Study Design and Oversight

The NTDS data were collected from June 2006 through September 2008, and the 100-Car Study data were collected from January 2003 through July 2004. The two studies used similar experimental methods, detailed descriptions of which have been reported previously.14,16

We used a case–cohort approach to compare the prevalence of each task in the seconds before a crash or near-crash with the prevalence of the task during randomly sampled control periods of driving. We conducted separate analyses involving novice drivers and experienced drivers.

In both studies, adults provided written informed consent, and adolescents (i.e., those under the age of 18 years) provided written informed assent. Both studies were approved by the institutional review board of Virginia Polytechnic Institute and State University.

Participants

In the NTDS, 42 newly licensed drivers (22 females and 20 males) from southwestern Virginia were recruited, and instruments were installed in their personal vehicles. At the initiation of the study, the mean (±SD) age of the participants was 16.4±0.3 years of age, and they had had a driver's license for 3 weeks or less. They received a total of $1,800 in monthly and end-of-study compensation for participation in the 18-month study.

In the 100-Car Study, 109 participants (43 women and 66 men) between the ages of 18 and 72 years (mean age, 36.2±14.4 years) from the Washington, D.C., area were recruited. The mean length of time that participants had been driving was 20.0±14.5 years. A total of 22 participants were compensated with the use of a leased vehicle, and 87 participants drove their own vehicles; the latter group received a total of $1,800 ($125 per month plus $300 at the end of the 12-month study).

Equipment

Instruments with the same data-acquisition systems (developed at the Virginia Tech Transportation Institute) were installed in vehicles in both studies. These systems included four cameras (forward view, rear view, view of the driver's face, and view over the driver's right shoulder) and a suite of vehicle sensors that included a multiaxis accelerometer, forward radar, a global positioning system, and a machine-vision lane tracker. Video and driving-performance data were collected continuously for the duration of the studies.15,17

Data Coding and Analysis

Highly trained analysts used threshold values obtained through a sensitivity analysis of the vehicle-sensor data (e.g., braking at more than 65 gravitational units)16 to identify potential crashes and near-crashes. The operational definition of a crash was any physical contact between the vehicle and another object for which the driver was at fault or partially at fault. (None of the crashes involved a death or serious injury.) The operational definition of a near-crash was any circumstance requiring a last-moment physical maneuver that challenged the physical limitations of the vehicle to avoid a crash for which the driver was at fault or partially at fault.

On the basis of prespecified criteria, we excluded events in which the driver was considered to be not at fault (108 events in the NTDS and 190 events in the 100-Car Study) and in which the driver was observed to be drowsy or under the potential influence of drugs or alcohol (7 events in the NTDS and 113 events in the 100-Car Study). The analyses included 31 crashes and 136 near-crashes among novice drivers and 42 crashes and 476 near-crashes among experienced drivers. Previous analyses have shown that near-crashes are reliable surrogates for crashes.18

Randomly sampled control periods that consisted of 6-second time segments during which the vehicle was moving faster than 5 mph were selected to represent typical or “normal” daily driving conditions. For each driver, sampling for control periods was stratified according to the number of miles the vehicle had traveled (in the NTDS) or the number of hours the person had driven (in the 100-Car Study). Thus, the number of control periods for each driver was proportional to either the distance of travel (e.g., one sample per 50 vehicle miles traveled) or the duration of travel (e.g., two samples per hour driven).17

Table 1. Table 1. Secondary Tasks Observed in the Studies.

Two analysts viewed the video footage before each confirmed crash or near-crash and identified and coded secondary tasks. Analysts also viewed the video footage of the randomly sampled control periods and recorded the performance of secondary tasks. The identified secondary tasks were organized according to the 10 categories listed in Table 1.15 Operational definitions of the tasks were identical in the two studies; texting was assessed only in the NTDS, since the 100-Car Study was performed before this activity was widely used.

A secondary task was included if it occurred within the 6-second duration of each sampled control period or within 5 seconds before or 1 second after the onset of the crash or near-crash. Coding continued for 1 second after the onset of the crash or near-crash to capture behaviors that continued because the driver was not aware of the onset of the crash or near-crash.

It was not considered feasible for analysts to be unaware of whether a crash or near-crash occurred, but they were unaware of the purpose of the analyses and recorded many variables in addition to performance of secondary tasks. Any disagreements among analysts were adjudicated by a senior researcher. Interrater reliability, which was determined by comparing the analysts' assessments of the performance of secondary tasks during control periods with the assessments of a senior researcher, was 88.4% in the 100-Car Study17 and 93.3% in the NTDS (see Tables S1 and S2 of Appendix 1 in the Supplementary Appendix, available with the full text of this article at NEJM.org).

Statistical Analysis

We used a mixed-effects logistic-regression analysis to estimate odds ratios for a crash or near-crash associated with each category of distracting task. We conducted separate regression analyses involving novice drivers and experienced drivers. A random intercept was assigned to each driver to incorporate within-driver correlations.

The prevalence of engagement in a secondary task was calculated per 3-month interval as the percentage of control conditions in which any recorded secondary task was observed. A mixed-effects linear-regression model was used to assess trends for performance of a secondary task over time by both novice and experienced drivers.