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Impact of Texting and Driving Vs Drinking and Driving on Driver's Performance

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In Ontario, distracted driving continues to be the leading cause of car accidents (Day, 2018). Distracted driving includes the acts of cell-phone use, eating or drinking, use of electronic devices such as radio, and audio players, distractions outside the vehicle, or even conversing with passengers (Road Safety Canada Consulting, 2011). The article “Mild Cognitive Impairment and driving: Does in-vehicle distraction affect driving performance?” explores the links between in-vehicle distractions and driving performance. 

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Through a simulated driving experience, cognitively intact individuals with no pathological conditions and MCI (Mild Cognitive Impairment) patients test the effects of distraction while driving. Mild Cognitive Impairment is used to describe an individual in the cognitive state between normal aging and dementia. MCI patients have weakened cognitive resources, but due to their well-preserved functionality, do not meet the criteria for dementia (Beratis, et al., 2017). 'Driving is a complex task that involves a wide range of cognitive skills, multi-sensory perception, and motor abilities' (Apolinario, et al., 2009), and is strongly linked to cognitive functioning, so the theory is that weakened or limited cognitive resources will affect one’s behavior while driving. Researchers hypothesized that characteristics of MCI will lead to said patients having a longer reaction time to unexpected incidents on the (simulated) road, compared to a cognitively intact individual. The levels of distraction tested in the experiment consisted of 1) undistracted driving, 2) conversing with passengers, and 3) conversing through a hand-held device.

In the experiment, a total of 25 participants (13 MCI patients and 12 cognitively intact individuals) were put through practice driving until they felt familiar with the driving simulator environment. The experiment consisted of three trials, with two differing unexpected incidents (the appearance of an animal on the road) at differing times in each trial. Four driving performance factors were measured for each participant, 1) mean speed, 2) speed variability, 3) reaction time at an unexpected incident, and 4) accident probability.

The study found that “patients with MCI were driving with lower average speed, showed less speed variability, and presented larger reaction times in the case of unexpected incidents, but did not possess an increased accident risk” (Beratis, et al., 2017). Aligned with the hypothesis, the largest factor for a participant’s increased reaction time, especially an MCI patient, was conversing on a hand-held device, whereas conversing with an in-car passenger did not significantly delay their reaction time.

What's Next?

To further this experiment, I would replicate the original experiment with the following changes. Added levels of distraction, i.e. texting, listening to loud music, and outside distractions. For example, compared to having a conversation on the phone (in original study), texting, which required one’s visual attention and motor skills in addition to the cognitive thinking process required in talking on the phone, should theoretically be more distracting, therefore resulting in a higher accident rate. I also believe there could be more forms of unexpected incidents, rather than only the appearance of an animal on the road (in original study). In the real world, a driver must mind an array of other things, like other vehicles coming into their lane; whether it’s intentional merging, or perhaps a distracted driver veering into your lane. Although not included in my summary, the original study used a 40-inch LCD screen with total field of view of 170°. I would rerun the experiment in virtual reality to allow a more authentic environment, leading to real-world-related results.

For the experimental population, I would choose a larger sample of people from different age groups, as opposed to only 25 participants with approximate mean age of 62, s.d.≈7.5 (in original study). My controlled variables will be level of distraction, age, MCI/non-MCI, frequency of driving, and if deemed ethical after further research, sobriety level. My dependent variables will include reaction time, mean speed, accident probability, breaking pattern, and direction shifting. Notice that I have added two measured variables. I believe their measurements are equally crucial to understanding exactly how much distraction will cause an increase in accidental probability. Breaking too suddenly/harshly cause vehicle(s) behind to have less reaction time, leading to rear-end collisions. Uncontrolled shifting in vehicle direction could mean veering into another vehicle’s lane, leading to side-collisions or head-on collisions. In a recent government report, single motor vehicle accidents saw the highest numbers in both fatal and personal injuries. After single motor vehicle accidents, rear-end collisions had the highest number of personal injuries; while approaching vehicle collisions saw the highest number in fatalities (Road Safety Research Office, Safety Policy and Education Branch, & Ministry of Transportation, 2019). My dependent variables will be measured for each useful combination of independent variables, i.e. non-MCI teenager texting vs non-MCI adult texting to determine age-related effects, etc. Experiments will run over multiple days to deter anomalies, and minimize confounding variables.

I hypothesize that individuals, especially MCI patients, will have an increased reaction time, reduced mean speed, greater shift in direction, and sharper breaking patterns as higher levels of distraction are posed. I hypothesize that, contrary to the original findings, there will be a scientifically significant link between distraction and accidental probability. I predict that we will notice trends within each age group, and frequency of daily driving group across both MCI and non-MCI participants. For example, more frequent drivers may possess the ability to avoid certain low-level errors caused by distractions as they have the muscle memory build-up from more driving experience. I expect that young adults will have the lowest accidental probability, while older MCI patients will have the highest accidental probability, under the same condition. If my results support my hypothesis, the theory that weakened or limited cognitive resources will affect one’s behavior while driving will be greatly strengthened. As well as a scientific link between distraction and accidental probability will be formed.

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