Jocelyn Ang

Drexel University, Philadelphia, Pennsylvania, United States

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Publications (6)10.65 Total impact

  • PM&R 10/2012; 4(10):S203. DOI:10.1016/j.pmrj.2012.09.669 · 1.66 Impact Factor
  • PM&R 10/2012; 4(10):S182. DOI:10.1016/j.pmrj.2012.09.615 · 1.66 Impact Factor
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    ABSTRACT: To identify cognitive predictors of driving performance after multiple sclerosis (MS). Prospective design examining predictive value of cognitive measures on driving performance. All data were collected in an outpatient research setting and an outpatient driver rehabilitation program. Participants were community-dwelling persons (N=66) with clinically defined MS (86% relapsing-remitting, 14% progressive) with a mean age of 43.47 years. All were active drivers who met vision requirements established by their respective states, and none required adaptive driving equipment. Not applicable. Participants were administered a comprehensive neuropsychologic assessment and a clinical behind-the-wheel (BTW) driving evaluation. Additional measures of driving performance included history of traffic violations and collisions (since MS onset). Logistic regression indicated that information processing speed (Symbol Digit Modality Test [SDMT]) was the strongest predictor of BTW performance. A logistic regression revealed that the strongest predictors of collision and violation frequency were visuospatial learning and recall (7/24 Spatial Recall Test [SPART 7/24]). These findings indicate that information processing and visuospatial skills are predictive of driving performance among persons with MS. These measures (SDMT and SPART 7/24) may serve as screening methods for identifying the potential impact of cognitive impairment on driving. Furthermore, the findings raise questions regarding the appropriateness of the BTW evaluation to evaluate driving difficulties accurately among individuals with MS.
    Archives of physical medicine and rehabilitation 03/2010; 91(3):465-73. DOI:10.1016/j.apmr.2009.09.026 · 2.44 Impact Factor
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    ABSTRACT: To examine the relationship between measures of visual dysfunction and driving performance in persons with multiple sclerosis (MS). Between-group comparison. All data were collected in an outpatient research setting. Persons (N=66) with MS of the relapsing remitting type (26 self-reporting visual difficulties; 40 self-reporting no visual difficulties) and 26 age- and sex-matched healthy controls. Not applicable. Measures of vision included visual acuity, depth perception, and color perception. Driving was measured using documented accident/violation rate and self-reported driving behaviors. Quantitative analysis only revealed that MS persons with self-reported visual difficulties performed significantly worse than healthy controls on color perception (Kruskal-Wallis; chi(2)(2)=8.89, P=.01). There were no group differences on driving behaviors, and correlational analysis revealed a lack of relationship between the selected visual (visual acuity, depth perception, color perception) and driving performance measures (documented accident/violation rate and self-limiting driving behaviors). Persons with MS who self-reported difficulties with vision had acceptable visual acuity, despite demonstrating impairment in color perception. The fact that visual acuity remains the most common measure for visual fitness to drive remains problematic. There is a need to further define measures of visual dysfunction relevant to driving among this clinical population.
    Archives of physical medicine and rehabilitation 02/2010; 91(2):315-7. DOI:10.1016/j.apmr.2009.09.021 · 2.44 Impact Factor
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    ABSTRACT: The current study examined driver behaviors and patterns among drivers with multiple sclerosis (MS) as a function of disease severity and in comparison to drivers without MS. Between-group comparisons of participants with and without MS and cohorts of MS groups at varying levels of severity. All data were collected in an outpatient research setting. Community-dwelling persons (n=66) with clinically definite MS who were active drivers and healthy controls (n=30) were included. Not applicable. Driving characteristics' measures included (1) driving frequency defined in days and miles driven per week, (2) self-reports of voluntarily limited driving behaviors, (3) changes in driving since MS diagnosis, and (4) self-rating as a driver. Driving performance was also assessed by using pass/borderline performance on a clinical behind-the-wheel evaluation. Mann-Whitney U tests showed a significant difference in driving frequency (P=.021) with MS participants reporting they drove fewer days per week than healthy control group. This was also seen between cohorts of MS drivers (P=.014), with high Expanded Disability Status Scale (EDSS) participants driving less frequently than the low EDSS group. Descriptive observations suggested that participants with moderate EDSS scores drove less and engaged in more self-limiting behaviors. Chi-square tests showed that participants with high EDSS scores were more likely to report changing their driving behavior after diagnosis (P=.01) and were more likely to not pass the behind-the-wheel evaluation (P<.001). The current findings suggest that as disease severity progresses, differences in frequency of driving (days per week) and the use of self-limiting driving behaviors may vary. Furthermore, the findings also raise questions regarding the overall sensitivity of the most commonly used clinical driving assessment method (the behind-the-wheel evaluation) to accurately capture driving capacity in the milder stages of the disease process. The results indicate the need to consider issues related to driving as MS severity progresses, and, given the progressive nature of MS and the concerns of the validity of the most common clinical driving assessment measures (the behind-the-wheel evaluation), repeated assessment of driving ability may be the most effective approach for identifying driving difficulties among persons with MS.
    Archives of physical medicine and rehabilitation 07/2009; 90(6):975-81. DOI:10.1016/j.apmr.2008.12.017 · 2.44 Impact Factor