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Methods: In a prospective cohort study of 252 participants with mild/moderate injury sustained in a road traffic crash, quality of life was measured 24 months following the baseline survey. A telephone-administered questionnaire obtained information on various potential explanatory variables. Health-related quality of life was measured using the European Quality of Life–5 Dimensions (EQ-5D) and Medical Outcomes Survey Short Form–12 (SF-12). Multivariable linear regression analyses determined the associations between explanatory variables and quality of life measures.
Results: Mean SF-12 physical component summary (PCS) and mental component summary (MCS) scores increased by 7.3 and 2.5 units, respectively, from baseline to 24-month follow-up. Each 10-year increase in baseline age was independently associated with 3.1-unit (P < .001) and 1.5-unit (P = .001) decrease in EQ Visual Analogue Scale (VAS) and SF-12 PCS scores at follow-up, respectively. Poor/fair compared to excellent pre-injury health was associated with a 0.16-, 21.3-, and 11.5-unit decrease in EQ-5D summary (P = .03), VAS scores (P = .001), and SF-12 PCS scores (P < .001), respectively. Baseline pain severity ratings and pain catastrophizing scores were inversely associated with 24-month EQ VAS scores (both P < .001). Each unit increase in baseline pain score (P = .001) and pain catastrophizing score (P = .02) was associated with a 1.0- and 4.6-unit decrease in SF-12 MCS scores at 24 months, respectively. Other observed predictors of quality of life measures (EQ-5D summary and/or VAS scores and/ or SF-12 MCS) included marital status, smoking, hospital admission, pre-injury health (anxiety/depression and chronic illness), and whiplash injury.
Conclusion: Sociodemographic indicators, pre-injury health, and biopsychosocial correlates were independently associated with health-related quality of life 24 months following a noncatastrophic road traffic crash injury. 相似文献
Method: Data from an existing naturalistic driving study were used. Fast Fourier transform (FFT) was applied for the frequency domain analysis to explore drivers' behavior pattern changes between nondistracted (prestarting of visual–manual task) and distracted (poststarting of visual–manual task) driving periods. Average relative spectral power in a low frequency range (0–0.5 Hz) and the standard deviation in a 10-s time window of vehicle control variables (i.e., lane offset, yaw rate, and acceleration) were calculated and further compared. Sensitivity analyses were also applied to examine the reliability of the time and frequency domain analyses.
Results: Results of the mixed model analyses from the time and frequency domain analyses all showed significant degradation in lateral control performance after engaging in visual–manual tasks while driving. Results of the sensitivity analyses suggested that the frequency domain analysis was less sensitive to the frequency bandwidth, whereas the time domain analysis was more sensitive to the time intervals selected for variation calculations. Different time interval selections can result in significantly different standard deviation values, whereas average spectral power analysis on yaw rate in both low and high frequency bandwidths showed consistent results, that higher variation values were observed during distracted driving when compared to nondistracted driving.
Conclusions: This study suggests that driver state detection needs to consider the behavior changes during the prestarting periods, instead of only focusing on periods with physical presence of distraction, such as cell phone use. Lateral control measures can be a better indicator of distraction detection than longitudinal controls. In addition, frequency domain analyses proved to be a more robust and consistent method in assessing driving performance compared to time domain analyses. 相似文献