliability was obtained with a separate sample of 39 older drivers. Lower comfort and poorer perceived abilities were significantly related to actual behavior: reduced exposure (km, duration) in general and at night, average and maximum radii from home and driving in challenging situations (such as on highways). Neither sex nor age was associated with any of the driving indicators. While longitudinal studies are required to determine temporality (when drivers change their behavior) and directionality (does lower comfort lead to driving restrictions or vice versa), this is the first study to demonstrate that driver perceptions are associated with actual self-regulatory practices. © 2010 Elsevier Ltd. All rights reserved.
1. Introduction Attempts to regulate older drivers through mandatory renewal requirements and licensing restrictions are controversial and vary widely between countries and across regions. There is no accepted standard (Langford and Koppel, 2006) and limited evidence that policies other than in-person renewal are effective in reducing fatality rates (Grabowski et al., 2004). It has also been argued that many older drivers are capable of regulating their own behavior (e.g., Eberhard, 1996) and there is substantial evidence that driving changes with age. Compared to younger drivers, older adults drive less often, closer to home (e.g., Benekohal et al., 1994; Collia et al., 2003), in the daytime, on weekdays and in familiar areas (e.g., Collia et al., 2003; Keall and Frith, 2004). Older drivers, particularly women, also tend to avoid driving at night, in bad weather, in rush hour, on highways, and making complex manoeuvres such as left-hand turns (Baldock et al., 2006; Benekohal et al., 1994; Charlton et al., 2006; Eberhard, 1996; Hakamies-Blomqvist and Wahlström, 1998). While often assumed that seniors change their behavior to compensate for declining
∗ Corresponding author. Tel.: +1 250 545 0140; fax: +1 519 746 2510. E-mail addresses: [email protected] (R.A. Blanchard), [email protected] (A.M. Myers). 0001-4575/$ – see front matter © 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.aap.2010.01.013
abilities (e.g., Eberhard, 1996), these patterns may simply reflect preferences and lifestyle, such as less need to drive or flexible schedules (Ball et al., 1998; Charlton et al., 2006). Older drivers are overly involved in certain types of crashes, such as at intersections and involving multiple vehicles (e.g., Ryan et al., 1998). Crash risk, however, may be reduced if drivers adopt safer driving practices commensurate with their skills (Eby and Molnar, 2009) and regulate their driving to reduce task demands (Kostyniuk and Molnar, 2008). Raising awareness and promoting appropriate self-regulatory practices through education programs and health care professionals (e.g., eye specialists) may also be cheaper than government intervention (Ball et al., 1998). For instance, Nasvadi (2007) found that three-quarters of participants said they changed their driving practices after attending a mature driver education program. Drivers can self-regulate by reducing exposure (frequency and distance), modifying their driving patterns (such as when and where they drive), avoiding more complex driving situations and ultimately by giving up driving completely (Ball et al., 1998; Charlton et al., 2006; Kostyniuk and Molnar, 2008; Myers et al., 2008). Researchers are attempting to understand the factors that influence the adoption of self-regulatory practices, and ultimately whether such practices reduce crash involvement and severity. While Ball et al. (1998) found that older drivers with visual and attention impairments were more likely to reduce their exposure
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(# days/week) and avoid particular driving situations, Kostyniuk and Molnar (2008) found that sex had a stronger influence on selfregulation than age and physical functioning (self-reported ability to walk 1/4 of a mile and vision). As noted by Kostyniuk and Molnar (2008), however, confidence may explain the gender effect. A growing number of studies are showing that driver perceptions (particularly confidence or comfort) may be a key determinant of self-regulatory practices in older drivers (Baldock et al., 2006; Charlton et al., 2006; MacDonald et al., 2008; Marottoli and Richardson, 1998; Molnar and Eby, 2008; Myers et al., 2008; Rudman et al., 2006). For instance, Charlton et al. (2006) found that confidence was more important than other variables in characterizing self-regulators. MacDonald et al. (2008), meanwhile, showed that perceived comfort and abilities were more strongly related to situational driving frequency and avoidance than objective driving abilities, consistent with Bandura’s (1986) social cognitive theory. Charlton et al. (2006) and Molnar and Eby (2008) found that, with the exception of night driving, older drivers (particularly men) were quite confident and relatively few self-regulated. Both studies used categorical confidence ratings and defined self-regulators as those who avoided one or more difficult driving situations; however, the measures themselves comprised different items. Some studies have used only one item to assess confidence (e.g., Nasvadi, 2007). Findings are difficult to interpret and compare as driver perceptions have not been clearly defined or consistently measured (Myers et al., 2008). Only the Driving Comfort Scales (DCSs) and the Perceived Driving Abilities (PDA) Scales have been systematically developed with older drivers and subjected to rigorous psychometric examination. Other tools to measure confidence have included only one item on night driving (e.g., Marottoli and Richardson, 1998; Molnar and Eby, 2008) or two at most (e.g., Baldock et al., 2006; Charlton et al., 2006). Older drivers were adamant that most driving situations were more challenging at night, leading to separate day (DCS-D) and night (DCS-N) Driving Comfort Scales. The DCS and PDA (current and change) scales have demonstrated hierarchiality, unidimentionality, person and item reliabilities, goodness of fit and interval properties, as reported in Myers et al. (2008) and MacDonald et al. (2008), respectively. The DCSs also have good test–retest reliability (Myers et al., 2008). Two scales to assess self-regulatory behavior – Situational Driving Frequency (SDF) and Situational Driving Avoidance (SDA) – were also developed with older drivers; however, only internal consistency has been reported (MacDonald et al., 2008). Molnar and Eby (2008) noted that some subjects initially described themselves as self-regulators but later reported avoidance, questioning the reliability of these self-reports. While it is important to have reliable measures of perceptions and avoidance, it is vital to have measures of natural driving behavior. Studies to date have relied on self-report measures of driving exposure and patterns, which are subject to recall and social desirability bias (Lajunen and Summala, 2003). Based on objective data, there is evidence that estimated driving distance (km) is inaccurate (Huebner et al., 2006; Blanchard et al., 2009) and that older drivers may not regulate as much as they say they do (Blanchard et al., 2009). This paper reports the findings from two studies. The purpose of the first was to gather further psychometric evidence on the perception and self-regulatory measures by replicating the test–retest reliability of the DCSs with a new sample and examining the stability of PDA, SDF and SDA scores. The purposes of the primary study were to (1) replicate prior findings concerning the association between driver characteristics, perceptions and self-reported regulatory practices; (2) extend this area of investigation to actual driving patterns; and (3) examine the correspondence between
self-reported and objective measures of driving. The third objective is the subject of a separate article (Blanchard et al., 2009). This paper addresses the associations between driver characteristics, perceptions and self-regulatory practices. As licensing requirements may influence self-regulation (Charlton et al., 2006; Rudman et al., 2006), it is important to note that this study took place in Ontario, Canada which has a mandatory in-person Senior Drivers Renewal Program (SDRP) for drivers once they turn 80 and every 2 years thereafter. 2. Methods 2.1. Participants For the psychometric examination, the DCSs, PDA, SDF and SDA scales were completed twice, approximately one week apart (M = 7.6 days, S.D. = 1.6), by 39 older drivers not involved in other studies. This sample ranged in age from 65 to 86 (M = 73.6, S.D. = 5.1), 53.8% were female and 38.5% had some post-secondary education. For the natural driving study, a sample of 61 current drivers with a valid license, living in Southwestern Ontario, mostly in urban/suburban areas (86.9%), were assessed between spring and early fall (described in Blanchard et al., 2009). To obtain drivers aged 80+, we purposefully recruited from the Ministry of Transportation of Ontario’s SDRP. To obtain a sample of drivers under the age of 80 we recruited from senior centres, recreation and apartment complexes in the same region and during the same timeframe. The sample (59% female) ranged in age from 67 to 92 (M = 80.4, S.D. = 5.5) with 57.4% age 80 or over. About half (56%) had completed some post-secondary education, but only 8.2% were still working (mostly part-time). Most rated their health as excellent or good (93.4%) and said they could walk a quarter of a mile (88.5%). Diagnosed health conditions most often reported were arthritis, rheumatism or osteoporosis (60.7%), followed by cardiac problems (54%) and vision disorders (36.1%). Almost half the sample had undergone cataract surgery (48.3%); however, no one rated their eyesight as “worse than most their age”. On average, the sample had 57.7 years (S.D. = 10.6) of driving experience. The most common driving problems reported over the past year were near misses (23%); only 3 (4.9%) were involved in a crash. The majority felt driving was extremely or very important (90.1%), 85.2% were the primary driver in their household, 31.3% said others relied on them to drive and only 16.4% had seriously thought about driving reduction (16.4%) or cessation (9.8%). 2.2. Measures 2.2.1. Self-reported driving habits, barriers and restrictions A questionnaire was used to obtain information on usual driving patterns (e.g., times of day, roadways) and preferences for getting around. Similar to Baldock et al. (2006), respondents were also asked to rate (on a 4-point Likert scale from “not at all” to “very much so”) the extent to which 11 factors presented a barrier or challenge to reducing when and where they drove. Total scores could range from 0 to 21, with higher values indicating more perceived challenges. The 14-item SDF and 20-item SDA scales were used to assess self-reported driving restrictions. The SDF assesses how often people drive in challenging situations, while the SDA asks people to check particular situations they try to avoid, if possible. Scores on the SDF can range from 0 to 56, while scores on the SDA can range from 0 to 20, with higher scores indicating greater frequency and greater avoidance of challenging driving situations, respectively (MacDonald et al., 2008).
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2.2.2. Perceived driving comfort and abilities Perceived driving comfort was assessed using the 13-item day (DCS-D) and 16-item night (DCS-N) Driving Comfort Scales. When rating their level of comfort on the 5-point scale (0%, 25%, 50%, 75%, 100%), respondents are asked to consider confidence in their own abilities and driving skills, as well as the situation itself and to assume normal traffic flow unless otherwise noted. Scores on the DCS-D and DCS-N scales can range from 0% to 100%, with higher scores indicating higher levels of comfort (Myers et al., 2008). Perceived driving abilities were assessed using the 15-item PDA scale (4-point rating scale from poor to very good) and the 15-item PDA change scale comparing one’s abilities to 10 years ago (4-point rating from “a lot worse” to “better”). Higher scores (range 0–45) indicate more positive perceptions and fewer declines, respectively (MacDonald et al., 2008). 2.2.3. Objective driving data As described in Blanchard et al. (2009), natural driving patterns were assessed using two recording devices, a CarChip E/X® (Davis Instruments, Hayward, CA) and an Otto Driving Mate® (Otto; Persen Technologies Inc., Winnipeg, MB). The CarChip plugs into the on-board diagnostic port, while the Otto (a small GPS device) is mounted on the dashboard. While both devices collect similar timestamped information, ‘cold starts’ at the beginning of trips (Duncan et al., 2009) can result in lost or missing GPS data, especially for short trips (Porter and Ash, 2008). Thus, the Otto data was used only to examine roadways and radius (i.e., distance from home). Restricted radius may be an important indicator of self-regulation that has not been previously examined. The CarChip, which has shown minimal error in distance recording (Huebner et al., 2006), was used for all other driving data. 2.3. Procedures During the first visit, the researcher obtained consent, asked participants to complete the background questionnaire and explained the trip logs (used to record who drove, number of passengers and weather conditions). The CarChip and Otto were then installed in the person’s vehicle and a set of trip logs placed in the car. Subjects were asked to drive as usual over the subsequent week. The researcher then met with participants again to pick up the devices and trip logs. Subjects were asked to complete, in order: the DCSs, SDF, SDA and PDA scales and the driving habits questionnaire, followed by an interview. 2.4. Analysis For the psychometric examination, internal consistency was calculated using Cronbach’s alpha, while test–retest reliability was examined using the intraclass correlation coefficient, model 2, 1 (ICC2,1 ) (Shrout and Fleiss, 1979). For the driving study, total scores were calculated for the perception (DCS and PDA) and self-regulation (SDF and SDA) scales, following the developers’ instructions (Myers et al., 2008; MacDonald et al., 2008). Data from the CarChips (retrieved for 58 subjects) and Otto (retrieved for 55 subjects) were downloaded and cleaned (i.e., removing trips with 0.0 km and by drivers not in the study) prior to analysis. To determine complete trips, segments were linked by cross-referencing CarChip data with logs and/or Otto data. Archives were consulted for daily times of sunrise/sunset and weather conditions (Blanchard et al., 2009). Using the GPS data and digital maps, roadways driven were examined, including freeways (multi-lane, divided with speed limits usually greater than 90 km/h) and highways (two-lane with speed limits 70 km/h greater). The average and maximum radii each
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person drove from his/her home were also calculated for each trip. A composite Frequency Index of actual driving in challenging situations was calculated based on 11 of the 14 situations depicted in the SDF scale, as described in Blanchard et al. (2009). Descriptive statistics were used to depict sample characteristics, perception scores, as well as self-reported and actual driving patterns. Comparative statistics (parametric and non-parametric, as appropriate) were used to examine associations between driver perceptions with self-reported and actual driving patterns. Gender and age group (<80 versus 80+) differences were examined throughout. 3. Results 3.1. Further psychometric support The DCS-D and DCS-N scales showed good test–retest reliability over the week (ICC2,1 = .89 and .92, respectively), while the PDA scales showed moderate test–retest reliability (ICC2,1 = .65 and .66). Internal consistency was better for the current (˛ = .92) than for the change PDA scale (˛ = .77). Both the SDF and SDA scales showed high internal consistency (˛ = .92 and .87) and good test–retest reliability (ICC = .89 and .86), respectively. 3.2. Driver perceptions Table 1 presents the scores on the driver perception measures. All scores were normally distributed except for the PDA change score and DCS-N item #1. This item (driving at night in good weather and traffic conditions) was examined separately as it may be indicative of progressive declines in comfort level (Myers et al., 2008). Overall the sample had a moderate level of driving comfort, given the theoretical mean of 50%. While correlated (r = .82, p < .001), DCS-N scores were significantly lower than DCS-D scores (paired-t(60) = 7.46, p < .001). Perceived driving abilities were generally good (theoretical mean 22.5) and not seen as changing much over the past 10 years. Perceived abilities were positively correlated with comfort level (DCS-D, r = .41, p < .001 and DCS-N, r = .45, p < .001), while PDA change scores were inversely related (DCS-D: = −.25, p < .01; DCS-N: = −.37, p < .01). Maintaining one’s current lifestyle was viewed as the primary barrier to driving reduction (rated “very much” by 63%), followed by location of shops and services (59%), difficulty with public transit (47%), not wanting to bother others (42%), availability of others (24%), others relying on them (24%) and physical difficulty getting places (22%). Men had higher comfort for both day and night driving (t(59) = 2.44, p = .001 and t(59) = 3.46, p = .001, respectively), including DCS-N item #1 (z = −2.59, p = .01). Although not shown in Table 1, those living in rural areas also had higher DCS-N (M = 72.1, S.D. = 24.3 versus M = 51.7, S.D. = 24.0, t(59) = 2.22, p = .05) and perceived barrier scores (M = 14.6, S.D. = 3.2 versus M = 10.5, S.D. = 6.1, t(57) = 2.93, p = .009). Subjects diagnosed with cataracts, glaucoma or macular degeneration had significantly lower comfort at night (t(59) = 2.50, p = .02), poorer perceived abilities now (t(59) = 2.19, p = .03) and compared to 10 years ago (z = −1.96, p = .05). Those taking prescription medications rated their abilities lower than those who were not (M = 31.9, S.D. = 6.2 versus M = 37.8, S.D. = 4.7; t(57) = 2.86, p = .02). Individuals whose physician had asked them about driving had lower scores on the DCS-N (t(57) = 2.15, p = .05), DCS-D (t(57) = 2.03, p = .06) and PDA (t(57) = 3.51, p = .002) scales, while those who had talked with an eye care professional had lower scores on the DCS-D (t(58) = 2.30, p = .03), DCS-N (t(58) = 3.18, p = .003) scales and DCS-N item #1 (z = −2.86, p = .004). Subjects who had thought about driving reduction also had significantly
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Table 1 Perception scores by sex and age group. Total sample (n = 61)
Sex
Age group
Men (n = 25)
Women (n = 36)
<80 (n = 26)
80+ (n = 35)
DCS-Da
68.9 (15.2) 30.8–100
74.2 (14.4) 44.2–98.0
65.2 (14.7) 30.8–100
68.9 (16.2) 44.2–100
68.8 (14.6) 30.8–98.1
DCS-Na
54.3 (24.8) 6.3–100
63.4 (25.3) 12.5–100
48.0 (22.7) 6.3–100
54.1 (26.6) 10.9–100
54.5 (23.9) 6.3–100
DCS-N #1b
83.6 (19.8) 25–100
90.0 (19.1) 25–100
79.2 (19.4) 50–100
87.5 (19.0) 50–100
80.7 (20.2) 25–100
PDA (current)
32.5 (6.3) 15–45
33.4 (6.3) 21–45
31.9 (6.3) 15–44
32.9 (7.4) 15–45
32.3 (5.3) 21–43
PDA change
19.1 (5.9) 2–44
19.0 (7.9) 2–44
19.1 (4.0) 15–31
19.4 (5.7) 2–31
18.9 (6.0) 7–44
Barriers
11.0 (6.0) 0–21
11.3 (6.6) 0–21
10.8 (5.6) 0–21
10.9 (6.6) 0–21
11.1 (5.7) 0–21
Note: Values denote mean (S.D.) and range. DCS-D, DSC-N: day and night Driving Comfort Scales, respectively; DCS-N #1: item 1 on DCS-N (night in good weather and traffic conditions); PDA: perceived driving abilities; barriers: barriers to driving reduction or cessation score. a Significant sex difference (p < .001). b Significant sex difference (p < .01).
lower night comfort (t(59) = 2.09, p = .05) and higher perceived barriers scores (t(57) = 2.10, p = .05). Other characteristics (e.g., age group, primary versus non-primary driver, education, self-rated health, number of health problems) were not significantly related to DCS, PDA or perceived barriers scores. 3.3. Self-reported driving patterns Compared to 10 years ago, 54.1% said that they drove less now, 29.5% about the same and 13.6% more. Reported frequency of driving in challenging situations was moderate (M = 30.2, S.D. = 9.0 out of 56) and significantly higher for men (M = 33.9, S.D. = 8.6 versus M = 27.8, S.D. = 8.6; t(58) = 2.69, p = .01). Avoidance of challenging situations was relatively low (M = 9.2, S.D. = 4.8 out of 20). While women had higher SDA scores (M = 9.9, S.D. = 4.6 versus M = 8.3, S.D. = 5.0), the difference was not significant. No age group differences emerged. 3.4. Actual driving patterns Table 2 shows the driving patterns of the sample over the week. There were no significant differences in exposure by month of
participation, age group or sex, although total distance by gender approached significance (t(56) = 1.80, p = .08). A total of 137.5 trips (33% of all trips) involved at least one passenger (up to four), averaging 2.37 trips (S.D. = 2.53) over the week. Most trip segments were on city streets (88%), followed by residential streets (50%). Fewer were on rural roads (13%), highways (7%) or freeways (5%). There were no significant differences in driving exposure (i.e., distance, duration or number of trips) by day of the week or weekend versus weekday. However, the sample drove significantly less at night (dusk to dawn) compared to the morning (dawn to 11:59 am; paired-t(57) = 5.82, p < .001) and afternoon (12:00 pm to 4:59 pm; paired-t(57) = 7.38, p < .001). People drove significantly more during off-peak hours (9 am to 4 pm, 7 pm to 6 am) than peak hours (6 am to 9 am and 4 pm to 7 pm), paired-t(57) = 6.88, p < .001. Only 16 people (56% men; 8 in each age group) drove at night over the week, anywhere from 1 to 5 days and from 2.7 to 129.4 km (average 25 km). Five people started their trip at night. Men drove more km (t(14) = 2.24, p = .05), trip segments (t(14) = 2.50, p = .03) and for longer durations (t(14) = 2.91, p = .02) at night than women. There was no significant difference in km at night by study month.
Table 2 One-week driving behavior by sex and age group. Total sample
Sex
Age group
Men
Women
<80
80+
Days
5.2 (1.9) 1–7
5.5 (1.9) 1–7
5.0 (1.8) 1–7
5.1 (1.9) 1–7
5.2 (1.9) 1–7
Trips
7.1 (3.9) 0.5–17
7.8 (4.3) 0.5–17
6.6 (3.5) 1–14
6.7 (5.3) 1–14
7.4 (4.2) 0.5–17
Stops
14.8 (10.1) 1–44
16.2 (11.6) 2–44
13.8 (8.9) 1–36
13.9 (10.1) 1–42
15.6 (10.1) 1–44
Duration (h:min)
4:07 (3:06) 0:07–13:20
4:46 (3:38) 0:22–13:20
3:39 (2:37) 0:07–10:10
3:43 (2:48) 0:07–10:10
4:25 (3:19) 0:19–13:20
Distance (km)
164.1 (158.4) 4.2–633.3
210.8 (190.7) 8.5–633.3
131.1 (123.6) 4.2–548.0
149.0 (149.2) 4.2–548.0
176.4 (166.8) 6.8–633.3
Average radius
7.6 (7.6) 1.0–45.1
9.6 (9.9) 1.0–45.1
6.2 (5.4) 1.8–23.1
7.6 (7.1) 2.0–26.2
7.4 (7.9) 1.0–45.1
Maximum radius
21.3 (27.4) 1.8–113.7
27.5 (32.6) 1.8–113.7
17.1 (22.9) 2.5–107.5
23.4 (31.9) 3.2–113.7
18.8 (23.6) 1.8–93.7
Note: Values denote mean (S.D.) and range.
R.A. Blanchard, A.M. Myers / Accident Analysis and Prevention 42 (2010) 1213–1219 Table 4 Associations between perceptions and indicators of actual driving behavior.
Table 3 Associations between perceptions and self-reported restrictions. DCS-D
DCS-N
DCS-N #1
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PDA
PDA change
SDA score SDF score
−.48a .45a
−.59a .58a
−.31b .54a
−.51a .51a
.21 −.13
Types of roads Rural t-value Freeways t-value Highways t-value
1.33 2.19b 3.13c
2.66c 2.85c 3.83a
1.74 2.65c 1.89
.33 1.18 1.57
.60 1.35 2.34b
Time of day At night t-value
3.38c
5.18a
2.77c
2.09b
.63
Note: DCS-D: day Driving Comfort Scale; DCS-N: night Driving Comfort Scale; DCSN #1: item 1 on DCS-N; PDA: perceived driving abilities; SDA: Situational Driving Avoidance scale; SDF: Situational Driving Frequency scale. Values presented are Pearson’s r for SDA and SDF, except for DCS-N #1 PDA change (Spearman’s ). tTests were used for types of roads and time of day, except for DCS-N #1 and PDA change (Mann–Whitney U-coefficients). a p < .001. b p < .05. c p < .01.
Those who drove at night generally drove greater distances (t(56) = 1.96, p = .06), longer durations (t(56) = 2.20, p = .04) and made more stops (t(56) = 2.14, p = .04) overall, compared to those who did not drive at night. “Night” drivers also scored significantly higher on the SDF scale (t(56) = 2.03, p = .05) and significantly lower on the SDA scale (t(56) = 3.38, p = .002) compared with “non-night” drivers. A total of 34 trips (by 25 individuals) occurred in the rain. Distance driven in the rain averaged 17.9 km (S.D. = 14.5), with men driving significantly more than women (t(23) = 3.53, p = .004). Weather conditions were also examined for the days people did not drive (106 instances); most (81%) were on days with favorable conditions (i.e., no rain or fog).
3.5. Associations between perceptions and patterns 3.5.1. Self-reported driving patterns As shown in Table 3, DCS and PDA scores were significantly related to SDF and SDA scores, in the expected direction. With respect to usual patterns (on the habits questionnaire), DCS-D and DCS-N scores were significantly higher for those who reportedly drove on freeways, highways and at night, compared to those who did not. Only DCS-N scores discriminated between people who did and did not typically drive on rural roads (higher comfort, more driving on such roads). Those who typically drive at night also had higher perceived driving abilities, while those who typically drive on highways perceived less change in their abilities.
Distance (km) Duration Radius (avg) Radius (max) # Trips # Stops # Days Night (km) Night (duration) Frequency Index
DCS-D
DCS-N
.24 .16 .25 .23 .05 .18 −.01 .29c .29c .15
a
.43 .39b .39b .30c .21 .34b .14 .35b .40b .40b
DCS-N #1 b
.36 .35b .37b .28c .26c .35b .24 .25c .23 .40b
PDA b
.36 .38b .22 .35b .34b .37b .29c .15 .13 .39b
Barriers .31c .30c .19 .26 .32c .30c .31c .30c .27c .34b
Note: DCS-D, DSC-N: day and night Driving Comfort Scales, respectively; DCS-N #1: item 1 on DCS-N (night in good weather and traffic conditions); PDA: Perceived Driving Abilities Scale; barriers: barriers to driving reduction or cessation score. All values are Pearson’s r, with the exception of DCS-N#1 (Spearman’s ). a p < .001. b p < .01. c p < .05.
3.5.2. Actual driving patterns As shown in Table 4, DCS-N, PDA and perceived barriers scores were significantly related to most driving indicators, including the Frequency Index. Those who drove closer to home had significantly lower night driving comfort levels (overall and in good weather) and perceived abilities (maximum radius only); daytime comfort level bordered on significance (p = .07). Lower DCS-D scores were significantly related to more trips as a passenger (r = −.23, p = .05). Those who drove at night had both significantly higher DCS-D (75.6 versus 66.5, t(59) = 2.17, p = .04) and DCS-N (70.9 versus 48.4, t(59) = 3.71, p < .001) scores. None of the indicators were significantly associated with PDA change scores. Finally, people were categorized by whether they scored above or below the midpoint (50%) on the DCS-D and DCS-N scales, respectively. As shown in Table 5, those who scored below the midpoint drove significantly less (km, duration, number of trips), closer to home and less often in challenging situations. The only non-significant variable was stops. 4. Discussion Prior findings that older drivers are generally confident, consider their abilities to be as good or better than others and that relatively few regulate their driving (e.g., Charlton et al., 2006; Marottoli and Richardson, 1998; Molnar and Eby, 2008), may be largely a function of how these constructs have been measured. Determining the extent to which older drivers self-regulate, as well as factors associated with the adoption of self-regulatory practices, require reliable and valid measures.
Table 5 Driving exposure and patterns in persons with low versus high daytime and nighttime comfort levels. DCS-D score
Distance (km) Duration (h:min) Radius (average) Radius (maximum) # Trips # Stops # Days Night (km) Night (duration) Frequency Index
t (p)
≤50%, n = 7
>50%, n = 51
70.7 (35.7) 02:36 (01:01) 3.9 (1.9) 8.5 (8.0) 5.6 (1.5) 8.6 (2.9) 5 (1.3) .62 (1.6) 00:02 (00:04) 4.8 (1.3)
176.9 (164.5) 04:17 (03:11) 7.9 (7.8) 22.1 (28.2) 7.3 (4.2) 15.8 (10.4) 5.2 (1.9) 7.9 (22.2) 00:09 (00:20) 7.5 (4.2)
3.98 (<.001) 2.84 (.008) 2.96 (.005) 2.65 (.01) 2.09 (.05) 3.95 (<.001) .25 (.81) 2.30 (.03) 2.55 (.01) 3.22 (.005)
Note: DCS-D, DSC-N: day and night Driving Comfort Scales, respectively. Values presented as M (S.D.).
DCS-N score
t (p)
≤50%, n = 26
>50%, n = 32
95.3 (113.7) 2:54 (2:36) 4.3 (2.6) 11.5 (17.0) 6.4 (3.4) 11.7 (8.8) 5 (1.8) .69 (2.9) 00:01 (0:05) 5.5 (3.2)
220.0 (168.8) 5:02 (3:06) 10.1 (9.1) 28.4 (31.5) 7.7 (4.4) 17.5 (10.5) 5.3 (2.0) 11.7 (26.8) 00:14 (00:23) 8.6 (4.1)
3.35 (.001) 2.86 (.006) 3.41 (.002) 2.57 (.01) 1.31 (.20) 2.27 (.03) .69 (.49) 2.35 (.03) 3.05 (.004) 3.15 (.003)
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Psychometric examination with a new sample of 39 older drivers provided further evidence that both driving comfort scales have good test–retest reliability (Myers et al., 2008) and new evidence concerning the reliability of the PDA, SDF and SDA scales. Although the PDA scales have good structural properties (MacDonald et al., 2008), they showed only moderate test–retest reliability. Internal consistency was better for the current than the change scale, as might be expected. Perceived driving ability has often been measured using only one item (e.g., Marottoli and Richardson, 1998). Drivers may be more likely to acknowledge areas of weakness on multi-item measures and ratings of specific (e.g., seeing signs at night) versus general abilities (e.g., overall vision) are more likely to correspond with objective measures such as contrast sensitivity (MacDonald et al., 2008). Consistent with previous studies, DCS-N scores were significantly lower than DCS-D scores, while both correlated with PDA scores in the expected direction (Myers et al., 2008; MacDonald et al., 2008). Significant relationships between driver perceptions (comfort and abilities) and usual self-regulatory practices (situational frequency and avoidance) were also in keeping with prior results (MacDonald et al., 2008). Rudman et al.’s (2006) model postulates that driving comfort, influenced by a multitude of intrapersonal, interpersonal and environmental factors, may be a key determinant of self-regulation. Furthermore, the decision to stop driving may be based on reaching a personally unacceptable level of comfort. Until now, however, investigation has been limited to self-reported driving practices. Lower night comfort scores (overall and in good conditions) were significantly related to reduced exposure (km and duration), average and maximum radius from home, night driving and driving in other challenging situations (Frequency Index). Comparatively, DCS-D scores were significantly related to only two indictors (night km and duration). However, those who scored below the midpoint on either the DCS-N or DSC-D scale were significantly more likely to restrict their driving. Comfort level at night may progressively decline to the point where people are uncomfortable even in good weather and traffic conditions (item 1 on the DCS-N scale) before daytime driving comfort is appreciably affected (Myers et al., 2008). While only 12% of the sample scored at or below the midpoint (≤50) on the DCS-D, 44.9% scored below the midpoint on the DCS-N scale. The sample had notably higher comfort scores for day versus night driving, as was expected. There is ample evidence that night driving is particularly problematic for older drivers. For instance, over half of Charlton et al.’s (2006) sample who reportedly avoided night driving specifically attributed this to vision-related issues, especially glare from lights. Other studies have also shown that vision disorders and impairments are more strongly related to self-regulation than cognitive or motor impairments (e.g., Ball et al., 2006; Molnar and Eby, 2008). Present participants diagnosed with vision disorders had significantly lower comfort at night, as well as poorer perceptions of driving abilities. Men had higher comfort scores which have been previously found in some studies (e.g., Myers et al., 2008), but not others (e.g., MacDonald et al., 2008). As suggested by Kostyniuk and Molnar (2008), confidence may explain the gender effect that has emerged in several studies on self-reported regulatory practices. In fact Charlton et al. (2006) found that inclusion of a confidence variable significantly reduced the contribution of other factors (gender, age, vision problems) in avoidance ratings. Unfortunately, our sample was too small to examine the relative contribution of driver characteristics and perception scores (as well as possible interactions), as predictors of actual driving patterns via regression modelling.
Generally people who volunteer for driving studies do not have significant cognitive, motor or attention deficits (Molnar and Eby, 2008) and may be more confident than older drivers in general. Our sample was older than those in many previous studies (over half were 80 or over). However, this 80+ cohort may not be representative since all had successfully completed provincial renewal requirement. Otherwise they would not have qualified for the present study (i.e., their license would have been suspended or revoked). Charlton et al. (2006) speculated that regular license retesting procedures may foster self-checking and, if so, one would expect to find more self-regulatory behavior among older drivers in jurisdictions with age-based requirements. However it must be kept in mind that not everyone holding a valid license actually drives and some might let their license lapse rather than undergo assessment (Langford and Koppel, 2006). In-person renewal may serve as a deterrent for the oldest group (85+) who doubt their capabilities (Grabowski et al., 2004). What is most unique about Ontario’s biannual SDRP for drivers 80+ is mandatory attendance at a group education session. The session reviews rules of the road, effects of medical conditions and medications, frequent types of collisions and strategies for avoiding or dealing with these situations. Driving education may positively or negatively affect driving confidence (Nasvadi, 2007; Owsley et al., 2004). Drivers with discrepancies between perceived and actual abilities (indicating lack of awareness) were found to be more confident and less likely to reportedly self-regulate MacDonald et al. (2008). Older drivers who overestimate their capabilities and have high levels of confidence may be more likely to exceed their limitations and pose a safety risk to themselves and others (Marottoli and Richardson, 1998; MacDonald et al., 2008). Older drivers are concerned about being reported to authorities and losing their licenses. Presenting a favorable image is particularly likely when people are asked about driving problems (Lajunen and Summala, 2003) or whether their driving practices changed post-intervention (e.g., Nasvadi, 2007). The use of invehicle devices is far less intrusive than using video cameras or in-car observers (Lajunen and Summala, 2003) and is more likely to capture natural driving behavior than road testing which typically takes place in daylight and good conditions (Molnar and Eby, 2008). In the follow-up interviews, our sample reported that the vehicle devices did not affect their driving; half the sample (55.7%) also said that their driving patterns over the study week were fairly typical. Various reasons (e.g., visitors, personal or family illness, special events) were given for driving more (9.8%) or less (34.4%) than usual. Circumstances (e.g., appointments, commitments) may largely dictate when and where people drive, although they may prefer not to drive at night, on highways, in rush hour or in bad weather (Blanchard et al., 2009). Perceived challenges or barriers to driving reduction were significantly associated with all objective indicators, except for radius from home. By the same token, a snapshot of driving over one week may not fully capture usual practices or seasonal fluctuations (Blanchard et al., 2009). Over half our sample said that they drove less now compared to 10 years ago, however, it is unknown what precipitated this change. People may simply prefer not to drive or have less need to drive as they age. Thus far, all studies on self-regulatory practices, including the present, have been cross-sectional. Only longitudinal studies can confirm the extent to which older adults actually change their behavior as they age and whether various types of adaptations reduce crash involvement. Such a study is currently underway in Canada following 1000 drivers aged 70+ for 5 years using sophisticated in-vehicle devices to monitor natural driving, reliable tools to assess driver perceptions and ministry records to examine crashes.
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5. Conclusions This is the first study to demonstrate that driver perceptions are significantly related to actual driving patterns using in-vehicle devices. Although driving was only monitored for one week with a small sample, the findings extend our knowledge of self-regulatory practices among older drivers and provide direction for future research. These findings need to be replicated with larger samples and using multivariate analyses. Other aspects of driving behavior, such as keeping up with the flow of traffic and following distance, also need to be examined. Also, prospective studies are required to determine temporality and directionality. While lower comfort and poorer perceived abilities (possibly mediated by noticeable declines in vision and other factors) may lead to self-imposed driving restrictions, it is equally plausible (and consistent with Bandura’s theory) that restricted driving may lead to reduced comfort and potentially reduced driving skills due to lack of practice. Acknowledgements The authors wish to thank Alex Crizzle for assistance collecting the test–retest data, as well as Dr. Michelle Porter for loaning us equipment and feedback on earlier drafts. References Baldock, M.R.J., Mathias, J.L., McLean, A.J., Berndt, A., 2006. Self-regulation and its relationship to driving ability among older adults. Accid. Anal. Prev. 38, 1038–1045. Ball, K., Owsley, C., Stalvey, B., Roeneker, D., Sloane, M., Graves, M., 1998. Driving avoidance and functional impairment in older drivers. Accid. Anal. Prev. 30, 313–322. Ball, K.K., Roenker, D.L., Wadley, V.G., Edwards, J.D., Roth, D.L., McGwin Jr., G., et al., 2006. Can high-risk older drivers be identified through performance-based measurements in a Department of Motor Vehicles setting? J. Am. Geriatr. Soc. 54, 77–84. Bandura, A., 1986. Social Foundations of Thought and Action: A Social Cognitive Theory. Prentice-Hall, Englewood Cliffs, NJ. Benekohal, R.F., Michaels, R.M., Shim, E., Resende, P.T., 1994. Effects of aging on older drivers’ travel characteristics. Transp. Res. Rec. 1438, 91–98. Blanchard, R.A., Myers, A.M., Porter, M.M., 2009. Correspondence between selfreported and objective measures of driving exposure and patterns in older drivers. Accid. Anal. Prev. doi:10.1016/j.aap.2009.09.018.
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