Morbidity and mortality associated with vehicular crashes are a major public health concern, accompanied by great fiscal and emotional costs. While Type 1 Diabetes Mellitus (T1DM) drivers have over twice as many driving mishaps as non-diabetic drivers, this is accounted for by a subgroup. What is needed is a way to identify those diabetic drivers who are at high-risk, and interventions that can reduce this risk. We demonstrated that this high-risk subgroup differs from the majority of T1DM drivers at three levels of risk. 1) General: they are more likely to take risks while driving, have a history of collisions and citations, consume more alcohol, have poorer general cognitive functioning, and live alone. 2) Physical: they require more glucose to maintain euglycemia, and release less epinephrine and experience greater driving impairment during mild hypoglycemia. 3) Diabetes management: they are more likely to be on a rigid insulin regimen, engage in risky diabetes management practices, drive at lower blood glucose levels, have a history of frequent severe hypoglycemia, and have more fear of hyperglycemia. Based on these findings, we developed Diabetes Driving Safety Training (DDST) to reverse or neutralize risk factors. Pilot testing demonstrated that DDST significantly reduced both risk factors and driving mishaps. This raises the exciting possibility that high risk T1DM drivers can normalize their risk, reducing future crashes and their negative sequelae. We propose a three phase project: 1) Reanalysis Phase will take advantage of our extensive and unique data set of T1DM drivers including >1,700 drivers from our international cross sectional survey, >600 who were assessed then followed for occurrence of driving mishaps, and >80 who drove during laboratory- induced euglycemia and progressive hypoglycemia. These data were previously used to determine group differences among drivers with/without mishaps. The Reanalysis Phase will use Item Response Theory with this data to develop Risk Profiles to quantify an individual's risk for future driving mishaps. 2) Assessment Phase will prospectively evaluate the sensitivity/specificity of this Risk Profile in predicting future driving mishaps with a large national sample of T1DM drivers. 3) Treatment phase is a randomized clinical trial where drivers identified as High Risk for future driving mishaps would receive one of three "doses" of treatment: 1) Static Information, 2) interactive, personalized Diabetes Driving Safety Training, or 3) this safety training plus motivational interviewing, with assessment of Risk Profiles pre and post intervention, and prospective follow-up for 12 months for driving mishaps. To maximize the possibility of future dissemination, treatment would be delivered over the Internet. The results of this project are intended to provide clinicians with a practical and accurate tool to identify high risk patients, provide these high risk drivers with effective treatment to reduce future driving mishaps and their associated costs, and help to decrease a prejudicial attitude often extrapolated to all T1DM drivers. Project Narrative Morbidity, mortality and fiscal consequences due to vehicular collisions are a huge public health problem in the United States, accounting for 119 deaths each and every day, estimated to have cost the U.S. economy in 2000 approximately $230.6 billion. A subgroup of drivers with Type 1 Diabetes Mellitus (T1DM) has been estimated to have 8 times more driving mishaps than the majority of safer T1DM drivers. This study is intended to assist in identifying these high risk T1DM drivers and then to successfully rehabilitate them so that their risk of future driving mishaps is no greater than that of the general public.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Research Project (R01)
Project #
5R01DK028288-27
Application #
8291415
Study Section
Behavioral Medicine, Interventions and Outcomes Study Section (BMIO)
Program Officer
Hunter, Christine
Project Start
1982-01-01
Project End
2014-05-31
Budget Start
2012-06-01
Budget End
2014-05-31
Support Year
27
Fiscal Year
2012
Total Cost
$669,453
Indirect Cost
$227,570
Name
University of Virginia
Department
None
Type
Schools of Medicine
DUNS #
065391526
City
Charlottesville
State
VA
Country
United States
Zip Code
22904
Cox, Daniel J; Singh, Harsimran; Lorber, Daniel (2013) Diabetes and driving safety: science, ethics, legality and practice. Am J Med Sci 345:263-5
Cox, Daniel J; Cox, Brian S; Cox, Jennifer (2011) Self-reported incidences of moving vehicle collisions and citations among drivers with ADHD: a cross-sectional survey across the lifespan. Am J Psychiatry 168:329-30
Gonder-Frederick, Linda A; Schmidt, Karen M; Vajda, Karen A et al. (2011) Psychometric properties of the hypoglycemia fear survey-ii for adults with type 1 diabetes. Diabetes Care 34:801-6
Cox, Daniel J; Kovatchev, Boris P; Anderson, Stacey M et al. (2010) Type 1 diabetic drivers with and without a history of recurrent hypoglycemia-related driving mishaps: physiological and performance differences during euglycemia and the induction of hypoglycemia. Diabetes Care 33:2430-5
Campbell, Laura K; Gonder-Frederick, Linda A; Broshek, Donna K et al. (2010) Neurocognitive Differences Between Drivers with Type 1 Diabetes with and without a Recent History of Recurrent Driving Mishaps. Int J Diabetes Mellit 2:73-77
Boren, Suzanne Austin; Clarke, William L (2010) Analytical and clinical performance of blood glucose monitors. J Diabetes Sci Technol 4:84-97
Cox, Daniel; Ritterband, Lee; Magee, Joshua et al. (2008) Blood glucose awareness training delivered over the internet. Diabetes Care 31:1527-8
Cox, Daniel J; Gonder-Frederick, Linda; Ritterband, Lee et al. (2007) Prediction of severe hypoglycemia. Diabetes Care 30:1370-3
Cox, Daniel J; Kovatchev, Boris; Vandecar, Karen et al. (2006) Hypoglycemia preceding fatal car collisions. Diabetes Care 29:467-8
Clarke, William L; Anderson, Stacey; Farhy, Leon et al. (2005) Evaluating the clinical accuracy of two continuous glucose sensors using continuous glucose-error grid analysis. Diabetes Care 28:2412-7

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