Outline of Results, Methodology, and Data Limitations
Phase II Study on Distracted Driving

Study results revealed that distractions are a common component of everyday driving.

Percentage of drivers engaging in potentially distracting activities during three hours of driving, and percentage duration of these activities when their vehicles were moving.
Potential Distraction % of Subjects % of Total Driving Time
Talking on cell phone 30.0 1.30*
Answering cell phone 15.7
Dialing cell phone 27.1
Eating, drinking, spilling 71.4 1.45
Preparing to eat or drink 58.6 3.16
Manipulating music/audio controls 91.4 1.35
Smoking (includes lighting and extinguishing) 7.1 1.55
Reading or writing 40.0 0.67
Grooming 45.7 0.28
Baby distracting 8.6 0.38
Child distracting 12.9 0.29
Adult distracting 22.9 0.27
Conversing 77.1 15.32
Reaching, leaning, etc. 97.1 3.78*
Manipulating vehicle controls 100.0
Other internal distraction 67.1
External distraction 85.7 1.62

*Combined categories


Data collection

The methodology developed for the field data collection activities entailed a camera unit, containing three miniature video cameras, that was mounted inside the vehicle just below the vehicle's rear view mirror. Two of the cameras were directed inside at the driver and front seat area of the vehicle, and the third was directed outside the vehicle straight ahead. A recording unit was generally placed in the trunk of the vehicle, and cables discretely run between the units.

The recording equipment was installed in the vehicles of 70 volunteer subjects, who were informed only that the study was being conducted to learn "how traffic and roadway conditions affect driving behavior." They were instructed to "drive normally" and scheduled to return one week later for removal of the equipment.

Data Coding

The resulting videotape data was coded using software (The Observer Video-Pro) specially designed for the coding and analysis of videotaped data. A coding scheme was developed along with selected contextual and outcome variables. A total of three hours of driving data was coded per subject.


The data were analyzed descriptively using the Video-Pro analysis software, and were also converted into SAS data files for further analysis. Given that the longitudinal nature of the data did not meet the assumptions for classic statistical analysis methods, confidence intervals for proportions and linear combinations of proportions were constructed using the bootstrap percentile method.

Data Limitations

Sample size:

There are a number of important limitations to this study. The relatively small sample size (70 drivers) and relatively small number of hours analyzed (3 out of 10 hours observed) could limit generalizability.

Coding variables:

Difficulty in objectively defining the various driver distraction and contextual/outcome variables also made it hard to achieve high levels of inter-rater reliability when coding the data. Some potentially important variables could not be coded at all.

Cognitive measurement:

Cognitive distraction was unable to be captured, which the literature suggests may pose the greatest risk to driving safety. Consequently, the study is not able to provide a definitive answer as to which activities, or which driver distractions, carry the greatest risks of crash involvement.