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Return to Driving
Return to driving is a common patient inquiry during rehabilitation from stroke. Inability to drive has an impact on a patient’s lifestyle and emotional well-being and leads to a strong feeling of loss for the patient (White et al., 2012). However, driving requires a minimum level of sensory, motor and cognitive functioning that is often compromised following a stroke. Common residual deficits preventing the resumption of driving include visual disturbances, hemiparesis and spasticity (White et al., 2012). Deficits found to be predictive of returning to driving include walking ability (p=0.001), upper extremity dressing scores (p<0.001), Berg Balance Scale scores (p<0.001), lower extremity Motricity Index scores (p<0.001) and FIM® cognitive scores (p<0.001) with the latter two items included in a predictive model (OR=1.03, 95% CI 1.01-1.05) with a 74.8% accuracy rating (Aufman et al. 2013).
As such, screening for potential deficits in driving ability may be needed to ascertain whether returning to driving is a viable option. Research from Akinwuntan et al. (2013) revealed that stroke patients and healthy participants differ significantly in driving ability with only 46.67% of stroke patients passing a driver simulation test compared to 93.75% of healthy participants. Barco et al. (2013) reported that older age (p=0.005), lower grip strength (p=0.018), higher visual acuity scores (p=0.029), slower brake reaction time (p=0.04), and higher scores on the nine-hole peg test (left hand p=0.027, right hand p=0.038), with Trail-Making Test A (TMT-A) being found to be predictive for on-road driving performance (AUC = 0.87). Similarly, Aslaken et al. (2013) also found that TMT-A scores were predictive of on-road performance (AUC = 0.81, sensitivity = 0.85, specificity = 0.72) along with simple reaction time (AUC = 0.78, sensitivity = 0.77, specificity = 0.77) and Grooved Pegboard task scores (AUC = 0.73, sensitivity = 0.82, specificity = 0.18).
Patients often overestimate their ability to drive after stroke (Heikkila et al., 1999). A population based case-control study from the United States found that a higher percentage of drivers diagnosed with stroke who had been involved in accidents (7.3%) compared to the percentage of drivers diagnosed with stroke who were not involved in accidents (4.1%). After adjusting for age, sex, race, and driving frequency, these findings were statistically significant (OR 1.9, 95% CI 1.0-3.9) (McGwin et al., 2000). The 2009 Canadian Medical Standards for Drivers state that patients who have had a stroke “should not drive for at least one month. They may be allowed to operate any motor vehicle after the one month waiting period provided there has been a good recovery, the condition has stabilized and there are no signs of impending recurrence and a neurological assessment indicates that they are functionally able.” The medical standards also recommend that a neurological report be filed prior to resuming driving and a road-test is recommended for any individual with residual motor deficits. Facilitating a patient’s return to driving, where applicable, is an important part of rehabilitation. Return to driving was found to be significantly associated with an increase in community reintegration at one year post-stroke (Finestone et al., 2010).
There is limited information available regarding the sensitivity and specificity of office-based driver performance screening tools. Two systematic reviews (Devos et al., 2011; Marshall et al., 2007) outlining the screening tools that are most predictive of a pass or fail during on-road testing have been completed. The Road Sign Recognition test and Compass, which are both part of the Stroke Drivers Screening Assessment (SDSA), the Trail Making Test part A and part B, the Rey-Osterreith Complex Figure Test, and the Useful Field of View (UFOV) test have been identified as useful tools. These reviews, however, were not stroke specific. Cognitive screening tools were found to be the most predictive of outcome (pass/fail) on an on-road test.
Similarly, there have been very few randomized controlled trials to evaluate interventions that may support a successful return to driving for patients post stroke. A literature review conducted by Classen et al. (2014) of 6 studies (including 5 RCTs) revealed that whilst there was a lack of evidence for cognitive and visual training interventions, driving simulator interventions were highly recommended and traffic theory knowledge tests were moderately recommended. Visual information processing training and simulator based training interventions have been assessed (Mazer et al., 2003; Crotty & George, 2009; Akinwuntan et al., 2005). No statistically significant differences were found between intervention and control groups for on-road driving performance with the use of the UFOV or Dynavision training. Visuoperceptual scores (Mazer et al., 2003), response time, visual scanning abilities and driving self-efficacy (Crotty & George, 2009) also remained comparable between groups. The simulator based intervention assessed by Akinwuntan and colleagues found statistically significant improvements in neuropsychological test results (P<0.05) and on-road driving assessments (P=0.03) for patients receiving the intervention compared to controls (Akinwuntan et al., 2005).
Return to Vocation
A patient’s pre-stroke vocation may have included work, school and/or volunteering and is particularly important to address in younger stroke survivors. Return to work is the most common vocation addressed in the literature, and has been found to improve the quality of life for both the patient and their spouse (Gabriele & Renate, 2009). A review by Morris and colleagues (Morris, 2011), found that psychological disorders, fatigue, and effects from the stroke that impair a patient’s ability to perform specific work tasks have been reported in the literature as barriers for a patients potential return to work (Morris, 2011).
A wide range of estimates for the proportion of patients who return to work after stroke have been found. A mean of 44% of patients returning to work was found across a set of studies included in a review by Daniel et al. 2009 (Daniel et al., 2009). Patients more likely to return to work include those who worked in white collar jobs as opposed to blue collar (Tanaka et al., 2011; Tanaka et al. 2014), who had a higher income and who had a higher level of education (Trygged et al., 2011). Modifications to previous working conditions (Wozniak & Kittner, 2002) and a supportive employer (Morris, 2011) have been found to help facilitate a patients return to work. A systematic review of vocational rehabilitation interventions for patients post stroke was inconclusive in drawing conclusions regarding their effectiveness (Baldwin & Brusco, 2011). The study included six retrospective cohort studies of varying intervention types and a high level of heterogeneity; no randomized controlled trials were identified.
Although pediatric stroke is relatively rare, school aged stroke survivors are likely to have educational needs that are not typically addressed in older patients. Parent reported outcomes of school aged children in a study by Ganesan and colleagues found that 53% of patients needed school related assistance (Ganesan et al., 2000) based on a population of 90 stroke survivors between the ages of three months and 15 years (Ganesan et al., 2000). The same study reported that 62% of participants experienced at least some neurological deficits when assessed at a mean of 2.07 years post stroke. Another study, although small (n=23), found similar results, with 65% of participants aged 0 to 12 at stroke onset having at least some cognitive deficits (Rodrigues et al., 2011). Participants with a history of stroke also performed worse on arithmetic, reading and writing school performance tests compared to a control group of students (Rodrigues et al., 2011).
Evidence suggests that there are significant changes in sexuality and sexual functioning for patients post-stroke. A study assessing the impact of stroke on a patient’s sexual functioning found that 64% of patients experienced difficulties (Kersten et al., 2002). Another study found that stroke survivors are significantly less satisfied with their sex life one year after stroke compared to a control group of individuals not having experienced a stroke (p=0.001) (Carlsson et al., 2007). Difficulties may include changes in libido, coital frequency, sexual arousal and sexual satisfaction (Korpelainen et al., 1999). These changes may be a result of physical or psychosocial reasons or because of the presence of co-morbidities and medication use. Further research by Bugnicourt et al. (2014) revealed that 30 of 104 patients experiencing sexual dysfunction with impairments in sexual activity significantly predicted by depression (OR 9.1, 95% CI 2.45-33.46, p=0.001) and the use of ACE inhibitors (OR 6.0, 95% CI 2.11-17.27, p=0.001). The fears and concerns of a patient’s partner have also been suggested to contribute to a patient’s decline in sexuality after stroke (Giaquinto et al., 2003).
Patients prefer to address sexuality with their physicians as opposed to other health care providers, to receive written material, and to initiate discussion early in the rehabilitation process (Stein et al., 2013). A study assessing a sexuality education intervention found that patients who received a short (40-50 minute) education session that outlined the changes that they can expect in their sexuality post-stroke, frequently asked questions and tips to avoid sexual dysfunction were more sexually active and experienced greater sexual satisfaction than patients who did not. Interventions addressing post stroke sexuality are limited. Only one intervention was identified, consisting of patient education sessions following discharge from hospital (Song et al., 2011). Patients who received this intervention reported being more sexually active and satisfied one month post-stroke compared to control patients (Song et al., 2011).
Leisure activity has been found to be markedly reduced for individuals post-stroke (Drummond, 1990). Eighty-seven percent of individuals in a study assessing participation one year after stroke reported at least one gap or incongruence between an activity they wanted to do but were not currently doing (Eriksson et al., 2012). The same study found that the most frequently cited occupational gaps were in leisure and social activities (Eriksson et al., 2012).
The definition of leisure activities can vary quite widely among individuals. However, established tools such as the Nottingham Leisure Questionnaire (NLQ) and the Occupational Gaps Questionnaire contain a list of possible activities. For example, leisure activities on the NLQ are defined as activities that “individuals do during their free time” and can include watching TV, gardening, cooking, dancing, photography, sports etc. (Drummond et al., 2001).
Decreased participation (defined as instrumental activities of daily living and leisure activities) was found to explain 50% of the variance in life satisfaction scores in a sample of 56 patients living in the community one year after stroke (Hartman-Maeir et al., 2007). A review by Nicholson et al. (2013) revealed that personal barriers such as physical difficulties and motivation, and environmental barriers including transportation and affordability were frequently cited by stroke patients as reasons for decreased participation. However, socializing, returning to driving, and ability to perform activities of daily living were cited as motivational factors (Nicholson et al., 2013). Another study assessing the effects of social activity in particular (one dimension of leisure activity) on life satisfaction post-stroke found that 6.9% of the variance in a participant’s level of life satisfaction was explained by level of social activity (Boosman et al., 2011). Individuals at risk of decreased social activity are typically younger, female, not living with a partner and have a lower functioning at one year post-stroke (Schepers et al., 2005).
Results from a meta-analysis assessing community occupational therapy interventions found that interventions were effective in improving patient outcomes (Walker et al., 2004). Type of intervention, be it leisure or activities of daily living (ADL) specific, generated positive results in the corresponding outcome measure (i.e. leisure specific interventions result in positive leisure activity outcomes but do not show a similar response in general ADL outcomes. Likewise, ADL specific interventions resulted in positive ADL outcomes but did not appear to influence leisure activity outcomes). Educational sessions alone have also demonstrated effectiveness in improving leisure outcomes for patients following a stroke (Desrosiers et al., 2007).