RESEARCH ARTICLE
Performance on the flanker task predicts driving cessation in older adults Hiroyuki Shimada1, Kazuki Uemura2, Hyuma Makizako1, Takehiko Doi1, Sangyoon Lee1 and Takao Suzuki3 1
Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Ōbu, Japan 2 Institution of Innovation for Future Society, Nagoya University, Nagoya, Japan 3 National Center for Geriatrics and Gerontology, Ōbu, Japan Correspondence to: H. Shimada, PhD, E-mail: [email protected]
Objectives: This study examined the predictive validity of flanker tasks on driving cessation in older drivers.
The flanker task comprises a set of response inhibition tests used to assess the ability to suppress responses. A total of 2805 older drivers aged ≥65 years at baseline participated in this study. We conducted several baseline assessments focused on physical and psychological health as well as cognitive performance. Fifteen months after the baseline measurements, we collected information about the current driving status of the participants. Results: Forty-eight participants (1.7%) ceased driving during the 15-month period following the assessment. Logistic regression models identified the following as significant predictors of driving cessation: performance on the trail-making test (parts A and B), digit symbol substitution test scores, story memory, and flanker task scores for the total, congruent, and incongruent conditions. The flanker task scores for the total, congruent, and incongruent conditions were significant predictors in the fully adjusted logistic model. Conclusion: The flanker task was more important than assessments of general cognition, including memory, attention, executive control, and processing speed, in predicting driving cessation. The flanker task may be useful for identifying driving cessation in older adults. Copyright # 2015 John Wiley & Sons, Ltd. Methods:
Key words: driving cessation; cognitive function; selective attention; older adults; flanker task History: Received 26 November 2014; Accepted 28 April 2015; Published online 11 June 2015 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/gps.4308
Introduction Older drivers often have cognitive limitations but wish to continue driving daily in order to maintain their independence. This is especially important, as older drivers may face greater obstacles to alternative sources of transportation, such as public transportation systems, taxis, and family support. Meanwhile, evidence from motor vehicle crash studies suggests that drivers with dementia are involved in twice as many crashes as cognitively intact older adults (Carr and Ott, 2010). Driving cessation is a major negative life event and can represent the loss of independence for many older adults (Dickerson et al., 2007). There are a number of negative consequences associated with driving cessation, such as elevated depressive Copyright # 2015 John Wiley & Sons, Ltd.
symptoms, cognitive decline (Choi et al., 2013), increased risk for long-term care institutionalization (Freeman et al., 2006), and mortality (Edwards et al., 2009). To prevent driving cessation and its negative consequences, medical practitioners are attempting to find techniques for the early identification of individuals with a high risk of driving cessation. In Japan, individuals aged 75 years or older who actively drove were 3.3 times more likely to have been involved in a vehicle crash in 2006 compared with the same statistic in 1996. Even when taking population increase (1.6 times) into account, the number of vehicle crashes in which older adults were involved has risen dramatically over the past decade. Several longitudinal studies have indicated that the following factors contribute to the decision to cease driving: Int J Geriatr Psychiatry 2016; 31: 169–175
H. Shimada et al.
170
older age, poor vision, poor self-rated health and physical function, diminished cognitive processing speed, poor memory and reasoning, and problems with instrumental functional abilities (Ackerman et al., 2008; Anstey et al., 2006; Edwards et al., 2008). Recent evidence has highlighted the importance of cognitive abilities in maintaining driving behaviors, as compared with a number of sensory measures (Edwards et al., 2008). One prospective study found that older drivers with poor cognitive abilities, including processing speed, immediate recall, and symbol recall, had a higher probability of driving cessation (Anstey et al., 2006). Another prospective study demonstrated that assessments of cognitive processing speed could provide valuable information about the subsequent risk of driving cessation (Edwards et al., 2008). This information can lead to strategies that help to delay driving cessation. It is increasingly evident that cognitive and physical interventions can enhance driving performance and safety in older adults (Edwards et al., 2009; Marottoli et al., 2007). For instance, older drivers with impaired cognitive processing speed who completed a processing speed training program were 40% less likely to cease driving over the subsequent 3 years (Edwards et al., 2009). We expected that information processing and selective attention are important predictors of maintenance of driving behavior among older adults. It has been reported that the aging of the frontal cortex leads to failures in inhibition of motor responses and visual attention, which increases the risk not only of fallrelated injury (Anstey et al., 2009) but also of driving errors in later life (Anstey and Wood, 2011). The flanker task comprises a set of response inhibition tests used to assess the ability to suppress responses that are inappropriate in a particular context. However, the relationship between inhibitory function and driving cessation in older adults remains unclear. This study examined the predictive validity of flanker tasks on driving cessation in older drivers. We hypothesized that the flanker task would be more strongly associated with driving cessation than other cognitive measurements in older adults. Methods Study population
Our national study assessed 5104 individuals aged 65 years and older (mean age 72 years) who were enrolled in the Ōbu study of health promotion for older people (Shimada et al., 2013). Each individual Copyright # 2015 John Wiley & Sons, Ltd.
was recruited from Ōbu, Japan, which is a residential suburb of Nagoya. Inclusion criteria required that each participant be aged 65 years or older and reside in Ōbu at the time of examination (August 2011–February 2012). We conducted a follow-up postal survey approximately 15 months after the baseline assessment (November 2012–May 2013) with an offer to assist in its completion. We excluded participants who did not drive at the time of baseline assessment, had missing driving and demographic data, had been diagnosed with neurological disorders, had certified long-term care insurance, and those who showed a functional decline in the activities of daily living such as eating, grooming, walking, stair climbing, and bathing. As such, we excluded 2299 of the 5104 participants. Data obtained from a total of 2805 older adults (mean age 70.9 ± 4.7 years, 65–93 years, 1781 men and 1024 women) were assessed. We obtained informed consent from all participants prior to their inclusion in the study, and the Ethics Committee of the National Center for Geriatrics and Gerontology approved the study protocol. Cognitive measures
Screening for cognitive performance included the Mini-mental state examination (Folstein et al., 1975) and the National Center for Geriatrics and Gerontology functional assessment tool (NCGGFAT) (Shimada et al., 2013). The NCGG-FAT includes multidimensional cognitive tasks, has high test–retest reliability, and has a moderate to high level of validity, as confirmed in community-dwelling older adults (Makizako et al., 2013). Study assistants who were certified as assessors of cognitive measurements assessed cognitive functions in the community, in venues such as community halls. All staff received training from the authors in the correct protocols for administering the assessment measures prior to commencing the study. Participants took an average of 40 min to complete the battery. The battery consisted of tasks that tested word list memory (immediate memory and delayed recall), story memory (delayed recognition), attention and executive function (tablet version of the trailmaking test, parts A and B), processing speed (tablet version of the symbol digit substitution test), visuospatial skills (figure selection), and selective attention (flanker task). In the flanker task, on a screen, participants viewed displays consisting of an arrow at a central fixation point, flanked by a pair of arrows on either side. In Int J Geriatr Psychiatry 2016; 31: 169–175
Flanker task predicts driving cessation
171
half of the trials, the flanking arrows pointed in the same direction as the central arrow cue (< < < < < or > > > > >; congruent condition); in the other half, the flanking arrows pointed in the opposite direction (< < > < < or > > < > >; incongruent condition). In the incongruent condition, the flankers were provided with conflicting information that caused interference, which is known to increase response errors. The direction of the central arrow and the flanker condition (congruent or incongruent) were presented in random order. We measured the reaction times for the total, congruent, and incongruent conditions in milliseconds and calculated the average reaction times required for one task in each condition. We asked the participants to indicate which direction the central arrow was pointing via a simple display touch. This task was based on the flanker paradigm described by Eriksen and Eriksen (1974).
Potential confounding factors
Based on previous studies, we selected four demographic variables, two measures of health status, three measures of physical activity, three physiological variables, and one psychological variable as possible confounding factors of driving cessation (Table 1). To measure physical activity, specifically the amount of time spent walking and the amount of time involved in sedentary behaviors, we used the following questions from the physical activity scale for the elderly: “On an average, how many hours per day did you spend walking?” and “On an average, how many hours per day did you engage in these sitting activities?” (Washburn et al., 1993). We measured grip strength in kilograms using a Smedley-type handheld dynamometer (GRIP-D; Takei Ltd., Niigata, Japan). We calculated the skeletal muscle index for each
Table 1 Baseline demographic, health, physical, and cognitive characteristics of non-drivers compared with drivers
Characteristic Demographic variables Age (years, mean ± SD) Sex, n (%) Male Female Number of family members (n, mean ± SD) Employee, n (%) Yes No Health status Medication (n, mean ± SD) Falls in previous year, n (%) Yes No Physical activity Walking time (min/day, mean ± SD) Sedentary time (min/day, mean ± SD) Outdoors activity (times/week, mean ± SD) Physiological variables Grip strength (kg, mean ± SD) 2 Skeletal muscle index (kg/m , mean ± SD) Walking speed (m/s, mean ± SD) Psychological variables Geriatric depression scale 15 (points, mean ± SD) Cognitive performance (mean ± SD) Mini-mental state examination (points) Story memory—immediate (points) Word memory—immediate (points) Trail-making test part A (s) Trail-making test part B (s) Digit symbol substitution test (points) Word memory—recall (points) Story memory—delayed recognition (points) Figure recognition (points) Flanker task—total mean time (s/task) Flanker task—congruent mean time (s/task) Flanker task—incongruent mean time (s/task)
Continued driving (n = 2757)
Driving cessation (n = 48)
p-value
70.8 ± 4.7
73.6 ± 5.7
Performance on the flanker task predicts driving cessation in older adults Hiroyuki Shimada1, Kazuki Uemura2, Hyuma Makizako1, Takehiko Doi1, Sangyoon Lee1 and Takao Suzuki3 1
Department of Preventive Gerontology, Center for Gerontology and Social Science, National Center for Geriatrics and Gerontology, Ōbu, Japan 2 Institution of Innovation for Future Society, Nagoya University, Nagoya, Japan 3 National Center for Geriatrics and Gerontology, Ōbu, Japan Correspondence to: H. Shimada, PhD, E-mail: [email protected]
Objectives: This study examined the predictive validity of flanker tasks on driving cessation in older drivers.
The flanker task comprises a set of response inhibition tests used to assess the ability to suppress responses. A total of 2805 older drivers aged ≥65 years at baseline participated in this study. We conducted several baseline assessments focused on physical and psychological health as well as cognitive performance. Fifteen months after the baseline measurements, we collected information about the current driving status of the participants. Results: Forty-eight participants (1.7%) ceased driving during the 15-month period following the assessment. Logistic regression models identified the following as significant predictors of driving cessation: performance on the trail-making test (parts A and B), digit symbol substitution test scores, story memory, and flanker task scores for the total, congruent, and incongruent conditions. The flanker task scores for the total, congruent, and incongruent conditions were significant predictors in the fully adjusted logistic model. Conclusion: The flanker task was more important than assessments of general cognition, including memory, attention, executive control, and processing speed, in predicting driving cessation. The flanker task may be useful for identifying driving cessation in older adults. Copyright # 2015 John Wiley & Sons, Ltd. Methods:
Key words: driving cessation; cognitive function; selective attention; older adults; flanker task History: Received 26 November 2014; Accepted 28 April 2015; Published online 11 June 2015 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/gps.4308
Introduction Older drivers often have cognitive limitations but wish to continue driving daily in order to maintain their independence. This is especially important, as older drivers may face greater obstacles to alternative sources of transportation, such as public transportation systems, taxis, and family support. Meanwhile, evidence from motor vehicle crash studies suggests that drivers with dementia are involved in twice as many crashes as cognitively intact older adults (Carr and Ott, 2010). Driving cessation is a major negative life event and can represent the loss of independence for many older adults (Dickerson et al., 2007). There are a number of negative consequences associated with driving cessation, such as elevated depressive Copyright # 2015 John Wiley & Sons, Ltd.
symptoms, cognitive decline (Choi et al., 2013), increased risk for long-term care institutionalization (Freeman et al., 2006), and mortality (Edwards et al., 2009). To prevent driving cessation and its negative consequences, medical practitioners are attempting to find techniques for the early identification of individuals with a high risk of driving cessation. In Japan, individuals aged 75 years or older who actively drove were 3.3 times more likely to have been involved in a vehicle crash in 2006 compared with the same statistic in 1996. Even when taking population increase (1.6 times) into account, the number of vehicle crashes in which older adults were involved has risen dramatically over the past decade. Several longitudinal studies have indicated that the following factors contribute to the decision to cease driving: Int J Geriatr Psychiatry 2016; 31: 169–175
H. Shimada et al.
170
older age, poor vision, poor self-rated health and physical function, diminished cognitive processing speed, poor memory and reasoning, and problems with instrumental functional abilities (Ackerman et al., 2008; Anstey et al., 2006; Edwards et al., 2008). Recent evidence has highlighted the importance of cognitive abilities in maintaining driving behaviors, as compared with a number of sensory measures (Edwards et al., 2008). One prospective study found that older drivers with poor cognitive abilities, including processing speed, immediate recall, and symbol recall, had a higher probability of driving cessation (Anstey et al., 2006). Another prospective study demonstrated that assessments of cognitive processing speed could provide valuable information about the subsequent risk of driving cessation (Edwards et al., 2008). This information can lead to strategies that help to delay driving cessation. It is increasingly evident that cognitive and physical interventions can enhance driving performance and safety in older adults (Edwards et al., 2009; Marottoli et al., 2007). For instance, older drivers with impaired cognitive processing speed who completed a processing speed training program were 40% less likely to cease driving over the subsequent 3 years (Edwards et al., 2009). We expected that information processing and selective attention are important predictors of maintenance of driving behavior among older adults. It has been reported that the aging of the frontal cortex leads to failures in inhibition of motor responses and visual attention, which increases the risk not only of fallrelated injury (Anstey et al., 2009) but also of driving errors in later life (Anstey and Wood, 2011). The flanker task comprises a set of response inhibition tests used to assess the ability to suppress responses that are inappropriate in a particular context. However, the relationship between inhibitory function and driving cessation in older adults remains unclear. This study examined the predictive validity of flanker tasks on driving cessation in older drivers. We hypothesized that the flanker task would be more strongly associated with driving cessation than other cognitive measurements in older adults. Methods Study population
Our national study assessed 5104 individuals aged 65 years and older (mean age 72 years) who were enrolled in the Ōbu study of health promotion for older people (Shimada et al., 2013). Each individual Copyright # 2015 John Wiley & Sons, Ltd.
was recruited from Ōbu, Japan, which is a residential suburb of Nagoya. Inclusion criteria required that each participant be aged 65 years or older and reside in Ōbu at the time of examination (August 2011–February 2012). We conducted a follow-up postal survey approximately 15 months after the baseline assessment (November 2012–May 2013) with an offer to assist in its completion. We excluded participants who did not drive at the time of baseline assessment, had missing driving and demographic data, had been diagnosed with neurological disorders, had certified long-term care insurance, and those who showed a functional decline in the activities of daily living such as eating, grooming, walking, stair climbing, and bathing. As such, we excluded 2299 of the 5104 participants. Data obtained from a total of 2805 older adults (mean age 70.9 ± 4.7 years, 65–93 years, 1781 men and 1024 women) were assessed. We obtained informed consent from all participants prior to their inclusion in the study, and the Ethics Committee of the National Center for Geriatrics and Gerontology approved the study protocol. Cognitive measures
Screening for cognitive performance included the Mini-mental state examination (Folstein et al., 1975) and the National Center for Geriatrics and Gerontology functional assessment tool (NCGGFAT) (Shimada et al., 2013). The NCGG-FAT includes multidimensional cognitive tasks, has high test–retest reliability, and has a moderate to high level of validity, as confirmed in community-dwelling older adults (Makizako et al., 2013). Study assistants who were certified as assessors of cognitive measurements assessed cognitive functions in the community, in venues such as community halls. All staff received training from the authors in the correct protocols for administering the assessment measures prior to commencing the study. Participants took an average of 40 min to complete the battery. The battery consisted of tasks that tested word list memory (immediate memory and delayed recall), story memory (delayed recognition), attention and executive function (tablet version of the trailmaking test, parts A and B), processing speed (tablet version of the symbol digit substitution test), visuospatial skills (figure selection), and selective attention (flanker task). In the flanker task, on a screen, participants viewed displays consisting of an arrow at a central fixation point, flanked by a pair of arrows on either side. In Int J Geriatr Psychiatry 2016; 31: 169–175
Flanker task predicts driving cessation
171
half of the trials, the flanking arrows pointed in the same direction as the central arrow cue (< < < < < or > > > > >; congruent condition); in the other half, the flanking arrows pointed in the opposite direction (< < > < < or > > < > >; incongruent condition). In the incongruent condition, the flankers were provided with conflicting information that caused interference, which is known to increase response errors. The direction of the central arrow and the flanker condition (congruent or incongruent) were presented in random order. We measured the reaction times for the total, congruent, and incongruent conditions in milliseconds and calculated the average reaction times required for one task in each condition. We asked the participants to indicate which direction the central arrow was pointing via a simple display touch. This task was based on the flanker paradigm described by Eriksen and Eriksen (1974).
Potential confounding factors
Based on previous studies, we selected four demographic variables, two measures of health status, three measures of physical activity, three physiological variables, and one psychological variable as possible confounding factors of driving cessation (Table 1). To measure physical activity, specifically the amount of time spent walking and the amount of time involved in sedentary behaviors, we used the following questions from the physical activity scale for the elderly: “On an average, how many hours per day did you spend walking?” and “On an average, how many hours per day did you engage in these sitting activities?” (Washburn et al., 1993). We measured grip strength in kilograms using a Smedley-type handheld dynamometer (GRIP-D; Takei Ltd., Niigata, Japan). We calculated the skeletal muscle index for each
Table 1 Baseline demographic, health, physical, and cognitive characteristics of non-drivers compared with drivers
Characteristic Demographic variables Age (years, mean ± SD) Sex, n (%) Male Female Number of family members (n, mean ± SD) Employee, n (%) Yes No Health status Medication (n, mean ± SD) Falls in previous year, n (%) Yes No Physical activity Walking time (min/day, mean ± SD) Sedentary time (min/day, mean ± SD) Outdoors activity (times/week, mean ± SD) Physiological variables Grip strength (kg, mean ± SD) 2 Skeletal muscle index (kg/m , mean ± SD) Walking speed (m/s, mean ± SD) Psychological variables Geriatric depression scale 15 (points, mean ± SD) Cognitive performance (mean ± SD) Mini-mental state examination (points) Story memory—immediate (points) Word memory—immediate (points) Trail-making test part A (s) Trail-making test part B (s) Digit symbol substitution test (points) Word memory—recall (points) Story memory—delayed recognition (points) Figure recognition (points) Flanker task—total mean time (s/task) Flanker task—congruent mean time (s/task) Flanker task—incongruent mean time (s/task)
Continued driving (n = 2757)
Driving cessation (n = 48)
p-value
70.8 ± 4.7
73.6 ± 5.7
