29 research outputs found
Injury crashes and the relationship with disease causing excessive daytime sleepiness
Objective: The objective of this study was to understand the relationship between some of the most common diseases that are known to contribute to excessive daytime sleepiness (EDS) and traffic injury crashes. Specific focus was on the relationship between disease and crash type (single-vehicle or multiple-vehicle crash) and between disease and injury severity.Methods: This registry-based study considered all passenger car drivers involved in a crash in Sweden between 2011 and 2016 who were 40 years or older at the time of the crash (n = 54,090). For each crash-involved driver, selected medical diagnoses registered from 1997 until the day before the crash were extracted from the National Patient Register. The drivers were assigned to 1 of 4 groups, depending on prior diseases: sleep apnea (SA; group 1, n = 2,165), sleep disorders (group 2, n = 724), Parkinson’s or epilepsy (group 3, n = 645) and a reference group (group 4, n = 50,556). Logistic regression analysis compared single-vehicle crashes with multiple-vehicle crashes and moderately/severely injured drivers with slightly/uninjured drivers.Results: Drivers with EDS-related diseases (groups 1–3) had higher probability of a single-vehicle crash than a multiple-vehicle crash compared to the reference group. The most sizeable effect was found for Parkinson’s/epilepsy with an odds ratio (OR) of 2.5 (confidence interval [CI], 2.1–3.0). For multiple-vehicle crashes, the probability of a moderate/severe injury was higher for drivers with other sleep disorders (OR = 1.5; CI, 1.0–2.2) and Parkinson’s/epilepsy (OR = 1.6; CI, 1.1–2.3) compared to the reference group.Conclusions: This study has made first steps toward understanding the relationship between some of the most common diseases that are known to contribute to EDS and crashes. Having Parkinson’s/epilepsy, in particular, elevated the probability of a single-vehicle crash compared to a multiple-vehicle crash. A single-vehicle crash was seen as indicative of causing a crash; thus, having Parkinson’s/epilepsy could be interpreted as a risk factor for crash involvement. Having Parkinson’s/epilepsy, as well as other sleep disorders, was also related to more severe outcomes in multiple-vehicle crashes, given that a crash occurred. This was not identified in single-vehicle crashes.</div
Performance indicators related to speed and lateral position divided by Condition (day and night); Case setting (quiet and loud) and first/second part of the drive and time on task (5-10-15-20-25-30-35 minutes).
<p>Performance indicators related to speed and lateral position divided by Condition (day and night); Case setting (quiet and loud) and first/second part of the drive and time on task (5-10-15-20-25-30-35 minutes).</p
Sleepiness and performance indicators for the first 35-minute drive divided by condition (Day and Night) as function of time on task (5-10-15-20-25-30-35 minutes) and type of sound (Quiet or Loud).
<p>Sleepiness and performance indicators for the first 35-minute drive divided by condition (Day and Night) as function of time on task (5-10-15-20-25-30-35 minutes) and type of sound (Quiet or Loud).</p
Sleepiness indicators related to subjective sleepiness and blink behaviour divided by Condition (day and night); Case setting (quiet and loud) and First/second part of the drive and time on task (5-10-15-20-25-30-35 minutes).
<p>Sleepiness indicators related to subjective sleepiness and blink behaviour divided by Condition (day and night); Case setting (quiet and loud) and First/second part of the drive and time on task (5-10-15-20-25-30-35 minutes).</p
Mixed Model Anova: condition (day/night); minute (0–35); sound (Quiet/Loud).
<p>F-values bold show significance. P-values and df in parenthesis.</p><p>Mixed Model Anova: condition (day/night); minute (0–35); sound (Quiet/Loud).</p
Facilitating the conversation: Fatigue countermeasure acceptance for autonomous shuttle operations [Abstract]
Facilitating the conversation: Fatigue countermeasure acceptance for autonomous shuttle operations [Abstract]</p
Mixed Model Anova: condition (day/night); Part (first/last); Minute (0–35); Sound (quiet/loud).
<p>F-values bold show significance. P-values in parenthesis.</p><p>Mixed Model Anova: condition (day/night); Part (first/last); Minute (0–35); Sound (quiet/loud).</p
Infrasonic spectra of recorded vehicle sounds in 1/3 octave bands.
<p>Infrasonic spectra of recorded vehicle sounds in 1/3 octave bands.</p