ACTIVITY 6-5 Evaluate Your Concentration During Lecture Classes Make a list of the problems or difficulties you experience in at least two of your lecture classes. External distractions such as a cluttered or uncomfortable study environment, noise, and other people are common causes of poor concentration when reading. Apr 27, 2016. Listening to traffic reports on the radio could be bad for your driving – you could even miss an elephant standing by the side of the road. Listening out for a traffic update on the radio suggests that the load on our hearing may be an important and overlooked contributor to driver distraction and inattention.'

Results The most common distracting activities during the most recent driving trip were lack of concentration (weighted percentage (standard error, SE) 71.8% (1.4%) of drivers); adjusting in‐vehicle equipment (68.7% (1.5%)); outside people, objects or events (57.8% (1.6%)); and talking to passengers (39.8% (1.6%)). On average, a driver engaged in a distracting activity once every six minutes. One in five crashes (21%) during the last three years, involving one in 20 drivers (5.0% (0.7%)), was attributed to driver distraction based on self‐report. In the population under study, this equated to 242,188 (SE 34,417) drivers. Younger drivers (18–30 years) were significantly more likely to report distracting activities, to perceive distracting activities as less dangerous, and to have crashed as a result. A driver distraction can be defined as any activity that diverts a driver's attention away from the task of driving. Although it is clear that distractions may affect a driver's control of a vehicle, little is known about drivers' exposure to distracting activities while driving and the impact of that exposure on road safety.

To begin, there is a lack of detailed and systematically collected information about the role of driver distraction in crashes among a representative group of drivers. Of the research that has been undertaken, some has been based on police reports of crashes., However, information on driver distraction is likely to be underreported or differentially reported, with the latter dependent on factors such as the severity of the crash, the type of distracting activity and the jurisdiction within which the crash occurred. Other research has been experimental in nature, examining in‐vehicle,,,, and outside factors., Although the findings of most of these studies have shown decrements in driving performance, translating findings from the laboratory into the real world is problematic. In a novel development, two naturalistic driving studies have been conducted recently to assess exposure to, and the adverse consequences of, distracting activities., These studies used cameras to monitor the behavior of drivers in their everyday driving. For example, Stutts and colleagues recorded the distracting activities of 70 drivers using data from three hours of driving collected over a one‐week period. All drivers undertook at least one type of distracting activity and, altogether, these drivers spent 14.5% of the total time their vehicles were in motion engaged in distracting activities, other than talking to passengers. Some types of distracting activities were associated with driving impairment, defined as eyes off the road, hands off the steering wheel, and lane drift.

However, the limitations of naturalistic driving studies require consideration. They may include small, non‐representative, volunteer samples;, limited number of recorded hours of driving; low interrater reliability in coding distracting activities (65–70% in Stutts et al ); difficulties in distinguishing near crashes from incidents; an inability to capture drivers' level of cognitive attention;, and problems in extrapolating driving impairment into crash risk. How often a distracting activity will result in a crash will be a function of several factors: the proportion of the drivers who engage in the activity, the frequency and duration of time those drivers spend on it, and the relative risk conferred by it.

We conducted a representative survey of drivers in two Australian states to explore some of these factors. This study was unique in several respects. First, we systematically quantified the frequency, and types, of distracting activities drivers engaged in during their most recent driving trip.

Second, we measured the adverse consequences resulting from distracting activities, including the role of driver distraction in crashes occurring in the preceding three years and the types of crashes that ensued. Accordingly, the survey provides estimates of the prevalence of distracting activities while driving and the extent to which driver distraction adversely affects road safety. Study population The survey was conducted in New South Wales (NSW) and Western Australia (WA). Both states have capital cities (Sydney and Perth, respectively), regional centers, and rural areas.

Participants were required to hold a driver's licence, be aged between 18 and 65 years, reside in either NSW or WA, have driven in the last month, and mainly drive a motorized vehicle other than a motorcycle on public roads. Based on data from the NSW Roads and Traffic Authority (RTA) and the WA Department of Planning and Infrastructure (DPI), the population of this group was 4.9 million in July 2003. Sample selection The sampling frame was the residential section of the Electronic White Pages in NSW and WA (Brylar's Australia on Disc Residential, May 2003). Households were stratified by state of residence and area of residence as defined by postcode data. Four pools of phone numbers were randomly selected (comprising Sydney, regional NSW, Perth, and regional WA). Individuals were stratified on the basis of age group (18–30, 31–49, and 50–65 years) and sex. Small groups (for example, regional residents) were oversampled and the overall sample size was calculated to take into account the stratification and powered to detect distracting activities with a prevalence of at least 10% (p = 0.05, precision = 0.05).

Survey questionnaire The questionnaire contained items to ascertain the demographic and driving characteristics of respondents, the frequency of distracting activities during the most recent driving trip, their effects as measured by driving errors, and the perceived risk of crash attributable to certain risk taking habits including distracting activities. Identifying data, such as name and date of birth, were not obtained. We collected information on numerous types of activities such as viewing outside objects, people and events; talking to passengers; adjusting, or reaching for, in‐vehicle equipment or objects; and daydreaming. We also collected data on crashes in the last three years including when the crash had occurred (month and year), details about the crash event, whether the crash was the result of a distraction, what the distraction had been and how it had contributed to the crash.

Survey conduct Telephone interviews were conducted between 20 October 2003 and 7 November 2003 by trained professional interviewers in the Survey Research Centre at The University of Western Australia (UWA). Up to eight calls were made to each residential number, with calls made at various times of the day and days of the week to increase the likelihood of making contact. Once contact was established, the interviewer provided an explanation for the call and determined the eligibility of household members for the survey. If multiple household members were eligible, the individual with the most recent birthday was interviewed.

A computer assisted telephone interview system was used to manage call‐backs and to enter and check data to allow only valid responses, such as within‐range replies. Automatic rotation of response categories to minimize response bias was used where relevant. The Human Research Ethics Committees at UWA and The University of New South Wales approved the study. Survey analysis The total numbers of licensed drivers in each of the 24 strata were obtained from the RTA and the DPI. These were used to calculate the appropriate weightings so as to produce population estimates (pop est; and their standard errors, SE). There were no statistically significant differences between the states for the key measures, including the frequency and types of distracting activities reported during the most recent driving trip and the proportion of drivers who had crashed in the last three years.

Accordingly, the data were pooled and the study population was defined as the population of drivers aged between 18 and 65 years in the states of NSW and WA. The data were analyzed in STATA Version 8.

Percentages are presented as weighted percentages (SE), unless otherwise stated. In certain circumstances, for example, when the denominator was not drivers but incidents or crashes, unweighted percentages are shown. Frequencies and proportions were calculated for categorical data and means, medians, and interquartile ranges (IQR) for continuous data. Pearson's correlations were calculated for continuous data following data transformation to approximate normality, if necessary. Weighted univariate χ 2 tests and weighted χ 2 tests for linear trend were used for categorical data.

Multiple logistic regression was undertaken to determine if there were significant differences in the frequency of reported distracting activities by sex, age group, and area of residence, after adjusting for length of trip and, where relevant, for presence of passengers. The Friedman and Wilcoxon signed rank tests were used to establish whether there were significant differences in the way drivers' perceived the level of risk conferred by different risk taking habits (each expressed on a five‐point ordinal scale, where 1 equalled no increase in risk of crash and 5 equalled extremely high increase in risk of crash). Distracting activities during the most recent driving trip The most common distracting activities reported by drivers during the most recent driving trip of ⩾5 minutes' duration were lack of concentration (71.8% (1.4%) of drivers; pop est. (SE) 3,494,335 (69,880)), adjusting in‐vehicle equipment (68.7% (1.5%); 3,344,526 (69,880)), viewing outside people, objects, or events (57.8% (1.6%); 2,812,114 (76,229)) and talking to passengers (39.8% (1.6%); 1,938,505 (76,429); table 4). In relation to new technologies, very few drivers used an electronic organiser (three drivers) or accessed email (one driver) while driving. The number of different types of distracting activities was positively correlated with the trip's duration (Pearson correlation 0.35, p.

Table 4 Types of distracting activities reported during the most recent driving trip of five minutes' or more duration Younger drivers were significantly more likely to report a number of distracting activities including lack of concentration (18–49 years: 75.7% v 50–65 years: 61.5%, p. Perceived risks Some distracting activities were not perceived to pose a serious threat to a driver's ability to control a vehicle. For example, seven in 10 drivers did not consider talking to passengers as dangerous (table 5).

However, nine in 10 drivers felt that writing text messages while driving was very dangerous (high or extreme risk). There was a significant difference in the perceived risk of a crash by rank order from most to least dangerous risk taking habit (Friedman test, p. Table 5 Drivers' perception of the increase in crash risk conferred by various distracting and other risk behaviors (listed in rank order from most to least dangerous) Younger drivers (18–30 years) rated most distracting activities as significantly less risky compared with their older counterparts. These activities included using mobile phones (handheld and hands‐free), writing text messages, reading maps, reaching for backseat objects, and viewing scenery (all p⩽0.03).

For example, in relation to writing text messages while driving, 37.3% of 18–30 year old drivers, 55.9% of 31–49 year old drivers, and 69.0% of 50–65 year old drivers felt that this was extremely risky. Males rated most risk taking habits as significantly less risky compared with females (all p⩽0.03), except for reaching for backseat objects (p = 0.15) and daydreaming (p = 0.17), where there was no significant difference by sex. Crashes resulting from self‐reported distractions There were 313 crashes among 266 drivers (20.1% (1.3%)) during the preceding three years. Sixty six drivers (5.0% (0.7%)) attributed one of their crashes to being distracted (21% of reported crashes; unweighted). This suggests that almost one quarter of a million drivers (242,188 (34,417)) in the study population will have had a crash due to a distraction in the last three years.

The types of distractions that were reported were lack of concentration (28, 42% of all distraction related crashes; unweighted); outside objects, people, or events (18, 27%); talking to passengers (7, 11%); adjusting in‐vehicle equipment (3, 5%); and other distractions (10, 15%). The major types of crashes that were reported by these drivers were rear‐end collisions (27 crashes, 41%; 24 of which involved the driver's vehicle hitting the vehicle in front), crashes while reversing (12, 18%), crashes due to loss of vehicular control by the driver (9 crashes, 14%), and crashes due to the driver failing to stop at an intersection (7 crashes, 11%). Younger drivers were significantly more likely to report a crash due to a distraction than older drivers (18–30 years: 7.7%, 31–49 years: 5.0%, 50–65 years: 2.3%, p = 0.01). There were no significant differences on the basis of sex, area or state of residence, and driving frequency. Discussion Our survey is one of the few worldwide to have examined the issue of driver distraction beyond the use of mobile phones while driving. Distracting activities while driving are highly prevalent.

In the most recent driving trip, most drivers will have talked to passengers; adjusted in‐vehicle equipment; reached for objects in the vehicle; or viewed people, objects, or events outside the immediate road environment. In the study population, we have estimated that 1.9 to 3.5 million drivers will experience at least one of the four most common types of distracting activity during a trip lasting five minutes or more. On average, a driver will engage in a distracting activity once every six minutes and, although the time spent on each activity may vary, exposure to distracting activities while driving is substantial. Furthermore, distracting activities can result in adverse outcomes. Of all driver errors, 17% were attributed to driver distraction in our survey.

We estimate that over 160,000 drivers in the study population will experience an error due to a distraction on any given trip of five minutes or more. Almost a quarter of a million drivers (5% of the driving population) are expected to have crashed while distracted in the last three years, and 21% of all crashes were due to driver distraction on the basis of self‐report.

Dibal Dld Software more. Younger drivers were significantly more likely to report distracting activities while driving, to perceive distracting activities as less dangerous, and to have had a crash due to a distraction. The latter association is supported by evidence from an American survey.

A review of police reported crashes between 1995 and 1999 in the American Crashworthiness Data System (CDS) revealed that 8.3% of drivers were distracted at the time of their crash. Outside objects, persons, and events (29.4% of distracted drivers); adjusting in‐vehicle audio equipment (11.4%); and passengers (10.9%) were the three distractions most often cited. However, the drivers' attention status was unknown in 35.9% of drivers, so the proportion of drivers distracted at the time of crash is almost certainly underestimated. In New Zealand, driver distraction was involved in at least 9.5% of police reported crashes during 2002 and 2003, with outside‐ and inside‐vehicle factors recorded approximately equally. Although crash data can provide information on the relative contributions of different types of distracting activities, risk estimates cannot be calculated because of a lack of exposure data.

We recently published a case crossover study which showed that drivers' use of a mobile phone up to 10 minutes before a crash was associated with a fourfold increase in the likelihood of having a serious crash. However, the risks associated with other driver distractions remain unclear. The use of a case crossover design to examine other distracting activities has been limited by the inability to validate self‐report about such activities before a crash and during equivalent control intervals. This is distinct from data about mobile phone use which can be cross referenced to phone company records.

Establishing risk estimates for the gamut of distracting activities that occur while driving is an important area for research. Moreover, investigation into the circumstances during which distracting activities present the greatest risk is also warranted. In the naturalistic driving study conducted by Stutts and colleagues, drivers were more likely to carry out distracting activities while a vehicle was stationary suggesting some discriminating capacity among drivers to choose safer periods.

A number of limitations should be noted. First, random digit dialling was not undertaken because it represented a more costly and less efficient method. Instead phone numbers were randomly selected from the residential Electronic White Pages, thus respondents were sampled only from those households with a landline and a phone book listing. Notably, there is evidence to suggest that using the Electronic White Pages as the sampling frame for health surveys does not introduce significant bias into health estimates.

Second, the survey's response rate approached 50% and the possibility of selection bias needs consideration. However, as almost all drivers have engaged in distracting activities while driving, it is unlikely that differential bias between respondents and non‐respondents would have had an appreciable effect. Third, self‐reported data may be subject to recall and social desirability bias. Reassuringly, in a study conducted among a cohort of young drivers in New Zealand, there was a high level of agreement between self‐reported crash details and police records.

Social desirability has not been shown to significantly influence self‐reported driver behavior, particularly in an anonymous setting such as the one we used., It is possible that there may have been some differential bias on the basis of factors such as age and sex. Importantly, any resultant bias would tend to underestimate the extent of the problem of driver distraction rather than overestimate it.

Implications for prevention Our survey has provided valuable data on drivers' exposure to distracting activities and the adverse outcomes that can result. While further research is needed to estimate the risk conferred by different activities and the circumstances during which these activities pose greatest risk, we believe that a strategy to minimize distracting activities while driving, with a focus on young drivers, is indicated in the meantime.

According to the National Highway Traffic Safety Administration (NHTSA), on-the-job crashes cost employers more than $24,500 per crash, $150,000 per injury, and $3.6 million per fatality. And distracted driving caused by hand-held mobile device use is an emerging contributor to these accidents.

Employers are often held liable in distracted driving cases because of a legal doctrine known as “vicarious liability,” which charges employers with legal responsibility if the negligent act is committed by an employee acting within the “general scope” of his or her employment. Also at stake for fleets is the risk of penalties and fees for violating state laws cracking down on cell phone use while driving. As of press time, 33 states, the District of Columbia, and Guam have banned text messaging for all drivers. Eight states, the District of Columbia, and the Virgin Islands prohibit all drivers from using hand-held cell phones while driving. In addition, several other states have anti-distracted driving legislation pending. For the latest listing of state laws banning cell phone use, go to.

While a growing number of fleets have enacted strict policies governing cell phone use while driving, the challenge is this: How do you enforce these policies — especially if you’re managing fleets consisting of dozens, or hundreds, or even several thousand drivers? Here are six mobile applications that automatically disable certain functionality within wireless devices while a vehicle is in operation, equipping fleets of all sizes with a practical tool to enforce policies that reduce distracted driving accidents and minimize risk exposure. All the above apps are good, but they have two big issues.

1 the passenger issue. All have some kind of over ride button to allow a passenger to operate the phone. This of course means a driver can just push the same button. And 2, battery drain. If it GPS based there is a battery issue.

Stoptxting. Download Electric Chubbyland Rapidshare Free Software. com has a ISSUED patent dealing with the battery drain issue, and they have a pending patent on the passenger issue. They think they have figured out how to allow a phone to automatically know if the phone is being used by a driver or a passenger and auto configure based on that alone. No more over ride buttons, and they have at least 3 issued patents, maybe as many as 5. And they are looking to licenses their patent portfolio. Stoptxting.com • 4.

James Nelson [ May 27, 2013 @ 12:16AM ].