Skip to main content
NCBI home page
The Journals of Gerontology Series B: Psychological Sciences and Social Sciences logoLink to The Journals of Gerontology Series B: Psychological Sciences and Social Sciences
. 2009 Apr 9;64B(3):348–355. doi: 10.1093/geronb/gbp017

Poor Sense of Direction Is Associated With Constricted Driving Space in Older Drivers

Kathleen A Turano 1,✉, Beatriz Munoz 1,2, Shirin E Hassan 3, Donald D Duncan 4, Emily W Gower 1,2, Karen B Roche 5, Lisa Keay 2,6, Cynthia A Munro 7, Sheila K West 1,2
PMCID: PMC2670254  PMID: 19359596

Abstract

The aims of this study were to determine whether perceived sense of direction was associated with the driving space of older drivers and whether the association was different between genders. Participants (1,425 drivers aged 67–87 years) underwent a battery of visual and cognitive tests and completed various questionnaires. Sense of direction was assessed using the Santa Barbara Sense of Direction (SBSOD) scale. Driving space was assessed by both the driving space component of the Driving Habits Questionnaire and log maximum area driven. Analyses were performed using generalized linear models. The SBSOD score was lower in women than in men and significantly associated with log driving area in women but not in men. The SBSOD score also showed a significant association with women’s self-reported driving restriction. The findings emphasize the need to explore the role of psychological factors, and include gender, in driving studies and models.

Keywords: Drivers, Driving restriction, Driving space, Gender, Navigational ability, Sense of direction

OLDER drivers have reported voluntarily reducing their driving either by stopping altogether or by restricting their driving to shorter distances, familiar areas, low-traffic areas, or to daytime and good weather conditions (Charlton, Oxley, Fildes, & Les, 2001; Hakamies-Blomqvist & Wahlstrom, 1998; Marottoli et al., 1993; Unsworth, Wells, Browning, Thomas, & Kendig, 2007). Some investigators have proposed that older drivers reduce their driving to compensate for the loss of function or increased stress and difficulty of driving (Bäckman & Dixon, 1992; Charlton et al., 2001; Hakamies-Blomqvist & Wahlstrom, 1998; Marottoli et al., 1993). In fact, studies have shown significant associations between self-report of driving reduction and decreased sensory function, cognitive function, or physical function (Anstey, Windsor, Luszcz, & Andrews, 2006; Ball et al, 1998; Brabyn, Schneck, Lott & Haegerstrom-Portnoy, 2005; Campbell, Bush, & Hale, 1993; Edwards et al., 2008; Freeman, Munoz, Turano, & West, 2005; 2006; Jette & Branch, 1992; Marottoli et al., 1993; Sims, Ahmed, Sawyer, & Allman, 2007).

Largely unexplored is any association between driving reduction and psychological traits (Lindstrom-Forneri, Tuokko, & Rhodes, 2007), which may be more closely associated with driving reduction than decreased physical function (Anstey et al., 2006). One such trait is perceived navigational ability. Persons who report a poor sense of direction are more likely to report that they worry about becoming lost (Bryant, 1982) and feel more anxious when lost compared with those who report a good sense of direction (Kozlowski & Bryant, 1977).

Thus, it is likely that wayfinding problems cause stress to the driver, and particularly older drivers, who perceive wayfinding to be more difficult than do younger drivers (Burns, 1998). In a focus group of older drivers, navigation difficulties were the most commonly reported problem (Sixsmith, 1990). In another study, older participants identified navigating in unfamiliar areas as the most challenging driving situation (Vrkljan & Polgar, 2007).

Given the negative emotions and difficulties associated with orienting and wayfinding in persons with a poor sense of direction, it is reasonable to expect that they would limit their travel more than persons with a good sense of direction. Indeed, some people with vision loss report limiting independent travel to known routes to avoid the stress of becoming disoriented or lost (Golledge, 1993). In those persons who already have a poor sense of direction, the potential stress is likely to be even more pronounced. However, driving cessation and reduction are associated with increased risks of mental and physical health problems (Fonda, Wallace, & Herzog, 2001; Marottoli et al., 1997). Those who cease driving are more likely than others of similar age, gender, mental, and physical functioning to be admitted to long-term care (Freeman, Gange, Muñoz, & West, 2006). Therefore, understanding the factors that affect driving reduction can be used to effectively counsel older adults regarding driving decisions and to design interventions that have the potential to balance mobility and safety in older adults.

One aim of this study was to evaluate whether perceived sense of direction is a predictor of driving restriction in older drivers. And because women are known to have higher navigational anxiety, uncertainty, and difficulty than do men (Burns, 1998; Kozlowski & Bryant, 1977; Lawton, Charleston, & Zieles, 1996), a second aim was to evaluate whether the association between perceived sense of direction and driving restriction is different between genders.

We hypothesized that perceived sense of direction is a predictor of driving restriction in older drivers and that the association between sense of direction and driving restriction is stronger in women compared with men. To test these hypotheses, we analyzed the association between the scores from the Santa Barbara Sense of Direction Questionnaire (SBSOD) and estimates of driving space, determined from the scores of the driving space component of the Driving Habits Questionnaire (Owsley, Stalvey, Wells, & Sloane, 1999), and the actual maximum area driven in driving episodes occurring during a 5-day period using data from older drivers in the Salisbury Eye Evaluation Driving Study (SEEDS). We performed gender-specific analyses.

METHODS

Population

SEEDS is a longitudinal study of vision, cognition, and driving behavior of older drivers living in the greater Salisbury metropolitan area. Salisbury is a semirural city on Maryland’s Eastern Shore, with a major freeway running north and south. The Eastern Shore itself is bounded on three sides by water. Leaving the area requires substantial driving in a north–south direction.

Postcards were mailed from the Maryland Department of Motor Vehicles to all registered drivers aged 67–87 years as of May 1, 2005, residing in the Greater Salisbury Metropolitan Area of Wicomico County to invite them to participate. Details on the sample and respondents were described previously (Zhang et al., 2007). In short, we were allowed to enroll those who responded affirmatively, a total of 1,425 older drivers. Data for this report were collected in Round 1 of the study (July 2005–June 2006).

The research followed the tenets of the Declaration of Helsinki, and informed consent was obtained from subjects. The Johns Hopkins Medical Institutions’ review board approved the research.

Procedure

Participants underwent a battery of cognitive and vision tests administered by trained technicians. For the purpose of this study, only the results of cognitive status in the general, attention, working memory, psychomotor speed, and visual search domains are reported.

Demographic information.—

Basic demographic data such as age, gender, race, level of education, and current medication use were obtained using questionnaires administered at the participants’ homes. Information about specific comorbidities was derived through medical history questionnaires and the Geriatric Depression Scale (GDS) questionnaire (Yesavage et al., 1983). The GDS is a 30-item self-report questionnaire used to identify the presence of depressive symptoms. The higher the GDS score, the greater the number of self-reported depressive symptoms. Participants also were asked about current use of medications for arthritis pain and the feeling of pain in the knees, hips, legs, or feet. A pain score was created counting the number of positive answers to these questions (scale 0–5, with 0 being no pain).

Cognitive measures.—

General cognitive status was assessed using the standardized Mini-Mental State Examination (MMSE). Lower MMSE scores indicate lower levels of cognition. The Brief Test of Attention (BTA) (Schretlen, Brandt, & Bobholz, 1996) was used to assess participants’ executive function, attention, and working memory. In this task, participants were required to focus their attention on a specified stimulus set and use their working memory to maintain it while withstanding distraction. Only the first 10 trials were administered; thus, the maximum score for this test was 10. Higher BTA scores indicate better performance on the divided attention test.

The Trail-making Test, Part B (Bowie & Harvey, 2006), was used to assess psychomotor speed, visual–spatial attention, and visual search. The time required to consecutively connect labeled circles that alternated between numbers (1–13) and letters (A–L) was recorded (maximum time-out score of 500 s). Longer times to complete the task indicate poorer performance.

The attentional visual field (AVF) was assessed using a divided attention task (Hassan et al., 2008). Participants were required to verbally report a simultaneously presented central and peripheral target (number) and to touch the touch screen monitor at the location of the peripheral target. The largest visual field (VF) extent at which the participant could locate the peripheral target and identify both the central and peripheral targets defined the AVF threshold. The average AVF extent is the mean of four thresholds, measured at each of four meridians. Higher thresholds indicate larger AVFs.

Vision function measures.—

Visual function was assessed using participants’ habitual spectacle or contact lens prescription. Binocular visual acuity (VA) was measured using a high contrast Early Treatment Diabetic Retinopathy Study (ETDRS) acuity chart (Ferris, Kassoff, Bresnick, & Bailey, 1982) with standard transillumination and 3-m testing. Binocular threshold VA was scored by assigning each correctly identified letter a value of −0.02 log minimum angle of resolution (MAR). The lower the log MAR value, the better the VA.

Monocular contrast sensitivity (CS) was assessed using the Pelli–Robson letter CS chart (Pelli, Robson, & Wilkens, 1988), with standard illumination at a 1-m test distance. The CS score was the number of letters correctly identified (“O” for “C” was accepted as correct).

Monocular VFs in both eyes were measured using the 81-point, quantify defect test strategy on the Humphrey Field Analyzer. This test assesses the static VF over a 60° radius VF using a Goldmann III target. For this analysis, the results from the two eyes were combined to create a binocular VF of 96 points. The number of missed points in the binocular field was tallied and used for analyses.

Self-reported sense of direction.—

In the present paper, the SBSOD (Hegarty, Richardson, Montello, Lovelace, & Subbiaha, 2002) was used to assess participants’ perceived sense of direction. The measure consists of statements about spatial and navigational abilities, preferences, and experiences in which participants rate their level of agreement on a 7-point scale (“strongly agree” to “strongly disagree”). The instrument was developed in response to the need for a standardized measure of sense of direction. Earlier measures had shown substantial variability in their association with the ability to find one’s way in the environment and to learn the layout of a large-scale space, skills referred to as environmental spatial ability (Hegarty et al., 2002). The instrument was designed with 27 items derived from previous instruments and statements made in a pilot study. A factor analysis of the results revealed 15 items associated with the item “My sense of direction is very good.” Those items were retained for the final 15-item instrument. Internal consistency, α, for the 15-item instrument was 0.88, and test–retest reliability, assessed by two administrations of the questionnaire 40 days apart, was 0.91 (Hegarty et al., 2002). Construct validity was demonstrated by significant correlations between the SBSOD scores and wayfinding performance; SBSOD scores were correlated with pointing errors in updating one’s location in space while moving in the environment (r = −.40) and with pointing errors to unseen landmarks (r = −.44) (Hegarty et al., 2002).

Half the items in the questionnaire are stated positively and the other half negatively. Therefore, prior to analysis, the scoring of the positively stated items was reversed so that higher ratings indicated a higher sense of direction for all items. Interval measures of sense of direction were estimated from the ordinal ratings of the agreement ratings by performing Rasch analysis (Wright & Masters, 1982) on the matrix of the 15-item ratings using Winsteps (Chicago, IL) (Linacre & Wright, 1997). With these models, a “person logit” can be defined as the difference between each person’s perceived sense of direction and the mean item measure. If the person logit is positive, the person’s perceived sense of direction is greater than the average sense of direction for the 15 items.

Self-reported driving space.—

Self-reported driving space was determined from the participants’ yes/no responses to whether they had driven to each of five specified regions in the past year, where each region is a certain distance (or spatial extent) from home: “outside the Mid-Atlantic Region,” outside the Eastern Shore, distant towns within the Eastern Shore, neighboring towns, or neighborhood only. Questions were adapted for the Maryland Eastern Shore from questions 29 and 31–34 on the Driving Habit Questionnaire (Owsley et al., 1999). Participants were categorized into the farthest region they had reported driving to.

Maximum area driven.—

Participants had their driving behaviors monitored for five consecutive days in their own vehicle using our novel Driving Monitor System (DMS), described previously (Baldwin, Duncan, & West, 2004). The DMS includes a Global Positioning System (GPS 18 LVC) unit (Garmin, Olathe, KS) that tracks the driver’s route during each driving segment, defined as when the car turns on to when it turns off. As an objective indicator of driving space, we used maximum area driven, computed as the area of an ellipse whose axes were the maximum miles driven in the north–south and east–west directions, as determined by the GPS. For some participants, DMS data are not available because the GPS failed to activate or the driving segments were so short that the estimate of miles driven was too small relative to the odometer reading.

Data Analyses

The t and chi-square tests were used to determine the characteristics that differed between the men and the women. Linear regression was used to determine the characteristics associated with sense of direction. Smoothed scatter plots (Lowess function in S) were used to examine the assumption of a linear relationship between continuous/interval predictors and the outcomes of interest (perceived sense of direction, maximum area driven). We modeled perceived sense of direction as a function of age, race, gender, education, MMSE, years of driving experience, and AVF.

To determine whether sense of direction was associated with driving space, we used regression models to evaluate two measures of driving space. Each regression was stratified by gender and controlled for AVF and years of driving experience, age, race, education, depression, pain, Trail-making Test, (Part B), and MMSE. First, the logarithm of the maximum area driven was evaluated as a function of perceived sense of direction using linear regression. Second, self-reported driving space was evaluated as a function of perceived sense of direction using multinomial logistic regression. This analysis calculates factors by which the odds of having driven to each region relative to having driven to the farthest region (outside the Mid-Atlantic region) are multiplied across levels of predictor variables. The two categories for the closest regions (“immediate neighborhood” and “neighboring towns”) were collapsed into one category due to the small sample size in the immediate neighborhood category. The likelihood ratio test was used to test for significance of the global effect of individual covariates on the model.

RESULTS

Of the 1,425 participants, 50% (712) were men. Men had lower scores on the GDS compared with women, but men had lower cognitive function, with lower MMSE and BTA scores, and longer times on Trail-making Test, Part B (Table 1). Men had better VA but slightly poorer VFs. There was no gender difference in the score for the AVF. Men had more years of driving experience, higher SBSOD scores, and a larger driving space (both reported and measured) than women.

Table 1.

Characteristics of the 1,425 Participants

Characteristics Men, N = 712 Women, N = 713 Overall, N = 1,425 Test Statistics
Demographics
    Age: M (SD), range 75.2 (5.2), 67–87 75.2 (5.3), 67–87 75.2 (5.2), 67–87 t = −0.06
    % Men — — 50.0
    % Blacks 114.7 111.4 113.1 χ2 = 3.52
    Years of education: M (SD), range 13.5 (2.9), 1–22 13.4 (2.4), 1–21 13.4 (2.7), 1–22 t = −0.60
General health
    GDS score: M (SD), range 3.4 (3.4), 0–23 4.1 (3.8), 0–21 3.7 (3.6), 0–23 t = 3.89***
    % With arthritis 47.9 66.8 57.3 χ2 = 51.8***
    Pain score (0–5): M (SD), range 0.8 (1.0), 0–5 0.9 (1.1), 0–5 0.9 (1.1), 0–5 t = 1.98
Cognitive function
    Mini-mental: M (SD), range 27.9 (2.1), 15–30 28.6 (1.6), 18–30 28.3 (1.9), 15–30 t = −7.15***
    Attention score: M (SD), range 6.1 (2.5), 0–10 6.8 (2.5), 0–10 6.5 (2.5), 0–10 t = 5.33***
    Trail-making Test—Part B in seconds: M (SD), range 136 (83), 37–500 124 (70), 37–500 130.1 (76.7), 37–500 t = −3.01**
    AVF extent in degree: M (SD), range 12.6 (5.6), 1.6–21.0 12.2 (5.0), 1.6–21.0 12.4 (5.3), 1.6–21.0 t = −1.11
Visual function
    Log Mar visual acuity: M (SD), range −0.02 (0.11), −0.19–0.48 0.00 (0.11), −0.19–0.52 −0.01 (0.11), −0.19–0.52 t = 3.52**
    Contrast sensitivity better eye: M (SD), range 35.3 (2.5), 17–43 35.0 (2.2) 24–41 35.2 (2.3) 17–43 t = −1.82
    Bilateral VF points missing: M (SD), range 2.6 (6.4), 0–66 1.7 (4.2), 0–37 2.2 (5.4), 0–66 t = −2.91**
Sense of direction
    SBSOD score in logits: M (SD), range 0.53 (0.53), 0.44–4.5 0.23 (0.48), −0.86–4.5 0.38 (0.53), −0.86–4.5 t = −11.23***
Driving
    Years of driving experience: M (SD), range 59.3 (5.9), 32–74 55.4 (8.0), 9–76 57.3 (7.3), 9–76 t = −10.42***
    Driving area in square miles: M (SD), range 994.8 (3,152), 0.09–55,925 595.2 (3,086) 0.02–65,613 795.5 (3,121) 0.02–65,613 t = −2.23*
    Driving space: % Neighborhood 1.0 5.7 3.4 χ2 = 156.3***
    Driving space: % Near towns 10.0 25.7 17.8
    Driving space: % Distant towns 10.5 20.1 15.3
    Driving space: % Eastern shore 26.1 22.4 24.3
    Driving space: % Mid-Atlantic 52.4 26.1 39.2
Open in a new tab

Notes: AVF = attentional visual field; GDS = Geriatric Depression Scale; SBSOD = Santa Barbara Sense of Direction Questionnaire; and VF = visual field.

*p < .05; **p < .01; ***p < .001.

Sense of Direction Scores

The shape of the distributions of the SBSOD scores was similar for men and women; however, the SBSOD logit scores for the men are significantly higher than for the women (p < .001). The median SBSOD logit scores were 0.44 and 0.17 for the men and women, respectively.

In a linear regression model adjusting for age, race, education, MMSE, years of driving experience, and AVF, being a man was associated with a 0.31 increase in the SBSOD logit score compared with being a woman (p < .0001; Table 2). Participants who were younger, and had more years of driving, more education, higher MMSE scores, and larger AVF had higher SBSOD scores.

Table 2.

Association of Demographic and Cognitive Factors With SBSOD Score in a Multivariate Linear Regression Model

Characteristic β
Age (per year) −0.011**
Blacks −0.025
Men 0.31***
Education (per year) 0.029**
MMSE (per 1 increase) 0.029**
Years of driving experience (per year) 0.005*
AVF per degree 0.008**
Open in a new tab

Notes: AVF = attentional visual field; MMSE = Mini-Mental State Examination; and SBSOD = Santa Barbara Sense of Direction Questionnaire.

*p < .05; **p < .01; ***p < .001.

Maximum Area Driven

Linear regression analyses of driving space (measured as log maximum area driven) as a function of SBSOD showed similar associations by gender (Table 3). For both men and women, driving space decreased with increasing age. Maximum area driven decreased an average of about 1.0 square mile each year of age. For men, driving space increased with increasing years of driving experience. Among men, the relationship between SBSOD and driving space was not statistically significant, but among women, for every 0.5-logit increase in the SBSOD score, maximum area driven increased by 0.213 log units (p < .01). This is equivalent to an estimated 53% increase in the square miles area of driving space per logit SBSOD increase. The overall results were essentially unchanged when the analysis was restricted to persons who reported they were the primary drivers or when driving space was defined as the maximum driving extent along the north–south meridians.

Table 3.

Association of SBSOD With Driving Space Measured as the Log Maximum Area Driven, Controlling for Demographic and Cognitive Factors in Multivariate Models, Stratified by Gender

Characteristics Men, β Women, β
Age (per yr) −0.089** −0.060***
Blacks −0.143 0.110
Education (per yr) 0.043 −0.037
GDS score (per unit) −0.026 −0.011
Pain (0–5) per unit increase −0.065 0.101
Trails B (per 10 seconds) −0.009 −0.024
Years of driving experience (per yr) 0.057* 0.016
SBSOD score (per 0.5-logit increase) 0.028 0.213**
AVF per deg −0.004 −0.001
Open in a new tab

Notes: AVF = attentional visual field; GDS = Geriatric Depression Scale; SBSOD = Santa Barbara Sense of Direction Questionnaire.

*p < .05; **p < .01; ***p < .001.

Self-Reported Driving Space

The characteristics of participants with small to large self-reported driving space are shown in Table 4. Participants who severely limited their driving space tended to be older, and women and to have more vision and cognitive deficits compared with those who drove more widely.

Table 4.

Descriptive Statistics (M and SD) of Participants’ Demographics and Cognitive and Visual Functions, Stratified by Reported Driving Space

Characteristics Reported Driving Space
Neighborhood Near Towns Distant Towns Eastern Shore Mid-Atlantic Region
Number of subjects (% of total) 48 (3.4) 254 (17.8) 218 (15.3) 346 (24.3) 559 (39.2)
Age: M (SD) 79.4 (4.1) 76.7 (5.3) 76.6 (5.1) 75.2 (5.2) 73.6 (4.8)
% Men 14.6 28.0 34.4 53.8 66.7
% Blacks 6.3 19.3 14.2 12.7 10.6
Education: M (SD) years 12.6 (2.6) 12.4 (2.6) 13.0 (2.7) 13.4 (2.4) 14.1 (2.6)
GDS: M (SD) 5.5 (5.6) 5.1 (4.1) 4.3 (3.6) 3.4 (3.0) 3.0 (3.3)
% With arthritis 68.8 59.5 62.4 54.6 55.1
Pain score: M (SD) (0–5) 1.2 (1.3) 1.03 (1.14) 0.92 (1.13) 0.77 (1.01) 0.87 (1.06)
MMSE: M (SD) 28.0 (2.3) 28.0 (2.1) 28.0 (2.0) 28.4 (1.6) 28.4 (1.9)
Trails part B (time in seconds): M (SD) 164 (91.7) 155.1 (95.4) 138.2 (82.0) 126.5 (72.2) 115.3 (61.2)
Years of driving experience (per year) 57.3 (8.4) 56.5 (9.3) 58.6 (7.7) 57.7 (6.5) 57.0 (6.40)
SBSOD score: M (SD) 0.10 (0.45) 0.12 (0.38) 0.24 (0.40) 0.39 (0.45) 0.57 (0.61)
Log Mar: M (SD) 0.03 (0.13) 0.02 (0.12) 0.01 (0.12) 0.01 (0.11) 0.03 (0.10)
Contrast sensitivity better eye: M (SD) 33.8 (2.4) 34.6 (2.5) 35.0 (2.3) 35.3 (2.2) 35.5 (2.2)
Bilateral VF points missing: M (SD) 3.4 (5.7) 3.0 (7.4) 2.3 (4.9) 1.8 (4.1) 1.8 (5.2)
BTA: M (SD) 5.9 (2.3) 5.7 (2.6) 6.2 (2.7) 6.7 (2.4) 6.8 (2.4)
Trail-making Test—Part B mean time (SD) 164 (91.7) 155 (95.4) 138 (82.0) 126 (72.2) 115 (61.2)
AVF extent mean (SD) 8.8 (5.0) 10.4 (5.5) 11.5 (5.3) 12.6 (5.2) 13.8 (4.8)
Open in a new tab

Note: AVF = attentional visual field; BTA = Brief Test of Attention; GDS = Geriatric Depression Scale; MMSE = Mini-Mental State Examination; SBSOD = Santa Barbara Sense of Direction Questionnaire; and VF = visual field.

Table 5 shows gender-specific adjusted odds ratios (ORs) of having driven to each region relative to having driven outside the Mid-Atlantic region (largest driving space) as a function of SBSOD. For men, associations were in the expected directions for most variables, suggesting restriction with increased age, lesser education, and more depressive symptoms. The global association with sense of direction did not reach statistical significance, but there was notable indication of association with restriction of driving to the neighborhood/near towns, with the odds of such a severe restriction decreasing (odds of 0.67, confidence interval [CI] = 0.46–0.96) for each 0.5-logit increase in the SBSOD score. Trends for association between the SBSOD score and restricting driving to distant towns and the Eastern Shore were in the expected direction, with decreasing odds of 0.79 (CI = 0.57–1.10) and 0.86 (CI = 0.71–1.04), respectively. Associations with age and depressive symptoms were strongest for restricting driving to the neighborhood/near towns, with ORs of 1.26 (per year; CI = 1.15–1.39) and 1.12 (per symptom; CI = 1.03–1.21), respectively. The number of years of driving experience showed decreasing odds of 0.89 (CI = 0.83–0.96) for restricting to the neighborhoods/near towns, for each year of driving, but increasing odds (not significant) of 1.12 (CI = 1.01–1.24) for restricting to the distant towns.

Table 5.

Association of SBSOD With Reported Driving Space, Controlling for Demographic and Cognitive Factors in Multivariate Models

Neighborhood and Near Towns, OR (95% CI)
 Distant Towns, OR (95% CI)
 Eastern Shore, OR (95% CI)
Men
 N = 78
 N = 75
 N = 186
Characteristics Women N = 224 N = 143 N = 160 χ2 (3)
Age (per year) Men 1.26 (1.15–1.39) 0.98 (0.87–1.10) 1.07 (1.00–1.15) 27.98***
Women 1.20 (1.13–1.27) 1.14 (1.08–1.22) 1.10 (1.03–1.16) 38.34***
Blacks Men 0.55 (0.20–1.52) 0.78 (0.32–1.94) 0.91 (0.48–1.73) 1.41
Women 2.42 (1.20–4.87) 2.06 (0.98–4.31) 1.54 (0.75–3.16) 6.64
Education (per year) Men 0.86 (0.77–0.95) 0.80 (0.72–0.88) 0.88 (0.82–0.95) 25.27***
Women 0.80 (0.72–0.89) 0.92 (0.82–1.02) 0.93 (0.84–1.03) 14.99**
GDS score (per unit) Men 1.12 (1.03–1.21) 1.08 (1.00–1.17) 1.03 (0.97–1.10) 9.30*
Women 1.05 (0.98–1.12) 1.03 (0.97–1.11) 0.98 (0.91–1.05) 4.94
Pain (0–5) per unit increase Men 0.80 (0.45–1.43) 0.77 (0.44–1.34) 0.83 (0.57–1.20) 1.57
Women 1.27 (0.81–2.02) 0.99 (0.61–1.59) 0.74 (0.47–1.16) 5.68
Trails part B (per 10 s. increase) Men 1.03 (0.99–1.06) 1.01 (0.97–1.04) 1.00 (0.97–1.02) 2.72
Women 1.04 (1.00–1.09) 1.03 (0.98–1.08) 1.02 (0.97–1.07) 4.20
Years of driving experience (per year) Men 0.89 (0.83–0.96) 1.12 (1.01–1.24) 0.99 (0.95–1.03) 18.01**
Women 0.97 (0.93–1.01) 1.00 (0.96–1.05) 1.00 (0.96–1.04) 5.75
SBSOD score (per 0.5-logit increase) Men 0.67 (0.46–0.96) 0.79 (0.57–1.10) 0.86 (0.71–1.04) 6.85
Women 0.43 (0.32–0.58) 0.58 (0.43–0.78) 0.72 (0.56–0.92) 30.81***
AVF per degree Men 0.97 (0.91–1.03) 0.95 (0.90–1.01) 0.99 (0.95–1.03) 3.19
Women 0.93 (0.88–0.98) 0.96 (0.90–1.01) 0.96 (0.91–1.02) 14.57**
Open in a new tab

Notes: Reference Mid-Atlantic region, number in reference category: men = 373; women = 186. AVF = attentional visual field; GDS = Geriatric Depression Scale; CI, confidence interval; OR = odds ratio; and SBSOD = Santa Barbara Sense of Direction Questionnaire.

*p < .05; **p < .01; ***p < .001.

Among women, associations with self-reported driving space, again, were in the expected directions for most variables, suggesting restriction with increased age, lesser education, smaller AVFs, and poorer sense of direction. The global association with race did not reach statistical significance, but there was notable indication of association with restriction of driving to the neighborhood/near towns, with increasing odds of 2.42 (CI = 1.20–4.87) for Black women. Unlike the men, women’s self-reported driving space was not globally associated with depression symptoms or driving experience. As expected, restricting driving to neighborhood/near towns, distant towns, and the Eastern Shore was associated with lower SBSOD scores (Table 5). The association with increasing education was strongest for restricting driving to the neighborhood/near towns with protective OR of 0.80 (per year; CI = 0.72–0.89).

DISCUSSION

This study aimed to evaluate the association between perceived sense of direction and driving restriction in older drivers and to determine whether the association differed between women and men. Our results showed that even after accounting for cognitive and other predictors, perceived sense of direction was still a significant predictor of driving space in older female drivers. A poorer sense of direction was significantly associated with restricting driving space among older female drivers for both measures of driving space (maximum area driven and self-report). The strong association between sense of direction and driving space in older women suggests that perceived spatial/navigational ability plays an important role in their driving restrictions. Older persons who report a poor sense of direction may find driving stressful, particularly in unfamiliar areas and/or in the presence of decreased sensory function. As a result they might restrict their driving to familiar areas, those closer to home.

Sense of direction has been shown to correlate (small to moderate correlations) with performance on wayfinding tasks, such as accuracy of pointing to unseen landmarks, accuracy of path choices, and speed at performing shortcuts (Cornell, Sorenson, & Mio, 2003). Whether sense of direction correlates with actual driving ability in older female drivers is open for future research. A positive correlation between the two factors would indicate that older female drivers make wise decisions regarding driving reduction. A lack of association between the two factors could suggest that decisions about driving reduction might be premature and done to reduce stress rather than compensate for poor driving.

Among men, the association between the SBSOD score and driving space was not as straightforward. The SBSOD score did not show a significant or appreciable association with (log) maximum area driven, but it did show a significant association with self-reported driving space in the most restricted areas, neighborhood/near towns. The discrepancy between the association of the SBSOD score and the two driving space measures may be due to differences in measurement between the two. The maximum area driven may have been underestimated because first, it represents 5 days of driving only, and second, we confined it to a single segment rather than trying to link driving segments and derive an absolute extent over an entire episode. Although there may be some misclassification, we feel that potential underestimation of driving space alone is unlikely to account for the discrepancy between driving space measures. A more likely possibility is the difference between the two measures in regard to the timescale of response. The self-reported driving space instrument queries a participant’s driving extent over the last year, whereas the driving space estimates driving extent from a 5-day sample. The abbreviated time sample may have been too short to capture long-distance trips. This explanation is plausible given that we told participants to drive their usual patterns during the week the device was installed; participants may have postponed longer trips to mimic usual driving habits. Also, the data show that men tended on average to drive longer distances. An alternative explanation is that the men inflated their reported sense of direction. However, several studies have shown that men compared with women actually have good spatial/navigational ability (Bryant, 1982; Cornell et al., 2003; Lawton et al., 1996; Montello, Lovelace, Golledge, & Self, 1999).

The finding that men had higher SBSOD scores than women is consistent with previous literature (Kozlowski & Bryant, 1977) and with findings that men have higher spatial confidence (Lunneborg, 1984) and lower spatial anxiety (Lawton et al., 1996) than women. If it is the case that older men have little difficulty or stress with navigation, then it is conceivable that sense of direction does not play as important a role in decisions about driving reduction for them as it does for women. This could explain the gender difference in the strength of association between sense of direction and driving space restriction.

The finding that women restrict their driving space to a greater extent than do men is consistent with the finding that women have a more limited life space, that is, travel distance from home (Peel et al., 2005), than do men. A recent longitudinal study showed that a severely constricted life space was associated with an increase in the mortality and frailty of older women and that even a slightly constricted life space was associated with a higher risk of becoming frail (Xue, Fried, Glass, Laffan, & Chaves, 2007). Factors associated with driving reduction in older adults include a perceived loss of independence (Buys & Carpenter, 2002), decrease in community activity participation (Marottoli et al., 2000), increased risk of isolation and depression (Fonda et al., 2001; Marottoli et al., 1997; Ragland, Satariano, & MacLeod, 2005), and increased risk of entering a long-term care institution (Freeman, Gange et al., 2006). The increased risk of health problems associated with limited travel highlights the need for intervention strategies aimed at reducing navigational stress and difficulty, particularly for women. Effective strategies may prevent older drivers from prematurely restricting their driving space just to reduce stress.

Navigational difficulty might be alleviated through the use of Intelligent Transportation Systems. Systems such as in-vehicle navigation systems have received positive responses from older adults in focus groups (Kostyniuk, Streff, & Eby, 1997; Oxley & Mitchell, 1995). In one focus group, participants found the systems useful and indicated their willingness to pay substantial prices for the devices (Oxley & Mitchell, 1995). They did point out the need to have prior hands-on training with the devices before using them on their own (Kostyniuk et al., 1997). Although these responses are promising, the devices that best afford safe driving will likely be those that are designed with age-related limitations in mind, particularly the loss of ability to divide attention, a known predictor of crash risk. Further research is needed to determine whether such devices can reduce the stress associated with navigating while driving and whether their use affects decisions about driving reduction.

The data in this study were derived from a population of older drivers located in a semirural city, where one has to drive a distance in the north–south direction to leave the area. Further research is needed to determine whether the findings generalize to older drivers who live in other geographic areas and terrain.

The findings in this study indicate that future driving studies with older adults should take into consideration the role that psychological factors play. Perceived ability can influence driving decisions, sometimes more strongly than physical or cognitive factors. The findings highlight strong gender differences with regard to driving restriction and perceived sense of direction and emphasize the need to include gender in driving studies and models.

FUNDING

The research was funded by a National Institutes of Health/National Institute on Aging grant (AG23110) to SKW; an Research to Prevent Blindness Althouse Special Scholars award to EWG; and an Australian National Health and Medical Research Council Postdoctoral Fellowship grant to LK.

References

  1. Anstey KJ, Windsor TD, Luszcz MA, Andrews GR. Predicting driving cessation over 5 years in older adults: Psychological well-being and cognitive competence are stronger predictors than physical health. Journal of the American Geriatric Society. 2006;54:121–126. doi: 10.1111/j.1532-5415.2005.00471.x. [DOI] [PubMed] [Google Scholar]
  2. Bäckman L, Dixon RA. Psychological compensation: A theoretical framework. Psychological Bulletin. 1992;112:259–283. doi: 10.1037/0033-2909.112.2.259. [DOI] [PubMed] [Google Scholar]
  3. Baldwin KC, Duncan DD, West SK. The driver monitor system: A means of assessing driver performance. Johns Hopkins APL Technical Digest. 2004;25:269–277. [Google Scholar]
  4. Ball K, Owsley C, Stalvey B, Roenker DL, Sloane ME, Graves M. Driving avoidance and functional impairment in older drivers. Accident Analysis & Prevention. 1998;30:313–322. doi: 10.1016/s0001-4575(97)00102-4. [DOI] [PubMed] [Google Scholar]
  5. Bowie CR, Harvey PD. Administration and interpretation of the Trail Making Test. Nature Protocols. 2006;1:2277–2281. doi: 10.1038/nprot.2006.390. [DOI] [PubMed] [Google Scholar]
  6. Brabyn JA, Schneck ME, Lott LA, Haegerstrom-Portnoy G. Night driving self-restriction: Vision function and gender differences. Optometry and Vision Science. 2005;82:755–764. doi: 10.1097/01.opx.0000174723.64798.2b. [DOI] [PubMed] [Google Scholar]
  7. Bryant KJ. Personality correlates of sense of direction and geographical orientation. Journal of Personality and Social Psychology. 1982;43:1318–1324. doi: 10.1037//0022-3514.43.6.1318. [DOI] [PubMed] [Google Scholar]
  8. Burns PC. Wayfinding errors while driving. Journal of Environmental Psychology. 1998;18:209–217. [Google Scholar]
  9. Buys L, Carpenter L. Cessation of driving in later life may not result in dependence. Australasian Journal on Ageing. 2002;20:152–155. [Google Scholar]
  10. Campbell MK, Bush TL, Hale WE. Medical conditions associated with driving cessation in community-dwelling, ambulatory elders. Journal of Gerontology. 1993;48:S230–S234. doi: 10.1093/geronj/48.4.s230. [DOI] [PubMed] [Google Scholar]
  11. Charlton J, Oxley J, Fildes B, Les M. Self regulatory behaviour of older drivers. 2001. Paper presented at the Road Safety Research, Policing and Education, Melbourne, Australia. [PMC free article] [PubMed] [Google Scholar]
  12. Cornell EH, Sorenson A, Mio T. Human sense of direction and wayfinding. Annals of the Association of American Geographers. 2003;93:399–425. [Google Scholar]
  13. Edwards JD, Ross LA, Ackerman ML, Small BJ, Ball KK, Bradley S, Dodson JE. Longitudinal predictors of driving cessation among older adults from the ACTIVE clinical trial. Journal of Gerontology: Psychological Sciences. 2008;63:P6–P12. doi: 10.1093/geronb/63.1.p6. [DOI] [PubMed] [Google Scholar]
  14. Ferris FL, Kassoff A, Bresnick G, Bailey I. New visual acuity charts for clinical research. American Journal of Ophthalmology. 1982;94:91–96. [PubMed] [Google Scholar]
  15. Fonda SJ, Wallace RB, Herzog AR. Changes in driving patterns and worsening depressive symptoms among older adults. Journal of Gerontology: Social Sciences. 2001;56:S343–S351. doi: 10.1093/geronb/56.6.s343. [DOI] [PubMed] [Google Scholar]
  16. Freeman EE, Gange SJ, Muñoz B. West SK. Driving status and risk of entry into long-term care in older adults. American Journal of Public Health. 2006;96:1254–1259. doi: 10.2105/AJPH.2005.069146. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Freeman EE, Munoz B, Turano KA, West SK. Measures of visual function and time to driving cessation in older adults. Optometry and Vision Science. 2005;82:765–773. doi: 10.1097/01.opx.0000175008.88427.05. [DOI] [PubMed] [Google Scholar]
  18. Freeman EE, Munoz B, Turano KA, West SK. Measures of visual function and their association with driving modification in older adults. Investigative Ophthalmology and Visual Science. 2006;47:514–520. doi: 10.1167/iovs.05-0934. [DOI] [PubMed] [Google Scholar]
  19. Golledge RG. Geography and the disabled: A survey with special reference to vision impaired and blind populations. Transactions of the Institute of British Geographers. 1993;18:63–85. [Google Scholar]
  20. Hakamies-Blomqvist L, Wahlstrom B. Why do older drivers give up driving? Accident Analysis and Prevention. 1998;30:305–312. doi: 10.1016/s0001-4575(97)00106-1. [DOI] [PubMed] [Google Scholar]
  21. Hassan SE, Turano KA, Muñoz B, Munro C, Lyketsos K, Zhang L, Bandeen-Roche K, West SK. Cognitive and vision loss affects the topography of the attentional visual field. Investigative Ophthalmology & Visual Science. 2008;49:4672–4678. doi: 10.1167/iovs.07-1112. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Hegarty M, Richardson AE, Montello DR, Lovelace K, Subbiaha I. Development of a self-report measure of environmental spatial ability. Intelligence. 2002;30:425–447. [Google Scholar]
  23. Jette AM, Branch LG. A ten-year follow-up of driving patterns among community-dwelling elderly. Human Factors. 1992;34:25–31. doi: 10.1177/001872089203400104. [DOI] [PubMed] [Google Scholar]
  24. Kostyniuk LP, Streff FM, Eby DW. The older driver and navigation assistance systems (No. UMTRI-97-47) Ann Arbor: University of Michigan: Transportation Research Institute; 1997. [Google Scholar]
  25. Kozlowski LT, Bryant KJ. Sense of direction, spatial orientation, and cognitive maps. Journal of Experimental Psychology: Human Perception & Performance. 1977;3:590–598. [Google Scholar]
  26. Lawton CA, Charleston SI, Zieles AS. Individual- and gender-related differences in indoor wayfinding. Environment and Behavior. 1996;28:204–219. [Google Scholar]
  27. Linacre JM, Wright BD. A user's guide to BIGSTEPS Rasch-model computer program. Chicago: MESA Press; 1997. [Google Scholar]
  28. Lindstrom-Forneri W, Tuokko H, Rhodes RE. Getting around town: A preliminary investigation of the theory of planned behavior and intent to change driving behaviors among older adults. Journal of Applied Gerontology. 2007;26:385–398. [Google Scholar]
  29. Lunneborg PW. Sex-differences in self-assessed, everyday spatial abilities: Differential practice or self-esteem? Perceptual and Motor Skills. 1984;58:213–214. doi: 10.2466/pms.1984.58.1.213. [DOI] [PubMed] [Google Scholar]
  30. Marottoli RA, Mendes de Leon CF, Glass TA, Williams CS, Cooney LM, Jr, Berkman LF. Consequences of driving cessation: Decreased out-of-home activity levels. Journal of Gerontology: Social Sciences. 2000;55:S334–S340. doi: 10.1093/geronb/55.6.s334. [DOI] [PubMed] [Google Scholar]
  31. Marottoli RA, Mendes de Leon CF, Glass TA, Williams CS, Cooney LM, Jr, Berkman LF, Tinetti ME. Driving cessation and increased depressive symptoms: Prospective evidence from the New Haven EPESE. Journal of the American Geriatrics Society. 1997;45:202–206. doi: 10.1111/j.1532-5415.1997.tb04508.x. [DOI] [PubMed] [Google Scholar]
  32. Marottoli RA, Ostfeld AM, Merrill SS, Perlman GD, Foley DJ, Cooney LM., Jr Driving cessation and changes in mileage driven among elderly individuals. Journal of Gerontology. 1993;48:S255–S260. doi: 10.1093/geronj/48.5.s255. [DOI] [PubMed] [Google Scholar]
  33. Montello DR, Lovelace KL, Golledge RG, Self CM. Sex-related differences and similarities in geographic and environmental spatial abilities. Annals of the Association of American Geographers. 1999;89:515–534. [Google Scholar]
  34. Owsley C, Stalvey B, Wells J, Sloane ME. Older drivers and cataract: Driving habits and crash risk. Journal of Gerontology: Medical Sciences. 1999;54:M203–M211. doi: 10.1093/gerona/54.4.m203. [DOI] [PubMed] [Google Scholar]
  35. Oxley PR, Mitchell CGB. Final report on elderly and disabled drivers information telematics. 1995. Dedicated Road Infrastructure for Vehicle Safety in Europe DRIVE II Project V2031 Elderly and Disabled Drivers Information Telematics EDDIT. Brussels: Commission of the European Communities CEC, Directorate General XIII Telecommunications, Information Industries and Innovation. [Google Scholar]
  36. Peel C, Baker PS, Roth DL, Brown CJ, Bodner EV, Allman RM. Assessing mobility in older adults: The UAB study of aging life-space assessment. Physical Therapy. 2005;85:1008–1019. [PubMed] [Google Scholar]
  37. Pelli DG, Robson JG, Wilkens AJ. The design of a new letter chart for measuring contrast sensitivity. Clinical Vision Sciences. 1988;2:187–199. [Google Scholar]
  38. Ragland DR, Satariano WA, MacLeod KE. Driving cessation and increased depressive symptoms. Journal of Gerontology: Medical Sciences. 2005;60:M399–M403. doi: 10.1093/gerona/60.3.399. [DOI] [PubMed] [Google Scholar]
  39. Schretlen D, Brandt J, Bobholz JH. Validation of the Brief Test of Attention in patients with Huntington's disease and amnesia. Clinical Neuropsychologist. 1996;10:10–95. [Google Scholar]
  40. Sims RV, Ahmed A, Sawyer P, Allman RM. Self-reported health and driving cessation in community-dwelling older drivers. Journal of Gerontology: Medical Sciences. 2007;62:789–793. doi: 10.1093/gerona/62.7.789. [DOI] [PubMed] [Google Scholar]
  41. Sixsmith J. Driving experiences and new technology: Evaluations and expectations of older drivers. 1990. Paper presented at the EC DRIVE Programme. London: Kings College. [Google Scholar]
  42. Unsworth CA, Wells Y, Browning C, Thomas SA, Kendig H. To continue, modify or relinquish driving: Findings from a longitudinal study of healthy ageing. Gerontology. 2007;53:423–431. doi: 10.1159/000111489. [DOI] [PubMed] [Google Scholar]
  43. Vrkljan B, Polgar JM. Driving, navigation, and vehicular technology: Experiences of older drivers and their co-pilots. Traffic Injury Prevention. 2007;8:403–410. doi: 10.1080/15389580701576423. [DOI] [PubMed] [Google Scholar]
  44. Wright BD, Masters GN. Rating scale analysis: Rasch measurement. Chicago: MESA Press; 1982. [Google Scholar]
  45. Xue QL, Fried LP, Glass TA, Laffan A, Chaves PH. Life-space constriction, development of frailty, and the competing risk of mortality: The Women's Health and Aging Study. American Journal of Epidemiology. 2007;167:240–248. doi: 10.1093/aje/kwm270. [DOI] [PubMed] [Google Scholar]
  46. Yesavage JA, Brink TL, Rose TL, Lum O, Huang V, Adey MB, Leirer VO. Development and validation of a geriatric screening scale: A preliminary report. Journal of Psychiatric Research. 1983;17:37–49. doi: 10.1016/0022-3956(82)90033-4. [DOI] [PubMed] [Google Scholar]
  47. Zhang L, Baldwin K, Munoz B, Munro C, Turano K, Hassan S, Lyketsos C, Bandeen-Roche K, West SK. Visual and cognitive predictors of performance on brake reaction test: Salisbury eye evaluation driving study. Ophthalmic Epidemiology. 2007;14:216–222. doi: 10.1080/09286580701502988. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from The Journals of Gerontology Series B: Psychological Sciences and Social Sciences are provided here courtesy of Oxford University Press

RESOURCES